Lives Versus Livelihoods: The Impact of the Great Recession on Mortality and Welfare

Overview

Research Question. Does the Great Recession reduce or increase mortality, and what are the welfare implications of incorporating recession-induced mortality changes into standard macroeconomic welfare frameworks?

Setting and Identification. The authors exploit spatial variation in the severity of the 2007–2009 Great Recession across 741 U.S. Commuting Zones (CZs), following the empirical design of Yagan (2019). The primary shock variable is the percentage-point change in the CZ unemployment rate between 2007 and 2009. The key identifying assumption is that no concurrent shocks to mortality coincide with the timing and geographic pattern of the Great Recession shock. Pre-trend evidence supports this: CZs subsequently harder hit experienced a slight relative increase in mortality before 2007, which is the opposite sign from the main effect, supporting the validity of the design.

Data. Mortality data come from CDC restricted-use death certificate microdata (2003–2016) covering the universe of U.S. deaths, combined with SEER population denominators. A 20 percent random sample of Medicare enrollees aged 65–99 provides an individual-level panel that directly addresses concerns about endogenous migration. The main outcome is the log age-adjusted CZ mortality rate; economic indicators come from BLS, BEA, and FHFA; air pollution data from the EPA AQS monitor network (PM2.5); morbidity from the BRFSS; nursing home characteristics from federal certification inspections.

Main Mortality Finding. A one-percentage-point increase in the local unemployment rate between 2007 and 2009 is associated with a 0.50 percent decline (SE = 0.15) in the annual age-adjusted mortality rate in 2007–2009, and a 0.58 percent decline (SE = 0.34) in 2010–2016; the two periods are statistically indistinguishable (p = 0.78). Because the national average unemployment rate rose by 4.6 percentage points, the Great Recession on average reduced the annual age-adjusted mortality rate by approximately 2.3 percent, with effects persisting for at least 10 years. The authors note this is equivalent to approximately two years of secular mortality improvement at the pre-recession trend pace of 1.1 percent per year. For a 55-year-old, the estimates imply that 1 in 25 gained an extra year of life from a shock of this magnitude.

Heterogeneity by Cause of Death. Mortality declines appear across most major causes. Cardiovascular disease (34 percent of 2006 deaths) declines by 0.65 percent per percentage-point unemployment increase (SE = 0.21) and accounts for approximately 48 percent of the total estimated mortality reduction. Motor vehicle mortality falls by 1.7 percent (SE = 0.56) and liver disease by 1.1 percent (SE = 0.43). Suicides show a statistically significant 1.7 percent decline (SE = 0.5) in the 2010–2016 period. The notable exception is cancer (the second-largest cause of death), for which the estimated effect is a precise null of 0.02 percent (SE = 0.11). The null cancer result is interpreted as a specification check: if mortality declines were spurious (e.g., driven by population mismeasurement), cancer mortality should also decline.

Heterogeneity by Demographics. Recession-induced mortality declines are similar in percentage terms across gender and race/ethnicity, and statistically equi-proportional across age groups (p-value for equality across 25–64 versus 65+: 0.76). Because mortality is heavily concentrated in the elderly, those aged 65 and over account for approximately 74.3 percent of averted deaths, roughly proportional to their 72.5 percent share of 2006 mortality. The most striking heterogeneity is by education: the entire mortality decline is concentrated among the approximately 52 percent of the population with a high school degree or less. The estimated 2007-2016 effect is −1.3 percent per percentage-point unemployment increase (SE = 0.56) for those with high school or less, compared to +0.34 percent (SE = 0.68) for those with more than high school (statistically distinguishable at p < 0.01).

Mechanisms. The authors distinguish internal effects (own reduced employment or consumption improving health) from external effects (externalities from reduced aggregate economic activity, holding own employment/consumption fixed). Evidence strongly favors external effects as the primary driver. Three-quarters of averted deaths accrue to the elderly, who experienced no direct income effects from the labor market shock. Moreover, the timing pattern—an immediate mortality drop that does not grow over time—is inconsistent with health-behavior channels (e.g., smoking cessation, improved diet) that would build up gradually. Direct tests find no statistically significant impact on self-reported health behaviors (smoking, drinking, exercise) and no impact on healthcare use among Medicare enrollees.

Among external channels, neither reduced spread of infectious disease nor improved nursing home staffing receives empirical support. Reduced air pollution (PM2.5) is identified as a quantitatively important channel. A one-percentage-point increase in CZ unemployment is associated with a 0.16 µg/m³ decline in PM2.5 (SE = 0.04), a 1.3 percent decline relative to the 2006 national average of 12 µg/m³. A mediation analysis (controlling for the PM2.5 shock) attenuates the estimated mortality effect by 37 percent, from −0.52 percent to −0.33 percent per percentage-point unemployment increase. Back-of-the-envelope calculations combining the PM2.5 decline with external estimates of PM2.5-mortality elasticities suggest pollution can explain 17 to 35 percent of total recession-induced mortality declines.

Lag Structure. Exploiting variation in the speed of post-recession labor market recovery (measured by 2010–2016 EPOP ratio changes) conditional on the initial shock, the authors find that mortality reductions persist in areas that have fully recovered economically by 2016, suggesting lagged mortality effects of the initial economic downturn beyond what contemporaneous economic conditions alone explain.

Welfare Analysis. The authors extend the Krebs (2007) consumption-based welfare cost-of-recessions model to incorporate endogenous mortality. For a 45-year-old with γ = 2 and a value of a statistical life-year (VSLY) of $250k (five times annual consumption), accounting for endogenous mortality reduces the willingness to pay to avoid all future recessions from 2.00 percent of average annual consumption to 0.91 percent—a reduction of approximately 55 percent. Starting around age 55, recessions become welfare-improving on net. For the Great Recession specifically, at age 55 endogenous mortality reduces the welfare cost by approximately 25 percent (from 2.39 to 1.80 percent of average annual consumption). Because mortality declines are concentrated among those with high school or less, accounting for endogenous mortality also substantially mitigates—and at older ages reverses—the finding that the Great Recession was more costly for the less educated.

Scope Conditions and Caveats. (i) The design captures only differential local effects, not nationwide impacts (e.g., stock market collapse, nationwide malaise). (ii) Mortality impacts may not generalize to milder recessions, though the relationship appears approximately linear in shock size. (iii) The analysis excludes morbidity, though limited evidence suggests morbidity is also pro-cyclical and roughly equi-proportional across ages. (iv) The welfare analysis begins at age 35 and does not account for longer-run mortality costs of recession entry for younger cohorts.

Q&A

Q1: What is the baseline empirical specification, and why does the design exploit cross-sectional variation rather than time-series panel regressions?

The estimating equation regresses the log age-adjusted CZ mortality rate on an interaction of the CZ-level Great Recession shock (2007–2009 unemployment change) with year indicators, plus CZ and year fixed effects, weighted by 2006 CZ population. The authors prefer this to the standard two-way fixed effects panel approach (area and year FE with contemporaneous unemployment rate) for three reasons: (1) it directly identifies the full dynamic lag structure of the shock rather than imposing contemporaneity; (2) exploiting a single spatially differentiated shock reduces risk of confounding from other concurrent area-level shocks; (3) the panel can be linked to individual-level Medicare data, allowing explicit control for endogenous migration, which the existing literature cannot do.

Q2: How does the paper address the concern that mortality rate declines might simply reflect unmeasured population outflows from hard-hit areas rather than genuine reductions in deaths?

The authors offer two main responses. First, cancer mortality shows a precise null effect despite being the second-leading cause of death; if unmeasured population losses were driving the results, cancer deaths should decline proportionally. Second, using the Medicare individual-level panel, they fix each enrollee’s location at their 2003 CZ and find a statistically significant mortality decline of 0.35 percent per percentage-point unemployment increase in the reduced-form (2007–2009 period). A control function approach that instruments current-year location with 2003 location yields an estimate of −0.37 percent (SE = 0.17), similar to the baseline −0.50 percent from the aggregate specification, confirming that migration bias is not the primary driver.

Q3: How long do the mortality reductions from the Great Recession persist, and does the paper identify whether these are contemporaneous or lagged effects?

The 2007–2009 period estimate is −0.50 percent per percentage-point unemployment increase and the 2010–2016 period estimate is −0.58 percent, and these are statistically indistinguishable (p = 0.78). To identify whether persistence reflects ongoing economic effects or true lagged mortality effects, the authors compare CZs with above- vs. below-median 2010–2016 EPOP recovery (conditional on initial shock decile). Both groups show similar 2010–2016 mortality declines despite the above-median recovery CZs having returned to pre-recession employment levels by 2016. This finding is consistent with lagged mortality effects of the initial economic downturn that persist independently of current economic conditions.

Q4: Are mortality reductions concentrated among individuals already near death (“harvesting”), or do they represent meaningful longevity gains?

The authors use a Medicare auxiliary model to predict counterfactual remaining life expectancy for each enrollee based on age, demographics, and chronic conditions. The marginal life saved has only about 6 percent lower counterfactual remaining life expectancy than a typical decedent of the same age, and this difference is statistically insignificant. Because effects persist over 10 years (not just days or weeks), short-run mortality displacement (harvesting) is not the operative concern. The 6 percent difference is also small enough that the authors do not adjust their welfare analysis for it.

Q5: What is the educational gradient in mortality impacts, and is it explained by age composition or other confounders?

Mortality declines are entirely concentrated among those with a high school degree or less: the 2007–2016 estimate is −1.3 percent per percentage-point unemployment increase (SE = 0.56) for this group versus +0.34 percent (SE = 0.68) for those with more than high school, distinguishable at p < 0.01. This gradient holds within age groups (confirmed in Appendix analysis), and further disaggregation shows no mortality declines for those with some college or college-or-more separately. In Medicare data, the elderly mortality effect is concentrated among the approximately 12 percent enrolled in Medicaid (a proxy for low income), reinforcing the socioeconomic concentration.

Q6: What evidence rules out improved health behaviors (increased exercise, reduced smoking, reduced alcohol) as the main mechanism?

Two types of evidence argue against this channel. First, three-quarters of averted deaths are among the elderly, who experienced no direct income or employment effects from the local labor market shock and would not plausibly change their health behaviors in response to someone else losing employment. Second, the mortality decline is immediate in 2007 and flat through 2016 rather than growing over time; smoking cessation, for example, takes 10–15 years to accumulate mortality effects. Direct tests of behavioral outcomes from BRFSS find no statistically significant impact on smoking, drinking, exercise, or flu vaccination rates, individually or pooled. The pooled average treatment effect on six morbidity measures is statistically significant and negative (suggesting morbidity improvements), but behavioral covariates show no movement.

Q7: What is the evidence for and against improved nursing home care as a mechanism?

Prior literature (Stevens et al. 2015; Konetzka et al. 2018; Antwi and Bowblis 2018) documents that recessions increase nursing home staffing and reduce nursing home deaths in earlier decades. However, the authors find no evidence for this channel in the Great Recession context. Estimated mortality impacts are virtually identical (approximately 0.5 percent per percentage-point unemployment increase) for the 7 percent of the elderly in nursing home care and the 93 percent not in nursing home care. Direct measures of nursing home staffing (direct-care staff hours per resident-day, highly skilled nurses ratio) show no statistically significant change in harder-hit areas: the point estimate for direct-care hours is −0.11 percent (SE = 0.22) in 2007–2009. Nursing home occupancy rates and resident characteristics also show no significant changes.

Q8: How is the quantitative importance of the air pollution channel estimated, and what are the two complementary approaches used?

Approach 1 (back-of-the-envelope): The authors combine their estimate that a one-percentage-point unemployment increase reduces PM2.5 by 0.16 µg/m³ with external estimates from Deryugina et al. (2019) of PM2.5’s effect on elderly daily mortality, rescaled to annual exposure. This calculation implies pollution explains 17–35 percent of total recession-induced mortality declines, depending on which Deryugina et al. mortality estimates are used. Approach 2 (mediation analysis): Adding the county-level PM2.5 shock as an additional control in the mortality regression attenuates the Great Recession mortality coefficient from −0.52 percent to −0.33 percent per percentage-point unemployment increase—a 37 percent attenuation. Both approaches are suggestive rather than definitive, as the mediation analysis requires the strong assumption that the recession shock and PM2.5 shock are conditionally independent of other unmeasured mediators.

Q9: What are the specific calibration parameters in the welfare model and how does the paper set the mortality decline parameter?

The authors extend Krebs (2007)’s income process calibration (pH = 0.03, pL = 0.05, dH = 0.09, dL = 0.21, g = 0.02, σ = 0.01, πH = 0.5) and use 2007 SSA life tables for age-specific mortality rates in normal times. The recession mortality parameter is set to dm = −0.015 for all ages, derived from a 3.1 percentage-point unemployment increase in a typical recession multiplied by the estimated 0.5 percent mortality decline per percentage-point. VSLY values are parameterized at two, five, or eight times annual consumption ($100k, $250k, or $400k at $50k annual consumption). Risk aversion γ takes values 1.5, 2, and 2.5. For the Great Recession-specific exercise, dmA = −0.023 (4.6 × 0.5 percent), dmHS = −0.037, and dmC = 0.0006.

Q10: How does accounting for endogenous mortality change the distributional welfare analysis of the Great Recession by education group?

Under exogenous mortality, the welfare cost of the Great Recession at age 35 is 2.89 percent of average annual consumption for those with high school or less versus 1.23 percent for those with more than high school—the less educated bear roughly twice the burden. Under endogenous mortality, the mortality declines are concentrated entirely among the less educated (dmHS = −0.037 vs. dmC ≈ 0), so accounting for mortality disproportionately offsets welfare losses for that group. By around age 65, the welfare costs of the Great Recession converge across education groups, and after age 65, the less educated bear lower welfare costs than the more educated, reversing the exogenous-mortality ranking. This result depends on the same education differential in mortality impacts that drives the main empirical finding.

Q11: What robustness checks demonstrate that the baseline mortality estimates are not driven by geographic or functional-form choices?

The baseline CZ-level estimate of −0.50 percent (SE = 0.15) is replicated almost exactly at the state level (−0.62, SE = 0.25) and county level (−0.49, SE = 0.10). A Poisson regression yields −0.45 percent (SE = 0.14). Dropping the top/bottom decile of CZs by shock size yields −0.46 percent (SE = 0.16). Adding Census-division-by-year fixed effects attenuates the estimate slightly to −0.38 percent (SE = 0.14) but retains statistical significance. Dropping CZs with high fracking activity and dropping the ten most populous CZs both produce estimates similar to baseline. Quartile regressions show monotone mortality reductions across quartiles of the unemployment shock, consistent with approximate linearity.

Q12: What does the expert survey reveal about prior beliefs, and how does the paper’s finding compare?

In a spring 2023 survey of over 300 experts, 50 percent predicted the Great Recession would increase mortality and only 27 percent predicted a decrease. Of those predicting a decrease, 93 percent gave a magnitude larger (in absolute value) than the paper’s negative point estimate of 0.50 percent per percentage-point unemployment increase, and 82 percent gave a prediction larger than the upper bound of the 95 percent confidence interval. This illustrates that the paper’s finding—mortality is meaningfully pro-cyclical during the Great Recession—was highly surprising to the empirical and policy economics community.

Key Concepts

Pro-cyclical mortality: The phenomenon whereby mortality rates fall during economic downturns and rise during expansions. The paper documents this for the Great Recession using a spatial identification strategy, in contrast to the time-series correlation that had weakened in the two decades before the Great Recession. The term “pro-cyclical” means mortality moves in the same direction as the business cycle (up in booms, down in recessions), implying recessions are associated with fewer deaths.

Internal vs. external effects (of recessions on mortality): The paper distinguishes internal effects—whereby an individual’s own reduced employment or consumption affects her own mortality—from external effects, which are changes in mortality from reduced aggregate economic activity that hold constant one’s own employment and consumption. This distinction has direct welfare implications: external effects (e.g., less pollution from lower industrial output) are genuine welfare improvements for people who did not lose income, while internal effects of behavioral change are mitigated by the envelope theorem if behavior is privately optimal.

Commuting Zone (CZ) shock: The paper’s primary treatment variable, defined as the percentage-point change in the CZ unemployment rate between 2007 and 2009. CZs are aggregations of counties (741 total) designed to approximate local labor markets. The median CZ experienced a 4.6-percentage-point increase, with substantial variation ranging from roughly 2.9 points (bottom quartile) to 6.7 points (top quartile).

Value of a Statistical Life-Year (VSLY): The dollar value placed on one additional year of life in expectation, used in the welfare calibration. In the paper’s framework it equals VSLY = bcγ − c/(γ−1), where b is a preference parameter governing the marginal utility of life-years. Results are reported for VSLYs of $100k, $250k, and $400k corresponding to two, five, and eight times average annual consumption of $50k, following Hall and Jones (2007).

Endogenous mortality in welfare analysis: The paper’s central theoretical contribution is augmenting the Krebs (2007) welfare cost-of-recessions framework to allow mortality to vary with the aggregate state of the economy. When mortality is endogenously lower in recessions, the willingness to pay to eliminate recession risk falls—and at high enough VSLY or old enough ages, recessions become welfare-improving because the mortality benefit outweighs the consumption cost.

Mortality displacement (harvesting): The possibility that short-run mortality declines merely reflect the premature death of already-frail individuals being slightly delayed, without meaningful longevity gains. The paper argues this is not the operative concern given 10-year persistence and uses auxiliary Medicare models to show marginal lives saved have only 6 percent shorter counterfactual life expectancy than average decedents of the same age.

PM2.5 mediation analysis: An empirical approach in which the county-level change in fine particulate matter (PM2.5, in µg/m³) between 2006 and 2010 is added as a covariate in the mortality regression. Under the assumption that the recession shock and the PM2.5 shock are conditionally independent of other unmeasured mediators, the attenuation in the recession-mortality coefficient when controlling for PM2.5 identifies the share of the mortality effect operating through the pollution channel. A 37 percent attenuation is found in the 2007–2009 period.