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Disability insurance benefits and labor supply decisions: evidence from a discontinuity in benefit awards


The effect of disability insurance (DI) benefits on the labor supply of individuals is a disputed topic in both academia and policy. We identify the impact of DI benefits on working full-time, working part-time or being out of the labor force by exploiting a discontinuity in the DI benefit award rate in Switzerland above the age of 56. Using rich survey data and a discrete endogenous switching model, we find that DI benefit receipt increases the probability of working part-time by approximately 32% points, decreases the probability of working full-time by approximately 35% points and has little effect on the probability of being out of the labor force for the average beneficiary. Looking at the treatment effect distribution, we find that male, middle- to high-income and relatively healthy DI beneficiaries are more likely to adjust their labor supply from full-time to part-time, whereas women, low-income and ill beneficiaries tend to drop out of the labor market. Our results shed new light on the mechanisms explaining low DI outflow rates and may help better target interventions.

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  1. This will be done using the Geweke, Hajivassiliou and Keane (GHK) algorithm that allows estimating higher-order dimensional cumulative normal distributions (Roodman 2011).

  2. Social Security Disability Insurance (SSDI) is one of the two main federal programs that provide cash assistance to the disabled in the USA. The other program is Supplemental Security Income (SSI), which was introduced in 1972 to provide a minimum level of income to impaired individuals.

  3. The elasticity of non-participation with respect to benefits is defined as the ratio of the change in labor supply relative to the change in potential benefits (Gruber 2000).

  4. We discuss this data limitation in more detail in Sect. 7. While our empirical strategy identifies an internally valid local average treatment effect, the fact that the number of DI recipients in the SHP is not representative for the entire Swiss population clearly poses limitations on the external validity of our results, in particular the labor market responses of younger individuals and those directly below the retirement age.

  5. See “Appendix A.1” for a detailed description of the construction of all variables.

  6. Non-response is less than 1% in the SHP for the key demographic and socioeconomic variables (e.g., age, gender, education). As for the remaining health, benefit receipt and satisfaction indicators relevant for our analysis, the non-response rate is about 20% stemming mostly from vulnerable groups (e.g., individuals with migration background, low-education households) which are therefore underrepresented in the survey [see Rothenbühler and Voorpostel (2016) for details on attrition in the SHP]. Note that these more vulnerable groups can be expected to show a less accentuated labor supply reaction than the generally more flexible and literate respondents in our sample, thus leaving us with an upper bound on the DI benefit effects reported in this paper.

  7. Unobserved confounders could be, for example, genetic endowments, risk preferences, time discounting, innate abilities, work motivation and general attitudes toward health and work.

  8. Note that the standard procedure in the RD literature of investigating the density of the forcing variable around the threshold does not work in our case because of the sampling design, which is a representative sample of the whole Swiss residential population, including all age groups.

  9. Pension reductions currently amount to approximately 5–8% for every additional year of early retirement.

  10. Parental education is considered medium to low if the educational attainment of the parents was at most compulsory schooling or having finished an apprenticeship (upper secondary education).

  11. We follow the standard approach in the literature by imposing variance unity on the error terms to normalize the scale of utility and thus ensure parameter identification.

  12. Age, age interacted with the instrument, gender, weight, height, number of children, foreigner status and six regional dummies according to the standard classification of the Swiss Federal Office of Statistics.

  13. Marital status, years of schooling, log household income, life satisfaction.

  14. Number of doctor visits, days of illness, physical activity, health impediments, medication needed, indication for self-assessed health and dummies for depression, back problems, weariness, headaches, sleeping problems.

  15. The specifications were also estimated for \(S=2000\) and \(S = 5000\), results available upon request.


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We are grateful to Magne Mogstad, Judit Vall Castello, Teresa Bago d’Uva, Bill Greene, Bruno Ventelou, Michael Gerfin, Kaspar Wüthrich, Eva Deuchert, Lukas Kauer, David Roodman, Mujaheed Shaikh and seminar participants at the iHEA World congress 2015 in Milan, the 2nd EuHEA student-supervisor conference 2015 in Paris, the 2014 SSPH+ Doctoral Workshop in Health Economics and Public Health in Lugano and the Applied Statistics Seminar at the University of Lucerne for helpful suggestions and comments.


for this project was provided by the Swiss National Science Foundation (Grant 100014/134526).

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Correspondence to Stefan Boes.

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1.1 A.1 Variable construction

The indicator for DI benefits is constructed from a question about DI benefit receipt in the past year and equals one for individuals who report to receive DI benefits and equals zero for those who report that they do not receive benefits. There is also information about the amount of DI pensions in the Swiss Household Panel, but we decided against using this variable because of many missing values and the likely presence of reporting error, which would bias the results. Moreover, our instrument likely has a stronger effect on the extensive margin of DI benefits than the intensive margin. The discrete labor supply variable takes three values for individuals who work part-time (1), full-time (2) or are unemployed or not in the labor force (3).

Regarding the control variables, we create a binary variable from the life satisfaction variable (originally scaled from “not satisfied” (0) to “completely satisfied” (10)), which is coded such that it is one for individuals with a satisfaction score of at least seven (the median value) and zero else. General health status ranges from “very well” (1) to “not well at all” (5) and is recoded as a dummy variable good health that takes the value one for individuals with a “very well” or “well” health status, and zero else. Physical activity is a binary variable indicating whether a person exercises for at least half an hour a week (1) or if that person remains inactive (0). Health impediments in everyday activities and medication needed in everyday functioning are measured on an 11-point scale from “not at all” (0) to “a great deal” (10). For both of these variables, we generated an indicator equal to one for individuals with a value of at least 5 in terms of severity of health impediments and medication needed for everyday functioning, and zero for all others. Depression, anxiety and blues are measured on a scale of “never” (0) to “always” (10). We constructed an indicator depression from this information that equals zero for all observations below 3, and one for the rest. The cutoff value of 3 is chosen here as the 75% quantile in the distribution of the original depression variable. Finally, indicators for back problems, weakness and weariness, sleeping problems and headaches are dummy variables, which were created in the way that they are one for observations that report that they are suffering “very much,” and zero for those who are suffering “not at all” or “somewhat” on the original three-point scale.

1.2 A.2 Simulated log-likelihood

The vector of model parameters \(\theta _j \equiv \left\{ \beta _j, \gamma _j \right\} \) and \(\Omega \) are estimated by maximizing a simulated log-likelihood function of the form,

$$\begin{aligned} {\hbox {SLL}}(\theta , \Omega ; x,y) =&\sum _{j = 1}^3 \sum _{n=1}^N d_{ij} D_i \log (\tilde{P}(y_i = j | D_i = 1)) \nonumber \\&+ \sum _{j = 1}^3 \sum _{n=1}^N d_{ij} (1-D_i) \log (\tilde{P}(y_i = j | D_i = 0)) \end{aligned}$$

where \(d_{ij}\) is an indicator for the choice taken by individual i, \(D_i\) is the indicator for the DI benefits and \(\tilde{P}(\cdot )\) is the simulated (conditional) choice probability.

Table 8 shows the discrete ES estimates for 1, 10, 100 and 1000 draws per observation, the number of iterations that were needed to reach convergence, the value of the pseudo-log-likelihood at the coefficient vector and the computation time in seconds. All simulations were ran on an Intel(R) Core(TM) i7-4790 CPU @ 3.60 GHz with 16GB RAM on Windows 10 using Stata/MP 14.0. Recall that the main results are based on 1000 draws per observation so that Table 8, column 4 is identical to the results presented in Sect. 6. For small numbers of draws, we know from asymptotic theory that the MSL estimator is not equivalent to the ML estimator and inconsistent (Gouriéroux and Monfort 1991). This is likely the case for the estimates corresponding to \(S = 1\) and \(S=10\) draws per observation. For such small S, the reduced computation time comes at the cost of inconsistent parameter estimates. However, as the number of draws is increased, the MSL estimates stabilize and remain basically unchanged. Table 8 suggests that estimates with as few as 100 random draws per observation produce reliable coefficient estimates that are close to those with \(S = 1000\). This is also relevant to know from a practical point of view since the differences in computation time are considerable: For the full specification, the computation time is roughly 7 minutes for \(S = 100\), but already 18 minutes for \(S = 1000\). If the number of draws is further increased,Footnote 15 the coefficient estimates hardly change but again come at the cost of a significantly higher computational time.

Table 8 Sensitivity checks on the number of simulation draws

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Müller, T., Boes, S. Disability insurance benefits and labor supply decisions: evidence from a discontinuity in benefit awards. Empir Econ 58, 2513–2544 (2020).

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  • Disability insurance
  • Labor market participation
  • Fuzzy regression discontinuity design
  • Discrete endogenous switching models
  • Maximum simulated likelihood

JEL Classification

  • J2
  • C35
  • C36