Returns to Education in Pakistan

There is an extensive empirical literature on returns to education that focuses both on developed and developing countries. Available literatures in developing countries compare the returns to academic education and vocational education [Nasir and Nazil (2000)], or seek to identify the impact of completing a given schooling cycle on earnings [Appleton (2001)]. The aim of this study is to contribute the literature by conducting a systematic analysis on returns to education and education inequality in Pakistan. In particular it asks to what extent inequality for different level of education vary across the wage distribution. In order to address simultaneously the two issue of return to education and education inequality, study adopt a quantile regression framework. A characteristic of the wage and salary structure of most countries is that people with more education tend to receive higher remuneration than those with less [Colclough (1982)]. To do so, the paper has used data drawn from Labour Force Surveys, conducted by Government of Pakistan for the time period between 1990 and 2003, which contains eight different surveys, using methodology developed by Agrist, et al. (2006), where weighted least squares interpretation of Quantile Regression is used to derive an omitted variables bias formula and a partial quantile regression concept, similar to the relationship between partial regression and OLS. Estimation uses personal and household characteristics, occupational and employment characteristics in order to assess the education inequality. Empirical estimates indicate that education inequality is much higher for the middle level educates compare to educate that has less education or high level education and qualifications. The education level coefficients decrease when different sets of exogenous variables are introduced in the estimation equation. Analysis also suggests the existence of the education inequality across different areas and regions and over the time it has increased.