Firm dynamics, monopsony, and aggregate productivity differences
What this paper finds — and why it matters
Layer 1: Overview
Research question and motivation. Firms are larger and grow faster over the life cycle in high-income countries, while labor markets in poorer countries are less competitive (employers hold more wage-setting power). The paper asks how important employer labor market power (monopsony) is for explaining cross-country differences in firm dynamics and aggregate productivity. The novelty is that beyond the standard static misallocation-of-workers channel, monopsony also distorts selection into entrepreneurship and productivity-enhancing technology adoption, potentially making the losses larger than prior static estimates suggest.
Data and setup. Stylized facts come from the World Bank Enterprise Surveys (WBES), an establishment-level survey of non-agricultural, non-financial private firms with at least 5 full-time permanent employees, covering more than 90 countries from 2006 to 2021, merged with World Development Indicators GDP per capita (2017 constant USD). The estimation sample restricts to countries that ever had GDP per capita above 25,000 USD and to manufacturing firms with non-missing sales/workers/material/capital data, yielding 37,096 firm-year observations across 31 middle- and high-income countries (poorest: Kazakhstan, 19,615 USD in 2009; richest: Ireland, 91,791 USD in 2020). Local labor markets are defined as location-industry (2-digit ISIC v3.1) pairs. The model is a dynamic general-equilibrium neoclassical-monopsony model with occupational choice (entrepreneur vs. wage worker), endogenous productivity investment, and Card-et-al.-style taste-for-employer (amenity) differentiation that gives firms wage-setting power. It is calibrated to the Netherlands (GDP per capita 54,275 USD; median wage markdown 1.301, implying firm-level labor supply elasticity 3.318) via method of simulated moments.
Main quantitative findings. Empirically, moving from poorer to richer countries in the sample, average firm age triples from 11 to nearly 30 years; annualized firm growth rises ~1.6 percentage points per year per doubling of GDP per capita; the share of firms doing R&D more than doubles (from ~15% to >40%); product innovation rises from 20% to 80% and process innovation from 20% to 50%; and median wage markdowns fall (from ~2.25 at 25,000 USD GDP per capita — workers paid ~55% below marginal product — to ~1.25 at 60,000 USD — paid 20-25% below). The calibrated model matches a right-skewed firm-size distribution, life-cycle growth, employer turnover, age distribution, and R&D share (sum of squared deviations between empirical and simulated moments = 1.7%). In counterfactuals raising the markdown from 1.2 to 3, average firm growth shrinks by more than half (from ~150% to ~50%), average firm size falls from ~60 to ~45 employees, the innovating share halves (from ~40% to ~25%), and average firm productivity is ~20% higher in competitive markets. Differences in wage markdown alone account for 25% of observed cross-country TFP variation (model TFP std dev 0.051 vs. data 0.201), and no less than 11% across robustness checks. In a Netherlands-vs-Greece decomposition, about 85% of the model-implied TFP gap is attributable to lower technology adoption, ~9% to distorted selection into entrepreneurship, and ~6% to static employment reallocation.
Mechanisms and implications. Labor market competition acts as a “skill-biased” force favoring high-productivity firms through three channels: (i) static labor reallocation toward high-productivity, low-amenity firms; (ii) improved selection into entrepreneurship (low-productivity high-amenity agents stop being able to profitably attract workers as ϵL rises); and (iii) higher returns to innovation. The policy implication is that raising labor market competition in less-developed economies could yield substantial productivity gains, and that prior static studies understate the cost of monopsony because they omit the dynamic investment/selection channels.
Layer 2: Deep Dive
What is the identification/calibration strategy and what are the main threats to it?
The model is calibrated to the Netherlands using a mix of externally set and internally estimated (method-of-simulated-moments) parameters. Externally: model period = 1 year; σν (Gumbel scale) normalized to 1; β = 0.961 (4% annual rate); δw = 0.025 (40-year working life); revenue elasticity of labor ξ = 0.333 (estimated via control function in Section 2); labor supply elasticity ϵL = 3.318 backed out from median markdown 1.301 via ϵL = 1/(µ−1). Six parameters {c_f, c_x, p_i, p_n, σ_z, σ_a} are estimated by MSM. The markdown itself is a key input and is estimated as the ratio of marginal revenue product of labor to wage, with revenue elasticity ξ from a standard control-function approach. Threats: the markdown estimate drives the whole quantitative exercise; the WBES sample is truncated at firms with ≥5 employees (biasing toward larger firms), addressed by re-estimating with imputed moments; and the cross-country counterfactual attributes all variation in ϵL to labor market power while holding all other parameters at Netherlands values, so other cross-country differences are not separately identified.
What are the three mechanisms and how are they distinguished quantitatively?
(1) Static labor allocation: lower competition raises marginal factor cost only for sufficiently high-productivity firms, reallocating employment toward less-productive, lower-paying employers. (2) Selection into entrepreneurship: when ϵL is low, amenities matter more for profits, letting low-productivity high-amenity agents profitably self-select into entrepreneurship. (3) Technology adoption: returns to innovation increase with ϵL, so weak competition lowers the share of firms investing. They are distinguished via a decomposition that sequentially fixes policy functions at benchmark levels: ~6% of the TFP loss is from employment allocation alone, ~85% from the distortion to innovation policy, and ~9% from distorted selection into entrepreneurship.
What heterogeneity across firms is documented?
Firms differ in entrepreneurial productivity z and amenity a. Average revenue product of labor rises with productivity and falls with amenities, and this dispersion is much steeper under weak competition: the elasticity of APL with respect to productivity is 0.31 in the baseline (Netherlands) vs 0.79 in the counterfactual (Greece), and with respect to amenities -0.28 vs -0.81. High-productivity, low-amenity firms face the biggest barriers in less-competitive markets and stay inefficiently small; low-productivity, high-amenity firms are propped up. Innovation distortion is concentrated among high-productivity firms.
What robustness checks are run and what do they show?
Four main checks, each reported as the share of cross-country TFP variation explained (data std dev 0.201): (1) Productivity-amenity correlation — allowing entrants to draw correlated (z,a) with σ_za = 0.296 (matching Sockin 2024’s 0.622 wage-satisfaction correlation) lowers explained variation to ~15% (model std dev 0.030), because correlation reduces scope for reallocation. (2) Costs in terms of labor instead of final goods (per Klenow and Li 2025) gives ~22% (std dev 0.044). (3) Imputed firm-level moments covering all firms (not just ≥5 employees) gives ~14% (std dev 0.028). (4) Over-identified alternative identification using size/age/R&D shares and annualized growth gives ~11% (std dev 0.023). The headline range is therefore 25% baseline, no less than 11% across checks.
How does this paper relate to and differ from closely related prior work?
It builds on static monopsony cost estimates: Berger et al. (2022, eliminating US labor market power raises average wage 48%, welfare +6% of lifetime consumption); Armangüé-Jubert et al. (2025, labor market power explains 15% of GDP-per-capita gap over development); Deb et al. (2022, less competition lowered US low/high-skill wages 12% and 11%); Amodio et al. (2025b, eliminating monopsony in Peru raises earnings 26%); Bachmann et al. (2022, monopsony caused a 10% aggregate productivity loss in East Germany). Its contribution is to add the entrepreneurial-selection and innovation channels, yielding larger losses than static studies, and to bridge the monopsony-cost literature with the misallocation literature (Restuccia-Rogerson, Guner et al., Hsieh-Klenow).
What are the policy implications and their scope conditions?
Raising labor market competition (higher firm-level labor supply elasticity) improves allocative efficiency, selection into entrepreneurship, and innovation, raising firm growth and aggregate productivity. Scope conditions: the quantitative results apply to middle- and high-income countries (sample restricted to those ever above 25,000 USD GDP per capita); the 25% headline depends on the assumption that initial productivity and amenities are independent (falls to ~15% under positive correlation); and the decomposition attributing 85% to innovation is specific to the Netherlands-vs-Greece comparison. The model treats labor supply elasticity differences as the sole varying parameter, so the counterfactuals isolate the labor-market-power channel rather than reproducing total cross-country income gaps.
What is the Netherlands-vs-Greece comparison specifically?
Greece has roughly half the GDP per capita of the Netherlands (29,000 vs 54,000 USD) and much weaker competition (wage markdown 2.623 vs 1.301, labor supply elasticity 0.616 vs 3.318). In the Greece counterfactual, average firm size is 26 vs 59 employees, life-cycle growth 84.5% vs 153%, average age 22.5 vs 30 years, and R&D investing share 18% vs 41%. Labor market competition differences explain 29% of the firm-size gap, 27% of the firm-age gap, and 74% of the R&D-share gap between the two countries.
What does the model get right that was not targeted?
The firm size and age distributions are not targeted yet are matched: in the data ~57.6% of firms have <20 employees and ~6.2% have >100; ~60% of firms are under 30 years old and ~10% over 60. The estimated parameters imply investing firms are 15% more likely to grow (p_i=0.649 vs p_n=0.499); innovation and operating costs equal ~43% and ~8% of average incumbent profits respectively; standard errors are small, indicating informative moments.