employment or to unemployment? The results obtained by the CASE survey indicated that the latter is closer to the truth, although one has to remember that any conclusions have been obtained based on cross-sectional data only.

Here we will have the opportunity to run the analysis exploiting the quasi-panel nature of the Polish LFS. It will enable us to observe the changes of the relative labor market position of those with higher probability of shadow employment in the booming labor market of Poland during most of the analyzed period. We will also be able to say something about the countercyclicality and pro-cyclicality of shadow employment in Poland.

# **2. Data and Definitions**

The data used for the analysis are as follows: the dedicated CASE survey performed in 2007 (later called CASE_2007) and the micro-files from the Polish LFS data from years 20032008.

The results of the CASE 2007 survey enable one to analyze shadow employment according to several definitions. According to the narrowest one, the shadow employees are only those working without any written contract, i.e. those with verbal agreements only. It is obvious that these workers neither pay any taxes and/or social contributions, nor are they covered by any rules regulating the standard working contract. Those who are self-employed running their business activities without being registered are a very similar group as they do not pay any taxes nor are protected by any legal regulations. The other groups are those having some kind of written contract or registration but either not paying any taxes and/or social contributions, or employees obtaining part of their salaries ―in envelope‖ or the self-employed hiding part of their revenue in order to avoid taxes.

The methodology of creating the proxy variables for shadow employment in the LFS data was based on the idea of predicting the probability of shadow employment for individual workers in LFS, using the previous estimated coefficients of the probabilistic models in the CASE_2007 database. This methodology is based on the idea of Propensity Score Matching; however in this case, different data sets are used for model estimation and for calculating the ―propensity scores‖.

The first step of the analysis was to select the groups of shadow workers to be used for the analysis, i.e., the groups of shadow workers to be later ―matched‖ by observations from the LFS data-set. For the reasons of analytical clarity, it was decided to use only those working without any written contract and those working with some form of written contract but not paying any taxes or social security contributions (later called shadow employees) and the same group extended to self-employed (or employers) with unregistered firms (latter called shadow workers). In either case, only those who were employed in the shadow sector in their main job have been taken into account. Further in text both of these groups are referred to as shadow workers according to basic definitions.

The next step was to identify the set of explanatory variables to be used in the ―matching models‖. The important limitation was that all the explanatory variables used in the model specification had to be transformable so as to achieve common definitions and categories for CASE_2007 database and for each and every LFS database used in the further course of the analysis (for years 2003-2007).