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). Separating physical and intangible capital, the paper finds that peer effects in physical investment do not spill over to a firm’s intangible investment, and vice versa. 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. Strategic competition (product-market substitution) does not explain the results.

Core results

#	Result	Locator	Magnitude as reported
R1	OLS: local peer total investment on firm total investment	Table 2 col 2, p. 6	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)

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 tests which one dominates.

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

Intangible investment rate (eq. 2):

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

Total investment rate (eq. 3):

I^{total}_{i,t} = I^{phy}_{i,t} + I^{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).

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

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. 6): $\hat{\beta} = 0.088^{***}$ (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.

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_{a/j/t} + \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). EA and industry fixed effects are included. Standard errors are clustered at the firm level. Coefficients multiplied by 100; sample 67,319 to 59,291 firm-years (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} = \sum_{k=-4}^{4} \beta_k \text{TaxInc}_{s,t+k} + \delta_i + \delta_t + \varepsilon_{i,t} \tag{6}

This regression traces the investment path four years before and after a state corporate income tax increase, with the tax increase year as the reference. Figure 2 (p. 8) shows flat pre-trends (no anticipatory effects) and declining investment in post-event years, supporting the parallel-trends assumption and the validity of the instrument.

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

$\Delta I_{i,t} = \alpha \Delta \hat{\bar{I}}_{-i,a,j,t} + \Delta X'_{i,t} \rho + \delta_{a/j/t} + \varepsilon_{i,t}$

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. 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.

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, or local-industry R&D ratio) exceeds the sample median of its local peers. 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.

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.