Who's Afraid of the Minimum Wage?: Rao & Risch (2026)

What this is. The paper’s core results, the identification strategy, and the estimating equations: enough to know what it found and how, without reading all 55 pages. To replicate or extend it, read the full source at the original.

TL;DR

Rao and Risch construct the first matched firm-worker-owner panel from the universe of U.S. pass-through tax returns, covering roughly 271,000 independent businesses in highly minimum-wage-exposed industries over 2010-2019. Using 19 state minimum wage increases between 2013 and 2016 as quasi-natural experiments and a stacked difference-in-differences design with 22 control states as clean comparators, they estimate how independent businesses accommodate higher wage floors. Firms do not lay off existing workers but modestly reduce part-time hiring, ending up with about 1.5 fewer employment relationships per year. They fully finance the higher wage bills through revenue growth: four years out, revenues rise 3.3% of baseline while profits are statistically indistinguishable from zero change. Firm entry falls roughly 2%, with surviving entrants positively selected for efficiency. At the individual level, low earners and young workers gain substantially in earnings (+18.9% and +21.8%) with essentially unchanged employment rates. Minimum wages redistribute from consumers to workers; owners escape the burden.

Core results

Magnitudes are as reported from the source PDF. Locators are in the form Table/Figure, page number. All coefficients are from the stacked difference-in-differences specification at event-year s+4 (four years after the initial minimum wage increase).

#	Result	Locator	Magnitude
R1	Firms reduce part-time hiring; no layoffs; employment falls ~2%	Figure II Panel A, p. 390	1.5 fewer employment relationships per firm per year; own-wage employment elasticity -0.245 (s.e. 0.134)
R2	Wage bills rise sharply after the minimum wage increase	Figure III Panel A, p. 392; Table II, p. 397	+0.0143 of baseline revenue (s.e. 0.00167); 1.43 cents per dollar baseline revenue
R3	Revenues rise and fully cover the added labor costs	Figure IV, p. 395; Table II, p. 397	+0.0331 of baseline revenue; 3.31 cents per dollar; consumers bear 100% of incidence
R4	Owner profits are unchanged: incidence falls on consumers, not owners	Figure V, p. 396; Table II, p. 397	0.0002 (s.e. 0.0029); null result; rules out losses larger than 0.37% of baseline revenues at 95% confidence
R5	Firm entry falls; total active firms decline ~2%	Figure VI, p. 401	-1.97% total active firms; entry rate -5.5%; exit flat (increase >0.53% ruled out at 95%)
R6	Value added per worker rises; stronger among entrants	Table III, p. 402	+4.6% across all firms (coeff. 0.0462, s.e. 0.0102); entrants +15.5% (0.1550, s.e. 0.0473); incumbents +3.1% (0.0313, s.e. 0.0149)
R7	Low-earning workers gain earnings with near-zero employment effect	Figure VII Panel A, p. 409; Table VI Panel A, p. 411	+$1,473 (+18.9%) at s+4; own-wage employment elasticity -0.013 (s.e. 0.036)
R8	Young workers gain earnings with stable employment; retention rises	Figure VII Panel B, p. 409; Figure VIII, p. 413; Figure IX, p. 415	+$1,995 (+21.8%) for ages 15-26 at s+4; employment elasticity 0.064 (s.e. 0.032); retention +1.30 pp for low earners

Overall (paper’s conclusion). Independent businesses are more adaptable than the conventional narrative about small-firm vulnerability to minimum wage hikes suggests. They absorb the cost shock by passing it through to consumers via revenue growth and by shedding the least productive firms from the industry, leaving owners whole and workers better off. The worker-reallocation channel (from independent businesses to larger C-corporations), analogous to what Dustmann et al. (2022) document for Germany, explains why firm-level employment reductions do not translate into individual-level unemployment.

Theory / model

The paper presents no formal model in the main text. A Cournot competition model with heterogeneous production technologies appears in Online Appendix O, following Besley (1989). The conceptual framework generates five empirical predictions that structure the analysis.

The paper connects to the long-standing empirical debate on minimum wage employment effects, complementing Card and Krueger (1995) and extending the who-pays analysis of Harasztosi and Lindner (2019) to independent U.S. businesses using matched tax data.

Under imperfect product market competition with fixed costs, a minimum wage acts as a differential labor cost shock: firms relying more heavily on low-wage labor face larger per-unit cost increases. The key predictions are:

1. Employment. Incumbent firms facing modest cost shocks need not reduce employment if they can pass costs forward. Employment reductions should be concentrated in part-time and low-earning positions.

2. Revenue pass-through. Under Cournot, market shares are proportional to margins. An industry-wide cost shock is easier to pass through than a unilateral price increase because the elasticity facing firms is the industry demand elasticity, not the firm demand elasticity. Revenue should rise to offset wage bill increases.

3. Profits. If revenue pass-through is complete, owner profits should be unchanged. Consumers bear the entire burden.

4. Entry deterrence. Higher fixed operating costs (relative to benefits) deter entry of firms that cannot cover the wage premium. The minimum wage raises the viability threshold, reducing entrant counts.

5. Positive selection. Firms that enter despite higher costs are more productive and efficient than the marginal entrants under the pre-reform wage floor, generating a positive shift in the productivity distribution of the industry.

Identification. The paper relies on state-level variation in minimum wage policy. Treatment states are the 17 states, Washington DC, and the city of Chicago that raised their minimum wages between 2013 and 2016. Control states are 22 states that enacted no minimum wage increase between 2011 and 2019, providing a set of clean controls (p. 385). The stacked design compares treated firms in each reform cohort to all control firms over event time, avoiding the negative-weight problem in staggered difference-in-differences that arises when previously treated units serve as controls (Callaway and Sant’Anna (2021); Goodman-Bacon (2021)). Pre-trend validation at s = -4 to s = -2 confirms parallel trends across all primary outcomes.

Method

The paper applies a panel stacked difference-in-differences design introduced in Cengiz et al. (2019) and extended here to a firm-level setting. It builds on difference-in-differences, panel-regression, and event-study estimators.

Firm-level estimating equation (equation 1, p. 385). For firm j in year t belonging to reform cohort c:

$$y_{jct} = \alpha + \sum_{s=-4,\, s \neq -1}^{4} \left(\beta_s \,\text{treat}_{jc} + \Gamma_s X_{jc}\right) \times \text{year}_{s=t} + \delta_{ct} + \psi_{jc} + \nu_{jct} \tag{1}$$

where $\text{treat}_{jc}$ is an indicator for firm j being in a treatment state in cohort c; $X_{jc}$ is a vector of baseline firm and market controls (size categories, value-added deciles, two-digit industry, county density quintiles, county employment-rate quintiles); $\delta_{ct}$ is a cohort-by-year fixed effect; $\psi_{jc}$ is a firm-cohort fixed effect; and $s = -1$ (year before the minimum wage increase) is the omitted base year. The DD estimator $\beta_s$ represents the differential average outcome between firms in treated and untreated states relative to the pre-reform base year. Standard errors are clustered at the state-by-cohort level.

For outcomes scaled by baseline revenue the dependent variable is $y_{jct} = z_{jct} / \text{revenue}_{j,s-1}$. For percent-change outcomes the dependent variable is $y_{jct} = z_{jct} / z_{j,s-1}$. Regressions are weighted by log baseline revenues.

Individual-level estimating equation (equation 2, p. 387). For individual i in year t belonging to cohort c:

$$y_{ict} = \alpha + \sum_{s=-4,\, s \neq -1}^{4} \left(\beta_s \,\text{treat}_{ic} + \Gamma_s V_{ic}\right) \times \text{year}_{s=t} + \delta_{ct} + \rho_{ic} + \nu_{ict} \tag{2}$$

where $V_{ic}$ are individual controls (age, age squared, county density quintiles, county employment-rate quintiles); $\rho_{ic}$ is an individual-cohort fixed effect (replacing the firm fixed effect). For binary employment outcomes the specification is a linear probability model (LPM), with coefficients interpreted as percentage-point changes for the treatment group relative to the control group.

Empirical specifications

All headline estimates are at event-year s+4 (four years after the initial minimum wage increase). The full event-study path (s = -4 to s = 4, omitting s = -1) is shown in the figures.

Firm-level analyses (Section IV). The primary sample is a balanced panel of 134,974 independent businesses in highly exposed industries in treatment and control states, measured in the base year. “Highly exposed” industries are four-digit NAICS industries where at least 1% of workers are paid less than the prevailing minimum wage, identified using CPS Monthly Outgoing Rotation Group data (CPS MORGs) for the pre-reform period. Restaurants alone account for 42% of minimum wage workers; together the selected industries cover more than two-thirds of minimum wage workers (pp. 382-383).

The main firm-level outcomes are:

• Wage bill / baseline revenue (Figure III; Table II)
• Revenues / baseline revenue (Figure IV; Table II)
• Owner profits / baseline revenue (Figure V; Table II)
• Number of employment relationships (Figure II; Section IV.A)
• Value added per worker (Table III)

The COGS and other-deductions items complete the income statement decomposition in Table II, which traces the incidence of each cost dollar:

Component	All exposed	Restaurants	Other/retail
Wage bill	0.0143	0.0201	0.0076
Revenue (financing)	0.0331	0.0294	0.0359
COGS (nonlabor costs)	0.0131	0.0053	0.0210
Other deductions	0.0035	0.0029	0.0039
Owner profits	0.0002	-0.0001	0.0003

Table II coefficients are scaled by baseline revenue at s-1; the three asterisk significance levels (p < .01, .05, .10) from the paper are omitted here; revenue and wage bill are significant at 1% for all-exposed and restaurants.

Extensive-margin analysis. The collapsed dataset aggregating firm counts by cohort, year, treatment status, and industry is used for entry, exit, and total active firm regressions (Section IV.E). Regressions are weighted by base-year firm counts and scaled by pre-reform firm counts.

Individual-level analyses (Section V). Two panels constructed from IRS administrative data:

• Low-earning workers: 2% random sample of individuals earning less than $20k in any industry in the year before the minimum wage increase (s-1) who were also earning less than $25k or not working in s-2.
• Young workers: 2% random sample of individuals ages 15-26 in the year before the minimum wage increase, regardless of employment status.

Individual employment outcomes use an LPM; earnings outcomes use the log of annual individual income estimated via equation (2) with individual-cohort fixed effects. Own-wage employment elasticities are estimated as the percent change in employment divided by the percent change in average annual earnings, calculated using the delta method.

Datasets used

Dataset	Role in paper	Wiki page
IRS administrative tax records (linked firm-worker-owner panel)	Business income tax returns (revenues, COGS, deductions, profits) + Form W-2 wage data linked to owners and workers; universe of U.S. pass-through firms 2010-2019	No page yet
CPS Monthly Outgoing Rotation Groups (CPS MORGs)	Identifies highly exposed industries by share of hourly workers paid below the prevailing minimum wage, pre-reform	No page yet

Sample: approximately 134,974 firms in highly exposed industries (balanced panel) or 271,000 firms per year (full unbalanced panel); 2% random samples of low-earning and young workers. Period: 2010-2019. Frequency: annual.

When to read the full paper

Read the original if you are: designing or evaluating minimum wage policy for a setting that covers independent businesses; extending the incidence decomposition to other cost shocks (payroll taxes, mandated benefits); studying how the IRS linked firm-worker-owner panel (detailed in Online Appendix L) was constructed; or exploring the productivity selection mechanism in the Cournot model of Online Appendix O. The locators above point to exact tables and figures.

Attribution and rights

Source: peer-reviewed, The Quarterly Journal of Economics 141(1), 2026. This distillation was extracted by an LLM on 2026-06-28 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Rao, Nirupama L., and Max Risch. “Who’s Afraid of the Minimum Wage? Measuring the Impacts on Independent Businesses Using Matched U.S. Tax Returns.” The Quarterly Journal of Economics 141, no. 1 (2026): 373-427. DOI: 10.1093/qje/qjaf053. (c) The Author(s) 2025. Published by Oxford University Press on behalf of President and Fellows of Harvard College. Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.