Many countries are turning to active labor market programs (ALMP) to increase individuals’ incentive to start a business and to reduce unemployment, but research on the effectiveness of such programs has produced mixed results and is still inconclusive at the macroeconomic level. This article examines the importance of ALMP targeted at entrepreneurship to explain cross-country differences in aggregate entrepreneurship rate. By using GEM data over the period 2002–2013 on OECD countries, our results show a positive impact of ALMP on the rate of necessity entrepreneurship but no significant effect on the rate of opportunity entrepreneurship. We further established that generous unemployment benefits reduce the positive outcome of ALMP on the aggregate rate of necessity entrepreneurship. Moreover, because most businesses started out of necessity do not create new jobs, we find that the economic spin-off of such programs in terms of unemployment reduction is very limited.
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Micro enterprises are defined as companies hiring less than 10 people. See European Commission report (2014) on start-up incentives in Europe
In the rest of the paper, we use the term ALMP to refer to start-up incentive policies, i.e., all types of policies targeted at entrepreneurship.
There is evidence that most entrepreneurs do not create additional jobs in the first years of operations and do not innovate (e.g., Acs et al. 2016; Åstebro and Tåg 2015; Shane 2008). This prompts some scholars to distinguish self-employment and small business activity from “real”—or “Schumpeterian”—entrepreneurship (Henrekson and Sanandaji 2014; Sanandaji and Leeson 2013). In this paper, we use the terms self-employment and entrepreneurship interchangeably.
See Parker (2004) chapter 4 for a review of the literature.
See European Employment Policy Observatory Review (2014) for a very detailed review of evaluation policy analysis on a large sample of countries.
Including the introduction of the highly mediatized “Me Inc.” instrument. “The media-friendly treatment of the subject of self-employment and buzzwords such as “Ich-AG” (“Me Inc.”) have considerably increased the acceptance and chances of launching a start-up from unemployment. It can be assumed that in 2003 and 2004, almost every unemployed person in Germany knew that he or she could get financial support when starting a business.” (Bergmann and Sternberg 2007: 217–218).
Start-up grants as in Finland, Spain, Germany, and France, income support programs as in Germany and the UK and training programs as in Italy. See European Commission Report (2014) for more details.
Other indexes have been used to capture generosity of unemployment benefits. Koellinger and Minniti (2009) measure the generosity of unemployment benefits by an index obtained by dividing the public spending on out-of-work income maintenance and support, measured in percent of GDP, by one plus the current employment rate.
For most countries, the replacement rate is relatively similar regardless of whether the previous wage or marital status are considered. In the United Kingdom, there is a strong discrimination between replacement rates depending on individuals’ marital status and previous wage. The replacement rate of a single earner is 20%, while the replacement rate of two earner married couples is 67%. We have run the analysis with net replacement rates based on different criteria (the replacement rate of single earner married couples without children and single earner married couples with 2 children having a previous wage of 67% of the average wage and 150% of the average wage). Our main findings are not altered by the choice of the variable. We report the results with the net replacement rate based on a 40-year-old single earner without children with a previous wage of 67% of the average wage because most studies, including OECD reports, base their conclusion on this category.
Unfortunately, the index does not allow unemployment generosity for all individuals in one country to be captured. In order to overcome this limitation, we introduce a measure of access to unemployment benefits defined as the ratio of unemployment benefit recipients to the number of unemployed (the database has been generously provided by David Grubb at the OECD). The variable is only available for 16 countries from 1999 to 2004, which reduces the sample in a significant way. More specifically, the coefficients are estimated on a sample of 45 observations, which does not provide consistent estimates.
This variable is captured by “social security contribution” in the remainder tables.
This variable is captured by “payroll tax” in the remainder tables.
See Parker (2009) for a survey of the literature.
The coefficient of the interaction term is unbiased only when the two parents are estimated simultaneously, otherwise not only is the interpretation difficult but ignoring parent variables may create biased estimates. This is due to multicolinearity problems that would raise standard errors and could create an endogeneity problem in which the interaction term is affected by the parent variable. We therefore estimate PIP and weighted means for equations that include the interaction term in a model when all of its base variables are also included.
We also interact social security contributions with start-up incentives. The coefficient of the interaction variable is negative but not significant. The other results remain similar. For clarity reasons, we only discuss and report the results based on the interaction term between NRR and start-up incentives.
We have tested for mediation effects with linear regression models, but did not find significant effects. The results are reported in an online appendix available on the first author’s website.
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Electronic supplementary material
Appendix A: Descriptive Statistics
Start-up incentive schemes vary considerably from one country to another. Lending programs at preferential rates are in place in almost all countries. However, other ALMPs are specific to some countries and can take different forms.
For example, in Finland, start-up incentives appear as a one-off grant of €32.66 per day for entrepreneurs with a feasible business plan. The aim of the start-up grants paid on a monthly basis is to replace unemployment benefits for unemployed people. Italy provides free grants and loans with reduced interest rates for investment and management expenditure, as well as training and technical assistance for the realization of investments in the first year of business activity. The programs are targeted at unemployed and young people between 18 and 35 years old. In the UK, income support is provided during the launch phase by means of a weekly allowance in addition to the amount of the minimum contribution. The Spanish government offers grants to cover specific set-up costs such as general operating costs and training costs to those planning to start a new venture. The German government launched two different non-cumulative schemes: (i) the bridging allowance which provides the same amount of unemployment benefits recipients would have received for a period of six months and (ii) a start-up subsidy which is a declining grant of €600/month in the first year, €360/month in the second year and €240/month in the third and last year. Finally, French unemployed workers have the option to accumulate unemployment benefits, business revenue and tax exemption from social security contributions for a year.
Some other countries have very low funds dedicated to business creation (close to zero). It is especially the case of countries with less protective labor market institutions such as Great-Britain (UK) and the United-States (US). Start-up incentive programs in the UK and US take the form of training and grants without being directly targeted towards unemployed individuals.
Figure 3 reports change in ALMP spending targeted at entrepreneurship over the period 1999–2013 for six European economies: Denmark, Finland, France, Germany, Spain and the United Kingdom. We decided to report descriptive statistics for those specific six countries because they present very different labor market outcomes, social security systems and start-up incentive spending, which allows us to show entrepreneurship trends in countries with different labor market institutions.
Figure 3a reports changes in the average replacement rate received in the first 12 months of unemployment over the period of observation. Spain and France are the two most generous countries, whereas the United-Kingdom is the country with the lowest net replacement rate. Figure 3d illustrates differences in social security contributions for different countries. The figure exhibits less protective labor market institutions in the United Kingdom as illustrated by the low rate of social security contributions compared to the other 5 countries which have social security contributions rate above 10%. Before the 2007 economic crisis, countries with the highest unemployment benefits and social security contributions were also those with the highest unemployment rates (see Fig. 3b). The conventional wisdom has been that there exists a positive relationship between high unemployment benefits and high unemployment. More specifically, persistent high unemployment is caused by the rigidity imposed by protective labor-market institutions. The economic crisis has changed the belief that unemployment benefits are the only culprit of persistent unemployment, as illustrated by the dramatic increase of unemployment rate trends observed in most countries reviewed after 2007. Across different countries, the economic recession and the increase in unemployment did not have a major impact in terms of start-up incentive spending as illustrated by Fig. 3c.
Among the countries reviewed, Spain and France increased ALMP expenditure in 2004 and 2006 respectively. In contrast, Germany has continuously reduced revenue allocation to ALMPs targeted at start-up incentives since 2005 but this was still very high until 2010. In stark contrast, the UK government decided to allocate only a very small amount of resources to the promotion of entrepreneurship.
First, we report the results with a fixed effect model without controlling for model uncertainty. The results are reported in Table 5.
Secondly, we perform a fixed effect model by including different proxies for social security contributions. Table 6 below describes the robustness variables used.
Table 7 reports the results with different proxies for social security contributions as detailed in Table 6. The first set of proxies are the employer and employee social security contribution rates derived from the OECD tax database (columns (2) and (3) in Table 7). In some countries flat rate structures are applied whereas in other countries progressive tax rates are in practice. In countries where the rate is progressive (i.e. different rates are applied for given threshold levels of revenue defined by the government) we assign a unique value defined as the average of the different rates. These indicators are not the ones retained in our baseline specification because they might suffer from an aggregation bias. We further provide two other proxies for social security contributions derived from the World Bank. The first variable measures the tax on income as a percentage of GDP defined as compulsory transfers for the central government for public purposes (columns (1) and (2)). Certain compulsory transfers such as fines, penalties and most social security contributions are excluded, which is why we include other social security variables in the regression. The second is a measure of tax on profit as a percentage of commercial profit. It includes the amount of taxes and mandatory contributions payable by businesses after accounting for allowable deductions and exemptions as a share of commercial profits. Taxes withheld (such as personal income tax) or collected and remitted to tax authorities (such as value added taxes, sales taxes or goods and service taxes) are excluded, which is why we include the regression income and corporate taxes. Column (4) reports the results of the baseline specification (fixed-effects model from Table 5).
Whatever the proxy retained, the coefficients of interest do not change either in sign or in significance.
Next, we report results from multilevel modeling, which includes individual characteristics stemming from the GEM surveys. Results for our variables of interest are substantively consistent with the results of our main analysis with the BMA model.
Turning to the analysis of the determinants of unemployment in t + 1 and t + 2, we report the results of the country fixed-effect model in Table 9.
Finally, we report results from a robustness test using entrepreneurship rates estimated through the fixed-effect model of Table 5 to predict the unemployment rate in t + 1 and t + 2. Results are again consistent with our main analysis and show that opportunity entrepreneurship contributes to reduce unemployment rates whereas necessity entrepreneurship does not have a significant effect, especially in t + 2. We interpret the positive coefficient of necessity entrepreneurship rate in column (1) as a positive correlation between unemployment and the pool of available necessity entrepreneurs in one country Table 10.
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Laffineur, C., Barbosa, S.D., Fayolle, A. et al. Active labor market programs’ effects on entrepreneurship and unemployment. Small Bus Econ 49, 889–918 (2017). https://doi.org/10.1007/s11187-017-9857-7
- Active labor market programs
- Start-up incentives
- Unemployment benefits