Understanding High-Wage Firms: Monopoly, Monopsony, and Bargaining Power
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation: Why do some firms pay persistently higher wages for observably similar workers, and what role do firms’ product-market power (monopoly/markups), labor-market power (monopsony/markdowns), and workers’ collective bargaining power play in shaping wages and welfare? Prior literature studies labor-market power as a driver of wages/profits but abstracts from product-market power and bargaining, while the markups literature abstracts from imperfect labor competition and bargaining. The paper unifies all three in one structural framework.
Central theoretical insight: A firm’s wage equals its marginal revenue product of labor (MRPL) times a “labor wedge” (the share of MRPL workers receive). The labor wedge decomposes into three components — price-cost markups, monopsony markdowns, and bargaining power — via equation (3): Lambda = kappa*(product market rents term) + (1-kappa)*lambda. With positive bargaining power (kappa>0) workers capture a share of markup-generated rents, so the labor wedge rises with markups (rent-sharing); this nests pure monopsony as the kappa=0 special case.
Data and setting: French administrative micro-data. Firm balance sheets (FARE, 2008-2019, DGFiP); firm-product output prices (EAP survey, 2009-2019, INSEE, manufacturing firms >=20 employees or sales >5m euros); matched employer-employee data (DADS, 1995-2018) which crucially includes hours worked. Firm wage premia estimated via a k-means/BLM grouped AKM regression (Bonhomme, Lamadon, Manresa 2019). Markups and labor wedges estimated with the production-function/production approach (De Loecker-Warzynski 2012; Yeh et al. 2022) using translog functions and an Ackerberg-Frazer-Caves control function, separating the two by noting markups distort all input demands while labor wedges distort only labor demand.
Two key empirical facts a standard monopsony model cannot explain: (i) high-wage firms charge higher output prices and markups; (ii) high-wage firms pay a larger share of MRPL as wages (higher labor wedges). Both persist within narrow industries and conditional on TFP, pointing to product quality and positive bargaining power.
Main quantitative findings (French manufacturing, 2016 unless noted): Median markup 1.32 (IQR 1.14-1.60). Median labor wedge 0.62 (median monopsony markdown 0.46) — the gap is due to bargaining power and markups. Workers capture about 12% of firm profits (bargaining power kappa ~ 0.12-0.14; falls to ~0.05-0.13 under IV correction). Median markdown 0.46 implies a median firm-specific labor supply elasticity of 0.85. Accounting for hours matters: median labor wedge is 0.62 with effective hours, 0.65/0.68/0.71 across specifications, rising to 0.71 when labor is measured by employment (near Yeh et al.’s 0.70-0.73 US figures) — so omitting hours upward-biases labor wedges.
Quantitative GE model (oligopoly/oligopsony, nested-CES, Atkeson-Burstein/Berger et al.): A 1% productivity shock has wage passthrough 0.97-0.99 versus 0.23 for an equal quality shock (because varieties are close substitutes, sigma=5.17), though quality still generates more wage-premium dispersion. Markups and markdowns reduce welfare by 46% in consumption-equivalent terms, with markups alone accounting for over 80%; misallocation explains about 63% of the markup welfare cost. Equalizing markups raises average wages 39% and wage variance 99% and welfare 24% (output-restriction effect dominates rent-sharing, so equalizing markups raises wage dispersion). Raising bargaining power from 0.12 to 0.50 matches the wage gains of removing markups but yields only 10% welfare gain (vs 38%); full bargaining power (kappa=1) raises welfare 13%, under one-third of the planner’s 46% gain. Bargaining power offsets the uniform-tax and misallocation distortions on labor demand but cannot fix markup distortions to capital/material demand.
Layer 2: Deep Dive
What is the core identification strategy for separating markups from labor wedges, and what are its main assumptions/threats?
The author applies the production approach: estimate translog production functions per 2-digit manufacturing sector (via two-step GMM with an Ackerberg-Frazer-Caves control function for unobserved productivity) to recover firm-specific output elasticities. Markups distort the demand for ALL inputs while labor wedges distort ONLY labor demand, so choosing materials as a flexible, price-taken input lets markups be identified from the material cost share (mu = alpha_m * PY/(Pm*M)) and labor wedges from the wage-bill-to-materials ratio scaled by elasticity ratios (eq. 4). Key assumptions/threats: materials must be a flexible input firms take prices for (examined in Appendix B.7-B.8); unobserved productivity must satisfy scalar unobservability and monotonicity in material demand; unobserved output and input prices bias elasticities — addressed using observed EAP output prices (measuring output in quantities) plus the De Loecker et al. (2016) input-price control function, and additionally controlling for firm wage premia because monopsony markdowns create unobserved labor-price variation. Markup variation driven by idiosyncratic demand uncorrelated with TFP is controlled via export status, market shares, firm age, and a 3rd-order price polynomial. Gandhi-Navarro-Rivers concerns about identifying material elasticities are addressed in Appendix B.9.
What is the new identification challenge for estimating bargaining power, and how is it solved?
The rent-sharing literature estimates bargaining power kappa by regressing wages on quasi-rents using instruments (export demand, patent shocks) assumed orthogonal to the worker’s reservation wage. But in this model, when kappa=0 workers earn an endogenous monopsony wage (lambda*MRPL) that moves with the SAME firm-specific shocks (productivity, quality, amenities) that shift quasi-rents — so standard instruments violate the exclusion restriction. The solution: instead of the wage equation, exploit the labor-wedge equation (3), which relates labor wedges to markups and avoids unobserved monopsony wages. Conditional on markdowns, variation in product-market rents identifies kappa (when kappa=0 product-market rents do not affect the labor wedge). This shifts the core challenge from unobserved monopsony wages to unobserved amenities (mirroring IC3 in the rent-sharing literature), handled by a theory-consistent control function in which employment and the wage bill jointly proxy for amenities under a monotonicity assumption (labor supply increasing in amenities). Under multiplicative separability of wages and amenities, markdowns do not depend directly on amenities, so unobserved amenities do not bias kappa at all.
What are the bargaining-power estimates across specifications?
Pooled OLS gives ~0.135; adding firm fixed effects ~0.124; adding the amenity control function (columns 3-4) ~0.124-0.135, indicating amenities have little direct effect on markdowns; instrumenting product-market rents with their lags to correct correlated measurement error (columns 5-6) gives 0.130 and 0.059. Baseline kappa is taken as ~0.12 (specification 4). All 2-digit sectors have kappa below 0.3. These align with the rent-sharing literature’s typical 0.05-0.15, though external innovation-based instruments tend to find ~0.30.
How does the paper measure firm wage premia and why not use standard AKM?
Standard AKM firm effects assume time-invariant firm effects and rely on worker mobility; short panels yield noisy estimates with upward-biased variance. The author needs time-varying premia (to measure effective labor over time). He uses the BLM (Bonhomme, Lamadon, Manresa 2019) k-means approach: cluster firms by the similarity of their internal wage distributions (by 2-digit sector over overlapping 2-year windows), then run an AKM-style regression with firm-GROUP effects that vary by year, identified by workers switching between firm-groups — greatly increasing the number of switchers. DADS-Postes is used for clustering (broad coverage) and DADS-Panel for the wage-premium regression.
What heterogeneity is documented across firms?
Firm wage premia dispersion accounts for 5.2% of wage dispersion; the 90-10 premium gap is ~30% (about 4 euros/hour, 25% of the median worker’s hourly wage), IQR 15%. Markdowns increase with firm wage premia (flat gradient) but DECREASE with firm size — larger firms have more monopsony power, consistent with oligopsony models. Firm-specific labor supply elasticities are 0.54/0.85/1.33 at the 25th/50th/75th percentiles. About 7% of firms have labor wedges above 1, and these tend to have much higher markups (rationalized by kappa>0). In the GE model, top-decile high-wage firms are ~15% more productive but have over 100% greater product quality than bottom-decile firms; amenities rise slightly more steeply with premia than productivity. Passthrough is substantially smaller for 90th-percentile firms (0.74 productivity, 0.18 quality) than for median/10th-percentile firms (~1.06/~0.26).
How is the dispersion of wage premia decomposed across sources of firm heterogeneity?
Introducing one source at a time into the GE model and comparing variance to baseline (Table 6): varying only product quality reproduces 161.5% of baseline variance, only TFP 153.3%, and only amenities 40.8%. Product quality is the largest single contributor to wage-premium dispersion, closely followed by productivity, then amenities.
Why does the productivity passthrough differ so much from the quality passthrough?
Total passthrough is 0.97 for a 1% productivity shock vs 0.23 for an equal quality shock (~4x). The decomposition (Table 5) attributes most of the gap to the direct effect (1.07 vs 0.26): with high within-market substitutability (sigma=5.17), consumers are very price-sensitive, so productivity (which lowers price) moves sales and labor demand far more than quality. Higher sigma raises productivity passthrough but lowers quality passthrough. For sufficiently low sigma the ranking can reverse. The variable-market-power channel also matters: higher productivity raises markups, increasing rent-sharing (+0.06 via labor wedge) but also output restriction (-0.09 via markup), with output restriction dominating; firm-size effects (sectoral price -0.10, sectoral wage +0.03) further adjust passthrough. Amenity shocks have direct effect -0.26 (mirror of quality) but total -0.28, amplified because better amenities lower hiring costs and expand the firm.
How does worker bargaining power affect welfare, and what are the limits?
Bargaining power offsets two distortions firm market power imposes on aggregate labor demand: a uniform tax (Lambda/mu, lowering labor demand proportionally) and a misallocation tax (Theta, from dispersion in wedges). There exists a kappa-bar that exactly cancels the uniform tax, and kappa-bar falls as markups rise (high markups make bargaining more effective). With full bargaining power and common markups, the markdown-driven misallocation tax is fully neutralized. BUT bargaining only acts through labor demand; markups also distort capital and material demand, which bargaining cannot fix. Quantitatively: raising kappa from 0.12 to 0.50 matches the wage gain of removing markups but yields only 10% welfare gain (vs 38%) and far less dispersion increase; full kappa=1 raises welfare 13%, under one-third of the planner’s 46% gain. So bargaining power is a partial, not full, remedy for firm market power.
What is the welfare accounting for markups vs markdowns?
Comparing the decentralized economy to the social planner’s (Table 7, column 3): eliminating both markups and markdowns raises wage-premium dispersion 113%, average wages 303%, and welfare 46% (consumption-equivalent). Over 80% of the welfare gain comes from removing markups. Equalizing markups alone (column 4) gives 24% welfare, +39% wages, +99% wage variance, implying ~63% of the markup welfare cost is misallocation. Equalizing markdowns alone (column 5) has little welfare effect (2%), though a wide markdown level reduces welfare significantly (column 2).
What robustness checks and caveats does the author flag?
Caveats: (1) Multiplication bias — mismeasured output elasticities enter both labor wedges and product-market rents multiplicatively, mechanically biasing kappa upward (Appendix B.10); IV with lags only fixes classical, not serially-correlated, measurement error. (2) Labor adjustment costs get absorbed into the labor wedge and bias kappa; firm fixed effects do not fully fix this (Appendix B.11). (3) The markdown estimation imposes that all markdown variation reflects firm size and amenities — more general than kappa=0 approaches but restrictive in this dimension. (4) The model uses collective (not individual) bargaining and abstracts from sequential-auction wage-setting (Cahuc-Postel-Vinay-Robin); robustness to hiring-wages-only following Di Addario et al. (2020) is shown (Appendix B). (5) Worker types assumed perfect substitutes; an Appendix E two-skill extension gives similar results. (6) Empirical patterns hold without TFPQ controls (Figure D.3) and by firm size (Figure D.4).
How does this paper relate to and differ from closely related prior work?
Versus the labor-market-power literature (Berger et al. 2022; Lamadon et al. 2022) it adds product-market power and bargaining, showing their pure-monopsony labor wedge is a kappa=0 special case. Versus the markups/welfare literature (De Loecker et al. 2020; Edmond et al. 2023) it adds imperfect labor competition and bargaining. Versus recent integrated product+labor power models that use wage-posting and no bargaining (Kroft et al. 2024; Deb et al. 2024), it adds the rent-sharing channel where markups raise (not just lower) the labor wedge. Versus production-approach markdown estimation (Yeh et al. 2022; Mertens 2020), it shows their estimates are labor wedges (not markdowns) once kappa>0, and that omitting hours upward-biases them. Versus the rent-sharing literature (Card et al. 2018; Kline et al. 2019; Van Reenen 1996), it shows their instruments violate exclusion under endogenous monopsony wages and proposes the labor-wedge-equation alternative. The closest exception incorporating unions is Azkarate-Askasua and Zerecero (2025).
What are the policy implications and their scope conditions?
Strengthening worker collective bargaining power can raise welfare mainly by offsetting markup-induced distortions to labor demand and redistributing rents, but it raises between-firm wage inequality and cannot restore full efficiency because it leaves markup distortions to capital/material untouched (full kappa closes under one-third of the planner gap). The wage effects of innovation depend on whether it improves productivity or quality and on the degree of product differentiation. Scope conditions: estimates are for French manufacturing under firm-level collective bargaining institutions (firms >=50 employees legally bargain annually); results rely on the production-approach assumptions (flexible/price-taken materials, scalar unobservability) and on data including hours and output prices that many countries lack — researchers should interpret labor-wedge/markup moments cautiously without hours data.