RETURN TO WORK AFTER A LUMP-SUM SETTLEMENT

Transcription

1 RETURN TO WORK AFTER A LUMP-SUM SETTLEMENT Bogdan Savych WC July 2012 WORKERS COMPENSATION RESEARCH INSTITUTE CAMBRIDGE, MASSACHUSETTS 1

2 COPYRIGHT 2012 BY THE WORKERS COMPENSATION RESEARCH INSTITUTE ALL RIGHTS RESERVED. NO PART OF THIS BOOK MAY BE COPIED OR REPRODUCED IN ANY FORM OR BY ANY MEANS WITHOUT WRITTEN PERMISSION OF THE WORKERS COMPENSATION RESEARCH INSTITUTE. ISBN (online) PUBLICATIONS OF THE WORKERS COMPENSATION RESEARCH INSTITUTE DO NOT NECESSARILY REFLECT THE OPINIONS OR POLICIES OF THE INSTITUTE S RESEARCH SPONSORS. 2

3 ACKNOWLEDGMENTS Many people have contributed their knowledge and advice to our effort of better understanding return to work after a lump-sum settlement in Michigan. Rick Victor at WCRI provided valuable guidance. Reviewers gave us helpful comments and ideas. In particular, Henry Hyatt and Erin Todd Bronchetti, the technical reviewers, were extremely valuable. Helpful comments were also received by many other reviewers. Linda Carrubba provided very helpful administrative assistance and edited the manuscript with great skill. Any errors or omissions remaining in this report are responsibility of the author. Cambridge, MA July

4 TABLE OF CONTENTS List of Tables 5 List of Figures 6 Executive Summary 7 1. Introduction Background on Settlements Role in Workers Behavior 13 Policy Background on Use of Settlements across States 13 Use of Settlements in Michigan 14 How Workers May Respond to Lump-Sum Settlements Data and Empirical Approach 19 Data Sources and Measures 19 Characteristics of Workers with Redemptions 20 Outline of the Empirical Approach Results 26 Change in Average Employment Rate after a Settlement 26 Employment Behaviors after a Settlement 27 employment exit among those who were working at the time of the settlement 28 employment entry among those who were not working at the time of the settlement 30 main contributors to employment change 32 Do Workers Behave Strategically? 34 does employment exit increase before a settlement? 34 do workers delay return to work before a settlement? 37 correlation of pre- and post-settlement employment patterns 39 Employment with At-Injury Employer 41 Role of Worker and Claim Characteristics in Employment Trends 42 employment trends by worker characteristics 42 employment trends by settlement type and amount 45 employment changes by injury type Discussion and Policy Implications 50 Implications for Public Policy 50 Interpretative Caveats and Further Research Needs 53 Technical Appendix 55 References 80 4

5 LIST OF TABLES 2.1 Selected Indemnity Benefits per Claim with More Than 7 Days of Lost Time in Wage-Loss States, 2007/2010 / Descriptive Statistics for Workers with an Indemnity Injury with and without a Settlement / Employment Patterns after the Settlement among Workers Who Were Employed at the Time of Settlement / Employment Patterns after the Settlement among Workers Who Were Employed at the Time of Settlement Based on Employment with At-Injury Employer or New Employer / Employment Patterns after the Settlement among Workers Who Were Not Employed at the Time of Settlement / Comparing Employment in the Settlement Quarter and Four Quarters after the Settlement / Employment Patterns in the Quarters before the Settlement for a Subsample of Workers Who Were Not Employed at the Time of Settlement / Employment Patterns in the Quarters before the Settlement for a Subsample of Workers Who Were Employed at the Time of Settlement / 40 TA.1 Regression Estimates / 56 TA.2 Predicted Employment by Subgroups of Workers in the Four Quarters after the Settlement / 59 TA.3 Employment Patterns in the Settlement Quarter and One Year after the Settlement Quarter / 61 TA.4 Characteristics of Workers Based on Employment Status in the Settlement Quarter / 64 TA.5 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement / 66 TA.6 Characteristics of Workers Who Were Not Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement / 68 TA.7 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year After the Settlement with At-Injury Employer or New Employer / 70 5

6 LIST OF FIGURES 4.1 Average Employment Rate before and after the Settlement with Confidence Intervals / Percentage of Employed Workers Who Exited Employment before and after the Settlement / Percentage of Employed Workers Who Exited Employment before and after the Settlement, Sample of Cases with at Least 7 Quarters between the Injury and the Settlement / Employment Exit from At-Injury Employer and New Employer before and after the Settlement / Percentage of Non-Employed Workers Who Returned to Work before and after the Settlement / Percentage of Non-Employed Workers Who Returned to Work before and after the Settlement, Sample of Cases with at Least 7 Quarters between the Injury and the Settlement / Average Employment Rate and Employment with At-Injury Employer before and after the Settlement / Predicted Employment by Age / Predicted Employment by Gender / Predicted Employment by Marital Status / Predicted Employment by Subgroups of Workers with Different Settlement Amounts / Predicted Employment by Claim Type / Employment by Subgroups Based on Number of Years between the Time of Injury and Settlement / Predicted Employment by Injury Type / 49 TA.1 Average Employment before the Settlement, by Groups of Workers Based on Number of Quarters between the Injury and the Settlement / 72 TA.2 Employment Trends across Cases with and without a Settlement / 76 TA.3 Employment Exit Rate / 76 TA.4 Employment Entry Rate / 77 TA.5 Employment Trends among Workers Who Were Employed with At-Injury Employer in Quarter 0 / 78 TA.6 Employment Trends among Workers Who Were Employed with New Employer in Quarter 0 / 78 6

7 EXECUTIVE SUMMARY Do lump-sum settlements of workers compensation benefits discourage return to work? This question is often raised in policy discussions about allowing or restricting settlements of workers compensation benefits. Some observers have expressed concern that receipt of a sizable lump sum will delay return to work because workers may feel less immediate need for income. Economists call this an income effect. System practices in some states may reinforce these incentives, as settlement agreements may require workers to leave their at-injury employer and seek a new job. Other observers of the workers compensation system indicate that the receipt of a lump-sum settlement may encourage return to work. Settlements provide workers with the sense of closure which motivates workers to restart their careers after the workers compensation process is over (Hyatt, 2010). This is often referred to as the closure effect. Some workers may be willing to accept lump-sum settlements in order to move on with their lives after an injury (Torrey, 2007a). Ultimately, it is an empirical task to determine whether these hypothesized effects are material and which of the economic behaviors may dominate. That is the goal of this study. We examined the concerns outlined above by exploring the following research questions: Did workers who were employed at the time of a workers compensation settlement stop working shortly after the receipt of the lump sum? Did workers who were not employed at the time of settlement find employment shortly after receiving a settlement? Following a sample of 2,138 workers injured in Michigan in 2004 who later received a lump-sum settlement, we found evidence consistent with both behaviors outlined above. Settlements created incentives to end employment (among workers who were employed) and incentives to re-enter employment (among those who were not employed). In particular, 30 percent of workers who were employed at the time of settlement exited employment and were no longer employed one year later. The majority of these workers (53 percent) left employment in the first quarter after the settlement. Employment exit behavior is more pronounced among workers who were working for their at-injury employer at the time of the settlement. About 41 percent of workers who were working for their atinjury employer at the time of the settlement left employment, while only 25 percent of workers who had found a new employer by the time of the settlement left employment one year later. 19 percent of workers who were not employed at the time of the settlement returned to work and were employed within a year after the settlement. These estimates of employment exit and entry behavior presented above suggest that a material fraction of workers change their employment after a settlement in Michigan. Furthermore, these findings suggest that system practices play an important role in shaping return-to-work outcomes. A commonly used practice of terminating employment with the at-injury employer as part of the settlement agreement directly contributes to some of the employment exits at the time of the settlement. 7

8 While the results above suggest that employment exit and re-entry behaviors are important, they are not sufficient to determine which of the behavioral responses dominates. This question is important for addressing concerns of policymakers about how these behaviors contribute to changes in average employment after a settlement. The changes in the average employment rate in our sample depended on the overall number of workers who either exited employment or returned to work after a settlement. Analysis for the full sample of workers suggests the following: Workers who were employed at the time of the settlement but exited employment shortly after the settlement represent about 7 percent of all workers who received a settlement payment. An even higher percentage of workers decided to enter employment. About 15 percent of the overall sample of workers were not employed in the settlement quarter and were employed a year after the settlement. These two patterns of responses contributed to a 7 percentage point increase in average employment after a lump-sum settlement. While 25 percent of workers in our sample were employed at the time of the lump sum, 32 percent of workers were employed one year after a settlement. This evidence in Michigan reveals the greater importance of the closure effect that workers may experience closing out a claim helps workers restart their careers which may have been stopped due to a disabling injury. An even more important finding is that many workers do not change their employment behavior after a settlement. The majority of workers in our sample did not work before a settlement and did not return to work within one year after a settlement. This finding may not be surprising since workers who received settlements in Michigan had suffered severe injuries. Another behavior that we examined in this report is whether workers may have delayed their return to work in anticipation of the settlement. If many workers are able to delay their return to work, and potentially shift bargaining power to their advantage, then the settlement costs may not reflect their true disability. We examined this behavior by comparing how many workers entered employment before and after a settlement quarter. We found little evidence of this behavior in the full sample of claims that we examined the dip in the employment re-entry rate in the settlement quarter was relatively small. At the same time, when we focused on workers with a longer duration of time between an injury and a settlement, we found a greater drop in the employment entry rate just before the settlement. Some workers in this subsample were able to delay their return to work and returned to work only after the settlement. We also examined whether behavioral responses to the settlement varied depending upon worker and settlement characteristics. We hypothesized, for instance, that responses to settlement may vary based on workers age. Older workers may be on a slower recovery path than younger workers. Older workers often face greater hurdles than younger workers in finding a new job. They also are more likely to consider retirement as a viable alternative (an option that is not available to younger workers). As a result, settlements may lead to different behavioral responses based on workers age. Consistent with these hypotheses, we found that responses to the settlements differed by age groups. While we found an increase in employment after a settlement for workers who were less than 55 years old at the time of the settlement, we found a slight decline in employment for workers who were over 55 years old. We found no material differences in employment trends by most other worker and claim characteristics. Employment increased after a settlement for a majority of worker and claim types. We found that employment increased for men as well as for women, for those who received smaller settlements 8

9 as well as larger settlements, and for those with objective as well as subjective injuries. This suggests that the impact of the closure effect does not vary much depending upon the claim and worker characteristics. 9

10 1 INTRODUCTION Since one of the goals of the workers compensation system is to get workers back to work after an injury, policymakers are often interested in understanding how various system features may shape incentives for return to work. This study contributes to the policy knowledge by exploring how incentives to return to work change after a workers compensation settlement. Conceptually, several potential responses may explain changes in employment after a settlement. On the one hand, a large lump-sum payment may discourage return to work. This is consistent with the economic theory that suggests that a sudden increase in non-work income may, under some conditions, lead to lower labor force participation and discourage return to work. Workers who are working at the time of the settlement may respond to the settlement by leaving their employment. Policymakers in some states express this concern when limiting lump-sum settlements (see discussion in Reville et al. 2001, p. 7 about policy approaches in New Mexico). On the other hand, some observers expect that a settlement may improve return to work and lead to higher employment. For instance, the perception in the policy community in some states is that settlements allow some workers to resolve their issues that are being disputed and then, potentially, seek another job (Belton, 2011). In other words, the option of receiving weekly benefits may limit workers desire to seek new or better employment. Consistent with these explanations, we may expect that some of the workers who are not working at the time of the settlement may find new employment after the settlement. Given conflicting conceptual explanations, it is an empirical task to determine how employment changes after a settlement and which of the potential responses dominate. This report examines employment changes after a settlement in Michigan. Existing literature provides some evidence about workers behavior after a settlement. Since much of this evidence as well as relevant policy concerns were reviewed in two recent studies (Hunt and Barth, 2010; Torrey, 2007b), we only briefly highlight empirical studies that focus on workers employment after a settlement. Morgan, Snider, and Sobol (1959) is one of the first studies that examined whether workers used lump-sum settlements for rehabilitation purposes. The authors were concerned that while lump sums provide incentives to rehabilitate, weekly benefits may offer disincentives to go back to work. Their analysis of survey responses suggested that very few workers used the settlement for rehabilitation purposes and most used them to pay off debt and meet living expenses. Evidence from a survey of workers in Maine suggests that at least some of the workers were able to return to work after a settlement, while other workers responded that their disability prevented them from working (Most and Most, 2007). They found that about half of the workers were working at the time of the settlement and about 25 percent of those who were not working returned to work after a settlement (Most and Most, 2007). A recent study of injured workers in California found that workers who received a settlement were more likely to increase employment compared with workers who received their permanent partial disability (PPD) benefits as 10

11 periodic payments (Hyatt, 2010). The author attributed this result to a closure effect, since no increase in employment was observed among similar workers who received their PPD benefits as regular weekly payments instead of a lump-sum payment. This sense of closure was also mentioned by some of the workers in Pennsylvania interviewed by Torrey (2007a) workers were willing to accept lump-sum settlements since they wanted to move on with their lives. This study contributes to the literature by providing an empirical analysis of workers return-to-work behavior after a settlement in Michigan. In this report we highlight how different employment behaviors may contribute to the changes in the aggregate employment rate in our sample after a settlement. Consistent with the conceptual arguments outlined above, some of the workers may choose to enter employment after a settlement, while other workers may choose to leave employment. Following a sample of workers with work-related injuries who eventually received lump-sum settlements, we focused on the following research questions: Did workers who were employed at the time of a workers compensation settlement stop working shortly after the receipt of the lump sum? Did workers who were not employed at the time of settlement find employment shortly after receiving a settlement? These two behavioral responses shed light on the policy implications of our analysis. If we found that many workers chose to leave employment after a settlement, then settlements are likely to provide disincentives for return to work. They may create expenses for employers and contribute to greater social costs. Some workers may underestimate their future income needs and may have to rely on public benefit systems for income support. Other workers may have greater bargaining power to extract a larger lump sum than their future income needs would require, which may create expenses for employers that are not necessarily consistent with the compensation goals of the workers compensation system. If few workers exit employment, then policymakers can disregard concerns that there may be significant disincentives to return to work after a settlement. Note that some workers may leave employment with the objective to retrain for a new occupation that accommodates their residual disability. Policymakers may also be concerned that some workers may choose to delay their return to work to increase the likelihood of receiving a settlement. If workers delay their return to work unnecessarily, it may increase employer costs due to larger settlements that may not reflect workers true disability. We also examined whether the behavioral responses that are mentioned above vary, depending upon worker and injury characteristics. This analysis helps clarify whether limitations on the use of lump sums may be appropriate in some circumstances. The scope of this report is limited to a descriptive analysis of the patterns of employment before and after a settlement. Data and policy restrictions limit the nature of the empirical questions that we can examine. Although we discuss multiple conceptual reasons for observed changes in employment after a settlement, we cannot attribute our results to a specific hypothesis. Furthermore, we have limited information about the reasons for changing employment before or after a settlement. We do not know why workers may choose to leave work, how they spend their settlement, and whether they continue to work in their preinjury occupations. Moreover, the nature of the available data only allows us to explore a relatively short window of employment after lump-sum settlements. Finally, we do not have much information about workers experiences in different parts of the economic business cycle. We observed employment only 11

12 through the end of 2008, reflecting only early experience in the Great Recession. The report is organized as follows. In the next chapter, we provide a background on the use of lump sums in Michigan and also set a framework for thinking about the potential effects of the settlement. Chapter 3 discusses data and empirical methods. Chapter 4 provides results. We offer concluding remarks and discuss policy implications in Chapter 5. Further discussion of the underlying methodology and alternative tests are provided in the Technical Appendix. 12

13 2 BACKGROUND ON SETTLEMENTS ROLE IN WORKERS BEHAVIOR This chapter of the report provides background information on settlements role in return-to-work behavior. We start by outlining policy approaches towards lump-sum settlements across states and highlight relevant system features in Michigan. Then, we discuss multiple reasons why workers may change their employment status after a settlement. POLICY BACKGROUND ON USE OF SETTLEMENTS ACROSS STATES Approaches toward settlements differ widely across states. A recent review of state regulations revealed that forty-three states allow compromise and release agreements in some form (Torrey, 2007a,b). 1 In these states, employers or carriers can rely on compromise settlements (or compromise and release agreements) to close workers compensation cases. In general, these settlements include an agreement between the employer and the injured worker about the amount of benefits to be paid, payment of the suggested amount in a lump sum, and a release of the employer from future liability. The specific system details, however, vary among states. State systems often differ based on when the settlement can occur and whether a case can be reopened after a settlement. States have different approaches towards allowing settlement of the future medical costs. Some states do not allow settlements for future medical costs, even though they may allow settlement of the indemnity benefits. Administration of the system also differs between states based on the role of the workers compensation agency in review and approval of a settlement, the need for a formal hearing before a settlement, and the standards used for settlement approval. These and many other policy dimensions are discussed in detail in Torrey (2007b). Policy approaches towards settlements are not static as state regulators periodically reevaluate the design of the workers compensation system. In the last few years, a number of states expanded the use of settlement agreements. In 2011, the state of Washington allowed settlements of future indemnity; however, only older workers can receive these settlements. In 2009, New Mexico expanded the number of conditions 1 Since the 2007 review, the number of states that allow settlements has increased. In 2011, the state of Washington allowed compromise and release agreements for some injured workers. 13

14 under which the settlements were allowed. The 2007 reforms in New York required that employers offer a settlement to injured workers within six months of reaching maximum medical improvement. As summarized in Torrey (2007b) and Hunt and Barth (2010), these regulatory changes reflect the general movement toward allowing compromise and release agreements in more cases, which has been observed across many states over the last few decades. USE OF SETTLEMENTS IN MICHIGAN Michigan is one of the states that allow settlements of future indemnity and medical payments. It is generally observed that in Michigan, settlements are most frequently used to close out payments for permanent disability claims. These agreements in Michigan are called redemptions, reflecting the fact that the executed agreement redeems the employer s liability for further compensation for the named injury or injuries. At least six months must elapse after an injury before the settlement can be provided. Redemptions in Michigan are reviewed by a Workers Compensation Magistrate (administrative law judge) who must find that the agreement is voluntary, serves the purposes of the Workers Compensation Act, is in the best interests of the injured employee, and that the injured employee is fully aware of the consequences of the agreement (Sec of the Act) reforms in Michigan allowed parties to waive redemption hearings and allowed magistrates to approve redemptions without a hearing. The nature of the settlements in Michigan reflects the wage-loss approach towards compensation for both temporarily and permanently disabling injuries. Wage-loss systems compensate earnings losses as they occur an approach that is different from impairment or loss of wage-earning capacity systems, which attempt to determine the degree of disability and estimate future earnings losses that will result (Barth and Niss, 1999). Although a wage-loss compensation plan appears simple to manage while the losses are still occurring, this approach is more difficult to implement when it comes to predicting future benefits. Longterm earnings losses are difficult to estimate with great precision. Perhaps as a result of these features, many permanent disability claims in Michigan result in lump-sum settlements. 2 Insurance companies may prefer to settle a case to end their liability, while workers may prefer to accept a settlement that closes out their involvement with the workers compensation system. A review by Hunt and Barth (2010) outlines arguments under which a worker may prefer to choose a one-time settlement over a stream of periodic payments. Multistate comparisons from the Workers Compensation Research Institute (WCRI) CompScope benchmarks series covering 16 large states (Savych, 2011) 3 help compare and contrast use of lump sums in Michigan and in other states. Compared with other states that do not use the wage-loss principle of compensation for permanent disability, the incidence of permanent disability benefits and/or lump-sum payments is very low in Michigan. According to the CompScope studies, only about 15 percent of all claims with more than seven days of lost time received such payments in Michigan, compared with a median of 45 percent in the non-wage-loss states (Table 2.1). Even among the CompScope sample of wage-loss systems, Michigan had a low incidence of such payments; compared with about 18 percent in Pennsylvania, 20 percent in Massachusetts, 22 percent in Louisiana, and 23 percent in Virginia (Savych, 2 In this report, we will use the two terms (lump-sum settlements and redemptions) interchangeably. 3 The study included the following states: California, Florida, Illinois, Indiana, Iowa, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, New Jersey, North Carolina, Pennsylvania, Texas, Virginia, and Wisconsin. 14

15 2011, p. 86). Note that virtually all payments for permanent disability in Michigan were paid as lump-sum settlements. Use of lump sums in Michigan may have reflected a higher underlying severity of the cases with lump sums. While fewer workers may have received lump sums in Michigan than in other wage-loss states, the settlement amount that workers received was higher. Injured workers received an average lump-sum payment of over $45,000. This measure was higher than in most other wage-loss states. This may suggest that cases with lump-sums settlements in Michigan may have included workers with more severe injuries than what we observed in other states. This observation is important for understanding how results from Michigan may apply to other states, since a different mix of injury severity may lead to different return-towork outcomes after a settlement. In comparison with the other wage-loss states in the CompScope benchmarks study (Louisiana, Massachusetts, Pennsylvania, and Virginia), Table 2.1 shows that Michigan had a lower duration of temporary disability (TD) and a moderately low average indemnity cost per claim. Table 2.1 Selected Indemnity Benefits per Claim with More Than 7 Days of Lost Time in Wage- Loss States, 2007/2010 LA MA MI PA VA 11-State Median a 2007/2010 claims with more than 7 days of lost time Average indemnity benefit per claim $19,959 $15,933 $13,590 $21,879 $16,551 $13,158 Average duration of temporary disability (weeks) PPD/LS claims as percentage of claims with more than 7 days of lost time 21.5% 20.0% 14.7% 18.3% 22.5% 45.2% Average PPD/lump-sum payment per PPD/lump-sum claim $35,362 $30,848 $45,926 $52,611 $32,487 $13,745 Claims with lump-sum settlement but no periodic PPD payments (percentage) 17.4% 14.8% 14.0% 17.2% 15.8% 13.0% Average lump-sum settlement per claim with lump-sum settlement but no periodic PPD payments $38,292 $37,210 $47,011 $54,306 $39,896 $19,801 Claims with both PPD and lump-sum payments (percentage) 0.8% 1.3% 0.2% 0.3% 1.2% 3.4% Average PPD/lump-sum payment per claim with PPD and lump-sum payments $83,342 $39,918 $61,534 $47,661 $42,591 $29,669 Claims with periodic PPD payments but no lump-sum settlement (percentage) 3.4% 3.9% 0.5% 0.8% 5.5% 22.5% Average PPD benefit per claim with periodic PPD payments but no lump-sum settlement $9,092 $3,772 $10,199 $17,096 $9,004 $6,831 Note: 2007/2010 refers to claims arising from October 1, 2006, through September 30, 2007, evaluated as of March 31, a States included in the 11-state median are the 11 non-wage-loss states included in CompScope Benchmarks, 12th Edition. The 11-state median is the state ranked 6th on a given measure; the state changes depending on the measure being evaluated. Key: PPD: permanent partial disability; PPD/LS: permanent partial disability or lump sum. Source: Savych (2011, p. 86) 15

16 HOW WORKERS MAY RESPOND TO LUMP-SUM SETTLEMENTS Next, we outline the main factors that may help explain why workers may change their employment status after a settlement. We draw insights from various conceptual models as well as previous empirical studies. These hypotheses will frame our presentation of the empirical results in Chapter 4. The extent of the worker s residual disability is perhaps the most important factor determining return to work after a settlement. Workers who are partially impaired at the time of the settlement may have limited return-to-work options after the claim is closed. Therefore, we would expect a low level of employment among workers who experience a permanent impairment as a result of an injury. This does not mean that workers with more severe remaining impairments would not be able to return to work. Some may be able to return to a part-time position or to a position that accommodates their disabilities these workers may contribute to a potential increase in employment after a settlement. The return to work may even improve with time, if the disability eases, or if workers are able to find jobs reflecting underlying impairment. Changes in employment after a settlement may also reflect workers taking opportunities to alter their careers. Some workers may choose to exit employment to retrain for a new job or occupation that accommodates their remaining disability. These employment exits may be desirable from the workers perspective if they are able to improve their longer-term employment opportunities. The settlements provide liquidity for workers to take time off from work to retrain for a new job. Although we have limited guidance from empirical studies on the role of the settlements in changing occupations (Krause, et al., 2001), this behavior can be observed if researchers observe a long window of post-settlement employment or if they have information on workers occupations. Without longer post-settlement data, it is not possible to know how many workers who exit employment return to work a few years after a settlement. Economic theory may provide one potential explanation for why workers may decrease employment in response to a lump-sum settlement. An extensive economic literature was developed to better understand how income assistance from other public programs may change willingness to work, and the lessons from this literature may apply to the workers compensation income benefits. 4 Extensive reviews of the literature are provided in Pencavel (1986), Blundell and MaCurdy (1999), and Killingsworth and Heckman (1986). For instance, many studies examine how workers may change their labor supply behavior in response to sudden changes in income (referred to as income shocks) due to an increase in spousal earnings, or large monetary payments such as a sudden increase in income due to an inheritance, or winning a lottery. Most of these studies found that positive income shocks tend to lead to lower labor earnings, with larger effects for older workers (Imbens, Rubin, and Sacerdote, 2001). Workers who experienced an increase in nonwork-related income tend to reduce their hours of work, or even exit employment. Consistent with this theory, we may expect that some of the workers who were working at the time of the settlement may have chosen to leave employment after a settlement. Workers who were not employed at the time of the settlement may have faced even lower incentives to return to employment after a settlement. A growing literature in economics provides evidence of income effects in the social insurance programs. Analysis of the Social Security Disability Insurance (SSDI) program shows that awarding of the 4 Note that lessons from the economic studies have to take into account the change in physical disability constraints that often accompany work-related injuries. Although the injury itself is a negative income shock, mostly due to changes in the physical abilities constraints, this negative income shock is often followed by a positive income shock reflected by a settlement. 16

17 SSDI benefits provides significant disincentives for return to work, even for people with impairments that were on the margin of allowance for SSDI benefits (Maestas, Mullen, and Strand, 2011). Substantial income effects are also found among workers who receive unemployment insurance benefits after job loss. Lumpsum severance payments as well as extension of potential duration of unemployment insurance benefits lead to lower return-to-work rates (Card, Chetty, and Weber, 2007; Chetty, 2008). Autor and Duggan (2007) demonstrated evidence of an income effect on the labor force participation rates of veterans corresponding with a change in their disability benefits. One study formally tested income effects on the labor supply of injured workers receiving workers compensation benefits. Hyatt (2011) found some evidence of income effects in the postinjury labor supply patterns of workers receiving temporary disability benefits in California. Economic literature also distinguishes between income and liquidity effects. Studies on the social insurance system (Chetty, 2008) suggest that the amount of benefits that workers receive is often small relative to their life-cycle income, so it may not induce large changes in labor supply due to the pure income effect. Instead, workers labor supply may change due to a liquidity effect. When workers cannot borrow against their future lump-sum payments, an income transfer may be able to influence labor supply. This is a common explanation for why the labor supply of social insurance claimants reacts to severance payments (Card, Chetty, and Weber, 2007) or the amount of cash the worker has in the bank (Chetty, 2008). Applying this evidence to workers compensation benefits, we can observe that large lump-sum payments often reflect a large proportion of the lifetime income a $50,000 payment may be equivalent to 5 percent of lifetime income, which may directly affect labor supply through an income effect. If this payment replaces a stream of future bi-weekly payments, workers may be more likely to respond to such a payment than the stream of regular payments. The strength of these economic incentives depends on the settlement amount and on the extent to which workers value their current consumption versus future wellbeing. In an extreme case, workers who care mostly about their current consumption are more likely to exit employment and use the settlement to replace lost earnings. 5 A common practice in Michigan is to terminate employment with the at-injury employer. As a result, we can expect that a majority of workers who are still employed with their at-injury employer at the time of the settlement will either exit employment or will look for a new job with a different employer. Note that this system practice should not have an effect on the workers who were already working for a different employer at the time of the settlement. Some of these workers might have found a job that accommodates their disabilities after an injury, while other workers might have taken a temporary position to help them out before the claim was closed. Change in employment among these workers does not reflect a response to system practices but rather reflects other factors that we mention in this section, such as economic incentives, which we discussed previously, or psychological responses to the settlements, which we discuss next. Change in employment after a settlement may also be attributed to the psychological concept of closure after the case is resolved. A compromise and release agreement eliminates any uncertainty about whether and how a workplace injury will be compensated. Given the stress of disputing a claim, it is not a surprise that a number of injured workers who were interviewed in Pennsylvania responded that they felt a sense of 5 Economic models of hyperbolic discounting and inconsistent time preferences may explain a decline in employment after a settlement. 17

18 relief when the claim was closed with a settlement (Torrey, 2007b). These workers were no longer concerned about the resolution of their case, they were no longer constantly reminded about their injury, and they were glad to move on with their lives. Hyatt (2010) suggests that this closure effect may explain an observed increase in employment among workers who received their PPD benefits as a settlement in California when compared with little change in the behavior of workers who received their PPD benefits as a stream of bi-weekly payments. Several potential mechanisms may contribute to this response to settlements. Workers who are uncertain about the ultimate amount of the settlement may choose to strategically avoid returning to work before the settlement. This incentive is strengthened by the common practice of terminating employment with the at-injury employer workers who do not expect to return to their at-injury employer may delay their search for a new job. Some of these workers may be concerned that their employment before the settlement may be interpreted as evidence that their injury is less severe than their true level of disability. These workers could potentially have returned to work before a settlement, but they did not do so until after a settlement. After their cases are resolved, these workers may move on by returning to work. Consistent with these conceptual mechanisms, we may expect that employment may increase after a settlement. 6 Workers who were not employed after an injury may choose to enter employment. Workers who are underemployed (or employed at less than desirable jobs) may choose to seek better employment. Responses to a lump-sum settlement may depend on worker characteristics. Workers age, gender, and marital status may influence whether and how workers are employed after the settlement. For instance, workers economic incentives to work tend to change with age older workers may find it more difficult to shift to a new occupation, and they may also be more likely to select retirement as an option after an injury. Furthermore, the speed of recovery from an injury may also differ by age, leading to differential employment changes after a settlement based on workers age. Marital status may also play an important role since labor supply decisions may depend on spousal employment and earnings. Workers with lower wages might be more likely to seek and accept settlement if they are not as attached to their jobs or their future prospects at that job are less promising. A worker s response may also depend on other characteristics, such as injury type, the amount of the settlement, and whether the settlement reflects a disputed past temporary disability. Large settlements provide greater economic incentives when workers make their decisions. Furthermore, settlements for disputed temporary disability may lead to a different response than a lump-sum payment for permanent disability or future temporary disability. These groups of cases may have different patterns of recovery at the time of the settlement. Workers who receive settlements only for disputed temporary disability may have a greater propensity to return to work after the settlement. We explore the role of these factors in Chapter 4. 6 Economists also suggest that a substitution effect may explain an increase in employment among workers who received weekly benefits up until the receipt of a lump-sum settlement. At the moment of the settlement, the relative price of leisure for these workers increases substantially because weekly benefits are no longer being received. In this case, it would not be surprising to find an increase in labor supply for some workers, for whom the change in the relative price of leisure/work dominates the income effect of the lump-sum transfer. On average, 22 percent of workers received benefits in the quarter before a settlement, reflecting a system feature that some time often passes between employers termination of temporary disability benefits and receipt of the settlement. These workers may change their employment in response to both income and a substitution effects. We found an increase in the settlements in both subgroups, suggesting that the closure effect is important even in the absence of the substitution effect. 18

19 3 DATA AND EMPIRICAL APPROACH DATA SOURCES AND MEASURES This analysis examines employment outcomes for a sample of workers who were injured in Michigan in 2004 and received a lump-sum settlement some time after their injury. We identified injured workers using the Detailed Benchmark/Evaluation (DBE) database maintained by WCRI. These claim records were provided to WCRI by national and regional payors, and include claims from private insurers, state insurance funds, and self-insured employers. The database and the processes used to clean and standardize the data are reported in earlier WCRI publications. 1 We received information on workers quarterly employment and earnings from the Michigan Unemployment Insurance Agency covering 24 quarters (6 years) from the beginning of calendar year 2003 through the end of calendar year These data were collected every quarter by the Michigan Unemployment Insurance Agency for all workers covered by the unemployment insurance program, so that if someone files for unemployment insurance benefits, the state knows what weekly benefit to pay the unemployed worker. The data cover about 97 percent of workers in the state. Almost all workers covered by unemployment insurance are also covered by workers compensation insurance, creating substantial overlap between the covered populations. Aside from those workers covered by federal workers compensation programs, virtually all workers and employers are covered by Michigan law. Only very small employers (those with less than three employees at any one time, or that do not employ one full-time worker for more than 13 weeks) are exempt. These records allow us to track employment of workers after their injury and after their lump-sum settlement. Note, however, that these employment records reflect only employment in Michigan. They do not capture potential employment if injured workers decide to move to a different state. This may suggest that the employment rate is potentially underestimated. These records also allow us to examine employment transitions for each worker. We are able to examine whether the worker returned to the at-injury employer before or after a settlement or whether the worker was able to find a new employer. For each worker we identified the employer at the time of the injury using the employer tax identifiers and tracked changes in those employer numbers over time. 2 When workers had two employers at the time of the injury, we identified their at-injury employer by finding whether other workers who were identified as injured in the DBE database working for the same employer 1 A full description of this data set can be found in Coomer et al. (2011). 2 Following previous studies, we adjusted for changes in employer numbers that are not likely to reflect a change in employer (Jacobson, Lalonde, and Sullivan, 1993; Benedetto et al., 2005). Sometimes the changes in employer numbers reflect reorganization, a merger, or change in ownership status. We identified those changes when the majority of workers stayed with the company even though the employer number changed. Those types of employment changes are not considered to be a change of employer. 19

20 were also recorded as working for the same employer in the Michigan employment and earnings dataset. We further limited the sample by including only claims with settlements before calendar year 2008 (the last year of the earnings and employment data). This assures that we observed each worker at the time of the settlement and at least four quarters after the settlement. Although this sample restriction eliminates concerns about limited comparability of the average employment measures over time, some of the workers who may be excluded from the sample may have had severe injuries and we may not have fully observed their behavior. 3 We focused our analysis on workers with a settlement, although workers without a settlement serve as a comparison group for selected examples in the Technical Appendix. For more detail about employment outcomes of workers without settlements, see Savych and Hunt (forthcoming). The analysis sample includes 2,138 workers with a lump-sum settlement. 4 The resulting dataset includes information on workers earnings before and after the settlement, as well as information on the injury, the workers themselves, and their employers. 5 Since the timing of settlement varies widely among cases, the observed post-settlement periods differ across workers. For workers who were injured in 2004 and received a settlement by the end of 2005, we observed at least three years of post-settlement employment behavior. For workers who received a settlement in the last quarter of 2007, we observed only four quarters of post-settlement employment behavior. CHARACTERISTICS OF WORKERS WITH REDEMPTIONS Table 3.1 provides background information for our sample by comparing characteristics of injured workers with and without a settlement. Most of the characteristics of the workers presented here rely on measures at the time of an injury. We found that workers with a settlement tended to be older the average age of workers with lump-sum settlements in our sample was 43 years, compared with 41 years among workers with no lump-sum settlements. This likely reflects an age-related increase in injury severity, or a greater likelihood of accepting a settlement if they are being offered one. In addition, we found that workers with lump-sum settlements tended to have lower preinjury earnings than those without a settlement workers with a settlement earned 15 percent less in the year prior to an injury than workers who did not receive a settlement after their work-related injury. This finding is consistent with the shorter tenure observed for workers with the settlement among workers with a settlement, 24 percent had been with the employer for only one or two quarters compared with 18 percent among workers with no settlements. We also found that workers who ultimately received settlements tended to be employed at smaller companies. Only 22 percent of workers with a settlement were working for employers with over 1,000 workers, compared with 27 percent for workers with no settlements. This finding likely reflects the additional costs of returning injured workers back to work for smaller companies, since they may have fewer opportunities to accommodate 3 Inclusion of the workers who received a settlement in 2008 may bias the estimates of the change in average employment. If these workers were included, they would contribute to the estimates of the average employment at the time of the settlement, but they would not be included in the estimates of the average employment one year after a settlement (since we do not have their earnings information in 2009). If we included these workers, we may overstate the extent of employment recovery after a settlement, since these workers were less likely to be employed at the time of the settlement. 4 We also retained information for 10,980 workers with an indemnity injury who did not receive a settlement in the time frame of this study. These workers are used for limited comparisons in this chapter and in the Technical Appendix. 5 The two data sets were merged in a way that ensured the confidentiality of both the workers compensation and unemployment insurance information. At no time did WCRI staff have access to individual identifiers. 20

21 various work restrictions. Estimates in Table 3.1 also suggest that workers who ultimately received lump sums tended to have more severe injuries. They spent more time on temporary disability and they had higher medical costs than workers with no lump sums. 6 Workers with lump sums spent on average 35 weeks on temporary disability, compared with 12 weeks for workers with no settlements. Workers with settlements were more likely to have injuries to their backs. It is not surprising, therefore, that they received more in indemnity benefits than workers without settlements. Workers in our lump-sum sample received over $60,000 as indemnity benefits, with an average lump-sum amount of over $48,000 (with the median lump-sum payment of $35,100). Table 3.1 Descriptive Statistics for Workers with an Indemnity Injury with and without a Settlement Percentage or Indemnity Injury, Indemnity Injury, Percentage Point with Lump Sum with No Lump Sum Difference Observations 2,138 10,980 Worker characteristics Age (mean) Age (median) Age group categories (percentage of cases) 15 to 24 years 4% 10% to 35 years 17% 22% to 45 years 32% 29% to 55 years 31% 26% to 65 years 14% 11% 3.4 Over 65 years 1% 2% -0.5 Percentage male 61% 68% -6.8 Percentage married 50% 50% -0.4 Preinjury average weekly wage (mean) $621 $ Preinjury average weekly wage (median) $533 $ Preinjury annual earnings (mean) $25,286 $29, Preinjury annual earnings (median) $22,240 $27, Tenure groups (percentage of cases) 1 or 2 quarters with the employer 24% 18% or 4 quarters with the employer 15% 15% 0.2 Over a year with the employer 59% 66% -6.6 Type of injury (percentage of cases) Neurologic spine pain 11% 4% 7.2 Spine sprains and strains 21% 15% 6.3 Fractures 5% 12% -7.5 Lacerations and contusions 5% 11% -6.0 continued 6 Note that about 3 percent of claims in the no lump-sum subsample in Table 3.1 were still open at the time of evaluation. Some of these cases may ultimately receive settlements which may increase their indemnity payments. 21

22 Table 3.1 Descriptive Statistics for Workers with an Indemnity Injury with and without a Settlement (continued) Percentage or Indemnity Injury, Indemnity Injury, Percentage Point with Lump Sum with No Lump Sum Difference Inflammations 9% 8% 0.6 Other sprains and strains 19% 19% 0.2 Upper extremity neurologic 4% 4% -0.5 Other injuries 26% 26% -0.2 Defense attorney is present 89% 5% 84.2 Detroit metropolitan area 44% 43% 0.6 Industry (percentage of cases) Manufacturing 26% 25% 1.0 Construction 12% 11% 0.7 Clerical and professional 8% 8% 0.5 Trade 11% 12% -0.9 High-risk services 22% 22% -0.6 Low-risk services 17% 14% 2.2 Other industry 4% 7% -2.8 Industry is missing 3% 4% -1.1 Average employer size at the time of an injury 1,662 1, Median employer size at the time of an injury Employer size groups (percentage of cases) 1 to 49 workers 20% 19% to 249 workers 33% 31% to 999 workers 25% 23% 2.1 Over 1,000 workers 22% 27% -5.8 Total medical payments (mean) $14,160 $7, Total medical payments (median) $6,380 $3, Total indemnity payments (mean) $61,038 $5,358 1,039.1 Total indemnity payments (median) $42,180 $2,038 1,970.0 Lump-sum settlement (mean) $48,087 n/a Lump-sum settlement (median) $35,100 n/a Weeks of temporary disability (mean) Weeks of temporary disability (median) Percentage with no temporary disability payments 43% 0% Number of quarters from the injury quarter to settlement (mean) 8 n/a Number of quarters from the injury quarter to settlement (median) 8 n/a Note: The sample includes workers who were injured in Michigan in calendar year Claim costs are evaluated as of December Key: n/a: not applicable. 22

23 Table 3.1 also highlights several important features of the system. First, workers did not receive lump sums right after an injury. On average, workers in our sample received lump-sum settlements 8 quarters after an injury. Within 6 quarters after the injury about 25 percent of workers received a settlement, and within 10 quarters after the injury about 75 percent of workers received a settlement. This suggests that changes in employment after a settlement are unlikely to reflect the initial disabling effects from an injury, although they may reflect potentially slow recovery if workers received their settlements before the extent of the permanency of their condition was established. Furthermore, Table 3.1 highlights important differences between settlements. While many workers received lump sums after initially receiving temporary disability benefits, in some cases workers received only a settlement. The latter may reflect claims where disability was initially disputed and the settlement may have included payments for past temporary disability as well as future wage losses. In 43 percent of cases, workers received only a settlement and did not receive regular temporary disability benefits. OUTLINE OF THE EMPIRICAL APPROACH We performed our empirical analysis in several steps, following the empirical questions that were outlined in Chapter 1. To examine employment changes after a settlement, we compared workers employment status in the settlement quarter and in the quarters after the settlement. This analysis helped us identify several types of workers based on their employment histories. Some workers were not employed at the time of the settlement and found employment after the settlement. Other workers were employed at the time of the settlement but later exited employment. Yet another group includes workers who did not change their behavior these workers were either consistently employed before and after the settlement or they were not working throughout the period. We started by examining the sample of workers who were employed at the time of the settlement and stopped working shortly after receipt of the lump sum. This pattern of employment change helps us understand how many workers may have exited employment after a settlement. We also examined the characteristics of these workers. For this discussion, we initially compared the differences in employment at the time of the settlement and one year after the settlement. We chose this time period to reflect medium-term adjustments in the labor supply, since it may take some time for injured workers to find a job. We can expect that the job search process in Michigan may take longer than in other states due to the relatively more depressed labor market. Furthermore, our focus on employment experience up to four quarters after a settlement reflect data limitations that we have complete employment records for all workers only through about four quarters after the settlement we did not observe employment information for some of the workers beyond the fourth quarter after the settlement. Therefore, employment measures beyond the fourth quarter after a settlement may reflect a selected sample these workers tended to receive settlements earlier after an injury. 7 We then extended the analysis by presenting employment exit rates in each of the quarters before and after the settlement. Similar to the comparisons above, this measure captures how many of the workers who were employed in a particular quarter were no longer employed in the following quarter. Unlike the 7 For instance, employment measures eight quarters after a settlement exclude workers who received settlements in 2007, since we only have employment data through

24 previous measure, which examined the employment transition one year after a settlement, this measure captures employment transitions on a quarterly basis. We provide this measure by estimating the fraction of workers who were not employed among all workers who were employed in the previous quarter. 8 An increase in employment exit in the settlement quarter may indicate that settlements lead to an immediate increase in the fraction of workers who stop working. We proceeded by examining the sample of workers who were not employed at the time of the settlement and entered employment after receipt of the lump sum. This pattern of employment change helps us understand how many workers may have returned to work after the settlement. As with the measure of employment exit discussed previously, we present estimates of employment entry at an annual as well as a quarterly basis. A quarterly measure of employment entry reveals whether workers who were not working in the previous quarter found a job in the subsequent quarter. We captured this by estimating the fraction of workers who became employed among all workers who were not employed in the previous quarter. 9 An increase in employment entry in the quarter after the settlement may indicate that settlements lead to an increase in the fraction of workers who re-entered employment. We also paid attention to the patterns of employment entry before a settlement quarter, since a dip in employment entry before a settlement may indicate that workers were delaying employment entry in anticipation of the settlement. These patterns of employment behavior (leaving employment and returning to work) also help us better understand changes in average employment after a settlement, which we present as part of our analysis. In particular, we compare average employment in each of the quarters relative to the settlement quarter for the full sample of workers with settlements in Michigan. The analysis outlined above is descriptive in nature since we do not have an appropriate comparison group of workers who did not receive a settlement. Differences in residual injury severity preclude comparisons with workers with a medical-only injury or workers with indemnity benefits without a settlement. Workers with and without a settlement may change their employment due to layoffs, childcare demands, going back to school, or the illness of a spouse. At the same time, workers with settlements may also change their employment in response to the settlement, as well as to the changes in unobserved residual injury severity. As a result, we cannot isolate the effect of the settlement by comparing workers with and without a settlement. Such an analysis can be conducted in states that allow multiple approaches for paying PPD benefits. Some states, for instance, allow PPD benefits to be paid either as a settlement or as a stream of periodic payments. The effect of the settlement may be identified by comparing these two groups of workers (as was done by Hyatt, 2010). Most of the payments for permanent injuries in Michigan, however, are paid as a lump-sum settlement, which limits the possibility of these types of comparisons. As a result, we are left with comparisons of employment patterns for the same workers before and after a settlement, which is a focus of the main body of this report, while some comparisons of labor force participation rates of workers who did and did not receive settlements is provided in Technical Appendix. Large changes in the measures around the quarters of the settlement may suggest the possibility of a response to the settlement. Focus on large changes in the measures of employment entry may help provide some information 8 The individual labor market exit measure is set to one in a quarter if the claimant worked in the current quarter and did not work in the next quarter, zero if the claimant worked in both current and next quarter, and missing otherwise. Basically, this measure reflects the fraction of workers who exit employment in a quarter. 9 The individual labor market entry measure is set to one in a quarter if the claimant did not work in the prior quarter and worked in the current quarter, zero if the claimant worked in neither the prior nor the current quarter, and missing otherwise. 24

25 about potential costs that lump sums may create. One may be concerned that workers may delay return to work in anticipation of the settlement. We may observe this phenomenon when examining quarterly employment rates in the quarters prior to the settlement. A drop in employment entry rates just before a settlement may imply that some of the workers choose to delay their return to work. In our final step, we examined whether employment changes differed by worker and claim characteristics. This analysis compares trends in average employment for different subgroups of workers based on age, tenure, gender, injury type, and settlement type. To isolate the effects of just the variables of interest, we constructed those employment trends based on linear regressions that controlled for the main characteristics. The details of this method are presented in the Technical Appendix. This method allowed us to examine the correlation between factors that are hypothesized to affect employment rates in each quarter. One advantage of the regression approach is that it isolates the effect of each separate factor from the effects of the other factors in the model. When reporting employment trends for this final step, we showed the effect of each factor by contrasting the predicted probability of being employed in each quarter in an average case under alternative values for the factor of interest. This approach holds constant all other factors in the model. To illustrate the effect of age on the likelihood of employment, we computed the predicted value for workers and claims that are otherwise identical, except that one group had workers who were under 35 years old, another had workers who were 35 to 44 yeas old, a third group included workers who were 45 to 55 years old, and a fourth group had workers who were over 55 years old. The role of workers age is reflected in the different employment rates in each age group after controlling for many other factors that may affect return to work. 25

26 4 RESULTS In this section of the report we discuss the results of our analysis. First, we provide an aggregate look at the issue by presenting changes in the average employment among all workers in our sample. Then, we provide details of the behavioral responses that may explain employment changes. We focus on two main behavioral responses to a settlement leaving employment and returning to work. In particular, we examine whether workers who were not employed at the time of settlement find employment shortly after receipt of the lump sum. We also examine whether workers who were employed at the time of settlement stop working shortly after the receipt of the lump sum. After that, we explore how employment trends may have differed by important worker and claim characteristics. This helps us better understand the role of workers characteristics in employment changes. For each of the findings we also discuss potential hypotheses that may explain the findings. CHANGE IN AVERAGE EMPLOYMENT RATE AFTER A SETTLEMENT We found that the average employment rate increased after a settlement. Employment estimates in Figure 4.1 suggest that while 25 percent of workers were employed in the quarter of the lump-sum settlement, 32 percent of workers were employed four quarters after the lump-sum settlement. 1 The difference between the periods is statistically significant at the conventional levels. 2 This transition is characterized by a gradual increase in average employment about 25 percent of workers were employed in the first quarter after the settlement, 28 percent were employed in the second quarter after the settlement, and 31 percent of workers were employed in the third quarter after the settlement. 1 We used the settlement quarter as a base employment measure in this analysis. In this chapter, we disregard the decline in employment in the quarters before a settlement observed in Figure 4.1. Average employment measures before a settlement may be misleading since the sample includes workers with different amounts of time between an injury and a settlement. Some workers in our sample received a settlement relatively quickly after their injuries, while other workers received settlements years after their injuries. Since all workers are employed at the time of the injury, the average employment rate before a settlement may be misleading if some of the workers might have had only two quarters between an injury and a settlement. Analysis in the Technical Appendix presents employment estimates by subgroups based on the number of quarters between an injury and a settlement. 2 Figure 4.1 provide estimates of the 95 percent confidence interval for the average employment rate in each quarter. The standard error of the estimate of the mean of a variable that only takes value of 0 or 1 (as is the employment measure used here) may be estimated using binomial distribution as p( 1 p) n, where p stands for the average value of the variable in question (expressed as a fraction) and n denotes the number of observations. The standard error of. 25( 1.25) 2138 = the employment in the settlement quarter is, and the standard error of the employment. 32( 1.32) 2138 = one year after a settlement is. Since the confidence intervals for the employment measures in settlement quarters and one year after the settlements do not overlap, the difference between the two measures is statistically significant. 26

27 Figure 4.1 Average Employment Rate before and after the Settlement with Confidence Intervals 40% Percentage Employed 35% 30% 25% 20% 15% 10% 27% 25% 25% 25% 28% 31% 32% 31% 31% 32% 33% 5% 0% Quarter from Settlement (settlement in quarter 0) % Employed 95% Confidence Intervals Notes: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. The 95 percent confidence interval reflects values of the average employment rate for which the difference between the estimate and the observed measure is not statistically significant at the 5 percent level. Estimates of the average employment rate presented in Figure 4.1 mask significant complexity of the employment behaviors underlying the average measure. Although we observed that the average employment rate increased by 7 percentage points within a year after a settlement, it does not mean that only 7 percent of workers changed their behavior. Likewise, even though we found no change in average employment between a settlement quarter and one quarter after a settlement, it does not imply that no workers had changed their employment. It is normal to expect that in every quarter some workers may choose to enter employment, while other workers may choose to stop working. We examine these patterns of behavior next. EMPLOYMENT BEHAVIORS AFTER A SETTLEMENT We explain employment trends in Figure 4.1 by exploring workers underlying behavior after a settlement. We examine different employment transitions based on the workers employment status in the quarter of the settlement. Workers who were already employed at the time of the settlement have the option of remaining employed or leaving employment. Workers who were not employed at the time of the settlement may choose to remain not employed or to return to work. 27

28 EMPLOYMENT EXIT AMONG THOSE WHO WERE WORKING AT THE TIME OF THE SETTLEMENT We learn about employment exits by focusing on the sample of workers who were employed at the time of the settlement. If many workers exit employment after a settlement, it may appear that a settlement may create disincentives for employment. We found that nearly one third of the workers who were employed at the time of the settlement were no longer employed one year after the settlement (see Panel A of Table 4.1). 3 As we have noted before, the observed employment exits cannot be fully attributed to the effect of the settlement. Workers without settlements often exit employment due to layoffs, increased childcare demands, retirement, desire for a better job, or going back to school. Our estimate, therefore, reflects a combination of these factors. We found that most workers exited employment quickly after the settlement. Panel A in Table 4.1 compares employment patterns among those who were employed at the time of the settlement but not employed one year later. Most of these workers (about 53 percent) exited employment after the settlement they were employed in the settlement quarter and not employed in any of the other quarters after the settlement. This finding reveals an important pattern of behavior that cannot be observed when examining average employment in Figure 4.1. It further emphasizes the need to examine employment exit behaviors when studying post-settlement employment outcomes. The strength of incentives to leave employment depends greatly on whether the worker was employed with the at-injury employer at the time of the settlement. Settlement agreements often terminate employment with the at-injury employer as part of the settlement workers who are working for their atinjury employer agree to leave their job. These agreements, however, do not apply to workers who work for a new employer (since they have already separated from their at-injury employer). As a result, differential responses between the two groups may reveal the role of the termination agreements in inducing employment exits after a settlement. Estimates in Panel B of Table 4.2 suggest that settlements may provide strong incentives to leave employment even in the absence of the termination clause. About 25 percent of workers who were working for a new employer at the time of the settlement were no longer employed a year later. This exit behavior may be attributed to the income effect as well as other non-settlement related factors. Among workers who were employed with a new employer and exited employment after a settlement (Panel C of Table 4.2), 34 percent exited in the first quarter after a settlement. This suggests that workers who did not have to leave their employer as part of the settlement agreement were slightly more likely to respond to the settlement by leaving employment in the first quarter after the settlement. Termination clauses of the settlement agreements even further increase likelihood that a worker will leave employment after a settlement. About 41 percent of workers who were employed with their at-injury employer at the time of the settlement left employment and were no longer employed one year later (Panel A of Table 4.2). Since, a quarter of workers left employment even in the absence of a termination provision, our estimates suggest that the termination clause of the settlement agreement increases the likelihood of employment exit by 16 percentage points. 3 This estimate of the fraction of workers who exited employment is higher than in a sample of comparable workers who did not receive a settlement. Of the workers without a settlement who were employed in a given quarter, about 15 percent were no longer employed one year later (Figures TA.5 and TA.6). 28

29 Table 4.1 Employment Patterns after the Settlement among Workers Who Were Employed at the Time of Settlement Employment Pattern Number of Workers % of Subgroup A. Employment patterns among those who were not employed one year after the settlement Was not employed one year after the settlement % Of which: Exited in the first quarter after the settlement and was not employed in any of the quarters after the settlement 85 53% Exited in the second quarter after the settlement and was not employed afterwards 28 18% Exited in the third quarter after the settlement and was not employed afterwards 17 11% Exited in the fourth quarter after the settlement 22 14% B. Employment patterns among those who were employed one year after the settlement Was employed one year after the settlement % Of which: Was employed in each of the quarters % Skipped one of the quarters of employment 42 12% Skipped two quarters of employment 20 5% Skipped three quarters of employment 15 4% Notes: The sample includes 525 workers injured in Michigan in 2004 who later received a lump-sum settlement and who were employed at the time of settlement. The estimates of the percentage of workers in each subgroup may not add up to a 100 since not all possible combinations of the employment patterns are included in this table. Estimates in Table TA.5 in the Technical Appendix provide greater context about the workers who exit employment by comparing characteristics of workers who stopped working to the characteristics of workers who remained employed after a settlement. While workers who exited employment were similar to the workers who stayed employed in many observed characteristics, there were several important differences in the worker and injury characteristics. For instance, workers who stopped working were more likely to be older. While about 8 percent of injured workers in our sample who remained employed were over the age of 55, 19 percent of those who stopped working were over the age of 55. In addition, workers who remained employed worked for larger employers at the time of the injury. Table 4.1 also helps examine employment patterns among workers who were employed in the settlement quarter and were employed one year later. Most of these workers were consistently employed throughout the period. About 79 percent of workers were employed in each of the quarters after a settlement. Many of the workers who remained employed had found new employers by the time of the settlement. About 75 percent of workers who were employed with a new employer at the time of the settlement remained employed one year later (Panel B of Table 4.2). Many of them remained working for the same employer. 29

30 Table 4.2 Employment Patterns after the Settlement among Workers Who Were Employed at the Time of Settlement Based on Employment with At-Injury Employer or New Employer Employment Pattern Number of Workers % of Subgroup A. Employment patterns among workers employed with at-injury employer Worker was employed with at-injury employer at the time of settlement % Of which: Was not employed one year after the settlement 73 41% Was employed one year after the settlement % Was employed with at-injury employer one year after the settlement 82 46% B. Employment patterns among workers employed with a new employer Worker was employed with a new employer at the time of settlement % Of which: Was not employed one year after the settlement 87 25% Was employed one year after the settlement % Was employed with at-settlement employer one year after the settlement % C. Employment patterns among those who were employed with a new employer at the time of settlement and were not employed one year after the settlement Number of workers who were employed with a new employer at the time of settlement and were not employed one year after the settlement 87 Of which: Exited in the first quarter after a settlement and was not employed in any of the quarters after the settlement 30 34% Exited in the second quarter after the settlement and was not employed afterwards 20 23% Exited in the third quarter after the settlement and was not employed afterwards 16 18% Exited in the fourth quarter after the settlement 15 17% Notes: The sample includes 525 workers injured in Michigan in 2004 who later received a lump-sum settlement and who were employed at the time of settlement. The estimates of the percentage of workers in each subgroup may not add up to a 100 since not all possible combinations of the employment patterns are included in this table. EMPLOYMENT ENTRY AMONG THOSE WHO WERE NOT WORKING AT THE TIME OF THE SETTLEMENT Another important behavioral response to the settlement is for workers to enter employment. We examine this response by focusing on workers who were not working at the time of the settlement and later become employed. This increase in employment for these workers may be consistent with the closure effect that we outlined in Chapter 2. These workers may feel relief that the claim is closed and may move on with their lives while seeking new employment opportunities that may accommodate their remaining disability. Our finding of an increase in employment after a settlement may also be consistent with workers strategic 30

31 decrease in employment in anticipation of a settlement. The increase in employment may also be a result of the regular changes in the labor market that may not be related to an injury. We cannot distinguish between these alternative explanations in our analysis. Analysis in Panel A of Table 4.3 suggests that about one in five workers who were not employed at the time of the settlement returned to work within a year after the settlement. Of the sample of workers who were not employed in the settlement quarter, about 19 percent were employed one year later. This entry into employment happened fairly gradually. About 28 percent of those workers who entered employment did so in the first quarter after a settlement, another 24 percent returned to work in the second quarter after the settlement, and 23 percent returned to work in the third quarter after a settlement. Not surprisingly, virtually all of the workers who returned to work found a new employer. This gradual increase in employment rate is consistent with a long job search process. The duration of the job search often increases in areas with higher unemployment rates even workers without injuries may take longer to find a new job. Therefore, it is not surprising to find that employment entry was not immediate in our sample. Table TA.6 in the Technical Appendix suggests that there are few differences in the characteristics of those workers who were not employed at the time of the settlement based on their employment one year later, although there are some important exceptions. As we have seen for other measures, age is an important predictor of whether workers became employed in the year after a settlement. Workers who return to work after a settlement were on average six years younger than workers who did not return to work (39 versus 45 years old). Workers in the two groups also differed based on the amount of settlement that they had received. Those who did not return to work received about $4,800 more as part of their settlement. Furthermore, workers who later returned to work were less likely to receive temporary disability payments before their settlement. Finally, workers who did not return to work were more likely to be married. This last factor may suggest the role that spousal income has in the household earnings. On most other measures, the two groups of workers were fairly similar. Table 4.3 Employment Patterns after the Settlement among Workers Who Were Not Employed at the Time of Settlement Employment Pattern Number of % of Workers Subgroup A. Employment patterns among those who were employed one year after the settlement Employed one year after the settlement % Of which: Entered employment in first quarter after settlement and remained employed 87 28% Entered employment in second quarter after the settlement and remained employed 76 24% Entered employment in third quarter after the settlement and remained employed 72 23% Entered employment in fourth quarter after the settlement 62 20% Of which: Returned to at-injury employer 6 2% Found a new employer % continued 31

32 Table 4.3 Employment Patterns after the Settlement among Workers Who Were Not Employed at the Time of Settlement (continued) Number of % of Employment Pattern Workers Subgroup B. Employment patterns among workers who were not employed one year after the settlement Not employed one year after the settlement 1,299 81% Of which: Was not employed in any of the quarters 1,187 91% Was employed in one of the quarters 64 5% Was employed in two of the quarters 39 3% Note: The sample includes 1,613 workers injured in Michigan in 2004 who later received a lump-sum settlement and who were not employed at the time of settlement. MAIN CONTRIBUTORS TO EMPLOYMENT CHANGE The results presented in the previous section highlight two ways of thinking about policy implications of our analysis. First, the estimates of the likelihood of employment entry or exit suggest that those behaviors are important for many workers. In fact, the finding that nearly a third of the workers who are employed at the time of the settlement were no longer employed one year later suggests that settlements provide a rather strong incentive to exit employment. Similarly, the estimates of the employment re-entry rates suggest that every fifth injured worker who was not employed at the time of the settlement returned to work one year after a settlement. Both of those behaviors are important for those interested in understanding how different behaviors are prevalent and how likely they are to contribute to potential changes in the aggregate employment. A second way to think about our estimates is to consider how different behaviors may contribute to the changes in average employment. This reflects policy interest in the number of workers who are involved in each behavior. It requires comparing the number of workers who exit employment after a settlement and the number of workers who decide to return to work after a settlement. The estimates of the percentage of workers who have different employment patterns are presented in Table 4.4. As before, we determined workers behavior after a settlement by comparing their employment at the time of the settlement and one year later. We found that the majority of workers did not change their employment status between these two points in time. About 61 percent of all workers were not employed at the time of the settlement and were not employed four quarters after the settlement quarter. Furthermore, about 17 percent of workers were employed at both points in time. 4 Estimates in Table 4.4 suggest that about 7 percent of all workers in our sample were employed at the time of the settlement and stopped working shortly after the receipt of the lump sum. 5 Furthermore, about 15 percent of all workers in our sample went from being without employment in the settlement quarter to 4 Of those workers who were employed at the time of the settlement and one year after the settlement, about 38 percent found a different employer (about 137 of the 365 workers in Table 4.2 found a different employer). 5 This estimate reflects that only those workers who were employed at the time of the settlement could exit employment. About 25 percent of workers in the full sample were employed at the time of the settlement (Figure 4.1). Of those, 30 percent exited within one year after a settlement (Table 4.1). 32

33 being employed one year later. 6 The two patterns of employment change discussed above act in the opposite direction when explaining average employment trends. The 15 percentage point increase in employment rate due to employment entry is partially offset by the 7 percentage point decline in employment due to employment exit. As a result of those two offsetting factors we can expect about an 8 percentage point increase in the average employment rate. 7 Although many of the effects discussed in Chapter 2 may play a role in explaining these behaviors, the closure effect may have greater importance in explaining changes in average employment after a workers compensation settlement. Unfortunately, we cannot attribute the change to a single hypothesized effect. The two approaches for determining the strengths of the incentives are important for a complete understanding of how employment may change after a settlement. Employment entry and employment exit rates are important behaviors that shape potential changes in employment. In our analysis for Michigan we found that those two behaviors play an important role. At the same time, the resulting change in average employment also depends on the number of workers who are employed at the time of the settlement. As we have shown, the number of workers who entered employment in Michigan was greater than the number of workers who exited employment (15 percent versus 7 percent) even though the estimated likelihood of employment re-entry was lower than the likelihood of employment exit (19 percent versus 30 percent). This resulted from a greater fraction of workers who were not employed at the time of the settlement and could re-enter employment. Expanding this thought experiment to other states requires understanding how many workers may be employed at the time of the settlement as well as the likelihood of employment exit and entry behaviors. 8 Table 4.4 Comparing Employment in the Settlement Quarter and Four Quarters after the Settlement Employed in the Settlement Quarter Employed in the 4th Quarter after the Settlement % of Workers No No 61% No Yes 15% Yes No 7% Yes Yes 17% Note: The sample includes workers injured in Michigan in 2004 who later received a lump-sum settlement. 6 This estimate reflects that only those workers who were not employed at the time of the settlement could enter employment. About 75 percent of workers in the full sample were not employed at the time of the settlement (Figure 4.1). Of those, 19 percent entered employment within a year after a settlement (Table 4.3). 7 The suggested 8 percentage point increase in employment differs from the estimated 7 percentage point increase in employment in Figure 4.1 due to rounding. 8 If the estimated exit and entry rates stay the same in other states, then a different level of employment at the time of the settlement may produce different implications about the changes in average employment. Note that the estimates we provide do not reflect a causal effect of the settlement, thus the estimates may differ in a state with a different mix of injuries or a different set of workers compensation features determining settlements. 33

34 DO WORKERS BEHAVE STRATEGICALLY? One question that is often asked about the effect of lump-sum settlements is whether workers strategically change their employment in anticipation of the settlement. In this section, we provide some illustrative evidence about the extent to which this behavior may occur. Given that we do not have a group of comparable workers without lump sums, we cannot provide a clean estimate of how lump sums may affect this behavior. To address this concern, we discuss how the results may change under various assumptions about the effect, and highlight which other conceptual explanations are consistent with the observed effects. DOES EMPLOYMENT EXIT INCREASE BEFORE A SETTLEMENT? Our first concern that we examine is whether workers stop working in anticipation of a settlement. We examine this behavior by focusing on how many workers stopped working in each of the quarters (Figure 4.2). Similar to the discussion of employment exits, this measure captures how many of the workers stopped working in a quarter they were employed in a given quarter and were no longer employed in the following quarter. 9 Estimates in Figure 4.2 suggest that workers became less likely to stop working after a settlement. In the quarters prior to the settlement, the employment exit rate was about 25 percent about 25 percent of all workers who were employed in the previous quarter would stop working. After the settlement quarter, the employment exit rate dropped to below 15 percent workers became less likely to exit employment. 10 This reduction in the likelihood of stopping work, however, does not imply that workers were previously exiting employment in anticipation of a settlement. Although some of the workers might have exited employment in anticipation of the settlement, other workers may have responded to other factors that may be important. Prior to the settlement many workers may still have been experiencing the lingering effects of the injury they may have come back to work only to discover that they could not perform all the duties. Furthermore, the sample of workers who remained employed changes over time, since the workers with the greatest residual severity may have already left employment, leading to the select group of perhaps healthier workers who were still employed. Unfortunately, we are not able to disentangle these different effects. 9 This measure is set to one in a quarter if the claimant works in the current quarter and does not work in the next quarter, zero if the claimant works in both the current and next quarter, and missing otherwise. 10 This decline in employment exit is statistically significant with the standard error of the difference of and a t- statistic of

35 Figure 4.2 Percentage of Employed Workers Who Exited Employment before and after the Settlement Percentage of Workers Who Stopped Working 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. Estimates in Figure 4.3 help isolate a spike in the likelihood that workers stopped working in the settlement quarter. This pattern may be attributed to two main effects some workers may have responded to monetary incentives associated with the settlements, while other workers may have exited employment since settlement agreements may terminate employment with the at-injury employer. Figure 4.3 presents estimates of the fraction of workers who stopped working each quarter in the subset of claims with at least seven quarters between an injury and a settlement. This sample restriction is needed to reduce the potential bias arising from stopping work as an initial response to an injury. Since workers in this sample were injured at least seven quarters prior to the settlement, the estimates near the settlement quarter are likely to reflect the effects of the residual severity as well as potential responses to the settlement. Estimates in Figure 4.3 suggest that the fraction of workers who stopped working spiked in the settlement quarter. While 20 to 25 percent of employed workers stopped working one to three quarters before the settlement, 28 percent of workers stopped working in the settlement quarter, although the increase in the employment exit rate between the settlement quarter and one quarter before the settlement is not statistically different from zero The standard error of the difference is with t-statistic of

36 Figure 4.3 Percentage of Employed Workers Who Exited Employment before and after the Settlement, Sample of Cases with at Least 7 Quarters between the Injury and the Settlement Percentage of Workers Who Stopped Working 35% 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Notes: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. The sample includes cases with at least 7 quarters between the injury and the settlement. The drop in exit rates that we observe in the quarter after the settlement in Figure 4.2 is explained mostly by a drop in exit rate from the at-injury employer. Figure 4.4 splits employment exit rates that are presented in Figure 4.2 between workers who exited from their at-injury employer and workers who exited from a new employer. We observed that the employment exit rate from the at-injury employer dropped between the quarter of the settlement and post-settlement quarters. Those workers who remained working for their at-injury employer after a settlement were less likely to leave their job. This drop in exit rate from the at-injury employer explains most of the decline in the exit rate for the full sample (Figure 4.2). Estimates in Figure 4.4 also reveal that the exit rate from new employers was relatively stable in the post-settlement quarters, although the estimates are a bit noisy. There was a spike in exits from new employers in the quarter of and immediately following the settlement, which may be consistent with the income effects hypothesis. 36

37 Figure 4.4 Employment Exit from At-Injury Employer and New Employer before and after the Settlement Percentage of Workers Who Stopped Working 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Employment Exit from New Employer Employment Exit Rate from At-Injury Employer Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. DO WORKERS DELAY RETURN TO WORK BEFORE A SETTLEMENT? Another concern that we raise in this report is whether workers delay return to work before a settlement. We examine this concern by tracking quarterly measures of employment entry (return to work) before and after a settlement. Ideally, we would compare these measures to a sample of comparable workers without a settlement. In the absence of a defensible comparison group, we are left with discussing how employment entry trends may develop in the absence of the settlement. One main assumption behind this discussion is that without a settlement we would expect that employment entry rates remain smooth over time there is no reason to expect that incentives to return to work change drastically from one quarter to another. Employment rates may either gradually increase or gradually decline over time. Deviations from these gradual changes prior to the settlements may suggest the possibility of a strategic response to the settlements. Note that this analysis only focuses on workers who may delay their return to work for a few quarters; we are not able to examine whether some workers may delay their return to work for a few years as a result of the settlement. The estimate for the full sample of workers in Figure 4.5 indicates a slight decline in employment entry in the settlement quarter, although the difference in the rates of employment entry is not statistically different from zero. 12 Furthermore, the cumulative increase in employment entry rate after the settlement 12 The standard error of the estimates of the average employment entry rate is one quarter before the settlement and in the settlement quarter. The difference between employment entry rates expressed as a frequency is

38 (presumably due to the closure effect), outweighs the relatively small decline in the employment entry rate observed in the settlement quarter. The increase in the employment entry rate of nearly 2 percentage points between the settlement quarter and one quarter after the settlement is statistically significant at the 5 percent level. 13 Estimates for the subsample of workers with at least seven quarters between an injury and a settlement in Figure 4.6 indicate a greater dip in employment entry prior to the settlement. The dip in employment entry in the quarters before the settlement suggests that some of the workers might have delayed return to employment. Estimates in Figure 4.6 suggest that the rate of return to work one quarter before the settlement was 1 2 percentage points lower than in the other quarters before the settlement. For instance, in the time between the third quarter before the settlement and the first quarter before the settlement, the employment entry rate declined 2 percentage points, a statistically significant change at a 5 percent confidence level against a one-sided hypothesis. 14 This suggests that workers with a longer duration between an injury and a settlement might be more likely to somewhat delay their return to work in anticipation of the settlement. Also note that the return-to-work rate rebounded in the quarters immediately after the settlement, suggesting that workers may have delayed return to work for only a few quarters. Figure 4.5 Percentage of Non-Employed Workers Who Returned to Work before and after the Settlement Percentage of Workers Who Returned to Work 12% 10% 8% 6% 4% 2% 0% Quarter from Settlement (settlement in quarter 0) Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. (or 0.77 percentage points). The standard error of the difference between the two proportions is , and the t- statistic is The standard error of the difference is and the t-statistic is The standard error of the difference between two measures is and the t-statistic is 1.892, leading to about a 3 percent p-value for the test that the employment entry rate decreased between the two quarters. 38

39 Figure 4.6 Percentage of Non-Employed Workers Who Returned to Work before and after the Settlement, Sample of Cases with at Least 7 Quarters between the Injury and the Settlement 9% Percentage of Workers Who Returned to Work 8% 7% 6% 5% 4% 3% 2% 1% 0% Quarter from Settlement (settlement in quarter 0) Notes: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. The sample includes cases with at least 7 quarters between the injury and the settlement. CORRELATION OF PRE- AND POST-SETTLEMENT EMPLOYMENT PATTERNS Next we examine whether those workers who changed employment prior to the settlement tended to have different employment outcomes after the settlement. Ideally, we could identify workers who strategically delayed their return to work or exited employment in anticipation of the settlement. Unfortunately, we do not know the specific reasons why workers changed their employment, and whether workers delayed their return to work. This task becomes especially difficult given the variety of employment patterns that workers may experience some workers may never return to work after an injury, other workers may return to work and stay employed, while others may come back and leave employment again. Absent specific identifiers for the workers who behaved strategically, we can only examine the outcomes of the workers who changed their employment behavior prior to the settlement. In particular, we examine whether workers who changed their employment status in anticipation of the settlement have different post-settlement employment experiences. We start by providing general information about workers pre-settlement employment experiences. Table 4.5 characterizes workers who were not employed at the time of the settlement based on their presettlement employment patterns into several groups. The majority of these workers (77 percent) were not employed in the three quarters prior to the settlement and were not employed in the quarter of the settlement. The rest of the workers were employed in some of the quarters before the settlement quarter and exited employment some time before the settlement. We also found that those workers who were employed and then exited employment were more likely to be employed in the fourth quarter after the settlement. At the same time, their post-settlement employment rate was fairly similar. With the exception of the workers 39

40 who were not employed in the first quarter before a settlement and the settlement quarter, the average employment rate was between 29 and 31 percent. Table 4.6 presents post-settlement employment information for workers who were employed in the settlement quarter, grouped based on their pre-settlement employment experiences. Most of these workers (52 percent of the subsample) were employed in the three quarters prior to the settlement and in the quarter of the settlement, with the rest of the sample fairly equally divided between all other subgroups. Post-settlement employment estimates suggest that workers who were employed in the majority of the presettlement quarters were more likely to be employed at the time of the settlement. One group of workers with a lower post-settlement employment rate in Table 4.6 includes workers who did not work for at least three quarters before a settlement and who first returned to work at the time of the settlement. Only 44 percent of workers in this group were employed in the fourth quarter after the settlement. These findings suggest a great degree of correlation between pre-settlement and post-settlement employment patterns. Table 4.5 Employment Patterns in the Quarters before the Settlement for a Subsample of Workers Who Were Not Employed at the Time of Settlement Employment in the Quarters before the Settlement Settlement Quarter % of Workers % Employed in the Fourth Quarter after the Settlement No No No No 77% 17% Yes No No No 8% 29% Yes Yes No No 6% 21% Yes Yes Yes No 4% 31% Other employment patterns 6% 30% Notes: The sample includes workers injured in Michigan in 2004 who later received a lump-sum settlement. The sample includes 1,564 workers who were not employed in the settlement quarter and had at least four quarters between an injury and settlement. Table 4.6 Employment Patterns in the Quarters before the Settlement for a Subsample of Workers Who Were Employed at the Time of Settlement Employment in the Quarters before the Settlement Settlement Quarter % of Workers % Employed in the Fourth Quarter after the Settlement Yes Yes Yes Yes 52% 77% No Yes Yes Yes 10% 73% No No Yes Yes 11% 65% No No No Yes 14% 44% Other employment patterns 12% 61% Notes: The sample includes workers injured in Michigan in 2004 who later received a lump-sum settlement. The sample includes 473 workers who were employed in the settlement quarter and had at least four quarters between an injury and settlement. 40

41 EMPLOYMENT WITH AT-INJURY EMPLOYER Finding an increase in employment after a settlement is notable given that most workers in Michigan do not return to their preinjury employment. As we show in Figure 4.7, the fraction of workers working for their at-injury employer was declining prior to a lump sum, so that only 8 percent of all workers were employed with the at-injury employer in the quarter of a lump-sum settlement. In other words, a third of those workers who were still employed in the settlement quarter were employed with their at-injury employer. The fraction working for the at-injury employer declined even further after the settlement quarter. Five percent or fewer workers were employed with the at-injury employer after a lump-sum settlement. These low rates of return to the at-injury employer are attributed to the prevailing practice of terminating employment with the company as part of the settlement agreement. Figure 4.7 Average Employment Rate and Employment with At-Injury Employer before and after the Settlement 35% 31% 32% 31% 31% 32% 33% Percentage Employed 30% 25% 20% 15% 10% 27% 14% 25% 11% 25% 25% 8% 28% 5% 5% 5% 4% 4% 4% 4% 4% 3% 0% Quarter from Settlement (settlement in quarter 0) % Employed % Employed with At-Injury Employer Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. It is also helpful to examine the characteristics of the workers who still stayed with their at-injury employer after the settlement. Estimates in Figure 4.7 suggest that workers who remained with their atinjury employer after the settlement stayed with their employer for a while. These workers may have had attributes that were valued by their employer, since their employment was not terminated as part of the settlement agreement. Not surprisingly, these workers tended to have longer preinjury tenure 12 percent of these workers stayed with the company for less than a year, compared with around 34 percent for workers who were no longer employed with their at-injury employer at the time of the settlement (Table TA.7 in the Technical Appendix). Furthermore, half of these workers who stayed with the at-injury 41

42 employer were working for a company with over 1,000 workers (compared with less than 30 percent among workers in other groups), which suggests that larger companies were more likely to offer needed accommodations for their workers. ROLE OF WORKER AND CLAIM CHARACTERISTICS IN EMPLOYMENT TRENDS Next, we examine how worker and claim characteristics may play a role in the relationship between settlements and workers employment. The figures presented in this section reflect estimates for the same claim while only varying the variable of interest. For instance, the trends across age groups were constructed while keeping all other claim features, except for age, constant. This helps focus the discussion only on the role of the measure of interest. For each of the variables, we also discuss potential conceptual reasons for observing a specific effect. Details of how the estimates were derived, and relevant tables, are provided in the Technical Appendix. EMPLOYMENT TRENDS BY WORKER CHARACTERISTICS As we mentioned in Chapter 2, the response to settlement may also depend on workers characteristics. Consistent with our expectations, Figure 4.8 shows that employment trends differed based on workers age. We found that employment increased after the settlement for workers who were under 55 at the time of the settlement and decreased slightly for workers who were over 55 years old. Among workers under 34 years old, employment increased from about 29 percent at the time of the settlement to about 44 percent within four quarters after the settlement. The employment of workers who were 35 to 44 years old increased from 29 to 39 percent. At the same time, employment for workers who were over 55 years old at the time of the settlement decreased after the settlement quarter. This different reaction of workers based on their age is important. It highlights different constraints that workers of different ages may face. Younger workers may be faster to recover after an injury and may have more opportunities to find a new job or even a new occupation. At the same time, older workers may have more difficulty finding a new job and face more constraints for changing an occupation (Chan and Stevens, 2001; Addison and Portugal, 1989). Furthermore, this result may reflect several other concerns that we have raised in this report. Older workers may have a greater residual injury severity, and their medical condition may not improve much after a settlement. In addition, older workers may also take into account retirement options when accepting a settlement, thus they are more likely to accept a settlement if they were planning to leave employment. Their incentives to return to work may be different than those of the younger workers. 42

43 Figure 4.8 Predicted Employment by Age 50% 45% 40% Percentage Employed 35% 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Age: Age: Age: Age: Over 55 Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. We found little difference in employment changes for other worker characteristics. Figure 4.9 shows employment trends by workers gender and Figure 4.10 shows employment trends by marital status. We may expect employment trends after an injury to differ based on these two measures. For instance, married workers may have other sources of income to rely on even after a settlement. Males may have different incentives to accept a settlement and to return to work after a settlement if they are primary earners in the household. Both of these figures suggest that employment increased across most subgroups of workers, suggesting that these characteristics play a small role in the differential responses to settlements. 43

44 Figure 4.9 Predicted Employment by Gender 40% 35% Percentage Employed 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Female Male Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. Figure 4.10 Predicted Employment by Marital Status 40% 35% 30% Percentage Employed 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Single Married Missing Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. 44

45 EMPLOYMENT TRENDS BY SETTLEMENT TYPE AND AMOUNT As we mentioned in Chapter 2, the response to a settlement may depend on settlement characteristics. Settlement amount is one such measure. Workers who receive larger settlements may be more likely to change employment in response to greater economic incentives. Furthermore, a larger settlement amount may also reflect a greater initial injury severity, and a greater opportunity for workers to recover after an injury. Thus, a larger settlement amount may mean both a more severe injury and a larger income/liquidity effect. Contrary to our expectations, we found little difference in employment trends across workers with different levels of settlements average employment increased among most subgroups of workers presented in Figure This figure presents employment trends for four groups of workers: those with up to $10,000 in a lump-sum settlement, those with $10,001 to $30,000 in a settlement, those with $30,001 to $70,000 in a settlement, and those with over $70,000 in a settlement. 15 Estimates in Figure 4.11 provide several important observations. First, we observed that workers with larger lump sums tended to have lower levels of employment at the time of the settlement. Among workers who received $10,000 or less in settlements, 41 percent of workers were employed at the time of the settlement. Among workers who received over $70,000 in settlements, just over 10 percent were employed at the time of settlement. This likely reflects that workers with higher settlements tended to have higher levels of disability and, as a result, lower levels of employment. Second, we also observed an increase in employment after a settlement across most of the subgroups of workers. Among workers who received settlements over $70,000, employment increased from about 10 percent in the settlement quarter to over 20 percent four quarters after the settlement. 16 This is consistent with the closure effect that may be observed across different levels of settlements. Estimates in Table TA.3 in the Technical Appendix also reveal that the difference in the employment change was driven mainly by the slight decrease in employment exit and an increase in employment entry with the settlement amount. 15 These earnings thresholds roughly approximate lump-sum settlement quartiles. 16 Table TA.2 in The Technical Appendix tests the differences in average employment between the settlement quarter and four quarters after the settlement for all subgroups. 45

46 Figure 4.11 Predicted Employment by Subgroups of Workers with Different Settlement Amounts 50% 45% 40% Percentage Employed 35% 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Up to $10,000 $10,001 up to $30,000 $30,001 up to $70,000 Over $70,000 Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. We also found that the response to settlements differs little based on the type of settlement. Figure 4.12 presents separate estimates for workers who only received a lump-sum settlement and workers who received payments for temporary disability as well as a settlement. Conceptually, we may expect to see a different response in those two groups. Settlements in claims with no previous temporary disability payments are likely to reflect payments for disputed past temporary disability, but not for future earnings losses. Contrary to our expectations, we observed an increase in employment for both groups of workers. Among workers who received only a settlement, employment increased from 24 percent at the time of the settlement to 29 percent four quarters after the settlement. Among workers with both settlements and periodic TD payments, the employment rate increased from 24 percent to 33 percent. As we show in Table TA.3 in the Technical Appendix, employment exit rates in these two groups were fairly similar, although a higher fraction of workers that received both settlements and periodic TD payments entered employment within a year after the settlement. One may also be concerned that there may be differences in the likelihood of employment with the atinjury employer based on the type of settlement. We found that in the settlement quarter, 8.2 percent of workers who received TD payments as well as a settlement were still working for their at-injury employer, and 8.5 percent of workers who only received a settlement (without periodic TD payments) were employed with their at-injury employer. This suggests that a dispute between a payor and a worker about the compensability of an injury is not correlated with employment with the at-injury employer at the time of the settlement. 46

47 Figure 4.12 Predicted Employment by Claim Type 40% 35% Percentage Employed 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Claims with Settlement and Periodic TD Claims with Settlement Only Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. Key: TD: temporary disability. We also found that employment increased across claims with different timing of settlements. Figure 4.13 suggests that average employment increased for all groups, regardless of whether the workers received the settlement within one, two, three, or four years after the injury. Figure 4.13 also highlights a difference in base employment rates at the time of the settlement. Workers who received their settlement earlier were more likely to be employed at the time of the settlement. For instance, of the few workers in our sample who received their settlement within a year after their injury, about 32 percent were employed at the time of the settlement. Of the workers who received a settlement four years after an injury, about 21 percent were employed at the time of the settlement. 47

48 Figure 4.13 Employment by Subgroups Based on Number of Years between the Time of Injury and Settlement 45% 40% 35% Percentage Employed 30% 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) 1 Year 2 Years 3 Years 4 Years Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. EMPLOYMENT CHANGES BY INJURY TYPE Injury type is another measure that some readers may expect to impact post-settlement employment. For instance, one may expect that those workers who have more subjective injuries may have a greater opportunity to behave strategically, since the extent of disability may be more difficult to observe. Contrary to those expectations, we found little difference in employment trends across different injury types. Estimates in Figure 4.14 suggest that employment increased for workers with both subjective as well as objective injuries. 48

49 Figure 4.14 Predicted Employment by Injury Type 35% 30% Percentage Employed 25% 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Objective Subjective Notes: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. The subjective injury group includes injuries categorized as spine (back and neck) sprains, strains, and non-specific pain; other sprains and strains; carpal tunnel; inflammations; and neurological spine pain. The objective injury group includes lower and upper extremity fractures; lacerations and contusions; hand lacerations; knee derangements; skin injuries; and other injuries. 49

50 5 DISCUSSION AND POLICY IMPLICATIONS IMPLICATIONS FOR PUBLIC POLICY How do our results inform public policy debates regarding allowing or restricting settlements of workers compensation benefits? Policy concerns often revolve around two main themes. One concern that is often raised is whether workers who accept a settlement are compensated for their loss equally well as those who are receiving periodic payments. Another concern often involves arguments about whether settlements create disincentives to return to work. This study provides evidence about the latter concern. Our analysis helps the reader understand the prominence of the disincentives for return to work after a settlement and how many workers may choose to exit employment. Our focus on employment exits after a lump sum reflects concerns about the potential disincentive effect of the settlements. These concerns often arise from the economic literature, which suggests that large lump-sum settlements may, under some conditions, lead to lower return-to-work rates. Workers who receive a settlement may feel less immediate need for income, and they may be willing to exit employment, even though they would not have done so without a settlement. Settlements may provide disincentives to return to work for some of the workers in our sample nearly a third of the workers in our sample who were employed at the time of the settlement left employment shortly after a settlement and did not return to work. For some of these workers settlements may have provided incentives to leave employment. If some of these workers underestimated their future income needs, they may have to rely on public benefit systems for income support (such as Social Security Disability Insurance or Medicaid), which may contribute to greater social costs. Note that some of the workers who exit employment may take the settlement as an opportunity to retrain for a less demanding occupation or for a job that fits their work restrictions. If some of the workers are able to return to work in a job that accommodates any of their residual disability, it may be a preferred outcome from the policy perspective. System practices in many states may reinforce these behaviors since settlement agreements often preclude workers from returning to their at-injury employer. Michigan workers who were working for their at-injury employer at the time of the settlement were more likely to stop working than workers who had found a new employer after an injury. About 41 percent of workers who were working for their at-injury employer at the time of the settlement left employment, while only 25 percent of workers who had found a new employer by the time of the settlement left employment one year later. The difference in the employment exit rate reflects the widely used practice that workers, as part of the settlement agreement, often agree not to return to the at-injury employer (presumably for additional consideration). While these provisions may be attractive to the employers, their benefit from a policy perspective is not clear. Many employers may be dominant sources of jobs in a given region, which limits employees ability to return to 50

51 their preinjury occupation. Furthermore, given that only a third of the workers who received a settlement were employed one year after the settlement, the practice of ceasing employment with the at-injury employer at the time of the settlement may discourage re-employment of some of the workers. Our results also suggest that some workers may respond to the settlements by returning to work. Nearly one in five workers in Michigan who were not employed at the time of the settlement returned to work within a year after the settlement. Similar behavior is highlighted by many observers of the workers compensation system. Many system participants interviewed by Belton (2011) expressed the belief that settlements allow some workers to resolve the issues that are being disputed and then, potentially, seek a new job. Several empirical studies provide evidence consistent with this interpretation. Torrey (2007a) found that some workers in Pennsylvania were willing to accept lump-sum settlements in order to move on with their lives after an injury. Hyatt (2010) found that workers in California who received their PPD benefits as a settlement were more likely to return to work than similar workers who received their PPD benefits as a stream of periodic payments. Policy concerns about settlements often go beyond the acknowledgement of the employment entry or exit behavior policymakers are also interested in how the two effects may contribute to the changes in the average employment after a settlement and which of the behaviors may dominate. Unlike our previous discussion, this discussion has to take into account how many people may exit or enter employment. Although some workers exit employment immediately after a settlement, we found that an even greater number of workers returned to work after a settlement. The magnitude of these two behavioral responses suggests that the policy concerns about incentives to stop working after a settlement may be offset by the return to work by some of the workers that may happen after the settlement. We found that average employment increased after a settlement, suggesting that employment entry after a settlement provided a more important force shaping workers behaviors. These findings suggest that in Michigan a potential closure effect (a return to work among those workers who are no longer concerned about the resolution of their case) may outweigh the potential disincentive effect from a large settlement, and thus policymakers need not be so concerned about the potential disincentives to work provided by settlements. Note that application of this finding to other states has to consider the importance of the employment exit and entry behaviors as well as the fraction of workers who are employed at the time of the settlement. We found an increase in average employment even though many workers had to find a new employer after an injury. Few workers in our sample remained employed with their at-injury employer after the settlement. These workers often have to search for a new employer and often a new occupation while competing with uninjured workers for a limited number of job openings. Since a job search is often a long process, re-employment outcomes may be improved if the settlements emphasize job opportunities that may still exist for the injured workers that accommodate their residual severity. Some of the workers may need to take time off to retrain; other workers may be able to apply their skills for other employers. While we found an increase in employment, a more important observation in this analysis is that many workers did not change their employment after a settlement. A great majority of workers in our Michigan sample stayed not employed they did not return to work before a settlement and they did not return to work within a year after a settlement. This finding may not be surprising since workers who received settlements in Michigan had suffered severe injuries. The policy debates for these workers often revolve around adequacy of the benefits if the benefits are not adequate to cover future earnings losses these workers may eventually need to rely on other sources of income support and on the support of their spouses. Lack of return to work among these workers may reflect large residual disability. In this case the 51

52 settlements may provide workers with liquidity to take the needed time off to recover and recuperate from their injury. To the extent that some of the workers may return to work later may reflect a desirable outcome from the social welfare standpoint. The policy discussion should also consider the possibility of getting some of these workers back to work. While this is likely to be a difficult task, many of these workers may benefit in the longer term from opportunities to return to productive employment. The disincentive effect of the settlement may also arise if workers choose to delay their return to work in order to improve their settlement prospects. Workers who delay return to work may have greater bargaining power to extract a larger lump sum than their future income needs may require, which may add costs to employers that are not necessarily consistent with the compensation goals of the workers compensation system. Our analysis suggests that in the full sample of claims, there is little evidence about a potential delay in return to work the dip in the employment re-entry rate in the settlement quarter is relatively small. We found a drop in the employment entry rate for workers with a longer duration of time between an injury and a settlement, which suggests that some of these workers may delay their return to work until their case is resolved. Cases that take longer to resolve allow for greater opportunity for some workers to affect their settlements. This raises the importance of having an expedited claim resolution process in determining post-settlement return to work opportunities and affecting system costs. Our analysis also provides lessons for policymakers concerned about whether a settlement should take worker and claim characteristics into account. Policymakers may be able to design policies that reduce the disincentive effect of the settlement, if they take the appropriate worker and claim characteristics into account. We found that age plays an important role in return-to-work behavior. While employment increased for workers who were less than 55 years old at the time of the settlement, employment declined slightly for workers who were over 55 years old. These findings are likely to reflect a number of factors that may be associated with age. Older workers may be slower to recover after an injury, they tend to have fewer opportunities to retrain for a new job, it may take them longer to find a new job, and they may also consider retirement as a viable alternative to returning to work. We found little difference in employment behavior across most other worker and claim characteristics, which suggests that responses to settlements are insensitive to most other claim features. Employment increased after a settlement for a majority of workers and claim types. We found that employment increased for men as well as for women, for smaller settlements as well as for larger settlements, and for objective injuries as well as for subjective injuries. This suggests that these claim characteristics may play a relatively minor role in affecting how workers behavior may change after a settlement. Our findings for Michigan are also consistent with recent empirical findings from California. Hyatt (2010) found that employment increased among workers who received settlements of PPD benefits. This consistency in findings is important since the two states use different approaches for compensating permanent disability. The two results, when considered together, suggest that the closure effect may occur in a variety of workers compensation systems. At the same time, the two states for which the analysis was conducted have unique system designs, and workers responses to settlements may differ in states with different system features. The application of policy lessons from this study to other states has to consider Michigan s unique features and the uniqueness of the set of workers who received a settlement. While these results are applicable to Michigan, the results in other states may differ due to the differences in system features and the types of workers who receive settlements. As mentioned in Savych (2011), fewer injured workers receive 52

53 settlements in Michigan than in other states. This suggests that workers who receive settlements in Michigan may have more severe injuries on average than may be observed in other states. This impacts our ability to generalize the results to other settings. Policy concerns about settlements also consider the adequacy of the benefits that workers receive. This reflects a concern about whether workers who accept a settlement are compensated for their loss equally well as those who are receiving periodic payments. Since the course of disability cannot be predicted with certainty, a settlement today may turn out to be inadequate (or excessive) in just a few years. We do not examine this concern in this report since we do not have data needed to estimate the longer-term consequences of an injury. Such an analysis would require a longer post-settlement analysis window. We focus mostly on the policy implications of the short-term changes in employment after a settlement. Furthermore, we do not know how pre- and post-settlement employment outcomes are related to earnings losses. For example, those injured workers who are working for their preinjury employer at the time of the settlement may or may not be suffering a wage loss. Some of the workers may or may not be on a light duty assignment. This may affect both the worker and employer incentives. A recent study by Savych and Hunt (forthcoming) examined the adequacy of the workers compensation benefits in Michigan. They found that in a sample of workers who received settlements, workers compensation income benefits replaced over 90 percent of the lost earnings in the four and a half years after an injury a higher replacement rate than what was found for other states. INTERPRETATIVE CAVEATS AND FURTHER RESEARCH NEEDS We are also mindful of a number of limitations of the analysis presented in this report. The scope of this report is limited to the descriptive analysis of the patterns of employment before and after a settlement. This reflects an absence of an appropriate comparison group that may reveal how employment would change without a settlement. Without an appropriate comparison group, our results may reflect a response to the settlement as well as to other factors that may be related to the postinjury recovery as well as factors that are not related to the injury. Furthermore, available data limit the nature of the empirical questions that we can examine. Although we raise multiple conceptual reasons for observed employment changes after a settlement, we cannot attribute our estimate to a specific hypothesis. Furthermore, we cannot estimate a causal effect of the settlements, since we do not know what would have happened had the workers continued receiving regular disability payments. In some cases workers could even influence whether they received a settlement, thus limiting our ability to provide a causal estimate of the effect of the settlement. Moreover, the nature of the available data only allowed us to explore a relatively short window of employment after a lump-sum settlement. It is also necessary to highlight other important policy questions that we were not able to examine due to the limitations in our data or lack of policy variation. First, future research could focus on how system features may interact to encourage or constrain return to work. For instance, some system participants suggested that strategic responses to the settlement (such as delay of employment entry) are often associated with attorney involvement in a claim. Our analysis did not examine this possibility, since we do not have any information to determine when an attorney was involved or the manner in which the dispute resolution and subsequent re-employment was affected. Furthermore, the administrative records that we used for our analysis do not reveal reasons why workers may accept lump-sum settlements and change their employment after a settlement. We do not 53

54 know why workers may have chosen to leave work, how they spent their settlement, whether they chose to get more training, and whether they were working in their preinjury occupations. A detailed survey analysis of workers satisfaction with the system and workers satisfaction with the process of settling a claim may help determine which aspects of the system may improve workers satisfaction and help them get back to work faster. Future analysis should also focus on the states with more variability in the way that settlements are administered. Greater variation in the types of settlements and approaches toward payments may help determine policies that are more beneficial for achieving the goal of return to work. This analysis should also consider the role that expansion of Medicare set-aside agreements may play in workers behavior. Future research should also consider roles that other public income support systems may play for this population. Some of the workers who receive settlements may later end up relying on the SSDI program or on Medicaid. It is important to understand how many workers may have to rely on these systems and in how many cases the prospect of future SSDI payments may limit potential for return to work after a settlement. Future analysis would benefit from records that link workers compensation claimants to SSDI records. Researchers should also consider examining a longer period of employment after a settlement. While our analysis examined return to work within one year after a settlement, a longer window of observations may sometimes by preferable. A lack of return to work within one year after a settlement may reflect workers taking time to recover and retrain for a new job. If some of the workers later return to work, this may reflect a desirable policy outcome. Without longer post-settlement data, it is not possible to know how many workers who exit employment return after the fourth post-settlement quarter. Finally, we do not have much information about workers experiences in the different parts of the economic business cycle or in the states with a different economic situation. Even before the Great Recession that started in December 2007, the unemployment rate in Michigan was higher than in many other states. Evidence from periods of economic recovery may provide different lessons for policymakers. 54

55 TECHNICAL APPENDIX EMPLOYMENT PREDICTIONS BASED ON LINEAR PROBABILITY MODELS Our analysis of the role of worker and claim characteristics in explaining employment changes after a settlement relies on the estimates from linear probability models. These regressions were used to adjust for possible differences in the claim, injury, worker, and employer characteristics. The linear probability model has a linear structure and can be written as a linear equation. 1 This approach fits a linear regression to data where a dependent variable, e.g., employment in a given quarter, takes on two possible values: 1 to indicate that the worker is employed and 0 to indicate otherwise. The coefficients from the linear probability model can be easily used to compute probabilities should the reader want to perform additional computations. We estimated separate linear models with the following structure: Y iq = α q + β q X i + e iq, for q [-2, 8] Where i: individual claim q: quarter since the settlement Y: employment, 1 if worker i is employed in quarter q, 0 otherwise α: a constant term β: represents the correlation between individual factors and employment outcome X i : individual, firm, and injury characteristics of claim i e: individual disturbances independent of X. We controlled for claim characteristics that may be important correlates of employment, including injury type, industry, firm size, and the worker s preinjury wage, age, tenure, marital status, and gender. We estimated separate regressions for each of the quarterly employment measures from two quarters prior to the settlement and up to eight quarters after the settlement. This flexible specification allowed our controls to play a different role in each of the quarters. We estimated this equation of interest using a linear probability model. This approach fits an ordinary least squares regression to the data. Our coefficients of interest (β) can be interpreted as a change in the probability of employment, given a specific measure, while controlling for other claim characteristics. We corrected standard errors for heteroscedasticity using the Huber-White estimator implemented in Stata. The coefficient estimates from the linear probability model are presented in Table TA.1. 1 An alternative approach to this analysis may use non-linear probability models, such as logit or probit. While such models may sometimes be preferable to the linear probability model, the estimates from the linear probability model are similar to the estimates from non-linear models when the average probability of employment is away from the tails of the distribution. The estimates from the linear probability model are easy to interpret, making it appealing in our circumstances. 55

56 56 Table TA.1 Regression Estimates Age at the time of injury Quarter before Settlement 2 1 Settlement Quarter Quarter after Settlement 2 3 Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE 15 to 34 years (base) 35 to 45 years (0.028) (0.028) (0.028) (0.029) * (0.030) (0.031) (0.032) 45 to 55 years * (0.028) ** (0.027) ** (0.027) ** (0.028) ** (0.030) ** (0.030) ** (0.031) Over 55 years ** (0.031) ** (0.031) ** (0.030) ** (0.030) ** (0.031) ** (0.031) ** (0.032) Gender is male (0.020) (0.020) (0.020) (0.020) ** (0.021) (0.021) (0.022) Marital status Single (base) Married (0.026) (0.025) (0.025) (0.024) (0.026) (0.026) (0.026) Missing (0.023) (0.022) (0.022) (0.022) (0.024) (0.024) (0.024) Annual preinjury earnings Less than $12,000 (base) $12,000 to $25, ** (0.027) 0.109** (0.026) 0.088** (0.025) 0.108** (0.026) 0.121** (0.028) 0.115** (0.029) 0.111** (0.029) $25,000 to $36, ** (0.030) 0.095** (0.030) 0.115** (0.030) 0.125** (0.030) 0.126** (0.032) 0.120** (0.033) 0.139** (0.033) Over $36, ** (0.032) 0.229** (0.031) 0.235** (0.031) 0.226** (0.031) 0.197** (0.033) 0.191** (0.034) 0.178** (0.034) Preinjury tenure 1 or 2 quarters with the employer (base) 3 or 4 quarters with the employer (0.030) (0.029) (0.029) (0.029) (0.031) (0.031) (0.032) Over a year with the employer (0.025) (0.024) (0.024) (0.025) (0.025) (0.026) (0.027) Detroit metropolitan area ** (0.021) ** (0.020) ** (0.020) (0.020) (0.021) ** (0.022) * (0.022) Lives close to border (0.040) (0.039) (0.037) (0.035) (0.038) (0.040) (0.040) Type of area Metropolitan area (base) Rural (0.031) (0.030) (0.030) (0.030) (0.032) (0.033) (0.032) Micropolitan area (0.039) (0.037) (0.036) (0.034) (0.037) (0.039) (0.039) Injury employer size 1 to 99 workers (base) 100 to 249 workers (0.025) (0.024) (0.024) (0.025) (0.027) (0.027) (0.027) 250 to 999 workers (0.025) (0.025) (0.024) (0.025) (0.026) (0.027) (0.027) Over 1,000 workers 0.071* (0.029) 0.090** (0.029) 0.084** (0.029) (0.028) (0.029) (0.030) (0.031) continued 1 4

57 57 Table TA.1 Regression Estimates (continued) Industry groups Quarter before Settlement Settlement Quarter after Settlement 2 1 Quarter Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Manufacturing (base) Construction ** (0.032) (0.033) (0.033) (0.034) (0.035) (0.036) (0.037) Clerical and professional (0.040) (0.038) (0.039) (0.036) (0.037) (0.038) (0.040) Trade (0.035) (0.036) (0.033) (0.034) (0.037) 0.098** (0.037) 0.093* (0.038) High-risk services (0.028) (0.027) (0.027) (0.026) (0.028) (0.029) (0.029) Low-risk services (0.030) (0.029) (0.029) (0.029) (0.030) (0.031) (0.031) Other industry (0.047) (0.045) (0.045) (0.048) 0.108* (0.050) 0.124* (0.052) (0.052) Industry is missing (0.054) (0.055) (0.049) (0.055) (0.054) (0.055) (0.055) Employed in companies with mass layoffs (0.036) (0.034) (0.034) (0.036) (0.036) (0.035) (0.037) Injury type Fractures (base) Neurologic spine pain (0.049) (0.044) (0.049) (0.045) (0.048) (0.049) (0.052) Spine sprains and strains (0.047) (0.043) (0.048) (0.043) (0.045) (0.046) (0.049) Lacerations and contusions (0.061) (0.055) (0.060) (0.056) (0.059) (0.059) (0.063) Inflammations (0.054) (0.050) (0.054) (0.049) (0.052) (0.053) (0.057) Other sprains and strains (0.048) (0.044) (0.048) (0.043) (0.046) (0.047) (0.050) Upper extremity neurologic (0.062) (0.057) (0.060) (0.059) (0.064) (0.064) (0.068) Other injuries (0.047) (0.043) (0.047) (0.042) (0.045) (0.046) (0.049) Settlement amount Less than $10,000 (base) $10,001 to $30, ** (0.029) ** (0.029) ** (0.029) ** (0.029) ** (0.030) ** (0.030) * (0.030) $30,001 to $70, ** (0.029) ** (0.028) ** (0.028) ** (0.028) ** (0.029) ** (0.029) ** (0.030) Over $70, ** (0.030) ** (0.029) ** (0.029) ** (0.029) ** (0.030) ** (0.031) ** (0.031) Settlement only, no TD payments (0.020) (0.020) (0.019) (0.019) (0.020) (0.021) (0.021) Years between an injury and settlement 1 year (base) 2 years ** (0.034) * (0.032) (0.032) * (0.032) (0.034) (0.034) (0.034) 3 years ** (0.034) ** (0.033) ** (0.033) * (0.033) (0.034) * (0.035) ** (0.035) 4 years ** (0.044) ** (0.040) ** (0.042) * (0.041) * (0.043) ** (0.044) * (0.045) Constant 0.595** (0.067) 0.492** (0.064) 0.480** (0.068) 0.485** (0.063) 0.535** (0.068) 0.520** (0.068) 0.583** (0.070) Observations 2,138 2,138 2,138 2,138 2,138 2,138 2,138 R-squared Notes: * significant at 5%; ** significant at 1%. Robust standard errors are shown in parentheses. The sample includes workers who were injured in Michigan in calendar year Key: Coef.: coefficient; SE: standard error; TD: temporary disability.

58 We used the estimates from the linear probability models to create predicted employment rates in each quarter under alternative values of the factor of interest. 2 These predictions are based on applying the regression estimates to the average case in our sample of workers with lump sums, which is described in Table 3.1, while setting the factor of interest to a certain value. For instance, to create employment trends for workers who were over 55 years old, we computed predicted employment by setting the variable that indicates workers who were over 55 years old to 1, setting other age variables to 0, and keeping the rest of the variables at the sample average. We repeated this exercise for all variables in the sample. REGRESSION ESTIMATES OF THE DIFFERENCE IN EMPLOYMENT This Technical Appendix provides tests of the differences in employment before and after the settlement across all groups of workers presented in our report. Table TA.2 presents the average employment in each of the subgroups that we present throughout the report in the settlement quarter and in the four quarters after the settlement. These estimates are consistent with what we present in the rest of the report. We found that employment tended to increase across most worker and claim characteristics after the settlement. The only group of workers with a decline in employment between the settlement quarter and four quarters after the settlement were workers who were over 55 years old at the time of the settlement (although the change for this group is not statistically different from zero). Descriptive analysis in Table TA.3 helps us understand the reasons behind employment changes presented in Table TA.2. 3 For each worker, we compared employment at the time of the settlement and one year after the settlement and estimated the fraction of workers who were employed in both periods, the fraction of workers who entered employment, and the fraction of workers who exited employment. These estimates help us better understand underlying behavioral reasons behind changes in employment in each of the subgroups based on worker characteristics. Estimates across workers in different age categories suggest that change in employment responsiveness with age was driven mainly by declines in employment entry with age. While the rates of employment exit were fairly similar across workers in different groups, the propensity of workers to enter employment declined with age. While 23 percent of workers under the age of 25 entered employment after the settlement, only 5 percent of workers who were older than 55 entered employment after the settlement (Table TA.3). The estimates in Table TA.3 provide several other important observations about how employment patterns relate to settlement characteristics. When comparing estimates of employment patterns across subgroups of workers with different settlement sizes, we found that workers with larger settlements were less likely to be consistently employed and were more likely to not be employed in both quarters. This suggests that the settlement size is correlated with the underlying injury severity and resulting employment patterns. Similar patterns of the results hold across subgroups of workers with different amounts of time between the injury and the settlement the longer the time between the injury and the settlement the higher the likelihood that the workers would not be employed in any of the quarters. 2 The linear probability model has a linear structure and can be written as a linear equation. In contrast, logit and probit probability models are non-linear. Such models are generally preferable to the linear probability model; however, the estimates from the linear probability models are similar to the estimates from non-linear models when the average employment rate is not in the tails of the distribution. The estimates from the linear probability model are easy to interpret, making it appealing in our circumstances. 3 Note that the employment change for each group implied by the estimates in Table TA.3 do not always correspond to the estimates presented in Table TA.2. Estimates in Table TA.2 provide regressions adjusted for employment probabilities, while estimates in Table TA.3 provide raw fractions of workers in each of the groups without adjusting for claim characteristics. These raw comparisons are appropriate to better understand the trends in employment for each subgroup of workers. 58

59 Table TA.2 Predicted Employment by Subgroups of Workers in the Four Quarters after the Settlement Age at the time of injury to 34 years (0.032) 35 to 45 years (0.024) 45 to 55 years (0.021) Over 55 years (0.026) Gender Male (0.017) Female (0.020) Marital status Single (0.024) Married (0.025) Missing (0.018) Annual preinjury earnings Less than $12, (0.027) $12,000 to $25, (0.023) $25,000 to $36, (0.029) Over $36, (0.030) Preinjury tenure 1 or 2 quarters with the employer (0.025) 3 or 4 quarters with the employer (0.034) Over a year with the employer (0.018) Detroit metropolitan area Yes (0.019) No (0.019) Lives close to border Yes (0.013) No (0.049) Type of area Rural (0.014) Metropolitan area (0.039) Micropolitan area (0.048) Injury employer size Quarters Relative to Settlement Quarter (settlement in quarter 0) Difference between Quarter 0 and 4 Standard Error 1 to 99 workers (0.022) 100 to 249 workers (0.026) 250 to 999 workers (0.025) Over 1,000 workers (0.032) continued 59

60 Table TA.2 Predicted Employment by Subgroups of Workers in the Four Quarters after the Settlement (continued) Industry groups Manufacturing (0.026) Construction (0.038) Clerical and professional (0.048) Trade (0.038) High-risk services (0.027) Low-risk services (0.032) Other industry (0.057) Industry is missing (0.064) Employed in companies with mass layoffs Yes (0.013) No (0.046) Injury type Quarters Relative to Settlement Quarter (settlement in quarter 0) Difference between Quarter 0 and 4 Standard Error Neurologic spine pain (0.062) Spine sprains and strains (0.034) Fractures (0.027) Lacerations and contusions (0.029) Inflammations (0.058) Other sprains and strains (0.045) Upper extremity neurologic (0.058) Other injuries (0.024) Settlement amount Less than $10, (0.032) $10,001 to $30, (0.026) $30,001 to $70, (0.022) Over $70, (0.023) Settlement type Lump-sum and other TD payments (0.017) Lump-sum settlement only (0.020) Years between an injury and settlement 1 year (0.041) 2 years (0.019) 3 years (0.020) 4 years (0.042) Notes: The sample includes 2,138 workers in Michigan who were injured in 2004 and later received a settlement. Estimates are predicted employment probabilities based on regression estimates from Table TA.1 Key: TD: temporary disability. 60

61 Table TA.3 Employment Patterns in the Settlement Quarter and One Year after the Settlement Quarter Age at the time of injury Number of Observations Not Employed in Both Quarters Employed in Both Quarters Employment Entry: Not Employed in Settlement Quarter and Employed One Year Later Employment Exit: Employed in Settlement Quarter and Not Employed One Year Later 15 to 34 years % 21.8% 22.8% 8.0% 35 to 45 years % 21.4% 17.3% 7.8% 45 to 55 years % 15.3% 13.5% 6.7% Over 55 years % 8.6% 4.9% 7.9% Gender Male 1, % 15.9% 13.9% 7.4% Female % 18.6% 15.8% 7.6% Marital status Single % 19.9% 14.8% 8.4% Married % 17.9% 12.1% 9.0% Missing % 15.0% 15.3% 6.5% Annual preinjury earnings Less than $12, % 13.9% 15.4% 5.6% $12,000 to $25, % 16.0% 15.9% 6.7% $25,000 to $36, % 16.2% 16.0% 7.6% Over $36, % 22.8% 11.1% 10.5% Preinjury tenure 1 or 2 quarters with the employer % 15.5% 15.8% 5.3% 3 or 4 quarters with the employer % 16.7% 14.7% 8.5% Over a year with the employer 1, % 18.0% 14.1% 8.4% Detroit metropolitan area Yes % 15.6% 14.3% 6.2% No 1, % 18.2% 15.0% 8.5% Lives close to border Yes % 17.0% 15.0% 9.5% No 1, % 17.1% 14.7% 7.3% Type of area Rural % 17.2% 11.9% 6.6% Metropolitan area 1, % 17.1% 14.8% 7.5% Micropolitan area % 14.5% 19.8% 9.3% Injury employer size 1 to 99 workers % 15.4% 16.0% 5.6% 100 to 249 workers % 14.1% 14.1% 7.7% 250 to 999 workers % 16.6% 12.9% 7.9% Over 1,000 workers % 23.0% 15.4% 9.5% continued 61

62 Table TA.3 Employment Patterns in the Settlement Quarter and One Year after the Settlement Quarter (continued) Industry groups Number of Observations Not Employed in Both Quarters Employed in Both Quarters Employment Entry: Not Employed in Settlement Quarter and Employed One Year Later Employment Exit: Employed in Settlement Quarter and Not Employed One Year Later Manufacturing % 14.5% 13.6% 9.9% Construction % 14.1% 16.9% 7.7% Clerical and professional % 23.7% 13.0% 6.5% Trade % 16.8% 20.8% 5.8% High-risk services % 18.6% 14.5% 5.8% Low-risk services % 17.2% 12.0% 9.0% Other industry % 22.2% 14.4% 3.3% Employed in companies with mass layoffs Yes % 16.2% 15.5% 4.1% No 1, % 17.1% 14.6% 7.7% Injury type Neurologic spine pain % 16.5% 11.3% 7.2% Spine sprains and strains % 12.7% 15.1% 3.3% Fractures % 16.6% 14.6% 9.9% Lacerations and contusions % 20.2% 14.7% 8.9% Inflammations % 21.3% 13.0% 3.7% Other sprains and strains % 20.3% 14.4% 7.5% Upper extremity neurologic % 15.0% 21.3% 5.0% Other injuries % 15.6% 14.7% 7.4% Settlement amount Less than $10, % 33.1% 12.7% 8.8% $10,001 to $30, % 21.7% 14.0% 7.4% $30,001 to $70, % 10.1% 15.5% 7.1% Over $70, % 5.3% 16.4% 6.8% Settlement type Lump-sum and other TD payments 1, % 14.9% 16.9% 7.7% Lump-sum settlement only % 19.9% 11.8% 7.2% Years between an injury and settlement 1 year % 27.2% 15.5% 9.9% 2 years % 17.4% 17.2% 8.5% 3 years % 14.5% 12.4% 6.3% 4 years % 13.6% 10.7% 4.1% Note: The sample includes 2,138 workers in Michigan who were injured in 2004 and later received a settlement. Key: TD: temporary disability. 62

63 DIFFERENCES IN WORKER CHARACTERISTICS BASED ON EMPLOYMENT PATTERNS Tables TA.4 through TA.7 provide information about workers characteristics based on the workers employment patterns. The estimates from these tables are discussed in the main body of the report. 63

64 Table TA.4 Characteristics of Workers Based on Employment Status in the Settlement Quarter Employed in the Settlement Quarter Not Employed in the Settlement Quarter Percentage or Percentage Point Difference Observations 525 1,613 Worker characteristics Age (mean) Age (median) Age group categories (percentage of cases) 15 to 24 years 7% 3% to 35 years 19% 17% to 45 years 38% 30% to 55 years 26% 33% to 65 years 10% 16% -6.0 Over 65 years 1% 1% 0.2 Percentage male 57% 62% -5.4 Percentage married 49% 50% -0.6 Preinjury average weekly wage (mean) $662 $ Preinjury average weekly wage (median) $562 $ Preinjury annual earnings (mean) $28,775 $24, Preinjury annual earnings (median) $25,520 $21, Tenure groups (percentage of cases) 1 or 2 quarters with the employer 19% 26% or 4 quarters with the employer 16% 14% 1.2 Over a year with the employer 64% 58% 6.3 Type of injury (percentage of cases) Neurologic spine pain 7% 13% -5.3 Spine sprains and strains 23% 21% 2.2 Fractures 4% 5% -0.2 Lacerations and contusions 5% 5% 0.1 Inflammations 10% 8% 1.5 Other sprains and strains 23% 18% 4.8 Upper extremity neurologic 3% 4% -0.9 Other injuries 24% 26% -2.2 Defense attorney is present 85% 90% -5.5 Detroit metropolitan area 39% 45% -6.5 Industry (percentage of cases) Manufacturing 26% 26% -0.4 Construction 11% 12% -1.9 Clerical and professional 10% 8% 2.4 Trade 10% 11% -1.3 High-risk services 21% 22% -0.4 Low-risk services 18% 16% 1.4 Other industry 5% 4% 0.2 Industry is missing 3% 3% -0.8 continued 64

65 Table TA.4 Characteristics of Workers Based on Employment Status in the Settlement Quarter (continued) Employed in the Settlement Quarter Not Employed in the Settlement Quarter Percentage or Percentage Point Difference Average employer size at the time of an injury 2,283 1, Median employer size at the time of an injury Employer size groups (percentage of cases) 1 to 49 workers 17% 21% to 249 workers 29% 34% to 999 workers 25% 25% 0.0 Over 1,000 workers 29% 19% 9.3 Total medical payments (mean) $10,605 $15, Total medical payments (median) $4,727 $7, Total indemnity payments (mean) $39,873 $67, Total indemnity payments (median) $20,900 $50, Lump-sum settlement (mean) $31,075 $53, Lump-sum settlement (median) $15,580 $43, Weeks of temporary disability (mean) Weeks of temporary disability (median) Percentage with no TD payments 47% 41% 6.0 Number of quarters from the injury quarter to settlement (mean) Number of quarters from the injury quarter to settlement (median) Employment patterns between an injury and settlement Number of quarters with a job between an injury and settlement (mean) Number of quarters with a job between an injury and settlement (median) Fraction of all postinjury quarters with a job 72% 23% 48.9 Percentage of workers with no employment after an injury 0% 30% Notes: The sample includes workers who were injured in Michigan in calendar year Claim costs are evaluated as of December Key: TD: temporary disability. 65

66 Table TA.5 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement Not Employed One Year after the Settlement Employed One Year after the Settlement Percentage or Percentage Point Difference Observations Worker characteristics Age (mean) Age (median) Age group categories (percentage of cases) 15 to 24 years 4% 8% to 35 years 20% 19% to 45 years 33% 39% to 55 years 25% 26% to 65 years 16% 7% 8.5 Over 65 years 3% 1% 1.7 Percentage male 57% 57% 0.4 Percentage married 54% 47% 6.9 Preinjury average weekly wage (mean) $677 $ Preinjury average weekly wage (median) $603 $ Preinjury annual earnings (mean) $28,149 $29, Preinjury annual earnings (median) $26,529 $25, Tenure groups (percentage of cases) 1 or 2 quarters with the employer 17% 20% or 4 quarters with the employer 16% 15% 0.9 Over a year with the employer 66% 64% 2.1 Type of injury (percentage of cases) Neurologic spine pain 5% 8% -3.5 Spine sprains and strains 28% 21% 7.6 Fractures 4% 4% 0.0 Lacerations and contusions 3% 6% -3.8 Inflammations 9% 10% -1.7 Other sprains and strains 23% 23% 0.1 Upper extremity neurologic 3% 3% -0.8 Other injuries 26% 24% 2.1 Defense attorney is present 87% 84% 2.8 Detroit metropolitan area 36% 40% -3.8 Industry (percentage of cases) Manufacturing 34% 22% 12.1 Construction 12% 10% 2.2 Clerical and professional 7% 11% -4.3 Trade 8% 11% -2.5 High-risk services 17% 23% -6.9 Low-risk services 20% 17% 3.1 Other industry 2% 6% -3.7 Industry is missing 2% 3% -1.1 continued 66

67 Table TA.5 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement (continued) Not Employed One Year after the Settlement Employed One Year after the Settlement Percentage or Percentage Point Difference Average employer size at the time of an injury 1,852 2, Median employer size at the time of an injury Employer size groups (percentage of cases) 1 to 49 workers 15% 18% to 249 workers 31% 29% to 999 workers 27% 25% 2.2 Over 1,000 workers 28% 29% -1.5 Total medical payments (mean) $14,572 $8, Total medical payments (median) $6,109 $4, Total indemnity payments (mean) $55,465 $33, Total indemnity payments (median) $34,000 $17, Lump-sum settlement (mean) $44,305 $25, Lump-sum settlement (median) $29,750 $13, Weeks of temporary disability (mean) Weeks of temporary disability (median) Percentage with no TD payments 41% 50% -8.9 Number of quarters from the injury quarter to settlement (mean) Number of quarters from the injury quarter to settlement (median) Employment patterns between an injury and settlement Number of quarters with a job between an injury and settlement (mean) Number of quarters with a job between an injury and settlement (median) Fraction of all postinjury quarters with a job 66% 75% -9.7 Percentage employed with at-injury employer in the settlement quarter 46% 29% 16.9 Notes: The sample includes workers who were injured in Michigan in calendar year Claim costs are evaluated as of December Key: TD: temporary disability. 67

68 Table TA.6 Characteristics of Workers Who Were Not Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement Not Employed One Year after the Settlement Employed One Year after the Settlement Percentage or Percentage Point Difference Observations 1, Worker characteristics Age (mean) Age (median) Age group categories (percentage of cases) 15 to 24 years 2% 8% to 35 years 15% 23% to 45 years 29% 37% to 55 years 34% 27% to 65 years 18% 4% 13.9 Over 65 years 1% 0% 1.0 Percentage male 64% 58% 5.7 Percentage married 52% 43% 9.0 Preinjury average weekly wage (mean) $610 $ Preinjury average weekly wage (median) $530 $ Preinjury annual earnings (mean) $24,260 $23, Preinjury annual earnings (median) $21,443 $22, Tenure groups (percentage of cases) 1 or 2 quarters with the employer 26% 26% or 4 quarters with the employer 14% 15% -0.3 Over a year with the employer 58% 57% 0.6 Type of injury (percentage of cases) Neurologic spine pain 13% 12% 1.2 Spine sprains and strains 21% 21% -0.5 Fractures 5% 4% 1.3 Lacerations and contusions 5% 4% 0.7 Inflammations 8% 9% -0.3 Other sprains and strains 18% 19% -1.4 Upper extremity neurologic 4% 5% -1.8 Other injuries 26% 26% 0.7 Defense attorney is present 91% 89% 1.6 Detroit metropolitan area 46% 43% 3.4 Industry (percentage of cases) Manufacturing 27% 24% 2.6 Construction 12% 14% -1.7 Clerical and professional 8% 7% 0.4 Trade 10% 15% -5.2 High-risk services 22% 21% 0.4 Low-risk services 17% 13% 3.4 Other industry 4% 4% 0.0 Industry is missing 4% 3% 0.4 continued 68

69 Table TA.6 Characteristics of Workers Who Were Not Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement (continued) Not Employed One Year after the Settlement Employed One Year after the Settlement Percentage or Percentage Point Difference Average employer size at the time of an injury 1,471 1, Median employer size at the time of an injury Employer size groups (percentage of cases) 1 to 49 workers 21% 23% to 249 workers 35% 32% to 999 workers 26% 22% 3.7 Over 1,000 workers 18% 23% -4.1 Total medical payments (mean) $15,254 $15, Total medical payments (median) $7,081 $8, Total indemnity payments (mean) $68,764 $64, Total indemnity payments (median) $51,380 $48, Lump-sum settlement (mean) $54,565 $49, Lump-sum settlement (median) $44,900 $40, Weeks of temporary disability (mean) Weeks of temporary disability (median) Percentage with no TD payments 43% 34% 8.8 Number of quarters from the injury quarter to settlement (mean) Number of quarters from the injury quarter to settlement (median) Employment patterns between an injury and settlement Number of quarters with a job between an injury and settlement (mean) Number of quarters with a job between an injury and settlement (median) Fraction of all postinjury quarters with a job 23% 27% -4.2 Percentage of workers with no employment after an injury 30% 28% 2.3 Notes: The sample includes workers who were injured in Michigan in calendar year Claim costs are evaluated as of December Key: TD: temporary disability. 69

70 Table TA.7 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement with At-Injury Employer or New Employer Employed in Settlement Quarter Not Employed One Year after the Settlement Employed One Year after the Settlement with At- Injury Employer Employed One Year after the Settlement with a New Employer Observations Worker characteristics Age (mean) Age (median) Age group categories (percentage of cases) 15 to 24 years 4% 1% 10% 25 to 35 years 20% 11% 21% 35 to 45 years 33% 35% 41% 45 to 55 years 25% 40% 21% 55 to 65 years 16% 12% 6% Over 65 years 3% 1% 1% Percentage male 57% 60% 56% Percentage married 54% 45% 48% Preinjury average weekly wage (mean) $677 $847 $597 Preinjury average weekly wage (median) $603 $796 $498 Preinjury annual earnings (mean) $28,149 $44,307 $24,489 Preinjury annual earnings (median) $26,529 $41,719 $21,595 Tenure groups (percentage of cases) 1 or 2 quarters with the employer 17% 4% 25% 3 or 4 quarters with the employer 16% 8% 17% Over a year with the employer 66% 88% 56% Type of injury (percentage of cases) Neurologic spine pain 5% 5% 10% Spine sprains and strains 28% 21% 20% Fractures 4% 4% 5% Lacerations and contusions 3% 6% 6% Inflammations 9% 11% 10% Other sprains and strains 23% 25% 22% Upper extremity neurologic 3% 1% 4% Other injuries 26% 27% 22% Defense attorney is present 87% 68% 89% Detroit metropolitan area 36% 48% 38% Industry (percentage of cases) Manufacturing 34% 23% 22% Construction 12% 5% 11% Clerical and professional 7% 15% 10% Trade 8% 10% 11% High-risk services 17% 22% 24% Low-risk services 20% 16% 17% Other industry 2% 10% 4% Industry is missing 2% 2% 3% continued 70

71 Table TA.7 Characteristics of Workers Who Were Employed in the Settlement Quarter Based on Their Employment Status One Year after the Settlement with At-Injury Employer or New Employer (continued) Employed in Settlement Quarter Not Employed One Year after the Settlement Employed One Year after the Settlement with At- Injury Employer Employed One Year after the Settlement with a New Employer Average employer size at the time of an injury 1,852 5,899 1,448 Median employer size at the time of an injury 285 1, Employer size groups (percentage of cases) 1 to 49 workers 15% 10% 20% 50 to 249 workers 31% 19% 32% 250 to 999 workers 27% 21% 26% Over 1,000 workers 28% 50% 23% Total medical payments (mean) $14,572 $7,056 $9,408 Total medical payments (median) $6,109 $3,007 $4,564 Total indemnity payments (mean) $55,465 $21,814 $36,394 Total indemnity payments (median) $34,000 $13,606 $19,900 Lump-sum settlement (mean) $44,305 $18,033 $27,442 Lump-sum settlement (median) $29,750 $9,991 $14,900 Weeks of temporary disability (mean) Weeks of temporary disability (median) Percentage with no TD payments 41% 56% 48% Number of quarters from the injury quarter to settlement (mean) Number of quarters from the injury quarter to settlement (median) Employment patterns between an injury and settlement Number of quarters with a job between an injury and settlement (mean) Number of quarters with a job between an injury and settlement (median) Fraction of all postinjury quarters with a job 66% 92% 71% Percentage of workers with no employment after an injury 0% 0% 0% Notes: The sample includes workers who were injured in Michigan in calendar year Claim costs are evaluated as of December Key: TD: temporary disability. 71

72 TRENDS IN AVERAGE EMPLOYMENT BEFORE A SETTLEMENT It is beneficial to understand how employment changes leading up to the settlement. A severe injury often leads to a large decline in employment and earning in the quarters after an injury, as many workers in our sample spent a long time away from work. Figure TA.1 shows employment trends between the injury and the settlement for groups of workers based on the time between the injury quarter and the settlement quarter. We focused on workers with 4, 7, 10, and 13 quarters between the injury quarter and the settlement quarter. 4 For each of the groups in this figure, the point of maximum employment reflects the quarter of an injury. 5 Consistent with the discussion of employment trends presented by Savych and Hunt (forthcoming), we observed an increase in employment leading up to an injury. This is explained by employment entry before an injury, since workers can only have a work-related injury if they are employed. While the main focus of the study is on employment changes before and after the settlement, Figure TA.1 helps us understand that, even before the settlement, many of the workers experienced large declines in employment. Figure TA.1 Average Employment before the Settlement, by Groups of Workers Based on Number of Quarters between the Injury and the Settlement 100% 90% Percentage Employed 80% 70% 60% 50% 40% 30% 20% 10% 0% Quarter before Settlement (settlement in quarter 0) 4 Quarters 7 Quarters 10 Quarters 13 Quarters Note: The sample includes workers who were injured in Michigan in calendar year 2004 who later received a lumpsum settlement. 4 This choice of subgroups helps us present earnings trends that are not distorted due to the averaging of workers employment with different experiences/maturities. Figures that average pre-settlement experiences of all workers would not be appropriate since they cannot distinguish between two factors: the fraction of workers with an injury and the reduction in employment after an injury. 5 Note that in some cases we do not see 100 percent employment in the quarter of an injury. This is due to injuries that were close to the beginning or the end of a quarter. 72

73 The similarities in employment profiles before an injury across the subgroups in Figure TA.1 are striking. Employment rates dropped sharply across most subgroups of workers in the first few quarters after an injury. This decline in the first few quarters after an injury reflects the temporary disability aspect of the injury. After that, the rates of decline slowed, although employment was still decreasing after the initial drop until the time of the settlement. This gradual decline raises questions about the extent to which delayed resolution of the claim may contribute to a drop in employment. These concerns are especially important for longer-term return to work, since many economic studies showed that long-term absences from work may have negative consequences for workers subsequent employment and earnings opportunities. CONTRIBUTION OF THE EMPLOYMENT ENTRY AND EXIT BEHAVIOR TO MEASURES OF AVERAGE EMPLOYMENT We found that two behavioral responses described in Chapter 4 contributed to an increase in employment after a settlement a decrease in the fraction of workers who stopped working (Figure 4.2) was accompanied by an increase in the fraction of workers who found new employment (Figure 4.5). Measures of employment entry and exit help us better understand employment transitions in each of the quarters. Consider, for example, the employment transition between the settlement quarter and the first quarter after the settlement. Estimates in Figure 4.1 suggest that the average employment changed relatively little between the two quarters. At the same time, the estimates of employment exit and entry suggest that quite a few workers changed their employment patterns. Consider, for instance, the 28 percent exit rate recorded for the settlement quarter (quarter 0). This means that 28 percent of those workers who were employed in this quarter will not be employed in the next quarter. Since about 25 percent of workers were employed in the settlement quarter, we can expect that 7 percent of all workers observed in the settlement quarter (quarter 0) exited employment the transition from being employed to not being employed in the next quarter. Furthermore, consider a 9 percent employment entry rate in quarter 1. This implies that 9 percent of workers who were not employed in the settlement quarter entered employment and became employed in the first post-settlement quarter. Since about 75 percent of workers were not employed in the previous quarter, this transition type affected about 7 percent of all workers in the sample. These two changes employment exit and employment entry offset each other, leading to no change in the average employment rate between the settlement quarter and the first quarter after the settlement. The drop in the employment exit rate in the first quarter after the settlement contributed to an increase in average employment between quarters 1 and 2 after the settlement. Similar to the previous quarter, the employment entry rate in quarter 2 was just below 10 percent nearly 10 percent of workers who were not employed in quarter 1 became employed in quarter 2. This behavior alone could have contributed to an increase in employment of about 7.5 percentage points. At the same time, the employment exit rate in quarter 1 was 15 percent (15 percent of all workers who were employed in quarter 1 were no longer employed in quarter 2). This implies that just over 3 percent of all workers exited employment between quarters 1 and 2. This employment exit rate was not enough to offset an increase in employment due to employment entry, and we observed an increase in employment rate from about 25 percent to about 28 percent between quarters 1 and 2 after the settlement. The smaller employment exit rate was no longer sufficient to offset an increase in employment due to new workers entering employment. In general, it is not surprising to observe some of this volatility in the labor market. Even in the normal labor market, workers leave employment, look for new jobs, and change employers. Our estimates in the 73

74 Technical Appendix show that workers who received settlements had lower rates of employment entry and higher rates of employment exit than workers who did not receive settlements. This suggests that workers who ultimately receive lump-sum settlements have very different patterns of employment entry and exit behavior than workers who do not receive settlements. COMPARISONS TO WORKERS WITHOUT SETTLEMENTS In this section, we compare employment outcomes for workers with lump-sum settlements to outcomes for workers who did not receive settlements. The objective of this discussion is to contrast the estimates presented in the rest of this report to the estimates that we may observe in a sample of workers with a greater attachment to the labor market. These comparisons should not be interpreted as a causal effect of the settlement they are likely to reflect the difference in the residual injury severity and claim development. MATCHING APPROACH The comparison groups of workers with medical-only injuries and workers with indemnity injuries who did not receive lump-sum settlements were created using propensity score matching methods. For each worker with the settlement, we found up to five workers without settlements who had similar propensity scores. This assures that workers who are chosen as comparison group workers are very similar, in terms of observed characteristics, to the workers with settlements. Matches between the groups take into account the following variables: age, gender, preinjury tenure, preinjury earnings, preinjury job attachment, industry, and employer size. With the intention of constructing a flexible specification, we included various functions of control variables: levels, squares, and interactions (by interacting some of the variables with measures of average preinjury earnings). The variables were also interacted with age. We conducted the analysis separately for the two separate control groups: workers with a medical-only injury and workers with an indemnity injury and no settlement. The logits were estimated in Stata using programs written by Becker and Ichino (2002). The dependent variable for the propensity match was receipt of the settlement. These models included routines to check the balancing properties of the model a technical requirement that workers with a lump sum and workers in the comparison groups have observed characteristics that are not statistically different from each other. For each of the workers from a lump-sum sample, we chose up to five workers with the closest propensity score. 6 CHOICE OF QUARTER 0 FOR WORKERS WITHOUT A SETTLEMENT One concern about using non-settlement workers as a comparison group is that these workers do not have a settlement quarter to which we peg their employment trends. While we observed the settlement quarter for workers with a settlement (denoted as quarter 0 in this Technical Appendix), this measure is not available for workers who did not receive a settlement. As a result, we had to choose an arbitrary quarter to serve as quarter 0 for our comparison group sample. In general, we wanted to choose quarter 0 for the comparison group to reflect what happens to employment trends at a time that is similar to the time after 6 Balance tables are available from the author upon request. 74

75 an injury that we observed for the settlement group. We tried several approaches that provided similar results. Our preferred approach was to choose quarter 0 for the comparison group so that the time between an injury and quarter 0 was the same for each of the matched pairs of comparison and lump-sum cases. We achieved this by first estimating the time between an injury and a settlement for each worker with a settlement. Then, we applied this time lag to every comparison case that was matched to the case with the settlement. This approach recognizes large variability in the time to settlement between cases. Some workers received settlements 6 quarters after an injury, while other workers may have received settlements 9, or even 12, quarters after an injury. For instance, if a case with a medical-only injury was matched to a settlement case where the settlement was 12 quarters after an injury, we chose the 12th quarter after an injury as quarter 0 for the settlement case. An alternative approach is to use the same quarter after the settlement for all workers in the control group. Since, on average, workers received settlements 8 quarters after an injury (Table 3.1), we pegged employment trends of the comparison group workers to the 8th quarter after an injury. This assures that we examined employment trends of these workers 8 quarters after an injury. This approach, however, may be problematic since it provides for a different role of economic conditions in the two subsamples. Even though the results from both approaches were similar, we chose to use the preferred approach for this analysis. OUTCOMES OF WORKERS WITH NO SETTLEMENTS Figure TA.2 presents employment trends for workers with a lump-sum settlement, workers with a medicalonly injury, and workers with an indemnity injury and no settlement. While we found that workers with a lump-sum settlement were less likely to be employed at the time of the settlement than workers in the two other groups, these groups followed different employment trends. The employment rate of workers without a settlement was declining, while the employment rate of workers with a lump-sum settlement was increasing. This likely reflects the differences across subgroups in the postinjury recovery stage. Most of the workers without a settlement had probably recovered and been back to employment for a while by nine quarters after an injury. Their employment trends were driven by other factors that led workers to leave employment. At the same time, workers with settlements were just starting their post-recovery employment stage, and some of them may have responded by increasing their employment after the settlement. Figures TA.3 and TA.4 present employment exit and entry rates of workers with and without a settlement. These comparisons reflect the role that residual injury severity may play in the worker s ability to go back to work. In general, we found that workers with a settlement had higher rates of employment exit, and lower rates of employment entry, than workers without a settlement. This is not surprising, since workers who receive a lump-sum settlement likely have a higher rate of residual disability, and higher rates of employment exit may be consistent with workers recognizing that their disability is not fully accommodated. 75

76 Figure TA.2 Employment Trends across Cases with and without a Settlement 90% 80% Percentage Employed 70% 60% 50% 40% 30% 20% 10% 0% Quarter from Settlement (settlement in quarter 0) Medical-Only Injuries Indemnity Injuries with Settlement Indemnity Injuries with No Settlement Note: The sample includes workers who were injured in Michigan in calendar year Figure TA.3 Employment Exit Rate 30% 25% Employment Exit Rate 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Medical-Only Injuries Indemnity Injuries with Settlement Indemnity Injuries with No Settlement Note: The sample includes workers who were injured in Michigan in calendar year

77 Figure TA.4 Employment Entry Rate 25% Employment Entry Rate 20% 15% 10% 5% 0% Quarter from Settlement (settlement in quarter 0) Medical-Only Injuries Indemnity Injuries with Settlement Indemnity Injuries with No Settlement Note: The sample includes workers who were injured in Michigan in calendar year SUBGROUPS BASED ON EMPLOYMENT WITH AT-INJURY EMPLOYER In this section of the Technical Appendix, we extend our discussion of employment patterns to whether the worker was working for the at-injury employer in the settlement quarter. While Tables 4.1 through 4.3 compare employment patterns for these workers at two points in time (settlement quarter and one year after the settlement), Figures TA.5 and TA.6 present employment trends for each of the quarters. Figure TA.5 shows the employment rate in the sample of workers who were employed with the atinjury employer at the time of the settlement. We also present estimates of employment for the comparison workers who did not have settlements. Since the sample includes only workers who were working for the atinjury employer at the time of the settlement (or were working for the at-injury employer in quarter 0, in the case of the comparison group), quarter 0 represents 100 percent employment for this sample. The average employment rate declined in both settlement and comparison groups after the settlement quarter. 77

78 Figure TA.5 Employment Trends among Workers Who Were Employed with At-Injury Employer in Quarter 0 Percentage Employed 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Quarter from Settlement (settlement in quarter 0) Indemnity Injuries with Settlement Medical-Only Injuries Indemnity Injuries with No Settlement Note: The sample includes workers who were injured in Michigan in calendar year Figure TA.6 Employment Trends among Workers Who Were Employed with New Employer in Quarter 0 Percentage Employed 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Quarter from Settlement (settlement in quarter 0) Indemnity Injuries with Settlement Medical-Only Injuries Indemnity Injuries with No Settlement Note: The sample includes workers who were injured in Michigan in calendar year

79 Figure TA.6 displays employment trends for workers who worked for a new employer in the settlement quarter. As we have mentioned in Chapter 2, workers who have left their at-injury employer and have found a new job are no longer affected by the requirement to leave their employer. Their response may reflect the change in employment in response to the economic incentives that the settlements may provide. It is also important to highlight that the differences between the settlement and comparison groups may not reflect a true causal effect of the settlements. Even though the analysis adjusted for many observed characteristics, a number of unobserved characteristics may limit the comparisons. Most importantly, the residual medical severity was likely to be very different between the groups. Furthermore, employed workers in the two samples may have had different motivation for work and may have faced different physical constraints on employment. Workers with medical-only injuries were likely to change their employment mainly in response to the changes in the labor market situation if they found a better job or if their job was eliminated. At the time, workers with settlements may have changed their employment due to the physical limitations that their injury may have imposed. 79

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82 Other WCRI Publications Medical Costs, Utilization, and Health Care Delivery designing workers compensation medical fee schedules. Olesya Fomenko and Te-Chun Liu. June wc compscope medical benchmarks for california, 12th edition. Rui Yang. May wc compscope medical benchmarks for florida, 12th edition. Rui Yang. May wc illinois medical cost drivers prior to 2011 reforms: compscope medical benchmarks, 12th edition. Evelina Radeva. May wc compscope medical benchmarks for louisiana, 12th edition. Carol A. Telles. May wc compscope medical benchmarks for maryland, 12th edition. Rui Yang. May wc compscope medical benchmarks for massachusetts, 12th edition. Evelina Radeva. May wc compscope medical benchmarks for michigan, 12th edition. Bogdan Savych. May wc medical benchmarks for minnesota, compscope 12th edition. Sharon E. Belton. May wc medical benchmarks for new jersey, compscope 12th edition. Carol A. Telles. May wc early impact of outpatient fee schedule reduction in north carolina: compscope medical benchmarks, 12th edition. Carol A. Telles. May wc compscope medical benchmarks for pennsylvania, 12th edition. Evelina Radeva. May wc monitoring the impact of reforms in texas: compscope medical benchmarks, 12th edition. Carol A. Telles. May wc medical benchmarks for virginia, compscope 12th edition. Carol A. Telles. May wc compscope medical benchmarks for wisconsin, 12th edition. Evelina Radeva. May wc why surgeon owners of ambulatory surgical centers do more surgery than non-owners. Christine A. Yee. May wc wcri medical price index for workers compensation, fourth edition (mpi-wc). Rui Yang and Olesya Fomenko. March wc hospital outpatient cost index for workers compensation. Rui Yang and Olesya Fomenko. January wc wcri medical price index for workers compensation, third edition (mpi-wc). Rui Yang. August wc prescription benchmarks, 2nd edition: trends and interstate comparisons. Dongchun Wang and Te-Chun Liu. July wc prescription benchmarks for florida, 2nd edition. Dongchun Wang and Te-Chun Liu. July wc prescription benchmarks for washington. Dongchun Wang and Te-Chun Liu. July wc interstate variations in use of narcotics. Dongchun Wang, Kathryn Mueller, and Dean Hashimoto. July wc impact of preauthorization on medical care in texas. Christine A. Yee, Philip S. Borba, and Nicole Coomer. June wc workers' compensation medical cost containment: a national inventory, April wc prescription benchmarks for minnesota. Dongchun Wang and Richard A. Victor. October wc prescription benchmarks for florida. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for illinois. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for louisiana. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for maryland. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for massachusetts. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for michigan. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for north carolina. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for new jersey. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for pennsylvania. Dongchun Wang and Richard A. Victor. March wc

83 prescription benchmarks for tennessee. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for texas. Dongchun Wang and Richard A. Victor. March wc prescription benchmarks for wisconsin. Dongchun Wang and Richard A. Victor. March wc fee schedules for hospitals and ambulatory surgical centers: a guide for policymakers. Nicole M. Coomer. February wc national inventory of workers compensation fee schedules for hospitals and ambulatory surgical centers. Nicole M. Coomer. February wc workers compensation medical cost containment: a national inventory. August wc wcri flashreport: information requested by medicare to support decision-making on medicare secondary payer regulations. Ramona P. Tanabe. April fr wcri medical price index for workers compensation, second edition (mpi-wc). Stacey M. Eccleston with the assistance of Juxiang Liu. June wc wcri flashreport: connecticut fee schedule rates compared to state medicare rates: common medical services delivered to injured workers by nonhospital providers. Stacey M. Eccleston. December fr wcri flashreport: what are the most important medical conditions in workers compensation. August fr wcri flashreport: what are the most important medical conditions in new york workers compensation. July fr wcri flashreport: analysis of illustrative medical fee schedules in wisconsin. Stacey M. Eccleston, Te-Chun Liu, and Richard A. Victor. March fr wcri medical price index for workers compensation: the mpi-wc, first edition. Stacey M. Eccleston. February wc benchmarks for designing workers compensation medical fee schedules: Stacey M. Eccleston and Te-Chun Liu. October wc analysis of the workers compensation medical fee schedules in illinois. Stacey M. Eccleston. July wc state policies affecting the cost and use of pharmaceuticals in workers compensation: a national inventory. Richard A. Victor and Petia Petrova. June wc the cost and use of pharmaceuticals in workers compensation: a guide for policymakers. Richard A. Victor and Petia Petrova. June wc how does the massachusetts medical fee schedule compare to prices actually paid in workers compensation? Stacey M. Eccleston. April wc the impact of provider choice on workers compensation costs and outcomes. Richard A. Victor, Peter S. Barth, and David Neumark, with the assistance of Te-Chun Liu. November wc adverse surprises in workers compensation: cases with significant unanticipated medical care and costs. Richard A. Victor. June wc wcri flashreport: analysis of the proposed workers compensation fee schedule in tennessee. Stacey M. Eccleston and Xiaoping Zhao. January fr wcri flashreport: analysis of services delivered at chiropractic visits in texas compared to other states. Stacey M. Eccleston and Xiaoping Zhao. July fr wcri flashreport: supplement to benchmarking the 2004 pennsylvania workers compensation medical fee schedule. Stacey M. Eccleston and Xiaoping Zhao. May fr wcri flashreport: is chiropractic care a cost driver in texas? reconciling studies by wcri and mgt/texas chiropractic association. April fr wcri flashreport: potential impact of a limit on chiropractic visits in texas. Stacey M. Eccleston. April fr wcri flashreport: are higher chiropractic visits per claim driven by outlier providers? Richard A. Victor April fr wcri flashreport: benchmarking the 2004 pennsylvania workers compensation medical fee schedule. Stacey M. Eccleston and Xiaoping Zhao. March fr evidence of effectiveness of policy levers to contain medical costs in workers compensation. Richard A. Victor. November wc

84 wcri medical price index for workers compensation. Dongchun Wang and Xiaoping Zhao. October wc wcri flashreport: where the medical dollar goes? how california compares to other states. Richard A. Victor and Stacey M. Eccleston. March fr patterns and costs of physical medicine: comparison of chiropractic and physician-directed care. Richard A. Victor and Dongchun Wang. December wc provider choice laws, network involvement, and medical costs. Richard A. Victor, Dongchun Wang, and Philip Borba. December wc wcri flashreport: analysis of payments to hospitals and surgery centers in florida workers compensation. Stacey M. Eccleston and Xiaoping Zhao. December fr benchmarks for designing workers compensation medical fee schedules: Stacey M. Eccleston, Aniko Laszlo, Xiaoping Zhao, and Michael Watson. August wc wcri flashreport: changes in michigan s workers compensation medical fee schedule: Stacey M. Eccleston. December fr targeting more costly care: area variation in texas medical costs and utilization. Richard A. Victor and N. Michael Helvacian. May wc wcri flashreport: comparing the pennsylvania workers compensation fee schedule with medicare rates: evidence from 160 important medical procedures. Richard A. Victor, Stacey M. Eccleston, and Xiaoping Zhao. November fr wcri flashreport: benchmarking pennsylvania s workers compensation medical fee schedule. Stacey M. Eccleston and Xiaoping Zhao. October fr wcri flashreport: benchmarking california s workers compensation medical fee schedules. Stacey M. Eccleston. August fr managed care and medical cost containment in workers compensation: a national inventory, Ramona P. Tanabe and Susan M. Murray. December wc wcri flashreport: benchmarking florida s workers compensation medical fee schedules. Stacey M. Eccleston and Aniko Laszlo. August fr the impact of initial treatment by network providers on workers compensation medical costs and disability payments. Sharon E. Fox, Richard A. Victor, Xiaoping Zhao, and Igor Polevoy. August dm the impact of workers compensation networks on medical and disability payments. William G. Johnson, Marjorie L. Baldwin, and Steven C. Marcus. November wc fee schedule benchmark analysis: ohio. Philip L. Burstein. December fs the rbrvs as a model for workers compensation medical fee schedules: pros and cons. Philip L. Burstein. July wc benchmarks for designing workers compensation medical fee schedules: Philip L. Burstein. May wc fee schedule benchmark analysis: north carolina. Philip L. Burstein. December fs fee schedule benchmark analysis: colorado. Philip L. Burstein. August fs benchmarks for designing workers compensation medical fee schedules: Philip L. Burstein. December wc review, regulate, or reform: what works to control workers compensation medical costs. Thomas W. Grannemann, ed. September wc fee schedule benchmark analysis: michigan. Philip L. Burstein. September fs medicolegal fees in california: an assessment. Leslie I. Boden. March wc benchmarks for designing workers compensation medical fee schedules. Stacey M. Eccleston, Thomas W. Grannemann, and James F. Dunleavy. December wc how choice of provider and recessions affect medical costs in workers compensation. Richard B. Victor and Charles A. Fleischman. June wc medical costs in workers compensation: trends & interstate comparisons. Leslie I. Boden and Charles A. Fleischman. December wc-89-5.

85 Worker Outcomes how have worker outcomes and medical costs changed in wisconsin? Sharon E. Belton and Te-Chun Liu. May wc comparing outcomes for injured workers in michigan. Sharon E. Belton and Te-Chun Liu. June wc comparing outcomes for injured workers in maryland. Sharon E. Belton and Te-Chun Liu. June wc comparing outcomes for injured workers in nine large states. Sharon E. Belton, Richard A. Victor, and Te-Chun Liu, with the assistance of Pinghui Li. May wc comparing outcomes for injured workers in seven large states. Sharon E. Fox, Richard A. Victor, and Te-Chun Liu, with the assistance of Pinghui Li. February wc wcri flashreport: worker outcomes in texas by type of injury. Richard A. Victor. February fr outcomes for injured workers in california, massachusetts, pennsylvania, and texas. Richard A. Victor, Peter S. Barth, and Te-Chun Liu, with the assistance of Pinghui Li. December wc outcomes for injured workers in texas. Peter S. Barth and Richard A. Victor, with the assistance of Pinghui Li and Te-Chun Liu. July wc the workers story: results of a survey of workers injured in wisconsin. Monica Galizzi, Leslie I. Boden, and Te-Chun Liu. December wc workers compensation medical care: effective measurement of outcomes. Kate Kimpan. October wc Benefits and Return to Work factors influencing return to work for injured workers: lessons from pennsylvania and wisconsin. Sharon E. Belton. November wc the impact of the 2004 ppd reforms in tennessee: early evidence. Evelina Radeva and Carol Telles. May fr factors that influence the amount and probability of permanent partial disability benefits. Philip S. Borba and Mike Helvacian. June wc return-to-work outcomes of injured workers: evidence from california, massachusetts, pennsylvania, and texas. Sharon E. Fox, Philip S. Borba, and Te-Chun Liu. May wc who obtains permanent partial disability benefits: a six state analysis. Peter S. Barth, N. Michael Helvacian, and Te-Chun Liu. December wc wcri flashreport: benchmarking oregon s permanent partial disability benefits. Duncan S. Ballantyne and Michael Manley. July fr wcri flashreport: benchmarking florida s permanent impairment benefits. Richard A. Victor and Duncan S. Ballantyne. September fr permanent partial disability benefits: interstate differences. Peter S. Barth and Michael Niss. September wc measuring income losses of injured workers: a study of the wisconsin system A WCRI Technical Paper. Leslie I. Boden and Monica Galizzi. November permanent partial disability in tennessee: similar benefits for similar injuries? Leslie I. Boden. November wc what are the most important factors shaping return to work? evidence from wisconsin. Monica Galizzi and Leslie I. Boden. October wc do low ttd maximums encourage high ppd utilization: re-examining the conventional wisdom. John A. Gardner. January wc benefit increases and system utilization: the connecticut experience. John A. Gardner. December wc designing benefit structures for temporary disability: a guide for policymakers Two-Volume Publication. Richard B. Victor and Charles A. Fleischman. December wc-89-4.

86 return to work incentives: lessons for policymakers from economic studies. John A. Gardner. June wc income replacement for long-term disability: the role of workers compensation and ssdi. Karen R. DeVol. December sp Cost Drivers and Benchmarks of System Performance compscope TM benchmarks for california, 12th edition. Rui Yang, with the assistance of Syd Allan. December wc compscope TM benchmarks for florida, 12th edition. Rui Yang, with the assistance of Syd Allan. December wc baseline for monitoring the impact of 2011 reforms in illinois: compscope TM benchmarks, 12th edition. Evelina Radeva, with the assistance of Syd Allan. December wc compscope TM benchmarks for louisiana, 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc compscope TM benchmarks for maryland, 12th edition. Rui Yang, with the assistance of Syd Allan. December wc compscope TM benchmarks for massachusetts, 12th edition. Evelina Radeva, with the assistance of Syd Allan. December wc compscope TM benchmarks for michigan, 12th edition. Bogdan Savych, with the assistance of Syd Allan. December wc benchmarks for minnesota, compscope TM 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc benchmarks for new jersey, compscope TM 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc baseline for evaluating impact of 2011 reforms in north carolina: compscope TM benchmarks, 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc compscope TM benchmarks for pennsylvania, 12th edition. Evelina Radeva, with the assistance of Syd Allan. December wc monitoring the impact of reforms and recession in texas: compscope TM benchmarks, 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc benchmarks for virginia, compscope TM 12th edition. Carol A. Telles, with the assistance of Syd Allan. December wc compscope TM benchmarks for wisconsin, 12th edition. Evelina Radeva, with the assistance of Syd Allan. December wc early impact of 2007 reforms in new york. Carol A. Telles and Ramona P. Tanabe. December wc compscope TM benchmarks for tennessee, 11th edition. Evelina Radeva, Nicole M. Coomer, Bogdan Savych, Carol A. Telles, Rui Yang, and Ramona P. Tanabe, with the assistance of Syd Allan. January wc baseline trends for evaluating the impact of the 2007 reforms in new york. Ramona P. Tanabe and Carol A. Telles. November wc updated baseline for evaluating the impact of the 2007 reforms in new york. Ramona P. Tanabe, Stacey Eccleston, and Carol A. Telles. April wc interstate variations in medical practice patterns for low back conditions. Dongchun Wang, Kathryn Meuller, Dean Hashimoto, Sharon Belton, and Xiaoping Zhao. June wc wcri flashreport: timeliness of injury reporting and first indemnity payment in new york: a comparison with 14 states. Carol A. Telles and Ramona P. Tanabe. March fr baseline for evaluating the impact of the 2007 reforms in new york. Ramona P. Tanabe, Stacey Eccleston, and Carol A. Telles. March wc why are benefit delivery expenses higher in california and florida? Duncan S. Ballantyne and Carol A. Telles. December wc compscope TM benchmarks: massachusetts, Carol A. Telles, Aniko Laszlo, and Te-Chun Liu. January cs compscope TM benchmarks: florida, N. Michael Helvacian and Seth A. Read. September cs-01-1.

87 wcri flashreport: where the workers compensation dollar goes. Richard A. Victor and Carol A. Telles. August fr predictors of multiple workers compensation claims in wisconsin. Glenn A. Gotz, Te-Chun Liu, and Monica Galizzi. November wc area variations in texas benefit payments and claim expenses. Glenn A. Gotz, Te-Chun Liu, Christopher J. Mazingo, and Douglas J. Tattrie. May wc area variations in california benefit payments and claim expenses. Glenn A. Gotz, Te-Chun Liu, and Christopher J. Mazingo. May wc area variations in pennsylvania benefit payments and claim expenses. Glenn A. Gotz, Te-Chun Liu, and Christopher J. Mazingo. May wc benchmarking the performance of workers compensation systems: compscope TM measures for minnesota. H. Brandon Haller and Seth A. Read. June cs benchmarking the performance of workers compensation systems: compscope TM measures for massachusetts. Carol A. Telles and Tara L. Nells. December cs benchmarking the performance of workers compensation systems: compscope TM measures for california. Sharon E. Fox and Tara L. Nells. December cs benchmarking the performance of workers compensation systems: compscope TM measures for pennsylvania. Sharon E. Fox and Tara L. Nells. November cs cost drivers and system performance in a court-based system: tennessee. John A. Gardner, Carol A. Telles, and Gretchen A. Moss. June wc the 1991 reforms in massachusetts: an assessment of impact. John A. Gardner, Carol A. Telles, and Gretchen A. Moss. May wc the impact of oregon s cost containment reforms. John A. Gardner, Carol A. Telles, and Gretchen A. Moss. February wc cost drivers and system change in georgia, John A. Gardner, Carol A. Telles, and Gretchen A. Moss. November wc cost drivers in missouri. John A. Gardner, Richard A. Victor, Carol A. Telles, and Gretchen A. Moss. December wc cost drivers in new jersey. John A. Gardner, Richard A. Victor, Carol A. Telles, and Gretchen A. Moss. September wc cost drivers in six states. Richard A. Victor, John A. Gardner, Daniel Sweeney, and Carol A. Telles. December wc performance indicators for permanent disability: low-back injuries in texas. Sara R. Pease. August wc performance indicators for permanent disability: low-back injuries in new jersey. Sara R. Pease. December wc performance indicators for permanent disability: low-back injuries in wisconsin. Sara R. Pease. December wc Administration/Litigation workers compensation laws as of january Joint publication of IAIABC and WCRI. Ramona P. Tanabe. March wc workers compensation laws, 3rd edition. Joint publication of IAIABC and WCRI. Ramona P. Tanabe. October wc avoiding litigation: what can employers, insurers, and state workers compensation agencies do?. Richard A. Victor and Bogdan Savych. July wc workers compensation laws, 2nd edition. Joint publication of IAIABC and WCRI. June wc did the florida reforms reduce attorney involvement? Bogdan Savych and Richard A. Victor. June wc lessons from the oregon workers compensation system. Duncan S. Ballantyne. March wc workers compensation in montana: administrative inventory. Duncan S. Ballantyne. March wc workers compensation in nevada: administrative inventory. Duncan S. Ballantyne. December wc

88 workers compensation in hawaii: administrative inventory. Duncan S. Ballantyne. April wc workers compensation in arkansas: administrative inventory. Duncan S. Ballantyne. August wc workers compensation in mississippi: administrative inventory. Duncan S. Ballantyne. May wc workers compensation in arizona: administrative inventory. Duncan S. Ballantyne. September wc workers compensation in iowa: administrative inventory. Duncan S. Ballantyne. April wc wcri flashreport: measuring the complexity of the workers compensation dispute resolution processes in tennessee. Richard A. Victor. April fr revisiting workers compensation in missouri: administrative inventory. Duncan S. Ballantyne. December wc workers compensation in tennessee: administrative inventory. Duncan S. Ballantyne. April wc revisiting workers compensation in new york: administrative inventory. Duncan S. Ballantyne. January wc workers compensation in kentucky: administrative inventory. Duncan S. Ballantyne. June wc workers compensation in ohio: administrative inventory. Duncan S. Ballantyne. October wc workers compensation in louisiana: administrative inventory. Duncan S. Ballantyne. November wc workers compensation in florida: administrative inventory. Peter S. Barth. August wc measuring dispute resolution outcomes: a literature review with implications for workers compensation. Duncan S. Ballantyne and Christopher J. Mazingo. April wc revisiting workers compensation in connecticut: administrative inventory. Duncan S. Ballantyne. September wc dispute prevention and resolution in workers compensation: a national inventory, Duncan S. Ballantyne. May wc workers compensation in oklahoma: administrative inventory. Michael Niss. April wc workers compensation advisory councils: a national inventory, Sharon E. Fox. March wc the role of advisory councils in workers compensation systems: observations from wisconsin. Sharon E. Fox. November revisiting workers compensation in michigan: administrative inventory. Duncan S. Ballantyne and Lawrence Shiman. October wc revisiting workers compensation in minnesota: administrative inventory. Carol A. Telles and Lawrence Shiman. September wc revisiting workers compensation in california: administrative inventory. Carol A. Telles and Sharon E. Fox. June wc revisiting workers compensation in pennsylvania: administrative inventory. Duncan S. Ballantyne. March wc revisiting workers compensation in washington: administrative inventory. Carol A. Telles and Sharon E. Fox. December wc workers compensation in illinois: administrative inventory. Duncan S. Ballantyne and Karen M. Joyce. November wc workers compensation in colorado: administrative inventory. Carol A. Telles and Sharon E. Fox. October wc workers compensation in oregon: administrative inventory. Duncan S. Ballantyne and James F. Dunleavy. December wc revisiting workers compensation in texas: administrative inventory. Peter S. Barth and Stacey M. Eccleston. April wc workers compensation in virginia: administrative inventory. Carol A. Telles and Duncan S. Ballantyne. April wc workers compensation in new jersey: administrative inventory. Duncan S. Ballantyne and James F. Dunleavy. April wc workers compensation in north carolina: administrative inventory. Duncan S. Ballantyne. December wc workers compensation in missouri: administrative inventory. Duncan S. Ballantyne and Carol A. Telles. May wc-93-1.

89 workers compensation in california: administrative inventory. Peter S. Barth and Carol A. Telles. December wc workers compensation in wisconsin: administrative inventory. Duncan S. Ballantyne and Carol A. Telles. November wc workers compensation in new york: administrative inventory. Duncan S. Ballantyne and Carol A. Telles. October wc the ama guides in maryland: an assessment. Leslie I. Boden. September wc workers compensation in georgia: administrative inventory. Duncan S. Ballantyne and Stacey M. Eccleston. September wc workers compensation in pennsylvania: administrative inventory. Duncan S. Ballantyne and Carol A. Telles. December wc reducing litigation: using disability guidelines and state evaluators in oregon. Leslie I. Boden, Daniel E. Kern, and John A. Gardner. October wc workers compensation in minnesota: administrative inventory. Duncan S. Ballantyne and Carol A. Telles. June wc workers compensation in maine: administrative inventory. Duncan S. Ballantyne and Stacey M. Eccleston. December wc workers compensation in michigan: administrative inventory. H. Allan Hunt and Stacey M. Eccleston. January wc workers compensation in washington: administrative inventory. Sara R. Pease. November wc workers compensation in texas: administrative inventory. Peter S. Barth, Richard B. Victor, and Stacey M. Eccleston. March wc reducing litigation: evidence from wisconsin. Leslie I. Boden. December wc workers compensation in connecticut: administrative inventory. Peter S. Barth. December wc use of medical evidence: low-back permanent partial disability claims in new jersey. Leslie I. Boden. December wc use of medical evidence: low-back permanent partial disability claims in maryland. Leslie I. Boden. September sp Vocational Rehabilitation improving vocational rehabilitation outcomes: opportunities for early intervention. John A. Gardner. August wc appropriateness and effectiveness of vocational rehabilitation in florida: costs, referrals, services, and outcomes. John A. Gardner. February wc vocational rehabilitation in florida workers compensation: rehabilitants, services, costs, and outcomes. John A. Gardner. February wc vocational rehabilitation outcomes: evidence from new york. John A. Gardner. December wc vocational rehabilitation in workers compensation: issues and evidence. John A. Gardner. June s Occupational Disease liability for employee grievances: mental stress and wrongful termination. Richard B. Victor, ed. October wc asbestos claims: the decision to use workers compensation and tort. Robert I. Field and Richard B. Victor. September wc-88-5.

90 Other workers compensation: where have we come from? where are we going?. Richard A. Victor and Linda L. Carrubba, eds. November wc recession, fear of job loss, and return to work. Richard A. Victor and Bogdan Savych. April wc wcri flashreport: what are the prevalence and size of lump-sum payments in workers compensation: estimates relevant for medicare set-asides. Richard A. Victor, Carol A. Telles, and Rui Yang. November fr the future of workers compensation: opportunities and challenges. Richard A. Victor, ed. April wc managing catastrophic events in workers compensation: lessons from 9/11. Ramona P. Tanabe, ed. March wc wcri flashreport: workers compensation in california: lessons from recent wcri studies. Richard A. Victor. March fr wcri flashreport: workers compensation in florida: lessons from recent wcri studies. Richard A. Victor. February fr workers compensation and the changing age of the workforce. Douglas J. Tattrie, Glenn A. Gotz, and Te-Chun Liu. December wc medical privacy legislation: implications for workers compensation. Ramona P. Tanabe, ed. November wc the implications of changing employment relations for workers compensation. Glenn A. Gotz, ed. December wc workers compensation success stories. Richard A. Victor, ed. July wc the americans with disabilities act: implications for workers compensation. Stacey M. Eccleston, ed. July wc twenty-four-hour coverage. Richard A. Victor, ed. June wc These publications can be obtained by visiting our web site at or by sending a written request by fax to (617) , or by mail to Publications Department Workers Compensation Research Institute 955 Massachusetts Avenue Cambridge, MA 02139

91 About the Institute The Workers Compensation Research Institute is a nonpartisan, notfor-profit research organization providing objective information about public policy issues involving workers compensation systems. The Institute does not take positions on the issues it researches; rather it provides information obtained through studies and data collection efforts that conform to recognized scientific methods, with objectivity further ensured through rigorous peer review procedures. The Institute s work helps those interested in improving workers compensation systems by providing new, objective, empirical infor mation that bears on certain vital questions: How serious are the problems that policymakers want to address? What are the consequences of proposed solutions? Are there alternative solutions that merit consideration? What are their consequences? The Institute s work takes several forms: Original research studies on major issues confronting workers compensation systems Original research studies of individual state systems where policymakers have shown an interest in reform and where there is an unmet need for objective information Sourcebooks that bring together information from a variety of sources to provide unique, convenient reference works on specific issues Periodic research briefs that report on significant new research, data, and issues in the field Benchmarking reports that identify key outcomes of state systems