The Impact of Health on Labor Supply near Retirement

A. Measuring Health Using Objective Measures

The health stock can be formalized by a combination of all health conditions (and combinations of conditions) that limit work, for k = 1,… K. These are typically labeled “objective” health measures because they represent medical health conditions that can be unambiguously identified; indeed, some surveys report only conditions that have been medically diagnosed and for which the respondent receives treatment.

Assuming a linear functional form, we write (3) and this expression can be replaced in Equation 1 to yield (4) where .

In practice, the simple specification in Equation 4 is sensitive to potentially serious measurement problems for four reasons. First, the number of observed conditions K^o is smaller than the total number of health conditions K since one can only ever observe a limited subset of the relevant medical conditions. This is true even if one has full access to medical records, as only diagnosable conditions under current technology can be observed. Health status can be decomposed into observed and unobserved objective conditions: (5) where q summarizes the contribution to health status of the K – K^o unobserved conditions. Consequently, the effect of health can only partly be determined. Second, not all health conditions are equally important for overall health and thus employment, a fact that is expressed by the multiple parameters . While some conditions may be so debilitating as to impair work completely (like strokes), others may have more limited consequences for work capacity (like diabetes). Hence, the magnitude of the estimated impact will depend critically on exactly which conditions are included. Third, estimates of the impact of specific observed conditions may be biased if unobserved conditions are related to observed ones. Fourth, most health measures only describe whether respondents suffer from certain conditions, not the severity of those conditions. This is a key source of measurement error biasing the estimated effects, potentially towards zero.

To put it more formally, consider the linear regression model of employment in Equation 1 and assume that the true health stock H* is a combination of two conditions, . For this discussion we also ignore the correlation between health and the X variables. We normalize the variance of the objective measures to equal that of H* and ensure that all variables are ordered in the same direction (say, higher values for better health) so that (α₁, α₂) ∈ [0, 1]² Suppose that is observed and measured without error, but is unobserved. In such case, the ordinary least squares (OLS) estimator of θ_H yields

If , then plim and will thus identify the effect of the first health condition, which is smaller than the impact of the global health measure (θ_H) under the assumptions stated above. Moreover, had one observed instead of , a different impact would be identified (specifically, θ_Hα₂).

In the likely case where the two health condition measures are positively correlated (with a second health condition being more prevalent among those who already suffer from the first health condition), then the estimated effect of health will be closer to the true overall effect (hence less biased) than under the case where they are uncorrelated. A prediction based on model estimates of how much changes in health status drive employment (as described below in Section III.G) will still be biased towards zero for two reasons: first, the likely attenuation bias in the estimated coefficient and, second, the failure to account for all the relevant variation in health in the presence of missing variables.

Applications that use objective health measures often combine information from numerous health conditions. This may attenuate the estimation bias but will generally not eliminate it. With many health measures, the formula for the asymptotic limits described above becomes more complex, although the key insight is the same—the index will understate the true causal effect of health on employment because it does not capture all relevant variation in health, and the extent of the bias depends on how strongly correlated the omitted variables are with the observed ones. In fact, using any linear combination of the observed health measures (such as the first principal component of the objective measures) will understate the true causal effect. The lack of detailed medical data on the severity of a condition can be viewed as a specific case of missing variables and will, as in the general omitted variable case, lead to attenuation bias.

In the empirical application, we use the complete set of medically diagnosed conditions (for which the respondent is getting treatment) common to the two data sets. These amount to ten objective measures in total. We have produced a parallel set of results by augmenting the set of objective measures with observed variables measuring activities of daily living (ADL), which are meant to capture general levels of health that may limit work. Our results are not sensitive to this choice.³

B. Measuring Health Using Objective Measures

Although we cannot observe H* directly, we do observe “subjective” measures for k = 1,… K^S. These are self-reported health measures that describe overall health status and provide an alternative to using objective measures to describe heath. The literature has interpreted the subjective measures as noisy measures of a single latent health stock H*. Thus, while the different objective measures describe different subcomponents of the health stock (as shown in Equation 3), the subjective measures are overall (noisy) measures of the single latent health stock. This idea can be formalized by the following set of measurement equations, which relate the observable subjective health indicators to the unobservable latent health index H*: (6) where u_k represents the measurement error in observed health variable k.

In practice, studies that model health as a latent variable typically use a single indicator of health (Bound et al. 1999; Bound, Stinebrickner, and Waidmann 2010; Disney, Emmerson, and Wakefield 2006). Instead, we use all the subjective measures of health that are contained in both the HRS and ELSA surveys, which total three, and extract a health index using principal component analysis.⁴ This is a natural approach if one wants to summarize the common information in many subjective measures, each being a noisy measure of the same latent health variable.⁵ It turns out that the results are not sensitive to the procedure used to extract the variation from the subjective measures; we show only results using principal components analysis in the main text (see Online Appendix Section 4.2 for some results using factor analysis).

Let H^S be the subjective health index constructed using the subjective health measures. The single index is a parsimonious approach that can be used in a variety of contexts; it is particularly useful when keeping the number of health variables low is paramount, such as for estimation of structural models of health. Moreover, the use of common variation across many subjective health measures (using approaches such as factor analysis or principal components analysis) helps mitigate the importance of measurement error if the noise across different variables is independent.

However, measurement error is unlikely to be completely eliminated by the use of many measures in constructing the health index. In particular, justification bias affecting all underlying subjective measures implies that measurement error is not classical. So we write (7)

If the unobserved component of employment (e) and the measurement error (v) are uncorrelated, estimates of the health effect θ_H will be biased towards zero. In the more likely event that (e, v) are positively related—those not working tend to report lower levels of health partly to justify their working status—the direction of the overall bias is ambiguous. Indeed, the OLS estimator of θ_H in Equation 1 using H^S to proxy H* has asymptotic limit: (8) which may be greater or smaller than the parameter of interest θ_H depending on the sign and relative size of Cov(e, v). O’Donnell, van Doorslaer, and van Ourti (2015) suggest that justification bias dominates and Cov(e, v) > 0, resulting in an upward biased estimate of θ_H. However, Stern (1989) and Dwyer and Mitchell (1999) do not find that justification bias dominates.

C. Using Instrumental Variables to Deal with Measurement Error and Justification Bias

Thus far we have seen that approaches using exclusively objective measures suffer from omitted variable bias and are likely to produce estimates of the impact of health that are downward biased. Approaches using only subjective measures suffer both from measurement error and justification bias, leading to estimates that could be either upward or downward biased. One way of dealing with the biases afflicting estimates based on subjective health measures is to use instrumental variables (IV). We have many potential instruments to choose from if measurement error and justification bias in the subjective measures are independent from objective health conditions, namely the entire set of objective health measures.

It is straightforward to see that any subset of the objective health measures can be used to instrument the subjective index. For simplicity, consider the case where we only have one objective measure (indexed k) and use it to instrument the subjective health index. The first stage regresses H^S on , and the estimated coefficient (call it ) converges in probability to

Recall that H* is a combination of all objective health conditions (as described in Equation 3), each of which has been standardized to have a variance equal to that of H*.

The predicted value of H^S is, therefore, . The second-stage IV estimate using the linear employment Equation 1 is

Under the IV exclusion restrictions, we can assess the importance of biases confounding estimates of θ_H based on objective measures (due to omitted variables) and based on subjective measures (due to measurement error and justification bias). We do this by comparing IV estimates to those obtained using only objective or subjective health measures.

It is straightforward to show that the instrumental variables approach is valid even if there is measurement error in the objective measures, so long as that measurement error is orthogonal to that affecting the subjective measures. In particular, this assumption requires that the justification bias generally associated with subjective measures does not permeate into responses to the survey questions on objective health measures. We discuss the plausibility of this assumption in the Online Appendix.

D. Tests of the Single Index Assumption

We now turn to discuss the plausibility of the single index assumption. The single index assumption states that there exists an index of multiple measures of selfreported health status, H^S, constructed as a composite measure of the subjective health variables, which contains all relevant health information for employment. Under this assumption, the objective measures impact employment only through their impact on H^S. This is a restriction on Model 1 in which the latent measure of health (H*) can be a function of multiple health conditions with varying implications for work capacity, as described in Equations 3 and 4. We use this restriction to derive a specification test below. Notice that measurement error and justification bias are not ruled out by this assumption. Indeed, we do allow for both sources of noise in H^S, as described in Equation 7. The single index assumption imposes that any measurement error in H^S (in Equation 7) is independent of H*:

The single index assumption underpins much of the empirical work on the impact of health on labor supply. In particular, it is critical in contexts where dealing with multiple health dimensions is impractical, such as in large structural models. We now use our methods to assess the validity of this assumption using data that is now becoming widely available in developed countries. To the best of our knowledge, this has not been done before.

First, we use our subjective measures. Under the single index assumption, all subjective measures of health are noisy measures of the same concept. Thus, each individual measure should have little predictive value for employment above and beyond a summary measure of all subjective variables. We test this assumption by including the second and third principal components of health in the employment model, in addition to the first principal component. Formally, we test the explanatory power of the added principal components.⁶

Second, we use the objective measures to assess the single index assumption. One simple point is that the single index assumption implies that the effect of health estimated using the index should not be smaller than that estimated using objective measures. This is because a correctly specified health index should capture all relevant health information for employment, while objective measures can only capture part of the relevant variation (as explained above). We therefore compare the magnitude of the health effects based on the single subjective health index and the full set of objective measures.

A slightly subtler point is that the instrumental variables approach with multiple instruments provides the means to test the validity of the single index assumption using a Sargan overidentification test (Hansen 1982). The intuition is simple: if the single index assumption is valid, all the objective measures (the instruments) should affect labor supply only through the subjective health index. For this reason, the IV residuals e^IV should not be correlated with the instruments. With ten objective measures, we have nine overidentification conditions.

In practice, we implement the test following the suggestion in Davidson and MacKinnon (2003). For the linear probability regression model in Equation 1, we construct the IV residuals: (9)

Under the single index assumption, we know that: (10)

So we regress the residual on all health objective measures and the exogenous variables X, and calculate the F-statistic associated with the hypothesis that all health coefficients are jointly equal to zero. For the latent index model of employment in Equation 2 we use the overidentification test developed in Lee (1992), based on the minimum distance estimator proposed in Newey (1987) (see also Rivers and Vuong 1988).⁷

E. Health Measures and the Estimation of the Effects of Other Determinants of Employment

Our focus is on obtaining consistent estimates of the impact of health on employment θ_H. Here we discuss how our various approaches to measuring health affect estimates of the effects of other drivers of employment θ_X. We point out that instrumenting subjective health using objective measures of health can deliver consistent estimates of θ_H but will not in general deliver consistent estimates of θ_X a result highlighted in Bound (1991).

In the context of a structural model such as that discussed in Section VI, health affects both employment and other choices and outcomes throughout life, including savings towards retirement, offered wages, and other financial incentives to retire (see also Gilleskie, Han, and Norton 2017). In the statistical model of employment in Equation 1, which can be derived from that structural model, these other economic variables are contained in X. However, since health status is not observed, the statistical model of employment needs to be completed with a description of how health is measured. We consider two alternative proxy measures, based on objective and subjective health measures as expressed in Equations 5 and 7, respectively. The error in these measures may be correlated with the other drivers of employment (X) in ways that bias the estimates of their effects. Moreover, since the nature of that error differs across health measures, so will its consequences for the estimation of the effects of X on employment.

To be more specific, consider first the use of an incomplete set of objective health measures as described in Section III.A. If the omitted health conditions are correlated with the covariates X in the employment Equation 1, the resulting estimates of θ_X will be biased. Consider estimates of the effect of age on employment. In our specification, the age coefficient is particularly interesting, as it summarizes the joint roles of changing preferences and monetary incentives to retire in driving employment of older workers. We expect it to be negative, as older people are increasingly less likely to work. If health deteriorates faster later in life and in ways that are not fully captured by observed objective health , then the age coefficient would partly encapsulate the effects of the deterioration of health along the unobserved dimensions . In this case, estimates of the age effects would be downward biased, away from zero, which means that one would overstate the effect of age on retirement.

The consequence of using a subjective health index for the direction and magnitude of the bias of the age effects on employment near retirement is more ambiguous. Suppose that justification bias dominates other sources of measurement error, so that Cov(e, v) > 0 in Equation 8; this will result in upward biased estimates of the impact of health on employment (θ_H). The mismeasurement of H* can affect estimates of the age effects in two ways that may partly cancel out. First, the bias in θ_H leads to an overprediction of the role of health deterioration with age in driving employment. In our regression model, this would be partly compensated by an age effect biased towards zero. That would be the only source of bias if age is independent of the justification error, but not otherwise. The second source of bias arises precisely if the measurement error in health status is correlated with age. For instance, one could think that the importance of justification bias fades with age if old age is widely accepted as a valid reason for not working, in which case younger workers underreport their health more strongly than older workers. This would bias the estimate of the age coefficient downwards or away from zero partly to compensate for the fact that subjective health underpredicts the true pace of health deterioration with age. The ultimate direction of the bias in θ_X would, in this case, be undetermined a priori. If, on the contrary, justification error becomes more important with age as more workers stop working, then instead subjective health would overpredict the true pace of health deterioration with age, and the two sources of bias would push the age coefficient in the same direction, towards zero.

Finally we note that instrumenting subjective health using objective health would remedy bias from the first source by producing consistent estimates of θ_H. However, it will have no impact on the second source of bias given that subjective health rather than actual health is used in the statistical model.

F. Cognition

Cognition is not only a determinant of productivity in work, it may also affect work capacity in a way that is not otherwise observed in objective and subjective health variables. It may, therefore, be a critical driver of labor supply, and we are interested in determining its effect. We therefore extend our model to control for cognition. We observe several measures of cognition, described in Section IV.D below. These are test scores, measured by the interviewer, and thus not subject to the sources of bias that may afflict health measures. Yet, our cognition measures will provide only an incomplete representation of cognitive ability, implying our estimates of the cognition effects may be biased towards zero. Denoting the latent cognition index by C*, the extended model is (11)

As in the case of health, we construct a parsimonious representation of cognitive ability under the single index assumption by summarizing the cognition variables in a single index using principal component analysis.⁸ When using this extended model, we supplement the initial conditions in X with cognition measured when each individual is first observed.

G. Comparable Measure of the Impact of Health and Cognition

To facilitate the comparison of results across the various specifications, we construct a global measure of the impact of health or cognition by predicting their cumulative impact on employment for the 20 years that span ages 50–70. The parameter we calculate is (12) where the upper bar represents average predictions from a fixed effects regressions of measures M (for health and cognition) and Y (for employment) on age. Hence, (for X = Y, M) is simply the average change in measure X that individuals experience between the ages of 50 and 70. The fixed effects net out differences across cohorts and attrition in the panel that could confound our estimates of individual-level decline in health, cognition, or employment.

Θ_M is a function of the estimated parameters. In the probit specification, it is the estimated marginal effect of health or cognition evaluated at the mean of all explanatory variables. In the linear model, Θ_M equals the estimate of θ_M, where M denotes the corresponding measure of health or cognition.⁹

In measuring changes in health and cognition as workers get older, we rely on the exact same measures that were used to estimate each model. So the change in health or cognition that we consider to calculate will depend on which specific measure was used in estimating Θ_M. For instance, we use changes in subjective health and in instrumented subjective health to quantify the impacts implied by estimates based on the respective measure. If the subjective health is afflicted by justification error that varies with age, then that age dependence will be reflected on our measure of health deterioration in the 20 years from age 50 based on the subjective index but not on its instrumented counterpart.

When using various measures of health and cognition together in the same regression model—such as, for instance, when estimating a model of employment on objective health measures—we use changes in each measure to calculate the single impact parameter (13) where j indexes the various health and cognition measures included in the employment regression model. Here again Θ_j is the marginal effect of health or cognition measure j evaluated at the mean of all covariates in the case of the probit model, or simply the estimate for the linear model. A similar metric has been used by French (2005). Cutler, Meara, and Richards-Shubik (2013) calculate the decline in employment not explained by declining health.

A. Employment Profiles

We now turn to our key outcome variable, employment. Figure 3 shows significant declines in employment for all three education groups for both genders, particularly after age 60. In ELSA, employment among men starts from a higher base than that of women and declines later; a sharp decline coincides with the state pension age (at 65 for men, 60 for women) in both groups. In contrast, both men and women experience similar declines in employment rates with age in the United States, where the early (62) and normal (66 for most of the sample period) retirement age is the same for the two genders. These profiles for the two countries are suggestive of the importance of retirement incentives in driving the decline in employment. Employment rates are flatter in the HRS than in ELSA, implying that a higher proportion of Americans than English are still working in their late 60s. Finally, the education gradient is much stronger in the United States than it is in England. Fewer high school dropouts are in work during their 50s in the United States than in England. This feature is likely to be linked to the differences in education attainment of Americans and English, with high school dropouts being a much larger, and hence probably less disadvantaged, group in England.¹¹

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Figure 3

ELSA Employment on Age, by Gender and Education

B. Objective Measures of Health

As described, we consider health variables in two broad categories, objective and subjective. Here we focus on the former. Table 2 summarizes the objective health measures we consider, which include reports of the health conditions for which respondents receive medical treatment (such as cancer or diabetes). For comparability, we only use variables that are present in both surveys.

The differences between the United States and England are stark; prevalence in the United States is larger for eight out the ten conditions for which the respondent is treated (top ten rows in the table) and is often twice or even three times larger in magnitude. For example, cancer prevalence is 3 percent in ELSA for both men and women, but the figures in the HRS are, respectively, 8 percent and 11 percent; diabetes prevalence is 9 percent and 6 percent for men and women in ELSA and is 19 percent and 17 percent in HRS; the numbers for arthritis are 23 percent and 34 percent in ELSA and up to 44 percent and 57 percent in HRS.

These reported health differences have been well documented before in Banks et al. (2006) and Banks, Keynes, and Smith (2016). They may reflect a combination of differences across the two countries, in health status, diagnosing rates, and respondents’ information about their health conditions. Meanwhile, gender differences are similar across the two countries. Typically, women are more likely to have arthritis and psychiatric problems, but are less likely to have suffered from a stroke, heart attack, or diabetes.

Panels A and C of Figure 4 show how the prevalence of arthritis changes between the ages of 50 and 70, by gender and education in England and the United States. The plotted lines show smoothed age trends using moving averages of three years. The clear positive gradient with age for all groups is indicative of how health deteriorates around the retirement age. This unsurprising finding justifies the focus on this age group of much of the economic literature on health and employment in developed countries. The graphs also show that the prevalence of arthritis is higher among women and those with less education in both countries. The latter is also typical of many health conditions— less educated and poorer individuals tend to report lower levels of health. However, the sharpest difference is that between England and the United States, with arthritis being much more prevalent for all groups in the United States.

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Figure 4

Prevalence of Arthritis by Age, Gender, and Education

Notes: MA(3) indicates a three-year moving average, and FE indicates fixed-effects estimates.

These figures may mask cohort differences in the prevalence of the disease. To deal with this, we net out fixed effects by estimating where h_it is a health outcome of interest for individual i aged t, α are the individual fixed effects (normalized to have mean zero in the population), and β_t are a full set of age dummy variables that capture health–age profiles net of fixed effects. We then plot the estimated age profile β_t. Note that this fixed effects specification captures all time invariant factors. For example, a cohort effect is just the average fixed effect of everyone within that cohort. In our application it is important to net out fixed effects particularly when looking at health profiles conditional on education because of the rapid increase in education attainment over the sample period, especially in England. Specifically, the shift towards more education implies that highly educated individuals in the older cohorts of our sample may be drawn from a more selected sample, with different health outcomes, than equally educated individuals from the younger cohort. The fixed effects estimator, which is identified by individual changes in health with age, eliminates the effects of such compositional changes on the level of health. In addition, because fixed effects track the same people over time, they addresses the issue of nonrandom attrition from the sample due to death or other reasons. Profiles for arthritis are shown in Panels B and D of Figure 4 for England and the United States, respectively. The patterns are similar to those in the raw data, but the age gradient is noticeably steeper for most groups. The full set of figures describing the prevalence of health outcomes by age is available in Section 2.2 of the Online Appendix.

C. Subjective Measures of Health

The indicators of subjective health are summarized in Table 3. These are variables of self-reported health, describing general health and whether it hinders work or the ability to perform normal daily activities. The means reported in the table show some interesting patterns. Responses to all questions are well aligned across the two countries, with English people reporting slightly better health than Americans but with much more modest differences than those observed for objective health measures. This is remarkable given the considerably higher prevalence of disease in the United States as described by the objective measures. It must be driven, at least to an extent, by large differences between the two countries in the way individuals report their own health. This is consistent with results in Banks, Keynes, and Smith (2016), showing that Americans set lower thresholds for good and excellent self-reported health than do the English, and in Kapteyn, Smith, and Van Soest (2007), showing that Americans set lower thresholds for being nondisabled than the Dutch.

Finally, the English tend to report lower levels of health as children than Americans do, with around 12 percent of ELSA respondents reporting bad health as a child compared to 7 percent of HRS respondents.

We summarize the subjective measures of health in a single index that we think captures well the global measure of health status, the first component from a principal component analysis of the three subjective health measures.¹² The age profiles of the index are shown in Figure 5. The patterns are much more similar across the two countries than those found for the objective measures. There is again a clear ordering by education group and a negative gradient with age. Removing fixed effects changes the patterns for the United States more than it does for England, by making the age profiles steeper.

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Figure 5

Single Subjective Health Index by Age, Gender, and Education

Notes: MA(3) indicates a three-year moving average, and FE indicates fixed-effects estimates.

D. Cognition

High-quality survey information on cognitive functioning only recently became available. It exists in both ELSA and HRS, with respondents being given a battery of cognitive tests. The literature on cognitive skills in adults (for example, Choi et al. 2014) has distinguished between measures of crystallized intelligence (which relies on accessing information from long-term memory) and fluid intelligence (the capacity to think logically and solve problems in novel situations, independent of acquired knowledge).¹³ Our focus is on fluid measures, primarily because they are available in both surveys across several waves,¹⁴ though also because previous studies have found that it is fluid and not crystallized intelligence that is positively correlated to labor outcomes (for example, Anger and Heineck 2010; Heineck and Anger 2010).

Both data sets include several cognitive measures of fluid intelligence. We focus on two of the tests in the survey alongside two of the instrumental activities of daily living (IADL) measures, which also reflect cognition. The measures are summarized in Table 4. The table shows that Americans do slightly worse in cognition tests than the English, with 10 percent (compared to 3 percent) reporting difficulty using a map, 4 percent (2 percent) reporting difficulty managing money, and average scores of 5.8 (6.1) and 4.8 (4.9) out of 10 in the recall and delayed recall tests.

Similar to the construction of our health index, we construct a cognition index that summarizes the information content of the four cognition variables using principal component analysis. The first principal component is plotted in Figure 6.¹⁵ In general, there is a clear worsening in cognition with age as assessed by this test. What is remarkable, however, is that the age profiles in ELSA are essentially flat once fixed effects have been removed (Panel B). This suggests that the deterioration in cognitive skills with age seems to be explained by compositional changes across cohorts in England: older individuals have lower cognition not because of their age, but because they were born into older cohorts with lower cognition over their life.¹⁶ The figure also shows evidence of a clear ordering by education group in the scoring of the recall tests, with the highest educated scoring best and the lowest educated scoring worst. Moreover, the gap between the high educated and the low educated is considerably larger in the United States.

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Figure 6

Cognition Index by Age, Gender, and Education

Notes: MA(3) indicates a three-year moving average, and FE indicates fixed-effects estimates.

A. The Effect of Subjective Measures of Health and Cognition on Labor Supply

Table 5 displays estimates of the effects of a one standard deviation improvement in the health or cognition indexes on employment. As described previously, the subjective health index is the first principal component of the three subjective health measures and the cognition index is the first principal component of the four cognition measures. Each cell in Panels A and B reports estimates from a separate regression; cells in the top and bottom halves of Panel C report, respectively, the cognition and health coefficients in regressions that control for both. Sample sizes are shown in the bottom panel.

The relationship between subjective health and employment is shown in Panel A. Estimates in Column 1 are for men in England; they are obtained from a set of education-specific regressions of employment on the subjective health index and a basic set of controls that only include a quadratic polynomial in age and year dummies. In ELSA, a one standard deviation improvement in the subjective health index is associated with 17.7 percent higher employment among high school dropout men; comparable estimates for high school graduates and college graduates are 11.0 percent and 7.1 percent, respectively.

However, estimates of the effects of subjective health on employment may be biased by unobserved factors that relate to both. For instance, individuals from poor backgrounds may have missed critical investments that foster good health as well as other skills required in work environments. If poor health and unobserved skill deficits lower employment rates later in life, then failure to control for skill will confound estimates of the employment effects of health. To deal with this sort of problem, we add a full set of initial conditions to the regression model, including health status during childhood, accumulated years of working experience, as well as health, cognition, employment, marital status, and nonhousing wealth when first observed in the sample. These variables capture existing heterogeneity at the start of the observation period that relates to both employment and health.

For men in ELSA, the new set of estimates controlling for initial conditions can be found in Column 2. The reported coefficients in Panel A measure the impact of changes in health on changes in employment during later working years. The effects of health roughly halve with the inclusion of initial conditions in the regression model, showing that indeed much of the relationship between health and employment among English men is spurious. We find very similar patterns for English women (see Panel A, Columns 5 and 6), although with estimates that are generally slightly smaller. The HRS estimates, meanwhile, are modestly larger than ELSA estimates but are less affected by the inclusion of initial conditions (Columns 3–4 and 7–8 for men and women, respectively).

Panel B shows equivalent estimates for the effects of cognition. These are always smaller than the effects of subjective health. In ELSA, a one standard deviation improvement in the cognition index of men is associated with 8.7 percent, 3.3 percent, and 1.3 percent higher employment rates among high school dropouts, high school graduates, and college graduates, respectively (Column 1, Panel B). Adding initial conditions to the regression model, which now include the cognition index but not the health index in the first observation period, considerably reduces the estimated effects. HRS estimates are larger and are again less affected by the inclusion of initial conditions. Estimates for women are very similar to those for men.

Panel C in Table 5 shows results for employment regressions on both the cognition and subjective health indexes. It shows that health remains a strong determinant of employment among older workers even when accounting for cognition, but that cognition plays a much more modest role (if any) after accounting for health. In line with findings in Panels A and B, Panel C also highlights the importance of controlling for permanent heterogeneity when estimating the impacts of cognition and subjective health on employment. We therefore focus exclusively on estimates from regression models that include initial conditions in what follows.

Table 6 displays estimates of the share in employment decline between ages 50 and 70 that can be explained by a decline in health and/or cognition over the same period. It uses the coefficients in Table 5 to calculate the percentage change in employment explained (δ in Equation 13). Estimates in Column 1 of Panel A show that the deterioration in health explains between 4.0 percent and 7.2 percent of the decline in men’s employment in ELSA. The impact is largest for the high school dropouts and falls with education. Column 1 in Panel C shows that these estimates are barely affected by the inclusion of cognition, in line with cognition having a negligible impact on the employment of older workers in England (see also Panel B). Contrasting Columns 1 and 3 shows that changes in health and cognition explain generally less of the changes in employment of women than men, particularly among those who leave education without qualifications.

Results for the HRS display similar patterns to those found in ELSA, only stronger (Columns 2 and 4 in Table 6). In particular, they suggest that both health and cognition play a role in explaining the decline in employment of American workers near retirement age, though the impact of health decline is about two to four times larger than that of cognition decline (Panels A and B). Moreover, cognition explains about two additional percentage points of the decline in employment when added to health in the same regression model (Panel C versus A).

The incremental value of cognition is tested in Table 7. Columns 1–4 show the change in explained share of employment decline induced by adding cognition in addition to health, in percentage terms relative to the effect of health alone; these numbers are obtained from comparing estimates in Panel C and A of Table 6. Columns 5–8 show p-values for testing the equality between the same two sets of estimates, with and without cognition. The results suggest that cognition modestly increases the explained employment decline in the HRS, but the differences are never statistically significant at a 5 percent level. In line with our earlier findings for ELSA, cognition plays no discernible role in driving employment in England.

By summarizing the information on subjective health in a single index, we may be discarding important information. Our subjective health index is constructed using three variables. In principle, each of the three variables could have independent explanatory power for employment beyond their contribution to the index. To test whether this is the case, we estimated alternative empirical specifications of the employment regression model and used them to predict the share of employment decline driven by health over the same 50–70 age range (δ in Equation 13). Table 8 displays estimates. Panel A reproduces Panel A in Table 6 and is the reference set of estimates, obtained using the single subjective health index. Panel B adds all three measures of subjective health separately to the employment regression; this has little effect on the estimates.¹⁷ Panel C includes only one of the subjective health variables directly measured in the questionnaire, the dichotomous variable for whether health limits work; estimates of the δs are modestly lower in this case, suggesting that this single measure misses some of the drivers of employment, or that there is significant measurement error in the variable. Online Appendix Section 4.3 shows estimates using the other subjective measures individually. The individual subjective measures always produce smaller and more variable estimates of the impact of health than the health index using all three measures. This suggests that a single health index, if properly constructed, is sufficient for capturing the effect of health on employment; however, a single subjective measure is not sufficient.¹⁸

Table 9 further quantifies the importance of accounting for more detailed subjective health information by comparing Panels B and C with Panel A of Table 8. Columns 1–4 detail the percentage differences between the estimates in these panels, using estimates in Panel A as baseline, while Columns 5–8 detail the p-values for testing their equality. The figures in the top panel reveal that the relative differences induced by fully accounting for the subjective health information are generally small and mostly negative. In most cases we fail to reject equality. In some cases we do reject, but the only rejection of a positive difference (which would indicate that the three measures separately contain more information for employment than the composite index) is for women with high school diploma in the HRS, for whom the relative difference is very modest.

However, the inspection of the bottom panel in Table 9 reveals that the information in a single observed measure significantly underrepresents the variation in subjective health relevant for employment, particularly in ELSA. For all groups in ELSA, the share of employment decline explained by changes in this measure is at least 50 percent lower than the same measure for the subjective health index. For the HRS, the use of the single measure “health limits work” also produces smaller effects of changes in health on employment than those produced by our health index, but the differences are smaller and only statistically significant at conventional levels for men.

Overall we find that the single subjective health index captures the variation in health that is responsible for the decline in the employment rates of older workers as well as more detailed measures of subjective health do. Our parsimonious yet complete representation of health is particularly useful in contexts that are only practical with lowdimensional specifications, such as in structural models of health, employment, and earnings. We therefore focus on results based on the single subjective health index in what follows.

B. Using Instrumental Variables to Address Justification Bias and Measurement Error in Subjective Health Measures

Subjective health measures can be afflicted by justification bias and measurement error that confound estimates of the effects of health on employment if subjective health is used as a proxy for health status. We address this problem by instrumenting it with the full set of objective measures. Objective measures focus on specific conditions and thus may provide an incomplete picture of health status, but they are likely to be strongly related to the subjective measures. Moreover, measurement error and justification bias in subjective health are likely to be unrelated to objective health. These features make the objective measures an ideal candidate for instrumenting the subjective health index. Since the direction of the bias resulting from using subjective health to proxy health status is indeterminate a priori, so is the direction of the correction from instrumenting it (see discussion in Sections III.B and III.C). Instrumental variables estimates should be smaller than their linear counterparts if justification bias dominates, while the opposite holds if attenuation bias dominates.

We start by testing the strength of the instruments when using the entire set of objective measures and then discuss how estimates of the effects of health on employment change with instrumenting. To test for weak instruments, we compare the F-statistics to Stock–Yogo critical values: we reject the null of no statistically significant relationship between the subjective health index and the objective health measures at the 5 percent significance level for all gender × education × country cells, whether or not cognition is included in the regression model of employment. This demonstrates that the objective measures are strong predictors of the subjective health index.

The IV estimates of the fraction of employment decline explained by health and cognition are shown in Table 10, in Panel A for the impact of health only and in Panel B for the joint impact of health and cognition. The estimates in both panels are very close; they are also overall similar to the OLS estimates of the impact of subjective health and cognition on employment in Table 8. They reveal that declining health can explain at most 15 percent of the decline in employment around retirement age and that cognition adds little to this and only for the HRS. What is also apparent from these estimates is that both health and cognition are stronger drivers of the employment choices for Americans than for the English. We further discuss this point in Section V.D.

The two panels of Table 11 compare the IV estimates in Panels A and B of Table 10 with their OLS counterparts, respectively, in Panels A and C of Table 6. The first four columns show the relative differences between the IV and OLS estimates, using OLS estimates as the baseline, and Columns 5–8 show the p-values for testing their equality. The results suggest that measurement error and justification bias do not seriously affect estimates, or at least they offset. The OLS estimates are of similar order of magnitude, albeit systematically smaller (hence the positive differences in Columns 1–4), than similar IV estimates. The null hypothesis that the OLS and IV estimates are equal is not rejected at conventional levels in most cases. In the couple of cases where it is rejected, which are both in the HRS, IV estimates are larger than their OLS counterparts.

For the IV approach to be valid, any measurement error affecting the objective measures must be orthogonal to that affecting the subjective measures. In particular, this rules out justification bias affecting both objective and subjective measures. It also rules out the possibility that detection of objective health conditions may be related to economic conditions, which might be the case if seeking medical attention is a choice affected by access to health insurance or if those with higher socioeconomic status are more likely to be aware of their health problems (for example, Johnston, Propper, and Shields 2009). We test the validity of the IV approach by restricting the number of objective instruments to represent only major conditions. These major conditions usually require medical attention, making it unlikely that people would wrongly report whether they suffer from one of them. The results from these estimates are shown in Online Appendix Section 4.3 and are not statistically different from the estimates using the full set of instruments. We therefore conclude that the measurement errors in our objective health measures and subjective health index are unlikely to be correlated. Our findings suggest that justification bias, which has been a major concern in the literature and is expected to bias estimates of the impact of health upwards, is either not very important or is more than compensated for by attenuation bias from measurement error in the subjective measures.

Table 12 provides additional evidence on the validity of the single index assumption using the overidentification restrictions supplied by the many instruments we are using. If the objective measures affect employment only through their effect on subjective health, then the IV residuals should not be systematically related to any of the objective health measures.

We implemented the test by regressing the IV residuals on all the objective health measures and all other explanatory variables in the employment regression and then calculating the F-statistic for the full set of objective measures (as suggested by Davidson and MacKinnon 2003; their Equations 9 and 10).¹⁹ The residuals were clustered at the individual level to account for serial correlation. In Table 12 we show the p-values for testing the null hypothesis that objective measures affect labor supply only through the subjective health (the IV exclusion restriction). The test results show that the exclusion restriction is rejected in the majority of the cases in the HRS, whether or not cognition is included in the regression model, but it is never rejected with the ELSA data.

One possibility is that the impact of health on employment varies with health conditions, in line with the argument that it is the serious and persistent conditions that most affect employment. We test whether this may be the case by restricting the objective instruments to a subset of major health conditions. These are heart problems, lung disease, and whether the individual has suffered a stroke or heart attack. When rerunning the test on this more homogeneous set of conditions we find much stronger support for the single index assumption. Online Appendix Table A17 shows that, whether or not cognition is included in the regression, the null is only rejected for three out of 12 cases (in all cases, better educated individuals from the HRS). This result suggests that the impact of changes in health may be more important if these are driven by the onset of more serious (and potentially long-lasting) health conditions.

C. Assessing Bias Due to Omitted Objective Health Measures

Objective health information is only collected for a subset of the relevant conditions, which is likely to result in downward biased estimates of the impact of health on employment, as discussed in Section III.A. Here we assess the bias when using only a limited set of objective measures to proxy for health. We estimated the alternative model of health as a function of the entire set of objective measures in Equation 4 to assess the severity of bias due to omitted objective measures; estimates using all objective measures can be found in Panel D of Table 13. Even when they are added in a fully flexible format, all objective measures together predict an employment decline that is generally smaller than the estimated effects based on the subjective heath index; see Table 14 for percent differences and p-values for testing the equality of predicted share in employment decline explained by objective and subjective measures. The differences are modest, although statistically significant for many groups, particularly in the HRS. For high school dropout women in both ELSA and the HRS, the share of the employment fall predicted by health is actually larger when using the full set of objective measures than when using the subjective health index; however, the differences are small and only statistically significant for the HRS data.

These results are consistent with the hypothesis that using only a limited set of objective measures provides an incomplete view of the health status affecting work capacity and underestimates the impact of health on employment (recall discussion in Section III.A). More generally, however, our predictions of the effects of health based on objective and subjective measures are much more similar than has been suggested in previous studies. Existing estimates based on objective measures used only a subset of the measures we use here and found that they produced much smaller estimates than subjective IV estimates. Bound (1991), for example, found that a single objective measure (future mortality) produced estimates of the effect of health that were only about one-tenth of the size of the subjective or IV estimates. Interestingly, but perhaps predictably, we now find that a comprehensive set of objective health measures available in the HRS and ELSA produces estimates that are much closer to the subjective IV estimates.

To further investigate the effects of using limited subsets of objective health measures, Panels A–C of Table 13 show estimates of the explained share of employment decline from regressions that gradually add more objective measures. The set of estimates in Panel A are based on a single health measure, specifically whether the individual reports that they have high blood pressure; estimates of the impact of health on employment in this specification are very small and are not statistically significant at conventional levels. These results align well with the findings in Bound (1991).²⁰ Surprisingly, the results in Panel B show that the estimates of the impact of health quickly converge to levels very close to those obtained when using the full set of objective measures by adding just three more measures of objective health that arguably capture a wide range of conditions (arthritis, psychiatric, and lung diseases). Further adding more conditions does not change the estimates much (Panels C and D).

D. Exploring between-Country Differences

Our estimates show that the share of decline in employment that is explained by declines in health is consistently greater in the United States than it is in England for all groups, often larger by a factor of approximately three. Here we decompose the differences in our main set of estimates for the United States and England, the δ parameters defined in Equation 13 and presented in Table 10. Table 15 uses an Oaxaca decomposition to describe how much of the difference δ_US – δ_ENGLAND is explained by differences in the impact of health and cognition on employment (θ), differences in deterioration in health and cognition (ΔH), and differences in the employment decline (ΔY). Breakdowns are provided for both sets of estimates from Table 10, depending on whether only health (Panel A) or also cognition (Panel B) are accounted for in estimating δ.²¹

The general picture for all cases is that the majority of the between-country differences in how much of the decline in health is explained by health or health and cognition can be attributed to differences in the impact of these variables on employment (θ); differences in the decline of health, cognition, and employment are less relevant. The role of the impact of health on employment is particularly dominant among men with less than college education, for whom it drives almost the entirety of the between-countries difference. For other groups, across countries differences in θs explain two-thirds or more of the differences in δs.

The larger response of employment to health in the United States may result from differences in the institutional backgrounds of the two countries shaping the employment responses to health around retirement age. For instance, the two countries differ in the provision of health insurance, which is universal in England but not in the United States, the generosity of disability benefits and the rigor of its entitlement rules, and the design of financial incentives to retire and their age-dependence. For example, the United States disability system, which provides a health-dependent benefit, is more generous than the English one and provides benefits only if beneficiaries do not work. Thus, unhealthy Americans have a strong incentive not to work. Compared to the United States, England provides more generous out-of-work benefits for reasons unrelated to health, such as unemployment benefits. All these institutions are expected to play an important role in determining retirement choices and their dependence on health. While establishing the importance of these channels certainly merits further research, this is beyond the scope of this paper.

Less than one-quarter of the difference for men, but more than one-quarter of the difference for women, can be explained by a larger employment drop in England among those in their 50s and 60s. Here we notice that employment drops sharply in England at the state pension age (60 for women, 65 for men), but it declines much more gradually and slowly in the United States (recall Figure 3). While this is likely related to differences in the retirement incentives for these age groups, it implies that Americans are more likely to work into older ages than the English. Hence, Americans may be more exposed to the onset of health conditions leading to retirement during their (longer) working lives. In turn, the English are more likely to be already retired when experiencing a similar deterioration in health.

A. The Model

We consider the problem of individuals deciding whether to work near retirement age. Our aim here is to focus on the simplest dynamic model that can represent the many ways in which health affects employment among older workers. In our model, individuals decide in each period whether to work, how much to consume, and how much to save for the future. They do so in a risky environment, where they face uncertainty in future health, wages, and preferences for working. Health-related benefits or disability benefits partially insure against income losses associated with bad health, but as with other social insurance instruments, they also change working incentives. Individuals may save to further insure themselves against economic consequences of health and other shocks. In what follows, we briefly formalize the model.

B. How Health Affects Employment of Older Workers

The addition of health to an otherwise stylized structural model of employment and savings exposes various channels through which health affects employment. In our simple model, a negative health shock reduces preferences for work, wages, and expected longevity, and it increases entitlement to benefits.²²

Formally, the structural labor supply function is (19) where θ is the set of all parameters in Equations 14–18. In the context of this labor supply function, we can see multiple pathways by which health deterioration with age may impact labor supply. In particular, we identify five channels through which bad health shocks can discourage work, all of which are expressed in our structural model:²³

1. Preferences. Bad health can raise the marginal utility of leisure relative to that of consumption (Capatina 2015). This is embodied in Equation 14, where health is allowed to interact with the utility value of working. For this reason, health H impacts employment directly in Equation 19.

2. Productivity. Bad health can lower workers’ productivity and resulting wages. This is represented in Equation 15, where function ω(.) captures the potentially negative impact of health on wages, so health may affect employment indirectly through wages W.

3. Disability insurance benefits. People in sufficiently bad health may qualify for benefits from disability programs. This is embodied in B, the benefit amount, and also in τ, the share of earned income that is taxed away. Those receiving benefits have incentives to reduce labor supply through several channels. First, the benefits provide income, allowing individuals to purchase more leisure. Second, in many countries the benefits are means-tested and sometimes limit work altogether. Moreover, in the United States, beneficiaries can receive Medicare or Medicaid health insurance depending on their working income, with excessively high income triggering the loss of benefits.

4. Expectations of future employment and earnings capacity. The persistent health process described Equation 17 implies that current shocks may have long-lasting effects on future health and thus future employment and earnings capacity. This changes the value of savings and, hence, that of employment.

5. Life expectancy. With shorter expected lifespans, individuals in bad health may not need to work as long to accumulate savings for retirement. This effect operates through the survival probability s(t, H) in Equation 18.

Most papers consider only a subset of these channels. For example, French (2005) and Capatina (2015) consider four of the five channels, excluding only disability benefits.

French, von Gaudecker, and Jones (2018) and Kitao (2014) account for disability benefits, but French, von Gaudecker, and Jones (2018) use a stylized model of disability benefits, and Kitao (2014) uses a very stylized model of demographic transitions and health insurance.

C. Approximation Model

We demonstrate how our simple reduced form model of employment, described in detail in Section III, can be derived as an approximation to the solution of the dynamic labor supply model. The process of doing so provides further clarity on the interpretation of the estimates in Section V.

In the structural model, the work decision is defined as (20)

To the extent that time periods are short, separability between leisure and consumption in the utility function implies that the marginal utility of consumption is only mildly affected by labor supply. This simply reflects consumption smoothing, as any additional income received in a period is consumed over time and, therefore, mostly saved in the period it is realized. But then, the additional income from work will be valued at the marginal value of assets (which, by the envelope condition, equals the marginal utility of consumption). We can then rewrite Equation 20 as where is the marginal utility of consumption, W_it(1 – τ_it) is the change in income induced by a move into work, τ_it is an abbreviation for τ_t(W_it, Y_it, H_it), and is the latent employment index. This is the discrete choice version of the marginal rate of substitution condition, a condition that holds exactly as the time periods become arbitrarily short.

In a cross-section, H_it may be correlated with Y_it, W_it, τ_it, or ξ_i, leading to biased estimates of θ₁ if these variables are not added to the regression model. This is for two reasons. First, while θ₁ is a deep parameter that represents how preferences for work change with health, its estimate will conflate other mechanisms, such as the indirect impact of health on employment through its effect on wages. Second, health is likely correlated with permanent individual characteristics that also determine employment, such as those settled in childhood through investments and other factors. We use initial conditions to address this second problem.

To proceed, we write the employment index for period t and an initial period 0: (21) (22)

We then combine these two equations to obtain the following expression: (23) where the approximation in the final line results from a simple Taylor series approximation. The key issue to notice here is that by using initial employment we were able to eliminate unobserved heterogeneity in preferences for work from the employment equation.

We now project growth rates in the marginal utility of consumption, wages, and taxes, weighted by the initial marginal of consumption and the initial after tax wage, on initial health, changes in health, initial employment index, and other exogenous variables:

In the above expression, Z_it summarizes variables that may affect changes in consumption, wages, or taxes, including time dummies and a second order age polynomial. Thus the coefficients on age in the projection of the rate of change in the tax rate (line 3) will partly capture how the work incentives change with age around retirement.

Replacing the above expressions in Equation 23 yields the key equation that motivates our empirical specification: (24) where (25)

The second line of Equation 25 shows that θ_H in Equation 24 measures a combined effect of the change in health on employment, arising both from its impact on preferences to work and, indirectly, through its impact on the marginal utility of consumption, wages, and the tax rate. Here the mechanisms discussed in the previous section are all represented—the direct impact through preferences is captured by θ_H; indirect effects through productivity and benefit entitlement are reflected on the parameters from the wages and tax projections, respectively; and changes in expected future health, lifespan, and consequent future value of work are reflected in the parameters from the projection of the marginal utility of consumption.

The final line of Equation 25 shows that the residual in Equation 24 is a function of the transitory shocks to preferences for work and the orthogonal residuals from the projection of the changes in the marginal utility of consumption, wages, and taxes on health, initial health, the initial employment index, and Z. If ζ_it follows a random walk, and innovations are uncorrelated with the initial value of health and , our procedure should produce consistent estimates.

The expression in Equation 24 can be trivially rearranged to match our empirical Specification 2, that we repeat here for reference

In the above equation, and as discussed in Section III, X includes all variables in Z (that is, time dummies and an age polynomial) and the initial condition in health H₀. The only difference relative to Equation 24 is that the initial index , which is not observed, is approximated by a function of initial employment Y_i0 and a set of other variables that determine initial labor market attachment, including working experience accumulated so far, wealth, marital status, and health in childhood.

Crucially for our purposes, the parameter of interest θ_H is the same in the two equations. Therefore, the key insight from this exercise is that, by controlling for initial health and employment while focusing on the effects of changes in health, we are able to eliminate bias in the estimation of θ_H that is induced by the potential correlation between unobserved heterogeneity in health, preferences for work and wages, (ψ_i, ξ_i φ_i). As a by-product, we are also capable of revealing the response mechanisms encompassed in the parameter θ_H.

Now suppose that, instead of controlling for initial conditions, we depart from Equation 21 and project the marginal value of the additional income on current health and the exogenous variables Z:

Replacing in Equation 21 yields (26) where . Equation 26 matches our empirical employment model without initial conditions.

The two empirical models in Equations 24 and 26 differ in two substantial manners. First, the parameters identified in each case, θ_H and , are not the same. While θ_H encompasses all five of the response channels we describe in Section IV.B, does not account for the indirect impact on employment of the contemporaneous effects of health on productivity and benefit entitlement. And second, by not exploiting longitudinal information, Equation 26 does not eliminate unobserved heterogeneity correlated with current health. As a consequence, estimates of are likely to be biased and we would expect the direction of the bias to be positive.

D. Using the Reduced Form Regressions to Assess Structural Models of Health

The findings from the reduced form model inform the structural work, just as the structural model can help us interpret the reduced form work. Our three key findings for structural modeling are as follows.

First, and most importantly, a carefully constructed single health index captures well the incentives for labor supply. We found relatively little evidence against the assumption of a single health index in our reduced form analysis, and this finding supports the use of a single index in structural models. In fact, the vast majority of life-cycle models that account for health consider only a single health index (see French 2005; French and Jones 2011; French, von Gaudecker, and Jones 2018; Braun, Kopecky, and Koreshkova 2015; De Nardi, Pashchenko, and Porapakkarm 2017; Pashchenko and Porapakkarm 2017; Aizawa and Fu 2017, as well as the references in their Footnote 24. Exceptions include Capatina, Keane, and Maruyama 2018 and Gustman and Steinmeier 2014). However, we found that the most commonly used measure of health in structural studies, which assesses whether the respondent has a health condition that limits work, understates the impact of health on labor employment modestly in the HRS and strongly in ELSA relative to our preferred measure.

Second, dynamics are important. Accounting for initial conditions, and thus exploiting more transitory fluctuations in health, reduces the estimated impact of health by about half in England and a quarter in the United States. Our model shows why this is important and reveals several channels through which changes in health affects changes in employment. We should point out that it is not obvious which of these channels are most important.

The model we described does not include all the channels by which health and employment may be related. For instance, it is conceivable that higher incomes cause better health. The Grossman (1972) model implies that those with higher income may be able to purchase better nutrition and healthcare, improving later health outcomes. The structural analyses of models allowing for both directions of causality is becoming increasingly common.²⁴ Another potential mechanism is embedded in the learning-by-doing model, whereby workers’ productivity on the job, and hence wages, increase with accumulated working experience.²⁵ In that case, bad health shocks that lower current employment affect future wages because of the loss in working experience. The consequent lower future wages negatively affect future employment, even if health recovers. We believe that more structural work is necessary to disentangle the various mechanisms by which employment and health are related.

Third, the differences between the United States and England in estimates are notable. We noted an important institutional difference between the two counties in that the U.S. disability system provides a relatively generous health benefit that is conditional on not working. Thus, unhealthy Americans have a strong incentive not to work. Compared to the United States, England provides more generous out-of-work benefits for reasons unrelated to health, such as unemployment benefits, but relatively less generous disability benefits. These institutional differences suggest that modeling the labor supply incentives of the disability insurance system is key to better understand how health affects employment decisions.