Open Competition, Workers Compensation Costs, and Injury Rates *

Transcription

1 Open Competition, Workers Compensation Costs, and Injury Rates * by Anthony J. Barkume and John W. Ruser U.S. Bureau of Labor Statistics May, 1998 * This paper has benefited greatly from the comments of Alan Krueger, Brooks Pierce, and seminar participants at the Center for Studying Health System Change, the Department of Treasury, the 1997 meeting of the American Risk and Insurance Association, the 1998 winter meeting of the Econometric Society, the 1998 meeting of the Risk Theory Society and the 1998 meeting of the Society of Labor Economists. The views expressed are those of the authors and do not reflect the policies of the U.S. Bureau of Labor Statistics.

2 1 Abstract Open Competition, Workers Compensation Costs, and Injury Rates Anthony J. Barkume and John W. Ruser Compensation Research Group Bureau of Labor Statistics 2 Mass. Ave. NE, Room 4130 Washington, DC (202) (Barkume) (202) (Ruser) Barkume_A@bls.gov Ruser_J@bls.gov Property and casualty lines of insurance have traditionally been subject to more regulatory price control than most goods in the US economy. However, beginning in the 1970s, some states began to deregulate these lines of insurance, dropping either mandatory pricing in concert by means of rating bureaus or, additionally, dropping regulatory prior approval of premiums. Economic theory is ambiguous about the impact of deregulation on price and product quality. While pricing in concert by itself might lead to higher premiums with uncertain quality effects, the general theory of regulation suggests that higher or lower rates and quality may arise when regulators set prices. This paper assesses the impact of rate deregulation on one of the major property/casualty lines workers compensation. The impact on price and one dimension of quality, loss control as measured by injury rates, is evaluated. Using longitudinal micro data on employers workers compensation premiums from the BLS Employment Cost Index program, the paper finds that the simultaneous elimination of rate bureau pricing and prior approval leads to a decline in premiums of 8.7 to 14.7 percent, while dropping only rate bureau pricing has little effect on premiums. Analysis of longitudinal injury data from the BLS Annual Survey of Occupational Injuries and Illnesses suggests that simultaneously dropping rate bureau pricing and prior approval reduces injury rates at most around 7 to 8 percent, with insignificant declines when only rate bureau pricing is eliminated.

3 2 I. Introduction and overview Property and casualty lines of insurance have traditionally been subject to more regulatory price controls than most goods in the US economy. Rate regulation has generally consisted of two characteristics: insurance regulators must approve rates ( prior approval ) and carriers must adhere to the rates filed with regulators by rating bureaus ( rate bureau pricing ). Beginning in the 1970 s, states have removed one or both of these restrictions in many lines of insurance. Some states have eliminated both regulatory characteristics, frequently termed the introduction of open competition, while others no longer allow rate bureau pricing, but maintain prior approval of individual insurer s rates. Economic theory is ambiguous about the effects of these regulatory requirements on insurance rates. By itself, rating bureau pricing with adherence requirements should stifle competition among insurers, leading to higher premiums (Joskow (1973)). However, where rate regulation requires rating bureau pricing, prior approval restrictions have also been in place. The general theory of regulation (Stigler (1971); Peltzman (1976)) suggests that regulators seek to redistribute wealth in such as way as to maximize political support. Prior approval restrictions can be used to raise or lower rates, and whether regulated rates favor insurers or those insured depends on the relative cost of organizing these groups for political action. Thus, theory suggests that it is an empirical question whether rates are higher or lower under regulation. What actions insurers take to control losses is an important dimension of product quality in the supply of insurance, but theory is also ambiguous about the effect of regulation on loss control. If rates are set above the competitive level, then regulated insurers may compete on service by providing excess loss control, resulting in fewer (but suboptimal) losses, and premiums that are lower than they otherwise would be. Conversely, rate suppression may weaken insurers lost control incentives, leading to greater losses and rates increases. Finally, the establishment of rates in concert based on actual costs may lessen incentives for loss control.

4 3 This paper empirically assesses the impact of rate regulation on property/casualty insurance, focusing on one of the major lines workers compensation. Deregulation of workers compensation did not start until the early 1980s, possibly because of the mandated requirement for coverage. We investigate the effect of this legislative development on both premiums and, as a proxy for loss control, injury rates. To investigate the impact on premiums, we use longitudinal data on employers workers compensation costs drawn from the micro data of the BLS s Employment Cost Index for 1981 to Since observations are available in states that did and did not institute regulatory change and since there are observations that spanned law changes, we can apply a treatment/control group methodology to assess the impact of deregulation on rates. The empirical results show that simultaneously dropping rate bureau pricing and prior approval leads to a large decline in workers compensation rates. Including a fixed effect for each job, and controlling for the level of workers compensation benefits and general time trends, the analysis shows that, after deregulation, rates fall nearly 9 percent after the first year. A larger effect (14.7 percent) is obtained when the job fixed effect is replaced with a set of dummies describing state, union status, two digit SIC industry classification, and occupation. The decline in premiums is much smaller (at most 3.5 percent) when rate bureau pricing is eliminated but prior approval of insurers rates is retained. To analyze the impact of deregulation on injury rates, we used data from the BLS Annual Survey of Occupational Injuries and Illnesses for 1987 to We created a longitudinal panel of aggregate observations defined by year, state, 2 digit SIC and establishment size class. Like the analysis of premiums, we employ a treatment/control group methodology. In non-self-insuring establishments, the empirical results indicate that simultaneous elimination of prior approval and rate bureau pricing reduces rates of days-away-from-work injuries by about 7 percent and reduces non-lost-workday injury rates by nearly 8 percent. Injury rates are not affected when rate adherence is abolished but prior approval is retained. Assuming that all injury costs decline in the same

5 4 proportion as injury rates, these injury rate declines explain about one-half of the decline in workers compensation premiums when weaker controls for job heterogeneity are included, indicating that there is a pure price effect of deregulation. In contrast, the fixed effect specification suggests that most of the premium decline is due to a decline in injuries. The empirical analysis fails to support the hypothesis that rate bureau pricing alone maintains rates above the competitive level. However, regulators prior approval authority, possibly in concert with rating bureaus, does raise premiums and injuries. This empirical analysis is broadly consistent with two theories of the effects of regulation. First, regulators may establish rates above the competitive level, shielding insurers that are inefficient in loss control. However, as suggested by Danzon and Harrington (1996), regulators may try to suppress rates, reducing incentives for loss control, in turn leading to higher premiums and more injuries. II. An overview of price regulation in workers compensation insurance Price regulation of workers compensation insurance is a state responsibility. 1 Nearly every state requires employers to purchase workers compensation insurance (unless employers qualify to self-insure), so that the cost of workers compensation insurance is an important regulatory concern. Before 1980, every state had a legal framework for regulation which Klein, Nordman, and Fritz (1993; p. 26) characterize as an administered pricing system. In this system, proposed workers compensation premiums had to be filed, reviewed, and approved by the state insurance regulator prior to their use; there was a legal requirement for prior approval of these rates. Furthermore, the process of developing, filing, and using rates constituted pricing in concert, since rates were actually filed on behalf of individual insurers by a rating bureau which collected information on losses from individual insurers in the state. If the rates proposed by the rating bureau were approved, states required individual insurers to adhere to those rates. 1 Until 1944, the U.S. Supreme Court had held that insurance was not part of interstate commerce. After this ruling was reversed, Congress acted to insure continued state responsibility for regulation in the 1945 McCarran-Ferguson Act, which exempted the insurance industry from the Federal anti-trust laws in any state which affirmatively regulated the industry.

6 5 Beginning in the early 1980s, a number of states began to introduce modifications in the traditional system of regulation for many different property/liability lines. These modifications were frequently termed competitive rating or open competition. As characterized by the National Association of Insurance Commissioners (NAIC), a competitive rating system has two essential components. 2 First, the prior approval requirement is replaced by a less restrictive filing requirement; the insurer can use rates after they have been filed with the insurance department (a so-called file and use stipulation). Secondly, the role of the rating bureau is substantially reduced; rating bureaus no longer file rates on behalf of insurers nor are insurers required to use rates developed by rating bureaus. With this second regulatory change, the rating bureau becomes an advisory organization which compiles loss data from insurers within the state and develops projections of costs associated with expected losses. Individual insurers can use these cost projections to develop their rates, but need not use any markups on loss costs suggested by the rating bureau. Thirty one states passed laws between 1982 and 1995 that introduced one or both of the elements of open competition eliminating prior approval requirements and pricing in concert through a rating bureau into workers compensation. 3 Twelve of these states introduced both aspects of a competitive rating system, while the other 19 states eliminated legal sanction for rate making in concert but retained the prior approval requirements on individual insurers. 4 Table 1 identifies the states making these legal changes, along with the effective dates of introduction as reported by the National Council on Compensation Insurance (NCCI), the organization which oversees rating bureau operations in most states. 5 2 The characterization below follows the model legislation developed by NAIC (model #775-1). Several individual states are characterized by NAIC as having competitive rating laws, but do not follow the NAIC model legislation. 3 There have been regulatory changes among states retaining the traditional system of regulation. Some states do allow insurers do file rates with the insurance commissioner which are deviations from the rate proposed by rating bureau. For more information on practices in particular states, see Klein, Nordman, and Fritz (1993;pp.26-27)). For our analysis, we ignore the differences within this group of states. 4 We base this breakdown on classification of laws made by the NAIC (see Table 1 for sources). 5 A difficult problem in doing empirical work in this area is that the NAIC and the NCCI use different definitions of open competition. NCCI defines open competition as a situation in which rating bureaus no longer file rates on behalf of insurers, whether or not insurers are subject to prior approval restrictions.

7 III. Theoretical effects of rate regulation on premiums and injury rates 6 1. Effects on workers compensation prices Joskow (1973) argued that state sanction of rating bureau pricing and related rate adherence requirements act to provide legal support for cartel action (pricing in concert) in a competitive industry. However, whether dropping rating bureau pricing lowers workers compensation premiums depends also on whether prior approval of rates is retained. Wherever rating bureau pricing has been allowed, state regulation has also retained prior approval restrictions on insurers; we can not observe the effect of rating bureau pricing independently from the effects of prior approval restrictions. Indeed, much of the analysis of the effects of traditional rate regulation in workers compensation insurance has stressed the use of prior approval to set rates either higher or lower than those which would prevail with open competition. The general theory of regulation of Stigler (1971) and Peltzman (1976) models regulators as seeking to maximize political support from competing groups. The implication of this model is that, depending on the relative strength of employers, workers, and insurers, regulators could reject proposed rates considered either too low or too high. 6 For example, in states where the affordability of workers compensation insurance was an important objective, regulators could act to suppress market-determined rates. In other states, where insurers political strength is relatively great, regulators might maintain rates that exceeded those arising in a free market. There are two implications of the view that prior approval restrictions provide regulators the power to establish rates. First, if rating bureau pricing is eliminated but prior approval authority is retained there should be no long term change in rates, so long as regulator preferences are unchanged and regulators make the necessary adjustments with elimination of rating bureau pricing to maintain the same target for rates. Second, because regulator preferences for high or low rates may vary across states, eliminating 6 See: Klein (1992), Carroll and Kaestner (1995), and Schmidle (1995).

8 7 both prior approval restrictions and rating bureau pricing could either increase or decrease rates on average with introduction of open competition. Prior approval restrictions may also influence insurer incentives and thus costs of providing workers compensation insurance. An important function of the insurance rating bureau is to pool the loss experience of individual insurers. Regulators having prior approval authority rely on these average cost measures to determine the reasonableness of insurer rate requests. When prior approval restrictions are combined with uniform prices enforced with rate adherence requirements, less efficient firms are insulated from price competition by more efficient firms, raising average costs. 7 Thus, by introducing more price competition, elimination of rating bureau pricing and prior approval restrictions should tend to lower rates through greater loss control by insurers. How open competition could induce greater loss control was discussed in a review of the introduction of open competition in Minnesota: It is widely believed that the increased competition resulting from deregulation will encourage insurers to experiment in rehabilitation and return-to-work programs, as well as to reward employers who are successful with such programs with lower premiums [Keefe (1988, p. 34)]. Danzon and Harrington (1996) show that insurer incentives may generate cost growth even when regulators use prior approval authority to attempt to lower rates. With lower rates, insurers tend to adjust their underwriting standards to insure only better risks. But, since states generally require workers compensation insurance coverage, employers rejected for voluntary underwriting must continue to be insured through a residual market in which insurers collectively bear the losses. Danzon and Harrington argue that regulator concerns for the affordability of coverage in the residual market will lead regulators to introduce cross subsidy schemes, raising overall insurance costs because of lessened incentives to control losses. There are relatively few empirical studies of the impact of rate deregulation on workers compensation costs, and these studies have not always distinguished the effects of dropping rating bureau pricing alone from effects due to the introduction of open

9 8 competition. Hunt, Krueger, and Burton (1988) estimated that the introduction of open competition in Michigan led to a 32 percent decline in the statewide average rate between 1978 and 1984, after accounting for the effects on prices due to changes in allowable benefits to injured workers over this period. Carroll and Kaestner (1995) studied the average effect among states introducing either type of deregulation between 1980 to 1987, finding that on average statewide prices fell 14 to 25 percent, depending on the price measure. In contrast, Schmidle (1995), examining more disaggregated data for a slightly longer time period, failed to find an effect of deregulation. However, neither the Carrol/Kaestner or Schmidle studies estimated separate effects for open competition from dropping rating bureau pricing alone. Danzon and Harrington (1996) showed that regulators suppression of rates is positively and significantly related to growth in claims costs up through All of these studies examined changes in statewide premium measures (Danzon and Harrington also studied rates by rating class) and only assessed the impact of the initial wave of deregulation (pre-1991). In contrast, the present study uses workers compensation cost data from firms and, analyzing data for a longer time period, includes a larger number of states that changed regulatory regime. 2. Effects on injury rates Despite the fact that the workplace injury rate is a central component of workers compensation insurance costs, there has been little study of how deregulation could affect workplace injuries. Most recently, Kaestner and Carroll (1997) argue that deregulation will affect injury rates to the extent that changes in premiums influence the effort employers will make on workplace safety. In this line of argument, if deregulation lowers premiums, then workplace injuries will tend to increase because the penalty on employers associated with experience rating is now lower. Examining state level data by major industry division from 1983 to 1988, Kaestner and Carroll find an increase in injury rates associated with regulation, a result these researchers argue is consistent with their findings on the price effects of deregulation (Carroll and Kaestner (1995)). 7 See Joskow (1973, pp ) and Williams (1986, p. 224).

10 9 However, the relationship between injury rates, the supply of insurance, and workers compensation costs must be examined further because injury rates affect insurer costs and thus rates. (If deregulation did increase injury rates, worker compensation costs would tend to rise, all other things being equal.) Although insurer discretion in changing the quality of services (such as waiting periods for payment of benefits) is limited in workers compensation insurance because the terms of benefits are set by statute 8, insurer reaction to regulatory change can influence workplace safety and thus the cost of providing insurance. Insurers can influence workplace safety by undertaking injury prevention efforts as part of a loss control program. Expenditures for loss control programs yield returns over time through lower injury rates and payment of benefits. If insurers always chose the long run least cost scale of investment in loss control (with long run marginal costs equal to long run average costs) then regulatory change would have no systematic long term effects on injury rates. (Then, insurers would have no incentive to change their scale of investment.) But, regulation may not lead insurers to invest in the least cost scale of injury prevention. We review three scenarios; in one case regulatory change lowers workers compensation premiums but raises injury rates; in the other two cases, both workers compensation premiums and injury rates could decline. The possibility that elimination of prior approval restrictions could reduce injury rates and workers compensation premiums is suggested by an analysis of Danzon and Harrington (1996, pp.16-17). They argue that, regardless of regulatory objectives, the prior approval process can discourage investments in loss control programs when regulators do not allow rate adjustments to reflect insurer expenditures on loss control. Even though future claim costs would be higher without loss control investments, the prior approval process can allow a pass-through of additional claim costs, lessening the incentives of insurers to undertake such investments. Thus elimination of prior approval requirements could induce expansion of loss control investments, a reduction in injury 8 See Klein, Nordman, and Fritz (1993, pp ).

11 10 rates, and subsequent declines in premiums due to lower aggregate benefit payments to injured workers. Deregulation could possibly lower premiums and raise injury rates if regulatory conditions induced excessive loss control expenditures here defining excessive expenditures as those for which the long run marginal costs exceed long run average costs. Samprone (1979) noted that when regulatory conditions created a price floor above the competitive price in a particular state insurance market 9 then nonprice competition by insurers would increase so long as insurer profits exceeded those in other state markets. As we noted above, many avenues of product quality competition are limited in workers compensation, so that insurers may compete for excess profits through use of more extensive workplace safety programs to attract employers. If deregulation then eliminated the price floor, loss control expenditures could conceivably decline and injury rates could rise. Finally, it was previously argued that prior approval coupled with rate bureau pricing may shield less efficient insurers from competition. One form this may take is inefficiencies in loss control. In this scenario, deregulation would lead to the exit of these less efficient insurers, with a resulting decline in injury rates and premiums. The following sections report the results of empirical work designed to measure the impact of rate deregulation on workers compensation premiums and injury rates. Our emphasis is to estimate net effects for all states undertaking regulatory change between 1982 and 1995, measuring premiums and injury rates at a very high level of detail within each of the states. IV. Data for analyzing premiums To measure actual transaction prices for the same insured risk over time, we utilize longitudinal micro data collected in the Employment Cost Index (ECI) program. Statistics from this program provide estimates for the total nonfarm economy (excluding 9 We can abstract from whether the price floor is generated from monopoly returns generated from rating bureau pricing, or implementation of regulatory objectives.

12 11 the Federal government, the self-employed, and private household workers) of levels and changes in wages and salaries and in employer costs per hour worked for 22 categories of benefits, including employer costs for workers compensation insurance. The ECI is a sample survey of establishments; within sampled establishments, employer cost data are obtained for 4 to 8 specific jobs (with selection based on probability proportionate to employment size). The data collection is longitudinal in design, obtaining a quarterly time series on labor costs for the same company job for four to six years. Until recently, the sample was replaced by industry panel, so that, for example, certain manufacturing industries are replaced one year, while wholesale trade is replaced another year. Data collectors are specifically instructed to obtain the net workers compensation cost for each sampled job, after allowance for experience modifiers, premium discounts and expense constants applicable to the particular employer. Where relevant, information on retrospective rating plans is also obtained. In some cases, workers compensation costs are not available for the sampled job, in which case the data collector is instructed to obtain similar cost information for a more aggregated category of labor within the establishment. For the present study, we restricted our data set to only those observations for which workers compensation costs were specific to the job. The ECI collects workers compensation costs both for self-insurers and for those establishments that purchase insurance. Restricting the data set to those observations for which the workers compensation quote is job-specific effectively eliminates self-insurers from the sample. One limitation of the ECI micro data is that no retrospective adjustment of net costs is made for changes in actual dividends paid on policies. Some analysts (e.g., Worrall (1986)) have argued that even with uniformity of insurance rates under the traditional prior approval/rating bureau system, insurers could still engage in price competition by using dividends to offer deferred rebates on the standard rate premium established by the rating bureau. Thus, it is possible with deregulation that rates could decline but that this decline is partially or fully offset by a decrease in policy dividends. If rate reductions were fully offset by dividend decreases deregulation would not affect the

13 12 net costs of insurance, only the timing of payments, but the ECI data could not identify this reduction. If price competition does systematically reduce dividends, then dividends should be lower in states requiring rating bureau pricing. To check for the relevance of this argument (and hence the importance of the ECI data not indicating changes in dividends) we examined dividend data by state for 1990 reported in Klein, Nordman, and Fritz (1993). The National Council on Compensation Insurance (NCCI) compiles data on average dividend adjustments to standard earned premiums for 38 states. We compared the 1990 NCCI data on premiums for those states which allowed the rating bureau to file rates and those states allowing insurers to file their own rates (and limiting the rating bureau to development of loss costs or advisory rates). Weighted by state written premium volume, the average dividend in states permitting rating bureau pricing (24 of the 38 states) was 3.27% while the average dividend in the other states not permitting rating bureau pricing was 3.74%. Using a t-test allowing different variances we cannot reject the hypothesis that these data came from the same population. We conclude that the lack of information on changes in dividends in the ECI measures of workers compensation cost is not a serious limitation for the purposes of the analysis. The data set we analyzed contains 115,709 observations for private nonfarm jobs, formed by merging the micro data for each June from 1981 to Since the data are longitudinal and sampled establishments are replaced in different years depending on industry, the data set contains between 1 and 7 observations per job for a total of 38,940 distinct jobs. To compare costs across both jobs and years, the workers compensation rate variable that we analyze is the job s workers compensation cost as a percentage of gross earnings. This was calculated from the ECI micro data as the product of workers compensation costs per hour worked times annual hours worked, divided by annual gross earnings. The natural logarithm of this relative cost measure is used in the pooled crosssection time-series analysis discussed in the next section. So as to focus on developments in the market for private insurance, we restricted the data set to states that did not have

14 13 monopolistic state funds. However, states that had state funds competitive with private insurers were retained in the data set. Following the approach of previous studies, the objective of our statistical design was to specify regression equations which would provide estimates of the effect of regulatory changes on rates, while (ideally) controlling for the effects of national insurance market developments over time, differences in the generosity of benefits across states over time, as well as differences in the riskiness of employment across occupations and employers. In the regressions, to control for effects other than those associated with the regulatory regime, we developed two alternative sets of categorical ( dummy ) variables derived from the ECI micro data. The first approach ( Dummy Variable Set A ) used ECI micro data describing the establishment or the job, including state, industry, occupation, and collective bargaining status. Dummy variables were created for 67 two digit SIC industry categories, 11 major occupational groups, collective bargaining status, and state. In addition, the time of observation was represented by a set of year dummies which reflect national trends in the cost of workers compensation. The second approach ( Dummy Variable Set B ) more fully exploits the longitudinal character of the data set to control for cross sectional variation between jobs. Since there are multiple observations on an individual job, average cost differences between each individual job can be accounted for by including a fixed effect for each job, using 38,939 dummy variables. This design controls for all possible cross sectional variation in rates. In those specifications that used fixed job effects, we also included year dummies to measure national insurance market developments and the measure of workers compensation benefits. Because jobs rotate in and out of the sample through time, the two ways of controlling for job heterogeneity lead to slightly different interpretations of the measured effect of regulatory change. Using job fixed effects (dummy variable set B) shows the change in workers compensation costs within a job, so that the impact of regulatory change can only be evaluated over the life of the job, or at most 6 years. For most jobs, the impact of change is evaluated for a much shorter duration than this. When dummy

15 14 variable set A is used in the regressions instead, the regressions measure the effect of regulatory change within similar jobs over a longer time span (which depends on when regulatory change occurred relative to the sample), where similar is defined by the dummy controls. These controls are not as strong as the fixed effects in controlling for cross-job heterogeneity, but allow us to see the longer run effect of regulatory change. Several dummy variables were created to assess the effect of changes in state rate regulation. One dummy variable, T1, took the value one when an observation fell within one year following the elimination of rate bureau pricing, while another dummy variable, T2+, took the value one when an observation fell more than one year after this change in rate regulation. T2+ measures the long run effect, while T1 measures the transitional impact. Since workers compensation policies tend to be rewritten annually, it may take some time for insurers to react to changes in the regulatory environment in making their pricing decisions. Further, the market itself may take some time to reach a new equilibrium. Including T1 in the analysis is intended to segregate these short run effects from the longer run impact of changes in the regulatory environment. Some states retained prior approval requirements after eliminating rate bureau pricing, while others simultaneously eliminated prior approval (see Table 1). We wanted to assess the effect on rates of these two regulatory approaches and to disentangle the impacts of prior approval restrictions and the use of rate bureau pricing. Therefore, we created two dummy variables, labeled T1 PRIOR and T2+ PRIOR, that indicated whether an observation was in a state that retained prior approval while eliminating rate bureau pricing. When these variables are included in the regressions, the original T1 and T2+ variables indicate the effect of jointly dropping rate bureau pricing and prior approval. Preliminary analysis indicated that the overall results were driven in part by legislative reforms that occurred in California in early This state dropped rate bureau pricing late in the period covered by the data set, while adopting other legislation that may have affected premiums. For this reason, in some regressions the T1 and T2+

16 15 effects were estimated separately for California. Further, the T1 PRIOR and T2+ PRIOR variables were set equal to zero for California for all years despite the fact that it retained prior approval, so these variables measure the differential impact of eliminating rate bureau pricing while maintaining prior approval in all states except California. Changes in workers compensation premiums are obviously affected by changes in the generosity of workers compensation benefits. To control for this effect, we created a variable that measures the generosity of benefits. This is the ratio of the state s maximum benefit for temporary total disability to the state s average weekly wage. It was created using information from the US Chamber of Commerce Analysis of Workers Compensation Laws (for ), the US Department of Labor State Workers Compensation Laws (for 1996) and information on state average weekly wages from the BLS Employment and Wages. This benefit variable varies by state and year. While benefits are affected by a variety of factors (such as the nominal replacement rate, and marital and dependency status) Butler and Appel (1990) argue that the benefit maximum explains most of the variation in expected benefits. Table 2 reports sample statistics for some of the variables in the data set. V. Empirical analysis of workers compensation premiums Table 3 reports regression estimates for equations explaining variation in the log of the ratio of workers compensation costs to gross earnings. As discussed above, we control for job heterogeneity in two ways in the analysis. In odd numbered columns, dummy variables are included for collective bargaining status, 2 digit SIC, major occupation, state, and year. In even numbered columns, controls are included for the specific job and for year. The latter fully exploits the longitudinal characteristics of the data and controls for all possible cross-sectional variation in workers compensation rates. It shows how rate deregulation affects rates within specific jobs. In contrast, the estimates in the odd numbered columns show how rate deregulation affects similar jobs, where similarity is defined by the controls used. The results are somewhat sensitive to which set of controls is used. As suggested above, this may stem from the fact that the

17 16 model with industry and occupation dummies can measure longer run impacts of rate deregulation in our unbalanced panel. Columns 1 and 2 show the effect of rate deregulation in all states pooled. Across all states, dropping rate bureau pricing is associated with a lower (and statistically significant) workers compensation cost per dollar of wages and salaries. The effect is greater in the second and subsequent years (T2+) than in the first year (T1), which is consistent with our hypothesis that the first year is transitional. Table 4 converts the results in Table 3 into percentage terms. The top panel of Table 4 shows that dropping rate bureau pricing led to a decline in rates of 4.9 to 5.8 percent in the first year and from 9.1 to 9.3 percent in subsequent years. Columns 3 and 4 of Table 3 segregate the estimates for California from those for the other states. As noted earlier, California dropped rate bureau pricing late in the study time period and concurrently introduced a number of other legislative reforms. The analysis shows that the combined effect of these changes had a much larger effect on workers compensation costs in California than in other states. The coefficients for the interactions of the California dummy with the T1 and T2+ variables show by how much the results for California differ from those of other states. The coefficients are large, negative and statistically significant, indicating that California rates dropped by much more than other states. However, even when California is removed from the pool of states, the analysis still shows that dropping rate bureau pricing had a statistically significant negative effect on rates after the first year following deregulation, though the magnitude of the effect is somewhat smaller than in the first two columns. The middle panel of Table 4 shows the percent effect on rates in California and in all other states. In the first and later years after deregulation premiums were between 25 and 28 percent lower in California than they otherwise would have been. In all other states, dropping rate bureau pricing in the second and later years reduced costs between 6.7 and 8.1 percent, while there were no statistically significant effects in the transitional year.

18 17 We previously noted that some states maintained prior approval of rates while eliminating rate bureau pricing. The differential effect on costs associated with these policy choices is shown in columns 5 and 6 of Table 3, which report regressions that separate states that did and did not eliminate prior approval. The coefficients on the T1 PRIOR and T2+ PRIOR variables show how much the results for states that retained prior approval (except California) differ from those that dropped prior approval, while the T1 and T2+ variables show the effects of dropping prior approval and rate bureau pricing. The results indicate that there is a significantly different long run effect on rates when prior approval is retained rates drop far less. The bottom panel of Table 4 shows the estimated percentage effects on rates for purchased workers compensation in states that dropped rate bureau pricing and that both did and did not retain prior approval. For states that dropped prior approval, the first year effect is statistically insignificant. However, in subsequent years the model that included fixed job effects indicated that rates dropped 8.7 percent, while the other specification suggested a larger decrease (14.7 percent). This larger effect may arise because the specification with fixed job effects measures the effects of rate deregulation over a shorter time span than the other specification. The effects of dropping rate bureau pricing are much smaller in states that retained prior approval. The model with job fixed effects indicates a decline of 3.5 percent in the long run but all the other coefficients estimating these effects are insignificant. While not the principal focus of this paper, Table 3 also reports how workers compensation costs as a fraction of gross earnings vary with the statutory benefit maximum. As expected, an increase the maximum leads to a statistically significant increase in premiums, with elasticities ranging from about.10 to.12 in the job-fixed effect specifications and.32 to.38 when less stringent controls are applied for job heterogeneity. Not reported in the tables is a lengthy list of coefficients for the control variables. These deserve some mention. Consistent with the industry-based development of rates

19 18 for workers compensation insurance (see Burton and Krueger (1986)), our estimates indicate large differences across industries. Consistent with aggregate time series, the time dummies show, everything else equal, that workers compensation costs as a fraction of gross earnings dropped between about 5 and 10 percent from June 1981 to June 1984 (depending on specification). From that time, until June 1994, they have risen about 80 to 90 percent, then dropped in the neighborhood of 10 percent between June 1994 and June Finally, union jobs are 16 percent costlier in terms of workers compensation than other jobs. It is likely that this reflects the greater riskiness of union jobs, which is inadequately controlled for by the major industry and occupation dummies. VI. Data for analyzing injuries The source of injury and illness ("injury") data for this study is an annual, nationally representative survey of about 250,000 private industry establishments conducted by the U.S. Bureau of Labor Statistics. 10 Surveyed establishments report annual counts for three types of nonfatal injuries, as well as the total annual hours worked by all employees, the number of employees, and the establishment s industry. The injury data are based on logs that establishments are required to maintain under the Occupational Safety and Health Act. 11 The injury and hours data are used to estimate nonfatal injury rates, expressed as the number of injury cases per 100 full-time worker years. In order of increasing severity, the three types of nonfatal injuries are: non-lostworkday, restricted-workday, and days-away-from-work. A non-lost-workday case is one where an injured worker returns to all of his duties on the day following the injury. A restricted-workday case involves some restriction on the amount of work that the 10 In addition to data on injuries, the survey collected data on illnesses. The survey seeks to measure the number of new work-related illness cases that are recognized, diagnosed, and reported during the year. Some conditions, for example, long-term latent illnesses caused by exposure to carcinogens, are often difficult to relate to the workplace and are not recognized and reported. In contrast, the overwhelming majority of newly reported illness cases are those which relate to workplace activities and are easy to identify. Injuries make up over 95 percent of all reported injuries and illnesses with days away from work. For convenience, I use "injuries" to mean both injuries and reported illnesses.

20 19 employee can perform on the day or days following the injury, but where the employee returns to work on the day following the injury. In contrast, when an employee sustains a days-away-from-work injury, he does not return to work on the day or days following the injury. As the means in Table 5 show, by far the most frequent cases are either non-lostworkday or days-away-from-work cases. For this study, a longitudinal data set spanning 1987 to 1995 was created, consisting of injury rates for cell-level observations defined by year, state, two digit SIC industry, and 8 employment size classes. Injury rates were calculated separately for the three injury types. Due to the unreliability of injury estimates for small cells, an observation was excluded when its estimated full-time annual equivalent employment was less than 100. Also excluded were a small number of outlier observations with very high injury rates (in excess of 1 injury per worker per year). The resulting data set consisted of 129,577 observations for 17,989 state, industry, and employment size class cells. Since these are annual data, in contrast to the quarterly ECI data analyzed previously, slightly different variables were created to indicate whether a particular observation was affected by rate deregulation. One dummy variable, T1, took the value one when an observation fell in the year following the elimination of rate bureau pricing, while another dummy variable, T2+, took the value one when an observation fell in the second or later year after this change in rate regulation. As with the previous measures, T2+ measures the long run impact on injury rates, while T1 measures the transitional effect. The variables constructed to study the impact of rate deregulation on injuries measure these impacts later than those utilized to study the impact on workers compensation premiums. This is reasonable since changes in injury rates often are the result of investments in safety that occur over time (Viscusi, 1979), so that one would not expect to see injury rates change as quickly as workers compensation premiums. 11 Establishments with 10 or fewer employees and those in certain low hazard industries are not required routinely to maintain injury logs except when they prenotified to do so because they have been selected for participation in the BLS survey.

21 20 The previous section indicated the importance in the empirical analysis of distinguishing between states that did and did not retain prior approval. To assess the importance of this distinction for injury rates, the dummy variables T1 PRIOR and T2+ PRIOR were created to measure the differential effect of retaining prior approval while dropping rate bureau pricing. As before, these are the interactions of the T1 and T2+ variables with a dummy indicating the retention of prior approval. When these variables are included in the regressions, the variables T1 and T2+ measure the effect of simultaneously dropping prior approval and rate bureau pricing. Also as before, since prior approval was essentially eliminated only when rate bureau pricing was dropped, it is not possibly to fully disentangle the impact on injuries of both rate bureau pricing and prior approval. Finally, unlike the previous analysis, no additional control was necessary for a differential effect in California, because the longitudinal injury rate data end in the year that California introduced legislative reforms -- while the T1 and T2+ variables would only measure effects in California in years outside the study period. An extensive body of research has established that increases in workers compensation benefits increase nonfatal injury rates (for example, see Ruser (1991)). The explanation given for this finding is that higher benefits increase workers incentives to report injuries and may reduce their care-taking incentives. In larger, experience-rated establishments, this may be offset by firms increased incentives to invest in safety and to keep workers off workers compensation. The previous empirical work suggests that the worker effects dominate. To control for these offsetting effects in the present study, the workers compensation variable utilized in the analysis of workers compensation premiums was also utilized here. VII. Empirical analysis of injury rates Table 6 reports the coefficients from regressions of injury rates on the dummies representing rate deregulation. Separate panels are displayed for each of the three types of nonfatal injuries. Also included in the regressions are the workers compensation benefit variable and two different sets of dummies to control for cell heterogeneity. The

22 21 control variables included in models 1 and 3 were dummies for employment size class, 2 digit SIC industry, state, and year, while year dummies and cell fixed effects were included in models 2 and 4. These latter two models provide the maximum amount of control possible for cell heterogeneity. Unlike the regressions reported previously, the dependent variables here were not logged, due to the presence of zero injury rates. Models 1 and 2 show that, when no distinction is made between states that did and did not retain prior approval, non-lost-workday and days-away-from-work injuries declined slightly in the long run after states dropped rate bureau pricing. However, the effect is small. Two or more years after the change, non-lost-workday cases decline only about.12 to.14 per 100 worker years and days-away-from-work cases decline about.07 to.08 per 100 worker years. Declines in the first year are even smaller, which is consistent with the hypothesis of that early years after a regulatory change are transitional. Table 7 expresses these declines in percentage terms. In the long run, dropping rate bureau pricing lowers non-lost-workday cases about 2.3 to 2.8 percent and days-awayfrom-work cases about 2.0 to 2.2 percent. Overall, there was no effect on restricted workday cases. Models 3 and 4 show that, consistent with the results for workers compensation premiums, the injury rate declines are present only in those states that simultaneously dropped both prior approval and rate bureau pricing. For both non-lost-workday and days-away-from-work injuries, the T2+ variable is negative and statistically significant, while the interaction of that variable with the dummy denoting retention of prior approval is positive, statistically significant, and about the same size in absolute value. Table 7 shows that, in the long run, dropping prior approval and rate bureau pricing reduces nonlost-workday injuries by about 6 percent, while reducing days-away-from work cases between 5.1 and 5.3 percent. Smaller percentage declines were experienced in the first year after deregulation for these injuries. Only very small percentage declines in injuries were experienced in the long run by those states that retained prior approval while dropping rate bureau pricing. Finally, there appears to be no statistically significant effect on the less frequent restricted workday injuries.

23 22 One finding in these results contrasts qualitatively with those presented for the change in workers compensation costs. Here the results for the fixed-effect and nonfixed-effect specifications are relatively close. This is because for any cell the injury data tend to span the entire time period studied. Thus, the fixed-effect specification measures effects of deregulation that are as long run as those measured by the non-fixed-effect specification. In contrast, in the cost regressions, the fixed-effect specification measures shorter run effects since this control is limited to the same job. The estimates just presented indicate the effect of rate deregulation on injury rates in all establishments. It is not possible with these data to estimate injury rates separately for firms that purchase workers compensation insurance from those that self-insure. Therefore, if one assumes that rate deregulation has no effect on self-insuring firms, the previous estimates underestimate the impact of rate deregulation on injuries in firms that purchase workers compensation. With some reasonable assumptions, it is possible to bound the magnitude of the effect on firms purchasing insurance. Let r o = the injury rate for all establishment r s = the injury rate for self-insuring establishments r p = the injury rate for establishments purchasing workers compensation s = the share of hours worked in self-insuring firms. Then the overall injury rate is simply the hours-weighted average of the injury rates in the two types of establishments: r = s r + ( 1 s) r. o s p Now consider the differential effect of a shock, denoted by x, such as a change in rate regulation regime. Assume that x can change the injury rate of establishments purchasing insurance and can change the fraction of firms that self-insure, but that x does not change the injury rate of self-insurers. Then,