QUANTITATIVE METHODS IN ECONOMICS Vol. XV, No. 2, 2014, pp. 349 – 358
THE INS AND OUTS OF UNEMPLOYMENT IN POLISH VOIVODESHIPS
Stanisław Jaworski Department of Econometrics and Statistics Warsaw University of Life Sciences – SGGW Department of the Prevention of Environmental Hazards and Allergology Medical University of Warsaw e-mail: stanislaw_jaworski@sggw
Abstract: This paper measures and compares the job-finding and separation transition rates of Polish voivodeships from 2005:Q1 to 2014:Q2. It uses readily accessible data of Local Data Bank in Poland: registered unemployment person by duration of unemployment and registered unemployment rate. The method of measurement stems from influential paper by Shimer on "Reassesing the Ins and Outs of Unemployment". The main sources of variability between the rates are investigated in terms of functional principal components.
Keywords: unemployment rate, Poisson process, smoothing, functional principal component data analysis
INTRODUCTION
Unemployment rate in Poland varied substantially across time but not across voivodeships over the last ten years. This phenomenon may occur as a result of variation in the rate at which workers flow the unemployment pool, variation in the rate at which workers exit the unemployment pool, or a combination of the two. It is largely these flows that drive the overall unemployment rate which is the main indicator of the health of the labour market. These flows generally follow a pattern in a typical business cycle. As the economy enters a downturn, separations start rising and job-finding rates start falling. These movements cause the overall unemployment rate to rise. The diagnosis of the labour markets should take into account the rates of reallocation of labour force. Reduced rates of worker turnover are usually associated with the more regulated labour markets. The reduced 350 Stanisław Jaworski turnover may contribute to higher unemployment. Lower turnover could reflect the fact that searching for jobs and workers has gotten costlier or harder, resulting in poorer worker-job matches, and therefore lower productivity. Low rates would indicate that firms and workers are settling for matches that are substantially suboptimal. What is optimal, of course, depends on the quality of the match and on the costs of searching. To learn more about these flows in Polish voivodeships, I track the job- separation and job-finding rates. I show that the split job-finding to job-separation rate is closely related to the unofficial distinction to Poland A and B. Although the distinction is oversimplified, it is widely acknowledged and discussed in Poland. It refers to the historical, political, and cultural distinction between west and east areas of Poland, with the west (Poland A) being significantly more economically developed. Historically it can be traced to the period of the partitions of Poland: the Prussian partition (Poland A), compared to the Austrian and Russian partitions (Poland B). Poland B encloses for example Podlaskie, Mazowieckie, Lubuskie, Łódzkie, Małopolskie and Podkarpackie voivodeships. I show that these voivodeships display the lowest rates of reallocation of labour force. In contrast I show that the west voivodeships have the highest rate of worker turnover.
METHODOLOGY
I relate to the method developed by Shimer (2012). The model relates to continuous time environment in which data are available at discrete dates. For every the interval will be referred to as 'period '. It is assumed that all ∈unemployed 0, workers ,find + a 1 job according to Poisson process with arrival rate and all employed workers lose their job according to Poisson = process − log 1 with − arrival rate , where is the job finding probability and is= the −log 1 separation − probability ∈ during 0,1 period . Two critical assumptions in ∈the 0,1 model should be underlined: (i) movements in and out of the labour force are ignored, (ii) arrival rates and are constant during period . For evidence that the assumptions are good approx imation of reality see Shimer (2012) and Elsby et al. (2013). Let and let denote the number of employed workers at time ∈ 0,1 , the number of unemployed workers at time , and workers + + who are employed at some time . For and , unemployment and short unemployment ∈ ,evolve + accordin g ∈ to {0,1, … } ∈ 0,1 = − (1) = − The Ins and Outs of Unemployment in Polish voivodeships 351
Note that and, under assumption (i), holds for every , where 0 = denotes 0 labour force in period . Thus+ equations = (1) have the following ∈ 0,1 solutions in discrete time : exp{− } = (2) { } = + exp {− − } We can estimate and for as we know and from available data. The second equation in ∈ {0,1, (2) implies … } that if 1 then the = unemployment rate ( will be denoted as ⋆ and be called the ≔ = flow steady-state unemployment rate ). The equation can be reformulated into the form