The origin of the wage function (13.11.1) can be traced to Jacob Mincer.46 As you can see, the wage function includes quantitative as well as qualitative or dummy variables. A priori, all these variables seem logical. Notice that the race variable has three categories: Hispanic, non-Hispanic whites, and non-Hispanic nonwhites (largely black or African-American); hence, there are two dummies. The left-out or reference category thus is non-Hispanic whites.

The data consist of 528 persons interviewed in 1985 as a part of the current population survey (CPS) periodically conducted by the U.S. Census Bureau. These data were originally collected by Berndt and were adapted by Arthur Goldberg. We have already discussed this source in Chapter 2. Keep in mind that the data are cross sectional.

A priori, hourly wage is expected to be positively related to education, life experience, marital status and union status and negatively related to Hispanic, race, gender, and region; again note that all comparisons are in relation to non-Hispanic whites. Consult any book on labor economics to learn more about the various determinants of hourly wages.47

Using the data, I asked my students to estimate the model (13.11.1). The regression results are given in Table 13.4. As you can see, all the variables in (13.11.1) have the expected signs, although not all variables are individually statistically significant. The R2 value of about 0.2826 might seem low, but such low R2 values are typically observed in cross-sectional data with a large number of observations. But this R2 value is statistically significant, since the computed F value of about 25.56 is highly significant, as its p value is almost zero: Remember that the F statistic tests the hypothesis that all the

46See J. Mincer, School, Experience and Earnings, Columbia University Press, New York, 1974.

47See, for example, George Borjas, Labor Economics, 2d. ed., McGraw-Hill, New York, 2000.



Dependent Sample: 1

Variable: HWAGE 528

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