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TOTAL FACTOR PRODUCTIVITY OF PAKISTAN’S KNITTED
GARMENT INDUSTRY AND ITS DETERMINANTS
A thesis submitted to The University of Manchester for the degree of
MPhil
in the Faculty of Engineering and Physical Sciences
2008
Muhammed Mushtaq Ahmed Mangat
School of Materials
2
Table of Contents
Abstract 10
Declaration 11
Copyright 11
Chapter One
Introduction 14-38
1.1 Productivity: An Everlasting Concept 14
1.2 Productivity Implications and Current Era 16
1.3 Productivity Measurements and its Significance 18
1.4 Pakistan Textile Industry and Productivity 20
1.5 Structure of Pakistan Knitted Garment Industry 30
1.6 Research Problem 32
1.7 Research Justification 33
1.8 Study Objectives 34
1.9 Scope of the Research 35
1.10 Type of Research 35
1.11 Research Methodology 35
1.12 Contribution of Study 37
1.13 Outline of the Thesis 37
Chapter Two
Total Factor Productivity: An Overview 38-142
2.1 Historical Perspective and Emergence of Productivity Movement 38
2.2 Productivity: a Multi Dimensional Term 51
3
2.3 Distinction and Interdependence of Productivity, Profitability, Performance,
Efficiency, Effectiveness 57
2.3.1 Productivity and Profitability 58
2.3.2 Performance, Efficiency, and Effectiveness 60
2.4 Theory and Emergence of Production Function: Foundation of TFP 62
2.5 Total Factor Productivity: a Discrete Concept 72
2.6 A Critical View of Production Function and TFP 76
2.7 Significance of TFP in Economic Perspective 81
2.8 Sources of TFP Growth 84
2.9 TFP and Industrial Engineers/ Business Managers 90
2.10 Productivity Measuring Approaches and Their Assumptions 96
2.11 Productivity Measurement and Its Classification 99
2.12 TFP at Firm Level: Evidence from Empirical Studies 123
2.13 Summary of TFP Review 139
Chapter Three
Data Collection and Research Methodology 143-171
3.1 Research Methods 144
3.2 Quantitative and Qualitative Data 144
3.3 Validity of Data Set 149
3.4 TFP Measuring Model 150
3.5 Selection of Determinants Affecting TFP of PKGI 155
3.6 Population and Sampling 160
3.7 Classification of PKGI 161
4
3.8 Correlation and Regression 161
3.9 Measurement of Correlation 162
3.10 Regression Analysis 164
3.11 Regression Coefficient and Regression Equation 165
3.12 Prediction and Prediction Errors 166
3.13 The Pitfalls and Limitations of Regression 167
3.14 Unequal Variability 167
3.15 Determining the Linear Regression Equation 168
3.16 Hypothesis Testing 169
3.17 Selection of Software for Statistical Analysis 169
3.18 Conclusion 170
Chapter Four
Data Analysis and Results 172-229
4.1 Profile of PKGI 173
4.2 Data Summarisation of PKGI 176
4.3 TFP of PKGI and Its Comparison 179
4.4 Hypotheses Testing 187
4.4.1 Assumptions for t-test 187
4.4.2 Mean Difference in TFP of Horizontal and Vertical Firms 188
4.4.3 Test of Mean of TFP at Aggregated Level 190
4.4.4 Test of Mean of TFP of Horizontal and Vertical Firms 191
4. 5 Correlation Test between TFP and Seven Independent Variables 195
4.6 Regression Analysis Assumptions (Vertical and Horizontal Firms) 200
5
4.6.1 Linearity and Data of Horizontal Firms 202
4.6.2 Linearity and Data of Vertical Firms 206
4.6.3 Data Normality (Horizontal and Vertical Firms) 210
4.6.4 Homoscedasticity Assumption 212
4.7 Weighted Least Squares Regression 214
4.7.1 Weighted Least Squares Regression (Horizontal Firms) 217
4.7.2 WLS Regression Equation (Horizontal Firms) 222
4.7.3 Weighted Least Squares Regression (Vertical Firms) 222
4.7.4 WLS Regression Equation (Vertical Firms) 226
4.8 Initial Conclusion from Results 227
Chapter Five
Conclusions and Recommendations 230-248
5.1 Pakistan Textile Industry: An Overview 230
5.2 Productivity and Performance 231
5.3 Selection of Independent Variables and Data Collection 231
5.4 Level of TFP of PKGI and Its Ranking 234
5.5 Hypothesis Testing 236
5.6 Correlation between TFP and Its Determinants 238
5.7 Contribution of Independent Variables in Variance of Dependent Variable 238
5.8 Regression Equation and Determinants Affecting TFP of PKGI (Horizontal
and Vertical Firms) 240
5.9 Suggestions and Recommendations 244
5.10 Limitations of the Study 247
6
5.11 Further Study 248
References 249
Appendix: 1 Data of Horizontal Firms 267
Appendix: 2 Data of Vertical Firms 268
Appendix: 3 Questionnaire 269
7
List of Tables
Table No. Description
1.1 Contribution of Pakistani Textile Industry to the Economy
(2004-5)
24
1.2 Share Percentage in World Clothing Exports and Ranking in Top
Clothing Exporting Countries
26
1.3 Growth Rate of Textile Related Commodities in Total Export from
Pakistan
27
2.1 Journey of Productivity Awareness in a Chronological Order 50
2.2 Chronological Order of Productivity Definition 56
4.1 Frequency of Firms Based on Type and Location (Population) 175
4.2 Frequency of Firms Based on Type and Location (Sample) 175
4.3 Descriptive Statistics of Dependent Variables 179
4.4 TFP of PKGI (at aggregate and disaggregate level) and Major
Industries of Pakistan
184
4.5 Independent t test Group Statistics 189
4.6 Independent t test Significance Values 189
4.7 One Sample t test Group Statistics (at Aggregated Level) 191
4.8 One Sample t test Significance Values (at Aggregated Level) 191
4.9 One Sample t test Group Statistics (Horizontal and Vertical Firms) 193
4.10 One Sample t test Significance Values (Horizontal and Vertical
Firms)
194
4.11 Correlation Matrix among Seven Independent Variables 197
8
(Horizontal Firms)
4.12 Correlation Matrix among Seven Independent Variables (Vertical
Firms)
198
4.13 Test of Normality of Data (Horizontal and Vertical) 211
4.14 Test of Homogeneity of Variances for Horizontal Firms 213
4.15 Robust Tests of Equality of Means for Horizontal Firms 213
4.16 Test of Homogeneity of Variances for Vertical Firms 213
4.17 Robust Tests of Equality of Means for Vertical Firms 214
4.18 Regression Analysis Model Summary (Horizontal Firms) 218
4.19 ANOVA Regression Analysis (Horizontal Firms) 218
4.20 Coefficients Regression Analysis (Horizontal Firms) 219
4.21 Regression Analysis Model Summary (Vertical Firms) 223
4.22 ANOVA Regression Analysis (Vertical Firms) 223
4.23 Coefficients Regression Analysis (Vertical Firms) 224
9
List of Figures
Figure No Description
4.1 Distribution of TFP (Horizontal Firms) 185
4.2 Distribution of TFP (Vertical Firms) 185
4.3 Distribution of TFP (At Aggregated Level) 186
4.4 Distribution of TFP (Manufacturing Sector Pakistan) 186
4.5 TFP and Share % of Labour Expenses in Total Cost (Horizontal
Firms)
202
4.6 TFP and Share (%) of Financial Expenses in Total Cost
(Horizontal Firms)
202
4.7 TFP and Share (%) of Fashion Goods in Total Production
(Horizontal Firms)
202
4.8 TFP and USA Market Share in Total Exports (Horizontal Firms) 204
4.9 TFP and Average FOB Price in US $ (Horizontal Firms) 204
4.10 TFP and Number of Stitching Machines (Horizontal Firms) 205
4.11 TFP and Sale Value in Million US $ (Horizontal Firms) 205
4.12 TFP and Share % of Labour Expenses in Total Cost (Vertical
Firms)
207
4.13 TFP and Share (%) of Financial Expenses in Total Cost (Vertical
Firms)
207
4.14 TFP and Share (%) of Fashion Goods in Total Production (Vertical
Firms)
208
4.15 TFP and USA Market Share in Total Exports (Vertical Firms) 208
4.16 TFP and Average FOB Price in US $ (Vertical Firms) 209
4.17 TFP and Number of Stitching Machines (Vertical Firms) 209
4.18 TFP and Sale Value in Million US $ (Vertical Firms) 210
Total Word Count: 61174
10
ABSTRACT
This study measures the Total Factor Productivity level of Pakistan‘s Knitted Garment
Industry, which is one of the most significant segments of the Pakistan Textile Industry. Data
covering financial and production variables were collected from government offices and through
an exploratory survey. The data were analysed with the help of SPSS software. This analysis
confirmed that the Total Factor Productivity of Pakistan‘s Knitted Garment Industry is
comparatively low. The current study attempts to assess the impact of seven different factors on
Total Factor Productivity. The significance of these factors was assessed with the help of
regression models. The analysis showed that some of selected variables have a direct relationship
with Total Factor Productivity. However, it is presumed that there are certain factors, particularly
factors covering qualitative areas of the industry, which might have a significant relationship but
are missing. Nevertheless, this study accomplishes one of its main objectives of providing a
theoretical framework through which to make the industry highly competitive. It is proposed,
however, that there is a need of another in-depth study to diagnose the relationship between Total
Factor Productivity and any factors missing from the current analysis.
11
DECLARATION
I declare that no portion of the work referred to in this report has been submitted in
support of an application for another degree or qualification at this or any other university or
institution of learning.
COPYRIGHT STATEMENT
i. The author of this thesis (including any appendices and/or schedules to this thesis)
owns any copyright in it (the ―Copyright‖) and s/he has given The University of
Manchester the right to use such Copyright for any administrative, promotional,
educational and/or teaching purposes.
ii. Copies of this thesis, either in full or in extracts, may be made only in accordance
with the regulations of the John Rylands University Library of Manchester.
Details of these regulations may be obtained from the Librarian. This page must
form part of any such copies made.
iii. The ownership of any patents, designs, trademarks and any and all other
intellectual property rights except for the Copyright (the ―Intellectual Property
Rights‖) and any reproductions of copyright works, for example graphs and tables
(―Reproductions‖), which may be described in this thesis, may not be owned by
the author and may be owned by third parties. Such Intellectual Property Rights
and Reproductions cannot and must not be made available for use without the
prior written permission of the owner(s) of the relevant Intellectual Property
Rights and/or Reproductions.
iv. Further information on the conditions under which disclosure, publication and
exploitation of this thesis, the Copyright and any Intellectual Property Rights
12
and/or Reproductions described in it may take place is available from the Head of
School of (insert name of school) (or the Vice-President) and the Dean of the
Faculty of Life Sciences, for Faculty of Life Sciences‘ candidates.
13
ACKNOWLEDGEMENTS
Particular and everlasting regards are owed to Professors Mr. Mike Bailey
Dr. Rukhsana Kaleem, Mr. Sajjad Tahir, Mr. Rehmatulla, Mr. M. Rashid and
Mr. Shahzad Ahmed, Mr. Ahmed Sidiqui, Mr. Ejaz Ahmed, Mr. Farooq Gillani, who instructed,
zealously encouraged, and helped in writing this investigative report. Many thanks go to the
Institute of Research Promotion, which offered its literature and helped to collect the required
data. In addition, many thanks to my mother, wife, and kids, who are a symbol of life and hope
and who stood side by side to furnish their consistent support throughout this period.
ABOUT THE AUTHOR
The author of this report earned a degree in textile engineering in 1981 from the
University of Engineering and Technology (UET) in Lahore, Pakistan. He received his Masters
Degree in Business Administration in 2001 from Hamdard University in Karachi, Pakistan. He
has worked in the textile industry for 27 years, spending most of his time on the manufacturing
and marketing of textile products, particularly apparels and textile auxiliaries. Lately, he has
been performing his duties as an assistant professor at the University of Management and
Technology (formally Institute of Leadership and Management) in Lahore. Additionally, he is
director of the Textile Productivity Centre, which plays an outstanding role in adding
improvements to the productivity of Pakistan‘s textile industry. He has 16 years of experience
teaching graduate students.
Chapter One: Introduction 14
CHAPTER ONE: INTRODUCTION
Globalization, a new phenomenon in today's world, is one of the main factors that
have emphasized the importance of productivity in modern business and economics.
Scarcity of resources, global competition, environmental awareness, high levels of
pollution, transitions in production processes, and time factors in business games have led
the emphasis on productivity in several disciplines. As a result, the debate over
productivity has taken place not only in production houses of manufacturing systems but
in social enterprises, behaviour science, and daily life. In this context, academic scholars
have introduced abundant concepts, new ideas, novel approaches, and new applications
based on socioeconomic factors (Kamimura, Bodeutsch, and Gayton, 1999; Sink, 1985;
Sumanth, 1998).
1.1 Productivity: An Everlasting Concept
Productivity received attention even in olden days, when most human beings used
fire for their work. Stones were an essential element for a cave man on the hunt.
Similarly, if one looks at developments in car manufacturing, he or she will come across
the same idea. Presently, every car-manufacturing firm is adopting modern technology to
engineer more miles from the fewest units of fuel. It seems that productivity is a universal
concept and a ubiquitous term, as concluded by Sink (1985). This is proved by the
earliest known records, which recorded year-to-year crop levels. What's more, regions,
nations, and states with higher productivity were more powerful and capable of
leadership. Such states, regions, villages, and cities were rich economically (Brinkerhoff
and Dressler, 1990; Sink, 1985). Competition always finds its place among rivals to
Chapter One: Introduction 15
increase productivity. History bears out several interesting stories of wars and the
movement of tribes from one region to other, all in the name of better productivity. The
run through has become more vital in the modern era where different firms, companies,
brands go all-out to enclose higher productivity growth. These companies transfer their
manufacturing units from one country to other. A good example of this is the migration
of Western companies to developing countries, where they ensure better and less costly
production facilities. The particular purpose of all this is to increase productivity and
compete in global markets. Chen, Liaw and Yeong (2001) write about the economic
growth in China and in Far East Asian countries, concluding that the remarkable growth
is a result of many factors, the most significant of which is productivity growth. Chen et
al further indicated that foreign investment has a major role in the development of China
and that the attraction of foreign investment in China is mainly because of better returns
on investment, which is the result of high labour and capital productivity in China.
Economic literature includes productivity definitions and concepts. People have
different viewpoints on it. In the beginning, the literature was inclined towards labour
productivity, followed by capital productivity. Now the focus is on total factor
productivity. The attention to productivity caused many changes both in business
practices and in environment. Now green productivity emerging from green movements
and the efforts of the Asian Productivity Organization and United Nations is an example
of its significance. A very common definition of productivity, however, is the ratio of
output to input. This may be partial, multifactor, or total factor productivity (OECD,
2005; Sink, 1985; Sumanth, 1998). Like many terms in business management literature,
at first it seems difficult to reach a consensus on the definition of productivity.
Chapter One: Introduction 16
Nevertheless, it seems there is disparity in words but there is a consistent theme when it
comes to definitions of productivity.
Productivity and production function are well integrated. Production function
explains how the inputs are converted into output. Economists have taken keen interest in
production function since the industrial era. Many authors have put forward theories of
production function and total factor productivity (TFP). As described by Humphrey
(1997), at least 18 economists hailing from seven countries either presented or reported
production function over a span of 160 years. All this was done before the famous
production function that was presented by Cobb-Douglas in 1927. Even after Cobb-
Douglas, several economists have presented production function models. These models
are used to measure productivity and TFP. On the other hand, economic literature
presents numerous models developed by business and industrial managers to measure
partial, multifactor, and TFP (see Section 2.1 for more details).
1.2 Productivity Implications and Current Era
Ali (1978) wrote that productivity implications continue to change with the
passage of time, but in the modern era, productivity‘s importance is somewhat high in
comparison with the past. Ali writes, "The recognition of productivity came later, as late
as the 40s when Rostas published his famous study about productivity in British and
American industries" (p. 9). Mahoney (1998) linked the existing industrial period and
productivity, writing, ―Every age has its slogans and energizing concepts. Among other
concepts, concern for productivity has characterised much of the current decade‖ ( p.
13).
Chapter One: Introduction 17
The values of resources used for any output classify the behaviour of individuals
towards its utilization. Productivity becomes a crucial issue when there is a lack of
meaningful resources. The most relevant example in this regard is the invention of new
automotive technology, which has reduced oil consumption. Continued development of
this technology helps to lower unaffordable prices of oil. It supports the statement given
by Mahoney, who said that the current era focuses on better utilizations of resources than
in the past. ―In Japan, productivity (seisansei) marched into public awareness in 1955,
since then, physical productivity of direct labour made out in manufacturing and its
auxiliary industries‖ (Taira, 1998, p. 40). After World War II, Japan embarked on its
activities in the industrial field and set up productivity centres. It is common knowledge
that Japan could not have made such an impressive performance without the concept of
high productivity.
Murugesh, Devadasan, and Natarajan (1997) commented on the changes in
business practices and essential requirements of the industrial era. According to
Murugesh et al., after the 1980s, a wide-ranging upsurge occurred in the production of
different concepts and philosophies related to productivity. Indeed, the industrial world
witnessed the emergence of technologies and managerial philosophies because of
increased competition. For the most part, this trend has gone unmatched over the last 20
years.
According to Mittal (2002), use of the most up-to-date technologies in business
radically improves productivity, and this is a vital factor in the surge in productivity
awareness. Savery (1998) pointed out the significance of productivity in the current
business environment. Savery explains that the battle cry of the 1980s and 1990s was a
Chapter One: Introduction 18
for multiplying the productivity of business organizations. This movement certainly
endured in the early 21st century. It becomes a key point when resources are limited,
especially with a deeper concern about environmental impact, high competition in the
market, application of free trade agreements, continuing technological improvements, the
use of information technology, and other factors such as cost of labour associated with
business practices.
1.3 Productivity Measurements and its Significance
The simplest definition of productivity measurement is the assessment, findings,
analysis, and gauging of the productivity of any firm, organisation, and industry, or the
whole economy of a country. Broken down, the primary meaning of productivity is a
ratio of output and input, but there are several definitions of productivity. According to
the Oxford Dictionary, ―measurement means to find the size, quantity, or degree of
something.‖ The word measurement is used here in the same sense. ―Productivity
measurement is defined as a set of management tools that are associated with the
assessment of organisation's productivity‖ (Hoque, 2000, p. 1278).
A sound understanding of the present-day productivity level is critical for
productivity enhancement. As discussed by Sumanth (1990), planning for improvement
of productivity is only possible when there is a solid knowledge available on the existing
productivity level. The impact of productivity measurement depends upon the business
environment. It becomes more decisive when there is always a want for resources, tough
and strict competition, rapid changes in business environment, and thin profit margins.
Chapter One: Introduction 19
According to Lord Kelvin (as cited in Ali, 1978), if one cannot measure and put
across a notion specifically, measurement or process in numbers, knowledge of the object
or process is of a very meagre and unsatisfactory nature. In this statement, measurement
means expressing any judgement numerically. If one cannot get across productivity
findings numerically, the knowledge is not sufficient. Drucker expressed his personal
opinions about productivity measurement in the following words, ―Without productivity
objectives, a business does not have direction. Without productivity measurement, a
business does not have control‖ (Parsons, 2000, p. 13).
Morris and Sink wrote that, ―Measurement fosters organizational learning when
management team becomes skilled at converting data to information and information to
knowledge‖ (Parsons, 2000, p. 13). It all favours the observation that productivity
measurement is one of the industrial management tools, particularly for monitoring,
controlling, and evaluating the performance of systems and employees. Accordingly, it is
apparent that measurement is a process that converts ideas, observations, and assessments
into some understandable numbers, so that one can make an informed judgement about
the observations. Russell said (as cited in Ali, 1978), that measurement is, in the most
general sense, any method by which a unique and reciprocal correspondence is
established between all or some of the magnitudes of kind and all or some of the numbers
— integral, rational, or real, as the case may be. If you cannot measure it, you cannot
improve it. ―The ultimate goal of productivity improvement as a driving force of
economic development is to improve the quality of life of the people‖ (Prokopenko,
1999, p. 5).
Chapter One: Introduction 20
Bernolak (1980) stated that the objective of productivity measurement is to find
how to produce output of desired goods and services within the minimum quantity of
human and physical resources. The measurement of output is the first major element in
productivity analysis. Therefore, it is evident that productivity measurement is a basic
necessity for the applicable analysis of productivity. No one can make useful comments
on the utilization of resources without analysing productivity (see Section 2.7 for more
details).
In the above paragraphs, there is a general discussion about the significance of
productivity and its implications in current era. In the following lines, there is a brief
discussion about productivity and its link with Pakistan‘s Textile Industry. This
discussion will help understanding the justification of this research.
1.4 Pakistan Textile Industry and Productivity
Pakistan came into being in 1947 and at that crucial time, the Pakistani economy
largely depended upon agriculture. As mentioned by Husain (2002), many large
industries were located in the geographic areas that became India after Partition.
However, considerable structural changes in the economy took place over the later part of
the 20th century. Husain wrote:
In 1950, agriculture production was 60% of the total GDP and 82% people were
living in rural areas and share of manufacturers merchandise in exports was zero,
while in 1996, agriculture had 26 % share in total GDP and manufactured goods
had 84% share in total exports from Pakistan. Pakistan was agrarian and large
dependence was on cotton, rice, jute, and wheat production. (2002, p. 9)
Chapter One: Introduction 21
One can glimpse the active re-configuration of Pakistani economy during that period.
In fact, the textile industry is one of the oldest industries on the subcontinent, but
unfortunately, a huge range of textile mills were in the Indian part of the subcontinent as
reported by All Pakistan Textile Mills Association (APTMA, 2006). APTMA further
illustrated the situation of the textile industry in Pakistan in 1947, writing that at the time
of Partition (in 1947), there were only 78,000 spindles and 3,000 power looms in the area
that became part of Pakistan, while in 2005-2006, there were 10,437,000 spindles,
155,000 rotors and 4,000 shuttle less looms installed in Pakistan. Besides, in 1947, cotton
production was only 6.876 Million Kg, and there was no manmade fibre production in the
area.
During [the] 1950s there was a strong need to develop local industrial capacity for
development not relying much on agriculture. In order to develop local industries,
[the] government of Pakistan provided generous fiscal incentives, heavy
protections, preferential access to foreign exchange, allocation of imports of
capital goods, and credit at low interest rates. (Husain, 2002, p. 12)
During the last 60 years, the Pakistani government made specific and focused policies to
develop industries in order to increase the share of manufactured goods in export —
particularly the share of textile goods instead of non-manufactured goods exports. This
industrial development was all designed to meet the growing requirements of foreign
exchange needed to import goods. As a result, Pakistan‘s economy had more
manufacturing share in GDP as compare to agriculture, with an average annual GNP
growth of 9.6% manufacturing growth, and 2.8% agricultural growth during 1947 to
1958 (Husain, 2002). As per the APTMA report , in 1959-60, there were 1,582,000
Chapter One: Introduction 22
spindles working in Pakistan, whereas there were only 78,000 spindles in 1947. It shows
an almost twenty-fold increase in spinning capacity in a span of 13 years. One of the
basic reasons for this tremendous growth was the availability of cotton at affordable
prices, which was the main raw material for spinning industry during the 1960s.
APTMA further states that in 1947, cotton consumed by Pakistan‘s spinning
industry was only 6.876 Million Kg, while it was 201.18 Million Kg in 1959-60, a
virtually 30-fold increase in just 13 years. In addition to that, cotton consumed by
Pakistan spinning industry in 2002-03 was 1.943 Billion Kg, which was 281 fold more
than the domestic consumption of cotton in 1947. This clearly gives a picture of the
government's major focus to develop the textile industry rather than relying on
agriculture. There is also evidence that comes from annual growth rate of agriculture and
manufacturing sectors. According to Husain (2002), these growth rates were 2.8% and
9.6%, respectively, from 1947 to 1958.
APTMA explains that in 1971-72, cotton manufacturing was 38.8% of total
exports, while raw cotton was 33.9% of total exports. Nevertheless, in 2004-05, cotton-
manufacturing share had jumped to 60.1%, and raw cotton share had come to 0.76% of
the total exports. This trend clearly shows that policies framed by government of Pakistan
worked well, and ultimately succeeded in developing local industry. The dependence on
agriculture exports decreased, and the share of manufacturing goods increased from nil in
1947 to 84% in 1996 (Husain, 2002). It can be assumed that this all happened in
consequence of the favourable government policies.
In the 1980s, textile-importing countries, primarily the U.S., Canada, and
European countries imposed quota restriction on the imports of textile goods, particularly
Chapter One: Introduction 23
on apparels. This became one of the main obstacles for exporting countries, mostly for
Pakistan, which heavily relied on textile exports. The institution of the General
Agreement on Trade and Tariff (GATT) in 1994 embarked on a process of eliminating
non-tariff barriers. The Agreement on Textiles and Clothing (ATC) established the
necessary process for elimination of quotas on textile imports (SMEDA, 2000).
As discussed by Small Medium Enterprises Development Authority (SMEDA,
2000), during the quota regime, every country officially allowed exporting a certain
quantity of textile products to U.S.A, Canada, and European countries. The GATT
abolished quota-based import restrictions by U.S.A, EU, and Canada on Jan 01, 2005.
This agreement granted permission and liberty to exporters from any country to export
any quantity of textile goods to importing countries, implying a stern competition in
approaching years.
SMEDA states that during 1980s and 1990s, the government of Pakistan
supported its textile industry by providing rebates on exports to make industry
competitive in international markets. Currently, the government has taken back most of
its financial assistance to the textile and clothing industry. It is presumed that having no
financial boost by the government of Pakistan, only better productivity can gradually
assist exporters to become competitive in the international and local market. The
expected competition requires improved productivity in the textile sector, which is the
core industry of Pakistan. In the textile industry, the clothing sector is more crucial due to
its value addition and high employment potential. Pakistan‘s Knitted Garment Industry
(PKGI) is one of the major clothing sectors. The economy largely depends upon the
Chapter One: Introduction 24
performance of textile industry since it has a two-thirds share in total exports and one-
third share in total employment (see Table 1.1 for more details).
Table 1.1
Contribution of Pakistani textile industry to the economy (2004-2005)
Exports 62.1% of total exports (U.S. $10.211
Billion)
Manufacturing 46% of total manufacturing
Employment 38% of total labour force
GDP 8.5% of total GDP
Investment U.S. $ 0.771 Billion
Market Capitalization (Listed
Companies) 5.11% of total market capitalization
Source: APTMA (2006)
Under the World Trade Organization (WTO), when every country is needed to
phase out tariffs completely, the Pakistan textile industry (PTI) faces brutal competition.
This is most likely due to poor performance of this sector. A survey conducted by the
Japan International Cooperation Agency (JICA), (as cited in SMEDA, 2000) supports
this observation. According to this detailed study, PTI is impeded by outdated production
facilities, low productivity, and high production costs because of the small scale of
operations. JICA made recommendations for all textile sub sectors (ginning, spinning,
weaving, wet processing, and clothing). Furthermore, JICA identified many problems
associated with Pakistan Textile Industry (PTI). As per the JICA report, there is a
significant flaw in government policies as well as in business practices in the textile
industry. Poor presentation is further supported by Sheikh, who said, ―There were many
Chapter One: Introduction 25
drawbacks on the part of the industry, such as internal weaknesses, structural imbalances,
technology gaps and neutralization of the incentives given by the government of
Pakistan‖ (Sheikh, 2001, p. 41). In addition, PTI is facing a severe shortfall of skilled
workers as well as trained and educated managers. JICA also pointed out ample lack of
research and development activities, in mills as well as at the government level. Despite
all the problems cited above, growth of PTI is high, but not satisfactory when put in
comparison to the growth rate of other regional countries (see Table 1.2).
It is clear from the data provided in Table 1.2 that several regional countries
performed better than Pakistan in 2005. In 1980, Bangladesh was not included in the list
of top 70 states ranked based on textile and clothing export values. Nevertheless, in 2005,
this country rose to the 10th position. This suggests that in an international scenario, the
performance of the Pakistani clothing industry had a declining trend when compared with
other major exporters in the region. On the other hand, if one views the performance of
the PTI in Pakistan, it looks quite outstanding. The gap between performance both at the
local and international level shows that there is a considerable room for improvement.
Table 1.3 shows growth of the Pakistan textile industry from 1971 to 2005.
Chapter One: Introduction 26
Table 1.2
Share Percentage in World Clothing Exports and Ranking in Top Clothing Exporting
Countries
Countries
1980 2005
Export
Value (U.S.
$ Millions)
Share in
World Exports
(%)
Ranking
Among
Clothing
Exporting
Countries
Export
Value (U.S.
$ Millions)
Share in
World Exports
(%)
Ranking
Among
Clothing
Exporting
Countries
Bangladesh 2 0.05 72 6,418 2.23 10
India 673 1.76 13 8,290 2.88 7
Pakistan 103 0.269 43 3,604 1.25 19
Sri Lanka 109 0.285 42 2,877 1.23 22
Source: WTO.org
Chapter One: Introduction 27
Table 1.3
Growth Rate of Textile Related Commodities in Total Export from Pakistan (Million
U.S.$)
Total
Exports
From
Pakistan in
1971-72
Share in
Total
Exports
in1971-72
(%)
Total
Exports
From
Pakistan
in 2004-
2005
Share in
Total
Exports
in 2004-
2005
(%)
Average
Growth
Rate In 33
Years (%)
Total Exports 590.70 14,391.00 10.16
Total Textile exports 429.50 72.80 8,834.00 61.39 9.60
Raw Cotton 200.50 33.98 110.00 0.76 -1.8
Yarn 127.50 21.61 1,450.00 10.08 7.65
Cotton and Blend Fabric 81.50 13.81 1,863.00 12.95 9.95
Tent & Canvas 1.90 0.32 67.00 0.47 11.40
Towels 6.10 1.03 0.00 0.00 0.00
Bed Wear 0.90 0.15 1,057.00 7.34 15.54
Other Made- Ups (including
towels)
1.20 0.20 986.00 6.85 22.55
Woven Garments 3.20 0.54 1,088.00 7.56 19.32
Knitted Garments 3.20 0.54 1,635.00 11.36 20.80
Other Textiles 4.20 0.39 578.00 4.02 16.09
Source: APTMA (2006)
The data provided in Table 1.3 supports several conclusions regarding exports of
a mixture of textile products:
1. The textile industry had a 72.8% share in total exports in 1971-72, which declined
to 61.39% in 2004-05. It shows the textile industry has failed to hold its
significant share in total exports of the country.
2. Raw cotton was a major export during 1971-72. It had a share of 33.98% of total
exports while its contribution came down to only 0.76% in 2004-05. This shows
Chapter One: Introduction 28
that the government policies promoted the establishment of a local industry that
converted this raw material into manufactured goods.
3. Share of cotton, yarn, and grey cotton cloth is trending downward in total exports.
Share of these items in total exports was higher in 1971-72 as in comparison with
2004-05. The raw material of the textile industry that was of less value added in
manufacturing of these items.
4. Yarn had 21.61% share in total exports in 1971-72, but these shares decreased to
10.08% in 2004-05, despite addition in spinning mills capacity. This indicates that
local industry converted yarn into fabric instead of exporting as bulk yarn.
5. There is no big change in the share of fabric in total exports. In 1971-72, the share
was 13.81%, while in 2004-05, it was 12.95%. During this period yarn export
decreased, which means that yarn consumption in Pakistan increased and more
value-added goods (clothing) exported.
6. Share of made-ups, bed wears and clothing (woven and knitted both) was 2.46%
in 1971-72, and it increased to 33.12% in 2004-05. This shows that total export of
value-added goods increased over a period of 33 years.
7. In 1971-72, textile raw material (raw cotton) export was 33.98% and share of
textile-manufactured goods was 38.82% of total exports, while in 2004-05, the
share of textile raw material reduced to 0.76%, which is quite trifling.
8. Growth rate of made-ups and clothing is much higher than yarn and fabric. Table
1.3 depicts that in 33 years Pakistan succeeded in converting its textile raw
material into manufactured goods. In addition, growth rate of value-added product
Chapter One: Introduction 29
is higher than less value-added products; particularly the share of apparel is higher
than yarn and fabric.
As per JICA (2006), in a scenario where there are no quota restrictions after 2004,
a duty-drawback facility will not be available to garment industry. In addition, the
government of Pakistan might not be able to protect its industry by putting tariff barriers
on textile and clothing imports. The only competitive strategy that could assist the textile
and clothing industry to survive is improved productivity. Better productivity is a great
challenge in the current business era and only a collaborative effort from government
side, academia, and industry can meet the challenge.
In light of the above discussion, it is apparent that the economy of Pakistan has
strong ties with the performance of textile sector. The textile sector mainly consists of
ginning, spinning, weaving, knitting, wet processing, made-ups, and apparel
manufacturing. One can observe a high growth rate during the last three decades, in the
sector of apparel manufacturing and made-ups (see Table 1.3). These sectors are
relatively higher benefit commodities and generate a high degree of foreign exchange.
Besides, the most important factor associated with these sectors is high employment
potential. At this time, massive unemployment is one of the main problems of Pakistan
because of high population growth and less industrial activities. The present
government's policies focus on improving employment opportunities; for this particular
purpose, the government is developing textile and garment cities throughout the country.
The government will generously provide all sorts of facilities on a priority basis to firms
interested participating in the garment business. It is anticipated by the apparel
manufacturers that these cities will attract more foreign investment to appear in Pakistan.
Chapter One: Introduction 30
Hopefully, many big international companies will establish their garment production
units in these cities so that garment industry positively affects the economy of Pakistan.
In previous pages, discussion is mainly about the role of textile in the economy of
Pakistan. It shows that textile industry is playing a significant role in the economy of
Pakistan. This study is to assess TFP of Pakistan‘s Knitted Garment Industry (PKGI) and
the impact of different factors on its growth. Based on the objective, it is imperative to
discuss in detail the structure of PKGI so that one could have an idea about the working
in the sector under discussion.
1.5 Structure of Pakistan’s Knitted Garment Industry
In 2004-05, PKGI had a 11.36% share in total exports of Pakistan, while in 1971-
72 it was 0.54%. Furthermore, PKGI has an 18.51% share in total textile exports. In
addition to that, it belongs to the group of products that have the highest growth rate (see
Table 1.3). As described by SMEDA (2000), this sector provides enormous employment
opportunities to skilled, semi-skilled individuals and even to unskilled people. Primarily,
this industry is export-oriented. This is due to the clothing customs of Pakistani society.
The main dress of Pakistani people is shalwar and qameez, while this industry produces
polo shorts, T-shirts, trousers, etc., which are not popular in Pakistan. Nevertheless,
young people living in cities are showing interest in knitted shirts and trousers. This
requirement is fulfilled by the left over goods after exporting the better quality products.
However, a few firms are producing for the local market. Their main products are vests
and undergarments. Such firms belong to cottage and unorganised sectors. The emphasis
of this research is only on the export-oriented sector. It is appropriate to have basic
Chapter One: Introduction 31
information about this sector, which is under discussed in the current study. The
following information has been derived from the unpublished reports provided by the
Pakistan Hosiery Manufacturing Association (PHMA), an official representative of PKGI
for this study only:
1. Over 900 export companies sent knitted goods abroad in the year 2003. The range
of export figures (value in U.S.$) is very extensive, as low as a few hundred U.S.$
to many millions U.S.$.
2. More than 90% of knitted garment export is manufactured by only 24% of the
total exporters (218 firms).
3. Three major cities of Pakistan: Lahore, Karachi, and Faisalabad account for more
than 98% share in total export of knitted garments. Their share percentage is 45%,
35% and 18%, respectively.
4. More than 255,000 individuals are working in this sector and there is a 10%
annual growth in job opportunities.
5. Several other industries are working for this sector, too such as stitching thread
manufacturers, packing material suppliers, etc.
6. More than 80% of knitted garments are exported to U.S., Canada, and other
European countries.
7. Many worldwide firms are establishing their plants in Pakistan due to the
accessibility of cheap labour, abundant raw material and soft environmental and
labour laws.
Chapter One: Introduction 32
1.6 Research Problem
As mentioned earlier, PKGI plays a key role in Pakistan's economy but the author
of this report could not find any such study that weighed its TFP and determinants. It is
an assumption that a minor change in this sector can significantly affect the exports of
Pakistan, since it has nearly 12% share of exports. To improve productivity, it is requisite
to dig up a profound knowledge of the existing productivity level and appropriately
identify significant factors that underpin productivity. Based on this observation, this
study plans to calculate the TFP level of the industry and identify major determinants that
correlate with TFP.
A many ways rally round improving the function of the industry. Government
subsidies or the relaxation of import duties from the importing countries has profound
effects on improving export capacity. According to the JICA (2006) report, government
support for raw material cannot solve the problem entirely. Rather, industry must
improve its own productivity. Furthermore, as per SMEDA (2000), until 2000,
government provided strong duty drawbacks (rebate on exports). In addition to that, there
were many tax exemptions available to the industry. All these efforts supported the
competitiveness of the textile industry. Ministry of Finance has announced a rebate of 6%
in form of research and development funds, but even this support will take some time to
decide the structural problems of the industry.
A pilot survey revealed that people dislike competition in the global market
without the help of government. However, the government is not ready to give additional
concessions to the industry. On the other hand, productivity of the industry is quite low,
as discussed by Majid (2000). Majid made a list of 110 states based on their
Chapter One: Introduction 33
competitiveness; Pakistan in 91st place. This shows how poor capacity has affected the
industry‘s ability to compete in the international market. Majid has not discussed PKGI
separately, but it can be presumed that the PKGI position is not significantly different
from other sectors. Majid also suggested improving the competitiveness and productivity
of the industry to acquire a better market share in the international market. These studies
indicate that better productivity in the textile industry will likely improve the economic
and social wellbeing of the Pakistan (See section 2.1 for more details)
1.7 Research Justification
This research stands upon the following justifications:
1. The productivity of PKGI has not assessed rigorously; it is significant to resolve
critical determinants.
2. PKGI is coping with tough competition in global and local markets. It also has
trouble with the elimination of quota restrictions, which occurred in 2005. As a
result, the competition has become more ruthless; in such circumstances, only
higher productivity can improve PKGI competitiveness.
3. This sector plays a noteworthy role in value addition of the textile products. At
this time, the PKGI has 11.36% share in the total export of Pakistan. This share
could be increased significantly by improving the productivity.
4. Pakistan is facing a massive problem of unemployment, and PKGI sector may
adequately provide abundant jobs to skilled and unskilled workers.
5. According to various reports published by the Export Promotion Bureau of
Pakistan, the world market of knitted garments is expanding day by day.
Chapter One: Introduction 34
Fortunately, there is a huge gap in supply and demand, which PKGI can partially
fill by improving productivity.
1.8 Study Objectives
The objectives of this research are:
1. To examine the TFP level achieved by PKGI (at aggregate level), vertical firms
and horizontal firms of PKGI
2. To compare TFP level of PKGI with other manufacturing sectors of Pakistan
3. To test the following hypotheses:
Ho Ha
µTFP of vertical firms = µ TFP of horizontal
firms
µTFP of vertical firms ≠ µ TFP of
horizontal firms
µ TFP of horizontal firms is less than or equal
to 1
µTFP of horizontal firms is greater than
1
µTFP of vertical firms is less than or equal to
1
µTFP of vertical firms is greater than 1
µTFP of PKGI at aggregate level is less than
or equal to 1
µ TFP of PKGI at aggregate level is
greater than 1
4. To identify the correlation between different determinants and TFP.
5. To develop a regression equation to answer the following questions:
(a)Which set of independent variables is able to predict TFP (a dependent
variable)?
(b) Which variable in a set of variables has highest contribution in the variance of
dependent variable?
Chapter One: Introduction 35
1.9 Scope of the Research
This report is an attempt to estimate the TFP level of PKGI and mark out such
determinants as affects its performance. For this purpose, primary and secondary data
have been collected from different sources. The source of primary data is a census of the
manufacturing industry in Pakistan, whereas secondary data was found through a survey
of the industry through a structured questionnaire.
1.10 Type of Research
This basic research is aimed at generating fundamental knowledge and a
theoretical understanding about basic internal and external processes linked with the
PKGI. This is not an applied research project focused on answering practical questions,
which would provide relatively immediate solutions. Basic and applied research can be
viewed as two endpoints on a research continuum, with the centre representing the
research applicable to both ends. Furthermore, it is also to be noted that for this research,
the deductive method has been used. As expressed by Johnson and Christensen (2006),
the deductive method involves the following three steps:
1. A statement of a testable hypothesis (based on theory or research literature).
2. Collection of data to test the hypothesis.
3. Determination of whether the data supports or rejects the hypothesis.
1.11 Research Methodology
As described by Johnson and Christensen (2006), there are currently three major
research paradigms in education and the social and behavioural sciences. They are
quantitative research, qualitative research, and mixed research. This study relies largely
Chapter One: Introduction 36
on the collection and analysis of quantitative data (see Section 3.1 for more details). In
order to assess the TFP of any firm or any industry, it is necessary to identify and collect
data on critical determinants. Data can be classified broadly in to two classes; time-series
and cross-sectional data. The PKGI is only 15 to 20 years old, which is quite short for
time-series data, and furthermore no authenticated data is available over this entire
period. Hence, cross-sectional data has been utilised. According to the unpublished data
provided by the Pakistan Hosiery Manufacturing Association, the official representative
association of PKGI, 900 firms exported knitted goods in 2003, and 218 out of 900 firms
represented 90% of knitted garment exports; 682 firms exported the remaining 10%. Due
to the insignificant share of the 682 firms, only the 218 firms with the majority share
were selected as the test population.
The knitted garment manufacturing process consists of three major processes: (a)
knitting, (b) wet processing, and (c) stitching. There are two models of garment
manufacturing firms, (a) vertical and (b) horizontal (non-vertical), existing in the
Pakistani market. Vertical firms have knitting, wet processing, and stitching facilities
under one roof, and horizontal firms have only stitching facilities. There are many
differences between vertical and horizontal firms in their production capacity, business
practices, capital invested, etc. All these firms are located in three major cities of Pakistan
— Karachi, Lahore and Faisalabad. Primary and secondary data were used for analysis.
The Total Productivity Model proposed by Sumanth, Sink, the American
Productivity Center, Craig, and Harris has been selected to assess TFP of the PKGI after
a thorough discussion. This model takes all inputs and outputs into account and gives a
true picture of the firms and industries where it is applied. The selection criterion is
Chapter One: Introduction 37
discussed in detail in chapter two (see Section 3.5 for more details). Analysis has been
carried out with the help of Statistical Product and Service Solutions (SPSS) software.
1.12 Contribution of Study
For the first time in Pakistan, the TFP of the PKGI has been estimated
quantitatively, and the related contribution of some determinants has been measured, too,
with the help of quantitative research methods. The decisive conclusion from this
research is that a well-timed analysis of PKGI has been made. This study may convince
the industry as well as the Pakistani government to take serious steps to increasing the
TFP of PKGI. Furthermore, results of this study can be useful for benchmarking of
related sectors and accurate assessment of TFP of PKGI in future. Finally, this report
provides a foundation for a more methodical analysis, integrating a broader range of
determinants.
1.13 Outline of the Thesis
This thesis has been separated into five chapters. Chapter two reviews appropriate
economic literature covering production function, productivity meanings and generic
concepts, productivity measuring models, disciplines/fields and classification of
productivity measuring approaches, and distinct production measurement studies.
Chapter three examines the research and data collection methods. Chapter four contains a
critical analysis of data information on the testing of the hypotheses. Chapter five
discusses the major findings and develops some policy guidelines for the PKGI.
Chapter Two: An Overview of Total Factor Productivity 38
CHAPTER TWO: AN OVERVIEW OF TOTAL FACTOR PRODUCTIVITY
In chapter one, a concise introduction of productivity, an overview of the textile
industry of Pakistan, and the objectives of this report were discussed. As a conclusion,
one can get a condensed idea about productivity and its importance in the economy of
Pakistan, particularly in Pakistan‘s Knitted Garment Industry (PKGI). This chapter is
dedicated to a synopsis of TFP, its emergence, development, dimensions, and criticism on
its genuineness, along with theories and ideas to measure productivity.
In the first half of the chapter, a debate embraces to elaborate the TFP concept,
the theoretical significance of TFP estimation issues that correlate with TFP
measurement, and an argument on the diverse approaches and methods to measure TFP.
It begins from the historical aspect of production functions and TFP, followed by an
argument on how this TFP caught the attention of macroeconomists, ending with the
journey of TFP from the macro to micro levels. This chapter discussed how TFP is
applicable at firm level as picked up by business managers and industrial engineers. In
the second part of the chapter, selection of the most apposite and appropriate TFP
measurement approaches applicable to TFP of PKGI is made.
2.1 Historical Perspective and Emergence of Productivity Movement
Productivity refers to the sensible and proper utilisation of inputs to create an
output in a production function, and it is the reason why it has remained on humans‘
minds throughout history. Sink (1985) also discussed it in the same way. Probably, better
productivity has been the first step in the development of economies, and it positively
contributed in the undeniable success of the world's leading states. The roots of
Chapter Two: An Overview of Total Factor Productivity 39
productivity are found in ancient records, where people were recording the crops‘ yields
on an annual basis. This was a way to measure and compare productivity of different
fields on seasonal basis (Brinkerhoff and Dressler, 1990; Monga, 2000; Sink, 1985;
Sumanth, 1998).
Struggle for better resources has been the basic quest of human beings, and they
have used several ways to fulfil this need, which led to the different revolutionary
movements. In the last three centuries, many ideologies were formed to improve the
living standard of people, including, for example, capital theory by Adam Smith and
Socialist theory by Karl Marx. In both cases, productivity remained the fundamental
stone to build whole theory. Nevertheless, there are drastic dissimilarities between the
two theories. Even today's open market economy also focuses on better economic
conditions through higher productivity. Contemporary scholars have added resource
productivity as one of the burning issues in the business world. This is obvious from the
Performance and Innovation Unit (PIU) UK, which acts directly under the supervision of
the Premiership of UK. This unit has been established for the same cause — namely
promoting the concept of resource productivity (PIU, 2001). Resource productivity, like
other concepts of productivity, means creating more output while utilising fewer
resources. Shortage of natural resources forces the businesses and academic and political
leaders to take serious steps in order to have a minimum input for a higher output. A
relevant example is the use of recycled material. In the current era recycling has become
a winning tool in the marketing of the consumer products. Based on this observation, it
seems that a strong movement is emerging to improve the current productivity level of
natural resources. The resource productivity concept may be best illustrated in case of oil,
Chapter Two: An Overview of Total Factor Productivity 40
where people strive to bring innovative methods of increasing oil productivity and are
searching for alternatives energy sources.
Nevertheless, productivity concepts have their origin centuries in the past and
have been promoted and redefined over the different periods in history. However, it looks
that after industrial revolution; the primary productivity concept developed in advanced
countries and less developed countries was gradually adopted to follow the proven and
tested practice available in the developed world.
Literature indicates that productivity is an ever-present concept but formally
discussed by Quesnay in 1776, followed by Litter in 1883, who defined productivity as
the ―faculty to produce,‖ which is the ultimately desire to produce. In the mid-20th
century, the Organization of European Cooperation and Development (OECD) defined
productivity as the quotient obtained by dividing input by one of the factors of production
identified by Sumanth (1998). In this way, it is possible to speak of productivity of
capital, investment, or raw materials, according to whether output is being considered in
relation to capital, investment, raw materials, etc.
Sumanth has elaborated the role of OECD in productivity growth in the developed
world. Sumanth says that the OECD definition of productivity carries the strong impact
of industrial revolution, as it intends to measure input in relation to output and divides it
into productivity of capital, investment, and raw material. During the 1950s, OECD made
many initiatives to promote concept of productivity in a newly industrialized world.
In the same period, many other Asian and European countries set up productivity
centres and encouraged the promotion of productivity efforts as discussed by Prokopenko
(1999). The United States is one of the countries that took many initiatives to promote
Chapter Two: An Overview of Total Factor Productivity 41
productivity concepts. The U.S. Department of Labor and Bureau of Labor Statistics have
gradually assisted many Asian and European countries to adopt practices of productivity.
The U.S. and UK played an important role in ongoing growth of productivity concepts,
particularly in its application in the industrial economy (Prokopenko, 1999).
Dewitt has also noted that productivity movement has a strong link with U.S.
industry. Dewitt wrote that the ―productivity concept was first recognized in the late
1800s and early 1900's when widespread efforts were made to improve the efficiency of
American industry‖ (Ali, 1978, p. 9).
This Dewitt argument advocates the relationship of productivity awareness with
industrial development, principally in the case of the U.S., as it intends to dish up
corporate motives of maximum profitability within minimum resources' utilization.
Another observation by Ali (1978) shores up the argument that productivity
awareness grew with sharp development of industrial activities. Ali wrote, ―The
recognition of productivity came later, as late as the 1940s when Rostas published his
famous study about productivity in British and American industries‖ (1978, p. 9). This
study was carried out during World War II. It reveals that both Nazi Germany and the
U.S. were the strongest productive industries at that time, capable of producing anything
more than thrice annually what British industry could produce. The higher productivity of
Nazi Germany provided them a cutting edge in wars over their competitors. Outcome of
this detailed study gave a warning call to the European countries and led to the
establishment of Anglo-American Productivity Council (AAPC).
The AAPC effectively implemented the Marshall Plan given by Marshall in 1948.
It was to raise markedly the productivity of European countries. The Marshall Plan called
Chapter Two: An Overview of Total Factor Productivity 42
for the foundation of productivity councils and centres in the all aid recipient countries
after the World War II 1(EANPC, 2008). Marshall‘s efforts show a high concern by U.S.
authorities towards productivity and their approach to it as a remedy for the adversity of
European countries. This discussion clearly indicates that higher productivity is believed
to be a measure of rehabilitation and progress by advanced countries in the early 1950s.
Campbell and Campbell (1998) have given in detail the journey of productivity
from its narrow concept to a wider acceptable approach. They argue that when the
concept of proliferation of productivity came, many behavioural scientists lavished their
attention on it and were captivated by it. Many authors embarked on research and
published academic papers on unlike dimensions of productivity. Different schools of
thoughts emerged because of academic conversation, emphasising innovation, original
experiments, new techniques, and latest methods to achieve higher productivity.
Campbell and Campbell further state that the concept of productivity ultimately
approaches the matter of urgency because a sense of urgency in human beings pushes
them to higher efficiency. They concluded that productivity has a direct impact on real
income, competitiveness in an international market, improvement in infrastructure and
finally defence capabilities of a country. Literature discloses a strong link of productivity
with daily life, at both macro and micro level and their effective role in improving living
standard to making useful strategies of national defence.
Corporate managers faced a major challenge in higher production so they
improved a distribution system to meet the latent demand of products in the early period
of the 1950s after WWII; this field of productivity converted into a new dimension when
1 European Union National Productivity Centers
Chapter Two: An Overview of Total Factor Productivity 43
managers shifted their focus from more selling to marketing in the early part of the
1960s. This modification in application of productivity concepts was experienced in the
oil crisis of 1973, when people began to talk about energy saving and using less to
produce more (Sumanth, 1998). Sumanth further stated that productivity became the
buzzword in corporate America and elsewhere in the world. All this is evidence for a
shift from production to productivity and a change in favourites among the corporate
sector within a span of few decades. Not only the corporate sector turned its face;
governments, particularly in developed countries, started programs, established institutes,
and organized conferences to make people aware of productivity. One can say that before
the 1950s, the business world‘s focus was on getting more production because there was
a huge demand in the market and less competition. However, after the 1960s, the focus
was less to produce maximum with minimum input to compete in international markets,
where there is severe competition, lower tariffs, and non-tariff barriers in international
transactions. Furthermore, there is a regular reduction in import duties under the umbrella
of the WTO regime.
Prokopenko (1999) has given a complete history of productivity movement
starting from the U.S. after WWII and its emergence in Europe and then in other parts of
the world, including underdeveloped countries. Prokopenko states that the productivity
concept was known for a long time, but it was formally taken as part of activities in the
1950s. The U.S. government took the first step by establishing a War Production Board
during WW II. The particular aim was to improve functioning of U.S. industries. After a
successful experience in the U.S., many European countries took initiative and started
their activities. This was all held under the Marshall Plan. However, the U.S. shared its
Chapter Two: An Overview of Total Factor Productivity 44
experiences and technology with many other countries. Prokopenko further tells that the
U.S. focus was efficiency-oriented productivity, which later synthesized with the
European concept of improvement by combining efforts of employer, employees, and
government. Numerous National Productivity Organisations were established in the
1950s all over Europe with the prime responsibility of ensuring that capitalism was the
only way of survival. In 1948, British Productivity Council was established and its main
function was to assist British industries for better productivity.
Prokopenko further expresses that during 1948 to 1952; nearly 66,000 high rank
UK executives visited the U.S. to learn the methods and techniques to improve
productivity. In fact, this practice generated an initiative in other European countries to
establish productivity organisations in Europe. To promote the coordination among
different NPOs, in 1953, European countries launched a centre in Paris. The objective of
this centre was to carry out goals of technology innovation, human respect, and
achievement of a better quality of life. Because of these efforts, many new organisations
emerged at the National Productivity Centre in Greece, which is largest in its nature in
Europe. In addition to that, local organisations based on regions became serviceable.
Prokopenko concluded that during this period many new slogans were promoted,
including, ‖Productivity is a state of mind,‖ ―Productivity is an attitude that seeks the
continuous improvement of what exists and can do better today than yesterday,‖ and
‖Tomorrow will be better than today.‖
Productivity awareness is the fundamental requisite of improvement. All efforts
started in the U.S. and later on, Europe became a vital part of this movement, showing
that the core objective of NPOs was to create awareness and sort out dissimilar activities.
Chapter Two: An Overview of Total Factor Productivity 45
Nevertheless, facilitation for technology innovation, help in skill development and
training of managers to acquire better tools of management were the main activities of
these organizations. These organisations did not establish different technology centres.
Rather, they assisted different industries to adopt the latest technologies (Prokopenko,
1999). In general, there was a general awareness about productivity, as it is clear from the
statement that productivity is a mindset that one can have better tomorrow than today,
which was adopted by European Productivity Centres.
Prokopenko wrote about the development of productivity centres in Japan, noting
Japan joined this race and established a Productivity Centre in 1955. In 1994, after a
merger of two sister organizations it converted into the Japan Productivity Centre for
Socio-Economic Development (JPC-SED). This centre contributed incredibly in the
development of Japan, particularly in the industrial sector. This centre arranged the first
visit of high executives in September 1955 to the U.S. to study productivity methods and
techniques. The objective behind this tour was to learn from U.S. experiences in the field
of production. In 1995, this centre organized its 50th anniversary with the declaration of
―Productivity Movement towards a Society Based on Mutual Trust and Vitality‖. This is
vital to note that the slogan is different from the original concept that this centre adopted
in 1955. At that time, the main objective of the centre was to facilitate Japan's industry to
have higher productivity and fill the gap between the performance of developed countries
and Japan.
Additionally, Prokopenko provided information about the emergence of Asian
Productivity Center. Prokopenko asserted that after JPC‘s successful experience, Japan
took the initiative to shape an association of different productivity organizations. It
Chapter Two: An Overview of Total Factor Productivity 46
became the base of Asian Productivity Organisation (APO) in 1961, based in Tokyo. To
begin with, few Asian countries were members of this organisation, but now there are 20
members of APO. Efforts in the field of productivity significance contributed in the
economy of Japan. Taira (1998) also supports this observation. Taira conveyed the
massage that after 1950s, there was a strong movement in Japan to increase productivity
however; focus was labour productivity along with resource productivity.
Japan entered the race of productivity with a full commitment and did good
efforts to minimize the gap of productivity with U.S. The U.S. felt that Japan was making
a dent in its economy based on productivity. In response, the U.S. established a National
Commission on Productivity that later added ―Work Quality‖ to its title to attract trade
union participation (Prokopenko, 1999). This was all to improve competitiveness of
home based manufacturing and service providers firms through better productivity. One
of the relevant examples is auto market. In 1980s, Japanese cars were becoming popular
in U.S. markets. This all compelled the U.S. government and industrialists to join hands
to compete with international companies.
Having assessed the strength of productivity in 1983, the White House organized
a conference with the title ―White House Conference on Productivity: An Opportunity for
Change.‖ First, four preparatory conferences were organized in different parts of the
country and the closing convention held in Washington. The focus of this conference was
to discuss growing concerns like government regulation, tax reform, capital investment,
human resources, private sector initiatives, and public sector management. The
conference concluded that productivity is vital to the United States economy and that the
productivity challenge can be met with good management, ongoing public awareness,
Chapter Two: An Overview of Total Factor Productivity 47
and a cooperative effort between government and industry (Aronson and Skancke, 1983).
It proved that productivity was a catch cry of the 1980s and was a point of focus at a
higher level for a well-developed and productive nation.
Countries in Latin America and the Caribbean region also felt the significance of
productivity, though much later than other developed countries. No outstanding activities
were observed in African countries in the field of productivity until 1990s. In this
context, the most active institution is National Productivity Institute (NPI) in South
Africa, established in 1965. It is a dynamic organisation in the region, according to
Prokopenko (1999). The existence of a productivity institute in South Africa might be
because of the presence of European people in government who had strong links with
Europe. Because of this connection, they started a productivity awareness movement
somewhat earlier than other African countries.
After the fall of Soviet Union, productivity awareness in the Eastern Europe and
in central Asia lost its way. Nevertheless, after 1990, a movement emerged focusing on
productivity. Before the collapse of Soviet Union, ILO started cooperation with the
Soviet Labour Ministry. As a result, the All Union Productivity Centre and eight other
productivity centres were set up in former Soviet Republics (Prokopenko, 1999). The
productivity movement gave many benefits to U.S. and then to Europe, Japan. The rest of
the world was observing and filling a gap between them and developed nations.
Consequently, other countries and regions started their campaigns in this field and
established productivity centres and organisations.
The above discussion shows the significance of productivity. Most probably, it is
assumed that today there is neither developed nor underdeveloped countries that have no
Chapter Two: An Overview of Total Factor Productivity 48
productivity organizations or productivity centres. It is noticeably different from other
associations, and APO is one of the examples, having 20 member states alone, mainly
from East and South Asia.
The survival of NPOs shows that there is a general conviction that for better
economic growth, productivity awareness is a key factor. Another example of association
is the European Association of National Productivity Centres (EANPC), which was
established in 1966 as a successor body to the European Productivity Agency. The main
objective of this body is to facilitate and increase exchanges of information and
experiences, and arrange co-operation among participating bodies. The headquarters of
this organization are in Brussels and its membership is open to all national productivity
centres and institutions. All the members are equal as being the member states of the
United Nations Economic Commission for Europe (EANPC, 2008).
As mentioned earlier, EANPC was established in 1966, mainly for Western
Europe, but currently many new countries have sought its membership, including Poland,
Russia, and the Ukraine. In late 1980s, Japan, which was in the list of developed
countries also contributed for the development and promotion of productivity movements
and assisted Hungary, Poland and Ukraine to establish National Productivity Centres.
There are many associations that are not fully capable of giving desired results, but there
is a serious concern about productivity, and it is growing every day. Currently there are
about 100 productivity institutions located all over the world (Prokopenko, 1999).
Prokopenko published this study in 1999. Therefore, it is this researcher‘s strong belief
there are many more centres and associations working today in this field.
Chapter Two: An Overview of Total Factor Productivity 49
With the start of 21st century, there is momentous growth in the productivity
movement. It is hard to find any country not focusing on productivity. Different awards
are being conferred upon the best performers. There are analysts of productivity gaps
who work online for public consumption and publish articles to promote productivity
awareness. Take the example of the Australian Government Productivity Commission,
which was established in 1998 after the merger of three different organizations. The main
objective of this merger was to enhance and strengthen the cohesive effort in the field of
productivity. The U.S. Senate offers productivity awards on an annual basis to best
performers, e.g., the Maryland-U.S. Senate Productivity Award and Award for
Productivity and Quality (SPQA). This award was established in 1982.
Another example of productivity preferences in the economy is the study
conducted by The London School of Economics in 2004. The School did an effort to find
out gaps in UK Productivity. This report is quite informative and gives a true depiction of
UK productivity. Such effort by a renowned business school is an indicator about the
seriousness of the nation in productivity measurement and improvement process.
The summary of the above discussion is that the movement, which started in the
early 1900s, gained momentum with the passage of time. Having travelled from firms
and offices to government offices, it has attracted everyone to have healthier productivity
than yesterday. Below, a summary of landmarks in the productivity movement is
presented in chronological order.
Chapter Two: An Overview of Total Factor Productivity 50
Table 2.1
Highlights of productivity awareness in chronological order
1766
Quesnay used the word productivity (Sumanth)
1883
Littre used the faculty to produce to describe productivity (Sumanth)
1880-1920
Spread of productivity concept in U.S.A (Dewitt)
Post WWII
Anglo-American Productivity Council was established to implement Marshal Plan
(Prokopenko)
1948
British Productivity Council formed (Prokopenko)
1950
OEEC defined productivity (Sumanth)
1953
Productivity Centre for Coordination established in Paris (Prokopenko)
1955
Japan Productivity Center was established
1961
Asian Production Organization was formed
1965
National Productivity Institute established in South Africa (Prokopenko)
1966
The European Association of National Productivity Centres was made (EANPC)
1969
World Confederation of Productivity Science (WCPS) in London
1980-1990
More than eight All Union Productivity Centres were erected in U.S.S.R.
1983
White House Conference on Productivity: An Opportunity for Change was organized
1992
Pan African Productivity Association in November 1992 by six African countries
1994
Merger of Japan Productivity Center and Socio Economic Congress of Japan
1998
Productivity Commission Australia was established under Productivity Commission Act
2005
50th
Anniversary of JPC-SED with the declaration ―Productivity Movement towards a
Society Based on Mutual Trust and Vitality‖
Chapter Two: An Overview of Total Factor Productivity 51
2.2 Productivity: a Multi Dimensional Term
Section 2.1 gave a glimpse of productivity awareness in the last three centuries. It
is obvious from the discussion that productivity is catching more and more attention
among all occupations. In addition, it is assumed that the future will witness serious,
focused, and results-oriented efforts in this field. This section is dedicated to discuss the
meaning, definition, concept and understanding of productivity in different parts of
history by authors, researchers, scholars, economists, and business managers.
As discussed in section 2.1, the term productivity was first used in 1766 by
Quesnay and in 1950. It was formally defined by OECD, as said by Sumanth (1990).
Literature is full of several definitions of productivity. People view in different ways and
with different angles.
Tangen (2005) wrote that productivity is like other terms in the field of economics
and business still needs more efforts for a comprehensive definition. Tangen further
states that it is a fact that productivity is one of the most significant factors affecting a
company's competitiveness. Nevertheless, it is often relegated to second rank, and
neglected or ignored by the people directly involved in operations. The purpose of
ongoing discussions is to have a better understanding of productivity and clarity of
observation and ideas related to productivity.
Productivity is largely a concern of people engaged in economic activities, and
they agree that productivity is a ratio of output to input. This concept has been explained
in different words and phrases. The complex concept of productivity covers changed
meanings and contexts. Such diverse views are mainly because of industry contexts in
which productivity is discussed. Industrial engineers view productivity as higher output
Chapter Two: An Overview of Total Factor Productivity 52
with less input, whereas environmental engineers consider productivity as less pollution
with more recycling of materials. Unwritten definitions create a common and shared view
about productivity. Productivity is commonly used when management explains the
strategic objectives of the firms and reveals the plans of the firms. Adding to this idea,
Tangen spelled out that for the purpose of developing methods of improvement there is a
need for mathematical formulas. These formulas serve to design a framework to make
certain changes to increase productivity. For better results, there is an essential demand
visibly make a note between a concept and a particular mathematical definition attached
to the main theme. It is mandatory to measure the characteristics of mathematical
definition.
Productivity is commonly defined as a ratio of a volume measure of output to a
volume measure of input use. It tells how efficiently and wisely resources are used to
enclose a certain output. The commonly understood meaning of the word productivity is
too general for use in specialized fields. Even within business, the definition of
productivity varies according to the aspect being studied (Thomas and Baron, 1994). It is
obvious from the Table 2.2 that there is a difference of words — otherwise the concept of
productivity is nearly identical in all contexts. The main emphasis is on the professional
utilization of resources in a production function. Better use of resources gives better
productivity to firms. If it is observed at the national level, it is a sign of high productivity
of the nation. Productivity could have a single factor, multiple factors, or total factors
developed on a generic system of circulation. Relationship between outputs to input is
universal and generic. Productivity does not only cover production but also covers
services.
Chapter Two: An Overview of Total Factor Productivity 53
The concept that productivity is a relationship between outputs from a given
system during or over a given period in time and inputs to that system during that same
period, should be generic and universal. The simplest meaning of productivity is the
relationship between goods produced or service provided and the resources consumed.
Productivity is also an indicator of the utilization of the resources. A right product at the
right time is an indicator of better productivity. If a firm is using resources in a proper
manner without wasting resources, it means the amount of undesired valuable products is
very low. Productivity has its functional meanings in a production function. It depicts the
whole process at an aggregate level. One can have a view of performance starting from a
departmental level to an aggregate level. It is an indicator of the performance of a nation
(Afzal, 2004; Lawlor, 1985; Sink, 1985; Sumanth, 1998).
Generally, it seems that different scholars and authors are agreeing that
productivity indicates how well resources are used to accomplish certain goals of the
firm.
Thomas and Baron (1994) have discussed the relationship between output and
input. They have studied the flaws present in the relationship and have concluded that
majority of concepts take productivity as a relationship between output and input. It
applies only to a production system. In an organization, there is a physical system that
develops a relationship and interdependency among various factors. They further argue
that the above definition of productivity relies on the acceptance of the stimulus-response
model of causality, which explains that input causes output. This concept creates a
prejudice towards the production function. This assumption leaves out other economic
and non-economic performance of output such as market goodwill, market share, new
Chapter Two: An Overview of Total Factor Productivity 54
production introduction, social services, etc. Such output is an intangible gain. It is a fact
that for all such achievements, there is a consumption of tangible inputs and such inputs
are fully taken into account while calculating the ratio. It shows that the relationship
between tangible output and tangible input does not give a true picture.
Thomas and Baron point out another lack in this relationship. They write that in
case partial productivity is discussed, one factor is only considered whereas input factors
cannot be studied in isolation. Productivity of one factor usually involves contributions
from other factors, just as labour productivity improvement because of latest technology
is the result of financing of new technology. Nevertheless, labour is still crucial to
productivity.
Stainer (1995) pointed out that the term productivity is often confused with the
term production, although both have a close relationship to each other. Production is
concerned with the operational activity of producing goods or services, while
productivity relates to the efficient utilization of inputs in producing prescribed outputs of
goods or services. Broman pointed out (as cited by Tangen, 2005), that what appear to be
dissimilar definitions of productivity are actually quite similar in nature. The reason for
this similarity is the contents associated with productivity. Ghobadian and Husband (as
cited by Tangen, 2005) have suggested that similar productivity concepts can be
classified into three main categories:
1. Technological concept. A ratio of output to input
2. Engineering concept. Relationship between actual and potential output
3. Economist concept. Efficiency of resources allocated
Chapter Two: An Overview of Total Factor Productivity 55
After having examined Table 2.2, one can find that there are different authors
who have explained productivity concepts and defined productivity, but there are subtle
differences that are based on the perception attached with the productivity. It shows that
context has a strong influence on productivity‘s meaning. It might be different for top
management and front line management. It may have dissimilar meaning when one is
discussing productivity in the context of a single machine, assembly line, unit level,
department level, or even at an individual level (Tangen, 2005). It may have different
meaning for employees, employers, government, and society. For employees, it may be
considered as more salary and wages; for employers more as profit; for government an
increase in taxes; and for society better employment chances (Baig, 2002).
Chapter Two: An Overview of Total Factor Productivity 56
Table 2.2
Chronological order of productivity definitions
1766: Productivity appeared first time in literature (Quesnay).
1833: Faculty to produce (Littre)
1950: Quotient obtained by dividing output by one of the factors of production
(OECD)
1955: Change in product obtained for the resources expended (Davis)
1962: Always a ratio of output to input (Fabricant)
1965: Functional definition for partial, total factor and total productivity (Kendrick and Creamer)
1967: Ratio between the wealth produced and input resources used in the process of production (ILO)
1971: Real output per hour of work (Herbert Stein)
1973: Productivity is the optimization of all available resources, investigation into the best-known
resources and generation of new resources through creative thinking, research and developing and
by using all possible improvement techniques and methods (M. R. Ramsay)
1976: A family of ratios of output to input (Siegel)
Productivity denotes the productiveness of the factors of production (British Institute of
Management Foundation)
1979: Total productivity- a ratio of tangible output to input (Sumanth)
1980: Productivity refers to the effectiveness of the work not its intensity (Sir John Hedley)
1984: Delivering the right product or service of the right quality at the right time with least expenditure of
resources (Scarf)
1985: At its simplest, productivity is the relationship between goods produced and sold or services
provided- the output, and the resources consumed in doing it –the input (Alan Power)
Productivity defines competitiveness (M. E. Porter)
Productivity is simply the relationship between the outputs generated from a system and inputs
provided to create those outputs (D. Scott Sink)
1990: Briefly, productivity reflects results as a function of effort (Brinkerhoff and Dressler).
1992: Productivity is the ratio of outputs produced to the input resources utilized in their production (Rick
L. Wilson)
1996: Productivity is about making the most efficient use of all resources and gaining the maximum
benefit from them (Joseph Prokopenko and Klaus North)
Chapter Two: An Overview of Total Factor Productivity 57
Green Productivity (GP) is a strategy for simultaneously enhancing productivity and
environmental performance for overall socio-economic development that leads to sustained
improvement in the quality of human life (APO).
1997: Productivity is a measure of the capacity of individuals, firms, industries or entire economies to
transform inputs to outputs (Industry Commission)
Productivity is a road to the competitiveness of enterprises, the economic development of countries
and welfare and well being of the nations (Gharneh)
Productivity relates to the efficient utilization of inputs in producing prescribed outputs of goods or
services (Alan Stainer).
1998: Productivity is an efficiency concept generally cast as ratio of output to input into some productive
process (Thomas A Mahoney)
Productivity is the ratio of output to input. Unfortunately, there is no place for management output
on an income statement (Richard Cardinali)
1999: Traditionally productivity is considered as ratio between input and output (Joseph Prokopenko)
2001: Technically productivity is the rate of output per unit of input (Social Policy Research Unit
Canada)
Sources: (Bheda, 2002; Brinkerhoff and Dressler, 1990; Cardinali, 1998; Gharneh, 1997;
Mahoney, 1998; Sink, 1985; Stainer, 1997; Sumanth, 1990)
2.3 Distinction and Interdependence of Productivity, Profitability, Performance,
Efficiency, and Effectiveness
To some extent, productivity, performance, effectiveness, efficiency and
profitability are synonymous in nature. Apparently, these terms are indicators of a firm's
level of achievement in a certain period and in a certain field, however in-depth analysis
shows that there is a subtle difference among the meanings and concepts of these terms.
Below, there is a discussion of the aforementioned terms. The discussion is designed to
develop a better understanding about these terms. This would serve in avoiding any
confusion during the academic study of productivity.
Chapter Two: An Overview of Total Factor Productivity 58
2.3.1 Productivity and Profitability. Productivity as discussed in previous
paragraphs is mostly considered the ratio of output to input by volume, whereas
profitability is a ratio of cost of production and total revenue collected by a firm. Stainer
(1995) has pointed out that a fundamental problem in using profitability ratios is that
often outdoor conditions affect them, which may bear no relationship to the efficient use
of resources. Furthermore, profit is an economic activity. One firm can have larger profits
in certain circumstances. For example, firms working in a monopoly situation can have
better profits than a firm operating in a highly competitive market. Stainer further states
that it is the case that some firms do not earn profits because of their use of resources but
due to some external causes, for example, being unable to navigate government
regulations or adjust to sudden and unanticipated changes in the business environment, or
due to very high consumer interest in the product or service.
Nevertheless, profitability has a strong link with productivity. Firms are of two
types: for profit (business concerns) and non-profits, such as welfare organisations,
educational institutions, and health centres. Non-profit organisations do not work
rigorously for profit; they benefit from the same productivity considerations in some
ways (Chapman, Murray, and Mellor, 1996). Nevertheless, profitability is a meaningful
function of productivity. However, it is true that incidents outside the firm's control can
substantially increase profit or loss. Such incidents are outside the scope of this study. In
general, high profitability is a consequence of high productivity.
The terms productivity and performance are commonly used within academic and
commercial circles, but they are rarely adequately defined or explained. Indeed, they are
often confused and considered to be interchangeable, along with terms such as efficiency,
Chapter Two: An Overview of Total Factor Productivity 59
effectiveness, and profitability (Tangen, 2005). Apparently, productivity, performance,
efficiency, effectiveness, and profitability are interdependent, but they always move in
one direction. As mentioned earlier, profitability is an economic function, which is
related with internal and external factors.
―Increased productivity does not necessarily lead to increased profitability in the
short term but the effect of increased productivity is more likely to be realised in terms of
long-term profitability‖ (Tangen, 2005, p. 39). It seems that productivity does not
guarantee profitability, but it is more likely that it will not reduce the profitability, and it
is expected that in the end, better productivity would contribute to high profits.
Miller (as cited by Tangen, 2005) is one of the scholars who tried to build up a
clear line between profitability and productivity. Miller defines profitability as the sum of
productivity and the price recovery. Price recovery is the ratio of unit price related to unit
cost. Bernolak (1980) makes it clearer by saying that an organization should combine
productivity and profitability ratios to give a more comprehensive depiction of their
performance. This will let them know the true reason for accumulated earnings. By doing
so, firms can make decisions to enhance productivity and profitability separately. All the
same, profitability is the dominant objective of the firms, and success and growth of any
business depends upon profitability. To get high rate of profits, from the shareholders'
point of view the inputs are converted into the productions to add value, which ultimately
contributes to achievement of high profits (Kolay and Sahu, 1995). It seems that
profitability cannot be ignored in pursuing better productivity, whereas the main agenda
of the firms is profit. Because of this, some time productivity is ignored and other means
are adopted to attain high profitability.
Chapter Two: An Overview of Total Factor Productivity 60
2.3.2 Performance, Efficiency and Effectiveness. ―People who claim to be
discussing productivity are actually looking at the more general issue of performance.
Productivity is a fairly specific concept while performance includes many more
attributes‖ (Thomas and Baron, 1994). There is a general confusion between productivity
and performance. Some people discussing productivity are actually discussing
performance. It shows that some time people are not able to find a difference between
productivity and performance.
―Performance is a broader term than productivity and it includes factors
that are not easily quantified, such as quality, customer satisfaction, and worker morale‖
(Thomas and Baron 1994). Measurement of productivity by dividing output with input is
quite easy but the measurement of performance is quite difficult. It covers many areas
that have qualitative and quantitative data such as customer satisfaction, etc. It envelops
many economical and non-economical areas like objectives of the firm. It may involve
speed, flexibility, market share, employees‘ satisfaction, and fulfilment of social
obligation. In addition to that, performance can be described as an umbrella term for all
concepts that are the main determinants of success or failure of the firm.
There is a common confusion regarding the term efficiency. It creates more doubts
with respect to productivity. Often it is used as a synonym of productivity. Efficiency is
commonly defined as the utilisation of resources for a certain output. It denotes how well
resources have used, and it affects the denominator (input) of a productivity ratio.
Nevertheless, efficiency explains the minimum resource level, which is theoretically
required for a certain operation or output versus actual consumption of the resources. It is
easy to calculate because in most of the cases it is related to time, capital, or other
Chapter Two: An Overview of Total Factor Productivity 61
specified inputs (Tangen, 2004). Sumanth (1998) has discussed it in detail. ―Efficiency is
the ratio of actual output generated to the expected (or standard) output prescribed and
efficiency does not necessarily imply productivity‖ (p. 13). In fact, efficiency is the actual
output versus standard output. There is a strong need to have a distinct view about
efficiency. Confusion between two terms can lead to unwanted decisions that can regress
the overall performance of the firm.
Tangen (2004) states that effectiveness is a term that is difficult to explain in
certain situations. Actually, it is biased towards the qualitative. It is associated with the
value for a customer and usually affects the numerator (output). It can be explained as the
ability to reach a desired objective. As in comparison to efficiency, it has a limited scope;
mainly it is related to assess the effectiveness of the organization. However, generally, in
management textbooks efficiency is described as to do the right things and effectives
doing the things right. As said earlier, productivity, profitability, performance, efficiency,
effectiveness are indicators of the function of the organization. They move in one
direction but are not interdependent. However, they are strong associates with each other.
As communicated by Sumanth, (1998), better efficiency does not guarantee better
productivity. In addition, it is worth noting that better productivity does not give the
assurance of high profitability. All these terms move side-by-side, but by putting hands
together as concluded by Tangen. It is obvious from all above discussion that there is an
ongoing debate about different terms and phrases. In addition to that, it looks that it is
quite difficult to reach on a conclusion. Below, however, definitions and concepts that are
commonly acceptable are given:
1. Productivity. always a ratio of output to input
Chapter Two: An Overview of Total Factor Productivity 62
2. Profitability. return on investment
3. Performance. over all achievements of the organization (qualitative and
quantitative)
4. Efficiency. ratio of actual production to the standard production or doing
right things
5. Effectiveness. ability of the firms to eventually reach accurate targets or
doing things right
The whole discussion could be concluded on the point that ―productivity is not
everything but in the long run, it is nearly everything‖, as said by Paul Krugman, an
American economist.
2.4 Theory and Emergence of Production Function: Foundation of TFP
Section 2.3 discussed the concepts and definitions of productivity and highlighted
various misconceptions surrounding it. The section offered debate aimed at clarifying the
productivity concepts, describing the different definitions, and associating similar and
dissimilar meanings, urging for strong consideration of context when discussing the
implications of productivity. In this part of the chapter, the subject matter is production
function and TFP. The debate covered below will encompass the significance and
emergence of production function and TFP. This discussion explains how aggregate
production function approach originated and the way economists and industrial engineers
have developed their research frameworks based on production function and TFP. The
rationale of the discussion is to develop thoughts about the concept of production
function and TFP, which is greatly linked with economies from the nation to firm levels.
Chapter Two: An Overview of Total Factor Productivity 63
This discussion will also throw a light on the emergence of TFP in the circle of industrial
engineers, who look at it with a different approach.
What does a man do in this world? This question has an implicit answer: work.
Man cannot live without desires and needs. This is the instinct of all human beings. The
level and intensity of desires and needs depends upon many factors, which include
environment, culture, society, level of awareness, norms, and values. This list is not
meticulous in nature. There are numerous factors that force human being to perform a job
or to do work. Drucker, a famous management consultant, says that work is a binding
force that keeps society together. Based on this observation, it is understandable that
human beings started working since their beginning and this activity has gradually
developed many types of bonds in society. It may be a relationship between an employee
and employer, seller and purchaser, taxpayer and tax collector, etc. Ancient caves, where
the people lived in the Stone Age, tell us the story of human beings about working and
products, which they produced and consumed. Primeval cities, tombs, temples, and
churches are understandable indicators that man is deeply involved in production. It may
be production of agriculture to satisfy the hunger, clothing to save from the weather
severity or to improve the aesthetic sense, houses to live, swords to fight, tools to have
better production from the existing resources etc.
Production is a result of an effort in which inputs are processed and converted into
another product. Output is obviously different from inputs and has higher value and high
usability. There is a need for a number of production factors such as technology, labour,
organisation, etc., and a system to covert inputs into an output. Nevertheless, in the case
of service, which is also a production function, more labour and less physical capital is
Chapter Two: An Overview of Total Factor Productivity 64
needed, whereas in some industries there is more need for physical stock and less labour.
In other words, production is a function of inputs (capital, labour, technology,
organisational set up etc). It is apparent that there is a clear and strong relationship
between inputs and outputs, and production function details the type of relationship
between inputs and outputs.
Production function tells how one can have maximum output from a set of inputs.
As a measuring tool, it revises minimum requirements to own a certain production. As
said earlier, production is an outcome of certain inputs, so based on this statement every
production process has a unique production function. Economists view production
function with few assumptions. They assume firms have a maximum level of efficiency
to keep themselves away from errors and wastage. In other words, economists presume
engineering and managerial problems of technical efficiency have worked out. It is the
reason why they focus on problems of allocated efficiency.
Relationship of physical output to physical inputs is based on production
functions. It is a non-monetary liaison since prices are not under the control of firms.
Many more factors control the prices of goods and services. In the case of a monopoly,
price phenomenon is different as compared to a challenging market, whereas production
function does not explain the whole production process. It keeps itself away from the
essential and inherent aspects of physical production processes, which include errors and
waste. It also ignores the crucial role of management of sunk cost investments and the
relation of fixed overhead to variable costs. The primary objective of the production
function is to discuss efficiency in the best possible role of factor inputs in production
and then result to distribution of income to those factors.
Chapter Two: An Overview of Total Factor Productivity 65
Production function models can also be used to measure marginal productivity of
different factors. Production functions proposed by different authors used a term ―output‖
in their models to express the production of all factors. In the Cobb-Douglas production
function model, Y is the output or outcome of the capital and labour. In Cobb-Douglas
model output is a function of labour and capital.
Generally, it is believed that human beings have always tried to get better output
from fewer resources. For this purpose, human beings invented a number of products that
contributed a great deal of amounts to the well-being of human beings. It may be a
machine, medicine, or entertainment instrument.
Humphrey (1997) describes the whole phenomenon of production function and its
significance and concludes that a systematic and academic process to discover the
correlation between influencing factors of production came under discussion with the
beginning of industrial age. Production has a strong link with economics of the firm,
industry, and nations. Based on this observation, economists consider production function
and productivity their main area of research.
Humphrey has put forward the curiosity of economists on production function. It
is obvious from the efforts of people since 1767 until the early 1900s. Even in the current
era, it is one of the topics that is highly debated and has caught attention from economic
scholars, government officials, political leaders, and those in the business world.
Humphrey further pointed out the landmarks in the development of the production
function concept. Humphrey said that there is a great deal of academic work that defines
production function in terms of an equation well before the Cobb-Douglas production
function, which has attracted the attention of scholars and researchers. The history of
Chapter Two: An Overview of Total Factor Productivity 66
production function can be traced in the work of A.R.J. Turgot in 1767. Until the early
1900s, there were many people who proposed different concepts and models of
production function, e.g. Malthus's iron law of wages, Ricardo's rent theory, the trend of
relative income shares in a growing economy, the first-order conditions of optimal factor
hire, Euler's theory of adding-up. Humphrey further explains that all these efforts
revealed their secrets through the production function, and it is important to note that von
Thune and Wicksell had proposed the Cobb-Douglas production function prior to Cobb
and Douglas. Humphrey divides the emergence of production function into seven stages
even before the presentation of the Cobb-Douglas model in 1928. Humphrey writes:
Each stage saw production functions applied with increasing sophistication. First
was the idea of marginal productivity schedules as derivatives of a production
function. Then numerical marginal schedules came whose integrals constitute
particular functional forms indispensable in determining factor prices and relative
shares. Thirdly, there appeared the path of breaking initial statement of the
function in symbolic form. The fourth stage saw a mathematical production
function employed in an aggregate neoclassical growth model. The fifth stage
witnessed the flourishing of microeconomic production functions in derivations of
the marginal conditions of optimal factor hire. Sixth was the demonstration that
product exhaustion under marginal productivity requires production functions to
exhibit constant returns to scale at the point of competitive equilibrium. Last was
the proof that functions of the type later made famous by Cobb-Douglas satisfy
this very requirement. In short, macro and micro production functions and their
appurtenant concepts—marginal productivity, relative shares, first-order
Chapter Two: An Overview of Total Factor Productivity 67
conditions of factor hire, product exhaustion, homogeneity and the like—already
were well advanced when Cobb and Douglas arrived. (1997, p. 54)
The above statement depicts the level of concern of economists in modelling the
production function. Since the start of industrial era, people involved in production and
particularly economists have shown an excessive concern with production and factors
affecting production. There was continuous research in the past three centuries to have a
better knowledge of factors, which had a significant correlation with production.
At least 18 economists from seven countries over a span of 160 years either
presented or described such functions before Cobb-Douglas. Seen in this
perspective, Cobb-Douglas and his contemporary successors represent
culmination of a long tradition rather than the beginning of a new one. (Humphrey
1997, p. 78)
Humphrey has critically examined various production functions and their applications.
He found that in different parts of the world, people were deeply involved in the
academic study of production function. It shows a path along which production function
concept travelled in last three centuries from a single factor to multi factors and finally
aggregated production function. Efforts in the field of production function are one
necessary output of industrial revolution, which has aged almost 300 years.
Mishra (2007) divides the history of production functions into three main parts. It
starts from Adam Smith (or even before), an economist of a particular mind set proposing
that a society based on self-governing management, capitalistic ideology, self-interest
guided agents operating in an institutional framework of personal property, market
economy, competition, gathering of capital, etc., is most practicable and steady. In the
Chapter Two: An Overview of Total Factor Productivity 68
second phase, Karl Marx questioned the efficacy of the capitalistic system and put
forward his theory of socialism. In third phase, neoclassical scholars attempted to defend
the capitalistic approach.
Walrasian general, equilibrium, Pareto-optimality of the competitive economy,
aggregate production function, marginal productivity theory of distribution,
product exhaustion theorem, and ultimately Harrod's, Solow's and von Neumann's
paths to expansion are only some major lemmas to prove they said grand
proposition. (Mishra, 2007, p. 14)
After surveying the economics literature, two landmarks hit upon the history of
production functions: first, the Cobb-Douglas production function, and second, the Solow
model in 1928 and 1957, respectively. Douglas computed the index numbers of total
numbers of manual workers (L) employed in American manufacturing between 1899 and
1922. Douglas also compiled fixed capital (C) for the same period and expressed in
logarithmic terms on a chart. Douglas added the index of physical production. After
developing a curve he found that there was around one quarter of distance between the
curve for labour, which increased the least (to162), and the curve of capital which had
increased to the most (to 431). He took 1899 as the base year and allotted 100 to this
year. Charles W Cobb, a mathematician assisted Douglas to give all indices a shape of
following equation:
Y = ALaKß,
Where:
Y = total production (the monetary value of all goods produced in a year)
Chapter Two: An Overview of Total Factor Productivity 69
L = labour input
K = capital input
A, a, and ß are constants determined by technology.
The Cobb-Douglas production function is based on the assumption of constant
returns to scale, meaning that if labour and capital input increases by 10%, output (Y)
will increase with the same percentage. This model also tells that if:
a + ß < 1, returns to scale are decreasing, and if
a + ß > 1, returns to scale are increasing.
Based on this perfect competition assuming, a and ß are representatives of labour
and capital's share of output. Furthermore, the exponents a and ß are output elasticity
with respect to labour and capital, respectively. This model explains the responsiveness
of output to a change in levels of either labour or capital used in production. This model,
influenced by statistical evidence, assumes that share of labour and capital over a period
remains constant. Critic of this model believe that it is doubtful. However, Douglas once
again wrote about the significance of the production function, which they proposed in
1928. Douglas (1976) has quoted some further studies, which also supported their
assumption that labour and capital had approximately same share as it calculated first
time. ―The results of this study lend further corroboration to the accuracy of the
production function as a description of manufacturing production and as a determinant of
the distribution of the product—which is a separate but allied subject‖ (Douglas, 1976, p.
913). This study once again proved the usability of the Cobb-Douglas model. There is a
Chapter Two: An Overview of Total Factor Productivity 70
great deal of criticism on this model and probably Robinson (1953) is the strongest critic
of this model. Nonetheless, still many scholars feel that this production function is useful.
Sharpe (2002) tells the story of economic theory of productivity and the
production function. Sharpe says economic theory went forward a stage beginning from
simple framework, which based on certain assumptions and was quite inflexible and
sometimes unworkable. With the passage of time, certain assumptions removed and
many more factors incorporated. This is evident from the development in production
function, which eventually took place from 1950 to 1990. For example, the addition of
technology as a main factor along with capital and labour were important. The main
contributors are Robert Solow, Moses Abramovitz and Dale Jorgenson (Sharpe, 2002).
As discussed by Sharpe (2002), Solow made a thriving effort to assess the
contribution of technology in growth.
He [Solow] did not measure the contribution of technological change to economic
growth directly, but rather measured it as a residual after the contribution of
labour and capital had been calculated. Solow characterized this residual as a
measure of our ignorance. (Sharpe, 2002, p. 37)
In reality, this was an abstraction to make it simple and allow facilitation to production
model users, which focus on long-term growth. Solow described the unexplained growth
due to technology as TFP. Literature survey shows that Solow is probably one of the
earliest scholars who used TFP phrase in a particular context and did venture to measure
TFP with the help of mathematics, putting forward a systematic way to measure TFP.
Chapter Two: An Overview of Total Factor Productivity 71
In recent years, a big change has occurred in production model assumption.
Landau, Taylor, and Wright (as cited by Sharpe, 2002) have divided these changes in
neoclassical models into five main areas, which are:
1. All firms work in a similar style in their endeavour to maximize profit
2. The neoclassical model also assumed perfect competition
3. The neoclassical model assumes that the secrets of technical progress are
available to all
4. The neoclassical model assumes that all industries are equally important
5. Recent research suggests that higher rates of accumulation and investment can
increase productivity growth, that there is no steady-state rate of growth and that
the inputs in the growth process act independently.
The above debate is an attempt to give a brief idea about the history of production
function. It seems that significance of production function is totally dependent on the
involvement of people in production functions. The contemporary era is called a
production era. Every day, manufacturers offer new products to satisfy the essential needs
and demands of people. Nevertheless, it is obvious from the above discussion that a
systematic study of factors affecting production or output has a history of nearly 300
years. The conclusion of the discussion can be that change in the production function
models is a continuous phenomenon. This is because there is a regular, continuous, and
drastic change in the business environment.
Chapter Two: An Overview of Total Factor Productivity 72
2.5 Total Factor Productivity: A Discrete Concept
―Total Factor Productivity is a very old and perhaps obsolete concept in
economics growth and development‖ (Chen, 1997, p. 20). Chen further expressed that
Tinbergen, a German economist, introduced this concept in 1942 and in 1957 Solow put
forward TFP concept through his empirical work and proposed a production model.
Solow model is a great breakthrough in the history of economics. However, there is a
never-ending debate in the economic literature about the origination of the TFP concept.
Many others measured, discussed, and promoted the TFP concept before Solow. Chen
has given a list of different authors who have worked and published their works on TFP
before Solow. Chen‘s statement supports the theme that TFP is one of the topics that has
been under discussion for a long time. ―Solow's 1957 paper was not so original, ‗not the
question, nor the data, nor the conclusion‘… The ‗new wrinkle‘ was the explicit
integration of economic theory into such a calculation and the use of calculus‖ (Chen,
1997, p. 20). This expression also supports the view that people were aware about the
TFP concept and its significance in the world of economics even before the presentation
of Solow Model. However, Solow gave an organised shape to this concept.
Uri (1984) defines the ―TFP as the residual of output growth not explained by the
weighted sum of growth in factor inputs (i.e. labour and capital)‖ (p. 555). This is the
exact definition proposed by Solow. In his model, he developed a method to distant
productivity growth due to technology from capital and labour. Besides the fact that
much studies about TFP halted after 1970s. In 1980s, supplementary stress was laid upon
labour and capital productivity. After 1990s, there is extra attention on this ―obsolete‖
topic, which has gained the full momentum everywhere. Today there is a tough
Chapter Two: An Overview of Total Factor Productivity 73
competition among nations to earn better TFP capacity and to rank well in the global
business world (Chen, 1997).
This discussion elaborates that TFP is an old concept and many people have
discussed this topic for many decades. Nevertheless, in the third quarter of the 20th
century TFP lost its importance, and other areas like quality of labour and capital were
moved ahead, as stated by Chen. During this period, however, the productivity concept
and its significance regressed, and more talks were made about the quality, which was the
most discussed topic in the fourth quarter of 20th century. However, it gained a
momentum once again in the late 1990s, and today, there is great attention to this topic. It
has becomes one of the most discussed topics in literature as well as in business world,
according to Chen.
―TFP is interpreted in the literature in different, mutually contradictory ways‖
(Lipsey and Carlaw, 2004, p. 1118). For example, Parente and Prescott (1994) and Hall
and Jones (1999) have put forward that TFP indicates people‘s living standard. It tells
about the prosperity of a country and shows performance of firms and industries.
Nevertheless, there is a recognized variation in the level of TFP among different
countries. Different authors have discussed two common reasons of variation — one
related to technology, capital, skill, etc., and the other belongs to government policies,
plans, transparency, level of corruption, availability of the institutes, focus of leadership,
social infrastructure, etc.
The aforementioned two reasons are interdependent. Accessibility of skilled work
force, modern technology, and useable knowledge are main contributors to TFP. On the
other hand, no one can ignore the role of government because it provides a platform to
Chapter Two: An Overview of Total Factor Productivity 74
attain skill, technology, and knowledge. Availability of technology depends upon the
government policies because the government is to facilitate firms in order to have better
technology, high skill, capital accumulation, and creation of knowledge. It looks that TFP
is a result of solid efforts of the industry, academia, and government, so it is impossible
to encompass a better TFP without a critical support from all three main partners.
Hulten (2000) explains the role of technology and capital in empirical growth
analysis. Hulten says that there are two main areas or tasks before the growth economist.
First, he must take into account the historical data of input and output, and, second, he
must assess the impact of technology or share of growth due to technological up
gradation. There is a discussion of source of growth analysis, which is the ―intellectual
framework‖ of TFP residual. It gives the whole concept of the TFP survey. This shows
that TFP is the area that remained the core subject matter of economists and their efforts
to analyse the reasons of growth has the points of diversity. It seems that with the passage
of time, the point of difference in measuring TFP and the reasons of growth is becoming
wider as it is observable from different TFP measurement efforts.
A vast empirical literature has attempted to sort out the capital-technology
dichotomy, but no clear assessment has emerged. Many of early studies favoured
productivity as the main explanation of output growth, and this view continue in
the ‗official‘ productivity statistics produced by Bureau of Labour Statistics.
(Hulten, 2000, p.2)
―Output per unit input, or TFP, is not deeply theoretical concept rather it is an
implicit part of a circular income flow model‖ (Hulten, 2000, p. 4). In this model, product
market establishes the price of the product (Pt) and the quantity (Qt) sold to the
Chapter Two: An Overview of Total Factor Productivity 75
consumers. The total value, which is Pt Qt, is equal to the expenditures of the consumer
and revenue of the producer. On the other hand, the factor market establishes the quantity
and price of input (labour and capital). Producers forfeit these factors and this amount is
equal to the gross income of the consumer. In this way, two markets are interconnected
with each other based on equality of revenue and cost; revenue for producer and cost for
consumer. This leads to the GDP index (Hulten, 2000). Hulten further argued that
economic well-being originating from the quality and quantity of consumed goods and
services is not the total used up because prices change from time to time, and ultimately
there will also be a change in the value of cost and revenue. The argument leads towards
an agreement of the TFP, which Hulten noted is not theoretical.
Contrary to economists who define TFP as a residual of growth, there is another
school of thought that takes TFP into account in a different way. Those in this school of
thought include Sumanth, Sink, and the American Production Centre. They take TFP as a
ratio of total tangible input and total tangible output. They convert the entire inputs and
outputs in monetary terms and weigh up the ratio. This approach is also much criticised,
and the main objection is the conversion of volume of input and output in dollar value, so
it is reasonable that the cost of inputs and price of output depend upon many factors that
are mostly not in the control of producers. Hulten tries to answer this problem and gives
suggestions that there should be an accounting model based on the volume keeping prices
constant and using any base year prices for valuing current input and output. It is a hope
that this model would hold a finer image of the TFP of the economy at macro level and of
a firm at micro level. The following items summarize the Hulten‘s observation of TFP:
Chapter Two: An Overview of Total Factor Productivity 76
1. The residual captures changes for output that can produce by a given quantity of
input.
2. Many factors may cause shift in TFP: technical innovation, organisational and
institutional change, shift in social attitude etc.
3. To the extent that the innovation affects, is the costless part of technical change
that it captures.
4. Various factors comprising the TFP do not measure directly, but lump together as
residual ―left over‖ factors.
5. When different assumptions meet, the residual is a valid measure for shift in the
production function however, it normally understates the importance of
productivity change in stimulating the growth of output. It is because the shift in
the function generally induces further movement along the function as capital
increases.
The discussion renders TFP as a concept, which has been the talk of economists
for a long time. Two main schools of thoughts take TFP in a different way. One takes it
as an unexplained contribution in productivity growth, and the other thinks of it as a ratio
of tangible output to tangible inputs. The coming pages discuss the variation in the TFP
concept.
2.6 A Critical View of Production Function and TFP
There is criticism on production function, most likely in the strongest manner
from Robinson: ―Moreover, the production function has become a powerful instrument of
miseducation‖ (1953, p. 81). Mishra (2007) concludes that Karl Max questioned the
Chapter Two: An Overview of Total Factor Productivity 77
classical thoughts, and eventually neoclassical thoughts came forward to defend classical
approaches. Finally, the neoclassical era ended in the 1970s with capital controversies
that opened several new avenues of production function. Prescott (1998) supports this
observation by putting a question in order to build up a new theory of TFP. Title of the
question is ―Needed: A theory of TFP.‖
The previous paragraphs show that TFP has been a well-recognised concept by
the economists for a long time and its significance is a well known, accepted fact.
However, at the same time a lot of controversy came with TFP. One of the major
controversies is the method of measuring TFP. As it is expressed by (Hulten, 2000), there
is another source of controversy arising from sins of ―omission‖ instead of
―commission‖. An economic critique points to unmeasured gains in product quality,
while an environmental critique points to the unmeasured cost of growth. TFP is one of
the topics that has been a concern among economists. Its importance was quite high in the
past and today it is one of topics that have attained the attention of almost all and sundry
economists, and there are number of studies to measure TFP of different economies.
Nevertheless, the main difference, which was clearly observed, is the methods and
techniques of measurement (Humphrey 1997; Mishra 2007; Prescott 1998; Robinson
1953).
Recently a conference criticising the Aggregate Theory with the title ―Economic
Growth and Distribution: on the Nature and Causes of the Wealth of Nations‖ was held.
In this conference, Felipe and McCombie (2007) criticised the thoughts of Prescott, who
put forward that there was a strong need to develop a theory of TFP. However, they
Chapter Two: An Overview of Total Factor Productivity 78
raised another question about the difference between rich and poor country in the
following words:
With the revival of the interest in growth theory since the 1980s, and, in
particular, the interest in applied work in this area, economists have returned to
the important question of why some countries are richer than others (p.196).
Is there a need for a theory that may identify the reason why some countries are
rich and others poor? To answer this question Prescott says that there is a need of another
theory of TFP, while Felipe and McCombie are of the view that the neoclassical growth
model is sufficient to answer this question. However, there is a problem with the
calculation of TFP. They write
We conclude that the tautological nature of the estimates of TFP lies at the heart
of an important question that the empirical literature on economic growth has
been dealing with during recent years. Hence, our arguments cast doubt on the
need for a theory of TFP.
After 1980, there is a great concern about the growth in economics and disparity
between rich and poor. People are trying to find out the answer of this question, and one
view is that this is due to the difference in TFP. This shows how TFP is associated with
the prosperity of nations.
Why is a concept as theoretically and empirically bankrupt as TFP so widely
embraced not only by mainstream development analysis but even by some
ecological economists? To answer this question requires analysis of the anti-
ecological ideologies and mystifications generated by capital production relations.
(Burkett, 2006, p. 184)
Chapter Two: An Overview of Total Factor Productivity 79
Burkett (2006) has developed a link between production function and
productivity. Burkett said that there is a growing ecological appraisal of the TFP concept.
In addition, TFP has a theoretical base, which connect it with aggregate production
function and which expresses in mathematical form showing output of different factors
employed in production function. It also tells the share of dissimilar factors, mainly
labour and capital in the output. While TFP growth is the amount of output growth,
which factors of input (also called residual) is not explained. Most commonly, TFP
growth is considered an upgrade in collective productivity of factor inputs, as driven by
the disembodied technological change. These factors are quite independent, they do not
influence other factors, and as a result, other factors also do not influence them.
―Generally speaking, there are two approaches to measuring TFP: the explicit use
of an aggregate production function for econometric estimations, and national income or
growth accounting approach which used discrete data and assume an aggregate
production implicitly‖ (Chen 1997, p. 20). TFP as a residual in growth accounting is an
arbitrary concept. Strictly speaking, it confines to disembodied exogenous and Hick-
neutral technological changes (Chen, 1997).
A trend is rising in the interest of measuring productivity at firm, plant or division
level. However, there are many difficulties attached with this measurement. Many
underlying assumptions are there that need satisfaction. There are several methods to
measure TFP, which should be acceptable to all (Soriano, Rao and Coelli 2003). This is
one of the questions when TFP is measured at firm level, division, or plant level.
Fare and Zelenyuk (2003) did efforts to solve this issue, and established a new
method in aggregating Farrell efficiency measures, and finally suggested some
Chapter Two: An Overview of Total Factor Productivity 80
conditions, which are necessary to estimate aggregate efficiency by using firm level
efficiency. Fare and Zelenyuk (2003, p. 616) write, ―To define the industry revenue
function and obtain an aggregation theorem, it is crucial that all firms face the same
output price vector.‖ It is not possible that in real business firms, which are producing
the same product, would have the same prices. Obviously, there is a difference in prices
and definitely a difference in cost of inputs. Aggregation of efficiency is only possible by
giving a relaxation in output prices (Soriano, Rao, and Coelli, 2003).
There are two assumptions that are used to get aggregate efficiency of different
firms. The first assumption states that all firms are equal in fixing on one price system
that is similar, whereas the second assumption says that, there is no reallocation of
resources. Without having these two assumptions, it is difficult to aggregate the
efficiencies (Soriano et al., 2003). This all discussion shows that TFP concept, which
originally was used for economies at nation level, can be used at the firm level, too. It
supports the concept that TFP, which originated to assess the performance of any
country, trickles down to the firm level.
But the aggregate production function is only a little less legitimate a concept that,
say, the aggregate consumption function, and some kinds of long-run macro
models, it is almost as indispensable as the alter for short run. As long as we insist
on practicing macro-economics we shall need aggregate relationship. (Solow,
1957, p. 312)
In previous pages, a discussion was made about factors responsible for better
productivity. Solow is the first author who put forward a simple way to measure the
effect of technological changes by using U.S. manufacturing data from 1909 to 1949. The
Chapter Two: An Overview of Total Factor Productivity 81
proposed production function by Solow is well recognised and much cited. This all shows
the significance of TFP (aggregate function) at macro level. Nevertheless, Nadiri (1970)
has put serious questions on the models of production function, questioning how changes
could measure due to technical alterations. Nevertheless, there are serious flaws taken
together in aggregate production function and doubts are quite high about its existence
and use for empirical studies. Even now, there is not an appropriate method to evaluate
the enormity and stability of its parameters. According to Nadiri (1970), there is evidence
which suggests that the specification of the form of the aggregate production function is
of secondary significance and not contribute significantly in the explanation of the
residual. This statement supports the idea of Robinson (1953), who said that aggregate
production had led us to miseducation about the whole process.
Though debate sheds light on the aggregate production function, which is the base
for all the models of production function, it is not acceptable to everyone because there
are many shortcomings in this model. Regardless, many authors and researchers are using
it continuously. Not all of the above discussion clarifies the differences among
economist who are discussing TFP and aggregate function. The purpose of the discussion
is to have an idea about the kind and level of difference among economists.
2.7 Significance of TFP in Economic Perspective
A positive and sustainable growth is the ultimate target for economies at macro
level and for firms at micro level. Particularly, its significance has increased in the
current scenario where globalisation has removed the economic borders and firms are
working in a global environment. Felipe (1997) elaborates the sources of growth and
Chapter Two: An Overview of Total Factor Productivity 82
concludes that primarily, growth stems from two sources: factor accumulation and
productivity growth. The importance of these two components is highly debated by both
fundamentalist and assimilation theorists. Felipe further explains that TFP is a
neoclassical concept that attempts to measure productivity, taking into account all factors
of production; thus, the underlying assumption is that labour is not the only input
(classical Ricardian labour theory of value). Second, TFP is a notion linked to the
aggregate production function, a neoclassical tool. Productivity, per se, is a technical
concept that refers to a ratio of output to input, in other words a measure of efficiency.
When referring to a single input (i.e. partial productivity), typically labour (Q/L), the
notion of productivity does not pose any problem. However, when more than one input is
to be taken into account (e.g., labour and capital), the problem that arises is how to weigh
each factor in the quotient. The arguments put forward by Felipe back up the viewpoint
of Hulten, who has been talked about in previous sections. Both are of the view that there
is a debate about the contribution factors in TFP. Both have the same opinion of the value
of TFP in contemporary scenario.
Cororaton (2002) wrote that the World Bank Economic Review has shown up the
significance of TFP. It has also published a series of its publications on the topic that
highlight the essential role of TFP in movement of economic growth in different
economies. The research seeks out the decisive role of factor accumulation in economic
growth while identifying some additional factors, which contribute significantly to the
economic growth of different countries. TFP is one of these factors. TFP, as a residual,
could be due to many factors. These may be the role of technology, better skill of
workers, strong infrastructure, established organization, and effective ways of turning
Chapter Two: An Overview of Total Factor Productivity 83
raw material into finished goods. Many authors have tried to incorporate these factors in
the growth of economies. Arguments by Cororaton support the debate about the role of
different factors in the uplift of TFP. In addition, Cororaton does not ignore the role of
factor accumulation and, at the same time, gives credit to technology and skill of the
workers.
The TFP gap among the developed countries, which was quite large around 1870,
is narrowing. As written by Greasley and Madsen (2006, p.527), ―Wide productivity
variation existed around 1870 and thereafter that technological congruence lessened the
gaps over the course of the twentieth century‖. Solow supported this argument, saying
that growth model, which predicts the gap between developed and underdeveloped
countries is becoming narrow. Greasley and Madsen (2006, p. 527), write,
This paradigm highlights the erosion of British and most especially of U.S.
productivity leadership, as trade, and circulation of capital, labour, and
knowledge, especially advantaged a group of socially capable followers,
particularly those countries with institutions and policy regimes which fostered
investment, work, and efficiency.
Felipe (1997) did a survey of the empirical literature on TFP and sources of
growth in East Asia. Felipe concludes that these studies build up awareness of
productivity in these countries. However, at the same time the theoretical concept of TFP
is rather weak and it generates many problems. Based on this, it seems as if the whole
studies were less reliable since different authors concluded different results from the
same data. Ultimately, there was a big variation in results from the same countries and
same periods. This variation may be due to the assumptions, data sources, methods, and
Chapter Two: An Overview of Total Factor Productivity 84
techniques to process data. The discussion in foregoing lines points out another problem
with the TFP concept. The conclusion can be that there is a dire need for another theory
to assess the economic growth of a country, as said by Prescott.
2.8 Sources of TFP Growth
How can people have a better TFP? A question arises. From time to time, many
economists have tried to respond to this, but it is all in vain. The economists divide on the
question of what is the source of TFP growth. Keeping this in mind, a sum up of views of
a few authors, who describe different reasons of growth, is presented below.
Hulten (2000) examines the economic growth of the U.S. and concludes that there
is a remarkable growth in TFP of U.S. in the last two centuries. However, this growth rate
is not smooth; rather it has an average growth of 1.7%. Having made much effort to
identify the reasons for the growth, people finally came up with different theories and
models. Some attached this growth with the growth of technology and organisational set
ups. Hulten further highlights that Marxian and neoclassical theories are of the view that
this growth is due to better productivity, which is the result of technology and
development of organisational structures. Another group considers investment in human
capital, knowledge, and in fixed capital as the reasons for growth. One thing is obvious
from all above discussion: There is a possible resemblance between the theories. The first
group that relates to the growth in TFP with the technology and organisational
development is the result of investment used to develop human capital and fixed capital.
Both theories have their own parameters.
Chapter Two: An Overview of Total Factor Productivity 85
Baier, Dwyer, and Tamura (2002) summed up the result of a vast survey and put
forward the results. Results of survey show that out of 145 countries, 24 had 8% average
output growth per worker associated with TFP growth. They divide TFP growth into nine
different regions, and out of these regions, TFP growth accounted for about 20% of
average output growth in three regions and between 10% and 14% in three other regions.
In rest of three other regions, TFP growth is negative on average. It shows that it is not
necessary all factors contribute positively. In fact, there is a big variation in contribution
by different factors in different countries. However, an average 8% labour productivity
growth associated with TFP shows that the rest of the growth is a result of other factors.
These may be due to technology, knowledge, accumulation factors, etc. This is a very
common question that how much growth in TFP is due to growth in workers‘
productivity and due to change in technology, institutional changes, and other factors.
Baier et al. (2002) further explained that there was much dissimilarity in the contribution
percentage. It starts from 34% and goes to negative input in some countries. However,
the role of different factors has not underlined. Accordingly, divergent factors in TFP
have glimpsed.
Giandrea (2006) has elaborated that a merger is another source of TFP growth.
Giandrea states that during last decades of the 20th
century, firms began to undertake
horizontal mergers in order to perk up TFP. Nearly 28,816 mergers took place only in the
U.S. during 1996-2005. The idea was that merger is a most suitable method to improve
TFP. Giandrea concludes that many studies show that these mergers have contributed in
achieving objectives and the main target was to achieve high TFP. However, at the same
time it reduced the competition among firms, while competition is a basic motive for
Chapter Two: An Overview of Total Factor Productivity 86
enhancing TFP. Since competition stirs up firms to adopt policies of cost cutting and
investment to get over the value of goods, it has shown that there is a positive correlation
between merger and TFP. Nevertheless, its impact varies from industry to industry
depending upon size, area, types of products etc. Merger is one of the sources of TFP that
boost up capital, both human and physical. It also furnishes superior access to technology
offering synergy to the production function.
There is a continuous debate among researchers about the role of capital in TFP.
Sometime it was overestimated. Stainer (1997) verifies this observation. He broadens
understanding by saying that many researches show when in U.S., productivity was low,
and capital investment was quite high and in some cases role of capital in productivity
overrated. Stainer further squeezes out that in the case of the U.S. and UK; decline in
productivity observed even there was an increase in capital investment. Moreover, if
there is an improvement in productivity hardly it is 10% due to capital. In this report, TFP
appears to yield a complete view of the industry since it takes all quantitative factors of
inputs and outputs. In addition, an effort to quantify the correlation between TFP and
several other factors has also made out. Determination of factors affecting TFP of the
industry can exercise to improve productivity. Stainer insists on a balance approach to
have better productivity. It seems that share of labour and capital for a better productivity
cannot be substitutes of each other; rather, they complement each other and at the same
time, other factors like government policies and organisational structure cannot be
overlooked.
Prescott (1998) investigates a big difference in income level of different countries
during 1996-2005. Prescott also points out a huge gap between labour productivity of a
Chapter Two: An Overview of Total Factor Productivity 87
rich and poor country, e.g. a worker of U.S. is 20 to 30 times more productive than a poor
country like Nigeria. There is even a big gap between the labour productivity within a
country among different firms. Many people argue the difference is due to capital and
technology but this is not imperative, the most important reason is the difference in TFP.
Prescott‘s argument based on this observation gave a new dimension to the significance
of TFP in the economics of the world. Prescott urges to develop a new thinking about
TFP to find out the actual reasons of the variation in productivity across the different
countries and firms. There are many ways to determine labour productivity not directly
but also indirectly by determining capital per worker (Prescott, 1998). Prescott
emphasizes to develop a new theory of TFP and for this purpose; he presents facts from
the industrial data. According to him, there is a huge difference in TFP across the
countries other than physical stock and technology differences. Prescott introduced a new
area for research in which reasons of discrimination are recognizable between poor and
rich countries. Prescott does not agree with the notion that capital per worker is one of the
reasons, rather he says, that large differences in output per worker that cannot be
accounted for by difference in capital per worker is the difference in TFP. Prescott further
states there is a direct link between capital per worker and TFP and it is obvious from the
level of TFP in rich countries.
Erosa and Hidalgo (2007) have also talked about the issue of a gap between poor
and rich countries. According to them, three main factors cause the gap: low down
aggregate in TFP, big differences in output per worker across industries, and high
employment shares in sectors with the lowest labour productivity. Besides having
Chapter Two: An Overview of Total Factor Productivity 88
discussed more grounds for the factors, they gave evidence by using capital market as an
indicator.
This discussion leads to the addition of many other causes of disparity between
poor and rich. Every proposed factor has a contribution, and it is not easy to make one or
few factors responsible for the gap. However, it looks that every factor (TFP, human
capital technology, capital per worker, worker productivity, skill, knowledge, physical
capital, government policies) has its own sharing. Nevertheless, share percentage varies
from country to country and firm to firm. It is evident, from the findings of Prescott
(1998), Erosa and Cabrillana (2007), Felipe and McCombie (2007), Greasley and Madsen
(2006), Grosskopf and Self (2006), and Giandrea (2006).
Sharpe (2002, p. 39) has identified following seven areas of productivity growth:
1. Rate of technical progress
2. Investment in physical capital
3. Quality of workforce
4. Size and quality of natural resources
5. Industrial structure and intersectional shifts
6. Macroeconomic environment
7. Microeconomics policy environment
Oguchi (2004) identifies and evaluates the role of TFP in economic prosperity of
the countries and verifies that TFP growth played a vital role in economic growth of most
economies while maturity in the quality of labour contributed greatly to economic
growth. In addition to that, accumulation of capital also was another factor that chipped
Chapter Two: An Overview of Total Factor Productivity 89
in to the economic growth. Finally, impact of capital became more significant when it
allocated to sectors that were more productive, Oguchi concluded.
A big discussion is also there about the role of technology in the change of TFP
values. Change in TFP is a result of change in returns that is much higher than the
opportunity cost of all activities, not technology alone. There is no chance that
technological change can precede without change in TFP. Both are interdependent and
difficult to separate (Lipsey and Carlaw, 2004). Economic literature points out three main
sources of TFP growth: physical capital, human capital, and technology. In traditional
growth model, investment in physical stocks is considered the most robust source of TFP
growth. After 1960, human capital captured the focus of people as being one of the most
effective growth sources. The literature also illustrates that after 1960, human capital was
a major source. Far East financial crises also proved it, showing that growth based on
factor accumulation was not as strong as based on human capital (Grosskopf and Self,
2006). Research by Grosskopf and Self also supports the argument of Stainer (1997),
who says that the bases of TFP growth are turning human capital reliance into other
factors. The physical capital does not provide the required support in all cases and all the
times.
The above discussion converges on one point that TFP growth is the reason for
economic growth. For a higher TFP, physical capital, human capital, and technology are
the primary contributors. Adding on to that, the author of this report asserts that although
there is a no denial of above stated factors, some important factors are missing. These
factors are qualitative in nature and are not of less value though measurement in
performing of their role is quite difficult. These factors are:
Chapter Two: An Overview of Total Factor Productivity 90
1. Government policies
2. Level of corruption in society
3. Political stability
4. Over all attitude of people towards work
5. Level of education
6. Historical background of nations
7. Law and order situation
8. Collective destination of country
9. Awareness about human dignity
10. Recognition of achievements
11. Respect for work
The factors given above are not conclusive in nature. There might be a number of
other factors, but one thing is sure; high TFP is a result of cohesive and comprehensive
effort.
2.9 TFP and Industrial Engineers / Business Managers
The above discussion depicts the history of TFP, which starts as a production
function and ends with neoclassical models. Economists whose emphasis was to identify
the factors mainly worked on TFP. There is a continuous development in the models of
the production function starting from a simple mathematical equation. It is evident from
the Cobb-Douglas model, which says that capital and labour are main contributors. Solow
modified it and added technology as a factor, which he called residual, or the growth
unexplained by capital and labour growth. In the last, Prescott demanded to consider TFP
Chapter Two: An Overview of Total Factor Productivity 91
theory to explain the current era growth and finally Landau, R., T. Taylor, and G. Wright
(as cited by Sharpe, 2002) introduced five more areas in production model. On the other
hand, a group of people defines TFP in a different approach. In this part of the chapter, a
discussion will cover the TFP concept, described by different authors who view TFP
from a different angle.
In TFP-related literature, there is another definition of TFP, which is very
different from the concept proposed by economists since the start of the industrial era. It
is a ratio of total tangible output and total tangible input as defined by Sumanth; Sink,
Craig, and Harris; Kendrick; Office of National Statistics; and American Productivity
Center. This definition is quite different from the most common definition mainly put
forward by economists. As seen in previous paragraphs TFP is considered as the residual
of the growth which other factors are not able to explain. It is the growth that embodies
technological and organizational contribution. In the following lines, there is a discussion
about this dimension of TFP.
Mahadevan (2002) explains the meanings of partial, multi and total factors
productivity in a different way. Partial productivity indicates the output contributed by a
single factor of production, like labour hours employed to produce a certain number of
shirts in a clothing mill. The multi factor productivity considers the joint use of the all
production inputs. Dissimilarity between TFP and multifactor productivity is that the
latter includes the joint productivity of labour, capital, and intermediate inputs, and the
former considers the joint productivity of labour and capital only (Mahadevan 2002).
Chapter Two: An Overview of Total Factor Productivity 92
Though people commonly use the terms multi and total factor productivity in
literature in order to interchange with each other, there is a great need to have a clear
point while considering these terms.
The official publication of the Office for National Statistics (ONS) which is the
agency of UK government has explicated the diversity between multi factor productivity
(MFP) and TFP. The ONS is responsible for compiling, analysing, and disseminating
economic, social, and demographic statistics about the UK. The agency takes ―multi
factor productivity as the residual contribution to output growth of an industry or
economy after calculating the contribution from all of its factor inputs. It is also
sometimes called Total Factor Productivity‖ (Camus, 2007, p. 182). Camus further
explains:
MFP can also be viewed as the unexplained difference between the growth in cost
of inputs and the growth in cost of output. MFP (Multi-factor productivity) –
sometimes called total-factor productivity (TFP) or growth accounting, apportions
growth in output to growth in the factor inputs, capital and labour, and growth in a
residual that represents disembodied technical change. Examples of such change
are increased knowledge through research and development (R&D) or
improvements in organisational structure or management.
It seems that there is a big difference in the meaning of MFP and TFP among the
two well-known and established organizations, the Asian Productivity Organization and
Office of National Statistics. This confusion has led some to differentiate between MFP
and TFP. Sink (1985) has also increased the level of difference. He introduced new
terminology to express the MFP and TFP and proposed Multi Factor Productivity
Chapter Two: An Overview of Total Factor Productivity 93
Measuring Model (MFPMM), which according to him takes all input as denominator and
used to asses the ratio between total output and total input. This is similar to the model
named total productivity proposed by Sumanth (1990). National Productivity Corporation
(1999) has also explained that TFP measures the synergy and efficiency of the utilisation
of both capital and human resources. It is also regarded as a measure of the degree of
technological advancement associated with economic growth. Higher TFP growth
indicates efficient utilisation and management of resources, materials and inputs
necessary for the production of goods and services. Sumanth (1990) and the American
Productivity Center both have developed their TFP model on this definition. According
to Felipe (1997), TFP is an attempt to measure productivity taking into account all factors
of production. Thus, the underlying assumption is that labour is not the only input
(classical Ricardian labour theory of value). Second, TFP is a notion linked to the
aggregate production function, a neoclassical tool. Productivity, per se, is a technical
concept that refers to a ratio of output to input; in other words, a measure of efficiency.
When referring to a single input (i.e. partial productivity), typically labour (Q/L), the
notion of productivity does not pose any problem. However, when more than one input is
to be taken into account (e.g., labour and capital), the problem that arises is how to
weight each factor in the quotient, Felipe concludes.
Lipsey and Carlaw (2001, p. 3) investigated in depth different definitions and
concepts of TFP and finally concluded that:
One group holds that changes in TFP measure the rate of technical change (Law,
Statscan, Krugman, and Young). We refer to this as the ―conventional view‖. The
second group holds that TFP measures only the free lunches of technical change,
Chapter Two: An Overview of Total Factor Productivity 94
which are mainly associated with externalities and scale effects (Jorgenson and
Griliches). The third group is sceptical that TFP measures anything useful
(Metcalf and Griliches). (2001, p. 3)
Sargent and Rodriguez (2000) have compared labour productivity and TFP.
According to them, both academics and policy makers commonly use the two competing
measures of productivity, which are labour productivity, an output per hour; and TFP,
which measures net productivity of the contribution of capital. They further state that
both measures have their own place, and that neither tells the whole story. TFP is more
useful over the long run, assuming that one is confident about the underlying growth
process and the quality of capital stock data. Labour productivity is more reliable in the
short run, when there is doubt about the underlying growth process, or when capital stock
data are unreliable. This statement reinforces the observation that there is no absolute
measuring system suitable for every condition.
Author of this report summarizes all above discussion in following points:
1. Production function is a very important concept that describes the relationship
between output and input assuming that firm is operating with highest efficiency
and after assuming that engineering problems have resolved. Nevertheless, in real
world, there is no chance that all engineering problems are solved and firm is not
facing any technical or engineering problem in its function.
2. History of production function is full of struggle of changes, introducing new
ideas, commenting on old adages, discovering new ways, presenting new models,
creating discrimination between poor and rich and so on (Humphrey, 1997;
Mishra, 2007).
Chapter Two: An Overview of Total Factor Productivity 95
3. Going back to the olden times, one can find that nearly 18 economists from seven
countries over a span of 160 years before Cobb Douglas discussed production
function as said by (Humphrey (1997).
4. Mishra (2007) divides history of production function into three main periods (1)
Adam Smith era or even before, (2) Carl Max time and (3) Neoclassical period to
defend capitalistic theory. Paul Douglas used data to create index that became the
base of Cobb-Douglas function model. Robert Solow Model (1957) explains the
share of technology in the theory.
5. Needed: Theory of TFP by Prescott (1998) was presented which initiated a
debate among economists.
6. Five more areas added to production models proposed by Landau, R., T. Taylor,
and G. Wright (as cited by Sharpe 2002)
7. TFP has two broad definitions; one it is a ―residual‖ or the growth embodied due
to factors, mainly technology other than capital and labour (Solow 1957) and next
describes the ratio of all inputs to all outputs (proposed by Sink, Sumanth, Hines,
Kendrick and Creamer, Craig and Harris, Office of National Statistics with minor
difference in nomenclature of terms).
8. TFP proposed by economists mainly used for long-term data analyst. Whereas,
TFP proposed by industrial engineers can be used for cross sectional data, as
proposed by Lipsey and Carlaw (2001).
9. There is a little attention of the researcher to identify the role of attitude of the
people. Parente and Prescott (1994) and Hall and Jones (1999) have mentioned
Chapter Two: An Overview of Total Factor Productivity 96
role of institution, corruption level, transparency in the growth of TFP but they
have ignored other qualitative factors that could hamper TFP growth.
10. It is proposed that there should be studies to assess the correlation between TFP
growth and the attitude, behaviours, preferences, culture, norms, values and even
religious beliefs etc.
2.10 Productivity Measuring Approaches and Their Assumptions
The concept of production function was discussed in the previous part of this
chapter, which explained the meaning and definition of TFP. This part also discussed
how it emerged from economic background to the side of industrial management.
Summarizing it the two broader definitions of TFP found, as per Solow, it is residual of
productivity growth unexplained by capital and labour input growth and as a ratio of total
tangible input and total tangible output as taken by Sink, Craig and Harris, and Sumanth.
Both schools of thoughts have shown their own concern about the TFP measurement
complexities. This part of the chapter has been dedicated to thrash out different
approaches to measure productivity. The intention of this debate is to comprehend
different ways and techniques to measure productivity at firm, industry and national
level. Additionally, criticism of different authors will put forward different productivity
measurement techniques. Moreover, this part will also cover classification of productivity
measuring methods.
Generally, people agree that productivity measurement is critical for economic
development. A very common and unanswered question in this regard is the availability
of most apposite methods to measure productivity. This question is becoming more
Chapter Two: An Overview of Total Factor Productivity 97
essential to be answered in the present scenario due to rapid and unexpected changes in
the world, particularly in the world of business. The volatile nature of the business world
has impeded the productivity measurement process to some extent. Based on its
significance for economic development, it now has become a challenge for the researcher
to build up a new and a comprehensive model. The literature survey bears out that it is an
unending story because the prime contribution in the business world is uncertainty, which
compels researchers to investigate such a productivity measurement method, which could
fulfil requirements of obtaining an accurate measurement.
In addition to that, factors affecting productivity level are increasing day by day,
and they create a big hurdle to cover all factors while measuring productivity. ―It is a
popular game among the researchers to find a suitable measure for denoting the
effectiveness of a set of manufacturing circumstances and using this measure to monitor
the changes‖ (Stark and Bottoms, 1980, p. 100). Many authors have recommended a
number of ways to measure productivity that are accessible in the literature. Selection of
technique and method to measure productivity is the most critical step. ―The range of
measurement approaches and measurement tools is quite large. As with other
productivity tools, the choice of an appropriate tool depends on the nature, scale, level
and phase of the investigation. There are even ‗political‘ considerations‖ (McKee, 2003,
p. 138). As expressed by McKee, selection of tools depends upon many factors. Based on
the observation it can assume that wrong selection of tools may cause some wrong
effects.
―Productivity estimates must be considered as approximate rather than precise‖
(Industry Commission, 1997, p. 28). Industry Commission further says, ―Measurement of
Chapter Two: An Overview of Total Factor Productivity 98
productivity is not an exact science. There is a range of important, but not fatal, technical
difficulties in measurement. Claims of precision in measurement of productivity are
inappropriate, especially at the national level‖. Industry Commission also discusses that
―productivity measures cover the market sector rather than the whole economy‖. The
above discussion establishes a view that one should never expect a high level of precision
while measuring productivity, as it is just estimation. No one can deem entire results as
incorrect; rather, one can say that results may be biased and based on some extraneous
data. This might be because there are several factors that can alter the situation and
generally intricate to identify the exact cause of change. For example, modern technology
can increase labour productivity and with passage of time, skill of workers will also be
increasing. At the end of the day, it would be highly difficult to estimate exactly the share
percentage of technology and learned skill of workers in high productivity. There are
several other questions like that which have been put forward by different authors. On the
other hand, various scholars and even institutions are trying to resolve and solve this
complication. Nonetheless, the productivity measuring is an ongoing process and as said
by Nadiri might end one day.
Maital and Vaninsky (2000) have pointed out a serious flaw in the productivity
measuring techniques. Maital and Vaninsky further acknowledge that commonly used
approaches may lead to results contradictory to straightforward economic reasoning, and
hence generating paradoxes. Moreover, they argue that there is a well-known paradox in
the literature of economics related to structural changes. The structural changes can
increase in the relative weight of inexpensive goods formally to generate an increase in
an average price. The work of Maital and Vaninsky tells that there is major confusion
Chapter Two: An Overview of Total Factor Productivity 99
attached with the modern measurement of productivity, which is based on structural
changes. This entire debate advocates that prior to studying and applying productivity
measurement process, it is mandatory to be ready to accept the outcome that the results
estimated are not fully correct. A hope survives that if the productivity of any firm or
industry is better, one can apply any method or technique to measure it and the results
will not be different. This can be explained with the help of an example of a natural scene
in which the viewer might see it as beautiful from his one angle he is using. ―All
measures of productivity considered are credible in the sense that highly productive
plants, regardless of measure, are clearly more profitable, less likely to close, and grow
faster‖ (Dwyer, 1996, p. 13). Dwyer‘s thoughts support the theme that a beautiful picture
always gives a better look irrespective the angle of observation.
2.11 Productivity Measurement and Its Classification
Several scholars have classified productivity-measuring techniques into different
classes and types in a diverse way. Difference in views might be due to many reasons but
the most appropriate reason could be the complexities associated with a productivity
measurement system. Based on this observation, classification of productivity measuring
techniques gives a blurred picture that is sometimes confusing. According to Maital and
Vaninsky, productivity measurement leads to some confusion and a paradox. Murugesh
et al. (1997) classified a productivity approaches into two main aspects, which is based
on a broad literature survey on productivity studies in manufacturing systems over the
last 20 years revealed:
Chapter Two: An Overview of Total Factor Productivity 100
1. The effort on productivity in the initial days was biased towards its improvement
only and, later, it shifted slowly to its management.
2. Reception of productivity measurement among the manufacturing community was
moderate during the early days of 1970s.
Murugesh et al. affords an argument that initially productivity was considered
essential to improvement but now it has become as one of the central functions of
management. It shows the shift of the productivity concept and benefits. It also supports
the fact that initially productivity was considered as the province of economists, but now
industrial engineers and business managers also take part in productivity measurement
and in its implication from business outlook. This is evident from the activities of many
productivity organizations. Examples of publications of OECD and Asian Productivity
Organizations are very clear indicators of this context. A major part of the publications of
the aforementioned two prominent organizations covers engineering and managerial part
of productivity. Another example is books from different authors, as Sumanth wrote a
book on productivity and titled it Productivity Engineering and Management. This
supports the theme proposed by Murugesh et al. that now productivity has become one of
the main functions of management.
Parsons (2001) divided productivity measurement into following seven different
methods/approaches:-
1. Control Panels
2. The Objectives Matrix -OMAX
3. The Balanced Scorecard
Chapter Two: An Overview of Total Factor Productivity 101
4. Productivity Accounting
5. Throughput Costing
6. Economic Value Added -EVA.
7. Integrated Business Control – IBC
Parsons has discussed above-cited methods in finer detail and concludes that no
performance measuring system is static whereas, measures are dependent on strategy and
action. Parsons further states that the choice of method mainly depends upon what is
most suitable to achieve the target or objectives of the whole process. Parsons has cited
the following quotation of John Kenneth Galbraith, which paints the true picture of the
productivity measurement phenomenon, ―To many it will always seem better to have
measurable progress towards the wrong goals than immeasurable progress towards the
right ones‖ (2001, p. 39). The discussion is based on the observation, which Parsons
views as part of the management. The focus of all approaches mentioned by Parsons
provides a clear picture to management and expects that it would be helpful for
management in an improvement process, which is the definitive goal of the firms.
Salinger (2001) divides productivity measurement approaches into the three
following categories:
1. Growth models, which attribute increased economic growth either to accumulate
physical or human capital or to increase efficiency of their use.
2. Neoclassical growth models which view technical progress as exogenously
determined.
Chapter Two: An Overview of Total Factor Productivity 102
3. Endogenous models which consider a range of structural and policy variables that
contributes to differences in technology endowment, investment, and knowledge
accumulation among countries.
These approaches are based upon the change associated capital, technology,
efficiency, and accumulation of knowledge and are suitable and appropriate in such
studies where time series data is available along with the data of technological,
knowledge accumulation change, and change in capital or investment. In addition to that,
these approaches are particularly helpful in calculating the benefits of new technology
and investment.
Mawson, Carlaw, and McLellan (2003) have classified productivity measurement
approaches into four categories:
1. The growth accounting approach
2. The index number approach
3. A distance function approach
4. Econometric approach
Each of these approaches is discussed below,
The growth accounting approach, The growth accounting approach is based upon
following four assumptions:
1. Technology and TFP term, is separable
2. Production function exhibits constant returns to scale
3. Producers behave efficiently in that they attempt to maximise profits
Chapter Two: An Overview of Total Factor Productivity 103
4. Markets are perfectly competitive with all participants being price-takers who can
only adjust quantities while having no individual impact on prices
The majority of statistical agencies that produces regular productivity statistics
use the index number approach. For example, the Australian Bureau of Statistics
calculates market sector multifactor productivity using the index number approach based
on a Törnqvist index, as does the U.S. Bureau of Labor Statistics.
The index number approach. The index number approach to calculating
productivity involves dividing an output quantity index by an input quantity index to
achieve a productivity index. Mawson et al. (2003, p. 20) has further stated that:
There are two main approaches to choose an index number formula: the economic
and axiomatic approaches. The former approach bases the choice of index
formula on a producer‘s underlying production technology, and therefore has
theoretical microeconomic underpinnings. The axiomatic approach bases the
choice of index formula on desirable properties that indexes should exhibit. Once
the index formula is chosen, consideration then needs to be given as to whether
the productivity index should be chained to reduce substitution bias associated
with fixed weight indexes.
This approach is under utilized as mentioned by Mawson et al. (2003), who propose that
index numbers application in productivity measurement process is quite useful.
Distance function approach. To measure TFP, an approach based on distance
function separates TFP into two components using an output distance function. More
generally, the distance function (which is the dual of the cost function) has discussed in
the consumer and production literature where duality concepts are used. In principle, this
Chapter Two: An Overview of Total Factor Productivity 104
technique enables a change in TFP to be deconstructed into changes resulting from a
movement towards the production frontier and shifts in the frontier. The output distance
function measures how close a particular level of output is to the maximum attainable
level of output that could be obtained from the same level of inputs if production is
theoretically 100% efficient. In other words, it represents how close a particular output
vector is to the production frontier given a particular input vector.
Econometric approach. The econometric approach for productivity measurement
involves the estimation of parameters of a specified production function (or cost, revenue,
or profit function, etc). Often the production function is expressed in growth rate and of
the contribution of specific determinates are then estimated to determine which
determinants are critical. One major advantage of the econometric approach is ability to
gain information on the full representation of the specified production technology. In
addition to estimates for productivity, information about other parameters of the
production technology is also obtained.
The above four approaches are useful and appropriate for a time series data
related to production and change in technology, capital etc. Additionally, these
approaches enable researchers to segregate productivity growth into separate
determinants, such as change due to the change in technology, capital, etc. These
approaches also help to assess the level of productivity of any firm, and the potential of a
firm with the same level of inputs. In other words, one can assess the potential
productivity of a category of firms. Econometric approaches are very useful to assess
productivity but this approach requires time series data and furthermore, these are based
on stringent assumptions.
Chapter Two: An Overview of Total Factor Productivity 105
Gharneh (1997) classifies productivity measurement into two main categories: (a)
production function and index numbers and (b) accounting models.
Production function is one way to assess productivity and is widely used by
economists. Many models are available to assess the productivity of any production
function and accountants are more concerned about different financial ratios. These
approaches are based upon a broad classification of productivity measurement
approaches. In fact, the two categories are widely used by the researchers as mentioned
by Gharneh. Both have their advantages and disadvantages. One must keep in mind the
limitations and assumptions when applying these approaches to productivity
measurement. The main objections to these approaches are below.
In physical numbers, items produced by certain numbers of labour units, or by
consuming certain amounts of raw material are assessed. It ignores the impact of
technology, the business environment, employee satisfaction, compensation system, etc.
These models assume that two firms can have same parameters at a time therefore any
minor change in these parameters can change labour productivity. This kind of
measurement is most useful for short-term productivity measurement process and for
partial factor productivity.
Another approach based on financial values of input and output has been objected
to that several outer factors of normal business control examples, such as the price of raw
materials or utilities, impact of government and international regulations, etc. Here, a
minor change in the price of raw material can alter the productivity, e.g. cost of furnace
oil to run boiler to get steam for dyeing. Nevertheless, various organizations use this
approach often and bring up results keeping all factors in view. Gharneh‘s classification
Chapter Two: An Overview of Total Factor Productivity 106
is too broad to ignore other methods as described by the Parsons. However, this
classification serves the purpose of the scholars who are interested in measuring
productivity. Mahadevan (2002) has classified measuring of TFP into two main
categories, the Frontier approach and the Non-Frontier approach. This classification
stands on econometrics.
According to Mahadevan (2002), frontier refers to a bounding function, or more
appropriately, a set of best obtainable positions. Thus, a production frontier traces the set
of maximum outputs easily to get to a given set of inputs and technology, and a cost
frontier traces the minimum achievable cost given input prices and output. The
production frontier is an unobservable function that is said to represent the 'best practice'
function, as it is a function bounding or enveloping the sample data.
The frontier and non-frontier categorisation is of methodological importance
since the frontier approach identifies the role of technical efficiency in overall firm‘s
performance, whereas the non-frontier approach assumes that firms are technically
efficient. Sink (1985) has put forward three approaches to measure productivity: (a)
Normative productivity measurement methodology (NPMM), (b) Multifactor
productivity measurement model (MFPMM), and (c) Multi-criteria Performance/
productivity measurement techniques (MCP/PMT). There is a brief discussion about the
aforementioned approaches in the following lines.
Normative productivity measurement methodology (NPMM). The normative
productivity measurement methodology focuses on the business behaviour of employees
at all levels. In fact, this approach acts for a spotlight to improve productivity with the
help of participative management philosophy.
Chapter Two: An Overview of Total Factor Productivity 107
Multifactor productivity measurement model (MFPMM). Multifactor of
production makes use of this approach, which are also called ―TFP‖ models, the APC
model named for the American Productivity Centre, developed the approach in 1977. As
per Sink (1985), this model is a measure of profitability.
Total Revenue (TR)
Measure of Profitability = ─────────
Total Cost (TC)
This model can be used to evaluate change in profitability of two different periods
with the help of following mathematical equation:
ΔTR
Measure of Profitability = ─────
ΔTC
Sink (1985) discusses this model in detail and gives an explanation that how to
use this model.
Multi-criteria performance/productivity measurement techniques (MCP/PMT).
The third approach presented by Sink (1985) is called ―Multi-criteria Performance or
Productivity Measurement Techniques‖. In this approach, a criterion is set for
performance or measurement and subsequently, the performance or productivity is
measured against this criteria set. The main advantage of this method is that setting of
criteria and measuring of performance are performed with the same methodology and
assumptions. This may be physical number of input and output or value of the input and
output.
Chapter Two: An Overview of Total Factor Productivity 108
Parsons (1980, p. 60) has proposed another approach called, ―Profitability
analysis in inter-firm comparison: a new approach‖. Parsons (1980) developed this
system with the help of the National Productivity Institute. According to this approach,
profitability of different firms is compared and finally the firms that are more profitable
are identified. In this case, productivity equates with profitability. Some scholars use the
term profitability synonymously with productivity.
Singh, Motwani, and Kumar (2000) divide productivity measurement approaches
into three categories, (a) index measurement, (b) linear programming, and (c)
econometric models.
Singh et al. (2000) surveyed the studies of different productivity measurement and
showed that 11 authors used the index measurement approach and eight researchers used
an econometric approach, while only four used a linear programming approach. Having
written concluding remarks, Singh et al. said:
The theoretical and empirical section of this paper clearly points out that there is
no one method for every company. However, in general productivity
measurement, as well as index and comparison, can provide an objective source
of information about long term operating trends, draw attention to problems of
performance, and inspire a useful exchange of ideas. (2000, p. 240)
The financial ratio approach is one of the most common, simple, and easy ways to
assess the performance of any organisation. Every organisation prepares its annual
account statements and its performance is measured in the light of these statements.
These ratios are the basic criteria for its share value in stock exchange. One should be
clear that such ratios are not indicators of performance and productivity. However, this
Chapter Two: An Overview of Total Factor Productivity 109
approach is widely used in the industry and is easier to identify with. In the APC model,
input and output values are put in the models and profitability of the firms is calculated.
In the case of financial ratios, the value of different inputs and outputs is used to measure
productivity. This may be called partial productivity, and is useful to focus on specific
determinants such as labour productivity, which is calculated with the total labour cost
and total revenue.
Dwyer (1996) used 12 different productivity measurement methods to assess
productivity at the manufacturing plant level. According to Dwyer, ―All measures of
productivity considered are credible in the sense that highly productive plants, regardless
of measure, are clearly more profitable, less likely to close, and grow faster‖ (1996, p.
13). In this way, Dwyer proposes that if plants were highly productive, no matter in
which way productivity is measured, the results would be parallel. Every measure will
prove that the plant is making profit, which is the ultimate goal of the plant owners.
Nevertheless, the assessment tool must be vigilantly selected to assess productivity in
each case and this all depends upon the objectives, capability, and data/resources
available. However, Dwyer favours regression models as a better predictor of plant
growth and survival than the factor shared-based measure of TFP.
The Centre for Inter-Firm Comparison UK (CIFC) has developed 103 different
ratios to assess performance. All these ratios are based on financial reports. CIFC also
publishes an Inter-Firm Comparison (IFC) of its members. CIFC helps the individual
organisations assess their positions in the market.
The following factors are used to weigh up the performance of the firms:
1. Total Capital Employed (Fixed assets, Sales Profits)
Chapter Two: An Overview of Total Factor Productivity 110
2. Different Ratios (Current Ratios, Quick Ratios, etc.)
The main theme of these ratios is to build a relation between diverse outcomes
and inputs of the firms. This is a valid way to verify the performance of any firm. In
accounts, these ratios are used to assess the present health of the firm and provide a
comparison with the past. It is evident from a deep look from the working of CIFC that
centre relies more on finance related matters whereas, economist always rely on the
volume not the value. Economists view profitability as an economic activity, which is
based on many factors and some of the factors, are not under the control of firm e.g.
government policies of taxes which may be different in different parts of the country. The
author of the report views that high productivity is required to get through high profits, if
not, then, high productivity will be of no value, not as a purpose of the firm. This
observation has its basis on the market economy theory of Adam Smith, which says that
the core objective of the firm is to gain profit. It is guided by self-interest and
maximization of profit.
Chen, Liaw and Yeong (2001) have proposed 15 different financial ratios to
assess the productivity of any firm. These 15 ratios cover most financial activities. By
using these ratios, one can assess the productivity of any firm. However, such approaches
do not illustrate the real picture about resource employment; rather these ratios portray
the financial health of the firms. Chen et al also supports the approaches proposed by
CIFC.
Sumanth (1990) has developed the Total Productivity Model, which is defined as
follows:
Chapter Two: An Overview of Total Factor Productivity 111
Total Tangible Output
Total Productivity = ───────────
Total Tangible Input
Sumanth takes this model similar to model presented by Craig and Harris. The
only major difference is that it offers greater clarity about the input and output.
According to Sumanth, there are five major outputs:
1. Value of finished unit produced
2. Value of partial units produced (work in process)
3. Dividend from securities
4. Interest from bonds
5. Other income
Sumanth (1990) further mentioned the following five major inputs:-
1. Human (labour cost)
2. Material
3. Capital
4. Energy
5. Other expense
Chapter Two: An Overview of Total Factor Productivity 112
This model is more comprehensive as compared to previously discussed models.
It appears to be more applicable. Apparently, the APC and Sumanth models are based
upon the total output and total input.
Bernolak (1980) used financial ratios to assess the performance of the firms and
finally concluded, ―There is no single measure or best measure of productivity, but rather
a whole family of measures‖. Boucher (1980) also used the Inter-Firm comparison
method to discuss productivity in Ireland. The Irish Productivity Centre regularly uses
this approach and prepares an Inter-Firm comparison based on financial indicators.
Buttgereit (1980, p. 41) has urged the application of inter-firm comparison based on the
following rationale: ―Inter-firm comparison meets both the wish to have information and
data concerning competitors, the market and its changing nature and the need for a
reliable and efficient management instrument‖. This depicts Inter-Firm comparison as
one of the most useful approaches to measuring productivity. Parsons believes that
traditional financial ratio analysis and value added measures will probably remain a part
of future National Productivity Institute (NPI) surveys, although they will assume a less
prominent position.
Harrington (1980) has also talked about the Inter-Firm comparison as a useful tool
to measure productivity. Gharneh (1997) used accounting models and indexing
numbering approaches to measure productivity and performance in the textile industry of
UK and Iran. Brinkerhoff and Dressler (1990) have also used the ratio of output and input
to measure TFP. However, they have documented many problems associated with this
process.
Chapter Two: An Overview of Total Factor Productivity 113
Ali (1978) has discussed in detail different productivity measurement approaches
and developed a chart. According to Ali (1978, p. 54):
From the discussion of productivity measurement at the company level it can be
found that there are great numbers of possible solutions to the problem of
measuring the productivity of an organisation ranging from purely physical to
monetary and from a simple ratio to an integrated model.
Kumbhakar, Hesmati, and Hjalmarsson (1999) have used different parametric
approaches to productivity measurement and finally concluded that there is always a
model selection problem in this type of study. Furthermore, they said that simulation
studies might improve our understanding of the properties of the different models, and
facilitate model choice.
There has been a drastic change in productivity measuring models since 1980. It
has spread out from the standard calculation of TFP towards more refined methods of
decomposition. Traditionally, TFP framework based on the technical efficiency, but now
there are several restrictions while using this framework such as constant rate to scale,
and allocating and technical efficiency. Different authors recommended a number of
techniques to overcome this issue (Brummer, Glauben, and Thijssen, 2002). Brummer et
al have extended the TFP framework and added technical change, technical efficiency,
allocating efficiency, regarding inputs and outputs and scale components. Based on this
outline, they measured the TFP of four European countries by using panel data from the
dairy sector. This shows a considerable concern of researchers in measuring TFP by
using different techniques. Nevertheless, Brummer et al concluded that there is a still call
for a more refined decomposition of productivity growth, which is essential for policy
Chapter Two: An Overview of Total Factor Productivity 114
makers. It tells the significance of the ignored components and supports the fact that
technology efficiency is not the only way to estimate TFP, as Solow did.
OECD (2005) lays down different ways of measuring productivity. It has also
published an inclusive manual that provides a great deal of knowledge for managers who
are interested to estimate productivity.
There are many different productivity measures. The choice between them
depends on the purpose of productivity measurement and, in many instances, on
the availability of data. Broadly, productivity measures can be classified as single
factor productivity measures (relating a measure of output to a single measure of
input) or multifactor productivity measures (relating a measure of output to a
bundle of inputs). (OECD, 2005, p. 3)
OECD further divides productivity measurement into two main categories at firm
and industry level. It has proposed four types of productivity measurement based on
input, labour (labour productivity), capital (capital productivity), labour and capital
(multifactor productivity), labour, capital, energy, materials and services (KLEMS
Multifactor productivity). OECD has also suggested different methods to measure
productivity.
In its productivity measuring manual, OECD discussed the weaknesses of
application and usages of econometrics in productivity measurement. OECD argued that
when economists measure productivity, they mostly rely on capacity of inputs and
outputs. They avoid assuming a relationship between production elasticity and income
share. This leads to possibilities with econometric techniques. There is a discussion about
the subsidy in order to adjust prices of inputs and outputs. It is quite possible that cost of
Chapter Two: An Overview of Total Factor Productivity 115
inputs increases more than the prices of output. Furthermore, there is a possibility that
technical changes may be different other than assumptions. Fully-fledged models raise
complex econometric issues and sometimes challenge the utility and strength of results.
OECD observation has put a serious question on the efficacy of the econometrics
approach in measuring productivity. It all shows that OECD prefers the accountant
approach over econometrics approach. Why OECD relies more on the accountant
approach rather than econometrics approaches requires more research.
There is another view about the application of econometrics and index
approaches. Hulten (2000), who points out that there is no valid reason to judge the
econometric and the index number approach as competitors, gives examples of synergism
that proved particularly productive. Hulten further says that one can have better results
when econometric methods are used further explain the productivity residual, hereby
reducing the ignorance about the ―measure of our ignorance‖. OECD especially views
econometrics in a different way.
Overall, econometric approaches are a tool that is best suited for academically
oriented, single studies of productivity growth. Their potential richness and
testable set-up make them a valuable complement to the nonparametric, index
number methods that are the recommended tool for periodic productivity
statistics. (OECD, 2005, p. 19)
All this discussion argues that productivity measurement is not a trouble-free process.
There is a great deal of complexities attached with it and final OECD argues that multiple
approaches give better results. Nevertheless, one should not forget that productivity
Chapter Two: An Overview of Total Factor Productivity 116
measurement is not a science; rather it is an estimate as expressed by Industry
Commission.
―Creating and implementing models incorporating the many key pieces of the
productivity puzzle that are becoming increasingly prominent in our ‗new era‘ of
enhanced technology and interdependencies, even for relatively traditional industries
such as those in the food system, is not straightforward‖ (Paul, 2003, p. 173). The above
argument seems to be a big deal of debate among scholars about the complexities of
productivity measurement. However, it is also a fact that people are still striving to reach
a point where maximum consensus can develop. The main cause of complexities is the
vibrant nature of new business era. Paul also sustains the argument and says that:
Recent studies focus on carefully constructing and interpreting data, recognizing
internal and external production structure characteristics and their interactions,
distinguishing between measured and effective input and output prices and
quantities, valuing non-marketed good and bad, identifying spill over effects, and
recognizing links among cost- and demand side drivers (p.173).
There are a number of efforts that have been tried by different authors to develop more
comprehensive model. However, on the other side, swift changes in the business world
are hampering all such efforts. It seems that both ends will move side by side. Change is
an inevitable phenomenon and curiosity to know the depth is the instinct of human
nature.
A number of methods are used to measure productivity. Each has its strengths and
weaknesses. Notably, there are five widely used techniques, two non-parametric and
three parametric: in order, (a) index numbers, (b) data envelopment analysis (DEA), (c)
Chapter Two: An Overview of Total Factor Productivity 117
stochastic frontiers, (d) instrumental variables (GMM), and (e) semi parametric
estimation (Biesebroeck, 2007). Biesebroeck used simulated samples of firms and then
analyzed the sensitivity of alternative methods to the way randomness is introduced in the
data generating process. Biesebroeck concludes,
When measurement error is small, index numbers are excellent for estimating
productivity growth and are among the best for estimating productivity levels.
DEA excels when technology is heterogeneous and returns to scale are not
constant. When measurement or optimization errors are no negligible, parametric
approaches are preferred. Ranked by the persistence of the productivity
differentials between firms (in decreasing order), one should prefer the stochastic
frontiers, GMM, or semi parametric estimation methods (p.529).
It looks from above discussion that there is a continuous anxiety about productivity
measurement. Biesebroeck focused on five major methods and finally concluded that
every model needed a specific environment for its suitability. It supports the theme of
OECD that selection of the methods depends upon the objective, data nature and the
structure of the firm or industry.
Ark (1996) addresses the issue of productivity and economic prosperity. Ark
argues explain that the significance of the productivity is quite high enough for economic
growth. However, there are certain assumptions for a better output. Unfortunately, these
assumptions are very rare particularly; following three assumptions are quite difficult to
meet:
1. Value added is a function of capital input, labour input and the level of
technology.
Chapter Two: An Overview of Total Factor Productivity 118
2. The production function is the same in all industries.
3. Producers face identical factor prices.
Ark further proposes an appropriate and a bottom up approach in this difficult
situation in which aggregate results lift up at the industry level. Ark puts forward this
approach and gives his support to the concept of productivity measurement at firm level.
Additionally, Ark argues that for the economy as a whole, value added is the preferred
output concept because it does eliminate ―double counting of intermediate inputs, such as
raw materials, energy inputs and business services, and is comparable to the domestic or
national product as shown in national accounts‖ (p.25). Ark further states that ―from a
theoretical point of view, the bottom-up approach in combination with gross output is
favoured; the question is how to aggregate productivity figures by industry to aggregate
levels‖( p.24). Ark debates other issues like labour and capital productivity. From all the
discussion, it seems that it is hard to fulfil all assumptions before applying and measuring
techniques. If one looks at the history of productivity measuring models in two centuries,
it is obvious that from Cobb-Douglas to Solow, all are relying on top down approach,
whereas Ark strongly recommends a bottom up approach. Based on survey of the
literature, the author of this report supports the theme of Ark and finds it the most
suitable for measurement. The strong argument in the favour of this approach is that
consistent and sustainable growth starts from the bottom and gives better results that are
ultimately contributing to the aggregate function of the economy.
Nadiri (1970) provides a better insight into the productivity-measuring
phenomenon. Nadiri concludes after a thorough survey of economic literature that in the
last few years there was a tremendous growth in the publications covering issues of
Chapter Two: An Overview of Total Factor Productivity 119
productivity measurement. The most significant advancements in this filed can be
categorised in three main areas: First, there are theories‘ endogenous technical changes
and attempts to explain the production and transmission of new knowledge. Second, there
is the formulation of general forms of production functions that are based on cost
function. It serves many purposes. Third, there is a serious attempt to segregate the pure
residual by attributing the growth of productivity due to change in the quality of inputs.
Nadiri further discusses the contribution of modern economics in this filed and draws a
result that there are still conceptual flaws which need further investigation. Nadiri
provides a complete list of such issues like substitution of factors, true assumptions of
production function models, etc. It all shows that the search for a comprehensive solution
is in progress but the main challenge is the instability in business and every day new
factor affecting production process.
The selection of the most suitable method to measure productivity is one of the
tribulations that industrial managers cope with. Particularly, partial productivity
measuring methods give a picture that is not authentic because it is affected by other
factors. For example, firms can have better technology by putting more capital and this
could change the labour productivity. A solution to the problem is provided by measuring
productivity, incorporating the totality of all outputs and all inputs (Saha, 1994). Saha
further writes that, ―Craig and Harris first suggested a model with this end in view. Their
algorithm determines the total productivity of an organization by dividing‘ the total
production of goods and services by the total resources consumed‖ (p.3).
Saha divides total productivity models into two leading categories: a model,
which takes into account ratio of tangible output to tangible inputs, and an alternative
Chapter Two: An Overview of Total Factor Productivity 120
model that takes into account the value added in the system. The value-added model does
not consider raw materials, parts and services purchased from outside the organization on
the premise that these represent tile fruits of someone else‘s labour and as such are ―an
obfuscation of one‘s own productivity efforts‖ (p.3).
Saha preferred TFP on partial productivity. Based on this assumption that TFP is
a better indicator than partial, it is obvious from Saha‘s discussion that partial
productivity measurement is commonly affected by many factors, which are sometimes
not in control of a firm. However, managers can have a better idea from TFP
measurement.
Industry Commission raised another crucial point in measuring aggregate
productivity. It writes,
The ―non-market‖ sector covers a number of activities in the services sector for
which output cannot be measured independently of inputs. For example, many
government services (public administration and defence) measure largely in terms
of the value of their labour inputs. Many financial services are similarly valued.
Ownership of dwellings has no corresponding inputs. For these ―non-market‖
activities, productivity growth estimates make little sense or are assumed by the
ABS to be zero (p.29).
Sumanth (1990) also takes into account this factor. Sumanth calls it intangible
input and intangible output. Correctly, there are certain factors that are also output of the
firm, but in aggregate function, these are not considered, e.g. market share, brand value,
position in the market, etc. All discussion shows that in productivity measurement by
economists or by industrial engineers the exact reflection of the performance of the firm
Chapter Two: An Overview of Total Factor Productivity 121
is not apparent. It is obvious that to enhance share in the market and to get a high position
among the competitors, there is a need for some tangible input whose output is intangible.
Such intangible output consumes the tangible inputs. While calculating productivity, all
inputs are taken as denominator, whereas not all outputs are taken as numerator. Results
based on such equation cannot represent a true picture since certain outputs are intangible
and are not accounted for. This argument facilitates the observation of Industry
Commission, which says that productivity measurement is not a science. It gives only
estimates— not actual position.
Productivity measurement is one of the main functions of the management. This
function provides assurance to survive in the competitive world. From the last ten years,
there was a growing concern about the productivity measurement schemes had devised to
measure productivity (Miller and Rao, 1989). Miller and Rao further state that one of the
most significant developments is the creation of a link between profitability and
productivity. This method has some advantages over traditional methods in which
productivity is calculated with different angles. The main difference is found in the
methods in which results are expressed in dollars and cents, in other words in ‗financial
language‘, which is more powerful and easily understandable by the managers
responsible to run the firm. This method guides management in a focused way and lends
a hand to managers so that they may understand the whole situation and develop
strategies to achieve better performance.
Miller and Rao have further stated that there are many models, which premise
profit-linked approach. Two of the most common and popular are the (a) profit-oriented
Chapter Two: An Overview of Total Factor Productivity 122
model by the Ethyl Corporation (PPP) and (b) the American Productivity Centre Model
(APC model).
The basic approach of these models is that the firm engenders profit through
productivity and price recovery. Whereas productivity is a measure of factual growth and
it brings a change in physical input and output quantities, while price recovery is the
extent to which firm passes the increase in cost of production to its customer. In this way,
firms keep their profit intact. This approach is more liked by the industrial engineers and
business executives since its results are in term of money and do not need any further
explanation. There are number of models which are based on this approach, like the total
productivity model by Sumanth, Sink, APC and Craig and Harris.
To measure productivity at firm level is obligatory because in most of the firms it
is well-documented phenomenon. All the same, there are many complexities attached
with the whole process. There is a lack of useful tools to measure productivity and always
a doubt on the effectiveness of available data. In most of the cases, available data is
misunderstood and ultimately wrong measuring of productivity comes out there (Wilson,
1994). Wilson proposed a new model to solve this issue and developed an improved
model for productivity measurement based on weighted multi-factor productivity index
(WMFPI) approach represented. Wilson used Analytic Hierarchy Process (AHP) and the
main objective of this process is to determine the relative importance of a set of activities
in either a single-criterion or multi-criteria decision setting. Additionally, AHP provides
an easy-to-use decision-making process to allow the manager(s) to settle on goal
preferences accurately, even in a group decision-making environment.
Chapter Two: An Overview of Total Factor Productivity 123
2.12 TFP at Firm Level: Evidence from Empirical Studies
It is quite obvious from the above discussion that TFP is a concept of which many
people have many different opinions. Generally, it is thought of as a macroeconomic
concept. The majority of scholars who put forward their theories have an economic
perspective and focus on measuring TFP at the national and international levels. In some
cases, scholars have made a serious effort to measure at international level, such as
Brummer et al., who measured the TFP of the dairy sector in different European
countries. Nevertheless, in the late 20th century people applied the TFP concept at the
firm level. In the following pages, there is a description of different empirical studies,
mostly conducted in the late 20th and early 21st century. This discussion will support the
significance of the study, which has been conducted to assess TFP at the firm level as
well as factors affecting its position.
As discussed in previous pages, the TFP model is preferred for this study. There is
a discussion about the selection of the most suitable TFP measuring model. The
conclusion was that TFP, as proposed by Sumanth in a model similar to that proposed by
Sink, Craig, and Harris, and APC would be used to measure TFP of PKGI with only
minor modification. Before proceeding, it is necessary to review empirical studies from
the literature that have been conducted at the firm level and used on the same or similar
models. The following discussion will offer a detailed literature survey, which covers the
different empirical studies conducted at the firm level. This whole discussion will provide
logical support for the notion that TFP is not a matter solely for macroeconomists, but
also for those studying firms. Nevertheless, literature provides points of view of different
scholars who prefer measurement of TFP at the firm level (the micro level) over the
Chapter Two: An Overview of Total Factor Productivity 124
macro level. The reason behind their preference is that micro level studies provide a basis
for action, and they are also helpful for making strategic decisions. An increasingly
voluminous literature assessing the TFP at firm level reveals the significance of TFP at
the firm level.
Wilson (1994) is one of the scholars who strongly recommend measuring
multifactor productivity at firm level. Wilson stated,
Each partial productivity (representing a unique resource) possesses a distinct
magnitude of influence on the overall productivity measure (i.e. a weighted
influence). Thus, the weighted multi-factor [It is also called Total Factor
Productivity] productivity model offers a multidimensional view of productivity
oriented towards the goals of the organization. (p. 50)
Wilson developed a new approach, and by using ―An Improved Method for Measuring
Productivity,‖ measured productivity of different firms. This shows that TFP is not a
concept that is only useful at macro level. It has been used on firms, too.
Zeed and Hagén studied the impact of ICT on TFP of the firms by using Swedish
Enterprises and finally concluded that ―if a larger part of the staff uses computers the firm
labour productivity is higher if controlled for capital intensity, education, industry and
size and a more advanced ICT use increases the firm TFP‖ (2008, p.22 ).
This study provides appropriate evidence that TFP is not only a concept useful at
macro level but also at the firm level.
Antonelli and Scellato (2007) conducted a study by using data from 7,020 Italian
manufacturing companies observed during 1996-2005. This study was to see the impact
of localised social interactions on TFP of the firms. The paper presents an empirical
Chapter Two: An Overview of Total Factor Productivity 125
analysis of firm level total factor productivity (TFP) for a sample of 7,020 Italian
manufacturing companies observed during years 1996-2005.
We show that changes in firm level TFP are significantly affected by localised
social interactions... Moreover, we find evidence suggesting that changes in
competitive pressure, namely the creative reaction channel, significantly affect
firm level TFP with an additive effect with respect to localised social interactions
deriving from knowledge spillovers. (Antonelli and Scellato, p. 1)
The above study was conducted in 2007, which supports the idea that TFP is becoming
popular among the people who are interested in applying this concept at the firm level.
Oyeranti (2000) has discussed in depth the concept of TFP. Oyeranti gave
arguments that productivity represents multifactor and total factor productivity.
Emerging literature on productivity measurement of late indicate that early
productivity measures revolve around the value of aggregate output per man hour
of labour input despite the problems associated with measuring labour input. At
the moment, productivity research has focused more on total factor productivity
(TFP) measures, where comprehensive aggregates of outputs and inputs are of
interest. (p. 13)
Oyeranti‘s emphasis is on developing a link between TFP and the firms. Oyeranti further
gives the three fundamental sources of TFP growth. He stated,
Arising from these three factors behind productivity changes are three possible
explanations for differences in total factor productivity. These are differences in
productive efficiency, the scale of production, and the state of technology,
Chapter Two: An Overview of Total Factor Productivity 126
depending on the specific assumptions that are made with respect to the
production function and the market conditions. (p. 14)
It is obvious from above statements that all three factors are associated with firm. The
statements show that to attain a better TFP, there is a strong need for change at the firm
level.
Bheda (2002) studied the productivity of the Indian apparel industry and applied
partial productivity methods to measure productivity of distinct factors. The author
collected data by interviewing people from selected firms throughout the country.
Thirteen different factors of production were taken into account. The data was analysed
with the help of ANOVA. Major findings from this study are:
1. Factories based in South India are more productive than in the North.
2. Small companies perform better than big firms do.
3. Exporters are more productive than domestic producers are.
4. Companies that export to the U.S.A perform better than those that export
to other countries.
5. Education has a direct, positive link with productivity.
6. Modern bundling systems improve productivity.
7. Factories using scientific methods of setting production standards are
more productive.
In this study, the author did not take into account the firms‘ financial strength and
management style. It is well established in the management sciences that satisfied
workers have better productivity and that workers‘ satisfaction mainly depends upon the
working environment, wage levels, and timely payment of wages. This has a strong link
Chapter Two: An Overview of Total Factor Productivity 127
with financial strategy and strength of firms. Firms with more fixed assets and less
working capital always face a shortage of funds to clear their outstanding expenses.
Moreover, management style also plays an important role in productivity. Bheda ignored
these factors.
Gharneh (1997) compared productivity and performance in textiles between the
UK and Iran. The focus of the Gharneh study was to estimate the labour productivity gap
between the UK and Iran. Gharneh found a significant gap between labour productivity in
the two countries. UK labour productivity was quite high as compared to Iran. Gharneh
cited numerous reasons for this gap. One of the main reasons was the closure of the big
UK manufacturing plants. UK textile manufacturers preferred to establish small factories
rather than large mills, and labour in small units was more productive than in big units. In
Iran, the government gave incentives to establish industry and provided numerous
protections to industry by putting heavy duties on imports. This gave employees in the
Iranian textile industry a feeling of security, which ultimately became one reason for low
labour productivity. At the same time, the UK textile industry was squeezed due to many
problems, and employees had to perform well to save the industry as a whole as well as
their jobs in particular.
Malley, Muscatelli, and Woitek (2003) carried out an international comparison of
TFP. They compared TFP across different sectors of G7 countries. They collected sector
wise data for the use of intermediate inputs and calculated gross output measures of TFP
growth and TFP levels. An accurate view of underlying TFP growth across the G7
economies was provided by the Malley et al. analysis. The conclusion was in the form of
the existing value added, and cyclically unadjusted measures that tended to overestimate
Chapter Two: An Overview of Total Factor Productivity 128
TFP growth were influencing the underlying technical progress. Keeping the importance
of the accurate measurement in mind, the economists stressed achieving stronger
measures of TFP. According to them, a key conclusion from the UK‘s perspective is that,
in manufacturing, its productivity gap with other major industrialized countries,
especially the U.S. and Germany, is still significant. This supports the findings of recent
studies based on value-added measures, but of course, the measure of the gap differs
depending on comparisons between individual countries. Generally, the data shows that
the UK was closing the gap in the mid-1990s but that a considerable amount of ground
still needs to be recovered.
The Industry Commission (1997) measured the partial productivity (labour
productivity) of Australia by using a financial and accountant data function. It made a
useful link between productivity and the living standard of the people. This study
analyzed direct data of GDP, working hours, and number of workers without applying
any econometrics. Their findings are very simple. Industry Commission concludes,
Productivity matters for growth. Productivity growth has accounted for about half
of the increase in Australia‘s output over the past three decades. Since a firm‘s
management performance can be evaluated in terms of financial ratios, efficient
management using financial factors is proposed as the key element for upgrading
a firm‘s productivity. This investigates productivity in terms of certain financial
factors of large-scale manufacturing firms in Taiwan. First determines several
influential financial factors using factor analysis (p. 10).
Chen et al. stated that that they have developed their results by applying fuzzy
clustering approaches for the purpose of categorization, with distinct characteristics for
Chapter Two: An Overview of Total Factor Productivity 129
financial factors and with the application of characteristics of productivity and financial
factors for each pattern. Chen et al. used data from the Taiwanese manufacturing
industry, and their data suggests they have used a financial ratio to determine the
productivity of the industry. Salinger (2001) conducted a survey of African industry and
used financial parameters to measure competitiveness and productivity. His views are
that financial matters are insufficient to develop an analysis and that there is a strong
need to add other factors.
―However, it should be noted that the growth of a country results from the growth
of industries, which comes from the growth of firms. Ultimately, the productivity growth
of a country is attributed to the productivity growth of firms‖ (Nishimura, Nakajima and
Kiyota, 2005, p.1). Nishimura et al. have discussed in depth the role of the firm for
national productivity improvement. They have studied productivity convergence at the
firm level. For this purpose, they used the index computing TFP method. Nishimura et al.
compared productivity across firms and time-series. They employ the multilateral index
method in computing TFP, as developed by Caves, Christensen and Diewert and
extended by Good, Nadiri, Roeller and Sickles (as cited in Nishimura et al.). The
outcome of their research is that there is a strong need to measure TFP at the firm level.
According to the authors, ―this paper has examined the growth of productivity at the firm
level, especially focusing on the effects of convergence…The productivity convergence
among firms exists not only in manufacturing but also in non-manufacturing industries‖
(Nishimura et al., p.17). The above discussion supports the general observation that TFP
is not a subject of economists only at the macro level but also at the personal and firm
levels.
Chapter Two: An Overview of Total Factor Productivity 130
Harris and Li (2007) conducted a study to measure the TFP of different exporting
firms in the UK. All this measure is at the micro level. They have identified firms having
high and low TFP, confirming:
that generally exporters and foreign-owned firms have on average higher levels of
TFP, but foreign-owned firms are not always better than UK-owned exporters and
exporting (as opposed to not selling abroad) by foreign-owned firms only seems
to confer a TFP advantage in half of the industries considered‖ (Harris and Li, p.
35).
This study was conducted in the start of the 21st century, which shows that people are
now adding firms to study TFP. One possible explanation of this study is that TFP is a
concept that is just as relevant at the firm level.
Duguet (2003) examined the contribution of incremental and radical innovations
to TFP growth at the firm level. Duguet held the view that ―radical innovators would be
the only significant direct contributors to TFP growth‖ (p. 20). For this study, Duguet
used data provided by the French Innovation Survey, which covers the period from 1986
to 1990.
This survey provides information on eight innovation types that firms can have
implemented, including five types about products and processes, as well as
information about eight knowledge sources used as the determinant of these
innovations. Finally, it also provides information about the motivation of firms‘
activities (market pull, technology push) and the innovative opportunities of their
line of business. (p. 5)
Chapter Two: An Overview of Total Factor Productivity 131
This study is another argument that goes in the favour of the newer, modern view
that TFP is equally applicable at firm level.
Cingano and Schivardi (2004) conducted a study of more than 30,000 firms in
Italy to identify the TFP growth of the firms. This data were provided by a consortium of
banks. Cingano and Schivardi explained, ―The TFP estimates in the L-S are obtained by
averaging over the firm level TFP so that the precision of the estimates increases with the
number of firms‖ (p. 14). It has been shown in Cingano and Schivardi‘s research that
TFP at firm level fulfills many of the business people‘s objectives. They support the view
that estimation of firm TFP and determination of its factors is one of the best techniques
and provides a guideline for the business community.
Chan, Krinsky, and Mountain (1989) have proposed another non-parametric way
to assess TFP at firm level. Chan et al. believes that estimation of TFP at firm level is
equally important. In addition, Chan et al. have supported the TFP definition conceiving
of it as a ratio of output to input.
For practitioners, however, TFP analysis has various advantages. TFP is a relative
measure showing how the ratio of total output to total input changes from one
period to the other. It is relatively inexpensive to perform and since the data are
displayed in an index number form, it is easy to identify anomalies in the data
(Chan et al., p. 337).
Aw and Chen (2001) made an effort to find evidence at the firm level to measure
the difference in TFP. They did so by using data of firms from Taiwan.
For each firm in the Taiwanese manufacturing data we construct an index of total
factor productivity (TFP) in each of the three census years 1981, 1986, and 1991.
Chapter Two: An Overview of Total Factor Productivity 132
We use this index as a single measure of the firms‘ relative efficiency, a proxy for
in the theoretical model. A TFP index captures many factors that can lead to
profit differences across firms, including differences in technology, age or quality
of capital stock, managerial ability, scale economies combined with size
differences, or differences in output quality. (p. 10)
Aw and Chen (2001) did not attempt to explain why TFP varies across firms but rather
focus on whether firms‘ relative efficiency is correlated with their decisions to enter, exit,
or remain in operation.
Probably, Burgess (1990) is one of the critics who very openly criticized
economists who measure productivity (TFP and multifactor productivity) by using
econometrics. As discussed above, Robinson (1953) has also commented on the
authenticity of the results provided by economists. Felipe (1997) has described that
people have produced different results from the same data. Burgess believed that the
results produced by economists are not perfect.
Economists would be the first to admit that their approach to productivity
measurement is far from perfect. Critics could point to the impact on productivity
of a large range of factors other than the two factors named above. The accuracy
of the ―estimates‖ are often open to question. However, economists are more
confident about their productivity estimates for manufacturing industry than they
are with their estimates for either public or service sectors. (Burgess, 1990, p.7)
This statement does not mean that all estimates are incorrect. Rather, it supports the point
of view of the Industry Commission of Australia (1997), which holds that the
productivity measurements should be taken as estimates.
Chapter Two: An Overview of Total Factor Productivity 133
Burgess (1990) believes that aggregate productivity is a result of productivity at
the firm level. This is a similar approach, which Ark (1996) proposed.
A point to be borne in mind is the ―batting average‖ concept. Because national
productivity is a function of the aggregation of the performances of individual
companies, national productivity could be significantly improved by ―getting rid‖
of low productivity organizations. (Burgess, 1990, p. 7)
The above discussion converges on one point that TFP is equally important at firm level.
It is easy to understand and business leaders can make quick changes to have better TFP.
Nevertheless, TFP at the macro level depicts TFP levels and trends in the long run and
provides useful data for economic leaders.
Productivity measurement at the factory level (the firm level) is significant on a
very small scale, but it also carries weight at the international level. Schmenner and Ho
(1989) conducted a study at international level. In this study, they compared productivity
of firms.
For the plant manager, so many potential remedies for increased factory
productivity are difficult to evaluate, much less to implement…This article
addresses this question by comparing factory-specific productivity data from all
over the world. It reports on three studies, which mailed the same survey to
factories in the US, South Korea, and to 30 other countries mainly concentrated in
Europe. (p. 16)
This study was conducted in the late 1920s. Schmenner and Ho work clearly depicted
that factory level productivity is gaining significance at every level.
Chapter Two: An Overview of Total Factor Productivity 134
The significance of productivity measurement at the factory level is well
recognized by various scholars. It is interesting to note here the Solow, a Nobel laureate
who presented a famous model with which to estimate TFP, also accepts the importance
of productivity measurement at the firm level. ―Increasingly, however, there have been
attempts to supplement aggregate calculations with micro-level information about
individual departments, firms or narrowly defined industries and their operations. A
variety of such studies has been done‖ (Baily and Solow, 2001, p.151). Baily and Solow
have quoted many studies carried out to estimate productivity (TFP) at the firm level.
They have mentioned a few studies, such as that by Jorgenson, Wagner, and Van Ark,
who carried out studies at the firm level. It is interesting to note that all such studies were
conducted at the end of the 20th century. At the same time, however, Baily and Solow
said that such studies cannot reduce the importance of TFP measurement at the aggregate
level.
One comforting conclusion is that a macroeconomic view of the economy is not
dramatically challenged by these studies. However, standard aggregate
productivity calculations say nothing about the deeper causes of observed
differences in total factor productivity from firm to firm or country to country.
The natural place to hunt for those causes is in detailed comparisons of firms or
groups of firms producing similar outputs but operating in different environments
and adopting different practices‖. (Baily and Solow, 2001, p. 152)
Productivity measurement at firm level is not without biases. There are many
issues attached to bias in productivity measurement. Misterek, Dooley and Anderson
(1992) have discussed many difficulties during productivity measurement at the firm
Chapter Two: An Overview of Total Factor Productivity 135
level. However, they categorically mentioned that measurement at the firm level is
required with decision-making. Misterek et al. wrote,
The purpose of this article is to examine the measurement of productivity at
"micro'' levels — the level of the firm, plant, office, work group, or individual
product — and to discuss the strengths and weaknesses of traditional productivity
measures, especially as they relate to managerial decision-making. The strengths
and weaknesses of productivity measures are explored, and their characteristics
are compared with those of the traditional measures of internal firm performance:
cost accounting measures (p. 29).
The observation of Misterek et al. supports the point of view of Baily and Solow that
productivity measurement at firm level is quite helpful in decision-making.
In the above discussion, most of the studies are at firm level. Still, in the final
analysis, the results were presented at the aggregate level. It is possible to conduct TFP
only for one company. Such studies can be conducted for any type of firm, whether
involved in production or service.
Mohanty and Rajput (1987) have conducted a study to measure TFP of a steel
mill. They described the objective of the study in the following words, ―The primary
objective of this study is to develop, apply and evaluate productivity measurement
methods for a company engaged in the manufacture of steel wire ropes‖ (p. 65). Mohanty
and Rajput have selected the TFP model, which is similar to the Craig and Harris model.
Furthermore, they developed a relationship between TFP and different variables, such as
capital, sale, energy consumption, etc.
Chapter Two: An Overview of Total Factor Productivity 136
It is particularly important to note that for the current study the same method has
been adopted as in the Mohanty and Rajput work. The researcher has collected financial
and non-financial data and calculated the TFP of every firm, developing a correlation (see
chapter 3 for further details). A review of the literature shows evidence that research
methods used for the current study and models are not new. They are already tried and
tested in various other studies.
TFP measurement at the aggregate level (macro level) represents the overall
picture of the industry or the whole economy. It is the sum of the TFP of individual firms.
Such aggregation raises many questions. The main doubt with respect to this aggregation
process surrounds the factors affecting TFP at firm level. These are different for different
firms. Raa (2005) has studied this problem and concluded that such simple adding does
not serve the issue—rather, it creates confusion.
Firms‘ productivity indices do not sum to the industry productivity index, except
when production is linear in the sense that marginal rates of substitution and
marginal rates of transformation are constant and these constants are common to
the firms. (Raa, 2005, p. 203)
Linear production is a particularly vague term. There is a low probability that
different firms may have linear production. Raa (2005) concluded, ―Aggregate
productivity is the sum of firm productivities and firm allocative efficiency changes. A
firm‘s allocative efficiency change is measured by its excess marginal productivities
(over and above the competitive economy wide ones), weighted by input changes‖ (p.
208).
Chapter Two: An Overview of Total Factor Productivity 137
Raa further explained that before adding to get aggregate results, many details are
required about the production function of individual firms, which is not possible.
TFP measurement is not only significant in measuring different contributing
factors. At the same time, though, it can be used to develop a correlation with other
factors, such as turnover of the firm. Gebreeyesus (2008) has conducted a study of
Ethiopian firms to develop a correlation between turnover and the TFP of the firms. ―In
this paper we provide empirical evidence on firm turnover and productivity differentials
in Ethiopian manufacturing based on firm level industrial census data‖ (Gebreeyesus, p.
126). This study also reinforces the observation that TFP at the firm level is not less
important.
The Malmquist index is commonly used to compare TFP of two different
economies. It has been widely used by economists at the macro level. But some
researchers, including Portela and Thanassoulis (2006), found it useful at the micro level.
They have used the Malmquist index to compare the TFP of different banks‘ branches (at
the firm level). It is interesting to note that they conducted this study in the early 21st
century, whereas Malmquist was proposed by Swedish academic Malmquist in the 1980s
and was mainly used to compare economies at macro level. Nevertheless, Portela and
Thanassoulis have made minor alterations to the index to measure TFP at firm level.
The literature is full of difficulties researchers face during productivity
measurement at the macro level. As discussed above, there are a number of factors that
influence productivity at the macro level. At the same time, analysis at the firm level can
present its own problems. Husband and Lee (1981) analyzed productivity of a plant by
using three different methods: (a) technical added value, (b) productivity costing, and (c)
Chapter Two: An Overview of Total Factor Productivity 138
TFP. ―The measurement of productivity at plant level is notoriously difficult. Much
depends on the precise definitions of input and output factors. Despite the difficulties
involved, company productivity measurement is an important factor in manufacturing
industry‖ (Husband and Lee, p. 32). The above statement clearly depicts that productivity
measurement at the firm level is not only important— rather, it is highly demanded.
Generally, economists are more interested in measuring productivity at the macro
level. On the other hand, industrial engineers, business managers, finance people, and
people associated with stock markets are more interested in conducting analyses at the
micro level. This is mainly due to their interest and stakes. But over time, economists
have been focusing more on the micro level in their analyses. ―More and more,
economists are looking at ‗micro‘ issues — that is, issues concerned with the firm, work
group, or individual — rather than ‗macro‘ issues (industry or national level) as causes of
the poor level of productivity growth‖ (Misterek et al., 1992, p. 29).
Misterek et al. have held the view that productivity measurement at the firm level
is not only the job of people directly involved in business but also economists who are
generally more at ease with the macro level.
Newman and Matthews (2006) conducted a TFP analysis of Irish dairy, using
data from firms. Their study shows that TFP measurement does not matter significantly
for industrial production system. Rather, it is equally important even in the livestock
industry.
The literature search outlined above yielded a vast amount of literature covering
many studies to estimate TFP at firm level. A closure look of all studies mentioned above
provides strong evidence that TFP models, which was initially developed by economist,
Chapter Two: An Overview of Total Factor Productivity 139
mainly used to estimate TFP at macro level, whereas, business managers and industrial
engineers prefer TFP estimation at firm level. For this purpose they developed different
TFP models. Nevertheless, in recent days, economists are also using firm level data to
estimate TFP. Apparently, it looks that both points of view are confronting each other. In
fact, it is not. The difference is due the diverse objectives. Based on this difference, they
adopt different strategies. Based on all above discussion, it can be concluded that TFP
estimation is quite possible at both levels; macro and micro. However, selection depends
upon the many factors e.g. research objectives, data availability, and capability of
researchers.
2.13 Summary of Productivity Review
This chapter discussed the concept and significance of productivity and its
emergence. It also covered history of production functions along with the critical views
of different people related to application of production functions. This chapter also put
forward long and full of similarities and contradiction approaches to measure
productivity along with concepts of partial, multifactor, and total productivity. From all
the above discussion, one can derive that productivity has several meanings and concepts,
depending on the user of the term. Productivity ranges from labour productivity to green
productivity; there is even an issue of social productivity, as expressed by Asian
Productivity Organization. There is a consensus in the literature that productivity is a
sustainable approach to prosperity. All developed nations rely on better productivity. It is
a common view that measurement of the current level of productivity is essential for
better productivity. Productivity measurement is a complex phenomenon. As discussed
Chapter Two: An Overview of Total Factor Productivity 140
in the previous pages, there are a number of approaches available in the literature and
used by different researchers to measure productivity.
It is quite clear from the above discussion that it is hard to reach a consensus on
the use of a particular model of productivity. Different authors have classified
productivity measurement approaches into different categories. This variation results
from different objectives of measuring productivity. Numerous models are available in
the literature to assess productivity. The suitability of the individual models depends
upon the measuring objectives and availability of data. In addition, the applicability of
the model varies according to the nature of the industry and level of productivity
measurement.
After the profound discussion of the different approaches, the author of this report
fully agrees with Gharneh about the two main categories of productivity measurement
mentioned in previous pages. Every firm by definition performs a function, usually to
produce goods or provide a specific service. Firms are recognised by their function, for
example a producer of textiles, producer of automobiles, etc. A firm that lacks production
consequently also lacks a function, which in turn means an absence of input and output.
In a production function, productivity is a ratio of output to input. However, productivity
may have different meanings in a different context.
As Gharneh said, all productivity measurement approaches related to the two
areas. First is index numbers, a measurement that is related to the production of goods. In
other words, it mainly covers the physical numbers, e.g. number of stitching workers in a
mill and number of shirts produced. Furthermore, these numbers are used in some
indexes. This approach has its merits and demerits. Second is the accounting model,
Chapter Two: An Overview of Total Factor Productivity 141
which deals with the amount used to produce any goods or services. As reflected in the
approaches mentioned above a great deal of divergence exists in different approaches of
productivity measurement yet a selection of the approach mainly depends upon the scores
of factors such as data availability, level, purpose, etc. Schreyer and Pilat (2001) explain
that the choice between the approaches depends on the purpose of productivity
measurement and, in many instances, on the availability of data.
Briefly, selection of the most suitable approach mainly depends upon the
following factors:
1. Purpose of productivity measurement
2. Resources available for productivity measurement
3. Potential of the people involved in productivity measurement
4. Organisational set up
5. Types of product and composition of market segments
6. Available data
The author of the report concludes that economists are interested in index
methods, which are highly useful for a long run production function and are loaded with
econometrics. Business managers and industrial managers find it difficult to understand
econometrics, as they are slaves of the accounting model, which is highly suitable for a
short run or even for cross- sectional data. It gives quick results and provides guidance
for immediate improvement. Business managers also prefer an accounting model, since
they and other stakeholders cannot wait for a long time. They need immediate action to
have a better profit. It may be an outcome of a better productivity or by using any other
means such as, change of location, new product line or even closing down the plants and
Chapter Two: An Overview of Total Factor Productivity 142
relying on outsourcing. After all, the goals of the firms are to have a high profit and they
like the measuring models that tell them about the level of profit and factors affecting
profit so that they may plan to have better profits. For this reason there are many studies
which have been conducted at a firm level. Literature provides evidence that interest to
measure productivity (partial, multi factor or total factor) at a firm level has an increasing
trend. In addition to that most of the studies have been conducted after 1980. It is also
apparent from the literature that such studies are widely available in literatures which
have been conducted in last in first decade of 21st century.
Chapter Three: Data Collection and Research Methodology 143
CHAPTER THREE: DATA COLLECTION AND RESEARCH METHODOLOGY
The previous chapter contained a mixture of models that different authors
proposed in order to measure productivity. It concluded that the selection of a particular
model depends upon the situation of business, availability of data, and objectives that
measure productivity. The observation taken from the discussion can finalise the
methodology for research in depth. This chapter attempts to rationalize research
methodology in detail including the selection of production variables, data collection
processes, finalisation of software for analysis, explanation of techniques and methods to
measure correlation, and measuring the strength of the correlation between dependent and
independent variables. As discussed in chapter one, there are five foremost objectives of
this research:
1. Measurement of TFP of PKGI (at aggregate and disaggregate level) and its
position in the ranking to other industries
2. Finding correlation and its strength between dependent variables (TFP) and
independent variables
3. Testing of hypotheses
4. Developing a model to predict TFP
5. Course of action to improve productivity of the industry
Keeping the aforementioned objectives in view when forming the research
methodology is discussed below.
Chapter Three: Data Collection and Research Methodology 144
3.1 Research Methods
As Johnson and Christensen (2006) have rightly said, there is not a single
approach that can be utilized in historically research to carry out it, although there is a
general set of steps typically followed. These include:
1. Recognizing the research topic and formulating its problems in question form
2. Data collection or literature review
3. Evaluation of material
4. Data synthesis
5. Report preparation
This report espouses similar processes as mentioned in the above lines. Earlier
research problem, research justification, and research objectives were discussed in detail
in chapter one (for more details see Section 1.6, 1.8). The researcher has developed a
hypothesis based on these findings. The literature review in the chapter two presented
not only plentiful definitions and concepts about productivity, but many other methods
for measuring productivity. The literature offers a good variety of models that can be
availed. After a comprehensive treatise, one single model was finalised. This chapter
explains data collection and the analytical method and its implications.
3.2 Quantitative and Qualitative Data
According to Johnson and Christensen (2006), for most of the 20th century, the
quantitative paradigm prevailed. During the 1980s, the qualitative paradigm came of age
as an alternative to the quantitative paradigm, and often was conceptualised as the polar
opposite of quantitative research. Finally, the modern roots of mixed research date back
Chapter Three: Data Collection and Research Methodology 145
the late 1950s. According to Johnson and Christensen (2006) mixed research became the
legitimate third paradigm with the publication of the Handbook of Mixed Methods in
Social and Behavioural Research by Tashakkori and Teddlie, although mixed research
had always been conducted by practicing researchers. They further state that presently
there are three major research paradigms in education (and in the social and behavioural
sciences: quantitative research, qualitative research, and mixed research. Quantitative
research primarily relies on collection of quantitative data, while qualitative research
relies on collection of qualitative data. On the other hand, mixed research involves
quantitative and qualitative methods or paradigm characteristics.
Additionally, there are two categories of quantitative research: experimental and
non-experimental research. Quantitative research is based upon variables that have
different values or categories. Opposed to constants, these do not differ. There are
numerous types of variables (Johnson & Christensen, 2006). Besides, dependent and
independent variables also exist in which independent variables are the presumed cause
of another variable, while dependent variables are the presumed effect or outcome of
another variable. Dependent variables get influence of independent variables in some
cases. There is an essential relationship between dependent and independent variables
and due to this affiliation one could not say that the dependent variable is not the outcome
of the independent variable(s).
In most cases, it is difficult to consider all variables because sometimes one
variable is affected by another variable. This gives the idea of intervening variables (also
called mediator or mediating variables) that take place between two other variables. For
example, in the case of TFP of PKGI, a better TFP is the result of capital investment,
Chapter Three: Data Collection and Research Methodology 146
which is used to pay for the high tech machines. Such variables are called intervening
variables. In this study, an effort has made to build up a relationship between the
dependent variable TFP and seven other independent variables. It is likely that there are
additional intervening variables.
It is essential to note down in experimental research that there is a manipulation of
dependent variables, whereas the non-experimental research carries no manipulation of
the dependent variables. Apparent relationships between the two variables in non-
experimental research do not necessarily indicate a definite relationship between
dependent and independent variables. Nevertheless, there could be numerous alternative
explanations for the relationship. Johnson and Christensen (2006) have explained in
detail characteristics of experimental and non-experimental research. Here is a summary:-
1. One can obtain much stronger evidence for causality emerging from
experimental research rather from non-experimental research (e.g., a strong
experiment is better than causal-comparative and correlation research).
2. One cannot wind up that a relationship is causal when there is only one
independent variable and one dependent variable in non-experimental research
(without controls). Therefore, the basic cases of both causal-comparative and
correlation research are severely flawed.
3. There are three necessary conditions to diagnose a relationship between
dependent and independent variables first, both variables must be related;
second, a proper time order must be established; and third the relationship
between the two must not take roots owing to extraneous or third variables.
Chapter Three: Data Collection and Research Methodology 147
In this research report, a relationship between dependent and independent
variables was ascertained. It is presumed that time series data can give better results than
cross sectional data. As discussed earlier, no time series data is available for PKGI,
therefore, cross sectional data were utilized in this situation. There might be confounding
and extraneous variables that strongly influenced the cause-effect statement. Since this is
non-experimental research, the influence of such variables cannot be brushed aside. As
discussed earlier in the case of experimental research, all of the above-mentioned criteria
was dealt with while on the other hand, in causal-comparative and correlation research,
which focuses on a relationship between two variables, statistics (regression analysis) can
be employed to conclude that two variables are related, as well as to determine the level
of the relationship. This report attempts to weigh the relationship between independent
and dependent variables and ascertain the meaning of the relationship. However, results
would have been better if this had been done as an experimental research.
Of the three types of research possible, experimental research produces the
informative results, though experimental research was not practicable for this study
because the objective of the research was to assess TFP levels of PKGI and its
determinants at industry level. Nevertheless, experimental study is quite effective in cases
of single firm so it is nearly impossible to have an experimental study at industry level.
In view of the research objectives, the use of cross sectional data and its implication of
relationships came into use.
In addition to measuring TFP, this research attempts to determine the impact of
different factors on TFP. To measure TFP, this report used quantitative data; however,
only to measure the impact of different variables on TFP. This research drew upon a
Chapter Three: Data Collection and Research Methodology 148
different set of variables. As discussed in the chapter two, the most common definition of
productivity is the ratio of output to input. Keeping this definition into reflection, there is
a strong need for quantitative data to measure TFP.
Nevertheless, certain qualitative factors might have a strong impact on TFP. To
accommodate certain questions such as level of education, managerial style of the firms,
level of skill, application of IT, and level of technology were added to the questionnaire.
During the pilot survey, it originated that a part of the data is unavailable, e.g. education
and skill level of the workers. Moreover, people are often reluctant to answer such
questions. Because of these constraints, it was decided to use quantitative data in order to
assess the impact of factors on TFP. As mentioned earlier, there is no alternative to
quantitative data. Even so, this researcher believes that qualitative data should be
included in this report, though, as explained earlier, the researcher excluded some
selected variables due to limitations in collecting data for the study.
The next section discusses in depth the procedure (research methodology) used to
achieve the objectives. This section will explain the rationale of the adopted research
methodology. The debate shows up the following points:
1. Selection of TFP measuring model
2. Finalisation of population
3. Sampling
4. Measuring of TFP
5. Comparison of TFP
6. Testing of hypotheses
7. Identification of factors affecting TFP of PKGI
Chapter Three: Data Collection and Research Methodology 149
8. Assessing impact of selected factors on TFP
9. Measuring importance of correlation between TFP and its determinants
10. Regression equation for prediction of dependent and independent variables
3.3 Validity of Data Set
In this research, primary and secondary data were used to measure the TFP of
PKGI and its determinants. It is necessary to confirm the validity of the data before using
it. Johnson and Christensen (2006) discussed in detail the impact of data validity in
research and outlined methods to check validity. According to them, every information
source must be evaluated for authenticity and accuracy because any source can be
affected by a variety of factors, such as prejudice, economic conditions, and political
climate. Evaluation is of two kinds that each source must pass:
1. External Criticism- is a process for determining the validity, trustworthiness, and
authenticity of the source.
2. Internal Criticism–this is a process for determining the accuracy of the
information contained in the sources collected. This is done by positive and
negative criticism.
Positive criticism refers to the confirmation that the meaning conveyed to the sources
has been understood. This is normally difficult because of problems of vagueness and
presence. The vagueness refers to uncertainty in the meaning of the words used in the
source and presence passes on to the assumption that the present-day connotation of
terms also applied in the past.
Chapter Three: Data Collection and Research Methodology 150
Negative criticism refers to establishing the authenticity and accuracy of the
content of sources used. This is difficult because it requires a judgment about the
accuracy and authenticity of the source. Firsthand accounts by witnesses to an event are
typically assumed reliable and accurate.
As per Johnson and Christensen (2006), historians often use three heuristics in
handling evidence: corroboration, sourcing, and contextualization. Corroboration or
comparing documents to each other determines whether they provide the same
information or not, and can be used to obtain information about accuracy and
authenticity. Sourcing, or identifying the author, date of creation of a document, and the
place it was created is another technique that is used to establish the authenticity or
accuracy of information. Contextualization, or identifying when and where an event took
place, is a technique used to establish authenticity and accuracy of information.
In the research report, all of three methods will be applied prior to using data that will
help confirm the legitimacy of the data.
3.4 TFP Measuring Model
The preceding part of this chapter discussed the concept, significance, application,
and measuring approaches of productivity. It covered partial, multifactor, and total
productivity. It is in evidence that productivity has lots of meaning and concepts,
depending upon the user of the term. Productivity ranges from labour productivity to
green productivity; there is even an issue of social productivity. The literature has
consensus that productivity is a sustainable approach to prosperity. All developed nations
rely on better productivity and it is a common observation that measurement of the
Chapter Three: Data Collection and Research Methodology 151
current level of productivity is essential for better productivity, and productivity
measurement is a complex phenomenon. As discussed in the previous pages, there are a
number of approaches available in the literature and used by different researchers to
measure productivity.
The discussion makes it quite clear that it is hard to reach an agreement about the
use of a particular model of productivity as different authors have classified productivity
measurement approaches into different categories. This variation results from different
objectives of measuring productivity and numerous models are available in the literature
to gauge productivity. The suitability of the individual models depends upon the
measuring objectives and availability of data. In addition, the applicability of the model
varies according to the nature of the industry and level of productivity measurement. It is
the reason why TFP models are a good choice because they have an effective function of
accounting. The rationale of selection of this model is put forward here yet there are some
other TFP models that different authors have proposed and which are based on
accounting functions. As said earlier they are not much different from each other, the fact
goes there are serious objections on TFP models and at the same time, there are people
who are in favour of these models. Amid this controversial situation, TFP models based
on accounting function have been selected. A list of some common TFP models is as
under:
1. American Productivity Centre Model (APC Model),
2. Sumanth Model (1990)
3. Tangerass Model (1980)
4. Craig and Harris Model (1973)
Chapter Three: Data Collection and Research Methodology 152
5. Sink Model (1985)
The TFP models have both advantages and disadvantages. A critical discussion on
TFP models supports the criteria of TFP model and its selection. Sumanth (1990)
describes the following advantages of the TFP model (total productivity model):
1. It provides both aggregate (firm level) and detailed (operational unit-level)
productivity indices.
2. It points out the operational units that are profit making.
3. It shows which particular input resources are being utilised inefficiently, so that
corrective actions can be taken.
4. It lends itself to mathematical treatment so that sensitivity analysis and model
validity become easier.
5. It is integrated with evaluation, planning, and improvement phases of the
productivity cycle. That is to say, the total productivity model offers for the first
time a way of not only measuring but also evaluating, planning, and improving
the overall productivity of the organisation as a whole, as well as its operation
units.
6. Management can have tighter control on total productivity of major operation
units, while providing routine control for the less critical operation units.
7. It provides valuable information to strategic planners in making decisions related
to diversification and phase-outs of product or services.
8. It helps firms to identify their position in the market.
9. It helps to identify the level of achievement to benefit the ultimate goal of better
total productivity.
Chapter Three: Data Collection and Research Methodology 153
One of the principal drawbacks of this model is that it does not indicate which
particular input is being used efficiently or inefficiently. Aggregate total productivity
does not help the firm‘s management to identify the productivity level of different
departments. It could be biased by the existence of investment outside production.
Carlaw and Lipsey criticise the TFP approach. They argue, ―TFP is not a measure
of technological change and only under ideal conditions does it measure the super normal
profits associated with technological change‖ (2003, p. 457). According to Carlaw and
Lipsey TFP is not the appropriate approach to measure productivity in all cases.
In spite of the constraints of the TFP model, many studies have worked to assess
its validity, including the above-mentioned points. Based on the above discussion and
keeping in view the study objective, availability of cross-sectional data, and data
restriction, the TFP model is the best option, and there are only minor differences among
the models, while their theme is alike. The total productivity model presented by
Sumanth, Sink, Craig and Harris, and APC was selected for the estimation of TFP of
PKGI.
The mathematical notation of Sumanth (1990) total productivity model is under.
Total Tangible Output
Total Productivity = ──────────
Total Tangible Input
Where:
Total Tangible Output = value of finished units produced+ value of partial units
produced + dividends from securities + interest from bonds + other income
Chapter Three: Data Collection and Research Methodology 154
Total Tangible Input = value of (human + material + capital + energy + other expenses)
input used
There is a need to cover data of total tangible input and tangible output for the
application of the TFP model in order to measure TFP. This data should travel over total
value of goods produced, value of goods in process, any other revenue generated by the
firm through input details, and covering the cost of material, energy, labour, and any
other cost incurred during the period under consideration. As mentioned in chapter one,
firms of PKGI are not bound to submit their financial reports to the Income Tax
Authority. What‘s more, majority of firms are not listed in the stock exchange however,
the Ministry of Industries and Production in Pakistan carries out detailed surveys of all
manufacturing industries and compiles an annual report. This survey is called the Census
of Manufacturing Industries (CMI). This data go to the Federal Bureau of Statistics of
Pakistan that publishes this report for common uses. During a visit to the Federal Bureau
of Statistics of Pakistan, it observed that the last survey conducted in 2000-01. This data
provides complete details about input and output of PKGI and 18,061 other firms of 181
different sectors during the year of 2000-01.
In 2000-01, the Government of Pakistan collected data from 18,061 firms from
181 different sectors, including textiles, steel, leather, etc. This data will be used to
measure the TFP at the sector level. Results will be used to make a comparison of TFP
among different sectors, as well as with PKGI. The purpose of this comparison is to
gauge the ranking of TFP of PKGI in the list of other manufacturing industries of
Pakistan.
Chapter Three: Data Collection and Research Methodology 155
3.5 Selection of Determinants Affecting TFP of PKGI
Numerous variables can affect the TFP of any firm. Different authors have used
different variables critically assess the productivity of the garment industry. For instance,
Bheda (2002) used 14 variables in his research on the Indian apparel industry.
Brinkerhoff and Dressler (1990) suggested the following variables to weigh up the
productivity:-
1. Ratio of total sales and total cost (TFP)
2. Ratio of satisfied customers and total expenses
3. Ratio of labour expenses and total satisfied customers
4. Production of energy cost
Au (1997) used firm size (production capacity) as a variable in determining
productivity of the Hong Kong garment industry and found a direct link between
productivity and firm size. Key and McBride (2003) have used outsourcing (production
on contracts) as a variable in productivity. Keeping in view the data availability, the
following variables have been taken into account to determine a relationship between
TFP and additional factors:
1. Share of Fashion Garments in Total Production (Basic and embellished)
2. Number of Stitching Machines (production capacity)
3. Share of Different Markets (export to different markets)
4. Share of Financial Expenses in Total Expenses (reliance on bank loans)
5. Share of Labour Expenses in Total Expenses
6. Average Product Price (FOB in U.S. $)
7. Total Sales in One Year (in U.S. $)
Chapter Three: Data Collection and Research Methodology 156
It is very important to argue about the selection criteria of these variables before
they are used for regression analysis. The following pages briefly discuss the variables
and their anticipated results.
Share of fashion garments in total production. Knitted garment manufacturers
produce various types of products for different segments of the market. The range of
products produced by PKGI clearly demonstrates it and this range can be divided into
two main categories: basic and embellish garments (basic and fashion garments). Basic
garments include polo shirts, T-shirts, and basic logo tees, none of which have
embroidery and printing. Embellished knitted garments have embroidery, printing,
piecing, etc. There is invariably a price difference between the two types of garments.
Furthermore, production processes differ and consequently profit margins are different
for basic and fashion garments. It was found during a survey that the majority of firms
were producing a combination of basic and fashion (embellished) garments. It is very
unusual that a firm would produce exclusively basic or fashion garments and it is
presumed that the product mix has a major impact on TFP. It is hypothesised that firms
having a mixture of the two types of garments will have a higher TFP.
Production capacity. Knitted garment producing firms can be classified into two
main categories: (a) vertical and (b) horizontal firms. The knitted garment production
process includes the knitting of fabric, finishing (bleaching, dyeing, printing, and
finishing of fabric), and stitching of garments. Firms that have knitting, finishing, and
stitching engagements under one roof are called vertical and firms having only a stitching
facility are called horizontal firms. They get their fabric knitted and dyed from the market
and do outsourcing for the fabric. The garment manufacturing capacity of any firm
Chapter Three: Data Collection and Research Methodology 157
primarily depends upon the number of machines it has, since garments are produced on
these machines. It is presumed that this factor has a significant impact on the TFP of the
firms. Wu (2002) has discussed the relationship between factory size and its
productivity. Wu recalled that more than 60 years ago Coase, an economist who was a
Nobel Prize winner, had explained meticulously why firms vertically integrated, as
opposed to individually buying and selling goods and services at every stage of
production. The primary reason Coase explained was inadequate information and basic
need to diminish transaction costs. Wu (2002) has further stated that in past, transaction
costs were high and, due to insufficient information, firms repeatedly preferred vertical
integration. Nevertheless, in the current era, when rapid access to information is readily
available and there is a tremendous reduction in transaction costs and time, firms find
attractive the option of concentrating on their strong points and outsourcing for other
goods and services.
Brush and Karnani (1996) have also supported the idea that firms should not
focus on big plants and prefer to have vertical units. According to their study, firms are
diverting to smart plants instead of developing huge projects. Recent popular business
literature has elaborated that there is a significant trend in U.S. firms to downsize.
Conventional manufacturing wisdom —the bigger the plant, the greater its efficiency—is
being seriously questioned. Numerous firms, including AT&T, FMC, and General
Electric are replacing huge manufacturing complexes with new, smaller plants.
Key and McBride (2003) have investigated the impact of contract production of
the U.S. Hog Industry on productivity. They found that the number of formal contracts
Chapter Three: Data Collection and Research Methodology 158
has increased from 11% to 34%, while output increased from 22% to 63%. Considering
these factors, an assumption says that capacity has a negative impact on TFP.
Market segment. PKGI is primarily an export-oriented industry and depends
exclusively upon export. This industry sells only overruns in the local market and its
chief markets are the U.S.A and Europe. More than 60% of goods are exported to the
U.S. market (APTMA, 2006). There is a fundamental difference between the U.S. and
European markets. U.S. buyers place big orders, in contrast to the smaller orders typical
of European buyers. The average price is also different in both markets. Besides, the
garment description differs. It is assumed that firms having a mixed share of both markets
will have a high TFP compared to firms that have only one market access. Bheda (2002)
has also used this variable in his research.
Share of financial expenses in total expenses. The interest rate of Pakistani banks
is quite high, when it is compared with banks in developed countries. During 1990, it was
more than 20% per annum. In 2000-01, the period under study, interest rates were
between 12% to 15%. The government of Pakistan offers working capital to exporting
firms at highly discounted rates under a scheme of export finance, which was discounted
to 6% in 2001. Firms that do not take advantage of this loan seek help from the market to
cover financial needs through purchasing of goods on credit. Purchase of goods from the
market on credit is expensive as compared to banks. Vendors charge more than 25%
extra to supply goods on credit. Firms with less working capital have to rely on market
credit. It is assumed that the TFP of firms that depend more a lot on bank loans is better
than others. Firms with more bank loans have to pay interest to banks. This is an expense
of the firm but its amount is less than the extra amount charged by the supplier if firm
Chapter Three: Data Collection and Research Methodology 159
gets goods and service at credit (payment after a certain period). Consequently, the share
of financial expenses of such firms is high compared to firms that do not rely upon bank
loans. It is assumed that firms relying on banks will be more productive.
Share of labour expenses in total expenses. The garment industry is a labour
intensive industry. There is a great variation in the percentage of the labour cost in
relation to the total cost of the garment. Perhaps there is a negative linkage between
labour cost and TFP. A greater share of labour cost in the total cost of the production of
garments means that firms are relying more on manual work than machine work or there
is less automation in the production system, e.g. a traditional bundle system in stitching
instead of a modern hanger system. Gottschalk (1978) shows that payment for labour at
higher than normal market rates do not mean a correspondingly higher productivity. In
fact, there are various reasons that control the relationship between marginal productivity
and the cost of labour in total production. Gottschalk (1978) has explained a relationship
between marginal productivity and factor payment. According to him, a sophisticated
statement of the marginal productivity theory recognizes that factor payments and
productivity differ at any moment in time for a number of reasons.
Average product price. PKGI exports garments at different prices depending upon
quality, quantity, delivery time, material, size, style, etc. It was observed during this
research that every firm exports garments at different prices and the average price is
directly linked with productivity. Bheda (2002) used product price or value as a variable
in assessing productivity. He gave the name ―product category‖ to this variable. This
hypothesis asserts that firms having very low or very high garment prices have a low
TFP, whereas firms producing garments of medium prices will have a higher TFP.
Chapter Three: Data Collection and Research Methodology 160
Total sales. Total Sales show the size of the business. Au (1997) has used this
variable to assess the productivity of the Hong Kong garment industry. A negative link
between sale volume and TFP is expected.
The discussion can infer a number of factors contributing to TFP; however, the
significance of each factor might differ. In this study, it is assumed that all of the above-
mentioned factors have contributed to the TFP and their significance can be judged with
the help of regression analysis.
3.6 Population and Sampling
As per the report of Pakistan Hosiery Manufacturing Association (2001), 218
firms out of 900 firms were responsible for 90% of the exports (for more information see
Section 1.5). These 218 firms were selected as the population for this survey, based on
their 90% share in total exports2. Based on this relevant information, it was decided to
ignore 682 out of 900 companies due to their insignificant share in total exports.
There are two types of firms, vertical and horizontal. Vertical-firms have knitting,
wet processing, and stitching facilities under one roof, while horizontal firms have only
stitching facilities. They do outsourcing for knitting and dyeing/printing (wet process).
Using a random selection process, 49 firms were selected for the survey, which
was 24.22% of the total population. Out of 49 firms, 33 are vertical firms and 16 are
horizontal firms. A questionnaire was developed and a survey was conducted. Some
firms were visited personally and a few were contacted by phone (see Appendix A).
2 Note that PKGI is mainly export oriented.
Chapter Three: Data Collection and Research Methodology 161
3.7 Classification of PKGI
For better results in the research, there should be a high degree of similarity
among the population. In this study, the population of 218 firms engaged in exporting
knitted garments. Vertical and horizontal firms differed widely, including in size,
capacity, business style, and marketing strategies. Consequently it was decided that both
types of firms, vertical and horizontal firms, would be treated separately for better results.
Lasserre and Ouellette (1988) have elaborated that there must be maximum similarities
among the sectors under study. They said that productivity measures are frequently used
to compare the performance of different sectors. Keeping all in view PKGI was classified
into the two following categories:
1. Industries having knitting, dyeing, and stitching facilities (vertical firms);
2. Industries having only stitching facilities (horizontal firms).
Both classes will be dealt with separately for accurate results (at disaggregate level).
However, to have an overall view of the PKGI, analysis at an aggregate level (combine
for vertical and horizontal) will also be done and results will be compared in order to
identify which sector performed best.
3.8 Correlation and Regression
Correlation and regression analyses help measure the relationship and strength of
the relationship between dependent and independent variables. Siegel (2000) suggests
three basic goals to keep in mind when studying relationships in bivariate data:
1. The first purpose of studying relationships is to describe and understand
the correlation between different production variables. This is the most
Chapter Three: Data Collection and Research Methodology 162
general goal, adequately providing background information to help
understand how the world works.
2. The second purpose is to predict new observations. In this process, one
can correctly predict any outcome based on the previous workings. Sales
of particular goods can be predicted based upon the previous period's
sales.
3. Finally, the process guides the management to mediate in the process for
adjustment and control. Based on this observation one can make necessary
changes in the whole process to achieve desired targets.
3.9 Measurement of Correlation
As said by Berenson and Levine (1998), correlation is a process in which the
strength of different variables is measured. It is not used to predict dependent variables;
rather it is used to evaluate the strength of alliance among different determinants.
Chaudhry and Kamal (2005) have mentioned that correlation, like covariance, is a
measure of the level to which any two variables differ in relation to one and other. In
other words, two variables are said to be correlated if they tend to all together diverge in
the similar direction. If one variable tends to enlarge while the other variable decreases,
the correlation is said to be negative or contrary, e.g. the volume of gas decreases as the
pressure increases.
According to Chaudhry and Kamal (2005), it is worth remarking that in
correlation one measures the strength of a relationship or interdependence between two
variables; both the variables are random variables, and are treated symmetrically, i.e.
Chapter Three: Data Collection and Research Methodology 163
there is no difference between dependent and independent variables. According to Siegel
(2000), correlation is a summary measure of the strength of a relationship. Like all
statistical summaries, the correlation is both helpful and limited. Siegel has further stated
that if the scatter plot shows either a well-behaved linear relationship or no linear
relationship, then the correlation provides an excellent summary of the relationship.
Nevertheless, if there are problems such as a non-linear relationship, unequal variability,
clustering, or outliers in the data, the correlation number could be misleading and as a
result, correlation will be measured among different variables, including dependent and
independent variables.
Siegel (2000) has mentioned two ways to identify and measure correlation
between two variables: (a) scatterplot or (b) by mathematical formulae.
According to Siegel a scatterplot displays each case or elementary unit using two
axes to represent the two factors. Berenson and Levine (1998) define six types of
scatterplots:
1. Positive linear relationship
2. Negative linear relationship
3. No relationship
4. Positive curvilinear relationship
5. U-shaped curvilinear relationship
6. Negative curvilinear relationship
As a result, the scatter plot will be designed with the help of SPSS software to see
correlation among different variables. Mathematically the correlation is computed for the
data using a straightforward but time consuming formula. The formula for the correlation
Chapter Three: Data Collection and Research Methodology 164
coefficient is based on bivariate data consisting of two measurements made on the first
elementary unit through the measurements made on the last one. The term in numerator
summation involves the interaction of the two variables and determines whether a
correlation will be positive or negative. The denominator merely scales the numerator so
that the resulting correlation will be an easily interpreted pure number between –1 and
+1, denoted by ―r ―. A correlation of +1 indicates a perfect straight-line relationship, with
higher values of one variable associated with perfectly predictable advanced values of
another. A correlation of –1 indicates a perfect negative straight-line relationship, with
one variable decreases while the other increases. There are three types of relationship:-
1. Linear relationship
2. No relationship
3. Non-linear relationship
Considering the above discussion, this research will measure the strength of
correlation. This measurement tells us how two variables are correlated. It is important to
note that this correlation cannot be used for predictive purposes. In addition, this process
does not distinguish between dependent and independent variables. In chapter four, the
strength of correlation will be measured with the help of SPSS software.
3.10 Regression Analysis
Regression analysis is a methodology used primarily for the purpose of
prediction. In this report, the main objective is to use regression analysis to develop a
statistical model that can be used to predict the values of a dependent or response variable
based on the values of an explanatory or independent variable.
Chapter Three: Data Collection and Research Methodology 165
Basic assumptions of linear regression are very important to discuss. Berenson
and Levine (1998) stated that there are four underlying assumptions that arise while using
regression analysis:
1. Normality
2. Homoscedasticity (constancy of the variance of a measure over the levels of
the factor under study)
3. Independence of errors
4. Linearity
The first assumption of normality requires that the value of Y be normally
distributed at each value of X. The second assumption homoscedasticity requires that the
variation around the line of the regression be constant for all variables of X. It means that
Y varies the same amount when X has low or high values. The third assumption,
independence of error, requires that the population and the error (the residual difference
between each observed and average predicted value of Y) should be independent for each
value of X. The fourth assumption, linearity, states that the relationship among variables
is linear in the parameters. Based upon this discussion, the analysis will be carried out
using multiple regressions.
3.11 Regression Coefficient and Regression Equation
Among all possible regression equations with various values of these coefficients,
these make the sum of squared prediction errors have the smallest possible values. The
regression equation or prediction equation is:
Chapter Three: Data Collection and Research Methodology 166
Y = a + b1 X1+ b2 X2+ b3 X3 +…...bk X k
Where:
Y = Dependent variable or explanatory variables
a = Constant
X1, + b2 X2+ b3 X3 +……….. X k = Independent Variables
b1 + b2 + b3 +………..bk = Regression Coefficient
As mentioned before, the regression model is a statistical equation for prediction.
For appropriate results, one should be able to interpret the regression coefficients.
3.12 Prediction and Prediction Errors
According to Berenson and Levine (1998), the regression lines serve only as an
approximate predictor of the mean value of Y for a given value of X. Hence, there is a
call for development of a measure of the variability of each observation around its mean.
The measure of variability around the line of regression (that is, standard deviation) is
called standard error of the estimate. The interpretation of the standard error of the
estimate is analogous to that of the standard deviation. Similarly, the standard deviation
measures variability around the fitted line of the regression.
Chapter Three: Data Collection and Research Methodology 167
3.13 The Pitfalls and Limitations of Regression
According to Berenson and Levine (1998), regression and correlation are perhaps
the most widely used and, unfortunately, the most widely misused statistical techniques
applied to business and economics. The difficulties come from the following sources:
1. Lack of awareness about the assumption of the least square regression
2. Not knowing how to evaluate the assumption of least square regression
3. Equating of correlation and causation
4. Not knowing what the alternatives to least squares regression are if a
particular assumption is violated
5. Use of a regression model without an adequate knowledge of the subject
matter.
As per Statsoft (2006), the chief conceptual limitation of all regression techniques
is that one can only ascertain relationships, but he or she is never sure about underlying
causal mechanisms. During data analysis in chapter four, these pitfalls will be kept in
mind before reaching any conclusion.
3.14 Unequal Variability
The technical word heteroscedastic (adjective) and heteroscedastic (noun) also
describe unequal variability. Siegel (2000) pointed out the unequal variability in the data
as another technical difficulty that, unfortunately, arises in business and economic data.
A scatterplot is said to have unequal variability when the variability on the
vertical axes changes dramatically with horizontal movement. Siegel has further stated
that the problem with unequal variability is that the places with high variability, which
Chapter Three: Data Collection and Research Methodology 168
represent the least precise information, tend to influence statistical summaries the most.
In such cases, the correlation coefficient will probably be unreliable.
Siegel (2000) has proposed two ways to address this problem. According to him,
the most common is transformation of data by using a logarithm, while the second way is
to develop a square root transformation.
3.15 Determining the Linear Regression Equation
As discussed above, scatterplot is the first step to assessing any relationship
between two different variables. This relation may be of many types. According to
Berenson and Levine (1998), one of the most important questions to address in regression
analysis involves the determination of the particular straight-line model that best fits the
model. According to Berenson and Levine, the least square method is a mathematical
technique that solves this problem.
According to Chaudhry and Kamal (2005), the principle of least squares consists
of determining the values for the unknown parameters that will minimise the sum of the
squares of errors (or residuals), where errors are defined as the difference between
observed values and the corresponding values predicted or estimated by the fitted model
equation.
The parameter values obtained will give the least sum of errors and are known as
least square estimates known residuals. According to the principles of least squares, such
values of a and b are determined that will minimise the sum of the squares of the
residuals. In other words, the best regression line is the one, which minimizes the sum of
the squares of the vertical deviations between the observed values and corresponding
Chapter Three: Data Collection and Research Methodology 169
values predicted by the regression model. Keeping all discussion in view in chapter four,
regression analysis will be carried out by using the least squares method so that best line
could be found to develop a regression equation.
3.16 Hypothesis Testing
In chapter one, a hypothesis was suggested. According to Siegel (2000), there is
always a pair of hypotheses, the null hypothesis (H0) and the alternative hypothesis (H1).
The null hypothesis represents the default possibility that one will accept unless presented
with convincing evidence to the contrary. Siegel (2000) explains that acceptance of the
null hypothesis is a very favoured position. It takes into account the benefit of the doubt.
In fact, one can end up accepting the null hypothesis without actually proving anything,
which can make the research weak and fragile. The null hypothesis is the more specific
hypothesis of the two. The null hypothesis is to be rejected only if there is convincing
statistical evidence that would rule out the null hypothesis as a reasonable possibility.
Rejecting the null hypothesis represents a stronger position than accepting the null
hypothesis. Results will be compared, taking null and alternative hypotheses into account,
and finally conclusions will be made.
3.17 Selection of Software for Statistical Analysis
There is an array of statistical analysis software available in the market. For this
study, the SPSS was selected and before selection of SPSS software, other software,
including Minitab and Stata, were checked. Results in all cases were the same, since the
same mathematical formulae were used, the main difference were the operation
Chapter Three: Data Collection and Research Methodology 170
procedure. SPSS software was found to be easy to use and understand. Accordingly,
SPSS software was chosen for this research.
3.18 Conclusion
This chapter focused primarily on finalising the research methodology. Before
attempting to fit a linear model to observed data, first determination ought to be made
whether there is a relationship between the variables of interest or not. This does not
necessarily imply that one variable causes the other (for example, higher SAT scores do
not cause higher college grades), but there is some considerable union between the two
variables. A scatterplot can be a helpful tool in determining the strength of the
relationship between two variables. If there is no appearance of a coalition between the
proposed explanatory and dependent variables (i.e., the scatter plot would not indicate
increasing or decreasing trends), then fitting a linear regression model to the data
probably will not provide a useful model. A valuable numerical measure of association
between two variables is the correlation coefficient, which is a value between -1 and +1,
indicating the strength of the association of the observed data for the two variables.
After detailed discussion, the following strategy for research analysis has been
finalised:
1. TFP will be calculated by using Sumanth‘s (1990) model of total
productivity of PKGI at aggregate and disaggregate levels;
2. Comparison will be made between TFP at aggregate and disaggregate
levels;
Chapter Three: Data Collection and Research Methodology 171
3. TFP of other sectors of the Pakistani industry will be calculated employing
the same model;
4. TFP of PKGI and other sectors will be compared;
5. Hypotheses will be tested;
6. Correlation between dependent and independent variables will be checked
out by using a scatter plot;
7. Data will be transformed with the help of a logarithm to minimise unequal
variability if required;
8. Normality, homoscedasticity, and linearity will be tested out before using
a regression model to fulfil the assumptions for regression analysis;
9. A regression equation will be derived by using the least squares method
and eliminating the less significant variables;
10. The least square weighted method will be applied if felt necessary.
Chapter Four: Data Analysis and Results 172
CHAPTER FOUR: DATA ANALYSIS AND RESULTS
Chapter three discussed research methodology in detail and finalised the approach
to collecting and analysing data. In line with the research methodology, primary and
secondary data were collected. Primary data were collected through a survey of the
industry using a structured questionnaire and secondary data were made available from
the Federal Bureau of Statistics of Pakistan. This chapter is aimed at data analysis and
reaching a conclusion based on results derived from the data analysis.
The following list of study objectives, as detailed in Chapter One, appears here
for ready reference:
1. To examine the TFP level achieved by:
2. PKGI (at aggregate level)
3. Vertical firms of PKGI
4. Horizontal firms of PKGI
5. To compare TFP level of PKGI with other manufacturing sectors of
Pakistan
6. To identify the correlation between different determinants and TFP
7. To develop a regression equation to answer the following questions: (a)
Which set of independent variables is able to predict TFP (a dependent
variable)? And (b) which variable in a set of variables has highest
contribution in the variance of dependent variable?
8. To test hypotheses (see Section 1.8)
To achieve the aforementioned objectives, the following procedures will be
adopted (see chapter three for further detail): -
Chapter Four: Data Analysis and Results 173
1. TFP of the PKGI will be calculated at aggregate and disaggregate levels
for vertical and horizontal firms with the help of the TFP models based on
accounting function of total productivity (see Section 3.4)
2. TFP of the 181 major industries (18061 reporting firms) of Pakistan will
be calculated and a comparison will be carried out among different
industries in Pakistan
3. Hypotheses will be tested to accept or reject the research claims
4. A correlation matrix will be made with the help of SPSS
5. Regression will be run to make a regression equation for prediction
purpose and to identify the significant contributors in the variance of TFP
6. Based on the outcome, a guideline will be provided for the better
productivity of the PKGI
4.1 Profile of PKGI
Before moving ahead, it is imperative to have a look on the profile of the industry
under discussion. As mentioned in Chapter One, PKGI can be divided into two main
sectors: organised and unorganised. A cottage or unorganised sector is quite small and
produces garments, mainly undergarments, for local consumption whereas the organised
sector is producing garments only to export to different parts of the world. It is important
to note that this study covers only the organised sector. The reason behind this selection
is the availability of required data for this study and its significance contribution in the
economy of Pakistan.
Chapter Four: Data Analysis and Results 174
As discussed in chapter one, PKGI is spread in three main cities of Pakistan:
Faisalabad, Karachi and Lahore. More than 98% of businesses are in these cities.
Furthermore, there are two main classes of PKGI; vertical and horizontal. Vertical firms
have knitting, dyeing, and apparel making facilities (cutting, stitching and finishing) all
under one roof. Horizontal firms have only apparel making facilities. Vertical firms
purchase yarn from spinning mills and convert into fabric, then dye it according to
demands and finally make apparels as per the demand of the customer. Horizontal firms
buy ready fabric or get its knitting and dyeing from commercial knitters and dyers
(outsourcing of fabric) and they do only cutting, stitching and finishing. Nevertheless, a
few horizontal firms have in house embroidery and small scale printing.
Table 4.1 and 4.2 have been made based on information collected from Pakistan
Hosiery Manufacturing Association, which is the only association of this sector. This is
secondary data, which is being used to have an overview of the PKGI. This information
would help a better understanding of the results. Table 4.1 tells about the population of
the PKGI, which is under study, and shows the total population of the PKGI under
discussion. As mentioned in Section 1.5, there are total 900 exporters but out of 900, the
major share nearly 90% is with 24% firms (218). Table 4.1 depicts that out of 218 firms,
81 firms (37.16%) firms are horizontal and rest 137 firms (62.84%) are vertical firms.
Furthermore, Table 4.1 speaks about the share of different cities in total
population. It is very clear that Lahore has 45% share of total firms, while Karachi has
second position.
Table 4.2 deals with firms selected as sample based on random sampling
techniques. According to this table, 33 firms (67.35%) are vertical firms, while 16 firms
Chapter Four: Data Analysis and Results 175
(32.65%) are horizontal firms. In addition to that, Table 4.2 shows that in sample data,
majority of the firms are established at Lahore whereas, Faisalabad has lowest share
(12.24%) in total sample data. Nevertheless, the ratio of horizontal and vertical firms in
population approximately matches with the ratio of horizontal and vertical firms in the
sample, collected for analysis.
Table 4.1
Frequency of firms based on type and location (population)
Types of Firms Faisalabad Karachi Lahore Total Percentage
Horizontal firms 17 48 16 81 37.16
Vertical firms 13 41 83 137 62.84
Total 30 89 99 218 100
Percentage 13.76 40.83 45.41 100.00
Source: Unpublished Report of Pakistan Hosiery Manufacturing Association
Table 4.2
Frequency of firms based on type and location (selected by using random sampling)
Types of Firms Faisalabad Karachi Lahore Total Percentage
Horizontal firms 3.00 11.00 2.00 16.00 32.65
Vertical firms 3.00 10.00 20.00 33.00 67.35
Total 6.00 21.00 22.00 49.00 100.00
Percentage 12.24 42.86 44.90 100.00
Chapter Four: Data Analysis and Results 176
4.2 Data Summarisation of PKGI
In previous paragraphs, an overview of the PKGI was given in the form of Tables
4.1 and 4.2. These tables give a picture of the PKGI. Table 4.3 has been constructed to
have an overall observation about the data. In this table, basic statistics about the
independent variable are given. This table gives the overall view of the data and tells
about the difference between the characteristics of PKGI at aggregate and disaggregates
level. One can derive the following information from this table:
1. Sale value in U.S. Million $, is an indicator of the level of business firm is doing.
It is more likely that firms having vertical setup have high sale volume from the
firms having horizontal setup. It is clear from the Table 4.3, that mean value of
vertical firm is 4.86, which is much higher that the sale volume of horizontal
firms, which is 1.65. It is also evident that horizontal firms have Standard
Deviation (1.34), less than they have the vertical firms (5.96), which show that
spread of data.
2. Clothing manufacturing is a highly labour intensive industry. It is more likely that
share of labour expenses (direct and indirect both) have a reasonable share as
compared to a spinning mill, which is capital intensive industry. Table 4.3 depicts
that mean value of labour expenses in case of horizontal firm (8.26) is less than
labour expenses of vertical firms (9.60). It is more likely due to the difference in
salaries, over head expenses, and high stitching machine to operator ratio. Table
4.3 also demonstrate that there is a significant difference in the minimum and
maximum values of both horizontal and vertical firms.
Chapter Four: Data Analysis and Results 177
3. Borrowing money from banks and paying interest is quite common among
business circle. It is here that firms get loan from banks and pay them interest
along with other financial charges. In this report, it is called financial expenses. It
is more likely that vertical firms, which are big in nature from horizontal firms,
have to get more loans from banks and resultantly, their financial expenses share
will be higher than horizontal firms. Table 4.3 demonstrates that vertical firms
have financial expense mean 3.73, where it is only 1.58, in case of horizontal
firms. It is a proof that vertical firms rely more on banks as compared to
horizontal firms. Furthermore, there is a huge difference in maximum values,
which are 3.60 and 13.14 for horizontal and vertical firms respectively.
4. Industry is making two types of products, simple for causal use and embellished
for people interested in fashion wear. Such clothes are printed, pieced, decorated
with appliqués, colourful embroidery. Table 4.3 demonstrates both types of firms
are producing a mix of above mentioned types of garments. There is not a
significant difference between mean values (48.44 and 52.58 for horizontal and
vertical firms respectively). However, there is understandable difference in SD.
Vertical firms have double value (22.75) than of horizontal firms (11.06). It
shows the spread of data.
5. The U.S. market has more than two third shares in total exports from Pakistan
(APTMA 2006). It is same in case of knitted garments exports. In both cases,
(horizontal and vertical) U.S. has more than two-third shares. It is obvious from
Table 4.3 that mean of exports to U.S. is 63.75% and 79.48% for horizontal and
Chapter Four: Data Analysis and Results 178
vertical firms respectively. It shows that both firms have behaved in a similar
way.
6. Average Free on Board Price (FOB) in U.S. $ is another measure of type of
business. It is obvious from the Table 4.3, that there is a minor difference in FOB
prices. Its mean value is 4.17 and 4.29 for horizontal and vertical firms,
respectively. It shows that it is not likely that vertical firms can charge more
prices. However, it is more likely that cost of production of vertical firms is
higher than horizontal firms. This observation is supported from the share of
finance and labour expenses in total cost of production, as discussed in previous
lines.
7. As discussed earlier that vertical firms have usually a bigger setup than horizontal
firms. This is supported by the figure on mean value of number of stitching
machines. There is drastic difference between horizontal (139.06) and vertical
firms (321.82). It shows that generally, vertical firms have more production
capacity as compared to horizontal firms.
Chapter Four: Data Analysis and Results 179
Table 4.3
Descriptive statistics of dependent variables
N Mean Std.
Deviation
Skewness Kurtosis
Min Max
Sale Value
(Million U.S. $)
Horizontal Firms 16 1.65 1.34 1.19 0.9 0.32 4.98
Vertical Firms 33 4.86 5.96 2.39 6.15 0.12 26.83
Aggregated Level 49 3.81 5.15 2.94 9.81 0.12 26.83
Share (%) of
Labour Expenses
in Total Cost
Horizontal Firms 16 8.26 5.1 -0.1 -1.41 0.63 16.03
Vertical Firms 33 9.6 4.76 0.47 0.62 0.67 22.96
Aggregated Level 49 9.16 4.86 0.24 -0.03 0.63 22.96
Share (%) of
Financial
Expenses in
Total Cost
Horizontal Firms 16 1.58 1.12 0.45 -0.8 0.02 3.6
Vertical Firms 33 3.73 3.15 1.42 1.72 0.16 13.14
Aggregated Level 49 3.03 2.84 1.8 3.41 0.02 13.14
Share (%) of
Fashion
Garments in
Total Production
Horizontal Firms 16 48.44 11.06 -0.39 3.62 25 75
Vertical Firms 33 52.58 22.75 -0.13 -0.54 0 90
Aggregated Level 49 51.22 19.67 -0.01 0.08 0 90
U.S.A Market
Share (%) in
Total Exports
Horizontal Firms 16 63.75 22.55 0.18 -0.53 25 100
Vertical Firms 33 79.48 22.14 -1.36 1.51 10 100
Aggregated Level 49 74.35 23.27 -0.74 -0.25 10 100
Average FOB
Price in U.S. $
Horizontal Firms 16 4.17 0.72 0.62 -0.62 3.25 5.5
Vertical Firms 33 4.29 1.03 1.01 -0.17 3 6.5
Aggregated Level 49 4.25 0.93 1.02 0.1 3 6.5
Number of
Stitching
Machines
Installed
Horizontal Firms 16 139.06 82.75 2.31 6.43 75 400
Vertical Firms 33 321.82 182.23 0.9 0.32 60 780
Aggregated Level 49 262.14 178.26 1.19 0.96 60 780
Source: Primary data collected through survey and secondary data from Pakistan Federal Bureau of
Statistics.
4.3 TFP of PKGI and its Comparison
As mentioned in Section 1.8, one if the objectives of this study is to calculate the
TFP of PKGI at aggregate and disaggregate level and then its comparison with other
sectors of Pakistan manufacturing industries. In the following pages, TFP has been
calculated. For this purpose, TFP models based on accounting function has been
calculated (Section 3.4) Mathematical equation of this model is as under:
Chapter Four: Data Analysis and Results 180
Total Tangible Output
Total Productivity
Total Tangible Input
Where:
Total Tangible Output = value of finished units produced+ value of partial units
produced + dividends from securities + interest from bonds + other income
Total Tangible Input = value of (human + material + capital + energy + other
expenses) input used
The above equation was used to calculate TFP of PKGI and other manufacturing
sectors of Pakistan. Total Tangible Input (total cost of production) was calculated by
adding all expenses (wages, salary bill, utility cost, financial expenses, raw material cost,
and miscellaneous expenses. All these expenses are given in Appendix 1 and 2 under the
heading of Total Cost of Production. Total Tangible Output means value of finished
goods, work in process, change in stocks and raw material, and other incomes. Total of
all above mentioned figures is available in Appendix 1 and 2 under the heading of Total
Sale Value.
Table 4.4 shows the results of the data analysis. It lists the TFP of PKGI at
aggregate and disaggregate levels and TFP of 18,063 firms from 181 different
manufacturing sectors of Pakistan. The Pakistan Federal Bureau of Statistics published
reports based on census of Pakistan‘s manufacturing industries in 2001. This report
(secondary data) was used to calculate the TFP of different sectors with the help of the
TFP models based on accounting function. The following results have been derived from
the analysis:
1. Arithmetic mean of the TFP of PKGI at aggregate level is 0.976, which is less
than one. Furthermore, the TFP at a disaggregate level for horizontal and vertical
Chapter Four: Data Analysis and Results 181
firms is 0.975 and 0.976 respectively, which is also less than one. This means that
the tangible output of PKGI at aggregate and disaggregate levels is less than the
tangible input. In other words, it can be said that PKGI as a whole completely
faced a loss in the year under study. As discussed in Chapter One, a TFP of more
than 1means that the TFP of the firm is high and less than 1means that the TFP is
low. In this study, the benchmark is one. A TFP of more than 1will be considered
as a high TFP, and vice versa. If the TFP is more than one, it depicts that the total
tangible output is greater than the total tangible input. In other words, one can say
that the firm produces more than its consumption. It also means that the firm
made a profit, since more output is a gain on input, which is the ultimately
purpose of the firm.
2. The TFP level of horizontal firms is 0.975, while the TFP of vertical firms is
0.976, which is slightly higher than the TFP level of horizontal firms. TFP level
of vertical firms is also same when compared with the TFP of PKGI at aggregate
levels. As a whole, however, there is not a significant difference among all three
values.
3. The median of horizontal firms is 1.01, while the median of vertical firms is
0.950. The median value of vertical firms obviously shows that more than 50% of
the firms have a TFP of less than one. Nevertheless, more than 50% of horizontal
firms have a TFP of greater than one. As a whole this indicates that horizontal
firms have better productivity than vertical firms at disaggregate level. In case the
data is heterogeneous, the median value becomes significant as compared to
mean. Median of horizontal and vertical tells the real picture. It is evidence that
Chapter Four: Data Analysis and Results 182
majority of horizontal firms earned profit. Whereas majority of vertical firms
faced loss in the year 2000-01. Nevertheless, median of TFP at aggregated level
is also less than1(0.98), which shows that more than 50% of the firms at
aggregated level have beard the loss since their output was less than the input.
4. Table 4.4 shows that the standard deviation of TFP at aggregate levels is 0.127,
which is greater than the horizontal firms (0.09). It shows that in the case of the
TFP at aggregate levels, firms vary more from the average. Table 4.4 also shows
that the standard deviation of horizontal firms is 0.091, which is much less than
the vertical firms. It shows that there is less variation in the case of horizontal
firms. In other words, it can be said that the horizontal firm‘s behaviour has more
similar as compare to the vertical firms.
5. According to the results, the skewness value of the TFP of vertical firms is 1.28
with a positive sign, while, in the case of horizontal firms it is 1.454 with a
negative sign. It shows that in the case of vertical firms, the majority of the firms
have a lesser TFP compared to the mean vale of TFP. In the case of horizontal
firms, skewness is negative, which means that the majority of the firms have a
TFP of more than the mean value of TFP. It can be inferred from these two values
that, as a whole, horizontal firms performed better than the vertical firms.
6. Table 4.4 shows that the value of kurtosis in all three cases is different. It is quite
high in the case of PKGI at aggregate levels and of vertical firms. However, it is
significantly less in the case of horizontal firms. High kurtosis value shows that
values are nearer to mean value. Whereas, low value shows that values are spread
and there are number of observations which are away from the mean value. This
Chapter Four: Data Analysis and Results 183
table shows that horizontal firms have more data spread as compared to vertical
firms.
7. Minimum and maximum TFP values indicate that range in case of horizontal
firms is less as compared to vertical firms. This is evidence that vertical firms
have more diverse performance as compared to horizontal firms.
8. As indicated in the above discussion, it has been observed that mean of TFP of
vertical firms is slightly higher than horizontal firms. However, at the same time,
its median is less, which shows that there are some outfitters in the data of
horizontal firms, which have reduced the mean value of TFP of horizontal firms,
and in case of vertical firms they have increased the mean value.
9. It is manifest from Table 4.4 that the mean of TFP of PKGI at aggregate level is
0.976, while the mean of 181 manufacturing industries is 1.47, which is nearly
50% higher than the TFP of PKGI. It is clear from the aforementioned values that,
as a whole, the manufacturing industry of Pakistan has performed well since its
total tangible output is greater than their tangible input. In other words, these
firms gain more than they consume. Based on the observation one can say that
these firms earned a profit. It is also obvious that the lowest TFP of 18,061 firms
is 0.34, which is quite less than one. The TFP of PKGI at aggregate and
disaggregate levels is less than one.
10. These results show that mean of TFP of the Pakistani manufacturing industry
(1.47) is significantly high in comparison with TFP of PKGI (0.976).
11. The Standard Deviation value of 181 manufacturing industries is 0.351, while
standard deviation of PKGI is 0.127. This indicates the variation in the data. It is
Chapter Four: Data Analysis and Results 184
obvious from these values that there is a lot of variation in the case of 181
manufacturing industries of Pakistan, while in the case of PKGI, there is more
similarity in the behaviour of different firms. Based on the above, it can be said
that the TFP of PKGI is lower than the TFP of other manufacturing industries in
Pakistan.
The above discussion is related to the TFP level achieved by the PKGI at
aggregate, disaggregates levels, and results in a comparison of the two. It can be deduced
that the TFP of PKGI is low compared to other industries in Pakistan. This scenario
shows that PKGI is not in a better shape and is facing a crisis. If this situation remains
then it is likely that many firms will have to file voluntary bankruptcy. (This observation
has been verified by the unpublished report of the Pakistan Hosiery Manufacturing
Association, the official representative of PKGI, that in 2005 more than 25% firms closed
and many more were intending to file bankruptcy).
Table 4.4
TFP of PKGI (at aggregate and disaggregate level) and major industries of Pakistan
PKGI
Horizontal
Firms
PKGI
Vertical
Firms
PKGI
Aggregated
Level
(Combined)
Major
Industries
of
Pakistan
N 16 33 49 181
Mean 0.975 0.976 0.976 1.470
Median 1.01 0.95 0.98 1.39
Std. Deviation 0.091 0.142 0.127 0.351
Skewness -1.45 1.28 1.03 2.24
Kurtosis 1.48 3.59 3.97 9.58
Minimum 0.75 0.72 0.72 0.34
Maximum 1.08 1.47 1.47 3.59
Chapter Four: Data Analysis and Results 185
Figure 4.1
Distribution of TFP (horizontal firms)
Figure 4.2
Distribution of TFP (vertical firms)
Chapter Four: Data Analysis and Results 186
Figure 4.3
Distribution of TFP (at aggregated level)
Figure 4.4
Distribution of TFP (manufacturing sector in Pakistan)
Chapter Four: Data Analysis and Results 187
4.4 Hypothesis Testing
The following research hypotheses were included in chapter one. The testing of
these hypotheses is part of the study objectives. Below, results of hypotheses testing are
given. Based on the results, the following hypotheses will be rejected or accepted. For
analysis purpose, SPSS software has been used.
Ho Ha
µTFP of vertical firms = µ TFP of horizontal firms µTFP of vertical firms ≠ µ TFP of horizontal firms
µ TFP of horizontal firms is less or equal to 1 µTFP of horizontal firms is greater than 1
µTFP of vertical firms is less or equal to 1 µTFP of vertical firms is greater than 1
µTFP of PKGI at aggregate level is less or equal to 1 µ TFP of PKGI at aggregate level is greater than 1
4.4.1 Assumptions for t-test. There is a big debate in the literature about the
assumptions made before applying the t test. There are different views but the most
important is that population from which sample has been drawn is normally distributed.
As said by Elliott and Woodward (2006) this assumption is rarely if ever precisely true in
practice. It depends upon the researcher and how he or she is concerned about this
assumption. Elliot and Woodward published three assumptions that are generally
considered before applying the t test.
1. If the sample size is small (less than 15), then one should not use the one-
sample t-test if the data are clearly skewed or if outliers are present.
2. If the sample size is moderate (at least 15), then the one-sample t-test can
be safely used except when there are severe outliers.
Chapter Four: Data Analysis and Results 188
3. If the sample size is large (at least 40), then the one-sample t-test can be
safely used without regard to skewness or outliers.
Elliot and Woodward (2006) further wrote that there is an obvious variation of
these rules throughout the literature and having large sample data is based on the central
limit theorem, which says that when sample size is moderately large, the sample mean is
approximately normally distributed even when the original population is not normal.
Chaudhry and Shahid (2005) have also discussed the assumptions for t test.
According to them, one should take care of the following assumptions;
1. Selection of samples should be based on random selection
2. The population from which samples has been drawn should be normal
3. In the case of two small samples, both the samples are selected randomly
from a population which should be normal and have same equal variances.
Table 4.2 tells that there are 49 firms under study. Out of these, 33 are vertical
and 16 are horizontal. Apparently, sample size in both cases is more than 15, which is
one of the fundamental requirements for t test. Based on the above assumption it was
decided to carry out the t test because the data fulfils the assumption — not fully, but to
some acceptable extent.
4.4.2 Mean difference in TFP of horizontal and vertical firms. As discussed in
chapter four, TFP is one of the key objectives of the firms. It is also discussed in previous
paragraphs that there are two types of firms; horizontal and vertical. Based on this
diversification, there is a need to know which type of firm or business style is better than
others. For this purpose the following hypothesis was developed:
Chapter Four: Data Analysis and Results 189
Ho: µTFP of vertical firms = µ TFP of horizontal firms
Ha: µTFP of vertical firms ≠ µ TFP of horizontal firms
For this purpose, Independent-Samples t test was carried out and the following
tables were obtained:
Table 4.5
Independent t test group statistics
Types of Firm N Mean Std.
Deviation
Std. Error
Mean
Total Factor
Productivity
Horizontal Firms 16.00 0.975 0.091 0.02
Vertical Firms 33.00 0.976 0.142 0.02
Table 4.6
Independent t test significance values
Independent
Samples Test
Levene's Test
for Equality of
Variances
t-test for
Equality
of Means
F Sig. t df Sig.
(2-tailed)
Lower Upper Lower Upper Lower
Total Factor
Productivity
Equal variances
assumed
1.57 0.22 -0.03 47.00 0.98
Equal variances
not assumed
-0.03 43.08 0.97
Chapter Four: Data Analysis and Results 190
Table 4.5 gives a group statistics of the firms under test. It is understandable from
Table 4.5 that there is a slight difference between mean values of horizontal and vertical
firms (0.975 and 0.976 respectively). However, this table shows that there is a significant
difference in standard deviation between two (0.91 and 1.42). Table 4.6 is the outcome of
the Levene test. After assuming that there is no equality in variance, its significance value
(0.978) is greater than 0.05. It shows that there is no significant evidence to reject the null
hypotheses, which says that there is no difference in the mean values of TFP of horizontal
and vertical firms.
From this result, it is clear that there is no significant difference in the TFP of
horizontal and vertical firms. It can be at the 5% level of significance (α=0.05) that it is
not necessary for better TFP to prefer horizontal or vertical type of firms.
4.4.3 Test of mean of TFP at aggregated level. TFP is a ratio of output to input.
In this equation, output is numerator and input is denominator. If the output and input
values are equal then it can be said that the output of a firm is equal to its input or one can
say that firm consumed same amount of input, which is equal to output. If the
denominator is smaller than the numerator, it means that firm has produce more and
consumed less and TFP will be more than 1. To check the TFP, whether it is greater
than1or not, the following hypothesis was made:
Ho: µTFP of PKGI at aggregate level is less or equal to 1
Ha: µ TFP of PKGI at aggregate level is greater than 1
For this purpose, One-sample t test was carried out and following Tables were
obtained:
Chapter Four: Data Analysis and Results 191
Table 4.7
One sample t test group statistics (at aggregated level)
N Mean Std. Deviation
Std. Error
Mean
Total Factor Productivity 49 0.976 0.127 0.018
Table 4.8
One sample t test significance values (at aggregated level)
One-Sample Test
Test Value = 1
T Df
Sig. (2-
tailed)
Lower Upper Lower
Total Factor
Productivity -1.34 48.00 0.186
Table 4.8 shows that the p value is 0.186, which is greater than 0.05 (α= 0.05). In
this case, the null hypothesis says that TFP of aggregated firm is equal or less than1 (one
tail). For this purpose, p value is divided by two and then the p value is compared with α
value, which is 0.05. Table 4.8 shows that p/2 is 0.093, which is greater than 0.05. Based
on this result there is no enough evidence to reject the Ho. Hence, it can be said at the
level of 5% significance that TFP of aggregated firms (combined horizontal and vertical
firms) is less than 1 or maximum equal to 1. It means that PKGI as a whole did not earn
profit.
4.4.4 Test of mean of TFP of horizontal and vertical firms. In previous pages, the
mean of TFP at aggregated level was checked, and it was found that there is no evidence
Chapter Four: Data Analysis and Results 192
to reject the null hypotheses that mean of TFP is less or equal to 1. This test is also
applied to check the hypotheses about the mean of TFP of horizontal and vertical firms.
To check the TFP of horizontal and vertical firms, whether it is greater than1or not, the
following hypothesis was made:
Ho:
µTFP of vertical firms is less or equal to 1
µTFP of horizontal firms is less or equal to 1
Ha:
µTFP of vertical firms is greater than 1
µTFP of horizontal firms is greater than 1
For this purpose, One-sample t test was carried out.
Chapter Four: Data Analysis and Results 193
Table 4.9
One sample t test group statistics (horizontal and vertical firms)
One-Sample
Statistics(a)
N Mean
Std.
Deviation
Std. Error
Mean
Total Factor
Productivity 16.00 0.975 0.09 0.02
Type of Firm = Horizontal Firms
One-Sample
Statistics(a)
N Mean
Std.
Deviation
Std. Error
Mean
Total Factor
Productivity 33.00 0.976 0.14 0.02
Type of Firm = Vertical Firms
Chapter Four: Data Analysis and Results 194
Table 4.10
One sample t test significance values (horizontal and vertical firms)
One-Sample Test(a)
Test Value = 1
T df Sig. (2-tailed)
Lower Upper Lower
Total Factor Productivity -1.10 15.00 0.29
Type of Firm = Horizontal Firms
One-Sample Test(a)
Test Value = 1
T df Sig. (2-tailed)
Lower Upper Lower
Total Factor Productivity -0.97 32.00 0.34
Type of Firm = Vertical Firms
Table 4.10 shows that the p value (divided by two) in both cases (horizontal and
vertical) is .145 and 0.17, respectively, which is greater than 0.05 (α= 0.05). In this case,
null hypothesis says that TFP of horizontal and vertical firms is equal or less than1 (one
tail). Based on this result there is not enough evidence to reject the Ho. Hence, it can be
said at 5% level of confidence that TFP of horizontal and vertical firms is less than 1 or
maximum equal to 1. It means that horizontal and vertical firms both did not earn a profit.
In this part of chapter, an effort was carried out to test the null hypothesis, which
is one of the main objectives of this research. It was found that there is no significant
difference between the mean value of TFP of horizontal and vertical firms. Furthermore,
Chapter Four: Data Analysis and Results 195
it was derived that PKGI at aggregate and disaggregated level have a TFP value of less
than one. It means that this sector faced a loss since it has consumed more and its output
is less. This is quite alarming for the sector and begs serious efforts to make it better. In
the next part of the chapter, correlation between TFP and other independent variables will
be checked.
4. 5 Correlation Test between TFP and Seven Independent Variables
Correlation is a statistical method used to determine the relationship between two
independent variables. This association does not ensure the dependency of the said two
variables on each other. It simply denotes the association. Its value (correlation
coefficient "r") is from +1 to -1. One with positive sign means that there is a perfect
positive association and1with negative signifier means there is a perfect negative
association between two variables. However, zero means no association, but in some
cases it shows that initially there was negative and then positive association occurred
between two variables. In fact, correlation analysis estimates the degree of association
between two or more than two independent variables. Parametric techniques of
correlation analysis are based on the assumption that for any set of variables taken under
a given set of conditions, variation in each the variable is random and always follows
normal distribution.
As discussed in chapter three, the knitted garment manufacturing process is
lengthy and complex. In this process, yarn is converted into fabric with the help of
knitting machines of various types. After knitting, this fabric is bleached, dyed, printed,
Chapter Four: Data Analysis and Results 196
and finished as per the requirements of the customer. Finally, the fabric is cut and
assembled (sewn), with the help of stitching machines, to make garments.
In the process of knitted garment manufacturing, there are many factors that can
affect the TFP of PKGI. Chapter three presents a detailed discussion about variables,
which can affect the TFP of PKGI. After in-depth discussions, the following seven
variables have been selected (see for more details Section 3.4) to measure their
correlation with the TFP of the PKGI:
1. Sale Value Million U.S. $
2. Share (%) of Labour Expenses in Total Cost
3. Share (%) of Finance Expenses in Total Cost
4. Share (%) of Fashion Garments in Total Production
5. U.S.A Market Share (%) in Total Exports
6. Average FOB Price in U.S. $
7. Number of Stitching Machines Installed
Since there are numerous dissimilarities between vertical and horizontal firms, it
has been decided to carry out analysis for vertical and horizontal firms separately.
Chapter Four: Data Analysis and Results 197
Table 4.11
Correlation matrix among seven independent variables (horizontal firms)
Total Factor
Productivity
Sale
Value
(Million
U.S. $)
Share (%)
of Labour
Expenses
in Total
Cost
Share (%)
of
Financial
Expenses
in Total
Cost
Share (%)
of Fashion
Garments
in Total
Production
U.S.A
Market
Share
(%) in
Total
Exports
Average
FOB
Price in
U.S. $
Number
of
Stitching
Machines
Installed
Total Factor
Productivity
Pearson
Correlation
1 -0.072 -.650(**) 0.084 -0.174 -0.191 0.082 0.003
Sig. (2-
tailed)
0.791 0.006 0.757 0.52 0.478 0.762 0.992
Sale Value
(Million
U.S. $)
Pearson
Correlation
-0.072 1 -0.106 -0.02 -0.312 -0.191 0.038 .542(*)
Sig. (2-
tailed)
0.791 0.696 0.94 0.239 0.478 0.889 0.03
Share (%) of
Labour
Expenses in
Total Cost
Pearson
Correlation
-.650(**) -0.106 1 0.305 0.469 0.171 0.132 -0.113
Sig. (2-
tailed)
0.006 0.696 0.251 0.067 0.526 0.627 0.678
Share (%) of
Financial
Expenses in
Total Cost
Pearson
Correlation
0.084 -0.02 0.305 1 0.089 0.015 0.013 -0.107
Sig. (2-
tailed)
0.757 0.94 0.251 0.744 0.956 0.962 0.694
Share (%) of
Fashion
Garments in
Total
Production
Pearson
Correlation
-0.174 -0.312 0.469 0.089 1 0.326 0.453 0.071
Sig. (2-
tailed)
0.52 0.239 0.067 0.744 0.218 0.078 0.794
U.S.A
Market
Share (%) in
Total
Exports
Pearson
Correlation
-0.191 -0.191 0.171 0.015 0.326 1 0.224 0.409
Sig. (2-
tailed)
0.478 0.478 0.526 0.956 0.218 0.405 0.115
Average
FOB Price
in U.S. $
Pearson
Correlation
0.082 0.038 0.132 0.013 0.453 0.224 1 0.309
Sig. (2-
tailed)
0.762 0.889 0.627 0.962 0.078 0.405 0.244
Number of
Stitching
Machines
Installed
Pearson
Correlation
0.003 .542(*) -0.113 -0.107 0.071 0.409 0.309 1
Sig. (2-
tailed)
0.992 0.03 0.678 0.694 0.794 0.115 0.244
Chapter Four: Data Analysis and Results 198
Table 4.12
Correlation matrix among seven independent variables (vertical firms)
Total Factor
Productivity
Sale
Value
(Million
U.S. $)
Share
(%) of
Labour
Expenses
in Total
Cost
Share
(%) of
Financial
Expenses
in Total
Cost
Share (%)
of Fashion
Garments
in Total
Production
U.S.A
Market
Share
(%) in
Total
Exports
Average
FOB
Price in
U.S. $
Number
of
Stitching
Machines
Installed
Total Factor
Productivity
Pearson
Correlation 1.000 0.081 -0.111 0.257 -0.217 -0.070 -0.025 0.070
Sig. (2-
tailed)
0.653 0.540 0.149 0.225 0.698 0.889 0.700
Sale Value
(Million U.S. $)
Pearson
Correlation 0.081 1.000 0.007 -0.149 0.470 0.285 0.248 0.847
Sig. (2-
tailed)
0.653 0.967 0.409 0.006 0.108 0.165 0.000
Share (%) of
Labour
Expenses in
Total Cost
Pearson
Correlation -0.111 0.007 1.000 -0.012 0.043 0.117 0.194 0.152
Sig. (2-
tailed)
0.540 0.967 0.945 0.810 0.516 0.279 0.400
Share (%) of
Financial
Expenses in
Total Cost
Pearson
Correlation 0.257 -0.149 -0.012 1.000 -0.112 -0.183 -0.061 -0.086
Sig. (2-
tailed) 0.149 0.409 0.945 0.536 0.308 0.734 0.636
Share (%) of
Fashion
Garments in
Total
Production
Pearson
Correlation -0.217 0.470 0.043 -0.112 1.000 0.311 0.653 0.446
Sig. (2-
tailed) 0.225 0.006 0.810 0.536 0.078 0.000 0.009
U.S.A Market
Share (%) in
Total Exports
Pearson
Correlation -0.070 0.285 0.117 -0.183 0.311 1.000 0.288 0.214
Sig. (2-
tailed) 0.698 0.108 0.516 0.308 0.078 0.104 0.231
Average FOB
Price in U.S. $
Pearson
Correlation -0.025 0.248 0.194 -0.061 0.653 0.288 1.000 0.311
Sig. (2-
tailed) 0.889 0.165 0.279 0.734 0.000 0.104 0.078
Number of
Stitching
Machines
Installed
Pearson
Correlation 0.070 0.847 0.152 -0.086 0.446 0.214 0.311 1.000
Sig. (2-
tailed) 0.700 0.000 0.400 0.636 0.009 0.231 0.078
Chapter Four: Data Analysis and Results 199
Table 4.11 indicates that in case of horizontal firms, there is a significant positive
association between (a) sale and number of stitching machines (r= .542) and (b) TFP and
share of labour expenses in total cost (r=-0.650) has a negative association with TFP.
Capacity has an obvious positive correlation with sale. This table further
demonstrates correlation between TFP and share of labour expenses in total cost
(r=-0.650). It shows that there is a negative and moderate correlation between TFP and
share of labour expenses in total cost of production. However, it looks that data is quite
independent and there is no enough evidence (significance of test) that there is
collinearity among the variables and any strong correlation between TFP (DV) and seven
other (IV) variables.
Table 4.12, indicates that in case of vertical firms, there is a significant
association between:
1. Sale and Number of Stitching Machines (r= 0.847)
2. Share of Fashion Goods in Total Production and Sale Value (r=470)
3. Fashion goods share and average FOB prices (r=0.653)
4. Share of fashion goods and number of stitching machines (r=0.446).
Above results support that there is a positive correlation between sale and
stitching capacity. Furthermore, it is apparent that fashion goods fetch high prices and
this would contribute in high volume of sale.
Tables 4.11 and 4.12 show that there is no significant correlation between TFP
and other independent variables. Only, in case of horizontal firms is there a significant
association (r=-6.50) between TFP and share of labour expenses in total cost of
production. Otherwise, there is no association between TFP and any other variable. This
Chapter Four: Data Analysis and Results 200
all shows that that data is quite independent. In the following pages, a regression will be
run to answer the research questions.
4.6 Regression analysis assumptions (vertical and horizontal firms)
There are two broad categories of regression analysis; linear regression and curve
estimation. Linear relationship can gives a picture of relationship between one dependent
and one or more than one independent variable. Curve estimation is normally carried out
between two variables; one dependent and other independent. In this report, there are
seven independent and one dependent variable. It is also important to note that curve
regression is algebraically defined function used to model relationships between
variables. For example, a demand function exhibits the demand for a product as a
function of the unit price, and a cost function expresses total cost as a function of the
number of products manufactured. Academically, these functions are often called models.
In this report, curve estimation was carried out to obtain an exponential model
from two data points. The purpose was to find the equation of the line or exponential
curve passing through them. However, it quite common that that many data points that do
not lie on one line or exponential curve. To solve this problem many other techniques
were applied, for example, exponential curve, quadratic curve and many others to find
out closest to passing through all of the points. In this report, before applying linear
regression, curve estimation was carried out and it was found that for most appropriate
results, linear regression should be adopted.
Chapter Four: Data Analysis and Results 201
1. It is obligatory that the following three assumptions of Ordinary Least Square
(OLS) be examined before doing a regression analysis (see Chapter Three for
more details): (a) linearity, (b) normality, and (c) homoscedasticity.
According to Statsoft (2006), in a situation when the assumption of
homoscedasticity is not supported, the dependent variable is transformed and again
checked for homoscedasticity. If the transformed variable demonstrates
homoscedasticity, it can be used for regression analysis.
There are three following methods of transformation: (a) logarithmic, (b) square
root, and (c) inverse transformation.
According to Zar, 2006), arcsine transformation is one method to transform data.
As per Siegel (2000), most common is the transformation of data with the help of
logarithms. Also, noting that according to Zar, dependent variables are transformed since
the transformation of independent variables makes no difference. According to Siegel, in
cases when there is unequal variability in the data, the inference will be unreliable. Too
much importance is given to the high-variability component of data and too little
importance to the more reliable low-variability component of data. Siegel further stated
that the use of advanced techniques of weighted regression analysis is also a way to
rebalance the importance of the observations. In an initial analysis, it was found that there
is a weak relationship between dependent and independent variables. Keeping in mind
and as per recommendation of Siegel, TFP, dependent variable was transformed with the
help of logarithms. It was observed that transformation did not make a significant
difference in results. Based on this observation, it was decided to use data as such.
Chapter Four: Data Analysis and Results 202
4.6.1 Linearity and Data of Horizontal Firms. As discussed earlier, data should
have a linear relationship for a purposeful regression model. If there is no linearity in the
data, a regression model will not be useful. Scatterplots are one way to check the linearity
of the data. In the following pages, scatter plots will be developed to observe the
relationship between TFP (dependent) variables and other determinants (independent
variables).
Figure 4.5
TFP and Share (%) of Labour Expenses in Total Cost (Horizontal Firms)
Chapter Four: Data Analysis and Results 203
Figure 4.6
TFP and Share (%) of Financial Expenses in Total Cost (Horizontal Firms)
Figure 4.7
TFP and Share (%) of Fashion Goods in Total Production (Horizontal Firms)
Chapter Four: Data Analysis and Results 204
Figure 4.8
TFP and U.S.A Market Share in Total Exports (Horizontal Firms)
Figure 4.9
TFP and Average FOB Price in U.S. $ (Horizontal Firms)
Chapter Four: Data Analysis and Results 205
Figure 4.10
TFP and Number of Stitching Machines (Horizontal Firms)
Figure 4.11
TFP and Sale Value in Million U.S. $ (Horizontal Firms)
Chapter Four: Data Analysis and Results 206
From Figure 4.5 to 4.11, it is obvious that there is a linear relationship between
TFP and seven independent factors. Nevertheless, Figure 4.10 shows that there is a
negligible linear relationship between TFP and number of stitching machines. It can be
assumed that in regression analysis TFP will not be dependent on number of stitching
machines or the capacity of production. Nevertheless, TFP has a moderate relationship
with sale.
As per Statsoft (2006), as is evident in the name multiple linear regression, it is
assumed that the relationship between variables is linear. In practice, this assumption can
virtually never be confirmed; fortunately, multiple regression procedures are not greatly
affected by minor deviations from this assumption. However, as a rule it is prudent to
consistently look at bivariate scatter plots of the variables of interest. It is obvious in the
above scatter plots that there is a linear relationship between TFP and six independent
variables. Nevertheless, strength of relationship varies a lot, which is obvious from the
value of R2 Linear, which is given along with the scatter plot. In conclusion, it can be said
that linearity is the basic assumption for the regression analysis, which is being
successfully fulfilled in this case.
4.6.2 Linearity and Data of Vertical Firms. In previous pages, there are seven
scatter plots, which tell the relationship between TFP (dependent variable) and seven
independent variables of horizontal firms. In the following pages, there are seven scatter
plots that have been developed by using TFP on X-axis and independent variables at Y-
axis. These scatter plots will be used to check the linearity of the data which is one major
assumption for regression analysis.
Chapter Four: Data Analysis and Results 207
Figure 4.12
TFP and Share% of Labour Expenses in Total Cost (Vertical Firms)
Figure 4.13
TFP and Share (%) of Financial Expenses in Total Cost (Vertical Firms)
Figure 4.14
Chapter Four: Data Analysis and Results 208
TFP and Share (%) of Fashion Goods in Total Production (Vertical Firms)
Figure 4.15
TFP and U.S.A Market Share in Total Exports (Vertical Firms)
Figure 4.16
Chapter Four: Data Analysis and Results 209
TFP and Average FOB Price in U.S. $ (Vertical Firms)
Figure 4.17
TFP and Number of Stitching Machines (Vertical Firms)
Figure 4.18
Chapter Four: Data Analysis and Results 210
TFP and Sale Value in Million U.S. $ (Vertical Firms)
As discussed earlier, data should have a linear relationship for a purposeful
regression model. If there is no linearity in the data, a regression model will not be useful.
Scatter plots are one way to check the linearity of the data. It is obvious from Figure 4.12
to 4.18 that the majority of the independent variables have a moderate relationship with
dependent variables (TFP). Based on all the above scatter plots, it is assumed that there is
a linear relationship between TFP and seven independent variables.
4.6.3 Data Normality (Horizontal and Vertical Firms). The second assumption for
the regression analysis is that there should be normality in the data. As per Siegel (2000),
it is assumed in multiple regressions that the residuals (predicted minus observed values)
are distributed normally (i.e. follow the normal distribution). There are many ways to test
the normality. Most common is a histogram with normal curve. Pallant (2007) prefers
other statistical tools to test the data normality. Nevertheless, in the real world it is quite
Chapter Four: Data Analysis and Results 211
hard to have a data set able to meet the whole list of assumptions. Pallant proposes the
Kolomogrovo-Smirnov Test to check the data normality. This test was applied with the
help of SPSS and produced Table 4.13.
Table 4.13
Test of normality of data (horizontal and vertical)
Types of Firm Kolmogorov-Smirnov(a)
Statistic Df Sig.
Sale Value (Million U.S. $) Horizontal Firms 0.222 16.000 0.034
Vertical Firms 0.238 33.000 0.000
Share (%) of Labour Expenses in
Total Cost
Horizontal Firms 0.169 16.000 .200(*)
Vertical Firms 0.067 33.000 .200(*)
Share (%) of Financial Expenses
in Total Cost
Horizontal Firms 0.132 16.000 .200(*)
Vertical Firms 0.187 33.000 0.005
Share (%) of Fashion Garments
in Total Production
Horizontal Firms 0.431 16.000 0.000
Vertical Firms 0.212 33.000 0.001
U.S.A Market Share (%) in Total
Exports
Horizontal Firms 0.191 16.000 0.122
Vertical Firms 0.258 33.000 0.000
Average FOB Price in U.S. $ Horizontal Firms 0.199 16.000 0.092
Vertical Firms 0.233 33.000 0.000
Number of Stitching Machines
Installed
Horizontal Firms 0.260 16.000 0.005
Vertical Firms 0.168 33.000 0.018
It is obvious from Table 4.13 that there is a variety of results. Kolmogorov-
Smirnov test gives the p values. Significance of p value (less than 0.05) provides
statistical evidence from which to reject the null hypotheses, which claims that data is
normally distributed. It is apparent from Table 4.13 that data is a mix of normally
distributed values and not normally distributed values. As said by Elliot and Woodward,
assumptions of normality and linearity are rarely met in practice. It depends upon the
researcher‘s level of concern about this assumption. Nevertheless, violation of
assumptions is very common in literature as described by Elliot and Woodward. Keeping
Chapter Four: Data Analysis and Results 212
all this in mind, it was preferred to continue with the testing of next assumption, i.e. test
of data homogeneity.
4.6.4 Homoscedasticity Assumption. Homoscedasticity is the third assumption. It
means that the variability in scores for one variable is roughly the same as all values of
the other variable, which is related to normality. When normality is not met, variables are
not homoscedastic. As per Siegel (2000), if there is no normality in the data, it means that
data has unequal variance and is not homoscedastic. This assumption means that the
variance around the regression line is the same for all values of the predictor variable (X).
The complement is called heteroscedasticity. The assumption of homoscedasticity
simplifies mathematical and computational treatment and may lead to good estimation
results (e.g. in data mining) even if the assumption is not true. Heteroscedasticity is
caused by lack of normality of one of the variables, an indirect relationship between
variables, or data transformation. Siegel (2000) contends that heteroscedasticity is not
fatal to an analysis — the analysis is weakened but not invalidated. Homoscedasticity is
evaluated for pairs of variables.
There are both graphical and statistical methods for evaluating homoscedasticity.
The graphical method is called a box plot. The statistical method is the Levene statistic,
which SPSS computes for the test of homogeneity of variances. There are many more
methods to check homoscedastic. According to Statsoft (2006), neither of the methods is
definitive.
The Levene test is one method to check the homoscedasticity (homogeneity of
variance). In this method, all independent variables are grouped and the homogeneity of
variance was checked. Tables 4.14, 4.15, 4.16, and 4.17 demonstrate that p value is less
Chapter Four: Data Analysis and Results 213
than 0.05, which shows that there is an enough statistical evidence to reject the
hypotheses, which claims that data is homogeneous. It is a proof that there is no
homoscedasticity in the data in both cases (horizontal and vertical).
Table 4.14
Test of homogeneity of variances for horizontal firms
Levene
Statistic df1 df2 Sig.
11.935 6 105 0.000
Table 4.15
Robust Tests of equality of means for horizontal firms
Statistic(a) df1 df2 Sig.
Welch 81.425 6 44.293 0.000
Brown-
Forsythe 38.942 6 17.928 0.000
a Asymptotically F distributed.
Table 4.16
Test of homogeneity of variances for vertical firms
Levene
Statistic df1 df2 Sig.
65.255 6 224 .000
Chapter Four: Data Analysis and Results 214
Table 4.17
Robust tests of equality of means for vertical firms
Statistic(a) df1 df2 Sig.
Welch 107.006 6 89.519 .000
Brown-Forsythe 90.261 6 34.093 .000
a Asymptotically F distributed.
The above analysis found that the data do not fulfil the assumptions for regression
analysis. In all cases, the p value is less than 0.05, which shows that test is significant and
there is a sufficient evidence to reject the research hypothesis, i.e. data is homogeneous.
4.7 Weighted Least Squares Regression
In previous pages, there is a discussion about the assumptions, which should be
tested before applying regression analysis. It was found that apparently there is not a
clear picture about the assumptions. Particularly, in case of homoscedasticity, there is a
serious violation. It seems that there is a partial fulfilment of assumptions. As said by
Elliot and Woodward, Guthrie, Filliben and Heckert (2008) and Pallant, in practice it is
quite hard properly fulfil all assumptions, and violation of the required level of
assumption is quite common. At the same time, Elliot and Woodward, Guthrie et al., and
Pallant have provided solutions to overcome such difficulties.
Garson (2008) wrote that one of the most significant assumptions of OLS
regression is homoscedasticity. Keeping its significance in view, Garson proposes
different solutions when there is a violation of homoscedasticity. As discussed above,
homoscedasticity means that the variance of residual error should be constant for all
Chapter Four: Data Analysis and Results 215
values of the independent(s). Garson concluded that presences of different error variance
at different ranges of their values will change the estimates of the regression coefficients.
In this situation, large standard errors for some ranges of the dependent and too small for
other ranges will occur and the power of significance tests will be reduced, which is to
say regression estimates will be inefficient.
Garson suggests that weighted least squares (WLS) regression compensates for
violation of the homoscedasticity assumption by weighting cases differentially. The
weighing process change the values of variables in such a way that values on the
dependent variable correspond to large variances on the independent variable(s) count
less and those with small variances count more in estimating the regression coefficients.
Consequently, cases with greater weights contribute more to the fit of the regression line.
Resultantly, the estimated coefficients are usually very close to what they would be in
OLS regression, but under WLS regression, their standard errors are smaller.
Garson further stated that sometimes WLS regression is used to adjust fit to give
less weight to values, which are considered as outliers and less importance to points,
which are considered less reliable. There are a number of ways to give weight to make
data appropriate for better regression analysis.
Guthrie et al have also explained the treatment of weights. Guthrie et al. wrote
that in situations where it is feasible to use weighting for every observation, it is
recommended to use the weighted least squares method to maximize the efficiency of
parameter estimation. This process would give less precisely measured points more
influence than they should have and would give highly precise points too little influence.
Guthrie et al. points out that weighted least squares regression does not say anything
Chapter Four: Data Analysis and Results 216
about the association with a particular type of function used to describe the relationship
between the process variables. It reflects the behaviour of the random errors in the model,
and it can be used with functions that are either linear or nonlinear in the parameters.
Applying weight to different values is quite complex. There are many ways and
techniques available in the literature to weight values. Guthrie et al. points out that the
size of the weight indicates the precision of the information contained in the associated
observation. For the provision of optimizing, the weighted fitting criterion to find the
parameter estimates facilitates the weights to determine the contribution of each
observation to the final parameter estimates. Furthermore, it is significant to note that the
weight for each observation is given keeping the relationship with weights of the other
observations; so different sets of absolute weights can have identical effects, Guthrie et
al. concludes.
Guthrie et al. elaborated on the advantages of WLS methods, writing that WLS
method is an efficient method, particularly when there are small data sets. It provides
facilities to provide different types of easily interpretable statistical intervals for
estimation, prediction, calibration, and optimization. In addition to that, weighted least
square has the ability to handle regression situations in which the data points are of
varying quality. If the standard deviation of the random errors in the data is not constant
across all levels of the explanatory variables, using weighted least squares with weights
that are inversely proportional to the variance at each level of the explanatory variables
yields the most precise parameter estimates possible, Guthrie et al. concluded.
It is a fact that WLS method has certain advantages, but at the same time, the
biggest disadvantage of weighted least squares is the assumption that the weights are
Chapter Four: Data Analysis and Results 217
known exactly. It is not possible in real applications. Estimated weights are often used.
Guthrie et al. commented on this, writing that it is difficult to assess the effect of such
estimated weights. It is a fact that small variations in the weights due to estimation do not
often affect a regression analysis or its interpretation. However, there are chances that
one might face very poor and unpredictable results. There are more chances of such
occurrence when the weights for extreme values of the predictor or explanatory variables
are estimated using only a few observations. Guthrie et al. warned that it is important to
remain aware of this potential problem, and to only use weighted least squares when the
weights can be estimated precisely relative to one another.
Keeping all above discussion in mind, in the following pages regression analysis
will be carried out with the help of weighted least squares methods. In the following
pages, Weighted Least Squares Regression is used to answer the following research
questions:
1. Which set of independent variables is able to predict TFP (a dependent variable)?
2. Which variable in a set of variables has highest contribution in the variance of
dependent variable?
As discussed in chapter three, there is a reasonable difference in the business
practices of horizontal and vertical firms. Based on that observation it was decided to
carry out the data analysis separately for horizontal and vertical firms. In the following
pages, regressions will be run separately for horizontal and vertical firms.
4.7.1 Weighted Least Squares Regression (Horizontal Firms). Horizontal firms
are those, which have only stitching facilities. In this study, there are only 16 firms,
which are called horizontal firms. Results of regression for horizontal firms are as under.
Chapter Four: Data Analysis and Results 218
Table 4.18
Regression analysis model summary (horizontal firms)
R R Square
Adjusted R
Square
Std. Error of the
Estimate
.944 .891 .836 .01049
Table 4.19
ANOVA regression analysis (horizontal firms)
Sum of
Squares Df Mean Square F Sig.
Regression .005 3 .002 16.337 .003
Residual .001 6 .000
Total .006 9
Chapter Four: Data Analysis and Results 219
Table 4.20
Coefficients regression analysis (horizontal firms)
Unstandardized
Coefficients
Standardized
Coefficients t Sig. Collinearity Statistics
B
Std.
Error Beta Tolerance VIF
(Constant) 1.036 .028 37.563 .000
Share (%) of Labour Expenses in Total Cost
-.015 .002 -1.401 -6.184 .001 .354 2.822
Share (%) of Financial Expenses in Total Cost
.022 .006 .699 3.815 .009 .541 1.848
Share (%) of Fashion Garments in Total Production
.002 .001 .564 2.632 .039 .396 2.524
a Dependent Variable: Total Factor Productivity
b Weighted Least Squares Regression - Weighted by Standardized Residual
The above three tables (Tables 4.18, 4.19, and 4.20) are the outcomes of the
regression run for the horizontal firms. There are a number of values given in the tables.
There is a brief interpretation of theses vales in the following paragraphs:
Table 4.18 is a summary of the regression and there three main values in this
table; R, R Square and adjusted R Square. Value of R Square is square of R. R Square is
the main outcome of the regression. As said by Pallant, R Square tells how much of the
variance in the dependent variable (TFP) is explained by the model (which includes the
seven independent variables). SPSS provided the ability to adopt a method of regression
analysis. There are five different methods listed. For this analysis, backward method has
been selected since it provides better results as compared to other methods.
Chapter Four: Data Analysis and Results 220
Table 4.18 tells that R Square value is 0.891. Expressed as a percentage, this
means that this model which has seven independent variables explains 89.1% of the
variance in dependent variable (TFP). This is quite a respectable result since it covers
more than 89% variance in the dependent variable due to the seven independent
variables.
SPSS also provides an Adjusted R Square value in the output. Pallant suggests
that when a small sample is involved, the R Square value in the sample tends to be a
rather optimistic overestimation of the true value in the population. In such cases the
Adjusted R square statistic ‗corrects‘ this value to provide a better estimate of the true
population value. Table 4.18 gives an Adjusted R Square value, which is 0.836. It means
that 83.6% variance in TFP is explained by the seven independent variables. It shows that
even after selecting the adjusted value the explanation of TFP is quite high.
To assess the statistical significance of the result it is obligatory to look at Table
4.19. This tests the null hypothesis that multiple R in the population equals zero. Table
4.19, gives p value 0.005, which is less than α value (0.05). It means that there is
sufficient evidence to reject the null hypotheses.
Table 4.20 tells which of the variables included in the model contributed to the
prediction of the dependent variable. This information is available in the output box
labelled Coefficients. There are standardised and un-standardised columns. Pallant states
standardised means that these values for each of the different variables have been
converted to the same scale so one can compare them. Pallant further states that when
researchers are interested in comparing the contribution of each independent variable
they have to compare values given under the standardised column. During comparison,
Chapter Four: Data Analysis and Results 221
one has to ignore the negative sign. The highest value indicates highest contribution.
However, at the same time, a researcher has to see its significance value. Results will
only be significant if p value is less than 0.05. In Table 4.20, Share (%) of Labour
Expenses in Total Cost (Independent Variable) has the highest value (-1.401). It shows
that this variable has the highest contribution. With a p value of .001, it can be said that
this variable does have a statistically significant contribution.
Table 4.20 furthers tells about the other contributors, i.e. Share (%) of Financial
Expenses in Total Cost and Share (%) of Fashion Garments in Total Production. These
two factors have 0.699 and 0.564 standardized coefficients, respectively. Furthermore,
these factors also have p values (0.009 and 0.039 respectively) that are less than 0.05.
These values provide evidence that these two factors have a significant impact on TFP.
To make a regression equation, values under the un-standardised column are
commonly used. Based on these values a regression equation has been made.
There are two values also appearing in Table 4.20: Tolerance and VIF (Variance
Inflation Factor). Tolerance value indicates how much of the variability of the
specificities independent variable is not explained by the other independent variables. It
is calculating by using the formula 1-R2 for all variables. Pallant states that its small value
indicates presence of high correlation among the variables, so means multicollinearity
exists. Less than 0.1 values is an indicator of multicollinearity. In Table 4.20, there is no
value less than 0.1, so it means that data is independent and there is no chance of
multicollinearity.
VIF value is just the inverse of the tolerance value. It is calculated by
dividing1with tolerance value. Its high value indicates the presence of multicollinearity.
Chapter Four: Data Analysis and Results 222
As said by Pallant, its value more than 10, so it is an indicator of multicollinearity. In
Table 4.20, it is less than 10, which means that VIF value also indicates that there is no
multicollinearity in the data.
4.7.2 WLS Regression Equation (Horizontal Firms). Based on the B value under
the un- standardized column in Table 4.20, following regression equation has been made:
TFP = 1.36 -0.015 SLE +0.022 SFE +0.002 SFG
Where:
TFP = Total Factor Productivity
SLE = Share of Labour Expenses in Total Cost
SFE = Share of Financial Expense in Total Cost
SFG = Share of Fashion Garments in Total Production
The regression coefficient explains the change in dependent variable, e.g. if there
is an increase of 1 in share of labour expenses in total cost firm its TFP will decrease by
1.015. It shows that increase in share of labour expenses in total cost firm will negatively
affect TFP of the firm. The above equation further explains that share of financial
expenses total cost has a positive impact on TFP. An increase of 1 on financial expenses
will increase 1.022 TFP. Nevertheless, an increase of 1 in share of fashion garments in
total production will increase 1.002 TFP.
4.7.3 Weighted Least Squares Regression (Vertical Firms). Vertical firms are
those that have knitting, dyeing, and stitching facilities. In this study, there are only 33
vertical firms. Results of regression analysis for vertical firms are as under.
Chapter Four: Data Analysis and Results 223
Table 4.21
Regression analysis model summary (vertical firms)
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .972 .946 .903 .04717
a. Dependent Variable: Total Factor Productivity
b. Weighted Least Squares Regression - Weighted by Standardized Residual
Table 4.22
ANOVA regression analysis (vertical firms)
Mode Sum of
Squares df Mean Square F Sig.
1 Regression .349 7 .050 22.392 .000
Residual .020 9 .002
Total .369 16
a. Dependent Variable: Total Factor Productivity
b. Weighted Least Squares Regression - Weighted by Standardized Residual
Chapter Four: Data Analysis and Results 224
Table 4.23
Coefficients regression analysis (vertical firms)
Unstandardized
Coefficients
Standardized
Coefficients T Sig.
Collinearity
Statistics
B Std. Error Beta Tolerance VIF B Std.
Error
(Constant) 1.137 .115 9.865 .000
Sale Value (Million US $) .026 .008 .617 3.128 .012 .155 6.449
Share (%) of Labour Expenses in Total Cost
-.006 .004 -.168 -1.427 .187 .436 2.295
Share (%) of Financial Expenses in Total Cost
.032 .005 .815 6.389 .000 .371 2.695
Share (%) of Fashion Garments in Total Production
-.005 .001 -.628 -5.408 .000 .447 2.235
USA Market Share (%) in Total Exports
.002 .001 .201 1.248 .244 .232 4.318
Average FOB Price in US $ .025 .021 .104 1.202 .260 .807 1.239
Number of Stitching Machines Installed
-.001 .000 -.431 -2.331 .045 .177 5.655
a Dependent Variable: Total Factor Productivity
b Weighted Least Squares Regression - Weighted by Standardized Residual
Chapter Four: Data Analysis and Results 225
The above three tables (Tables 4.21, 4.22, and 4.23) are the outcomes of the
regression run for the vertical firms. There are a number of values given in the tables.
Table 4.21 shows that the R Square value in this case is 0.946. This means that
this model, which has seven independent variables, explains 94.6 percent of the variance
in dependent variable (TFP). This is quite a respectable result since it covers more than
94% variance in the dependent variable due the seven independent variables.
Table 4.21 gives the Adjusted R Square value, which is 0.903. It means that
90.3% variance in TFP is explained by the seven independent variables. It shows that
even after selecting the adjusted value the explanation of TFP is quite high.
To assess the statistical significance of the result it is obligatory to see in the
Table 4.22, labelled ANOVA. This tests the null hypothesis that multiple R in the
population equals zero. Table 4.22 gives p value 0.000, which is less than the α value
(0.05).
Table 4.23 tells which of the variables included in the model contributed to the
prediction of the dependent variable. The highest value indicates highest contribution.
Nevertheless, at the same time, one has to see its significance value. Results will be only
being significant if p value is less than 0.05. In Table 4.23, share of financial expenses
has the highest value (.815). Financial Expenses is the expenses which firms are liable to
pay banks against loans. It shows that share of finance in total cost has the highest
contribution. One has to consider test significance before reaching any conclusion. Table
4.23 shows that the above mentioned variable has p value 0.000, which is less than 0.05,
which means contribution of this factor is statistically significant.
Chapter Four: Data Analysis and Results 226
There are two values also appearing in table 4.23: Tolerance and VIF (Variance
Inflation Factor). There is no value less than 0.1 in the column of Tolerance and no value
more than 10 in the VIF column. It shows that there is no multicollinearity in the data.
4.7.4 WLS Regression Equation (Vertical Firms) Based on the B value under the
un- standardized column in Table 4.23, following regression equation has been made:
TFP =0.137 + 0.026 SV -0.006 SLE +0.032 SFE -.005SFG+0.002 USAM +0.025 FOB
-0.001 NSM
Where:
TFP = Total Factor Productivity
SV = Sale Value (Million U.S. $)
SLE = Share of Labour Expenses in Total Cost
SFE = Share of Financial Expense in Total Cost
SFG = Share of Fashion Garments in Total Production
USAM= U.S.A Market Share in Total Exports
FOB= Price in US $ Free on Board
NSM= Number of Stitching Machines Installed
The regression coefficient explains the change in dependent variable, e.g. if there
is an increase of 1 in sale value of the firms its TFP will increase 1.026. It shows that
increase in sale will positively affect TFP of the firm. The above equation further
Chapter Four: Data Analysis and Results 227
explains that share of labour in total expenses and share of fashion garments in total
production can create a negative impact on TFP.
4.8 Initial Conclusion from Results
This chapter analysed data as per the research methodology described in chapter
three. Data related to total output and total input of PKGI (vertical and horizontal) and
181 major industries in Pakistan were collected, and the TFP was calculated with the help
of the TFP models based on accounting function.
Since productivity is a comparison phenomenon, it was decided that a TFP of
more than1would be considered as high productivity and less than1as low productivity. It
was repeatedly observed from the results that the TFP of PKGI is low (less than one). It is
also less than the average TFP level for 181 manufacturing sectors within Pakistan. It was
also observed that the TFP of vertical firms is better than the TFP of PKGI at aggregate
levels. In contrast, the TFP of horizontal firms is slightly less than the TFP calculated at
aggregate levels. Nevertheless, the majority of horizontal firms have a TFP of more than
one, which means that the majority of horizontal firms performed better compared to
vertical firms (see Table 4.1, 4.2, 4.3).
In the second part, the correlation between dependent and independent variables
was calculated. For this purpose, primary and secondary data were collected through a
survey of the industry and from the Federal Bureau of Pakistan. Correlation and
regression analysis were carried out separately for vertical and horizontal firms. Three
basic assumptions were checked before using the regression analysis. The transformation
Chapter Four: Data Analysis and Results 228
process was used to minimize the unequal variability in the data. Moreover, the most
advanced technique of weighed regression was applied.
A few research hypotheses that were tested showed no significant difference in
the mean value of TFP of horizontal and vertical firms. Furthermore, it was also shown
that TFP of PKGI at aggregated level is not more than1 (see Table 4.4 to 4.11).
Correlation among independent variables was carried out separately for horizontal and
vertical firms and it was found that there is not a serious correlation among variables. It
shows that data is quite independent. It was also confirmed through regression and found
that there is no multicollinearity in the data.
A statistical equation (model for prediction) is the outcome of the exercise. (For
more details see Section 4.6). This equation is based upon the regression analysis. As
discussed earlier, such an equation can be used to predict the dependent as well as the
independent variables. Such models, including this one, have many weaknesses. The
main weakness of the equation is that there are additional variables that can intervene or
mediate the function, referred to as intervening and mediating variables that are not taken
into account. It is assumed that taking into account all factors of production is nearly
impossible. For example, the technology level of the firm can influence the results, as
discussed in chapter two. Productivity has a direct and positive association with the level
of technology used by firms. Such factors can influence the TFP of the firm.
Furthermore, skill and education levels of the workers have a positive impact on the TFP,
as discussed in chapter two.
It must be acknowledged that precise predictions based on this model are quite
difficult. However, this model can be used to predict the TFP of the knitted garment
Chapter Four: Data Analysis and Results 229
manufacturing firms to provide some idea of the factors influencing the TFP of the PKGI.
The main objective of regression was to answer the two questions posed in chapter one.
The first question was to identify the factor that has the highest contribution in the
variance of TFP. In the case of horizontal firms it was found that there are three factors
which have high value (in column of standardized values) but by looking at their
significance value, it was observed that only share of labour expenses in total cost has a
significant value (Standardized Beta= -1.401). In case of vertical firms, it was observed
that share of financial expenses has the highest contributor in TFP (Standardized Beta=
0.815).
The second question was to develop an equation based on regression that could be
used to predict the TFP. Two equations were developed, which explains the impact of
any change in the interdependent variables on TFP. Before running regression, normality,
linearity, and homogeneity were tested, and it was found that data partially fulfils the
assumptions. However, for better results, the WLS technique was used. This technique
improved the results and helped in developing a regression equation. In the data, it was
observed that some independent variables have significant relationships with dependent
variables.
In this analysis few tangible factors were taken into account. It is believed that
there are certain additional factors that are not included in this analysis can influence the
TFP of PKGI at aggregate and disaggregate levels. For example, missing factors include
technology level and worker‘s skill level, and both can strongly impact TFP. Data
covering intangible factors might also have a strong impact on the TFP.
Chapter Five: Conclusion and Recommendations 230
CHAPTER FIVE: CONCLUSIONS AND RECOMMENDATIONS
This report is the outcome of research conducted to measure the Total Factor
Productivity (TFP) level of Pakistan‘s Knitted Garment Industry (PKGI) and its
determinants. It was also part of the study to develop acceptable guidelines for the PKGI
to achieve high productivity. Previous chapters were dedicated to a theoretical
background of production function and productivity, research methods, and finally data
analysis. This chapter will cover the whole study and will provide concluding thoughts.
In the following pages, there is a summary of the complete report and a guideline for the
industry.
5.1 Pakistan Textile Industry: An Overview
PKGI is one of the major sectors of the Pakistan Textile Industry (PTI). In 2004-
2005, it had 11.36% share of total exports from Pakistan, which was the highest share
among all value added products exported from Pakistan. Its growth rate in exports is
20.80%, which is the second highest among all products being exported from Pakistan
(see Table 1.3). The Pakistan Textile Industry (PTI) as a whole has a significant share in
the economy of Pakistan. In 2004-2005, it had a 62.1% share in exports and 38% of the
labour force of Pakistan was employed in the textile sector. The clothing sector provides
a huge employment opportunity for skilled, semiskilled, and unskilled workers. This
sector adds value in the products being exported from Pakistan (see Table 1.1).
As discussed in chapter one, the PTI has shown a growth rate of 9.6% per annum
in exports from 1971 to 2005. That is quite satisfactory when compared to exports from
other sectors of Pakistan. However, when export performance of PTI is compared with
Chapter Five: Conclusion and Recommendations 231
other countries it becomes obvious that many countries from South Asia have had
stronger growth than Pakistan in the international textile and clothing trade. Although
Pakistan is the fourth largest country in cotton growing, with more than 113 Million
spindles, the performance of PTI is comparatively lower than countries like Bangladesh
and Sri Lanka, which do not even have natural resources such as cotton. Furthermore,
these countries do not have strong spinning and wet processing industries. They import
all raw materials and then export it after adding value (see Table 1.2).
5.2 Productivity and Performance
There are many ways to improve performance, as discussed in detail in chapter
two. Better productivity is one of the preferred methods for improved performance. It
was concluded that there is a strong link between productivity, performance, and
prosperity (see Section 2.7).
It is evident from the literature that for improved productivity, measurement of
the current level of productivity is essential. Furthermore, it is important to identify the
factors affecting productivity. For this study, the TFP approach was selected since this is
one of the most advocated approaches in the literature and many studies have been
conducted using this approach (see Section 2.5).
5.3 Selection of Independent Variables and Data Collection
The PKGI is a relatively new industry. In 1972, it held only 0.54% share of the
total exports, while in 2004-5, it held more than 11.36% share of the total exports. This
industry is primarily export oriented. Few firms do local business. The export data
Chapter Five: Conclusion and Recommendations 232
revealed that real activities of this sector started after 1995. The period for time series
data from 1995 to 2005 is limited. Furthermore, only export related data were available
for this period (see Table 1.3).
The industry survey and secondary data show that the PKGI is comprised of 900
firms. Major business is in three main cities of Pakistan, and more than 90% of business
is with 218 firms (24.22% firms). Based on this observation, it was preferred to consider
these 218 firms as the population for the survey and to discount the remainder of the
firms since their individual share in exports was negligible.
There are three major departments involved in the knitted garment manufacturing
process: (a) knitting, (b) wet processing (dyeing and finishing), and (c) stitching. There
are two types of firms: (a) vertical and (b) horizontal. In a vertical set up, firms have in
house knitting, wet processing, and stitching facilities. In a horizontal set up, firms have
only a stitching facility. Such firms outsource for knitting and wet processing.
There is considerable variation in the approach of vertical and horizontal firms. It
was observed from the literature that for best results the firms should be as similar as
possible. Keeping in view the prerequisite of the research methodology, it was decided to
assess the TFP and its determinants of both sectors separately. However, results of both
sectors were compared in order to evaluate which sector functions most effectively. A
mathematical model was used to calculate the TFP, for which the Sumanth Model was
selected. (see Section 3.4).
The TFP model selected requires total tangible input and output data. Different
firms were visited and it was found that a vast majority of the firms are private limited,
means that they are not listed on the stock exchange and do not publish their financial
Chapter Five: Conclusion and Recommendations 233
reports. Furthermore, not all firms that export more than 80% of their goods are required
to submit financial reports to the government. In addition to that, they refused to provide
data to this researcher. The Pakistani government is supposed to conduct an annual
survey of the firms. However, for unknown reasons this survey is not conducted every
year. The last survey was conducted in 2001. The government office was approached by
this researcher, who collected data on 49 randomly selected firms. Out of the 49 firms, 33
firms have a vertical set up and 16 firms have a horizontal set up. These data sufficed to
measure the TFP of the PKGI at aggregate and disaggregate levels. Based on a thorough
survey of the literature and feedback from the industry through a sample survey, the
following seven factors were selected to create a structured questionnaire (see Section
3.5).
1. Sale Value (Million U.S. $)
2. Share (%) of Labour Expenses in Total Cost
3. Share (%) of Labour Expenses in Total Cost
4. Share (%) of Financial Expenses in Total Cost
5. U.S.A Market Share (%) in Total Exports
6. Average FOB Price in U.S. $
7. Number of Stitching Machines Installed
The primary data were collected through an exploratory survey of selected
industries based on random sampling and secondary data from the Pakistan Federal
Bureau of Statistics. SPSS software was selected for analysis purposes based on its ease
of use (see Section 3.17).
Chapter Five: Conclusion and Recommendations 234
5.4 Level of TFP of PKGI and Its Ranking
As discussed in chapter one, one of the core objectives of the current study was to
calculate the TFP of the PKGI. This measure has to be established to compare its ranking
to other manufacturing industries in Pakistan. Sumanth‘s model (1990) provides a ratio of
output to input, but it is difficult to comment on this ratio without comparing it to other
ratios. For this purpose, data from 18,061 firms, drawn from 181 different sectors of the
Pakistani manufacturing industry, were collected in the same time. It was their TFP that
was compared with TFP of PKGI.
The TFP of the PKGI was also calculated at aggregate (both vertical and
horizontal) and disaggregate (of vertical and horizontal firms separately) levels. Analysis
showed that the average TFP of 18,061 firms from 181 different sectors is 1.47, while the
TFP of the PKGI at the aggregate level is only 0.976. The TFP of vertical and horizontal
firms is 0.976 and 0.975, respectively. In comparison to other sectors of Pakistan, the
TFP of the PKGI is over 50% less, which clearly indicates that this sector is performing
at 50% less than other sectors (see Table 4.4).
As discussed in chapter one, nearly 62% of shares in exports from Pakistan are
related to the textile industry, although the textile sector has a low TFP. Such a scenario
indicates that major contributors of Pakistan‘s economy have a low TFP, which indicates
low performance of these sectors that would ultimately affect the prosperity of Pakistan.
It is also evident from the results that there is not a significant difference between
the TFP at aggregate and disaggregate levels. At aggregate levels the TFP is 0.976, while
at disaggregate levels it is 0.976 and 0.975 for vertical and horizontal firms, respectively.
This shows that the TFP is low compared to other industries (see Table 4.4).
Chapter Five: Conclusion and Recommendations 235
It was assumed that horizontal firms would have a better TFP, but results
indicated no significant difference. However, by looking at normal curves in both cases,
it becomes clear that there is a significant difference in skewness values. For vertical
firms it is 1.028, while for horizontal firms it is -1.45. This means that the majority of
vertical firms have a TFP of less than 1, and the majority of horizontal firms have a TFP
of more than 1. As a whole, horizontal firms performed better than vertical firms did.
This suggests that outsourcing can improve productivity. From the above analysis one
can extrapolate the following results (for more details see Table 4.4):
The TFP of PKGI is low comparative to other sectors. There is a comparison of
PKGI at aggregate and disaggregate level with TFP of 18061 industries from 81 sectors
of Pakistan manufacturing sector. Table 4.4 shows that TFP of PKGI is 0.976, whereas
TFP of other sectors of Pakistan manufacturing sector is 1.47.
The TFP of the PKGI is less than 1. If input and output values are equal then, as
per Sumanth‘s (1990) model, TFP will be 1. It means that firm has not gained anything
and at the same time has not lost anything. For this research, ―1‖ was taken as a
benchmarking value. More than1means the TFP is high and less than1means the TFP is
low. In addition, if the TFP is plus 1, the firm‘s output is more than its input —in other
words the firm is earning a profit. In the case of a TFP of less than 1, the firm‘s output is
less than its input, meaning the firm is taking a loss. Table 4.4 shows that PKGI at
aggregate and disaggregate level have TFP less than 1, which means that at both levels
PKGI had faced losses and did have low TFP. As said earlier1was the benchmark. In
both cases Table 4.4 shows that TFP is less than 1, which supports the statement that
PKGI at both levels has low TFP. Nevertheless, the majority of the horizontal firms have
Chapter Five: Conclusion and Recommendations 236
a TFP of more than 1, while the majority of vertical firms have a TFP of less than1
(Mean of horizontal firms is less than the median and mean of vertical firms is greater
than medium). Based on this observation it can be said that horizontal firms performed
better. This might be due to the outsourcing of two out of three processes involved in
knitted garment manufacturing (see Table 4.4).
5.5 Hypothesis Testing
One of the objectives of the research was to test the hypothesis mentioned in
Section 1.8. The following research hypotheses were supposed to be tested:
Ho Ha
µTFP of vertical firms = µ TFP of horizontal firms µTFP of vertical firms ≠ µ TFP of horizontal firms
µ TFP of horizontal firms is less or equal to 1 µTFP of horizontal firms is greater than 1
µTFP of vertical firms is less or equal to 1 µTFP of vertical firms is greater than 1
µTFP of PKGI at aggregate level is less or equal to 1 µ TFP of PKGI at aggregate level is greater than 1
To test the aforementioned hypothesis, one-sample t and independent t tests were
carried out with the help of SPSS (see Table 4.5, 4.6). The following results have been
derived from the outcome of the SPSS analysis:
There is no significance difference in the mean value of TFP of horizontal and
vertical firms. It shows that although there is a big difference in the business pattern,
there is no difference in outcome. From the survey it was found that a majority of the
mills are vertically integrated. Generally, people are of the view that vertical integration
pays more as compared to horizontal firms. However, this analysis shows that there is no
difference in TFP of horizontal and vertical firms (see Table 4.5 and 4.6). Nevertheless,
Chapter Five: Conclusion and Recommendations 237
median of horizontal firm is higher (1.01) than vertical firms (0.95). The median indicates
the number of firms in the upper and lower half. In the case of horizontal firms, the
median is greater than mean (Table 4.4) more than 50% mills have TFP greater than
mean value (0.975), whereas in case of vertical firms it is less than the mean value
(0.976). These figures demonstrates that there is no significant difference in mean values,
but median indicates that majority of the horizontal firms performed better than vertical
firms.
TFP is a ratio of output to input. Output values indicate a relationship between
input and output. If output (numerator) is equal to input (denominator), the result will be
1. This result indicates that firms consumed equal to their revenue (production). In
accounting terms, one can say that these firms do not earn profit. Nevertheless, if the
value is less than 1, it means that firm has consumed more and yield is less since the
denominator is higher than numerator. Keeping that in mind, null hypotheses were
developed. These hypotheses state that TFP of PKGI is less or equal to 1. Results show
that TFP of horizontal, vertical, and at aggregated level is not greater than one. This was
tested with the help of a one-sample t test (see Tables 4.7, 4.8, 4.9. 4.10).
The above discussion supports the common observation of industry leaders that
PKGI is in crisis. Based on this observation, it is recommended that the solution does not
lie in having horizontal or vertical firms; rather, there is a need to make this sector
productive by adopting the most modern techniques.
Chapter Five: Conclusion and Recommendations 238
5.6 Correlation between TFP and its Determinants
The second objective of this study was to identify determinants affecting the TFP.
As discussed earlier, seven different determinants were selected before conducting the
survey. Primary data covering this data were collected and the correlation between the
TFP and selected determinants was assessed with the help of SPSS software (see Section
4.5).
The correlation between the TFP and different factors is markedly different for
vertical and horizontal firms because there are significant differences between vertical
and horizontal firms, particularly in business practices, firm size, and infrastructure. In
the case of horizontal firms, there is a correlation between TFP and share of labour
expenses in total cost of production (see Table 4.11), Pearson Correlation -.650. It was
also observed no other variable has a significant correlation with TFP. Additionally, in
cases of vertical firms, Table 4.12 depicts that no independent variable has a significant
correlation with the dependent variable, TFP. All these demonstrate that data does not
retain multicollinearity.
5.7 Contribution of Independent Variables in Variance of Dependent Variable
As mentioned in chapter one, one of the objectives of this study is to identify the
independent variables that have high contribution to the variance of TFP. It is presumed
that the answer to this question will provide a direction for the PKGI. Based on this result
PKGI can plan to focus on this point, and it is expected that such focus will help improve
TFP of firms, which is the ultimate goal. Nevertheless, highly contributing factors are
Chapter Five: Conclusion and Recommendations 239
different for horizontal and vertical firms. In the current study, both will be discussed
separately.
In the case of horizontal firms, the share of labour expenses in total cost of
production has the highest standardised coefficient Beta (-1.401), with t value -6.184 and
p value 0.001 (see Table 4.20). It shows that labour expenses in total cost of production
are the highest contributing factor. As discussed in chapter one, garment manufacturing is
a labour intensive industry. Although there is a lot of automation a lot of people are still
employed to make garments. Keeping this in mind, it is suggested that industry should
focus on minimising the share of labour expenses in total cost and trying to have more
automated and efficient machines.
Vertical firms have a different highest contributing factor. It is clear from Table
4.23 that share of financial expenses in total cost of production has the highest
contributing factor in TFP variance. It has standardised coefficient Beta (.815), with t
value 6.389 and p value 0.000. This is evidence that horizontal and vertical firms have
different pattern. As discussed in Section 1.5, vertical firms have bigger setups when
compared to horizontal setups.
Due to bigger setup, such firms always need more capital to run the business. For
this purpose, they rely on loans from banks. As per the policy of the government of
Pakistan, exporters are provided loans nearly at the 50% interest rate as compared to
general market rates. This is called export-refinance loan. Firms can increase their
working capital by having this loan at discounted rates and ultimately such loan increase
the financial strength of the firms, which is highly required for a smooth business. Based
on the above discussion it can be concluded that for horizontal firms, labour expenses
Chapter Five: Conclusion and Recommendations 240
share in cost of production is the highest contributor. Horizontal firms should try to
minimise the labour expense and rely on automated machines, where there is less need
for workers. However, for vertical, relying on the bank is the single factor that would
have a reasonable and significant contribution to a better TFP. Based on this result, it is
recommended that vertical firms should rely more on banks for credit because it is
comparatively cheap because of government subsidies.
5.8 Regression Equation and Determinants Affecting TFP of PKGI (Horizontal and
Vertical Firms)
As mentioned in Section 1.8, one of the objectives of this study is to develop a
model for the prediction of TFP. For this purpose, a regression was run and following
two different models were developed for horizontal and vertical firms separately (Section
3.10).
For horizontal firms (see Table 4.18, 4.19, 4.20):
TFP = 1.036 -0.015 SLE +0.022 SFE +0.002 SFG
For vertical firms (see Table 4.21, 4.22, 4.23):
TFP =0.137 + 0.026 SV -0.006 SLE +0.032 SFE -.005SFG+0.002 USAM +0.025 FOB
-0.001 NSM
Where:
TFP = Total Factor Productivity
SV = Sale Value (Million U.S. $)
SLE = Share of Labour Expenses in Total Cost
Chapter Five: Conclusion and Recommendations 241
SFE = Share of Financial Expense in Total Cost
SFG = Share of Fashion Garments in Total Production
USAM= U.S.A Market Share in Total Exports
FOB= Price in US $ Free on Board
NSM= Number of Stitching Machines Installed
In horizontal firms, three determinants, labour expenses, financial expenses, and
share of fashion goods, have a significant impact on the TFP of the PKGI. Nevertheless,
in the case of vertical firms, there are seven independent variables that can be used for the
prediction of TFP. As discussed in chapter four, a regression equation is used to explain
the variance in the dependent variable (TFP) with the help of an independent variable. In
addition, R2 value tells how much of the variance in the dependent variable (TFP) is
explained by the model. Adjusted R2 values are 0.836 and 0.903 for horizontal and
vertical firms, respectively. These values indicate that in case of horizontal firms 83.6%,
variance in TFP can be explained by the independent variables and it is 90.3% for vertical
firms. One can use a regression equation for the prediction of dependent or independent
variables (Siegel, 2000). Pallant described this usage of regression and states, writing that
multiple regression is not just one technique but a family of techniques that can be used
to explore the relationship between one continuous dependent variable and a number of
independent variables or predictors (usually continuous). Pallant further explained that
Chapter Five: Conclusion and Recommendations 242
the basis of multiple regression is correlation. However, this gives results that are more
sophisticated and explore the interrelationship among a set of variables.
There are two different models mentioned above for horizontal and vertical firms.
However, it is obvious that different factors have different contribution and it is natural.
This contribution is explained by the coefficient values given in the column under the
heading of B along with standard error values (Table 4.20 and 4.23) and above two
models (regression equations) have been developed based in these tables. Following
results have been derived from these two regression equations:
In the case of vertical firms, there is significant contribution of sale value in the
prediction of TFP. The equation indicates that if there is an increase of 1 in sale value of
the vertical firms, its TFP will increase 1.026. It shows that an increase in sale will
positively affect TFP of the firm. This clearly depicts that vertical firms should endeavour
to increase their sale. Nevertheless, sale value has no significant strength to explain the
TFP in the case of horizontal firms. As discussed in Section 1.5, that vertical firms are
bigger in size (production facilitates, number of employees, capital involved etc) as
compared to horizontal firms. Usually, their fixed expenditures are quite high. Obviously,
higher fixed expenditure demand that firms should have high sale volume with a higher
contribution margin to cover the fixed expenditure and contribute to better TFP. This
model supports the general notion that big firms have to focus to increase their sale since
they need a lot to cover the fixed expenses.
Both equations show a negative relationship between TFP and share of labour in
total expenses. However, their coefficient is different in both models i.e. it is -0.015 and
-0.006 for horizontal and vertical firm respectively. It is commonly known that garment
Chapter Five: Conclusion and Recommendations 243
manufacturing is a labour intensive industry, although there is a lot of innovation and
automation in clothing manufacturing. Still, labour expenses in total production are
between 10% to 15% (see Table 4.3). It is obvious from the model that an increase of 1 in
labour expenses can decrease 1.015 and 1.006 TFP of the horizontal and vertical firms. It
suggests that PKGI as a whole should move towards automation and modernisation of the
stitching process instead of relying on manual work. One example is the auto clipper,
which clips the hanging thread after the completion of the stitching process. It is a
modern development whereas in traditional machines, the operator has to do it manually.
These equations show that in both cases, the Share of Financial Expenses (SFE) in
the total cost has a positive relationship with the TFP. This model explains that with the
increase of 1in the share of financial expenses, the TFP will increase by 1.22 and 1.032
for horizontal and vertical firms, respectively. The share of financial expenses has the
highest coefficient value. This shows that the financial cost in the total cost of production
has a significant impact on the TFP. Based on the result it can be suggested to PKGI to
take more help from the scheme provided by the government of Pakistan of discounted
interest rates, rather than relying on any other expensive source of capital.
It is also obvious from the regression equation that share of fashion garments in
total production has a positive impact on TFP of horizontal and negative on vertical
firms. However, its coefficient is different (.002 and -0.005). It appears true that fashion
garments are of more value than simple garments. A higher fashion garment share in total
production means a high value addition. The regression model depicts the link between
fashion garments and the TFP. According to this model, vertical firms should produce
less fashion products to attain better productivity. There is a need of more sophisticated
Chapter Five: Conclusion and Recommendations 244
machines and highly skilled labour for fashion goods production. Nevertheless, fashion
goods production will help horizontal firms increasing TFP.
The share of U.S. market in total production has a positive impact on TFP of
vertical firms, whereas it has no relationship in the case of horizontal firms. The U.S. is
the biggest market of clothing in the world. It imports in huge quantities, whereas
Europe, which is second after the U.S., imports small quantities. Based on this pattern,
one can presume that U.S. customers take quantity discounts from suppliers. Based on
this model it can be suggested that vertical firms should not look markets other than USA
to have a better TFP.
FOB price, means the offered price of supplier in U.S. $ free on board. This
model suggests that in the case of horizontal firms there is no relationship; however,
vertical firms have an association with FOB price. The model depicts that an increase of
1 in FOB price can increase 1.025 TFP of vertical firms. Based on this discussion,
vertical firms should select products with high FOB prices.
5.9 Suggestions and Recommendations
The PKGI is not in a healthy state, as is clear from the figures discussed in
chapter four. Almost 50% of the firms have faced a loss during the period of 2000–2001.
In addition, those firms who earned a profit had a profit percentage less than the generally
applicable rate of interest paid by the banks. It is widely expected that many firms will
face bankruptcy if there are no changes in the current situation, meaning a dramatic
improvement in the TFP. Based on the outcome of this research, the following
suggestions can be made:
Chapter Five: Conclusion and Recommendations 245
1. There is no significant difference in mean value of TFP. However, the majority of
horizontal firms performed better than the vertical firms (see Tables 4.4). Based
on this test, it is recommended that there should be independent units for knitting,
dyeing, and stitching. In the same company such departments should work as
strategic business units or cost centres. Every department should enjoy full liberty
in formulating its own business strategy.
2. The clothing industry is a labour intensive industry. Labour expenses are near
10% of the total expenses (see Table 4.3). This research has proved that share of
labour expenses in total cost has a negative correlation with TFP with horizontal
and vertical firms (see Tables 4.11 and 4.12). Based on these results, it is
recommended that industry should unanimously consider production systems
where less labour is required— for example, automatic and high-speed machines,
hanger system in stitching halls, laser cutting machines, automatic laying systems,
etc. It is expected that less share of labour expenses in total production will lead to
better TFP.
3. Regression analysis provides evidence that firms relying on borrowing loans from
banks have higher TFP (see Tables 4.20 and 4.23). Pakistan provides funds to
exporters at discounted rates. This model suggests that both horizontal and
vertical firms should have more loans from banks to cover their expenses rather
than relying on market credit. Market credit is costly compared to bank charges;
particularly in the modern era when the Pakistani government offers export re-
finance loans at 50% of normal banking rates.
Chapter Five: Conclusion and Recommendations 246
4. In the case of vertical firms, the amount of sale contributes significantly to the
variance of TFP (see Table 4.23). Based on these results it is recommended to
vertical firms that they should focus on increasing their sales. Nevertheless, TFP
of horizontal firms do not have any direct and positive relationship with sales.
(see Table 4.20).
5. Data (see Table 4.3) shows that firms produce a mix of simple and fashion goods.
Table 4.20 and 4.23 show that increasing the share of fashion goods in production
has negative impact for vertical firms and positive impact on horizontal firms.
Based on this results it is recommended that horizontal firms should focus on
fashion goods. Whereas, vertical firms prefer to more basic garments. This
supports the problem associated with PKGI (Section 1.4 and 1.5), that the
manufacturing sector of PKGI is not competitive enough to face the international
competition. Horizontal firms should focus on fashion goods because fashion
goods quantities are small and these can be properly managed in small mills. But
for vertical firm it is recommended to focus basic garments instead of high
fashion garments for better TFP.
6. Table 4.23 suggests that vertical firms should focus on markets U.S. for better
TFP. This table shows that there is a positive correlation between TFP and U.S.
market share in the case of vertical firms. The U.S. is the biggest clothing
consumer in the world. However, at the same time, they do not offer a good price
as compared to Europe. European buyers purchase short runs but they offer a
good price. Based on this observation, it is suggested that vertical firms should
not change their focus from the U.S. to Europe, which is the second biggest
Chapter Five: Conclusion and Recommendations 247
importer in the world. This is mainly due to the high volumes required by the
vertical firms to meet its fixed expenses.
5.10 Limitations of the Study
There are a number of variables, which can substantially contribute to the TFP of
any firm or industry, as is the case with the PKGI. Data collection of all determinants of
the PKGI was not possible. Data related to seven determinants were collected through a
survey and from the government. There are many more influencing factors that might
have a significant impact on the TFP — for example, management style, workers‘
knowledge, skill level, and many more. Initially an attempt was made to collect such
information with the help of a structured questionnaire. However, during the pilot survey,
it was noted that people are reluctant to adequately provide such information and, in
some cases, do not have such data as workers‘ education level, etc.
As mentioned, the PKGI is exempt from filing accounts statements and annual
income tax reports. Since virtually every firm is a private limited firm that does not have
public shares, they are not bound to publish their accounts annually. Consequently, this
researcher opted to use financial data from 2000-2001 collected by the Pakistani Ministry
of Industries, and data covering seven production variables collected through a survey for
the measurement of the TFP. It was hoped this would allow the identification of factors
affecting the TFP of the PKGI. It was found during analysis that there should be
additional factors included in the analysis since the seven determinants used were
insufficient. Furthermore, after 2001, no surveys were conducted to collect financial
information from this sector.
Chapter Five: Conclusion and Recommendations 248
5.11 Further Study
The following suggestions are made for further study:
The Total Productivity model takes all tangible output and input to measure the
TFP. It is plausible that the production system of a firm is highly productive, but financial
costs or earnings on capital invested in other firms might alter its TFP. Keeping this in
mind, it is suggested that there should be another study, which would focus only on the
production process. Such a study would reveal a more accurate picture of the TFP of the
firms.
There is a need to measure productivity of vertical firms by splitting up the whole
operation (knitting, wet processing, and sewing).
In this study, many significant determinants are missing, such as management
style, workers‘ skill level, working environment, government policies, etc. It is suggested
that another study should be conducted in which additional factors would be included to
identify the significance and impact of different factors.
This research can be concluded as follows, in the words of H. James Harrington:
―Do not start an improvement process to improve customer satisfaction or employee
morale. It will do that, but the real reason you need an improvement process is to increase
profits‖.
To increase profit, one has to increase productivity.
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Appendices 267
Appendix 1:
Data of Horizontal Firms
TFP Sale
Value in Million
US $
(Total Output)
Total Cost of
Production in Million
US $ (Total
Input)
%
Share of Labour
Expenses in
Total Cost
% Share of
Financial Expenses in
Total Cost
% Share of
Fashion Goods in Total
Production
USA
Market Share
(%)
FOB
US $
No of
Stitching Machines
1 Karachi 0.99 0.38 0.38 13.10 1.20 75.00 95.00 5.50 85
2 Karachi 0.90 0.50 0.56 14.88 3.40 50.00 100.00 3.50 120
3 Lahore 0.98 1.09 1.11 9.02 1.46 50.00 70.00 5.50 150
4 Lahore 1.03 1.53 1.49 10.65 2.91 50.00 60.00 3.50 75
5 Karachi 1.00 0.32 0.32 7.18 0.79 50.00 50.00 3.50 75
6 Faisal A 1.03 3.28 3.18 1.45 1.72 25.00 50.00 3.50 85
7 Karachi 1.08 2.97 2.75 4.07 1.03 50.00 100.00 4.50 400
8 Faisal A 1.03 0.71 0.69 10.64 3.60 50.00 25.00 4.50 85
9 Karachi 1.05 0.66 0.63 0.63 0.72 50.00 50.00 3.50 80
10 Faisal A 1.00 0.97 0.97 2.94 2.60 50.00 80.00 4.00 150
11 Karachi 1.04 1.52 1.46 4.03 0.40 25.00 60.00 4.00 100
12 Karachi 0.81 2.98 3.68 11.09 1.95 50.00 80.00 5.00 220
13 Karachi 1.01 4.98 4.93 10.30 2.20 50.00 30.00 4.50 200
14 Karachi 0.89 0.53 0.60 16.03 1.08 50.00 60.00 4.00 100
15 Karachi 1.01 1.31 1.30 2.21 0.02 50.00 50.00 4.50 150
16 Karachi 0.75 2.60 3.47 13.97 0.14 50.00 60.00 3.25 150
Appendices 268
Appendix 2
Data of Vertical Firms
TFP Sale
Value in Million
US $
(Total Output)
Total Cost
of Production
in Million
US $ (Total
Input)
%
Share of Labour
Expenses
in Total Cost
% Share
of Financial
Expenses
in Total Cost
% Share
of Fashion Goods in
Total
Production
USA
Market Share (%)
FOB
US $
No of
Stitching Machines
TFP
1 Lahore 0.85 3.37 3.96 14.81 1.91 50.00 95.00 4.50 250
2 Faisal A 0.81 1.44 1.78 16.40 4.28 50.00 10.00 3.50 300
3 Lahore 0.99 3.93 3.97 13.56 4.35 75.00 100.00 4.00 400
4 Karachi 0.98 4.60 4.69 9.09 0.56 75.00 95.00 3.50 175
5 Karachi 1.05 2.05 1.95 3.86 2.33 40.00 90.00 3.50 225
6 Faisal A 0.93 0.68 0.73 4.72 6.62 25.00 50.00 3.50 200
7 Lahore 0.76 2.52 3.32 7.26 1.66 50.00 90.00 3.50 200
8 Lahore 0.95 0.55 0.58 9.87 2.16 50.00 50.00 3.50 125
9 Karachi 1.19 6.47 5.44 16.98 3.49 25.00 90.00 3.50 450
10 Lahore 0.94 1.48 1.57 6.50 0.50 0.00 100.00 3.00 125
11 Faisal A 1.47 0.81 0.55 11.66 13.14 25.00 50.00 4.50 200
12 Lahore 1.22 9.42 7.72 7.47 1.65 50.00 98.00 4.50 400
13 Karachi 1.02 4.70 4.61 0.67 9.41 50.00 70.00 3.50 500
14 Karachi 1.05 2.05 1.95 3.86 2.33 50.00 80.00 4.50 250
15 Lahore 0.90 1.90 2.11 13.82 8.60 50.00 90.00 4.50 250
16 Karachi 0.88 8.73 9.92 14.08 0.52 25.00 55.00 4.00 600
17 Lahore 1.05 13.18 12.55 8.82 4.80 75.00 90.00 4.00 600
18 Lahore 0.86 1.24 1.44 6.83 10.83 25.00 95.00 3.25 100
19 Karachi 1.11 0.84 0.76 5.59 3.32 25.00 40.00 3.50 200
20 Lahore 0.92 3.52 3.83 11.43 2.80 90.00 100.00 6.00 200
21 Lahore 0.93 26.83 28.85 10.39 2.44 75.00 95.00 4.50 750
22 Lahore 0.85 0.53 0.62 4.50 4.80 50.00 50.00 3.50 60
23 Lahore 0.93 9.78 10.52 10.78 4.76 75.00 70.00 4.50 400
24 Lahore 0.98 0.12 0.12 22.96 2.51 25.00 100.00 3.50 280
25 Lahore 0.90 3.12 3.47 12.61 8.01 90.00 95.00 6.50 400
26 Karachi 0.94 9.47 10.07 13.01 3.16 75.00 80.00 6.00 500
27 Lahore 1.01 6.17 6.11 11.23 3.20 75.00 90.00 5.50 400
28 Karachi 0.96 1.85 1.93 6.60 1.53 50.00 80.00 3.50 400
29 Lahore 1.07 2.08 1.94 1.77 0.16 50.00 60.00 4.00 200
30 Lahore 1.08 1.77 1.64 9.17 0.96 50.00 80.00 6.25 150
31 Karachi 0.86 1.73 2.01 9.07 3.57 50.00 90.00 3.50 150
32 Lahore 1.05 21.33 20.31 5.60 2.43 90.00 100.00 5.50 780
33 Lahore 0.72 2.05 2.85 11.71 0.34 75.00 95.00 6.50 400
Appendices 269
Appendix 3
S. No. -----
Dear Sir
Many thanks in advance for your cooperation. The purpose of this survey is to establish links
between the productivity of the firms and other factors, like, firm location, size, and market and
marketing methodology etc. We assure you that all information will be kept confidential and will
only be used for academic purpose.
Once again thanks
Yours truly,
Mushtaq Mangat
Appendices 270
Part One
Company Information
Company Name
Address
Location (distance from the city centre)
Contacts
Ph
Mobiles
Fax
E mail
Web site
Type of company: sole proprietor, partnership, private limited, public limited
Year of establishment
Any other information
Part #02
Operational capacity
Appendices 271
Q# 01
What is your per day operational capacity of the following products?
Knitting
Kgs/day
Dyeing
Kgs/day
Finishing
Kgs/day
Printing
Kgs/day
No of
stitching
machines
Stitching No of
pieces (based
on simple polo
shirt)
Part #02
Operational capacity
Q# 01
What is your per day operational capacity of the following products?
Knitting
Kgs/day
Dyeing
Kgs/day
Finishing
Kgs/day
Printing
Kgs/day
No of
stitching
machines
Stitching
No of pieces (based on
simple polo shirt)
Q#02
What is the origin of your different machines?
Origin Knitting Dyeing Finishing Printing Stitching Remarks
Local
%
EU, U.S.A,
Japan
%
Korea,
Taiwan,
China
%
Q#03
What are your production types and their ratios?
S# Product Type Share Remarks
1 High fashion 2 Fashion 3 Basic
Appendices 272
Q# 04
How many people are working in this organisation?
S.No Description Number of
Employees 1 On salary
2 On piece rate
3 On daily basis
Q# 05
What are the qualifications of the following persons?
S. No Position Education No man for this post Remarks
1 Chief Executive
2 GM Production
3 Knitting master
4 Dyeing manager
5 Printing master
6 Stitching head
7 Head PPC
8 Head Merchandiser
9 HR manager
Appendices 273
Q# 06
Which one is your major market and also let us know the share of different markets. Where
your products are being exported?
S.NO Market Share% 1 U.S.A, Canada 2 Europe 3 Local 4 Any other
Q# 07
What is the average price of your products? U.S.
$ FOB: ----------
Q#08
How many buyers are you dealing with at a time and what was the share of direct and through
buying office sales in the last year?
Description Numbers Percentage Remarks
Number of buyers
Number of orders
Through buying office
Direct marketing
Sold to your own office
abroad
Local
Any other
Appendices 274
Q#09
Do you have certification?
S# Yes/No ISO 9000 ISO 14000
Q#10
Share of contracted and salaried stitching operators
Descriptions Share On salary On piece rate
Q#11
Share of male and female workers in stitching
Descriptions Share% Women Men
Q#12
Do you have any formal training system for the following people?
Descriptions Yes/No Managers Supervisors Workers
Q#13
Please let us know the involvement of your directors in the daily functioning.
1-Very High
2- High
3- Normal
4- No involvement
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