Mandatory CSR Spending and Firm Risk: Chauhan, Ghosh & Jadiyappa (2026)

What this is. A distilled skeleton of Chauhan, Ghosh and Jadiyappa (2026), extracted by an LLM from the published PDF. Read the original to replicate or extend.

TL;DR

India’s Companies Act 2013 (Section 135) created a mandatory CSR regulation, requiring firms with net profit above INR 50 million, net worth above INR 5 billion, or revenue above INR 10 billion to spend at least 2% of their three-year average net profit on specified CSR activities. The paper exploits this as a quasi-natural experiment. The treatment group comprises 662 Indian non-financial listed firms that did not engage in CSR before the regulation but began doing so afterward; the control group comprises 268 firms that remained below all statutory thresholds and never engaged in CSR. Using a difference-in-differences (DiD) design over 2010-2019, the paper reports an 8.1% increase relative to pre-regulation beta in the firm-fixed-effects specification (0.061/0.75) and a 12.7% increase relative to mean beta in the univariate estimate (0.112/0.88). Three complementary robustness strategies (propensity-score matched DiD, a multivariate regression discontinuity design, and tail-risk measures MES and delta CoVaR) all confirm the direction. The paper identifies operating leverage as the primary transmission channel: mandatory CSR outlays act as quasi-fixed costs, raising firms’ degree of operating leverage (DOL) and making earnings more sensitive to macroeconomic fluctuations.

Core results

#	Result	Locator	Magnitude as reported
R1	Univariate DiD: treatment firms exhibit higher beta post-regulation	Table 2, p. 8	DiD = 0.112*** (p<1%); ~12.7% above sample mean beta of 0.88
R2	Multivariate DiD (OLS + FE): baseline panel regression	Table 3 cols. 1-6, pp. 8-9	CSR Dummy x Treatment Firms: 0.117*** (t=4.49) no controls; 0.098*** (t=3.754) with controls; 0.061** (t=2.325) with firm FE
R3	PSM-matched DiD corroborates baseline	Table 5 cols. 1-2, p. 10	0.078** (t=2.571) without firm FE; 0.100*** (t=4.155) with firm FE
R4	MRDD: Beta is higher at the CSR eligibility cutoff	Table 6, p. 12	Discontinuity at M=0: 0.241-0.292 (BW ±0.50, all significant); 0.318-0.427 (BW ±0.10, all significant)
R5	Treatment firms exhibit higher marginal expected shortfall	Table 7 cols. 1-2, pp. 14-15	DiD = 0.344** (t=2.305) col. 1; 0.392*** (t=3.222) col. 2
R6	Treatment firms exhibit higher delta CoVaR	Table 7 cols. 3-4, pp. 14-15	DiD = 1.886*** (t=4.65) col. 3; 0.841** (t=2.243) col. 4
R7	Results are consistent with an operating-leverage channel from CSR to beta	Table 9 cols. 1-2, p. 17	Triple interaction (CSR Dummy x Treatment Firms x ΔDOL): 0.013*** (t=3.556) col. 1; 0.013*** (t=2.632) col. 2
R8	Treated firms’ profits are more sensitive to GDP growth post-regulation	Table 10 cols. 1-2, p. 19	Triple interaction (CSR Dummy x Treatment Firms x GDP growth): 0.007** (t=2.296) col. 1; 0.006** (t=2.213) col. 2
R9	CSR participation and spending intensity rose after the mandate	Table 1, p. 6	CSR-active firms rose from 38% in 2013-2014 to 48% in 2014-2015 and 56% in 2018-2019; CSR/net profit rose from 1.67% to 2.85%
R10	CSR expenditure did not significantly predict ESG performance	Table A2, p. 22	CSR expenditure/net profit coefficient = 2.971 (t=1.048), not significant; N=1,893
R11	Voluntary CSR performance was negatively associated with beta before the mandate	Table A3, p. 23	CSR performance = -0.003* (t=-1.743) with industry FE and -0.002** (t=-2.065) with firm FE; N=1,152
R12	Post-mandate CSR-performance interaction was negatively associated with beta for non-mandated firms	Table A4, p. 24	CSR performance x CSR Dummy = -0.0373*** (t=-2.741); N=163
R13	Non-mandated voluntary spenders cut CSR spending after the mandate	Section 6.3, p. 13	Mean CSR spending fell from 5.27% to 4.23% of net profit, a 19.7% reduction
R14	Previously voluntary spenders outside the mandate had a larger post-mandate beta increase	Table A5, p. 24	CSR Dummy x TreatVolCSR = 0.083*** (t=3.197); N=6,487
R15	CSR spending was associated with beta in the post-mandate period, but not on its own	Table A6, p. 25	CSR expenditure/net profit = 0.0248 (t=0.279), not significant; interaction with CSR Dummy = 0.208** (t=2.167); N=16,994
R16	Industries with higher mandate exposure saw a larger increase in industry beta	Table A7, p. 25	CSR Dummy x Industry with Higher Treatment Firms = 0.119** (t=2.094); N=1,194 industry-years. The Appendix A7 exposure description conflicts with §7.2, p. 14
R17	Baseline beta effect persisted with governance controls	Table A8, p. 26	CSR Dummy x Treatment Firms coefficients range from 0.058** to 0.061** across specifications
R18	Mandatory CSR increased operating leverage	Table 8, p. 16	CSR Dummy x Treatment Firms x ln(Sales) = 0.023** (t=2.270), controls included; N=7,712
R19	The DOL-beta relationship was concentrated among financially constrained firms	Table A9, pp. 26-27	CSR Dummy x Treatment Firms x ΔDOL = 0.029*** (t=3.109) for constrained firms; -0.008 (t=-0.850), not significant, for unconstrained firms
R20	Unconstrained firms adjusted financial leverage with DOL changes	Table A9, pp. 26-27	CSR Dummy x Treatment Firms x ΔDOL = -0.002 (t=-0.990), not significant, for constrained firms; -0.012*** (t=-4.692) for unconstrained firms
R21	Treated firms increased advertising intensity	Table A10, p. 27	CSR Dummy x Treatment Firms = 0.002** (t=2.376); N=8,671
R22	Advertising intensity attenuated the mandate’s beta effect	Table A10, p. 27	Treatment Firms x CSR Dummy x advertising expense/sales = -3.349*** (t=-2.935); N=8,671
R23	Event-study evidence showed no significant pre-trend except in 2013, followed by positive post-mandate coefficients	Figure 1, p. 8	The paper reports no significant treatment-control beta differences in pre-period years except 2013; post-mandate coefficients are significantly positive
R24	MRDD estimates at the intermediate bandwidth also show a positive beta discontinuity	Table 6, p. 12	For M in [-0.25, 0.25]: conventional = 0.161 (z=1.9419**); bias-corrected = 0.3936 (z=4.7475***); robust = 0.3936 (z=3.1223)
R25	The treatment effect also appears when partial spenders are used as the comparison group	Section 5.4, p. 9	The text reports a significantly larger beta increase for treatment firms, but says the estimates are not reported and available upon request
R26	Loss-making firms continued CSR activity during the post-regulation period	Section 4.3, p. 5	Among 269 loss-making firm-years, average CSR spending was 0.4% of sales
R27	Unconditional mean beta was similar for treatment and control firms	Table 1, p. 6	Treatment mean = 0.885, control mean = 0.871, difference = 0.014 and statistically insignificant

Overall (paper’s conclusion). Across all specifications and outcome measures, firms subject to India’s mandatory CSR spending regulation exhibit significantly higher systematic risk than non-subject firms in the post-regulation period. The evidence supports the mechanism that mandatory CSR introduces quasi-fixed costs, raises firms’ operating leverage, and amplifies their sensitivity to aggregate economic fluctuations. The findings imply that universal CSR mandates can impose costs on firms at the expense of shareholders, in contrast to the risk-reducing role of voluntary CSR documented in prior literature.

Theory / model

The paper has no formal model. It tests the theoretical prediction of Albuquerque et al. (2019) in a mandatory CSR setting. In their framework, CSR investment enables firms to cultivate consumer loyalty and reduce demand elasticity, thereby stabilizing revenues and lowering systematic risk. A key implication of their model is that these risk-mitigating benefits depend on CSR remaining a relatively selective differentiating practice: as adoption becomes widespread, the strategic distinctiveness erodes and the insurance mechanism dissipates. Their model therefore predicts a reversal in the CSR-risk relationship once CSR adoption becomes ubiquitous.

The paper argues that India’s 2013 mandate creates precisely this condition. Before the regulation, roughly 35-38% of Indian listed firms engaged in CSR voluntarily, using it as a differentiation signal. After the regulation, participation rose to 48-56%. This shift from selective adoption to universal compliance simultaneously (i) dilutes CSR’s differentiation value, eroding the demand-stabilization channel, and (ii) imposes new quasi-fixed costs on formerly non-CSR firms, raising their degree of operating leverage.

The operating leverage channel follows Harjoto (2017): when firms cannot pass CSR costs onto customers or offset them with higher contribution margins, CSR expenditures function as fixed costs. Higher fixed costs raise the operating leverage ratio (DOL), amplifying the sensitivity of operating profit to sales fluctuations. Because sales fluctuations co-move with aggregate demand, higher DOL translates into higher systematic risk (equity beta).

The tested hypothesis (Section 3.2, p. 4): firms subject to mandatory CSR regulation would experience higher levels of systematic risk than firms not subject to the mandatory CSR regulation in the post-regulation period.

Method

The primary estimator is a panel DiD comparing treatment firms (those legally compelled to begin CSR spending after 2014) against control firms (those consistently below all statutory thresholds). Standard errors are clustered at the firm level.

Parallel trends validation. An event-study specification (eq. 2 below) tests whether pre-regulation beta differences between treatment and control firms are statistically zero. Figure 1 (p. 8) reports no significant pre-treatment differences except in 2013, with positive and significant post-treatment coefficients.

PSM-matched DiD. To address observable pre-period differences, each CSR-exposed firm is matched (nearest-neighbor with replacement) to a non-exposed control on Leverage, Tangibility, Sales Growth, Market-to-Book ratio, and Firm Age during the pre-shock period (Section 5.5, p. 7). The matched sample comprises 5,258 firm-year observations.

MRDD. Following Manchiraju and Rajgopal (2017), a multivariate regression discontinuity design (MRDD) constructs a composite binding score M = min(R1, R2, R3), where R1 = (Profit - 50)/50, R2 = (Book value - 5,000)/5,000, and R3 = (Sales - 10,000)/10,000 (all thresholds in INR millions). Firms with M > 0 are treated; M < 0 are controls. The rdrobust command (Calonico et al., 2014) provides bias-corrected and robust inference (Section 5.6, pp. 8-9).

Tail-risk measures. MES and delta CoVaR (Section 7.3, pp. 14-15) are used as alternative outcome measures. MES captures a firm’s average return on days when the market falls in the bottom 5% of its distribution; delta CoVaR captures how much the system’s downside VaR worsens when a firm moves from its median to its distressed state. Both are estimated over the same DiD framework as eq. 1.

DOL mediation. A two-stage approach identifies the operating leverage channel (Section 8.2, pp. 17-18). Stage 1 estimates each firm’s ΔDOL from a regression of log EBIT on log Sales interacted with the CSR Dummy. Stage 2 tests whether post-mandate changes in DOL account for the increase in beta, via a triple interaction (CSR Dummy x Treatment Firms x ΔDOL) in the beta regression.

Empirical specifications

Main DiD regression (eq. 1, p. 6), producing R1-R2:

\text{Beta}_{it} = \alpha + \beta_1\text{CSR Dummy}_t + \beta_2\text{Treatment Firms}_i + \beta_3(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) + \beta_4\text{Size}_{it} + \beta_5\text{ROA}_{it} + \beta_6\text{Tangibility}_{it} + \beta_7\text{MB}_{it} + \beta_8\text{Leverage}_{it} + \beta_9\text{Firm Age}_{it} + \varepsilon_{it} \tag{1}

where $\text{Beta}_{it}$ is equity beta estimated from daily returns against the NSE Nifty 50 index (minimum 100 trading days per year); $\text{CSR Dummy}_t = 1$ for 2015-2019 (post-regulation) and 0 for 2010-2014; $\text{Treatment Firms}_i = 1$ for the 662 firms legally compelled to begin CSR spending. The coefficient of interest is $\beta_3$. The baseline sample contains 8,671 firm-years from 930 firms. Table 3 columns 1-2 use year and industry fixed effects, columns 3-4 use year and firm fixed effects, and columns 5-6 use year-by-industry and firm fixed effects; all report standard errors clustered by firm (Table 3, pp. 8-9).

Parallel trends event study (eq. 2, p. 7):

\text{Beta}_{it} = \sum_{p \neq 0} \delta_p (D_p \times \text{Treatment Firms}_i) + X_{it}\gamma + \mu_i + \lambda_t + \varepsilon_{it} \tag{2}

where $D_p$ is a year dummy (omitted base year: 2014), $\mu_i$ are firm fixed effects, and $\lambda_t$ are year fixed effects. Pre-regulation coefficients $\delta_{-4}, \delta_{-3}, \delta_{-2}, \delta_{-1}$ should be indistinguishable from zero; post-regulation coefficients $\delta_1$ to $\delta_5$ are expected to be positive. The event study uses the firm-year panel and firm-clustered inference (Figure 1, p. 8).

MES definition (eq. 3, p. 14), producing R5:

\text{MES}_{j,t} = \frac{1}{N_t} \sum_{d \in D_t} R_{j,d} \tag{3}

where $D_t$ is the set of trading days on which the market falls in the bottom 5% of its return distribution, $N_t$ is the number of such days in year $t$, and $R_{j,d}$ is firm $j$‘s return on day $d$. MES is multiplied by $-1$ so higher values indicate greater systemic vulnerability.

Delta CoVaR (eqs. 4-6, p. 14), producing R6. The tail-event VaR for firm $j$ is defined by:

\Pr\!\left(r_j \leq \text{VaR}_{j,5\%}\right) = 5\% \tag{4}

The system’s CoVaR conditional on firm $j$ being in distress:

\Pr\!\left(r_{\text{fs}} \leq \text{CoVaR}_{\text{system}|j},\; r_j = \text{VaR}_{j,5\%}\right) = 5\% \tag{5}

\Delta\text{CoVaR}(r_{\text{fs}}, j, 1\%) = \text{CoVaR}(r_{\text{fs}}, j, 1\%) - \text{CoVaR}(r_{\text{fs}}, j, 50\%) \tag{6}

Delta CoVaR is estimated via quantile regression conditioning on firm $j$‘s median vs distressed state, with lagged market return, lagged market volatility (VIX), Treasury bill yield, and term premium as state variables.

The MRDD running variable used for the cutoff estimates in Table 6 (p. 12) is the minimum of the three normalized statutory threshold distances:

$M = \min\left(\frac{\text{Profit}-50}{50},\frac{\text{Book Value}-5000}{5000},\frac{\text{Sales}-10000}{10000}\right)$

The treatment cutoff is $M=0$, with firms above it assigned to treatment. The paper reports conventional, bias-corrected, and robust local-polynomial estimates for bandwidths 0.50, 0.25, and 0.10, using rdrobust.

DOL estimation (eq. 7, p. 17), providing the baseline DOL measure:

\ln(\text{EBIT}_{it}) = \alpha + \beta \ln(\text{Sales}_{it}) + \varepsilon_{it} \tag{7}

where $\beta$ is the firm’s degree of operating leverage.

Extended DOL with CSR mandate (eq. 8, p. 17), producing the operating-leverage estimate in R18 (Table 8):

\begin{aligned} \ln(\text{EBIT}_{it}) = \alpha &+ \beta_1\text{CSR Dummy}_t + \beta_2\text{Treatment Firms}_i + \beta_3(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) \\ &+ \beta_4(\text{Treatment Firms}_i \times \ln\text{Sales}_{it}) + \beta_5(\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \ln\text{Sales}_{it}) + X_{it}\gamma + \varepsilon_{it} \end{aligned} \tag{8}

$\beta_5$ captures whether mandated firms become more sensitive to sales in the post-regulation period. The estimate 0.023** (t=2.270, Table 8 col. 2) implies a 1% change in sales corresponds to a ~2.3% larger change in operating profit for treatment firms after the mandate.

First-stage DOL mediation (eq. 9, p. 18), estimated per firm to recover ΔDOL:

\ln(\text{EBIT}_{it}) = \alpha + \beta_1\text{CSR Dummy}_t + \beta_2(\text{CSR Dummy}_t \times \ln\text{Sales}_{it}) + \beta_3\ln\text{Sales}_{it} + X_{it}\gamma + \varepsilon_t \tag{9}

Here $\beta_2$ measures the firm’s change in DOL (ΔDOL) in the post-regulation period. The coefficient on $\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \ln\text{Sales}_{it}$ in Table 8 confirms ΔDOL is positive and significant for mandated firms.

Second-stage DOL mediation (eq. 10, p. 18), producing R7 (Table 9):

\text{Beta}_{it} = \alpha + \beta_1\text{CSR Dummy}_t + \beta_2(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) + \beta_3(\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \Delta\text{DOL}_i) + \beta_4\text{Treatment Firms}_i + X_{it}\gamma + \varepsilon_{it} \tag{10}

$\beta_3$ tests whether firms with larger post-mandate increases in DOL exhibit correspondingly higher systematic risk. The estimate 0.013*** (t=3.556, Table 9 col. 1) is economically significant. The paper reports that, with SD(ΔDOL) = 2.4, a one-standard-deviation rise in ΔDOL is associated with a beta increase of 0.312; however, 0.013 × 2.4 equals 0.0312, so the reported multiplication appears to contain a factor-of-ten error.

Profit cyclicality (eq. 11, p. 19), producing R8 (Table 10):

\Delta\text{ROA}_{it} = \alpha + \beta_1\text{CSR Dummy}_t + \beta_2(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) + \beta_3(\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \text{GDP growth}_t) + \text{Controls} + \varepsilon_{it} \tag{11}

$\beta_3$ captures whether mandated firms’ operating profits become more sensitive to GDP growth after the regulation. The estimate 0.007** (Table 10 col. 1) confirms that CSR-exposed firms are more vulnerable to aggregate economic shocks, consistent with higher systematic risk via the operating leverage channel.

Additional estimating specifications (Tables A5-A10, pp. 24-27). These specifications reuse the firm-year beta DiD structure with firm and year fixed effects and firm-clustered standard errors unless otherwise stated. The A5 voluntary-spender comparison and A8 governance-control regressions use the 2010-2019 firm-year sample; A5 has 6,487 observations, while A8 has 7,509-7,548 observations across columns. The table descriptions report firm-clustered t-values.

For the outcome-measure robustness specifications (Table 7, pp. 14-15), the paper substitutes MES or delta CoVaR for beta in the baseline DiD. The MES sample has 7,882 firm-years and the delta CoVaR sample has 7,723. The table reports firm-clustered standard errors; specifications vary between year plus industry fixed effects and year plus firm fixed effects:

$\text{Systemic Risk}_{it} = \alpha + \beta(\text{Treatment Firms}_i \times \text{CSR Dummy}_t) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

The CSR performance checks use firm-level beta as the outcome. Table A2 (p. 22) relates CSR expenditure/net profit to the Bloomberg ESG Index for 1,893 observations over 2011-2017 with firm and year fixed effects. Table A3 (p. 23) estimates the pre-mandate relation over 1,152 observations with year and either industry or firm fixed effects. Table A4 (p. 24) estimates the non-mandated firm relation, including the post-period interaction, for 163 observations with firm and year fixed effects. All three use controls and firm-clustered standard errors:

$\text{CSR Performance}_{it} = \alpha + \beta\text{CSR Expenditure/Net Profit}_{it} + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

$\text{Beta}_{it} = \alpha + \beta\text{CSR Performance}_{it} + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

$\text{Beta}_{it} = \alpha + \beta_1\text{CSR Performance}_{it} + \beta_2(\text{CSR Performance}_{it} \times \text{CSR Dummy}_t) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

The non-mandated voluntary-spender test (Table A5, p. 24) estimates:

$\text{Beta}_{it} = \alpha + \beta(\text{TreatVolCSR}_i \times \text{CSR Dummy}_t) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

The direct spending test (Table A6, p. 25) includes CSR expenditure intensity and its post-period interaction:

$\text{Beta}_{it} = \alpha + \beta_1\text{CSR Expenditure/Net Profit}_{it} + \beta_2(\text{CSR Expenditure/Net Profit}_{it} \times \text{CSR Dummy}_t) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

The industry exposure test (Table A7, p. 25) uses industry beta and industry/year fixed effects; the table has 1,194 industry-year observations and reports firm-clustered standard errors:

$\text{Industry Beta}_{jt} = \alpha + \beta(\text{Higher Treatment Exposure}_j \times \text{CSR Dummy}_t) + \lambda_t + \mu_j + \varepsilon_{jt}$

For the constrained/unconstrained splits (Table A9, pp. 26-27), the beta and leverage regressions are estimated separately by group with year and firm fixed effects, firm-clustered standard errors, and 3,638 constrained versus 3,938 unconstrained firm-years:

$Y_{it} = \alpha + \beta_1(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) + \beta_2(\text{CSR Dummy}_t \times \Delta\text{DOL}_i) + \beta_3(\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \Delta\text{DOL}_i) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

where $Y_{it}$ is beta in Panel B or financial leverage in Panel A. For the advertising tests (Table A10, p. 27), the advertising-intensity outcome and beta outcome use year and firm fixed effects, firm-clustered standard errors, and 8,671 observations:

$\text{Advertising/Sales}_{it} = \alpha + \beta(\text{CSR Dummy}_t \times \text{Treatment Firms}_i) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

$\text{Beta}_{it} = \alpha + \beta(\text{CSR Dummy}_t \times \text{Treatment Firms}_i \times \text{Advertising/Sales}_{it}) + X_{it}\gamma + \lambda_t + \mu_i + \varepsilon_{it}$

Datasets used

Dataset	Role in paper	Wiki page
CMIE Prowess	Primary source: firm financials (profit/loss, balance sheet, financial ratios, share prices, stock returns), annual 2010-2019 for all Indian non-financial listed firms	no page yet
Bloomberg ESG Index	CSR performance measure for pre-mandate firms and non-mandated firms in Tables A2-A4	no page yet

Sample: 8,671 firm-year observations from 930 unique firms (2010-2019), of which 662 treatment firms (6,332 firm-years) and 268 control firms (2,339 firm-years). Beta estimated from daily returns against the NSE Nifty 50 index; firm-years with fewer than 100 trading days excluded. All variables winsorized at the 2% tails.

Rajgopal and Tantri (2023) noted that firms that voluntarily spent more than 2% of average profits on CSR prior to the mandate subsequently reduced their CSR expenditures; the paper excludes these voluntary pre-spenders from the treatment group to keep it clean.

When to read the full paper

Read the original when studying: (1) the causal effect of mandatory ESG or CSR regulations on corporate risk, using India’s Section 135 as a quasi-natural experiment with a clean statutory eligibility rule; (2) the two-stage DOL mediation design (eqs. 7-10) for tracing a compliance-cost channel through to systematic risk; or (3) MRDD methodology applied to multi-threshold eligibility rules following the composite score approach (Table 6). The appendix (Table A1, p. 21) defines all variables; Tables A2-A10 provide the full robustness suite (pre-mandate CSR orientation, voluntary spenders, direct CSR-spending regressions, industry concentration, alternative systematic risk measures, confounding governance reforms, and advertising substitution).

Attribution and rights

Chauhan, Y., Ghosh, C., and Jadiyappa, N. (2026). Mandatory CSR spending and firm risk: New evidence from regulatory intervention in India. Journal of Corporate Finance, 98, 102965. https://doi.org/10.1016/j.jcorpfin.2026.102965

Copyright 2026 Elsevier B.V. All rights reserved, including those for text and data mining, AI training, and similar technologies. This page is an LLM-distilled extract (not human-verified, not reproduced). Access the original at doi.org/10.1016/j.jcorpfin.2026.102965.