determinants of basic needs fulfillment the case...

DETERMINANTS OF BASIC NEEDS FULFILLMENT

THE CASE OF PAKISTAN

Muhammad Azhar Khan

Reg. No. 100-SE/PhD/S05

Supervisor: Prof. Dr. Nasim S. Shirazi

Co-supervisor: Prof. Dr. Asad Zaman

School of Economics

International Institute of Islamic Economics

International Islamic University Islamabad

2012

DETERMINANTS OF BASIC NEEDS FULFILLMENT

THE CASE OF PAKISTAN

Muhammad Azhar Khan

Reg. No. 100-SE/PhD/S05

Supervisor: Prof. Dr. Nasim S. Shirazi

Co-supervisor: Prof. Dr. Asad Zaman

A Thesis Submitted to International Institute of Islamic

Economics(IIIE), International Islamic University (IIU) Islamabad,

Pakistan in partial fulfillment of the requirements for the award of the

Degree of Doctor of Philosophy in Economics

2012

DECLARATION

I, Muhammad Azhar Khan, Registration No. 100-SE/PhD/S05, a student of PhD in

Economics at International Institute of Islamic Economics (IIIE), International Islamic

University Islamabad (IIUI) Pakistan hereby declare that the work embodied in this

thesis entitled Determinants of Basic Needs Fulfillment The Case of Pakistan is the

result of original research and has not been submitted for any degree in any other

University or Institution.

---------------------

Muhammad Azhar Khan

i

ABSTRACT

This study investigates the impact of different socio economic indicators on basic

needs fulfillment in Pakistan. Basic needs gap index (BNGI) is dependent variable and

is used as proxy of basic needs fulfillment. Ordinary least squares (OLS) and two

different versions of empirical Bayes techniques have been applied on the time series

data of eight different regions of Pakistan with rural and urban bifurcation for the period

1979 – 2008. Significant factors are figured out of ten explanatory variables: per capita

income, per capita savings, remittances (domestic and foreign), human capital index,

household size, ratio of income of top 20 percent to bottom 20 percent , share of income

held by bottom 20 percent , higher education , unemployment, and dependency ratio.

Our final model comprises of the following four explanatory variables, per capita

income, human capital index, share of income held by bottom 20 percent, and

unemployment. It is found that per capita income and income held by bottom 20 percent

are highly correlated with BNGI in all the regions of Pakistan. It is also observed that

share of income held by bottom 20% is also a significant variable that affect BNGI.

Human capital index and unemployment showed mixed and sometimes contrasting

results for rural and urban regions. Income distribution is more uneven in urban areas

as compared to the rural areas. In the case of human capital, there is a considerable

difference in rural and urban areas of Pakistan.

Growth for the sake of growth is meaningless unless it reduces the miseries of

the masses. To make every person part of development process, it needs to ensure

that no one is underprivileged and marginalized. This can only be done when all the

basic needs of the individuals are met. To improve the indicators of basic needs

fulfillment it is important to improve the income share held by the poorest 20% people,

which is in accordance to the MDGs. This requires strong political will at the part of the

policy makers, the government officials, and the political parties.

ii

ACKNOWLEDGEMENTS

All the praise and thanks to Allah Almighty Who enabled me to accomplish this

challenging task; and may peace and blessings be upon His prophet Muhammad

(PBUH), the role model for the humanity.

Following the tradition of the prophet Muhammad (PBUH) in thanking people who

do us a favor, I would like to thank many people who contributed in my research work.

I owe a special note of gratitude to my supervisors Dr. Asad Zaman and Dr

Nasim Shah Shirazi whose invaluable support and consistent encouragement was a

source of motivation for me through the twists and turns of my belated research work.

Their unflagging enthusiasm for my work has been inspirational.

I would also wholeheartedly like to express my thanks and gratitude to Dr Eatzaz

Ahmad, Dr. Hafiz Muhammad Yasin, Dr Atiq ur Rehman, and Muhammad Siddique for

their support in my research work. I am also indebted to all my teachers, especially Dr.

Asad Zaman and Dr. Shaukat Niazi (Late). Thanks are also due to Muhammad Khan

(Deputy Secretary-KPK) for his invaluable support and prayers for my success.

My colleagues and friends, Muhammad Zahid, Mehtab Ahmad Abbasi, Mudassar

Nazir, Liaqat Ali, Shahid Razzaque, Qammar Abbas, Tahir Masood Bhatti, Khalid

Mahmood, and Malik Naseer Hussain deserve my heartfelt thanks. I am also gratified to

university staff especially Niaz Ali Shah, Tauqir Ahmad and Zaheer Ahmad. I am

thankful to HEC (Pakistan), and Higher Education Department (KPK) for extending the

financial support to pursue my doctoral studies.

I wish to express my appreciation and gratitude to my noble parents, especially

to my dear departed father. I also extend my appreciation to my wife and children for

their continuous support, help and prayers during the completion of this work.

iii

This Thesis is Dedicated to

My Family,

My Source of Inspiration

for Higher Studies.

iv

TABLE OF CONTENTS

Abstract i

Acknowledgements ii

Dedication iii

Table of contents iv-vi

List of Tables vii-ix

List of Figures x

List of Abbreviations xi

CHAPTER 1: INTRODUCTION 1-13

1.1 Background of the Study 5

1.2 Statement of the Problem 8

1.3 Objectives of the Study 9

1.4 Motivation for and Significance of the Study 10

1.5 Methodology in Brief 12

1.6 Organization of the Study 12

CHAPTER 2: LITERATURE REVIEW 14-25

2.1 Growth and Inequality 14

2.2 Growth and Poverty 17

2.3 The BNF Approach to Poverty 20

2.4 Empirical Studies on the BNF Approach 23

CHAPTER 3: THEORETICAL BACKGROUND 26-52

3.1 Growth, Development and Income Distribution 26

3.2 Poverty and Income Inequality 29

3.3 Different Approaches to Poverty 34

3.4 Measures of Poverty 44

3.5 Concluding Remarks 51

CHAPTER 4: DATA AND VARIABLES 53-114

4.1 Limitations of the Grouped Data 53

4.2 Data Sources 54

4.3 Variables Suggested for the Preliminary Model 56

v

4.4 Construction of Basic Needs Gap Index (BNGI) 87

CHAPTER 5: MODEL SPECIFICATION AND METHODOLOGY 115-146

5.1 Model Selection 116

5.2 Empirical Model 134

5.3 Estimation Approaches 139

CHAPTER 6: EMPIRICAL FINDINGS AND ANALYSIS 147-182

6.1 Rural Areas 149

6.2 Urban Areas 156

6.3 Overall Areas 162

6.4 Rural-Urban Analysis using Aggregate Prior 169

6.5 Sensitivity Analysis 178

CHAPTER7: FINDINGS AND CONCLUSION 183-193

7.1 Overview of the Study 183

7.2 Summery And Findings 185

7.3 Conclusions and Policy Recommendations 189

REFERENCES 194-209

APPENDICES 210-229

Appendix I-A 210

Appendix I-B 211

Appendix I-C 212

Appendix I-D 213

Appendix II-A 214

Appendix II-B 215

Appendix II-C 216

Appendix II-D 217

Appendix III-A 218

Appendix III-B 219

Appendix III-C 220

vi

Appendix III-D 221

Appendix IV-A 222

Appendix IV-B 223

Appendix IV-C 224

Appendix IV-D 225

Appendix IV-E 226

Appendix IV-F 227

Appendix IV-G 228

Appendix IV-H 229

vii

LIST OF TABLES

No. Table Description Page

4.1 Per Capita Income (Monthly) 62

4.2 Per Capita Savings (Monthly) 63

4.3(a) Percentage of Total Monthly Income by Foreign (F) Remittances

64

4.3(b) Percentage of Total Monthly Income by Foreign (D)

Remittances

65

4.3(c) Percentage of Total Monthly Income by Foreign (F+D)

Remittances

66

4.4 Household Size 69

4.5 Percentage Distribution of Earners (Both Sexes) by Degree

Level Edu:

70

4.6 Dependency Ratio 73

4.7 Labour Force Unemployment Rate (Un) 74

4.8 Share of Income held by bottom 20 Percent 78

4.9 Ratio of Income of top 20 % to bottom 20 % 79

4.10 Human Capital Index 84

4.11 Average Monthly Expenditure / Household (Rs) 92

1.12 Average Monthly Income / Household (Rs) 93

4.13 Percentage Distribution of Monthly Income Among

Households by Quintiles Pakistan

96


Households by Quintiles Punjab

97


Households by Quintiles Sindh

98


Households by Quintiles KPK

99


Households by Quintiles Balochistan

100

4.18 Poverty (Head Count Ratio)

104

viii

4.19 Construction of the BNGI 105

4.20 BNGI for all Regions of Pakistan 107

5.1 Correlations, Means and Standard Deviations Aggregate Rural and Urban Areas

118

5.2 Correlations, Means and Standard Deviations Overall Areas

119

5.3 Correlations, Means and Standard Deviations Rural Areas (four provinces)

121

5.4 Correlations, Means and Standard Deviations Urban Areas (four provinces)

122

5.5 Static Panel Model for Rural Areas 124

5.6 Static Panel Model for Urban Areas 125

5.7 Static Panel Model for Overall Areas 127

5.8 Static Panel Model for Aggregate Rural Urban Areas 128

5.9 Rural Areas (Auto Selected Model) 129

5.10 Urban Areas (Auto Selected Model) 131

5.11 Overall Areas (Auto Selected Model) 132

5.12 Aggregate Rural and Urban Areas (Auto Selected Model) 132

6.1a OLS and Empirical Bayes Estimates for Rural Punjab 151

6.1b OLS and Empirical Bayes Estimates for Rural Sindh 152

6.1c OLS and Empirical Bayes Estimates for Rural KPK 154

6.1d OLS and Empirical Bayes Estimates for Rural Balochistan 155

6.2a OLS and Empirical Bayes Estimates for Urban Punjab 157

6.2b OLS and Empirical Bayes Estimates for Urban Sindh 158

6.2c OLS and Empirical Bayes Estimates for Urban KPK 160

6.2d OLS and Empirical Bayes Estimates for Urban Balochistan 161

6.3a OLS and Empirical Bayes Estimates for Overall Punjab 163

6.3b OLS and Empirical Bayes Estimates for Overall Sindh 164

6.3c OLS and Empirical Bayes Estimates for Overall KPK 165

6.3d OLS and Empirical Bayes Estimates for Overall Balochistan 167

6.3e Summary of Results 168

6.4a OLS and Empirical Bayes Estimates for Rural Punjab (using

aggregate rural and urban prior)

170

ix

6.4b OLS and Empirical Bayes Estimates for Urban Punjab (using


171

6.4c OLS and Empirical Bayes Estimates for Rural Sindh (using


172

6.4d OLS and Empirical Bayes Estimates for Urban Sindh (using


173

6.4e OLS and Empirical Bayes Estimates for Rural KPK (using


174

6.4f OLS and Empirical Bayes Estimates for Urban KPK (using


175

6.4g OLS and Empirical Bayes Estimates for Rural Balochistan

(using aggregate rural and urban prior)

176

6.4h OLS and Empirical Bayes Estimates for Urban Balochistan

(using aggregate rural and urban prior)

177

x

LIST OF FIGURES

No. Figure Description Page

4.1 Human Capital for Overall Regions 85

4.2 Human Capital for Rural Regions 86

4.3 Human Capital for Urban Regions 87

4.4 BNGI. Pakistan Rural and Urban 109

4.5 BNGI. Punjab Rural and Urban 110

4.6 BNGI. Sindh Rural and Urban 111

4.7 BNGI. KPK. Rural and Urban 112

4.8 BNGI. Balochistan Rural and Urban 113

xi

LIST OF ABBREVIATIONS

Abbreviation. Description

B20 Share of Income Held by Bottom 20 Percent

BNF Basic Needs Fulfillment

BNGI Basic Needs Gap Index

CZ William J. Carrington and Asad Zaman

D Domestic Remittances

EB Empirical Bayes

F Foreign Remittances

GDP Gross Domestic Product

HCI Human Capital Index

HCR Head Count Ratio

HE Higher Education

HP Cheng Hsiao and M. Hashem Pesaran

HS Household Size

IMF International Monetary Fund

KPK Khyber Pakhtunkhwa

MDGs Millennium Development Goals

OLS Ordinary Least Squares

PGI Poverty Gap Index

PSLM Pakistan Social and Living Standards Measurement Survey

Rem Remittances (Domestic and Foreign)

SPGI Squared Poverty Gap Index

Spc Per Capita Savings

T2B Ratio of Income Top 20% to Bottom 20 %

UNDP United Nations Development Programme

WTO World Trade Organization

Ypc Per Capita Income

1

CHAPTER 1

INTRODUCTION

In the second half of the 20th century massive economic growth and substantial

development took place in the world, but all the people did not equally benefit from this

development due to uneven distribution of wealth and income. Most of the developing

countries, particularly the poor ones, are facing various problems like macroeconomic

imbalance, poverty, extremely high dependence on agriculture, and uneven income

distribution among the classes. Macroeconomic imbalance includes high rate of

unemployment, inflation, negative balance of payments, exchange rate depreciation,

debt burden, low and inconsistent growth rates of gross domestic product, and low rates

of industrialization. The most important factors, relevant to this study, are the poverty

and unequal distribution of income which lead to socio-political and economic instability.

The rich and the affluent live a luxurious life whereas the poor are unable to meet their

basic needs. This inequality also leads to various socio-political problems.

According to the World Bank Report (2000), 2.72 billion people were living on

less than $2 a day in 1990. However, by 1998, the number of the poor people getting

less than $2 per day raised to 2.80 billion. This shows an increase of around 100 million

people living in adverse poverty within eight years despite an increase in income level

by an average of 2.5 percent per annum. According to a report of The United Nations

Development Programme (UNDP-2004), the high income group with 19 percent of the

world population received 84 percent of the global income in the year 2002. The report

indicates that the six richest countries in the world, with only 11 percent of the world

2

population, received two-third of the global income in the same year. It is also reported

that the total revenues of the world’s top 11 largest corporations during the year 2002

was about US$2 trillion, which is equivalent to twice the aggregated income of all the

low income countries and the ratio of income of the richest 20 countries to the poorest

20 countries is 40:1.

Milanovic and Yitzhaki (2002) observe that majority (97%) of the world poor

population is living in developing countries. The World Development Indicators

(WDI:2008) shows that in 2005, the highest quintile i.e. the richest twenty percent of the

world, accounted for 76.6% of total private consumption and the poorest quintile just

consumed 1.5%. Likewise, the poorest 10% of the world population accounted for just

0.5% of the total private consumption as against the wealthiest 10% people who could

consume around 59% of the aggregate. According to the World Bank Report (2008), the

fast emergent economies of Asia will be having more than 600 million rural population

living in extreme poverty conditions. Despite the fact that rural-urban migration is

prevailing, however, poverty seems to be the overriding phenomenon for quite a few

more decades. Moreover, the report says that agriculture will be the key factor for

poverty reduction in the 21st century, since about 75% of the poor people in the

developing countries are living in rural areas. About 2.1 billion of these people are living

on less than 2 Dollars a day and 880 million on less than 1 Dollar a day, all depending

on agriculture for their subsistence. According to world bank (2008), in 2004, 2.6 billion

people live on less than $2 per day, with three quarters of them in rural areas. In 2008,

1.29 billion people live on less than $1.25 per day which accounts 22.4 percent of total

population.

3

Given this situation as well as the rate of growth in population, the developing

countries ought to strive for high and sustainable rate of growth in aggregate output,

and to reduce macroeconomic imbalances and socio-economic inequalities, failing

which the problems of abject poverty and extreme inequalities will keep on worsening in

the developing countries leading to other socio-political problems including terrorism.

The much debated relationship between economic growth and inequality has

gained considerable space in development research. In his 1955 article, often referred

to as the inverted-U hypothesis, Simon Kuznets predicted that inequality is likely to fall

after having reached to some climax during the earlier period of development. This

rising trend in the inequality in the early periods followed by a downfall later on was

supposed to be associated with structural changes that took place because of changes

in technology and labor productivity as the laboring class will shift overtime from the less

productive traditional (agricultural) sector to more productive (industrial) sector. This

phenomenon of structural transformation and trickle-down effect has been highlighted

by Arther Lewis (1950’s). A fairly good number of studies supported these hypothesis

including Kravis (1960), Oshima (1962), Adelman and Moris (1971), Ahluwaila (1974)

etc. However, the inverted-U hypothesis tested for individual countries could not be

proved. Anand and Kanbur (1984), Field (1989), Deninger and Squire (1996) found no

evidence of such a curve to exist in individual countries.

Dagdeviren and Weeks (2001) stated that income redistribution is not a

necessary condition for poverty reduction. According to author, aggregate growth itself

is capable of reducing poverty, although redistribution can support achieve the target of

4

poverty reduction along with growth. The study suggested that growth combined with

redistribution would be more effective to reduce poverty. Deininger and Squire (1996)

presented the theme of income redistribution with a new data set.

Inequality in Pakistan has been a key issue in development strategies and social

reforms. However, the basic fallacy lies in measurement of inequality. In majority of

cases, the researchers lay stress on income or consumption inequalities in Pakistan

and a considerable literature on these lines prevails. Further, a number of different

measures for inequalities of income is available. Different researchers have therefore

employed different tools in their studies of income inequalities and poverty, keeping in

view the coherency of data sets available. The first attempt to measure income

inequalities in Pakistan is attributed to Haq (1964). The study analyzed income

inequalities within the highest income group, based on income tax data for 1948-49

through 1960-61. This was followed by a chain of studies, the worth mentioning, among

others, are Bergan (1967), Khandker (1973), Naseem (1973), Allaudin (1975) and Ayub

(1977). All these studies used the Gini coefficient as a measure of inequality.

An important problem associated with the estimation of income inequalities is the

selection of suitable units of measurement. Only few studies have preferred to use

‘household’ as a unit of measurement; while some others have used the ‘individual’ as a

unit. Another group of researchers have considered the ‘per adult equivalent

expenditure’ as the unit of measurement. These include Jehle (1990), Haq (1998),

Anwar (2003) and Jamal (2003) etc.

5

Keeping in view the importance of income distribution, this study aims at

evaluating the ‘basic needs gap’ for different regions of Pakistan, with rural-urban break

up. After the descriptive analysis, the study tries to estimate the possible determinants

of the basic needs gap index (proxy of basic needs fulfillment) in Pakistan. The earlier

studies available in this area of research discuss only the basic needs fulfillment in the

context of cross country analysis. However, there are some serious reservations about

determination and assessment of basic needs, since different countries having different

socio-political and cultural environments. Moreover, such empirical studies are very few

in number, obviously due to non-availability of uniform and consistent data sets for

developing countries. The present study takes the rural, urban and overall classification

of the four provinces of Pakistan based on the HIES (Household Integrated Economic

Survey) data from 1979 to 2008. Luckily, the data on the relevant variables are available

in Pakistan from HIES survey, and so we endeavor to conduct research in this rarely

explored area.

1.1 Background of the Study

The two concepts of economic growth and economic development are used as

synonyms for the general reader to perceive a steady increase in the availability of

goods and services measured by the GNP per capita. Although there is no hard and

fast line of demarcation to separate growth and development because boundaries of

both are overlapping, however both the areas differ in scope and coverage. Growth

theory focuses on the factors responsible for enhancement of national or per capita

income. The focal point of the theory of development, on the other hand, is the overall

6

socio-economic and institutional setup that advances overtime. Economic development

means improvement in education, health and other facets of human welfare. It also

encompasses the elements like life expectancy, infant survival rate, literacy rate, human

capital development and conservation of the environment etc.

The growth rate of income is vital to the course of economic development and it

plays the role of an engine. Following the momentum achieved through growth in

income, the development carriage moves forward. If the process of growth is persistent

and sustainable overtime, this leads to rebuilding and modernization of relevant

institutions in due course, improvement in democratic norms, equity in the distribution of

resources, decline in poverty and general progress in the standards of living. Topics like

income, savings, labor force, technological progress, capital formation and investment

etc. are discussed under the domain of growth. On the other hand, the impact of growth

on the whole socio-economic composition is discussed in the development literature. It

discusses the questions, for example, whether or not the GNP growth is equitably

passed on to different segments of the society; whether the standard of living gets

improved overtime or otherwise; what is happening to poverty and if there is any trade

off between growth and poverty; what are other factors of development beside growth

(social, political) etc. Thus economic development is much broader concept than

economic growth. Moreover, economic growth can be measured quantitatively whereas

only qualitative indices may be available to measure the pace of economic

development. Both the disciplines come closer to each other when distributional aspect

of income among different groups of society is discussed.

7

The basic needs fulfillment (BNF) approach to development is never opposed to

an expansion in GNP; rather its aim is to minimize poverty through the vehicle of

growth. However, certain doubts were raised and magnified in the literary circles as if

the BNF was possible at the cost of growth. However this line of thinking has been

historically and empirically proved wrong. The BNF approach may require a revision of

the development priorities, stringent redistributive measures, reallocation of resources

across production sectors, shifts in technological choices and readjustments in the

structure of the economy. However, an integration of all these components into a unified

and comprehensive development plan may be hazardous and challenging because their

net outcome may not always be welcomed by the powerful and political elites. This

argument explains the reluctance often shown by the policy makers in developing

countries while considering the BNF approach central to the development planning. The

much enchanted slogans and practices of globalization, privatization and liberalization

in transitional and other developing countries have failed in achieving the targets; rather

the economic conditions of the citizens of these countries have further worsened. The

BNF approach has been neglected and almost shown its exit from the literature.

According to Hasan, Z (1997), even the Islamic economists are confined to the juridical

aspects of BNF with little attention to its operational side. As this study is concerned

with the case of Pakistan, it covers not only descriptive analysis to appraise different

concepts of basic needs but also compares different regions of Pakistan in terms of

BNF indices and explores their linkage with other important variables.

8

1.2 Statement of the Problem

The idea that the basic needs of all human beings should be fulfilled before the

relatively less important wants of a few are met with, is the well established and widely

advocated principle of all major religions of the world. The BNF approach is concerned

with eradicating mass deprivation; an apprehension that has always been at the heart of

development programs.

In spite of the claims of curing poverty and inequalities, most of the developing

countries pursue the neoclassical agenda of free market (laissez-faire) that takes into

consideration efficiency, globalization, and privatization process blindly but ignores

equity in distribution. This strategy has promoted the dominance of poverty in most of

the developing economies. The problem can be tackled if the basic needs objective is

recovered and put at the centre of the development discourse. However to accomplish

this agenda, a strong will on the side of policy makers and political leaders is the

necessary condition needed.

This study aims at constructing and analyzing the Basic Needs Gap Index (proxy

of basic needs fulfillment) for different regions of Pakistan. The operational implications

of BNF and the possible and significant determinants of this target in Pakistan are also

explored. This is a need of the hour in the context of developing countries, including

Pakistan where the problem of poverty is severe. The empirical work done so far on the

issue and mechanism of BNF is very scarce. As discussed above, the main reason is

the non-availability of consistent data. To our good fortune, we have several editions of

9

the HIES at hand that contain comprehensive information about food, clothing, shelter,

health, education and many other economic and social variables.

The developed world is highlighting and struggling for the human rights; like the

freedom of expression, rights to have demonstrations and rights to detention etc;

whereas, developing and poor nations are facing acute deficiency of food, clothing and

shelter for masses, which should be at the top of development agenda in these

countries. The present study explores the basic needs fulfillment situation in different

parts of Pakistan and then steps forward to identify the factors responsible for fulfillment

of these needs, keeping in view the peculiar features of different regions. Finally the

study provides valuable information to the policymakers, think tanks and academicians,

public authorities and researchers in this area.

1.3 Objectives of the Study

Most of the prominent economists stressed on increase in GDP and it was

believed that the trickledown effect will take care of all problems, but some people like

Stiglitz (2002) are skeptical of this outcome. So there is a dire need to rethink alternative

solutions to address the problem of poverty. The basic needs approach to economic

development is one way of addressing the problem of poverty. The objective is to

enable people earn their livelihood with honor or obtain the basic necessities of life like

nutrition, housing, water and sanitation, followed by appropriate education and health

facilities so as to increase their productivity.

10

The study primarily aims to examine the factors responsible for poor performance

of BNF in different regions of Pakistan. The focus of the study is to investigate the

empirical relationship between basic needs gap index with per capita income, human

capital, status of employment and income distribution in Pakistan. The objectives of the

study are specifically explained as under:

To compute the Basic Needs Gap Index (BNGI) for Pakistan.

To explore and analyze the extent and severity of BNF situation in different

regions of Pakistan.

To investigate the empirical relationship between BNGI and per capita income,

unemployment, and the income share held by the bottom 20 percent of

population.

To compute and explore the impact of basic factors like human capital on

poverty, which is essential to get rid of the current mess that most of the

developing economies are facing.

1.4 Motivation for and Significance of the Study

When we see poverty amidst plenty, it is utmost necessary for a planner,

government in chair, political leader, and a researcher to think of seriously and to find

answers to this problem. For a student of Economics, this topic is much interesting to be

investigated. When we judge the situation with the usual yardstick of economic growth,

we see a spectacular, unprecedented, and appreciable success in GDP growth of many

countries after the World War II. However when we look at the world through the lens of

human characteristics, it is far less successful in elimination of poverty elimination and

11

reduction of income inequalities. The statistics reveal tremendous income disparities

across the nations and within different regions of nations. Different strategies to

accelerate development have been adopted overtime using the growth oriented

neoclassical approach, but is outcome is not encouraging. It is unfortunate that even

knowing the causes of failures, the governments and planners in the developing

countries follow the same policies time and again. Policies of the World Bank and IMF in

the form of Washington consensus and globalization are followed, which have failed in

most of the developing and transitional economies. So there is a dire need for the policy

makers in these countries to rethink and to focus on the reduction of poverty and

disparities. These burning issues inspired me to work on the topic of poverty and to

contribute something positive to the discipline.

On the one hand, there is unprecedented development in the field of science &

technology, trade & commerce, political liberty, human rights, longevity etc., yet

remarkable deprivation, destitution, and oppressions do exist around us. Given this

situation there is dire need to strive for high and consistent growth in aggregate output

on one hand and to reduce macroeconomic imbalances and socio economic

inequalities on the other, which are at the extremes in developing countries.

Hicks and Streeten (1979), while reviewing various social indicators, found that

the use of social and human indicators was capable to accelerate growth provided the

main targets include the fulfillment of basic needs. The present study focuses on this

possible approach to development, which seems to be the most important, live and

timely debate of the developing economies.

12

1.5 Methodology in Brief

The current study is directed to analyze empirically the issue of basic needs

fulfillment (BNF) using the data of different regions of Pakistan and the focus is to find

the factors responsible for this goal. Here the basic needs gap index is used as prime

variable and three different estimation techniques are used to find appropriate and

plausible parameters. These techniques include the ordinary least squares [OLS], the

empirical Bayes subdivided into Hsiao and Pesaran approved (2004) and Carrington

and Zaman (1994). The basic unit of analysis and comparison is the rural and urban

areas of the four provinces of Pakistan. Descriptive analysis of the regions is carried out

and we particularly focused on the spatial analysis to see the present and past state of

BNF and its linkage with the poverty.

The empirical Bayes technique is believed to give precise results particularly

when sample is small where the results of OLS analysis are imprecise. The present

study uses HIES data from 1979 to 2007-08.

1.6 Organization of the Study

The study is split into six chapters. The present introductory chapter is followed

by a review of the relevant literature that discuses the findings of different studies

focusing on the basic needs fulfillment approach. We present an overview the debate

on growth and development, which is followed by a brief account of studies on poverty

and income inequalities. The important empirical studies carried out on basic needs

fulfillment are also discussed. Chapter-3 provides the theoretical background to clarify

different approaches to handle the problem of abject poverty. Chapter-4 deals with data

13

and the description of variables and construction of different indices used in the

analysis. Chapter-5 deals with the empirical framework and methodology. It comprises

the discussion of the empirical model and different estimation approaches to the use of

empirical Bayesian technique. Chapter-6 is central and presents the empirical results

and analysis. These results are obtained by using three different techniques and the

findings are discussed group and region wise (rural, urban, overall, aggregate rural-

urban). The final chapter is reserved, as usual for conclusions and policy implications.

At the end, references are given.

14

CHAPTER 2

LITERATURE REVIEW

Equity in income distribution and fulfillment of basic needs have always enjoyed

high priority in major religions of the world. The prominent economists and social

philosophers have considered fulfillment of basic needs as an important factor of social

integration and a key to real economic development. Although, the issue of basic needs

fulfillment has lost its importance to some extent in the industrialized countries during

the 19th and 20th centuries of high growth and prosperity, however it remains a live topic

of discussion in the developing countries, which is emotionally argued at all forums and

investigated at academic sites. In this chapter, we briefly review the literature concerned

with different strategies of economic growth and development.

2.1 Growth and Inequality

Economic growth and development gained tremendous attention in 1950‟s. The

contributions of the Noble Laureate Arthur Lewis (1954) and others are worth

mentioning in this regard. The terms economic growth and development are used as

synonyms and perceived as continuous increase in the level of income along with an

uplift in the standard of living and development of the associated social, political and

economic institutions. Lewis (1954) contended that inequality was good for development

and economic growth, since saving is necessary for capital formulation, which in turn

leads to high employment, elimination of poverty and economic growth.

15

Another Nobel Laureate, Simon Kuznets (1955) argued from the historical facts

of European development that inequality worsened in the initial stage but later on

improved. The major deriving force behind this behaviour was presumably the structural

changes that occurred due migration of labour from poorer and less productive

traditional sectors to more productive industrial sectors. This line of thinking got

popularity under the title of inverted-U hypothesis, which was supported by a number of

studies conducted by prominent researchers like Harry Oshima (1962), Adelman and

Morris (1971), Felix Pankert (1973), Ahluwalia (1974), Robinson (1976) and Ram

(1988). However, the empirical evidence collected later on as well as the experience

gained during the later half of 20th century did not support the inverted-U hypothesis.

For instance, Ashwani Saith (1983), S. Anand and SMR Kanbur (1986), Gustav

Papanek (1987) argue against the existence of the hypothesis.

Subsequent research shows that there is no strong relationship between GNP

growth and the distribution of income. High growth rate does not necessarily worsen the

distribution of income as shown by Gustav Papanek and Oldrich Kyn (1986). However,

World Development Report 1991 provides evidence that higher growth is more often

associated with lower inequality. Greater equality leads to improved nutrition, more

employment and greater output growth, for instance the studies by Partha Dasgupta

and Debraj Ray (1987) and Roberto Parotti (1996) provide this argument. On the

contrary, Deininger and Squire (1996) attempted a comprehensive test and confirmed

that there was no systematic relationship between growth and inequality for individual

countries. On the hand, two classic technical articles by Abhijit V. Banerjee and Andrew

16

F. Newman (1993) and Oded Galz and Joseph Zaira (1993) address the mechanism by

which higher inequality may lead to lower growth or income.

Mahmood (1984) worked out income inequalities for urban and rural households

of Pakistan by using Gini coefficient, coefficient of variation, Theil entropy index and

Atkinsons indices. He used the time series data, ranging from 1963-64 to 1978-79, and

derived from household income and expenditure survey. The study revealed higher

intensity of income inequalities in urban area as compared to rural areas throughout the

study period. The study traced a declining trend in income inequalities up to the year

1968-69 in all regions. However rising trend persisted from 1970-71 onward in urban

Pakistan.

Kruijik (1986) estimated household income inequality in Pakistan, its all four

provinces and rural- urban break up of each province by employing Theil index. The

study used data for 1979. The coefficient of Theil index revealed the incidence of

income inequality was highest in KPK followed by Sindh, Punjab and Balochistan

respectively. According to the study urban income inequality was higher in urban areas

of all the provinces.

Ahmad et al, (1989) measured inequality in household income and expenditure

using Gini Coefficient, coefficient of variation, log-variance and Atkinson‟s indices and

different inequality aversion parameters for 1979 and 1984-85. According to the study,

inequality has increased from 1979-1984-85, but this increase was very trivial.

Jefri at (1995) analyzed inequality between urban and rural areas of Pakistan for

191 through 1979 by estimating Gini coefficient and income shares of the richest and

17

the poorest 20% households. The findings of study divulged that income inequality

improved slightly during 1979-88 but grew exorbitantly in 1990-91. According to study

inequalities in urban Pakistan have been persistent as compared to rural Pakistan.

Anwar (2003) analyzed inequalities for the period 1998-99 to 2001-02 by

incorporating household composition and using micro data of HIES. The study

measured inequality in per equivalent consumption expenditure for Pakistan as whole,

rural-urban cohorts of Pakistan as well as of provinces by estimating Gini coefficient.

The interesting feature of the study was adjusting data for household size and

composition by assigning weights to all members of household. The study observed

decrease in inequalities in three out of four provinces. According to study, Sindh and

overall Pakistan witnessed increase in inequality for the study period}.???

2.2 Growth and Poverty

The trickle-down effect in growth oriented approach didn‟t materialize because

the distribution side was altogether missing in the original policies. By now, there is

consensus among the researchers that mere emphasis on GDP growth is sufficient for

the process of some meaningful results. Instead, the reduction of inequalities, alleviation

of poverty and focus on basic needs fulfillment are the primary objectives of welfare and

development. The focus of research has shifted gradually during 1970‟s onwards while

measuring economic development. Anderson and White (2001) emphasize on the

pattern of growth and distribution. They derived data from 143 countries and found that

the growth effect dominates, but distribution also proved to be significant. In over a

18

quarter of cases, distribution played a stronger role than growth in increasing the

income for the poor.

Norman L. Hicks (1970) stressed on formulation of effective policies to promote

better distribution and reduce poverty since these policies are likely to stimulate growth

rather than retarding it. During 1980‟s and 1990‟s, the „Washington consensus‟

remained the advice in which fiscal strictness, privatization, and market liberalization

were the main ingredients. In 1980‟s, the economies of the Latin American countries

mostly suffered the grave problems of budget and BOP deficit, inefficient government

enterprises, protection policies, high inflation etc. However, the policies suggested by

the ‘Washington Consensus‟ were to be carried out in the developing countries where

the markets were imperfect and state intervention was enormous. As such the results

were not encouraging. Montek S. Ahluwalia, Nicholas G. carter and Hollis B. Chenery

(1979) also emphasize on the dimensions of poverty and inequality which formulating

policies for growth and development.

According to Afxentiou (1990), the unattended and uncontrolled poverty is a

threat to the social world fabric since this creates tensions among classes that may get

out of state control. The structure of asset ownership has led to more inequality of

income and wealth in many countries. It has prevented the poor to reap their shares

from growth benefits. The international organizations like ILO and the World Bank

advocate a re-orientation of development policies to deal with the problem of poverty

directly. Hence, emphasis on fulfillment of basic needs became a slogan in all

developing countries in late 1970‟s and early 1980‟s.

19

Joseph E. Stiglitz (2001) argued that the trickledown was a phenomenon that

never materialized in the democratic world. While quoting the example of USA, he noted

that although meaningful reductions in poverty cannot be attained without robust

economic growth, the converse is not true, i.e. it is not necessary that growth will

necessary benefit all. He showed that many third world and the transitional economies

were badly affected due to the haphazard process of privatization, liberalization and

stabilization initiated by the International Monetary Fund (IMF) and World Trade

Organization (WTO). He also advocated a reforms agenda for the betterment of life in

these countries where protests of trade unions and citizens from all walks of life were

going on against the strategy of liberalization.

Arvind (2006) notes that there is a wide realization among different segments of

the developed countries that India‟s economic growth has failed in reducing poverty. He

further states that poverty and inter-state disparities have increased over time. Even in

the presence of high growth rate, interregional disparities in income and poverty have

increased overtime.

The main focus of the capabilities approach as proposed by Amartya Sen (1995)

focuses on capabilities rather than consumption. He gave theoretical framework for the

provision of priorities in public policies. He suggests that social arrangement ought to be

evaluated keeping in view the level of freedom to enhance the valuable capabilities of

citizens. He refers to Alfred Marshal (1890) for the concept of human capital as the

personal wealth that includes all the energies, qualities and skills of the individual.

Likewise, Fisher (1906) had argued that labor participation in production was helpful for

20

enhancement of economic growth and poverty alleviation. He also draws upon Blaug

(1972) who stated that educated person was better in production process due to his

capabilities as compared to the uneducated person.

According to the UNDP report (2004), people are the real wealth of nations.

Indeed, the basic purpose of development is to enlarge human freedoms. The process

of development can expand human capabilities by expanding the choices that people

have to live full and creative lives. And people are both the beneficiaries of such

development and the agents of the progress and change that bring it about. This

process must benefit all individuals equitably and build on the participation of each of

them. Capability refers to a person‟s freedom to promote or achieve valuable

functioning.

2.3 The BNF Approach to Poverty Alleviation

According to Dudley Seers, (1969; 1972), due to the diminished faith in the

trickle-down effect, it was widely held that per capita GNP was not a proper yardstick to

measure development and an index of social welfare of the masses. As mentioned

above, the basic needs approach gained popularity and adopted as a strategy for

economic development during the late 1970‟s and early 1980‟s. The strategy focused

on essential needs necessary for long term physical well being, usually in terms of

consumption goods. In the words of Ghai (1977), efforts were made by the International

Labor Organization (ILO) at the „World Employment Conference 1976‟, which aimed to

redesign the global order through strategies that made the fulfillment of basic needs of

the poor the central focus for national and international efforts.

21

Streeten (1980) defends the Basic Needs Fulfillment approach by defining it as

the one that spells out (in considerable detail) human needs in terms of health, food,

education, water, shelter, transport and non-material needs like participation, culture,

identity and a sense of purpose in life and work, which interact with the material needs.

Further, Streeten says that “if we judge policies by reduction of suffering, the criterion of

basic needs fulfillment scores higher than that of reducing inequality, eliminating or

reducing unemployment or alleviating poverty.”

The basic needs approach is also used for the measurement of absolute poverty.

It attempts to define the absolute minimum resources necessary for long-term physical

well-being, usually in terms of consumption goods. consumption is considered a better

indicator than income as mentioned by A.V. Banerjee and E. Duflo (2006) , and Deaton

(2004). The poverty line is then defined as the amount of income required to satisfy

these needs .these personal needs include food, shelter, clothing, public service, health,

education, and safe drinking water etc. However, different agencies use different lists of

basic needs for the purpose of constructing development indices.

Hicks and Streeten (1979) are of the view that the use of social and human

indicators is most important supplement to GNP, especially if work on social indicators

is done in areas central to the basic needs approach, specially the human capital, which

could be instrumental in increasing productivity and growth in output. They are of the

view that GNP per head and related concepts are used to measure development

process in developing countries, whereas these are not sufficient indicators of

development and poverty elimination and fulfillment of basic human needs are goals

22

that should show up in a measure of development. Some early studies describing the

growth poverty nexus cast doubt on the capabilities of growth in reducing poverty,

unless expanding GNP is not complemented by social and human factors, it fails to

translate to the poor.

Some critiques also arise from various quarters of development economics. Main

attack on basic needs approach is that it is mighty difficult to identify a universal set of

basic needs which vary country to country and man to man. According to Weigel (1986),

a main question in the disagreement surrounding the basic needs approach to

economic development concerns the complicatedness of discovering a universal set of

basic needs which is capable of cross-cultural application. He further argues that the

apparent theoretical infeasibility of the basic needs approach stems from well-known

deficiencies in our current stock of economic, political and ethical paradigms,

particularly in the presence of rationality assumption.

Another critique states that by emphasizing activities that are essentially

consumption oriented, the basic-needs approach implies a reduction in the rate of

growth. However, Norman Hicks finds that the countries, which had performed well on

basic needs in 1960‟s had also shown growth rate above the average during the spell

1960-1973 and amelioration in basic needs during the same spell are also mutually

correlated with accelerated growth rates of GNP. Further, he ruled out the case that

higher growth in output was a cause of better basic needs conditions.

23

2.4 Empirical Studies on the BNF Approach

The existing literature in the area of basic needs fulfillment also includes a few

empirical studies, besides theoretical underpinning. These studies have been carried

out for different countries for different time spans and space and covering different

dimensions of basic needs. The wok of Stewart (1980) is worth mentioning where he

explored two observations that favored BNF. The first is the pattern of growth and the

resultant distribution of income that manifested an egalitarian labor deepening patterns

of growth, catering for the purchasing power of the poor to ensure sufficient nutrition

and others requirements for them. The second observation is on the nature of

government interventions that is necessitated in order to provide basic needs in the

form of both the levels of services and subsidies. The aim of such provision of services

and their distribution is to increase the income of the poor.

Hopkins and Hoeven (1983) investigated the role of economic growth and

income distribution in determining the basic need requirements. The study introduced

four basic determinates in this context. These included the level of per capita GDP,

number of years lapsed after independence, dependence on material exports and the

rate of economic growth and income distribution. According to the study, greater

number of years after getting independence could be associated with good

performance. On the other hand, dependence on mineral exports could be associated

with poor performance. The paper disclosed that both, rate of economic growth and

income distribution were insensitive to basic need performance. The study suggested

that policies promoting income distribution did not positively reduce a country‟s ability to

24

meet basic needs and that just acceleration of even appropriate economic growth would

not be dependable tool to solve the problem of poverty.

Hassan (1997) explored the correlation between basic needs and GNP per

capita for seven Muslim countries at three points in time (1987, 1990, and 1994). The

study used Basic Needs Gap Index (BNGI) as proxy for basic needs. This variable was

used as dependent variable. BNGI measures the extent by which the mean

consumption level of the poor, which equals their mean income, saving assumed to be

zero falls short of the mean national expenditure on basic needs. The study used

income, growth of income, net workers remittance and defense expenditures as

independent variables. Five basic needs food, clothing, shelter, Medicare and education

were included in the index. It measured the difference between the expenditure on

these basic needs by poor people and the mean national expenditure on them as a ratio

of the later on each year on a 0-1 scale. The study showed no correlation between the

BNGI and the GNP per capita or its rate of growth at any point in time. More over the

study ascertained that BNGI varied considerably over time and among countries. The

study found inverse relationship of BNGI (BNGI directly) with the pace of growth

between time splits 1987-90 and 1990-90. The study recommended multidimensional

sustained effort and political will and government intervention to carry into effect the

agenda of meeting basic needs.

Kipanga (2007) attempted to find the determinants of BNF based on data taken

from 27 developing countries from 1990 to 2002 in three groups as (1990-93, 1994-97,

1998-2002). The paper also used BNGI as the proxy of basic needs fulfillments. The

paper employed OLS pooled least squares techniques to find the determinations of

25

BNF. The study performed three types of analyses, firstly, the study made comparison

through the use of BNGI performance group averages, secondly cross country analysis

was done; and lastly the study employed multivariate regression test for variables.

Shirazi (1995), assessed the impact of various factors including Sadaqat on

poverty. Results conclude that the more Sadaqat to the poor, the less probability of a

household being poor. The Study also found that probability of a household being poor

was negatively related to the number of earners, educational level of the head and it

was positively related to the household size. Household in Punjab are relatively poor.

There are mixed feelings about the causes of poverty in developing countries.

Hassan (1997) and Navaratnam (2003) are of the view that affluent class is not ready to

sacrifice their growth prospects. They are least concerned with the living condition of

poor and lower middle income class. Most of the existing literature on the assessment

of basic needs fulfillment is concerned with reduction of poverty and inequality and very

few studies on this topic cover the basic needs approaches which provide basic tools to

analyze the relationship between BNGI and inequality.

In this research, the focus will be to analyze and compare the situation of basic

needs fulfillment in different parts of Pakistan. This study is very important because the

basic needs approach is envisaged as a dynamic instrument of development within the

framework of the current international economic order. This approach clearly tells

whether the benefits of development are passed on to the poor or otherwise. In

Millennium Development Goals (MDGs) redistribution of income along with growth is the

policy prescription for the developing countries.

26

CHAPTER 3

THEORETICAL BACKGROUND

This chapter is devoted to a brief review of different concepts of economic

development and their inter-relationships. The objective to provide a rationale for this

study while focusing on the Basic Needs Fulfillment approach (BNF).

3.1 Growth, Development and Income Distribution

Growth and development are the closely related terms that convey more or less

the same message to the general reader. However, where growth theory concentrates

on the factors responsible for uplifting the gross and per capita incomes, the theory of

development focuses on the overall socioeconomic structure and institutional set-up

that move ahead with the passage of time. The growth rate of income is central to the

process of economic development. The relationship between growth and development

resembles that of an engine and the carriage. Following the impetus of growth in

income/ output, the entire social and institutional structure of an economy begins to

improve in all directions. If the growth process sustains overtime, the social structure

moves gradually towards modernization, democratic attitudes, broadness in outlook

along with equity in distribution, reduction in poverty and general improvement in the

standard of living.

If we look at the very concepts of economic growth and development in

chronological order, it is revealed that they gained tremendous attention in 1950‟s after

the World War II. The cherished contributions of the Noble Laureate Arthur Lewis and

27

others are worth mentioning in this regard. Heavy emphasis was laid on economic

growth in the capacity of being a powerful tool for poverty eradication. The term

economic growth was perceived as continuous increase in the volume of goods and

services, measured by the GNP per capita. The underlying rationale was an

improvement in the quality of life through the trickle-down effect. Lewis (1954)

contended that inequality was good for development and economic growth, since only

rich could save and invest; and saving was necessary for capital formulation, which in

turn would lead to high employment, elimination of poverty and uplifting the standard of

living. The major driving force behind this phenomenon was presumably the structural

changes that occurred because of labor migration from poorer and less productive

traditional sectors to more productive industrial sectors.

Another Nobel Laureate, Simon Kuznets (1955) argued from the historical facts

of European development that with constant growth of per capita income overtime, the

inequality gets worsened in the initial stage but gets improved later on. This line of

thinking got popularity under the title of inverted-U hypothesis, which was supported by

a number of studies conducted by prominent researchers like Adelman and Morris

(1971), Ahluwalia (1974), Robinson (1976) and Ram (1988). However, the empirical

evidence collected later on as well as the experience during the later half of 20th century

did not support the inverted-U hypothesis. For instance, Deininger and Squire (1996)

attempted a comprehensive test and confirmed that there was no systematic

relationship between growth and inequality for individual countries.

28

The trickle-down effect in growth oriented approach didn‟t materialize because

the distribution side was altogether missing in that strategy. By now, there is sufficient

consensus among the development economists that the mere growth of income may be

necessary but never a sufficient condition and therefore a true representative of

development. Instead, alleviation of poverty, reduction of inequalities in income

distribution and the fulfillment of basic needs are the objectives that signify the extent of

development. The focus of research shifted gradually during 1970‟s onwards from

„GDP growth‟ to „growth with distribution‟ to „poverty reduction and basic needs

satisfaction‟, while suggesting strategies for economic development. Now the

distribution pattern and human capital receive more attention of researchers in the area.

Anderson and White (2001) express the pattern of growth and distribution in the

following words:

“The growth of income can be decomposed into a growth effect and a distribution effect.

Using data from 143 growth episodes, it is found that the growth effect dominates, but

distribution is important in a significant minority of cases. In over a quarter of cases,

distribution played a stronger role than growth in increasing the income for the poor.

Moreover, if there is no systematic relationship between growth and distribution, then it

is clearly better to have growth that is pro-poor rather than not in order to achieve

international poverty reduction targets.”

The purpose of sustainable economic development is to look for human

development and quality of environment. Sustainable development means use of

resources today by the people to satisfy their needs and improve their quality of life in

29

the present while safeguarding the ability of future generations to meet their own needs.

Current developments should not be on the cost of future generation. A better quality of

life means a higher standard of living which universally is measured in terms of income

level consumption expenditure and uses of available resources and technology. There

is a principle of equity Inherent in the concept of sustainable development. In order to

achieve economic and environmental goals, social goals – such as universal access to

education, safe drinking water, health care and economic opportunity – must also be

achieved, which increase quality of life and fruits should be distributed equally with the

future generations.

The senior vice president, chief economist of the World Bank and the winner of

the Nobel prize for economics 2001, Joseph E. Stiglitz in his book titled “Globalization

and its Discontents” –(2001) argues that, trickledown economics was never much more

than just a belief, an article of faith. He quotes the example of America:

“The economy grew in the 1980‟s and those at the bottom saw their real incomes

decline……..It is true that sustained reductions in poverty cannot be attained without

robust economic growth, the converse is not true: growth need not benefit all. It is not

true that „a rising tide lifts all boats‟ Sometimes a quickly rising tide especially when

accompanied by a storm that dashes weaker boats against the shore smashing them to

smithereens.”

3.2 Poverty and Income Inequality

Despite significant improvement and growth over the past half a century, extreme

poverty and gross inequalities remain widespread in the developing world. The fruits of

30

development have failed to reach the poor segments of the society, while the rich and

elite class is the prominent beneficiary. The reason is obvious. If the richer class owns

most of the productive assets (including agricultural lands), leading to higher income

and propensity to save and invest, they will naturally reap the benefits of growth through

the normal market mechanism based on towards functional distribution. The rents and

rentals of the physical assets as well as interest and profits of the financial capital and

business will accrue to this class that comprises the landlords, capitalists and business

class. The labouring class will reap the benefits of its time rented out in the form of

wages. The factor of production called „labour‟ is the weakest of all other factors since it

is human time that evaporates and cannot be stored for some useful employment.

Therefore, inequality is likely to increase with the process of growth and poverty has to

sustain overtime.

In fact for many countries, as noted by Todaro (2003), there is no particular

tendency for inequality to change much at all in the process of economic development.

Inequality seems to be a rather stable part of a country‟s socioeconomic make-up. This

inequality cannot be corrected peacefully and through a democratic process since it

involves a redistribution of productive assets. This can be possible only through forces

working outside the premises of market mechanism, through substantial upheavals and

social revolutions as in the case of former USSR, China, Japan, Tiawan and Korea.

It was asserted in the „Program of Action‟ at the Cairo International Conference

on Population and Development (1994) that “despite decades of development efforts,

both the gap between rich and poor nations and inequalities within nations have

31

widened over time;…….., and that the widespread poverty remains the main challenge

to development efforts”1.

Poverty is a multidimensional phenomenon and it requires multidimensional

policies and programs to eradicate it from this planet. Well-being of the individuals has

been conceptualized within multiple paradigms. It can be considered as command of a

person over resources and opportunities to earn. The people may be more

economically well off if they have more opportunities to manipulate and to enjoy the

commodities like food, clothing, and shelter, and other essentials of life. In this situation,

people will be less vulnerable to weather-shocks and income variations. Thus poverty

means either lack of command over commodities in general or a specific type of

consumption that deems essential for a reasonable standard of living in a society or lack

of an ability to function in a society.

Poverty is a social evil and it is generally understood as hunger, squalor, no

proper shelter, no access to education and health facilities, no security and access to

justice. Some time the lack of freedom of expression, the shortage of time and

restlessness is also considered as poverty. Wealth is useless if an individual cannot

purchase peace of mind and tiny pleasures of life. Defining poverty objectively is a futile

exercise because social researchers and reformers never agreed on a common line

which covers all the dimensions of poverty.

1 Referred to by Todaro & Smith: Economic Development – 8

th Edition (2003)

32

Poverty influences and is influenced by so many factors, in other words it is

caused by so many factors where as it also gives birth to many evils. Poverty prevails

unevenly among regions of developing world. It is observed that poverty is found more

in certain groups. Women and children are more victim of poverty. The incidence of

poverty is also observable among ethnic group and minorities. The term “Poverty„‟ in its

immediate sweep, is however, concerned with the poor and proletariat. Strictly

speaking, poverty may be defined as the proportion of people whose income falls below

a specific poverty line, generally known as head count ratio, the income gap. Poverty is

a deprivation of essential assets and opportunities to which every human being is

entitled. A deep postmortem of poverty reveals that it is not merely the command over

goods or services or calories intake; rather it is more complicated phenomenon. Thus

one can think of poverty from a non monetary2 perspective while measuring its different

components.

Another interesting characteristic of poverty is that sometime low incomes go side

by side with other forms of dispossession. For example in Mexico life expectancy for

poor 10 percent of population is 20 years less than for the richest 10 percent (Meir).

Poverty differs from inequality; poverty defines the absolute standard of living of a part

of society whereas inequality points to relative living standards across the whole

society. If a person has all income of nation, this is an example of maximum inequality.

2 Non Monetary approaches to measure poverty include Calories consumed per person per day, Food

consumption as a fraction of total Expenditure, Measures of outcomes rather than inputs and

Anthropological method.

33

In such case poverty would be high. Conversely, if all have same income there will be

no inequality, but poverty may be zero or maximum.

According to Afxentiou (1990), the unattended and uncontrolled poverty is a

threat to the social world fabric. Unattended poverty creates tensions among classes

and countries. The structure of asset ownership and its associated economic and

political power has led to more inequality of income and wealth. Supposedly, this

structure of asset ownership prevents the poor from benefiting from growth. The

objections against these economic disparities were raised by international organizations

like ILO and the World Bank. These advocates of re-orientation of economic

development stressed upon dealing with the poverty directly and gave this process the

name of basic needs. Hence, concentration on the basic needs became a slogan of

action, and this was referred to as a new theory of economic development by its

exponents while others termed it modestly as a new approach to development. In

subsequent period i.e. late 1980‟s, another approach Human Development also gained

popularity and given considerable weight.

3.2.1 Measuring Inequality

Inequality in income distribution can be measured in many ways. The „Personal

or size-distribution‟ looks at the gross income received by the individuals, irrespective of

whether it is earned or un-earned and the way it is received. A common method is to

divide the total population into successive quintiles or deciles according to ascending

income levels and then to estimate the proportion out of aggregate income each group

of people is deriving. The income shares of different groups are then mutually compared

34

to have an idea of inequalities in distribution. For instance, the income share of top 20%

of population (highest quintile) is compared with that of the bottom 20% (lowest quintile).

A more dis-aggregation, for instance deciles instead of quintiles, gives a clearer picture

of inequality in distribution.

A commonly used aggregate measure of inequality is the Gini Coefficient, which

is the ratio of the areas covered by the line of equality (hypothetical distribution) and the

Lorenz curve3 (actual distribution) to the total area beneath the line of equality, taken as

unity. The value of this coefficient lies between zero and unity. Thus a value nearer to

zero reflects more equity in distribution and a value nearer to unity shows the converse.

The coefficient of variations may also be used as a measure of inequality. Despite a few

short comings, the Gini coefficient is considered as the best indicator of macro-

inequality, particularly across countries and geographical regions.

3.3 Different Approaches to Poverty

A specific minimum level of income needed to satisfy the basic physical needs of

life (food, clothing and shelter etc) in order to ensure continued survival is defined as the

poverty line. Some problem however, arise when we recognize that the minimum

subsistence levels will vary from country to country and society to society, and even

individual to individual; reflecting different physiological, social and economic

requirements. One common methodology to avoid these problems is to define an

3 As referred to by Todaro (2003), Conard Lorenz, an American Statistician devised the convenient and

widely used diagram/curve in 1905 to show the relationship between population groups and their

respective income shares.

35

international poverty line and then attempt to estimate the purchasing power parity or

equivalent of that amount in terms of local currency. This amount was estimated by the

World Bank as US $ 370 per individual (1993 constant dollars) on annual basis, or 1.08

dollars per day. This became popular and commonly referred to as 1$ per day or

equivalent amount as a general yardstick.

This approach to poverty in terms of the basic needs fulfillment became popular

in the 1970‟s. The concern shifted from inequalities in distribution to the eradication of

absolute poverty, particularly by concentrating on basic human needs. It was generally

recognized that mass poverty can coexist with a high degree of equality and it is

possible to find ways and means to reduce mass poverty even if gross inequalities

prevail. Eradication of poverty through the provision of basic needs became the popular

political slogan4. Extensive Research started on this line of thinking with the pioneering

work of Norman Hicks and Paul Streeten (1979)5. In contrast to the common mass

perception of the basic needs as well as political slogan (food, clothing and shelter), the

economists have generally concentrated on identification of the measurable basic

needs to include food, education, health, clean drinking water and sanitation; on the

different indicators to be used for the assessment of basic needs and on the estimation

of cost involved for provision of these needs to the society at large.

4 Roti, Kapra, Makan was the slogan of PPP in 1970 elections of the „united‟ Pakistan.

5 “Indicators of Development: The Search for a Basic Needs Yardstick” – World Development Volume 7

(1979)

36

According to United Nations Development Program (2004), people are the real

wealth of nations. Indeed, the basic purpose of development is to enlarge human

freedoms. The process of development can expand human capabilities by expanding

the choices that people have to live full and creative lives. And people are both the

beneficiaries of such development and the agents of the progress and change that bring

it about. This process must benefit all individuals equitably and build on the participation

of each of them. Capability refers to a person‟s freedom to promote or achieve valuable

functioning.

The main focus of the capabilities approach expounded by Amartya Sen focuses

on capabilities rather than consumption. In his book “Inequality re-Examined” (1995), he

gave theoretical framework for the provision of priorities for public action. He argues that

the evaluation of the social arrangements should be made according to the level of

freedom of citizens to enhance the valuable functioning; and the main objective of this

development is the enhancement of valuable capabilities. Marshal (1890) defined the

human capital as person‟s personal wealth which included all his/her energies, qualities

and skills which had helped for his/her economic activities. Fisher (1906) said that labor

force participation in production was considered as a capital, which was helpful for

enhancement of economic growth and poverty. Solow (1956) analyzed long run growth

model. According to this model, countries use their resources efficiently and their return

diminishes as capital and labor input increase. Blaug (1972) stated that educated

person was better in production process as compared to the uneducated person

because only skilled worker had capability to increase production as he envisaged the

problems in better way and solved them.

37

3.3.1 Assessment of Poverty

To draw a line between poor and non poor is cumbersome and odd job for the

economists. So far many efforts have been made by the researchers to explain the

phenomenon of poverty and to assess its magnitude, breadth and depth. However,

there is no universal agreement among the researchers on drawing such a line of

demarcation, or the so called poverty line. As defined above, the „income poverty line‟

refers to the income sufficient enough to purchase the minimum basic needs, and this is

considered as the indicator of welfare in developed countries. But the case of

developing countries is much different where poverty is intense and distribution is more

asymmetric. In these countries, the consumption per capita is often considered to be the

preferred estimate of welfare. Some researchers regard the expenditure on

consumption per adult equivalent to be the appropriate poverty line in order to capture

differences in basic needs due to age. Other popular measures of welfare include the

calories consumption per person per day, food consumption as proportion of total

expenditure and nutritional status.

All the measures of poverty are evaluated in relation to some norms. For

example, we deem life expectancies in some countries to be low in relation to those

attained by other countries at a given date. The concept of poverty has evolved

historically and varies largely from culture to culture. Keeping in view the specific

national priorities and normative concepts of welfare, every country has its own criteria

to distinguish poor from non poor. The perception of an acceptable minimum level of

consumption tends to change as countries grow and become richer. The most popular

38

approach to measure wellbeing of people is to define a poverty line based on

expenditure function. It defines the minimum amount of resources required for achieving

a given standard of living. There are two approaches to construct poverty line. In the

first approach, poverty line is based on the household size, and it is adjusted to the

price differentials people face in different parts of the country, and also to the

demographic composition. The second approach is to draw a single poverty line for all

individuals. The per capita poverty line or level of income is then adjusted for

differences in prices and household composition. Therefore, it is pertinent to elaborate

different types of poverty lines. The common international poverty is roughly $1 per day

at 1993 purchasing power parity (PPP).

(i) Absolute Poverty line

Absolute poverty line is given in terms of standard of living. It takes into account

the minimum level of needs that are deemed necessary for survival. The poor are then

cut-off as those who do not possess this income. Here problem arises that there is no

common and concurrent definition of standard of living. All absolute poverty lines are set

in terms of goods and services. The commodity based poverty line is defined by Z;

which is a measure of the indirect utility. If utility is derived from commodities and

commodities demand depends on purchasing power, then utility level depends entirely

on this power, while assuming the prices to remain stationary:

( )U f Y or 1( )Y f U

39

This implies that for a specific level of utility, there is some income or purchasing power

or an effective expenditure level in the background (defined by Z) that is must to

achieve the specific utility level. If the quantity of goods or the level of utility so

attainable is sufficient to avoid poverty, then

1( )ZZ f U

Putting the same things in the other way round, given a poverty line that is absolute in

the space of welfare (i.e. gives Uz) there is a corresponding absolute commodity based

poverty line. Certain problems are associated with the commodity based poverty lines,

which are summarized below:

1. The referencing problem: what is the appropriate value of (Uz), the utility line.

2. The identification problem: given (Uz), what is the exact value of (Z) the

commodity value of poverty line?

For given UZ (standard of living) there is a corresponding absolute commodity based Pz.

Absolute poverty line does not vary from country to country, hence is a good tool to

compare poverty across countries.

40

(ii) Relative Poverty Line

This measure defines „poverty‟ in terms of amount tat falls below some relative

poverty threshold expenditure. According to Peter Townsend ( ), poor are poor

because their actual resources fall short of the resources held normally by other

individual in family or the society in which they live. The relative approach to define

poverty takes a moderate view. When society transforms (grows) overtime, the

perception of necessities fulfillment and hence the perception of wellbeing also

changes. Hence, if income of every household within a society increases uniformly but

the mechanism of income distribution remains the same, the perception of relative

poverty will also remain unchanged. Relative poverty line is revised upward as country

becomes well off. This form of poverty line varies from country to country.

(iii) Objective poverty line

Objective poverty line is set such that it enables an individual to achieve certain

capabilities, including health, active life and full participation in the society. Two

methods have gained popularity in recent years to develop the objective poverty line.

These are, food energy intake method, and the cost of basic needs method.

Food Energy Intake Method

According to this method, a certain level of consumption expenditure/ income is

arrived at that allows the household to obtain enough food to meet his energy

requirements.

( )k f Y which implies 1( )Y f k

41

Where, k is the level of adequate food energy intake sufficient at the margin. Now given

the minimum adequate level of calories intake mink , we have.

1

min( )Z f k

The shortcoming of this approach is that this method has no potential to concede

the relationship between food energy and income, hence suggesting the same poverty

line for urban and rural areas. However, a large number of researchers have arrived at

the conclusion that for a specific level of food intake, the poverty line in the rural areas

would be lower than in the urban areas. This is termed as the rural-urban problem.

Secondly, a rise in the relative prices of food items leads people to shift away from food

to non-food consumption. This results in poverty line to rise up. This is known as the

relative price problem.

The Cost of Basic Needs Method

The cost of basic needs method takes into account a consumption bundle that is

deemed to be adequate, comprising both food and non food components, and then

estimates the cost of this bundle for each sub group (urban rural, each region etc.). In

this approach, poverty line is calculated in the following manner.

1. The nutritional requirement for a healthy person give by 2100 to 2300 calories per

day is considered uniform for all.

2. The expenditure required to meet this food requirement is estimated. It uses a diet

that reflects the habits of household who are near to the poverty line. This food

42

component may be denoted by FZ , the calculation of which may not be easy,

particularly if diets and prices vary widely across the country.

3. Non food component ( NFZ ) is added and the basic poverty line is symbolized as:

BN F NFZ Z Z

No concordant method has yet been developed to measure the non-food component of

poverty line. However, the poverty line developed for South Korea (KIHASA 2000)

measures the cost of non-food items, as the average spending by the household in the

poorest two fifth of the income distribution.

Ravallion (1998) measured an upper poverty line by an assessment of the

income level at which the household would buy 2100 calories of food and other

necessary non-food items, and a lower poverty line by measuring the income level at

which households could just buy enough food and have no money left to buy non food

items. Given the two extremes; a household may typically buy the non-food items so

where in between. Ravallion, suggests that one might compromise on measure of the

non-food items at the midpoint of these two extreme.

This objective poverty line approach is criticized on various grounds. Due to

limitation and shortcomings, the idea of a subjective poverty line was introduced. Under

this approach, people are asked to define poverty line and the extent of poverty is then

measured by using this line. This entails different answers from different persons

according to their preferences. These can be plotted in order to get a best fit line

through econometric techniques.

43

Guarau Datt of World Bank has analyzed the Philpine data and concluded that

self rated poverty lines are high. They have climbed up rapidly overtime. The self rated

Pz (poverty line) given by poor household are slightly lower than those of the non poor

households. There is clear urban/ rural difference in the perception of the poverty line.

With the urban households setting, a money poverty line is estimated at about twice the

level of rural households. A plain explanation for this behaviour could be that inequality

prevails more in urban areas, which raises the expectations of urban people.

The choice of poverty line affects the qualification of poverty. All approaches

yield different pattern of change in poverty resource line to a great extent. For example,

in Pakistan, the policy makers do not consider a minimum standard for all citizens as

policy goal because resource constraints do not permit them act likewise.

3.3.2 Properties of Poverty Measures

Before going to discuss various measures of poverty it might be appropriate to

discuss briefly the desirable properties of a good measure. A good measure of poverty

should carry the following characteristics.

Focus: The poverty index should be independent of the income of the non poor.

Symmetry: The index should not change if any two individual exchange

incomes.

Population Invariance: Adding up or subtracting two or more identical

population should not change the poverty level. This means that poverty remains

unchanged if identical populations merge together.

44

Monotonicity: This property has three dimensions.

Poverty level must not decrease if poverty line is shifted upward.

If the proportion of poor people increases then poverty should increase.

An increase in the income of poor should decrease the poverty index.

Transfer: This property was propounded by Sen (1973). According to the

principle a regressive transfer of income between two poor people should

increase the poverty index and vice versa. The principle requires that a

progressive transfer of income between two individuals, that moves the recipient

across the poverty line, should decrease the poverty index and vice versa.

Additive Decomposability: This property implies that the poverty index should

be such that overall poverty could be related to the part of the population.

Define Limits: The poverty index should be such that it has well defined limits. In

order to make comparison easy. Generally the value of poverty index falls

between 0 and 1, where zero reflects no poverty and 1 reflects perfect poverty.

Once the poor have been distinguished from the non poor, the problem creeps up as to

how to measure poverty. Also how much poor are poor? The subsequent section of

study serves the different measures of poverty.

3.4 Measures of Poverty

Prominent economists an researchers have devised different statistics for measuring

the extent of poverty on national/regional level. Here we discuss the most important

formulations.

45

(a). Head Count Ratio (HCR)

The „Head count ratio‟ expresses the number of poor as a proportion of the

population living below the poverty line. It is denoted by Po, Mathematically,

0

1( )

p

i

NP I Y Z

N N

N= Total Population. Np= No of poor, Z is poverty line, Yi is income of household.

I(.) is an indicator function that takes on a value of 1 if the expression (Yi < Z) holds

true and zero otherwise. If Yi falls short of poverty line (Zi), then I(.) equals to 1 and

household will be reckoned as poor. The big advantage of HCR recognized by

researchers is that:

It is plain (easy) to construct;

It is simple to use; and

Its interpretation is not fuzzy as this measure has well defined limits.

However, this measure is not free from demerits. It disadvantages are:

It has nothing to do with intensity of poor or depth of poverty; this implies that

head count ratio does not translate clearly as how poor they are.

It violates the principle of transfer badly, transfer of income between two or more

households does not change the status of poverty that is, and they remain below

poverty line.

46

The estimates by HCR are not computed for households rather these are

calculated for individuals. In order to calculate the percentage of household it is

assumed that all household members maintain the same level of well being.

(b). Poverty Gap Index

Poverty gap index indicates the extent to which individuals fall below the poverty

line (by whatever way it is defined). It is denoted by P1, and expressed as under:

1

1 NGP

N Z

NG is the income gap and it measures the intensity of poverty it is given as:

( )( )N i iG Z Y Y Z

Y is income of the household, and Z is the poverty line.

Value of poverty gap lies between zero and one. A zero poverty gap implies no one is

poor and unit gap indicates that every individual requires an amount of money equal to

poverty line to get the minimum standard of living.

This measure estimates the cost of eradicating poverty, as it spells how much

income households require in order to bring them at a level above the poverty line.

Verily, this index is very helpful in evolving the poverty alleviation budget, by providing

the valuable information about minimum resources required dealing with the precarious

situation of poverty. This measure however, lacks to assimilate the effect of such

income transfer between poor where transfer does not make anyone non poor. This

47

measure potently satisfies the axiom of monotoncity and violates transfer property and

could not assign a greater weight to the income gap of poorer person.

(c) Squared Poverty Gap

The Squared poverty gap is just a weighted sum of poverty gaps, where weights

are the proportionate poverty gaps themselves. It is denoted by P2. Mathematically,

2

2

NGP

N

, where G is income gap given by:

( )( )N i iG Z Y Y Z , where Z is the poverty line.

This index solves the problem of inequality among the poor as more weight is given to

poorer amongst the poor. This measure has no upper limit making it difficult to interpret.

(d) Foster, Greer-Thorbecke Index (FGT)

Foster, Greer and Thorbecke (1984) introduce a more flexible and plausible

poverty measure. This measure is flexible and can readily be made more or less

sensitive to poverty. This index can be disaggregated for population sub groups.

Mathematically

1( 0)

a

n

a

GP where a

N N

where ( )( )n i iG Z Y Y Z and is measure of sensitivity of the index of poverty. Z is

the poverty line. HCR, PGI and SPGI are limiting cases of FGT, such that 1 , it gives

48

the poverty gap. 2 it becomes the squared poverty gap and so on. FG index can be

disaggregated for population sub groups and the contribution of each group to overall

poverty can be worked out. FGT has following distinct properties.

For all 0 the measure is strictly decreasing in the living standard of poor.

For 1 it implies the poorer one is, the increase in his measured poverty due to fall in

the standard of living will be deemed greater.

The measure is strictly convex in income and weakly convex for 1 .

FGT index satisfies the properties of monotonicity and transfer for 1 and violate the

transfer sensitivity axiom at 1 but satisfies this property for 2 . The FGT

measure is also additively decomposable with population share weight.

(e) Sen Index

A.K. Sen (1973) put forward an index which contains the effects of the number of

poor, depth of their poverty and poverty distribution with in the group symbolically. It is

given by:

0 1 1p

s pP p GZ

The definitions are as under:

P0= Head count Ratio (HCR)

P = Mean income of poor

49

PG = Gini coefficient of inequality among the poor, and 0 1PG

The values of Sen index lie between zero and one: 0 1SP , 1SP means everyone is

poor and 0SP means no one is poor. Different factors contribute individually to the

Sen index. The income gap represents poverty as measured by the proportionate gap

between the mean income of poor and the poverty line income. The Gini coefficient

measures the inequality among the poor and head count expresses the proportion of

population below poverty line.

Sen Index is considered as a comprehensive indicator of poverty, however it

cannot be decomposed into its constituent components. Therefore, it is rarely used in

practice. The Sen Index satisfies the properties of monotenity and transfer and has the

ability to give greater weight to the income gap of poorer person but it violates both

transfer and sensitivity axioms.

(f) The Sen- Shorocks-Thon Index

Another index satisfying all desirable properties is the Sen-Shorock-Thon index.

It is given as:

0 1 1SSTP P P G

This index is the product of HCR, PGI and the Gini coefficient. The Gini coefficient is

close to indicating inequality in incidence of poverty gap. It is a decomposable measure.

All measures of well-being and poverty are imperfect, but this is not an argument

to avoid measuring poverty. Instead one has to do this practice but with caution.

50

Inconsistency between two indices can be illustrated by comparison of India and

Indonesia. Both countries differ only negligibly in terms of Gini index (India=32.5,

Indonesia=34.3). However, 79.9% of the Indian population lives on below $2 a day,

while 52.4% of the population of Indonesia subsists at the same level of poverty (World

Bank, 2004a:55, 61).

(g) The Basic Needs Gap Index

Paul Streeten is considered as the pioneer of the basic needs fulfillment

approach. For the first time, he described why basic need policy, the feasibility of its

implementation, the search for a suitable yardstick to measure the role of governments

and political activists in its administration and management is crucial. Streeten (1980)

defends this approach by defining it as the one that spells out (in considerable detail)

human needs in terms of health, food, education, water, shelter, transport and non-

material needs like participation, culture, identity and a sense of purpose in life and

work, which interact with the material needs. He says, “if we judge policies by reduction

of suffering, the criterion of basic needs fulfillment scores higher than that of reducing

inequality, eliminating or reducing unemployment or alleviating poverty.”

The Basic Needs Gap Index (BNGI) is used by researchers as a proxy of basic

needs fulfillment (BNF), for instance by Hassan (1997) and Kipanga (2007). BNGI

represents the dependent variable, which is measured by the following formula:

ptntBNG

51

This measures the difference between the mean expenditure on basic needs nt

in the region and the mean income of the poor in that region, pt . The mean income of

the poor generally equals their consumption and therefore the study takes income into

consideration instead of consumption.

When the above equation is expressed as a ratio of nt , this gives the index:

ntptntBNGI ][ , which implies ntptBNGI 1

Generally the income of the poor is less than the mean expenditure on basic

needs ( pt nt ) and the index lies between zero and one. However, in rare cases, it

may happen that income of the poor exceeds the level expenditure on the basic needs

( pt nt ). In such a situation, BNGI will be less than zero. The smaller value of index

indicates better performance and higher value shows the worst condition of the region.

We postpone the construction of BNGI till chapter-4 on data and variables.

3.5 Concluding Remarks

During 1980‟s and 1990‟s the „Washington consensus‟, also known as the “neo-

liberal” policy prescriptions, remained the general advice for the developing countries in

which fiscal discipline, privatization and market liberalization were the main ingredients.

These policies were suggested in response to the real problems in Latin Americas. The

governments concerned were facing the problems of deficit, inefficiency of the public

sector enterprises, enactment of protection policies, high inflation and unemployment

etc. Obviously, there would have been definite gains if these policies could be

52

implemented in letter and spirit in the right direction. But the problem was that most of

these policies became the victim of futile exercise at the government levels rather than

serious efforts leading to more equitable and sustainable growth. The suggested

policies were supposed to be carried out in the developing countries where the markets

were more often imperfect and information system very inefficient that result into the

failure of invisible hand6.

Poverty is a complex phenomenon and it has many dimensions. In case of

Pakistan, as also in other developing countries, majority of people have low income and

they also suffer from lack of access to basic needs. Pakistan is more relying on

remittances to alleviate poverty, which permit families to maintain or enhance

expenditure on basic needs like food, clothing, housing and health. It also allows

masses to increase expenditure on durables and non durable goods, real estates and

on human capital accumulation; and thus to enhance their living standards.

The nutshell of all discussion is to answer three questions. These are:

1)- How can standard of living be increased?

2)- What is meant by minimal standard of living?

3)- Which single index or measure can be used for overall severity of poverty?

6 The free market ideology dates back to Adam Smith who argued that market forces drive the

economy to efficient outcomes. However the theory ignored the highly restrictive conditions of the

perfect competition and complete markets, which are seldom found in the developing world.

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CHAPTER 4

DATA AND VARIABLES

For any empirical analysis, reliable and disaggregated data is crucial. Data

availability and its quality is a major hurdle in testing various economic phenomena. For

better and consistent results, one needs reliable data over a longer period of at least

thirty years. To see the basic needs gap (BNG) and its determinants across different

regions of Pakistan, the appropriate economic unit would be district. We are relying on

the data compiled by the Federal Bureau of Statistics [FBS], Government of Pakistan

under the title “Pakistan Social and Living Standards Measurement Survey (PSLM)

(Provincial/District)” formerly called the HIES. The requisite published data is available

at district level only for three years 2004-05, 2005-06 and 2006-07. Therefore we are

compelled to use the provincial level data of HIES.

4.1 Limitations of the Grouped Data

Although grouped data allows us to see changes in basic needs requirement

over longer continuous periods and to identify the pattern of changes in basic needs

gaps across different regions of Pakistan; however grouped data has certain limitations.

For instance, it displays only the mean income and share of quintiles or deciles. It leads

to the assumption that all the individuals within a group have the same income

inequality, which may not be realistic. Obviously, this may distort the estimates of

income distribution within each region.

The study uses most of the data from HIES/PSLM, which is subject to two types

of errors i.e. sampling and non-sampling errors. To overcome the problem of sampling

54

errors, FBS provides training to enumerators and other staff members. FBS also uses

latest data entry (software) programs equipped with the built-in consistency checks.

To control for the non sampling errors is somewhat difficult job in such mass level

surveys, because most of people are illiterate especially in rural areas who do not keep

any record of their income and expenditure. The goods are most often exchanged

through barter and agricultural products are not weighed properly, which leads to

incorrect estimation of the household income and expenditure. Last but not least is the

exclusion of the poorest household from the sample who are nomads and do not have

permanent dwellings.

We are considering different regions of Pakistan and the data is derived from the

same sources, so the nature of biases (if any) will remain the same, i.e. the sampling

errors will be uniformly distributed across all the regions, and therefore the validity of

results will not be affected.

4.2 Data Sources

As mentioned above, the main source of data for the present study is the

household income and expenditure survey (HIES). The survey started in July 1963 at

national level and continues to date with normal intervals of 3 years with the exception

of some gaps. In 1990-91, the document was renamed to Household Integrated

Expenditure Survey (HIES) and the questionnaires were revised according to the

needs.

HIES is not available for the year 1993-94 (due to the loss of some

questionnaires or for some technical reasons). However the required data for that year

55

used in this study is taken from “Fifty Years of Pakistan”, a publication of the Federal

Bureau of Statistics, Government of Pakistan.

The available Issues are as follows (20 surveys):1963-64, 1964-65, 1965-66,

1968-69, 1969-70, 1970-71,1971-72, 1979, 1984-85, 1985-86, 1986-87, 1987-88, 1991

(only Pakistan- R/U), 1992-93, 1996-97, 1998-99, 2001-02, 2004-05, 2005-06 and

2007-08. This data is available in two formats; the aggregate data in published form (for

all the above mentioned years) and micro data (soft form) available since 1987-88.

For the years 1963-64 to 1971-72 and 1991-92, HIES published data set is

available at national level only, whereas it is available at provincial level with rural

urban bifurcation from the year 1979 onwards. This study covers all the four provinces

(settled areas), i.e. Punjab, Sindh, KPK and Baluchistan. The federally administered

tribal areas, the mountainous northern areas and Azad Kashmir are not included in the

present study due to the non availability of data and differences in economic

characteristics from rest of the country. The four provinces account for major share in

population of the country and provide sufficient information for the purpose of this study.

Another document, the Pakistan Integrated Household Survey (PIHS) published

by the FBS, Government of Pakistan Islamabad (1995-96, 1996-97, and 1998-99)

provides data on the social indicators at the national and provincial levels. The major

indicators for health and education are taken from this survey.

In addition to the above mentioned sources, some other important documents

have also been consulted, like the Pakistan Economic Survey published by the Finance

Division, Government of Pakistan (various issues), the Demographic and Health

56

Surveys published by the National Institute of Population Studies, The Pakistan Labor

Force Survey published by the FBS, Various issues of the Fifty Years of Pakistan

published by the FBS, World Development Reports and World Development Indicators

published by the World Bank, Human Development Reports published by the United

Nations Development Program (UNDP), Data banks from International Monetary Fund

(IMF), and Social Development in Pakistan (various issues) published by the Asian

Development Bank. Different papers and publications are also consulted for data

consistency, which include Amjad. R and Kemal. A.R (1997), Cheema I.A. (2005), Irfan.

M. (2007), Jamal.H. (2006), Qureshi.S.K and Arif. G.M, (2001), Ellahi, Mahboob, Khan

S.R. Rafi (1999), Shirazi N.S (1993), Zaidi S.A (2000).

4.3 Variables Suggested for the Preliminary Model

The preliminary model is discussed in detail in the next chapter. The dependent

variable is the Basic Needs Gap Index (BNGI), which is expected to depend on the

following ten (10) explanatory variables. The model may be written in the general format

as under.

BNGI = f(YPc, SPc, Rem, HS, HE, DR, Un, B20, T2B, HCI)

The intuitive list of explanatory variables is given below. However, it is possible that

some of these variables may be insignificant, which will become clear after conducting

the specification tests.

1. Ypc = average monthly per capita income

2. Spc = average monthly per capita savings

3. Rem. = remittances (domestic and foreign % of monthly income)

4. HS = household size

57

5. HE = higher education (BA/BSc both sexes)

6. DR= dependency ratio

7. Un. = labor force unemployment rate

8. B20= share of income held by bottom 20%

9. T2B= ratio of income of top 20% to bottom 20 %

10. HCI = human capital index

As discussed above, we have derived the relevant data from various issues of

HIES/PSLM. According to HIES (2007-08), household is either a single person or group

of persons, who live together and share cooking and other essentials of living. In the

multi-person household, the individuals may pool the whole amount of income or a part

for general consumption. The main feature is that they reside together and don‟t have

alternative residence. The main sampling unit in all surveys is the household.

Detailed discussion of the concerned data and justification of these explanatory

variables is given below. The construction of dependent variable, being very complex, is

postponed till the next section.

4.3.1 Per Capita Income (Monthly in Rs) [Ypc]

Per capita income is a powerful indicator representing economic growth of a

country. The per capita income is obtained by dividing national income (usually GDP) by

population of a country. It roughly indicates how fast an economy is flourishing. In

contrast to the gross GDP, the per capita income also covers the demographic aspect

of economy and so it is considered to be more comprehensive indicator.

The role of national per capita income in reduction of poverty is one of the most

discussed topics. Many studies found strong statistical association between per capita

income and poverty indicators. According to World Bank (1999), relationship between

58

infant mortality rate and GNP per capita, ratio of female to male literacy1 and per capita

income, and between average consumption and the incidence of income poverty is

negative. This indicates that as per capita income rises, the national poverty indicators

fall. The present study includes per capita income as one of the potential regressors for

the preliminary model.

Per Capita Income for country level as well as for the four provinces with rural

urban bifurcation is shown in Table 4.1. A glance at the data reveals that per capita

income for the economy increases over the study period. This gives the evidence that

GDP grew at higher rates than population. Interestingly, rural per capita income grew

more moderately as compared to the urban per capita income. Per capita income trends

for all the four provinces synchronized with the trend shown by economy as a whole.

However, the per capita income depicts a downward trend after 2005-06.

4.3.2 Per Capita Saving (Monthly in Rs) [Spc]:

Saving is regarded as basis for capital formation. Countries which succeeded in

setting aside a reasonable fraction of income for saving and investment have managed

to develop miraculously. For achieving a high average growth in real GDP, the saving

rate needs to be in line with the investment requirements. Since investment leads to

capital formation and an increase in productivity, then the per capita saving has

conceptually positive impact on poverty reduction. High rates of saving and investment,

and rising productivity are the foundation for rapid and sustainable growth, which

indirectly improve the living standard of the inhabitants. We included this variable in the

1 (non-income measure of poverty)

59

tentative list of regressors, although there is a difference of opinion among economists

about the causal relation of saving and growth.

Table 4.2 presents data on per capita saving for Pakistan and for the four

provinces with rural-urban break-up. It is apparent from the Table that PC monthly

saving increased by 125% for Pakistan as a whole, by 341% and 153% for rural and

urban Pakistan respectively. This significant increase in saving and obviously a

substantial increase in income of people living in rural area of Pakistan might be due to

their immigration to foreign countries in search of employment and their remittances

sent home. This led to a significant increase in their propensity to save. Same increase

is traceable for rural Punjab and rural Sindh, which is 1000% high in 2007-08 as

compared to 1979-90. On the other hand, per capita saving fell drastically in KPK and

Balochistan except for rural KPK and urban Balochistan, where marginal increases in

per capita saving is substantial.

4.3.3 Remittances (domestic and foreign as % of monthly income) [Rem]:

Remittances are very important source of income for poor and developing

countries. In some cases it is more than 20% of GDP (Global Economic Prospects

2011). As the remittances are directly received by the households, they are expected to

reduce poverty directly. The impact of remittances on poverty reduction can be

understood from both micro and macro perspectives. However to capture this impact,

there is no formal framework. Chimhowu et al, (2005). Uruci and Gedeshi (2003) found

that about 70 percent of international legal migrants send their money in order to

support the essential needs of family.

60

The present study also used remittances (Rem) as a determinant of BNGI.

Remittances are sum of both foreign remittances (F) and domestic remittances (D).

Data on foreign remittances (F) and domestic remittances (D) is not available separately

before 1990. This data is available in combined form under the column “gifts and

assistance” (GAs) for 1980s figures. This ratio of “foreign remittances” to “gifts and

assistance” (F/GAs) is taken for 1990s onwards and this ratio is multiplied by the 1980s

figures to get foreign remittances out of “Gifts and Assistance”. In the same way, values

of D are obtained and sum of the two (i.e. F and D) gives us estimates of remittances

(Rem). However, separate data for gifts and assistance, foreign remittances and

domestic remittances is available in the HIES from 1992-93 onwards.

In our preliminary model, we use the combined foreign and domestic remittance

as one of the regressor. Table 4.3 (a) presents foreign remittances (F) as percentage of

total monthly income. This percentage witnesses no increase up to 1987-88. It suddenly

increased in 1990s and kept on increasing at steady rate up to 2005-06 but it shows a

downward trend thereafter. Only for Balochistan, the percentage of total monthly income

by foreign remittances reflects an increase beyond 2005-06.

Table 4.3 (b) presents percentage of total monthly income by domestic (D)

remittances. It is apparent that during 1980s no considerable change is seen. However,

from the very beginning of the 1990s, it jumped to 4.1% for overall Pakistan and to

5.53% and 1.76% respectively for rural and urban areas. The change in percentage of

total monthly income by domestic remittances exhibits same pattern in Punjab and KPK.

However, Sindh and Balochistan depict a different story. In these provinces the change

is steady rather than by strides.

61

Table 4.3 (c) reports the percentage of monthly income by total remittances

(F+D). It is obvious that no significant change is observable till 1987-88. However, an

unbelievable change is witnessed from 0.65% in 1987-88 to 7.01% in 1992-93. This

sudden increase in Rem can be attributed to immigration of labor force to Middle East. It

oscillated down for the spell 1993-94 and 1996-97, but again it gained momentum and

showed consistent rise.

62

Table 4.1 Per Capita Income (Monthly) (Rs) CPI 2005=100

PAKISTAN PUNJAB SINDH KPK BALOCHISTAN

YEARS Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban

1979 1128 929 1402 1062 935 1305 1230 841 1514 1215 1023 1599 1079 876 1292

1984-85 1242 1105 1563 1182 1079 1430 1372 1054 1714 1331 1231 1929 1201 1171 1484

1985-86 1241 1106 1562 1210 1110 1748 1358 1056 1693 1157 1113 1424 1274 1208 1622

1986-87 1277 1123 1613 1221 1084 1548 1438 1149 1760 1215 1174 1429 1377 1356 1485

1987-88 1253 1091 1644 1217 1088 1576 1384 1011 1817 1173 1137 1343 1301 1250 1593

1992-93 1305 1133 1738 1297 1175 1646 1408 1020 1898 987 923 1456 1156 1118 1417

1993-94 1287 1073 1776 1275 1131 1629 1388 919 2080 1041 964 1405 1123 1059 1467

1996-97 1387 1271 1650 1455 1385 1626 1514 1241 1772 1039 996 1308 1175 1097 1464

1998-99 1337 1110 1882 1335 1122 1863 1482 1100 2338 1064 964 1639 1434 1409 1706

2001-02 1241 1038 1737 1261 1095 1673 1290 905 1892 1073 995 1533 1217 1133 1615

2004-05 1435 1166 2017 1449 1211 1965 1552 1092 2171 1219 1107 1786 1286 1175 1715

2005-06 1671 1460 2084 1765 1612 2085 1719 1292 2157 1428 1303 2098 1091 1018 1322

2007-08 1569 1342 2034 1648 1502 1965 1628 1067 2269 1315 1226 1768 1048 881 1479

Source: HIES/PSLM (various issues)

63

Table 4.2 Per Capita Savings (Monthly) (Rs) CPI 2005=100



1979 88 36 164 68 45 112 96 8 160 149 24 397 110 39 186

1984-85 85 65 131 78 60 110 64 26 105 153 102 448 114 111 142

1985-86 78 71 95 84 83 101 65 38 95 46 32 128 176 167 222

1986-87 54 41 81 44 32 45 55 28 81 46 42 68 214 217 201

1987-88 60 34 122 52 35 100 79 10 158 36 25 90 147 135 219

1992-93 38 -7 256 60 26 154 25 -71 148 -62 -85 111 99 87 182

1993-94 28 5 82 51 31 100 -15 -63 47 -3 -15 61 43 22 199

1996-97 143 176 67 201 244 92 87 150 26 18 15 42 104 86 172

1998-99 72 46 136 102 74 171 64 43 107 -44 -65 83 111 103 174

2001-02 79 46 159 113 73 212 27 12 50 18 -17 217 130 93 306

2004-05 84 32 195 96 37 225 60 8 129 87 49 279 49 16 175

2005-06 221 265 135 240 328 55 250 306 193 149 89 474 81 72 109

2007-08 195 159 268 225 233 207 231 86 396 54 33 161 66 4 225


64

Table 4.3 (a) Percentage of Total Monthly Income by Foreign (F) Remittances

PAKISTAN PUNJAB SINDH KPK Balochistan


1979 0.30 0.31 0.38 0.39 0.30 0.58 0.14 0.03 0.19 0.47 0.63 0.25 0.12 0.22 0.07

1984-85 0.39 0.29 0.67 0.37 0.29 0.52 0.59 0.10 1.08 0.28 0.27 0.34 0.54 0.56 0.31

1985-86 0.33 0.23 0.61 0.34 0.24 0.61 0.40 0.06 0.74 0.30 0.29 0.35 0.25 0.22 0.44

1986-87 0.37 0.29 0.60 0.41 0.33 0.57 0.40 0.06 0.76 0.27 0.25 0.39 0.27 0.31 0.09

1987-88 0.30 0.21 0.54 0.33 0.26 0.49 0.37 0.03 0.75 0.16 0.16 0.21 0.14 0.14 0.14

1992-93 2.91 2.65 3.34 2.96 2.5 3.85 1.78 0.47 2.68 5.8 6.27 3.66 1.22 1.25 1.05

1996-97 2.15 1.63 3.04 2.25 1.43 3.98 1.12 0.33 1.64 3.54 3.49 3.79 2.04 2.03 2.06

1998-99 3.24 3.15 3.37 3.38 2.78 4.26 0.93 0.14 1.5 9.06 9.44 7.74 1.29 1.45 0.35

2001-02 3.12 3.13 3.12 3.36 3.04 3.87 0.87 0 1.58 7.33 7.52 6.6 1.97 2.17 1.29

2004-05 3.58 3.45 3.75 3.84 3.08 4.87 1.37 0.76 1.79 8.02 8.57 6.3 2.04 2.15 1.76

2005-06 4.42 5.08 3.51 5.13 5.09 5.19 0.72 0.33 0.96 9.42 10.71 5.12 1.56 1.61 1.43

2007-08 4.31 5.48 2.74 4.83 5.35 3.95 0.44 0.06 0.65 10.48 11.91 5.45 1.74 1.8 1.66

Source: HIES/PSLM and Labour Force Survey (various issues)

65

Table 4.3 (b) Percentage of Total Monthly Income by Domestic (D) Remittances



1979 0.35 0.44 0.31 0.48 0.48 0.48 0.13 0.09 0.13 0.53 0.75 0.22 0.09 0.18 0.04

1984-85 0.46 0.41 0.55 0.46 0.47 0.43 0.57 0.28 0.76 0.32 0.32 0.30 0.41 0.44 0.19

1985-86 0.40 0.33 0.50 0.43 0.39 0.51 0.38 0.18 0.52 0.34 0.35 0.31 0.19 0.17 0.28

1986-87 0.44 0.41 0.48 0.51 0.54 0.47 0.38 0.17 0.53 0.30 0.29 0.35 0.21 0.24 0.06

1987-88 0.35 0.30 0.44 0.41 0.41 0.40 0.36 0.09 0.53 0.18 0.18 0.19 0.11 0.11 0.09

1992-93 4.1 5.53 1.76 4.87 6.12 2.43 0.55 0.67 0.47 9.23 10.13 5.14 -0.09 0.11 -1.18

1996-97 3.04 4 1.36 3.39 4.17 1.73 0.37 0.08 0.56 6.84 7.53 3.57 0.03 0.06 -0.07

1998-99 3.67 5.32 1.32 4.4 6 2.02 0.09 0.33 -0.08 10.4 12.27 3.99 -0.08 -0.09 -

2001-02 3.97 5.43 1.83 4.59 5.69 2.82 0.13 0.13 0.16 10.71 12.61 3.57 0.47 0.27 1.15

2004-05 4.29 5.74 2.49 5.19 6.61 3.3 0.96 0.9 1 8.76 10.01 4.85 1.21 1.38 0.76

2005-06 3.84 5.34 1.75 4.61 5.64 2.94 0.07 0.13 0.03 8.69 10.57 2.45 0.8 0.96 0.42

2007-08 4.05 5.65 1.89 4.86 6.14 2.73 0.38 0.13 0.51 8.55 9.94 3.67 0.34 0.28 0.42


66

Table 4.3 (c) Percentage of Monthly Income by Remittances (F+D)



1979 0.65 0.74 0.69 0.87 0.79 1.06 0.27 0.12 0.32 0.99 1.38 0.47 0.20 0.40 0.11

1984-85 0.85 0.70 1.22 0.82 0.76 0.95 1.16 0.37 1.84 0.60 0.59 0.65 0.95 0.99 0.50

1985-86 0.73 0.57 1.11 0.77 0.63 1.11 0.78 0.25 1.26 0.64 0.64 0.66 0.45 0.39 0.73

1986-87 0.81 0.70 1.08 0.93 0.87 1.05 0.78 0.23 1.29 0.57 0.54 0.74 0.48 0.55 0.15

1987-88 0.65 0.51 0.97 0.74 0.66 0.89 0.72 0.12 1.28 0.35 0.34 0.40 0.25 0.26 0.23

1992-93 7.01 8.18 5.10 7.83 8.62 6.28 2.33 1.14 3.15 15.03 16.4 8.8 1.13 1.36 -0.13

1993-94 7.18 8.29 5.59 7.84 8.22 7.18 2.9 2.23 3.29 15.37 17.16 8.23 2.5 2.8 0.9

1996-97 5.19 5.63 4.40 5.64 5.60 5.71 1.49 0.41 2.2 10.38 11.02 7.36 2.07 2.09 1.99

1998-99 6.91 8.47 4.69 7.78 8.78 6.28 1.02 0.47 1.42 19.46 21.71 11.73 1.21 1.36 0.35

2001-02 7.09 8.56 4.95 7.95 8.73 6.69 1 0.13 1.74 18.04 20.13 10.17 2.44 2.44 2.44

2004-05 7.87 9.19 6.24 9.03 9.69 8.17 2.33 1.66 2.79 16.78 18.58 11.15 3.25 3.53 2.52

2005-06 8.26 10.42 5.26 9.74 10.73 8.13 0.79 0.46 0.99 18.11 21.28 7.57 2.36 2.57 1.85

2007-08 8.36 11.13 4.63 9.69 11.49 6.68 0.82 0.19 1.16 19.03 21.85 9.12 2.08 2.08 2.08


67

4.3.4 Household Size (HS)

As poverty is multi dimensional and complex phenomenon, various Economic,

social, political and demographic factors influence it. Household size is intimately

connected with poverty status. It is observed that in countries like Pakistan the extended

family has diverse effects on poverty. In large family, the young members continue to

live with parents even after adulthood and marriages. They contribute their earning to

common pool of family. In the extended family, the young and old aged have full

insurance and security by living in combined family. They derive their livelihood from

common pool during their unemployment spell. This hinders mobility both in space and

in occupation. Due to large proportion of dependents and least participation of women in

income generating activities, the poverty persists in joint families. The system of

inheritance, which divides land and other assets among the heirs, also affects poverty to

some extent. Large family having more dependents and less earning hands is certainty

prone to poverty. Conceptually the household size is positively related with poverty.

Table (4.4) presents households size for Pakistan and the four provinces with

rural urban bifurcation. For Pakistan, the household size at the outset of study period is

6.1 and household size increases marginally up to 6.58 toward the end of the study

period. It is worth mentioning that household size remained higher in rural Pakistan as

compared to urban Pakistan till 1996-97. However, a reverse pattern is visible after this

period till the end of study period and almost same pattern is observed in Sindh and

KPK. The household size remained low in rural areas of Balochistan as compared to

urban regions throughout the study period.

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4.3.5 Higher Education (BA/BSc both sexes) (HE)

Higher literacy rate and primary education is argued to be important for economic

growth and development. In many developing countries, secondary and higher

education is neglected due to emphasis on primary education by donor agencies.

According to Mamphela Ramphele, managing director for human development,

World Bank (2002), “There is no way we can succeed in the eradication of poverty if the

developing world is not part of knowledge creation, its dissemination and utilization to

promote innovation. Higher education is a critical factor in making this possible and

must be part of any development strategy.”

When David Lavin, the Grawemeyer Award winner in education 2009, was asked

that “how can we break the generational cycle of poverty?” he answered that you make

sure that the disadvantaged, especially women, have access to a college education.

By higher educaton we mean the percentage of earners out of the total labor

force, having degree level (BA/BSc) education. It also implies that degree holder with no

employment are excluded since they have no impact on the dependent variable. Data

for the higher education (Having BA/BSc degree) is obtained from HIES. Data for the

year 1993-94 is not available and is filled with five year moving average. Table (4.5)

demonstrates the percentage distribution of earners (both sexes) by degree level

education. A glance at the table asserts that distribution was more skewed towards

urban Pakistan in 1979. Distribution is awfully low in rural Pakistan. In all provinces this

percentage increases at rapid rates as in urban areas as compared to rural area. This

however is not surprising because infrastructure lacks in rural Pakistan. As a result of

which migration at mass level is taking place from rural to urban area.

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Table 4.4 Household Size



1979 6.1 6 6.4 6 5.9 6.2 6.4 6.3 6.5 6.3 6.1 6.7 6.3 5.8 7

1984-85 6.21 6.05 6.65 6.28 6.15 6.64 6.19 5.85 6.59 6.37 6.25 7.11 5.33 5.22 6.44

1985-86 6.34 6.17 6.77 6.2 6.07 5.56 6.66 6.31 7.1 6.68 6.65 6.83 5.71 5.59 6.47

1986-87 6.46 6.32 6.79 6.31 6.19 6.63 6.71 6.49 6.98 6.82 6.77 7.13 6.22 6.09 6.92

1987-88 6.3 6.16 6.66 6.18 6.06 6.52 6.41 6.15 6.73 6.83 6.75 7.31 5.9 5.76 6.9

1992-93 6.4 6.3 6.66 6.55 6.44 6.78 6.42 6.19 6.73 7.1 7.23 6.26 5.83 5.66 7.16

1993-94 6.21 6.18 6.4 6.29 6.16 6.664 6.21 6.11 6.02 7.02 7.15 6.86 5.62 5.56 6.96

1996-97 6.21 6.14 6.37 6.13 6.04 6.38 5.87 5.48 6.29 7.11 7.18 6.72 5.85 5.71 6.46

1998-99 6.77 6.82 6.65 6.5 6.48 6.54 6.74 6.87 5.57 7.8 7.84 7.63 7.5 7.43 7.5

2001-02 6.96 7 6.87 6.54 6.5 6.63 7.54 7.87 7.08 7.66 7.67 7.55 7.63 7.56 7.96

2004-05 6.75 6.8 6.63 6.55 6.56 6.54 6.71 6.84 6.54 7.71 7.69 7.77 6.88 6.79 7.27

2005-06 6.83 6.93 6.65 6.46 6.43 6.53 7.02 7.57 6.53 7.96 8.03 7.61 7.51 7.28 8.4

2007-08 6.58 6.72 6.31 6.33 6.35 6.28 6.5 6.97 6.04 7.63 7.71 7.23 7.75 7.59 8.17


70

Table 4.5 Percentage Distribution of Earners (Both Sexes) by Degree Level Edu:


YEARS Overall Rural Urban YEARS Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban

1979 0.72 0.13 1.6 1979 0.52 0.11 1.31 1.46 0.2 2.39 0.45 0.09 1.13 0.52 0.24 0.83

1984-85 1.83 0.7 4.68 1984-85 1.68 0.53 2.74 3.64 0.43 7.75 0.98 0.45 4.06 4.37 3.95 8.02

1985-86 1.82 0.55 5.1 1985-86 1.37 0.44 4.02 3.36 0.68 7.07 1.1 0.8 2.91 1.11 0.55 4.37

1986-87 2.03 0.48 5.76 1986-87 1.47 0.3 4.55 4.02 0.89 8.27 1.32 0.74 4.2 0.79 0.47 2.46

1987-88 2.09 0.59 5.87 1987-88 1.57 0.54 4.54 3.9 0.46 8.53 1.29 0.84 3.34 1.71 1.15 5.17

1990-91 2.05 1.03 4.46 1992-93 1.6 0.75 4.25 4.52 1.35 8.88 1.74 1.1 6.48 0.96 0.39 5.51

1992-93 2.24 0.89 6.05 1993-94 1.69 0.62 4.75 4.98 1.36 9.52 1.52 0.96 4.87 1.2 0.62 4.4

1993-94 2.38 0.9 6.1 1996-97 2.14 0.92 5.68 7.51 2.77 12.4 1.76 1.17 5.46 1.36 0.46 5.24

1996-97 3.16 1.22 8.02 1998-99 1.85 0.74 5.04 5.23 2.14 10.13 2.28 1.79 5.1 2.78 2.36 5.81

1998-99 2.8 1.27 6.89 2001-02 2.2 0.91 5.85 4.81 1.91 10.36 2.78 2.02 7.11 3.17 2.33 7.72

2001-02 3.02 1.36 7.55 2004-05 3.86 2.16 8.14 9.34 3.94 17.53 4.59 3.47 10.42 3.61 2.03 11.88

2004-05 5.34 2.71 11.63 2005-06 2.89 1.1 7.19 6.73 2.11 12.34 3.95 3.18 8.26 3.45 2.26 8.24

2005-06 4.03 1.67 9.18 2007-08 3.43 1.71 7.55 6.93 2.7 12.47 4.24 3.26 9.49 3.84 1.48 10.43

2007-08 4.41 2.11 9.45


71

4.3.6 Dependency Ratio (DR)

Dependency ratio relates the number of children (0—14 years) and elders (65

years and above) to the working age population (15—64 years), expressed as

percentage. Dependency ratio has different implications; for instance, high ratio is a

sign of aging population, overburdened pension, social security requirements to the

older and non working population. The ratio is given below:

0 14 65100

15 64

number of people aged and those aged and aboveDependency Ratio

number of people aged

This can be further disaggregated as:

0 14100

15 64

number of people agedChild Dependency Ratio


65100

15 64

number of people aged and aboveAged Dependency Ratio


Larger number of dependents in the form of children and elderly members is

equivalent to smaller number of income earners or a smaller per capita income in the

household. This ratio allows quantify the burden on members of the labor force. One

might expect that a high dependency ratio would be correlated positively with the level

of household poverty. This ratio is considered a preferred social and demographic

indicator as compared to the household size since the later ignores the number of

earners. For example, large sized households with many earners may suffer low per

capita income whereas small sized households with only one significant earner may

enjoy relatively high per capita income. However, there is a difference of opinion among

the researchers regarding the relationship between the dependency ratio and the level

of poverty or inequality in distribution.

72

Idrees (2006) could found no relationship between dependency ratio and

inequality. He is of the view that, changes in the dependency ratio do not translate into

corresponding changes in incomes per adult-equivalent. Imran Sharif Chaudhry,

Shahnawaz Malik and Abo ul Hassan (2009) are of the view that dependency ratio has

a significant impact on a household‟s well being. The results support the hypothesis that

poverty will be more severe among the households with higher dependency ratio.

According to Njimanted, Godfrey Forgha (August 2006), the dependency ratio is

negatively related to poverty. The study shows that 10 percent increase in these

variables will result into 0.7164 percent fall in poverty.

Dependency ratio for Pakistan and the four provinces with rural urban bifurcation

is given in Table 4.6, which shows a mixed trend. Rural dependency ratio is much

higher than the urban. This fact is also observed by Idrees (2006). The obvious reason

is that rural Pakistan draws livelihood mainly from agriculture, which is characterized by

open and disguised unemployment. The segment of population between the age 1-14

and over 65 is more densely distributed in rural Pakistan than the urban regions. One of

the main reasons of prevalence of higher poverty in rural areas is the higher

dependency ratio. Dependency ratio was very high during 1979 in Pakistan as well as in

provinces except in case of Balochistan, where the ratio in rural Punjab and urban KPK

stood at 100. For the Pakistan as a whole, there is about 15% decline in dependency

ratio from 1979-80 to 2007-08. The ratio declined more drastically in Punjab by about

22% during the same period. A rise in the ratio in case of rural Sindh and rural

Balochistan is serious feature. We may conclude that dependency ratio is not falling

with a rate sufficient enough to make a dent in poverty.

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Table 4.6 Dependency Ratio


Year Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban

1979 97 99 93 99 100 96 93 97 87 112 115 100 90 91 86

1984-85 98 101 92 93.48 95.61 88.12 92 98 84 102.3 105.3 86.48 94 95 92

1985-86 95.3 98 90 98 101 94 96 103 91 105 110 99 106.3 109.2 92.1

1986-87 100 103 94 95 98 89 95 103 87 109 113 90 107 109 105

1987-88 98 102 89 93 97 85 95 105 86 109 114 86 113 115 103

1992-93 98 104 86 96 100 86 94 105 86 110 115 86 108 110 100

1993-94 96 102 84 94 99 84 92 103 83 108 113 85 106 107 100

1996-97 97 101 90 95 97 91 93 99 87 110 114 91 96 108 107

1998-99 76 96 86 86.9 92.3 76.7 88 100 79 85.6 88.6 81.5 108 108 109

2001-02 86 92 75 82 91 73 88 98 77 96 105 81 102 106 96

2004-05 84 92 71 82 88 72 84 97 72 93 96 79 98 100 92

2005-06 81 89 69 81 88 69 84 98 71 92 94 80 97 99 93

2007-08 82 92 72 77 78 67 83 99 74 92 95 78 92 95 83

Source: HIES/PSLM and Labour Force Surveys (various issues)

74

Table 4.7 Labour Force Unemployment Rate (Un)


Year Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban Overall Rural Urban

1979 3.0 3.6 5.2 3.5 4.3 6.2 1.8 1.8 4.0 3.3 3.5 4.6 1.5 2.2 2.2

1984-85 4.0 3.7 5.7 4.5 4.3 6.7 2.7 2.5 4.2 4.2 3.9 6.1 1.6 1.5 4.1

1985-86 3.7 3.3 4.0 4.2 3.5 4.9 2.4 1.6 2.2 3.9 3.8 4.2 1.6 1.4 2.0

1986-87 4.7 4.2 5.2 5.4 4.6 6.4 3.0 2.0 2.7 4.8 4.7 5.1 1.5 1.4 1.9

1987-88 6.0 5.4 7.6 7.2 6.4 9.5 3.5 2.1 4.9 5.9 5.7 7.1 1.4 1.1 3.5

1992-93 4.5 4.1 5.6 5.3 4.7 7.3 2.3 1.7 3.1 5.2 5.2 5.3 2.2 2.1 2.5

1993-94 4.5 4.0 5.8 5.5 4.6 8.1 2.4 2.0 3.0 7.1 6.9 8.3 2.8 3.0 2.1

1996-97 5.7 5.3 6.7 6.2 4.7 9.8 2.8 2.1 3.5 8.8 8.7 9.3 2.5 2.1 4.4

1998-99 5.6 4.7 7.6 7.7 6.2 11.7 2.8 2.1 3.7 11.7 11.7 11.8 5.9 5.7 7.0

2001-02 7.8 7.2 9.1 7.9 7.0 10.0 5.1 3.2 6.9 13.1 12.7 15.0 6.3 6.3 6.2

2004-05 7.3 6.4 9.2 5.8 4.8 8.3 4.3 2.9 5.7 12.0 12.1 11.6 3.1 2.4 5.6

2005-06 6.1 5.4 7.5 5.1 4.3 7.0 3.4 2.3 4.5 9.6 9.2 11.9 2.6 2.2 3.9

2007-08 5.0 4.5 6.0 5.1 4.5 6.6 3.1 2.0 4.3 8.9 8.7 10.0 2.7 2.1 4.8

Source: Labour Force Survey and Social Development in Pakistan (various issues)

75

4.3.7 Labor Force Unemployment Rate (Un)

Unemployment is considered one of the biggest obstacles to rapid economic

growth and poverty reduction. High unemployment can be attributed to high population

growth and aimless educational system. Population is growing at an alarming rate in

Pakistan and thus responsible for adding more people to the pool of unemployed and

poverty. It has various dimensions; on the one side unemployment-to-population ratio

and labor force participation rates are dismal and on the other hand, the female to male

unemployment ratio is crucial. Unemployment rates are higher in urban areas as

compared to rural areas.

Data for the labor force unemployment rate is obtained from Social Development

in Pakistan (various issues) and is presented in Table 4.7. Unemployment in 1979 was

not so much alarming as is evident by the figures 3.0 for overall Pakistan, 3.6 for rural

areas and 5.2 for urban areas. There has been massive influx of people in the labor

force over the past thirty years, for which our economy has no potential for absorption.

That is why the labor force unemployment rate grew by a figure of 66 percentage points

from 1979 to 2007-08. This increase is 25 percentage points for rural areas whereas it

is 16 percentage points for urban areas. Similarly labor force unemployment rate for

rural Punjab grew slowly by an average of 4.6% as compared to 5.5% for overall Punjab

and 6.45% for urban Punjab. The values for the year 1985-86 and 1986-87 are

interpolated using three years moving average. For overall Pakistan and all the

provinces in general, the labor force unemployment rate remained high in urban areas

as compared to rural areas. This rate remained high in KPK for most of the time,

whereas the rural Sindh and rural Balochistan enjoyed a lower rate during the study

76

period. A sharp increase in the unemployment rate is observed in almost all regions of

Pakistan after the year 2000. This increase might be due to many factors including the

9/11 incident. Pakistan remained the front line ally of the US and allied forces against

war on terror in Afghanistan and own tribal areas. However, in the 2007-08, all the

regions of Pakistan witnessed downward trend in labor force unemployment rate.

4.3.8 Share of Income held by Bottom 20% Population (B20)

Distribution of income is closely related with economic growth and poverty. Some

researchers like Lewis (1954) were of the view that inequality promotes saving,

investment and growth, which in turn tends to reduce poverty. However, the empirical

evidence revealed that inequality has a negative impact on development - see for

instance Alesina (1993) and Roberto Perotti (1996)

There are several ways to measure inequality. Traditionally, the size distribution

is measured in terms of relative amounts received by 10% or 20% of income earners.

The present study incorporates the share of income held by the bottom 20% of the

households (B20) as one of the regressor for its significance towards poverty. Table 4.8

presents this picture. For the year 1979-80, this share was 12.3% for overall Pakistan

(14.1% for rural areas and 10.6% for urban areas). The share however, reduced by the

year 2007-08 to 7.9% for overall Pakistan (10.5% for rural areas and 3.0% for urban

areas). Same trend prevails for urban areas of all the provinces. However, the said

share in overall and rural Balochistan has increased sharply during this period.

Likewise, rural Sindh has shown slight improvement in the indicator concerned over the

data period but with some fluctuations.

77

4.3.9 Ratio of Income of Top 20% to Bottom 20 % (T2B)

Equity and fair distribution provides a great motive for participation in a

development process. The individuals will pursue a life of their choice without any

feelings of deprivation. According to World Development Report (2006), “Institutions and

policies that promote a level playing field, where all members of society have similar

chance to become socially active, politically influential and economically productive;

contribute to sustainable growth and development. Greater equity is thus doubly good

for poverty reduction.”

A frequently used measure of inequality is the ratio of the share of income

received by the 1st quintile (top/richest 20%) to the share of income received by the 5th

Quintile (bottom/poorest 20%). The intuitive list of regressors includes this ratio as

explanatory variable and it is expected to be positively related with BNGI. These results

are similar to Kipanga (2007), where it was observed that group averages varied

positively with the levels of BINGI performances.

Table 4.9 depicts the ratio of income of top 20% to bottom 20% for Pakistan and

all provinces. This measure gives a clearer picture of income inequality. The data shows

some interesting features. Almost 90% increase in the ratio (from 3.1 in 1979-80 to 5.9

in 2007-08) is visible for Pakistan. Increase in this measure for rural Pakistan is a

somewhat moderate, but there is unprecedented increase in this ratio for urban

Pakistan (from 4.3 to 22.5) and urban Sindh (3.8 to 57.2) during the study period. The

ratio increased steadily up to 2007-08 with spikes in some years. In contrast, the rural

Sindh and rural Balochistan have shown a clear downward trend.

78

TABLE 4.8 Share of Income held by bottom 20 Percent

Pakistan Punjab Sindh KPK Balochistan


1979 12.2 14.1 10.6 12.1 13.5 10.6 12.7 17.2 12.0 11.7 13.7 8.7 13.3 16.0 12.0

1984-85 9.7 12.4 10.8 10.3 12.4 10.3 11.0 15.3 13.8 8.1 11.2 8.9 8.7 11.6 13.0

1985-86 12.0 13.2 10.4 12.0 12.9 10.7 11.6 14.5 11.1 13.0 13.4 10.8 12.8 13.3 12.2

1986-87 11.8 13.2 10.1 12.1 13.4 10.1 12.3 14.7 11.7 11.5 11.7 11.2 15.5 15.6 16.9

1987-88 12.0 13.4 12.0 11.9 13.0 11.4 13.2 17.3 12.6 13.5 12.4 12.7 15.3 15.8 14.5

1992-93 11.7 12.8 9.7 11.4 12.3 9.7 10.4 13.5 9.5 13.4 14.1 10.7 13.6 13.0 12.2

1993-94 9.4 11.5 7.5 11.3 10.9 9.5 11.7 14.0 9.9 10.7 12.0 7.9 11.4 13.0 10.3

1996-97 10.6 11.3 10.9 10.8 10.7 10.8 11.4 12.8 10.9 12.1 12.4 11.4 10.8 14.8 13.0

1998-99 7.9 9.9 8.2 8.4 10.2 8.0 9.4 10.1 9.4 8.1 10.3 8.8 10.2 11.8 11.6

2001-02 9.7 13.0 4.8 8.8 10.6 5.9 9.5 18.1 3.0 14.0 16.3 5.2 10.2 11.2 7.1

2004-05 9.3 13.8 3.7 9.9 14.0 4.5 6.5 13.0 2.1 12.9 15.1 5.8 8.8 10.2 5.3

2005-06 7.6 10.4 2.2 5.9 7.7 2.0 10.3 18.9 1.9 10.2 11.9 3.4 17.2 20.2 6.4

2007-08 7.9 10.5 3.0 7.5 9.1 4.0 8.6 19.2 1.2 6.9 7.9 2.6 17.4 24.3 5.4

Source: Own calculations till 1998-99 using data from HIES/PSLM (various issues)

79

TABLE 4.9 Ratio of Income of Top 20 % to Bottom 20 %

Pakistan Punjab Sindh KPK Balochistan


1979 3.1 2.1 4.3 3.0 2.3 4.0 3.2 1.4 3.8 3.6 2.4 4.9 2.6 1.6 3.3

1984-85 4.5 2.7 4.2 4.0 2.7 4.2 3.9 1.9 3.2 6.4 3.4 6.2 4.6 2.7 2.7

1985-86 3.2 2.5 4.4 3.1 2.6 4.2 3.5 2.0 4.1 2.8 2.5 4.2 2.8 2.5 3.4

1986-87 3.3 2.5 4.6 3.1 2.4 4.7 3.4 2.1 3.9 3.3 3.1 4.0 2.3 2.2 2.1

1987-88 3.3 2.4 3.6 3.3 2.6 4.0 2.9 1.4 3.4 2.6 2.9 3.3 2.0 1.8 2.4

1992-93 3.9 3.1 5.5 4.1 3.4 5.4 4.8 2.7 5.7 2.9 2.6 4.4 2.6 2.8 3.5

1993-94 4.9 3.2 7.4 3.7 3.5 5.5 4.0 2.2 5.3 3.6 3.0 6.5 3.2 2.5 4.2

1996-97 4.6 4.0 4.3 4.4 4.6 3.5 3.9 3.1 4.3 3.5 3.3 4.2 3.8 2.1 3.1

1998-99 7.3 5.0 7.2 6.8 4.7 7.5 5.7 4.9 6.2 7.4 4.8 6.6 5.4 4.5 3.9

2001-02 4.3 2.3 12.4 5.0 3.3 9.9 4.9 1.3 20.9 2.0 1.3 10.1 2.7 2.0 6.6

2004-05 4.8 2.1 17.2 4.6 2.4 13.8 7.8 1.7 33.2 2.4 1.5 9.5 3.5 2.3 9.3

2005-06 5.9 3.2 31.6 8.2 5.0 34.9 4.1 0.7 36.6 3.6 2.8 15.2 1.2 0.8 5.8

2007-08 5.9 3.4 22.5 6.7 4.6 17.8 5.3 0.8 57.2 5.0 3.7 21.6 1.0 0.3 6.3

Source: Own calculations till 1998-99 using data from HIES/PSLM (various issues)

80

4.3.10 Human Capital Index (HCI)

In addition to physical capital, human capital (HC) is now considered to an

important determinant of economic growth and well-being of the masses. Human capital

is a comprehensive concept that comprises good health and physique and the level of

education, training, acquisition of technical skills and experience. The economists

consider the expenditure made or the funds allocated to education, training and medical

care as investment in human capital (HC).

It is difficult to measure human capital or to compare it across individuals or

different regions. However, the level of health and education are conventionally

considered as significant determinants of HC. The present study follows the same

convention and attempts to build an index for use in the analysis. In this context, we

follow the convention used in the construction of Human Development Index (HDI) vide

the Human Development Report (UNDP-1997).

The main indicators used are the educational attainment and health status. The

educational attainment index captures the effects of literacy rate and combined

enrollment rate, whereas the health index includes the effects of both infant survival rate

and crude birth rate that determine the life expectancy at birth.

(a) Educational Attainment Index (EAI)

Educational attainment index (EAI) is calculated by using zero as a minimum

level and 100 percent as a maximum level of education attainment. Here two third

81

weight is assigned to percentage of literates in labor force (denoted by l ) and one

third weight to combined enrollment rates (denoted by e ).

2 3 1 3

100

l e

EAI

(b) Health Status Index [HSI]

The second part of the human capital is health status, which is measured by the

life expectancy at birth. This indicator has appeared to be significant in many cross

country growth analyses (Bloom and Canning 2000, 2001). Due to the non availability of

relevant data for different regions of Pakistan, we adopted an indirect route. The infant

mortality rate (IMR) and the crude birth rate (CBR) are assumed to be good

determinants of life expectancy at birth. For this purpose, we took data on the three

indicators from 1960 to 2007(panel data set, total 73 observations) for the South Asian

region (Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) and fitted

the following regression:

77.3416 0.1538( ) 0.15923( )Life Expectancy CBR IMR

By putting the values of crude birth rate and infant mortality rate we obtained the

life expectancy for different regions of Pakistan. To obtain health status index, the

standard procedure given in UNDP, Human Development Report (1997) is followed.

The minimum expected life is 25 years and the maximum is 85 years.

tan 25

85 25

Life Expec cyHSI

82

(c) Human Capital Index

Human capital index (HCI) is obtained by simply taking the average of

educational attainment index (EAI) and health status index (HSI): HCI2

EAI HSI

Using the above formula for the computation of human capital index, the relevant

indicator for different regions of Pakistan is obtained, as shown in Table 4.10. The

relevant information shows that conditions in urban areas are better in this regards than

the rural areas .This trend is also present in the initial period and remains persistent

during the whole period of the study.

The obvious reason is the higher literacy rate in the urban areas, which imparts

awareness in the individuals about health and education. Moreover and as discussed

earlier, there is a visible difference in the income and expenditure of the people residing

in the urban and rural areas. Further the better facilities for health and education depict

better results for urban areas in terms of human capital. The table shows this significant

difference, where the mean human capital index for rural and urban areas of Pakistan is

0.46 and 0.64 respectively.

The overall regions of provinces with reference to human capital index may be

ranked in the sequence: Sindh, Punjab, Khyber Pakhtoonkhwa (KPK), and Balochistan.

This ranking also shows that the urbanization is the major determinant of this variable.

Difference between the last and the initial values in the different regions shows higher

rate of catch up in the rural areas of Pakistan as compared to urban areas. At provincial

level, this difference is not persistent. For instance, Punjab and KPK followed the same

trend as that of Pakistan. However, KPK rural area showed significant progress in

83

human capital where in 1979 value of HCI was 0.33 and in 2007-08 it appeared as 0.58.

These facts are also depicted in the following figures.

84

Table 4.10 Human Capital Index



1979 0.42 0.35 0.51 0.41 0.35 0.51 0.45 0.35 0.54 0.39 0.33 0.48 0.40 0.35 0.49

1984-85 0.42 0.35 0.54 0.42 0.36 0.54 0.43 0.32 0.55 0.42 0.38 0.48 0.30 0.26 0.47

1985-86 0.45 0.40 0.58 0.45 0.40 0.58 0.47 0.37 0.60 0.44 0.41 0.55 0.35 0.32 0.48

1986-87 0.48 0.42 0.61 0.48 0.42 0.60 0.48 0.40 0.61 0.46 0.42 0.56 0.38 0.34 0.52

1987-88 0.49 0.42 0.62 0.49 0.43 0.60 0.51 0.38 0.64 0.49 0.45 0.58 0.39 0.35 0.56

1992-93 0.53 0.47 0.68 0.51 0.45 0.65 0.54 0.40 0.69 0.48 0.45 0.63 0.40 0.35 0.60

1993-94 0.54 0.48 0.69 0.53 0.48 0.67 0.56 0.42 0.72 0.53 0.51 0.67 0.44 0.41 0.63

1996-97 0.55 0.49 0.69 0.55 0.50 0.68 0.55 0.44 0.71 0.55 0.53 0.65 0.50 0.48 0.65

1998-99 0.57 0.52 0.68 0.56 0.51 0.69 0.56 0.47 0.72 0.57 0.55 0.68 0.50 0.48 0.67

2001-02 0.54 0.50 0.63 0.57 0.52 0.66 0.57 0.49 0.69 0.54 0.52 0.64 0.48 0.45 0.62

2004-05 0.57 0.51 0.70 0.56 0.51 0.68 0.57 0.47 0.68 0.56 0.55 0.63 0.50 0.46 0.64

2005-06 0.56 0.51 0.68 0.61 0.56 0.72 0.58 0.47 0.71 0.55 0.53 0.62 0.49 0.46 0.59

2007-08 0.58 0.53 0.68 0.59 0.55 0.68 0.58 0.49 0.70 0.59 0.58 0.67 0.54 0.50 0.66

Source: Own Estimation

85

Human Capital Index for overall areas is plotted against years in Figure 4.1,

where on the average, HCI is 0.41 at the initial level and in 2007-08 this value is 0.57.

Figure 4.2 shows that the average HCI for rural areas is 0.34 for the year 1979 and 0.53

for 2007-08. Figure 4.3 reveals that in 1979 average human capital index was 0.51 and

in 2007-08 this value rises to 0.68.

Figure 4.1 Human Capital for Overall Regions

The above figure depicts the situation of overall provinces of Pakistan. There is a

persistent increase from 1979 to 1998-99. Initially, there is no wide gap among regions,

but just after 1979 overall Balochistan took downward jump and remained below the

average throughout the study period. After 1984-85, there is an upward trend that

persists till 1996-97. After that, mixed trend is observed, where some values increased

while others showed downward trend.

86

Figure 4.2 Human Capital for Rural Regions

In the above figure, human capital index for the year 1979 indicates almost the

same standard of health and education in rural section of the country. In general, all

regions observed upward trend overtime, but after 1996-97 the rural Balochistan and

rural Sindh showed stagnant trend, whereas rural Punjab and rural KPK followed more

or less the previous trend. For most of the time, human capital index in rural Balochistan

remained low as compared to other regions, whereas HCI in rural KPK remained

dominant throughout the period.

Figure 4.3 depicts the level of human capital index in urban areas. As expected,

the urban areas are achieving higher level of human capital than the rural areas. Here

urban Sindh dominates throughout (due Karachi metropolitan); which is a clear

indication that standard of health and education in urban Sindh remained well

87

throughout the study period. Like rural areas, the urban regions also show upward trend

till 1998-99. After 1998-99 almost all the regions show slight downward trend especially

in case of urban KPK and urban Balochistan. However both these regions catch up with

urban Punjab and urban Sindh in 2007-08.

Figure 4.3 Human Capital for Urban Regions

4.4 Construction of Basic Needs Gap Index (BNGI)

The basic needs gap index (BNGI) is a measure of poverty level in that it shows

the gap or deprivation level or the distance at which the poor households are standing

before they could attain the objective of basic needs fulfillment. This has been used by

different researchers as indicator and yardstick for policy makers to think of appropriate

88

strategies to attain the objective of basic needs fulfillment (BNF), for instance by Hassan

(1997) and Kipanga (2007). BNGI represents the dependent variable, which is

measured by the following formula:

ptntBNG (4.1)

This measures the difference between the mean expenditure on basic needs nt in the

region and the mean income of the poor in that region, pt .

When equation (4.1) is expressed as a ratio of nt , this gives the index:

ntptntptntBNGI /1][ (4.2)

Generally the income of the poor is less than the mean expenditure on basic needs

( pt nt ) and the index lies between zero and unity. However, in rare cases, it may

happen that income of the poor household exceeds the expenditure on the basic needs

( pt nt ). In such a situation, BNGI will be less than zero. In the present study, it

factually happened. Out of 104 observations for rural and urban areas, we have three

values less than zero. The smaller value of index indicates the better performance; and

the higher value of index shows the worst condition of the region.

To evaluate the mean income of the poor ( pt ), the headcount index as a

measure of poverty ( ) is used for finding the income share ( ) going to a proportion

of population concerned. This can be estimated by using the tables of percentage

distribution of monthly income among the households by quintiles. The following

relationship gives the mean income of the poor.

89

pt Y

. (4.3)

The parameter (α) can be estimated by the following formula,

1 2 1 /L L L n a (4.4)

Where 1L and 2L are the lower and upper limits of the income for population in poverty

respectively. Here n is the value that varies with (people in poverty). If it is above 20

and below 40, the value of n will be 20, and if the value of is more than 40, n will be

40 and so on. Here a assumes the value of 20 if the limits are quintiles; and 10 if the

limits are deciles.

Now to evaluate nt , we need two parameters. The parameter (a ) is the

proportion of consumption expenditure (C ) to household income (Y ), i.e. /a C Y .

The parameter (b ) is the proportion of expenditure on basic needs ( B ) to total

consumption expenditure (C ), i.e. /b B C . Thus we have the equation:

nt tabY (4.5)

Keeping in view the above, the construction of basic needs gap index requires a

lot of information. Some of the data may be readily available, whereas in most cases

one has to evaluate different variables and construct certain parameters. We discuss

the necessary data before we could construct the key variable of our study.

4.4.1 Specification of Basic Needs (B)

Household expenditure is the amount spent by the household on goods and

services for consumption. It also includes own produced goods and those received in

90

kind as remuneration. Household consumption expenditure can be classified as “paid

and consumed” and “unpaid and consumed”, like the wages and salaries received in

kind and consumed, the own produced goods and consumed, and the receipts in the

from of assistance, gifts, dowry, inheritances and other sources.

Published data of HIES includes the following main items under consumption

expenditure:

i) food beverages and tobacco vii) fuel and lighting

ii) apparel, textile and footwear viii) education

iii) transport and communication ix) medical care

iv) cleaning, laundry and personal appearance x) religious functions etc

v) recreation and entertainment xi) litigation expenses

vi) rent xii) miscellaneous expenditure

Out of the above, the following four (4) items are selected as the basic needs and

which constitute about 80% (plus) of expenditure in the poor households:

i) Food, Beverages and Tobacco iii) Rent, Fuel and Lighting

ii) Apparel, Textile and Footwear iv) Health and Education

The average monthly expenditure per household for overall Pakistan and the four

provinces with rural urban bifurcation is shown in Table 4.11.

The average monthly expenditure per household is synchronized with average

monthly income per household. Oscillation in average monthly expenditure reveals that

marginal propensity to consume (MPC) of general public remains more or less the

same. Sharp rise in income in later parts of 2000s can be attributed to seemingly

prudent macroeconomic policies of the military-led regime that could not be sustained

91

by the present government. The year 2007-08 shows downward trend in both income

and expenditure in almost all areas except urban Balochistan. It is apparent from the

Table that in case of Punjab, both income and expenditure increased up to 1986-87, but

after that both the variables oscillated and could not gain momentum.

For the purpose of this study, some information about expenditure on education

and health is needed. The requisite data set on health expenditure and medical care is

not available as a separate entity. The same is therefore extracted from the

miscellaneous expenditures, where expenditure on medical care is given as percent of

miscellaneous expenditure. To obtain the data on healthcare, the ratio of miscellaneous

expenditure to total household expenditure could be multiplied by percent fraction of

miscellaneous expenditure allocated to medical care:

Health as % of total expenditure = [(misc exp/total exp) × % of medicare in misc exp]

As far as the case of education is concerned, the expenditure going to education

is available for the 1986-87 onward, but there is no separate entry in HIES for

expenditure on education before this year. This data is available in column “personal

effects” for the year 1979 and is available in column “entertainment, recreation and

education” for the years 1984-85 and 1985-86. First, we estimated the ratio of both,

“expenditure on education” and “expenditure on recreation” to the gross “expenditure on

personal effect” and using this ratio, we estimated the expenditure on education for the

years 1979 to 1985-86. From 1986-87 onward the data for “expenditure on education” is

available as a separate variable.

Education as % of total expenditure = [(Personal effect/total exp) × % of education in

personal effect or entertainment exp].

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Table 4.11 Average Monthly Expenditure / Household (Rs) CPI 2005=100

PAKISTAN Punjab SINDH KPK BALOCHISTAN


1979 6343 5356 7922 5963 5252 7393 7260 5244 8797 6716 6098 8053 6106 4855 7739

1984-85 7189 6294 9521 6932 6269 8765 8095 6014 10601 7506 7055 10525 5794 5535 8647

1985-86 7375 6388 9930 6981 6236 9157 8609 6423 11343 7423 7192 8850 6267 5819 9063

1986-87 7904 6839 10408 7425 6513 9966 9286 7279 11714 7973 7661 9701 7236 6935 8880

1987-88 7515 6515 10134 7200 6381 9622 8369 6154 11167 7765 7510 9159 6809 6423 9482

1992-93 8105 7181 9865 8102 7400 10119 8874 6756 11777 7447 7288 8423 6160 5835 8842

1993-94 7814 6596 10847 7698 6773 10190 8718 6000 12241 7333 6998 9218 6069 5769 8827

1996-97 7727 6722 10085 7688 6891 9784 8376 5976 10981 7258 7045 8506 6264 5773 8346

1998-99 8559 7257 11609 8015 6789 11065 9559 7260 12428 8641 8068 11872 9923 9709 11491

2001-02 8089 6947 10840 7510 6645 9687 9528 7028 13039 8083 7758 9940 8290 7864 10418

2004-05 9121 7712 12079 8857 7696 11383 10013 7415 13351 8724 8137 11715 8515 7870 11201

2005-06 9901 8282 12960 9849 8256 13258 10310 7459 12825 10181 9752 12358 7587 6890 10189

2007-08 9042 7948 11144 9007 8061 11040 9086 6839 11310 9622 9200 11619 7616 6661 10244


93

Table 4.12 Average Monthly Income / Household (Rs) CPI 2005=100



1979 6880 5571 8973 6372 5519 8090 7872 5296 9838 7656 6243 10712 6800 5080 9043

1984-85 7714 6686 10392 7422 6637 9496 8493 6167 11292 8478 7695 13713 6401 6113 9560

1985-86 7869 6826 10573 7500 6740 9719 9042 6662 12019 7728 7403 9727 7272 6755 10496

1986-87 8249 7099 10955 7706 6710 10262 9652 7457 12282 8288 7945 10188 8568 8256 10273

1987-88 7891 6724 10949 7522 6592 10275 8873 6216 12228 8009 7678 9819 7678 7198 10993

1992-93 8349 7140 11572 8495 7567 11163 9037 6314 12774 7009 6672 9116 6740 6328 10147

1993-94 7990 6629 11369 8018 6965 10853 8622 5612 12522 7308 6894 9637 6312 5890 10208

1996-97 8612 7803 10512 8918 8364 10373 8890 6799 11143 7390 7151 8787 6875 6266 9455

1998-99 9049 7573 12512 8675 7268 12181 9988 7556 13021 8301 7557 12503 10752 10472 12799

2001-02 8636 7267 11933 8249 7120 11092 9728 7119 13392 8219 7631 11577 9283 8567 12853

2004-05 9685 7929 13371 9488 7941 12854 10413 7467 14196 9395 8516 13879 8849 7980 12470

2005-06 11413 10119 13859 11400 10365 13615 12066 9777 14084 11369 10464 15966 8194 7414 11102

2007-08 10326 9018 12836 10430 9540 12341 10585 7436 13703 10031 9451 12783 8125 6689 12081


94

4.4.2 Household Income (Y)

Household income is the sum of monetary income and income “in kind”. The

household income comprises receipts from all sources like wages and salaries,

share/rent received from agricultural land/crop, income from farming and crop

production (like the rents earned on agricultural equipments: tractors, tube wells, animal

carts etc), income from livestock farming, poultry sold or slaughtered for domestic, bee

hives and fishery, the income earned as rents from non-agricultural property and leasing

of equipments, transfer receipts and assistance which includes Zakat, insurance claims,

pensions, gifts and grants etc. and all other sources including sale of assets, domestic

remittances, foreign remittances etc.

Average monthly income per household for different regions as well as for rural

urban segments is shown in the Table 4.12. It shows that average monthly income has

increased for the year 1984-85, 1985-86, 1986-87, 1990-91, 1998-99, 2004-05 and

2005-06; whereas it has declined for the remaining years. This rise was very sharp in

2005-06. Average monthly income per household for rural areas portrays the same

pattern as for the overall Pakistan. However, the urban areas show a consistent trend

which is not observable for the rural areas of Pakistan.

The income gap between the rural and urban areas is visible, and which

remained permanent feature over the whole study period. One of the possible reasons

might be the selective privatization and liberalization of the economy, leading to a

sustained increase in GDP of around 6 % per annum during the 1980‟s decade. The Zia

ul Haq regime and the successive governments emphasized the development of rural

sector and backward areas by distributing funds through the elected representatives.

95

4.4.3 Distribution of Income (Construction of Quintiles)

Income distribution is considered as an important factor of social integration by

the researchers. For the construction of quintiles, the data on monthly income of

different classes is derived from different issues of HIES. The first quintile is computed

by taking the ratio of cumulative income and cumulative population to obtain monthly

income for bottom 20%. The second, third, fourth, and the highest quintiles are

constructed. For some values of α in the formula for finding BNGI, we need the first

decile or income share going to the bottom 10% population. Quintiles after the year

2000 are given in the HIES published data.

Table 4.13 shows income distribution by quintiles for overall, rural and urban

Pakistan. The overall picture shows a dismal position where the distribution is getting

more unequal overtime. The share of income for the first quintile goes down by 33%

(12.2 to 7.9) over the period 1979-90 to 2007-08, where this decrease is 26% and 71%

respectively for the rural and urban areas. However, this decrease is very large and

consistent in case of urban areas, whereas in the rural areas of Sindh and Balochistan

an increase in the share of income is observed .This change is more vivid after 2005-

06. The highest quintile (top 20% share of income) shows an increase overtime, except

for rural Sind, overall and rural KPK, and rural Balochistan.

So, we can conclude that the poor is getting poorer and the rich getting richer

overtime. There is an increase in share of income held by the highest quintile in all the

cases. This increase is 22% for the overall Pakistan; and 18% and 51% respectively for

rural and urban areas.

96

Table 4.13 Percentage Distribution of Monthly Income Among Households

by Quintiles PAKISTAN

OVERALL

RURAL URBAN

Years 1st 2nd 3rd 4th 5th 1st 2nd 3rd 4th 5th 1st 2nd 3rd 4th 5th

1979 12.2 14.1 15.6 19.9 38.2 14.1 16.4 17.6 21.7 30.2 10.6 12.3 14.0 18.2 45.0

1984-85 9.7 13.8 15.8 17.3 43.3 12.4 16.7 18.6 19.1 33.3 10.8 13.4 14.3 15.7 45.7

1985-86 12.0 14.8 16.8 18.4 38.1 13.2 16.3 18.1 19.6 32.8 10.4 12.8 14.9 16.5 45.4

1986-87 11.8 15.0 16.5 17.8 39.0 13.2 16.7 18.0 19.2 32.9 10.1 12.5 14.7 15.8 46.8

1987-88 12.0 14.6 16.3 17.7 39.4 13.4 16.3 18.3 19.4 32.6 12.0 13.7 15.2 16.0 43.0

1990-91 9.2 12.2 16.3 15.7 46.6 4.3 11.3 16.5 23.2 48.9 10.2 14.1 14.5 15.3 45.9

1992-93 11.7 13.1 14.6 15.4 45.2 12.8 14.4 15.9 16.6 40.2 9.7 11.0 12.6 13.8 53.0

1993-94 9.4 11.9 16.1 16.9 45.7 11.5 15.3 16.2 20.6 36.4 7.5 11.6 11.8 13.7 55.4

1996-97 10.6 11.8 12.8 16.2 48.6 11.3 12.5 13.5 16.9 45.7 10.9 13.0 14.1 15.2 46.8

1998-99 7.9 9.1 11.7 13.2 58.0 9.9 11.1 14.1 15.3 49.5 8.2 9.4 11.1 12.3 59.0

2001-02 9.7 12.7 15.7 20.3 41.7 13.0 16.4 18.8 22.3 29.6 4.8 7.2 11.2 17.3 59.5

2004-05 9.3 11.9 14.5 19.7 44.6 13.8 16.9 18.2 22.0 29.2 3.7 5.8 9.9 16.9 63.8

2005-06 7.6 11.4 15.4 20.6 45.0 10.4 14.3 18.6 23.5 33.2 2.2 5.5 9.1 14.8 68.5

2007-08 7.9 11.2 14.5 19.7 46.8 10.5 13.9 17.5 22.5 35.6 3.0 6.1 8.7 14.3 67.8

Source: Own calculations till 1998-99 using HIES/PSLM data (various issues) .

97


by Quintiles PUNJAB

OVERAL

RURAL URBAN


1979 12.1 14.6 16.1 20.8 36.3 13.5 16.1 17.5 22.0 31.0 10.6 12.8 14.6 19.1 42.9

1984-85 10.3 14.3 16.6 18.1 40.7 12.4 16.3 18.5 19.2 33.6 10.3 13.6 15.2 17.2 43.7

1985-86 12.0 14.9 17.1 18.5 37.5 12.9 16.0 18.2 19.5 33.4 10.7 12.6 15.0 16.8 44.9

1986-87 12.1 15.1 16.9 18.1 37.9 13.4 16.8 18.2 19.5 32.0 10.1 12.3 14.9 15.8 46.9

1987-88 11.9 14.6 16.3 17.9 39.3 13.0 16.0 18.1 19.5 33.4 11.4 12.2 14.3 16.0 46.1

1992-93 11.4 12.8 14.5 15.2 46.1 12.3 13.9 15.6 16.0 42.2 9.7 11.1 12.8 14.0 52.4

1993-94 11.3 13.7 15.4 17.2 42.3 10.9 13.1 17.5 20.9 37.6 9.5 10.2 11.1 16.8 52.3

1996-97 10.8 11.8 13.8 15.9 46.9 10.7 11.6 12.8 15.9 49.1 10.8 20.8 15.2 16.0 37.2

1998-99 8.4 9.1 12.3 13.8 56.4 10.2 11.7 12.6 17.2 48.2 8.0 9.2 10.9 11.7 60.1

2001-02 8.8 11.9 15.1 20.7 43.5 10.6 14.7 17.2 23.1 34.5 5.9 7.3 11.8 16.9 58.2

2004-05 9.9 11.4 13.5 19.5 45.7 14.0 15.3 15.4 21.6 33.8 4.5 6.3 11.0 16.6 61.7

2005-06 5.9 9.4 15.3 21.0 48.4 7.7 11.3 18.3 24.4 38.3 2.0 5.1 8.8 13.6 70.5

2007-08 7.5 10.0 13.7 18.3 50.5 9.1 11.7 15.9 21.9 41.5 4.0 6.4 8.7 10.0 71.0

Source: Own calculations till 1998-99 using HIES/PSLM data (various issues)

98


By Quintiles SINDH

OVERALL

RURAL URBAN


1979 12.7 13.2 14.8 18.4 40.9 17.2 17.9 19.0 21.5 24.5 12.0 11.8 13.6 16.9 45.7

1984-85 11.0 13.8 15.3 16.7 43.2 15.3 17.3 19.0 19.9 28.5 13.8 14.9 13.7 13.7 43.8

1985-86 11.6 14.4 16.0 17.7 40.4 14.5 17.3 18.7 20.1 29.4 11.1 13.7 14.0 16.0 45.2

1986-87 12.3 14.2 15.3 17.0 41.2 14.7 17.0 17.8 19.3 31.1 11.7 12.8 13.9 15.6 46.0

1987-88 13.2 14.7 16.1 17.2 38.7 17.3 18.7 19.5 20.1 24.3 12.6 13.9 15.0 16.1 42.4

1992-93 10.4 12.0 13.2 14.7 49.8 13.5 15.3 16.3 17.9 37.0 9.5 10.8 12.1 13.3 54.3

1993-94 11.7 11.8 13.8 16.0 46.7 14.0 18.1 17.7 18.8 31.4 9.9 11.5 12.2 14.1 52.3

1996-97 11.4 12.3 15.3 16.3 44.6 12.8 14.4 14.9 18.4 39.6 10.9 13.2 13.8 15.2 46.8

1998-99 9.4 11.1 12.7 13.4 53.4 10.1 11.3 12.9 15.9 49.9 9.4 9.1 11.6 11.8 58.1

2001-02 9.5 11.1 14.1 18.8 46.6 18.1 17.4 19.6 20.8 24.1 3.0 6.4 9.9 17.2 63.5

2004-05 6.5 9.9 13.7 19.7 50.3 13.0 18.0 22.9 24.1 22.1 2.1 4.4 7.5 16.7 69.4

2005-06 10.3 13.8 16.6 16.8 42.5 18.9 23.3 24.7 20.0 13.1 1.9 4.6 8.8 13.7 71.0

2007-08 8.6 12.2 13.6 19.7 46.0 19.2 23.2 21.7 20.7 15.2 1.2 4.5 7.9 19.1 67.4


99


By Quintiles KPK

OVERALL

RURAL URBAN


1979 11.7 13.2 14.4 18.5 42.3 13.7 15.6 16.7 20.9 33.2 8.7 10.1 25.0 13.5 42.7

1984-85 8.1 11.5 14.2 14.8 51.4 11.2 15.4 17.7 17.4 38.2 8.9 10.1 12.7 13.4 54.9

1985-86 13.0 15.6 16.9 18.5 36.1 13.4 16.2 17.3 19.0 34.0 10.8 12.9 15.1 16.0 45.2

1986-87 11.5 15.6 17.0 17.8 38.1 11.7 16.2 17.3 18.3 36.5 11.2 12.8 15.8 15.5 44.7

1987-88 13.5 16.1 17.0 17.7 35.7 12.4 15.2 17.8 18.2 36.4 12.7 14.3 15.5 15.9 41.5

1992-93 13.4 15.1 16.2 16.9 38.5 14.1 15.8 16.8 17.3 36.1 10.7 12.2 14.1 15.4 47.5

1993-94 10.7 15.8 16.7 19.0 37.9 12.0 15.3 16.1 20.9 35.7 7.9 12.3 13.1 15.5 51.2

1996-97 12.1 13.2 14.2 18.5 41.9 12.4 13.6 14.4 19.2 40.4 11.4 11.8 13.3 15.5 48.0

1998-99 8.1 9.5 10.3 12.7 59.4 10.3 12.3 12.7 15.0 49.6 8.8 10.4 9.9 12.4 58.5

2001-02 14.0 18.4 19.4 20.7 27.5 16.3 20.5 21.5 20.9 20.8 5.2 10.7 11.4 20.1 52.7

2004-05 12.9 18.6 19.4 18.3 30.8 15.1 21.6 21.7 18.5 23.1 5.8 9.4 12.1 17.9 54.9

2005-06 10.2 15.8 13.4 23.7 36.9 11.9 17.5 14.1 23.2 33.4 3.4 9.3 10.5 26.0 50.9

2007-08 6.9 14.0 18.8 26.3 34.1 7.9 15.8 21.1 26.1 29.2 2.6 6.3 8.9 27.1 55.2


100


By Quintiles BALOCHISTAN

OVERALL

RURAL URBAN


1979 13.3 14.6 16.5 21.0 34.6 16.0 17.5 19.3 21.3 25.8 12.0 12.5 15.1 21.3 39.1

1984-85 8.7 16.1 15.9 19.2 40.1 11.6 19.4 18.6 19.1 31.3 13.0 15.9 16.2 19.6 35.3

1985-86 12.8 14.9 16.9 20.2 35.2 13.3 15.5 17.3 20.9 32.9 12.2 13.2 15.3 17.7 41.6

1986-87 15.5 14.2 16.9 18.1 35.3 15.6 14.1 17.1 18.1 35.1 16.9 15.0 16.0 17.4 34.7

1987-88 15.3 16.7 18.5 19.0 30.4 15.8 17.3 19.1 19.2 28.5 14.5 15.5 17.2 17.4 35.4

1992-93 13.6 15.3 17.1 18.4 35.6 13.0 14.7 16.8 19.8 35.8 12.2 13.5 13.6 17.4 43.3

1993-94 11.4 14.1 16.4 21.1 37.0 13.0 15.6 17.7 21.4 32.3 10.3 12.3 14.2 20.5 42.7

1996-97 10.8 13.6 15.5 18.9 41.1 14.8 16.1 17.4 20.0 31.7 13.0 14.7 15.7 16.8 39.8

1998-99 10.2 10.8 12.0 12.0 54.9 11.8 11.2 12.0 11.8 53.1 11.6 13.9 13.0 15.9 45.6

2001-02 10.2 16.8 22.9 22.6 27.6 11.2 19.0 24.9 23.1 21.9 7.1 9.3 16.1 21.1 46.5

2004-05 8.8 13.8 18.6 28.2 30.5 10.2 16.3 20.8 29.4 23.4 5.3 7.3 12.8 25.0 49.6

2005-06 17.2 19.3 20.3 23.1 20.2 20.2 20.5 21.3 22.7 15.3 6.4 15.3 16.5 24.5 37.3

2007-08 17.4 22.8 20.9 21.8 17.1 24.3 23.2 21.7 23.5 7.3 5.4 22.3 19.6 18.7 34.0


101

Table 4.14 depicts the situation of Punjab, which is more or less similar to that of

overall Pakistan. In case of Sindh rural as shown in Table 4.15, the results are unusual

where the data shows 12% increase in the percentage share of income of the poor over

time and 38% decrease in the highest quintile. The middle three quintiles share more

than 65% of income and the highest quintile shows very unusual low values of 38% and

15% for the years 2005-06 and 2007-08 respectively.

In Sindh urban case, we have the opposite trend in the first quintile with a huge

decrease in the income share i.e. 12% in1979 to 1% in 2007-08. On the other hand,

there is an increase of income share by about 8% in the highest quintile. Although all

the urban areas show a wide gap between income shares of the poor and the rich; but

this gap is very large in case of Sindh urban. The reason might be that urban Sindh

includes Karachi, the largest trading and industrial city of Pakistan, where the middle

class is gaining weight.

The percentage distribution of monthly income by quintiles for KPK is given in

Table 4.16. The income share of both the first and the fifth quintile have decreased for

overall KPK, while the share of the middle quintiles has increased overtime. KPK urban

shows a huge decline in the share of income held by the first quintile i.e. 70% decrease,

whereas an increase in the share held by the highest quintile is observed by 29%.

Balochistan portrays a different picture when it is compared with other regions of

Pakistan. Table 4.17 depicts the picture of income distribution. Balochistan overall

shows an increase in the income share of poor by 30% but the highest quintile shows a

decline by 50%. Year 2007-08 shows a value of 17.13 for the highest quintile, which is

102

quite unusual when compared to other parts of the country. Balochistan rural presents

more unusual results and the situation seems just opposite when we compare the

values of the initial and final years. Here, the 1st quintile shows a decrease from 16.0 to

10.2 up to the year 2004-05 and then a sudden increase to 24.3 by the year 2007-08.

Likewise, the highest quintile shows somewhat constant but fluctuating trend up to

2004-05 but a sudden drop from 23.4 to 7.28 by the year 2007-08. This may be due to

problems in data collection and compilation on account of security issue and

deteriorating law and order situation. Another probable reason behind this unusual

change might be the policies adopted by the Musharaf regime; where funds were

generously provided by the federal government for the least developed areas of

Balochistan.

4.4.4 The Poverty Status of Households (The Headcount Ratio)

One component that is used during the construction of BNGI is Head Count Ratio

for the different regions of Pakistan. Data about head count ratio, especially at provincial

level with rural urban bifurcation, is not available from a single source. The data on

poverty indices is available from early 1990‟s onward. Prior to this period, there were

certain individual studies that attempted to estimate the poverty indices; which is surely

a great contribution in this area of research. These include Amjad.R and Kemal. A.R

(1997), Cheema. I. A. (2005), Irfan. M. (2007), Jamal.H. (2006), Qureshi.S.K and

ARIF.G.M, (2001),Ellahi Mahboob, Khan S.R. Rafi (1999), Shirazi.N.S (1993), Zaidi S.A

(2000).

As mentioned earlier, the HIES data for 1990-91 is only for all Pakistan level,

therefore we have one extra entry. For the year 1985-86 and 1986-87, we could not find

103

data on the headcount index and it is interpolated by using weighted average formula2.

Data for the last two entries is obtained by using the 5-year moving average.

Table 4.18 shows head count ratio (HCR) for Pakistan and the four provinces

with rural-urban bifurcation. If this measure of poverty is reliable, then the poverty

remains more or less the same over the long study period for overall Pakistan. However

there is a visible difference in urban and rural area. There is a slight increase in HCR in

rural areas but a significant reduction in poverty in urban areas, from 25.9 in 1979 to

19.7 in 2007-08.

Punjab (overall, rural and urban) shows approximately the same situation of

poverty for the year 1979. Total Punjab and rural area shows slight decrease in HCR

figures, whereas a significant reduction in poverty can be seen for urban Punjab. In

case of Sindh, a significant reduction in HCR can be seen for all the three cases (total,

rural and urban). Here the pattern of change is similar to Punjab. The situation in

Balochistan is more or less similar to that of Punjab and Sindh. However, the situation

of poverty in KPK worsened over time both in overall and rural areas. However, the

urban areas of KPK recorded a slight reduction in HCR like other urban regions of

Pakistan.

Generally speaking, the urban areas of Pakistan have enjoyed some reduction in

poverty (HCR), whereas the situation in rural areas either remained same or got

worsened in some cases. The poverty figures measured as the HCR remained low in all

regions of Pakistan for the year 1987-88.

2 0.7 weight to the previous value and 0.3 to next to previous value) in the same way for the year 1986-

87, 0.7 weight to the proceeding value and 0.3 to next to the proceeding value is given.

104

Table 4.18 POVERTY (Head Count Ratio)

PAKISTAN PUNJAB SINDH NWFP BALOCHISTAN

YEARS Total Rural Urban YEARS Total Rural Urban Total Rural Urban Total Rural Urban Total Rural Urban

1979 30.7 32.5 25.9 1979 35.1 35.3 35.1 38.2 40.3 37.5 34.9 35.1 34.5 33.70 36.30 32.00

1984-85 18.3 21.1 11.1 1984-85 19.0 21.3 12.8 15.3 22.2 7.0 9.6 9.0 7.5 27.5 28.5 17.0

1985-86 22 24.5 15.6 1985-86 23.83 25.5 19.49 22.17 27.63 16.15 17.19 16.83 15.6 29.36 30.84 21.5

1986-87 21.8 24.8 14.5 1986-87 21.21 23.43 14.70 13.64 18.79 7.17 20.94 21.67 15.99 14.54 14.86 12.21

1987-88 16.6 19.6 8.7 1987-88 19.9 22.6 11.9 9.5 14.6 3.1 15.5 16.0 12.4 9.3 10.0 4.40

1990-91 34 36.9 28 1992-93 24.25 25.37 21.24 23.29 28.56 16.65 33.62 34.91 24.37 26.77 26.21 30.44

1992-93 25.5 27.6 20.0 1993-94 28.55 32.95 17.01 21.5 30.24 11.33 36.37 38.22 25.31 34.36 36.75 15.62

1993-94 28.2 33.5 15.4 1996-97 24.66 27.84 16.61 15.39 19.22 11.77 40.23 42.36 26.92 37.69 41.61 22.98

1996-97 25.8 30.2 15.8 1998-99 31.62 34.62 24.24 26.01 34 15.57 41.28 43.72 27.13 21.55 21.34 22.94

1998-99 31.1 35.1 21.4 2001-02 32.24 35.86 23.33 35.32 45.07 20.06 41.47 43.61 29.05 35.49 37.45 26.18

2001-02 34.5 39.3 22.7 2004-05 24.3 28 16.3 18.3 23.7 11 32.1 34.1 21.9 26.7 28.8 18.5

2004-05 23.9 28.1 14.9 2005-06 28.21 31.58 20.12 23.75 30.49 14.6 38.77 40.94 26.25 30.35 32.3 22.65

2005-06 28.8 33.2 18.7 2007-08 29.38 32.83 21.29 26.54 34.25 15.54 38.28 40.47 26.026 27.91 29.19 22.54

2007-08 29.8 34.2 19.7

Source: HIES and Miscellaneous sources

105

Table 4.19 Construction of the BNGI (Overall Punjab) Y

EA

RS

AV

G.M

ON

TH

LY

IN

CO

ME

/ H

.H

(Y)

AV

G: M

onth

ly C

ons: E

xp: / H

H (

C )

Cons. E

xp.

as %

of H

H I

ncom

e

a=

C/Y

Fo

od,

bevera

geg &

tobacco

Appare

l, t

extile

and footw

ear

Rent

+F

uel &

Lig

htin

g

Health +

Ed

ucatio

n

Exp:

on B

asic

Needs a

s %

of C

ons:

b

=(B

/C)*

100

People

in

Povert

y a

s %

of

Tota

l

Popula

tio

n λ

(H

C)

Lim

its o

f In

com

e (

%)

for

(%

) of P

op.

in P

ov. (B

ased o

n R

ela

vant Q

uin

tile

s

/ D

ecile

Valu

es)

Estim

ate

d %

of In

com

e

with

Corr

espondin

g %

of P

eople

in

Povert

y α

Ént =

a*b

*Yt

Y¯

pt =

(α/λ

) Y

t

BN

GI

=1

- (Y

¯pt/É

nt)

Y C a=C/Y b Λ L1 L2 α Ént Ýpt BNGI

1979 6372 5963 0.94 50.6 10.0 15.5 2.0 0.78 35.1 12.1 14.6 14.0 4656 2544 0.45

1984-85 7422 6932 0.93 47.9 7.7 16.3 3.9 0.76 19.0 4.4 10.3 7.1 5257 2756 0.48

1985-86 7500 6981 0.93 47.4 7.8 16.9 3.8 0.76 23.8 12.0 14.9 14.0 5293 4409 0.17

1986-87 7706 7425 0.96 45.5 7.6 17.4 3.6 0.74 21.2 12.1 2.0 6.4 5499 2335 0.58

1987-88 7522 7200 0.96 45.1 8.0 17.7 3.7 0.75 19.9 5.8 11.9 8.9 5366 3346 0.38

1992-93 8495 8102 0.95 48.3 8.7 20.1 4.1 0.81 24.3 11.4 12.8 11.7 6581 4091 0.38

1993-94 8018 7698 0.96 48.6 8.6 20.2 4.7 0.82 28.6 11.3 13.7 12.4 6325 3471 0.45

1996-97 8918 7688 0.86 48.5 8.5 19.7 4.2 0.81 24.7 10.8 11.8 11.0 6224 3989 0.36

1998-99 8675 8015 0.92 47.8 8.4 21.8 8.5 0.86 31.6 8.4 9.1 8.8 6928 2416 0.65

2001-02 8249 7510 0.91 47.9 7.2 21.5 7.9 0.85 32.2 8.8 11.9 10.7 6347 2734 0.57

2004-05 9488 8857 0.93 48.0 9.2 20.9 7.1 0.85 24.3 9.9 11.4 10.2 7549 4001 0.47

2005-06 11400 9849 0.86 42.9 6.2 23.1 7.8 0.80 28.2 9.4 15.3 11.8 7874 4773 0.39

2007-08 10430 9007 0.86 43.5 5.9 22.6 7.7 0.80 29.4 10.0 13.7 11.7 7174 4167 0.42


106

4.4.5 The Construction of BNGI

Table 4.19 shows the procedure for and construction of the dependent variable

BNGI, taking the data of overall Punjab. The first column contains years whereas the 2nd

and 3rd columns show the average monthly income and average monthly consumption

per household denoted by Y and C respectively. To find the mean expenditure on basic

needs nt , we need a and b . Column 4 shows consumption expenditure as percentage

of household income which is represented by „α‟ while column 5 through 8 contains

expenditure on basic needs that is (1) food, beverages and tobacco, (2) apparel, textile

and footwear, (3) rent, fuel and lighting, (4) health and education as percent of total

consumption. Sum of the above entities is given in column 9 and is denoted by „b‟.

The people in poverty (as % of total population i.e. head count ratio) is

denoted by „λ‟ and is given in column 10. Lower L1 and upper Limits L2 of Income

against population in poverty, both expressed in percentage is mentioned in column

11. In column 13, the parameter „α‟ is estimated as percent of income with

corresponding percent of people in poverty using Equation 4.4. nt and pt are

calculated by using formulas contained and shown in column 13 and 14. The last

column contains values of BNGI, as dependent variable.

In the same way BNGI for overall, rural and urban areas of Pakistan and each

province is constructed and the values for all these regions are given in Table 4.20.

The analysis follows in the next section.

107

Table 4.20 BNGI for all Regions of Pakistan

PAKISTAN PUNJAB SINDH KPK

BALOCHISTAN


1979 0.41 0.38 0.38 0.45 0.41 0.51 0.52 0.45 0.54 0.48 0.47 0.52 0.42 0.39 0.45

1984-85 0.33 0.12 0.31 0.48 0.16 0.39 0.40 0.08 0.51 0.50 0.28 0.64 0.42 0.27 0.28

1985-86 0.23 0.21 0.41 0.17 0.23 0.44 0.27 0.26 0.14 0.17 0.32 0.65 0.33 0.33 0.18

1986-87 0.23 0.26 0.40 0.58 0.16 0.42 0.29 0.54 0.53 0.28 0.28 0.61 0.01 -0.005 -0.06

1987-88 0.35 0.32 0.15 0.38 0.17 0.31 0.56 0.47 0.56 0.32 0.37 0.23 0.46 0.45 0.49

1992-93 0.40 0.42 0.48 0.38 0.37 0.37 0.42 0.43 0.50 0.53 0.54 0.39 0.34 0.32 0.42

1993-94 0.54 0.50 0.41 0.45 0.53 0.49 0.33 0.40 0.27 0.52 0.55 0.55 0.53 0.51 0.34

1996-97 0.42 0.44 0.44 0.36 0.41 0.47 0.42 0.37 0.37 0.25 0.28 0.47 0.54 0.04 0.21

1998-99 0.66 0.63 0.50 0.65 0.60 0.56 0.53 0.61 0.50 0.53 0.45 0.58 0.40 0.31 0.33

2001-02 0.56 0.49 0.70 0.57 0.51 0.64 0.62 0.04 0.81 0.07 0.03 0.63 0.42 0.37 0.56

2004-05 0.47 0.34 0.70 0.47 0.35 0.76 0.68 0.29 0.86 0.35 0.29 0.60 0.50 0.46 0.72

2005-06 0.54 0.22 0.62 0.39 0.52 0.77 0.31 -0.13 0.88 0.45 0.44 0.87 0.20 0.17 0.55

2007-08 0.54 0.32 0.55 0.42 0.51 0.65 0.46 0.12 0.92 0.55 0.50 0.91 0.10 0.02 0.55


108

4.4.6 Analysis of Basic Needs Gap Index

The basic needs gap index is given in Table 4.20 for different regions of

Pakistan. The behaviour of BNGI is also depicted graphically. The index for rural and

urban Pakistan is shown in Figure 4.4. The indices emanate from 1979 with a value of

0.38. The gap between rural and urban Pakistan widened immediately in succeeding

years. The index for rural Pakistan fell drastically while that for urban areas surged

modestly. A fair explanation for decline in the rural index is that local elections were held

in 1979. More emphasis was laid on rural development and enormous funds were

distributed through local representatives. This led to an increase in economic activity,

employment opportunities and increase in production.

Zia‟s regime specifically targeted backward and rural areas of Pakistan and

special measures were taken to uplift the less developed regions. According to Tahir et

al (1997), involvement of former USSR in Afghanistan from 1979 to 1987-88 remained

the major reason of foreign capital flows during this period. The economy grew with a

sustained rate of around 6% during the 1980s decade, which caused a reduction in

income inequality. This study further argues that export of manpower to Middle East in

mid 1970s resulted into large remittances, which reached to peak figures in 1982-83,

and contributed to reduction in rural and urban inequality. In 1988 a structural

adjustment program was initiated under the auspices of IMF. Some reforms were

introduced, which included increase in tax rates and withdrawal of subsidy on certain

items, specifically on the agricultural inputs and production. These reforms adversely

affected the rural segments of Pakistan. BNGI fell to its lowest ebb (0.15) in 1987-88 but

109

there is an increasing trend after 1988. It is conservable fact that latter governments

carried out different sort of programs according to their policy priorities.

Some governments wanted to attack poverty through social action programs

while others tried to alleviate it through distribution of funds among the public

representatives. However, no government managed to make dent in poverty through

long run viable and sustained development policies. Five breakeven points are

traceable if one observes the figure. The gap between rural and urban BNGI beyond

2001-02 is continuous, although not uniform, and it lasts till the end of the study period.

BNGI touches 0.7 points during 2005-06 and the gap tends to converge after 2005-06.

Figure 4.4 BNGI. Pakistan Rural and Urban

Same relationship of BNGI between rural Punjab and urban Punjab is traceable

in Figure 4.5. However BNGI line for urban Punjab emanates from 0.5 point and for the

rural Punjab it starts from 0.4 points. Both rural and urban Indices showed downward

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trend up to 1990. Rural index travels beneath urban index at large up to 1992-93 and it

lies above the urban index between 1992-93 and 1993-94. Rural index falls below urban

index perceptually after 2001-02. This trend resembles BNGI for overall Pakistan urban

and rural areas. The spell ranging from 1987-88 to 1998-99 exhibits quite interesting

results regarding BNGI. The country embarked on the path of democracy during this

spell. Ironically, democratic governments changed frequently without completing their

tenures. It was a severe blow for the country‟s economy because every new political

government rolled back the ongoing projects and changed the direction of their policies

spoiling the process of economic growth. Due to these oscillatory strategies, BNGI for

rural and urban Punjab coincides with each other. The same relationship can be

observed for rural and urban BNGI. During this spell, BNGI kept increasing moderately.

Figure 4.5 BNGI. Punjab Rural and Urban

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Figure 4.6 BNGI. Sindh Rural and Urban

BNGI for urban and rural Sindh is depicted in Figure 4.6. A parallel fall in urban

and rural BNGI is obvious up to 1984-85. BNGI for both rural and urban Sindh twisted

around 0.4 points. These indices began diverging after 1988-89. Urban BNGI rose

persistently. This index tends to 1.0 point whereas rural BNGI plumbed down and even

it became negative in 2006-07.

In Figure 4.7 BNGI for rural and urban KPK has been portrayed. BNGI for rural

KPK stemmed from 0.5 and it rolled up touching the value of 0.68, afterwards, it turned

down, reaching 0.2, then further showing oscillatory trend fall to zero in 2001-02, but it

started climbing again parallel to urban KPK. BNGI for urban KPK started from 0.5 and

contrary to its rural counterpart it went above rural KPK and remained high till 1985-86.

Then it remained below for the period of 1986-87 to 1993-94. After this, there is

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divergence till 2001-02. Contrary to other provinces, there is a parallel increase in BNGI

for rural and urban KPK from 2001-02 to 2007-08.

Figure 4.7 BNGI. KPK Rural and Urban

BNGI for rural and urban Balochistan has been displayed in Figure 4.8, where

both rural and urban BNGI chased each other. BNGI constructed for rural and urban

Balochistan has zigzag trend. Both fall up to 1986-87, increase and then again follow

each other. In 1986-87 rural BNGI went almost zero whereas urban BNGI lagged

behind to 0.2. Both indices increase up to 2004-05 and then start decreasing.

Summing this discussion up, the research concludes that the urban BNGI on average

lies above rural BNGI for the study period.

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Figure 4.8 BNGI. Balochistan Rural and Urban

Very few studies have been done for inequalities at province level and

especially with rural urban bifurcation. Our results are more or less same as that of

earlier studies for the overall areas. However comparison of rural and urban areas

gives us clearer picture.

As mentioned by Idrees (2006) about earlier studies and their results.

“The official estimates do not provide any estimates regarding incidence of

income inequality within provinces of Pakistan. However, Kruijk (1986), Ahmad

and Ludlow (1989), Ahmad (2000) and Talat (2003) are few major efforts in this

regard each of these studies was conducted for shorter period of time. In general

these studies show that in late I970's and early 1980's the income inequality has

been maximum in the province of KPK. In 1990 's the maximum income

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inequalities prevailed in Sindh followed by Punjab and then NWFP. Baluchistan

in general had the least degree of income inequalities during all surveyed years. ”

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CHAPTER 5

MODEL SPECIFICATION AND METHODOLOGY

Our preliminary/intuitive model includes ten (10) explanatory variables against

the dependent variable, which is the basic needs gap index (BNGI). The model can be

written in the general format as under:

1

K

it ki kit it

k

Y X

The dependent variable (BNGI) is denoted by „Y‟, where the subscript „i‟ stands for the

region (rural/urban in the province concerned) and „t‟ stands for the time period. The

explanatory variables are generally denoted by „X‟, where the disturbance term is given

by „v‟, with all the standard classical assumptions. Detailed discussion and justification

of the explanatory variables is given in Chapter-4. The model may be written in the

simple linear form as follows (Eq. No 5.1):

0 1 2 3 4 5 6 7 8 9 10Re 20 2it i i it i it i it i it i it i it i it i it i it i it itBNGI YPC SPC m HS HE DR Un B T B HCI V

However, it is possible that some of these variables may be irrelevant or insignificant.

Whether the suggested model is just fitted and whether all the explanatory variables are

theoretically consistent and satisfy the general criterion of “weakly exogenous

regressors”? All these questions need to be answered carefully and the model

specification bias must be avoided. We discuss different possibilities to answer these

questions in the next section.

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5.1 Model Selection

The procedure for selecting explanatory variables needs proper attention; on one

hand we want our model to be parsimonious and simple, and on the other hand, it

should incorporate all the essential variables since exclusion of any relevant variable

might cause missing variable bias. We adopt general to simple methodology for

selection of appropriate and theoretically relevant variables in the model. Alternatively,

the variables which do not contribute significantly to dependent variable should be

excluded. In order to get an appropriate model, there are number of procedures and all

of which have their merits and demerits. There is no clarity in literature as to which of

the proposed methods is superior. The present study uses three methods to select

appropriate regressors for the model. These methods are;

Descriptive statistics (Correlation),

Static panel, and

Impulse Saturation

The variables that are insignificant by all of these methods are not included in the

model. Alternatively, if a variable is supported by at least one of the three methods, we

will include it in the final analysis. This is because if we drop a relevant variable, it will

result into biased specification. On the other hand, if we include an extra and less

significant variable, the results of model will remain unbiased and consistent. The

details of these methods and the results are summarized in the forthcoming sections.

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5.1.1 Statistical Analysis

For this study, ten determinants of the basic needs gap have been proposed;

namely the per capita income(Ypc), per capita savings (Spc), remittances both domestic

and foreign (Rem), household size (HS), higher education (HE), dependency ratio (DR),

unemployment rate (Un), share of income going to bottom 20% of population (B20),

ratio of income of top 20% to bottom 20 % (T2B), and human capital represented by the

health and education index (HCI)

To get relevant regressors for the final model, both economic theory and the

statistical significance need to be considered. The relevance and importance of the

proposed explanatory variables has been discussed in the previous chapter on data and

variables. So far as the statistical significance is concerned, we have to study the

correlation among the variables under reference. The common wisdom leads us to

determine as to which variables should be retained and which ones to be excluded.

Obviously, the explanatory variables having high correlation with the dependent variable

should be considered and those which have high correlation among themselves should

be excluded. The results of this investigation are shown below:

(i) Aggregate Rural and Urban Areas

The results are shown in Table 5.1. Looking at the Table, the variables like per

capita income, human capital, ratio of income of top 20% to bottom 20%, share of

income held by bottom 20%, higher education and unemployment show relatively high

value of absolute correlation with BNGI; if the standard is absolute value of r > [0.4].

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Of these six variables, the per capita income (YPc) is the most important one

since the remaining five variables are also correlated with it. Because of its importance,

it will be inappropriate to exclude this variable. Next we see that B20 is linearly

correlated with HCI and T2B; HE is linearly correlated with HCI, T2B and B20; and Un is

linearly correlated with HCI and B20. If the two variables, namely T2B and HE, are

excluded, the final model will be good fit if Ypc, B20, HCI and Un are incorporated. The

specification will assume the following form:

0 1 2 3 420it i i it i it i it i it itBNGI Y HCI B Un v (5.2)

Table 5.1 Correlations, Means and Standard Deviations

Aggregate Rural and Urban Areas

Variables BNGI Ypc Spc Rem HCI HS T2B B20 HE Un DR

BNGI 1.00

Ypc 0.43 1.00

Spc 0.24 0.61 1.00

Rem 0.22 -0.06 -0.15 1.00

HCI 0.43 0.77 0.31 0.25 1.00

HS 0.07 0.03 0.11 0.39 0.38 1.00

T2B 0.64 0.64 0.35 0.04 0.49 0.00 1.00

B20 -0.80 -0.72 -0.43 -0.20 -0.62 -0.12 -0.72 1.00

HE 0.43 0.82 0.36 0.00 0.78 0.23 0.63 -0.66 1.00

Un 0.46 0.43 0.28 0.36 0.57 0.31 0.29 -0.56 0.37 1.00

DR -0.04 -0.23 -0.22 0.30 -0.09 0.16 -0.10 0.07 -0.16 -0.12 1.00

Mean 0.36 1400.88 112.58 4.13 0.53 6.70 5.77 11.22 4.00 4.75 101.67

S. Deviation 0.24 355.84 105.21 5.37 0.12 0.66 8.09 4.12 3.64 2.76 78.56

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(ii) Overall Areas (combined Provinces)

In case of overall areas, most of the independent variables have low correlation

with BNGI. The results are shown in Table 5.2. If we consider the minimum criterion of

absolute correlation as r > [0.1]; then the following seven regressors can be considered

as significant: Rem, HCI, T2B, B20, HE, Un and DR. Interestingly, the per capita income

has low correlation with the dependent variable, but this feature can be ignored and YPc

cannot be excluded due to its importance and that all other significant variables are

highly correlated with it.

Table 5.2 Correlations, Means and Standard Deviations Overall Areas


BNGI 1.00

Ypc 0.07 1.00

Spc -0.06 0.63 1.00

Rem 0.24 -0.14 -0.24 1.00

HCI 0.23 0.39 -0.07 0.50 1.00

HS 0.06 -0.13 -0.26 0.56 0.55 1.00

T2B 0.61 0.57 0.19 0.20 0.42 -0.03 1.00

B20 -0.70 -0.52 -0.17 -0.24 -0.37 0.01 -0.90 1.00

HE 0.10 0.60 0.08 0.03 0.54 0.23 0.34 -0.34 1.00

Un 0.23 0.12 0.03 0.38 0.48 0.31 0.31 -0.35 -0.01 1.00

DR -0.31 -0.54 -0.23 -0.18 -0.60 -0.10 -0.56 0.53 -0.56 -0.33 1.00

Mean 0.39 1291.53 88.28 4.63 0.50 6.63 3.92 11.19 2.88 3.87 95.97

S. Deviation 0.18 179.29 68.90 0.36 0.07 0.63 1.56 2.45 1.94 1.70 9.01

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Next we look at the horizontal position, i.e. values in a row from left to right. It is

observed that Remittances and HCI are highly correlated ( 0.5r ) and one variable

ought to be deleted; same is case for HE and HCI pair; and for Un and HCI pair. On the

other hand, DR is highly correlated with HCI, T2B, B20 and HE and likewise B20 and

T2B are highly correlated, Keeping in view these considerations, if Rem, DR, HE and

either T2B or B20 are excluded, the problem of multi-collinearity is solved to a great

extent. The model will assume the final form to include YPc, HCI, B20, Un as significant

explanatory variable and look similar to equation 5.2 shown above.

(iii) Rural Areas (of the four Provinces)

The results are shown in Table 5.3. In the rural areas, all the variables with the

exception of T2B and B20 have low correlation with the dependent variable (BNGI).

Therefore a criterion of r > [0.13] is considered for the purpose. This pinpoints the

variables Spc, Rem, HCI, T2B, B20, and Un to be significant. For possible conflicts in

data, the per capita income shows lower correlation with the dependent variable.

However, this variable cannot be excluded for reasons explained above and further its

close associate, namely the per capita saving has some acceptable correlation with

BNGI.

Next we look at the cross section of the selected variables to see their mutual

correlation. The correlation between HCI and Rem is high [ 0.67r ], and between B20

and T2B is high as usual [ 0.86r ]. If we exclude the variables Rem, and T2B, the final

model may be „good fit‟ and should comprise the four regressors, namely: YPc instead

of Spc, HCI, B20, and Un. The model may be written as equation 5.2 shown above.

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Next we look at the horizontal position, i.e. values in a row from left to right. It is

observed that Remittances and HCI are highly correlated ( 0.5r ) and one variable

ought to be deleted; same is case for HE and HCI pair; and for Un and HCI pair. On the

other hand, DR is highly correlated with HCI, T2B, B20 and HE and likewise B20 and

T2B are highly correlated, Keeping in view these considerations, if Rem, DR, HE and

either T2B or B20 are excluded, the problem of multi-collinearity is solved to a great

extent. The model will assume the final form to include YPc, HCI, B20, Un as significant

explanatory variable and look similar to equation 5.2 shown above.

Table 5.3 Correlations, Means and Standard Deviations Rural Areas (four provinces)


BNGI 1.00

Ypc -0.02 1.00

Spc -0.16 0.80 1.00

Rem 0.23 0.08 -0.15 1.00

HCI 0.13 0.20 0.03 0.67 1.00

HS -0.10 -0.08 -0.19 0.56 0.70 1.00

T2B 0.60 0.50 0.20 0.32 0.27 -0.08 1.00

B20 -0.60 -0.49 -0.14 -0.34 -0.17 0.13 -0.86 1.00

HE -0.11 0.20 0.06 0.34 0.42 0.49 -0.06 -0.04 1.00

Un 0.28 0.18 0.01 0.46 0.50 0.33 0.35 -0.40 0.06 1.00

DR 0.10 -0.16 -0.23 0.34 0.18 0.25 0.27 -0.14 0.04 0.00 1.00

Mean 0.28 1118.91 62.69 4.92 0.44 6.57 2.61 13.51 1.34 3.43 116.50

S. Deviation 0.21 156.05 84.99 6.73 0.08 0.72 1.13 3.11 1.03 1.58 109.18

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(iv) Urban Areas (of the four Provinces)

Finally, we repeat the exercise for the urban areas of the four provinces. The

results are shown in Table 5.4. In this case, most of the proposed variables have

relatively high correlation with dependent variable when compared to the rural areas. If

the limit of r>0.4 in absolute value is set the criterion, then Ypc, Rem, HCI, T2B, B20,

HE, Un and DR turn out to be significant so far as their relationship with BNGI is

concerned.

Table 5.4 Correlations, Means and Standard Deviations

Urban Areas (four provinces) Variables BNGI Ypc Spc Rem HCI HS T2B B20 HE Un DR

BNGI 1.00

Ypc 0.47 1.00

Spc 0.29 0.25 1.00

Rem 0.44 0.15 -0.02 1.00

HCI 0.45 0.54 -0.24 0.48 1.00

HS 0.12 -0.33 0.25 0.15 -0.06 1.00

T2B 0.73 0.65 0.25 0.17 0.44 -0.14 1.00

B20 -0.93 -0.57 -0.29 -0.45 -0.54 -0.16 -0.79 1.00

HE 0.44 0.66 0.01 0.16 0.63 0.04 0.58 -0.60 1.00

Un 0.41 0.06 0.10 0.76 0.31 0.24 0.12 -0.42 0.02 1.00

DR -0.66 -0.61 0.00 -0.51 -0.51 0.16 -0.62 0.72 -0.50 -0.40 1.00

Mean 0.44 1682.84 162.47 3.34 0.62 6.83 8.93 8.93 6.66 6.07 86.84

S. Deviation 0.25 263.13 100.27 3.41 0.07 0.58 10.51 3.72 3.36 3.05 9.79

However, when the mutual correlation among these variables is considered, we

observe that B20 and T2B are highly correlated as usual [ 0.79r ]. Likewise, HE has

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high correlation with HCI [ 0.63r ] and B20 [r=-0.60], Un and Rem are highly

correlated [ 0.76r ], finally DR and B20 are highly correlated [ 0.72r ]. The high

correlation of some variables with per capita income can be ignore for plausible

reasons. Keeping in view these results, HE, Rem, T2B and DR can be excluded from

analysis. The model assumes the final form as under to include four explanatory

variables, Ypc, HCI, B20 and Un, and the equation for the purpose of regression

analysis will look similar to 5.2 shown above.

5.1.2 Static Panel Models

Consider a panel description of the relation between dependent and independent

variable given as it it itY X , where is vector of coefficients. The coefficients are

assumed to be constant, i.e. they do not vary over time and space. Under this

assumption, the panel specification becomes a static model.

Although the assumption of invariability of the coefficients for all cross-sections is

not reasonable due to heterogeneity among the cross-section, the model has the

capability to capture co-variation of the regressors and regressand. Furthermore, as the

sample size increases when we merge all the cross-sections, the degree of freedom

also increases, assuming the coefficients to remain unchanged. Therefore the model

provides precise estimates of the parameters. The model in equation 5.1 is estimated

using static panel model technique (OLS method) for four different groups of data, i.e.

Rural Areas, Urban Areas, Overall Areas, and aggregate Rural Urban Areas. The

results are discussed below:

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(a) Rural Areas (of the four Provinces)

The regression results for the four rural areas are shown in Table 5.5.

Table 5.5 Static Panel Model for Rural Areas Dependent Variable BNGI

Coefficient Std.Error t-value t-prob

Ypc -0.001005*** 0.0002015 -4.99 0.000

Spc 0.00052*** 0.0001345 3.88 0.000

Rem 0.000312 0.003780 0.0825 0.935

HCI 0.2627 0.4473 0.587 0.560

HS -0.01829 0.03197 -0.572 0.570

T2B 0.1016*** 0.02102 4.84 0.000

B20 -0.0304** 0.01207 -2.52 0.016

HE 0.00708** 0.002911 2.43 0.019

Un 0.00124 0.01560 0.0794 0.937

DR -0.00034*** 9.068e-005 -3.79 0.000

Constant 0.54853*** 0.2355 6.57 0.000

Sigma 0.1485222 Sigma^2 0.02205884

R^2 0.5872018

RSS 0.90441231308 TSS 2.190931

No. of observations 52 No. of parameters 11

Using robust standard errors

AR(1) test: N(0,1) = 1.194 [0.232]

AR(2) test: N(0,1) = -0.2814 [0.778]

Note: Statistical significance is indicated by asterisk signs.

*** Significant at 1% level, ** Significant at 5% level, * Significant at 10% level.

_______________________________________________________ Calculations are based on Pc-give (Ox Metrics)

The above results have been obtained for the rural areas (balanced panel). The

objective is to arrive at the suitable specification. Per capita income (Ypc), per capita

savings (Spc), ratio of income of top to bottom 20% (T2B), share of income held by the

bottom 20 % (B20), Higher Education (HE), and Dependency Ratio (DR) appear to be

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statistically significant using robust standard errors. The value of R2 indicates that model

is moderately fit. Since P-value of AR(1) and AR(2) tests are greater than 10 % (as well

as 1% and 5%), so we do not reject the null hypothesis of presence of 1st and 2nd order

autocorrelation, particularly between Ypc and Spc and between B20 and T2B.

Therefore, the variables Spc and T2B ought to be deleted from the model. The final

equation will again look like equation 5.2.

(b) Urban Areas (of the four Provinces)

Table 5.6 Static Panel Model for Urban Areas Dependent Variable BNGI


Ypc -9.47855e-005*** 2.962e-005 -3.20 0.003

Spc 0.0001104* 6.162e-005 1.79 0.081

Rem 0.00213 0.006736 0.316 0.753

HCI 0.102 0.1411 0.722 0.474

HS -0.027 0.01850 -1.50 0.142

T2B 0.00048 0.001046 0.464 0.645

B20 -0.0679*** 0.006084 -11.2 0.000

HE -0.0109*** 0.003807 -2.87 0.007

Un -0.00283 0.002990 -0.947 0.349

DR -0.00057 0.0008155 -0.706 0.484

Constant 1.43814*** 0.2828 5.09 0.000

Sigma 0.09468026 Sigma^2 0.008964352

R^2 0.8821278

RSS 0.3675384232 TSS 3.1181106731



AR(1) test: N(0,1) = 0.5297 [0.596]

AR(2) test: N(0,1) = -0.08453 [0.933]

___________________________________________ Calculations are based on Pc-give (Ox Metrics)

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Table 5.6 shows results for the urban areas obtained, using the static panel

method. According to this regression, the per capita income (Ypc), per capita savings

(Spc), share of income held by the bottom 20 % (B20), and Higher Education (HE)

appear to be statistically significant. However, the income per capita and saving per

capita may be highly correlated as indicated by AR (1) and AR (2) tests. Similarly, the

human capital (HCI) may be better variable than higher education (HE). The high value

of R2 is indicative of good fit. It is therefore safe to consider the model given by equation

5.2 to be appropriate.

(c) Overall Areas (of the four Provinces)

Table 5.7 indicates the estimation results of overall areas through static panel. It

is clear from the table that per capita income (Ypc), per capita savings (Spc), ratio of

income of top to bottom 20% (T2B), and share of income held by the bottom 20 %

(B20), are significant at 1% level. By omitting the insignificant variables, there is no

danger of specification bias. However, Ypc and Spc may be highly correlated as also

T2B and B20. Therefore, it is safe to delete Spc and T2B to arrive at the appropriate

model, which may look like equation 5.2. The value of R2 (0.65) is indicative of

reasonably good fit.

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Table 5.7 Static Panel Model for Overall Areas Dependent Variable BNGI


Ypc -0.00071*** 9.221e-005 -7.73 0.000

Spc 0.00049*** 0.0001492 3.29 0.002

Rem -0.004 0.004079 -0.981 0.332

HCI 0.623 0.5089 1.22 0.228

HS -0.0013 0.02001 -0.0630 0.950

T2B 0.0153*** 0.005011 3.05 0.004

B20 -0.0654*** 0.007193 -9.09 0.000

HE 0.00232 0.01247 0.186 0.854

Un -0.0127 0.009939 -1.29 0.206

DR -0.000164 0.001513 -0.108 0.914

Constant 1.68827*** 0.1194 14.1 0.000 Sigma 0.1188651 Sigma^2 0.01412891

R^2 0.6504891

RSS 0.57928543997 TSS 1.6574174423



AR(1) test: N(0,1) = 0.7601 [0.447]

AR(2) test: N(0,1) = -0.9316 [0.352]

--------------------------------------------------------------------------

Calculations are based on Pc-give (Ox Metrics)

(d) Aggregate Rural and Urban Areas (of the four Provinces)

Table 5.8 shows the results for aggregate rural and urban areas of the four

provinces. Interestingly only three variables, namely the per capita income (Ypc), ratio

of income of top to bottom 20% (T2B), and share of income held by the bottom 20 %

(B20), are significant. Due to high correlation of B20 and T2B, the latter may be dropped

from the analysis. Instead, we will prefer to adopt the model as per equation 5.2, which

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is supported by the tests mentioned above. Again, in this case, the high value of R2

(0.718) indicates that the model is good fit.

Table 5.8 Static Panel Model for Aggregate Rural Urban Areas Dependent Variable BNGI


Ypc -0.000303** 0.0001161 -2.61 0.011

Spc 2.99004e-005 0.0001695 0.176 0.860

Rem -0.000689065 0.003415 -0.202 0.841

HCI 0.332325 0.2553 1.30 0.196

HS -0.0242700 0.01992 -1.22 0.226

T2B 0.007185*** 0.002396 3.00 0.003

B20 -0.05055*** 0.005299 -9.54 0.000

HE -0.00433 0.003510 -1.23 0.220

Un 0.00395 0.005955 0.663 0.509

DR -8.30044e-005 0.0001200 -0.692 0.491

Constant 1.30175 *** 0.1689 7.71 0.000

Sigma 0.1339398 Sigma^2 0.01793988

R^2 0.7181438

RSS 1.6684085454 TSS 5.9193598365



AR(1) test: N(0,1) = 0.9214 [0.357]

AR(2) test: N(0,1) = -0.8521 [0.394]

__________________________________________


5.1.3 Impulse Saturation

The Impulse Saturation is the most advanced technique developed by David F.

Hendry and others at the London School of Economics in the year 2008. This technique

takes into account multiple criteria simultaneously, including the significance of

regressors, model adequacy, and the forecast performance. The estimation starts with a

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General Unrestricted Model (GUM) and then the variables are dropped one after the

other, provided they do not adversely affect the three criteria mentioned above. This

technique can be applied to any model using Pc-Give software. The present study

employs the above software proposed by Doornik (2009). The print-out is quite lengthy

and we reproduce only the final equation, which is also known as auto-selected model.

Other initial calculations are omitted for the precision purpose. The results of the model

for rural, urban, overall areas and aggregate rural and urban areas are given below in

Tables 5.9, 5.10, 5.11 and 5.12. We discuss the results briefly.

(1) Rural Areas (auto-Selected Model)

Table 5.9 Rural Areas (Auto Selected Model)

Dependent Variable BNGI Coefficient Std. Error t-value t-prob Partial R^2

Constant 1.1523 0.3092 3.73 0.0005 0.22

Ypc -0.0006 0.0002 -4.07 0.0002 0.26

T2B 0.0826 0.0360 2.30 0.0261 0.10

B20 -0.0294 0.0129 -2.28 0.0268 0.10

Sigma 0.144455 RSS 1.00162744

R^2 0.54283 F(3,48) = 19 [0.000]**

Log-likelihood 28.9053 DW 1.57


Mean(BNGI) 0.2825 Var(BNGI) 0.0421333

-------------------------------------------------------------------------------------------


The results reported in Table 5.9 indicate that out of the ten explanatory

variables, only three are significant, i.e. the per capita income (Ypc), ratio of income of

top to bottom 20% (T2B), share of income held by the bottom 20 % (B20). The program

has omitted all other variables automatically. The value of R2 shows average fit whereas

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Durbin Watson statistics shows that null hypothesis can neither be accepted nor

rejected. Small value of variance of BNGI however suggests that it is consistent.

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(2) Urban Areas (auto-Selected Model)

The results for urban areas for same number of observations are reported in

Table 5.10. Interestingly only two variables are reported to be significant and pass all

the tests of model selection, namely the share of income held by the bottom 20 %

(B20), Higher Education (HE). The value of R2 indicates that the model is very good fit

while DW value (1.93) suggests that null hypothesis of „No Autocorrelation of first order‟

cannot be rejected in the data. As stated earlier, we may keep the per capita income for

its significance on theoretical basis. Likewise, we may substitute the human capital

index (HCI) for higher education, being more comprehensive.

Table 5.10 Urban Areas (Auto Selected Model) Dependent Variable BNGI Coefficient Std.Error t-value t-prob Part.R^2

Constant 1.1338 0.0616 18.40 0.0000 0.87

B20 -0.0685 0.0041 -16.60 0.0000 0.85

HE -0.0130 0.0046 -2.83 0.0068 0.14

Sigma 0.0880839 RSS 0.380179475

R^2 0.878074 F(2,49) = 176.4 [0.000]**



Mean (BNGI) 0.435712 Var (BNGI) 0.0599637

-----------------------------------------------------------------------------------------------------


(3) Overall Areas (auto-Selected Model)

When overall areas are taken into account and the data confronted to the test for

model selection, the final reports obtained are shown in Table 5.11. As evident, the per

capita income (Ypc) and share of income held by the bottom 20 % (B20) come out to be

132

significant. All other measures R2, DW, Var (BNGI), F-test are reasonably satisfactory to

accept the model.

Table 5.11 Overall Areas (Auto Selected Model) Dependent Variable BNGI Coefficient Std.Error t-value t-prob Part.R^2

Constant 1.6443 0.1939 8.48 0.0000 0.59

Ypc -0.0004 0.0001 -3.91 0.0003 0.24

B20 -0.0673 0.0076 -8.81 0.0000 0.61

Sigma 0.114144 RSS 0.638415411

R^2 0.614813 F(2,49) = 39.11 [0.000]**




----------------------------------------------------------------------------------------------------------


(4) Aggregate Rural-Urban Areas (auto-Selected Model)

Table 5.12 Aggregate Rural and Urban Areas

Dependent Variable BNGI Coefficient Std.Error t-value t-prob Part.R^2

Constant 1.2373 0.1179 10.50 0.0000 0.52

Ypc -0.0002 0.0001 -4.41 0.0000 0.16

T2B 0.0065 0.0024 2.71 0.0078 0.07

B20 -0.0521 0.0052 -10.00 0.0000 0.50

Sigma 0.131443 RSS 1.72772233

R^2 0.708123 F(3,100) = 80.87 [0.000]**




-------------------------------------------------------------------------------------------------------------


The results for aggregate rural and urban areas are presented in Table 5.12.

Here the per capita income (Ypc), ratio of income of top to bottom 20% (T2B), and

133

share of income held by the bottom 20 % (B20) appear to be statistically significant.

Value of R2 indicates that model is good fit. DW statistic shows that we cannot accept

the hypothesis of autocorrelation to be present in the data.

The results for different regions of Pakistan using the above mentioned

techniques suggest different forms of the model. In most of the cases, the income per

capita (Ypc), the human capital index (HCI), the share of income held by the poor at the

bottom 20 % of population (B20) and unemployment rate (Un) appear to be significant.

The appropriate model will again look like that given in equation 5.2, reproduced below.

0 1 2 3 420i pc i i i i iBNGI Y HCI B Un V (5.2)

The objective of the present study is to empirically analyze the determinants of

basic needs fulfillment (BNF) using the data from different regions of Pakistan. The

study empirically reviews the basic needs approach to development and poverty

reduction strategies. We have employed different estimation techniques in a quest to

arrive at the appropriate and plausible determinants of basic needs gap index. There is

also a descriptive analysis of the different regions of Pakistan analyzing the present and

past state of basic need fulfillment and its linkage with the poverty. The analysis is

primarily focused on the comparison among various regions of Pakistan (rural as well as

urban). Based on the data set, and model restrictions, suitable econometric techniques

are used and compared to obtain the reliable estimates in the next sections.

134

5.2 Empirical Model

The relationship between the dependent and independent variables can be

summarized via panel representation, reproduced below:

1

K

it ki kit it

k

Y X

where

1,2,3....,i N and 1,2,3....,t T

In the above relation, KX is kth regressor and the subscript „ i „ denotes the region

concerned and „ t „ denotes variation overtime. The study will employ two econometric

techniques to estimate the model given in (5.2). The first one is Region Specific Least

Square (RSLS) method that provides the initial estimation. The second one is the

application of the Bayesian (empirical Bayes) procedure that utilizes the OLS estimates

as priors. This technique has two alternative versions, the first is due to Carrington &

Zaman (1994) and the second is due to Hsiao and Pesaran (2004). The rationale for

using these econometric techniques, the estimation procedure and simplifying

assumptions are explained in the next section.

5.2.1 Classical Bayes vs. Ordinary Least Square (OLS)

In case of ordinary least square (OLS), prior information about the parameters is

ignored and the parameters are assumed to be fixed. In contrast, the Bayes method

includes the prior knowledge about the parameters and has an improvement over the

OLS estimators; it further suggests a subjective explanation of statistics as opposed to

an objective analysis. For example, if we are interested in estimation of consumption

function, we may follow the least squares as well as the Bayes procedure. In case of

135

classical least squares, the parameters of the function are estimated and they are

assumed to possess certain characteristics, called the BLUE properties (best linear

unbiased estimator). The model assumes that the true population parameters are

constant or not varying. However, the consumption habits in the economy are random

and likely to change over time. Moreover, the parameter estimates should have some

desirable asymptotic properties, i.e. if sufficiently large data are available the estimates

tend to converge to the true value of the population parameter.

In the classical (OLS) case, the emphasis is on the estimator itself and the

statistics that describe it, whereas in Bayesian analysis, the explanation of an estimator

is fairly different. Contrary to making a point estimate, the Bayesian method formulates

a posterior density function for the data, which is different from a sampling distribution.

This can be understood with reference to a prior confidence about what one

believed. It is normally discussed as the odds a researcher would give when taking bets

on the true value of data. Therefore, one needs to identify the initial degree of belief

while using empirical evidence as a means of changing that belief. Thus, the Bayesian

approach, draws upon a prior density function as well as a posterior density function,

which is a stark improvement over the classical (OLS) approach.

According to Greene (2004), if the empirical results conflict with the theory, such

results are not accepted by the classical approach and these are liable to collapse in

order to uphold the theory. But in case of the Bayesian approach, theory can be

reformulated. The existing evidence is assembled and compared with the theory, beliefs

are formulated and based upon the existing evidence, then further evidence is collected

136

and previous beliefs are compared with the new evidence. In this way, the beliefs

regarding the theory are revised.

Berger (1985) mentioned the following advantages of Bayesian analysis:

Firstly, contrary to classical estimation, the Bayes method assumes that the

parameters are random with prior density. Due to this characteristic, Bayesian

estimation is considered suitable for panel data.

Second, the Bayesian method offers a natural way of comparing and contrasting

prior belief with the available evidence (data). The parameter estimates obtained

through the classical method in case of panel data can be used as prior. That is why

this is suitable technique for panel data models.

Third, the Bayesian estimates are more precise when compared with the

classical estimates i.e. the Bayesian estimates have smaller standard errors, and hence

the results are more reliable.

Fourth, the Bayesian procedure gives precise and reliable results even in case of

small samples whereas the classical procedure, estimates are consistent only

asymptotically, i.e. in large samples.

5.2.2 Bayesian Estimation Procedure

Suppose the model is given below in the compact format, where Y denotes the

dependent variable, the set of independent variables is given by the matrix X (of order

nxk), β is the column vector of the parameters and ε is the vector of random error.

137

Y= X

The maximum likelihood (ML) estimator of β is given by

1ˆ ( )X X X Y

Under the standard assumptions, ̂ has the following density

2 1ˆ ˆ~ ( , (X X) ) where the variance is given by 2

ˆ ˆ( ) ( )ˆ

( )

Y Y

T K

The Bayesian estimation procedure assumes the population parameter to be

randomly described by the density function ~ ( , )N where and are called

priors since they represent our prior knowledge about the parameters.

The posterior estimate is then the weighted average of the prior and data mean and is

given by

2 1 1 2 1ˆ ˆ ˆ( ) ( (X X) ) ( (X X) , )B E (5.3)

and the posterior variance is given by

1 1

2

1ˆ( ) ( )Var X X

2

1X X

is the inverse of the variance of the data density, called the precision of data

density, and 1 is the precision of the priors. The variance of posterior density is given

by: 1 1

2

1ˆ( ) ( )BayesVar X X

(5.4)

138

so that posterior precision is 1 1

2

1( )X X

This is the sum of the prior and the data precision. Therefore the Bayesian estimator is

more precise than the data and the prior.

5.2.3 Classical Bayes vs. Empirical Bayes

For the most part of the research work, the economists have generally avoided to

use the Bayesian methods. The reasons for this attitude are not because they object to

the underlying philosophical nature of subjectivist probability; instead, there are practical

reasons. Zaman, (1996) noted three major problems with the use of Bayesian

estimators in practice given below:

First, the Bayesian models may have unbounded risk, depending on choice of

priors. If prior is precise enough, the improvement over maximum likelihood

estimator is substantial. If the prior is less precise, then the improvement over ML

estimator is very small and therefore fruitless.

Second, there is the problem of choice of hyper parameters. The classical

Bayesian procedure for the choice of hyper parameters is arbitrary and there are

no specific rules for choosing priors.

Third, sometimes the conflict between data and prior creates problems for

investigators.

Empirical Bayes method is especially formulated to avoid these three difficulties

by making the selection of the priors only after looking at the data, and fixing the values

of the priors according to those of the data. Empirical Bayes technique gives more

139

precise results particularly when sample is small and where the OLS method is

imprecise (Carrington & Zaman, 1994). Due to this imprecision of OLS estimation, the

present study is also using empirical Bayes to substantiate the OLS estimation.

Bayesian estimation takes advantage of the prior knowledge obtained through OLS or

M.L. estimation. Empirical Bayes uses aggregate information from the data as prior and

hence gives precise estimates. Techniques for application of Bayes estimators are

given in Zaman (1996) and Geweke (2005).

5.3 Estimation Approaches

As discussed above, the present study uses three different approaches to find

the estimates. These approaches are as follows:

Ordinary least square (OLS) [Region Specific Least Square].

Empirical Bayes following Hsiao and Pesaran (2004)

Empirical Bayes following Carrington and Zaman (1996) and

The imprecision problem of OLS estimates, if any, can be overcome by employing the

empirical Bayes. We discuss briefly the two versions of the empirical Bayes

methodology in the following lines.

5.3.1 Empirical Bayes due to Hsiao and Pesaran (2004)

As the model (equation5.2) consumes a lot of degrees of freedom and lacks

explanatory power, therefore this type of model can be estimated by imposing certain

assumptions about the parameters to get the predictive power.

Consider the following model:

140

' '

it it it i i ittY Z W

where

1,2,3....,i N and 1,2,3....,t T (5.5)

itZ and itW are vectors of exogenous variables with dimension ( &l p ), where

1i

n is the average of all individual coefficients, and i i is the deviation

of individual specific effects from the common effects. i is assumed to be random with

mean 0 and variance .

In practical situations, '

itZ and '

itW may be equal to the vector of regressors '

itX ; in

which case the model reduces to

' ( )it it it itY X (5.6)

This is similar to assuming that it it

with

We further assume that the parameter value doesn‟t change with time, in which case we

get it i

, and i is independently distributed multivariate normal with (0, )i N

The model finally reduces to

itit it itY X , (5.7)

which looks similar to equation 5.1 that we wanted to estimate. This formulation is

useful to decompose the coefficients into general and region specific effects. Under the

simplifying assumptions discussed above, the final empirical model can be formulated

as:

Y Z W U (5.8)

141

where 1

1

2

NT

N

y

yy

y

1

1

2

T

i

i

i

iT

y

yy

y

, 1

1

2

NT

N

U

UU

U

,

1

2

1

i

i

iT

iT

u

uU

u

,

1

2

NT l

N

Z

ZZ

Z

,

1

2

i

i

iT l

iT

Z

ZZ

Z

1

2

0 0

0NT Np

N

W

W W

W

'

1

'

2

'

i

i

iT p

iT

w

wW

w

and

1

2

1Np

N

Is the proportion of coefficients of regressor that doesn‟t change with regions and i

contains information on region specific effects.

142

Following Hsiao and Pesaran (2004), it is assumed that:

Prior distribution of and i are independent. That is ( , ) ( ). ( )P P P

The prior for is flat, which states that we have no information about , i.e.

( )P constant.

Prior Distribution of is known i.e. (0, )NN I

Under these assumptions, and given and , we have ( , )Y N Z W C ,

where cov( )C . The empirical Bayes estimates the prior parameters from the

marginal distribution, which in turn is derived by Hsiao and Pesaran (2004) as under:

( , ( ) ))NY N Z C W I W (5.9)

The matrix Z contains the exogenous regressors as defined in equation 5.8. Therefore,

can be obtained as least square estimate of by regressing Y on Z. However, we

should have estimates of C and to compute posterior estimates of parameters. Now

if the classical Bayes procedure is followed, value of and C is computed as modal

value of and C for the arbitrary choice of α and . The iteration starts by assuming

some arbitrary value of and , for any given value of and , one can compute

value of C and using many such values of and .

The mode of the values of C and are chosen as the hyper parameters.

143

The empirical Bayes procedure differs from the classical Bayes in that the hyper

parameters are also estimated from data.

Therefore, the variance covariance matrix of i given by can be estimated as

1( )( )i i

N

(5.10)

Now to estimate C we have to find out estimated covariance matrix of errors for all cross

sections. Since errors are assumed iid, the covariance matrix takes the form;

2

1

2

2

0 0

0 0

0 0

T

T

I

C I

Where 2

/i i T K and is given in equation (5.7).

when we have and C the posterior estimates of individual effects and their respective

variance covariance matrix is given by,

1 1 1 1 1 1 1 1 1 1 1 1{ [ ( ) ] ( )} { [ ( ) ] }NW C C Z Z C Z Z C W I W C C Z Z C Z Z C Y

(5.11)

and 1 1 1 1 1 1 1{ [ ( ) ] ( )}ND W C C Z Z C Z Z C W I

(5.12)

5.3.2 Empirical Model Following Carrington and Zaman

According to Carrington and Zaman (1994), the relationship between dependent

and independent variables can be summarized in m regression models of the form,

it it i iY X

1,2,...,i m (5.13)

144

where itX is the matrix of regressors for region i , itY is the dependent variable, and

i is the vector of coefficients for the region, which is assumed to be randomly

distributed with some mean and variance: ~ ( , )i N where is vector of the

common effects for all the regions.

The OLS estimate of equation (5.13) is then 2 2 ' 1, , ( )OLS

i i i i i i iN X X

where

' 1 '( ) .OLS

i i i i iX X X Y (5.14)

Empirical Bayes assumes that the true parameter values for the individual

regions are inter related and that i has a normal prior distribution of the form

[ , ] ( , ).i N (5.15)

The exchangeability condition assumes some centralized point around which the

individual region parameters are likely to be normally distributed. This provides a

justification for cross regional analysis within a country instead of cross country

analysis, where exchangeability condition is weak.

Having exchangeability assumption, we specify the hyper parameters and

and find empirical Bayes estimates 'i s , where the Bayesian estimator is given by:

1 2 ' 1( ),Bayes OLS

i i i i i iD X X (5.16)

where 2 ' 1

i i i iD X X (5.17)

145

This is the weighted average of the OLS estimate, and the assumed prior mean, where

the weights are the estimated variances of the OLS estimators and the assumed prior

variance.

Empirical Bayes (EB) allows and to be estimated directly from the provinces

distribution of the OLS parameters and the estimate of is found by,

1

' '

2 21 1

1 1T T

i i i i

i ii i

X X X Y

(5.18)

which is weighted average of the province specific OLS estimates, where the weights

are inversely related to the parameters estimated variance.

To find , we follow Blattberg and George (1991) and Carrington and Zaman

(1994), and restrict the off diagonal elements of to be zero so that

1 2 7( , ,..., ),diag which assumes no prior covariance among the coefficients.

2 2

1

1( )

1

T

i ij j i ij

i

aT

(5.19)

We then re-estimate the 'i s with (5.18, 5.19) and re-estimate each element of with

1

' 1 ' 1

2 21 1

1 1( ( ) ) ( ( ) )

T T

j i i j i i i j i ij

i ii i

X X X Y

(5.20)

Having solutions of (5.16), (5.19) and (5.20), the estimated variance of the posterior

distribution of the

'i s are obtained as:

146

1

11 2 ' 1var( ) ( )EB

i i i iX X

(5.20)

This is smaller than the variance of the OLS estimator. This precision is due to the

increased information incorporated into the model.

Here we assess and determine impact of various factors on BNGI across

different regions (rural/urban) of Pakistan. Various regressors like per capita income

Ypc, human capital index HCI, ratio of income of richest 20% to bottom 20% T2B, share

of income held by bottom 20% B20, higher education HE and unemployment Un are

included to see their impact on BNGI. However the final model includes only four

regressors i.e. Ypc, HCI, B20, and Un. The results are reported in the next chapter,

employing all the techniques of analysis.

147

CHAPTER 6

EMPIRICAL RESULTS AND ANALYSIS

This chapter presents results obtained by confronting the data to model

discussed in chapter 4 and 5 via the estimation techniques namely the OLS and

empirical Bayes. The model is reproduced for ready reference (Equation 5.2):

itititititpcit UnBHCIYBNGI 432,10 20

As already discussed, most of the data is drawn from various issues/editions of

the HIES/ PSLM/ Economic Survey/ Labour Force Survey and other official documents.

However, some of the composite variables, particularly BNGI and HCI, have been

constructed. The subscript ‘i’ stands for the region concerned and ‘t’ for time.

The results are presented under four different categories, i.e. (1) rural areas, (2)

urban areas, (3) overall areas province-wise, and (4) aggregate rural-urban areas. The

last category (aggregate rural-urban areas) is included for the advantage of having the

maximum number of observations, i.e. 104. Moreover, this category provides different

priors and different estimates for application of empirical Bayes techniques. This

difference also indicates that common priors cannot be taken in the estimation of rural,

urban and overall regions.

Apparently, the three approaches to estimation are based on similar

assumptions, but methodologically one is different from the other so far as precision of

results is concerned. OLS is widely used and it is appropriate for large samples.

However, it may lead to imprecise results particularly when the sample size is small.

148

Owing to these shortcomings in the least-squares method, the present study also

employs empirical Bayes approach that generates more precise estimates.

Another obvious reason to estimate and compare the results obtained through

the OLS with empirical Bayes is that the latter satisfies the parsimony criterion required

for model selection. This criterion states that simple explanation should always be

preferred to the complex one. In econometrics literature, if one obtains similar values of

R-Square in two regression models, the model with fewer explanatory variables needs

to be preferred. A practical advantage of parsimony is that one is unlikely to run up

against the degree of freedom problem. The number of observations in time series is

rarely large, so the lack of parsimony frequently leads to imprecise estimates. Still

another advantage of empirical Bayesian technique is that it can explain the results of

other models, specifically the Least Squares method.

The credibility and usefulness of any study can be judged by the major findings,

which are primarily based on the empirics. By adopting suitable methodology and

reaching at some meaningful results, one can assess the economic condition of the

country/region concerned. The economy of Pakistan is agro-based and majority of the

population is rural. Therefore, the rural sector should be given more importance. At the

outset, the empirical findings of the rural regions of Pakistan are discussed. We have

found the results of rural and urban areas to be more informative and meaningful when

taken separately as compared to the results of overall and aggregate rural-urban case.

We may expect the outcome or the impact of explanatory variables on the

dependent variable on the basis of economic theory. The dependent variable (BNGI)

149

measures the extent or depth of poverty. Naturally, the level of income per capita should

affect poverty negatively. Similarly, the share of income held by the bottom 20% of the

population should be negatively related to the dependent variable, i.e. the higher this

share, the lower will be poverty. On the other hand, the rate of unemployment should

have a positive impact on the extent of poverty, i.e. the higher the rate of

unemployment, deeper is the poverty. How the volume of human capital (education

level and health status) affect the dependent variable is a little bit complicated

phenomenon. If higher human capital leads to higher job opportunities/employment and

income generation, then the poverty level should go down. All these theoretical

predictions are supported only by the results obtained from urban Punjab.

The chapter consists of five sections. Section 6.1 is about results of rural areas

and section 6.2 is for the urban areas; whereas overall provinces are discussed in 6.3.

Section 6.4 presents the results of rural and urban areas by using the priors obtained

from the aggregate data i.e. eight rural-urban regions. Section 6.5 is devoted to

sensitivity analysis.

6.1 Rural Areas

As explained above, we analyze the data of rural areas of the four provinces

separately. So far as Bayesian estimation is concerned, we use the prior information

obtained from the data of all four rural regions in each case.

6.1 (a) Rural Punjab

We begin our analysis with the results obtained for rural Punjab, which are

shown in Table 6.1(a). First column shows the results of ordinary least squares (OLS).

The second column displays the results of empirical Bayes technique following Hsiao

150

and Pesaran (HP). The third column reports the results obtained through empirical

Bayes technique as suggested by Carrington and Zaman (CZ). The values of R-square

i.e. 0.82 in OLS, 0.75 in HP, and 0.80 in CZ are reasonably high reflecting the

appropriateness of our model.

The coefficient of per capita income (Ypc) is highly significant for both OLS and

CZ but it is insignificant in the case of HP. In column one, OLS estimates imply that a

unit increase in per capita income will result in decreasing the basic needs gap index by

0.001 units. The estimated coefficients of human capital index (HCI) for OLS, HP, and

CZ are statistically significant at different levels and indicate positive relationship with

BNGI for rural Punjab, which goes against the common wisdom. The share of income

held by bottom 20 percent (B20) shows a negative and significant impact on BNGI.

This result supports the theoretical perception. The results on unemployment (Un) show

that this factor does not carry any significant relationship with BNGI1. The negative sign

is contradictory to the conventional economic theory; however the reason behind this

might be the disguised employment in rural areas or some problems in data. Haider

(2006) is also skeptical for the data on unemployment in Pakistan.

1 Although it is significant at 10 % in OLS model (p=0.0658)

151

Table 6.1(a)

OLS and Empirical Bayes Estimates for Rural Punjab

Dependent Variable: BNGI

Least Squares Empirical Bayes HP Empirical Bayes CZ

CONSTANT 1.66** 1.09 1.49***

standard error 0.56 0.6693 0.3748

T- value 2.97 1.63 3.99

P- value 0.018 0.141 0.004

Ypc -0.001** -0.0005 -0.0008**

standard error 0.00 0.0004 0.0002

T- value -3.32 -1.35 -3.35

P- value 0.01050 0.2149 0.01007

HCI 2.7041*** 1.6205* 2.0139**

standard error 0.80 0.8159 0.64

T- value 3.38 1.99 3.13

P- value 0.0096 0.0822 0.0140

B20 -0.075*** -0.0624** -0.0712***


T- value -3.52 -2.62 -4.57

P- value 0.0078 0.0306 0.0018

Un -0.082* -0.0290 -0.053

standard error 0.04 0.0492 0.03205

T- value -2.13 -0.60 -1.68

P- value 0.0658 0.5671 0.1307

R Square 0.82 0.75 0.80

Note: Statistical significance is indicated by asterisk sign.

***Significant at 1% level. ** Significant at 5% level.* Significant at 10% level.

6.1 (b) Rural Sindh

The estimates for rural Sindh are reported in Table 6.1(b). Conventionally, per

capita income is considered as an important variable that can reduce basic needs gap

index. The coefficient of this variable (Ypc) in rural Sindh, as expected, has negative

152

relationship with BNGI. Here (Ypc) is highly significant in case of OLS and CZ; whereas,

it is significant at 10 percent level in (HP).

Table 6.1(b)

OLS and Empirical Bayes Estimates for Rural Sindh


OLS Empirical Bayes HP Empirical Bayes CZ

CONSTANT 2.1*** 1.598** 1.84***

standard error 0.41 0.6290 0.3159

T- value 5.15 2.54 5.83

P- value 0.0008 0.03459 0.00030

Ypc -0.001*** -0.0007* -0.00096***

standard error 0.00 0.0004 0.00026

T- value -3.84 -1.89 -3.67

P- value 0.0049 0.09478 0.00629

HCI 1.44* 0.5683 1.319*

standard error 0.73 0.7889 0.60228

T- value 1.96 0.72 2.19

P- value 0.08510 0.49183 0.05977

B20 -0.058*** -0.0566** -0.053***

standard error 0.01 0.0214 0.01238

T- value -4.44 -2.65 -4.28

P- value 0.0022 0.02948 0.00267

Un -0.19* -0.0448 -0.169*

standard error 0.09 0.0574 0.08312

T- value -2.24 -0.78 -2.04

P- value 0.0550 0.45720 0.07516

R Square 0.81 0.65 0.81


The Human capital index (HCI) is positively correlated with BNGI in rural Sindh.

This implies that with an improvement in human capital index, BNGI widens. Again, this

result goes against the common wisdom. In other words, as education level and health

of people improves, the gap between the rich and the poor increases and the poor fail to

153

escape poverty line in rural Sindh. One plausible reason of such relationship might be

that people affording higher level of human capital belong to the middle and upper

class. In other words, health and education are not much important for the poor people

in Sindh or they have rare access to such facilities. The impact of the share of income

held by bottom 20% (B20) is negative with BNGI and the variable is highly significant.

The basic needs gap index is positively related by unemployment level in rural Sindh as

also observed in the case of rural Punjab.

6.1(c) Rural KPK (Khyber Pakhtunkhwa)

The OLS and empirical Bayes estimates for rural KPK are illustrated in Table

6.1(c). The results for Ypc are somewhat poor. Though the algebraic signs associated

with this variable are according to the prior expectations but the variable is insignificant

in reducing the poverty gap in rural KPK.

Contrary to the case of rural Punjab and rural Sindh, HCI bears the expected

negative sign in case of KPK, but it is statistically insignificant. The share of income held

by bottom 20% is significant for OLS and HP estimations. The unemployment rate has

positive relationship with BNGI (except in case of CZ) but is statistically insignificant.

The value of R-square signals that model is poorly fitted.

To summarize the results, all the variables carry the expected signs but in most

cases, they are insignificant so far their impact on the dependent variable is concerned.

The reason for this outcome may be problems in data or the vicious circle of poverty

which is very severe in case of KPT and Baluchistan.

154

Table 6.1(c)

OLS and Empirical Bayes Estimates for Rural KPK



CONSTANT 2.79** 1.94** 1.58***


T- value 2.99 2.59 3.65

P- value 0.017 0.032 0.006

Ypc -0.00102* -0.0006 -0.00058

standard error 0.00 0.0004 0.00031

T- value -2.10 -1.44 -1.82

P- value 0.068 0.188 0.10700

HCI -1.44 -1.169 -0.416

standard error 0.79 0.8021 0.54252

T- value -1.83 -1.46 -0.77

P- value 0.104 0.182 0.46421

B20 -0.06* -0.049* -0.027

standard error 0.03 0.0258 0.0222

T- value -1.87 -1.90 -1.24

P- value 0.09825 0.09428 0.249

Un 0.021 0.0333 -0.0222


T- value 0.40 0.64 -0.49

P- value 0.701 0.539 0.634

R Square 0.51 0.35 0.39


6.1(d) Rural Balochistan

The estimated results for rural Balochistan are shown in Table 6.1(d). These

reflect similar picture as shown for rural Punjab and rural Sindh with the exception of

positive sign associated with unemployment. Although it is statistically insignificant but

155

the sign is correct it is in accordance with the conventional theory. The value of R-

square is smaller for rural Balochistan as compared to rural Punjab and rural Sindh.

Table 6.1(d)

OLS and Empirical Bayes Estimates for Rural Balochistan



CONSTANT 1.93*** 1.48* 1.65***


T- value 3.90 2.28 4.67

P- value 0.00450 0.05205 0.00160

Ypc -0.001** -0.0006 -0.0008***

standard error 0.00 0.0004 0.00023

T- value -3.22 -1.66 -3.53

P- value 0.01230 0.13513 0.00768

HCI 0.018 -0.1050 0.292

standard error 0.69 0.7941 0.57876

T- value 0.03 -0.13 0.50

P- value 0.97891 0.89747 0.62740

B20 -0.047** -0.044* -0.045***

standard error 0.02 0.0219 0.01333

T- value -3.07 -2.01 -3.40

P- value 0.01541 0.07955 0.00931

Un 0.032 0.040 0.027

standard error 0.04 0.0489 0.03283

T- value 0.92 0.82 0.81

P- value 0.38406 0.43543 0.44054

R Square 0.72 0.63 0.71


156

6.2 Urban Areas

The results obtained by OLS and empirical Bayes estimation for urban areas of

Pakistan scattered through the four provinces are shown in Table 6.2(a) to Table 6.2(d).

As explained above, we use the the prior information obtained from the data of all four

urban regions in the Bayesian estimation.

6.2 (a) Urban Punjab

Table 6.2(a) shows results for urban Punjab.

If we compare results for urban and rural Punjab, we come to conclude that all

the coefficients carry the correct signs but only one variable (B20) is statistically

significant. Ypc bears the correct sign for both areas, but it is insignificant in case of

urban area. However, HCI is negatively related to BNGI in urban Punjab, which is

according to expectations. This variable is statistically significant for empirical Bayes

(CZ). The share of income held by bottom 20% tells the same story for both rural and

urban areas of Punjab, which implies that the nature and impact of income distribution is

similar. Unemployment level in urban areas is positively related with the basic needs

gap, which is in accordance with the prior expectation. A high value of R- square signals

that model is good fit.

6.2 (b) Urban Sindh

Findings regarding the relationship of Ypc, HCI, B20, and Un with BNGI for urban

Sindh are given in Table 6.2(b). The coefficient on Ypc bears negative sign and is

statistically insignificant. The coefficient on HCI is negative for all the methods

employed, as compared to the positive values of rural Sindh and is significant in CZ.

157

The impact of unemployment is positive in case of OLS and empirical Bayes (CZ),

whereas it is negative for empirical Bayes (HP). These contradictory results pose some

questions about the quality of data about unemployment in Pakistan. The estimated

results regarding the share of income held by bottom 20%, presents similar trends for

both rural and urban areas.

Table 6.2(a)

OLS and Empirical Bayes Estimates for Urban Punjab


OLS

Empirical Bayes HP

Empirical Bayes

CZ

CONSTANT 1.59*** 1.41** 1.33***

standard error 0.3890 0.4299 0.1783

T- value 4.10 3.28 7.51

P- value 0.0030 0.0110 0.0001

Ypc -0.0002 -0.00012 -0.0001

standard error 0.0002 0.0002 0.0001

T- value -0.90 -0.59 -1.43

P- value 0.3900 0.5708 0.1896

HCI -0.31 -0.25 -0.18**

standard error 0.7649 0.6191 0.0603

T- value -0.40 -0.40 -2.96

P- value 0.7004 0.7009 0.0183

B20 -0.08*** -0.07*** -0.07***

standard error 0.0118 0.0111 0.0075

T- value -6.82 -6.68 -9.83

P- value 0.0001 0.0002 0.0000

Un 0.011 0.003 0.010

standard error 0.0148 0.0169 0.0109

T- value 0.77 0.20 0.93

P- value 0.4621 0.8445 0.3794

R Square 0.93 0.92 0.92


158

Table 6.2(b)

OLS and Empirical Bayes Estimates for Urban Sindh



CONSTANT 1.54** 1.29** 1.30***

standard error 0.5665 0.4545 0.1892

T- value 2.72 2.83 6.85

P- value 0.0264 0.0221 0.0001

Ypc -0.0001 -0.0001 -0.0001

standard error 0.0002 0.0002 0.0001

T- value -0.51 -0.28 -1.04

P- value 0.6237 0.7833 0.3286

HCI -0.39 -0.21 -0.18**

standard error 0.8363 0.6438 0.0603

T- value -0.46 -0.32 -2.97

P- value 0.6553 0.7581 0.0180

B20 -0.07*** -0.07*** -0.07***

standard error 0.0121 0.0112 0.0074

T- value -5.87 -6.05 -9.09

P- value 0.0004 0.0003 0.0000

Un 0.0008 -0.0010 0.0081

standard error 0.0327 0.0200 0.0275

T- value 0.02 -0.05 0.29

P- value 0.9807 0.9602 0.7767

R Square 0.91 0.91 0.91


6.2(c) Urban KPK

OLS and empirical Bayes estimate for urban KPK are given in Table 6.2(c).

It shows somewhat different results from rural KPK. Here Ypc have positive sign in case

of OLS and HP, while it is negative in case of CZ and is insignificant in all the cases.

HCI is significant and bears the expected sign in case of CZ, and is insignificant in other

cases. Unemployment shows negative relationship with BNGI in case of HP and CZ but

159

it is insignificant. The impact of B20 on the poverty level (BNGI) is significant and

according to prediction of economic theory.

6.2.(d) Urban Balochistan

Results for urban Balochistan are given in Table 6.2(d). The sign of coefficients

of per capita income, human capital index, share of income held by bottom 20%, and

unemployment are in accordance with expectations in all the three approaches adopted.

HCI is significantly related with the basic needs gap index in CZ approach. Though

unemployment bears positive sign in both rural and urban areas, but it is statistically

insignificant. The impact of B20 on the poverty level (BNGI) is strongly significant and

according to prediction of economic theory.

160

Table 6.2(c)

OLS and Empirical Bayes Estimates for Urban KPK



CONSTANT 0.44 0.97* 1.18***

standard error 0.7678 0.4993 0.1881

T- value 0.5700 1.9427 6.2597

P- value 0.5843 0.0880 0.0002

Ypc 0.0002 0.00003 -0.0001

standard error 0.0002 0.0002 0.0001

T- value 0.78 0.16 -0.51

P- value 0.4601 0.8775 0.6232

HCI 0.14 -0.11 -0.18**

standard error 0.7348 0.6061 0.0600

T- value 0.19 -0.18 -3.04

P- value 0.8504 0.8604 0.0160

B20 -0.0388* -0.0559*** -0.0572***

standard error 0.0186 0.0124 0.0100

T- value -2.09 -4.50 -5.71

P- value 0.0703 0.0020 0.0004

Un 0.0029 -0.0001 -0.0002

standard error 0.0152 0.0169 0.0105

T- value 0.19 0.00 -0.02

P- value 0.8534 0.9976 0.9858

R Square 0.79 0.76 0.76


161

Table 6.2(d)

OLS and Empirical Bayes Estimates for URBAN Balochistan


OLS Empirical Bayes HP

Empirical Bayes

CZ

CONSTANT 1.45** 1.24** 1.32***

standard error 0.5845 0.4643 0.1863

T- value 2.49 2.66 7.06

P- value 0.0377 0.0287 0.0001

Ypc -0.0004 -0.0001 -0.0002

standard error 0.0003 0.0002 0.0002

T- value -1.13 -0.63 -1.15

P- value 0.2909 0.5444 0.2821

HCI -0.0127 -0.0720 -0.1744**

standard error 0.6253 0.5938 0.0600

T- value -0.02 -0.12 -2.91

P- value 0.9843 0.9065 0.0197

B20 -0.0587*** -0.0638*** -0.0591***

standard error 0.0125 0.0112 0.0116

T- value -4.68 -5.69 -5.10

P- value 0.0016 0.0005 0.0009

Un 0.0191 0.0051 0.0179

standard error 0.0325 0.0200 0.0291

T- value 0.59 0.25 0.61

P- value 0.5737 0.8066 0.5558

R Square 0.84 0.83 0.83


162

6.3 Overall Areas

Results for overall regions are given in Tables 6.3(a) to 6.3(d). The objective of

this analysis is to investigate whether the results for decomposed sections, in rural

urban split are compatible or otherwise with the estimated results for overall areas. The

situation of BNGI in rural Pakistan is significantly different from urban areas. Some

factors that play a vital role in reducing BNGI in the urban communities have very little

effect in rural areas.

6.3 (a) Overall Punjab

The empirics for overall Punjab are given in Table 6.3(a). As far as the

relationship between per capita income and the gap between basic needs is concerned,

it is negatively related with basic needs gap index as per expectation. The t-ratio

confirms that per capita is highly significant for OLS and both for empirical Bayes (CZ)

as well as (HP). These results are more consistent with rural areas of Punjab.

HCI shows positive sign, as was in the case of rural Punjab, and is statistically

significant in OLS and CZ approaches. Due to dominance of population concentration in

rural areas, the overall results are not very different from the results of rural areas only.

Share of income held by bottom 20% follows the same pattern as it is in rural and urban

Punjab and is highly significant. An interesting finding of the study is that the rate of

unemployment is negatively related with the reduction of poverty (basic needs gap

index) in overall Punjab as was observed in the case of rural Punjab.

The behaviour of rural and overall areas of Punjab is similar. This is because the

great part of population is residing in rural areas, having a great impact on picture of

overall Punjab.

163

Table 6.3(a)

OLS and Empirical Bayes Estimates for Overall Punjab



CONSTANT 1.30** 1.31** 1.61***

standard error 0.5160 0.4781 0.1353

T- value 2.52 2.74 11.91

P- value 0.03556 0.02541 0.000002

Ypc -0.0015*** -0.0007** -0.0012***

standard error 0.0004 0.0003 0.0003

T- value -3.94 -2.41 -4.43

P- value 0.0043 0.0428 0.0022

HCI 4.14*** 1.44 2.60**

standard error 1.2103 0.8709 0.9107

T- value 3.42 1.66 2.86

P- value 0.0090 0.1360 0.0212

B20 -0.07** -0.06*** -0.08***

standard error 0.0214 0.0174 0.0094

T- value -3.12 -3.58 -8.00

P- value 0.0143 0.0072 0.00004

Un -0.07* -0.03 -0.04

standard error 0.0317 0.0289 0.0260

T- value -2.16 -0.95 -1.52

P- value 0.0625 0.3692 0.1665

R Square 0.77 0.32 0.71


6.3 (b) Overall Sindh

The regression results for overall Sindh are given in Table 6.3(b). These results

are quite consistent in terms of sign and size with the rural Sindh. Per capita income in

both the cases is negatively related with the poverty BNGI and is highly significant. HCI

is insignificant and bears positive sign as was the case with rural Sindh. However, this is

164

contrary to the behaviour of urban Sindh. The coefficient for the variable B20 (share of

income held by bottom 20% population), is highly significant in reducing poverty.

Unemployment has negative connectivity with basic needs gap index both in rural Sindh

and overall Sindh but it is statistically insignificant.

Table 6.3(b)

OLS and Empirical Bayes Estimates for Overall Sindh



CONSTANT 2.06*** 1.69*** 1.66***

standard error 0.5432 0.4692 0.1356

T- value 3.80 3.59 12.24

P- value 0.0052 0.0071 0.000002

Ypc -0.0009*** -0.0006* -0.0008***

standard error 0.0003 0.0003 0.0002

T- value -3.51 -2.25 -3.61

P- value 0.0080 0.0544 0.0069

HCI 1.36 0.68 1.38*

standard error 0.7564 0.7374 0.6299

T- value 1.80 0.92 2.19

P- value 0.1099 0.3865 0.0598

B20 -0.09*** -0.07*** -0.07***

standard error 0.0197 0.0165 0.0117

T- value -4.35 -4.47 -6.26

P- value 0.0024 0.0021 0.0002

Un -0.05 -0.03 -0.04

standard error 0.0386 0.0289 0.0360

T- value -1.31 -0.97 -1.02

P- value 0.2279 0.3591 0.3371

R Square 0.80 0.58 0.78


165

Table 6.3(c)

OLS and Empirical Bayes Estimates for Overall KPK



CONSTANT 2.10** 1.71** 1.63***

standard error 0.8357 0.5140 0.1380

T- value 2.51 3.32 11.84

P- value 0.0361 0.0105 0.000002

Ypc -0.0003 -0.0001 -0.0001

standard error 0.0004 0.0003 0.0002

T- value -0.71 -0.51 -0.66

P- value 0.5000 0.6230 0.5294

HCI -1.04 -1.02 -0.54

standard error 0.7906 0.7344 0.5018

T- value -1.32 -1.39 -1.09

P- value 0.2229 0.2007 0.3095

B20 -0.08*** -0.07*** -0.07***

standard error 0.0235 0.0169 0.0149

T- value -3.38 -4.18 -4.73

P- value 0.0096 0.0031 0.0015

Un 0.0098 0.0119 -0.0040

standard error 0.0403 0.0292 0.0359

T- value 0.24 0.41 -0.11

P- value 0.8138 0.6937 0.9143

R Square 0.66 0.32 0.64


6.3(c) Overall Areas KPK

The OLS and empirical Bayes estimate for overall KPK are presented in Table

6.3(c). Per capita income in KPK has inverse relation with the basic needs gap index as

expected. This result is consistent with rural KPK but contradictory to urban KPK.

Human capital index for overall KPK is negatively related to basic needs gap index,

which is supported by theory. Again this result is consistent with rural KPK, which

166

implies that the behaviour of majority residing in rural areas dominates the urban

behaviour. The share of income held by bottom 20% is highly significant and more or

less similar to urban KPK. Unemployment rate is statistically insignificant, and the

coefficient obtained by CZ approach differs in sign when compared to OLS and

empirical Bayes (HP).

6.3(d) Overall Balochistan

The results for overall Balochistan are given in Table 6.3(d). When the three

estimation techniques are employed to data for overall Balochistan, the correlation of

two variable, namely the per capita income and share of income held by bottom 20

percent, with the dependent variable is not only strong (significant) but also according to

theory. So far as the variable human capital index is concerned, its correlation with

basic needs gap index is weak (insignificant) and also contradictory to theoretical

expectation. Once again, the results show that human capital has to do little with

poverty reduction in case of Baluchistan.

The results suggest that unemployment has a positive correlation with poverty

measured by the basic needs gap index; and this is in line with theory. The OLS gives

correct but insignificant relationship. However, in case of empirical Bayes (HP) and

(CZ), the impact is not only insignificant but even the signs are not correct.

167

Table 6.3(d)

OLS and Empirical Bayes Estimates for Overall Balochistan



CONSTANT 2.10*** 1.72*** 1.67***

standard error 0.4575 0.4532 0.1338

T- value 4.59 3.80 12.48

P- value 0.0018 0.0052 0.0000

Ypc -0.0009** -0.0006* -0.0007***

standard error 0.0003 0.0003 0.0001

T- value -3.19 -2.09 -4.75

P- value 0.0127 0.0700 0.0015

HCI 0.41 0.09 0.69

standard error 0.5334 0.6938 0.4311

T- value 0.77 0.13 1.59

P- value 0.4612 0.9008 0.1505

B20 -0.069*** -0.067*** -0.066***

standard error 0.0121 0.0152 0.0115

T- value -5.72 -4.39 -5.76

P- value 0.0004 0.0023 0.0004

Un 0.0050 -0.0007 -0.0032

standard error 0.0282 0.0275 0.0263

T- value 0.18 -0.03 -0.12

P- value 0.8644 0.9802 0.9057

R Square 0.86 0.69 0.85


Summary of above results for the rural, urban and overall areas is given in Table

6.3(e). Where real per capita income , and share of income held by bottom 20 % has

pre-dominantly negative relationship. Human capital and Unemployment rate appeared

to be positively related with the BNGI in urban areas.

168

Table: 6.3(e) SUMMARY OF RESULTS 1

2

Explanatory Variables

Expected Theoretical Relationship with BNGI

Depicted Relationship General Relationship in Empirical Analysis

PUNJAB SINDH K P K BALOCHITAN

Rural Urban overall Rural Urban overall Rural Urban overall Rural Urban overall

Real Per capita Income

Ypc

(-)

(-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (+) OLS (-) OLS (-) OLS (-) OLS (-) OLS Pre-dominant (-)

(-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (+) HP (-) HP (-) HP (-) HP (-) HP

(-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ

Human capital Index

HCI

(-)

(+)OLS (-) OLS (+) OLS (+)OLS (-) OLS (+) OLS (-) OLS (+) OLS (-) OLS (+)OLS (-)OLS (+) OLS Most Likely

(+) for urban

areas (+) HP (-) HP (+) HP (+) HP (-) HP (+) HP (-) HP (-) HP (-) HP (-) HP (-) HP (+) HP

(+) CZ (-) CZ (+) CZ (+) CZ (-) CZ (+) CZ (-) CZ (-) CZ (-) CZ (+) CZ (-) CZ (+) CZ

Share of Bottom 20% Population in

Income

B20

(-)

(-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-) OLS (-)OLS (-) OLS Pre-dominant

(-) (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP (-) HP

(-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ (-) CZ

Rate of Unemployment

Un

(+)

(-)OLS (+) OLS (-) OLS (-) OLS (+) OLS (-) OLS (+)OLS (+) OLS (+) OLS (+)OLS (+)OLS (+) OLS Most Likely (+)

for urban areas

(-) HP (+) HP (-) HP (-) HP (-) HP (-) HP (+) HP (-) HP (+) HP (+) HP (+) HP (-) HP

(-) CZ (+) CZ (-) CZ (-) CZ (+) CZ (-) CZ (-) CZ (-) CZ (-) CZ (+) CZ (+) CZ (-) CZ

169

6.4 Rural-Urban Analysis using Aggregate Prior

This section presents an empirical analysis from different angles so as to

ascertain the relationship of regressors and regressand more precisely. We analyze the

rural and urban behavior in the four provinces, however using the prior (new) obtained

from all the eight areas for Bayesian estimation only. Core objective of the present study

is to investigate and quantify the relationship of per capita income, human capital index,

share of income held by bottom 20%, and unemployment rate with the level of poverty

reflected by the basic needs gap index in different regions of Pakistan for the period

1979 to 2007-08. The results for rural-urban aggregate areas are presented in Table

6.4(a) to 6.4(h).

6.4 (a) Rural Punjab

Results for rural Punjab, as depicted in Table 6.4(a), confirm again that results

based on empirical Bayes (CZ) are unambiguously precise as indicated by smaller

values of standard errors of respective variables. Only a slight difference in values of

coefficients computed by empirical Bayes HP can be observed. Value of R-Square

indicates that fit is moderately good in all the cases.

170

Table 6.4(a) OLS and Empirical Bayes Estimates for Rural Punjab

(using aggregate rural-urban Prior)



CONSTANT 1.67** 1.16 1.49***

standard error 0.5601 0.7186 0.4017

T- value 2.97 1.61 3.71

P- value 0.0178 0.1457 0.0060

Ypc -0.0011** -0.0005 -0.0008**

standard error 0.0003 0.0004 0.0003

T- value -3.32 -1.49 -3.07

P- value 0.0105 0.1747 0.0153

HCI 2.70*** 1.95* 1.88**

standard error 0.8002 0.9060 0.6589

T- value 3.38 2.15 2.86

P- value 0.0097 0.0638 0.0213

B20 -0.0755*** -0.0675** -0.0714***

standard error 0.0214 0.0230 0.0164

T- value -3.52 -2.93 -4.35

P- value 0.0078 0.0189 0.0024

Un -0.0820* -0.0389 -0.0494

standard error 0.0385 0.0504 0.0328

T- value -2.13 -0.77 -1.51

P- value 0.0658 0.4630 0.1697

R Square 0.82 0.65 0.80 ***Significant at 1% level. ** Significant at 5% level.* Significant at 10% level.

The analysis of results is given at the end.

171

6.4 (b) Urban Punjab

Table 6.4(b)

OLS and Empirical Bayes Estimates for Urban Punjab




CONSTANT 1.60*** 1.25* 1.50***

standard error 0.3890 0.6572 0.3181

T- value 4.10 1.90 4.71

P- value 0.0034 0.0940 0.0015

Ypc -0.0002 0.0001 -0.0002

standard error 0.0002 0.0003 0.0002

T- value -0.91 0.19 -1.07

P- value 0.3900 0.8544 0.3146

HCI -0.31 -0.34 -0.13

standard error 0.7649 0.9066 0.6298

T- value -0.40 -0.37 -0.20

P- value 0.7004 0.7210 0.8471

B20 -0.081*** -0.077*** -0.079***

standard error 0.0118 0.0187 0.0104

T- value -6.82 -4.12 -7.55

P- value 0.0001 0.0033 0.0001

Un 0.011 0.023 0.009

standard error 0.0148 0.0433 0.0137

T- value 0.77 0.54 0.67

P- value 0.4621 0.6059 0.5215

R Square 0.93 0.23 0.93 ***Significant at 1% level. ** Significant at 5% level.* Significant at 10% level.

172

6.4 (c) Rural Sindh

Table 6.4(c)

OLS and Empirical Bayes Estimates for Rural Sindh (using aggregate rural-urban Prior)



CONSTANT 2.11*** 1.62** 1.89***

standard error 0.4093 0.6627 0.3317

T- value 5.15 2.45 5.69

P- value 0.0009 0.0399 0.0005

Ypc -0.0011*** -0.0007* -0.0010***

standard error 0.0003 0.0004 0.0003

T- value -3.84 -1.89 -3.56

P- value 0.0049 0.0956 0.0074

HCI 1.44* 0.98 1.17*

standard error 0.7332 0.8950 0.6154

T- value 1.96 1.09 1.90

P- value 0.0852 0.3074 0.0944

B20 -0.058*** -0.059** -0.053***

standard error 0.0131 0.0192 0.0125

T- value -4.44 -3.05 -4.27

P- value 0.0022 0.0157 0.0027

Un -0.19* -0.10 -0.16*

standard error 0.0868 0.0721 0.0836

T- value -2.24 -1.40 -1.96

P- value 0.0555 0.1993 0.0860

R Square 0.82 0.70 0.81


173

6.4 (d) Urban Sindh

Table 6.4(d) OLS and Empirical Bayes Estimates for Urban Sindh




CONSTANT 1.54** 1.15 1.41***

standard error 0.5665 0.7193 0.3954

T- value 2.72 1.60 3.57

P- value 0.0264 0.1479 0.0073

Ypc -0.0001 0.0001 -0.0001

standard error 0.0002 0.0003 0.0002

T- value -0.51 0.40 -0.69

P- value 0.6237 0.6983 0.5114

HCI -0.39 -0.33 -0.16

standard error 0.8363 0.9385 0.6515

T- value -0.46 -0.36 -0.25

P- value 0.6553 0.7307 0.8071

B20 -0.071*** -0.068*** -0.070***

standard error 0.0121 0.0187 0.0100

T- value -5.87 -3.62 -6.92

P- value 0.0004 0.0068 0.0001

Un 0.0008 0.0155 0.0034

standard error 0.0327 0.0493 0.0303

T- value 0.0249 0.3153 0.1121

P- value 0.9807 0.7606 0.9135

R Square 0.91 0.43 0.91


174

6.4 (e) Rural KPK

Table 6.4(e) OLS and Empirical Bayes Estimates for Rural KPK




CONSTANT 2.80** 2.04** 1.66***

standard error 0.9367 0.8426 0.4798

T- value 2.99 2.42 3.46

P- value 0.0174 0.0417 0.0085

Ypc -0.0010* -0.0005 -0.0006

standard error 0.0005 0.0004 0.0003

T- value -2.10 -1.14 -1.72

P- value 0.0688 0.2891 0.1244

HCI -1.44 -1.23 -0.60

standard error 0.7864 0.9017 0.5591

T- value -1.83 -1.37 -1.07

P- value 0.1043 0.2087 0.3149

B20 -0.060* -0.053* -0.029

standard error 0.0323 0.0265 0.0230

T- value -1.87 -1.99 -1.27

P- value 0.0983 0.0814 0.2413

Un 0.0205 0.0208 -0.0166

standard error 0.0517 0.0552 0.0455

T- value 0.40 0.38 -0.36

P- value 0.7017 0.7163 0.7252

R Square 0.51 0.39 0.42


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6.4 (f) Urban KPK

Table 6.4(f) OLS and Empirical Bayes Estimates for Urban KPK




CONSTANT 0.44 0.27 0.97*

standard error 0.7678 0.8204 0.4443

T- value 0.57 0.33 2.18

P- value 0.5843 0.7476 0.0605

Ypc 0.0002 0.0004 0.00003

standard error 0.0002 0.0003 0.0002

T- value 0.78 1.24 0.17

P- value 0.4601 0.2494 0.8699

HCI 0.14 -0.05 -0.20

standard error 0.7348 0.8906 0.5127

T- value 0.19 -0.05 -0.39

P- value 0.8504 0.9580 0.7048

B20 -0.0388* -0.0402* -0.0498***

standard error 0.0186 0.0217 0.0147

T- value -2.09 -1.86 -3.38

P- value 0.0703 0.1007 0.0096

Un 0.0029 0.0164 0.0034

standard error 0.0152 0.0435 0.0144

T- value 0.19 0.38 0.23

P- value 0.8534 0.7159 0.8209

R Square 0.79 0.06 0.77


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6.4 (g) Rural Baluchistan

Table 6.4(g) OLS and Empirical Bayes Estimates for Rural Balochistan




CONSTANT 1.93*** 1.52** 1.68***

standard error 0.4960 0.6957 0.3745

T- value 3.90 2.19 4.48

P- value 0.0046 0.0604 0.0021

Ypc -0.0010** -0.0006 -0.0008***

standard error 0.0003 0.0004 0.0002

T- value -3.22 -1.66 -3.41

P- value 0.0123 0.1350 0.0092

HCI 0.019 0.054 0.118

standard error 0.6928 0.8722 0.5902

T- value 0.0273 0.0622 0.2000

P- value 0.9789 0.9519 0.8465

B20 -0.047** -0.047** -0.043**

standard error 0.0153 0.0200 0.0136

T- value -3.07 -2.34 -3.18

P- value 0.0154 0.0475 0.0130

Un 0.032 0.038 0.033

standard error 0.0353 0.0502 0.0331

T- value 0.92 0.75 0.98

P- value 0.3841 0.4726 0.3542

R Square 0.72 0.64 0.72


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6.4 (h) Urban Baluchistan

Table 6.4(h) OLS and Empirical Bayes Estimates for Urban Balochistan




CONSTANT 1.45** 1.11 1.39***


T- value 2.49 1.51 3.46

P- value 0.0377 0.1684 0.0086

Ypc -0.0004 -0.0001 -0.0004

standard error 0.0003 0.0004 0.0003

T- value -1.13 -0.24 -1.25

P- value 0.2909 0.8149 0.2472

HCI -0.013 -0.090 0.054


T- value -0.02 -0.11 0.11

P- value 0.98 0.92 0.92

B20 -0.059*** -0.056** -0.058***

standard error 0.0125 0.0189 0.0121

T- value -4.68 -2.96 -4.80

P- value 0.0016 0.0181 0.0013

Un 0.019 0.031 0.017

standard error 0.0325 0.0489 0.0306

T- value 0.59 0.63 0.56

P- value 0.5737 0.5478 0.5893

R Square 0.84 0.64 0.84


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In the tables [6.4.(a) – 6.4(h)], results for aggregate rural-urban areas are given with

more or less the same results; however few points are worth mentioning:

OLS results for different regions as expected remain same.

Results obtained by empirical Bayes (HP) and empirical Bayes (CZ) slightly

differ, but are not statistically significant.

Results for rural areas are mostly the same in size and level of significance even

in case of both HP and CZ techniques (except rural Balochistan).

In case of urban areas results differ substantially in magnitude and even sometimes

coefficients bear inverse signs. However such coefficients in both categories are

insignificant.

6.5 Sensitivity Analysis

The requirement of a good research is to measure both dependent and

independent variables without any errors. This implies that statistic on the variables of

model used are accurate. It is assumed that they are not hypothetical estimates,

interpolated, extrapolated or smoothed by any systematic method. In practical exercise

such a situation is rare. We may face non response errors, reporting errors and

computing errors. Error in measurement gives rise to specification bias. If error of

measurement is in dependent variable, OLS estimates are still unbiased. However

variances and standard errors are different. This means that variances and standard

errors with error in measurement are larger than in the case where there are no such

errors in measurements. In case, errors measurement is in the regressors, OLS

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estimates are not only biased but also they are inconsistent, that is, they remain biased

even if the sample size increases indefinitely.

A good deal of research is that it does not contend with results and findings

obtained by using one estimation technique. It also does not hinge upon one modeling

aspect of research that it has compact ways to compare the results. According to

Hendry and Richard (1983), the results of a research can be termed satisfactory if

research is based on following criteria.

Results support the theory, Data is coherent, Model used by a particular study

encompasses all rival models, and Regressors are uncorrelated with stochastic

term.

However, the researcher is liable to commit various specification errors. One of

such errors is the measurement errors. For the case of exposition and computational

expediency, it is assumed that dependent as well as independent variables are

measured without any error. In practice a researcher encounters various errors in

measurement because of the simple fact that computing errors as well as reporting

errors creep in due to manipulation of data and there are grave consequences of errors

of measurements.

Keeping in view these concerns of errors of measurement the present study

analyses and compares the initial results with the results generated from data which

underwent some sort of manipulation.

Human capital index (HCI) is obtained by simply taking average of educational

attainment index (EAI) and health status indices (HSI):

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HCI2

EAI HSI

OR

HCI 0.5* 0.5*EAI HSI

Since the weights are arbitrary, someone might think of assigning more weight to EAI

than HIS or vice versa. Our target is to analyze impact of such procedure in our results.

Therefore we change the weights of EAI and HSI, and obtain HCI-1 and HCI-2 as given

below,

HCI-1 0.45* 0.55*EAI HSI

HCI-2 0.55* 0.45*EAI HSI

Using the above mentioned HCI, HCI-1 and HCI-2, we find estimates by using

three different techniques, i.e. OLS, Empirical Bayes (HP), and Empirical Bayes (CZ).

These estimates are given in Appendix I-A to Appendix IV-H.

Appendix I-A, shows the comparison of results for OLS and empirical Bayes estimates

for rural Punjab. Results for constant term are more or less same. No changes are

palpable with the exception that empirical Bayes (HP) turns significant in both HCI-1

and HCI-2 cases. Value and sign of the coefficient of Ypc also remain about same in all

three cases, however in case of HCI-1, significance level increases in all three

techniques.

Coefficient of HCI is nearly unchanged. This variable is significant at 99% for empirical

base (CZ) in case of HCI-1 and at 95% in case of initial and HCI-2. B20 is highly

significant in both HCI-1 and HCI-2. This shows that a change in HCI to the extent of

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about five percent both upward and downward, does not lead to change the initial result

drastically. It is indicative from the value of R-squared that regression fits equally good

in all three cases. From results reported in Appendix I-A, it can safely be concluded that

if the data on HCI is manipulated upward or downward, results do not tend to change all

along. If there had been any errors of measurement in one of the explanatory variable,

that is HCI, the results would have undergone a substantial change. So it can be

asserted with certainty that data for this segment of research is reasonably coherent

and all regressors particularly HCI are weak, implying that regressors are uncorrelated

with stochastic disturbance term.

Appendix I-B also shows somewhat same picture where magnitude and sign of

coefficient are almost same except slight difference of significance in only one case i.e.

HCI-1 Empirical Bayes (CZ). In Appendix I-A, most of the time, value of the coefficient,

sign and significance also remain same. Only B20 gets significant in Empirical Bayes

(HP) in both cases. Appendix I-A shows results for the rural Balochistan, where slight

difference in significance of the variable is observed. In case of HCI-1 and in Empirical

Bayes (HP), human capital changes the sign but this variable is insignificant in all three

cases. Value of the R squared is almost same in all situations.

Appendix II-A to Appendix II-D contain results for Urban areas and here also results

show same pattern and most of the time sign and magnitude remain the same while

slight difference in significance is observed in some cases. Human capital also has the

same picture and in urban Balochistan it varies sign. In both cases and with three

econometric techniques coefficients of HCI remain significant as it was in initial results

with OLS and Empirical Bayes (H.P).

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In cases of overall provinces, results are quoted in Appendix III-A to Appendix III-

D. Coefficients of all the variables have same sign and almost same magnitude. Only

slight difference in significance level is observed. The value of R-Squared is almost

same in most of the cases.

Results for the rural and urban areas are given in Appendix IV-A to Appendix IV-

H. In this case we observed the same pattern as it was in the case of separate rural and

urban case.

From the results presented above, it is clear that the estimates of the techniques

applied in this study are robust and do not change significantly. If the weights of EAI and

HSI are changed in the composition of HCI, the magnitude and direction of the

coefficients of various techniques remain same. However, little difference was observed

in the significance level of the variables in the study. Therefore we conclude that

redefining HCI with different weights of EAI and HSI will not change the results

presented in the study.

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CHAPTER 7

SUMMARY & CONCLUSIONS

This chapter contains three sections. In the first section, a brief overview of the

whole exercise is presented. The second section precisely explains the main findings of

the study regarding the basic needs fulfillment situation prevalent in different parts of

Pakistan. It also covers the rural and urban comparison and factors responsible for the

gaps in basic needs fulfillment. Section-3 presents the conclusions and some policy

recommendations briefly.

7.1 Overview of the Study

The present study aimed at discussing the operational implications of basic

needs and exploring the possible and significant correlates of basic needs in Pakistan.

It contributed towards the computation of basic needs gap indices (BNGI) for the first

time in Pakistan and analyzed various factors that could possibly affect the depth and

breadth of poverty as reflected by the BNGI. The research covers four provinces of

Pakistan with rural and urban bifurcation (thus total eight regions) over the period 1979

to 2008. Other areas like Azad Kashmir, FATA, and Gilgit-Baltistan are not included in

the study due to non availability of consistent data for these areas. The previous studies

based on cross country empirical analysis faced the main criticism of having no

common yardstick to measure basic needs for all the countries due to differences in the

socio-economic and cultural situations. However, this study is free from this sort of

observation and the basic needs yardstick is presumably same for all areas of Pakistan

due to similar socio-economic, cultural and political conditions. Initially, ten variables

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were considered that could be assumed to affect BNGI one way or the other. However,

after applying different techniques and keeping in view the theoretical considerations,

only four variables appeared to be more relevant for detailed empirical analysis. These

were the per capita income of the households, human capital index, share of income

held by the bottom 20 % and unemployment rate.

The study considered food, clothing, shelter, health, and education as the basic

needs and derived the necessary information from the official sources like the Economic

Survey of Pakistan, the HIES and PSLM, the Labour Force Survey and some other

publications. Some composite variable like the human capital index, quintiles of income

distribution and the BNGI were constructed from a variety of other variables. The data

used in this analysis covered a period of 1979-80 to 2007-08, comprising 13

observations over time and 4 observations over the cross section. The HIES information

is available generally after 3 years.

After disappointment from the results of (neoclassical) growth models and the

emergence of endogenous growth theory, human capital received tremendous attention

in the research work along with physical capital. Proponents of the endogenous growth

theory held the view that well trained and educated persons were more productive and

capable of using the new technology efficiently. Consequently, the returns to investment

(in human and physical capital) will be increasing rather than decreasing. This

investment in human capital will lead to higher productivity of labour, which in turn will

result into substantial increase in income and remunerations. Therefore, we

incorporated human capital as an important determinant of BNGI along with other usual

185

variable like the per capita incomes, the unemployment rate and the share of bottom

20% population in the distribution of income.

After descriptive comparison of BNGI among different regions and particularly

between rural and urban areas, the model was empirically analyzed through appropriate

techniques, following different estimation techniques given below:

Ordinary least square (OLS).

The Empirical Bayesian, with two alternative approaches:

Approach due to Hsiao and Pesaran (2004)

Approach due to Carrington and Zaman (1996)

7.2 Summary of Findings

Average monthly income per household is one of the determinants of basic

needs gap index. It has generally increased over the years 1984-85,1985-86, 1986-87,

1990-91, 1998-99, 2004-05 and 2005-06, but showed downward trend for the remaining

years.

Income gap between the rural and urban areas is visible, and remained a

permanent feature over the whole study period. Urban Sindh and urban Balochistan

showed high growth in income over the period concerned.

The percentage distribution of monthly income among households by quintiles for

the different regions of Pakistan also shows a dismal situation. The general picture

shows a downward trend for the share of the first quintile (lowest 20% of population);

186

however this decrease is very large and consistent in the urban areas. In contrast, an

increase in the share of income for this quintile is observed for the rural areas of Sindh

and Balochistan. In case of the highest quintile (top 20 % population), an increase in

income share has generally been observed overtime for most of the regions. These

facts about income disparities are also observable when one looks at the relative share

in incomes of the top 20% population (richest) to the bottom 20% (poorest).

Construction and analysis of the performance of basic needs indices for different

regions of Pakistan is also integral part of this study. It is obvious from the respective

Tables and Figures given in chapter-4, that initially all the regions started from more or

less the same level of basic needs. After that, till 1986-87, most of the regions showed

downward trend or at least remained below the urban regions. In the subsequent period

till 1998-99, there are fluctuations in basic needs gap index in both rural and urban

regions and generally the gap increased during this period.

Beyond 1998-99, there is a clear and visible split in rural and urban areas and

this trend persists till the end. This factual observation that inequality in urban Pakistan

remained high as compared to the rural areas has also been made by other studies, for

instance, Idrees (2006). During this spell, rural areas observed a decline in basic needs

gap index while urban areas witnessed converse trend, obviously due to constant

migration of people to big cities and the prevalence of open unemployment in the urban

areas. This gap widened till the end the study period except in rural KPK, where both

rural and urban areas show an increase in BNGI after 2001-02. Situation in urban Sindh

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and urban KPK is alarming where a persistent increase in BNGI is observable since

1993-94.

The health index and education attainment indices lead to the construction of the

human capital index (HCI). The mean human capital for rural areas turns out to be 0.46

and that for urban areas to be 0.64, obviously due to better health and education

facilities available in the urban areas. There is continuous rise in HCI for almost all the

regions; however, this rise is significant during the period 1985-86 to 1993-94. The

provinces can be ranked in the following sequence with reference to human capital

index: Sindh (highest), Punjab, KPK, and Balochistan (lowest). At provincial level,

however, this difference is not persistent. For instance, Punjab and KPK followed the

same trend as that of Pakistan. However, significant progress in human capital is visible

in rural KPK, where in 1979 value of HCI was 0.33 and in 2007-08 it appeared as 0.58.

The main objective of the present study is to investigate and quantify the

correlation of various variables with BNGI in different regions of Pakistan. After

exclusion of certain less significant and impotent variables, the final model includes four

variables as discussed above, namely, the per capita income, human capital index,

share of income held by bottom 20%, and unemployment rate.

Empirical Bayes (CZ) gives precise results when compared to OLS and empirical

Bayes (HP). This fact can be observed in Tables (6.1a - 6.4h), where standard error is

smaller in case of CZ as compared to OLS and HP.

The result reveal that per capita income and BNGI are negatively correlated in

both rural and urban areas of Pakistan. Although this is in accordance with our prior

188

expectations; however for most of the time this impact is insignificant for urban areas

but significant for rural areas.

The results obtained for aggregate rural and urban area slightly differ, especially

for those variables, which are statistically not significant. Results for rural areas are

mostly same in size and level of significance even in case of both HP and CZ

techniques (except for rural Balochistan). However, in case of urban areas, the results

differ substantially in magnitude and even sometimes the coefficients bear inverse

signs. Therefore, our main focus remained on rural, urban and overall regions.

HCI is negatively related to BNGI in urban areas and supports the prior

information. This variable is statistically significant for empirical Bayes (CZ) in all urban

areas. This implies that due to an improvement in HCI, people get more opportunities to

earn and fulfill their basic needs. HCI shows mixed relationship with BNGI in rural areas

except KPK, where there is a direct relationship between human capital index and

BNGI. In rural Punjab and rural Sindh, it is statistically significant.

The share of income held by bottom 20% appeared as one of the strong factors

that is negatively correlated with the BNGI. This shows that an increase in share of

income held by bottom 20% and the betterment of the masses in terms of basic needs

fulfillment, go hand in hand. For most of the events, this variable is highly significant for

both the rural and urban areas.

The rate of unemployment shows mixed results. In case of urban areas, its

relationship is positive with BNGI, which is in accordance with the prior expectations;

189

while in rural areas, except rural Balochistan, it negatively affects BNGI for obvious

reasons. However, this variable appeared statistically insignificant in most of the cases.

7.3 Conclusions and Policy Recommendations

After the failure of growth oriented approach in the late 1970s, there evolved a

consensus among the researchers that benefits of growth should be passed on to the

poor. Redistribution of income and accelerated economic growth is now considered as

policy prescription for the developing countries to eradicate poverty and hunger from the

world. At the international level, the Millennium Development Goals (MDGs) have been

formulated by the United Nations. According to this declaration, everyone is sovereign,

has the right to live with dignity, and the rights to meet the basic standards of living.

First goal of this declaration is to eradicate extreme poverty and hunger. Two

main targets to achieve this goal are:

1) Reduce poverty between 1990 and 2015 by 50% (in 1990 proportion of population

below the poverty line was 26.1% and target for 2015, is 13%).

2) Reduce the proportion of people who suffer from hunger by 50% by this period.

Poverty reduction received great attention as the key objective of development

policies. However, due to its multidimensional nature, no final definition and

measurement is agreed upon. Despite this serious shortcoming, the present study

employed a popular method to measure poverty via the yardstick of basic needs gap

indicator for different regions of Pakistan. This research is unique of its nature in this

respect.

190

The study found tangible gaps in income, expenditure and savings, among rural

and urban areas of Pakistan. Gap between the highest and the first quintile is widening

in most of the regions, which is not good for the social fabric of the country. In particular,

this uneven distribution is mostly observed in the urban areas.

All regions of Pakistan witnessed a substantial and consistent increase in per

capita income during study period. Surprisingly this growth in GNP per capita led to

worsening of distribution. This phenomenon supports the Kuznets hypothesis that

distribution of income worsens at early stages of economic development. Inequality

persisted and remained unabated in Pakistan due to the fact that our economy has not

yet attained the climax after which inequality will tend to decline. A prompt political

dispensation can curtail the acute problem of poverty even in the presence of growing

inequalities. Such political will is needed from policy makers, government official and

political stalwart of the country.

Based on its findings and analysis, the study will put forward the following

plausible and concrete suggestions and policy implications:

1. Human capital appears to be a strong and significant factor to make dent in

poverty on one side and to act as a natural catalyst for growth and equity in distribution

of income. The central policy must emphasize on creating demand for innovation and

indigenous capability building technology. This requires Pakistan to allocate appropriate

allocation for higher education. Access to quality education is instrumental to strengthen

scientific infrastructure. Special attention needs to be given to reduce large urban-rural

and gender disparities in enrollment rates at the secondary level. Strategies are also

191

needed to improve the educational profile of poor so that they can take advantage of

increasing opportunities in the job market available across the globe.

The government needs to focus on providing good quality basic health services

particularly in rural areas of Pakistan in which the condition of masses is miserable. The

present bias against poor regions and between rural and urban areas needs to be

reversed. Government is required to enter into operational partnerships with local

communities and NGOs to provide health care to poor segment of society.

Unemployment rate is an important factor responsible for poverty and a hurdle

towards fulfillment of basic needs. Government needs to revise its strategies on

patterns that encourage labor intensive growth. Such activities in agriculture,

manufacturing and export oriented sectors need to be encouraged so as to generate

more employment and ensure the spillover effects of growth for poor. GDP growth per

employed person and employment rate are among targets to achieve the first objective

(i.e. to eradicate extreme poverty and hunger) of MDGs. The positive relation between

unemployment and BNGI is observed in rural areas of Pakistan. Besides data issues,

disguised employment in villages is one cause of this positive relationship. Hence it is

recommended to create job opportunities in the rural areas so that masses could meet

their basic needs and to discourage further migration to big cities, which is now creating

grave socio-economic and political problems.

Economic growth remained respectably appropriate during the first six-seven

years of last decade. However, this robust economic growth remained concentrated in

relatively skilled-intensive sectors of finance, telecommunication and information

192

technology (IT), which favored only those with high income who could afford higher

education. Unskilled workers and those with low education level failed to integrate

themselves in the sectors of high growth during this period. Sectors like housing and

construction, SMEs are relatively labor intensive with high employment elasticity. These

sectors need to be given special priority for rapid expansion to ensure some gain to the

poorer sections of the society. Direct attack on poverty and inequality needs to reinforce

the social security nets that are already in progress.

On one hand, increased health facilities and innovations has enhanced the life

expectancy considerably; but on other hand the increased food production due to

technological advancement has failed to address the problems of hunger and

malnutrition in developing countries. The study has found a wide and persistent gap

between rural and urban segments of the society in terms of human capital. This bias

not only creates social and moral degradation and so many other evils in the society,

but also causes further disparity in the region. Unplanned urbanization makes the cities

overcrowded, which lack adequate infrastructure to accommodate influx of labour to

urban regions. When observed in terms of interregional disparity, the study found that

Balochistan is lagging behind other provinces in human capital index and this trend is

prevalent in both rural and urban areas.

Different regions of Pakistan have some differences on socio-cultural and

political basis. These differences are natural, but prolonged differences in economic

conditions among and within regions are posing some serious problems. Pakistan has

already paid the price in 1971. The sense of economic deprivation and exclusion is also

193

dangerous that is liable to bring about so many social evils in the society. Growth for the

sake of growth is meaningless unless it reduces the suffering and miseries of the

masses. To make every person part of development process, it needs to ensure that no

one is deprived and marginalized.

Research in economics greatly relies on data but reliable and disaggregated data

for spatial analysis in most of the developing countries is not available. Although this

study covered provinces of Pakistan with rural urban bifurcation, however there is still

scope to investigate these issues at district level. We can hope for availability of quality

data that is more dis-aggregated in future, which will enhance the quality of research.

194

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APPENDICES

Appendix I-A OLS and Empirical Bayes Estimates for RURAL PUNJAB

(Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares

Empirical Bayes (HP)

Empirical Bayes (CZ)

Least Squares



Least Squares



CONSTANT 1.66** 1.09 1.49*** 1.61** 1.19** 1.48*** 1.72** 1.24** 1.51***

standard error 0.56 0.67 0.37 0.54 0.50 0.37 0.59 0.51 0.38

T- value 2.97 1.63 3.99 2.98 2.40 4.01 2.92 2.42 3.96

P- value 0.018 0.141 0.004 0.018 0.043 0.004 0.019 0.042 0.004

Ypc -0.0010** -0.0005 -0.0008** -0.001*** -0.0006* -0.0008*** -0.0010** -0.0005 -0.0008**

standard error 0.0003 0.0004 0.0002 0.0003 0.0003 0.0002 0.0003 0.0003 0.0002

T- value -3.3225 -1.3468 -3.3504 -3.4970 -2.0012 -3.5765 -3.0891 -1.8336 -3.0791

P- value 0.0105 0.2150 0.0101 0.0081 0.0804 0.0072 0.0149 0.1041 0.0151

HCI 2.704*** 1.6205* 2.0139** 2.78*** 1.47* 2.17*** 2.57** 1.31* 1.81**

standard error 0.800 0.816 0.644 0.773 0.652 0.638 0.828 0.647 0.644

T- value 3.379 1.986 3.127 3.601 2.247 3.396 3.102 2.020 2.806

P- value 0.010 0.082 0.014 0.007 0.055 0.009 0.015 0.078 0.023

B20 -0.075*** -0.062** -0.071*** -0.075*** -0.062*** -0.072*** -0.076*** -0.061*** -0.071***

standard error 0.021 0.024 0.016 0.021 0.018 0.015 0.022 0.018 0.016

T- value -3.522 -2.621 -4.570 -3.633 -3.486 -4.699 -3.381 -3.357 -4.440

P- value 0.008 0.031 0.002 0.007 0.008 0.002 0.010 0.010 0.002

Un -0.082* -0.029 -0.053 -0.081* -0.025 -0.057 -0.081* -0.026 -0.049

standard error 0.039 0.049 0.032 0.036 0.041 0.031 0.041 0.042 0.033

T- value -2.129 -0.597 -1.684 -2.226 -0.622 -1.847 -1.974 -0.610 -1.465

P- value 0.066 0.567 0.131 0.057 0.551 0.102 0.084 0.559 0.181

R Square 0.82 0.75 0.8 0.84 0.76 0.82 0.8 0.72 0.78

211

Appendix I-B OLS and Empirical Bayes Estimates for RURAL SINDH (Dependent Variable: BNGI)

Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 2.1*** 1.598** 1.84*** 2.0882*** 1.6694*** 1.8475*** 2.1077*** 1.6909*** 1.8371***

standard error 0.41 0.6290 0.3159 0.3884 0.4699 0.3067 0.4093 0.4775 0.3228

T- value 5.15 2.54 5.83 5.3762 3.5530 6.0245 5.1495 3.5408 5.6905

P- value 0.0008 0.03459 0.00030 0.0007 0.0075 0.0003 0.0009 0.0076 0.0005

Ypc -0.001*** -0.0007* -0.00096*** -0.0012*** -0.0007** -0.0010*** -0.0011*** -0.0007** -0.0009***

standard error 0.00 0.0004 0.00026 0.0003 0.0003 0.0003 0.0003 0.0003 0.0003

T- value -3.84 -1.89 -3.67 -4.1242 -2.6127 -3.9631 -3.8442 -2.5079 -3.4193

P- value 0.0049 0.09478 0.00629 0.0033 0.0310 0.0042 0.0049 0.0365 0.0091

HCI 1.44* 0.5683 1.319* 1.6146* 0.4873 1.4891** 1.4397* 0.3380 1.1415*

standard error 0.73 0.7889 0.60228 0.7107 0.6458 0.5969 0.7332 0.6259 0.5969

T- value 1.96 0.72 2.19 2.2718 0.7546 2.4947 1.9637 0.5400 1.9124

P- value 0.08510 0.49183 0.05977 0.0527 0.4721 0.0372 0.0852 0.6039 0.0922

B20 -0.058*** -0.0566** -0.053*** -0.059*** -0.0574*** -0.0547*** -0.0580*** -0.0561*** -0.0517***

standard error 0.01 0.0214 0.01238 0.0124 0.0162 0.0118 0.0131 0.0164 0.0129

T- value -4.44 -2.65 -4.28 -4.7662 -3.5486 -4.6185 -4.4352 -3.4138 -4.0138

P- value 0.0022 0.02948 0.00267 0.0014 0.0075 0.0017 0.0022 0.0092 0.0039

Un -0.19* -0.0448 -0.169* -0.2045** -0.0388 -0.1813* -0.1944* -0.0325 -0.1583

standard error 0.09 0.0574 0.08312 0.0829 0.0501 0.0796 0.0868 0.0502 0.0861

T- value -2.24 -0.78 -2.04 -2.4668 -0.7738 -2.2764 -2.2393 -0.6472 -1.8391

P- value 0.0550 0.45720 0.07516 0.0389 0.4613 0.0524 0.0555 0.5356 0.1032

R Square 0.81 0.65 0.81 0.84 0.67 0.83 0.8 0.65 0.79

212

Appendix I-C OLS and Empirical Bayes Estimates for RURAL KPK (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 2.7967** 1.949** 1.5869*** 2.8114** 1.7208** 1.5780*** 2.7967** 1.7399** 1.5955***

standard error 0.9367 0.7524 0.4349 0.9519 0.5508 0.4381 0.9367 0.5587 0.4298

T- value 2.9857 2.5914 3.6486 2.9535 3.1244 3.6015 2.9857 3.1143 3.7119

P- value 0.0174 0.0320 0.0065 0.0183 0.0141 0.0070 0.0174 0.0144 0.0059

Ypc -0.0010* -0.0006 -0.0006 -0.0010* -0.0005 -0.0006 -0.0010* -0.0005 -0.0006

standard error 0.0005 0.0004 0.0003 0.0005 0.0003 0.0003 0.0005 0.0003 0.0003

T- value -2.1012 -1.4362 -1.8154 -2.0658 -1.6524 -1.7823 -2.1012 -1.6731 -1.8564

P- value 0.0688 0.1889 0.1070 0.0727 0.1371 0.1126 0.0688 0.1328 0.1005

HCI -1.4400 -1.1696 -0.4169 -1.4875 -0.8916 -0.4008 -1.4406 -0.8825 -0.4266

standard error 0.7864 0.8020 0.5425 0.8252 0.6491 0.5648 0.7864 0.6271 0.5196

T- value -1.8318 -1.4582 -0.7680 -1.8025 -1.3736 -0.7097 -1.8319 -1.4073 -0.8210

P- value 0.1043 0.1828 0.4642 0.1091 0.2068 0.4981 0.1043 0.1970 0.4354

B20 -0.0603* -0.049* -0.0276 -0.0604 -0.0471** -0.0274 -0.0604* -0.0483** -0.0279

standard error 0.0322 0.0258 0.0222 0.0325 0.0193 0.0223 0.0323 0.0196 0.0221

T- value -1.8710 -1.8970 -1.2400 -1.8571 -2.4419 -1.2249 -1.8710 -2.4690 -1.2617

P- value 0.0982 0.0943 0.2494 0.1004 0.0404 0.2555 0.0983 0.0388 0.2426

Un 0.0205 0.0330 -0.0220 0.0206 0.0267 -0.0232 0.0205 0.0268 -0.0215

standard error 0.0517 0.0519 0.0451 0.0522 0.0434 0.0453 0.0517 0.0437 0.0449

T- value 0.3970 0.6408 -0.4939 0.3953 0.6161 -0.5112 0.3970 0.6139 -0.4801

P- value 0.7017 0.5396 0.6346 0.7029 0.5550 0.6230 0.7017 0.5564 0.6440

R Square 0.51 0.35 0.39 0.51 0.29 0.38 0.51 0.3 0.4

213

Appendix I-D OLS and Empirical Bayes Estimates for RURAL BALOCHISTAN (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.93*** 1.4821* 1.6460*** 1.93*** 1.5063** 1.6383*** 1.9321*** 1.5017** 1.6535***

standard error 0.4950 0.6490 0.3500 0.5012 0.4856 0.3547 0.4960 0.4923 0.3493

T- value 3.8954 2.2800 4.6710 3.8520 3.1017 4.6187 3.8955 3.0503 4.7333

P- value 0.0045 0.0521 0.0016 0.0049 0.0146 0.0017 0.0046 0.0158 0.0015

Ypc -0.0010** -0.0006 -0.0008*** -0.0010** -0.0006* -0.0008*** -0.0010** -0.0006* -0.0008***

standard error 0.0003 0.0004 0.0002 0.0003 0.0003 0.0002 0.0003 0.0003 0.0002

T- value -3.2165 -1.6618 -3.5346 -3.2069 -2.2232 -3.5237 -3.2165 -2.2001 -3.5522

P- value 0.0123 0.1351 0.0077 0.0125 0.0569 0.0078 0.0123 0.0590 0.0075

HCI 0.0180 -0.1050 0.2920 0.0239 0.0002 0.3306 0.0189 -0.0320 0.2533

standard error 0.6928 0.7941 0.5788 0.7163 0.6338 0.5977 0.6928 0.6115 0.5558

T- value 0.0273 -0.1330 0.5047 0.0333 0.0003 0.5532 0.0273 -0.0524 0.4557

P- value 0.9789 0.8975 0.6274 0.9742 0.9998 0.5953 0.9789 0.9595 0.6607

B20 -0.0470** -0.0440* -0.0454*** -0.0471** -0.0469** -0.046*** -0.0470** -0.0453** -0.0448***

standard error 0.0153 0.0219 0.0133 0.0155 0.0166 0.0136 0.0153 0.0168 0.0130

T- value -3.0675 -2.0078 -3.4040 -3.0337 -2.8163 -3.3797 -3.0675 -2.7014 -3.4394

P- value 0.0154 0.0796 0.0093 0.0162 0.0226 0.0096 0.0154 0.0270 0.0088

Un 0.0325 0.0401 0.0266 0.0323 0.0326 0.0253 0.0325 0.0336 0.0280

standard error 0.0353 0.0489 0.0328 0.0357 0.0413 0.0332 0.0353 0.0415 0.0324

T- value 0.9208 0.8210 0.8115 0.9061 0.7906 0.7632 0.9208 0.8109 0.8635

P- value 0.3841 0.4354 0.4405 0.3913 0.4520 0.4673 0.3841 0.4409 0.4130

R Square 0.72 0.63 0.71 0.72 0.63 0.71 0.72 0.63 0.71

214

Appendix II-A OLS and Empirical Bayes Estimates for URBAN PUNJAB (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.59*** 1.41** 1.33*** 1.59*** 1.3629** 1.3013*** 1.5935*** 1.4020*** 1.3645***

standard error 0.3890 0.4299 0.1783 0.41 0.4747 0.1287 0.3756 0.4119 0.2058

T- value 4.10 3.28 7.51 3.91 2.8713 10.1147 4.2423 3.4035 6.6295

P- value 0.0030 0.0110 0.0001 0.0046 0.0208 0.0000 0.0028 0.0093 0.0002

Ypc -0.0002 -0.00012 -0.0001 0.00 -0.0001 -0.0001 -0.0002 -0.0001 -0.0001

standard error 0.0002 0.0002 0.0001 0.00 0.0002 0.0001 0.0002 0.0002 0.0001

T- value -0.90 -0.59 -1.43 -1.01 -0.6440 -1.5042 -0.8221 -0.7351 -1.3208

P- value 0.3900 0.5708 0.1896 0.3431 0.5376 0.1710 0.4348 0.4833 0.2231

HCI -0.31 -0.25 -0.18** -0.22 -0.1948 -0.1564 -0.3686 -0.2522 -0.1928

standard error 0.7649 0.6191 0.0603 0.81 0.7084 0.1230 0.7231 0.5767 0.1105

T- value -0.40 -0.40 -2.96 -0.28 -0.2750 -1.2717 -0.5097 -0.4373 -1.7450

P- value 0.7004 0.7009 0.0183 0.7889 0.7903 0.2392 0.6240 0.6734 0.1191

B20 -0.08*** -0.07*** -0.07*** -0.08*** -0.0726*** -0.0730*** -0.0805*** -0.0738*** -0.0747***

standard error 0.0118 0.0111 0.0075 0.01 0.0119 0.0067 0.0118 0.0110 0.0081

T- value -6.82 -6.68 -9.83 -6.76 -6.0798 -10.9534 -6.8400 -6.7233 -9.2450

P- value 0.0001 0.0002 0.0000 0.0001 0.0003 0.0000 0.0001 0.0001 0.0000

Un 0.011 0.003 0.010 0.01 0.0022 0.0098 0.0125 0.0035 0.0104

standard error 0.0148 0.0169 0.0109 0.01 0.0178 0.0110 0.0147 0.0167 0.0109

T- value 0.77 0.20 0.93 0.69 0.1249 0.8887 0.8503 0.2121 0.9552

P- value 0.4621 0.8445 0.3794 0.5106 0.9037 0.4001 0.4199 0.8373 0.3675

R Square 0.93 0.92 0.92 0.92 0.86 0.92 0.93 0.83 0.92

215

Appendix II-B OLS and Empirical Bayes Estimates for URBAN SINDH (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.54** 1.29** 1.30*** 1.5111** 1.2460** 1.2771*** 1.5474** 1.3139** 1.3080***

standard error 0.5665 0.4545 0.1892 0.5995 0.4962 0.1325 0.5399 0.4276 0.2232

T- value 2.72 2.83 6.85 2.5206 2.5111 9.6349 2.8659 3.0731 5.8592

P- value 0.0264 0.0221 0.0001 0.0358 0.0363 0.0000 0.0210 0.0153 0.0004

Ypc -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001 -0.0001

standard error 0.0002 0.0002 0.0001 0.0002 0.0002 0.0001 0.0002 0.0002 0.0001

T- value -0.51 -0.28 -1.04 -0.6147 -0.3755 -1.1142 -0.4227 -0.4188 -0.9211

P- value 0.6237 0.7833 0.3286 0.5559 0.7171 0.2976 0.6837 0.6864 0.3839

HCI -0.39 -0.21 -0.18** -0.2740 -0.1655 -0.1597 -0.4637 -0.2724 -0.1947

standard error 0.8363 0.6438 0.0603 0.9121 0.7239 0.1232 0.7670 0.5850 0.1105

T- value -0.46 -0.32 -2.97 -0.3004 -0.2286 -1.2966 -0.6046 -0.4656 -1.7619

P- value 0.6553 0.7581 0.0180 0.7715 0.8249 0.2309 0.5622 0.6539 0.1161

B20 -0.07*** -0.07*** -0.07*** -0.071*** -0.067*** -0.066*** -0.07*** -0.0681*** -0.067***

standard error 0.0121 0.0112 0.0074 0.0123 0.0120 0.0069 0.0119 0.0108 0.0077

T- value -5.87 -6.05 -9.09 -5.7853 -5.5925 -9.6955 -5.9256 -6.2755 -8.6874

P- value 0.0004 0.0003 0.0000 0.0004 0.0005 0.0000 0.0004 0.0002 0.0000

Un 0.0008 -0.0010 0.0081 0.0010 -0.0012 0.0087 0.0008 -0.0008 0.0075

standard error 0.0327 0.0200 0.0275 0.0330 0.0210 0.0272 0.0324 0.0195 0.0276

T- value 0.02 -0.05 0.29 0.0301 -0.0578 0.3213 0.0242 -0.0389 0.2725

P- value 0.9807 0.9602 0.7767 0.9768 0.9553 0.7562 0.9813 0.9700 0.7921

R Square 0.91 0.91 0.91 0.91 0.88 0.91 0.91 0.87 0.91

216

Appendix II-C OLS and Empirical Bayes Estimates for URBAN KPK (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 0.44 0.97* 1.18*** 0.5333 1.1092* 1.2199*** 0.3530 0.9479* 1.1450***

standard error 0.7678 0.4993 0.1881 0.7864 0.5529 0.1323 0.7453 0.4772 0.2221

T- value 0.5700 1.9427 6.2597 0.6781 2.0061 9.2235 0.4736 1.9865 5.1557

P- value 0.5843 0.0880 0.0002 0.5168 0.0798 0.0000 0.6484 0.0822 0.0009

Ypc 0.0002 0.00003 -0.0001 0.0002 0.0000 -0.0001 0.0002 0.0000 0.0000

standard error 0.0002 0.0002 0.0001 0.0002 0.0002 0.0001 0.0002 0.0002 0.0001

T- value 0.78 0.16 -0.51 0.6795 -0.0950 -0.7418 0.8659 -0.0045 -0.3433

P- value 0.4601 0.8775 0.6232 0.5160 0.9267 0.4794 0.4118 0.9965 0.7402

HCI 0.14 -0.11 -0.18** 0.0262 -0.2502 -0.1855 0.2519 -0.0810 -0.1999

standard error 0.7348 0.6061 0.0600 0.7450 0.6886 0.1205 0.7187 0.5701 0.1090

T- value 0.19 -0.18 -3.04 0.0352 -0.3633 -1.5400 0.3506 -0.1421 -1.8331

P- value 0.8504 0.8604 0.0160 0.9728 0.7258 0.1621 0.7350 0.8905 0.1041

B20 -0.038* -0.056*** -0.0572*** -0.039* -0.059*** -0.058*** -0.0383* -0.0555*** -0.055***

standard error 0.0186 0.0124 0.0100 0.0189 0.0134 0.0092 0.0183 0.0122 0.0108

T- value -2.09 -4.50 -5.71 -2.1022 -4.4278 -6.3984 -2.0894 -4.5535 -5.1483

P- value 0.0703 0.0020 0.0004 0.0687 0.0022 0.0002 0.0701 0.0019 0.0009

Un 0.0029 -0.0001 -0.0002 0.0043 0.0012 -0.0008 0.0015 -0.0003 0.0007

standard error 0.0152 0.0169 0.0105 0.0151 0.0179 0.0106 0.0152 0.0167 0.0107

T- value 0.19 0.00 -0.02 0.2867 0.0664 -0.0730 0.0971 -0.0180 0.0684

P- value 0.8534 0.9976 0.9858 0.7816 0.9487 0.9436 0.9250 0.9860 0.9471

R Square 0.79 0.76 0.76 0.79 0.65 0.75 0.79 0.61 0.76

217

Appendix II-D OLS and Empirical Bayes Estimates for URBAN BALOCHISTAN (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 1.45** 1.24** 1.32*** 1.42** 1.229** 1.2861*** 1.4803** 1.2682** 1.3338***

standard error 0.5845 0.4643 0.1863 0.61 0.4969 0.1315 0.5592 0.4447 0.2191

T- value 2.49 2.66 7.06 2.31 2.4733 9.7772 2.6470 2.8518 6.0883

P- value 0.0377 0.0287 0.0001 0.049 0.0385 0.0000 0.0294 0.0214 0.0003

Ypc -0.0004 -0.0001 -0.0002 0.00 -0.0002 -0.0002 -0.0004 -0.0002 -0.0002

standard error 0.0003 0.0002 0.0002 0.00 0.0002 0.0002 0.0003 0.0002 0.0002

T- value -1.13 -0.63 -1.15 -1.13 -0.7153 -1.2267 -1.1299 -0.7771 -1.1156

P- value 0.2909 0.5444 0.2821 0.2926 0.4948 0.2548 0.2913 0.4594 0.2970

HCI -0.0127 -0.0720 -0.1744** 0.05 -0.0831 -0.1429 -0.0593 -0.1356 -0.1794

standard error 0.6253 0.5938 0.0600 0.66 0.6559 0.1210 0.5882 0.5560 0.1091

T- value -0.02 -0.12 -2.91 0.07 -0.1267 -1.1810 -0.1009 -0.2439 -1.6445

P- value 0.9843 0.9065 0.0197 0.9455 0.9023 0.2715 0.9221 0.8134 0.1387

B20 -0.058*** -0.063*** -0.0591*** -0.06*** -0.0634*** -0.0589*** -0.0590*** -0.0643*** -0.0592***

standard error 0.0125 0.0112 0.0116 0.01 0.0117 0.0116 0.0124 0.0110 0.0116

T- value -4.68 -5.69 -5.10 -4.59 -5.4272 -5.0726 -4.7694 -5.8278 -5.0958

P- value 0.0016 0.0005 0.0009 0.0017 0.0006 0.0010 0.0014 0.0004 0.0009

Un 0.0191 0.0051 0.0179 0.02 0.0050 0.0168 0.0201 0.0059 0.0186

standard error 0.0325 0.0200 0.0291 0.03 0.0203 0.0290 0.0325 0.0199 0.0292

T- value 0.59 0.25 0.61 0.55 0.2465 0.5801 0.6177 0.2966 0.6374

P- value 0.5737 0.8066 0.5558 0.5955 0.8115 0.5778 0.5539 0.7744 0.5417

R Square 0.84 0.83 0.83 0.84 0.81 0.83 0.84 0.79 0.83

218

Appendix III-A OLS and Empirical Bayes Estimates for OVERALL PUNJAB (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.30** 1.31** 1.61*** 1.145* 1.298** 1.579*** 1.4585** 1.3724** 1.636***

standard error 0.5160 0.4781 0.1353 0.5128 0.4799 0.1602 0.5301 0.4556 0.1227

T- value 2.52 2.74 11.91 2.2329 2.7048 9.8611 2.7511 3.0125 13.3358

P- value 0.03556 0.02541 0.000002 0.0560 0.0269 0.0000 0.0250 0.0167 0.0000

Ypc -0.0015*** -0.0007** -0.0012*** -0.0014*** -0.0007** -0.0012*** -0.0015*** -0.0007** -0.0011***

standard error 0.0004 0.0003 0.0003 0.0004 0.0003 0.0003 0.0004 0.0003 0.0003

T- value -3.94 -2.41 -4.43 -4.0469 -2.3148 -4.5038 -3.7569 -2.3310 -4.2738

P- value 0.0043 0.0428 0.0022 0.0037 0.0493 0.0020 0.0056 0.0481 0.0027

HCI 4.14*** 1.44 2.60** 4.1798*** 1.3036 2.6737** 4.0217** 1.5576* 2.4365**

standard error 1.2103 0.8709 0.9107 1.1827 0.8594 0.8984 1.2419 0.7934 0.9139

T- value 3.42 1.66 2.86 3.5340 1.5169 2.9760 3.2383 1.9632 2.6660

P- value 0.0090 0.1360 0.0212 0.0077 0.1678 0.0177 0.0119 0.0852 0.0285

B20 -0.07** -0.06*** -0.08*** -0.0617** -0.0626*** -0.0747*** -0.0712** -0.0648*** -0.0749***

standard error 0.0214 0.0174 0.0094 0.0210 0.0174 0.0099 0.0222 0.0167 0.0093

T- value -3.12 -3.58 -8.00 -2.9453 -3.6002 -7.5812 -3.2121 -3.8738 -8.0671

P- value 0.0143 0.0072 0.00004 0.0186 0.0070 0.0001 0.0124 0.0047 0.0000

Un -0.07* -0.03 -0.04 -0.0638* -0.0232 -0.0388 -0.0711* -0.0292 -0.0381

standard error 0.0317 0.0289 0.0260 0.0297 0.0277 0.0251 0.0339 0.0276 0.0269

T- value -2.16 -0.95 -1.52 -2.1447 -0.8398 -1.5429 -2.0991 -1.0597 -1.4128

P- value 0.0625 0.3692 0.1665 0.0643 0.4254 0.1614 0.0690 0.3202 0.1954

R Square 0.77 0.32 0.71 0.78 0.33 0.72 0.75 0.35 0.7

219

Appendix III-B OLS and Empirical Bayes Estimates for OVERALL SINDH (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 2.06*** 1.69*** 1.66*** 1.946*** 1.6313*** 1.6544*** 2.1635*** 1.7365*** 1.6718***

standard error 0.5432 0.4692 0.1356 0.5106 0.4531 0.1600 0.5655 0.4594 0.1230

T- value 3.80 3.59 12.24 3.8126 3.6002 10.3433 3.8254 3.7801 13.5925

P- value 0.0052 0.0071 0.000002 0.0051 0.0070 0.0000 0.0051 0.0054 0.0000

Ypc -0.0009*** -0.0006* -0.0008*** -0.0010*** -0.0007** -0.0009*** -0.0009** -0.0005 -0.0007**

standard error 0.0003 0.0003 0.0002 0.0002 0.0003 0.0002 0.0003 0.0003 0.0002

T- value -3.51 -2.25 -3.61 -3.9801 -2.5859 -4.1190 -3.1691 -1.7413 -3.2355

P- value 0.0080 0.0544 0.0069 0.0041 0.0323 0.0033 0.0132 0.1198 0.0120

HCI 1.36 0.68 1.38* 1.6835* 0.9416 1.6477** 1.0661 0.4821 1.1260

standard error 0.7564 0.7374 0.6299 0.7376 0.7176 0.6121 0.7505 0.7018 0.6292

T- value 1.80 0.92 2.19 2.2824 1.3122 2.6918 1.4206 0.6870 1.7895

P- value 0.1099 0.3865 0.0598 0.0519 0.2259 0.0274 0.1932 0.5115 0.1113

B20 -0.09*** -0.07*** -0.07*** -0.083*** -0.073*** -0.074*** -0.088*** -0.074*** -0.073***

standard error 0.0197 0.0165 0.0117 0.0183 0.0158 0.0112 0.0208 0.0164 0.0122

T- value -4.35 -4.47 -6.26 -4.5404 -4.6180 -6.6009 -4.2433 -4.5468 -5.9877

P- value 0.0024 0.0021 0.0002 0.0019 0.0017 0.0002 0.0028 0.0019 0.0003

Un -0.05 -0.03 -0.04 -0.0551 -0.0330 -0.0438 -0.0457 -0.0256 -0.0301

standard error 0.0386 0.0289 0.0360 0.0357 0.0274 0.0336 0.0407 0.0288 0.0376

T- value -1.31 -0.97 -1.02 -1.5464 -1.2068 -1.3057 -1.1227 -0.8891 -0.8010

P- value 0.2279 0.3591 0.3371 0.1606 0.2620 0.2279 0.2941 0.3999 0.4462

R Square 0.8 0.58 0.78 0.83 0.65 0.82 0.78 0.5 0.75

220

Appendix III-C OLS and Empirical Bayes Estimates for OVERALL KPK (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 2.10** 1.71** 1.6*** 2.102** 1.7301*** 1.6212*** 2.1005** 1.6787*** 1.6490***

standard error 0.8357 0.5140 0.1380 0.8408 0.5044 0.1650 0.8308 0.4981 0.1244

T- value 2.51 3.32 11.84 2.5002 3.4303 9.8276 2.5285 3.3702 13.2503

P- value 0.0361 0.0105 0.000002 0.0369 0.0090 0.0000 0.0353 0.0098 0.0000

Ypc -0.0003 -0.0001 -0.0001 -0.0003 -0.0001 -0.0001 -0.0003 0.0000 -0.0001

standard error 0.0004 0.0003 0.0002 0.0004 0.0003 0.0002 0.0004 0.0003 0.0002

T- value -0.71 -0.51 -0.66 -0.6828 -0.5374 -0.5995 -0.7286 -0.0606 -0.7258

P- value 0.5000 0.6230 0.5294 0.5140 0.6056 0.5654 0.4870 0.9532 0.4887

HCI -1.04 -1.02 -0.54 -1.0623 -1.0446 -0.5331 -1.0273 -1.0930 -0.5516

standard error 0.7906 0.7344 0.5018 0.8111 0.7229 0.5153 0.7710 0.6879 0.4895

T- value -1.32 -1.39 -1.09 -1.3097 -1.4451 -1.0345 -1.3324 -1.5889 -1.1269

P- value 0.2229 0.2007 0.3095 0.2267 0.1864 0.3312 0.2194 0.1507 0.2925

B20 -0.08*** -0.07*** -0.07*** -0.0793*** -0.0709*** -0.0703*** -0.0796*** -0.0689*** -0.0710***

standard error 0.0235 0.0169 0.0149 0.0235 0.0165 0.0151 0.0235 0.0166 0.0148

T- value -3.38 -4.18 -4.73 -3.3703 -4.3092 -4.6528 -3.3887 -4.1579 -4.7852

P- value 0.0096 0.0031 0.0015 0.0098 0.0026 0.0016 0.0095 0.0032 0.0014

Un 0.0098 0.0119 -0.0040 0.0098 0.0117 -0.0045 0.0098 0.0133 -0.0035

standard error 0.0403 0.0292 0.0359 0.0404 0.0282 0.0360 0.0402 0.0286 0.0358

T- value 0.24 0.41 -0.11 0.2415 0.4142 -0.1259 0.2448 0.4668 -0.0981

P- value 0.8138 0.6937 0.9143 0.8153 0.6896 0.9029 0.8128 0.6531 0.9243

R Square 0.66 0.32 0.64 0.66 0.33 0.64 0.66 0.63 0.64

221

Appendix III-D OLS and Empirical Bayes Estimates for OVERALL BALOCHISTAN (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 2.10*** 1.72*** 1.67*** 2.089*** 1.7105*** 1.6705*** 2.114*** 1.7060*** 1.6794***

standard error 0.4575 0.4532 0.1338 0.4616 0.4432 0.1581 0.4530 0.4424 0.1213

T- value 4.59 3.80 12.48 4.5269 3.8596 10.5651 4.6674 3.8560 13.8448

P- value 0.0018 0.0052 0.0000 0.0019 0.0048 0.0000 0.0016 0.0048 0.0000

Ypc -0.0009** -0.0006* -0.0007*** -0.0009** -0.0005* -0.0007*** -0.0009** -0.0004 -0.0007***

standard error 0.0003 0.0003 0.0001 0.0003 0.0003 0.0001 0.0003 0.0003 0.0001

T- value -3.19 -2.09 -4.75 -3.1663 -2.1639 -4.5183 -3.2241 -1.6684 -4.8855

P- value 0.0127 0.0700 0.0015 0.0133 0.0624 0.0020 0.0122 0.1338 0.0012

HCI 0.41 0.09 0.69 0.4279 0.1158 0.7056 0.3935 0.0521 0.6541

standard error 0.5334 0.6938 0.4311 0.5393 0.6758 0.4351 0.5246 0.6554 0.4265

T- value 0.77 0.13 1.59 0.7935 0.1714 1.6216 0.7500 0.0795 1.5338

P- value 0.4612 0.9008 0.1505 0.4504 0.8682 0.1435 0.4747 0.9386 0.1636

B20 -0.069*** -0.067*** -0.066*** -0.069*** -0.067*** -0.0671*** -0.0688*** -0.0654*** -0.0657***

standard error 0.0121 0.0152 0.0115 0.0121 0.0148 0.0117 0.0120 0.0150 0.0114

T- value -5.72 -4.39 -5.76 -5.7113 -4.5341 -5.7530 -5.7357 -4.3737 -5.7728

P- value 0.0004 0.0023 0.0004 0.0004 0.0019 0.0004 0.0004 0.0024 0.0004

Un 0.0050 -0.0007 -0.0032 0.0046 -0.0018 -0.0037 0.0055 -0.0001 -0.0023

standard error 0.0282 0.0275 0.0263 0.0281 0.0264 0.0262 0.0282 0.0271 0.0263

T- value 0.18 -0.03 -0.12 0.1647 -0.0685 -0.1421 0.1949 -0.0030 -0.0872

P- value 0.8644 0.9802 0.9057 0.8733 0.9471 0.8905 0.8503 0.9977 0.9327

R Square 0.86 0.69 0.85 0.86 0.7 0.85 0.86 0.7 0.84

222

Appendix IV-A OLS and Empirical Bayes Estimates for RURAL PUNJAB (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.67** 1.16 1.49*** 1.6053** 1.1354** 1.5443** 1.7189** 1.2169** 1.5061***

standard error 0.5601 0.7186 0.4017 0.5382 0.4323 0.5094 0.5893 0.4333 0.4171

T- value 2.97 1.61 3.71 2.9825 2.6264 3.0318 2.9171 2.8088 3.6109

P- value 0.0178 0.1457 0.0060 0.0175 0.0303 0.0163 0.0194 0.0229 0.0069

Ypc -0.0011** -0.0005 -0.0008** -0.0011*** -0.0004 -0.0007** -0.0010** -0.0004 -0.0007**

standard error 0.0003 0.0004 0.0003 0.0003 0.0002 0.0003 0.0003 0.0002 0.0003

T- value -3.32 -1.49 -3.07 -3.4970 -1.8488 -2.7540 -3.0891 -1.6987 -2.7835

P- value 0.0105 0.1747 0.0153 0.0081 0.1017 0.0249 0.0149 0.1278 0.0238

HCI 2.70*** 1.95* 1.88** 2.7830*** 1.3893** 1.5668** 2.5677** 1.1500* 1.6746**

standard error 0.8002 0.9060 0.6589 0.7728 0.6019 0.5634 0.8277 0.5746 0.6624

T- value 3.38 2.15 2.86 3.6012 2.3083 2.7811 3.1023 2.0014 2.5282

P- value 0.0097 0.0638 0.0213 0.0070 0.0498 0.0239 0.0146 0.0803 0.0354

B20 -0.075*** -0.0675** -0.0714*** -0.0749*** -0.0632*** -0.0751*** -0.0760*** -0.0642*** -0.0714***

standard error 0.0214 0.0230 0.0164 0.0206 0.0147 0.0197 0.0225 0.0150 0.0171

T- value -3.52 -2.93 -4.35 -3.6328 -4.3076 -3.8188 -3.3808 -4.2692 -4.1770

P- value 0.0078 0.0189 0.0024 0.0067 0.0026 0.0051 0.0096 0.0027 0.0031

Un -0.0820* -0.0389 -0.0494 -0.0811* -0.0261 -0.0399 -0.0807* -0.0224 -0.0441

standard error 0.0385 0.0504 0.0328 0.0364 0.0285 0.0314 0.0409 0.0294 0.0341

T- value -2.13 -0.77 -1.51 -2.2258 -0.9153 -1.2702 -1.9737 -0.7624 -1.2940

P- value 0.0658 0.4630 0.1697 0.0567 0.3868 0.2397 0.0839 0.4677 0.2318

R Square 0.82 0.65 0.8 0.84 0.62 0.78 0.8 0.64 0.77

223

Appendix IV-B OLS and Empirical Bayes Estimates for URBAN PUNJAB (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 1.60*** 1.25* 1.50*** 1.5893*** 1.2668** 1.5337*** 1.5935*** 1.3305*** 1.5093***

standard error 0.3890 0.6572 0.3181 0.4061 0.4328 0.3764 0.3756 0.3819 0.3144

T- value 4.10 1.90 4.71 3.9133 2.9271 4.0743 4.2423 3.4843 4.8005

P- value 0.0034 0.0940 0.0015 0.0045 0.0191 0.0036 0.0028 0.0083 0.0014

Ypc -0.0002 0.0001 -0.0002 -0.0002 -0.0001 -0.0003 -0.0002 -0.0001 -0.0002

standard error 0.0002 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

T- value -0.91 0.19 -1.07 -1.0076 -0.3000 -1.3384 -0.8221 -0.2624 -0.9875

P- value 0.3900 0.8544 0.3146 0.3431 0.7718 0.2176 0.4348 0.7997 0.3523

HCI -0.31 -0.34 -0.13 -0.2233 -0.0337 -0.0180 -0.3686 -0.2050 -0.2018

standard error 0.7649 0.9066 0.6298 0.8067 0.6841 0.5764 0.7231 0.5646 0.6008

T- value -0.40 -0.37 -0.20 -0.2768 -0.0492 -0.0311 -0.5097 -0.3630 -0.3359

P- value 0.7004 0.7210 0.8471 0.7889 0.9619 0.9759 0.6240 0.7260 0.7456

B20 -0.081*** -0.077*** -0.079*** -0.081*** -0.0705*** -0.0803*** -0.0805*** -0.0725*** -0.0788***

standard error 0.0118 0.0187 0.0104 0.0120 0.0130 0.0117 0.0118 0.0119 0.0105

T- value -6.82 -4.12 -7.55 -6.7600 -5.4305 -6.8754 -6.8400 -6.0787 -7.5220

P- value 0.0001 0.0033 0.0001 0.0001 0.0006 0.0001 0.0001 0.0003 0.0001

Un 0.011 0.023 0.009 0.0102 0.0075 0.0077 0.0125 0.0096 0.0103

standard error 0.0148 0.0433 0.0137 0.0148 0.0227 0.0131 0.0147 0.0216 0.0137

T- value 0.77 0.54 0.67 0.6884 0.3289 0.5857 0.8503 0.4443 0.7553

P- value 0.4621 0.6059 0.5215 0.5107 0.7507 0.5742 0.4199 0.6686 0.4717

R Square 0.93 0.23 0.93 0.93 0.44 0.92 0.93 0.6 0.93

224

Appendix IV-C OLS and Empirical Bayes Estimates for RURAL SINDH

(Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



2.11*** 1.62** 1.89*** 2.0882*** 1.468*** 2.1118*** 2.1302*** 1.536*** 1.8985***

standard error 0.4093 0.6627 0.3317 0.3884 0.4039 0.3720 0.4280 0.3922 0.3445

T- value 5.15 2.45 5.69 5.3762 3.6345 5.6771 4.9772 3.9182 5.5113

P- value 0.0009 0.0399 0.0005 0.0007 0.0066 0.0005 0.0011 0.0044 0.0006 Ypc -0.0011*** -0.0007* -0.0010*** -0.0012*** -0.0005* -0.0010*** -0.0011*** -0.0005* -0.0009**

standard error 0.0003 0.0004 0.0003 0.0003 0.0002 0.0003 0.0003 0.0002 0.0003

T- value -3.84 -1.89 -3.56 -4.1242 -2.1518 -3.8741 -3.6015 -2.1679 -3.3180

P- value 0.0049 0.0956 0.0074 0.0033 0.0636 0.0047 0.0070 0.0620 0.0106

HCI 1.44* 0.98 1.17* 1.6146* 0.6620 1.0017* 1.2510 0.4962 0.9823

standard error 0.7332 0.8950 0.6154 0.7107 0.6232 0.5384 0.7435 0.5808 0.6128

T- value 1.96 1.09 1.90 2.2718 1.0622 1.8604 1.6826 0.8542 1.6031

P- value 0.0852 0.3074 0.0944 0.0527 0.3191 0.0999 0.1310 0.4178 0.1476

B20 -0.058*** -0.059** -0.053*** -0.059*** -0.061*** -0.058*** -0.057*** -0.0608*** -0.0524***

standard error 0.0131 0.0192 0.0125 0.0124 0.0124 0.0123 0.0137 0.0123 0.0130

T- value -4.44 -3.05 -4.27 -4.7662 -4.9154 -4.7087 -4.1661 -4.9291 -4.0192

P- value 0.0022 0.0157 0.0027 0.0014 0.0012 0.0015 0.0031 0.0012 0.0038

Un -0.19* -0.10 -0.16* -0.2045** -0.0305 -0.1657* -0.1837* -0.0263 -0.1526

standard error 0.0868 0.0721 0.0836 0.0829 0.0383 0.0780 0.0901 0.0381 0.0867

T- value -2.24 -1.40 -1.96 -2.4668 -0.7977 -2.1251 -2.0387 -0.6907 -1.7606

P- value 0.0555 0.1993 0.0860 0.0389 0.4480 0.0663 0.0758 0.5093 0.1163

R Square 0.82 0.7 0.81 0.84 0.62 0.82 0.8 0.62 0.79

225

Appendix IV-D OLS and Empirical Bayes Estimates for URBAN SINDH (Dependent Variable: BNGI)


Least Squares



Least Squares



Least Squares



CONSTANT 1.54** 1.15 1.41*** 1.5111** 1.1469** 1.4125** 1.5474** 1.2023** 1.4292***

standard error 0.5665 0.7193 0.3954 0.5995 0.4570 0.5207 0.5399 0.4021 0.3924

T- value 2.72 1.60 3.57 2.5206 2.5094 2.7129 2.8659 2.9901 3.6422

P- value 0.0264 0.1479 0.0073 0.0358 0.0364 0.0265 0.0210 0.0173 0.0066

Ypc -0.0001 0.0001 -0.0001 -0.0001 0.0000 -0.0002 -0.0001 0.0000 -0.0001

standard error 0.0002 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

T- value -0.51 0.40 -0.69 -0.6147 -0.0510 -0.9095 -0.4227 0.0668 -0.5929

P- value 0.6237 0.6983 0.5114 0.5559 0.9606 0.3897 0.6837 0.9483 0.5696

HCI -0.39 -0.33 -0.16 -0.2740 -0.0156 -0.0158 -0.4637 -0.1963 -0.2516

standard error 0.8363 0.9385 0.6515 0.9121 0.6928 0.6091 0.7670 0.5675 0.6132

T- value -0.46 -0.36 -0.25 -0.3004 -0.0225 -0.0259 -0.6046 -0.3458 -0.4103

P- value 0.6553 0.7307 0.8071 0.7715 0.9826 0.9800 0.5622 0.7384 0.6923

B20 -0.071*** -0.068*** -0.070*** -0.0710*** -0.0633*** -0.0704*** -0.0708*** -0.0642*** -0.0695***

standard error 0.0121 0.0187 0.0100 0.0123 0.0129 0.0118 0.0119 0.0115 0.0101

T- value -5.87 -3.62 -6.92 -5.7853 -4.8863 -5.9736 -5.9256 -5.5680 -6.9077

P- value 0.0004 0.0068 0.0001 0.0004 0.0012 0.0003 0.0004 0.0005 0.0001

Un 0.0008 0.0155 0.0034 0.0010 0.0085 0.0019 0.0008 0.0087 0.0034

standard error 0.0327 0.0493 0.0303 0.0330 0.0319 0.0324 0.0324 0.0281 0.0301

T- value 0.0249 0.3153 0.1121 0.0301 0.2656 0.0576 0.0242 0.3085 0.1121

P- value 0.9807 0.7606 0.9135 0.9768 0.7972 0.9555 0.9813 0.7656 0.9135

R Square 0.91 0.43 0.91 0.91 0.6 0.91 0.91 0.68 0.91

226

Appendix IV-E OLS and Empirical Bayes Estimates for RURAL KPK (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 2.80** 2.04** 1.66*** 2.8114** 1.567** 1.9650** 2.7823** 1.554** 1.6854***

standard error 0.9367 0.8426 0.4798 0.9519 0.4881 0.7302 0.9229 0.4731 0.4861

T- value 2.99 2.42 3.46 2.9535 3.2124 2.6911 3.0147 3.2844 3.4673

P- value 0.0174 0.0417 0.0085 0.0183 0.0124 0.0275 0.0167 0.0111 0.0085

Ypc -0.001* -0.0005 -0.0006 -0.001* -0.0002 -0.0007 -0.001* -0.0002 -0.0006

standard error 0.0005 0.0004 0.0003 0.0005 0.0002 0.0004 0.0005 0.0002 0.0003

T- value -2.10 -1.14 -1.72 -2.0658 -0.7942 -1.7512 -2.1337 -0.9219 -1.7629

P- value 0.0688 0.2891 0.1244 0.0727 0.4500 0.1180 0.0654 0.3835 0.1159

HCI -1.44 -1.23 -0.60 -1.4875 -0.7603 -0.5950 -1.3956 -0.6681 -0.6118

standard error 0.7864 0.9017 0.5591 0.8252 0.6191 0.5672 0.7509 0.5643 0.5385

T- value -1.83 -1.37 -1.07 -1.8025 -1.2281 -1.0490 -1.8587 -1.1839 -1.1361

P- value 0.1043 0.2087 0.3149 0.1091 0.2543 0.3248 0.1001 0.2704 0.2888

B20 -0.06* -0.053* -0.029 -0.0604 -0.053*** -0.0391 -0.0603* -0.0535*** -0.0299

standard error 0.0323 0.0265 0.0230 0.0325 0.0158 0.0280 0.0320 0.0158 0.0231

T- value -1.87 -1.99 -1.27 -1.8571 -3.3720 -1.3935 -1.8834 -3.3765 -1.2950

P- value 0.0983 0.0814 0.2413 0.1004 0.0098 0.2010 0.0964 0.0097 0.2314

Un 0.0205 0.0208 -0.0166 0.0206 0.0087 -0.0132 0.0204 0.0069 -0.0154

standard error 0.0517 0.0552 0.0455 0.0522 0.0319 0.0466 0.0513 0.0317 0.0454

T- value 0.40 0.38 -0.36 0.3953 0.2713 -0.2827 0.3980 0.2167 -0.3392

P- value 0.7017 0.7163 0.7252 0.7029 0.7930 0.7846 0.7011 0.8338 0.7432

R Square 0.51 0.39 0.42 0.51 0.2 0.43 0.52 0.21 0.43

227

Appendix IV-F OLS and Empirical Bayes Estimates for URBAN KPK (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares



Least Squares



CONSTANT 0.44 0.27 0.97* 0.5333 0.9310 0.5986 0.3530 0.7367 0.9314*

standard error 0.7678 0.8204 0.4443 0.7864 0.5572 0.6187 0.7453 0.4866 0.4451

T- value 0.57 0.33 2.18 0.6781 1.6710 0.9675 0.4736 1.5142 2.0927

P- value 0.5843 0.7476 0.0605 0.5168 0.1333 0.3616 0.6484 0.1684 0.0697

Ypc 0.0002 0.0004 0.00003 0.0002 0.0002 0.0001 0.0002 0.0002 0.0000

standard error 0.0002 0.0003 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002 0.0002

T- value 0.78 1.24 0.17 0.6795 0.7165 0.6523 0.8659 0.9658 0.2040

P- value 0.4601 0.2494 0.8699 0.5160 0.4941 0.5325 0.4118 0.3624 0.8435

HCI 0.14 -0.05 -0.20 0.0262 -0.3645 0.0301 0.2519 -0.1826 -0.1386

standard error 0.7348 0.8906 0.5127 0.7450 0.6386 0.5372 0.7187 0.5389 0.5079

T- value 0.19 -0.05 -0.39 0.0352 -0.5709 0.0561 0.3506 -0.3387 -0.2729

P- value 0.8504 0.9580 0.7048 0.9728 0.5838 0.9567 0.7350 0.7435 0.7918

B20 -0.038* -0.0402 -0.049*** -0.039* -0.0504** -0.0419** -0.0383* -0.0463** -0.0498***

standard error 0.0186 0.0217 0.0147 0.0189 0.0156 0.0175 0.0183 0.0141 0.0148

T- value -2.09 -1.86 -3.38 -2.1022 -3.2202 -2.3905 -2.0894 -3.2870 -3.3649

P- value 0.0703 0.1007 0.0096 0.0687 0.0122 0.0438 0.0701 0.0111 0.0099

Un 0.0029 0.0164 0.0034 0.0043 0.0099 0.0033 0.0015 0.0067 0.0024

standard error 0.0152 0.0435 0.0144 0.0151 0.0230 0.0139 0.0152 0.0216 0.0143

T- value 0.19 0.38 0.23 0.2867 0.4306 0.2393 0.0971 0.3088 0.1690

P- value 0.8534 0.7159 0.8209 0.7816 0.6781 0.8169 0.9250 0.7654 0.8700

R Square 0.79 0.06 0.77 0.79 0.29 0.79 0.79 0.45 0.77

228

Appendix IV-G OLS and Empirical Bayes Estimates for RURAL BALOCHISTAN (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares



Least Squares Empirical Bayes (HP)




CONSTANT 1.93*** 1.52* 1.68*** 1.9307*** 1.3530** 1.8494*** 1.9332*** 1.3872** 1.6933***

standard error 0.4960 0.6957 0.3745 0.5012 0.4267 0.4695 0.4917 0.4160 0.3770

T- value 3.90 2.19 4.48 3.8520 3.1712 3.9393 3.9314 3.3347 4.4912

P- value 0.0046 0.0604 0.0021 0.0049 0.0132 0.0043 0.0043 0.0103 0.0020

Ypc -0.001** -0.0006 -0.0008*** -0.001** -0.0005* -0.0009** -0.001** -0.0005* -0.0008***

standard error 0.0003 0.0004 0.0002 0.0003 0.0002 0.0003 0.0003 0.0002 0.0002

T- value -3.22 -1.66 -3.41 -3.2069 -1.9421 -3.2074 -3.2224 -2.0486 -3.4121

P- value 0.0123 0.1350 0.0092 0.0125 0.0881 0.0125 0.0122 0.0747 0.0092

HCI 0.019 0.054 0.118 0.0239 0.4018 0.1097 0.0143 0.3491 0.0845

standard error 0.6928 0.8722 0.5902 0.7163 0.5636 0.5412 0.6668 0.5186 0.5682

T- value 0.0273 0.0622 0.2000 0.0333 0.7129 0.2027 0.0215 0.6732 0.1487

P- value 0.9789 0.9519 0.8465 0.9742 0.4962 0.8445 0.9834 0.5198 0.8855

B20 -0.047** -0.047** -0.043** -0.0471** -0.0507*** -0.0470** -0.0470** -0.0496*** -0.0430**

standard error 0.0153 0.0200 0.0136 0.0155 0.0130 0.0141 0.0151 0.0128 0.0134

T- value -3.07 -2.34 -3.18 -3.0337 -3.9043 -3.3339 -3.1050 -3.8779 -3.2180

P- value 0.0154 0.0475 0.0130 0.0162 0.0045 0.0103 0.0146 0.0047 0.0123

Un 0.032 0.038 0.033 0.0323 0.0087 0.0299 0.0326 0.0097 0.0335

standard error 0.0353 0.0502 0.0331 0.0357 0.0293 0.0319 0.0348 0.0291 0.0327

T- value 0.92 0.75 0.98 0.9061 0.2967 0.9363 0.9373 0.3345 1.0271

P- value 0.3841 0.4726 0.3542 0.3913 0.7742 0.3765 0.3760 0.7466 0.3344

R Square 0.72 0.64 0.72 0.72 0.52 0.72 0.72 0.55 0.72

229

Appendix IV-H OLS and Empirical Bayes Estimates for URBAN BALOCHISTAN (Dependent Variable: BNGI) Initial Results HCI-1 HCI-2

Least Squares







CONSTANT 1.45** 1.11 1.39*** 1.42** 1.182** 1.373** 1.4803** 1.2309** 1.409***

standard error 0.58 0.73 0.40 0.6124 0.4554 0.5238 0.5592 0.4249 0.3974

T- value 2.49 1.51 3.46 2.3136 2.5943 2.6208 2.6470 2.8969 3.5457

P- value 0.0377 0.1684 0.0086 0.0494 0.0319 0.0306 0.0294 0.0200 0.0076

Ypc -0.0004 -0.0001 -0.0004 -0.0004 -0.0001 -0.0004 -0.0004 -0.0002 -0.0004

standard error 0.0003 0.0004 0.0003 0.0003 0.0002 0.0003 0.0003 0.0002 0.0003

T- value -1.13 -0.24 -1.25 -1.1263 -0.5790 -1.1535 -1.1299 -0.6696 -1.2275

P- value 0.2909 0.8149 0.2472 0.2927 0.5786 0.2820 0.2913 0.5220 0.2545

HCI -0.013 -0.090 0.054 0.0467 -0.0797 0.1046 -0.0593 -0.1049 0.0043

standard error 0.63 0.85 0.50 0.6622 0.5920 0.5099 0.5882 0.5310 0.4824

T- value -0.02 -0.11 0.11 0.0705 -0.1346 0.2051 -0.1009 -0.1976 0.0089

P- value 0.98 0.92 0.92 0.9456 0.8963 0.8426 0.9221 0.8483 0.9931

B20 -0.059*** -0.056** -0.058*** -0.058*** -0.055*** -0.0578*** -0.059*** -0.0563*** -0.0584***

standard error 0.0125 0.0189 0.0121 0.0127 0.0123 0.0122 0.0124 0.0119 0.0120

T- value -4.68 -2.96 -4.80 -4.5896 -4.4847 -4.7380 -4.7694 -4.7135 -4.8577

P- value 0.0016 0.0181 0.0013 0.0018 0.0020 0.0015 0.0014 0.0015 0.0013

Un 0.019 0.031 0.017 0.0179 0.0158 0.0165 0.0201 0.0152 0.0181

standard error 0.0325 0.0489 0.0306 0.0325 0.0276 0.0311 0.0325 0.0276 0.0307

T- value 0.59 0.63 0.56 0.5527 0.5738 0.5304 0.6177 0.5504 0.5878

P- value 0.5737 0.5478 0.5893 0.5956 0.5819 0.6102 0.5539 0.5971 0.5729

R Square 0.84 0.64 0.84 0.84 0.66 0.84 0.84 0.71 0.84

determinants of basic needs fulfillment the case...

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