away from work toward shorter durations for these injuries. Alternatively, those with more severe cases of these injuries may not be able to perform any kind of work.
This pattern in restricted work usage was consistent with the relative severity of fractures and carpal tunnel cases with median work loss of 21 and 34 days, respec-
tively, relative to sprains and strains with a median work loss of six days.
Table 1 shows that shorter state waiting periods to qualify for income replacement are associated with higher use of restricted work. States with ve- or seven-day
waiting periods were less likely to observe restricted workdays than states with three- day waits. Since states with shorter waiting periods were more likely to have com-
pensable injuries, this pattern suggests that rms may be using restricted work to control workers’ compensation costs.
A. Probability of restricted workdays
Tables 2–5 present the results from the rst-stage reduced-form estimation of the probability of receiving restricted workdays, for each of the four injury categories
complete results are available from the authors on request. We believe that higher benets will increase employers’ incentives to offer restricted work schedules to a
larger pool of injured workers than they might otherwise have especially for hard- to-observe injuries.
17
In addition, they may prompt large, experience-rated employers to offer restricted workdays more often so as to avoid any premium hikes resulting
from having more compensable work loss. We examine this issue in more detail by estimating models separately for large and small rms.
To facilitate comparison with other research using workers’ compensation data, we also present results for a smaller sample of potentially compensable cases whose
days away from work are longer than their state waiting periods. Note that while our theoretical model referred to the income replacement ratio, we separate out the
components of this ratio, the weekly temporary disability benet TTDBEN and the net weekly wage in our empirical work to allow these two variables to have different
effects.
Controlling for differences in the waiting period and worker, injury, and establish- ment characteristics, higher weekly benets were positively related to the probability
of working on a restricted schedule for sprains, strains, and carpal tunnel cases though the effect was strongest for back sprains. For fractures, higher benets re-
duced the probability of restricted work but this effect was statistically insignicant. The effect of weekly benets was larger for compensable cases of all injuries except
other sprains.
18
Controlling for differences in weekly benets, longer waiting periods signicantly reduced the likelihood of receiving restricted workdays for all the injury types includ-
ing fractures, and for small and large rms. This was consistent with employers’ efforts to control their workers’ compensation costs by reducing the likelihood that
their injured workers would qualify for benets. These efforts would be more likely in states with shorter waiting periods that made it easier for injuries to qualify for
17. Note that in our data, the weekly benet has been calculated by state, age, occupation, and sex, and may therefore vary even among workers in the same state.
18. This can be seen more easily from the top panel of Table 7.
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Table 2 Univariate Probit Estimates of Restricted Workday Provisions—Back Sprains
a
Standard errors in parentheses 250 or Fewer
More than 250 Compensable
All Workers
Workers Cases
N 5 1,1524 N 5 6,771
N 5 4,753 N 5 6,151
Variable 1
2 3
4 Average reported employment
,512 0.522 0.105
0.454 0.108 —
0.579 0.134 13–50
0.093 0.051 0.068 0.052
— 0.117 0.068
51–100 20.009 0.043
20.016 0.043 —
0.075 0.058 251–500
0.037 0.040 —
— 0.079 0.055
501–1,000 0.176 0.049
— 0.115 0.052
0.185 0.067 1,001–2,500
0.274 0.057 —
0.214 0.061 0.253 0.078
2,501–30,000 0.182 0.078
— 0.111 0.083
0.065 0.107 State union rate
23.242 0.335 22.860 0.449
23.626 0.514 22.962 0.471
Days-away-from-work rate 21.625 0.227
21.269 0.249 23.573 0.568
21.415 0.311 Log activity
0.097 0.062 0.090 0.079
0.122 0.100 0.019 0.084
State waiting period 20.068 0.008
20.063 0.010 20.077 0.012
20.054 0.010 TTDBEN
100 0.223 0.044
0.235 0.060 0.207 0.066
0.262 0.060 Average net weekly wage
20.101 0.034 20.091 0.046
20.123 0.051 20.132 0.047
a. Other covariates include age, sex, race, tenure, event and body part of injury, 13 occupation groupings, and detailed industry categories.
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Table 3 Univariate Probit Estimates of Restricted Workday Provisions—Carpal Tunnel Syndrome
a
Standard errors in parentheses 250 or Fewer
More than 250 Compensable
All Workers
Workers Cases
N 5 6,947 N 5 2,533
N 5 4,414 N 5 6,194
Variable 1
2 3
4 Average reported employment
,512 0.587 0.291
0.319 0.302 —
0.531 0.294 13–50
20.052 0.100 20.137 0.104
— 20.025 0.105
51–100 20.174 0.067
20.201 0.068 —
20.182 0.071 251–500
0.114 0.045 —
— 0.122 0.048
501–1,000 0.178 0.049
— 0.061 0.049
0.201 0.053 1,001–2,500
0.252 0.058 —
0.139 0.058 0.247 0.062
2,501–30,000 0.168 0.078
— 0.059 0.079
0.124 0.084 State union rate
22.221 0.394 21.245 0.673
22.805 0.497 22.330 0.420
Days-away-from-work rate 23.406 0.432
22.175 0.532 25.589 0.736
22.955 0.447 Log activity
0.177 0.094 0.058 0.144
0.289 0.129 0.173 0.099
State waiting period 20.066 0.009
20.059 0.015 20.071 0.012
20.069 0.009 TTDBEN
100 0.089 0.060
0.172 0.106 0.026 0.074
0.124 0.065 Average net weekly wage
20.054 0.047 20.065 0.083
20.031 0.058 20.052 0.051
a. Other covariates include age, sex, race, tenure, event, and body part of injury, 13 occupation groupings, and detailed industry categories.
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Table 4 Univariate Probit Estimates of Restricted Workday Provisions—Other Sprains
a
Standard errors in parentheses 250 or Fewer
More than 250 Compensable
All Workers
Workers Cases
N 5 11,208 N 5 6,459
N 5 4,749 N 5 5,882
Variable 1
2 3
4 Average reported employment
,512 0.283 0.109
0.217 0.112 —
0.332 0.144 13–50
0.119 0.059 0.089 0.055
— 0.145 0.072
51–100 20.012 0.045
20.025 0.046 —
20.038 0.063 251–500
0.059 0.041 —
— 0.110 0.056
501–1,000 0.099 0.049
— 0.027 0.053
0.167 0.066 1,001–2,500
0.131 0.055 —
0.078 0.061 0.250 0.074
2,501–30,000 0.161 0.076
— 0.085 0.081
0.251 0.104 State union rate
23.287 0.349 23.486 0.479
22.985 0.522 23.276 0.493
Days-away-from-work rate 21.919 0.234
21.563 0.259 23.313 0.547
21.693 0.306 Log activity
0.076 0.059 0.085 0.081
0.034 0.088 0.214 0.079
State waiting period 20.063 0.008
20.066 0.012 20.060 0.012
20.052 0.011 TTDBEN
100 0.079 0.046
0.045 0.062 0.115 0.070
0.039 0.062 Average net weekly wage
20.027 0.035 20.015 0.046
20.036 0.053 20.023 0.048
a. Other covariates include age, sex, race, tenure, event, and body part of injury, 13 occupation groupings, and detailed industry categories.
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Table 5 Univariate Probit Estimates of Restricted Workday Provisions—Fractures
a
Standard errors in parentheses 250 or Fewer
More than 250 Compensable
All Workers
Workers Cases
N 5 6,448 N 5 4,154
N 5 2,294 N 5 4,761
Variable 1
2 3
4 Average reported employment
,512 0.452 0.112
0.378 0.116 —
0.374 0.131 13–50
0.152 0.059 0.119 0.060
— 0.155 0.069
51–100 20.002 0.056
20.014 0.057 —
0.014 0.067 251–500
0.108 0.054 —
— 0.101 0.065
501–1,000 0.167 0.069
— 0.041 0.074
0.191 0.083 1,001–2,500
0.178 0.080 —
0.076 0.087 0.107 0.098
2,501–30,000 0.350 0.118
— 0.246 0.125
0.357 0.138 State union rate
22.470 0.439 22.945 0.578
21.576 0.700 22.659 0.536
Days-away-from-work rate 22.368 0.324
21.971 0.348 24.479 0.862
22.032 0.375 Log activity
0.165 0.073 0.108 0.091
0.267 0.134 0.185 0.086
State waiting period 20.064 0.010
20.058 0.013 20.076 0.017
20.073 0.012 TTDBEN
100 20.017 0.054
0.067 0.069 20.130 0.091
0.034 0.065 Average net weekly wage
0.009 0.039 0.003 0.049
20.014 0.066 0.001 0.048
a. Other covariates include age, sex, race, tenure, event, and body part of injury, 13 occupation groupings, and detailed industry categories.
workers’ compensation. The persistence of these strong negative effects for compen- sable cases in Column 4 may be more reective of the greater severity of such cases
in states with longer waiting periods, making any kind of return to work less likely for them.
The coefcients on establishment size in Column 1 of Tables 2–5 show that work- ers in rms with more than 250 employees were signicantly more likely to have
restricted workdays than smaller rms, with larger effects for larger rms. Firms with less than 12 employees appeared to be similar to larger rms in their use of
restricted work. These size effects may reect the effect of experience rating in the workers’ compensation system that links larger rms’ premiums more closely with
their actual injury experience. Thus, larger rms would be more inclined to use restricted work to reduce the size of their workers’ compensation claims. Large rms
may also be more likely to have light duty jobs that lend themselves easily to re- stricted work arrangements. The substitution of restricted work for work-loss days
also may be common for very small rms who cannot afford to hire and train tempo- rary replacement workers.
We explore differences by rm size more fully in Columns 2 and 3 where we present estimates from separate models for rms with less or more than 250 employ-
ees. Weekly benets were statistically signicant for small rms in carpal tunnel cases, and for large rms in cases involving other sprains and strains. Only for back
sprains did weekly benets maintain their statistical signicance for both size catego- ries. This result is puzzling since we expected stronger benet effects for larger rms.
However, the pattern of injuries for which benets had positive effects suggests that employers had a greater incentive to offer restricted workdays as a monitoring tool
for hard-to-verify injuries with the greatest potential for moral hazard while observ- able injuries like fractures were less likely to be associated with restricted work.
The coefcient on our measure of economic activity in each establishment showed that for all four injury categories, the greater the proportion of fully employed work-
ers, the more likely workers were to receive restricted work. Employers might have been more anxious to get their injured workers back on the job when the work load
was high, and the benets of their restricted work abilities outweighed the costs of their alternative work arrangements.
Restricted work arrangements were more likely in rms with better injury records. Workers in rms with a higher days-away injury rate were less likely to experience
restricted work for sprains, carpal tunnel, and fractures. This negative effect of the injury rate appeared stronger for rms with more than 250 workers. If our two-digit
industry dummies did not fully control for the within-industry variation in injury rates, the negative effect of the injury rate might reect industry-level differences.
Firms with a high rate of days-away-from-work cases have tended to be in the min- ing, manufacturing, or transportation industry groups Bureau of Labor Statistics
1996. Jobs in these industries are likely to be more physically demanding and hence may require costlier accommodations than in industries with lower injury rates.
The state union membership rate had a strongly negative effect on the likelihood of restricted work for all four injury categories. As suggested by Hirsch, Macpherson,
and Dumond 1997, unionized workers may receive more guidance from their union representatives and be better protected against employer monitoring and penalties
for ling claims.
Among other variables whose coefcients are not reported here, there was a slight pattern in favor of longer tenure workers being more likely to receive restricted work.
Controlling for tenure, the probability of receiving restricted workdays decreased with age though this effect was statistically signicant only for carpal tunnel cases.
Our results suggest that injuries to older workers caused greater reductions in their ability to perform any work, reducing the net benet of restricted work schedules.
Finally, women were signicantly more likely to receive restricted work than men for all types of sprains and strains; there was no single pattern of race differences
across the injury types.
To summarize, our probit results show that higher disability benets increased the use of restricted work for three out of four injury types, though some effects
were statistically insignicant when small and large rms were analyzed separately. As predicted by our model, benet effects were greatest for back sprains, which are
more subject to moral hazard issues than other injuries Robertson and Keeve 1983; Dionne and St. Michel 1991; Butler, Durbin, and Helvacian 1996; Ruser 1998.
Longer state waiting periods uniformly reduced the likelihood of restricted work provisions.
B. Results from the Duration Model