Model-based econometric techniques of the shadow economy estimation have been increasingly popular, but a systematic approach to getting the best of their complementarities has so far been missing. We review the dominant approaches in the literature-currency demand analysis and MIMIC model-and propose a hybrid procedure that addresses their previous critique, in particular the misspecification issues in CDA equations and the vague transformation of the latent variable obtained via MIMIC model into interpretable levels and paths of the shadow economy. We propose a new identification scheme for the MIMIC model, referred to as 'reverse standarization'. It supplies the MIMIC model with the panel-structured information on the latent variable's mean and variance obtained from the CDA estimates, treating this information as given in the restricted full information maximum likelihood function. This approach allows avoiding some controversial steps, such as choosing an externally estimated reference point for benchmarking or adopting other ad hoc identifying assumptions. We estimate the shadow economy for up to 43 countries, with the results obtained in the range of 2.8-29.9% of GDP. Various versions of our models remain robust as regards changes in the level of the shadow economy over time and the relative position of the analysed countries. We also find that the contribution of (a correctly specified) MIMIC model to the measurement of trends in the shadow economy is marginal as compared to the contribution of the CDA model, confirming the scepticism of some previous literature towards this method.

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We identify determinants of large disparities in local unemployment rates in Poland using panel data on NUTS-4 level (poviats). We find that the disparities are linked to local demographics, education and sectoral employment composition rather than to local demand factors. However, the impact of determinants is not homogenous across poviats. Where unemployment is low or income per capita is high, unemployment does not depend on the late working-aged share in the population but does depend relatively stronger on the share of early working-aged. Where unemployment is high or income per capita is low, unemployment does not depend on education attainment and is relatively less responsive to investment fluctuations. Where small farms are present, they are partial absorbers of workers laid off due to investment fluctuations. JEL classification: C23, J23, R23