Women in Charge: Lewellen (2025)

What this is. The paper’s core results, the datasets it assembles, and the empirical specifications behind the compensation and turnover findings: enough to know what it found and how, without reading all 55 pages. To replicate or extend it, read the full source at doi:10.1111/jofi.13455.

TL;DR

The paper uses 19 years of IRS Form 990 filings for U.S. nonprofit hospitals (2000-2018) to study female hospital CEOs: what kinds of hospitals they lead, how they make decisions under stress, and how boards compensate and replace them. Contrary to the gender-differences literature, female CEOs do not run hospitals with safer financing, lower investment, or more charitable orientation, and they respond to the 2008 financial crisis identically to male peers. However, female CEOs earn 32% lower unconditional pay (7.8% within hospital after controls), receive flatter pay-for-performance incentives (5.3 pp vs. 15.0 pp pay rise from bottom to top performance quintile), and are fired more often after poor performance (3.6 pp higher departure rate in the bottom quintile). The patterns are consistent with hospital boards perceiving female CEOs as less productive, whether due to true differences or biased beliefs.

Core results

Magnitudes and significance are as reported in source tables; \*/\*\*/\*\*\* = 10%/5%/1%.

#	Result	Locator	Magnitude
R1	No evidence female CEOs match or shift hospitals toward safer financing or lower investment: coefficients on leverage, cash, securities, and investment are statistically insignificant across all specifications	Table II, pp. 2212, 2214	One-SD increase in leverage raises female CEO probability by an insignificant 0.48 pp (col. 2); can reject a decline of more than 0.70 pp; similar bounds for cash and investment
R2	No evidence female CEOs lead hospitals with greater charitable orientation: Medicaid Ratio, Charity Care, and Medicare Ratio are all insignificant with hospital FE; Contributions positive at 10%	Table III, pp. 2213, 2215-2217	Contributions coefficient: 0.83 pp per 1-SD increase (col. 8); all other benevolence measures insignificant within hospital
R3	Hospitals with male and female CEOs respond identically to the 2008 financial crisis: interaction Post_Crisis x Female is insignificant for revenue, profit margin, investment, employment, salaries, and Medicaid Ratio	Table V, pp. 2221-2222; Figure 3, p. 2222	Female hospitals cut revenue by 3.0 pp, males by 2.9 pp; profit margin declines 1.6 pp vs. 1.3 pp; investment 7.2 pp vs. 6.9 pp; none of the differences is significant
R4	Female CEOs earn substantially lower pay: unconditional gap is 32.2%, falling to 12.5% after controlling for hospital size, and further to 7.8% with hospital FE and full controls	Table VII, p. 2225; Table VI, p. 2224	Female CEO coefficient: -0.322*** (col. 1), -0.125*** (col. 2), -0.078** (col. 5) in log pay regressions
R5	Pay-for-performance sensitivity is significantly flatter for female than male CEOs: men’s pay rises 15.0 pp from worst to best performance quintile; women’s only 5.3 pp	Table VIII, p. 2229; Figure 4, p. 2230	Female CEO x Perf_Quintile5: -0.096** (0.046) in col. 4; -0.105** (0.048) with age dummies in col. 5
R6	Turnover-performance sensitivity is significantly higher for female CEOs: incremental departure rate in the bottom quintile is 3.6 pp higher for women than men (forced turnover); 3.2 pp for all turnovers	Table IX, pp. 2232-2233; Figure 5, p. 2233	Marginal effect of Female CEO x Perf_Quintile1: 3.6 pp (col. 4, forced, significant at 5%); 3.2 pp (col. 8, all turnovers, significant at 5%)
R7	Board gender composition does not explain the pay gap: correlation between female board share and female CEO indicator disappears with hospital FE; the gap is not systematically smaller where female trustees are more common	Table X, pp. 2238-2239	Female Directors Non-CEO: 0.146*** (col. 2) without FE; 0.011 (col. 4) with hospital FE; Panel B interaction with gender pay gap insignificant

Overall (paper’s conclusion). Hospitals led by male and female CEOs look similar on observable financial and operational measures, pursue similar strategies, and respond similarly to external shocks. At the same time, compensation and turnover decisions are made as though female CEOs are less productive: they receive lower pay, flatter incentives, and steeper performance-conditioned dismissal probabilities. The evidence is consistent with boards holding biased beliefs about CEO ability, though true productivity differences cannot be ruled out.

Theory / model

The paper has no formal structural model. The tests are motivated by two classes of theory:

Agency and efficiency benchmark for pay. The paper follows the assignment models of CEO compensation (Gabaix and Landier (2008), Tervio (2008)) as an efficiency benchmark: in competitive markets for CEO talent, pay reflects the CEO’s marginal product of skill. A gender gap in pay, under this lens, reflects a perceived productivity difference. Boards may correctly assess CEO ability (true skill gap) or may hold biased priors (stereotype-based beliefs), consistent with Bayesian learning models of CEO turnover (Hermalin and Weisbach (1998), Taylor (2010)). The unconditional gender gap documented here is broadly consistent with the findings of Bertrand and Hallock (2001) for S&P 1500 executives, extended here to the CEO level within a single industry over a longer panel.

Principal-agent incentive theory. The moral hazard framework of Holmstrom and Milgrom (1987) predicts that optimal incentive steepness (pay-for-performance sensitivity) is higher for less risk-averse, higher-effort, or higher-productivity CEOs. Flatter incentives for women are consistent with boards perceiving them as more risk-averse or less productive. The matching between CEO type and board beliefs also predicts lower firing thresholds for lower-prior-ability CEOs, which is consistent with the finding that female CEOs are fired more often at the bottom of the performance distribution.

Identification. The core hospital-level regressions exploit within-hospital variation over time (hospital fixed effects) to absorb time-invariant confounders. For the financial crisis analysis, k-nearest-neighbor matching on 2007 hospital attributes creates treatment and control samples that are balanced on observables immediately prior to the shock, attenuating concerns about selection on trends. The crisis itself is a plausibly exogenous, unexpected shock to hospital finances (stock market crash, credit crunch, demand decline via unemployment) that provides a high-stakes setting where CEO preferences, if present, should be most visible. The data infrastructure for the crisis analysis follows Adelino, Lewellen, and McCartney (2021) and Adelino, Lewellen, and Sundaram (2015), who also use IRS Form 990 and AHA data to study hospital investment and governance decisions.

The results contradict prior evidence from European private-firm samples. Faccio, Marchica, and Mura (2016) find that firms led by female CEOs take less risk and choose safer financing; Huang and Kisgen (2013) find that female executives make fewer acquisitions and issue less debt. The hospital evidence shows no such financing or investment differences, consistent with the view that self-selection into top executive roles reduces or eliminates gender differences in behavior documented in broader populations (Adams and Funk (2012)). Similarly, Matsa and Miller (2013) find that Norwegian board quotas lead to labor hoarding, interpreted as a benevolence effect of female board members; the hospital tests of Medicaid mix, charity care, and employment response to the crisis find no analogous channel.

Method

Hospital fixed-effects panel regressions (Tables II, III, VII, VIII). The main estimating equation across all parts of the paper is:

$$Y_{ht} = \alpha + \beta \, \text{Female}_{ht} + \gamma X_{ht-1} + \mu_h + \tau_t + \varepsilon_{ht} \tag{1}$$

where $Y_{ht}$ is an outcome for hospital $h$ in year $t$ (leverage, log CEO pay, Charity Care, etc.), $\text{Female}_{ht}$ is an indicator for a female CEO, $X_{ht-1}$ is a vector of lagged hospital controls (log service revenues, profit margin, growth in services, contributions, density decile of the HSA, system membership, CEO tenure, and multiple positions), and $\mu_h$ and $\tau_t$ are hospital and year fixed effects. Standard errors are clustered at the hospital level throughout. The coefficient $\beta$ identifies the effect from within-hospital CEO gender transitions (pp. 2213-2214, 2225).

Matching + DiD for the financial crisis (Table V). For the crisis test, the paper constructs a matched sample using k-nearest-neighbor matching ($k = 3$, matching with replacement) on 2007 hospital attributes: service revenues, HSA population density rank, investment, revenue growth, and system membership. The matched sample has 134 treated (female CEO in 2007) and 271 control (male CEO in 2007) hospitals. The estimating equation is:

$$Y_{ht} = \alpha + \delta \, (\text{Female}_h \times \text{Post\_Crisis}_t) + \mu_h + \tau_t + \varepsilon_{ht} \tag{2}$$

estimated on the window 2006-2011, where $\text{Post\_Crisis}_t = 1$ for 2009-2011. The coefficient $\delta$ is identified from the differential within-hospital change from pre- to post-crisis between the matched treatment and control groups, a difference-in-differences design (p. 2220).

Probit for CEO turnover (Table IX). The turnover regressions are probit models:

$$\Pr(\text{Turnover}_{ht} = 1) = \Phi\!\left( \alpha + \beta_1 \, \text{Female}_{ht} + \sum_{q=1}^{4} \beta_q \, \text{Perf\_Quintile}_{qht} + \gamma \, (\text{Female}_{ht} \times \text{Perf\_Quintile1}_{ht}) + \delta X_{ht} + \mu_h + \tau_t \right) \tag{3}$$

where $\Phi$ is the standard normal CDF, performance quintiles are formed by year and hospital size bin based on the lagged profit margin (Perf_Quintile5 = best is excluded; Perf_Quintile1 = worst is the main interaction), and the equation is estimated separately for forced turnover (CEO aged 60 or younger at departure) and all turnover. Marginal effects are reported (pp. 2231-2234).

Pay-for-performance regressions (Table VIII). The pay sensitivity analysis runs OLS of log CEO pay on female dummy, performance quintile indicators, and their interactions:

$$\ln(\text{CEO Pay}_{ht}) = \alpha + \beta_1 \, \text{Female}_{ht} + \sum_{q=2}^{5} \beta_q \, \text{Perf\_Quintile}_{qht} + \sum_{q=2}^{5} \gamma_q \, (\text{Female}_{ht} \times \text{Perf\_Quintile}_{qht}) + \delta X_{ht} + \mu_h + \tau_t + \varepsilon_{ht} \tag{4}$$

where $\gamma_5$ on the Female x Perf_Quintile5 interaction captures the differential pay-performance slope at the top of the distribution (pp. 2228-2230, Table VIII).

Empirical specifications

• Matching and selection tests (Tables II, III, Sections IV and V). Panel OLS (equation 1) with and without hospital FE; sample 23,611 to 25,513 hospital-years depending on completeness. Controls include log service revenue, profit margin, growth in services, density HSA, competition rank. Tables II and III report coefficients on the female CEO indicator for financial risk (leverage, cash/assets, securities/assets, investment) and patient orientation (Medicaid, Medicare, Charity Care, Contributions) respectively. The within-hospital specifications (cols. with hospital FE) are the headline tests.

• Financial crisis DiD (Table V). Matched sample, 2006-2011, hospital and year FE, N = 2,251-2,287 hospital-years depending on variable. Dependent variables: growth in services, profit margin, investment, growth in personnel, growth in salaries, Medicaid Ratio. The interaction Post_Crisis x Female is the parameter of interest in Panel A; Panels B and C show results separately for the treatment and control samples.

• Gender pay gap (Table VII). Log CEO pay on Female CEO indicator, year FE, progressively adding: log service revenues (col. 2), full hospital controls (col. 3), density and system dummies (col. 4), hospital FE (col. 5), CEO age dummies. N = 16,193-20,520 hospital-years depending on pay data availability. Panel B replicates for the post-2008 consistent-reporting subsample (2009-2018).

• Pay-for-performance (Table VIII). OLS (equation 4), hospital and year FE, full controls including tenure and multiple positions; N = 16,193-18,066. Profit margin quintiles defined within year x hospital size bin. Robustness: Table IA.V adds interactions with size and HSA density to test whether urban/size confounds drive the result; coefficients remain significant.

• Turnover-performance (Table IX). Probit (equation 3), hospital and year FE, full controls; forced turnover sample (age 60 or younger) N = 3,176-18,348; all turnover N = 4,030-24,314. Marginal effects reported for Female CEO and the key interaction Female CEO x Perf_Quintile1. Robustness: Table IA.VI includes hospital size and density interactions; Table IA.VII documents subsequent career outcomes of departing CEOs (11% of male departures become CEOs elsewhere vs. 6% for female departures, differences often significant).

Datasets used

Dataset	Role in paper	Wiki page
IRS Form 990 filings (via Candid/Guidestar 1999-2014; IRS website 2015-2018)	Primary source for hospital financials, CEO/officer names, titles, salaries, board composition; 2000-2018	IRS Form 990
AHA Annual Survey Database (Dartmouth Institute, 2000-2018)	Hospital services, operations, system affiliation	AHA Annual Survey (licensed)
Healthcare Cost Report Information System (HCRIS / CMS, 2011-2018)	Charity Care and Uninsured Discounts spending	HCRIS
Dartmouth Atlas of Health Care	Hospital Service Area (HSA) geographic market definitions; demographic characteristics from 2010 U.S. Census	Dartmouth Atlas
CMS patient outcome metrics (mortality, readmissions, patient satisfaction)	Nonfinancial hospital performance proxies used in supplementary tests; available from 2008-2009	CMS quality
Radaris, LinkedIn, hospital websites (manual collection)	CEO biographical data (age, education, career history) for a subsample of 2,202 CEOs	no page yet

Sample: 1,981 hospitals and 25,762 hospital-years from 2000 to 2018; 4,353 CEOs (819 female). Financial ratios are winsorized at the 2% level. CEO compensation data cover approximately 80% of hospital-year observations. The matched crisis sample covers 134 female and 271 male CEO hospitals, observed 2006-2011.

When to read the full paper

Read the original if you are: researching gender gaps in executive compensation or turnover (Tables VII, VIII, IX have precise specifications and robustness); building on the nonprofit hospital setting (Internet Appendix Section II has a detailed comparison with for-profit CEO markets); extending the analysis to other nonprofit sectors; studying how boards update beliefs about CEO quality (the discussion in Section VI.D synthesizes the compensation and turnover patterns against learning and agency theories); or investigating whether board gender composition affects executive gender outcomes (Table X and the negative result on female directors, Section VII).

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(4), August 2025, pp. 2199-2253. Publisher: Wiley on behalf of the American Finance Association. DOI: 10.1111/jofi.13455. Licensed under Wiley Terms and Conditions (paywalled; not CC licensed). This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. Extract-only: no PDF hosted.

Lewellen, Katharina. “Women in Charge: Evidence from Hospitals.” The Journal of Finance 80, no. 4 (August 2025): 2199-2253. DOI: 10.1111/jofi.13455. © 2025 the American Finance Association.