Demographics and Entrepreneurship Evidence from Germany and India

Demographics and Entrepreneurship Evidence from Germany and India Authors Munish Kumar Doctoral Candidate, Sociology Group, Indian Institute of Management, Calcutta Raveendra Chittoor Doctoral Candidate, Strategic Management Group, Indian Institute of Management, Calcutta Sinnakkrishnan Perumal Doctoral Candidate, Management Information Systems Group, Indian Institute of Management, Calcutta Corresponding Author s Address E-mail Address: munish@iimcal.ac.in Address: Phone Numbers: Munish Kumar Home: 91-33-9831316925 Room No. 107, Family Hostel-I Office: 91-33-24532734 IIM Calcutta Joka, Diamond Harbour Road Fax Number: Kolkata 700104 Office: 91-33-22821498 1

Demographics and Entrepreneurship Evidence from Germany and India Abstract This paper attempts to examine the empirical evidence on the impact of three critical demographic factors namely, migration, population structure and higher education on entrepreneurial activity, in the cross-cultural context of Germany and India. It follows a unique approach by using state level data from the two countries. The statistical analyses of the secondary pooled data from twenty states in India and fifteen states in Germany have supported the hypotheses that in-migration and population structure have a significant and positive relationship with entrepreneurial activity. No statistically significant relationship is found between higher education and entrepreneurial activity. Implications and limitations of the study are highlighted and directions for further research are indicated. Key words: Demographics, entrepreneurial activity, Germany, India INTRODUCTION There is increasing empirical evidence to suggest that the source of economic growth for many nations is entrepreneurial activity (Audretsch & Fritsch, 2003). However, there is still a strong need for empirical support on the various theoretical factors that are hypothesized to foster entrepreneurial activity. With scholars questioning the applicability and validity of theory in global settings, many national level empirical studies are needed in different geographical and cultural contexts. This paper attempts to examine the empirical evidence on the impact of three critical demographic factors namely, migration, population structure and higher education on entrepreneurial activity, in the cross-cultural context of Germany and India. Germany and India have different levels of entrepreneurial activities (GEM report, 2002). They also share some interesting commonalities and differences in demographics. Some of these commonalities and differences have been associated with entrepreneurship in the literature. These are: migration (Aldrich & Waldinger, 1990; Constant, Shachmurove, & Zimmermann, 2004), higher education (Baumol, 2005; Chander & Thangavelu, 2004) and population structure (Wagner & Sternberg, 2004). Hence, it would be interesting to study the impact of these variables on entrepreneurial activities in both the countries. 2

One of the important common threads running across India and Germany is that both are relatively young nations in their present political forms, although both have had centuries of rich cultural history. Germany as a nation has undergone a lot of changes. It has been part of Roman Empire, Austro-Hungarian and Prussian empire. After Second World War, the country was split into two nations. Recent changes in the geo-political environment of the country include reunification of Germany after the breaking of Berlin Wall and formation of European Union. Similarly, India as a nation has undergone many changes, historically as well as in recent times.. It underwent numerous unifications and divisions before and after British Empire. India was reunified as a nation during colonial period. Other nations such as Myanmar, Pakistan, Ceylon and Bangladesh were carved out of India s territories. The nation in its present form was created by uniting big provinces and small principalities together (which ran into thousands in number) after the British left the country. Even within the present geo-political boundaries of nation, there have been continuous divisions with many states split up into two or more states. The continuous unifications and divisions in the two nations have led to a lot of flux of population within and across borders, especially during these times of transitions. This influx has resulted in important macro trends at social, political and economic level. For example, partition of Bengal led to a sudden upsurge in economic activities in Bengal and so was the case with partition of Punjab. Germany too witnessed a huge increase in economic activities in the post Second World War era. The second commonality between Germany and India is that both have strong higher education systems. This is unexpected as the two countries are at the two extremes on scale of economic development. Germany enjoys almost hundred percent literacy while India is struggling with its literacy program with only half of its population being able to read and write. However, amongst those who attend educational institutions, a significant proportion pursues higher education in India. This has been possible by the large number of schools and universities of higher learning established on Nehruvian ideals. Also, there is an interesting trend of increased emphasis on technical education in India. The number of institutes imparting technical education has increased rapidly with some of them equipped with excellent facilities. Germany also has sound institutions, devoted to higher education especially technical education. 3

Besides the above commonalities, there are many differences between India and Germany. Demographically, notable among them is the difference between the population structures of the two countries. Indian population structure constitutes a large proportion of youth of less than thirty years of age. In sharp contrast to India, the German population structure has a large population that is aging. The proportion of people in the working age is decreasing for Germany while it is increasing for India. Given these commonalities and differences between the two nations, an important question that arises in relation to entrepreneurship is, What kind of impact do migration, population structure and education have on entrepreneurial activities in these countries?. Examining such a research question using state level data that takes into account various contextual factors within a country, instead of country level data, will be a unique attempt. In this paper, we examine the state level data on demographic measures of the 20 states of India and 15 states of Germany and analyse their relationship with entrepreneurial activity in these states. The results indicate a statistically significant and positive relationship between migration and entrepreneurship activity as well as between population structure and entrepreneurship activity. These empirical results gain significance not only due to the study s focus on the across-state context of India and Germany but also due to the focus on within-the-country flux across two countries. The results, though are significant, should be taken as suggestive rather than confirmatory. The rest of the paper is organised as follows. The next two sections review the extant literature and develop specific hypotheses related to the impact of different demographic variables on entrepreneurship activity. The following section deals with data sources, operationalization of variables, empirical analysis and results. The last section discusses the implications of the findings for research and practice, and suggests future research directions. 4

DEMOGRAPHICS AND ENTREPRENEURSHIP Migration At the outset, it is imperative that we clarify the definition of migration. The term migration is used to denote movement of human beings from one geographical locality to another. The locality could be local region, state or nation. In-migration is migration into the region whereas out-migration is migration out of the region. Transitory migration is migration of people for short term and not with the intention of settling in the new region. Table 1 below summarises this. Table 1 is about here Migration has long been associated with entrepreneurship by scholars studying the entrepreneurship phenomenon. Numerous studies have been done to explore the relationship between migration and entrepreneurship (Gershon, 2000; Light & Bhachu, 1993). Modern nations like America, Australia, Canada, Israel and many others are built as a result of efforts of migrant population. This is largely true for Germany and India as well. Migrants in these two nations have created organizations and generated the wealth. In India, the city of Kolkata blossomed because of the merchant community of Marwaris, who migrated from Rajasthan and in the recent years, due to the influx of Bangladeshi migrants. In the city of Delhi, the economic activities are undertaken by migrants from west Punjab, now a part of Pakistan. This is also true for Jew migrants in Germany before the Second World War. Hence, if entrepreneurship is defined in terms of business activity, then in-migration has been one of the strong co-relates of entrepreneurship. However, if we look around the world, there have been quite a few exceptions to the positive relationship between in-migration and entrepreneurship. Not all migrant groups have shown entrepreneurial drive in the same capacities. One example of such a success is that of the Punjabis in U.K (Frederking, 2004). Other groups, especially African Americans have not been that successful in carrying out the entrepreneurial activities (Bates 1996). The conclusion that could be drawn from these studies is that mere migrant status is not enough for a person to become an entrepreneur. There are other factors that influence entrepreneurial activities of the 5

migrants like the strength of migrant network, knowledge sharing among the migrant network, size of network, etc. Migrant population, especially when it is in minority, is in a disadvantageous position and hence the normal routes of mobility are blocked to this population (Hagen, 1971). The migrant population usually has poor education, poor linguistic skills, and lack of understanding of cultural ethos and local knowledge (Barret, Jones and McEvoy, 1996). Entrepreneurs try to compensate for disadvantages by working hard and long hours leading to creation of enterprise. Because of the disadvantageous position, the members in the migrant population also develop stronger ties with each other. The ties help in accessing and exploiting the social capital available from the migrant population (Aldrich and Waldinger, 1990). The country of origin becomes the ethnic source for migrant population through mutual trust and enforcement of norms. The ties are not only advantageous for identification of opportunities but also for developing opportunities for entrepreneurship. These ties are important source of ideas, opportunities, finance and human resources (Honig, 1998)). Hence, the blocked mobility and social capital available are important concepts in the context of migrant entrepreneurship. Organization creation to generate self employment is one of the various ways of mobility available to migrants to establish themselves in the new locality. Given this literature, we would like to test, in context of both Germany and India, the relationship between in-migration and entrepreneurial activity. Hence, we propose: Hypothesis 1a: Keeping other things constant, higher in-migration would lead to higher entrepreneurial activity in various states of the two countries. As soon as the migrants arrive in a new region, they face blocked mobility and in some cases, hostilities in the new regions. However, this push usually is not adequate to start entrepreneurial activity which needs knowledge of local conditions. The knowledge could be of market forces, government regulations, demographic structure, customer preferences, culture, etc. Besides familiarity with the local conditions, familiarity with migrant network also takes time, before the migrant network could be exploited for starting a venture. In other words, there is a time lag between the time migrant arrives in a new locality and the time migrant understands the local conditions and migrant network. Based on this, we hypothesise that: 6

Hypothesis 1b: The impact of in-migration on the rate of entrepreneurship would occur with a time lag. Education Education is the institutional way of providing human capabilities. Education helps people in building competencies that could be harnessed for creating successful new ventures. Higher education has special role in enhancement of capabilities. This is especially true of high technology entrepreneurship as most high technology ventures require capabilities that could be developed through institutions of higher learning (Cooper, 1970). Based on this, we hypothesize that: Hypothesis 2: A higher percentage of population receiving higher education would lead to higher entrepreneurial activity in various states of the two countries Population Structure The second demographic indicator that we selected was that of population structure, which is quite different for the two countries. Indian population is younger while the German population is aging. Entrepreneurship as an activity requires considerable amount of energy and this could be provided by young people. In addition, entrepreneurship requires capabilities as well. The capabilities could be built through formal as well as informal ways. Both means of developing capabilities require time. Hence, a person would be able to create an enterprise only after the capabilities have been developed. With the assumption that the development of capabilities through socialization requires a person to be of at least 15 years of age, we hypothesize that: Hypothesis 3a: The population structure of a state will have an impact on its entrepreneurial activity. Hypothesis 3b: A higher percentage of population in the range of sixteen to forty-five years would lead to higher entrepreneurial activity in the state. 7

METHODOLOGY Data Data availability and collection pose particular challenges in the context of developing economies such as India. We used secondary sources for collecting all the data for the study. The data on Indian states was collected from the Centre for Monitoring Indian Economy (CMIE), Indian Census conducted by the Union government of India and the website www.indiastat.com. State-wise data on Germany was collected from www.destatis.de organized by the Federal Statistical Office of Germany. All the data pertains to the period 2003-2004 except the migration data, which was for the year 2001 in the case of India and which was a three-year average (ending 1997) for Germany and per capita data, which was for the year 2002 in the case of India. In all, data for twenty Indian states and fifteen German states were considered 1 yielding a total sample size of thirty five. Measures All the measures were zero-mean normalized to enable comparison across Indian and German states. Entrepreneurial Activity: We chose entrepreneurial activity, which is a more stable measure compared to rate of entrepreneurship given the lack of time series data. Number of companies registered in each of the states as per the Companies Act of India was used to measure entrepreneurial activity in India, while the total number of enterprises in each of the German states was used as a comparable measure in the case of Germany. Migration: Several measures of migration have been used in literature such as in-migration, outmigration, net migration or percentage of foreign-born population in the total population and so on. To measure the flux created by migration at the state level we used in-migration (sum of domestic and foreign) measured as the proportion of in-migrants to the total population of the state. We were not able to separate transitory migration from the data we have. 1 Berlin and Delhi were excluded as they were found to be outliers. Data on other Indian states were not available. 8

Population Structure: The percentage of people belonging to the bracket of 16-45 years in the total population of the state was used as a measure of the population structure 2. Higher Education: Number of pupils enrolled in Standard XII and above (in case of India) and secondary school and above (in case of Germany) as a percentage of all eligible people was used as a measure of enrolment into higher education. Per capita gross domestic product (GDP) of the states was included as the control variable. Per capita GDP is found to have a high correlation with all socio-economic factors and hence it is, by itself, sufficient to control for all possible confounds. EMPIRICAL RESULTS Table 2 reports the Pearson correlation coefficients for the sample data. The hypotheses were tested through ordinary least squares (OLS) regression. Entrepreneurial activity was modelled as a function of in-migration, population structure, higher education and per capita GDP. Collinearity diagnostics were performed by examining bivariate correlations and variance inflation factors (VIFs). All required assumptions for regression equations such as independence of errors and normality of the distribution of errors were checked for and were met. Table 2 is about here The results of OLS regression estimation are reported in Table 3. The Durbin-Watson statistic of 2.29 indicates that the observations are independent with no auto correlation. VIF values for all variables are less than 2 indicating the absence of multi-collinearity. The overall regression equation is statistically significant (p <.001). The results provide support to Hypothesis 1a with migration showing positive and significant beta coefficient (β =.38, p <.02). As the migration data considered for the analysis was with a time lag, Hypothesis 1b is also supported. No statistically significant relationship is found between higher education and entrepreneurial activity (β = -.08, p <.62) resulting in a lack of support for Hypothesis 2. However, we found 2 In the case of Germany, data was available for the age bracket of 15-44 years 9

differences in correlation between Germany and India. For Indian states, the correlation was 0.7 and for Germany, it was -0.4. The results provide strong support to Hypothesis 3a and 3b with population structure showing positive and significant beta coefficient (β =.46, p <.005). Table 3 is about here DISCUSSION AND CONCLUSION This study examined the impact of in-migration, population structure and higher education on the entrepreneurial activity of various states in India and Germany through a unique approach of using state-level data. The statistical analyses of the secondary pooled data from twenty states in India and fifteen states in Germany have supported the hypotheses that in-migration and population structure have a significant and positive relationship with entrepreneurial activity. The results vindicate the findings of earlier studies related to migration and entrepreneurship. The study also supports likely use of variables like fear and insecurity resulting in entrepreneurship as suggested by GEM studies. Role of social capital of migrant networks is also supported, though indirectly. Role of youth and energy which is important for combining different resources in creation of enterprises is also vindicated. The hypothesis that higher education will have a positive impact on entrepreneurship is not supported. There could be many reasons for this. First, this could be due to the fact that separate data related to technological entrepreneurial activity which is likely to be fostered by higher education was not available and hence not considered for analysis. Also, the way higher education was measured in the study, as percentage of enrolment in the secondary education and above, may not be the most appropriate way. It may be a good idea to ratify the results in future by taking the percentage of pupils enrolled in tertiary and technical education as the chosen measure. Second, result could change with bigger sample size. Finally, the relationship between education and entrepreneurship may not be linear as hypothesized in this study. On the policy front, the study has important implications in terms of fostering entrepreneurship through in-migration and higher percentage of young population. 10

We recognize a number of possible limitations to this study and hence the conclusions drawn are only suggestive and by no means definitive. First, the sample used for the study is relatively small. Second, the study is cross-sectional in nature and does not capture the dynamics introduced due to the time factor. Third and most importantly, the study uses pooled state level data from two countries and hence ignores the influence of many important country level differences. Similar studies can be replicated with larger sample of state level data within and across countries. Richer insights could be obtained by using longitudinal studies and factoring in cross-country differences. 11

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TABLE 1 Types of Migration Migration State National International Transit Non-Transit Transit Non-Transit Transit Non- Transit In-migration Out Migration In-migration Out Migration In-migration Out Migration In-migration Out Migration In-migration Out Migration In-migration Out Migration 14

TABLE 2 Pearson Correlation Coefficients a Variable 1 2 3 4 1. Entrepreneurial Activity b 2. In-migration b.53*** 3. Higher education b.21.20 4. Population structure b.58***.18.35* 5. Per capita GDP b.56***.47**.25 +.47** a N=35 b Zero-mean normalized + p <.10 * p <.05 **p <.01 *** p <.001 15

TABLE 3 Results of OLS Regression with Entrepreneurial Activity as the Dependent Variable a Variable β t VIF In-migration b 0.38 2.69* 1.31 Higher education b -0.08-0.58 1.17 Population structure b 0.46 3.16** 1.40 b Per capita GDPP 0.18 1.17 1.61 Number of observations 35 F 9.19*** R 2 0.55 Adjusted R 2 0.49 Durbin-Watson 2.29 a The Table reports standardized coefficients. VIF values indicate no multi-collinearity. b Zero-mean normalized * p <.05 **p <.01 *** p <.001 16