The Earnings and Labor Supply of U.S. Physicians

Overview

Research Question. What do U.S. physicians earn, how is that earnings variation structured across geography and specialty, and how much does government healthcare payment policy shape those earnings and — through them — physicians’ labor supply and long-run talent allocation?

Data. The paper builds a novel administrative panel by merging the universe of U.S. federal individual income tax returns (2005–2017) with: the National Plan and Provider Enumeration System (NPPES) physician registry; Medicare billing records with procedure-level Relative Value Unit (RVU) rates (2012–2017); restricted-use American Community Survey responses; Social Security Administration demographic records; and medical school ranking and graduation data. The main sample covers 11.6 million physician-year observations for 965,000 unique physicians aged 20–70.

Earnings Facts. In 2017, average physician total individual income was $350,000 (median $265,000); the distribution is right-skewed — the top 1% of age-40–55 physicians averages $4.0 million. Physicians in aggregate earned $297 billion in pre-tax dollars, equaling 8.6% of total U.S. healthcare spending. The age-earnings profile is steep: earnings are approximately $60,000 during residency, rise to roughly $185,000 by the early thirties, and peak near $425,000 at age 50. Business income — systematically underreported in survey data (ACS estimates are approximately $140,000 lower than tax data during peak career years, almost entirely due to non-reporting of business income) — accounts for nearly one-quarter of earnings at age 50. Earnings differ sharply across specialties: primary care physicians average $201,200 (ages 40–55), about half the sample mean, while surgeons earn roughly twice as much.

Geographic Pattern. Contrary to the pattern for lawyers and workers broadly, physician earnings are not highest on the coasts. A movers-based event study (physicians who changed commuting zones once during 2005–2017) finds that roughly 70% of the cross-location income difference is driven by place rather than worker composition. A two-way fixed-effects variance decomposition reveals pronounced negative physician-location sorting: high-earning physicians tend to locate in lower-income commuting zones, while lower-earning physicians locate in higher-income areas — the opposite of the pattern for lawyers. Medicare’s relatively weak adjustment of reimbursement rates for local costs (the empirical elasticity of the Geographic Adjustment Factor to median household income is 0.09, versus 0.33 for a broader local price index) can, by the authors’ estimates, account for approximately one-third of this unusual geographic earnings pattern.

Government Influence — Medicare Price Changes. Using procedure-specific RVU changes as a simulated instrument for each physician’s Medicare price exposure, the authors find that a 10% increase in the Medicare price instrument leads to a 2.4% increase in professional earnings of physicians aged 40–55. The behavioral supply response is substantial: physicians bill 4.4% more RVUs (supply elasticity of 0.4 after netting out the mechanical component), of which 3.9% reflects more unique procedures and the rest a shift toward higher-paid procedures. Nearly all of the procedure-level supply increase (3.4 out of 3.8 percentage points) comes from treating additional patients rather than more frequent treatment of existing patients. Converting to pass-through: physicians retain $62 of each $100 in additional Medicare spending directly, or approximately $25 of each $100 of any insurance spending once Medicare’s documented spillover into private insurance rates is accounted for. For physicians aged 56–70, a 10% increase in earnings driven by reimbursement changes reduces retirement probability by 0.5 percentage points in that year.

Government Influence — ACA Insurance Expansion. Using county-level variation in pre-ACA uninsurance rates (as of 2013) as a source of differential exposure to the ACA’s Medicaid expansions and Marketplace subsidies (in 24 states expanding Medicaid in 2014 or early 2015), the authors estimate that a 10 percentage point higher baseline uninsurance rate led to 3.9% higher physician earnings four years post-expansion. Scaling by the first stage (a 10 p.p. higher uninsurance rate translating to 4.96 p.p. higher insurance coverage post-expansion), the implied elasticity of physician earnings to the insurance rate is 0.41. The ACA expansion also reduced retirement probability — a 10 p.p. higher insurance coverage rate leads to a 1 p.p. decline in retirement probability — consistent with a medium-run retirement-to-income elasticity of approximately −1.1. In aggregate, 6% of the $110 billion in annual ACA insurance expansion spending accrued to physicians personally, slightly below their 8.6% baseline share of healthcare spending.

Talent Allocation. Specialty choice is sticky and entry-restricted. The authors estimate a discrete-choice model of specialty choice using graduates of top-5 medical schools — physicians with effectively unconstrained specialty access — and an aggregate model using USMLE Step 1 score buckets as ability proxies. At the top of the ability distribution, higher specialty earnings strongly attract physicians: increasing primary care physicians’ hourly income from $98 to $168 per hour (the level of medicine subspecialists) would raise the share of top-5 medical school graduates choosing primary care by approximately 20 percentage points (nearly doubling their representation in primary care). Moving down the USMLE score distribution, the earnings coefficient falls monotonically and turns negative for the lowest score groups — consistent with the model’s prediction that entry restrictions cause higher-paying specialties to displace lower-ability applicants as earnings rise, rather than simply attracting more entrants. A more modest counterfactual — raising internal medicine earnings to dermatology levels — raises the average USMLE score in internal medicine by 10 points (from 230.2 to 239.6).

Scope Conditions. The earnings estimates are for the period 2005–2017. Pass-through estimates use a short-run price instrument; long-run pass-through may differ depending on private market spillovers and entry. The ACA analysis is restricted to 24 early-expanding states. The specialty-choice model is estimated on medical graduates entering the residency match; the extensive margin of entering medicine itself is not modeled. Health outcome effects of changing physician ability distributions are not estimated.

Q&A

Q1: What is the level and composition of physician earnings in the tax data, and how do they compare to survey-based estimates?

In 2017, average physician total individual income was $350,000 and median was $265,000; the top 1% of age-40–55 physicians earned $4.0 million on average, more than twice the average of the top 5%. Business income constitutes nearly one-quarter of earnings at age 50 and is concentrated among top earners: 80% of physicians in the top 1% have business income exceeding $25,000, versus 35% overall. ACS survey data for the same physicians underestimate earnings by approximately $140,000 (roughly one-third of the administrative mean) during peak career years, driven entirely by non-reporting of business income on the extensive margin.

Q2: What share of total U.S. healthcare spending do physician earnings represent, and what does this imply for policy?

Physicians in aggregate earned $297 billion pre-tax in 2017, equaling 8.6% of total U.S. healthcare spending (approximately $913 of the average American’s $10,611 annual healthcare expenditure). After applying a 30% income tax rate, after-tax physician earnings equal approximately 6% of total healthcare spending, or roughly 1% of GDP. The authors note this provides an upper bound on the magnitude of savings available from policies aimed at reducing physician incomes as a strategy for lowering overall healthcare spending.

Q3: How does the age-earnings profile of physicians evolve, and what drives growth during peak years?

Physician earnings average approximately $60,000 during residency, rise to roughly $185,000 by the early thirties, and peak near $425,000 at age 50, before declining gradually to approximately $270,000 in the late 60s. Growth during peak earning years (ages 40–55) is driven almost entirely by business income: average wages are approximately flat at $285,000 across this age range, while business income and the probability of filing Schedule C rise steadily.

Q4: How large and unusual is the geographic pattern of physician earnings, and what is the causal role of location?

Physician earnings are highest in lower-income states (not on the coasts), unlike lawyers and the broader workforce. A movers event study finds that approximately 70% of the cross-commuting-zone income difference is attributable to location rather than worker characteristics; within specialty the estimate rises to approximately 85%. A two-way fixed-effects variance decomposition (with limited-mobility-bias corrections following Andrews et al. 2008 and Kline et al. 2020) reveals pronounced negative physician-location sorting, with the corrected covariance between individual and location effects being 0.6–0.8 times the variance of location effects in magnitude but opposite in sign — a pattern that reverses to positive sorting when the same methods are applied to lawyers.

Q5: What instrument is used to identify the causal effect of Medicare price changes on physician earnings, and why is it valid?

The authors construct a physician-year “Medicare price instrument” by fixing each physician’s service mix at its 2012–2017 average and then multiplying those fixed quantities by annually-updated RVU rates, summing over services. Because the fixed quantity weights exclude behavioral responses, and because national RVU changes from CMS periodic reviews affect physicians differentially according to their pre-determined service mix, variation across physicians and over time is plausibly exogenous to individual physicians’ income shocks. Year-by-specialty fixed effects absorb common specialty-level price trends.

Q6: What are the magnitudes of the earnings and labor supply responses to Medicare price changes?

A 10% increase in the Medicare price instrument raises earnings of 40–55 year-old physicians by 2.4% (reduced-form), with a 2SLS elasticity of income to billed RVUs of 0.17. The total-RVU billing coefficient of 1.437 implies a supply elasticity of 0.437 (subtracting 1 for the mechanical component). At the procedure level, a 10% price increase for a specific code leads to 3.8% more billings for that code, of which 3.4 percentage points reflects treating additional patients. For physicians aged 56–70, a 10% earnings increase reduces that year’s retirement probability by 0.5 percentage points.

Q7: How does the ACA insurance expansion affect physician earnings and retirement, and what is the implied pass-through?

Counties with a 10 percentage point higher pre-ACA uninsurance rate saw 3.9% higher physician earnings by 2017 (four years post-expansion). Scaled by the first stage (4.96 p.p. higher coverage), the elasticity of physician earnings to insurance coverage is 0.41. A 10 p.p. higher insurance coverage rate leads to a 1 p.p. lower retirement probability post-expansion (medium-run elasticity of retirement to income of approximately −1.1). In aggregate, 6% of $110 billion in annual ACA expansion spending — roughly $7.1 billion, or about $8,400 per physician — accrued to physicians.

Q8: How does the earnings-specialty choice relationship vary across the physician ability distribution?

In the individual-level discrete-choice model estimated on top-5 medical school graduates (likely unconstrained in specialty choice), the coefficient on hourly earnings is 0.014. In the aggregate score-group model, the implied earnings coefficient is 0.016 for USMLE scores above 260 and declines monotonically to −0.008 for scores at or below 190. This negative coefficient for low scorers is consistent with the theoretical prediction that higher earnings attract high-ability physicians, leaving fewer slots for lower-ability applicants due to binding entry restrictions — not a reversal of preferences.

Q9: What are the quantitative implications for specialty choice if primary care incomes were raised to subspecialty levels?

Raising primary care hourly income from $98 to $168 (the level of medicine subspecialists) would increase the share of top-5 medical school graduates choosing primary care by approximately 20 percentage points (about 48% would enter primary care, versus the current share), nearly doubling their representation. Nearly half of these reallocations would come from procedural specialties. An analogous exercise raising internal medicine earnings to dermatology levels shifts the average USMLE score in internal medicine from 230.2 to 239.6 — a 10-point increase — as higher-scoring applicants displace lower-scoring ones within a fixed slot constraint.

Q10: What is the pass-through from Medicare reimbursements to physician earnings, and how does it compare to rent-sharing elsewhere?

Direct estimates imply physicians retain $62 of each $100 in additional Medicare spending. Accounting for Medicare’s documented spillover into private insurance rates (following Clemens and Gottlieb 2017), the pass-through drops to $25 per $100 of total insurance spending. The authors note this is substantially higher than the modest rent-sharing found for average workers in response to firm-level shocks (Card et al. 2018), but comparable to rent-sharing with high-skilled workers benefiting from patent rents (Kline et al. 2019).

Q11: Can Medicare’s geographic pricing policy explain the unusual geographic earnings pattern for physicians?

The elasticity of Medicare’s Geographic Adjustment Factor (GAF) to commuting zone median household income is 0.09, compared to 0.33 for a broader local price index. Using the authors’ short-run estimate that a 10% increase in Medicare prices raises earnings by 2.4%, a counterfactual simulation shows that if the GAF-to-income elasticity rose to 0.33 (aligning Medicare rates with the general cost-of-living gradient), the geographic physician earnings pattern would more closely resemble that of lawyers. The authors estimate that the gap in Medicare’s local cost adjustment explains approximately one-third of the unusual physician earnings geography, conditional on the short-run pass-through estimate.

Q12: How does the theoretical model of specialty choice and entry restrictions guide the empirical predictions?

The model features a unit mass of physicians with heterogeneous ability (Pareto-distributed) and idiosyncratic specialty preferences (exponentially distributed). Physicians choose whether to specialize in period 1; government sets reimbursement rates in period 2; physicians choose labor supply in period 3. With a fixed number of residency slots, higher specialty earnings raise the ability cutoff for entry (rationing by ability). This generates a key nonmonotonic empirical prediction: higher-ability physicians respond positively to earnings increases (choosing a specialty more frequently), while lower-ability physicians respond negatively (displaced by the shift upward in the ability cutoff). The model also implies that demand shocks are not moderated by contemporaneous entry, so incumbents capture the full rent — motivating the estimated pass-through.

Key Concepts

Medicare Price Instrument (Simulated RVU Instrument). A physician-year measure of Medicare payment exposure constructed by holding each physician’s service mix fixed at its 2012–2017 average and multiplying those fixed quantities by time-varying national RVU rates, then summing across services. This purges the instrument of behavioral responses, creating exogenous cross-physician variation in price exposure arising from the interaction of fixed service mix with national RVU policy changes.

Relative Value Unit (RVU). The unit by which Medicare defines and reimburses each physician service in the Physician Fee Schedule. RVUs are intended to reflect the time, effort, and resources required to provide each service, but are subject to periodic review by CMS’s RVU Update Committee (RUC) and influenced by political factors. Changes in RVUs translate directly into changes in Medicare reimbursement rates for affected services.

Pass-Through (Reimbursement to Earnings). The share of an additional dollar of Medicare (or insurance) spending that accrues to physicians personally as earnings, after accounting for practice costs, intermediaries, and behavioral responses. The paper estimates $62 per $100 of direct Medicare spending or $25 per $100 of total insurance spending (the latter accounting for Medicare’s spillover into private rates).

Negative Physician-Location Sorting. The empirical finding — robust to limited-mobility-bias corrections — that higher-ability (higher-earning) physicians disproportionately locate in lower-income commuting zones, while lower-earning physicians concentrate in higher-income areas. This is the opposite of the pattern for lawyers and for worker-firm matching in the broader labor literature. The paper attributes part of this pattern to Medicare’s incomplete geographic adjustment of reimbursement rates.

Ability Cutoff (am) in Residency Matching. In the paper’s theoretical model, the minimum ability level required to gain entry into a restricted-entry specialty. Because the number of residency slots is fixed, the cutoff rises when a specialty’s relative earnings increase (attracting more high-ability applicants), displacing lower-ability physicians who would otherwise have entered. This makes the earnings-specialty relationship nonmonotonic across the ability distribution.

Business Income (Pass-Through Entity Income). Income from physician-owned practices organized as sole proprietorships, S-corporations, or partnerships, reported on Schedule C or through pass-through entities rather than on Form W-2. In the tax data, business income accounts for nearly one-quarter of physician earnings at career peak and is the main source of earnings for top physicians, but is systematically underreported in survey data (ACS), leading to a roughly one-third underestimate of total earnings during peak years.

Geographic Adjustment Factor (GAF). A Medicare policy parameter that multiplies the national RVU rate to adjust physician reimbursements for local input costs (specifically physicians’ work, practice expenses, and malpractice). The paper documents that the GAF’s elasticity to local median household income is 0.09 — far below the 0.33 elasticity of the general local price index — constituting an effective subsidy to rural and lower-income markets relative to higher-income areas.