Koşullu ve Koşulsuz Kantil Regresyon Modelleri Türkiye’de Ücret Eşitsizliği Hakkında Farklı Ne Söylüyor?

Bu çalışmanın amacı, Türk işgücündeki ücret eşitsizliğini genelleştirilmiş Mincer ücret denklemi çerçevesinde, koşullu ve koşulsuz kantil regresyon yöntemleri ile tahmin ederek karşılaştırmalı olarak analiz etmektir. Bu amaç için 2018 Türkiye Hanehalkı Bütçe Anketi verileri analiz edilmiştir. Bu çalışma, Türkiye’de Mincer ücret denklemini koşulsuz kantil regresyon yöntemi ile inceleyen ve koşullu ve koşulsuz kantil regresyon tahmin sonuçlarını karşılaştırmalı olarak analiz eden ilk çalışmadır. Analiz sonuçlarına göre, koşullu kantil regresyon yönteminin ücret eşitsizliğini olduğundan daha az belirlediği, koşulsuz kantil regresyon yöntemine göre ise ücret eşitsizliğinin daha fazla olduğu belirlenmiştir. Sonuçlar, Türk işgücü piyasasında ücret eşitsizliğinin olduğunu ve ücret eşitsizliğinin ücret seviyesinin düşük olduğu kesimlerde daha fazla olduğu hakkında kanıtlar sunmaktadır.

What Do Conditional and Unconditional Quantile Regression Models Tell Us Something Different About Wage Inequality in Turkey?

This study comparatively analyzes wage inequality in the Turkish labor force by estimating the generalized Mincer wage equation with quantile regression methods using the 2018 Turkish Household Budget Survey data. This is the first study on wage inequality in Turkey that includes a comparative analysis of conditional and unconditional quantile regression methods. The results indicate that conditional quantile regression estimates wage inequality to be lower than it actually is. In contrast, in unconditional quantile regression, wage inequality is higher. The results further provide evidence of wage inequality in the Turkish labor market and suggest that wage inequality is higher in low-wage segments.

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