This paper examines change in wage gaps in urban China from 1988 to 2008 by estimating quantile regressions on CHIPS data. It applies the Machado and Mata (2005) decomposition, finding sharp increases in inequality largely due to changes in the wage structure. During 2002–08, changes in the...

This study examines the determinants of educational outcome in eight selected MENA countries. The complicated structure of the TIMSS data has been considered carefully during all the stages of the analysis employing plausible values and jackknife standard error technique to accommodate the...

This paper investigates gender inequality of academic achievement using mean and quantile decomposition analysis in eight selected MENA countries. We use data from TIMSS 2007 to decompose the test scores gap between boys and girls at the eighth grade. There is a mixed picture of gender...

How rapidly will child undernutrition respond to income growth? This study explores that question using household survey data from 12 countries. In addition, data on the undernutrition rates since the 1970s available from a cross-section of countries are employed in this investigation. Both...

How rapidly will child undernutrition respond to income growth? This study explores that question using household survey data from 12 countries. In addition, data on the undernutrition rates since the 1970s available from a cross-section of countries are employed in this investigation. Both...

Girls completing primary schooling in Cote d'Ivoire are less likely to go to secondary schools because of inferior performance in the examination used to ration access. The gender gap in educational achievement varies with household economic status, with poverty inhibiting girls' performance...

Purpose – Penn World Tables (PWT) data on output measured at international prices are the data most frequently used in cross‐country growth regressions. These data are subject to revision, and the amendments can be substantial for a minority of countries, although negligible for most. The...