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 minimum-wage policy changes across 17 states, Washington, DC, and Chicago between 2013 and 2016, plus 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%); employment is essentially unchanged for low earners and modestly higher for young workers. 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. Unless stated otherwise, post-policy estimates 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; text p. 389	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; text p. 394; 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; text pp. 400, 402	-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; text pp. 376, 409-410; Online Appendix Figure I.11	+$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; text pp. 410, 414; Online Appendix Figure I.11	+$1,995 (+21.8%) for baseline ages 15-26 at s+4; employment elasticity 0.064 (s.e. 0.032); retention +1.30 pp for low earners
R9	Reduced hiring is concentrated among very low-earning workers	Figure II Panels B-C, text p. 391	Missing hires consist entirely of workers who would have earned less than $3,900 annually; 67% would have earned less than $1,000
R10	Costs rise across categories, with nonlabor cost increases differing by industry	Table II, p. 397; text pp. 397-398	Wage bill: 0.0201 for restaurants, 0.0076 for other/retail; COGS: +0.0131 overall, +0.0053 for restaurants, +0.0210 for other/retail; other deductions: +0.0035 overall, +0.0029 for restaurants, +0.0039 for other/retail, all as shares of baseline revenue
R11	Entrants are positively selected on productivity	Table IV, p. 403	Entrants are 0.0411 more likely to rank in the high-productivity quartile (s.e. 0.0128) and 0.0581 less likely in the low-productivity quartile (s.e. 0.0176)
R12	Aggregate point estimates are positive for revenue and value added; wage bills rise weakly significantly and profits are unchanged	Table V, p. 405	Aggregates scaled by baseline revenue: revenue 0.0263 (s.e. 0.0294, ns), wage bill 0.0112* (s.e. 0.0061), material costs 0.0133 (s.e. 0.0168, ns), profits -0.0012 (s.e. 0.0025, ns), value added per worker 0.0805* (s.e. 0.0467); * p < .10
R13	Average outcomes among all active firms rise in the unbalanced panel	Table V, p. 405	Average log effects: revenue 0.0432 (s.e. 0.0048), wage bill 0.0767 (s.e. 0.0054), material costs 0.0266 (s.e. 0.0059), profits 0.0278 (s.e. 0.0083), value added per worker 0.0464 (s.e. 0.0044)
R14	Unexposed industries show no employment response in the placebo test	Online Appendix Figure H.8, reported text p. 399	Employment relationships estimate -0.191 (s.e. 0.152) at s+4; no differential pre- or post-policy trends in other firm outcomes
R15	Young workers become more likely to be employed	Figure VIII, p. 413; text p. 410	Employment probability +1.39 percentage points for the average baseline 15-26 year old
R16	Low-earning workers shift from independent firms to large C-corporations	Table VI Panel A, p. 411	For workers initially in exposed industries, employment at exposed independent firms -0.0126 (s.e. 0.0055), at large C-corporations +0.0173 (s.e. 0.0065)
R17	Employed teenagers hold fewer jobs after the increase	Text p. 419	0.045 fewer jobs at s+4 (s.e. 0.015), from an average and median of 1.6 jobs
R18	Firms shift toward lower material costs relative to revenue	Table IV, p. 403; text p. 404	Low material-cost quartile probability: entrants +0.0324 (s.e. 0.0217), incumbents +0.0194 (s.e. 0.00569), all firms +0.0179 (s.e. 0.00585); entrants are 0.0267 less likely to have high material costs (s.e. 0.0124)
R19	Profit shares fall among incumbents while entrant profit shares are statistically unchanged	Table III, p. 402	Incumbent profits/revenue -0.0064 (s.e. 0.00251); wage bill/revenue +0.0091 (s.e. 0.00163); material costs/revenue -0.0033 (s.e. 0.00215); entrant profits/revenue +0.0070 (s.e. 0.0111)
R20	Compensation gains reach workers earning above the minimum-wage equivalent	Figure III Panel B, p. 392; text p. 393	Annual compensation rises for workers earning $3,900-$35,000, with the largest gains among workers earning around a full-time minimum wage; no earnings reductions appear higher in the distribution
R21	Entrants and incumbents shift toward higher wage-bill-to-revenue quartiles	Table IV, p. 403	Entrants: low quartile -0.0236 (s.e. 0.00781), high quartile +0.0271 (s.e. 0.00775); incumbents: high quartile +0.0310 (s.e. 0.00502); all firms: high quartile +0.0237 (s.e. 0.00434)
R22	Worker earnings show no differential pre-trend before minimum wage changes	Figure VII, p. 409; text p. 408	Pre-reform earnings coefficients are described as precise zeroes for event years s=-4 through s=-2
R23	Worker outcomes remain similar around a 2010 placebo event	Online Appendix Figures I.13-I.14; text p. 414	Earnings and employment paths in treatment and control states are described as very similar around the placebo event

Overall (paper’s conclusion). Independent businesses are more adaptable than the conventional narrative about small-firm vulnerability to minimum wage hikes suggests. They accommodate the cost shock through revenue growth, while lower entry among less productive firms and positive selection among entrants reshape the industry. This reduces the number of firms without evidence of higher exit. The paper cannot separate price pass-through from demand reallocation or quantify their relative contributions. The worker-reallocation channel (from independent businesses to larger C-corporations), analogous to what Dustmann et al. (2022) document for Germany, helps explain why firm-level employment reductions do not translate into individual-level unemployment; increased retention also contributes to this pattern.

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 analyses at s = -4 to s = -2 assess the plausibility of parallel trends across the 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 $y_{jct} = z_{jct} / \text{revenue}_{jc,s-1}$. For percent-change outcomes the dependent variable is $y_{jct} = z_{jct} / z_{jc,s-1}$. 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.

For the individual panel estimates in equation (2), standard errors are clustered by state-by-cohort. The main samples are a 2% random sample of low-earning individuals and a 2% random sample of people ages 15-26, with individual-cohort and cohort-by-year fixed effects. Binary employment outcomes use the LPM; earnings are measured annually, with log earnings used for the own-wage elasticity calculation (pp. 387, 407-408).

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)

For Figure II’s employment-composition estimates, the authors use equation (1) with outcomes for entrants, separations, and employment counts by worker age and annual earnings. These regressions include firm-cohort and cohort-by-year fixed effects and the baseline firm and market controls described above; they are weighted by base-year firm employment, winsorize employment counts at the 99th percentile, and cluster standard errors by state-by-cohort (Figure II, p. 390). The other balanced-panel firm specifications weight by log baseline revenue and winsorize raw firm outcomes at 1% in each tail (Table II, p. 397).

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.

The paper’s sample description and Table I label the young-worker panel ages 16-26 (pp. 382, 387), while the Figure VII and VIII captions label their baseline group ages 15-26 (pp. 409, 413). The age-specific estimates above follow the figure captions; the PDF does not reconcile the difference.

Individual employment outcomes use an LPM. The main earnings estimates use annual income levels; log annual earnings enter the own-wage elasticity calculation, which divides the percent change in employment by the percent change in earnings using the delta method.

Reconstruction (not printed in the paper): collapsed firm-count specification (Section IV.E; Figure VI, p. 401). The authors describe collapsing firm records to cells indexed by reform cohort, year, treatment status, industry, and market characteristics, then estimating event-time changes in active-firm, entrant, and exit counts:

$$Y_{gct} = \alpha + \sum_{s=-4,\,s\neq -1}^{4} \left(\beta_s\,\text{treat}_{gc} + \Gamma_s X_{gc}\right) \times \text{year}_{s=t} + \delta_{ct} + \nu_{gct}$$

Here $Y_{gct}$ is the number of active firms, entrants, or exits in cell $g$; $X_{gc}$ includes baseline two-digit industry, county-density quintile, county-employment-rate quintile, and commuting-zone pre-period firm-churn controls, each interacted flexibly with year. The specification has cohort-by- year fixed effects; it is weighted by the base-year firm count in each cell, and standard errors are clustered by state-by-cohort. The sample is the collapsed firm-count data from highly exposed industries in treatment and control states.

Reconstruction (not printed in the paper): quartile-transition specification (Table IV, p. 403). For each baseline measure $k$ and quartile $q$, the outcome is an indicator that firm $j$ belongs to that quartile at event time $s+4$. This expresses the paper’s stated use of equation (1)‘s event-time design and controls:

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

The fixed effects are firm-cohort and cohort-by-year. Controls are baseline industry, county-density quintile, and county-employment-rate quintile, flexibly interacted with year. The unbalanced panel has about 271,307 active firms per year; standard errors are clustered by state-by-cohort. Table IV does not state a regression weight.

Reconstruction (not printed in the paper): aggregate specification (Table V, p. 405). For the aggregate row, state-by-industry totals of each income-statement item are scaled by baseline total revenue and estimated over event time:

$$Y_{gct}^{\text{agg}} = \alpha + \sum_{s=-4,\,s\neq -1}^{4} \beta_s\,\text{treat}_{gc}\times\text{year}_{s=t} + \delta_{ct} + \nu_{gct}$$

The observations are state-by-industry totals; regressions are weighted by baseline total revenue and standard errors are clustered by state-by-cohort. The table note identifies cohort-by-year fixed effects for the average-log specification below, but does not enumerate additional fixed effects for the aggregate regression. For the unbalanced firm-panel row, the specification is the firm-level event-study equation (1), with cohort-by-year fixed effects and two-digit industry, county-density-quintile, and county-employment-rate-quintile controls flexibly interacted with year. It uses about 271,307 active firms per year, clusters standard errors by state-by-cohort, and estimates average log impacts. The aggregate row is suggestive because its state-level cells provide relatively few observations.

Worker-transition and teenage repeated-cross-section specifications (Table VI, pp. 411-412). For the low-earning worker panel, equation (2) is estimated as an LPM for indicators of employment in each industry and employer type at $s+4$. The specification includes individual-cohort and cohort-by-year fixed effects, age and age-squared, and county-density and county-employment-rate quintiles flexibly interacted with year. It clusters standard errors by state-by-cohort; panel-row regression samples range from 842,201 to 1,485,775 observations. The repeated cross-section of teenagers uses the same controls and cohort-by-year effects, without individual fixed effects; its two row samples are 5,338,697 and 2,370,428 observations. These estimates compare employment outcomes from $s-1$ to $s+4$. Figure IX’s retention regression uses the low-earning panel, an LPM, individual-cohort and cohort-by-year fixed effects, the same age and market controls, and state-by-cohort clustered standard errors (Figure IX, p. 415).

As an identification check, the worker earnings event-study coefficients for the pre-reform years $s=-4$ through $s=-2$ are described as precise zeroes (Figure VII, p. 409; text p. 408). A separate placebo exercise assigns a 2010 minimum-wage event, at least three years before the first actual increase; the paper reports very similar earnings and employment paths between treatment and control states in that exercise (Online Appendix Figures I.13-I.14; text p. 414).

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 2026-10-04 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.