Secondly, existence of a systematic relationship between any of the independent variables such as schooling and the error term in an Ordinary Least Squares regression will lead to bias in the estimates. In this case, effects of ability/heterogeneity must be random in the sample and normally distributed to avoid positive correlation (between schooling and ability) leading to a higher than estimated return to education. Card explored the relationship between education and earnings, and explicitly analysed the heterogeneity between schooling of twins in contrast to their earnings. The assumption in the study was that twins would have the same ability and other external influences so that differences in wages could be more accurately associated with differences in education. Card used a pooled sample of 198,075 men and women aged from 16 – 66 during the years 1994 to 1996. The study targeted the twins having 10, 12 and 16 years of schooling, and the earnings differences between them. An Ordinary Least Squares regression analysis was used to inspect the human capital return with hourly, weekly and annual earnings as alternative variables. The study findings explored an interesting impact of an instrumental factor family background (Altonji) on the schooling and earning of their children which had 30% variation in the earnings, similarly college education differences and location near college or university had some significant influence over schooling and hence earnings. Birth season was only an insignificant instrumental factor (Card). The Ordinary Least Squares estimator results by Card suggested a difference of around 10% in the...
This study has analysed returns to education in the UK. This was done using the BHPS data from 1991-2008 by using an OLS regression model adapted from previous studies. The dependent variable was logarithm of hourly wage while the predictor variable tested was years of schooling, controlling for other factors. The control variables were age, sex, race and region. Diagnostic tests was conducted to check for heteroskedascity and also the presence of serial correlations and both were found not to affect the model hence the regression was run.
The analysis shows that the number of years in schooling had a positive and significant effect on the logarithm of hourly wage (about 8% per year of schooling) which suggests that there were positive returns to education in the sample. This is consistent with a number of studies that have analysed the returns to education. From the R-squared results, the model accounted for 24% of the variance in hourly wages. Thus, the regression did not explain most of the variance in hourly wages but points to the fact that the number of years of schooling is a good predictor of returns in education.