Examining Earnings of U.S. Physicians Using Tax Return Information

Disclaimer: Any opinions and conclusions expressed herein are those of the presenter and do not necessarily reflect the views of the U.S. Census Bureau. All results were approved for release by the Census Bureau’s Disclosure Review Board, authorization numbers CBDRB-FY19-241, CBDRB-FY19-248, CBDRB-FY19-277, CBDRB-FY19-327, CBDRB-FY19-278, CBDRB-FY20-078, CBDRB-FY2020-CES005-015, CBDRB-FY20-177, CBDRB-FY2020-CES005-019, CBDRB-FY2020-CES005-024, CBDRB-FY2020-CES010-016, CBDRB-FY2020-CES005-035, CBDRB-FY2020-CES005-037, CBDRB-FY2020-CES010-021, CBDRB-FY20-349.

Federal Committee on Statistical Methodology: 2021 Research and Policy Conference

November 3, 2021

Presenter: Victoria Udalova, PhD, Senior Economist and Manager of the Enhancing Health Data (EHealth) program at the U.S. Census Bureau

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Acknowledgements

Enhancing Health Data (EHealth) team at the U.S. Census Bureau

Results are from my paper titled “Who Values Human Capitalists’ Human Capital? The Earnings and Labor Supply of U.S. Physicians” jointly with Joshua Gottlieb (University of Chicago), Maria Polyakova (Stanford University), Kevin Rinz (U.S. Census Bureau), and Hugh Shiplett (University of British Columbia) (https://www.census.gov/content/census/en/library/working-papers/2020/adrm/CES-WP-20-23.html)

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Enhancing Health Data (EHealth) is a research program that identifies opportunities to enhance health records with existing survey and administrative records data available at the Census Bureau

Our team links multiple data sources to create new data products and conduct research aimed at improving measurement of patient and provider characteristics

Enhancing Health Data at the Census Bureau

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Goal is to develop a new framework for linking multiple existing data sources on healthcare providers and their places of employment

Better understand the structure of the U.S. healthcare system and where healthcare providers are employed and located

Produce new data products on characteristics of healthcare providers and businesses 

Create gold-standard survey frame of healthcare providers and businesses

Initial focus is on physicians and their employers

Attach additional physician characteristics from Census and IRS data: race/ethnicity, employment status, earnings

Identify physicians’ employers from the IRS tax filings data

Attach additional employer characteristics from Business Register and Economic Census: industry, employer size/age, payroll, geography, legal form of organization, ownership, revenue/expenses

Developing Provider-Business Linkage Framework

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Goal is to develop a new framework for linking multiple existing data sources on healthcare providers and their places of employment

Better understand the structure of the U.S. healthcare system and where healthcare providers are employed and located

Produce new data products on characteristics of healthcare providers and businesses 

Create gold-standard survey frame of healthcare providers and businesses

Initial focus is on physicians and their employers

Attach additional physician characteristics from Census and IRS data: race/ethnicity, employment status, earnings

Identify physicians’ employers from the IRS tax filings data

Attach additional employer characteristics from Business Register and Economic Census: industry, employer size/age, payroll, geography, legal form of organization, ownership, revenue/expenses

Developing Provider-Business Linkage Framework

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Restricted NPPES Data at the Census Bureau

National Plan and Provider Enumeration System (NPPES) - registry of all healthcare providers and organizations with a National Provider Identifier (NPI)

Under agreement with the Centers for Medicare & Medicaid Services (CMS) (PI: Udalova)

Personally Identifiable Information (PII) is used to assign internal anonymized Census Bureau linkage keys, Protected Identification Keys (PIKs)

Cumulative list of all providers with an NPI who were active on April 28, 2018 (the date of the current NPPES pull)

Type 1 providers are individuals (physicians, nurse practitioners, etc.)

Type 2 providers are organizations (hospitals, clinics, etc.)

PIKs/NPIs allow for linkages across person-level data sources

EINs allow for linkages across business-level data sources

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This Project’s Objectives

Describe physician earnings and compare to survey data

Existing evidence relies on survey data with many measurement challenges 

Determine what influences physician earnings

Examine how physician earnings affect labor supply

Observe direct labor supply measures

Why you should care:

Survey measurement of top-earning occupation (issues of top-coding and complicated income structures)

Growing income inequality, physicians common in top 1%

Spending on physician services is 20% of U.S. healthcare spending

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This Project’s Objectives

Describe physician earnings and compare to survey data

Existing evidence relies on survey data with many measurement challenges 

Determine what influences physician earnings

Examine how physician earnings affect labor supply

Observe direct labor supply measures

Why you should care:

Survey measurement of top-earning occupation (issues of top-coding and complicated income structures)

Growing income inequality, physicians common in top 1%

Spending on physician services is 20% of U.S. healthcare spending

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Data

NPPES data to identify physicians

Merge NPPES with universe of individual federal income tax returns (Forms 1040/1099-SSA/W-2)

W-2s from 2005–2017, aggregated across EINs

Location – state and county of residence

Panel income data

Merge with Census Numident data to add date of birth, date of death, and sex

Merge with Census Bureau’s American Community Survey (ACS) responses

Self-reported occupation (physicians) and income

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How are Physicians Paid in the U.S.?

Complicated; well studied from the payer perspective (fee for service, capitation, etc.)

Not a lot of work on physicians’ personal earnings sources

Physician incomes come through a diverse and changing mechanisms

best studied using tax data

3 common payment models:

Model 1 – employed physicians with only W-2 income (common in large organizations such as academic medical centers)

Model 2 – only sole proprietorship income on Form 1040’s Schedule C

Model 3 – hybrid, includes pass-through entity, usually S-corporation or partnership (W-2, Form 1040’s Schedules E and K-1)

Important to capture both wages and business income 

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Income Measures from Tax Data

Wages from IRS Form W-2

Business income = Adjusted Gross Income (AGI) minus W-2 wages, taxable interest, dividends, Social Security

Total professional earnings = wages + business income

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Establishing Basic Facts About Physicians’ Earnings

Our baseline sample in 2017 = 863,000 physicians

Mean earnings in 2017 = $344K/year

Median earnings in 2017 = $255K/year

Physicians’ personal incomes account for 8.5% of U.S. total health spending

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High Earnings and Steep Life-Cycle Profile

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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One Quarter of Physician Households are in Top 1%

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Location of Physician Households in the U.S. Income Distribution

Half of physician households are in the top 2% of the U.S. income distribution

Significant variation by specialty (50% of surgeons vs. 14% of primary care physicians are in the top 1%)

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Top Earners Among Physicians

Average earnings of the top 1% of physicians is $3.9 million annually

12 times the average annual earnings across all physicians

Business income is crucial for the top 1% of earners

91% of these physicians report large business income (compared to 32% among all physicians)

78% of these physicians’ income is business income (compared to 32% among all physicians)

Top earners are more likely to be in high-paying specialties

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Importance of Business Income:

Only 68% of Income Reported on W-2

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

Substantial Heterogeneity Across Physicians

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Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

Substantial Heterogeneity Across Physicians

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Across specialties (primary care physicians $243K, surgeons $500K+)

Time

Space

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Peak Age (45-50) Annual Earnings by Specialty

Earnings across specialties can be explained by training length and work hours

But there are some exceptions: for example, radiologists earn nearly twice as much as neurologists despite working fewer hours

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Average Annual Income for Top and Bottom Earning Specialties

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Variation in Mean Earnings Across States

Highest average physician earnings are not in states with the highest incomes for other workers, highest costs of living, or productivity

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Growth in Mean Earnings Over Time

Source: Authors’ calculations based on NPPES (2018), IRS tax returns (2005-2017), and Census Numident (2018).

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Administrative vs. Survey data (Same Physicians)

Source: Authors’ calculations based on NPPES (2018), ACS (2001-2017), IRS tax returns (2005-2017), and Census Numident (2018).

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Mean earnings for the same physicians: $362K (tax data) vs. $237K (ACS)

During physicians’ most productive years, age 40-55, ACS underestimates annual income by about $130,000-$150,000

Represents about a third of physicians’ average annual earnings

The difference between the two measures is driven by fewer physicians reporting any business income in the survey data

Average wages are similar between tax and survey data

Self-employment income is substantially lower in ACS

Only 20% of physicians report positive self-employment income in ACS, while according to tax data 76% have positive business income

Administrative vs. Survey Data (Same Physicians)

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Conclusion

Underreporting of income in survey data, especially business income of top earners

Tax records allow for new, more granular estimates

Tax records along with Census and CMS administrative records can be used to describe the complexities in organizational and employment arrangement of healthcare providers

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Thank you!

For more information about this project and to access the research paper:

https://www.census.gov/programs-surveys/ehealth/information/examining-earnings.html

For more information about other EHealth projects: https://www.census.gov/programs-surveys/ehealth.html

Victoria Udalova, PhD

Senior Economist

Manager of the Enhancing Health Data (EHealth) program

U.S. Census Bureau

victoria.m.udalova@census.gov

301-763-7101

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