Determinants and Success of Self-Employment

  • Frank Reize
Part of the ZEW Economic Studies book series (ZEW, volume 25)


The empirical model is divided in two different stages of transition (see Figure 4) to address the questions: Who is entering self-employment, how successful is selfemployment and can the grant of bridging allowance affect the success of the chosen employment opportunity. In a first stage, unemployment duration is investigated to obtain the determinants of self-employment. As possible exit states from unemployment self-employment, paid-employment and out-of-the-labour-force are regarded. It is also possible that the individual stays unemployed during the whole observation period. In such a case the unemployment spell is rightcensored. Therefore, in the first stage of the analysis unemployment and out-oflabour force are different employment states.
Figure 4

Different Stages of the Analysis


Hazard Rate Mass Point Unobserved Heterogeneity Kernel Density Estimation Occupational Choice 
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  1. 53.
    For an extensive discussion of evaluation methodologies see Chapter 4.Google Scholar
  2. 55.
    For a survey on duration models see e.g. Petersen (1995).Google Scholar
  3. 56.
    The derivation of the discrete hazard rate model follows Steiner (1997). He applies a similar model on unemployment duration. For the application of this model see also Steiner (2001).Google Scholar
  4. 57.
    The non-proportional specification has less restrictive assumptions than proportional hazard rate models, which assume that effects of the covariates on the hazard rate are independent of duration. See e.g. Kalbfleisch and Prentice (1980) or Lancaster (1990) for more details.Google Scholar
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    For an application see Jenkins (1995).Google Scholar
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    It should be well noted that transition only occurs during t =Tik and that for right-censored spells δij for t =Tik.Google Scholar
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    See e.g. Stata (2000), Handbook Release 6.0, commands “xtlogit” or “quadchk” for a discussion on the reliability of the Gauss-Hermite-Quadrature method.Google Scholar
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    To facilitate writing the following refers to the unemployment model. Hence index j is suppressed for the unobserved heterogeneity.Google Scholar
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    See Sin and White (1996) for a survey on various IC.Google Scholar
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    The estimator “mlogXmX” is programmed using Stata’s (Version 6.0) d2-method. Computation time on a Athlon 1.3 GHz is about 5 days for the unemployment model with four mass points for the Sample-All. The programme code together with a help-file is available by the author upon request.Google Scholar
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    Lee (1996) suggests a correction for the computation of the difference of the vari-ance-covariance matrices which always yields a positive test statistic. However, Lee’s procedure is much more complex than the procedure proposed by Small and Hsiao (1985).Google Scholar
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    For the derivation of the fixed-effects estimator see e.g. Greene (1997). According to the Hausman test the fixed-effects estimator is preferred over the random effects es-timator for this analysis.Google Scholar
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    For a detailed description of the GSOEP see Scholar
  14. 71.
    Hence, it should be well noted that the region of residence of an individual is con-structed as time-varying variable (see below).Google Scholar
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    As the results for the Sample-West are similar to those for the Sample-All the discussion is concentrated on the Sample-All. Descriptive statistics for the Sample-West can be found in the Appendix in Table 41 and Table 42.Google Scholar
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    Earnings of the paid-employed were estimated using 16 cross-sections obtaining 16 different parameter vectors. Due to a smaller sample size, self-employment earnings were estimated on a pooled sample using GEE methods (see Liang and Zeger, 1986). These estimates were obtained using a reduced form instead of a structural selection model to control for labour market participation. A possible heteroscedasticity prob-lem may arise from the fact that SPIR and IRR, respectively, are estimated variables. Therefore, the standard errors for these variables may be inconsistent in the duration analysis. To address this problem bootstrapped standard errors could be used in prin-ciple. However, bootstrapping is not applicable to this analysis as computation of the multinomial logit model with random effects takes between one and five days per model. See Appendix A.l for the calculation of the SPIR and IRR.Google Scholar
  17. 74.
    I.e. unemployment-benefits/[Probability(being self-employed)*expected-self-employ-ment-income + Probability(being paid-employed)*expected-employment-income)].Google Scholar
  18. 75.
    The UV-ratio and the GDP growth rate proved to approximate demand pull and un-employment push best. Several other measures were also tested, like the unemploy-ment rate, GDP growth on quarterly data, number of insured employees in a certain sector etc.Google Scholar
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    The further discussion of Section 6.5 is concentrated on the results for the Sample-All. Results for the Sample-West are similar. The interested reader is referred to Table 43 and Tables 45 to 50 in Appendix A.2.Google Scholar
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    To save time of computation, the testing of the significance of specific variables was done within the model of no unobserved heterogeneity. For the exit into self-employment a significance level of 20% is used, because of the relative small number of observations for this state.Google Scholar
  21. 78.
    See Table 44 in the Appendix A.2.Google Scholar
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    For the estimation results neglecting unobserved heterogeneity see Table 44 in Appendix A.2.Google Scholar
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    Part of this effect may be due to legal requirements according to the German economic and trade regulations. In the craft sector as well as in some professional occu-pations (for example lawyer, doctor) special examinations or vocational degrees, and in the banking and insurance sector some minimum requirements with respect to initial capital, are a necessary precondition for becoming self-employed. However, in principle everybody is allowed to start a business in most parts of the private sector of the economy.Google Scholar
  24. 81.
    Doubling the expected income from self-employment is equal to doubling the SPIR and halving the IRR.Google Scholar
  25. 82.
    For the construction of the GSOEP samples see the GSOEP Desktop Companion (2000).Google Scholar
  26. 83.
    The corresponding graphs for the Sample-West can be found in the Appendix, Fig-ure 25 and Figure 26.Google Scholar
  27. 84.
    The studies by Taylor (1998, 1999 and 2001) on the duration of self-employment do not test for duration dependence, as he employs the Cox proportional hazard model.Google Scholar
  28. 85.
    1,900 DM represents the 10%-and 6,000 DM the 90%-Percentile.Google Scholar
  29. 86.
    Estimation results are displayed in Table 13.Google Scholar
  30. 87.
    Based on several simulations, it was not possible to isolate a specific individual or a specific type of individuals which cause these implausible values.Google Scholar
  31. 88.
    Kernel density estimations for the other samples can be found in the Appendix, Fig-ure 27 to Figure 29.Google Scholar
  32. 89.
    Results for the sample of the initially paid-employed are omitted because of the implausible estimation results for the hypothetical income from self-employment.Google Scholar
  33. 90.
    The results for the regression of the time-invariant variables on the fixed effects can be found in the Appendix in Table 47.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Frank Reize
    • 1
  1. 1.KfW BankengruppeKSb Volkswirtschaftliche AbteilungFrankfurt am MainGermany

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