work. Women are paid less, on average, and yet may have an opportunity cost of going back to work that is at least as high as for men. It is also possible that women
are more responsive to changes in wages Kahn and Griesinger 1989; Sicherman 1996; Galizzi 2001. As a result, once they realize that the injury is leading them
to jobs with lower wages or lower wage growth, they may be more likely to take longer to return to work or to leave the labor force.
Alternatively, on the demand side of the market we have already noted that em- ployers may be more willing to employ or reemploy men after their injuries because
of discriminatory beliefs or because employers can obtain better predictions of men’s skills or mobility behaviors. Also, women may be easier to replace, once they are
off work because of an injury, because of their average lower accumulation of job specific human capital. At the same time, it is possible that women’s jobs are more
flexible, allowing women to work fewer hours post injury.
A. Wage Decomposition
As a first step toward understanding gender disparities in losses, we examine the extent to which observed personal, job, employer, and injury characteristics account
for these disparities. To do this, we apply an extension of the Oaxaca-Blinder decom- position Oaxaca 1973; Blinder 1973.
Neumark’s 1998 general version of the decomposition can be written as: 4
W ¯
m
⫺ W
¯
f
⫽ X¯
m
⫺ X
¯
f
b ⫹ [X¯
m
b
m
⫺ b
⫺ X¯
f
b
f
⫺ b
] Where W
¯
m
and W ¯
f
are gender-specific average wages, X¯
m
and X¯
f
are vectors of aver- age characteristics, b represents the nondiscriminatory wage structure referring to
the pooled male-female labor force, while b
m
and b
f
are gender-specific wage struc- tures. The first term in Equation 4 represents the portion of the male-female wage
disparity that is due to differences in characteristics. The second term, in the square brackets, is the portion of wage differentials unexplained by differences in character-
istics, and often is used to measure discrimination. The first part of the term in brackets represents the male advantage ‘‘nepotism’’ compared with the nondiscriminatory
wage structure. Similarly the second part of that term represents the female disadvan- tage ‘‘discrimination’’. Although these and related measures often have been used
as a measure of employer gender discrimination, they also could reflect gender differ- ences in worker responses to injury or the impact of unmeasured covariates.
As Neumark 1988, Oaxaca and Ransom 1994, and others have pointed out, the decomposition is not unique and, indeed, there are an infinite number of possible
decompositions, each depending on a model of employer discrimination that gener- ates a nondiscriminatory wage. Moreover, each decomposition may produce a differ-
ent estimate of the proportion of the wage differential explained by differences in characteristics. We choose here to follow Neumark’s model, which derives the non-
discriminatory wage structure as the consequence of employers’ utility deriving in part from the gender composition of the labor force they employ. Based on the as-
sumption that the employers’ utility function in homogenous of degree zero in male and female labor inputs, Neumark shows that the nondiscrimination wage structure
is estimated by wage regression on the pooled male and female labor force. This is the approach that we use to study post-injury losses.
We adapt the Oaxaca-Blinder-Neumark method to decompose injury related losses. Therefore, we apply it to injury-related losses estimated from Equation 3, and not to
wages. We allow the loss coefficients to vary in the quarter of injury and in each of the four subsequent quarters and to have a long-term trend. Therefore, our new specification
differs from Equation 4 in having the gender difference in losses as the dependent variable and in allowing losses to change with time after injury:
4a L¯
mk
⫺ L¯
fk
⫽
冱
k⫽ 1,5
F
k
{X¯
mk
⫺ X
¯
fk
b
k
⫹ [X¯
mk
b
mk
⫺ b
k
⫺ X¯
fk
b
fk
⫺ b
k
]} Here, L
k
is the estimate of the injury-induced change in earnings in period k relative to the quarter of injury derived from the results of Table 3; X¯
k
is a vector of mean characteristics in period k, and b
k
is a vector of coefficients for the period k relative to the injury. Of course, the m and f subscripts represent male and female. Variables
without subscripts refer to the entire population. As in Equation 3, the impact of injury on wages in a given quarter is the sum of the F
k
for that quarter and the preceding ones.
B. Results of Decomposition
