An Economic View of Corporate Social Impact: Allcott, Montanari, Ozaltun & Tan (2026)

What this is. The paper’s core results, datasets, and theory: enough to know what it found without reading all 44 pages. To replicate or extend it, read the full source at the original (open access on the Wiley platform under AFA/Wiley terms). Replication files and the survey instrument are available at allcott.stanford.edu/research.

TL;DR

The paper proposes an economic definition of corporate social impact as the social welfare loss that would result from a firm’s exit. Using a 3,500-person survey (fielded July and November 2021) combined with standard industrial organization and labor economics methods, it estimates social impact for 74 large firms across 12 U.S. industries. Consumer surplus is by far the largest component, dwarfing profits, worker surplus, and externalities. Firm size is the strongest driver of total impact; product differentiation (own-price elasticity) drives impact per dollar of revenue. Existing ESG ratings from CSRHub and Just Capital are essentially uncorrelated with these welfare-grounded estimates. Cigarette companies have negative social impact in the model; Walmart’s grocery business has by far the largest positive impact ($151 billion/year).

Core results

Magnitudes and significance are as reported. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Consumer surplus dominates all other components of corporate social impact	§VI.C, Figure 7, p. 317–318	Consumer surplus accounts for the overwhelming share of weighted individual impact per dollar of revenue across all differentiated industries; profits receive much less weight (welfare weight on profits ≈ 0.12 because high-income people own most equity); worker surplus is small because average total compensation is only about 22% of revenues
R2	Firm size is highly correlated with social impact (R² = 0.89)	Figure 5, p. 316	Log-log plot of unweighted individual impact vs. revenue across 74 firms; firms excluded are the two cigarette companies plus Frontier and Spirit Airlines (negative impact)
R3	Product differentiation (own-price inelasticity) drives impact per dollar of revenue	Figure 6, p. 316	Unweighted impact/revenue ranges from about 0.2 to over 1.0 across firms; much of this variation explained by own-price elasticity from the survey
R4	Walmart’s grocery business has by far the highest individual social impact in the sample	Table VI Panel A, p. 319	Weighted individual impact: Walmart $150.54 billion/year (rank 1); Philip Morris: -$16.78 billion/year (rank 74, most harmful)
R5	Oil companies have the highest social impact per dollar of revenue due to global supply inelasticity	Table VI Panel B, p. 319	Weighted impact/revenue: Conoco, Eni, Total, Shell, Chevron all at 1.50–1.51; the large consumer surplus arises because oil exit raises prices substantially given inelastic global demand (elasticity ≈ -0.14)
R6	Cigarette companies have negative social impact due to large internalities ($2.77/$ sales)	§VI.A, Table IV, pp. 311, 314	Internality for cigarettes ≈ $2.77 per dollar of sales (vs. externality ≈ $0.12/$ sales); internality-adjusted consumer surplus is negative; Philip Morris -$16.78 billion/year, Reynolds -$13.72 billion/year (Table VI)
R7	Shares of industry impact are considerably larger than individual firm impacts, especially in industries with inelastic aggregate demand	§VI.C, p. 318	When all auto firms exit, consumers must find entirely different forms of transportation; individual BMW exit allows substitution to other auto firms; the gap is largest for toothpaste, groceries, and smartphones (most inelastic aggregate demand per Figure 2)
R8	ESG ratings from CSRHub and Just Capital are essentially unrelated to the welfare-grounded impact estimates	Figure 9, §VII, pp. 322–323	Scatterplot of weighted individual impact/revenue vs. CSRHub and Just Capital ratings shows little relationship; Internet Appendix Table IA.VII also shows other rating systems (Refinitiv, S&P) are not closely correlated with each other or with the paper’s estimates

Overall (paper’s conclusion). Consumer surplus is the primary driver of corporate social impact. Impact investors should consider devoting more attention to firms that deliver more consumer surplus, especially for lower income people. Making more differentiated products that more consumers want to buy is the key to social impact in this framework (p. 324). Where Chatterji et al. (2015) document that ESG ratings disagree substantially with each other, this paper shows that they are also uncorrelated with its welfare-grounded estimates.

Datasets used

Dataset	Role in paper	Wiki page
Original 3,500-person consumer and worker survey (Lucid and Cint panels, July and November 2021)	Firm-level price response, aggregate price response, worker price response, satisfaction, income	Proprietary/custom survey; not a public dataset; no page
NielsenIQ Homescan and Statista Consumer Market Outlook	Consumer packaged goods revenues and market shares	no page yet
U.S. Department of Transportation DB1B	Airline revenues	no page yet
Wards (auto revenues)	Auto revenues	no page yet
Winsight (grocery revenues)	Grocery revenues	no page yet
Technomic (restaurant revenues) and Statista / Statcounter (smartphone revenues)	Restaurant and smartphone revenues	no page yet
Compustat	Revenues and employment for publicly traded firms	WRDS / Compustat (licensed)
InfoUSA	Firm-level county employment counts	no page yet
American Community Survey (ACS), 2010-2019	Occupation and county employment distributions for worker surplus estimation	ACS
Rystad Energy	Oil production and operating expenses for the seven oil supermajors across all oil fields worldwide (2018)	Rystad Energy (licensed)
U.S. EPA supply-chain CO2 emission factors (Ingwersen and Li 2020)	Production externalities from CO2 emissions	EPA Supply Chain GHG
Piketty, Saez and Zucman (2018) distributional national accounts	After-tax income distribution for social marginal welfare weights	no page yet
C-corporation ownership data (Piketty et al. 2018)	Profit distribution across income percentiles	no page yet
National Household Travel Survey (2017)	U.S. gasoline consumption by income (for oil consumer surplus welfare weights)	no page yet

Sample: 74 firms; 12 industries (autos, airline, beer, cereal, cigarettes, grocery, oil, restaurant, smartphone, soda, toothpaste, yogurt); survey n = 3,544 valid respondents after screening.

Theory / model

Building on the concept of enterprise impact in Brest and Born (2013), the paper operationalizes social impact as the welfare loss from firm exit. The paper builds a micro-founded partial equilibrium model. There are $N$ people indexed by $i$ with income-earning ability $\theta_i$. Each person $i$ has quasilinear utility additively separable in consumption, labor, and externality (eq. 1, p. 292):

$$U_i(y; p, w(\theta_i)) = U_i\!\left( \sum_m \sum_{t \in T_m} \sum_{j \in J_m} (u_{ijt} - p_j)\, y_{ijt} + \pi_i + \sum_{fl} (u_{ifl} + w_{ifl}(\theta_i))\, y_{ifl} - \Phi \right)$$

where $y_{ijt}$ are binary purchase indicators, $y_{ifl}$ are binary employment indicators, $\pi_i$ is person $i$‘s share of redistributed profits, and $\Phi$ is the per-capita externality. For markets with behavioral biases (cigarettes, soda), consumers maximize perceived utility $\tilde{U}_i$ where $u_{ijt}$ is replaced by $u_{ijt} + \gamma_j$; $\gamma_j < 0$ is a negative internality. Consumer choice is (eq. 2, p. 292):

$$y^* = \operatorname*{argmax}\, \tilde U_i(y; p, w(\theta_i))$$

Firm $f$‘s profits are (eq. 3, p. 292):

$$\Pi_f(p) = \sum_{j \in J_f} \left[ p_j q_j(p) - C_j(q_j) \right]$$

Per-capita externality (eq. 4, p. 292):

$$\Phi = \frac{1}{N} \sum_m \sum_{j \in J_m} q_j(p)\, \phi_j$$

Social welfare is the Pareto-weighted sum of indirect utilities (eq. 5, p. 293):

$$W(p, w) = \sum_i \omega_i V_i(p, w(\theta_i))$$

Individual impact. Firm $f$‘s individual impact is the welfare loss from its exit if all other firms remain (eq. 6-7, p. 293):

$$\Delta W_f(X) := W(p^{X_0}, w^{X_0}) - W(p^{X_0 \setminus f}, w^{X_0 \setminus f})$$ $$\Delta W_f^{\text{Individual}} = \Delta W_f(F)$$

Share of industry impact. Defined as firm $f$‘s Shapley value for the social welfare loss if the entire industry exited. With $R_m$ the set of orderings of firms in market $m$ and $P_f^R$ the set of firms preceding $f$ in ordering $R$ (eq. 8, p. 293):

$$\Delta W_f^{\text{Shapley}} = \frac{1}{F_m!} \sum_{R_n} \Delta W_f(P_f^R)$$

Social marginal welfare weights. Following Saez (2002), welfare weights are inversely proportional to after-tax income (eq. 9, p. 294):

$$g_i = \kappa\, a(z_i)^{-\rho}, \qquad \kappa = \frac{N}{\sum_i a(z_i)^{-\rho}}$$

where $a(z_i)$ is after-tax income and $\rho = 1$ as benchmark (log utility). When $\rho = 0$, all people receive equal weight and $W$ is total surplus.

Identification. This is a structural quantification exercise, not a quasi-experimental design. Demand parameters are identified by survey moments (substitution, income-firm, aggregate price response), combined with aggregate market-share matching (BLP contraction). The partial equilibrium framework assumes each firm is a small share of the labor market and that intermediate inputs are produced at constant marginal cost.

Method

The estimation proceeds in three parallel modules: (i) differentiated product markets via BLP demand, (ii) oil market via price-taking competitive fringe, and (iii) labor markets via linear probability model. It builds on blp-demand, method-of-simulated-moments, and shapley-value-allocation. The differentiated-product demand follows the random-coefficient logit framework of Berry, Levinsohn, and Pakes (1995). The estimation strategy for differentiated-product markets using micro data builds on Berry, Levinsohn, and Pakes (2004).

Differentiated product markets (Sections IV.A-B, pp. 302-305). Representative utility for income group $z$ and firm $f$ is (eq. 12, p. 303):

$$\begin{aligned} V_{zf}(p_f, v_i) &= \eta(-p_f + u_{ift}) - \epsilon_{ift} \\ &= -\eta p_f + \xi_f + \gamma_f + A_i \zeta_f + \sigma_f v_{if} + \sigma_n v_{in} \end{aligned}$$

where $\eta$ is a market-level price scaling factor, $\xi_f + \gamma_f = \delta_f$ is firm $f$‘s mean utility, $\zeta_f$ is an income-firm interaction parameter, and $\sigma_f$, $\sigma_n$ are standard deviations of firm-specific and inside-good random coefficients. Income group $z$‘s choice probability (eq. 13, p. 303):

$$P_{zf}(p) = E_v\!\left[ \frac{\exp(V_{zf}(p_f, v_i))}{1 + \sum_{k \in F_m} \exp(V_{zk}(p_k, v_i))} \right]$$

Consumer surplus loss from firm $f$‘s exit (eq. 15, p. 304):

$$\Delta CS_f(X_0) = N \sum_z \mu_z\, g(z)\, T_m \left[ \widetilde{CS}_{zm}(p^{X_0}) - \widetilde{CS}_{zm}(p^{X_0 \setminus f}) - \sum_f \gamma_f \left( P_{zf}(p^{X_0}) - P_{zf}(p^{X_0 \setminus f}) \right) \right]$$

Estimation moments (MSM, p. 305). Three sets of micro-moments identify the structural parameters ($\Theta^m = \{\eta, \zeta, \sigma_f, \sigma_n\}$):

Income-firm moments (informative about $\zeta_f$):

$$g_f^{\text{inc}} = \left( \sum_i \omega_i \chi_{im} \right)^{-1} \sum_i \omega_i \chi_{im} \left[ (A_i F_{if} - B_i F_{if}) - \frac{\mu_A P_{Af}(p^0) - \mu_B P_{Bf}(p^0)}{1 - P_0(p^0)} \right]$$

(eq. 16, p. 305)

Substitution moments (informative about $\eta$ and $\sigma_f$):

$$g_f^{\text{sub}} = \left( \sum_i \omega_i \chi_{im} F_{if} \right)^{-1} \sum_i \omega_i \chi_{im} F_{if} \left[ H_{if} - \frac{P_f(p_f')}{P_f(p^0)} \right]$$

(eq. 17, p. 305)

Outside-good moments (informative about $\sigma_n$):

$$g^{\text{out}} = \left( \sum_i \omega_i \chi_{im} \right)^{-1} \sum_i \omega_i \chi_{im} \left[ O_i - \frac{1 - P_0(p')}{1 - P_0(p^0)} \right]$$

(eq. 18, p. 305)

Parameters are estimated by minimizing $G^m(\Theta^m)' G^m(\Theta^m)$. Marginal costs are backed out from Nash-Bertrand first-order conditions (eq. 10, p. 302):

$$p_f - C'_f = \frac{q_f}{-\,\dfrac{\partial q_f(p)}{\partial p_f}}$$

Counterfactual equilibrium prices $p^X$ are found by fixed-point iteration (Conlon and Gortmaker 2020).

Oil market (Section IV.D, pp. 308-309). Oil is treated as an undifferentiated globally traded commodity with price-taking firms. Market clearing (eq. 19):

$$D(p^X) = S(p^X; F)$$

Consumer surplus loss from firm $f$‘s exit under linear demand (eq. 20):

$$\Delta CS_f(X_0) = \tfrac{1}{2}\left( D(p^{X_0 \setminus f}) + D(p^{X_0}) \right)\left( p^{X_0 \setminus f} - p^{X_0} \right)$$

Labor markets (Section V, pp. 311-314). Worker surplus per worker assuming linear labor supply. Normalized surplus relative to outside option (eq. 22-23, pp. 311-312):

$$\frac{u_{ifl} + w_{ifl} - u_{i0} - w_{i0}}{w_{ifl}} = \frac{\epsilon_{ifl}}{\alpha x_{ifl}}, \qquad \epsilon \sim U(0,1)$$ $$E_i[ WS_{ifl} ] = \int_0^1 \frac{w_{ifl}\, \epsilon}{\alpha x_{ifl}}\, d\epsilon = \frac{w_{ifl}}{2 \alpha x_{ifl}}$$

Total worker surplus loss from firm $f$‘s exit (eq. 24, p. 312):

$$\Delta WS_f = \sum_{l \in L_f} \sum_{i \in fl} \frac{w_{ifl}}{2 \alpha x_{ifl}}$$

Empirical specifications

Labor supply regression (Table V, p. 313). The labor supply arc elasticity $\alpha$ is estimated from a linear probability model of whether workers leave if their employer cuts salaries by 10% (eq. 27, p. 312):

$$\Pr(L_i = 1) = (0.1\,\alpha)\, x_{ifl}$$

where $x_{ifl}$ includes: annual earnings $w_{ifl}$ (from survey), college degree indicator, major occupation indicators (management/business/science reference), natural log of firm total employment in county (from InfoUSA), natural log of labor market size (employment in occupation-county cell, from ACS), and a constant. Standard errors in parentheses; n = 1,302 employed non-self-employed respondents. Wages divided by 0.69 to convert from compensation to wages (U.S. DOL 2023).

Key estimates from column (3): constant = 0.448 (SE 0.079)***, total compensation ($10,000) = -0.014 (SE 0.002)***, college degree = -0.078 (SE 0.032)**, ln(firm employees in county) = 0.025 (SE 0.006)***; $R^2 = 0.064$. (Table V, p. 313.)

Product market estimation (Section IV.B-C, pp. 304-306). MSM estimation per market. Sample restricted to firms with at least 25 survey respondents as customers; all other firms in market pooled as “other” firm. Baseline prices $p^0 = 1$. Parameter identification: firm $\zeta$ estimated from share of purchases by high- vs. low-income consumers (income-firm moments); market $\eta$ and $\sigma_f$ from share of customers who would still buy after a 25% price increase (substitution moments); $\sigma_n$ from share of inside-good consumption retained if all prices double (outside moments). Berry (1994) contraction mapping used to match aggregate market shares in every iteration. (p. 305.)

Externality and internality calibration (Section IV.F, Table IV, pp. 310-311). No regression; values imported from prior literature. Production externalities: U.S. EPA supply-chain CO2 emission factors valued at $190/metric ton (U.S. government social cost of carbon, 2020). Consumption externalities: beer $33.60 per liter of pure alcohol (Herrnstadt et al. 2015); cigarettes $0.64/pack (DeCicca et al. 2020); soda 0.85 cents/oz; autos and oil include lifetime CO2 emissions discounted at 3%. Cigarette internality $= (1 - \beta) \times H^c$ = (1 - 0.67) * $44.40 per pack $\approx$ $14.65/pack (Chaloupka et al. 2019; Gruber and Koszegi 2001); soda internality 0.93 cents/oz (Allcott et al. 2019a).

Robustness checks (Section VI.E, Figure 8, p. 320). Six panels vary: social cost of carbon doubled to $380/ton; cigarette internality halved; soda internality doubled; more inelastic labor supply assumed. Main results are qualitatively unchanged across all panels.

When to read the full paper

Use the original article if you are: constructing your own social impact measure and need the full derivations (Sections I-V); replicating the demand or labor surplus estimates (replication code at allcott.stanford.edu/research); extending the framework to new industries or markets; auditing a specific firm-level estimate (Internet Appendix Table IA.V has all 74 firms); or comparing against the Harvard Business School Impact Weighted Accounts methodology (Section VII). Where Serafeim, Trinh, and Zochowski (2020) use accounting methods to monetize impact, this paper uses demand estimation instead, yielding different results. The locators above point to the exact table or figure.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026. Copyright © 2025 the American Finance Association. Open access on the Wiley platform under Wiley/AFA terms; no Creative Commons licence was confirmed (Crossref returns only the Wiley VOR terms URL). This distillation was extracted by an LLM on 2026-05-31 and is not human-verified or independently reproduced. The PDF sidebar carries a “Creative Commons License” watermark, but no CC attribution block appears in the article and no CC URL appears in Crossref metadata; the rights signal is therefore flagged as conflicted (rightsSignalConflict: true).

Allcott, Hunt, Giovanni Montanari, Bora Ozaltun, and Brandon Tan. “An Economic View of Corporate Social Impact.” The Journal of Finance 81, no. 1 (February 2026): 285–328. DOI: 10.1111/jofi.70004. © 2025 the American Finance Association. This page is an extract-only distillation by the Institute for Automated Research: core results re-expressed; changes were made. No verbatim PDF is mirrored here.