panels each enabling us to observe the situation of individuals over two consecutive years. 5 such data sets have been created for years 2003/2004 to 2007/2008.

At first, I tried to analyze whether working in shadow employment increases the probability of losing or quitting a job and whether this interdependence changed over the analyzed period. This has been performed by estimating the series of probit models in which the probability of transition out of employment between ―t‖ and ―t+1‖ has been set as the dependent variable and estimated probability of working in shadow employment<sup>2</sup> (―shadow‖) in period ―t‖ has been one of explanatory variables. Apart from that, I used the set of standard control variables, all for period ―t‖ such as: region, age, gender, education level, and characteristics of employing company.

Three versions of the model have been estimated for each year. One in which both the ―contract‖ variable and the ―shadow‖ variable have been added to model specification, and two with only one of those variables used.

The estimated coefficients on control variables have been as expected - a negative relationship between education level and transition probability, and the lowest transition probability for those in prime age and higher probabilities for females.

The contract variables are significant and similar for both specifications for all years (see Table 2). All employment contracts increase the probability of transition out of employment as compared to full time and open term contracts. It also seems that between years 2003-2007, when the labor market situation was clearly improving, the influence of the fixed term employment on the probability of losing a job was weakening, and then it started to gain importance. What makes this result unexpected is that this tendency started to change already in 2007 when the labor market was still blossoming.

Being a self-employed employee negatively influenced the probability of losing a job until 2006, but afterwards it lost its significance. This may mean that the booming economy strongly awards those deciding to run their own business, be it registered or unregistered. Most probably if our analysis continued until the crisis year, the estimated coefficients for self-employment would become significant again, but with the opposite sign.

The estimated coefficients for the ̳shadow‘ variable seriously depend on model specification. Due to its high correlation with contract variable, it appears not to be significant for transition probability out of employment if both variables (i.e. ―shadow‖ and ―contract‖) are used in model specification. They become significant only if the contract variable is excluded from the analysis.

As one could expect, working in shadow economy positively influences the probability to lose employment. It seems also that in the period where labor market situation was improving (years 2003-2007), the relative situation of those in the shadow sphere also tended to improve, although in absolute terms it was still much worse as compared to the legally employed. In 2008, when one could already feel the first symptoms of the crisis, the relative probability of losing a job increased for those in the shadow sphere.

It means that the labor market situation of those working in any non-standard working arrangement - be it in shadow economy or even registered - is much more dependent on the general situation of the labor market than for those working full time and an open term basis.

> 2 The probabilities of shadow employment predicted by probit models with full specification and basic definition of shadow employment have been used in all estimations below.