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3.3. Sensitivity analysis
The results are robust to controlling for household non-labor income (total family
income minus the labor income of each spouse), and to the exclusion of the few
observations from the “poverty/SEO sample” or the “immigrant sample.” We also
perform our estimation restricting our sample to couples where the wife is working;
here too, results yield the same pattern of associations between the body size and the
age and the educational level of each spouse. Including dummy variables for
occupational categories (professional-managerial; service; sales; agriculture; crafts;
transportation; or military) of the husband, or for each spouse when restricting the
sample to dual-earner couples, to account for on-the-job physical activity, does not alter
our main findings either. The same can be said when controlling for parental education
of both husbands and wives to account for family background characteristics. Finally,
we also consider black couples. We still find comparable correlations between spouses’
BMIs and penalties for women’s weight, even though imprecisely estimated. However,
our estimates should be interpreted with caution, as in the PSID blacks are
disproportionately over-represented in low-income households (“poverty/SEO
sample”).
Since estimating averages of the wife’s BMI conditional on several
characteristics is not necessarily informative about the tails of the BMI distribution, we
also explore the determinants of the likelihood of being obese or underweight for both
wives and husbands in Tables 9 and 10. First, we replicate the same pattern of
regressions while replacing each spouse’s BMI with obesity; the obesity variable takes
the value 1 if the individual is obese and 0 otherwise. The results closely match those
reported in Tables 3 and 4. Second, we construct an underweight indicator that takes the
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