Abstract
Offering unemployed individuals a subsidy to become self-employed is a widespread active labor market policy strategy. Previous studies have illustrated its high effectiveness to help participants escaping unemployment and improving their labor market prospects compared to other unemployed individuals. However, the examination of start-up subsidies from a business perspective has only received little attention to date. Using a new dataset based on a survey allows us to compare subsidized start-ups out of unemployment with regular business founders, with respect to not only personal characteristics but also business outcomes. The results indicate that previously unemployed entrepreneurs face disadvantages in variables correlated with entrepreneurial ability and access to capital. Nineteen months after start-up, the subsidized businesses experience higher survival, but lag behind regular business founders in terms of income, business growth and innovation. Moreover, we show that expected deadweight effects related to start-up subsidies occur on a (much) lower scale than usually assumed.
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Notes
We use the term “non-subsidized” in the sense that individuals did not receive the start-up subsidy under scrutiny. However, this does not exclude receipt of other support, such as subsidized loans and counseling.
Banks tend to screen individuals with respect to their human capital in the sense that it is negatively correlated with credit default risk, which renders individuals with higher human capital more capable and thus better access to credit.
The survival-of-the-fittest mechanism states that due to competition and market selection, relatively high performing start-ups survive while low performing firms drop out the market (see Fritsch 2008).
This relies on the existence of asymmetric information, i.e., individuals who apply for the subsidy have more information than the institution that pays the subsidy. Once the subsidy is approved, the institution has no influence on the effort of the applicant. See Paulson et al. (2006) as an example for how moral hazard induces financial constraints on start-ups.
In Germany, every individual who has been in employment subject to social security for at least one out of the last three years is eligible for unemployment benefit I. The amount of the benefit consists of 60 % (67 % with children) of the last net wage and is basically paid for a period of 12 months, with the exception of older individuals (see Caliendo and Hogenacker 2012).
Without program participation, the individuals would loose their unemployment benefit entitlement given that they start their own business and hence work full-time.
In order to be eligible to SUS, founders have to set up their businesses full-time. Therefore, we compare them to all business start-ups that were also set up full-time.
The KfW start-up monitor is an annual cross-section population survey, which currently contains 50,000 individuals between 18 and 65 years. The microcensus is an annual representative survey capturing 1 % of the German population and currently contains around 700,000 individuals. For further information, see KFW Bankengruppe (2012) and Fritsch et al. (2012).
Subject to German law, liberal professions are defined as professions that require “higher vocational education or creativity,” such as medical occupations (e.g., physicians, dentists), consultants (e.g., lawyers, tax accountants), technical or scientific occupations (e.g., engineers, architects) and the cultural sector (e.g., writer, musicians).
In Germany, 80 Chambers of Industry and Commerce and 53 Chambers of Crafts exist in total.
The commercial register contains firms who are actively involved in trading activities (so that large firms tend to be overrepresented). Its main objective is to provide security to business partners in the sense that they can rely on recorded firm-specific characteristics such as name, legal form, location, executive directors and the ability to pay liabilities.
We note that having access to only one particular quarter of entrants might restrict the external validity of the results if the composition of business founders would change significantly over time. However, comparing the distribution of certain characteristics (e.g., age, education, migration, unemployment duration) across different quarters of entries into the subsidy program (based on the statistic of the Federal Employment Agency) does not show significant differences.
According to the reporting system of the German Kreditanstalt für Wiederaufbau, of all business start-ups in Germany, 21.4 % self-reported having started out of unemployment in 2009 (KfW Bankengruppe 2010).
Out of the initial sample of 2,303 individuals, 132 business founders were excluded from the data because they started out of unemployment. Out of the remaining sample of 2,171 observations, a further 642 founders who started their self-employment part-time were excluded.
See Caliendo and Künn (2015) for evidence on subsidized start-ups out of unemployment by females.
The German Federal Statistical Office reports for 2009 that 55 % of female entrepreneurs work 40 hours/week or more while this amounts to 86 % for male founders.
The KfW Bankengruppe (2009) reports that among all founders who started a business in full-time in Germany in 2009, 80 % invested capital at start-up (which is very similar to our estimation sample) from which 10.5 % invested 50,000 Euro and more (which is in the middle of the two groups under scrutiny).
This means that all subsidy recipients in the first quarter of 2009 (N = 31,365) created approximately 27,500 jobs until the end of 2010.
The capital-intensive first part of the subsidy payment, i.e., unemployment benefit plus lump-sum payment of 300 Euro/month, has already expired for 10 months, and the optional second part, consisting of the lump-sum payment of 300 Euro/month only, for four months.
See, e.g., Bundesministerium für Arbeit und Soziales und Institut für Arbeitsmarkt und Berufsforschung (2011).
We do not expect that misreporting is a big issue here because each respondent was informed (by a letter and at the beginning of each interview) that their answers will be treated absolutely anonymous and public institution such as the Employment Agency will have never access to the data.
We neglect results for the subgroup of 21.3 % that is potentially affected by deadweight effects using the broad definition (see Table 4) as we cannot assume that this group would have started out of non-unemployment (and hence belong to regular business founders). Here, the adequate control group would consist of non-subsidized start-ups out of unemployment, which is difficult to create as almost no unemployed person starts a business without the subsidy in Germany. However, point estimates using our available control group indicate a similar pattern as for the share of 8.6 %. Results are available online in the Supplementary Appendix (available at http://ftp.iza.org/dp8817_supplement.pdf).
See Caliendo and Kopeinig (2008) for a detailed discussion on the assessment of the matching quality and for an explanation of applied measures.
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Acknowledgments
The authors thank Mirjam van Praag and two anonymous referees for helpful comments and suggestions. We further thank participants at the 2013 IECER in Brescia, the 2013 ESPE conference in Aarhus, the 2013 IZA Summer School and seminars at University of Potsdam and University of Jena for helpful discussions and comments. Financial support of the Institute for Employment Research (IAB) in Nuremberg under the research grant No. 1143 is gratefully acknowledged. We further thank the Chambers of Industry and Commerce, and Chambers of Crafts for their active support in constructing the data. A Supplementary Appendix is available online at: http://ftp.iza.org/dp8817_supplement.pdf.
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Appendices
Appendix 1
Appendix 2
1.1 Details on the Implementation of the Matching Procedure
This section contains details on the implementation of the propensity score matching in order to align the group of regular business founders toward the group of subsidized start-ups in terms of observable characteristics. First of all, we estimate the propensity score \(P(D=1|X)\) to start a business out of unemployment and therefore receive the subsidy using probit models. Table 8 shows the results of the probit estimation. We observe that particularly age, professional education, industry-specific experiences, labor market history, intergenerational transmission, regional characteristics and capital investment decisions at start-up significantly influence the probability of starting a business out of unemployment with subsidy receipt. In addition, Fig. 3 shows the distribution of the estimated propensity scores. Although the estimated propensity scores of subsidized business founders overlap the region of estimated scores for regular business founders to a large extent, there is only limited overlap in the tails of the distribution. To ensure that we only compare subsidized business founders to regular business founders with similar values of the propensity score, we exclude 29 subsidized business founders that have propensity score values above (below) the maximum (minimum) value of the regular business founders.
To finally align the group of regular business founders toward the group of subsidized start-ups, we apply a kernel matching. In fact, we apply an Epanechnikov Kernel with a bandwidth of 0.06. This offers the advantage of increasing efficiency by using the full set of regular business founders to construct the individual counterfactual outcome of previously unemployed business founders. Moreover, Kernel matching allows us to use bootstrapping in order to calculate standard errors and draw statistical inference. In this study, we use 200 replications to calculate standard errors (as suggested by Efron and Tibshirani 1993). Table 9 shows different measures to assess the quality of the applied matching procedure, i.e., whether the matching successfully balances the distribution of observable characteristics between both groups.Footnote 25 Based on a simple t test, it can be seen that the number of variables with significant differences in sample means between the subsidized and regular founders significantly declines after matching. As results from the t test allow for an assessment in terms of bias reduction in the marginal distribution of observable characteristics, we additionally provide the mean standardized bias (MSB) as suggested by Rosenbaum and Rubin (1985). We observe that the MSB is 16 % before matching, whereas our matching procedure significantly reduces the respective MSB down to 4 %. This is below the suggested threshold of 3–5 % by Caliendo and Kopeinig (2008) and therefore indicates a successful matching. In a final step, we also re-estimate the propensity score using the matched sample and compare it to the initial propensity score estimation. Given that the matching is able to balance the samples of subsidized and regular founders, we would expect a sizeable reduction in the Pseudo-\(R^2\) between both regressions (Sianesi 2004). Indeed, this is confirmed by Table 9, showing very low Pseudo-\(R^2\) for the matched sample estimation. Finally, we conclude that the applied matching procedure significantly reduces differences in observable characteristics between subsidized and regular business founders.
Propensity Score Distributions—Subsidized Business Founders versus Regular Business Founders.
Note: Depicted are distributions of estimated propensity scores for subsidized business founders out of unemployment and regular business founders (i.e., non-subsidized business founders out of non-unemployment) based on probit estimations as shown in Table 8
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Caliendo, M., Hogenacker, J., Künn, S. et al. Subsidized start-ups out of unemployment: a comparison to regular business start-ups. Small Bus Econ 45, 165–190 (2015). https://doi.org/10.1007/s11187-015-9646-0
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DOI: https://doi.org/10.1007/s11187-015-9646-0