for these factors, two more variables were added in the regression model ( _CORR, REGQUAL_ ).<sup>35</sup> Economic theory and empirical research described in previous sections indicate expected effect of the variables used in our regressions. Policies that distort effective functioning of labor markets―high minimum wages and excessive employment protection legislation ―should increase attractiveness of the shadow economy. In several instances―impact of trade unions, labor taxes and income support during unemployment―neither theory nor empirical research provide unambiguous predictions, as summarized in Table 2. Pair-wise correlations between the dependent variables and independent variables are given in Annex 5.

**_Table 2. Expected effect of explanatory variables on dependent variables_**

|EPL|positive|
|---|---|
|MW|positive|
|TU|positive/negative|
|TAX|positive/negative|
|LMPA|negative|
|LMPP|positive/negative|
|GDPPC|negative|
|FISF|negative|
|BUSF|negative|
|CORR|negative|
|REGQUAL|negative|

The basic data sample S1 covers only four NMS. Therefore it was not possible to run a separate analysis for this group of countries. Only the differences in the role of institutions between the whole group of countries and the old member countries and its implications for the NMS were examined using a modified Chow test (see also Cazes and Nesporova, 2003).<sup>36</sup> Similar test was also applied to the extended data samples S2 and S3. These two samples cover more NMS which allows for running separate regressions but still, the samples suffer from lower data reliability given by less accurate and relevant indicators with different underlying methodology and implied limited robustness of the results. Therefore, variation between the NMS and old member countries might be studied only partially given the above mentioned limitations.

> 35 Some authors (e.g. Lehmann and Muravyev, 2009) include the macro environment and policy variables in a lagged form. The underlying logic is that it is reasonable to expect the outcome of interest in time _t_ to be more related to the hypothesized causal variable in time _t-1_ . In our estimations, we assume that the environment prevailing at the time the decision is being made has the major effect on decision-making of economic subjects and, therefore, we do not use lagged form of these control variables. Furthermore, there exists substantial inertia in development of macroeconomic and policy environments, which reduces potential differences in outcomes of these two approaches. Nevertheless, we considered this eventuality as well and we checked robustness of our results by utilization of models with lagged macro environment and policy variables. The results were not altered significantly by this step.

36 We used a modified version of the test hypotheses and statistics, because number of observations in the NMS group is smaller than the number of parameters, _nNMS_ < _k,_ and thus we can not use the standard methods in this case. We test the hypothesis _H0_ : _E_ ( _y_ | _X_ ; βOE) = _E_ ( _y_ | _X_ ; βNMS). This is done by calculating the statistic _SSRT_  _SSROE F_  _nNMS_  _F (nNMS, nOE – k). SSROE nOE_  _k_

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