The Influence of Institutional Factors on the Level of New Business Activity of Local Firms in Emerging and Non-Emerging Economies

Master Thesis for International Business and Management

The Influence of Institutional Factors on the Level of New Business Activity of

Local Firms in Emerging and Non-Emerging Economies

By Gert-Jan Pronk

s2048884 MSc Thesis IB&M

March 2013

Supervisor: A.N. (Andreea) Kiss Co-assessor: dr. R.W. (Rudi) de Vries

Global Economics and Management Department

UNIVERSITY OF GRONINGEN Faculty of Economics and Business

The influence of institutional factors on the level of new business activity of firms in emerging and non-emerging economies

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University Of Groningen Faculty Of Economics And Business

Abstract

This research investigates whether the institutional development level of emerging

economies influences the level of new business activity of local firms within emerging

economies. While the main focus of this thesis includes emerging economies, the

empirical part will also include non-emerging economies. Currently, researchers and

policymakers have limited knowledge about what factors drive local entrepreneurship

towards higher levels in their own domestic market and what possible influences the

governments can have in stimulating entrepreneurial activity of local start-up firms. By

building on Scott`s (1995) three-pillar institutional framework, the regulative, normative,

and cognitive aspects of 8 emerging economies will be measured. The literature part

including the institutional components lies at the basis for including the correct

institutional variables extracted from the National Global Entrepreneurship Monitor

(GEM) databases. By analyzing data of 8 emerging economies and 35 non-emerging

economies this thesis will try to find evidence of whether the institutional development

level of these economies influences the level of new business activity of local start-up

firms in their own domestic markets. Control variables are added and include 3 economic

variables; unemployment rate, total GDP per capita and population growth.

TABEL OF CONTENT

INTRODUCTION--- ---4

2.0 LITERATURE REVIEW ---9

2.1. Institutional development level---9

2.2. The regulative or institutional components of institutions ---12

2.3. The normative or social components of institutions ---12

2.4. The cognitive components of institutions ---13

2.5 EMERGING ECONOMIES ---14

3.0 HYPOTHESIS DEVELOPMENT---17

3.1 The regulative component ---18

3.2 The cognitive component ---20

3.3 The normative component ---22

4.0 CONCEPTUAL MODEL---24 5. RESEARCH METHODOLOGY ---25 5.1 Data sources ---26 6. DATA COLLECTION---27 6.1 Dependent variable ---27 7.0 INDEPENDENT VARIABLES ---28 7.1 Regulative components---28 7.2 Cognitive components ---29 7.3 Normative components ---30 8. CONTROL VARIABLES ---31

9. DATA DETAILS AND MISSING DATA ---34

INTRODUCTION

The past two decades the global economy changed in a way that the former protected economies became liberalized and therefore integrated in the worldwide economies (Aulakh & Kotabe, 2008). This change reflects a rise in international business studies who are interested in researching emerging economies with their changed competitive environments. Varying institutional profiles can be found in these emerging economies and these profiles play an important role in fueling international business research (Clercq et al., 2010). This work will focuses on the influence of the institutional development level of emerging economies in relation with the level of new business activity1 within emerging economies. While the literature part of this thesis will include emerging economies related literature, a number of non-emerging economies will be included in the empirical part in order to verify the quality of the data used for this thesis. To my knowledge no studies included a sample of emerging economies in an attempt to find a relationship with the level of institutional development and new business activity rates.

Entrepreneurial activity can be the cure for stagnant economies, can increase the economic welfare in former planned economies and can stimulates economic growth in many other emerging economies (Bruton, 2008). Despite the importance of entrepreneurial activity within emerging economies, scholars today have limited knowledge about what factors influence high rates of entrepreneurship in an economy. Governments also struggle in promoting entrepreneurship which can stimulate the growth of an economy (Spencer and Gomez, 2002). However the importance of emerging economies is reflected in the rise of international studies including this topic, research in entrepreneurship can still be critiqued as almost exclusively

focusing on developed economies such as the European and North American countries (Bruton, 2008). Also, few studies focus on the role of the institutional development level of emerging economies in relation with new business activity in emerging economies (Estrin & Prevezer 2010). For emerging economies, the evidence about how and why some start-up firms succeed is less well established and this emphasizes the importance of examining the role of institutional factors (Estrin & Prevezer, 2010). Currently, researchers and policymakers have limited knowledge about what factors drive local entrepreneurship towards higher levels in their own domestic market and what possible influences the governments can have in stimulating entrepreneurial activity of local start-up firms (Spencer and Gomez, 2002).

For local firms starting up businesses in emerging economies it is of strategic importance to identify the institutional development level of an emerging economy because this influences the performance of the firm, and knowledge about the institutional development level can maximize returns and minimize investment risks (Chan et al., 2008). For the emerging economy itself, Nickell (1996) argues that the start-up of local firms can stimulate efficiency and productivity, and these local firms together can offer innovative ideas. Also according to Cohen and Klepper (1992), an increase in competition can be expected together with an increase in firm diversity. When an increasing number of start-up firms operate from emerging economies it is most likely that these firms will compete with each other by offering diverse products (Cohen and Klepper , 1992).

largest share of job creation together with the highest growth in employment rates. Therefore, the entrance of local start-up firms in emerging economies is expected to increase the availability of jobs, which will stimulate economic growth of an emerging economy (Kirchoff and Phillips, 1988). Therefore, governments, investors and researchers may have interest in assessing the level of new business activity of local start-up firms within emerging economies in relation with the institutional development level.

The understanding of the institutional context within an emerging economy is crucial for a local start-up firm as it can vary greatly from those in more developed economies and even between emerging economies (Bruton, 2008). And it is this variety in institutional environments according to De Clercq (2010) which gives input for international business research. Despite the growing importance of emerging economies, research about institutional factors influencing the local start-up of firms in emerging economies is limited (De Clercq, 2010). The above reasoning leads to the formulation of to the main research question:

“To what degree does the institutional development level affect the new business activity of local start-up firms within emerging and non-emerging economies?”

entrepreneurial activity on a National level within different countries. Its main goal is to measure what influence entrepreneurial activity has on National economic growth. Further information about GEM will be included in the methodology part of this thesis. By comparing institutional development level data extracted from the Gem databases, this research will compare a total of 8 emerging economies and 35 non-emerging economies for a total of 4 to 9 years. Also three control variables; GDP per capita, unemployment and population growth rates, will be included which are further discussed in the methodology part of this thesis. While this work includes a dependent variable which is country level based, the number of respondents is limited to the economies included.

This thesis will seek to contribute and better understand the effects of the institutional development levels of emerging and non-emerging economies in relationship with new business activity rates from local start-up firms. Given the fact that the institutional development level is responsible for influencing the allocation of local start-up firms within economies, investigating whether the institutional development level influences the decision-making of start-up firms seems necessary.

LITERATURE REVIEW AND HYPOTHESES

Institutional development level

This work is based on two streams in the literature published by William Baumol (1990, 1993, 2005) and Douglass North (1990, 1994, 1997, 2005). Both were responsible for providing theoretical information about entrepreneurial development in environments that differ (for example: emerging versus developed economies). North argues that entrepreneurs and therefore local start-up firms are the main cause of change within an economy. Local start-up firms will have to adapt their strategies and activities in a way that they will fit within the institutional framework which enables them to avoid limitations and make use of opportunities. Each economy has its own formal rules which ideally are formed to stimulate exchange and to reduce transaction costs. Since these institutions and formal rules are created by individuals or groups in ways which might differ, they do not necessary operate according to the interest of the social wellbeing (North 1990). This reasoning is followed by Baumol (1990). He describes institutions as the structural base that provides local start-up firms the incentives for differing economic activity. Both authors agree that local start-up firms will ‘weigh the incentives’ which are present in the economy in the form of regulations, norms and cultural aspects. Both authors also agree that the institutional environment plays an important role in attracting entrepreneurial activity (local start-up firms) and that in the event of a weak institutional environment the level of entrepreneurs operating form these economies will be less than in institutional environments that are more favorable.

differ from developed economies in ways that they lack market information that is reliable. Also unpredictable government actions can hinder local start-up firms from operating efficiently. (Chan et al., 2008). These elements, combined with the lack of efficient bureaucracy create an environment with several “institutional voids” (Khanna and Palepu, 2000). These institutional voids result in transaction costs which are more costly and also make transformations less efficient for local start-up firms (Henisz and Zelner, 2005). Transaction costs in emerging economies are higher due to the fact that firms need to protect their assets form dispossession (Chan et al., 2008). Therefore, local start-up firms that focus their operations in countries with low institutional development levels are more likely to face costly market transactions, whereas local start-up firms operating from developed economies with higher institutional development levels can benefit from the advantages presented by this developed economy (Chan et al., 2008). Institutions which are better developed lower the search, production and transactions costs (North, 1990). Also, the more favorable the institutional development level is, the more positive the profitability is for international start-up firms (Bergara et al., 1998).

The regulative or legal components of institutions

This component reflects the existing laws and rules in a specific region or a country that promote specific types of behaviour, and these laws and rules are known to influence behaviour and constrain others (Kostova & Roth, 2002). The regulative component of institutions in the context of new business creation also refers to the degree in which tax collection system are efficient (Estrin et al.,2006). An inefficient tax system will result in a burden for local firms starting up businesses and running them. The regulative component also refers to the presence of governments and their policies supporting local firms starting up businesses and their quest for resources (Reynolds et al., 2005), and the ease for getting licences and permits (Djankov & Murrell, 2002). Laws and regulations can make the decision making process of entrepreneurs more efficient in a way that these ‘rules’ indicate which responsibilities the owner has. Laws and regulations also tend to lower the risk involved with starting up a new business (Spencer and Gomez, 2002). Requirements which are complex will hinder the activities of businesses. The findings of Djankov et al. (2002) support the view that institutions with higher levels of regulative burdens are not supportive for positive outcomes such as improved quality of products. Instead, the result support that countries with high regulative burdens have higher levels of corruption and the size of the unofficial economy is larger (Estrin & Prevezer, 2010). Also, high regulative burdens hinder the attraction and creation of firms (Klapper et al., 2006).

The normative or social component of institutions

that shape interaction’, this includes formal rules and enforcement mechanisms and implicit rules that can take the form of taboos, customs, codes of conduct, routines and so on (North, 1990, pp. 6, 43, 83). Therefore the normative component can be used to measure the degree in which the countries inhabitants ‘welcome’ entrepreneurial activity. When an emerging economy scores low on the normative components this would suggest that the countries inhabitant don’t accept entrepreneurial activity.

The cognitive component of institutions

inhabitants should possess the knowledge about how to start up a business and how to manage risk. This knowledge can differ between countries in a way that in some, hardly any knowledge is available about how to run a business (Zucker, 1991). In these low cognitive countries, little is known about how to start and run a business. Training programs and the amount of assistance new businesses get from the government when starting up a business can contribute to the cognitive environment and stimulate entrepreneurial skills (Dana, 1987; Hawkins, 1993).

Emerging economies

important economies worldwide. By the year 2025, McKinsey & company (2012), expects that the annual consumption within emerging economies will reach the number of $30 trillion making it one of the most important possible growth markets for start-up firms. Also, Wilson & Purushothaman (2003) argue that by the year 2050, the “BRIC” economies; Brazil, China, India and Russia will outperform the economies of France, Germany, Japan, United Kingdom and the United States in growth rates making them larger.

Fig.1. Emerging economies in 2011

Source: Kearney (2012) p.162.

‘emerging economy’ is common used in the literature, there is no agreement about its theoretical or operational definition (Kearney, 2012). International financial institutions are mostly responsible for assigning the classification of emerging economies to a certain country. According to the World Bank a country is named ‘emerging’ when “its GDP falls below a certain hurdle that changes through time” (Bekaert et al., p.429. 2002). The obvious idea behind the term is that a country emerges from a less developed status country to a status which is more developed and therefore is emerging. However, while the interest in emerging economies is growing for both businesses and researchers on the global stage, plus the role local start-up firms play in fueling their economy, the understanding of local start-up firms operating from emerging economies is limited (Kiss et al., 2011).

Emerging economies tend to have a large diversity in cultural, language and political aspects (Kearney, 2012). Surprisingly, as is argued by Kearney (2012), most of the physical financial infrastructures of emerging economies are well-developed. The physical financial infrastructure includes different types of banks such as central and commercial banks and the stock exchanges. While these physical infrastructures seem to be well-developed the processes and systems supporting them are less well-developed. Important processes and systems which tend to be less well-developed within emerging economies include regulation, accounting, governance and its liquidity (Kearney, 2012). These less-well developed processes and systems lead to greater uncertainty and increase the risk of operating from emerging economies. So although the liberalization of these emerging economies opened up possibilities for businesses, there are still burdens to overcome for firms starting up businesses in these economies.

engines of structural change (Clercq et al, 2010). Entrepreneurial activity has long been viewed as the engine that stimulates innovation and will strengthen the economic development of an economy (Reynolds, 1997). Looking back in time at the history of the development of the industries of both Great Britain and the United States (former emerging economies) Casson (1990) argues that the rapid industrialization in these countries was made possible because entrepreneurial activity was allowed to increase. Still today, entrepreneurial activity brings new life into different economies worldwide and has facilitated growth in many other economies (Oviatt & McDougall, 1994). Considering the above, scholars still struggle in explaining why rates of entrepreneurial activity are different across countries and economies (Aronson, 1991).

An important element for emerging economies to be either attractive or not attractive for local start-up firms is the degree to which these economies have hindering or supportive institutional factors (Estrin & Prevezer, 2010). This concept will be tested in this thesis in the following part of this work and will include the three components view introduced by Scott (1995) These components, instead of the cultural view, nowadays seem more valid for researcher to implement in their studies as it is a much broader concept covering a much wider area then the beliefs and norms distinctive for individuals within a country.

HYPOTHESIS DEVELOPMENT

then in developed economies. When referring to emerging economies with its underdeveloped institutional development level, the discussed regulative, normative and cognitive components can act as institutional burdens affecting the local start-up firms operating from these emerging economies. Therefore it is highly likely that there will be a relationship between the degree of institutional burdens and the level of new business activity of local start-up firms within emerging economies. Since a relationship is expected in any given economy, this work will also include non-emerging economies.

The regulative component

business, the uncertainty of the regulative framework together with the likelihood of corruption will drive the start-up costs up which result in less favorable circumstances for starting up a business (Boettke and Coyne, 2003). Also missing intellectual property rights, corruption and untrustworthy regulations and laws might hinder entrepreneurial activities (Aidis et al., 2008). Since the literature suggests that the regulative environments can either encourage or discourage entrepreneurial activity, this work assumes that the regulative component of the institutional theory is related to the rate in which entrepreneurial activity takes place within an economy. Therefore this work assumes that;

Hypothesis 1. Supportive regulative components will positively influence the level of new business activity of local start-up firms within emerging and non-emerging economies.

The cognitive component

from Krueger et al., (2000) a person’s capability in undergoing entrepreneurial actions is also a very important element. Just recognizing an opportunity is not enough; it is the combination of recognizing opportunities and knowledge that, when combined form the ideal combination of an individual to start-up a new business (McMullen et al., 2008). Therefore it is of great importance that individuals have a certain amount of knowledge which can help them to successfully start-up a new business. The combinations of willingness, knowledge and intentions have been studied before and resulted to be of influence to entrepreneurial activity (Alvarez and Barney, 2007). The degree in which individuals are alert towards opportunities or are confident in their own skills to start up a business, are also strengthening the degree in which individuals are likely to start up a business (Arenius and Minniti, 2005). Therefore, the knowledge and skill sets of entrepreneurs are likely influencing the decision making related to starting up a business. Based on the above, this work argues that the extent in which an economy’s educational system gives attentions to entrepreneurship-related topics is likely to be of influence for the rate in which entrepreneurial activity takes place in a later stage, and therefore assuming that;

The normative component

of normative burdens are measured within an economy, it is likely that the inhabitants of this economy are unsupportive and disliking new business activity within their country. Therefore the normative component can be used to measure the degree in which the countries inhabitants ‘welcome’ entrepreneurial activity. When an economy scores low on the normative components this would suggest that the countries inhabitant don’t accept entrepreneurial activity. The above reasoning in combination with the findings from the literature review therefore suggests that;

Hypothesis 3. Supportive normative components will positively influence the level of new business activity of local start-up firms within emerging and non-emerging economies

CONCEPTUAL MODEL

While covering the institutional development theory this thesis argues that the work of Scott (1995) highlights the three most important components for researching the institutional development level of any given economy. By including the regulative, normative and cognitive components of the institutional development level, this thesis will try to measure if there is a relationship between these three factors and the rate of new business activity within emerging and non-emerging economies. The methodology part of this thesis will further discuss the chosen variables for the regulative, cognitive and normative institutional development components indicated in the conceptual model as R1,R2,C1,C2,N1,N3 as well as the control variables. Conceptual model

R

NEW BUSINESS OWNERSHIP RATES

(NBOR)

RESEARCH METHODOLOGY

In line with the classification of Scott’s (1995) three pillars concept as discussed in the literature review this thesis will include the regulative, normative and cognitive components of emerging economies and tests whether a relationship exists between the institutional development level of emerging economies and the level of new business activity of local start-up firms from these emerging economies. To do so this thesis will include data from the GEM databases2. In line with the findings from the literature review, the chosen institutional variables from the GEM databases will be based on the findings from the literature review.

2

DATA SOURCES

In order to study the rate in which entrepreneurial activity takes place within an economy, the entrepreneurship literature offers multiple options to measure this. While multiple options are offered, the usefulness of these measurements in measuring the new business foundation rates across economies remains questionable (Ahmad and Hoffmann, 2008). This is caused due to the fact that scholars today have multiple measures to choose from; individual reports which are selected randomly or data from business registries (Stenholm et al, 2013). These two types of data collection methods have their limitations since the type of measurements do vary between countries, making it difficult to analyze this data between countries and economies. The OECD was one of the first databases that included data about self-employment across all their member countries. Reynolds et al., (2005) argue that due to the limitations of the data of the OECD, the call for reliable date became apparent and was delivered by the Global Entrepreneurship Monitor (GEM). The datasets and models used by the GEM are promising (Valliere, 2010). The Global Entrepreneurship monitor (GEM) investigates the entrepreneurial activity on a National level within different countries. The 3 main objectives of the GEM research program according to their homepage are:

-“To measure differences in the level of entrepreneurial activity between countries”

-“To uncover factors leading to appropriate levels of entrepreneurship” -“To suggest policies that may enhance the National level of entrepreneurial activity”

within a specific country to provide measures of the influences entrepreneurs have on the economic development level of the country. The GEM data is unique in that is consists of data which can be compared with other countries. While all countries do collect data on self- employment none of these datasets can be used for country comparison since their definitions about self-employment differ. The main strength of GEM data therefore lies in the fact that is delivers data which is harmonized and therefore can be used to compare multiply countries without having to deal with different measurement variables. Therefore this data is suitable for this research as it will compare data between different emerging economies.

DATA COLLECTION Dependent variable

Independent variables

Regulative components

The measure utilized for the regulative component of the institutional development level is the regulative 1 and regulative 2 variable extracted from the GEM database. Since there is no known variable to measure the degree of regulative development level in an emerging or any economy the two regulative variables used for this thesis are based on the literature review. For the regulative components the literature review indicated that (see H1) it is likely that government rules, laws and decisions are influencing the level of new business activity of local start-up firms. REG 1 extracted from the GEM database refers back to the decisions made by the government and REG 2 refers back to the rules and laws. Both REG 1 and REG 2 scores are based on a 5 point likert scale indicating that 1,00= “Completely false” 2,00= “Somewhat false”3,00= “Neither true nor false”4,00= “Somewhat true”5,00= “Completely true”.

- REG 1: In my country, the support for new and growing firms is a high priority for policy at the National government level

- REG 2: In my country, new firms can get most of the required permits and licenses in about a week

Cognitive components

The measure utilized for the cognitive component of the institutional development level is the cognitive 1 (COG 1) and cognitive 2 (COG 2) variable extracted from the GEM database. Since there is no known variable to measure the degree of cognitive development level in an emerging or any economy the two cognitive variables used for this thesis are based on the literature review. For the cognitive components the literature review indicated that (see H2) the inhabitants should possess a certain type of knowledge to start-up businesses on their own. Also the degree in which the availability of training programs are offered in helping local entrepreneurs in starting up a business refers back to a cognitive component and is therefore considered as worthy of investigating.. COG 1 extracted from the GEM database refers back to the degree in which training programs support entrepreneurs in founding their new business. COG2 refers back the degree in which the entrepreneurs possess the right amount of knowledge to start –up a business on their own. Both COG 1 and COG 2 scores are based on a 5 point likert scale indicating that 1,00= “Completely false” 2,00= “Somewhat false”3,00= “Neither true nor false”4,00= “Somewhat true”5,00= “Completely true”.

- COG 1: In my country, the level of business and management education provide good and adequate preparation for starting up and growing new firms

- COG 2: In my country, many people know how to start and manage a small business

Normative components

The measure utilized for the normative component of the institutional development level is the normative 1 (NORM 1) and normative 2 (NORM 2) variable extracted from the GEM database. Since there is no known variable to measure the degree of normative development level in an emerging or any economy the two normative variables used for this thesis are based on the literature review. - For the normative components the literature review indicated that (see H3) that the inhabitants of this economy are unsupportive and disliking new business activity within their country. Therefore the normative component can be used to measure the degree in which the countries inhabitants ‘welcome’ entrepreneurial activity. NORM 1 extracted from the GEM database refers back to the degree in which inhabitants respect and welcome entrepreneurs. NORM 2 refers back the degree in which inhabitants see entrepreneurs as resourceful and supportive individuals Both NORM 1 and NORM 2 scores are based on a 5 point likert scale indicating that 1,00= “Completely false” 2,00= “Somewhat false”3,00= “Neither true nor false”4,00= “Somewhat true”5,00= “Completely true”.

- NORM 1: In my country, successful entrepreneurs have a high level of status and respect - NORM 2: In my country, most people think of entrepreneurs as competent, resourceful individuals

CONTROL VARIABLES

Although this research argues for a relationship between institutional factors affecting the amount of local start-up firms starting up businesses, there might be other factors explaining this phenomenon. It is therefore necessary to include control variables which are known to be of influence to new business foundation. This study includes three economic factors as control variables as possible variables influencing new business foundation.

Unemployment, total (% of total labor force)

While the definition of unemployment differs per country, the most common explanation refers to the share of the labor force that has no work but is searching for and is available to work. The unemployment data for this thesis is collected from the World Bank databases. Wildeman et al. (1998) suggest that within less developed economies, the unemployment rates are higher compared with developed economies and that in economies with high unemployment rates the possibility of self-employment will increase. It therefore seems likely that individuals within low employment opportunity economies are more likely to start-up their own business. Data about unemployment as total % of the labor force is extracted from the World bank databases3.

GDP per capita (current US$)4

“Gross domestic product (GDP) is the monetary, market value of all final goods and services produced in a country over a period of a year” (van den Berg, 2008, p-117). When the GDP is correlated for inflation the real GDP per capita becomes visible and this GDP per capita is often used as a measurement for judging the health of an economy within a specific country. The study

3

See http://data.worldbank.org/indicator/SL.UEM.TOTL.ZS for details 4

of Wildeman et al. (1998) indicated that economies with high per capita GDP, and therefore having a healthy economic environment are unsupportive for local starting-up firms. Within these high per capita GDP economies the level of business activities are on a much higher level due to the use of advanced technologies than compared with less developed economies were less advanced technologies are needed in order to succeed. Therefore it is likely that the higher the per capita GDP rate of an emerging economy, the more likely it is that the number of local start-up firms will be less. Data about GDP per capita is extracted from the World Bank databases5

Population growth (annual %)

The annual growth of the population can be described as the exponential growth of the midyear population (World Bank, 2012). This demographic factor seems to be of influence on entrepreneurial activity as Armington and Acs (2002) found a positive relationship between population growth and entrepreneurship. Reasons so include that fact that a growing population increases the possibilities for entrepreneurial activity due to a consumer market which is becoming larger. Data about population growth is extracted from the World Bank databases5

To summarize, this study expects that high unemployment rates, low per capita GDP and an increasing population will stimulate local start-up firm activity within their own domestic market. Therefore these variables will act as control variables. The following table shows an overview of all the included variables and descriptions used for the analyses.

5

Overview

Component variable Description Source

Dependent variable New business foundation rate Independent variables Regulative Cognitive Normative Control variables GDP per capita Total unemployment Population growth NBOR REG 1 REG 2 COG 1 COG 2 NORM 1 NORM 2 GDP UNEMPLOYMEN T POPULATION GROWTH

-The percentage of the 18-64 population who are currently an owner-manager of a new business, ie., owning and managing a running business that has paid salaries, wages, or any other payments to the employers for more than three months, but not more than 42 months

- In my country, the support for new and growing firms is a high priority for policy at the National government level

- In my country, new firms can get most of the required permits and licenses in about a week

- In my country, the level of business and management education provide good and adequate preparation for starting up and growing new firms

-In my country, many people know how to start and manage a small business

- In my country, successful entrepreneurs have a high level of status and respect - In my country, most people think of entrepreneurs as competent, resourceful individuals

- GDP per capita (current US$)

- Unemployment, total (% of total labor force)

-Population growth (annual %)

DATA DETAILS AND MISSING DATA

The data for this study is collected from the annual datasets6 of GEM and includes data covering the (2001-2009 period). While the GEM offers multiple datasets, the data necessary for this research was extracted from the datasets consisting of National level data which include institutional level elements (regulative, cognitive and normative) collected from respondents from multiple economies. All results from the National datasets are average values per country where the minimum response rate is 2000. While the countries included in these National level datasets include both developed and emerging economies the emerging economies had to be separated. In order to clarify which economies can be considered “emerging“ this work will include the emerging economies as stated by the Morgan Stanley Capital International (MSCI) emerging market index. The following information about the MSCI index is gathered from their website7. “The MSCI index is able to measure the equity market performance within emerging economies. The MSCI index is also called a “free float-adjusted market capitalization index and includes the following 21 emerging economies: Brazil, Chile, China, Colombia, Czech Republic, Egypt, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, South Africa, Taiwan, Thailand, and Turkey*. The MSCI is constantly reviewing the countries included by using a framework which is called the MSCI Market Classification Framework. In order to classify the emerging economies development level, variables such as size, liquidity and market accessibility are constantly gathered and examined. When necessary, the list of emerging economies is reviewed and changed.

6

MSCI Emerging Markets Index - Country Coverage6

The economies included in the National level datasets differ each year. After separating the emerging economies from the datasets for a time span of nine years the following overview becomes visible (Figure 1). The emerging economies highlighted in the MSCI Emerging Markets Index are included. Brazil is the only economy for which data is available in all the National datasets covering the nine years (2009-2001) period.

---Insert figure A here ---

6

The next separation of the data is based on the degree in which the new business ownership rate data is available. This data is extracted from the key-indicators data7 from the GEM website:

--- Insert figure B here ---

The remaining emerging economies that will be included in further analyses are Brazil, Chile, China, Hungary, Korea, Mexico, Russia and South Africa. Only these economies have data available for four years or more on National level data and new business ownership rates: Brazil 9 years, Chile 7 years, China 4 years, Hungary 6 years, Korea 4 years, Mexico 4 years, Russia 5 years and South Africa 7 years. Therefore all economies with missing data will be excluded for further analysis and only the economies with complete data will be included. This resulted in a total of 46 cases (N46) which will be included in further analysis. The same rules were applied when gathering data for the non-emerging economies. This resulted in the list of the following non-emerging economies: United States, Greece, Netherlands, Belgium, France, Spain, Italy, United Kingdom, Denmark, Sweden, Norway, Germany, Peru, Argentina, Colombia, New Zealand, Singapore, Thailand, Turkey, Canada, Japan, Ireland, India, Finland, Serbia, Croatia, Ecuador, Uruguay, Hong Kong, United Arab Emirates, Israel, Jamaica, Switzerland, and Australia

7

RESULTS

Since this study is interested in the relationship between multiple independent variables, (regulative, cognitive and normative institutional variables and 3 types of control variables) and their possible relationship with the dependent variable new business organization rate (NBOR) this thesis will use multiple regressions taking into account different dummy year variables to measure the influence of time. The Multiple Regression test is performed using SPSS software. In order to measure the influence of the institutional variables the NBOR was taken as the dependent variable, and the institutional components and control variables as independent variables. The first results will illustrate the descriptive statistics of all the included variables. Secondly, the coefficients results of emerging and non-emerging economies will be given, only taking into account the control variables and the dependant variable NBOR. Third, both the control variables and the independent variables will be included in the analyses. Each independent institutional variable will be measured individually against the three control variables in model 1, 2 and 3. Model 4 will analyses all variables at the same time. The final step includes the dummy year variables to measure the influence of time. In order to verify the quality of the results a test for multicollinearity is performed.

Table A1.

Means, standard deviations, and correlations ( Emerging economies) Mean S.D. 1 2 3 4 5 6 7 1. NBOR 0.4339 2.94277 1 2. REGMEAN 2.1626 .39676 -.266 1 3. COGMEAN 2.2151 .72550 -.229 -.071 1 4. NORMMEAN 3.5776 .32722 .419** -.004 .219 1 5. GDP 7176.8474 3954.79947 -.139 .058 .534** -.065 1 6.UNEMPLOYMENT 10.1130 8.21994 -.468** .203 -.117 -.030 -.368* 1 7. POPULATION GROWTH 0.7635 .58126 .250 -.006 -.323* .462** .379** .364* 1 Valid sample size: N 46

* P < .05. ** P < .01.

Table A2.

Means, standard deviations, and correlations (Non-emerging economies) Mean S.D. 1 2 3 4 5 6 7 1. NBOR 4.0133 3.05963 1 2. REGMEAN 2.4764 .60040 -.176* 1 3. COGMEAN 2.4006 .81604 .118 .079 1 4. NORMMEAN 3.4330 .57783 095 .155* .426** 1 5. GDP 27667.8109 17795.15593 -.370** .399** .202** .016 1 6.UNEMPLOYMENT 7.3167 3.33718 -.027 -.445** -.009 -.045 -.508** 1 7. POPULATION GROWTH 1.0224 1.72370 .171* .142* .175* .267** .081 -.225** 1 Valid sample size: N 190

Correlation tests will indicate to what degree the variables are correlated with each other and therefore testing for multicollinearity. Table A1 including the emerging economies shows a positive correlation for the NORMMEAN ( .419** ) and a negative correlation for unemployment ( -.468** ). Both are correlated at the significance level of P < .01. Table A2 includes the non-emerging economies and shows a negative correlation for the REGMEAN (-. 176*) and a positive correlation with population growth ( .171*) both correlated significant at the P < .05. GDP (-. 370*) shows a negative correlated relationship with NBORat P < .01.

According to the rule of thumb discussed by Hinkel et al., (2003), having a correlation coefficient of P > 0.7 á 0.8, indicates an issue related to multicollinearity. If we look at the results of both figure A1 and A2 then no indications can be found which indicate the problem of multicollinearity.

The following part will include the results of the multiple regressions. First tests are performed without taking into account the measurements for the institutional components (REGMEAN, COGMEAN and NORMMEAN), and the dummy years. These dummy years will later function as a robustness check where the influence of years is taking into account. The results of the multiple regression tests only including the control variables, are illustrated in figure A3 (emerging economies and non-emerging economies). The standard error is included between brackets.

--- Insert table A3 here ---

For the non-emerging economies the GDP has the largest standardized coefficient Beta of -. 481 and is significant at p < .01. Unemployment is also tested significant p < .01., and population growth scores a significance of p < .01.

The following results include both the control variables and the institutional components (REGMEAN, COGMEAN and NORMMEAN). The standard error is included between brackets. These results are illustrated in table A4 (emerging economies) and A5 (non-emerging economies):

Table A4

Regression results emerging economies

Model Model 1 Model 2 Model 3 Model 4

Table A4 includes coefficients results of emerging economies taking into account the control variables, the independent variables REGMEAN, COGMEAN AND NORMMEAN, and the dependant variable NBOR. Model 1 from table A4 includes all three control variables and the REGMEAN variable. Model 2 includes the same control variables but tests the COGMEAN individually and model 3 tests the NORMMEAN individually. Model A4 includes all three control and independent variables. Comparing the three institutional MEANS the NORMMEAN has the largest standardized coefficient Beta of, 406 and is significant at p < .01.. COGMEAN has the second largest standardized coefficient Beta of -, 306 and is significant at p < .01.. Interestingly, both the GDP and population growth variables are not significant anymore. REGMEAN is also of no significance (, 128).

Table A5

Regression results non-emerging economies

Model Model 1 Model 2 Model 3 Model 4

Constant 8.105 5.884 6.719 7.709 (1.330) (.940) (1.401) (1.594) Control variables GDP -.456** -.541** -.482** -.524** (.000) (.000) (.000) (.000) UNEMPLOYMENT -.245** -.257** -.222** -.279** (.074) (.070) (.071) (.073) POPULATION GROWTH .266** .233** .268** .241** (.249) (.247) (.260) (.256) Independent variables REGMEAN -.081 -.070 (.383) (.380) COGMEAN .195** .219** (.243) (.265) NORMMEAN .017 -.060 (.347) (.374) Number of observations 190 190 190 190 Adjusted R2 0.268 0.267 0.264 0.299 F-statistic 18.338** 21.164** 17.928** 14.448** * P < .05. ** P < .01.

The final results function as a robustness check and include the dummy year variables. These dummy year variables function to test for the influence of different years. The results of these tests are illustrated in figure 5A and 5B for both the emerging and non-emerging economies.

--- Insert Table A6 here ---

Table A6 includes coefficients results of emerging economies taking into account the control variables, the independent variables REGMEAN, COGMEAN AND NORMMEAN, Dummy year variables and the dependant variable NBOR. Comparing the three institutional MEANS the COGMEAN has the largest standardized coefficient Beta of -1.135 and is significant at p < .01. The remaining institutional MEANS are not significant (p > .05). Both the GDP and population growth variables are not significant. Unemployment is significant at p < .01 and has a standardized coefficient Beta of -. 639.

SUMMARIZING RESULTS Table A3

The results show mixed findings for both the control variables and the institutional components being of effect to NBOR. When only testing the relationship of the control variables and the influence towards NBOR’s and excluding other independent variables, the results indicate a significant relationship for both the unemployment (β= -.708 p <.01) and population growth (β= .416 p < .01) variables. Including non-emerging economies resulted in significant results for all three control variables.

Table A4 & A5

Table A6

DISCUSSION

This study examined the relationship of the three institutional components and new business organization rates within emerging and non-emerging economies. The results as presented in table A4 (emerging economies), show only significant results for the NORMMEAN variable and therefore supporting hypothesis 3. The support for hypothesis 3 indicates that, within emerging economies, the status and respect entrepreneurs receive from the inhabitants of this economy stimulate them in starting up a business. Also, in an economy where entrepreneurs are considered competent and resourceful individuals, this increases the level of new business activity. Entrepreneurs seem to be sensitive for the mindset of the inhabitants within emerging economies. The results of table A4 also show a significant result for the COGMEAN variable. As the expected relationship form hypothesis 2 was positive, the results indicate a negative relationship between COGMEAN and NBOR. This indicates that both knowledge about how to start up a business and educational attention is negatively influencing the degree in which entrepreneurs decide to start-up a successful business on their own within emerging economies. Hypotheses 1, including the regulative institutional components did not find supportive results for both the emerging and non-emerging economies. These findings suggest that government intensions in supporting an increase in new business activity are of no influence within both emerging and non-emerging economies.

“entrepreneurs of the year” award which according to Gnyawali and Fogel (1994), can contribute in stimulating the attitudes towards entrepreneurial activities.

This study included control variables which are studied before and were tested to be of influence towards entrepreneurial activity. Within the emerging economies the control variable unemployment was only tested significantly for negatively influencing the NBOR. It therefore seems that within emerging economies with growing unemployment rates, individuals are not tempted in doing self-employment. This negative relationship of unemployment and NBOR is also valid within the non-emerging economies group and will be discussed into more detail in the following part of the discussion. This part will include the discussion of the results from the non-emerging economies group together with the findings of a rather similar research performed by Spencer and Gomez (2002).

The study performed by Spencer and Gomez (2002) also investigated the influence of the three institutional components on both the percentage of self-employed people and the percentage of small firms in a country. Their study included two different dependent variables (self-employment and small firms or new listings) and a mix of different economies8. The data was gathered through surveys where the average response rate was 4.7 reviewers for the 23 countries accounting for almost 110 responses in total. In line with the suggested relationship of the three institutional components and the level of NBOR from this study, the study of Spencer and Gomez (2002) also expected all three components to be of positive influence towards the level of self-employment and small firms. The following tables illustrate both the expected relationships and the result from the study of Spencer and Gomez (2002) and my thesis:

8

Table 7

Overview Spencer and Gomez (2002)

Variable Expected relationship Results

Regulative Self-employment + Self-employment -** Small firms + Small firms +*

Cognitive Self-employment + Self-employment + Small firms + Small firms +*

Normative Self-employment + Self-employment + Small firms + Small firms -

GDP Self-employment - Self-employment -**

Small firms - Small firms -

Unemployment Self-employment + Self-employment + Small firms + Small firms +**

* P < .05. ** P < .01.

Table 8

Overview of my results (non-emerging economies)

Variable Expected relationship Results

Regulative NBOR: + NBOR: -

Cognitive NBOR: + NBOR: +**

Normative NBOR: + NBOR: -

GDP NBOR: - NBOR: -**

Unemployment NBOR: + NBOR: -**

* P < .05. ** P < .01.

from Audretsch, (1995) and Jovanovic (1982) who suggest that higher unemployment rates will result in less new business activity.

LIMITATIONS

PRACTICAL CONTRIBUTIONS

CONCLUSION

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APPENDIX

Table of content

Table A1: descriptive statistics emerging economies---62

Table A2: descriptive statistics non-emerging economies---63

Table A3: correlations emerging economies---64

Table A4: correlations non-emerging economies---64

Table A5: emerging economies (control variables) ---65

Table A6: non-emerging economies (control variables) ---66

Figure A: emerging economies and availability of national datasets---67

Table A1 (continued). Emerging economies Descriptive statistics Variables

N Mean Min Max

St. deviation Dependent variable: NBOR 46 4.9913 0.8 10 2.94277 Control variable: GDP 46 7176.8474 1135.45 19028.01 3954.7995 UNEMPLOYMENT 46 10.113 2.5 31.20 8.21994 POPULATION GROWTH 46 0.7635 -0.46 1.83 0.58126 Independent variable: REGMEAN 46 2.1626 1.5 2.94 0.39676 COGMEAN 46 2.2151 1.12 3.17 0.7255 NORMMEAN 46 3.5776 2.91 4.07 0.32722 Table A1.

Table A2 (continued). Non-emerging economies Descriptive statistics Variables

N Mean Min Max

St. deviation Dependent variable: NBOR 196 4.0133 0.3 24.5 3.05963 Control variable: GDP 197 27667.811 480.21 95189.87 17795.156 UNEMPLOYMENT 191 7.3167 1.2 18.3 3.33718 POPULATION GROWTH 197 1.0224 -1.48 14.78 1.7237 Independent variable: REGMEAN 197 2.4764 1.19 3.76 0.6004 COGMEAN 197 2.4006 0.93 3.86 0.81604 NORMMEAN 197 3.433 0 4.64 0.57783 Table A2.

Table A3

Regression results emerging/ non-economies (control variables) Economy Emerging non-emerging Control variables GDP -.241 -.481** (.000) (.000) UNEMPLOYMENT -.708** -.221** (.045) (.071) POPULATION GROWTH .416** .273** (.637) (.248) Number of observations 46 190 Adjusted R2 0.431 0.267 F-statistic 12.359** 24.002** * P < .05. ** P < .01. Table A4

Regression results emerging economies

Model Model 1 Model 2 Model 3 Model 4

Table A5

Regression results non-emerging economies

Model Model 1 Model 2 Model 3 Model 4

Table A6

Regression results emerging/non emerging economies (including dummy years)

Region Emerging economies Non-emerging economies