J44 | Macro Paper Warehouse

Peer Effects and the Gender Gap in Corporate Leadership

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

This paper investigates whether exposure to a larger share of female peers during an MBA program causally affects the gender gap in senior corporate leadership positions. The research question is motivated by the persistent underrepresentation of women in top management: in S&P 1500 companies, women hold only 6% of CEO positions despite comprising 40% of the workforce.

The authors merge administrative data from a top-10 U.S. business school (graduating classes 2000–2018, excluding 2009) with public LinkedIn profile data covering full employment histories, firm-level data from multiple sources including InHerSight crowdsourced female-employee ratings, and a 2023–2024 alumni survey of female graduates. Senior management is defined as Vice President, Director, Senior Vice President, or C-level executive, identified from exact job titles in LinkedIn CVs.

Identification exploits the quasi-random assignment of incoming MBA students to one of eight sections of approximately 60 students each, based on alphabetical order with balance checks on gender, undergraduate institution, and ethnicity. This assignment generates exogenous variation in the share of female section peers (mean 34%, standard deviation 4 percentage points). Randomization tests following Guryan et al. (2009) and Caeyers and Fafchamps (2021) confirm the assignment is as good as random. The estimating equation is a linear-in-means model with class, year, and class-by-year fixed effects interacted with gender, plus individual and section-level controls.

The paper first documents a baseline gender gap: despite 96% of both male and female MBA graduates entering management within 15 years, women are 24% less likely than men to hold senior management positions. This gap emerges immediately after graduation, persists for at least 15 years, and is partly attributable to lower promotion rates from first-level management (43% of women in first-level management transition to senior management within five years, versus 57% of men).

The main causal finding is that a 4 percentage point (1 SD) increase in the share of female MBA section peers increases the probability of a woman holding a senior management position by 8.4% (a 3.3 percentage point increase off a 39.1% baseline), equivalent to a 26% reduction in the management gender gap. There is no corresponding effect for men. The effect emerges as early as two years post-graduation, peaks around year seven, and persists through the 15-year horizon.

The increase is concentrated in female-friendly firms, defined as those with above-median ratings on InHerSight metrics including maternity leave generosity, flexible work schedules, and professional support. Women with more female peers are significantly more likely to transition into female-friendly firms 6 to 10 years after graduation — a period coinciding with prime childbearing years — where they subsequently attain senior management roles. The effect on senior management in female-friendly firms is statistically distinguishable from the null effect in non-female-friendly firms (p-value = 0.03). The results are largest in male-dominated industries (consulting, tech, finance) where women face greater barriers to informal networks.

A survey of 283 female MBA alumnae (10% response rate) reveals three mechanisms: (i) information sharing, especially gender-specific advice about employer policies and culture; (ii) higher ambitions and self-confidence through role modeling and emotional support; and (iii) increased perceived support from male MBA peers as female section representation rises. Corroborating the information-sharing channel, women with more female peers are more likely to work at the same firms as their female section peers, particularly when those firms are female-friendly.

A counterfactual exercise shows that reallocating the existing stock of female students so that all sections have at least 34% women would yield 2 to 5 additional female senior managers per graduating class (a 2.4% to 8.4% increase), holding the total number of female students fixed.

Q: What is the baseline gender gap in senior management among MBA graduates, and how does it evolve over time? A: Female MBA graduates are 24% less likely than male graduates to hold senior management positions in the 15 years after graduation. The gap emerges immediately after the MBA and persists for at least 15 years without closing. At year 15, 74% of men hold a senior management position compared to 59% of women.

Q: How is female peer share defined and what is its distribution across sections? A: Female peer share is the proportion of female students in an individual’s assigned MBA section of approximately 60 students, excluding the individual themselves. The average section female share is 34% with a standard deviation of 4 percentage points. The distribution ranges from 19% at the 1st percentile to 45% at the 99th percentile, with the interquartile range spanning approximately 32% to 36%.

Q: What is the main causal estimate of female peers on women’s senior management probability? A: A 4 percentage point (1 SD) increase in female section peer share increases the probability of a woman holding a senior management position by 8.4% (3.3 percentage points off a 39.1% mean), averaged across the 15 post-MBA years. This translates to a 26% reduction in the management gender gap. There is no statistically significant effect on men.

Q: When does the effect of female peers emerge and how does it evolve dynamically? A: The effect on women emerges as early as two years after MBA graduation and grows over time, peaking around seven years post-graduation. The effect is persistent across the 15-year horizon studied. Estimates become less precise toward the end of the sample period as recent cohorts contribute fewer observations.

Q: How do female-friendly firms mediate the main result? A: The main effect is entirely concentrated in female-friendly firms (those with above-median InHerSight ratings). The coefficient on female peer share is positive and significant for senior management in female-friendly firms, and statistically indistinguishable from zero in non-female-friendly firms. The difference between the two coefficients is significant at p = 0.03.

Q: What is the mechanism linking female peers to female-friendly firm transitions? A: Women with more female peers are significantly more likely to be employed at female-friendly firms 6 to 10 years after graduation, a window corresponding to prime childbearing years. This suggests female peers facilitate sorting into supportive firm environments when family-work tradeoffs become most acute. Once at female-friendly firms, women attain senior management positions at higher rates.

Q: Does the increase in female senior managers reflect easier paths (smaller firms, lower pay, non-P&L roles)? A: No. The effect is significant for both small (under 500 employees) and large (over 5,000 employees) firms, with no significant effect on the firm size of employment itself. There is no consistent pattern of women being promoted in firms with higher or lower average compensation. The increase in female senior managers includes those with Profit and Loss responsibilities, indicating these are substantive management positions.

Q: In which industries is the effect largest, and what does this imply? A: The effect is concentrated in male-dominated industries (consulting, tech, finance), with no significant effect in female-dominated industries (consumer goods, healthcare). The difference between coefficients is significant at the 3% level. Entry rates into male-dominated industries are not significantly affected, suggesting the mechanism is higher promotion rates within these industries rather than differential sorting into them. The authors interpret this as evidence that female MBA networks are most valuable where women face greater barriers to informal workplace networks.

Q: What does the survey evidence reveal about mechanisms? A: Among 283 survey respondents (10% response rate), three mechanisms emerge: information sharing about gender-specific employer attributes and policies; raising ambitions and self-confidence through role modeling; and increased perceived support from male MBA peers as section female share rises. Women with more female peers are also more likely to work at the same firms as their female section peers, especially female-friendly ones, consistent with referral and information-sharing channels.

Q: Does the effect operate through greater attachment to the corporate pipeline (fewer career breaks, higher entry into management)? A: No. Female peers do not significantly affect employment rates, career break incidence, entry into first-level management positions, or self-employment rates. The results thus reflect higher promotion rates from first-level management into senior management, not changes in pipeline attachment.

Q: What do the randomization tests show about identification validity? A: Two randomization tests confirm as-good-as-random assignment. Following Guryan et al. (2009), the section-level leave-out mean female share is not significantly different from zero after controlling for the class-level leave-out mean. Following Caeyers and Fafchamps (2021), after netting out the asymptotic exclusion bias, the female share coefficient is insignificant across all specifications. A simulation test (Bietenbeck 2020) finds no statistically significant difference between the actual and simulated within-class female share distributions.

Q: What placebo tests are conducted and what do they show? A: Two placebo tests are run. First, 1,000 random reassignments of students to sections within the same class show the true estimated effect for women lies outside the distribution of placebo effects, while the null effect for men lies within it. Second, estimating the main equation for up to three years before MBA enrollment finds no consistent pre-treatment effect of female share on future female graduates, supporting the identification strategy.

Q: What is the counterfactual policy exercise and what does it imply? A: Holding the total number of female students fixed, reallocating them so that all sections contain at least 34% women would yield 2 to 5 additional female senior managers per graduating class (a 2.4% to 8.4% increase). This assumes nonlinearity in the relationship and suggests meaningful gains from rebalancing section composition without increasing overall female enrollment.

Q: How do the results compare to the Thomas (2021) finding that more male peers raise female MBA earnings? A: The authors note several differences: Thomas (2021) focuses on starting earnings while this paper studies senior management positions over 15 years; the two studies use different universities and time periods; and this paper employs gender-by-cohort fixed effects to account for time trends in female labor market outcomes. The authors suggest these design and outcome differences explain the divergent findings.

Section peers: Students assigned to the same MBA section of approximately 60 students who take core classes together and form the primary peer network; sections are assigned quasi-randomly based on alphabetical order with balance adjustments, generating exogenous variation in gender composition.

Female-friendly firms: Firms with above-median ratings on InHerSight, a crowdsourced platform where female employees rate employers on metrics including maternity leave generosity, flexible work schedules, mentorship programs, and female representation in management; defined in this paper’s own terms as firms whose cultures and policies help women balance work-family responsibilities and support career advancement.

Senior management: Positions defined as Vice President (VP), Director, Senior Vice President (SVP), or C-level executive, identified using keyword matching on exact job titles from LinkedIn CVs; distinguished from first-level management (managers and supervisors) and representing the upper rungs of the corporate management ladder.

Female share (treatment variable): The proportion of female students among an individual’s section peers, excluding the individual themselves (leave-out mean); averaged 34% with a 4 percentage point standard deviation across sections, after residualizing by graduating class.

Management gender gap: The 24 percentage point (24%) difference in the likelihood of female versus male MBA graduates holding senior management positions within 15 years of graduation; emerges immediately post-MBA and does not close over the observed horizon.

Information sharing mechanism: The channel through which female MBA peers provide gender-specific advice and information about employer policies, culture, and female-friendliness that is otherwise difficult to observe; evidenced by the co-location of women with more female peers at the same female-friendly firms as their section peers.

Exclusion bias: The systematic negative correlation between an individual’s own characteristic and her leave-out peer mean that arises mechanically when individuals cannot be their own peer under assignment without replacement; addressed via the Caeyers and Fafchamps (2021) correction in randomization tests.

The Productivity of Professions: Evidence from the Emergency Department

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

This paper studies the productivity of nurse practitioners (NPs) versus physicians performing overlapping tasks in Veterans Health Administration (VHA) emergency departments (EDs), exploiting a quasi-experiment created by the VHA’s December 2016 grant of full practice authority to NPs. The identification strategy instruments patient assignment to NPs versus physicians using quasi-random variation in the number of NPs on duty on a given ED-day, conditional on ED-by-time-category fixed effects. The sample covers 1.1 million ED visits across 44 VHA EDs from January 2017 to January 2020, seen by 1,348 physicians and 156 NPs. The instrument is validated by demonstrating balance in patient observable characteristics across values of the instrument, stability of IV estimates across 256 combinations of patient covariate controls, and absence of spillover effects from NP presence onto physician performance.

On average in the ED setting, NPs increase patient length of stay by 11 percent (approximately 18 additional minutes) and raise the cost of the ED visit by 7 percent (approximately $66 per visit). NPs raise the 30-day preventable hospitalization rate by 0.25 percentage points, a 20 percent increase relative to the mean. No statistically significant effect on 30-day mortality is detected (95 percent confidence interval: -0.34 to 0.11 percentage points). OLS estimates carry the opposite sign because NPs are assigned healthier patients in observational data; the IV design corrects for this selection.

The average NP-physician performance gap varies systematically by case complexity and severity. For the highest-complexity quartile of cases (by Elixhauser comorbidities), NPs increase ED costs by 12 percent and length of stay by 28 percent. For cases at or above the 95th percentile of severity (based on 30-day mortality by diagnosis), NPs increase ED costs by 25 percent, length of stay by 99 percent, and admissions by 26 percentage points (42 percent relative to the mean), while reducing 30-day preventable hospitalization by 3 percentage points — suggesting that NPs’ higher care intensity partially offsets worse intrinsic skill for the most severe cases. For lower-complexity cases, the cost and length-of-stay gaps are smaller, but NPs still significantly raise preventable hospitalizations.

NPs exhibit clinical decision-making patterns consistent with lower diagnostic skill: they are more likely to order consults (2.6 percentage points, or 11 percent of the mean), CT scans (1.2 percentage points, or 8.3 percent), and X-rays (2.0 percentage points, or 6.9 percent). NPs lower opioid prescriptions by 1.8 percentage points (20 percent of the mean) and raise antibiotic prescriptions by 4.0 percentage points (6.3 percent of the mean), consistent with threshold adjustment under lower diagnostic skill with asymmetric error costs. Downstream, patients treated by NPs incur similar opioid use disorder rates despite lower opioid prescribing, and higher infection-related return visit rates despite higher antibiotic prescribing.

Counterfactual analysis finds that allocating one quarter of ED patients to NPs increases net spending by $129 million per year to the VHA after accounting for NPs’ lower wages (approximately half of physicians’). However, deploying NPs exclusively to the least-complex quarter of cases reduces net spending to approximately one-fifth of this amount.

A distributional analysis deconvolving provider-specific IV estimates reveals that within-profession productivity variation substantially exceeds the average between-profession gap. The interquartile range in annual spending attributable to provider productivity within each profession is approximately $900,000, roughly three times the mean annual spending difference between the average NP and the average physician. A randomly chosen NP outperforms a randomly chosen physician in up to 38 percent of pairs. Within professions, individual provider productivity shows essentially no relationship with wages or case complexity assigned, whereas between professions, case assignment and wages are strongly sorted by professional class.

Q: What is the core research question? A: The paper asks whether NPs and physicians, who perform overlapping tasks in the ED but differ sharply in training, selectivity, and pay, differ in productivity, and how that average between-profession difference compares to productivity variation within each profession. It also asks what mechanisms drive any observed gap and how case assignment responds to provider skill differences.

Q: What is the identification strategy and why is it credible? A: The authors instrument patient assignment to NPs with the number of NPs on duty on the ED-day, conditional on ED-by-year, ED-by-month, ED-by-day-of-week, and ED-by-hour fixed effects. Credibility rests on: provider schedules being set months in advance, decoupling NP availability from arriving patient characteristics; patient characteristics being well balanced across values of the instrument conditional on fixed effects; IV estimates being stable across all 256 covariate-control combinations; and on-duty physician and NP characteristics also being balanced across the instrument.

Q: What are the main average effects of NPs on resource use? A: IV estimates show NPs increase patient length of stay by 11 percent (approximately 18 minutes) and ED cost by 7 percent (approximately $66 per visit). There is no significant average effect on inpatient admissions in the overall sample, though NPs significantly raise admissions for high-severity cases.

Q: What is the effect of NPs on patient health outcomes? A: NPs raise 30-day preventable hospitalizations by 0.25 percentage points, a 20 percent increase relative to the mean. The 95 percent confidence interval for 30-day mortality is -0.34 to 0.11 percentage points, implying no statistically significant mortality effect in the overall sample.

Q: Why do OLS and IV estimates have opposite signs? A: In observational data, NPs treat healthier patients than physicians: NP patients are younger (60.7 versus 62.5 years), have fewer Elixhauser comorbidities (3.2 versus 3.7), and have fewer prior inpatient stays (0.4 versus 0.7). This selection causes OLS estimates of NP effects to be negative. The IV corrects for this by exploiting quasi-random variation in NP availability; IV estimates are stable across all combinations of patient controls, consistent with the instrument being orthogonal to unobservable patient health.

Q: How does the NP-physician performance gap vary with case complexity and severity? A: For the highest-complexity quartile, NPs increase length of stay by 28 percent and ED costs by 12 percent without a significant preventable hospitalization effect. For cases at or above the 95th severity percentile, NPs increase length of stay by 99 percent, ED costs by 25 percent, and admissions by 26 percentage points (42 percent relative to the mean), while reducing 30-day preventable hospitalization by 3 percentage points. For lower-complexity quartiles, NPs show smaller cost and length-of-stay effects but significantly raise preventable hospitalizations, suggesting the higher care intensity at high severity compensates for lower skill.

Q: What does the heterogeneity by severity imply for optimal case assignment? A: The pattern is consistent with skill-task matching: NPs have a comparative and absolute disadvantage in complex cases, so optimal assignment directs less complex cases to NPs and fewer patients to NPs when physicians are more available. Empirically, NPs are indeed assigned healthier patients from the available pool, and are assigned a modestly smaller share when the ED is less busy.

Q: What mechanisms explain the average NP-physician gap? A: Three mechanisms are examined. First, experience: a one-standard-deviation increase in specific experience is associated with a 5.8 percent decline in the NP-physician length-of-stay gap, and general experience with a 10 percent decline; however, experience does not significantly narrow the preventable hospitalization gap. Second, information acquisition: NPs order more consults, CT scans, and X-rays, consistent with compensating for lower diagnostic skill. Third, prescription thresholds: NPs reduce opioid prescribing by 20 percent and raise antibiotic prescribing by 6.3 percent, consistent with threshold adjustment under asymmetric error costs, but downstream outcomes are not improved correspondingly.

Q: What do prescription patterns and downstream outcomes reveal about NP diagnostic skill? A: NPs prescribe fewer opioids yet patients treated by NPs obtain similar downstream opioid use disorder rates; NPs prescribe more antibiotics yet patients treated by NPs have higher rates of return visits with infections. This pattern is consistent with NPs exhibiting higher rates of both false positives and false negatives, not merely adjusted thresholds, suggesting genuinely lower diagnostic skill rather than threshold differences alone.

Q: What do counterfactual cost calculations show? A: Allocating one quarter of ED patients to NPs raises non-wage spending by $197 million per year to the VHA; after accounting for NP wages being half of physician wages (approximately $120,000 versus $240,000 per year), net cost is still $129 million per year. Restricting NP deployment to the least-complex quarter of cases reduces net spending to approximately one-fifth of this amount, illustrating that targeted case assignment substantially improves NP cost-effectiveness.

Q: How large is within-profession productivity variation relative to between-profession differences? A: The interquartile range in annual spending attributable to provider productivity within each profession is approximately $900,000, roughly three times the mean annual spending difference between the average NP and the average physician. A randomly chosen NP outperforms a randomly chosen physician in up to 38 percent of random pairs. The authors conclude that, despite stark differences in training and selection between professions, within-profession variation dominates.

Q: Is individual provider productivity reflected in wages or case assignment within professions? A: Within each profession, provider productivity shows essentially no relationship with wages or with the complexity of assigned cases. This contrasts sharply with between-profession patterns, where professional class strongly predicts both wages (NPs earn approximately $120,000 per year versus $240,000 for physicians) and assigned case complexity. The authors interpret this as evidence of informational and organizational frictions in recognizing individual productivity within professional classes, and note that professional class is a far stronger predictor of pay and case assignment than is individual productivity.

Q: How do complier characteristics relate to the broader patient population? A: Compliers — cases whose provider type is determined by the instrument — are healthier than the average case: younger, with fewer comorbidities, fewer prior inpatient stays, and lower predicted mortality. Never-takers are riskier than the average case. There are no always-takers since patients cannot be assigned to NPs on days when no NPs are on duty.

Q: How does this paper relate to the literature on NP scope-of-practice laws? A: The scope-of-practice literature estimates general-equilibrium effects of allowing NPs greater autonomy, including labor reallocation between professions. This paper instead estimates the partial-equilibrium causal effect of assigning a patient to an NP versus a physician, holding the broader labor market fixed. The two literatures are complementary: the heterogeneity findings here suggest that scope-of-practice expansions may be more beneficial in lower-complexity primary care settings where the NP-physician performance gap is smaller.

Q: What are the policy implications of the findings? A: Three implications are highlighted. First, the efficiency of using NPs depends critically on case assignment: deploying NPs on the least-complex cases reduces net costs to approximately one-fifth of indiscriminate deployment. Second, the substantial overlap between NP and physician productivity distributions provides support for NP use in less complex settings even within the ED context. Third, within-profession productivity variation far exceeding between-profession differences suggests that individual-level productivity assessment, rather than professional class, may be a more accurate guide to case assignment and compensation.

Quasi-experimental variation in NP availability: The identification strategy exploits day-to-day variation in the number of NPs scheduled to work in a given VHA ED, conditional on ED-by-time-category fixed effects, as an instrument for whether a patient is assigned to an NP versus a physician. Schedules are set months in advance, rendering the NP count orthogonal to arriving patient characteristics conditional on those fixed effects.

30-day preventable hospitalization: A standardized quality-of-care outcome defined by the Agency for Healthcare Research and Quality, measuring hospitalizations occurring within 30 days of ED discharge that are classified as preventable given adequate prior outpatient management. Used by the paper as the primary downstream health outcome beyond the ED visit itself.

Elixhauser comorbidities: A set of 31 binary indicators for chronic conditions (e.g., cancer, diabetes) based on medical histories in the prior 365 days, used in this paper to measure and stratify case complexity into quartiles for heterogeneity analysis.

Productivity distributions within professions: Provider-specific productivity estimates derived from a just-identified IV model that instruments assignment to individual providers by indicators for on-duty providers, then deconvolved into underlying distributions using the Efron (2016) and Kline-Rose-Walters (2022) method. These distributions characterize the spread of productivity within each professional class, separate from measurement error.

Prescription threshold adjustment: The mechanism, formalized in Chan, Gentzkow, and Yu (2022), by which providers with lower diagnostic skill optimally adjust treatment thresholds in response to asymmetric costs of false-positive versus false-negative errors. In this paper’s application, NPs lower the opioid prescription rate (where false positives carry higher costs: addiction and overdose) and raise the antibiotic prescription rate (where false negatives carry higher costs: untreated infection), but downstream outcomes do not improve correspondingly.

Skill-task matching: The organizational economics principle (Acemoglu and Autor 2011) that efficiency requires assigning more complex tasks to higher-skilled workers. The paper documents that between professions, case assignment broadly follows this principle (NPs receive less complex patients on average), but within professions, essentially no matching between individual provider productivity and case complexity is observed.

Full practice authority (VHA, December 2016): The VHA policy that allowed NPs to treat patients independently without physician supervision at VHA facilities, superseding state-level restrictions. This policy change defines the start of the paper’s sample period and establishes the institutional context in which the quasi-experiment occurs, as it removed the requirement for physician oversight that previously constrained NP independence.