regional dummy variables, it has a strongly significant quadratic impact on reading and mathematics scores, the impact being stronger for those with higher school attainment" (p.380). These results, showing the significance of the interaction terms, reveal that schooling years work in conjunction with other factors. The interaction with Raven's score shows that more talented individuals benefit more from school years-as judged by the labor market. The interaction with age captures the secular decline in the quality of education. Persons who attended school many years ago were able to enhance their earnings per year of schooling more than recent cohorts. The result that the regional dummies, which are insignificant in themselves, show strong and significant variation in the attainment of reading and mathematical skills across regions, when interacted with years of schooling, point to the importance of regional difference in the quality of schools.

The total effect of education on earnings were calculated from theses estimated equations as shown in equation (3). The rate of return to school years, calculated at the mean value of both schooling and Raven's score was higher in the public but no longer zero in the private sector. In the former they ranged between 5.4 percent and 6.2 percent, and in the latter between 3.4 and 6.3. As already noted, the rates of return rise with age are higher in Accra reflecting the higher school quality in the capital city at all age groups.

# **5.3 Family Background, Private-Public Choice and Returns to Education**

Canagarajah and Liang (1996) address a different set of issues in the study of private-public differences in the returns to education. They consider the labor market all together, including the self-employed, the private sector employees, and those employed in the public sector. We can estimate separate earnings functions for these three categories, but the resulting estimates might be biased because we are not allowing for the selectivity factors which govern the allocation of the workers to the t-hree types of employment.

The authors propose. a model in which parents' occupational and educational backgrounds affect the individual's relative employment opportunity in the different types of job. The selection equation is a multinomial logit estimate of sectoral choice in which the explanatory variables are education and age of the individuals, as well as the parental background factors, and regions of the country. The equation applied to the 1991-2 GLSS data set provided estimates of the expected sign for almost all the variables. Thus middle school and above education helped job-seekers enter the wage sector as opposed to self-employment, as did different kinds of diplomas and certificates from training. Using Accra as a benchmark, residence in other regions decreases the probability of entering the public sector, but increases the probability of selfemployment.

Family background had a small but significant effect on the job-seekers' sectoral choice. In particular the parents' white collar occupation-clerical, professional or managerial-increased the probability of public employment. When the earnings 33