Local Peer Effects and Corporate Investment: Bao & Goetz (2026)

What this is. A distilled skeleton of Bao and Goetz (2026). Read the original at https://doi.org/10.1016/j.jcorpfin.2025.102935 to replicate or extend.

TL;DR

Bao and Goetz study how a firm’s investment is shaped by the investment of neighboring peer firms within the same local Economic Area (EA) and Fama-French industry. Using a large panel of U.S. public firms from 1989 to 2014, OLS results confirm a positive correlation between a firm’s investment and local peer firms’ average investment, consistent with Dougal et al. (2015) and Bustamante and Fresard (2021). To establish a causal link, the paper exploits staggered increases in U.S. state corporate income tax rates. Because EAs span multiple states, a tax increase in one state depresses investment in the taxed state without directly affecting investment conditions for peer firms in other states of the same EA. The resulting variation in peer investment is used as an instrument in a 2SLS framework. 2SLS results confirm a positive causal peer effect: a one-standard-deviation increase in instrumented peer investment raises a firm’s total investment by roughly 1.57 percentage points (about 6.9% of average total investment). For physical and intangible capital, peer investment affects firms’ investment in the same type, with no significant cross-type effects. This type-specificity is consistent with managers learning from peers who invest in the same type of capital. Further, peer effects in physical investment are stronger among firms with weaker information precision (higher earnings or equity volatility relative to local peers), and peer effects in intangible investment are stronger in knowledge-intensive local industries, consistent with a learning mechanism. The evidence is more consistent with learning than with the competing strategic-substitutes prediction; the paper does not separately identify that channel.

Core results

#	Result	Locator	Magnitude as reported
R1	OLS: local peer total investment on firm total investment	Table 2 col 2, p. 7	0.088*** (SE 0.016); 0.407 pp per 1-SD peer investment change
R2	2SLS causal peer effect on total investment (fraction-of-peers IV)	Table 5 col 1, p. 12	0.772*** (SE 0.285); 1.57-pp increase per 1-SD instrumented peer investment (~6.9% of mean total investment)
R3	First-difference OLS: state corporate income tax rise on firm total investment	Table 3 col 1, p. 9	-0.629** (SE 0.258), coefficients x100; approximately -63 bp drop
R4	2SLS: local peer physical investment on firm physical investment	Table 10 col 2, p. 17	0.628*** (SE 0.235)
R5	2SLS: local peer intangible investment on firm intangible investment	Table 10 col 4, p. 17	0.869** (SE 0.425)
R6	No cross-type peer effect: intangible peer investment on firm physical investment	Table 11 Panel A col 1, p. 18	-0.115 (SE 0.197), not significant
R7	Signal precision moderates physical peer effect (equity-vol interaction)	Table 12 Panel A col 1, p. 20	Interaction: 0.914*** (SE 0.298); base peer effect: 0.497*** (SE 0.147)
R8	Descriptive level and composition of investment in the main sample	Table 1, p. 6	Total investment mean 0.227 (SD 0.202; N = 75,858); physical mean 0.074 and intangible mean 0.147; 42.182 local peers on average
R9	Tax-cut effect is not robust across first-difference specifications	Table 3, p. 9	Tax-cut estimates range from -0.350 (SE 0.300) to 0.438 (SE 0.372) without controls and -0.497* (SE 0.280) to 0.396 (SE 0.329) with controls; no consistent significant effect
R10	No anticipatory investment trend before state tax increases; investment falls afterward	Figure 2, p. 8	Pre-increase coefficients are not statistically significant; investment declines after the tax increase
R11	Instrument-timing check: firm investment and neighboring tax policy do not predict tax increases	Table 4, p. 10	Home-state level -0.083 (SE 0.140), neighbor level -1.100 (SE 0.711), home-state growth 0.132 (SE 0.137), neighbor growth -0.295 (SE 0.617); neighbor TaxInc 0.006 (SE 0.025), TaxCut 0.002 (SE 0.012)
R12	Main local peer effect holds with the two predicted-investment instruments	Table 5 Panel A cols 2-3, p. 12	State-specific IV: 0.829*** (SE 0.290); state-industry IV: 1.082*** (SE 0.327)
R13	Peer effects survive exclusions for customer and subsidiary tax exposure	Table 6, p. 13	Excluding major customers: 0.833*** (SE 0.279), 1.060*** (SE 0.300); excluding subsidiaries in taxed states: 1.650** (SE 0.809), 1.906** (SE 0.750)
R14	Nearby tax increases predict lower investment, but expansion-opportunity proxies do not moderate the effect	Table 7, p. 14	Neighbor_TaxInc: -0.004* (SE 0.003), -0.006** (SE 0.003), -0.005 (SE 0.004); interactions: 0.009 (SE 0.006), 0.003 (SE 0.005), neither significant
R15	Local-demand checks do not explain the peer effect	Table 8 Panels A-B, p. 15	Tradable/nontradable interaction estimates 0.004 (SE 0.543), -0.349 (SE 0.507), -0.704 (SE 0.767), -0.519 (SE 0.609), all insignificant; effects stay positive after excluding sales-tax rises (0.955***, SE 0.346; 1.079***, SE 0.351) or personal-income-tax rises (0.823**, SE 0.338; 0.914**, SE 0.365)
R16	Main peer effect holds in firm fixed-effects 2SLS	Table 9, p. 16	1.930*** (SE 0.746), 2.052*** (SE 0.669), 2.233*** (SE 0.765)
R17	Physical peer investment does not affect firm intangible investment	Table 11 Panel A cols 3-4, p. 18	-0.441 (SE 0.478) and -0.065 (SE 0.224), neither statistically significant
R18	Same-type peer effects vary with a firm’s primary capital intensity	Table 11 Panel B, p. 18	Physical peers on physical investment: -0.101 (SE 0.179) for high-intangible firms, 1.433*** (SE 0.404) for high-physical firms; intangible peers on physical investment: 0.149 (SE 0.179), 0.058 (SE 0.314); physical peers on intangible investment: -0.080 (SE 0.614), 0.225 (SE 0.148); intangible peers on intangible investment: 1.956** (SE 0.792), 0.269* (SE 0.152), respectively
R19	Signal-precision moderation is strong for physical investment and weak for intangible investment	Table 12 Panel A col 2 and Panel B cols 1-2, p. 20	Physical peer x ROA volatility: 1.928*** (SE 0.670), base 0.112 (SE 0.223); intangible interactions: -0.060 (SE 0.641) and 0.561 (SE 0.374), both insignificant
R20	Knowledge spillovers strengthen intangible peer effects, not physical peer effects	Table 12 Panels A-B cols 3-4, p. 20	Intangible interaction: 0.815* (SE 0.456), base 0.506 (SE 0.457); local-industry interaction 1.244*** (SE 0.387), base 0.517** (SE 0.244); physical interactions -0.906 (SE 0.968) and -0.463 (SE 0.646), insignificant
R21	Level fixed-effects specification also finds positive local peer association	Table 2 col 1, p. 7	0.184*** (SE 0.021)
R22	The three excluded instruments predict peer investment and pass first-stage relevance tests	Table 5 Panel B, p. 12	Coefficients: -0.017*** (SE 0.002), 0.842*** (SE 0.058), 0.681*** (SE 0.040); KP Wald F-statistics: 130.4, 211.7, 283.3
R23	First-stage instruments predict both physical and intangible peer investment	Table 10 Panel B, p. 17	Physical instrument coefficients: -0.012*** (SE 0.001), 1.351*** (SE 0.106), 1.072*** (SE 0.053); intangible: -0.007*** (SE 0.001), 0.750*** (SE 0.066), 0.541*** (SE 0.031); KP F-statistics: 151.6, 161.7, 403.8 (physical), 40.63, 128.1, 303.2 (intangible)

Overall (paper’s conclusion). Local peer firms exert a positive causal influence on a firm’s investment behavior. This result is robust to alternative IV constructions, exclusion of indirect tax-spillover channels (customer-supplier links, subsidiaries in taxed states), local expansion opportunity concerns, local demand shocks, and alternative clustering of standard errors. The type-specificity of peer effects (R4, R5, R6) and the learning-incentive heterogeneity (R7 and Table 12 Panel B) are consistent with managers learning from peers who invest in the same type of capital, particularly when information about future investment conditions is scarce.

Theory / model

The paper has no formal model. It derives sign predictions from two competing theoretical mechanisms and examines evidence consistent with each.

Learning / information sharing (positive peer effects). Research on social learning argues that managers can infer information about future conditions by tracking the investment behavior of neighboring peers (Scharfstein and Stein (1990); Bikhchandani et al. (1992)). When a manager’s own signal about the future is noisy and informational asymmetries are significant, observing peers’ investment reduces uncertainty and induces correlated investment behavior (strategic complements). This force predicts same-sign peer effects that are stronger when the learning incentive is high (weaker own signal precision, i.e., higher earnings or equity volatility) and when knowledge can plausibly diffuse locally (higher R&D intensity of the peer set).

Strategic product-market competition (negative peer effects). An increase in local investment may raise the price of shared local inputs and intensify product-market competition, inducing neighboring firms to reduce investment (Dixit (1980); Gal-Or (1987)). This force predicts negative peer effects.

Type-specificity prediction. Drawing on learning theories, the paper hypothesizes that peer effects in physical investment influence a firm’s physical investment but not its intangible investment, because observing a neighbor’s factory-building decision provides a clearer signal about physical investment conditions than about R&D conditions, and vice versa. This prediction is supported by Table 11 (R6 above).

The identification assumption is that a neighboring state’s decision to raise corporate income taxes is exogenous to the investment of firms in other states of the same EA. Table 4 (p. 10) shows that state-level aggregate investment and neighboring states’ aggregate investment do not predict state tax increases, and that neighboring states’ tax policies are not correlated with a home state’s decision to raise taxes, supporting exogeneity of the instrument.

Method

The paper applies 2SLS within a first-difference panel framework. The first-difference transformation eliminates time-invariant firm-level unobservables; EA, industry, and year fixed effects absorb remaining common variation.

Investment measures (PDF p. 4, Appendix A):

Physical investment rate (eq. 1):

I^{\text{phy}}_{i,t} = \frac{\text{capx}_{i,t}}{K^{\text{total}}_{i,t-1}} \tag{1}

Intangible investment rate (eq. 2):

I^{\text{int}}_{i,t} = \frac{\text{R\&D}_{i,t} + (0.3 \times \text{SG\&A}_{i,t})}{K^{\text{total}}_{i,t-1}} \tag{2}

Total investment rate (eq. 3):

I^{\text{total}}_{i,t} = I^{\text{phy}}_{i,t} + I^{\text{int}}_{i,t} \tag{3}

where $K^{total}$ is the replacement cost of physical capital (Compustat item ppegt) plus intangible capital, both estimated following Peters and Taylor (2017).

Firm total capital combines the replacement cost of physical capital (Compustat item ppegt) and intangible capital estimated following Peters and Taylor (2017) (PDF p. 4, footnote 8).

Instrumental variables. Three instruments capture the exogenous component of the average peer investment change induced by state corporate income tax changes. The first-stage instruments exploit variation across states within the same cross-state EA:

1. Fraction of local peers affected ($\%\text{LocalPeersAffected}$): the fraction of firm $i$‘s local peers located in a state that raises corporate income taxes in year $t$. A higher fraction produces a larger negative shock to average peer investment.

2. Predicted state-specific $\Delta \bar{I}$: the coefficient on the tax increase dummy from equation (5) is estimated state by state to recover the state-specific investment effect of a tax rise; the average predicted peer investment change across other local peers is then computed.

3. Predicted state-industry-specific $\Delta \bar{I}$: the same procedure at the state-industry level, capturing heterogeneity in how the tax shock transmits across industries.

All three instruments are highly significant in first-stage regressions (KP Wald F-statistics: 130.4, 211.7, and 283.3 for the three 2SLS specifications in Table 5, p. 12), satisfying instrument relevance. The first-stage coefficients on the fraction-of-peers instrument (-0.017***) and the predicted investment changes (+0.842***; +0.681***) have the expected signs (Panel B, Table 5).

The cross-type peer effect analysis in Section 6.2 uses separate instruments for physical and intangible peer investment. The signal-precision heterogeneity analysis in Section 6.3 interacts the instrumented peer investment with above-median dummy variables for equity volatility, ROA volatility, and local-industry R&D intensity.

Empirical specifications

Table 2 col 1: level fixed-effects benchmark (PDF p. 7). The dependent variable is the firm’s total investment rate and the focal regressor is leave-one-out total investment of its peers in the same EA and industry:

$I_{i,t} = \beta \bar{I}_{-i,a,j,t} + \gamma_1 \bar{I}_{a,-j,t} + \gamma_2 \bar{I}_{-a,j,t} + \alpha_i + \alpha_a + \alpha_j + \lambda_t + \varepsilon_{i,t}$

The fixed-effects specification uses firm, EA, industry, and year fixed effects as indicated in the table; standard errors are clustered by firm. The full sample includes 75,858 firm-years. Table 2 col 1 reports the positive peer coefficient in R21. The preferred first-difference form is equation (4).

Eq. (4): Benchmark first-difference OLS (PDF p. 5)

\Delta I_{i,t} = \beta \Delta\bar{I}_{-i,a,j,t} + \Delta X'_{i,t} \rho + \delta_{a/j/t} + \varepsilon_{i,t} \tag{4}

where $\Delta I_{i,t}$ is the annual change in firm $i$‘s total investment rate; $\Delta\bar{I}_{-i,a,j,t}$ is the change in average investment rate of firm $i$‘s local peers in the same EA $a$ and Fama-French 12 industry $j$, excluding firm $i$; $X'_{i,t}$ includes two additional controls for the general industry investment trend (firms in the same industry outside the EA) and the local area investment trend (firms in the same EA but different industries); $\delta_{a/j/t}$ are EA, industry, and year fixed effects. Standard errors are clustered at the firm level. Estimated on 75,858 firm-years (level model) and 64,675 firm-years (first-difference model, Table 2).

OLS peer effect (Table 2 col 2, p. 7): $\hat{\beta} = 0.088^{***}$ (SE 0.016), implying a 0.407 pp increase in total investment per one-SD increase in local peer investment. OLS does not allow causal interpretation due to common local latent factors.

Table 2 col 2 uses 64,675 differenced firm-years, EA, industry, and year fixed effects, and firm-clustered standard errors (PDF p. 7).

Eq. (5): Tax effect first-difference OLS (PDF p. 8)

\Delta I_{i,t} = \beta_1 \text{TaxInc}_{s,t-1} + \beta_2 \text{TaxCut}_{s,t-1} + \gamma \Delta X_{i,t} + \delta_{j,t} + \delta_a + \varepsilon_{i,t} \tag{5}

where $\text{TaxInc}_{s,t-1}$ ($\text{TaxCut}_{s,t-1}$) equals 1 if state $s$ increases (decreases) its corporate income tax rate in year $t-1$, and 0 otherwise; $X_{i,t}$ includes firm-level controls (Tobin’s Q, cashflow, log assets) and macroeconomic state-level controls (GSP growth, unemployment, union penetration, population growth, per capita income growth). Industry-by-year and EA fixed effects are included. Standard errors are clustered at the firm level. Coefficients are multiplied by 100; samples are 67,319 to 59,291 firm-years for the full sample and 28,802 to 25,476 for the multi-state-EA subsample (Table 3). A tax increase reduces total investment by approximately 63 basis points ($\hat{\beta}_1 = -0.629^{**}$, SE 0.258, Table 3 col 1). This is consistent with Mukherjee et al. (2017), who find state tax increases reduce innovative investment, and motivates using the tax shock as an instrument for peer investment.

Eq. (6): Pre/post event dynamics (PDF p. 9)

I_{i,t} = \beta_{-4}\text{TaxInc}_{s,t-4} + \beta_{-3}\text{TaxInc}_{s,t-3} + \cdots + \beta_3\text{TaxInc}_{s,t+3} + \beta_4\text{TaxInc}_{s,t+4} + \delta_i + \delta_t + \varepsilon_{i,t} \tag{6}

This event-study traces investment four years before and after state corporate tax rises, with the event year as the reference category (PDF pp. 8-9). Firm and year fixed effects are included and standard errors are clustered at the firm level. Figure 2 (p. 8) shows no significant pre-event trend and declining investment after the tax rise. The sample follows the tax-response specification in Table 3.

Table 4: State tax-rise timing check (PDF p. 10). The state-year specification regresses an indicator for a corporate tax rise on home-state and neighboring-state investment levels or growth, neighboring-state tax changes, and the paper’s state controls:

$\text{TaxRise}_{s,t} = \theta_1 \text{HomeInvest}_{s,t-1} + \theta_2 \text{NeighborInvest}_{s,t-1} + \theta_3 \Delta\text{HomeInvest}_{s,t-1} + \theta_4 \Delta\text{NeighborInvest}_{s,t-1} + \theta_5 \text{NeighborTaxChanges}_{s,t-1} + \Gamma' Z_{s,t} + \alpha_s + \lambda_t + u_{s,t}$

The reported columns vary the investment-level, investment-growth, and neighboring-tax regressors. State and year fixed effects are included, and standard errors are clustered at the state level. Table 4 uses 1,171 to 1,248 state-year observations depending on the specification.

2SLS second stage (Table 5, p. 12)

$\Delta I_{i,t} = \alpha \Delta \widehat{\bar{I}}_{-i,a,j,t} + \Delta X'_{i,t} \rho + \delta_a + \delta_j + \delta_t + \varepsilon_{i,t}$

where $\Delta \hat{\bar{I}}_{-i,a,j,t}$ is the instrumented change in peer investment from one of the three first-stage IV constructions. The sample is restricted to firms in EAs spanning more than one state (3,871 firms, 28,066 firm-years). Year, industry, and EA fixed effects are included. Standard errors are clustered at the firm level. All three IV specifications yield positive and significant peer effect estimates (0.772***, 0.829***, 1.082*** across the three columns of Table 5 Panel A). Economic magnitude: 1.57 pp increase in total investment per one-SD of instrumented peer investment (Table 5 col 1 discussion, p. 12), equal to about 6.9% of average total investment.

The corresponding first stage replaces the endogenous change in peer investment with one excluded instrument at a time (Table 5 Panel B, p. 12):

$\Delta\bar{I}_{-i,a,j,t} = \pi Z_{i,t} + \Delta X'_{i,t}\kappa + \delta_a + \delta_j + \delta_t + v_{i,t}$

Here $Z_{i,t}$ is the fraction of local peers affected by a tax increase, the state-specific predicted leave-out peer investment change, or the state-industry-specific predicted change. The first-stage sample has 28,066 observations, year, EA, and industry fixed effects, and firm-clustered standard errors. KP Wald F-statistics are 130.4, 211.7, and 283.3.

Physical and intangible outcomes (Tables 10-11, PDF pp. 17-18). The paper re-estimates the second stage by replacing the dependent variable and the instrumented peer investment with the relevant capital type. For the cross-type tests, it includes both peer investment types:

$\Delta I^{c}_{i,t} = \alpha_{c,c}\Delta\widehat{\bar{I}}^{c}_{-i,a,j,t} + \alpha_{c,c'}\Delta\widehat{\bar{I}}^{c'}_{-i,a,j,t} + \Delta X'_{i,t}\rho + \delta_a + \delta_j + \delta_t + \varepsilon_{i,t}, \quad c,c' \in \{\text{phy},\text{int}\}$

Each peer investment type is instrumented using its corresponding tax-based predicted peer-investment change. The estimation uses first differences, year, EA, and industry fixed effects, and firm-clustered standard errors. Table 10 uses 28,090 physical-investment observations and 28,010 intangible-investment observations; Table 11 Panel A has 27,492 to 27,552 observations, while Panel B uses 13,038 to 14,455 observations by capital-intensity split.

Signal precision / learning heterogeneity (PDF p. 19)

$\Delta I_{i,t} = \beta_1 \Delta \bar{I}_{-i,a,j,t} \times \text{Above}_{i,t} + \beta_2 \Delta \bar{I}_{-i,a,j,t} + \Delta X'_{i,t} \rho + \delta_{a/j/t} + \varepsilon_{i,t}$

where $\text{Above}_{i,t}$ is a dummy equal to 1 if the firm’s equity volatility or ROA volatility exceeds the median among its local peers, if its industry R&D stock ratio exceeds the median across industries, or if its local share of industry-year R&D stock (excluding the firm) exceeds the sample median. A significant positive $\beta_1$ indicates stronger peer effects for firms with weaker own information precision. Physical investment: $\hat{\beta}_1 = 0.914^{***}$ (SE 0.298) for equity volatility; intangible investment: $\hat{\beta}_1 = 1.244^{***}$ (SE 0.387) for local-industry R&D stock ratio (Table 12, p. 20). The peer investment variable is instrumented using the state-industry predicted IV throughout.

For Table 12 (PDF p. 20), the dependent and endogenous peer-investment variables are physical in Panel A and intangible in Panel B. The four moderators are above-median equity volatility, ROA volatility, industry R&D stock ratio, and local-industry R&D stock ratio. The state-industry predicted peer-investment change instruments the endogenous peer level and its interaction with the moderator. Every specification includes year, EA, and industry fixed effects and firm-clustered standard errors; observations range from 22,359 to 28,090.

Datasets used

Dataset	Role in paper	Wiki page
CRSP/Compustat Merged	Firm-level investment (capx, R&D, SG&A), total capital (ppegt), assets, Tobin’s Q, cashflow, stock returns and equity volatility; NYSE, AMEX, NASDAQ; 1989-2014	WRDS
BEA Economic Areas	Geographic definition of local peer groups as regional markets; 2004 BEA boundaries; cross-state EAs identify the IV subsample (Fig. 1, p. 5)	no page yet
Fama-French 12 industries	Industry classification for peer group construction and industry fixed effects	Ken French library
State corporate income tax rates	Exogenous investment shock; Heider and Ljungqvist (2015) panel of 121 U.S. state tax changes 1989-2011, extended to 2014 using Tax Foundation data	no page yet
State macroeconomic controls	GSP growth (BEA), unemployment rate (BLS), union penetration (Hirsch and Macpherson 2003), population growth and per capita income growth (Census)	no page yet

Sample. OLS sample: 9,099 publicly listed U.S. firms on NYSE, AMEX, or NASDAQ with non-missing total investment data, fiscal years 1989-2014 (75,858 firm-years). Firms with fewer than five local peers in a given year are excluded. Average firm assets: $3.0 billion; average total investment rate: 22.7% of total capital (one third physical, two thirds intangible). Average number of local peer firms per EA: 42. Approximately 47.9% of sample firms are headquartered in cross-state EAs. 2SLS subsample: 3,871 firms, 28,066 firm-years (cross-state EAs only). All variables winsorized at the 0.5 percentile in each tail.

When to read the full paper

Read the full paper if you need:

• A causal IV design for local peer effects in corporate investment using state corporate income tax shocks (Tables 5-9), including robustness for indirect tax-spillover channels (Table 6), local expansion opportunities (Table 7), local demand shocks (Table 8), and fixed-effects alternatives (Table 9).
• Evidence on the type-specificity of peer effects: physical capital peers affect physical investment but not intangible investment, and vice versa (Table 11), with heterogeneity by firm operational strategy (Panel B).
• Tests of the learning-from-peers mechanism via signal-precision and knowledge-spillover proxies (Table 12).
• The cross-state EA identification strategy, which can be adapted to other firm-level outcomes affected by local conditions.

Attribution and rights

Paywalled. Access at https://doi.org/10.1016/j.jcorpfin.2025.102935. No open-access or CC license found in Crossref metadata (checked 2026-06-26; Elsevier TDM and STM-ASF licenses only). Rights held by Elsevier B.V.

Citation: Bao, Y. and Goetz, M. R. (2026). Local peer effects and corporate investment. Journal of Corporate Finance, 97, 102935. https://doi.org/10.1016/j.jcorpfin.2025.102935

This page is LLM-distilled, not human-verified, and not a reproduction of the paper. All quantitative results are extracted from the source PDF with source locators.