Value without Employment: Barkai & Panageas (2025)

What this is. The paper’s core results, the economic model connecting the ARPL-to-MRPL ratio to aggregate trends, and the key equations: enough to understand what it found and why, without reading all 46 pages. To replicate or extend, read the full source at doi.org/10.1111/jofi.13505.

TL;DR

Young firms’ employment contribution has fallen sharply since the early 1980s, but their contributions to aggregate sales and stock market value have not fallen similarly. The ratio of market-value contribution to employment contribution of young-firm IPO cohorts has more than doubled over 1985 to 2014 (Figure 1, p. 3734). Pitchbook exit-value data and NETS establishment-level data corroborate this pattern for the broader universe of firms. The divergence implies a rising average-to-marginal revenue product of labor (ARPL-to-MRPL ratio) for recent young-firm cohorts: these firms earn similar or greater revenues per dollar of market value while employing far fewer workers.

The paper introduces this feature into a standard model of dynamic firm heterogeneity (monopolistic competition, heterogeneous productivity, endogenous bankruptcy), shows it jointly explains a large set of empirical trends including the labor share decline, and then proves analytically that a 40% drop in young-firm employment contribution translates into only a 4.8% drop in steady-state consumption (under the base-case assumption that the shift reflects lower labor intensity). Even in the worst case where the shift reflects a rise in economic rents, the long-run consumption decline is bounded at 8.1%, roughly five times smaller than the employment decline.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Employment contribution of recent IPO cohorts fell, but market-value and sales contributions did not show a similar decline	Figure 1, Panels A-D, pp. 3733-3734	Log employment contribution of 2010-2014 cohort bin is 0.71 lower than 1985-1989 cohort bin; log sales contribution is only 0.11 lower; log market-value contribution is 0.07 higher
R2	The ratio of market-value contribution to employment contribution has more than doubled	Figure 1, Panel D, p. 3734	Normalized log ratio (1985-1989 = 0) rises by about 0.8 log points for the 2010-2014 cohort bin relative to the earliest cohort
R3	Recent cohort deflated exit values (Pitchbook) are at least as large as those of earlier cohorts, controlling for age	Figures 3-4, pp. 3737-3738	With the exception of the 1995-1999 cohort (dot-com era outlier), each successive cohort bin has deflated exit values similar to or larger than its predecessors at the same cohort age
R4	Young firms (post-2005) exhibit slower employment growth at acquired establishments relative to older acquirers, conditional on year, industry, state, and establishment age and size	Tables II-IV, pp. 3743-3745	Young Acquirer coefficient: -0.024** to -0.039*** in the first specification (Table II); Young Acquirer x Post-2005 interaction: -0.148*** to -0.161*** (Table III); coefficient is stable with establishment age and size controls (Table IV)
R5	A single change in the ARPL-to-MRPL ratio (modeled as a rise in rent share, i.e., lower xi*) simultaneously reproduces the labor share decline, stable investment share, falling young-firm employment share, declining job creation and destruction, and rising market-value-to-employment ratio	Figures 6-9, pp. 3755-3758	Model labor share declines from 0.60 to 0.54 (data: 0.60 to 0.54); young-firm employment share falls from ~22% to ~8% (data: ~18% to ~10%); investment share remains roughly flat; job creation and destruction rates each decline by ~4-5 pp
R6	The elasticity of steady-state consumption to a decline in young-firm output is approximately 12%, so a 40% drop in new-firm output implies only a 4.8% decline in consumption	pp. 3727, 3761	Under base-case parameters (xi = 0.93, alpha = 0.62), the pass-through factor (1 - xi) / (alpha xi) is approximately 12%; a 40% drop in new-firm output implies a 4.8% consumption decline
R7	In the worst case (rise in economic rents), the long-run consumption decline from the observed dynamics is bounded at 8.1%	pp. 3727-3728, 3764	Markup-driven scenario (Barkai (2020) estimates): 8.1% drop in steady-state consumption between old and new steady states, which is roughly five times smaller than the 40-50% decline in young-firm employment
R8	The multisector model quantifies the total steady-state consumption impact at -8.1%, with the main negative term being the rent-share channel (-14.2%), partially offset by positive factor-reallocation effects (+12.4%)	Table VI, p. 3767	dC^SS/C^SS = -0.081; Term 1 (new-firm output) = -0.047; Term 2 (rents) = -0.142; Term 3 (factor intensity) = -0.016; Term 4 (factor reallocation) = +0.124

Overall (paper’s conclusion). Young firms are not weaker than their predecessors in terms of value creation, only in terms of employment creation. This divergence implies a higher ARPL-to-MRPL ratio for recent cohorts. Attributing this shift to a rise in rents (as in Decker et al. (2016a), De Loecker, Eeckhout, and Mongey (2021)) provides a unified explanation for the labor share decline, declining business dynamism, and declining job flows. Akcigit and Ates (2023) document the same dynamism trends; this paper argues those trends are not mirrored in market values and thus cannot be interpreted as pure firm weakness. Decker et al. (2020) show that firms have become less responsive to idiosyncratic shocks; the model captures this as lower $\xi^*$ for new-type firms, implying a reduced standard deviation of employment changes for new cohorts. De Loecker, Eeckhout, and Unger (2020) provide the sector-level markup estimates used in the multisector model calibration. Gutierrez and Philippon (2017) document investmentlessness; consistent with the model, the investment share responds only modestly. Haltiwanger et al. (2017) provide employment growth dispersion data used to calibrate productivity volatility $\sigma$. Even under this worst-case interpretation, large declines in young-firm employment imply only moderate long-run declines in aggregate consumption, because a substantial part of the decline in new-firm output is an amplified transitory factor adjustment rather than a permanent loss.

Theory / model

The economic setting is monopolistic competition with heterogeneous intermediate-goods producers (Section II, pp. 3746-3750). Time is continuous. A representative final-goods firm assembles intermediate inputs using a Kimball (1995) aggregator (eq. 7, p. 3747):

$$1 = \int_{i \in I} \mathcal{Y}\!\left(\frac{y_{it}}{Y_t}\right) di \tag{7}$$

where $y_{it}$ is the quantity of intermediate good $i$ at time $t$, $Y_t$ is aggregate output, and $\mathcal{Y}(\cdot)$ satisfies $\mathcal{Y}(1)=1$, $\mathcal{Y}'>0$, $\mathcal{Y}''<0$. In the Dixit-Stiglitz special case $\mathcal{Y}(x) = x^{\xi}$ with $\xi \in (0,1)$, this reduces to the familiar CES aggregator (eq. 8, p. 3747):

$$Y_t = \left(\int_{i \in I} y_{it}^{\xi}\, di\right)^{1/\xi}. \tag{8}$$

Each intermediate producer uses a Cobb-Douglas production function (eq. 12, p. 3748):

$$y_{it} = Z_{it} k_{it}^{1-\alpha_i} l_{it}^{\alpha_i}, \tag{12}$$

where $l_{it}$ is labor, $k_{it}$ is capital, $\alpha_i \in (0,1)$ is the labor intensity (allowed to vary across firms), and $Z_{it}$ is firm-specific productivity following a geometric Brownian motion (eq. 13, p. 3748):

$$dZ_{it} = \mu Z_{it}\, dt + \sigma Z_{it}\, dW_{it}. \tag{13}$$

Firms must also pay a fixed overhead labor cost $\bar{l}$ per unit time (the “operating leverage” that drives endogenous bankruptcy). They face an exogenous death shock at rate $\lambda > 0$. The first-order conditions for capital and labor equate marginal revenue products to factor prices (eqs. 15-16, p. 3749):

$$(1-\alpha_i)\!\left(1 - \frac{1}{\varepsilon_i}\right) p_{it} Z_{it} k_{it}^{-\alpha_i} l_{it}^{\alpha_i} = r_t^K, \tag{15}$$ $$\alpha_i\!\left(1 - \frac{1}{\varepsilon_i}\right) p_{it} Z_{it} k_{it}^{1-\alpha_i} l_{it}^{\alpha_i - 1} = w_t, \tag{16}$$

where $\varepsilon_i \equiv -\mathcal{Y}^{\prime}\!\left(\frac{y_{it}}{Y_t}\right) / \!\left[\frac{y_{it}}{Y_t} \mathcal{Y}^{\prime\prime}\!\left(\frac{y_{it}}{Y_t}\right)\right]$ is the absolute demand elasticity. In the Dixit-Stiglitz case, $\xi_i = 1 - \frac{1}{\varepsilon_i}$ is the sum of factor shares, and the markup is $\frac{1}{\xi_i} - 1$. The ARPL-to-MRPL ratio for firm $i$ follows directly from (16) (eq. 17, p. 3749):

$$\frac{\frac{p_{it} y_{it}}{l_{it}}}{\frac{\partial(p_{it} y_{it})}{\partial l_{it}}} = \frac{1}{\xi_i \alpha_i}. \tag{17}$$

A high ARPL-to-MRPL ratio therefore corresponds to a small $\xi_i$ (large rent share) or a small $\alpha_i$ (low labor intensity), or both.

Representative household. The household maximizes (p. 3750):

$$\mathrm{E}_t\!\left(\int_t^{\infty} u(C_s) e^{-\rho(s-t)}\, ds\right), \quad u(C) = \frac{C^{1-\gamma}}{1-\gamma},$$

with discount rate $\rho > 0$ and inverse IES $\gamma > 0$. Goods market clearing requires $C_t + I_t = Y_t$; capital evolves as $\dot{K}_t = -\delta K_t + I_t$.

Identification and counterfactual. Section II.D (pp. 3751-3754) considers a transition experiment where from time $t_0$ onward a fraction $1 - e^{-\vartheta(t-t_0)}$ of arriving firms are “new-type” with $\xi^* < \xi$. The model is calibrated to match the labor share and employment volatility of young firms from $t_0 = 1983$ onward (Table V, p. 3753).

Method

The paper combines two methods: a descriptive-empirical analysis of cohort contributions (applied to Compustat, Pitchbook, and NETS) and a dynamic structural model solved analytically in steady state plus numerically along the transition path.

The paper builds on dynamic-general-equilibrium (the Kimball-aggregator model with Cobb-Douglas production and GBM productivity) and panel-regression (the NETS establishment-level switcher regressions). The analytical steady-state solution exploits a time-age-cohort decomposition and the Kimball demand structure to obtain closed-form expressions for wages, output, and the cross-sectional distribution of productivity (eqs. 19-22, pp. 3750-3751).

ARPL-to-MRPL decomposition. Equation (6) (p. 3746) expresses the ARPL-to-MRPL ratio as a function of two elasticities:

$$\frac{\text{ARPL}_i}{\text{MRPL}_i} = \frac{1}{1 + \frac{y_i}{p_i}\frac{\partial p_i}{\partial y_i}} \times \frac{1}{\frac{l_i}{y_i}\frac{\partial y_i}{\partial l_i}} = \frac{1}{1+\text{markup}^{-1}} \times \frac{1}{\text{labor intensity}}. \tag{6}$$

Lemma 1 (cohort divergence, p. 3739). Under a time-age-cohort decomposition, the discrepancy between the log change in market-value contribution and the log change in employment contribution of young firms equals the log change in the (employment-weighted) ARPL-to-MRPL ratio of young firms versus the whole economy (eq. 2, p. 3739):

$$\Delta\!\log\!\left(\frac{P_{t,t}}{\sum_{s \leq t} P_{t,s}}\right) - \Delta\!\log\!\left(\frac{l_{t,t}}{\sum_{s \leq t} l_{t,s}}\right) = \Delta\!\log\!\left(\frac{\sum_{i,t} \omega^i_{t,t}(\delta^i_{t,t}-1)}{\sum_{i,s \leq t}\omega^i_{t,s}(\delta^i_{t,s}-1)}\right), \tag{2}$$

where $\delta^i_{t,s} \equiv \frac{p^i_{t,s} y^i_{t,s} / l^i_{t,s}}{w_t}$ is the ARPL-to-MRPL ratio of firm $i$ born at time $s$.

Proposition 1 (aggregate implications, p. 3760). In the single-sector economy, as the discount rate $\rho \to 0$, the percentage change in steady-state consumption is (eq. 23, p. 3760):

$$\frac{dC^{SS}}{C^{SS}} = \frac{1}{\alpha}\frac{1-\xi}{\xi}\frac{dY^{\text{new}}}{Y^{\text{new}}} - \left(\frac{1-\xi}{1-\xi(1-\alpha)}\right)\frac{d\alpha}{\alpha} + \frac{1}{\alpha}(G - D)\frac{d\xi}{\xi}, \tag{23}$$

where $G \equiv \int_{z^*}^{\infty}(\tilde{g}(z) - \tilde{m}(z))\log y(z)\,dz$ and $D \equiv (1 + \frac{1}{\xi}) + \alpha\frac{\xi(1-\alpha)}{1-\xi(1-\alpha)} - (1-\alpha)$, with $\tilde{g}(z)$ and $\tilde{m}(z)$ the stationary and entering revenue distributions. The factor $\frac{1-\xi}{\alpha\xi}$ in the first term is approximately 12% under baseline parameters, quantifying why a large drop in new-firm output passes through only modestly to consumption.

Proposition 2 (multisector, p. 3765) generalizes (23) to $N^S$ sectors with sector-specific $\xi^S$ and $\alpha^S$ (eq. 30, p. 3765), adding a fourth term that captures factor reallocation across sectors.

Empirical specifications

Three separate empirical exercises establish the employment-value divergence.

Compustat cohort contributions (Section I.A, pp. 3731-3734). The paper forms five-year IPO cohort bins for all Compustat nonfinancial U.S. public firms founded within 10 years of their IPO, 1985 to 2014 (7,565 firms, 82,823 firm-year observations). Employment, sales, and market-value contributions of each cohort are expressed as shares of aggregate totals among all Compustat firms in the same year and summed within bins (Figure 1). No regression estimator is used here; the results are descriptive cohort-share series.

Pitchbook exit values (Section I.B, pp. 3735-3738). For firms exiting by IPO or M&A, post-money valuations are deflated by aggregate stock market capitalization and traced by cohort age. This is also a descriptive exercise, not a regression.

NETS establishment switchers (Section I.D, pp. 3740-3745). The headline regression is:

$$\log L_{t+1} - \log L_{t-1} = \beta_1\,\text{YoungAcquirer}_{it} + \beta_2\,(\text{YoungAcquirer}_{it} \times \text{Post-2005}) + \gamma_t + \delta_{j \times k \times s} + \varepsilon_{it}, \tag{OLS}$$

where the dependent variable is the log change in employment from year $t-1$ (before acquisition) to year $t+1$ (after acquisition) for a target establishment. YoungAcquirer equals one if the acquiring firm is less than eight years old. Fixed effects include year $\gamma_t$ and year $\times$ four-digit SIC $\times$ state $\delta_{j \times k \times s}$. Standard errors are clustered by year $\times$ SIC4 $\times$ state. Sample: 213,792 acquisitions, 1998 to 2014 (Table I, p. 3743).

The first specification (Table II) pools the full sample. The main specification (Table III) adds the YoungAcquirer $\times$ Post-2005 interaction term. Table IV adds establishment age bin dummies and log-employment in year $t-1$ as controls to address differential selection by young acquirers.

Results: YoungAcquirer coefficient = -0.024** to -0.039***; Post-2005 interaction = -0.148*** to -0.161*** (Tables II-III); results are stable with age and size controls (Table IV).

Datasets used

Dataset	Role in paper	Wiki page
Compustat (via WRDS)	U.S. public firm employment, sales, and market value by IPO cohort bin, 1985-2014	WRDS (licensed)
PitchBook	Exit valuations (IPO and M&A) for private and public U.S. firms by founding-year cohort, 1990-2019	PitchBook (licensed)
National Establishment Time Series (NETS)	Establishment-level employment and ownership changes (acquirer age), 1998-2014; 213,792 acquisitions	NETS (licensed)
Census Business Dynamics Statistics (BDS)	Aggregate employment share of young firms (ages 0-5), firm-size distribution, job creation and destruction rates, 1983-2019	U.S. Census Bureau public data products
BEA National Accounts (GDP-by-Industry, Fixed Asset Tables)	Labor share, investment share, and value added by sector for model calibration and multisector analysis	NIPA

Sample for Compustat analysis: 7,565 firms, 82,823 firm-year observations, 1985-2014, annual frequency. NETS sample: 213,792 acquisitions, 1998-2014. BDS and BEA coverage: 1983-2019 (model transition period).

When to read the full paper

Use the original if you are: studying the connection between business dynamism and the labor share and want the analytical propositions; examining the welfare implications of rising markups or declining labor intensity; building a calibrated model of firm dynamics with heterogeneous markups; or using the NETS switcher design to measure young-firm employment behavior. The Internet Appendix contains proofs, the elastic labor supply extension, the numerical algorithm for the transition path, and sector-level parameter estimates.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6), December 2025. This distillation was extracted by an LLM on 2026-06-03 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). Barkai, Simcha, and Stavros Panageas. “Value without Employment.” The Journal of Finance 80, no. 6 (December 2025): 3725-3770. DOI: 10.1111/jofi.13505. Copyright 2025 The Author(s). 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.