We study the importance of bunching in the context of tip-income reporting by workers at full-service, single-unit restaurants in the United States. Using tax reports at both the individual and the employer levels, we show that reported tip income varies with minimum-wage laws that provide an incentive for tipped workers to report some, but not necessarily all, of their tips. As a result, reported tips bunch at the minimum required threshold. We quantify missing tips due to bunching at nearly $63 million per year in 2018 dollars, on average over the period 2005-2018. Bunching is stronger for jobs at small employers and in the earlier part of the time series and declined monotonically from 2010 to 2018. Using restaurant-level revenue data, we also estimate the total value of unreported tips assuming an average tip rate of 12%. We find that tips are missing throughout the distribution. All told, missing tips exceed $4 billion per year, implying that bunching explains only 1.5% of all missing tips.
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Tip of the Iceberg: Tip Reporting at U.S. Restaurants, 2005-2018
November 2024
Working Paper Number:
CES-24-68
Tipping is a significant form of compensation for many restaurant jobs, but it is poorly measured and therefore not well understood. We combine several large administrative and survey datasets and document patterns in tip reporting that are consistent with systematic under-reporting of tip income. Our analysis indicates that although the vast majority of tipped workers do report earning some tips, the dollar value of tips is under-reported and is sensitive to reporting incentives. In total, we estimate that about eight billion in tips paid at full-service, single-location, restaurants were not captured in tax data annually over the period 2005-2018. Due to changes in payment methods and reporting incentives, tip reporting has increased over time. Our findings have implications for downstream measures dependent on accurate measures of compensation including poverty measurement among tipped restaurant workers.
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AI Exposure and Adoption Among U.S. Firms
September 2026
Authors:
John Haltiwanger,
Lucia Foster,
Martha Stinson,
Emin Dinlersoz,
Cheryl Grim,
Zoltan Wolf,
Sabrina Wulff Pabilonia,
Matthew Dey,
Sean Wang,
Aditya Pande,
Peter B. Meyer
Working Paper Number:
CES-26-61
Measures of exposure to artificial intelligence (AI) are widely used to study where AI is likely to affect firm and worker outcomes, yet limited evidence exists on how closely exposure relates to realized AI adoption at the firm level. We examine this relationship by linking 14 firm-level exposure measures, constructed from occupational exposure estimates in the literature and occupational employment shares from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, to direct measures of firm AI adoption from the Census Bureau's Business Trends and Outlook Survey. Exposure is positively and statistically significantly associated with adoption, but explains only a modest share of its variation. The strength of the relationship varies considerably across standardized exposure measures: a one-standard-deviation increase in firm-level exposure is associated with a 4'11 percentage point higher firm adoption probability, falling to 1'8 percentage points after controlling for year and sub-sector fixed effects. The exposure'adoption relationship is also heterogeneous across firm-size classes and sectors. Exposure is thus an informative but incomplete signal of adoption. More broadly, the results have implications for the interpretation of exposure-based measures in studies of the economic effects of AI.
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Measuring the Effects of the Tipped Minimum Wage Using W-2 Data
June 2016
Working Paper Number:
carra-2016-03
While an extensive literature exists on the effects of federal and state minimum wages, the minimum wage received by tipped workers has received less attention. Researchers have found it difficult to capture the hourly wages of tipped workers and thus assess the economic effects of the tipped minimum wage. In this paper, I present a new measure of hourly wages for tipped servers (wait staff and bartenders) using linked W-2 and survey data. I estimate the effect of tipped minimum wages on the wages and hourly tips of servers, as well as server employment and hours worked. I find that higher mandatory tipped minimum wages increase that portion of wages paid by employers, but decrease tip income by a similar percentage. I also find evidence that employment increases over lower values of the tipped minimum wage and then decreases at higher values. These results are consistent with a monopsony model of server employment. The wide variance of tipped minimum wages compared to non-tipped minimums provide insight into monopsony effects that may not be discernible over a smaller range of minimum wage values.
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Labor Market Effects of the Affordable Care Act: Evidence from a Tax Notch
July 2017
Working Paper Number:
carra-2017-07
States that declined to raise their Medicaid income eligibility cutoffs to 138 percent of the federal poverty level (FPL) under the Affordable Care Act (ACA) created a "coverage gap'' between their existing, often much lower Medicaid eligibility cutoffs and the FPL, the lowest level of income at which the ACA provides refundable, advanceable "premium tax credits'' to subsidize the purchase of private insurance. Lacking access to any form of subsidized health insurance, residents of those states with income in that range face a strong incentive, in the form of a large, discrete increase in post-tax income (i.e. an upward notch) at the FPL, to increase their earnings and obtain the premium tax credit. We investigate the extent to which they respond to that incentive. Using the universe of tax returns, we document excess mass, or bunching, in the income distribution surrounding this notch. Consistent with Saez (2010), we find that bunching occurs only among filers with self-employment income. Specifically, filers without children and married filers with three or fewer children exhibit significant bunching. Analysis of tax data linked to labor supply measures from the American Community Survey, however, suggests that this bunching likely reflects a change in reported income rather than a change in true labor supply. We find no evidence that wage and salary workers adjust their labor supply in response to increased availability of directly purchased health insurance.
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EARNINGS ADJUSTMENT FRICTIONS: EVIDENCE FROM SOCIAL SECURITY EARNINGS TEST
September 2013
Working Paper Number:
CES-13-50
We study frictions in adjusting earnings to changes in the Social Security Annual Earnings Test (AET) using a panel of Social Security Administration microdata on one percent of the U.S. population from 1961 to 2006. Individuals continue to "bunch" at the convex kink the AET creates even when they are no longer subject to the AET, consistent with the existence of earnings adjustment frictions in the U.S. We develop a novel framework for estimating an earnings elasticity and an adjustment cost using information on the amount of bunching at kinks before and after policy changes in earnings incentives around the kinks. We apply this method in settings in which individuals face changes in the AET bene.t reduction rate, and we estimate in a baseline case that the earnings elasticity with respect to the implicit net-of-tax share is 0.23, and the .xed cost of adjustment is $152.08.
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The Distributional Effects of Minimum Wages: Evidence from Linked Survey and Administrative Data
March 2018
Working Paper Number:
carra-2018-02
States and localities are increasingly experimenting with higher minimum wages in response to rising income inequality and stagnant economic mobility, but commonly used public datasets offer limited opportunities to evaluate the extent to which such changes affect earnings growth. We use administrative earnings data from the Social Security Administration linked to the Current Population Survey to overcome important limitations of public data and estimate effects of the minimum wage on growth incidence curves and income mobility profiles, providing insight into how cross-sectional effects of the minimum wage on earnings persist over time. Under both approaches, we find that raising the minimum wage increases earnings growth at the bottom of the distribution, and those effects persist and indeed grow in magnitude over several years. This finding is robust to a variety of specifications, including alternatives commonly used in the literature on employment effects of the minimum wage. Instrumental variables and subsample analyses indicate that geographic mobility likely contributes to the effects we identify. Extrapolating from our estimates suggests that a minimum wage increase comparable in magnitude to the increase experienced in Seattle between 2013 and 2016 would have blunted some, but not nearly all, of the worst income losses suffered at the bottom of the income distribution during the Great Recession.
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Resilience After Climate Shocks: Evidence From SBA Disaster Loans
September 2026
Working Paper Number:
CES-26-58
We show that natural disasters generate persistent declines in U.S. county employment and net job creation. We then examine whether emergency credit can mitigate these effects. Linking the universe of SBA disaster-loan applications to Census Bureau records, we exploit a credit-score screening rule introduced in 2016 and implement a fuzzy regression discontinuity design around the score cutoff. Firms just above the threshold are substantially more likely to receive government-backed loans, and approval increases employment relative to declined applicants, with effects persisting over subsequent years. The adjustment operates primarily through lower job destruction, while reduced firm exit and stronger job creation also contribute. Overall, SBA lending preserved an estimated 9.8% of the jobs that would otherwise have been lost in disaster-affected counties.
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Self-Employment Income Reporting on Surveys
April 2023
Working Paper Number:
CES-23-19
We examine the relation between administrative income data and survey reports for self-employed and wage-earning respondents from 2000 - 2015. The self-employed report 40 percent more wages and self-employment income in the survey than in tax administrative records; this estimate nets out differences between these two sources that are also shared by wage-earners. We provide evidence that differential reporting incentives are an important explanation of the larger self-employed gap by exploiting a well-known artifact ' self-employed respondents exhibit substantial bunching at the
first EITC kink in their administrative records. We do not observe the same behavior in their survey responses even after accounting for survey measurement concerns.
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Graduating Into Disruption: Labor Market Outcomes for AI-Exposed College Majors
September 2026
Working Paper Number:
CES-26-56
How are new college graduates affected by the rise of artificial intelligence, and what can this tell us about the mechanisms behind AI's overall labor market effects? We use administrative records on college graduates to observe how economic outcomes among college majors with differing levels of labor market AI exposure have evolved since large language models became available. We find that post-graduation employment, earnings, and job switching patterns among the most AI-exposed college majors began to diverge immediately following the introduction of ChatGPT in late 2022. In regression-adjusted estimates, the most AI-exposed decile of college majors saw their likelihood of initial employment decline by five percentage points, while full-quarter initial earnings declined by thirteen percent. This earnings decline is comparable in magnitude to the earnings losses associated with graduating into a large recession. Roughly half of the decline in earnings is attributable to lower earnings within the industry sectors that employ these graduates, with the remainder resulting from a shift in the industry mix into lower-wage sectors such as restaurants and retail. The effects attenuate as graduates move further from labor market entry but remain substantial for the most exposed majors.
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Labor Market Concentration, Earnings Inequality, and Earnings Mobility
September 2018
Working Paper Number:
carra-2018-10
Using data from the Longitudinal Business Database and Form W-2, I document trends in local industrial concentration from 1976 through 2015 and estimate the effects of that concentration on earnings outcomes within and across demographic groups. Local industrial concentration has generally been declining throughout its distribution over that period, unlike national industrial concentration, which declined sharply in the early 1980s before increasing steadily to nearly its original level beginning around 1990. Estimates indicate that increased local concentration reduces earnings and increases inequality, but observed changes in concentration have been in the opposite direction, and the magnitude of these effects has been modest relative to broader trends; back-of-the-envelope calculations suggest that the 90/10 earnings ratio was about six percent lower and earnings were about one percent higher in 2015 than they would have been if local concentration were at its 1976 level. Within demographic subgroups, most experience mean earnings reductions and all experience increases in inequality. Estimates of the effects of concentration on earnings mobility are sensitive to specification.
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