Assessing the intended and unintended. effects of disability insurance experience. rating, the case of the Netherlands

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1 Assessing the intended and unintended effects of disability insurance experience rating, the case of the Netherlands Nynke de Groot and Pierre Koning December 15, 2014 Abstract In the Netherlands, employers bear most of the costs of disability insurance through experience rating. However, there is little evidence on the effects of experience rating in DI. In this study, we estimate the intended and unintended effects of the removal of experience rating for small employers using a difference-in-difference approach. We find that the removal of experience rating caused an increase of the inflow into DI of about 14% for small employers, while the outflow from DI decreased by 3% for these employers. After an extension of the sick leave period before DI, the effects of DI experience rating become substantially smaller. We do not find a clear effect of experience rating on employment growth. Keywords: Disability insurance, experience rating, differences-in-differences JEL-code: H22, I12, C23 Corresponding author. VU University Amsterdam, Department of Economics, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands. Tel: (31 20) VU University Amsterdam, IZA and Ministry of Social Affairs and Employment.

2 1 Introduction According to many studies, one of the most important conditions for preventing work disability is that workers should receive timely interventions and work adaptations (OECD (2010)). In this respect, a key role should be played by employers that facilitate the return to work from sickness (Autor and Duggan (2010)). Following this line of reasoning, using disability insurance (DI) premiums that are experience rated may be an effective measure to increase employers awareness of DI benefit costs, thus reducing the number of DI beneficiaries. Similar to discussions on employment protection legislation, however, opponents of experience rating also point at unintended effects when employers become more reluctant to hire workers. The size of insurance risks that is transferred to employers may even raise the risk of bankruptcies, particularly when firms are small. So far, however, the literature on experience rating in particular its unintended effects is still limited (Tompa et al. (2012)). In the last few decades, many OECD countries changed their policy regarding disability insurance benefits. They shifted their approach from just paying benefits to individuals with disability to a more active employment-orientated policy. It is a widely-shared view that employers are key players in keeping workers from become disabled and facilitating their return to work from sickness (OECD (2010)). Experience rating could be an effective measure to increase employers awareness and responsibility and thereby reduce the number of DI beneficiaries. However experience rating could also have unintended effects on employers, employers who have to pay high premiums could be forced to fire employees, change their hiring procedure or even go bankrupt. In this context, the Netherlands provides an interesting setting to study the both intended and unintended effects of experience rating. After the Dutch number of individuals with disability insurance benefits reached a peak in the nineties with eight percent of the labor force receiving DI benefits, the Dutch government has 2

3 implemented several changes to reduce the number of DI beneficiaries. One of those measures was the introduction of experience rating for employers to finance DI benefits in Most countries that provide Workers Compensation use experience rating to finance disability benefits, whereas the Netherlands and Finland are the only countries with experience rating for public DI benefits. In the Netherlands the employer DI premium is based on the DI costs of their (former) workers. The DI premium is capped at a minimum and a maximum. To study the effects of experience rating, this paper exploits two reforms that changed the coverage of the experience rating scheme. This allows us to use a difference-in-difference design that takes advantage from the fact that experience rating was abolished for small and medium-sized employers between 2003 and We study whether experience rating reduced the DI inflow and increased DI outflow rates which can well be regarded as the intended effects. At the same time, we also assess the impact on employment growth. In this context, special focus will be on the effect of experience rating in the years prior to 2005 and thereafter; in this year, the sickness benefits period that precedes the DI benefit period and for which employers are fully financially responsible was extended from one to two years. As a result, the composition of workers that claimed DI benefits may have changed, as well as the overall incentives employers had to prevent inflow into the DI scheme. In the empirical analysis, we use matched administrative data on employers and (former) employees between 1999 and We combine these data with administrative data on DI spells as well as other demographic and labor market characteristics. This results in a dataset with over 360,000 unique employers and almost 13 million workers eligible for DI benefits. Our empirical findings are generally in line with expectance. Prior to the extension of the sickness benefits period, we find that experience rating reduces inflow into DI and increases outflow from DI. However, after the extension in 2005 these effects become substantially smaller and insignificant. 3

4 This indicates that the incentives for employers to reduce sick leave are already so large that the additional incentive targeted at the period after sick leave does not have an additional effect. The evidence on the unintended effects of experience rating is less convincing, initially we find that experience rating decreases employment growth but these results are very sensitive to the selection of the data. Therefore for now we can not conclude that experience rating has an unintended effect on employment growth. This paper adds to a literature on experience rating that is still limited. For the Netherlands, Koning (2009) studies the unanticipated effects of experience rating of employers who experienced an increase in their DI premium while Van Sonsbeek (2013) estimates the effect of experience rating as well as effects of other policy changes in the Netherlands, using aggregated data. Both find that experience rating reduced the inflow into DI, with an estimated impact of 15% of the DI inflow rate. Both Kyyrä and Tuomala (2013) and Korkeamäki and Kyyrä (2012) study the effect of experience rating exploiting a pension reform in Finland. The first study finds limited effects of experience rating on the inflow into the disability scheme for older workers while the latter study does find significant effects of experience rating for older workers on both the inflow into sick leave and the transition from sick leave into disability retirement. 1 Experience rating is more widespread in private Workers Compensation (WC) schemes than in DI schemes that are provided publicly. Most studies on experience rating in WC focus on outcome measures like fatality and injury rates. Generally, this literature supports the hypothesis that experience rating reduces disability claims costs (see Hyatt and Thomason (1998) or Ruser and Butler (2009) for a 1 The literature on experience rating of sickness benefits is closely related to the literature on DI experience rating. Fevang et al. (2011) show that the removal of the requirement to pay for the first 16 days of pregnancy-related-absences led to higher incidences of absence amongst pregnant women and increased the return rate for women who were absent longer than 16 days. They also argue that the removal of experience increased the employability of young women. Böheim and Leoni (2011) find that sickness benefits experience rating led to a decrease in both incidence and duration of sickness spells. 4

5 survey). 2 In addition, however, there is also evidence on unintended effects. Campolieti et al. (2006) for example observe that the decrease in disability claims happened specifically for less severe claims.kralj (1994) shows that experience rating stimulated employers to change their safety practices but also invest more in claims control. In this respect, several qualitative studies address the pressure that workers experience not to report their injury (Ison (1986), Lippel (1999), Strunin and Boden (2004)). 3 When comparing these findings with the Dutch DI system, it should be stressed that the ability that employers have to deter claims is only limited. In particular, DI claims follows automatically after a period of sickness (Koning and Lindeboom (2014)). At the same time, the Dutch DI scheme provides insurance for both occupational and non-occupational risks and DI benefit costs are experience rated over a time window of ten years. As such, the risks that are imposed on employers are relatively high, raising the question how employers will respond in such a setting. This paper proceeds as follows. In the next section we describe the Dutch DI system and in section 3 we discuss the method of experience rating. In section 4 we present our data. We discuss our identification strategy in section 5 and present the results from the estimations in section 6. Section 7 concludes. 2 For the US, Ruser (1985, 1991) finds experience rating to decrease the incidence of injury rates. More recently, Seabury et al. (2012) find that experience rating increases the return to work rates. Bruce and Atkins (1993) argue that experience rating has decreased fatality rates in Canada, whereas Campolieti et al. (2006) show that the introduction of experience rating in British Columbia led to a decrease in disability claims. 3 Compared to WC and DI experience rating, the literature on experience rating within the US unemployment system (UI) is more extensive. Generally, the evidence shows that UI experience rating reduces the number of layoffs (see e.g. Topel (1983) and Anderson and Meyer (1994)), with the largest effects during recessions (Card and Levine (1994)). European studies show similar results; see Skedinger (2011) for a survey. There is also evidence that experience rating has unintended side effects. In particular, studies show a negative effect on employment (Lazear (1990), Addison and Teixera (2005) and Kugler and Pica (2008)) and on the entry level of new firms (Addison and Teixera (2005)) 5

6 2 Institutional setting The Dutch DI scheme differs from most DI schemes in other countries in various aspects, making it one of the most generous disability insurance systems (OECD (2010). The level of the benefits is based on the difference between the pre-disability (covered) earnings and the residual earning capacity, where the residual earning capacity is the income the individual could earn conditional on his or her disability. This means that disability is measured as a percentage, rather than an all or nothing condition. Moreover, the Netherlands is the only EU-country where the DI program covers all workers against all incomes losses that result from both occupational and non-occupational injuries (LaDou (2011)). The generosity of the Dutch DI program makes it rather susceptible to moral hazard problems. In particular, for both employers and workers the scheme may be an attractive alternative pathway into unemployment (Koning and van Vuuren (2007) and Koning and van Vuuren (2010)). Since the introduction of the generous DI scheme WAO in 1967, the Dutch DI stock had been increasing and the DI inflow stayed persistently high (Figure 1). After the eighties the Dutch government implemented various reform plans attempting to increase employers and workers incentives to decrease the DI volume (Figure 2). One of the first reforms was the privatization of the sickness benefit program in 1996, making employers fully responsible for the first year of sickness benefits of their workers. In 1998, employer s incentives were enhanced by the system of DI experience rating. The DI premium for Dutch employers was now based on the actual amount of DI costs of their (former) workers. 4 The responsibility of employers was further increased in 2002, when a more stringent system of gatekeeping was introduced. Employers became responsible for the work resumption of sick workers in their first sick year and had to draft a rehabilitation plan with the sick worker. The Employee Insurance Agency could fine the employers for insufficient effort with 4 We will explain the calculation of the DI premiums in the next section. 6

7 Figure 1: Stock and inflow of workers in Disability Insurance ( ) Figure 2: Timeline of recent changes in the Dutch DI scheme the payment at most one more extra year of sick leave benefits. Experience rating for small employers was abolished in 2003, in 2003 small employers paid a base premium and after 2003 they paid a premium based on the average DI costs per employee in their sector. The definition of a small employers also changed in 2004: before a small employer had a wage sum of at most 15 times the average Dutch wage sum, after 2004 this became 25 times the average wage sum. In 2004 all employers experienced an extension of the payment of sickness benefits from one to two years. In addition, the rules for DI eligibility became more strict, meaning that workers were more likely to be found able to work and received lower DI benefits. The stricter rules applied for new entrants into DI, but also for current DI beneficiaries younger than 50. 7

8 A large reform of the Dutch DI system took effect in This reform entailed the start of two different types of DI benefits: the IVA (Income scheme for Fully Disabled) benefit for the full and permanently disabled and the WGA (Act for Partially Disabled workers) benefit for partial or temporarily full disability. An IVA benefit is 75% of previous gross earnings, while the WGA benefit covers 70% of previous gross earnings but the unemployment rules apply here. After eligibility to UI benefits expires the WGA benefits is based on the minimum wage. The reform only applied to individuals who suffered disability in 2004 or later, although the entitlement of most individuals younger than 45 was re-assessed under the rules of the new scheme. With the new types of DI benefits, a new form of experience rating also started. As of 2008 the premiums for the old WAO benefits are no longer experience rated and only the premiums of the new partially and/or temporarily benefits (WGA) are experience rated. This applies to all employers, also the small employers. The premium of the WAO and IVA is replaced by a uniform premium. 5 The experience rating incentive for the WGA benefit increased relative to the old scheme. The risk premium of an employer is now based on the first ten years of WGA costs of its former workers, instead of five years in the earlier situation. In effect, it seems that the overall incentive of experience rating has not changed substantially a result of the introduction of the new DI scheme. 3 Experience rating in the Netherlands In this section we explain the calculation of the experience rated DI premium of Dutch employers. First we discuss the method of calculation at the introduction of experience rating in 1998, and then we give an overview of the calculation of the premiums over the years. Moreover we will make an assessment of the size of DI 5 Since only new and re-examined spells would get IVA or WGA benefits, in the first years the WAO costs were the majority of the DI costs. 8

9 experience rating as a percentage of the wage sum of an employer. The experience rated DI premium of Dutch employers corresponds to the average premium that is needed to cover the expected expenditures on DI benefits, corrected for the employers own disability risk. This disability risk is based on the historical disability cost percentage of their (former) workers. The disability costs are equal to the DI benefit costs of a number of recent cohorts of workers that worked for the employer; in 1998 this time window was five years. The premiums for the other cohorts are set to sectorial averages. The disability costs are divided by the average wage sum of the employers over the same time window to calculate the disability cost percentage. 6 Both the disability costs and the wage sum are registered with a delay of two years: d t = T s=0 S t 2,t 2 s T s=0 W t 2 s/(t + 1) (1) where S a,b are the disability costs of an employer in year a for recipients that entered into the program at time b (a b ), and W a the insured wage sum at time a. Since the time-window is five years, T = 4. The wage sums are averaged over this time window which diminishes the effect of the volatility in wage sums. 7 The experience rated premium p of employer i in year t is equal to: p it = p t + c t + f t (d it D t ) (2) 6 In some cases, the information that is needed to calculate the disability risk may be incomplete, e.g. for starting firms, or when for some period there are no workers at a particular firm. The disability cost percentage is then calculated over the longest available time window, and subsequently rescaled to a time window of five years. 7 This way of smoothing also results in some cross subsidization of the experience rating system: when multiplying the disability cost percentage with the current wage sum, employers with high wage sum growth rates will pay more than their disability costs, and downsizing firms less than that. 9

10 where p t is the average premium for all employers and c is a correction to compensate for potential differences in premiums and actual DI costs because of start-ups and bankruptcies and the use of a minimum and maximum premium. The difference between the individual DI risk and the average DI risk is captured by d it D t, which is multiplied by the factor f t which corrects for the difference in DI risk between the observed time window and the actual DI risk. f t would be equal to one if the actual DI risk was exactly the DI risk in the time window. 8 The final employer DI premium is capped by a minimum and a maximum premium. This means that every employer pays a uniform minimum premium, even the employers for whom f t (d it D t ) < c t p t. The capping of the premium means that the experience rating system is incomplete to some extent: higher disability costs result in proportionate increases in the DI premium up to the maximum premium, but over-users do not pay the additional costs they impose on their system. The values of the minimum and maximum premium vary with respect to firm size, the argument for this is that small firms are more susceptive to (exogenous) variation in their DI cost percentage. In particular, small firms are defined as those with a wage sum that is smaller than the average wage sum per worker in the Netherlands, multiplied by 15 (workers). First, the maximum premiums are set equal to four times the average premium for large firms and to three times the average premium for small firms. Then, using an iterative algorithm, the minimum premiums and the correction for the average premium (c) are set at the level that balances the total disability costs with the collected premiums. 9 As DI cost percentages of small firms are more likely to be bounded by the maximum, the minimum premium is higher for small firms. The introduction of experience rating was combined with the possibility for employers to opt out from the public system and bear the risk on their own. Opting out 8 Since the DI risk decreased over the years, f t was smaller than one in most years. 9 Minimum premiums are needed to finance the DI costs over employers paying the maximum premium. 10

11 implied that employers became responsible for prevention and reintegration activities of their workers. When opting out, employers could choose between self-insurance and warrant the continued payment of DI benefits that were already ongoing, or private insurance. Employers were not allowed to switch back from private to public insurance for at least three years after opting out. Hassink et al. (2014) show that opting out had no effect on DI inflow rates and for that reason we do not expect opting out to change the incentive of DI experience rating. 3.1 Experience rating over the years The calculation method of experience rating did not change substantially between 1998 and 2011, with the exception of the removal of experience rating for small firms between 2003 and 2007 and the increased time window after However the parameters for calculation of the experience rated premiums can differ a lot from year to year, as we can observe from table 1. In the first years of experience rating only very recent information on DI risks were available. As more and more information became available, the average premium, time window factor and minimum and maximum premiums increased. With the introduction of the new type of benefits in 2006 the premiums decreased again, as the new permanent DI spells were not experience rated anymore. In 2010 the time window factor increased substantially because the employment agency made an error in the calculation of the time window factor in the years before. In 2008 and 2009 the factor had been set too low, and this was compensated in later years. In Figure 3 we show the distribution of the premiums for all employers. The majority of the small employers pay the minimum premium. In the years around 5% of the small employers paid the maximum premium, in this percentage decreased to around 3%. While most small employers pay either the minimum or maximum premium, the share of large employers that pay a premium between the minimum and maximum premium is substantially larger, before

12 Table 1: Parameters for calculation of the premiums over the years Average premium Average premium after correction Time window factor Average DI risk Minimum premium, small employers Maximum premium, small employers Minimum premium, large employers Maximum premium, large employers

13 Figure 3: Distribution of the experience rating premiums, by size of employers. the majority of the large employers pay a premium between the minimum and maximum premium. Remember that experience rating for the old DI was removed in After that year, only the new temporarily or partially DI benefits (WGA) were experience rated. This resulted in an increase of large employers paying the maximum premium, as of 2009 most large employers pay the minimum premium. 4 Data To estimate the effects of DI experience rating we use data from Statistics Netherlands covering the period from 1999 to Statistics Netherlands provided administrative individual data on DI benefits and employment spells and administrative employer data. The data cover the total Dutch population of individuals and firms. We are able to match individuals to their employers using a unique identifier and thus construct a dataset with combined individual data on employment spells, dis- 13

14 ability benefits and employer characteristics. For the empirical analyses we need to classify a firm as small, medium-sized or large by the level of the wage sum in the year t We can aggregate the individual wage sums to the level of the employer but there are two limitations to this approach. 11 The first limitation is that the employers in the employee data are not necessarily equal to the employers that pay the premium. Statistics Netherlands measures the employer at a higher level than the employment agency does. An example is a chain of supermarkets, where Statistics Netherlands combines the supermarkets to one large employer while the employment agency regards them as different employers with different risk premiums. Fortunately, we know exactly for which employers there is a difference between Statistics Netherlands and the employment agency and the majority of employers are measured in a similar way. 12 We remove all the employers without a one-to-one relationship between the employer in the data of Statistics Netherlands and the employment agency. 13 The second limitation is that the calculation of the wage sum based on employment spell could come with a measurement error. We can observe the measurement error for the employers that can be merged to the data from the employment agency. This is reported in table 2. The size of the large majority of the firms is correctly assigned. In 2001, we wrongly classify 3.3% of the small firms as large. For large firms this is substantially higher, 9.1% in The measurement error decreases 10 We will identify the effect of experience rating by using a difference in difference approach. This is explained in the next section. 11 An alternative would be to use data from the employment agency with their classification of the size of the firm. The main drawback of this data is that a unique identifier is missing in the data for the years before 2009, which implies that we can not merge the employer data to the employee data for those years. We can use information on overlapping wage sums and sector to solve this problem, but this implies that we can only use the employers that still existed in We lose a substantial amount of employers with this approach (around 90%) and moreover the balanced panel suffers from potential selection bias as we only select firms that did not go bankrupt or merged with another firm. For this reason we disregard this approach for our main analysis and only use it as a robustness check. 12 For example in % of the employers in the employment agency data corresponds to exactly one employer in the data of Statistics Netherlands, 7% to two employers, 2% to three or more employers. 13 We will check for the sensitivity of our data selection by repeating the analyses with datasets where we keep all employers. 14

15 Table 2: Percentage of employers with incorrect size based on employee data, % Small, measured as large % Large, measured as small over time, in 2011 the percentages are 0.5% for small firms and 4.6% for large firms. Since we are using a difference-in-difference approach and the percentage of wrongly classified firms is relatively small, we do not expect a large estimation bias. If anything, we would underestimate the potential effects of the removal of experience rating for small firms. Table 3 summarizes the main characteristics of the different datasets. We only present the odd years. In the full sample the number of employers decreases from about 290,000 in 2001 to 230,000 in If we only include employers with a oneto-one relationship between statistical unit and fiscal identify we lose around 20% of the employers but about 30% of the employees. This means that we mostly lose large firms. The distribution of the sector of the firms also change for the two selections. When we select employers with a one-to-one relationship we lose relatively more firms in the industrial sector and keep more firms in the health and food sector for the years after The inflow into DI as a percentage of (former) employees is decreasing over time. We also see a decrease in the percentage of employees with DI benefits. The selection of the employers with a one-to-one relationship leads to a dataset of less risky employers in terms of DI risk. Both the percentage of individuals with DI benefits and the inflow into DI is lower for the two selections. This could be related to the differences in the distribution of the sectors of the employers, since the sector 15

16 health has one of the highest percentage of individuals with DI risk in our full sample. 5 Estimation strategy The aim of experience rating was to decrease inflow into DI and to increase outflow out of DI. In this paper, we want to test whether experience rating had these proposed effects on DI, but also for a potential indirect effect of experience rating: an effect on the probability of bankruptcy. We will use a difference-in-difference approach. Recall that experience rating was removed for small and medium-sized employers in 2003, and that it was re-introduced for all employers in This allows us to separate potential time effects from the effect of the removal of experience rating, given that the common trend assumption holds. 14 Before 2005, a sick worker would receive sickness benefits for one year before applying for DI benefits. As of 2005, this period has been extended to two years. This implies that the composition of the inflow into DI before 2005 differs from that of 2005 and later. Moreover, the effect of experience rating could very will differ between the two periods, as the incentive to reduce the first period of sickness has increased and in addition it will be more difficult for employers to encourage their workers to return to work after two years of sick leave than after one year. For that reason we split our analyses in two parts: before and after Because there was hardly any inflow into DI in the year 2005, we drop this year from our analyses. 15 We therefore have two different treatment groups: the small employers for which experience rating was removed in , and the small employers in A different estimation strategy could be to estimate the effects using a regression discontinuity design. This approach is less appropriate in our case, since many employers in a close interval around the threshold switch from being classified as small to large. We would expect smaller effects of experience rating for this group of employers than for employers that are consistently classified as small or large. 15 Individuals who started collecting sickness benefits in 2004 would not enter DI because of the introduction of the second sick year. Only individuals who started collecting sickness benefits before 2004 but did not receive DI benefits in 2004, for example because of an appeal of an earlier dismissal to DI benefits, could enter DI in

17 Table 3: Descriptive statistics of the data for the full and selected sample, for the years 2001 to 2011 (only odd years are shown). Full sample One-to-one employers Number of employers 291, , , , , , , , , , , ,609 Number of employees 9,324,591 8,133,415 7,701,461 5,213,766 6,858,500 7,274,021 6,706,592 5,524,063 5,581,915 3,213,487 4,108,275 3,533,867 Average of employer size , % of large firms Sector (%) - Trade Industrial Business Health Food Characteristics workers Age Male Immigrant Permanent contract Average earnings (e) 20,384 21,977 22,644 24,454 26,726 27,247 19,972 21,512 22,252 23,285 26,024 27,478 DI characteristics Number of individuals 275, , , , , , , , ,095 80,718 81,338 69,179 with disability benefits DI, % of employees % WAO % WGA % IVA % Fully disabled Inflow into disability 93,658 65,808 23,318 34,875 29,417 30,054 65,018 40,828 14,267 11,044 11,381 9,590 Inflow, % of employees Outflow from disability 32,169 35,718 37,158 17,122 12,290 11,429 22,149 22,345 22,886 5,705 4,913 4,015 Outflow, % of employees Average benefits (e) 6, ,432 12,327 13,485 14,290 6, ,567 12,328 13,469 14,321 17

18 before experience rating was reintroduced. 5.1 DI inflow model Our empirical model is a difference-in-difference model where large employers are the control group and the small and medium-sized employers the treatment group. Since experience rating is an incentive for the employer, we aggregate the individual data to the level of the employer. We specify a regression model to estimate the effect of the removal of experience rating for small and middle-sized employers on the percentage of inflow into DI of workers (Y it ) working at employer i in the year of risk (t 1 before 2005, t 2 after 2005). We estimate the model separately for the period before and after We specify the model Y it = α + κ s S s it + δd it + βx it + µ t + ρ i + ɛ it (3) The variable D is the treatment dummy: this variable is equal to 0 if the firm is classified as large and for all firms in the years 1999,2000, 2001 and 2002 (before the removal of experience rating) and in the years 2008, 2009, 2010 and 2011 (after re-introduction of experience rating). D is equal to one if the firm is small or medium-sized and the year is between 2003 and We include dummies for size S s to control for differences in the level between small, middle and large firms. The vector X contains both employer characteristics (dummies for sector, average wage) and workers characteristics (average age, percentage of men, percentage of immigrants). We estimate the model including different specifications of X.The time trend µ t is specified using dummy variables for every year. This controls for calendar time variation in inflow probabilities and is identified by the control group of large employers. ρ i are the time-averages of the explanatory variables including the number of observations of each employer. 18

19 Since our dependent variable is a fraction between zero and one, we will estimate equation 3 with the fractional probit model described in Papke and Woolridge (2008). 16 The regression includes the time-averages of the explanatory variables and the standard errors are obtained using 25 bootstrap replications. Since we are using an unbalanced panel of employers, we follow the approach suggested by Woolridge (Woolridge (2010)) and also include the number of observed years as an explanatory variable. 5.2 DI outflow model We use the data on the level of the employees to estimate the effect of experience rating on outflow, since the majority of firms do not have any (former) employees who actually receive DI benefits. Moreover we would lose information by aggregating the data to the level of the employer, like the exact duration of DI benefits and the number of employees that receive DI benefits. We model the duration of DI benefits using a hazard rate model. We estimate the effects before and after 2005 separately. The model describes the exit from DI benefits for an individual who entered DI at calendar time t, with an employer of size S and employer and worker characteristics X, θ(τ t, X) = λ(τ)ϕ(s, t, X, D) (4) where λ(τ) denotes duration dependence in outflow from DI benefits. The treatment is again measured by variable D, which is 0 if an employer is exposed to experience rating and 1 otherwise. We include calendar time indicators to control for time variation in outflow. We use Cox partial likelihood method to estimate the hazard rate. We artificially censor all DI spells after two years, since experience rating is 16 We estimate the model using the generalized estimating equation described in Papke and Woolridge (2008). 19

20 expected to have an effect in the first years after inflow into DI and we want to avoid the influence of the reassessment of disability eligibility that generally takes place 2-3 years after inflow to DI. The outflow from DI also decreases after time, 21% of the inflow in 2001 left DI within the first year, 8% in the second year and only 5% in the third year Employment growth model We measure employment growth as the ratio between the number of employees in the current year, divided by the number of employees in the previous year. We restrict the employment growth to be between 0.5 and 2, as some small firms can have very large or small ratios and we do not want to give them too much weight. Since we have a limited dependent variable we use the panel data tobit model with random effects to estimate our model. We include the same explanatory variables as in the model for DI inflow, Y it = α + κ s S s it + δd it + βx it + µ t + ρ i + ɛ it (5) where ρ i are now the random effects of the employers. 5.4 Testing for anticipation and common trend Suppose employers anticipate the experience rating threshold and try to keep the wage sum just below the threshold to avoid experience rating. This could bias our estimation results. However for an employer this is not straightforward, since the threshold is set in the year before the actual year of experience rating and this threshold is applied to the wage sum of two years ago (which the employer can no 17 In addition, in the years the beneficiaries of the old scheme (WAO) younger than 50 years where reassessed under the stricter rules of the new scheme. We also reduce the influence of these reassessments with the censoring after two years. 20

21 longer manipulate). Therefore, an employer would have to make an expectation of the threshold. Moreover the removal and re-introduction of experience rating were implemented quite suddenly and with short notice. Using graphical analyses we do not find evidence of anticipation of the threshold. If there would be anticipation we would observe a higher number of employers just below the threshold than just above the threshold, but the distribution across years is evenly spread. We identify the effect of experience rating by comparing small firms and mediumsized firms (treatment group) with large firms (control group). This identification hinges on a common trend between small and medium-sized employers and large employers. For the DI inflow and outflow, the common trend assumption implies that sick or disabled individuals who used to be employed at a small firm respond the same to economic conditions than individuals who used to be employed at large firms. This does no appear to be a strong assumption. The assumption seems much stronger for employment growth, as it could for example well be that the effect of an economic crisis on employment growth differs for small and larger firms. The upper figure in Figure 4 shows the inflow into DI as a percentage of the total numbers of employees for small, medium-sized and large employers in the years Before the reform we observe the same trend in inflow for the different sizes of employers. The second figure shows the percentage of the DI inflow that leaves DI within the first year. We observe a different panel for the medium-sized employer, but since the yearly DI inflow of indivuals who used to work at a medium-sized firm is relatively small we can not draw any conclusions from this. For small employers the pattern looks similar as that of large employers. The bottom panel shows the average employment growth. 18 For the year 2002 we observe a much larger drop in employment growth for large firms than for smaller firms. This could indicate that our common trend assumption is violated, we will now test for this formally. We use a placebo test to test whether the common trend is indeed not violated. 18 We can not observe the employment growth for 1999 since we do not have data on

22 Figure 4: Inflow into DI, outflow from DI within first year and employment growth, by size of the employer 22

23 Table 4: Estimated placebo effects before the actual reform to test for common trends. Effect on inflow Effect on outflow Effect on employment growth No characteristics * (-0.149) (0.037) (0.031) With workers and employers characteristics (0.054) (0.058) (0.031) In this placebo test, we pretend that the removal of experience rating for small and medium-sized employers occurred in 2001 instead of We create a placebo dummy which is 1 if the firm is small or medium-sized in the years 2001 or We estimate equations 3, 4 and 5 for the years where we substitute D it by the placebo dummy. Tabel 4 provides the estimated coefficient of the placebo dummy for different specifications. We do not find a significant effect of the placebo dummy, except for the regression on outflow without employer and employee characteristics. This significant effect disappears when we control for characteristics and therefore we do not reject the common trend assumption Estimation results 6.1 Estimation results Table 5 shows the main results of the analysis on the data of the employers. We find a significant positive effect of the removal of experience rating on inflow into DI for small firms before For medium-sized firms the coefficient is positive but 19 Since we need information on the years before 2001, we use the data from the employment agency to measure the size of the employer. The downside to this approach is that we can only account for the firms that still existed in As a robustness check, we also performed the placebo test in the period after reintroduction of experience rating ( ). We do not find evidence that the common trend is violated for that period 20 Bankruptcy could be another unintended effect of experience rating. For this variable we do reject the common trend assumption and are not able to estimate the effects on bankruptcies using our difference-in-difference approach. In a next version of the paper we will estimate the unanticipated effects of experience rating on bankruptcie using a similar method as in Koning (2009). 23

24 not significant. Since we estimate the inflow model using fractional probit, we can not interpret the coefficients directly. However we can estimate the average partial effect of experience rating for small firms before 2005, which is equal to This means that the removal of experience rating increases the fraction of inflow into DI for small firms with Given that the average fraction of inflow into DI for small firms before the removal of experience rating was , this implies a relative increase of 14%. After 2005, we find a weakly significant effect on inflow for small firms which is smaller than the effect before 2005, the average partial effect is equal to which implies an increase of 9%. This is consistent with our hypothesis that after the extension of the sickness benefits from one to two years all in 2005 it has become more difficult for employers to influence the inflow into DI and the employers already have a large incentive the reduce the first sick leave period. For DI inflow we do not find significant effects of the size of the firm for the years before 2005, while we find positive effects of small and medium-sized firms for the years after This could mean that differences in size of the firm only occur after the second year of sickness, or that the incentive to minimize sickness is stronger for larger firms, since smaller firm are more likely to insure themselves against sickness of their employees. The coefficients of the other variables are in line with what we expect: firms with older employees, a lower average wage and in the sectors construction and transport have a higher inflow into DI. We find significant positive effects of the removal of experience rating on the employment growth before 2005, indicating that experience rating decreases employment growth (5. However in the period after 2005 we observe a significant negative effect for small firms. This would imply that experience rating with extended sick leave benefits increases employment growth, but this contrasting result could also be due to a violation of the common trend assumption. In our placebo test the common trend assumption was just not rejected, with a p-value of 12%. In the next section we will perform some robustness checks. 24

25 Table 5: Estimated effects of the removal of experience rating for small and mediumsized employers on inflow into DI and employment growth Inflow DI Employment growth Before 2005 After 2005 Before 2005 After 2005 Removal of experience rating, small firm 0.053** 0.031* 0.012** ** (0.011) (0.017) (0.003) (0.003) Removal of experience rating, medium firm ** (0.013) (0.026) (0.005) (0.005) Small firm ** 0.205** 0.202** (0.056) (0.089) (0.003) (0.007) Middle-sized firm ** 0.019** 0.098** (0.031) (0.049) (0.005) (0.006) Workers characteristics Average age 0.005** 0.003* ** ** (0.001) (0.002) (0.000) (0.000) Percentage of men ** ** (0.052) (0.088) (0.004) (0.004) Percentage of immigrants ** 0.057** (0.055) (0.092) (0.005) (0.005) Percentage of single households ** ** (0.040) (0.064) (0.011) (0.011) Percentage of single parents ** (0.040) (0.073) (0.012) (0.012) Percentage of parents 0.073** ** 0.013** (0.028) (0.048) (0.003) (0.002) Average wage (x1000) ** ** ** ** (0.001) (0.001) (0.004) (0.005) Sector Agriculture 0.097** 0.103** - - (0.022) (0.043) Industry 0.203** 0.168** 0.044** ** (0.017) (0.040) (0.012) (0.009) Government 0.150** ** 0.100** (0.031) (0.078) (0.018) (0.018) Construction 0.385** 0.341** 0.025** (0.020) (0.040) (0.011) (0.012) Trade 0.137** 0.104** (0.017) (0.031) (0.010) (0.008) Food (0.019) (0.045) (0.014) (0.011) Transport 0.229** 0.185** 0.062** ** (0.018) (0.037) (0.018) (0.013) Financial 0.207** 0.169* (0.061) (0.091) (0.053) (0.019) Business 0.154** 0.116** 0.061** (0.020) (0.039) (0.011) (0.009) Education 0.171** * (0.022) (0.040) (0.022) (0.032) Healthcare 0.133** 0.078** ** (0.014) (0.039) (0.011) (0.010) Other ** 0.020** (0.009) (0.008) Average partial effect removal of ER, small ** * - - (0.002) (0.0001) Average partial effect removal of ER, medium (0.0002) (0.0002) Year effects 25 Yes Yes Yes Yes Observations 253, , , ,881 * significant at a level of 10%, ** significant at a level of 5%

26 To estimate the effects on DI outflow we use the data on the individuals that entered DI between 2001 and We only select individuals for whom we know the employer. We estimate equation 4 using the Cox proportional hazard model. Table 6 shows the estimated coefficients of these estimations, where we censor the DI spells after one or two years. We only observe significant effects for small employers before 2005: the removal of experience rating decreased the exit rate from DI. This corresponds with a increase of the survival rate after one year with 2.6% (from 76.9% to 79.6%) and after two years with 2.5% (from 69.4% to 71.8%). In general we find that individuals who worked for small firms are less likely to exit DI. For medium-sized employers we do not observe any significant effects. The coefficients are in line with the literature: older individuals, women, immigrants, individuals with a low previous wage, single parents and individuals without children are less likely to exit DI. Because we are using data on the level of the individual we can estimate the effect of experience rating on DI outflow for different subgroups. Table 7 shows the main estimated coefficients for the outflow within one year for individuals with a different DI percentage and DI benefits. We observe larger effects for the individuals who are partially disabled (less than 80%), as well as individuals with higher DI benefits. This could imply that the effects of experience rating are strongest for individuals with more possibilities to return to work and that employers focus on the individuals with higher DI benefits since they have the largest effect on the DI premium Robustness checks As a robustness check we repeat the estimations for the different datasets. The results of these estimations are given in table 8 in the appendix. We use three different selections: our preferred selection of employers with a one-to-one relationship as in the previous estimation ((column 3 and 6), the balanced panel of firms that were 21 The estimated coefficients do not differ for gender, age or sector. 26

27 Table 6: Estimated effects of the removal of experience rating for small and mediumsized employers on outflow from DI First year Second year Before 2005 After 2005 Before 2005 After 2005 Removal of experience rating, small 0.867** 0.904** (0.024) (0.021) (0.088) (0.083) Removal of experience rating, medium (0.046) (0.042) (0.190) (0.164) Small firm 0.959* 0.966* (0.020) (0.019) (0.061) (0.047) Medium-sized firm (0.034) (0.032) (0.117) (0.089) Workers characteristics Age, ** 0.845** * (0.027) (0.022) (0.104) (0.089) Age, ** 0.710** 0.666** 0.658** (0.024) (0.018) (0.084) (0.072) Age, ** 0.561** 0.602** 0.536** (0.020) (0.015) (0.076) (0.059) Age, ** 0.469** 0.521** 0.536** (0.018) (0.013) (0.067) (0.059) Man 1.043** ** 1.068* (0.014) (0.011) (0.045) (0.036) Immigrant 0.909** 0.945** 0.902** 0.892** (0.013) (0.011) (0.040) (0.032) Single household ** 0.844* (0.046) (0.038) (0.088) (0.080) Couple ** 0.819** (0.043) (0.036) (0.082) (0.076) Single parent ** 0.775** (0.049) (0.042) (0.092) (0.081) Has children 1.143** 1.139** 1.139** 1.131** (0.015) (0.013) (0.059) (0.049) Wage, ** 1.129** (0.018) (0.014) (0.047) (0.038) Wage, ** 1.278** 1.224** 1.223** (0.023) (0.018) (0.067) (0.056) Wage, ** 1.418** 1.344** 1.405** (0.030) (0.025) (0.096) (0.082) Wage, ** 1.438** 1.828** 1.815** (0.044) (0.039) (0.176) (0.143) Wage, > ** 1.372** 1.942** 1.943** (0.050) (0.045) (0.203) (0.169) Sector dummies Yes Yes Yes Yes Year effects Yes Yes Yes Yes Observations 124, ,434 33,127 33,127 * significant at a level of 10%, ** significant at a level of 5% 27

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