The first three columns of table 1 show the estimated correlations from regressing observed wages on potential wages using a modified version of equation (1), which omits the unnecessary cohort controls. The estimates show sizable correspondences between potential wages and observed wages that are precisely estimated. The coefficients are 0.426 ($p<0.05$) for women, 0.481 (male, $p$$<$ 0.01) for men, and 0.833 ($p$$<$ 0.01) for the relative wage. In appendix A.4 I show that the addition of variation by occupation increases the magnitude of this correlation four-fold and that updating reduces the estimated standard errors by 10%.15 I also show that the results are robust to using a more succinct list of occupations, using alternative definitions of the marriage market or using Census wages.

Table 1.

Relationship between Potential Wages and Observed Wages

Correlation with ActualCross-Effects?
RelativeFemaleMaleFemaleMale
ln Rel. Wage (Potential) 0.833***
(0.225)
ln Female Wage (Potential)  0.426**  0.571*** −0.232
(0.202)  (0.186) (0.255)
ln Male Wage (Potential)   0.481*** −0.228 0.613***
(0.156) (0.277) (0.203)
Partial $R$-squared 0.067 0.039 0.041
Observations 1,064 1,064 1,064 1,064 1,064
Correlation with ActualCross-Effects?
RelativeFemaleMaleFemaleMale
ln Rel. Wage (Potential) 0.833***
(0.225)
ln Female Wage (Potential)  0.426**  0.571*** −0.232
(0.202)  (0.186) (0.255)
ln Male Wage (Potential)   0.481*** −0.228 0.613***
(0.156) (0.277) (0.203)
Partial $R$-squared 0.067 0.039 0.041
Observations 1,064 1,064 1,064 1,064 1,064

This table shows the coefficients from estimating equation (1), omitting cohort controls. The dependent variable is shown in the column heading. Standard errors are clustered at the state level, and cells are weighted by the female population in the cell. $*$$p$$<$ 0.10, **$p$$<$ 0.05, and ***$p$$<$ 0.01.

Sources: Potential wage: 1970 Decennial Census, 1980–2011 March CPS, Wages: 1980–2000 Decennial Censuses, 2010 ACS.

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