Probability of restricted workdays

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 Žve- 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 Žrms may be using restricted work to control workers’ compensation costs.

A. Probability of restricted workdays

Tables 2–5 present the results from the Žrst-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 beneŽts 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 Žrms. 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 beneŽt 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 beneŽts 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 beneŽts re- duced the probability of restricted work but this effect was statistically insigniŽcant. The effect of weekly beneŽts was larger for compensable cases of all injuries except other sprains. 18 Controlling for differences in weekly beneŽts, longer waiting periods signiŽcantly reduced the likelihood of receiving restricted workdays for all the injury types includ- ing fractures, and for small and large Žrms. This was consistent with employers’ efforts to control their workers’ compensation costs by reducing the likelihood that their injured workers would qualify for beneŽts. 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 beneŽt 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. W ae hre r and M iller 979 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. h e Journal of H um an R es ourc es 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. W ae hre r and M iller 981 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. h e Journal of H um an R es ourc es 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 reective 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 coefŽcients on establishment size in Column 1 of Tables 2–5 show that work- ers in Žrms with more than 250 employees were signiŽcantly more likely to have restricted workdays than smaller Žrms, with larger effects for larger Žrms. Firms with less than 12 employees appeared to be similar to larger Žrms in their use of restricted work. These size effects may reect the effect of experience rating in the workers’ compensation system that links larger Žrms’ premiums more closely with their actual injury experience. Thus, larger Žrms would be more inclined to use restricted work to reduce the size of their workers’ compensation claims. Large Žrms 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 Žrms who cannot afford to hire and train tempo- rary replacement workers. We explore differences by Žrm size more fully in Columns 2 and 3 where we present estimates from separate models for Žrms with less or more than 250 employ- ees. Weekly beneŽts were statistically signiŽcant for small Žrms in carpal tunnel cases, and for large Žrms in cases involving other sprains and strains. Only for back sprains did weekly beneŽts maintain their statistical signiŽcance for both size catego- ries. This result is puzzling since we expected stronger beneŽt effects for larger Žrms. However, the pattern of injuries for which beneŽts 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 coefŽcient 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 beneŽts of their restricted work abilities outweighed the costs of their alternative work arrangements. Restricted work arrangements were more likely in Žrms with better injury records. Workers in Žrms 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 Žrms 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 reect 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 Žling claims. Among other variables whose coefŽcients 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 signiŽcant 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 beneŽt of restricted work schedules. Finally, women were signiŽcantly 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 beneŽts increased the use of restricted work for three out of four injury types, though some effects were statistically insigniŽcant when small and large Žrms were analyzed separately. As predicted by our model, beneŽt 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