Spatial Labour Market Matching

Abstract

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.

What Affects Efficiency in Labour Market Matching at Different Territorial Aggregation Levels in Poland?

“…At more disaggregated levels the impact of certain stock and flow variables decreased (apart from the unemployment inflow, which became more positive) and returns to scale decreased (from constant to decreasing). Decreasing returns to scale may suggest that local labour markets treated separately are not efficient enough and that spatial interactions should be taken into consideration (compare also Antczak et al ., ). Antczak et al .…”

“…Antczak et al . () proved that workers often commute to the other regional labour markets at NUTS‐4 level; in terms of public employment intermediation, unemployment inflow generates congestion in adjacent labour markets, whereas vacancies attract job seekers from adjacent markets, what generates positive externalities.…”

“…At more disaggregated levels the impact of certain stock and flow variables decreased (apart from the unemployment inflow, which became more positive) and returns to scale decreased (from constant to decreasing). Decreasing returns to scale may suggest that local labour markets treated separately are not efficient enough and that spatial interactions should be taken into consideration (compare also Antczak et al, 2016). Antczak et al (2016) proved that workers often commute to the other regional labour markets at NUTS-4 level; in terms of public employment intermediation, unemployment inflow generates congestion in adjacent labour markets, whereas vacancies attract job seekers from adjacent markets, what generates positive externalities.…”

“…At NUTS-3 level economic entities creation is again especially important. It may be connected to the fact, that at this level intraregional commuting to work is very popular in Poland (Antczak et al, 2016). New enterprises attract labour supply within the given region.…”

Efficiency in Spatially Disaggregated Labour Market Matching

“…We The job queuing model yielded better results than the stock-flow one at the NUTS-3 level. Here, the yearly growth rate of GDP 5 regions with lowest efficiency were located in eastern Poland. In the rest of the country, there was no visible spatial pattern in the efficiency of matching.…”