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We analyse to what extent spatial interactions affect the labour market matching process. We apply spatial econometrics methods (including spatial panel Durbin model), which are rarely used in labour market matching analysis. We use the data on stocks and the inflows of unemployed individuals and vacancies registered at public employment offices. We conduct the analysis at the NUTS-3 and the NUTS-4 levels in Poland for the period 2003-2014. We find that (1) spatial dependency affects matching processes in the labour market; (2) both close and remote spatial interactions influence the results of the matching process; (3) spatial indirect, direct, and total spillover effects determine the scale of outflows from unemployment; and (4) spatial modelling is a more appropriate approach than classic modelling for matching function.

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We test whether the discouraged worker or added worker effect prevails in the Polish labour market. The discouraged worker effect implies that the participation rate is procyclical with respect to the GDP and countercyclical with respect to the unemployment rate. The added worker effect yields contrary findings. We analyse the period 1994–2014 with quarterly data. We focus on the working age population, both males and females. We apply a range of methods to obtain robust results, some of which have never been employed to resolve this problem. They include ad hoc filtration, spectral analysis, unobserved component model, time-varying parameter model, and frequency domain regression. The results indicate that the added worker effect prevails in most of the business cycle frequencies. It is significant and varies over time. It is true for both males and females. It is considerably stronger in contractions than in expansions. In low business cycle frequencies, the discouraged worker effect prevails. Although the last case is rare, it proves the heterogeneity of labour force behaviour over the business cycle.

I identify which theoretical model (random, stock-flow, or job queuing) best describes the matching mechanism in the labour market in Poland. The purpose of this work is to formulate policy recommendations aimed at increasing the number of matches. I use monthly registered unemployment data for the period January 1999-June 2013 and econometrically correct for temporal aggregation bias in the data. I extend known solutions and apply them directly to a job queuing model. Job seekers (from the pool) seek work among old and new job posts, but only a small fraction of the newly unemployed individuals find work quickly. Vacancies are the driving force in aggregate hiring, but the inflow is more important than the stock. The random model has greater explanatory power, although the results do not negate the nonrandom model. Hence, better information and higher inflows (especially of job offers) should facilitate matching.