An analysis of the determinants behind having an additional job by employees

Apart from having the main job, many people in Poland decide to take additional jobs. There are many potential factors which determine having a second job. These include varied needs of individuals, such as the desire to improve their material status, family situation, or the opportunities arising from human capital. In this study, apart from the aforementioned needs, the features of individuals, such as age, sex, place of residence and the features of the main workplace have been included. Unfortunately, some determinants of the studied phenomenon cannot be clearly observed or are generally unobservable. Hence, the models with unobservable heterogeneity, which were used in this study, are of particular importance in modelling this type of phenomena. The purpose of this paper was to show the demographic profile of a two-job worker. This has been done by the assessment of the impact of selected determinants on having an additional job. Furthermore, the scale of the impact of the studied determinants has been compared in the case of women and men. The study used the Bayesian logistic regression model

Socioeconomic aspects of long-term unemployment in the context of the ageing population of Europe: the case of Poland

In view of the ageing populations of Europe, an important current challenge for labour markets is to increase the professional activity of those social groups whose participation in the labour market is insufficient. This work focuses on people unemployed for 12 months or more. The main purpose of this study is designation of those social groups which have the greatest problems with getting out of long-term unemployment, and assessment of the consequences of long-term unemployment, depending on its duration, in the context of the ageing society. We have investigated this problem using the example of Poland, which suffers from particularly low fertility rates. In this study, data from the Labour Force Survey (L.F.S.) for Poland have been used to model the duration of long-term unemployment. In the analysis the accelerated failure time models (A.F.T.) in the Bayesian approach have been used. Our results show, among other things, that difficulties in getting out of long-term unemployment mainly affect women and people who have children or who looked after children directly before the start of their job search. This, in consequence, may deepen the problem of the decrease in labour supply caused by the ageing populatio

INFORMATIVE VERSUS NON-INFORMATIVE PRIOR DISTRIBUTIONS AND THEIR IMPACT ON THE ACCURACY OF BAYESIAN INFERENCE

In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling

An analysis of the determinants behind having an additional job by employees

Apart from having the main job, many people in Poland decide to take additional jobs. There are many potential factors which determine having a second job. These include varied needs of individuals, such as the desire to improve their material status, family situation, or the opportunities arising from human capital. In this study, apart from the aforementioned needs, the features of individuals, such as age, sex, place of residence and the features of the main workplace have been included. Unfortunately, some determinants of the studied phenomenon cannot be clearly observed or are generally unobservable. Hence, the models with unobservable heterogeneity, which were used in this study, are of particular importance in modelling this type of phenomena. The purpose of this paper was to show the demographic profile of a two-job worker. This has been done by the assessment of the impact of selected determinants on having an additional job. Furthermore, the scale of the impact of the studied determinants has been compared in the case of women and men. The study used the Bayesian logistic regression model

Modelling the occupational and educational choices of young people in Poland using Bayesian multinomial logit models

Binomial logit models are commonly used in the analysis of the situation of respondents on the labour market. Consequently, in most cases researchers consider two states: of being unemployed and employed or economically inactive and active. This paper focuses on the situation of young people aged 18 to 29 on the labour market in Poland. A major part of the people who comprise the studied group are still in education or combine education with work. Therefore, the participants of the research were divided into the following groups: the employed and not learning, those combining education with work, the unemployed, learners/students only, and those economically inactive and not at school. The model allowing an analysis which includes both the most common division into working and nonworking persons as well as the division proposed in this study is a nested logit model. This model has a hierarchical structure and is a special case of a multinomial logit model. In this paper, all models were estimated within the Bayesian approach. The findings show that continuing education by young people may result from their problems with finding a job; moreover, combining work with education is not the preferred form of professional activity. In addition, the study examines the inequalities observed on the Polish labour market

THE ANALYSIS OF CHANCES OF YOUNG AND MIDDLE-AGED PEOPLE FOR HAVING A JOB USING BAYESIAN LOGISTIC REGRESSION MODEL

The aim of this article is to analyze the chances of having a job using Bayesian logistic regression model. In this study both young and middle-aged people have been considered. The individual characteristics of economically active people have a significant impact on their labour market status. In this research the commonly studied set of features has been extended by adding the following characteristics: marital status, financial situation of the household, health assessment and the fact of living with parents in the case of young people. In this study, Bayesian logistic regression model has been used. The Bayesian approach enabled us to incorporate information from previous studies

Analiza determinant posiadania dodatkowej pracy przez pracowników najemnych

Apart from having the main job, many people in Poland decide to take additional jobs. There are many potential factors which determine having a second job. These include varied needs of individuals, such as the desire to improve their material status, family situation, or the opportunities arising from human capital. In this study, apart from the aforementioned needs, the features of individuals, such as age, sex, place of residence and the features of the main workplace have been included. Unfortunately, some determinants of the studied phenomenon cannot be clearly observed or are generally unobservable. Hence, the models with unobservable heterogeneity, which were used in this study, are of particular importance in modelling this type of phenomena. The purpose of this paper was to show the demographic profile of a two-job worker. This has been done by the assessment of the impact of selected determinants on having an additional job. Furthermore, the scale of the impact of the studied determinants has been compared in the case of women and men. The study used the Bayesian logistic regression model.Wiele osób w Polsce oprócz posiadania podstawowego miejsca pracy podejmuje inną dodatkową pracę. Istnieje wiele potencjalnych czynników determinujących posiadanie dodatkowego zatrudnienia. Można do nich zaliczyć indywidualne potrzeby jednostek, takie jak chęć poprawy statusu materialnego, sytuację rodzinną, czy też możliwości wynikające z posiadanego kapitału ludzkiego. W niniejszym badaniu, oprócz powyższych uwzględniono ponadto takie cechy jednostek, jak: wiek, płeć, miejsce zamieszkania oraz charakterystyki podstawowego miejsca pracy. Niestety niektórych determinant badanego zjawiska nie można dobrze zaobserwować lub są one generalnie nieobserwowalne. W związku z tym szczególne znaczenie w modelowaniu tego typu zjawisk mają, wykorzystane w niniejszej pracy, modele z nieobserwowalną heterogenicznością. Celem niniejszego artykułu było pokazanie profilu demograficznego pracownika, który oprócz swojej głównej pracy, wykonywał jeszcze jakąś inną pracę. W toku przeprowadzonych badań dokonano oceny wpływu wybranych determinant na posiadanie dodatkowej pracy przez pracowników najemnych. Ponadto porównano skalę wpływu badanych cech w przypadku kobiet i mężczyzn. W badaniu wykorzystano bayesowski model regresji logistycznej

Fertility analysis of women in Poland using Bayesian Poisson regression model

Celem niniejszej pracy jest zbadanie zachowań prokreacyjnych Polek poprzez identyfikację czynników je determinujących z wykorzystaniem metod bayesowskich. W pracy zastosowano bayesowski model regresji Poissona. Wybrany model umożliwił określenie kierunku i skali wpływu wybranych czynników na liczbę dzieci posiadanych przez kobiety. Natomiast podejście bayesowskie dało możliwość włączenia do modelu informacji a priori oraz lepsze oszacowanie parametrów modelu. W estymacji wykorzystano metody Monte Carlo oparte na łańcuchach Markowa, a w szczególności próbnik Gibbsa. Badanie przeprowadzono na podstawie danych indywidualnych pochodzących z polskiego badania retrospektywnego „Przemiany rodziny i wzorce dzietności w Polsce” (1991). W analizie płodności kobiet uwzględniono następujące czynniki: miejsce zamieszkania, wykształcenie, fakt pozostawania w związku małżeńskim, zatrudnienie oraz wyznanie. Otrzymane rezultaty porównano z dotychczasowymi wynikami badań dla Polski i innych krajów.The primary objective of the work is to use Bayesian methods to investigate women fertility in Poland and identify key factors influencing it. Bayesian Poisson regression model has been used in the analysis. The model allows determining factors that have a significant impact on the number of children born. Moreover Bayesian approach makes it possible to incorporate a priori knowledge and improve the estimation of model parameters. The model has been estimated using Markov chain Monte Carlo method with Gibbs sampling. The work has been based on the Polish study ”Family changes and Fertility Patterns in Poland” (1991). The following attributes have been considered in the analysis of women fertility: place of living, education, marital status, employment and religion. The results have been compared with the results of related research for Poland and other countries