In this research work we investigate which factors influence the probability of leaving the unemployment state among people registered in the District Labor Office in S lupsk. The multiepisode hazard models with time-varying variables are suitable tools for this analysis. We introduced the changing labor market structure into the risk model. The main results achieved show that the job finding process depends on the historical time of the entry into the unemployment state and the actual historical time. Also, the specific individual characteristics of people unemployed, such as gender, age, marital status, place of residence, education level, influence the probability of exiting the unemployment state. There is a greater tendency to leave the unemployment state when the person doesn’t receive the unemployment benefit. The participation in the vocational training doesn’t increase the transition rate into employment.
The aim of this paper is to analyze the duration of unemployment periods of people using the survival analysis. The hazard model can be a suitable tool for the analysis of unemployment period duration. Estimating the multiepisode risk model with time-varying covariates we calculate the direct risk of leaving the unemployment in the next instant. Hazard models usually comprise not only present duration of the phenomenon as a significant determinant for the probability of its occurrence, but also other observable characteristics of individuals such as gender, age, marital status, place of residence, education level, level of disability. Duration models with time-varying covariates are proper tools for the analysis of the influence of the unemployment benefit received at the risk of leaving unemployment. Additionally, we assess the impact of vocational training on unemployment duration as well. The following hypotheses are put forward by us to be verified using hazard models: old age gives smaller re-employment possibilities; women are less likely to be employed than men; higher education gives greater employment possibilities; there is a greater tendency to leave the unemployment state if the registered person receives no unemployment benefit; vocational training raises the transition rate from unemployment; there is a smaller probability of exiting unemployment state for people entitled to social welfare. To estimate, we use data from the District Labor Office in Słupsk in Poland from January 1990 to July 2007.
The goal of the study is to prove the effectiveness of training programs directed to the unemployed on the local labor market in Poland. We estimate a semiparametric hazard model to assess the impact of training on the individual’s unemployment duration. To resolve the potential sample selection problem, the participation in a training program is instrumented using a probit model. The main question of this paper is whether the training significantly raises the transition rate from the unemployment into the employment state.
W pracy analizowana jest długość czasu trwania zatrudnienia wśród ludności wiejskiej. Szacując modele hazardu wyznaczamy bezpośrednie ryzyko tego, że zatrudniony przestanie wykonywać swą pracę w danym przedziale czasowym. Modele hazardu pozwalają uwzględniać takie charakterystyki badanych osób, jak płeć, wiek, poziom wykształcenia, miejsce zamieszkania, czy status zatrudnienia jako determinanty dla prawdopodobieństwa zaprzestania zatrudnienia. Wskazujemy różnice w aktywności ekonomicznej ludności wiejskiej w porównaniu z miejską oraz identyfikujemy różnice pomiędzy województwami. W analizie wykorzystujemy dane z Badania Aktywności Ekonomicznej Ludności Polski.
EN
This paper discusses the duration of employment periods in the rural population. Estimating the risk models, the direct risk of leaving the job is calculated. We estimate hazard rate models to assess the effect of such factors as: gender, age, education level, place of residence and employment status, on the individual’s employment duration. We establish differences between economic activity of people in rural and urban areas and those between residents of rural areas from various polish provinces. To estimate, we use data from the Labour Force Survey in Poland.
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