An Evaluation of Wage Subsidy Employment Program in Albania Cover Image

An Evaluation of Wage Subsidy Employment Program in Albania
An Evaluation of Wage Subsidy Employment Program in Albania

Author(s): Arjeta Vokshi, Eda Spahiu
Subject(s): Economy, Business Economy / Management, Socio-Economic Research
Published by: Институт за икономически изследвания при Българска академия на науките
Keywords: active labour market programs; Logistic Regression (GLMNET); Decision Tree model; employment; gender

Summary/Abstract: There has been a long debate over the role of Active Labour Market Policies (ALMPs) in reducing unemployment. Numerous empirical studies have tried to evaluate the impact of these policies on employment, using both micro econometric and macro econometric approaches. Many countries spend a considerable part of their budgets investing in Employment Promotion Programs, which represent one of the main types of ALMPs. The success of Employment Promotion Programs in general depends on several factors, such as the broad economic situation, the duration of the program and the program type. This paper investigates the impact of the wage subsidy program on the employment outcome of its participants in Albania. This is done using a parametric logistic model and a non-parametric model, and by comparing the two models in terms of variable importance. The models have been applied to the latest database available of program participants from the National Agency for Employment and Skills. Both models take into consideration the gender variable and interpret the results accordingly. The Decision Tree model demonstrated better performance compared to Logistic Regression on evaluation metrics. The gender variable remains almost equally significant in both models. In the Albanian literature, there is a gap related to ALMP evaluations in general. This study contributes to the empirical evaluation of ALMPs in Albania, adding to the general analysis a gender perspective through the incorporation of a gender variable in the statistical regressions.

  • Issue Year: 2026
  • Issue No: 7
  • Page Range: 106-118
  • Page Count: 13
  • Language: English
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