Although a number of papers in the literature have shown the employment and wage differences between individuals receiving disability benefits and non-disabled individuals, not much is known about the potential employment and wage losses that disabled individuals suffer before being officially accepted into the disability insurance system (DI). Therefore, in this paper we compare individuals that enter into the DI system due to a progressive deterioration in the health status (ordinary illness) to similar non-disabled individuals. Our aim is to identify the differences in employment and wages between these two groups before disabled individuals are officially accepted into the DI system. We combine matching models and difference-in-difference and we find that the wage (employment) growth patterns of both groups of workers become significantly different three (five) years before entering the DI system. More specifically, our estimates suggest that 1 year before entering the system, there is a difference of 79 Euros/month in the wages of the two groups (8.3% of average wage) as well as a 7.8% point difference in employment probabilities.
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See also Malo et al.  for a comparative study of the wage differentials for diferent types of disabled workers across European countries.
Of course, individuals receiving disability benefits are only a subset of all those who would report a disabling condition. Thus, our results must be interpreted as providing evidence of earnings and employment loses only for this subgroup of disabled individuals.
Singleton  also performs a small part of the analysis using a sample of disability insurance beneficiaries. However, he does not estimate the earnings loss of individuals before they are accepted into DI as he reports that individuals applying for DI have negligible earnings around the time of disability onset (as the system requires the individual to be unable to work in order to be accepted into DI, which is different from the Spanish case).
Income is evaluated yearly. The income threshold in 2010 was set at 4,755.80 Euros/year for an individual living alone. This amount is adjusted if the individual lives with other members.
Although the receipt of DI occurs after going through an objective medical examination, there is literature showing the existence of opportunistic behavior in insurance markets as a results of asymetric information (see Fortin & Lanoie  for a survey and Martin-Roman & Moral  &  for two recent examples on Spain).
There is a fourth degree of disability benefits (permanent limited disability) but this type of benefits is already extinguished and it only consists on a one-time lump-sum payment. .
57% of claimants are in the partial disability system, 40% of claimants in the total disability system and 3% are severely disabled.
There was a reform in the calculation of the level of disability benefits for ordinary illness introduced in 2008. After the reform, there was a percentage that depended on the number of years contributed to the system that was multiplied by the regulatory base. As this change only affects individuals whose source of the disability is an ordinary illness, which could have an effect on the incentives to enter the DI system for this group of workers, in the robustness check section we will perform the same analysis but excluding the years after 2008 in order to have a sample period without any important reform of the DI system.
Benefit = regulatory base * percentage.
This means that the only individuals that are missing from this database are those who were inactive in 2010 and did not receive any kind of contributory benefit (such as disability, orphan, widow, etc.). Furthermore, the sample is representative for 2010 but, as exit from the disability system is extremely low (0.01%), we are confident that the sample is also representative for the other years included in the analysis.
Before being accepted into the permanent DI system, the individual needs to spend some time in the temporary disability system while he/she receives the prescribed treatment. There is a maximum period of 18 months that each individual can spend in temporary DI. Therefore, we exclude the 18 months prior to the observed entry into permanent DI to make sure that we are capturing the labour market situation of the individual before going through any of the two DI systems.
In any case, even if some of our treatment groups experiences a sudden health shock instead of a progressive health deterioration, our estimates would represent a lower bound of the true effects as some of our treatment group would not be really treated. However, as presented in the robustness check section, when we use as an alternative control group individuals entering DI due to a working accident (sudden health shock) our results are almost identical than our baseline results. This suggests that common illness and working accidents are two different types of disabilities with a different degree of progressivity in health deterioration.
In an alternative specification, we have tried the results by summing the number of days worked during a year and adding up wages received during all the months employed in that year. Then, we divide this number by the total number of months worked so as to obtain a monthly measure. The results of this alternative specification go in the same direction and are available upon request.
Our dataset does not provide information on the type of diseases that is causing the disabling condition. However, we use data from the Survey of Individuals with Disabilities in 2011 to observe that 25% of the sample of individuals that receive disability benefits in 2011 is due to osteo-articular problems, 12% are due to neuromuscular problems, 18% due to mental illness and 10% to cardiovascular, immunologic and respirator diseases. These types of diseases are likely to entail a progressive deterioration of the health status until reaching the severity level required to access the permanent disability system.
The descriptive statistics are taken in the last period before the individual becomes disabled. However, most of the variables relate to personal characteristics that are time-invariant (such as education, gender, nationality, etc.).
The variable sequence is composed of a set of dummy variables that indicate the 5 possible sequences of years that we have in the sample (when year t = 0 happens in 2006, 2007, 2008, 2009 or in 2010). See Table 1 above.
See Heckman and Horz , Heckman, Ichimura and Todd (1997) and Blundell and Costa Dias (2002) are some of the articles that explain how to evaluate certain treatments using matching procedures.
Of course, there could be some other events that change over time differently for our treatment and control group (i.e. individuals in the treatment group could start smoking at a higher rate than individuals in the control group during the 10 years included in our sample). Although we think these systematic differences in the treatment and control group that appear over time (in a 9 year period) are unlikely, we include them in our definition of “progressive health deterioration” and acknowledge that the impacts on employment and wages can be due directly to the disabling condition or to any health deterioration that is determining the existence of a disability at the same time.
We choose to show the results from 10 years before entering the DI system because, as it can be seen in Figs. 2 and 3, the differences in both employment and wages between the two groups of individuals only start appearing from 5 or 4 years before getting access into DI.
On average wages at t = − 10 were about 883,2 euros per month for those individuals who will not enter the DI system and 904,3 euros per month for those that enter the DI system due to ordinary illness. Then, if wages of workers that do not enter the system increased by 15.2% in t = − 1 this gives an average wage of 1041.8 euros per month. On the other hand, the increase in wages for those that enter the DI system is 9.1% which gives an average wage of 963.6 euros in t = − 1. Therefore, that gives us a difference of 78.2 euros.
As there is no element (a priori) to choose one matching technique over the others we show the results of the same model but using kernel matching and stratification matching in Table 12 in the Appendix.
We do not use wages but a proxy for wages, the contributory base, over which the contributions to the Social Security administration are calculated and paid. As it often occurs with Social Security records, the difference between contributions and wages is that contributions are top- and bottom-coded, that is, they are censored. Although for the entire MCVL this is a significant problem, as Bonhomme and Hospido  mention such an issue is likely not to be empirically relevant in our case as our sample does not include neither top nor bottom wages earners (bottom earners are typically concentrated in the non-contributory DI system).
Alternatively, we calculate wages adding the wages received in all the months worked and dividing it by the total number of months worked so to obtain a monthly measure. The results of this alternative exercise go in the same direction and are available upon request.
The average wage for individuals in our sample that will enter the DI system due to an ordinary illness is 943.5 Euros/month in t = − 1.
As before, the results of the estimations using kernel matching as well as stratification matching are reported in Table 13 in the Appendix.
Abadie, A., Drukker, D., Herr, H., Imbens, G.: Implementing matching estimators for average treatment effects in Stata. Stata J. 4, 290–311 (2004)
Abadie, A., Imbens, G.: Simple and bias-corrected matching estimators for average treatmen efects in STATA. Stata J. 1(1), 1–18 (2002)
Becker, S., Ichino, A.: Estimation of average treatment effects based on propensity scores. Stata J. 2(4), 358–377 (2002)
Blundell, R., Costa, Dias M.: Alternative approaches to evaluation in empirical microeconometrics. Port. Econ. J. 1, 91–115 (2002)
Bonhomme, S., Hospido, L.: The cycle of earnings inequality: evidence from Spanish social security data. Econ. J. 127(603), 1244–1278 (2017)
Bound, J.: The health and earnings of rejected disability insurance applicants. Am. Econ. Rev. 79(3), 482–503 (1989)
Cervini-Plá, M., Silva, J.I., Vall, Castello J.: Estimating the income loss of disabled individuals: the case of Spain. Empir Econ. 51(2), 809–829 (2016)
Charles, K.K.: The longitudinal structure of earnings losses among work-limited disabled workers. J. Human Resour. 38(3), 618–646 (2003)
Chen, S., Van Der Klaauw, W.: The work disincentive effects of the disability insurance program in the 1990s. J. Econom. 142(2), 757–784 (2008)
Contoyannis, P., Rice, N.: The impact of health on wages: evidence from the British Household Panel Study. Empir Econ 26(4), 599–622 (2001)
French, E., Song, J.: The effect of disability insurance receipt on labor supply. Am. Econ. J. Econ. Poli. 6(2), 291–337 (2014)
Fortin, B., Lanoie, P.: Incentive effects of workers’ compensation Insurance: a survey. In: Dionne, G. (ed.) Handbook of insurance, pp. 421–458. Kluwer Academic, Boston (2000)
Garcia Gomez, P., López, Nicolás A.: Health shocks, employment and income in the Spanish labour market. Health Econ. 15, 997–1009 (2006)
García-Gómez, P., van Kippersluis, Hans, O’Donnell, Owen, van Doorslaer, Eddy: Long-term and spillover effects of health shocks on employment and income. J. Human Resour. 48(4), 873–909 (2013)
Halla, M., Zweimüller, M.: The effect of health on earnings: quasi-experimental evidence from commuting accidents. Labour Econ. 24, 23–38 (2013)
Heckman, J., Hotz, J.: Choosing among alternative methods for estimating the impact of social programs: the case of manpower training. J. Am. Stat. Assoc. 84, 862–874 (1989)
Heckman, J., Ichimura, H., Todd, P.: Matching as an econometric evaluation estimator: evidence from evaluating a job training program. Rev. Econ. Stud. 64, 605–654 (1997)
Jenkins, S.P., Rigg, J.A.: Disability and disadvantage: selection, onset and duration effects. J. Soc. Poli. 33(3), 479–501 (2004)
Jolly, N.A.: The impact of work-limiting disabilities on earnings and income mobility. Appl. Econ. 45(36), 5104–5118 (2013)
Kidd, M., Sloane, P., Ferko, I.: Disability and labour market: an analysis of British males. J. Health Econ. 19, 961–981 (2000)
Lechner, M., Vazquez-Alvarez, R.: The effect of disability on labour market outcomes in Germany. Appl. Econ. 43, 389–412 (2011)
Lundborg P, Nilsson M, Vikström J.: Socioeconomic Heterogeneity in the effect of health shocks on earnings: evidence from population-wide data on swedish workers. IZA Working Papers No. 6121 (2011)
Maestas, N., Mullen, K.J., Strand, A.: Does disability insurance receipt discourga work? Using examiner assignment to estimate causal effects of SSDI receipt. Am. Econ. Rev. 103(5), 1797–1829 (2013)
Malo, M.A., Pagán, R.: Wage differentials and disability across Europe: discrimination and/or lower productivity. Int. Labour Rev. 151(1–2), 43–60 (2012)
Marie, O., Vall-Castello, J.: The employment effects of increasing disability benefits: a regression discontinuity approach. J. Public Econ. 96(1–2), 198–210 (2012)
Martín-Román, Á.L., Moral, A.: Moral Hazard in Monday claim filing: evidence from Spanish sick leave insurance. BE J Econ Anal Poli 16(1), 437–476 (2016)
Martín-Román, Á.L., Moral, A.: A methodological proposal to evaluate the cost of duration moral hazard in workplace accident insurance. Eur. J. Health Econ. 18(9), 1181–1198 (2017)
Mok, W.K.C., Meyer, B.D., Charles, K.K., Achen, A.C.: A note on the longitudinal structure of earnings losses among work-limited disabled workers. J. Hum. Resour. 43(3), 721–728 (2008)
OECD.: Sickness, Disability and Work: Keeping on Track in the Economic Downturn: Background Paper (2009)
Rubin, D.B.: Matching to remove bias in observational studies. Biometrics 29, 159–183 (1973)
Rubin, D.B.: Estimating casual effects of treatments in randomised and non-randomised studies. J. Educ. Psychol. 66, 688–701 (1974)
Rosenbaum, P., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)
Rosenbaum, P., Rubin, D.B.: Reducing bias in observational studies using subclassification on the propensity score. J. Am. Stat. Assoc. 79, 516–524 (1984)
Singleton, P.: The dynamic relationship between disability, earnings and disability insurance application and receipt. Econ. Lett. 124(3), 374–377 (2014)
Von Wachter, T., Song, J., Manchester, J.: Trends in employment and earnings of allowed and rejected applicants to the social security disability insurance program. Am. Econ. Rev. 101(7), 3308–3329 (2011)
Walker I, Thompson A.: Disability, wages and labour force participation: Evidence from UK panel data. Keele Department of Economics Working Paper No. 96/14 (1996)
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Cervini-Pla, M., Vall Castelló, J. The earnings and employment losses before entering the disability system. Eur J Health Econ 19, 1111–1128 (2018). https://doi.org/10.1007/s10198-018-0960-8
- Disabled individuals
- Employment and wage loss
- Disability benefits