J14 | Macro Paper Warehouse

Firm Accommodation After Workplace Disability: Labor Market Impacts and Implications for Subsidy Design

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

This paper studies (1) how firm accommodation decisions respond to financial incentives in the context of workplace disability under workers’ compensation, (2) what the causal effect of accommodation is on workers’ subsequent labor market outcomes, and (3) whether the equilibrium level of accommodation is socially efficient, and what the welfare implications of wage subsidies for accommodation are.

Empirical Context and Data

The analysis uses the universe of Oregon workers’ compensation claims from 2005 through 2017 — over 131,000 disabling claims — linked to longitudinal quarterly earnings records from the Oregon Employment Department. The setting exploits Oregon’s Employer at Injury Program (EAIP), which subsidizes employers who provide “transitional work” accommodations (primarily through wage subsidies) to workers with temporary workplace disabilities. EAIP accounts for roughly 25 percent of claims on average, with the wage subsidy component representing over 96 percent of EAIP expenses.

Identification Strategy

The authors exploit a policy change in July 2013 that reduced the EAIP wage subsidy rate from 50 percent to 45 percent. They construct a firm-level “exposure” measure — the fraction of a firm’s claims that used EAIP in a baseline period (2005–2009) — and estimate a continuous difference-in-differences specification in which the interaction of exposure and a post-2013 indicator instruments for accommodation. The identifying assumption is strong parallel trends: firms with low baseline exposure are unlikely to respond to the subsidy reduction, while high-exposure firms respond more, generating cross-firm variation in accommodation rates after 2013. An MTE framework (Heckman and Vytlacil 2005) is then used to explore heterogeneous treatment effects along an unobserved resistance-to-treatment dimension.

Main Empirical Findings

The subsidy reduction from 50% to 45% decreased accommodation rates by 2.9 percentage points (9.3 percent) for claims in firms with average exposure, implying a subsidy elasticity of accommodation of 0.9.
The policy change led to a 0.95 percentage point decrease in employment and a $120 decrease in quarterly earnings four quarters after disability for claims in average-exposure firms (roughly 1.3–1.5 percent declines relative to means), with no significant effect on worker turnover to other firms.
IV estimates of the effect of accommodation itself (using predicted EAIP as instrument) show accommodation increases the probability of employment four quarters after disability by 33 percentage points and increases quarterly earnings by approximately $4,100.
The MTE analysis reveals negative selection on gains: workers with workplace disabilities who are least likely to receive accommodation have the highest potential gains from it, driven largely by severe disabilities with high accommodation costs.
Descriptive and IV evidence is consistent with accommodation operating primarily as general human capital investment: accommodation has no statistically significant effect on the probability of moving to a new firm, and earnings gains are not systematically lower for workers who change employers after accommodation.

Structural Model and Counterfactual Findings

A two-period frictional labor market model with risk-averse workers, risk-neutral firms, Nash bargaining, imperfect experience rating in workers’ compensation, and firm accommodation as human capital investment is developed and estimated. Two inefficiency sources are identified: (1) a human capital externality — because accommodation builds general human capital, firms cannot capture the full surplus when workers separate, reducing accommodation incentives; and (2) a fiscal externality — imperfectly experience-rated firms do not fully internalize the workers’ compensation cost savings from accommodation, further depressing it below the efficient level. Counterfactual simulations show:

Eliminating wage subsidies (from 50% to 0%) reduces accommodation rates from 33% to 11%, leading to a 7% decline in post-disability employment and a 15% decline in post-disability quarterly wages (roughly $1,358).
A revenue-neutral reform eliminating wage subsidies reduces average welfare and the welfare of more than 90% of workers.
Welfare gains from the subsidy are larger for low-skilled workers than high-skilled workers.
Conditional on experiencing disability, eliminating wage subsidies decreases welfare by about 10%, while increasing the subsidy to 100% raises welfare for disabled workers by around 30%.
Firm profit is maximized at a subsidy rate around 80%, after which higher taxes offset accommodation gains.

Layer 2 — Q&A

Q1: What is the Employer at Injury Program (EAIP), and how does it differ from standard workers’ compensation?

A1: EAIP is an optional component of Oregon’s workers’ compensation system that subsidizes employers for the costs of accommodating workers with temporary disabilities during a transitional return-to-work period. Unlike standard workers’ compensation premiums (which are experience-rated at the firm level), EAIP is funded through a flat payroll tax on all firms that is not experience-rated — meaning firms that use EAIP do not pay higher premiums. The wage subsidy component accounts for over 96 percent of EAIP expenses; other reimbursable costs (worksite modifications up to $5,000, retraining up to $1,000, clothing up to $400) are rarely used. Eligible employers must be the employer at which the disability occurred, and accommodation is limited to a transitional period during which workers cannot simultaneously receive time-loss benefits.

Q2: How is firm-level “exposure” constructed, and what is the rationale for using it as an instrument?

A2: Exposure is the fraction of a firm’s workers’ compensation claims that used EAIP during a five-year baseline period from 2005 to 2009 — a separate historical period chosen to reduce volatility and avoid mean-reversion. The rationale draws on prior work (Aizawa et al., 2022) showing that firm fixed effects account for nearly 25 percent of variation in accommodation, far more than worker or disability characteristics (1 and 3 percent, respectively), suggesting permanent firm-level heterogeneity in the relative benefits and costs of accommodation. Firms with zero historical exposure are unlikely to change accommodation behavior in response to a subsidy reduction, while high-exposure firms respond more, creating differential quasi-experimental variation in accommodation rates after July 2013.

Q3: What are the first-stage and reduced-form results from the DID specification?

A3: The first-stage DID coefficient shows that a ten-percentage-point increase in exposure is associated with a one-percentage-point decrease in EAIP take-up after 2013, implying a 2.9 percentage point decrease for claims in firms with average exposure (mean 0.27). The corresponding reduced-form results show a 0.35 percentage point decrease in employment four quarters post-disability and a $45 decrease in quarterly earnings for every ten-percentage-point increase in exposure, scaling to 0.95 percentage points and $120 at average exposure. There is no statistically significant effect on the probability of moving to a new firm. Pre-trend tests show parallel accommodation trends across exposure terciles prior to 2013, supporting the identifying assumption.

Q4: What do the IV estimates imply about the causal effect of accommodation on labor market outcomes?

A4: Under the exclusion restriction that the subsidy change affects labor market outcomes only through accommodation, the IV estimates imply that receipt of accommodation increases the probability of employment four quarters after disability by 33 percentage points (against a mean of 72 percent) and increases quarterly earnings by approximately $4,100 (against a mean of $7,807). There is no significant effect on the probability of working at a new firm four quarters later. The authors note these large estimates reflect local average treatment effects for compliers — workers whose accommodation status was changed by the instrument — who disproportionately have high unobserved resistance to treatment and high accommodation returns, explaining the magnitude.

Q5: What does the MTE framework reveal about the distribution of accommodation effects and selection?

A5: The MTE curves show that workers with the highest unobserved resistance to treatment (least likely to receive accommodation) have the highest potential employment and earnings gains from accommodation. This negative selection on gains arises because these workers tend to have worse employment outcomes in the untreated state, consistent with more severe disabilities commanding higher accommodation costs. IV weights are concentrated at high-resistance values, explaining the large IV estimates. Negative selection on gains is also found along observable dimensions: workers in self-insured firms, healthcare support occupations, women, and those with wounds/cuts/burns show larger gains but lower likelihood of receiving accommodation.

Q6: What evidence supports characterizing firm accommodation as general rather than firm-specific human capital investment?

A6: Three pieces of evidence point toward general human capital. First, the IV estimate shows accommodation has no statistically significant effect on the probability of working at a new firm four quarters after disability. Second, a triple-interaction specification (DID interacted with new-firm indicator) yields suggestive evidence of even larger earnings gains for workers who move to a new firm post-accommodation, though this is not statistically significant — a pattern inconsistent with firm-specific human capital. Third, the subset of claims that receive non-wage EAIP benefits (worksite modifications, retraining) do show lower mobility, but this comprises fewer than 5 percent of the sample, meaning the predominant form of investment in the context is general in nature.

Q7: What are the two sources of market inefficiency in accommodation identified in the model?

A7: The first is a human capital externality operating through worker turnover. Because accommodation builds general human capital that workers carry to new employers, a firm accommodating a worker does not capture the portion of future surplus that accrues to future employers upon separation. In a Nash bargaining framework with lack of commitment, this dynamic inefficiency is larger when industry-wide turnover rates are higher — consistent with the descriptive finding that accommodation rates are strongly negatively associated with industry separation rates. The second is a fiscal externality from imperfect experience rating: firms whose workers’ compensation premiums are not fully linked to their own claim costs do not fully internalize the cost-savings from accommodation (i.e., reduced time-loss benefit payments), leading them to accommodate at inefficiently low rates.

Q8: How is heterogeneity incorporated in the structural estimation, and what do the estimated parameters show?

A8: The model incorporates observed heterogeneity (firm insurance status, worker skill type — measured by pre-disability wages — firm baseline exposure, and pre/post policy change) and unobserved heterogeneity mapped to the MTE framework’s unobserved resistance to treatment. Indirect inference matches cross-sectional accommodation rates, earnings by subgroup, and the DID coefficients. Key findings: net output during the disability period is negative (accommodation is a costly short-run investment), while post-disability output is higher for accommodated workers. Low-skilled workers experience larger productivity gains from accommodation than high-skilled workers. Accommodation cost shock variance is lower for higher unobserved types, meaning high-gain workers are also more sensitive to subsidy changes, consistent with the large IV estimates. The model fits the DID coefficients for accommodation, employment, and wages well.

Q9: What do the counterfactual simulations show about the welfare effects of varying the subsidy rate?

A9: Eliminating wage subsidies from the current 50% rate reduces the accommodation rate from 33% to 11% and lowers post-disability employment by 7 percentage points and post-disability quarterly wages by 15% ($1,358). From a welfare perspective, eliminating subsidies in a revenue-neutral reform reduces average ex-ante worker welfare and lowers welfare for more than 90% of workers. Conditional on experiencing disability, eliminating subsidies reduces welfare by about 10% while raising the subsidy to 100% increases welfare of disabled workers by around 30%. Firm profit is increasing in the subsidy rate up to about 80%, then decreases. Ex-ante worker welfare gains from the current 50% subsidy relative to no subsidy are modest in consumption-equivalent terms (at most 0.6% increase in consumption), partly because the disability probability is low (2.2%) and because unaccommodated workers still receive two-thirds wage replacement through time-loss benefits.

Q10: What distributional implications do wage subsidies have across worker and firm types?

A10: Welfare gains from higher wage subsidies are larger for low-skilled workers than high-skilled workers, so the subsidy has a redistributive dimension beyond efficiency correction. Welfare gains are also larger for workers in imperfectly experience-rated firms, where the fiscal externality creates the greater wedge from the efficient level. Self-insured firms, which already internalize workers’ compensation cost savings and thus accommodate closer to the optimal rate, benefit less from the subsidy and can even be made worse off if subsidies are set very high (since they bear higher flat payroll taxes with smaller marginal accommodation gains). The fraction of worker-firm matches experiencing welfare gains exceeds 90% under the benchmark subsidy level, indicating broad rather than narrowly concentrated gains.

Q11: How do the experience-rating channel and the worker-turnover channel interact in comparative statics?

A11: Model comparative statics show that reducing the job-to-job transition rate of workers with disabilities to one-quarter of its estimated value substantially raises accommodation rates, and this effect is more pronounced for imperfectly experience-rated firms than for self-insured firms. This occurs because self-insured firms already have a strong incentive to accommodate (to reduce workers’ compensation premiums), so turnover is less marginal for them. Forcing all firms to be self-insured (perfect experience rating) would substantially increase accommodation rates in currently imperfectly rated firms. Lowering the accommodation cost during the disability period (increasing net output during the disability period) also raises accommodation rates for both firm types.

Key Concepts

Firm Accommodation (EAIP): In this paper’s specific sense, accommodation refers to a firm’s decision to offer a worker with a temporary workplace disability “transitional work” — alternative tasks, modified duties, or flexible arrangements — during their recovery period, funded in part through Oregon’s Employer at Injury Program wage subsidy. Accommodation is distinct from simple early return to work; it functions as a form of human capital investment by potentially providing skill development opportunities and preventing human capital depreciation.

Exposure (Instrument): A firm-level continuous measure defined as the fraction of a firm’s workers’ compensation claims that used EAIP during a five-year baseline period (2005–2009). Exposure captures permanent, time-invariant firm-level propensity to accommodate, and is used to construct a difference-in-differences instrument for the causal effect of accommodation by interacting exposure with a post-2013 indicator (when the subsidy rate was cut from 50% to 45%).

Imperfect Experience Rating: The degree to which a firm’s workers’ compensation insurance premium adjusts to reflect that firm’s own claims costs, rather than being set at an industry average. Fully experience-rated (self-insured) firms internalize 100% of claim costs and thus have strong incentives to accommodate. Partially experience-rated firms face a fiscal externality: because their premiums do not fully reflect their own time-loss benefit expenditures, they do not capture all the cost savings from accommodating workers, leading to under-accommodation relative to the social optimum.

Human Capital Externality (Dynamic Inefficiency in Accommodation): The mechanism — analogous to Acemoglu and Pischke (1999) and Fang and Gavazza (2011) — by which worker turnover reduces firms’ incentives to invest in general human capital (here, accommodation). When accommodation raises workers’ general productivity, part of the future surplus from this investment accrues to future employers upon job-to-job separation. With Nash bargaining and lack of commitment (re-bargaining in the second period), the accommodating firm cannot capture this surplus, creating a dynamic inefficiency that is more severe in high-turnover industries.

Negative Selection on Gains: The empirical finding, established via the MTE framework, that workers with workplace disabilities who are least likely to receive accommodation (highest unobserved resistance to treatment) have the largest potential employment and earnings gains from accommodation. This pattern arises because workers with more severe disabilities have high accommodation costs (making firms unwilling to accommodate them) but also face far worse counterfactual labor market outcomes without accommodation, creating large potential gains.

Marginal Treatment Effect (MTE): Following Heckman and Vytlacil (2005), the treatment effect of accommodation evaluated at a specific quantile of unobserved resistance to treatment — defined here as the propensity score value at which a worker is indifferent between treatment and non-treatment. The MTE curve maps out the full distribution of treatment effects and reveals who benefits (and by how much), how IV estimates are weighted averages over this distribution, and which compliers drive the large IV estimates.

General vs. Firm-Specific Human Capital (in Accommodation Context): Accommodation is characterized as general human capital investment if the productivity and earnings gains it produces are transferable across employers — i.e., if accommodated workers who move to new firms retain their wage gains. It is firm-specific if gains are tied to the current match. In this paper, general human capital is supported by the null effect of accommodation on new-firm employment probability, suggestive evidence of non-lower (possibly larger) earnings gains for new-firm movers, and the observation that fewer than 5% of claims use non-wage EAIP benefits associated with firm-specific investment.

Revenue-Neutral Counterfactual: A counterfactual policy experiment in which the wage subsidy rate for accommodation is varied while imposing that both the time-loss benefit program and the EAIP wage subsidy program remain budget-balanced. Higher subsidy rates raise firm accommodation, reduce time-loss benefit payouts (lowering base premiums for imperfectly experience-rated firms), but require a higher flat EAIP payroll tax on all firms, some of which is passed through to workers via lower first-period wages.

The Effect of Provider Diversity on Racial Health Disparities: Evidence from the Military

Mon, 01 Jan 0001 00:00:00 +0000

This paper asks whether racial concordance between patients and medical providers — specifically, whether Black patients are treated by Black physicians — improves use of preventive care and reduces mortality among patients with chronic, manageable diseases. The authors argue that trust and communication deficits along racial lines cause Black patients to underuse low-cost, life-saving preventive care, and that increasing the share of Black providers addresses this deficit.

The authors use data from the Military Health System (MHS) Data Repository covering fiscal years 2003–2013, encompassing roughly 9.6 million beneficiaries. A distinctive feature of the MHS is that active-duty providers are themselves MHS beneficiaries, so their race is observed in the same eligibility files used for patients — overcoming the typical absence of provider-race data in claims databases. The study focuses on four chronic, deadly but manageable conditions: diabetes, hypertension, hypercholesterolemia, and clinical atherosclerotic cardiovascular disease. Preventive care is measured by medication fill-days for condition-appropriate generic drugs, HEDIS-recommended Comprehensive Diabetes Care compliance, and (for a subset) blood pressure control. Mortality is tracked across the full sample period.

The identification strategy exploits quasi-random variation in provider racial composition induced by across-base moves. The MHS setting generates abundant moves driven by DoD personnel management needs — not by patient health or preferences. Using a movers-only differences specification (analogous to Finkelstein et al. 2016), the authors compare differential changes in outcomes for Black versus non-Black patients who move to bases with larger versus smaller increases in the share of Black providers. This design includes fixed effects for both sending and receiving bases, controlling flexibly for regional quality differences. The estimand is an intent-to-treat effect among patients living within 10 miles of a base (who use on-base care 66% of the time).

The findings are consistent across all four disease samples. For diabetes, a move-induced one-standard-deviation increase in the share of Black diabetes providers is associated with a roughly 6 additional metformin fill-days per year (approximately 16% relative to the mean) and a 3 percentage-point increase (roughly 8% relative to the mean) in Comprehensive Diabetes Care compliance for Black relative to non-Black patients. Mortality falls by 0.4 percentage points — a 33% relative decline — for Black relative to non-Black diabetes patients following such a move.

Pooling across all four chronic-disease samples, a one-standard-deviation move-induced increase in the Black provider share is associated with approximately 3 additional fill-days of relevant preventive medication and a roughly 0.2 percentage-point reduction in mortality — approximately 15% relative to the mean mortality rate — for Black relative to non-Black patients.

A decomposition analysis combining the paper’s estimates with medical-literature parameters on the mortality effects of preventive medications finds that between 55% and 69% of the concordance mortality effect across the four disease samples can be attributed to improved medication adherence alone, with the remainder attributed to other aspects of the provider-patient relationship (e.g., lifestyle effects, other preventive care).

Scope conditions: results are local to MHS movers, who are on average slightly younger and healthier than non-movers, potentially understating concordance benefits for the full population. The MHS covers over 3% of all Black U.S. residents, but beneficiaries may differ from the general population. The paper measures Black patient / Black provider concordance specifically; it does not establish a symmetric concordance effect for non-Black patients. The concordance effect estimated is relative — it captures how much Black patients benefit more than non-Black patients from moving to a higher Black-provider-share base. A system-wide spillover mechanism (non-Black providers improving care for Black patients when working alongside more Black providers) cannot be ruled out and would also be consistent with the core concordance motivation.

Q: What is the central research question and why is the MHS an advantageous setting? A: The paper asks whether racial concordance between providers and patients causes Black patients to use more preventive care and achieve better health outcomes, focusing on the trust and communication channel. The MHS is advantageous because active-duty providers are themselves MHS beneficiaries, making their race observable — a feature absent in most claims databases. Across-base moves are driven by DoD staffing needs rather than patient health or preferences, providing quasi-random variation in provider racial composition. The system offers complete claims data covering both on- and off-base care, allowing full mortality tracking.

Q: How does the empirical strategy address selection concerns that plague prior concordance studies? A: Prior studies face selection problems from Black patients choosing different doctors than white patients and from residential segregation concentrating Black patients and Black physicians in regions with distinct care quality. The movers-based differences specification directly addresses both problems: it uses only patients who move across bases, comparing how the same individual’s outcomes change relative to non-Black patients experiencing the same move, as a function of the move-induced change in the Black provider share. Inclusion of fixed effects for both sending and receiving bases accounts flexibly for regional quality differences. Balance tests on observable patient characteristics show no differential sorting of Black versus non-Black patients toward high-Black-provider-share bases.

Q: What specific preventive care and outcome measures are used for each disease? A: For diabetes, the primary measures are annual metformin fill-days and Comprehensive Diabetes Care (CDC) compliance — defined as receiving HbA1c testing, a retinal eye exam, and medical attention for nephropathy in the focal year — plus blood pressure control (available only from 2009 onward for on-base patients). For hypertension, the measures are annual fill-days of WHO-recommended antihypertensives (thiazides, ACEs/ARBs, or long-acting dihydropyridine CCBs) and blood pressure control. For hypercholesterolemia, the measure is fill-days of antilipemic agents, bile acid sequestrants, and statins. For atherosclerotic cardiovascular disease, the HEDIS statin therapy receipt indicator is used. Mortality is tracked across all four samples.

Q: What are the main quantitative results for the diabetes sample? A: A move-induced one-standard-deviation increase in the share of Black diabetes providers is associated with approximately 6 additional metformin fill-days annually for Black relative to non-Black patients (roughly 16% relative to the mean). Compliance with Comprehensive Diabetes Care increases by 3 percentage points for Black relative to non-Black patients (roughly 8% relative to the mean). Mortality falls by 0.4 percentage points for Black relative to non-Black patients — a 33% relative decline — in connection with the same one-standard-deviation increase in Black provider share.

Q: What are the pooled results across all four chronic-disease samples? A: Pooling across diabetes, hypertension, hypercholesterolemia, and atherosclerotic cardiovascular disease, a one-standard-deviation move-induced increase in the Black provider share is associated with approximately 3 additional preventive medication fill-days per year for Black relative to non-Black patients. The pooled mortality effect is a 0.2 percentage-point reduction — roughly 15% relative to the mean mortality rate — for Black relative to non-Black patients.

Q: How much of the concordance mortality effect operates through medication adherence? A: The decomposition combines the paper’s estimated concordance effects on medication fill-days with medical-literature estimates of the mortality impact of each additional fill-day. For the diabetes sample, increased metformin adherence (4.2 additional fill-days) explains approximately 58.8% of the 0.4 percentage-point concordance mortality effect, with the residual 41.2% attributed to other channels such as lifestyle changes or other preventive care. Across all four disease samples, the medication fill-day channel explains between 55% and 69% of the respective concordance mortality effects.

Q: What specification checks do the authors conduct to validate causal identification? A: The authors conduct five main checks. First, balance regressions show that move-induced changes in Black provider share are not differentially related to baseline patient characteristics for Black versus non-Black patients. Second, regressions of the probability of moving on initial Black provider share and its interaction with patient race yield a near-zero concordance coefficient (0.008, SE 0.023), indicating no differential sorting. Third, regressions of post-move on-base care share on the concordance interaction term yield a near-zero coefficient (0.002, SE 0.003), indicating no differential race-specific selection into on-base care. Fourth, a distance falsification test shows that concordance coefficients are near zero and statistically insignificant for patients living more than 10 miles from the base. Fifth, event-study dynamics show no pre-move divergence in preventive care adherence between Black and non-Black patients, with a positive divergence emerging only after the move to a higher Black-provider-share base.

Q: How does the paper separate a concordance effect from a pure Black-physician-quality effect? A: The paper estimates a “first stage” specification on the subsample receiving on-base care (where provider race is observed), regressing the change in the probability of visiting a Black provider on the move-induced change in Black provider density. The results show an approximately one-to-one relationship between higher Black provider availability and increased visits to Black providers for all patients, with only a modest differential by patient race. This confirms that non-Black patients also see more Black providers when Black provider density rises, allowing the interaction specification to isolate concordance from a pure physician-quality effect.

Q: How do the authors assess the potential role of spillover effects? A: The authors acknowledge they cannot rule out that some of the estimated concordance effect arises through system-wide spillovers — for instance, non-Black providers on bases with more Black colleagues may improve their care for Black patients through peer learning or information transmission. They note that even if such a spillover mechanism operates, it is still consistent with the paper’s core concordance motivation, because provider-knowledge deficiencies about treating Black patients are among the theorized channels of racial discordance.

Q: What do the results imply for the overall racial mortality gap? A: Among MHS beneficiaries aged 20–65, Black beneficiaries are roughly 38% more likely to have diabetes and die over the sample period than non-Black beneficiaries; this gap appears driven primarily by higher diabetes prevalence rather than a within-diabetes mortality gap. Applying the diabetes concordance mortality estimate (a 0.4 percentage-point reduction), the authors calculate that a one-standard-deviation increase in the Black provider share would reduce the overall diabetes mortality gap from 38% to approximately 21% — a substantial narrowing driven by the concordance effect operating through conditional-on-prevalence outcomes.

Q: What are the policy implications of the findings? A: The results imply that investments in increasing physician workforce diversity could meaningfully reduce racial mortality disparities in the United States, particularly for chronic diseases manageable through preventive medication. The paper notes the results are relevant to affirmative action policies in medical school admissions, specifically the pending Supreme Court cases Students for Fair Admissions v. University of North Carolina and Students for Fair Admissions v. Harvard at the time of writing. The MHS population covered in the study includes over 3% of all Black U.S. residents, so the policy stakes extend substantially beyond the military context.

Q: What are the limitations of the study regarding generalizability? A: Movers in the chronic-disease samples are on average about four years younger and 0.2 percentage points less likely to die than non-movers, suggesting the local average treatment effect for movers may understate concordance benefits for the full population. The MHS population may be healthier overall than the general population, though conditioning on chronic-disease patients mitigates this concern. The paper covers only Black-patient/Black-provider concordance; concordance effects for other racial and ethnic groups are not estimated. The estimate of the concordance coefficient technically captures how much the Black patient / Black provider concordance effect exceeds the non-Black patient / non-Black provider concordance effect, meaning the absolute magnitude of Black concordance benefits is understated if non-Black concordance effects are also positive.

Racial concordance: In this paper’s usage, the match between the race of a patient and their treating physician — specifically Black patient / Black provider pairing — theorized to improve care through trust, communication, and reduced provider knowledge deficiencies about Black patients.

Provider Black share: The fraction of outpatient office visits for a given chronic condition at a given military base that are attended by Black active-duty providers, used as the base-level treatment variable; varies across bases from zero to approximately 20 percentage points in the pooled sample.

Movers-based differences specification: An identification strategy that restricts to patients who relocate across military bases exactly once during the sample period and estimates the differential change in outcomes for Black versus non-Black patients as a function of the move-induced change in the base’s Black provider share, including fixed effects for both the sending and receiving base.

Intent-to-treat (ITT) effect: The concordance estimate as applied to all patients living within 10 miles of a base — regardless of whether they actually received on-base care — to avoid selection bias from differential race-specific decisions to seek care on versus off base.

Comprehensive Diabetes Care (CDC): A HEDIS composite measure requiring receipt of all three of the following in the focal year: HbA1c testing, a retinal eye exam, and medical attention for nephropathy (via microalbumin exam, ACE/ARB therapy, or nephropathy treatment).

Medication fill-days: Annual days of supply dispensed for condition-appropriate generic medications (metformin for diabetes; thiazides/ACEs/ARBs/CCBs for hypertension; antilipemic agents, bile acid sequestrants, and statins for hypercholesterolemia; statins for atherosclerotic cardiovascular disease), used as the primary preventive care adherence measure.

Decomposition of concordance mortality effect: A calculation that uses the paper’s estimated concordance effect on medication fill-days, combined with medical-literature estimates of the mortality impact per fill-day, to determine what share of the total concordance mortality effect passes through medication adherence versus other channels (lifestyle, other preventive care).

The Effects of an Aging Population on the Structure of Bank Assets and Liabilities

Mon, 01 Jan 0001 00:00:00 +0000

Using 2001-2022 annual data on U.S. commercial and savings banks matched with county-level demographic data, this paper shows that banks operating in areas with older populations—measured by the deposit-weighted proportion of seniors (individuals over 65) in the counties where the bank has branches—issue more retail deposits and less wholesale funding, pay relatively lower retail deposit rates with greater stickiness across maturities, and experience smaller deposit withdrawals when market interest rates rise. On the asset side, these banks hold significantly more securities and fewer loans (particularly small business and residential mortgage loans) with longer maturities, substantially raising their asset-liability maturity gap. These findings are consistent with a lifecycle model in which seniors demand risk-free retail deposits as an investment vehicle while exhibiting lower borrowing demand, combined with the localization of banks’ deposit-taking and lending. The paper instruments for a bank’s senior exposure using projected county-level senior population shares constructed from historical state-level fertility rates and county-level cohort change rates by race and sex, mitigating concerns about endogenous bank location relative to contemporaneous economic conditions.

Summary based on a working paper version, AI-assisted and human-reviewed. See the linked published article for the authoritative version.

In depth

Q1. How is a bank’s exposure to seniors measured, and why is this measure preferred?

A bank’s ’exposure to seniors’ is defined as the deposit-weighted senior population share of all counties where the bank operates branches, using each county’s deposits at that bank as weights; this measure is preferred because it captures the bank’s actual demographic exposure to older depositors while accounting for the relative importance of each local market to the bank. The paper instruments for this measure using projected county-level senior population shares derived from historical demographic data (state-level fertility rates by race, historical county-level cohort change rates by race and sex), which are orthogonal to the contemporaneous economic conditions that could cause population migration and confound the results.

Q2. How does senior exposure affect retail deposit rates and stickiness?

Banks with greater senior exposure pay significantly lower interest rates on retail time deposits, and the spread between an equivalent-maturity competitive market rate and the bank’s retail deposit rate widens by more as market rates rise, indicating greater deposit rate stickiness; this effect is especially pronounced at longer maturities (24- and 60-month CDs), where seniors’ preference for deposits as an investment vehicle rather than a transaction account gives banks greater market power. Moreover, these banks’ deposits are less likely to be withdrawn when the Federal Funds Rate rises, despite lower and slower-adjusting deposit rates, consistent with seniors’ lesser sensitivity to interest rate differentials (limited recall in monitoring rates, as in Kahn, Pennacchi, and Sopranzetti 1999).

Q3. How does senior exposure affect the composition and maturity of assets?

Banks exposed to more seniors hold significantly more securities and fewer loans—particularly small business loans and residential mortgages—and choose securities and loans with much longer maturities, which substantially raises their asset-liability maturity gap. The lifecycle model predicts this: in markets with older populations, the demand for loans is lower (seniors are net savers, and local businesses benefit from greater labor supply in younger areas), leaving the bank’s retail deposit surplus to be invested in securities. The long-maturity asset allocation is supported by the bank’s stable retail deposit base, which is less sensitive to market rate movements (increasing the effective duration of deposits beyond their stated maturity).

Q4. What are the macroeconomic implications as populations age?

The paper’s findings predict economically important changes in banks’ future asset-liability structures as U.S. populations age: aggregate bank loan-to-asset ratios should decline, security-to-asset ratios rise, retail deposit shares increase, wholesale funding shares decrease, and the banking system’s aggregate asset-liability maturity gap should widen—with corresponding implications for banks’ interest rate risk exposure and the transmission of monetary policy through the bank lending channel. The demographic shift is projected to continue: the U.S. share of the population over 65 is predicted to reach 22% by 2050, while the EU’s share is projected at 28% and China’s share of those over 60 is projected at 40% in 2050, making these dynamics relevant across advanced economies.

Key concepts

bank exposure to seniors : the deposit-weighted proportion of individuals over age 65 in the counties where a bank has branches; the paper’s key explanatory variable, capturing how much of the bank’s deposit base is drawn from an older population. deposit rate stickiness : the slower adjustment of retail deposit rates to changes in equivalent-maturity competitive market interest rates; greater stickiness implies a widening of the deposit rate spread as market rates rise; found here to be more pronounced for banks with higher senior exposure. asset-liability maturity gap : the difference between the bank’s asset average maturity and its deposit average maturity; measures the bank’s exposure to interest rate risk; found here to be significantly larger for banks with higher senior exposure due to longer-maturity assets and stable retail deposit funding.