extended abstract of the master thesis - uclouvain · farinaceous products. regarding the ranking...
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Extended abstract of the master thesis
Country characteristics driving innovation adoption in the
European food and drink industry
This is an extended abstract of the master thesis, the final version is not yet ready, however
this abstract is and extended description of the work. Some parts still need to be finalized.
Introduction
The adoption of innovation has become a critical determinant of productivity and survival in
the European food industry which operates in a turbulent market, characterized by global
competition, and fast changing demands for sustainability of production and transparency of
chain processes. Consumer’s attitudes towards food have undergone a permanent change,
demand for healthier, locally produced products and environment friendly products make the
competition in the food industry more intense than ever, and manufacturers must be
continually alert in order to remain relevant for distribution channels. So, it becomes evident
that the European food industry has to adapt to these fast changing circumstances and that its
innovativeness has to be enhanced.
Due to their key role in modern society adoption and diffusion of innovations have been
studied within various disciplines, for example economics (e.g. Mansfield 1961; Stoneman &
Ireland, 1983), sociology (e.g. Rogers, 1962), geography (e.g. Brown, 1981), medical
sociology (e.g. Coleman, Katz & Menzel 1957), cultural anthropology (e.g. Barnett 1953) and
marketing (Bass, 1969; Gatignon & Robertson, 1985; Gatignon & Robertson 1989; Robertson
& Gatignon, 1986). Still incomplete
The European food industry
1. Structure
The food industry plays an important role in the economy of the EU. According to the Data
and trends of the European food and drink industry report ( 2013-2014), the food industry is
the largest manufacturing sector in EU with a turnover of €1.048 billion, which represents
14.6% share of turnover in the EU manufacturing industry ( Eurostat, 2011). The sector
generates a value added of €206 billion, a contribution of around 1.8% to EU gross value
added. The EU food and drink industry is a key job provider and a relatively stable employer.
It is a direct employer of 4.24 million people with jobs spread across all Member States,
mostly in rural areas. The EU food and drinks industry’s share of employment in the EU
manufacturing industry is around 15.5%, and it is ranked ahead of the automobile, machinery
and equipments and fabricated metal products ( Eurostat, 2011)). The food and drink industry is
a diversified sector. It is characterized by a wide range of company sizes with SMEs accounting for a
large share of the activity (Table 1).
SMEs in the EU food and drink industry (%)
Micro
companies
(% in total)
Small
companies 10-19
(% in total)
Small
companies 20-49
(% in total)
Medium sized
companies
(% in total)
Total SMEs
(% in total)
Turnover 8.2 5.2 9.7 28.5 51.6
Value added 8.9 6.1 9.2 24.6 48.8
Number of
employees
16.9 9.6 11.7 26 64.3
Number of
companies
78.8 10.8 5.8 3.8 99.1
Eurostat, 2011
The diversity of the sector is not only in term of company’s size, but also in term of
production and activity. It contains a variety of sub sectors ranging from meat processing to
dairy production and drinks. The top 5 sub-sectors are: bakery and farinaceous products, meat
sector, dairy products, drinks and various food products category, they represent 75% of the
total turnover and more than four fifths of the total number of employees and companies. The
meat sector contribute with the biggest share of turnover, around 20%. However, the bakery
and farinaceous products contribute with the biggest share of employment (32%). At the
national level, the sector ranks among the top three manufacturing industries in terms of
turnover and employment in several Member States. It ranks first in France, Spain, the UK,
Denmark and Belgium. Germany, France, Italy, the UK and Spain are the largest EU food and
drink producers (Table 2).
Food and drink industry data as published by FoodDrinkEurope National Federations ,
2012
Employment
ranking in
manufacturing
Turnover
€billion
Value added
€billion
Number of
employees
1,000
Number of
companies
Australia - 19.2 4.8 63 3,740
Belgium 1 47.5 6.8 89 4,768
Bulgaria 2 4.7 0.8 96 5,667
Cyprus 1 1.5 0.4 13 845
Czech
Republic
4 10.9 2.4 103 9,207
Denmark 1 26.2 3 54 1,600
Estonia 2 1.6 0.3 13 458
Finland 3 11 2.6 33 1,693
France 1 160.9 23.6 495 13,500
Germany 4 169.3 33.5 555 5,970
Greece - 11.2 1.4 65 1,180
Hungary 2 8.7 1.9 95 4,971
Ireland 1 22 6.9 43 689
Italy 3 130 24 386 6,850
Latvia 1 1.6 0.3 25 838
Lithuania 1 3.6 0.6 42 1,327
Netherland 1 66.6 15 133 4,751
Poland 1 49.7 9 396 14,330
Portugal 1 14.5 2.9 110 10,500
Romania 1 10.7 2.2 184 8,355
Slovakia 3 3.8 0.7 29 210
Slovenia 5 2 0.4 13 617
Spain 1 90.2 26.8 440 29,196
Sweden 5 19.5 4.6 55 3,600
United
Kingdom
1 114.1 29.7 406 7,766
Eurostat, 2011
2. World markets
EU exports increased by 13.2% in 2012 compared to 2011, while imports remained almost
unchanged. Amongst the top ten export destinations, the highest growth rates can be
observed for EU food and drink exports to China, Australia, Saudi Arabia and Japan, with
rates increasing by 30%, 18%, 16% and 15% respectively. The strongest growth rates for
food and drink imports were observed in Russia, Ukraine and Malaysia. NAFTA remains
the EU’s largest trading partner by region, followed by EFTA and Mercosur. Export
growth was strongest for oils and fats, spirits, prepared animal feeds, bakery and
farinaceous products. Regarding the ranking of EU food and drink imports, the largest
increase was recorded for processed tea and coffee, mineral waters and soft drinks, and
oils and fats. The EU drinks, meat and dairy sectors reached a combined export market
share of close to 50% (Table 3).
Exports and imports by sub-sector, 2011-2012 (€ million)
Exports
2011 2012 12/11 %
Imports
2011 2012 12/11 %
Drinks 22,325 25,706 15 4,682 4,907 5
Spirits
Wine
Mineral
waters and
soft drinks
8,475 10,176 20 1,134 1,219 7
8,112 8,867 9 2,400 2,491 4
2,409 2,761 15 771 883 15
Various
food
products
16,457 18,661 13 9,881 10,053 2
Chocolate
Processes
tea and
coffee
4,644 5,235 13 2,354 2,091 -11
1,940 2,111 9 1,588 1,919 21
Meat
products
10,382 11,249 8 7,110 6,975 -2
Dairy
products
8,787 9,488 8 769 816 6
Fruit and
vegetable
products
4,377 4,981 14 7,564 7,739 2
Oils and
fats
3,673 4,538 24 15,544 17,343 12
Prepared
animal
feeds
2,451 2,883 18 734 682 -7
Bakery and
farinaceous
products
2,968 3,498 18 540 570 6
Fish and
seafood
products
2,970 3,419 15 15,649 15,733 1
Grain mill
and starch
products
2,614 2,828 8 1,550 1,547 0
Eurostat
In the first half of 2013, EU exports increased by 4% compared to the same period in
2012. The value of imports remained almost unchanged. EU exports increased most
rapidly for the Balkans, the ASEAN, EFTA and Mercosur trading blocs. EU export
growth was highest for chocolate and confectionery, prepared meals and dishes, and fish
and seafood products.
The EU remains the leading exporter of food and drink products despite its shrinking
market share in global food and drink trade. A similar loss in market share was also
observed for other traditional exporters such as the USA, Canada and Australia. Countries
such as Brazil, Thailand, Indonesia and India have been continuously increasing their
export market share in recent years. The EU regained market share in a number of
traditional markets and future growth prospects look favourable in Brazil, China, Japan
and in emerging countries. The EU share in global food and drinks exports is 16.1%,
however it is 14% for imports (Figure 1,Figure2).
3. R&D and innovation
The world’s top 61 leading food and drink companies collectively invested €8.7 billion in
R&D in 2012. Out of these 61 companies, 17 are based in the EU and invested €2.3
billion in 2012. Among those 7 EU companies, we find 5 in NL, 4 in UK, 3 in DE and one
for each following country: FR, DK, FI, BE and IE. The share of EU R&D investment of
food and drink industry output is around 0.27% in 2013 (Eurostat).
16.10%
12%
7.60%
7.50%4.90%
4.60%
4.50%
4.40%
4.20%
3.90%
3.10%2.80% 2% 1.60% 1.60%
The share of the top exporters of food and drink
products, 2012
EU USA china Brazil Thailand
Indonesia India Argentina Malaysia Canada
New zealand Australia Mexico Turkey Chile
14.60%14%
8.70%6.80%
4.20%4.20%
2.70%2.50%2.50%
2.20%1.90%1.90%
1.60%1.50%1.50%
0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%
USA
Japan
Russia
South Korea
Mexico
Malaysia
Indonesia
Thailand
The share of the top importers of food and drink products, 2012
Drivers of innovation can be divided into 15 trends, grouped along five axes,
corresponding to general consumer expectations: pleasure, health, physical, convenience
and ethics. Pleasure, including variety of sense and sophistication, is by far the leading
axis with a 57% share in 2013. Dairy products are the leaders in innovation, followed by
ready-made meals which surpass soft drinks and rank second in 2013. The most
innovative food sectors in EU in 2013 are ranked as follow: dairy products, ready-made
meals, soft drinks, savory frozen products, biscuits, meat, delicatessen, poultry, Appetizer
grocery products, chocolate products, cheeses, condiments and sauces.
Conceptual framework
1. The concept of innovation
As innovation may involve a wide range of different types of change depending on the
environment studied, so these different changes resulting from different forms of
innovation are varying on different teams, departments and disciplines. Therefore,
innovation has been discussed variously across a range of disciplines such as human
resource management, operations management, entrepreneurship, research and
development, information technology, engineering, design, marketing and strategy.
However, there is no general agreement about the way of defining and measuring
innovation. Thus, each of these different disciplines proposes definitions for innovation
that fit better with the dominant paradigm of the discipline. As Damanpour and Schneider
(2006, p. 216) state: “Innovation is studied in many disciplines and has been defined from
0
5
10
15
20
25
0
5
10
15
20
25
30
35
USA EU Japan Switzerland New Zealand South Korea
R&D private investment in the food and drink industry for the world's top companies, 2012
R&D investment (€billion) Share of world regions(%)
Number of companies
different perspectives”. This diversity of definitions leads to a situation in which there is
no clear and unique definition of innovation. Also, there is no unique way of measuring
innovation, some researches are based on published R&D expenditures and patent data
(Brschi, 1999; Malerba and Orsenigo, 1995). While other researches rely on
measurements deduced from detailed surveys among companies. In this part we offer
some examples of definitions of innovation in order to draw some similarities and
differences among them and to understand how they vary between disciplines. This part is
based on the article of Baregheh and al who highlight the requirements for clarification of
defining innovation by arising fundamental questions: what are the key definitions of
innovation? How do these vary between different disciplines? What are the similarities
and differences? Is it possible and helpful to construct a universal definition?
As early as 1965, Thompson’s early and straightforward definition simply states:
“Innovation is the generation, acceptance and implementation of new ideas, processes
products or services” (1965, p.2). More recently, another definition of innovation was
proposed by West and Anderson (1996) and reformulated by in 2008 by Wong et al.
(2008, p. 2): “Innovation can be defined as the effective application of processes and
products new to the organization and designed to benefit it and its stakeholders”. This last
definition seems to be more or less similar to the previous one, however Kimberly in
1981, introduced a new concept for innovation by distinguishing between different stages,
he states that : “There are three stages of innovation: innovation as a process, innovation
as a discrete item including, products, programs or services; and innovation as an attribute
of organizations.”(1981, p.108). For other researchers, they focus more on the newness of
the innovation as a key factor to define it rather than on its stages. For instance, Van du
Ven et al. (1986) state that, “As long as the idea is perceived as new to the people
involved, it is an ‘innovation’ even though it may appear to others to be an ‘imitation’ of
something that exists elsewhere”. Some others join both newness and stages of change in
order to provide a more detailed definition of innovation. The most famous quotation
reflecting this association is the one stated by Damanpour in 1996:“Innovation is
conceived as a means of changing an organization, either as a response to changes in the
external environment or as a pre-emptive action to influence the environment. Hence,
innovation is here broadly defined to encompass a range of types, including new product
or service, new process technology, new organization structure or administrative systems,
or new plans or program pertaining to organization members”.(1996, p.694). Many other
variations in the definition of innovation arise from different disciplinary perspectives, we
present here a list of definitions gathered by Baregheh and al in their paper: Towards a
multidisciplinary definition of innovation:
Business and management: 18 definitions from 1966 to 2007.
Economics: nine definitions from 1934 to 2004.
Organization studies: six definitions from 1953 to 2008. .
Innovation and entrepreneurship: nine definitions from 1953 to 2007.
Technology, science and engineering: 13 definitions from 1969 to 2005.
Knowledge management: three definitions from 1999 to 2007.
Marketing: two definitions from 1994 to 2004.
On the basis of the key attributes highlighted throughout the previous definitions of
innovation, a diagrammatic definition of “innovation” is proposed in the paper of Baregheh
and al. The diagram incorporates the six attributes identified as being common to the various
disciplinary definitions of innovation.
Creation
Generation
Implementation
Development
Adoption
Organizations
Firms
Customers
Social systems
Employees
Developers
Technology
Ideas
Inventions
Creativity
Market
Succeed
Differentiate
Compete
Product
Service
Process
Technical
New
Improve
Change
Stages
Nature Type Aim
Social Means
Innovation process
Innovation adoption:
In this work we focus on innovation adoption, it means that we try to study the main
characteristics driving the adoption of innovation. The following part present an overview of
adoption of innovation theory. We need to understand how innovation adoption has been
presented through the previous research works in order to have a good framework for this
present work. The process of innovation adoption has been studied for over 30 years, and one
of the most popular adoption models is presented by Rogers in his book, Diffusion of
Innovations (Sahin, 2006). Much research from a broad variety of disciplines has used the
model as a framework. Dooley (1999) and Stuart (2000) mentioned several of these
disciplines as political science, public health, communications, history, economics,
technology, and education, and defined Rogers’ theory as a widely used theoretical
framework in the area of technology diffusion and adoption. For Rogers (2003), adoption is a
decision of “full use of an innovation as the best course of action available” and rejection is a
decision “not to adopt an innovation” (p. 177). Rogers defines diffusion as “the process in
which an innovation is communicated thorough certain channels over time among the
members of a social system” (p. 5). As expressed in this definition, innovation,
communication channels, time, and social system are the four key components of the
diffusion of innovations.
Product and process innovation
Product and process are considered as prime manifestations of innovativeness by
organizations or firms. Acoording to the three-stage model propsed by Abernathy and
Utterback in 1978, who explained the rate of product and process innovations during the
development of an industry, the type of innovation adopted corresponds to the developmental
stage of the industry. A ‘product’ is a good or service offered to the customer or client and a
‘process’ is the mode of production and delivery of the good or service (Barras, 1986).
Thus, product innovation is defined as new products or services introduced to meet an
external user or market need, product innovation can be the result of changes in the
organizational structure of the company. This can be illustrated by an example from the food
industry in which food quality may be improved through a more efficient organization of the
firm’s safety control. Also, new products could be seen as a result of new market segments
exploitation. For instance, over the last decades, the food industry have targeted many new
market segments starting from organic food, nutritional food up to ready-made meals.
However, product innovation is largely associated with changes in processing. Process
innovation is defined as new elements introduced into an organization’s production or service
operations (e.g., input materials, task specifications, work and information flow mechanisms,
and equipment) to produce a product or render a service (Ettlie and Reza, 1992; Knight, 1967;
Utterback and Abernathy, 1975). The distinction between product and process innovations is
crucial because depending on that, the process of innovation adoption can differ. Product
innovations has to do more with the outputs that are introduced for the benefits of customers
taking into account customer needs, design, packaging. While process innovations require
firms to introduce new tools, devices, procedures, knowledge by applying technology in order
to improve the efficiency of product development and commercialization (Ettlie et al.,
1984).In other words, product innovations have a market focus and are primarily customer
driven, while process innovations have an internal focus and are primarily efficiency driven
(Utterback and Abernathy, 1975).
Determinants of innovation
1. At the firm level
Diverse determinants of innovation have been identified through the past researches,
ranging from micro-economic characteristics and inter-firm linkages to macro-economic
performance. The age of the company, is one of the variables that was mostly analysed,
literature on the relationship between the age of the company and innovation go back to
Shumpeter ( 1934) who is considered to be the founding father of the theory of innovation
dynamics (Malerba and Orsenigo, 1995). In his first work “The theory of Economic
Development”, Shumpeter studied the European industrial structure in the late nineteenth
century which was at that period dominated by small firms. He figured out that new
entrance to the market is easier for firms bringing new technology, new ideas, new
products and new processes, therefore, existing firms with previous innovation and old
ways of production and distribution are excluded from the market. This dynamism was
called the creative destruction or the Shumpeter Mark I pattern of innovation. Donner des
exemples de résultats pr l’age. Firm size have been also analysed as a determinant of
innovation. The relationship between firm’s size and innovation goes back too to
Shumpeter’s second work (Capitalism, Socialism and Democracy, 1942), in this work
Shumpeter claimed that large firms are more likely to innovate than small firms. He
argued that large firms have accumulated knowledge and advanced experience in R&D
projects, this allow them to create barriers to entry for new firms. This finding was stated
as the Shumpeter Mark II pattern of innovation. Later, this relationship has been widely
studied (Antonelli and Calderini, 1999; Breschi, 1999; Le Bars et al., 1998; Malerba and
Orsenigo, 1995). However, the debate on the relationship between company size and
innovation is still ongoing, empirical studies using different measurements of innovation
have led to apparently contradictory conclusions. Those contradictions are also due to
different sampling methods which in most of the cases take data across industries instead
of looking at the industry specific patterns of innovation.
Character of innovation in general and in the food industry
In economy, innovation is regarded as one of the main determinants of economic growth,
national progress and competitiveness. Moreover, innovation is crucial to help
surmounting global challenges such as climate change and sustainable development.
There is a lack of information on the subject of product innovations adoption regarding
agricultural and food companies in comparison with other non-agro-food companies. So, in
order to identify the factors associated with innovation adoption for agricultural and food
products, it is necessary to conduct an extensive literature review of what has been written
regarding the factors driving innovation adoption in general. This distinction is very important
as there are huge differences between the characteristics of agricultural and agro-food
products and the non-agro-food products. The food industry is by tradition local or regional
providing products using traditional production technology. This traditional way of
production have been appreciated by consumers who are concerned by the nature, the origin
and the safety of food they eat. Nowadays, the food sector in undergoing huge technological
changes affecting consumer’s behaviors. Those changes can be divided into two main
categories: Information and communication technology, and biotechnology and
bioengineering. Information technology offers opportunities for direct contact between
producers and consumers, thus in the food market, traceability is becoming increasingly
important to ensure safety and quality and to meet consumers’ expectations. For
biotechnology, it is not new, it was used in many traditional production process for example
beer, wine and cheese, however, new biotechnological knowledge based on scientific research
in medicine, chemistry and biology have given researchers and product developers new tools
offering revolutionary opportunities. The changes didn’t affect only the consumer attitudes, in
fact international competition and the structural changes taken place in the European scene
reinforce the need of innovation. The introduction of new products is regarded as an essential
element of competition between food companies and the successful management of new
product becomes a key determinant of business performance. As a result, innovation is
considered as a major source of competitive advantage for a food company.
Previous research
Starting from Schumpeter’s works (1934, 1942), many theoretical as well as empirical studies
have sought estimate the role played by internal and external factors in determining the
propensity and intensity of firm innovation. Among the internal factors, attention has focused
on firm size, age, entrepreneurial, know-how and firm experience, as well as some
organizational features linked to the management-property relationship and the structure of
decisional processes. External factors, these include market size and demand growth.
Previous empirical studies on innovation adoption are mainly based on monographic studies
which quantify innovation diffusion by looking at the adoption of a specific technology (ICT,
seed, medical technologies for instance). All these works focus only on firms for just one
country and, the conclusions are restricted to the specific national cases.
Few studies consider different countries covered by CIS probably due to the lack of
homogeneity found in the data available (some sectors missing in some countries, different
classification of sectors in the different countries, among other problems).
Research objectives and questions
1. Research question: General country characteristics affect the introduction and
adoption of new food products.
2. Aim of the study: To find out the appropriate model to estimate the relationship
between country characteristics and the number of new food products introduced into
the market.
Data
For the independent variables which are :
Gross domestic production
Population with tertiary education
R&D in food and drinks
Foreign direct investment
Environmental expenditures
Annual wages
Final households expenditures on food and drinks
Government investment
Export of food
Total health expenditures
*FDI
Foreign direct investment (FDI) is a category of cross-border investment in which an investor
resident in one economy establishes a lasting interest in and a significant degree of influence
over an enterprise resident in another economy. Ownership of 10 percent or more of the
voting power in an enterprise in one economy by an investor in another economy is evidence
of such a relationship. FDI is a key element in international economic integration because it
creates stable and long-lasting links between economies. FDI is an important channel for the
transfer of technology between countries, promotes international trade through access to
foreign markets, and can be an important vehicle for economic development. The indicators
covered in this group are inward and outward values for stocks and flows.
*Average wage
Average wages are obtained by dividing the national-accounts-based total wage bill by the
average number of employees in the total economy, which is then multiplied by the ratio of
the average usual weekly hours per full-time employee to the average usually weekly hours
for all employees. This indicator is measured in USD constant prices using 2012 base year
and Purchasing Power Parities (PPPs) for private consumption of the same year.
*HH final consumption expenditure on food and drink ( millions of euro), current prices
This comparative table includes statistics on the final consumption expenditure of households
broken down by the COICOP (Classification of Individual Consumption According to
Purpose) classification and by durability. Final consumption expenditures comprise different
variables such as clothing and footwear, health, food expenditure, transport, education,
durable goods, final consumption expenditure, social protection and more. Data are
internationally comparable by following the System of National Accounts 1993 (SNA 1993)
and are expressed in millions of national currency. Data are presented from 1950 onwards.
*Total health expenditure as % of gross domestic product
Sum of General Government and of Private Expenditure on Health. Estimates for this
indicator were produced by WHO. The estimates are, to the greatest extent possible, based on
the National Health Accounts classification (see the World Health Report 2006 for details).
The sources include both nationally reported data and estimates from international
organisations like IMF, WB, UN and OECD. Therefore they may somewhat differ from
official national statistics reported by countries.
Data were collected from OECD, Eurostat, WHO. From the period: 2004 until 2013 for 7
european countries: Belgium, France, Germany, Hungary, Italy, Spain and UK .
The choice of those variables was based on the literature review and on the availability of
data.The choice of the countries was also based on data availability. For the dependent
variable ( Number of new products launched between the period 2004-2013), we collect data
from Mintel GNPD dataset (Global New Products Database). It is an
online database of new fast moving consumer goods, also known as consumer packaged
goods. Over one million records from more than 50 countries
provide product ingredients, nutrition facts, packaging, distribution and pricing information.
Added in 2007, Mintel GNPD IRIS allows users to differentiate successful and unsuccessful
product launches.
The following graph represent the number of new products launched into the market for the 7
European countries between 2004 and 2013:
Methodology
We make a panel data regression, all the regression are done using stata 13. We make three
regressions: Pooled regression model, Fised effects model and Random effects model.
1. Pooled regression model:
I pool all 70 observations toghether and run the model neglecting the across section and time
series nature of data. The major problem with this model is that it does not distinguish
between the various countries that we have. In other words, by combinig 7 countries by
pooling, we deny the heterogeneity that may exist among countries.
I assume all countries are same.
2. Fixed effects model or LSDV Model:
It allows for heterogeneity among 7 countries to have its own intercept value. The term fixed
effect is due to the fact that although the intercept may differ across countries, but it does not
vary over time. That is it is time invariant.
0
1000
2000
3000
4000
5000
6000
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Germany UK France Italy Spain Hungary Belgium
3. Random effects model
Here our 7 countries have a common mean value for the intercept.
After estimating the 3 models, I apply Hausman test to check which model is suitable.
Hausman test:
Null hypothesis: Random effects model is appropriate.
Alternative hypothesis: Fixed effects model is appropriate.
If I got a statistically significant P-value, I shal use fixed effects model, otherwise random
effects model. It means that if:
P-value < 5% : I reject null hypothesis and accept the alternative.
P-value > 5% : I don’t reject null hypothesis.
Prelimenary results
1. First regression: Pooled regression model:
Number of obs = 70
F(10,59) = 17.96
Prob > F = 0.0000
R-squared = 0.7108
Root MSE = 683.67
_cons 4822.869 1993.953 2.42 0.019 832.9778 8812.76
totalhealthexpenditureasofgdp -546.3508 238.957 -2.29 0.026 -1024.503 -68.19882
exportfood .0296781 .0142212 2.09 0.041 .0012215 .0581346
governmentinvestment -10.98423 42.25538 -0.26 0.796 -95.53705 73.56859
finalhhconspexponfoodanddrinks .0100629 .0059818 1.68 0.098 -.0019065 .0220324
annualavgwages -.1264479 .0557774 -2.27 0.027 -.2380583 -.0148375
foreigndirectinvestment -5.43545 6.348237 -0.86 0.395 -18.13824 7.267342
rdinfoodpdtandbeverages -2.55e-07 1.99e-07 -1.28 0.207 -6.54e-07 1.44e-07
popwithtertiaryeducation 88.99785 20.84907 4.27 0.000 47.27896 130.7167
envexp 6.010443 2.427053 2.48 0.016 1.15392 10.86697
grossdomesticspendingonrd 1439.905 526.1883 2.74 0.008 387.0048 2492.806
numberofnewproducts Coef. Std. Err. t P>|t| [95% Conf. Interval]
Among the 10 independant variables, we have 7 varibales which are significant:
( P-value <5%). For the time being, I should not accept the results of this regression because I
cannot assume that all countries are same.
2. Second regression: Fixed effects model
Group variable: country
R-sq within= 0.7625
Between=0.0073
Overall= 0.0481
Nbr of observations= 70, Nbr of groups=7
Obs per group: min 10; Avg 10; Max 10
Corr(u_i, xb) = -0.8632
F(10,53) = 17.01
Prob > F= 0.0000 P-value is too small ( less than 5%), it means that all the
coefficients of this model are not equal to zero.
3. Third regression: Rnadom effects model
rho .97183172 (fraction of variance due to u_i)
sigma_e 427.52769
sigma_u 2511.1902
_cons -3871.473 3211.069 -1.21 0.233 -10312.06 2569.115
totalhealthexpenditureasofgdp 325.6786 231.2383 1.41 0.165 -138.1266 789.4838
exportfood .0493796 .0158547 3.11 0.003 .017579 .0811801
governmentinvestment -21.71537 33.72745 -0.64 0.522 -89.36406 45.93333
finalhhconspexponfoodanddrinks -.0235317 .018108 -1.30 0.199 -.0598518 .0127883
annualavgwages -.0733396 .1023552 -0.72 0.477 -.278638 .1319588
foreigndirectinvestment -12.82752 5.186165 -2.47 0.017 -23.22965 -2.425392
rdinfoodpdtandbeverages -4.07e-07 1.66e-07 -2.45 0.017 -7.40e-07 -7.45e-08
popwithtertiaryeducation 208.4443 89.13767 2.34 0.023 29.6568 387.2318
envexp -6.385398 2.653247 -2.41 0.020 -11.70714 -1.063658
grossdomesticspendingonrd 1768.021 971.0091 1.82 0.074 -179.5771 3715.618
numberofnewproducts Coef. Std. Err. t P>|t| [95% Conf. Interval]
Number of obs = 70
Group variable: country
Number of groups = 7
Obs per group: Min =10; Avg =10; Max =10
R-sq: within = 0.4202
between = 0.9490
overall = 0.7527
Wald chi2(10) = 179.59
Prob > chi2 = 0.0000 P-value is too small ( less than 5%), it means that all the
coefficients of this model are not equal to zero.
corr(u_i, X) = 0 (assumed)
4. Hausman test
*Null hypothesis: Random effects model is appropriate
*Alternative hypothesis: Fixed effects model is appropriate
rho 0 (fraction of variance due to u_i)
sigma_e 427.52769
sigma_u 0
_cons 4822.869 1993.953 2.42 0.016 914.7924 8730.945
totalhealthexpenditureasofgdp -546.3508 238.957 -2.29 0.022 -1014.698 -78.00356
exportfood .0296781 .0142212 2.09 0.037 .0018051 .0575511
governmentinvestment -10.98423 42.25538 -0.26 0.795 -93.80326 71.83479
finalhhconspexponfoodanddrinks .0100629 .0059818 1.68 0.093 -.0016611 .021787
annualavgwages -.1264479 .0557774 -2.27 0.023 -.2357696 -.0171261
foreigndirectinvestment -5.43545 6.348237 -0.86 0.392 -17.87776 7.006865
rdinfoodpdtandbeverages -2.55e-07 1.99e-07 -1.28 0.202 -6.46e-07 1.36e-07
popwithtertiaryeducation 88.99785 20.84907 4.27 0.000 48.13442 129.8613
envexp 6.010443 2.427053 2.48 0.013 1.253506 10.76738
grossdomesticspendingonrd 1439.905 526.1883 2.74 0.006 408.595 2471.215
numberofnewproducts Coef. Std. Err. z P>|z| [95% Conf. Interval]
Prob > chi2= 0.8942 : Here the probability value is high ( more than 5%), meaning that I
cannot reject the null hypothesis: The random effects model is the appropriate.
I double check with the Breush and Pegan LM test for random effects model: Null hypothesis
here is pooled regression model is appropriate and alternative hypothesis is random effects
model is appropriate: Both tests confirm that random effects model is appropriate.
Next step of the work
In the next step, we would like to do the same work as before, but this time we will
decorticate the new food products introduced to the market by type of claims: Health claim,
environmental claim and convenience claims. And to see if there is a change in the results
obtained before.
To be finalized