JOURNAL OF APPLIED MICROECONOMETRICS <p><strong>The Journal of Applied Microeconometrics (ISSN: 2791-7401)</strong> is a peer-reviewed open access journal covering any issues in theoretical and applied microeconometrics. The journal also covers quantitative research in microeconomics. Journal of Applied Microeconometrics aims to serve as a platform for high quality research in applied microeconometrics. The scope of the Journal includes any papers dealing with identification, modelling, estimation, testing and prediction issues encountered in the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for microeconometric data i.e. cross sectional data, repeated cross- sectional data, pool data, cohort and panel data etc. The journal also accepts case study articles written for both developing and developed countries. The language publication of the journal is <strong>English. </strong>It is published<strong> 2 times</strong> a year as SUMMER (June) and WINTER (December) periods.</p> HOLISTENCE PUBLICATIONS en-US JOURNAL OF APPLIED MICROECONOMETRICS 2791-7401 Returns to education: Empirical evidence from Kyrgyzstan <p>The aim of this study is to identify the returns to education in Kyrgyzstan, with special reference to employment type and gender differences. The empirical analysis of this study is based on Life in Kyrgyzstan (LiK) survey data collected in 2016. The sample for analysis is constructed with employees and self-employed persons aged 18-65, who indicated their monthly income from employment. According to the empirical outputs, there is a wage premium for higher education such that the marginal return to education for women is higher than men.</p> Burulcha Sulaimanova Copyright (c) 2021 JOURNAL OF APPLIED MICROECONOMETRICS 2021-12-29 2021-12-29 1 2 73 84 10.53753/jame.1.2.01 Customer churn analysis in banking sector: Evidence from explainable machine learning models <p>Although large companies try to gain new customers, they also want to retain their old customers. Therefore, customer churn analysis is important for identifying old customers without loss and developing new products and making new strategic decisions for retaining customers. This study focuses on the customer churn analysis, that is a significant topic in banks customer relationship management. Identifying customer churn in banks will helps the management to classification who are likely to churn early and target customers using promotions, as well as provide insight into which factors should be considered when retaining customers. Although different models are used for customer churn analysis in the literature, this study focuses on especially explainable Machine Learning models and uses SHapely Additive exPlanations (SHAP) values to support the machine learning model evaluation and interpretability for customer churn analysis. The goal of the research is to estimate the explainable machine learning model using real data from banking and to evaluate many machine learning models using test data. According to the results, the XgBoost model outperformed other machine learning methods in classifying churn customers.</p> Hasraddin Guliyev Ferda Yerdelen Tatoğlu Copyright (c) 2021 JOURNAL OF APPLIED MICROECONOMETRICS 2021-12-29 2021-12-29 1 2 85 99 10.53753/jame.1.2.03 Income inequality-labor productivity relationship: CS-ARDL approach <p>With the acceleration of globalization, “Reducing Inequalities”, which is the 10th of the sustainable development goals, has started to attract more attention in the world. Many factors lead to inequality. Therefore, inequality requires consensus and strength at the interdisciplinary, local, national, and international levels. The leading indicator of inequality is income inequality. Its measurability and widespread impact are sources of its importance and priority. Unfair income distribution might have unfavorable effects on employees such as being more reluctant to work and the well-being of workers. In addition, if workers believe they earn less than they deserve, this might negatively affect the labor productivity. Ultimately, this process may cause countries to reduce their production output.</p> <p>This study aims to explore the link between income inequality and labor productivity among 31 countries in Europe with the period of 2005-2019. To do this, a cross-sectional auto-regressive distributed lag model (CS-ARDL) is employed. According to the results, wage inequality damages the productivity of labor. A 1% increase in the wage inequality reduces labor productivity by 0.16%. Moreover, the unequal income distribution has an explanatory power of approximately 33% on the decrease in productivity. This helps to determine the possible effects of the unequal income distribution leading towards two targets. These targets are to create an efficient wage structure and eliminate the destructive effects of inequality, respectively. In terms of the policy effectiveness, simultaneous application of tools may be more beneficial.</p> Hilal Bağlıtaş Copyright (c) 2021 JOURNAL OF APPLIED MICROECONOMETRICS 2021-12-29 2021-12-29 1 2 101 111 10.53753/jame.1.2.02