Addressing Potential Concerns with Cost of Living Measures
In Column 4, we add to the sample 40 women without observed wages whose spousal earnings fall between the 75
th
and 89
th
percentiles in the distribution of earn- ings. We continue to fi nd a positive and statistically signifi cant black wage premium.
Thus, we conclude that the black- wage premiums documented in the prior literature remain after accounting for selection. We now examine whether the estimated pre-
mium is robust to including two important omitted variables: cost of living and years of education.
Subsequent rows of Table 3 present wage gap estimates that also control for the cost of living in an area and a respondent’s years of education. In Table 1, we showed that
blacks live in CZs characterized by higher cost of living, whether we consider mean housing rents or higher wages paid to the least mobile occupations. We now present
estimates that control for either of these two measures of cost of living. This is similar to the approach that DuMond, Hirsch, and Macpherson 1999 took for individuals
residing in a MSACMSA. In Panel 2a of Table 3, we add a control for the average monthly rent in the CZ where the respondent lives. We fi nd that the black wage pre-
mium estimate falls substantially in all specifi cations Columns 1–4.
As Lang and Manove 2011 show, relative wages of black workers are overstated when we control for AFQT score but not years of education. In the third results row
of Table 3, we control for years of education in addition to local costs of living. The additional control for education completely erases the estimated black wage premium.
For the OLS results in Column 1, the coeffi cient on black turns slightly negative a wage penalty, although the conditional racial wage gap is essentially zero. The same
result holds in our median regression estimates that account for selection in Columns 3 and 4: Controls for cost of living and education in addition to quadratics in age
and AFQT score yield no evidence of a black wage premium. We note that the 95 percent confi dence intervals for the conditional wage gap are somewhat large, and
we are unable to reject substantial wage premiums as high as 0.087, in Column 2 or wage penalties as low as –0.089, in Column 4. Nevertheless, these specifi cations
show clearly that the very large black wage premiums estimated in prior studies are not at all robust to reasonable controls for cost of living and education.
In Panels 3a and 3b of Table 3, we show that results are similar when we instead control for cost of living with a measure of average wages in “low- mobility” occupa-
tions. Differential cost of living for black and white women explains away a large share though not all of the estimated black wage premium. When we also control for
a respondent’s years of education, we again fi nd no evidence of a wage premium.
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