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This paper investigates the dynamic relationship between self-employment and unemployment rates. On the one hand, high unemployment rates may lead to start-up activity of self-employed individuals (the “refugee” effect). On the other hand, higher rates of self-employment may indicate increased entrepreneurial activity reducing unemployment in subsequent periods (the “entrepreneurial” effect). This paper introduces a new two-equation vector autoregression model capable of reconciling these ambiguities and estimates it for data from 23 OECD countries between 1974 and 2002. The empirical results confirm the existence of two distinct relationships between unemployment and self-employment: the “refugee” and “entrepreneurial” effects. We also find that the “entrepreneurial” effects are considerably stronger than the “refugee” effects.

Linking unemployment to self-employment dates to at least Oxenfeldt (1943), who argues that individuals confronted with unemployment and low prospects for wage-employment will turn to self-employment as a viable alternative. This is an extension of Knight's (1921) view that individuals decide between three states — unemployment, self-employment and employment. Although the actual decision is shaped by the relative prices of these three activities, implied is the prediction of a positive correlation between self-employment and unemployment. This simple theory of income choice has been the basis for a range of studies focusing on the decision of individuals to become self-employed (Parker, 2004, Grilo and Thurik, 2005 and Grilo and Irigoyen, 2006). Specifically, this theory suggests that increasing unemployment leads to increasing start-up activity because the opportunity cost of starting a firm has decreased (Blau, 1987, Evans and Boyan, 1989, Evans and Leighton, 1989, Evans and Leighton, 1990 and Blanchflower and Meyer, 1994). This effect has been referred to as the unemployment push, refugee or desperation effect. There is, however, an important counterargument to this theory: The unemployed tend to possess lower endowments of the human capital and entrepreneurial talent needed to start and sustain a new firm. This, in turn, would suggest that high unemployment may be associated with a low degree of self-employment. High unemployment rates may also imply lower levels of personal wealth which also reduce the likelihood of becoming self-employed ( Johansson, 2000 and Hurst and Lusardi, 2004). Lastly, high unemployment rates may correlate with stagnant economic growth leading to fewer entrepreneurial opportunities ( Audretsch, 1995, Audretsch et al., 2002a and Audretsch et al., 2002b). The counterarguments above suggest that entrepreneurial opportunities are not just the result of the push effect (the threat) of unemployment but also of the pull effect produced by a thriving economy as well as by past entrepreneurial activities. Indeed, while some scholars argue that unemployment influences start-up activity, others claim that the reverse holds true. Firm start-ups hire employees, resulting in subsequent decreases in unemployment (Lin et al., 1998 and Pfeiffer and Reize, 2000). Furthermore, increased entrepreneurial activity may influence country-wide economic performance (van Stel et al., 2005). For example, entrepreneurs enter markets with new products or production processes (Acs and Audretsch, 2003). They also increase productivity by increasing competition (Geroski, 1989, Nickell, 1996 and Nickell et al., 1997). They also improve our knowledge of what is technically viable; what consumers prefer; and of how to acquire the necessary resources by introducing variations of existing products and services in the market. The resulting learning process speeds up finding the dominant design of product–market combinations. This learning does not just come from experimenting entrepreneurs: Knowledge spillovers play also an important role (Audretsch and Keilbach, 2004). Lastly, entrepreneurs are inclined to work longer hours and more efficiently as their income is closely related to their working effort. [See Carree and Thurik (2003) for a survey of the (positive) effects of entrepreneurship on economic growth.] A counterargument to this view points out that low survival rates combined with the limited growth of most small firms implies that the employment contribution of start-ups is very low. As Geroski (1995) has documented, the penetration rate, or employment share, of new-firm start-ups is remarkably low. In other words, the contribution of entrepreneurial activities to the reduction of unemployment is very limited at best. The available empirical evidence, unfortunately, presents similar ambiguities and reflects these two conflicting theories. Some studies have found that unemployment is associated with increased entrepreneurial activities while others have found that entrepreneurial activity and unemployment are inversely related (Thurik, 1999). Evans and Leighton (1990), for example, found that unemployment is positively associated with the propensity to start new firms, but Garofoli (1994) as well as Audretsch and Fritsch (1994) found that unemployment is negatively related to firm start-up.1Carree (2002) found no statistically significant relationship between unemployment and the number of establishments. In reviewing early empirical evidence relating unemployment rates to new-firm start-up activity, Storey (1991, p. 177) concludes, “The broad consensus is that time series analyses point to unemployment being, ceteris paribus, positively associated with indices of new-firm formation, whereas cross sectional, or pooled cross sectional studies appear to indicate the reverse. Attempts to reconcile these differences have not been wholly successful.” Audretsch and Thurik (2000) present empirical evidence that an increase in the number of business owners reduces the unemployment rate. They identify an “entrepreneurial” effect in terms of the positive impact on employment from new-firm entry. However, Blanchflower (2000), examining OECD countries, finds no positive impact of self-employment rates on GDP growth. Carree et al., 2002 and Carree et al., 2007 suggest that countries with relatively low self-employment rates benefit from increased self-employment in terms of GDP growth, but that countries with relatively high self-employment rates do not. Consequently, there are not just theoretical reasons, but also empirical evidence, albeit contested, that while unemployment causes increased self-employment, self-employment causes reduced unemployment. Unravelling the relationship between self-employment and unemployment is crucial because policy is frequently based on assumptions that do not reflect the described ambiguities. The purpose of the present paper is to try and reconcile the ambiguities found in the relationship between unemployment and start-up activity. We do this by introducing a simple two-equation vector autoregression model where changes in unemployment and self-employment are linked to subsequent changes in those variables for a panel of 23 OECD countries. The organization of this paper is as follows. We start by providing additional background on the “entrepreneurial” effect and present an algebraic model which forms the basis for our regression exercises. In the following sections the algebraic model is extended to a two-equation vector autoregression (VAR) model, which will be used to test the “entrepreneurial” and “refugee” effects. We also present the data and methodology employed to estimate the VAR model. Finally, in the last two sections we discuss the estimation results and draw conclusions.

The small business sector, and hence self-employment, has become increasingly important to modern OECD economies as they attempt to generate economic growth and employment. New and small firms have emerged as a major vehicle for entrepreneurship to thrive (Audretsch and Thurik, 2001). The present paper shows the important role that changes in self-employment can play in reducing unemployment. As public policy turned to entrepreneurship to generate employment and economic growth, policy makers have turned to the academic literature seeking guidance. The advice they have found is ambiguous at best, conflicting and contradicting at worst. While some studies find a positive link between unemployment and start-up or self-employment rates, as a result of what we refer to in this paper as the “refugee” effect, other studies find evidence supporting a negative link between unemployment and start-up or self-employment rates, as a result of what we call the “entrepreneurial” effect. These two findings suggest radically different policy approaches. On the one hand, the literature focusing on the decision to become an entrepreneur suggests that public policy can reduce unemployment by providing instruments to promote entrepreneurship but does not necessarily stimulate economic growth. This literature implies policies encouraging the unemployed to become entrepreneurs. On the other hand, literature suggesting that by generating economic growth, entrepreneurship will mitigate unemployment results in policy focusing on instruments inducing high-growth entrepreneurship. The disparate recommendations resulting from these literatures have resulted in ambiguous implications for public policy concerning entrepreneurship. Even further ambiguities emerging from the literature concerning the link between self-employment and unemployment involve the business cycle. Studies reveal a positive impact of economic downturns, which encourages unemployed workers to become self-employed, but also a positive impact of economic upturns, where growth opportunities induce an increase in entrepreneurial activity. The unemployed do not enjoy the benefits of a paid job and will tend to search for one, “pushing” people into self-employment. However, low unemployment is likely to coincide with a lively market demand for products and services “pulling” the (un)-employed towards self-employment (Parker, 2004). Thus, there is both a “recession-push” and a “prosperity-pull” aspect of the relation between unemployment and self-employment. Overall, the relationships between self-employment and unemployment are fraught with complexity resulting in confusion and ambiguity for both scholars and policy makers. This paper attempts to unravel these complex relationships. Explicitly modelling self-employment and unemployment within the context of a simultaneous relationship, this paper uses a rich data set of OECD countries for a recent period to identify that the relationship between unemployment and self-employment is, in fact, both negative and positive. Changes in unemployment clearly have a positive impact on subsequent changes in self-employment rates. At the same time, changes in self-employment rates have a negative impact on subsequent unemployment rates. The latter is even stronger than the former. Because these are dynamic inter-temporal relationships, previous studies estimating contemporaneous relationships have confounded what are, in fact, two relationships each working in opposite directions and with different time lags. Our model shows that it is crucial to allow for different and variable time lags. It shows that both the effect of self-employment on unemployment and that of unemployment on self-employment are rather long. This is one of the reasons why policy makers – favouring quick responses and results – have been slow to discover the prominent role of entrepreneurship in the economy. An additional finding of our analyses is that the impact of entrepreneurial activity on macro-economic performance increases with per capita income. This is also found in van Stel, Carree and Thurik (2005) where an entirely different data set is used. Hence, the many policy initiatives of the highly developed European countries to stimulate entrepreneurship seem justified. One limitation of our research, which is inherent to working with country data, is that we cannot directly trace the factors that influence the probability of moving from unemployment to self-employment at the micro level. For instance, heterogeneity across individuals (concerning education, former experience, etc.) is of great importance when we want to explain the success rate of exiting unemployment. Likewise, concerning the “entrepreneurial” effect, we know that heterogeneity across individuals plays a role as well. For instance, research at the micro level shows that education levels of entrepreneurs positively influence the probability of achieving firm growth (Congregado et al., 2005). In our study this heterogeneity is aggregated away into self-employment and unemployment statistics at the country level. This shortcoming can only in a limited way be addressed by incorporating possible additional variables determining self-employment and unemployment rates, thereby extending the VAR model to a VARX-model. Notwithstanding the above limitation, the results of this study are of significant policy importance because policy often aims at achieving desirable effects at the economy-wide level. For this purpose it is important to understand the relations at the macro-economic level, as studied in the present paper. For instance, Germany, a country with high unemployment, recently adopted policies designed to encourage unemployed individuals to exit unemployment by self-employment (Audretsch et al., 2007). However, as the current paper shows that the “refugee” effect is relatively small, one might wonder if such policies are worthwhile. Based on the larger “entrepreneurial” effect we suggest that it might be more effective to encourage entrepreneurship in general as higher levels of entrepreneurial activity significantly lower subsequent unemployment levels. In other words, unemployed individuals may have a bigger chance to escape unemployment by way of being hired by (new) entrepreneurs than by way of trying to start and maintain a new firm. This, in turn, may be related to the – on average – relatively low human capital levels of unemployed individuals making them less competent to run a firm (van Stel and Storey, 2004). Thus, the results of this paper unequivocally suggest that public policy to generate jobs and reduce unemployment would be best served by focusing more on innovative and high-growth entrepreneurship than on inducing the unemployed into entering into self-employment.