|R sq.|0.8007|0.8601|
|---|---|---|
|N|18|17|
|Method|IV 2SLS|IV 2SLS|
|Wald test Chi<sup>2</sup>statisticsp-value|0.0000|0.0000|

_Note: *** significant at 1% level, ** significant at 5% level, * significant at 10% level. IV 2SLS - two stage least squares procedure with instrumental variables on cross-sectional sample; robust standard errors utilized. The detailed description of all the models applied in our analyses is given in Annex 4. Source: OECD, Eurostat, World Bank, Heritage Foundation, IZA, own calculations._

# _<u>Models 3 and 4 – Small firms and self-employed</u>_

Models 3 and 4 analyze effects of labor market institutions on the share of workers in small firms (employing fewer than ten employees) and share of self-employed. Both measures should reflect labor market restrictions that presumably push workers into self-employment or prevent firms to expand beyond certain size, approximated here by the number of employees. Table 5 and 6 offer estimation outcomes of Model 3 and Model 4 for all three samples. The results indicate that our model explain the two chosen indicators weakly, particularly in case of Model 4 (self-employers). The poor explanatory power of regressions is probably caused by much more complex relationship between the labor market indicators chosen as dependent variables and the size of the shadow economy or shadow employment, as it was already discussed in previous sections of our paper.

Results of Model 3 are not statistically very robust, despite explaining a solid part of variability in dependent variable (R-squared reaching from 35% to 90%). Moreover, the Model 3 results are not consistent with results of Models 1, 2 and 5 (see also below). As in Models 1 and 2, the strictness of employment protection legislation seems to have a positive effect on indicators of shadow economy―with every grade of EPL strictness, the share of employment in small firms increases by almost 10 percent.

Level of labor taxation seems to reduce share of small firms’ employment in the labor force, confirming the apparent negative relationship between shadow market indicators and labor taxation detected in models 1 and 2.<sup>39</sup> Effect of passive labor market policy expenditures is ambiguous; business freedom tends to increase employment in small firms. Effects of other variables―trade union density, active labor market policy expenditures, minimum wage, economic development, fiscal freedom, control of corruption, and regulatory quality―are statistically insignificant in baseline sample S1. In samples S2 and S3, active labor market policy expenditures seem to have a negative effect on employment in small firms in new member states, but it has insignificant effects in old EU member states. **Further, minimum wage exhibits a negative effect on the dependent variable, which might be a reasonable result for pure legal employment in small companies, but fails to meet our hypotheses regarding the effect on shadow employment, similarly to results of previous models** .

Results of Model 4 relating the development of explanatory variables to self-employers proxy of shadow employment are not robust and the effect of independent variables (mostly significant) varies a lot between the particular samples and subsamples to which the model was applied.

> 39 We examined this relationship further by utilization of more specific dependent variable stating the share of labor force employed in firms with fewer than five employees, which should have a closer relationship to examined shadow employment. The source of these figures was EU-SILC and they concern time period 20052007. Regression estimation results confirmed the negative relationship between employment in small firms and labor taxation. The results thus stay inconclusive as regards the effect of this variable on shadow employment. Regression estimation results for the share of labor force employed in firms with fewer than five employees used as dependent variable are available from the authors upon request.

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