Ethnicity and sheepskin effects in the returns to education in Sri Lanka: A conditional quantile analysis

Purpose – The main purpose of this paper is to investigate the sheepskin effects in the returns to education in the Sri Lankan labor market by ethnicity. Design/methodology/approach – The study is based on the latest Sri Lankan Consumer Finance and Socio-economic Survey. The study employs the quantile regression method for each conditional quantile wage group, rather than the mean regression analysis used in most labor market analysis. The quantile regression technique fits hyperplanes through out the conditional wage and is ideal for characterizing the entire wage distribution. The standard Mincerian wage equation was estimated for the full sample of male workers and separately for the two main ethnic groups in Sri Lanka. Findings – The empirical findings are broadly encouraging. Quantile regression results suggest that average returns to education for both ethnic groups differs significantly from the returns at the two extreme ends of the wage distribution. In general, the returns to education are positive for both groups, but the returns are higher for Sinhalese workers than for Tamil. An increasing trend in returns to education is evident for both ethnic groups when moving up wage distribution. Sinhalese workers experience higher returns to education than for Tamil especially at the bottom of the wage distribution, but the difference becomes less at the upper part of the distribution. Estimated results with spline in years of education suggest that returns to secondary education are higher for Sinhalese workers, but the returns to tertiary education are greater for Tamil workers at the upper part of the wage distribution. Findings indicate that returns to experience are also higher for Sinhalese workers than for Tamil workers. Originality/value – This is the first study that examines sheepskin effects in the returns to education by ethnicity in Sri Lanka in a Mincerian framework, employing quantile regression models.