NEW BEGINNINGS: BUSINESS START-UPS BY DISMISSED INDIVIDUALS. Ana Venâncio 1

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1 NEW BEGINNINGS: BUSINESS START-UPS BY DISMISSED INDIVIDUALS Ana Venâncio 1 Abstract: For long, several industrialized countries have promoted entrepreneurship as a way out of unemployment. In this paper, we analyze the effect of job dismissals on the quantity and quality of entrepreneurial activity with the goal of identifying the subset of dismissed workers that should be encouraged by policy makers. To the best of our knowledge, we are the first to evaluate entrepreneurial activity among dismissed employees. To shed light into these issues, we use data from Portugal. There are two reasons why the examination of Portuguese dismissed entrepreneurs holds particular promise. First, Portugal experiences high levels of necessity entrepreneurs and has enact several policies to promote entrepreneurial activity among unemployed workers. Second, we combine micro-level data on firms and their employees using a matched employer-employee dataset. We have detailed information about all workers dismissed between 15 and 2005 who decided to establish a new venture between 16 and 2006, and their background history. As individuals and firms are cross referenced by a unique identifier, the data make it possible to trace the mobility of workers across firms and match founders with their firm characteristics. Our preliminary results suggest that job dismissals are a huge driver of firm creation, particularly within male, middle-aged (30-3 years), and low-educated individuals. Except for education, formerly dismissed entrepreneurs are from demographic categories that are most likely to be entrepreneurs. Start-ups established by dismissed workers have longer survival prospects in comparison to ventures not created out of unemployment. The main determinant of ventures survival and growth is their initial size. Keywords: Necessity entrepreneurship, job dismissals, involuntary turnovers, firm entry, firm performance, survival, job creation 1 The author is grateful to the Portuguese Ministry of Employment and Social Security and Gabinete de Estratégia e Planeamento (GEP) for giving us access to the matched employer-employee data. Financial support was provided by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through the project through the project CMU-PT/Etech/0036/

2 I. Introduction For many years now, the unemployment rate in the industrialized countries has been increasing. To solve the unemployment problem and at the same time foster economic growth, governments have adopted policies to foster firm formation by unemployed workers. 2 The aim of those policies is to shift founders out of unemployment and indirectly create further jobs in the newly-founded firms. Yet, previous research suggests that these ventures do not generate indirect jobs and do not enhance economic growth. Relatively to other entrepreneurs, previously unemployed founders have lower endowments of human capital and entrepreneurial talent (Poschke, 2010). In fact, their businesses are small, less profitable (Block and Wagner, 2010), with limited growth prospects but with high survival rates (Baptista, 2007; Poschke, 2010). Although these entrepreneurs are not perceived as engines of economic growth or job creation, it is unequivocal their size, particularly in the Southern European countries (Baptista, 2007; Poschke, 2010). According to Global Entrepreneurship Monitor (GEM) (GEM, 2011), Portuguese necessity entrepreneurs account for 17.8% of the total nascent entrepreneurs and Spanish and Greece necessity entrepreneurs account for 25.% and 25.4%, respectively. 3 In this paper, we analyze the entrepreneurial intent of dismissed workers - individuals that permanently and involuntary lost their jobs due to firm closure - and evaluate the performance of their ventures. To the best of our knowledge, we are the first to evaluate quantity and quality of entrepreneurial activity among dismissed workers. For that purpose, we start by evaluating whether job dismissals trigger individuals to transition to entrepreneurship and we analyze their demographic and education characteristics and work experience. Next, we compare start-ups established by formerly dismissed workers with those of other start-ups. Our measure of performance includes venture survival and employment growth. As predicted by evolutionary economics and human capital theory, historical antecedents are expected to determine the relationship between start-ups performance and pre-entry capabilities of necessity entrepreneurs. To shed light into these issues, we use data from Portugal. There are two reasons why the examination of Portuguese dismissed entrepreneurs holds particular promise. First, as noted before Portugal experiences high 2 Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Great Britain, Ireland, Luxemburg, the Netherlands, Norway, Sweden and the US are some examples of countries that adopted programs to promote entrepreneurship among unemployment workers (OECD, 15). 3 In contrast, in the Northern countries these percentages are smaller. For example, in Norway and Sweden, necessity entrepreneurship accounts only for 4.3% and 6.1% respectively. 2

3 levels of necessity entrepreneurs and this percentage has been increasing due to the rise on the unemployment rate in the last decade. The unemployment rate rose from 4.5% in 2000 to 12.04% in 2010, partially due to the country inability to compete with low-wage economies. Additionally, policy makers have been actively enacting legislation to promote entrepreneurial activity among unemployed workers. One of such policies is allowing unemployed workers who want set up their own firm to receive upfront their unemployment benefit. Between 2000 and 2010, more than 33,000 unemployed workers used this option. The expenditure during this period was about 301 million. Second, we combine micro-level data on firms and their employees using a matched employer-employee dataset. We have detailed information about all workers dismissed between 15 and 2005 who established ventures between 16 and 2006, and their background history. As individuals and firms are cross referenced by a unique identifier, the data make it possible to trace the mobility of workers across firms and match founders with their firm characteristics.. Our preliminary results suggest that job dismissals are a huge driver of firm creation, particularly within male, middle-aged (30-3 years), and low-educated individuals. Except for education, formerly dismissed entrepreneurs are from demographic categories that are most likely to be entrepreneurs. Start-ups established by dismissed workers have longer survival prospects in comparison to ventures not created out of unemployment. The main determinant of ventures survival and growth is their initial size. Implications of this research for policy-makers, financial investors and academics are significant. From a policy perspective, it is relevant to understand the determinants of growth and survival for new ventures founded by dismissed workers, particularly for countries that actively promote entrepreneurship by unemployed workers. This study provides insights into the ability of those policies to stimulate the formation of business with growth potential. Financial investors want to determine which subset of new firms is more likely to succeed and repaid their debt. New information on survival and growth makes their calculations more accurate and helps to avoid mistakes on their decisions. This research will also interest academia by providing new evidence on the determinants of entrepreneurial activity. The remaining sections of this paper are organized as follows. Section 2 reviews the literature. In section 3, we describe briefly the incentives available to unemployed individuals in Portugal. Next, in section 4 we present our data and some descriptive statistics. Section 5 explains our empirical methodology and presents the results. Section 6 concludes the paper. 3

4 II. Literature Review Previous literature devotes a considerable amount of research on understanding the relationship between entrepreneurship and unemployment. To distinguish between entrepreneurs that voluntary pursue an opportunity and entrepreneurs with no other employment opportunities available (Reynolds et. al. (2002), the concepts of opportunity and necessity entrepreneurs were introduced. Individuals who leave their jobs voluntarily to pursue an opportunity in the market and establish a new firm, are classified as opportunity entrepreneurs. Those individuals deliberately move through several jobs to acquire the required set of skills, which will be used in their new firm. In contrast, unemployed individuals are pushed into entrepreneurship by external factors as they do not have other alternative occupational choice. They are known as necessity entrepreneurs (Block and Wagner, 2010). As the unemployed rate increases, the number of necessity entrepreneurs also increases (Blau, 187; Evans and Leighton, 10; Evans and Jovanovic, 18 and Blanchflower and Meyer, 14). 4 Several explanations have been presented to explain this positive link or refuge effect between unemployment and entrepreneurship. According to the income choice theory, the opportunity cost of starting a firm decreases for unemployed individuals and consequently they are more likely to transition to entrepreneurship (Blau, 187; Evans and Jovanovic, 18; Evans and Leighton, 10; Blanchflower and Meyer, 14; Baptista and Thurik, 2007). Moreover, entrepreneurship becomes the only viable occupation choice (Remeikiene and Startiene, 200) and the only way to secure a job (Désiage, 2010). In fact, some studies even go further suggesting that necessity entrepreneurs are very similar to regular employees. They usually establish wage-substitution businesses with no intention of creating high-growth ventures (Shane, 2008). Their businesses are usually small (Poschkle, 2010), less profitable (Block and Wagner, 2010), with limited growth prospects but with high survival rates (Baptista, 2007; Poschke, 2010) relatively to other ventures. Necessity entrepreneurs hire fewer employees than opportunity entrepreneurs and their businesses are more likely to be affected by lack of financial and 4 However, the relationship between unemployment and entrepreneurship might be on both directions (Audretsch et al, 2002; Remeikiene and Startiene, 200). There is a push (unemployment stimulates entrepreneurship) and a pull (entrepreneurship reduces unemployment) effects. Some studies suggest that entrepreneurship leads to a reduction in the unemployment rate, the Schumpeter effect (Garofoli (14); Audretsch and Fritsch, 14; Thurik 2003; Audretsch et al, 2001) while others suggest a positive effect. Yet, Thurik et al. (2008) find that the refuge effect (direct effect) is considerably smaller than the Schumpeter effect (inverse effect). Nonetheless, as suggested by Remeikiene and Startiene (200) the link between entrepreneurship and unemployment is likely to be determined by economic and cultural factors such as religions, cultural differences, gender gap and the economic situation within a country. 4

5 administrative support and by unfavorable economic climate (Bhola et al, 2006). Typically, necessity ventures are established in low-income sectors 5 (Block and Wagner, 2010) and are less successful (Amit and Muller, 15). Consequently, firms established by necessity entrepreneurs are not seen as engines of economic growth or job creation (Acs, 2006; Rosa et al, 2006; Shane 2008). Explanations for firm s lack of performance include lack of time to look for good opportunities, to make detailed business plans or to seek advice (Baptista and Karaoz, 2006) and lack of innovative idea. As for demographic and educational characteristics, necessity entrepreneurs are typically middle-aged (Wagner, 2005; Block and Sandner, 200), mostly male (Poschke, 2010) and previously unemployed. Older unemployed individuals face higher difficulties in returning to the labor market; therefore they are more likely to create their own job (Giacomin et al, 2011). Additionally, male jobseekers seem to be more willing than women to start a business to quit inactivity. In terms of education, necessity entrepreneurs are on average less educated relatively to opportunity entrepreneurs (Bhola et al, 2006; Robichaud et al, 2006). However some studies find no difference between the education level of both type of entrepreneurs (Block and Sandner, 200; Block and Wagner, 2006; Amit and Muller, 15, Désiage, 2010). The most common push factor that influences an individual to start a business is unemployment, particularly job dismissals and lay-offs. 6 These two latter events lead individuals to reframe their risk perception (Wood, 2011). To compensate for their physiological and financial damage, unemployed individuals establish a new venture to regain control of their life (Wood, 2011) and at same time solve their labor problem. According to Wagner (2005) within the necessity entrepreneurs group, one in two is unemployed and their entrepreneurial intent usually increases with the duration of unemployment spell (Wood, 2011). Nevertheless, Désiage (2010) also suggests that short-term unemployed individuals are also likely to create a new business. Other studies look at founders characteristics such as risk aversion (Wagner, 2007; Block et al. 200, Bhola et al. 2006; Amit and Muller, 15), family ties 7 (Amit and Muller, 15; Block and Sandner, 2007) and individual s satisfaction (Block and Koellinger, 200; Block, 2007). Whereas Block et al. (200) and Bhola et al. (2006) argue that opportunity entrepreneurs are more willing to take risks than necessity entrepreneurs; Amit and Muller (15) suggest that the risk attitude between these two kinds of entrepreneurs is similar. In terms of satisfaction, prior studies find that necessity entrepreneurs are less satisfied than other entrepreneurs (Block and 5 Nevertheless, Amit and Muller (15) suggest necessity and opportunity entrepreneurs select similar type of industry. 6 However, unemployed individual from an entrepreneurial family are less likely to start a business because of the fear of the family s judgment (Giacomin, 2011; Bhola et al., 2006). 7 Amit and Muller (15) find no significant differences on the family size, number of children and in the financial status between the two groups of entrepreneurs. 5

6 Koellinger, 200) which might be a consequence of their lower preference for self-employment (Bhola et al, 2006) and lower financial returns. Necessity entrepreneurs earn, on average, less than opportunity entrepreneurs (Block and Wagner, 2010) and might not value some aspects of being an entrepreneur such as uncertainty, higher responsibility and more risk (Block and Sandner, 200). In the next section, we will briefly describe the incentives provided to unemployed individuals in the Portugal to create their own firm. III. Entrepreneurial Incentives to Unemployed Workers in Portugal Labor market policies in Portugal strongly support initiatives to found new firms by unemployment individuals. In addition to credit programs with low interest rates and advice and counseling programs, the government also allows business founders who are entitled to receive unemployment benefits to receive the entire compensation upfront (lump sum grant). 8 This latter policy instrument was introduced in 185 (see article 27 th of Decreto-Lei 20/85 and Portaria 264/85) and currently, it is regulated by Portaria 58/2011 and Despacho 7131/2011. Figure 1 presents the evolution of the number of beneficiaries and the total amount granted under the upfront compensation policy. Between 2000 and 2010, the amount granted rose from 27 million euros to more than 45 million euros and the number beneficiaries is around three thousand individuals per year. With this policy, the government intends to alleviate capital constraints by providing sufficient capital to fund the formation of a new firm by unemployed individuals. Above that, a larger capital investment allows founders to pursue more attractive and profitable opportunities (Meager, 15:14). The upfront compensation is granted to persons entitled to receive the usual unemployment allowances, who intend to start a business on their own and present a viable business plan to the Portuguese Institute of Employment and Professional Education (IEFP - 8 Similar aid programs also exist in Denmark, Great Britain, France, Spain and other European countries (Meager, 16). In France and Spain, the government aid is similar to the one provided in Portugal. A lump sum grant is provided to unemployed individuals who intend to start their own business. In Germany, the government provides a bridging allowance in addition to the unemployment allowance. The Danish programs select highly qualified, male and short-term unemployed workers and provide personal support and advisory service. Since 185, several laws have been enacted to regulate the upfront payment policy. In general, the policy did not suffer any significant changes. The major changes occurred in the eligibility rules to the unemployment compensation. To facilitate the coordination of the policies connected to job creation, a new regulation (Portaria n.º 16-A/2001) brought together several dispersed regulatory instruments (Decreto-Lei nº34/6 and 18/6, Portaria nº 476/5, 414/6 and 247/5) including the upfront payment policy. 6

7 Instituto do Emprego e Formação Profissional). 10 To be eligible for the unemployment benefit, individuals need to be employed for at least 24 months during the two years prior to unemployment. 11 The upfront compensation is determined by the duration of benefit, which in turn depends on founder s age and years contributed to Social Security system. The maximum duration ranges from 270 days for those aged less than 30 years to 00 days at age 45 or older. Consequently, this policy privileges old individuals with long career histories. Recent graduates or young workers are only eligible to counseling and technical services. Nevertheless, it should be noted, that by using this instrument, the unemployed workers lose their rights to unemployment benefits in case of business failure. Consequently, the decision to start a firm is taken with caution and those that embrace them seek a successful business. More recently in 1, the government introduced a special line of credit with low interest rates for unemployed workers who want to start a new venture (see 21 st article of the Decreto-Lei 132/ and Portaria 85/200). Currently, this line of credit applies to new ventures with a maximum of 10 employees and investments between and The interest rate contracted varies between 1.5% and 3.5%, depending on the conditions defined with the bank (see Portaria 58/2011). This policy applies to unemployed workers enrolled in the employment services for more than nine months, young individuals seeking their first job aged between 18 and 35 with a minimum of secondary education, self-employed individuals with monthly income below the minimum wage and individuals who never had a job. V. Data Our analysis relies in a matched employer-employee database, a mandatory survey collected by Portuguese Ministry of Employment and Social Security that covers all firms and employees in the Portuguese private sector from 186 to 200. Each firm and individual is referenced by a unique identifier; therefore with these data we 10 Within 30 days, the IEFP evaluates the business plan and if accepted, the IEFP contacts the Social Security services to insure the upfront payment of the unemployment benefit. 11 There are two categories of unemployment benefits available in Portugal: unemployment insurance and unemployment assistance. Individuals who do not fulfill the requirements associated with unemployment insurance can draw unemployment assistance if they have more than six months insured employment in the year preceding unemployment. In both cases, access to unemployment assistance hinges on per capita family income; only those whose per capita income is less than 80% of the minimum wage qualify for unemployment assistance. The value of the benefit is equal to 65% of daily average remuneration of the last two years and cannot exceed 75% of the net remuneration 12 The duration of the loan is seven years with a two year grace period of capital and no payment of interests in the first year. The interest rate is Euribor 30 days, plus 0.25%, with a minimum of 1.5% and maximum 3.5%. Recently there are two projects undergoing MICROINVEST and INVEST+. While the first funds in entirely investments of 20,000, INVEST + targets projects between 20,000 and 200,000 funding 5% of total investment and 50,000 per job created. 7

8 can follow individuals across firms and time. The database has comprehensive information at the individual and firm level. For each firm, the following data are available: year of creation, location, size and industry. At the individual level, the database contains information on gender, age, date of hire, education and occupation. With this data, we create two samples: entrepreneurs and workers samples. Before explaining the construction of these two samples, we start by describing the steps required to select the dismissed workers. The matched employer-employee data includes 63,64 part-time and full-time workers that lost their job between 15 and 2005 (excluding the year 2001) 13 due to firm closures. We restrict the sample to full-time workers aged between 20 and 55 in the year prior to displacement, to exclude retired and pre-retired individuals. Additionally, we select this age range because of the different behavior for young and old individuals. As suggested in the literature, displaced workers are defined as individuals with established work histories that are involuntarily separated from their jobs by mass layoff or firm closure (Kletzer, 18; Hamermesh, 18). Unfortunately, our database does not allow us to distinguish the reasons underlying workers separation: job leaving (quits), firing or displacements. In order to minimize this ambiguity, we only consider dismissed workers when their displacement year coincides with firm's shutdown year. Exits occur when firms do not appear in the database in the next years. 14 To insure that a firm truly shutdowns and does not merge with other firms, we exclude from the database workers that appear in the database after the displacement period with an admission year in the new firm less than the separation year minus one. 15 After the displacement, we do not impose any restriction: an individual can be either unemployed, reemployed in a part-time or full-time job, or can establish a new venture. To reduce the number of multiple observations for each individual, we exclude workers with multiple job losses and we only look at their first entrepreneurial event. We end up with a 376,134 dismissed individuals; the majority of them (62%) are re-employed while only 2% of them establish a new venture between the period of 16 and 200. Table 1 presents the characteristics of the dismissed sample separately for entrepreneurs and non-entrepreneurs. Dismissed individuals are mostly male (57%), low educated and are 35 years old on average. Their monthly income prior to their displacement is 654, on average. However, the sample of entrepreneurs is younger (33 years old), more educated and has a higher percentage of males (72%). On average, entrepreneurs earn larger monthly earnings (726 ). Table 2 presents the characteristics of new firms established by dismissed 13 We exclude the year 2001 because there are no data available for workers for that year. 14 However, some firms might temporarily exit from the database because, for instance, they do not deliver the survey before the due date. To minimize this problem, we classify a firm exiting in year t if it is present in the database in year t-1 and absent until 200 (or absent at least for four consecutive years). 15 For example, we exclude workers that were displaced in year 15 and appear in the database in the post-displacement period with an admission year in the new job of 13 or less. 8

9 entrepreneurs between the period of 16 and Start-ups are small and employ on average five workers. After the first three years, the average number of employees rises to six but approximately 1,400 firms fail corresponding to a mortality rate of 4%. To construct the entrepreneurs sample, we select from the matched-employer employee database all individuals that established a new venture between 16 and 2006, with age between 20 to 55 and with known labor market histories. To maintain consistency, we only consider their first entrepreneurial event. Next, we merge this latter sample with the dismissed entrepreneurs sample and we exclude all individuals that were not working before becoming an entrepreneur. In the end, our entrepreneurs sample includes dismissed entrepreneurs and paidworker entrepreneurs individuals that were working before transition to entrepreneurship. This sample allows us to compare the characteristics of start-ups by formerly dismissed workers with start-ups which were not created out of unemployment. In addition, we draw a 20% random sample of all individuals who were full-time employees between 15 and 2005, within the same age range (20 to 55) and with known labor market histories. More specifically, we identify their career history to determine if they transition to entrepreneurship from 16 and Then, we merge this latter sample with the dismissed workers sample. This sample allows us to compare the characteristics of previous dismissed founders with non-dismissed founders. In total, we have a sample of 1,053,523 individuals, of whom 11,453 are entrepreneurs. Table 3 presents the descriptive statistics separately for dismissed and nondismissed individuals. 6. Methodology and Results We begin by exploring if dismissal events trigger individuals to become entrepreneurs, by estimating an equation in which the dependent variable is the probability that any individual in our data founds a start-up in a given year. Specifically, we start with the simple linear probability model: Our results are robust to a probit or logit model.

10 where is an indicator variable that equals 1 if individual i establishes a firm in year y, and 0 if that individual does not open a firm. Our variable of interest is, an indicator variable that equals one for dismissed workers. We include also a vector founder characteristics X: gender, which equals 1 for men, 0 for women; four indicator variables for the founder's age, partitioned at 30, 40 and 50; and education, which is taken to be very low for those never completing elementary school, low for those that attended junior high school, medium for those with a high school diploma or equivalent, and high for those reporting bachelor's degree or more advanced degree. In reporting the estimated coefficients, our omitted categories are individuals aged 20-2 and with very low education. Since entrepreneurial activities are bound to vary according to the regional economic activity, we include a measure of gross domestic product (GDP),, by start-ups entry year and for 30 NUTS (Nomenclature of Unit for Territorial Statistics) regions defined for Portugal. We also include time-dummies to control for each cohort of individuals and county of residence dummies for where the individual lived to control for variations in business activities across geographic areas. Standards errors for this and all subsequent regression are robust. Previous research has found strong associations between job dismissals and entrepreneurship. Thus, we expect to be significantly positive. The estimates for this specification are presented in Column (1) of Table 4. As expected, dismissal events are associated with an increase in the probability an individual becomes an entrepreneur. These results are based on a 20% random sample of the database of more than one million individuals, but fewer than 10,300 individuals establish a new venture. Given the small fraction of the sample that ever becomes an entrepreneur (less than 0.7%), the coefficient on the dismissal dummy is associated with an marginal increase of about 3%. As for demographic and education variables, we can infer that male, middle aged (age between 30 and 3), and well-educated individuals are relatively more likely to transition into entrepreneurship than are other individuals. The next key question addressed in this paper is: what are the distinctive characteristics of dismissed entrepreneurs? To evaluate this issue, we interact each of the demographic and educational variables with D (our dismissal indicator variable) and add these interactions to the previous Equation (1). The coefficient estimates are reported in Column (2) of Table 4. Except for education, these results suggest that the dismissed entrepreneurs are from demographic categories that were previously likely to initially be entrepreneurs. Similarly, dismissed entrants appear older, are more likely to be male but are slightly less educated. Relative to the omitted category (very low educated individuals), the fraction of low, middle and high educated entrepreneurs increases by 0.5%, 0.3% and 0.2%, respectively. 10

11 Next, using only the entrepreneurs subsample, we apply the proportional hazard model proposed by Cox (172) to assess survival. The main advantage of the Cox proportional hazards model is that the vector of coefficients can be estimated without imposing any assumption on or estimating the baseline hazard function. The hazard rate for the ith subject in the data is ( ) ( ) ( ) where ( ) is the baseline hazard function, is a vector of explanatory variables and is the vector of coefficients to be estimated. We include a vector of founder characteristics described previously (age, gender, education), industry experience variable which equals 1 for founders that previously work on the same four-digit industry digit code, 0 otherwise and geographic/regional experience variable which equals 1 for founders that previously work on the same region (municipality). We also have a variable that gives the firm s initial size, S and we control for industry effects by including two-digit industry dummies. Table 5 presents the estimates. We intend to evaluate the characteristics of successful dismissed entrepreneurs. Before we discuss the estimates, it should be noted that the table reports hazard ratios rather than coefficients. Therefore, a ratio larger than 1 increases the hazard (the chance of exit on next period) while a ratio smaller than 1 decreases the hazard. As we can analyse from Column (1), new ventures that were established by dismissed workers are more likely to survive. The determinants for the survival of new businesses founded are the size of the new entrant and regional and industry experience. This indicates the importance of knowledge spill overs from the industry and region. These characteristics are similar to the ones presented by opportunity-based entrepreneurs. The next key question addressed in this paper is: what are the distinctive characteristics that ensure the survival of dismissed entrepreneurs? To evaluate this issue, we interact each of the demographic and educational variables with D (our dismissal indicator variable). The coefficient estimates are reported in Column (2) of Table 5. Firms initial is the distinctive characteristics that ensures the survival of dismissed entrepreneurs. Finally, we evaluate the performance of the surviving firms established by dismissed entrepreneurs. We specify the equation for employment growth as: 11

12 The dependent variable is the logarithm difference between the level of employment in moment T (where T=1, 3, 5 years) and the initial level of employment. As mentioned before, the vector of variables includes the demographic characteristics of the founder (gender, age, education and experience). The estimated results on new venture growth are presented in Table 6 using OLS. Column 1, 2 and 3 present the employment growth after one, three and five years, respectively. As expected ventures established by older and less educated founders perform worse. Relatively to younger founders (age between 20 and 2), older individuals (age between 50 and 55) reduce employment by 4.5%, 8.3% and 12.6% after one, three and five years, respectively. In contrast to the results presented on survival, industry and regional experience negatively affect job creation. VII. Conclusion Stimulating entrepreneurial activity and reducing unemployment is on the agenda of policy makers. The paper uses Portuguese micro-level data to evaluate the quantity and quality of business formation by dismissed entrepreneurs. By comparing dismissed and non-dismissed workers, we find that job dismissals are a huge driver of new firm creation, particularly within middle-aged (35-3 years), male and less educated individuals. Except for education, dismissed entrepreneurs are from demographic categories that are similarly likely to be entrepreneurs. The fraction of low-educated entrepreneurs increases and those entrants appear to be older. Another important finding is that surviving dismissed entrepreneurs present exactly the same characteristics as opportunity-based entrepreneurs. Male, middle aged individuals with higher education are more likely to survive. Although most of policies to promote firm creation among unemployed workers target older individuals, our results suggest that younger dismissed entrepreneurs are more likely to contribute to job creation Bibliography Acs, J Zoltan (2006), How Is Entrepreneurship Good for Economic Growth?, MIT Press 1(1) Alvarez SA, Busenitz LW The Entrepreneurship of Resource-based Theory. Journal of Management 27: Amit, R. and Muller, E. (15), " Push" and" Pull" Entrepreneurship, Journal of small business and entrepreneurship

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16 Figure 1 Beneficiaries (left scale) and the total amount granted under the upfront benefit payment policy (in thousands 2010 euros), Source: IEFP 16

17 Table 2 Descriptive statistics for new firms established between 16 and 2006 Obs Mean Std. Dev. Survival Dummy variable: 1 for start-ups that did not stop operation 3 years after entry. Size_0 No. of employees at the entry year Size_1 No. of employees in the second year of the start-up Size_3 No. of employees in the fourth year of the start-up Size_5 No. of employees in the sixth year of the start-up

18 Table 2 Descriptive statistics of the Dismissed Sample All Sample Unemployed Workers Re-employed Workers Entrepreneurs Variable Definitions Obs Mean Std. Dev. Obs Mean Std. Dev. Obs Mean Std. Dev. Obs Mean Std. Dev. Gender Dummy variable equaling 1 for 376, , , , (Male) male and 0 for female individuals. Age of the individual in dismissed Age 376, , , , date. Dummy variable equaling 1 for Very Low individuals who never attended or 372, , , , Education completted the elementary school. Low Education Medium Education High Education Foreign Tenure Income Dummy variable equaling 1 for individuals that attended junior high school. Dummy variable equaling 1 for individuals reporting a high school diploma or vocational school degree. Dummy variable equaling 1 for individuals with bachelor, masters or doctoral degrees. Dummy variable equaling 1 for non-portuguese individuals. Years of tenure prior to the displacement year Monthly regular earnings prior to the displacement year (200 Euros). 372, , , , , , , , , , , , , , , , , , , , , , , ,

19 Table 3 Descriptive statistics of workers and entrepreneurs sample All Sample Variable Definitions Obs Mean Gender (Male) Age Very Low Educatio n Low Educatio n Medium Educatio n High Educatio n Foreign Tenure Income Dummy variable equaling 1 for male and 0 for female individuals. Age of the individual in dismissed date. Dummy variable equaling 1 for individuals who never attended or completed the elementary school. Dummy variable equaling 1 for individuals that attended junior high school. Dummy variable equaling 1 for individuals reporting a high school diploma or vocational school degree. Dummy variable equaling 1 for individuals with bachelor, masters or doctoral degrees. Dummy variable equaling 1 for non- Portuguese individuals. Years of tenure prior to the displacement year or prior to the year considered Monthly regular earnings prior to the displacement year (200 Euros) or prior to the year considered 1,043,08 6 1,043,08 6 1,041,50 1,041,50 1,041,50 1,041,50 Std. Dev , ,038, , Non-Displaced Individuals Displaced Individuals Workers Entrepreneurs Workers Entrepreneurs Std. Std. Std. Std. Obs Mean Obs Mean Obs Mean Obs Mean Dev. Dev. Dev. Dev. 57,6,86 73,76 1, ,6 6 57, , , , , , , ,86,86 8,86 8,86 8,86 8,82 3,80 6 7, , , , , , ,7 2 73, , ,48 5 1,48 4 1,48 4 1,48 4 1,48 4 1,47 7 1,

20 Table 4 Impact of job dismissals on entrepreneurial activity and type of entrepreneurs Decision of Becoming an Entrepreneur (1) (2) Dismissed Individuals (D=1) 0.00*** (0.000) Gender (1 Male) 0.005*** 0.005*** (0.000) (0.000) Age *** (0.000) (0.000) Age ** (0.000) (0.000) Age *** *** (0.000) (0.000) Low education 0.005*** 0.004*** (0.000) (0.000) Medium education 0.008*** 0.007*** (0.000) (0.000) High education 0.008*** 0.008*** (0.000) (0.000) (Gender)xD 0.007*** (0.001) (Age 30-3)xD 0.003*** (0.001) (Age 40-4)xD 0.002* (0.001) (Age 50-55)xD 0.002* (0.001) (Low education)xd 0.005*** (0.001) (Medium education)xd 0.003*** (0.001) (High education)xd 0.002*** (0.001) Ln(GDP) 1.085*** 1.043*** (0.008) (0.007) Constant (.) (2.74) Observations 1,040,280 1,040,280 Adjusted R-squared Note: The table reports estimated coefficients for Equation (1). The dependent variable equals 1 if the individual is an entrepreneur, 0 otherwise. Year and county fixed effects are included but not reported. Robust standard errors are in parentheses. *** denotes statistical significance at 1%, ** significance at 5%, * significance at 10%. 20

21 Table 5 Hazard Rate of Survival for Ventures Established by Dismissed Entrepreneurs (1) (2) hazard ratio hazard ratio D 0.753*** (0.053) Gender (1 Male) 0.802*** 0.78*** (0.03) (0.041) Age (0.047) (0.04) Age (0.068) (0.074) Age (0.136) (0.157) Low education (0.062) (0.067) Medium education (0.072) (0.077) High education (0.080) (0.084) Industry Experience 0.87*** 0.886** (0.041) (0.045) County Experience 0.84*** 0.851*** (0.03) (0.042) Ln(Size) 0.756*** 0.780*** (0.025) (0.027) (Gender)xD (0.131) (Age 30-3)xD (0.14) (Age 40-4)xD 1.03 (0.187) (Age 50-55)xD 0.03 (0.287) (Low education)xd (0.122) (Medium education)xd 0.40 (0.070) (High education)xd 0.88 (0.06) (Industry Experience)xD 0.27 (0.11) (County Experience)xD 0.84 (0.126) (Ln(Size))xD 0.812** (0.068) Observations 35,86 35,86 21

22 Note: The table reports hazard ratios of a Cox hazard model. Year, county and industry fixed effects are included but not reported. *** denotes statistical significance at 1%, ** significance at 5%, * significance at 10%.. 22

23 Table 6 Employment Growth Rates for Surviving Ventures Established by Dismissed Entrepreneurs Growth_1 Growth_3 Growth_5 (1) (2) (3) Gender (1 Male) 0.037*** 0.044** (0.011) (0.020) (0.021) Age *** *** -0.15*** (0.020) (0.02) (0.025) Age *** *** *** (0.024) (0.035) (0.034) Age *** -0.16*** *** (0.027) (0.036) (0.043) Low education (0.028) (0.035) (0.037) Medium education (0.030) (0.038) (0.048) High education 0.123** 0.441*** 0.172** (0.054) (0.082) (0.06) Industry Experience *** *** *** (0.012) (0.01) (0.022) County Experience *** *** -0.36*** (0.026) (0.035) (0.045) Ln(JC) *** *** *** (0.002) (0.003) (0.002) Constant *** 0.821*** (0.563) (0.111) (0.103) Observations 2,755 28,14 21,360 R-squared Note: The table reports coefficients associated with Equation (3). Year, county and industry fixed effects are included but not reported. *** denotes statistical significance at 1%, ** significance at 5%, * significance at 10%. 23