J2 | Macro Paper Warehouse

Manager Pay Inequality and Market Power

Mon, 01 Jan 0001 00:00:00 +0000

This paper asks whether managers are paid for market power. Bao, De Loecker, and Eeckhout build a general equilibrium model in which firms compete oligopolistically in goods markets (following Atkeson and Burstein 2008) while managers are allocated to firms through a competitive matching market (following Gabaix and Landier 2008 and Tervio 2008). The model identifies two distinct channels through which market power and firm size jointly determine executive compensation: a market power channel, whereby a more productive firm charges a higher markup given its output level, and a firm size channel, whereby higher total factor productivity expands output given markups. Because manager ability and firm type are complementary inputs into TFP, assortative matching arises: high-ability managers sort into high-type firms, amplifying both productivity dispersion and markup dispersion across firms.

The authors estimate the model year-by-year using Simulated Method of Moments on Compustat data covering 1994 to 2019, targeting ten moments including the average salary share, markup distribution, employment, and manager compensation levels. Firm-level markups are estimated using the production approach of De Loecker, Eeckhout, and Unger (2020). The ExecuComp variable TDC1 — encompassing salary, bonus, restricted stock grants, and option grant values — measures manager pay. Finance, insurance, and real estate sectors (SIC 6000–6799) are excluded.

Main findings: market power accounts for on average 45.8% of total manager pay over the sample period, rising from 38.0% in 1994 to 48.8% in 2019. Over the full period, average CEO compensation (net of reservation utility) roughly doubled, from approximately $2.94 million to $6.43 million. Of the $3.49 million cumulative increase, $2.02 million (57.8%) is attributed to rising market power, with the remainder ($1.47 million) due to the firm size channel. The market power channel’s dominance is concentrated among top managers: for the highest-ranked managers in 2019, 80.3% of pay is attributable to market power, and nearly all of their pay growth since 1994 stems from the market power channel. For lower-ranked managers, pay is determined primarily by the firm size channel and has been roughly flat over the period.

Within the market power channel, changes in technology — specifically increasing dispersion in firm-level TFP — are the dominant factor, contributing $1.33 million (65.9% of total market power channel growth). The increasing importance of manager ability (rising parameter alpha) contributes an additional $1.14 million through the market power channel. Within the firm size channel, TFP change accounts for 70.1% ($1.03 million) of growth, but the large effects from rising alpha and rising complementarity (gamma) are substantially offset by increasing dispersion in firm type. Structural estimates confirm that the average number of firms per market declines from 4.40 to 3.15, and firm-type dispersion (sigma_z) rises from 0.51 to 0.77, both consistent with rising market power over the period.

A counterfactual economy with no market power — firms priced at marginal cost — would yield a social welfare gain of 58.4% on average. The welfare cost of market power in 1994 could be offset by a 33.8% TFP increase; by 2019 the required TFP offset had risen to 51.7%. Without any market power, even the most talented managers would earn only their reservation utility, because firms earn zero profits regardless of productivity, eliminating the complementarity-driven matching surplus that makes top managers valuable. This confirms that superstar manager pay is intrinsically tied to the existence of market power in goods markets, not solely to firm size.

Scope conditions: the model applies to publicly listed US firms covered by Compustat and ExecuComp. The mechanism relies on Cournot competition within oligopolistic markets, assortative matching between managers and firms, and complementarity between manager ability and firm type (elasticity of substitution gamma estimated to be negative throughout the sample). The findings on market power share apply to CEOs specifically; the authors argue the same logic extends to all managerial positions with span-of-control over other workers, which encompasses roughly one-fifth of the workforce.

Q: What are the two channels through which manager pay is determined in the model, and how do they differ mechanically? A: The market power channel captures how a given level of TFP translates into higher markups — more productive firms charge more above marginal cost — thereby increasing profits per unit of output. The firm size channel captures how higher TFP expands the quantity of output a firm produces, increasing total profits through scale rather than through price-cost margin. Both channels raise profits and thus the marginal product of managers, but they operate through distinct economic mechanisms: one through pricing power and the other through productive scale.

Q: What is the empirical magnitude of the market power channel’s contribution to manager pay levels and growth? A: Market power accounts for an average of 45.8% of total manager pay over 1994–2019, rising monotonically from 38.0% in 1994 to 48.8% in 2019. For the total pay increase of $3.49 million over the period, $2.02 million (57.8%) is due to the increase in market power, with the remaining $1.47 million attributable to the firm size channel.

Q: How does the market power channel’s importance vary across the manager ability distribution? A: For the highest-ranked managers, 80.3% of total pay in 2019 is attributable to market power, and nearly all of their pay growth since 1994 runs through the market power channel. For the lowest-ranked managers, pay is almost entirely explained by the firm size channel and has been approximately flat over the period. This heterogeneity arises because top managers sort into high-markup firms through assortative matching, making their compensation disproportionately dependent on those firms’ market power.

Q: How does the model generate assortative matching between manager ability and firm type? A: Manager ability and firm type are complementary inputs into TFP (the CES aggregator with elasticity of substitution gamma less than one), which makes the matching output supermodular. In a frictionless matching market with transferable utility, supermodularity guarantees that high-ability managers match with high-type firms in equilibrium (Proposition 1). This positive assortative matching then amplifies productivity and markup dispersion, since the most productive firms become even more productive and gain larger market shares.

Q: What structural changes drive the rising importance of market power in manager pay over time? A: The dominant factor within the market power channel is changes in technology, specifically increasing firm-type dispersion (sigma_z rising from 0.51 to 0.77), which contributes $1.33 million or 65.9% of market power channel growth. The rising importance of manager ability (alpha, the weight on manager ability relative to firm type in the TFP aggregator) contributes another $1.14 million. The number of firms per market declines from an average of 4.40 to 3.15, further reducing competitive pressure and amplifying the markup premium for high-productivity firms.

Q: What does the counterfactual with no market power (first-best pricing) imply for manager pay and social welfare? A: Without market power, firms price at marginal cost and earn zero profits regardless of productivity, which eliminates the surplus from manager-firm matching. All managers would earn only their reservation utility, which is negligible relative to actual compensation. Social welfare would increase by 58.4% on average. The efficiency cost of market power — measured as the TFP increase needed to offset welfare losses — rose from 33.8% in 1994 to 51.7% in 2019, indicating a worsening welfare distortion over the period.

Q: How are markups measured, and what is their trend in the data? A: Markups are not directly observable and are estimated using the production approach of De Loecker, Eeckhout, and Unger (2020), which recovers firm-level price-cost margins from production data without requiring price data. Average markups in the Compustat sample rose from 1.53 in 1994 to 1.78 in 2019. The reduced-form elasticity of manager pay with respect to markups (controlling for firm characteristics, year, and firm fixed effects) increased substantially: in 2019 a one-percent increase in firm-level markup raises manager pay by 0.41 percent, which is 70.1% larger than the effect estimated in 1994.

Q: How does the paper handle the identification challenges inherent in regressing manager pay on markups? A: The reduced-form regression (with firm fixed effects, year effects, and interactions of year dummies with markups) documents a robust positive correlation but cannot establish causality due to reverse causality and omitted-variable bias. The paper addresses this by embedding the markup-manager pay relationship in a structural model where both are jointly determined by primitives — technology, market structure, and manager ability — and estimating those primitives via Simulated Method of Moments. The quantitative decomposition into market power and firm size channels derives from the model structure rather than from identifying variation in an instrumental variables sense.

Q: What do the matching model estimates reveal about manager-firm complementarity over time? A: The estimated elasticity of substitution between manager ability and firm type (gamma) is negative throughout the sample, confirming complementarity. Gamma was relatively stable before declining sharply from -2.22 in 2014 to -3.55 in 2019, indicating that manager ability and firm type became substantially more complementary in the latter part of the sample. The importance-of-manager parameter alpha is small (consistent with Gabaix and Landier 2008) but generally increasing, suggesting managers play an expanding role in determining firm-level TFP over time.

Q: What are the broader macroeconomic and distributional implications of the findings? A: Because approximately one-fifth of workers supervise other workers, the market-power-driven premium in managerial pay has implications beyond CEO compensation for the shape of the earnings distribution. The rise in top-1-percent income is identified as an efficiency concern, not just an equity concern: the best managers are hired by high-markup firms where they generate profits for shareholders but disproportionately little additional social value. Assortative matching between top managers and top firms widens the productivity gap between competitors, increasing market power and deadweight loss — the social return to managerial talent is therefore below the private return in equilibrium.

Market Power Channel: The component of manager pay attributable to how a firm’s TFP raises its markup — the ratio of output price to marginal cost — given the level of output. Distinct from the firm size channel; operates through pricing power rather than scale.

Firm Size Channel: The component of manager pay attributable to how a firm’s TFP expands output quantity given markups. Increasing output scale raises total profits and thus the marginal product of the manager even absent any change in price-cost margins.

Assortative Matching: The equilibrium allocation of high-ability managers to high-type firms, arising because manager ability and firm type are complementary inputs into TFP (supermodular matching output). Matching is determined in a frictionless market with transferable utility.

Markup: The ratio of output price to marginal cost, equal to the inverse of the price elasticity of demand under the nested CES preference structure. Endogenously determined by the firm’s sales share within its oligopolistic market and the elasticities of substitution within markets (eta) and across markets (theta).

Manager-Firm Complementarity: The property that manager ability and firm type are imperfect substitutes with elasticity of substitution gamma less than one in the TFP aggregator. Complementarity is the necessary condition for positive assortative matching and for the supermodularity of matching surplus.

Span of Control (Lucas 1978): The mechanism by which a manager raises the productivity of all workers under supervision, so that a more able manager generates a proportionally larger productivity gain the larger the firm. Provides the microfoundation for why firm size amplifies the value of manager ability.

Market Structure: The number of firms in each oligopolistic sub-market (Ij), which varies across markets and over time. Together with the distribution of firm-level TFP within a market, market structure determines how much competitive pressure limits markup extraction. Average firms per market declines from 4.40 to 3.15 over 1994–2019.

Minimum Wages, Efficiency, and Welfare

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Research question. Can minimum wages improve welfare through efficiency — by correcting monopsony-driven under-employment — and, if so, by how much? What is the optimal minimum wage, and how much of the welfare gain from a higher minimum wage comes from efficiency versus redistribution?

Model and methodology. The paper develops a tractable general equilibrium oligopsony model with heterogeneous workers (four types: non-high-school, high-school, college workers, and capital owners) and heterogeneous firms (varying in total factor productivity), embedded in a continuum of local labor markets where firms compete strategically in Cournot fashion. Firms face downward-sloping labor supply curves; their market power generates wages below the marginal revenue product of labor (markdowns). The model is calibrated to US data using the Census Longitudinal Business Database (LBD, 2014), the Bureau of Labor Statistics Current Population Survey (CPS, 2019), and the Survey of Consumer Finances (SCF). Key calibration targets include: average firm size of 22.83 workers (LBD), 29 percent of workers earning below $15/hr (CPS), labor and capital income shares, and household-level earnings and capital income ratios. The model is validated by quantitatively replicating four strands of empirical evidence: (i) reallocation effects of the German minimum wage introduction (Dustmann et al., 2021); (ii) employer spillover responses to Amazon’s voluntary $15 minimum wage (Derenoncourt et al., 2021); (iii) wage distribution compression evidence from Brazil (Engbom and Moser, 2021); and (iv) heterogeneous employment effects by market concentration (Azar et al., 2019).

Three channels for efficiency gains. The model identifies three mechanisms through which a minimum wage can improve efficiency under oligopsony: (1) a direct effect in which constrained firms with monopsony markdowns increase wages and expand employment toward the competitive level (Region II firms); (2) a spillover effect in which unconstrained competitor firms narrow their own markdowns in response to constrained firms’ increased wages and market shares; (3) a reallocation effect in which employment is shifted away from low-productivity firms (which enter Region III — constrained on labor demand) toward more productive firms.

Main findings on efficiency versus redistribution. Under the $15.12/hr minimum wage that maximizes social welfare under utilitarian weights (population-share weights), less than 5 percent of the welfare gains come from improved efficiency, while more than 95 percent come from redistribution. When the government is additionally given access to budget-neutral lump-sum transfers that fully address redistribution goals, the efficiency-maximizing minimum wage narrows to a range of approximately $7.50–$10.00 per hour, which is robust across social welfare weight specifications. The welfare gains attributable to efficiency alone are approximately 0.16–0.20 percent in consumption-equivalent terms, representing only about 1–2 percent of the welfare gains achievable in an economy with no labor market power at all (which would be 15.26 percent in consumption-equivalent terms under the same conditions with optimal transfers).

Why efficiency gains are small. Three structural reasons limit efficiency gains: (i) low-productivity firms — which are the firms most affected by a binding minimum wage in Region II — have endogenously narrow markdowns even absent a minimum wage, because they face more elastic labor supply and command small market shares; (ii) the calibrated production function has relatively flat marginal revenue product of labor schedules (decreasing returns parameter α = 0.940), so once firms enter Region III, employment rationing occurs rapidly; (iii) the large, high-productivity firms with the widest markdowns are not materially affected by the minimum wages of their small, low-wage competitors because those competitors have small market shares — making spillovers quantitatively negligible even though the model matches empirical cross-employer wage elasticities.

Optimal minimum wages under alternative frameworks. Without transfers and under utilitarian weights, the optimal minimum wage is $15.12. Without transfers but under Negishi weights (which rationalize the observed competitive equilibrium and load approximately 62 percent of weight on college workers and owners versus their 35 percent population share), the optimal is $6.97. Under a 97 percent weight on high-school graduates, the optimal rises to $18.32. With optimal lump-sum transfers, the optimal collapses to $7.76–$10.11 regardless of social welfare weights — a range robust across Frisch elasticity variants (ϕ ∈ {0.30, 0.62, 0.86}), regional decompositions (low, medium, and high income US states), short-run capital-fixed scenarios (where the optimum declines by approximately $1 under utilitarian weights), and the removal of household heterogeneity entirely (which yields an optimum of $7.74).

Distributional proxies versus welfare. Wage inequality (college–non-college log wage premium, cross-sectional variance of log wages) and the labor income share are monotonically improving as the minimum wage rises, even as welfare is hump-shaped and eventually declining. A rise in the minimum wage from $7.50 to $15 reduces the college–non-college log wage premium from 0.53 to 0.43 (roughly one-fifth), reduces the cross-sectional variance of log wages by nearly half, and raises the aggregate labor income share by approximately 3 percentage points — all while welfare (under utilitarian weights with no transfers) reaches its maximum at $15.12 and then declines. These standard proxies therefore do not reliably indicate welfare.

Scope conditions. All results are long-run steady-state comparisons unless otherwise noted. Results assume no price passthrough and a unit elasticity of substitution between capital and labor. The paper abstracts from capital–labor substitution responses and occupational choice. The redistribution channel quantified here is specific to the utilitarian welfare criterion and to the existing distribution of capital and profit income, in which owners (6 percent of households) earn 92 percent of dividends.

In depth

Q1. What are the three regions of firm behavior in response to a binding minimum wage, and what are their efficiency implications?

A: A firm can be in one of three regions. In Region I the minimum wage is not binding: the firm pays its optimal monopsony wage and employment is inelastically below the competitive level. In Region II the minimum wage binds and exceeds the firm’s optimal monopsony wage, but labor supply at the minimum wage still falls short of labor demand: employment and efficiency improve as the shadow markdown narrows. In Region III the minimum wage exceeds the competitive wage, so unconstrained labor supply would exceed demand: the firm rations employment and the rationing constraint binds, reducing efficiency. At the boundary of Region II and Region III, the shadow markdown equals one and the firm is at its efficient employment level. Only a firm-specific minimum wage targeting each firm’s competitive wage could deliver economy-wide efficiency.

Q2. How does the paper define and use “shadow wages” to characterize equilibrium?

A: The shadow wage for a firm is the effective wage that rationalizes equilibrium employment given rationing constraints. Formally, when a firm rations employment (Region III), households act as if facing a shadow wage equal to the actual minimum wage multiplied by a rationing factor p < 1 (the Lagrange multiplier on the rationing constraint, normalized as a fraction). Shadow wages aggregate across firms into market- and type-level shadow wages via CES aggregation. The key insight is that shadow wages, not observed wages, are allocative: aggregate labor supply for each worker type is determined by the type-level shadow wage, not by the minimum wage that firms actually pay. This allows the paper to express aggregate efficiency via two wedges — the aggregate shadow markdown (capturing average market power) and a misallocation term — without tracking all firm-specific constraints individually.

Q3. What are the two aggregate efficiency wedges and how do they behave as the minimum wage rises?

A: The two wedges are: (i) the aggregate shadow markdown µ̃, which is a productivity-weighted average of firm-level shadow markdowns and measures the extent to which aggregate wages fall short of marginal revenue products; and (ii) the misallocation term ω, which measures whether employment is allocated toward more productive firms and equals one when all shadow markdowns are identical. As the minimum wage rises from zero, µ̃ initially narrows (improving efficiency) because firms in Region II expand toward their competitive employment level and constrained firms’ market shares rise, tightening the residual labor supply of unconstrained competitors and narrowing their markdowns. But as the minimum wage rises further, Region III rationing causes shadow markdowns to widen rapidly — first for low-productivity firms and then progressively for more productive ones — so µ̃ turns back downward. The misallocation term ω first improves as low-productivity firms are pushed out, but then worsens because rationing at intermediate-productivity firms redirects employment from high- to medium-productivity firms.

Q4. What does the model validation exercise on the German minimum wage (DLSUB 2021) show?

A: The paper calibrates the model to the German context by setting a minimum wage of $8.95/hr equivalent to 48 percent of the pre-reform median wage — matching Germany’s 8.50 euro introduction in 2015, where 15 percent of workers earned below the threshold. The model produces employment effects that are slightly positive (consistent with empirical findings of no disemployment), average wage increases consistent with both constrained and unconstrained firms raising wages, a negative elasticity of the number of operating firms with respect to minimum wage exposure (correctly signed, moderately smaller than data), and a positive elasticity of average firm size with respect to exposure (slightly larger than the data). The reallocation direction — small unproductive firms shrinking and workers moving to larger, more productive firms — matches the data qualitatively and within the range of data estimates across specifications.

Q5. What does the Amazon spillover replication (DNWT 2021) show, and what does it imply about the minimum wage spillover channel?

A: Derenoncourt et al. (2021) estimate a cross-employer wage elasticity of 0.26: when Amazon raised wages by approximately 18.1 percent, competitors raised wages by 4.7 percent on average. The model replicates this by treating Amazon as the largest (or second-largest) firm in each market, exogenously narrowing its markdown by a fraction ζ calibrated to deliver an 18.1 percent wage increase. Competitors in the model raise wages through the strategic interaction mechanism: Amazon’s higher wage and market share tightens competitors’ residual supply curves, inducing them to narrow their own markdowns. The model matches the 0.26 cross-employer elasticity when Amazon is the largest firm in markets with at least 36 competitors, or the second-largest in markets with at least 12. Critically, the authors note that this empirical evidence concerns responses to a large firm raising wages; for minimum wages the question is whether large firms respond to their small wage competitors, which the model shows they do not substantially, because small firms have negligible market shares.

Q6. How does the paper separate efficiency from redistribution, and what is the key methodological innovation?

A: The paper gives the government access to budget-neutral, unrestricted lump-sum transfers across households in addition to the minimum wage. With transfers available, the government can use them to meet any redistributive objective encoded in arbitrary social welfare weights. Whatever is left for the minimum wage to do must be purely efficiency-improving. The paper shows (via aggregation theorems) that optimal lump-sum transfers can be computed in closed form for any social welfare weights, and that the social welfare maximizing allocation subject to transfers can be decentralized by transfers that sum to zero across households. Under this framework, the efficiency-maximizing minimum wage lies between $7.50 and $10.00 per hour regardless of whether utilitarian, Negishi, or 97 percent high-school-weighted social welfare functions are used — collapsing the original $0–$31 range to a tight interval.

Q7. How are Negishi weights computed, and why are they important for interpreting the results?

A: The Negishi weights are the social welfare weights under which a planner would choose the observed competitive equilibrium with zero lump-sum transfers. They are computed by inverting the planner’s first-order conditions: for the competitive equilibrium to be optimal under some set of weights, the implied consumption ratios must match observed data. The calibrated Negishi weights assign a combined weight of approximately 62 percent to college workers and owners, who constitute only 35 percent of the population. This means the competitive equilibrium is disproportionately aligned with higher-income households. A utilitarian planner, which weights households by population shares, therefore sees large scope for redistribution toward non-college workers — which is exactly why the utilitarian-optimal minimum wage is $15.12 and why 94 percent of its welfare gains come from redistribution rather than efficiency.

Q8. What are the quantitative welfare gains from the efficiency-maximizing minimum wage, and how small are they relative to the potential gains from eliminating monopsony?

A: With optimal lump-sum transfers, the welfare gains from the efficiency-maximizing minimum wage are approximately 0.16–0.20 percent in consumption-equivalent terms, robust across social welfare weight specifications, Frisch elasticity variations, and regional decompositions. The welfare gains associated with an economy in which all firms’ markdowns are set to one (no labor market power at all), also evaluated with optimal transfers, are 15.26 percent in consumption-equivalent terms. The efficiency-maximizing minimum wage therefore recovers approximately 1–2 percent of the potential welfare gains from eliminating monopsony. Equivalently, the efficiency gains correspond to roughly a 0.1 percent increase in TFP. These gains are small despite the model matching all empirical evidence on the channels through which efficiency gains could occur.

Q9. How do employment effects of minimum wages vary by market concentration, and why?

A: In concentrated markets (upper tercile of HHI), firms have larger monopsony markdowns, so a binding minimum wage pushes them into Region II — where employment expands — over a wider range of minimum wage values before entering Region III. This produces large, positive employment effects in concentrated markets. In less concentrated markets, firms already have narrow markdowns (they are closer to competitive), so even small minimum wage increases push them into Region III, where employment contracts. The model replicates the statistically significant positive effects in high-concentration markets and negative effects in low-concentration markets documented by Azar et al. (2019), for initial minimum wages below approximately $8/hr. At higher initial minimum wages, however, even high-concentration markets exhibit negative employment effects as more firms enter Region III.

Q10. What does the robustness exercise for Mississippi reveal?

A: Mississippi has the lowest per capita income in the US, and a $15 minimum wage would bind for 41.3 percent of its workers (versus 29.4 percent nationally). Despite this, the model finds that Mississippi would benefit from a $15 federal minimum wage under utilitarian weights, and the Mississippi-specific optimal minimum wage is $14.89 — nearly identical to the national optimum. The reason is an offsetting compositional effect: while Mississippi has lower average wages (pushing toward a lower optimal), it has a larger share of high-school graduates (63 percent versus 52.8 percent nationally) who prefer higher minimum wages (around $17 in the model). These two forces wash out, producing a stable optimal close to the national figure.

Q11. What happens to common empirical proxies for inequality and worker power as the minimum wage rises?

A: The college–non-college log wage premium declines from 0.53 to 0.43 (a fall of roughly one-fifth) as the minimum wage rises from $7.50 to $15. The cross-sectional variance of log wages falls by nearly half over this range, driven equally by declining within- and between-type inequality. The aggregate labor income share rises by approximately 3 percentage points, and the share of output created in non-high-school jobs paid to non-high-school workers rises by 7 percentage points. All of these proxies are monotonically improving in the minimum wage throughout, even as aggregate welfare under the model’s social welfare function is hump-shaped and declining past the optimum. The paper concludes that observations of declining inequality or a rising labor share are consistent with falling welfare, so these proxies cannot serve as reliable welfare indicators.

Q12. How does the short-run (fixed-capital) analysis differ from the long-run baseline?

A: In the short run, capital at each firm is fixed at the type-specific level chosen under a zero minimum wage. This creates sharper decreasing returns in labor (parameter γα rather than α̃), overhead costs that can make operation unprofitable, and a narrower range of minimum wages over which firms remain in Region II. The result is that firms in the short run enter Region III at lower minimum wages than in the long run, limiting the range of efficiency gains. Quantitatively, the efficiency-maximizing optimal minimum wage declines by approximately $1 under utilitarian weights (from about $10 to about $9 in the short-run exercise) and by only about $0.20 under Negishi weights. The robustness conclusion is that the difference between short- and long-run optimal minimum wages is modest, and the main finding that efficiency gains are small is preserved.

Key Concepts

Shadow wage (w̃ᵢⱼ): The effective wage that rationalizes a firm’s equilibrium employment in the presence of a minimum wage. When labor is rationed at firm ij (Region III), the shadow wage equals the actual minimum wage multiplied by a rationing factor pᵢⱼ < 1, where pᵢⱼ is derived from the Lagrange multiplier on the household’s rationing constraint. The shadow wage is allocative — it determines labor supply decisions — while the observed minimum wage wage is not. When the rationing constraint is slack (Regions I and II), the shadow wage coincides with the observed wage.

Shadow markdown (µ̃ᵢⱼ): The ratio of a firm’s shadow wage to its marginal revenue product of labor. In Region I (unconstrained), this equals the standard monopsony markdown. In Region II (constrained, on the labor supply curve), the shadow markdown narrows as the minimum wage increases, moving the firm toward its efficient employment level. In Region III (constrained, on the labor demand curve), the shadow markdown equals the rationing multiplier pᵢⱼ and widens, reflecting efficiency losses from rationing. An aggregate shadow markdown µ̃ is computed as a productivity-weighted average of firm-level shadow markdowns across all firms in the economy.

Misallocation wedge (ω): A productivity-weighted measure of how well employment is allocated across firms. In an efficient allocation with identical shadow markdowns, ω = 1. When high-productivity firms have wider markdowns than low-productivity firms (the baseline oligopsony outcome), ω < 1 because employment is directed away from productive firms. A minimum wage can improve ω by shrinking low-productivity firms but worsens it when high-productivity firms enter Region III and are over-rationed relative to medium-productivity firms.

Oligopsony with Cournot competition: The specific form of labor market power in this model. In each local labor market (defined as a NAICS 3-digit industry × commuting zone cell), a finite number of firms compete strategically in employment quantities, taking their competitors’ employment levels as given (Cournot assumption). Each firm has an upward-sloping labor supply curve derived from nested CES household preferences, and exercises a markdown on the marginal revenue product of labor. This differs from monopsony (one firm) or perfect competition (infinitely many firms), and generates both direct effects and spillover effects of minimum wages.

Negishi weights: The vector of social welfare weights under which the observed competitive equilibrium allocation would be the solution to a social planner’s problem with zero lump-sum transfers. In this model, the calibrated Negishi weights assign roughly 62 percent combined weight to college workers and owners (who constitute only 35 percent of the population), reflecting the fact that the market equilibrium allocates a disproportionate share of consumption to high-income households. The Negishi weights are used both to identify the gap between market outcomes and utilitarian objectives (motivating redistribution) and as one alternative normative benchmark.

Efficiency-maximizing minimum wage: The minimum wage that maximizes social welfare when the government additionally has access to budget-neutral lump-sum transfers across households. Because transfers can be optimized to handle any redistributive objective encoded in any arbitrary social welfare weights, the minimum wage under this framework serves solely to improve productive efficiency. In the calibrated model, the efficiency-maximizing minimum wage is approximately $7.50–$10.00 per hour, robust to social welfare weight specifications, Frisch elasticity variations (ϕ ∈ {0.30, 0.86}), and regional income differences.

Rationing constraint (n̄ᵢⱼₖ): A firm-specific, type-specific upper bound on the labor a household may supply to a firm in equilibrium. These constraints are taken as given by households and determined in equilibrium by firms’ labor demand decisions. When the minimum wage is above the firm’s competitive wage (Region III), the firm’s labor demand is less than what households would want to supply at that wage, so the rationing constraint binds. The binding rationing constraint generates the shadow wage discount (pᵢⱼ < 1) and is the mechanism by which high minimum wages reduce efficiency in the model.

Politics at Work

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1 — Overview

Research Question

Do individual political views shape firm behavior and labor market outcomes in the private sector? Specifically, do business owners sort copartisan workers into their firms, and does employers’ political discrimination drive this sorting?

Data and Setting

The paper studies the complete Brazilian formal labor market over 2002–2019, assembling a novel longitudinal worker-firm-owner-party matched dataset from three administrative sources: (1) RAIS (Relação Anual de Informações Sociais), the universe of formal-sector workers (87 million unique workers, 7.6 million unique firms); (2) the Receita Federal do Brazil (RFB) and Cadastro Nacional de Empresas (CNE), containing business ownership structures for all registered firms; and (3) the Tribunal Superior Eleitoral (TSE) registry of all party members (19.3 million individuals) over 2002–2019. Matching these sources yields political affiliation for 11.4% of all private-sector owners and 7.8% of all private-sector workers in the sample. Party affiliation in Brazil requires an active registration step and is interpreted as a signal of strong and visible political views, distinguishing affiliated from unaffiliated individuals who likely hold milder views. The 35 parties in the sample are highly fragmented; the top 7 account for nearly 70% of all party members.

Main Findings

Political assortative matching. Using a likelihood ratio index (Eika et al., 2019; Chiappori et al., 2020), the paper finds that workers and owners belonging to the same party are on average about twice as likely to match in the labor market relative to random matching. Once within-municipality geographical sorting is accounted for, this figure falls to approximately 55% excess probability of copartisan matching, and increases over time: from 1.41 in 2002–2006 to 1.67 in 2016–2019. A dyadic regression approach — constructing all worker-firm dyads within industry-municipality labor markets and controlling for shared gender, race, age, and education — confirms the result: across all years, a politically affiliated worker is between 41% and 75% more likely to be employed by a copartisan owner than by an owner affiliated with a different party. Political assortative matching is driven both by higher hiring probabilities (range: 32%–59% more likely for copartisans, hiring margin only) and by longer tenure: copartisan workers stay in the firm roughly 5.5% longer than otherwise comparable workers of a different party, even within the same firm and hire-year (column 3 of Table 2). In every year and by every method, the degree of political assortative matching exceeds that of gender (15%–31% excess probability under dyadic approach) and race (approximately 3.4%), which are themselves both positive and significant.

Mechanisms: political discrimination. Three sets of evidence point to employer political discrimination as a relevant driver. First, in the administrative micro-data: assortative matching decreases strongly with firm size — it is more than twice as large in firms with up to 10 employees than in medium firms and more than six times as large as in firms with more than 50 employees — and is stronger for higher occupational layers and for jobs requiring above-median social skills or interpersonal relationships. Political assortative matching is, if anything, larger for parties not in power locally, inconsistent with a patronage mechanism. An event study of 5,262 owners who switched party finds a sharp increase of about 0.2 standard deviations in hires from the new party and a corresponding drop in hires from the old party at the time of the switch, with the share of workers from the new party rising by roughly 5 percentage points persistently. Second, an incentivized resume rating (IRR) field experiment (150 business owners; nondeceptive design) shows that owners rate copartisan resumes 0.213 points higher on a 1–7 Likert scale (a 7.4% increase relative to the mean rating for different-party resumes, statistically significant at p < 0.05), with no significant effect on perceived candidate acceptance probability. Third, a representative survey of 891 owners and 1,003 workers finds that belief-based and taste-based discrimination are ranked as the leading explanations by both groups; 47% of owners and 58% of workers agree with the belief-based discrimination statement. Additionally, 29% of surveyed owners (22% say “Yes” and 7% “In some cases”) explicitly reveal that political views affect their hiring decisions.

Real consequences. Conditional on employment, copartisan workers are promoted faster: they are 0.448 percentage points more likely to be promoted from white-collar to managerial positions (against a base rate of 2.58%) and 0.44 percentage points more likely to be promoted from blue-collar to white-collar positions (base rate 2.98%). Workers from a different party than the owner face a promotion penalty of 0.104–0.180 percentage points for white-collar-to-manager promotions. On wages, copartisan workers earn 3.9% more than unaffiliated coworkers within the same firm and year (firm-year FE specification); the effect is 2.8% when restricting to the same occupation within the firm. Workers from a different party earn 1.6% less. Decomposing by tier: managers (copartisan premium 1.6%), white-collar workers (3.4%), blue-collar workers (1.5%). Despite better outcomes, copartisan workers are 2.1 percentage points (2.3% relative to the mean) less likely to be educationally qualified for their occupation, conditional on firm-year and controlling for a full set of demographics. Finally, a higher share of copartisan workers in the prior year is associated with lower firm employment growth (estimated β = −0.071), corresponding to approximately a 1 percentage point gap in annual growth rate for a one-standard-deviation difference in copartisan share — substantial relative to an average annual growth rate of 10%.

Scope Conditions

All findings pertain to the formal private sector in Brazil over 2002–2019. Political affiliation in the Brazilian system requires an active step and signals strong views; results apply to the approximately 7.8%–11.4% of workers and owners who are party-registered. The field experiment sample is limited to 150 business owners affiliated with major Brazilian parties who were actively seeking to hire. The firm growth result is explicitly characterized as suggestive, without a source of exogenous variation.

In depth

Q1. What is the likelihood ratio index and what does it show for political matching in Brazil?

The likelihood ratio index measures how many times more likely a match between a worker and owner of the same party is, relative to the expected frequency under random matching (conditional on the population shares of each party). Across 2002–2019, the unconditional index ranges from 1.56 to 1.85, implying workers and employers of the same party are on average about twice as likely to match as under random matching. After accounting for geographic sorting within municipalities, the index ranges from approximately 1.41 (2002–2006 average) to 1.67 (2016–2019 average), showing a clear increasing trend. The corresponding gender and race indexes average about 1.2 and 1.35, respectively, in the basic specification, both significantly lower than the party index in every year of the sample.

Q2. How do the dyadic regression estimates control for omitted characteristics, and what do they find?

The dyadic regression constructs all possible worker-firm pairs within each municipality-industry labor market in a given year. The dependent variable is an indicator for whether worker i is employed by firm f. The key coefficient of interest is the differential probability of employment for a copartisan pair relative to a different-party pair, controlling for indicators for shared gender, race, age bracket, and education level, as well as worker occupation fixed effects and experience. This controls for the concern that politically affiliated individuals share non-political traits that correlate with employment choices. After these controls, a politically affiliated worker is 41%–75% more likely (depending on year) to be employed by a copartisan owner than by a different-party owner. The effect stems primarily from copartisan workers being preferentially hired (not just from unaffiliated owners preferring any affiliated worker indiscriminately). The analogous dyadic estimate for shared gender is 15%–31% and for shared race is approximately 3.4%, both lower than the party estimate in all years.

Q3. How is political assortative matching decomposed into hiring versus retention margins?

To isolate the hiring margin, the authors estimate the dyadic regression restricting to newly hired workers (not present in the firm in year t-1). They find that the probability of being hired by a copartisan owner is 32%–59% higher than by a different-party owner across years. The retention (tenure) margin is estimated by regressing the share of subsequent years a worker remains at the firm on partisan alignment at the time of hire. In the most stringent specification (year-of-hire × firm fixed effects), copartisan hires stay 5.5 percentage points longer (as a share of post-hire years) than different-party hires from the same firm and hire-year cohort. Both margins are significant, and both exhibit stronger political sorting than equivalent estimates for gender or race.

Q4. What is the evidence against political patronage as the primary driver of political assortative matching?

If political patronage (parties pressuring owners to hire copartisans) were the main driver, we would expect political assortative matching to be stronger when the owner’s party is in power locally, as those parties have greater leverage over business owners. The authors estimate a modified dyadic regression distinguishing between cases where the owner’s party is in the ruling coalition of the municipal mayor or state governor versus not in power. The results show that political assortative matching is, if anything, larger for parties not in power. This is inconsistent with patronage being the dominant mechanism and consistent with the discrimination channel being driven by owner preferences rather than external political pressure.

Q5. What does the event study of owner party changes show?

The event study tracks 5,262 owners who switch party affiliation during 2002–2019, comparing their firms to control firms in the same market whose owners remain affiliated to the original party. At the time of the switch, there is a sharp increase of approximately 0.2 standard deviations in hires from the owner’s new party and a corresponding sharp decrease in hires from the old party. Hires from other parties and unaffiliated hires also decline modestly. The share of the workforce affiliated with the new party increases by roughly 5 percentage points and remains elevated in subsequent years. Because nonpolitical network ties (shared school, neighborhood, sports team) are unlikely to dissolve abruptly when an owner changes party, this design provides additional evidence that the change in hiring is driven by a direct change in the owner’s political preferences rather than by network overlap.

Q6. What was the design of the incentivized resume rating experiment and why does it identify political discrimination?

The experiment was conducted with 150 Brazilian business owners recruited from the administrative data (who are already known to be affiliated with one of six major parties), targeting owners with active hiring interest through a leading job platform. Owners rated 20 synthetic resumes with fully randomized features (education, experience, training, skills, formatting). Sixteen resumes had no partisan cues; two contained cues signaling copartisanship with the rating owner; two signaled a party from the opposite side of the political spectrum. Incentives were provided by committing to send respondents real job-seeker profiles from the platform chosen by machine learning based on revealed preferences. Because all resume features other than the partisan cue were randomized, the experiment shuts down shared nonpolitical networks and patronage as explanations; the only channel is the employer’s direct preference for the candidate’s partisan affiliation. The response rate was 11% and the survey was conducted March–May 2022.

Q7. What is the quantitative magnitude of the field experiment result?

Owners rate copartisan resumes 0.213 points higher on the 1–7 Likert scale relative to resumes from the opposite side of the political spectrum (statistically significant at p < 0.05), representing a 7.4% increase relative to the mean rating of different-party resumes (2.950). When resume-level controls (gender, high-skill experience flag, years of experience, programming skills, training) are added, the estimate is 0.254. There is no statistically significant effect on owners’ perceived likelihood that a candidate would accept a job offer (coefficient 0.150–0.158, not significant), suggesting that the observed difference in interest ratings reflects a genuine direct preference for copartisans, not an expectation that copartisans are more likely to accept.

Q8. What do the survey findings add about mechanisms and the prevalence of political discrimination?

The survey of 891 owners and 1,003 workers (response rate 26.84%) presents five candidate mechanisms and asks respondents to evaluate each. Both groups rank belief-based discrimination (owners believe copartisans would be more productive) as the most likely explanation: 47% of owners and 58% of workers partially or strongly agree. Taste-based discrimination is second (36% owners, 52% workers agree), followed by networks (39% owners, 49% workers). Patronage and workers’ preferences attract little agreement from either group. Among owners ranked by single strongest agreement, 29.7% most strongly agree with belief-based discrimination and 22.0% with taste-based, while 29% of all surveyed owners explicitly stated that political views do affect their hiring decisions. These patterns are broadly similar regardless of the respondent’s own political affiliation status.

Q9. How large are the political promotion and wage premia, and how do they compare to gender and race effects?

For promotions, copartisan white-collar workers are 0.448 percentage points more likely to be promoted to manager (relative to unaffiliated co-workers hired in the same firm-year), against a base promotion rate of 2.58% — an effect of approximately 17% of the mean. For blue-collar-to-white-collar promotion, the copartisan premium is 0.44 percentage points against a base rate of 2.98%. For wages, copartisans earn 3.9% more than unaffiliated co-workers within the same firm and year; restricting to the same occupation within the firm, the premium is 2.8%. The political wage premium (3.9%) exceeds the gender wage premium (1.5%) and the race wage premium (1.0%) in the same specification. Workers from a different party than the owner earn 1.6% less than unaffiliated co-workers within the same firm-year.

Q10. Are copartisan workers better qualified than those they displace, and what does this imply for firm performance?

Copartisan workers are significantly less qualified in terms of education relative to their occupation: they are 2.1 percentage points less likely to be educationally qualified for their position than their unaffiliated co-workers within the same firm-year (2.3% relative to the mean qualification rate of 93.2%), with the largest effects for managers. Workers of a different party show only a small and economically negligible qualification gap. The fact that copartisans are paid more, promoted faster, and yet are less qualified is consistent with political discrimination substituting for competence in personnel decisions. The qualification shortfall is specifically attributed to copartisanship and not to shared gender, race, age, or education between owner and worker, as those coefficients are economically small.

Q11. What is the evidence on firm growth and what are the limitations of that evidence?

Firms with a higher share of copartisan workers in the prior year grow less. The estimated coefficient β = −0.071, and a one-standard-deviation difference in the copartisan share is associated with approximately a 1 percentage point gap in annual employment growth, relative to a mean growth rate of 10%. The specification compares firms of the same size and with the same number of affiliated workers in the same year. The result is robust to adding municipality and municipality-industry fixed effects. The authors explicitly characterize this evidence as suggestive, noting the absence of an exogenous source of variation in political discrimination. The negative association is more consistent with taste-based discrimination (Becker, 1957) — in which politically homogeneous firms sacrifice productivity for the owners’ amenity of employing copartisans — than with accurate belief-based discrimination.

Q12. How is political assortative matching distributed across parties and does it depend on party ideology?

The likelihood ratio index shows large assortative matching across the entire political spectrum. For most years, relatively more ideologically extreme parties — on the left (PT, PDT) and on the right (PP, DEM) — display higher assortative matching than more centrist parties (PMDB, PSDB). This pattern is consistent with stronger partisan identity at the extremes leading to stronger preferences for copartisan workers, but the paper does not formally model the mechanism behind this heterogeneity.

Q13. What is the role of workers’ preferences as opposed to employers’ discrimination, and how can wages distinguish them?

If workers have a preference for working with copartisan owners (treating this as a job amenity), compensating differentials theory would predict a negative wage premium for copartisan workers — they would accept lower wages in exchange for working with like-minded owners. The data show the opposite: copartisan workers earn significantly more, not less, than their unaffiliated co-workers. This evidence is inconsistent with workers’ preferences being the primary driver of political assortative matching, and is instead consistent with employers’ discrimination. The survey evidence corroborates this: both owners and workers assign low priority to the “workers’ preferences” mechanism.

Key Concepts

Political assortative matching: The phenomenon by which workers and business owners belonging to the same political party are matched in the labor market at rates significantly exceeding what would occur under random matching within the local labor market. Measured via the likelihood ratio index and dyadic regressions that control for shared demographic characteristics. In this paper, political assortative matching is larger in magnitude than assortative matching along gender or racial lines.

Likelihood ratio index (S): A measure of assortative matching defined as the weighted sum of the ratios of observed same-party co-occurrence probabilities to their expected probabilities under random matching. S > 1 indicates positive assortative matching. The paper uses both a basic version and a geography-adjusted version that computes the index within municipalities to control for geographic concentration of party membership.

Dyadic regression: A regression approach that constructs all possible worker-firm pairs within a defined labor market (municipality × 2-digit industry) to estimate the differential probability that a worker is employed by a copartisan firm relative to a different-party firm. The key advantage is the ability to control simultaneously for multiple shared demographic characteristics between worker and owner, accounting for the correlation of assortative criteria.

Incentivized resume rating (IRR) experiment: A nondeceptive field experiment design (following Kessler et al., 2019) in which business owners rate synthetic resumes with fully randomized characteristics. Truthful rating is incentivized because respondents are told that their revealed preferences will be used to select real job-seeker profiles sent to them by a partner platform via machine learning. This design allows direct identification of employer preference for copartisan candidates while ruling out alternative channels such as shared nonpolitical networks or patronage.

Political wage premium: The percentage wage difference earned by copartisan workers relative to unaffiliated co-workers within the same firm-year (and occupation), after controlling for a full set of socio-demographic characteristics. A positive political wage premium is the paper’s primary piece of evidence that workers’ compensating differentials cannot explain political assortative matching, since amenity-based sorting would predict a negative premium.

Political promotion premium: The differential probability that a copartisan worker is promoted to a higher organizational layer (blue-collar to white-collar, or white-collar to manager) relative to an unaffiliated co-worker hired in the same firm and year, net of demographic controls.

Educational mismatch (Qualified): An indicator variable equal to one if a worker’s educational level meets or exceeds the educational level required by their specific occupation in the CBO (Classificação Brasileira de Ocupações) classification. Used to assess whether politically favored (copartisan) workers are less competent along this observable dimension.

Belief-based discrimination vs. taste-based discrimination: Two distinct theoretical channels for employer political discrimination. Belief-based discrimination (Phelps, 1972; Arrow, 1973) occurs when employers perceive copartisans to be more productive — e.g., because shared political views reduce intra-firm conflict. Taste-based discrimination (Becker, 1971) occurs when employers have a direct utility-affecting preference for copartisan workers, independent of productivity beliefs. The paper treats these as observationally distinct from patronage and network overlap, and uses the negative correlation between political homogeneity and firm growth as suggestive evidence favoring the taste-based channel.