Wage and productivity gaps - evidence from Ghana

The author uses a unique data set (combining information about individual workers with information about the firms employing them) to jointly estimate production functions and wage equations. This approach allows her not only to assess the marginal impact on wages of demographic and other characteristics but also to compare how these variables affect productivity among various groups of workers. Among her findings: 1) Female employees are paid less than male employees, but this negative wage premium does not reflect commensurately lower productivity. 2) Employees'experience is reflected equally in wages and in productivity differentials over the worker's life cycle. Wages and productivity both increase, but at a decreasing rate. 3) The more training and education workers have, the higher their wages and the greater their productivity. 4) Productivity differences can be demonstrated for five levels of education completed. The productivity gap is greater than the wage gap. 5) Returns to education are similar across gender, sector, and level of unionization, but they are lower for unskilled workers than for skilled workers. 6) Training supplied by outside providers (as opposed to in-house training) is associated with higher wages but appears to have no (immediate) impact on productivity. 7) Trade union members'wages are in line with productivity. Both wages and productivity are higher for union members than for non-union members.