Recently, in most developed economies, the average age of the workforce has been growing rapidly. Therefore, the questions arise how will it affect the level of wages and the shape of age-productivity and age-wage profiles. The aim of the paper is to analyse the relationship between changes in the age structure of the employment and wages of individuals in minor occupational groups. Using individual data from the Structure of Earnings Survey in Poland in 2006-2014 we created an unique database of individual wages and the characteristics of employed in occupational groups at 3-digit level of classification. In our analysis we used an extended version of Mincerian wage model where both the characteristics of employees (education, work tenure, age, gender, and type of employment contract) and employers (size and ownership sector) were taken into account. The results for the whole sample indicate a significant and negative relationship between the proportion of older workers in employment in a given occupational group and individual wages. However, when the analyses were performed separately for each of the 1-digit occupational groups, the results varied significantly. In those groups where knowledge and qualifications of employees are more important than physical strength had to be updated permanently, an increase in the number of the older workers raises the average wages.
The paper makes a comparison of the results of the application of two-sided and one-sided versions of the Hodrick-Prescott filter on GDP data concerning 27 EU Member States. Based on the results, the overall finding is that, contrary to its assumed advantages, the one-sided filter does not help overcome endpoint unbiasedness. Quite the opposite, it rather spreads and consolidates the endpoint bias that plagues the two-sided version over the entire filtered data. In addition, regression-based results on the influence of the second, third, and fourth moments of the GDP acceleration rates on the differences between onesided and two-sided HP trends are presented.
The purpose of this empirical study is to find the relationship between economic growth and foreign direct investment (FDI) in the Commonwealth of Independent States (CIS) and Central and Eastern European Countries (CEECs) using endogenous technological change model. First, we combine the CIS and CEECs into one group to test our hypothesis, and then we test each group separately to account for heterogeneity and draw a conclusion whether FDI is indeed a driving force of the economy. Panel data have been used from 2003 to 2014 and different panel estimation methods have been applied. Additionally, we use the Generalized Method of Moments (GMM) panel estimator to control for endogeneity problem. The present study finds that FDI is an important factor explaining economic growth in the pooled group and CEECs, although it is not significant in the case of CIS.
The paper presents two algorithms as a solution to the problem of identifying fraud intentions of a customer. Their purpose is to generate variables that contribute to fraud models’ predictive power improvement. In this article, a novel approach to the feature engineering, based on anomaly detection, is presented. As the choice of statistical model used in the research improves predictive capabilities of a solution to some extent, most of the attention should be paid to the choice of proper predictors. The main finding of the research is that model enrichment with additional predictors leads to the further improvement of predictive power and better interpretability of anti-fraud model. The paper is a contribution to the fraud prediction problem but the method presented may generate variable input to every tool equipped with variableselection algorithm. The cost is the increased complexity of the models obtained. The approach is illustrated on a dataset from one of the European banks.
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