CEO Stress, Aging, and Death: Borgschulte, Guenzel, Liu & Malmendier (2025)

What this is. The paper’s core results, the two empirical strategies (DiD apparent-aging and stratified Cox mortality hazard), and the datasets. Enough to know what was found and how, without reading the full 42 pages. To replicate or extend, read the original at doi.org/10.1111/jofi.13497.

TL;DR

Using two quasi-experimental sources of variation in CEO job demands, the paper documents that managerial stress causes accelerated visible aging and higher mortality. First, in a difference-in-differences design applied to 3,002 facial images of Fortune 1000 CEOs during the Great Recession, industry distress exposure makes CEOs look roughly one year older (eventually 1.1 to 1.2 years after 2012). Second, in a stratified Cox hazard model on 1,900 CEOs from Forbes Executive Compensation Surveys (1975 to 1991), industry distress raises the mortality hazard by about 15%, equivalent to 1.1 fewer years of chronological life. Third, the staggered passage of antitakeover (Business Combination) laws across U.S. states in the mid-1980s, which reduced monitoring intensity, is associated with a 16 to 21% lower mortality hazard per year of BC law exposure, implying roughly a two-year longevity gain for the average protected CEO. The effects are of similar magnitude across both stress proxies and consistent with a causal interpretation: neither compensation nor CEO tenure fully accounts for the health costs, suggesting the market does not price them in.

Core results

Magnitudes and significance as reported; \*/\*\*/\*\*\* = 10%/5%/1%.

#	Result	Locator	Magnitude
R1	Industry distress raises CEO apparent age (DiD, post-2006)	Table III col. (1), p. 3420	+0.806 years (SE 0.382, \*\*)
R2	Distress-induced apparent aging grows over time, reaching 1.0-1.2 years at post-2012 horizon	Table III cols. (3)-(4), p. 3420; Figure 4, p. 3418	0.634 (insig.) in 2007-2011; 1.049\*\* to 1.183\*\*\* from 2012 onward
R3	Industry distress raises CEO mortality hazard by ~15%	Table IV (cols. 1-6), p. 3426	Average hazard coefficient 0.136; hazard ratio exp(0.136) = 1.145; equivalent to 1.1 years older
R4	BC antitakeover law exposure (binary) reduces CEO mortality hazard	Table V cols. (1)-(4), p. 3432	Coefficients -0.198 to -0.234 (\*\* to \*\*\*); average -0.217
R5	Each additional year of BC law exposure reduces mortality hazard ~3.8%	Table V cols. (5)-(8), p. 3432	Coefficients -0.037 to -0.040 (\*\*\*); average -0.039
R6	BC law protection equivalent to being ~2 years younger; no compensating pay differential found	p. 3433; Internet Appendix Table IA.XXIV	Hazard ratio shift corresponds to mortality rate of a CEO 2 years younger; pay effect insignificant and positive
R7	Kaplan-Meier survival curves: ~67% of distressed CEOs die within 30 years of appointment vs. ~32 years for nondistressed	Figure 5, p. 3425	Visually left-shifted survival curve; 1-year mortality at median CEO age pushed from 1.337% to 1.532%
R8	BC law Kaplan-Meier: 1980s cohorts with BC exposure right-shifted vs. same-era no-BC cohorts; 1970s and 1980s no-BC curves nearly identical	Figure 6, p. 3431	25% cumulative mortality reached ~25 years (no BC) vs. ~28-30 years (BC) after appointment

Overall (paper’s conclusion). Heightened job demands in the form of industry-wide distress and stricter corporate monitoring impose significant personal health costs on CEOs: faster visible aging and shorter lives. The effects are of similar magnitude whether identified by economic distress shocks or by variation in governance intensity from antitakeover laws. For context, Sullivan and Von Wachter (2009) estimate that job displacement raises the mortality hazard by 10 to 15% and reduces life expectancy by 1 to 1.5 years in a general male population; the industry-distress estimate here is of comparable magnitude, but operates through an opposite channel (more, not less, work effort). The absence of a compensating pay differential suggests the market does not fully account for these costs, pointing to an underappreciated private cost of CEO service.

Theory / model

The paper has no formal structural model. The economic framework builds on the notion that work-related stress arises when job demands exceed available coping resources (Lazarus and Folkman (1984), p. 3403). In the CEO context, this is operationalized through two contrasting shocks: industry-wide distress (temporary demand increase) and antitakeover law protection (permanent demand decrease). Both shocks affect the intensity of CEO job demands without directly imposing financial hardship on the CEO, which allows identification to isolate health effects from income effects that confound most stress-and-health studies. Prior work by Bertrand and Mullainathan (2003) introduced antitakeover-law variation as a proxy for CEO monitoring intensity; this paper re-deploys that variation to study health outcomes rather than managerial behavior.

The biological mechanism is that chronic stress triggers cortisol and other hormonal responses, causing cellular damage that manifests as visible aging (p. 3423). Apparent age is validated as a clinical biomarker for mortality (Christensen et al. (2004), Christensen et al. (2009)): differences between apparent and chronological age predict short-term and long-term mortality even when physicians know the chronological age, and correlate with physical functioning, cognitive performance, and leucocyte telomere length (p. 3413). The paper’s identification tests parallel pre-trends in both the DiD and the Kaplan-Meier analysis (Figures 4 and 6) and rule out picture-management and image-selection confounds through a battery of robustness checks.

Industry distress identification. An industry is distressed in year $t$ if the median firm’s forward-looking two-year stock return falls below $-30\%$ (Babina (2020)). The distress indicator for CEO $j$ equals 1 if the CEO’s firm was in a distressed industry in 2007, 2008, or both (the Great Recession crisis years); it does not update after the CEO departs. Treatment status is orthogonal to pre-crisis aging trends (Figure 4, p. 3418) and to image sharpness (Internet Appendix Table IA.III).

Antitakeover law identification. Business Combination (BC) laws passed staggered across 33 U.S. states between 1985 and 1997 (Figure 1, p. 3413). Laws apply by state of incorporation, not state of headquarters, reducing concern that local economic conditions drive the results. The constitutionality of BC laws was established by a 1989 federal ruling, strengthening the exogeneity argument (p. 3412).

Method

Part 1: Apparent-age estimation. Apparent age is estimated from CEO facial images using the deep CNN of Antipov et al. (2016), trained on more than 250,000 images and winner of the 2016 ChaLearn Looking At People competition (p. 3414). The model is an ensemble of 11 sub-networks (bagging in the style of Breiman (1996)) and outputs a $100 \times 1$ probability vector over ages 0 to 99; the apparent-age point estimate is the expected value of this distribution. The software is validated within the CEO context by comparing 250 random pairs of CEO images to human assessments; agreement is ~70% overall and ~90% when the software-estimated age gap is in the top tercile (p. 3414).

The outcome variable is the apparent-age gap:

$$\text{Apparent Age Gap}_{i,j,t} = \widehat{\text{Apparent Age}}_{i,j,t} - \text{Chronological Age}_{j,t}$$

where $i$ indexes an image, $j$ a CEO, and $t$ a time bin (p. 3418).

Part 2: Cox proportional hazards model. Mortality is estimated using stratified Cox (1972) proportional hazards models. CEOs enter the risk set when they take office and exit at death or the October 1, 2017 censoring date. The baseline hazard $\lambda_{0,j}(t)$ is allowed to vary across Fama and French (1997) 49 industries (p. 3424).

Empirical specifications

Apparent-aging DiD (R1, R2). The pre-versus-post graphical test uses time-bin indicators interacted with the distress indicator (equation 1, p. 3418):

$$\text{Apparent Age Gap}_{i,j,t} = \beta_0 + \sum_{\substack{t \in T \\ t \neq 2005\text{-}06}} \beta_{1,t} \cdot \text{Industry Distress}_j \times \mathbb{1}_t + \boldsymbol{\beta}_2' \mathbf{X}_{i,j,t} + \delta_t + \theta_j + \varepsilon_{i,j,t} \tag{1}$$

The main regression collapses post-crisis to a single indicator (equation 2, p. 3419):

$$\text{Apparent Age Gap}_{i,j,t} = \beta_0 + \beta_1 \cdot \text{Industry Distress}_j \times \mathbb{1}_{[t > 2006]} + \boldsymbol{\beta}_2' \mathbf{X}_{i,j,t} + \delta_t + \theta_j + \varepsilon_{i,j,t} \tag{2}$$

where $\mathbf{X}_{i,j,t}$ includes image-level controls for smile, mood, self-confidence, style, side face, logo, glasses, magazine quality, lighting, natural pose, pre-2007 industry shock experience, and pre-2007 CEO tenure. CEO fixed effects $\theta_j$ absorb time-invariant facial characteristics. Standard errors are clustered at the three-digit SIC level. Observations are weighted by image sharpness (Laplacian). Sample: 3,002 images of 453 CEOs.

Mortality hazard, industry distress (R3, R7). The Cox hazard model stratified by FF49 industry is (equation 3, p. 3424):

$$\ln \lambda(t \mid \text{Industry Distress}_{i,t}, \mathbf{X}_{i,t}) = \ln \lambda_{0,j}(t) + \beta \cdot \text{Industry Distress}_{i,t} + \boldsymbol{\delta}' \mathbf{X}_{i,t} \tag{3}$$

where $\text{Industry Distress}_{i,t}$ equals 1 if CEO $i$ has experienced industry distress (forward-looking 2-year median firm return $< -30\%$) in year $t$ or any prior year. Controls include chronological age, linear or fixed time effects, and state-of-headquarters location fixed effects. Sample: 1,900 CEOs, 58,034 CEO-year observations; standard errors clustered at 3-digit SIC.

Mortality hazard, BC law indicator (R4, R6, R8). The BC law binary specification is (equation 4, p. 3429):

$$\ln \lambda(t \mid BC_{i,t}, \mathbf{X}_{i,t}) = \ln \lambda_{0,j}(t) + \beta \cdot I(BC_{i,t}) + \boldsymbol{\delta}' \mathbf{X}_{i,t} \tag{4}$$

Mortality hazard, BC cumulative exposure (R5). The cumulative-exposure specification counts years of BC law coverage until year $t$ (equation 5, p. 3430):

$$\ln \lambda(t \mid BC_{i,t}, \mathbf{X}_{i,t}) = \ln \lambda_{0,j}(t) + \beta \cdot BC_{i,t} + \boldsymbol{\delta}' \mathbf{X}_{i,t} \tag{5}$$

The BC analyses restrict to CEOs appointed before the BC laws were enacted (1,605 CEOs) to address selection; standard errors are clustered at the state-of-incorporation level. Both BC specifications add a first-generation antitakeover law exposure control following Karpoff and Wittry (2018).

Datasets used

Dataset	Role in paper	Wiki page
CEO Apparent Aging Data Set: 3,002 Getty Images / Google Images photos of 453 Fortune 1000 CEOs (2006 cohort), dated images, 2000-2016	Outcome (apparent-age gap); identified by ML apparent-age CNN	No page yet
CEO Mortality Data Set: Forbes Executive Compensation Surveys 1975-1991 (Gibbons and Murphy (1992)); hand-collected birth and death dates from Ancestry.com for 2,361 of 2,720 CEOs at 1,501 firms; tenure from Execucomp and NYT “Business People”	Outcome (mortality/longevity); treatment (industry distress, BC law exposure)	Forbes exec comp
CRSP (via WRDS): stock returns, PERMNO identifiers, historical SIC codes; used to construct annual industry-distress panel	Industry distress definition (median 2-year forward return < -30%) and sample restriction	WRDS / CRSP (licensed)
Compustat (via WRDS): assets, employees; Comphist / Compustat Snapshot for historical state of incorporation	Firm controls; historical state-of-incorporation for BC law assignment	WRDS / Compustat (licensed)
BC law passage dates, by state: Cheng, Nagar, and Rajan (2004); Cain, McKeon, and Solomon (2017); Karpoff and Wittry (2018)	Treatment variable (BC law indicator and cumulative exposure)	No page yet
Antipov et al. (2016) deep CNN apparent-age software (Oxford VGG architecture)	Apparent-age estimation from facial photos	No page yet
Human Mortality Database (2019)	Benchmark mortality rates for economic significance comparisons	No page yet

Sample periods: apparent-aging analysis, 2000-2016 (images) / Fortune 1000 cohort 2006; mortality analysis, 1975-2017 (CEO-year panel, censoring October 1, 2017).

When to read the full paper

Use the original if you are: extending the apparent-aging ML approach to other executive samples or professional groups (see Section II and Internet Appendix Section II.A for the CNN architecture and image-processing details); replicating the mortality analysis (full robustness tables are in Internet Appendix Sections III-IV, with 22+ additional specifications); studying the BC law / antitakeover identification in detail (Sections IV.D and Internet Appendix Tables IA.XIX-IA.XXIII follow Karpoff and Wittry (2018) exhaustively); or examining the pay-and-health compensating-differential calibration (Section IV.E and Internet Appendix Section IV).

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6), December 2025, pp. 3401-3442. This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Borgschulte, Mark, Marius Guenzel, Canyao Liu, and Ulrike Malmendier. “CEO Stress, Aging, and Death.” The Journal of Finance 80, no. 6 (December 2025): 3401–3442. DOI: 10.1111/jofi.13497. © 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.