Social Connectedness in Bank Lending: Rehbein & Rother (2025)

What this is. The paper’s core results, the economic hypotheses (information channel vs. favoritism), and the main empirical specifications with equations: enough to understand what was found and how, without reading all 51 pages. To replicate or extend it, see the full source at the original and the replication archive at the Harvard Dataverse.

TL;DR

Rehbein and Rother exploit geographic variation in the Facebook Social Connectedness Index (SCI) to show that bank lending is shaped by the social ties between bank and borrower counties. A 10% increase in social connectedness between counties is associated with roughly 6-9% higher cross-county SME loan volumes and a similar increase in mortgage loan volumes, after controlling for physical distance, cultural dissimilarity, and a broad set of geographic and economic factors. The relationship is stronger when screening incentives are high (e.g., no government guarantee, not securitized) and is absent for fintech lenders whose decisions are algorithm-based. Loans to high-connectedness borrowers carry lower interest rates, higher LTV ratios, and lower delinquency and default rates. Banks with more socially connected loan portfolios have higher ROA and ROE. At the aggregate level, borrower counties more socially proximate to bank regions receive more lending and experience higher GDP growth and employment, especially if they are small-firm intensive, patterns also supported by a shale-boom natural experiment.

Core results

Magnitudes and significance are as reported in the paper; \* = 10%, \*\* = 5%, \*\*\* = 1%.

#	Result	Locator	Magnitude
R1	County-to-county SME loan volume increases strongly with social connectedness; the relationship persists across a broad array of geographic and economic controls	Table 2, col. 1 (baseline); Table 3, col. 11 (full controls), pp. 2773-2775	Baseline elasticity 0.91*** (SE 0.06); with all controls 0.64*** (SE 0.06); R2 rises from 0.70 to 0.91; the authors argue omitted-variable bias would need to be unusually large
R2	Mortgage loan volumes also strongly increase with social connectedness	Table 2, col. 8, p. 2773	Elasticity 1.10*** (SE 0.05); robust to OLS, alternative clustering, HQ-based bank location
R3	High social connectedness is associated with lower interest rates on originated mortgages	Table 7, col. 2, p. 2787	Coefficient on log(SCI) = -0.497* (SE 0.259); 1-SD increase in log(SCI) lowers rate by ~1 bp
R4	High social connectedness is associated with lower delinquency and default rates	Table 7, cols. 3-4, p. 2787	Delinquency coefficient -0.004*** (SE 0.001); default coefficient -0.002** (SE 0.001); 1-SD change implies 0.8 pp lower delinquency and 0.4 pp lower default
R5	Bank ROA increases with portfolio social connectedness	Table 10, Panel A, col. 1, p. 2793	Coefficient 0.05*** (SE 0.02); 1-SD increase (1.4 log-units) raises ROA by 0.07 pp; effect larger for heavy SME lenders
R6	Bank ROE increases significantly with portfolio social connectedness	Table 10, Panel A, col. 2, p. 2793	Coefficient 0.74*** (SE 0.27); 1-SD increase raises ROE by 1.04 pp; nonperforming loans decrease (coefficient -0.35***, SE 0.10)
R7	Borrower counties more socially proximate to bank regions have higher real GDP growth, particularly in small-firm counties	Table 12, cols. 2-3, p. 2797	Baseline coefficient 1.719*** (SE 0.553); 10% increase in social proximity raises GDP growth by ~0.3 pp for counties at 95th percentile of small-firm share (= 10·(1.660+0.060·19)/100); with a positive and significant GDP interaction for high-small-firm counties in the shale-boom natural-experiment analysis (Table 13, col. 3)
R8	Employment is also higher with social proximity to bank regions, concentrated in small-firm counties	Table 12, cols. 4-5, p. 2797; Table 13, cols. 4-5, p. 2800	10% higher social proximity: coefficient 0.021*** (SE 0.006) on log(employment); twice as large at high small-firm-share counties; the shale-boom natural experiment also shows an employment increase
R9	County characteristics shape the SME lending gradient: it is larger in rural, volatile-industry, and small-firm counties and smaller in high-GDP counties and where large banks predominate	Table 4, cols. 1-5, p. 2780	Interaction coefficients: rural 0.08* (SE 0.04); high GDP per capita -0.18*** (0.04); volatile industries 0.13*** (0.04); high small-firm share 0.21*** (0.06); high large-bank share -0.13** (0.06)
R10	Screening incentives moderate the mortgage connectedness gradient: government-guaranteed, securitized, and low-LTV loans show smaller connectedness effects	Table 5, cols. 1-3 and 5-7, pp. 2782-2783	Interactions: guaranteed -0.14* (SE 0.06), securitized -0.10* (0.05), low LTV -0.08* (0.04); with county-pair FE: -0.09* (0.05), -0.04 (0.05, not significant), -0.11*** (0.04)
R11	Fintech lending is nearly unrelated to connectedness, consistent with the proposed loan-officer screening channel	Table 5, cols. 4 and 8, pp. 2782-2783	Traditional lender coefficient 1.29*** (SE 0.14), fintech interaction -0.99*** (0.20); with county-pair FE fintech interaction -1.58*** (0.38)
R12	High-connectedness mortgage borrowers have higher loan-to-value ratios	Table 7, col. 1, p. 2787	Coefficient on log(SCI) = 0.205*** (SE 0.077)
R13	Social connectedness is associated with greater dispersion in mortgage interest rates, consistent with more differentiated borrower assessment	Table 8, col. 3, p. 2789	Coefficient = 1.41** (SE 0.70), controlling for SDs of FICO, LTV, DTI, first-time-buyer status, and loan amount
R14	Banks with more socially connected loan portfolios have lower nonperforming loan ratios	Table 10, Panel A, col. 3, p. 2793	Coefficient -0.35*** (SE 0.10); a 1-SD increase (1.4 log units) is associated with a 0.49 pp decrease
R15	Shale-boom natural-experiment results support real lending effects: loan volume and employment rise, and GDP response is concentrated in small-firm counties	Table 13, cols. 1-5, p. 2800	Social proximity to shocked banks: loan volume 0.047** (SE 0.020); GDP 0.146 (0.240, not significant) in column 2 and -0.119 (0.244, not significant) in column 3, with GDP × small-firm share 0.103*** (0.024); employment 0.006*** (0.002) in column 4 and 0.005*** (0.002) in column 5, interaction 0.000 (not significant)
R16	After controlling for social connectedness, physical distance no longer predicts SME lending	Table 2, col. 4, p. 2773	Physical-distance coefficient 0.07 (SE 0.21), not significant; social-connectedness coefficient remains 0.96*** (SE 0.09)

Overall (paper’s conclusion). Social connectedness between bank and borrower regions is a distinct and economically important dimension of the geography of bank lending. It is not subsumed by physical or cultural distance and explains lending patterns consistent with both an information channel and favoritism, though the increase in bank profitability may indicate that reduced information frictions dominate favoritism on average.

Theory / model

The paper has no single formal theoretical model. It motivates the analysis with two competing hypotheses.

Information hypothesis. Social connections reduce information asymmetries between bank and borrower regions. Loan officers in socially connected counties obtain soft information about local economic conditions (through their own or their networks’ acquaintances in the borrower region) and can make better lending decisions. Formally, if social connectedness $\text{SCI}_{i,j}$ increases, the precision of banks’ private signal about borrowers in region $j$ rises. Better-screened borrowers receive lower rates and have lower delinquency; banks earn higher ROA. This prediction aligns with the classical framework of Diamond (1984) and Boot (2000) on delegated monitoring.

Favoritism hypothesis. Social connections may also lead to conscious or unconscious preferential treatment of borrowers in connected regions. Haselmann, Schoenherr, and Vig (2018) document rent-seeking in elite networks; the paper considers whether a similar mechanism operates at the population level. Unlike the information channel, favoritism predicts that banks’ loan profitability might not improve (and could fall) if resources flow to less creditworthy but socially connected borrowers. The paper tests both channels by examining loan terms, loan performance, and bank profitability jointly (Section 2.6).

Identification logic. The social connectedness measure (Facebook SCI, equation 1, p. 2766) is cross-sectional (2016) and predetermined relative to the 2017 lending outcomes in the baseline. It is not randomly assigned, so the authors proceed in two ways. First, they add an extensive set of geographic and economic controls including the physical and cultural distance controls emphasized by Degryse and Ongena (2005) and Agarwal and Hauswald (2010), and use the Oster (2019) coefficient-stability argument to argue that omitted-variable bias is unlikely. Second, for the real-effects results (Section 5.2), they exploit shale-boom liquidity shocks (Gilje, Loutskina, and Strahan (2016); Gilje (2019)) as a natural experiment: unanticipated increases in deposits at shale-exposed bank branches raise lending potential for banks with branches in boom counties, and counties socially connected to those banks receive more lending, without being directly affected by the boom.

Method

The paper has no standalone theoretical model. Its empirical framework tests two channels: social ties may reduce information asymmetry by conveying soft information about borrowers, or they may enable favoritism. The paper compares loan quantities, borrower terms and performance, and bank profitability to distinguish the implications. It also introduces a county-pair cultural-distance measure (Section 2.1, pp. 2767-2769), formed by combining standardized differences across four categories of cultural traits; this is described in prose rather than a numbered equation.

Facebook’s county-pair Social Connectedness Index is defined as (Equation 1, p. 2766):

$$\text{social connectedness}_{i,j} = \frac{\text{number of friendship links}_{i,j}}{\text{population}_i \cdot \text{population}_j} \cdot \text{scaling factor}. \tag{1}$$

The county-pair PPML loan-volume model is (Equation 2, p. 2770):

$$\text{volume of loans}_{i,j} = \exp\left[\beta \cdot \log(\text{social connectedness})_{i,j} + \gamma_1 \cdot \log(\text{physical distance})_{i,j} + \gamma_2 \cdot \text{cultural distance}_{i,j} + M_{i,j} + \alpha_i + \alpha_j\right] \cdot \epsilon_{i,j}. \tag{2}$$

For loan-type heterogeneity the paper estimates (Equation 3, p. 2781):

$$\text{volume of mortgage loans}_{i,j,k} = \exp\left[\beta \cdot \log(\text{social connectedness})_{i,j} \cdot \text{loan type}_k + \text{distance percentile FE}_{i,j} \cdot \text{loan type}_k + \alpha_{i,j}\right] \cdot \epsilon_{i,j,k}. \tag{3}$$

The portfolio connectedness measure is a loan-count-weighted average (Equation 5, p. 2790):

$$\text{portfolio social connectedness}_{b,y} = \sum_i \sum_j \text{social connectedness}_{i,j} \cdot \frac{\#\,\text{loans}_{b,i,j,y}}{\text{total }\#\,\text{loans}_{b,y}}. \tag{5}$$

The bank-profitability specification extends the empirical model of Loutskina and Strahan (2011) by adding portfolio connectedness and distance measures. Bank profitability is related to that measure and controls by (Equation 6, p. 2791):

$$\text{profitability}_{b,s,t} = \beta \cdot \log(\text{portfolio social connectedness})_{b,t} + \gamma_1 \cdot \log(\text{portfolio physical distance})_{b,t} + \gamma_2 \cdot \text{portfolio cultural distance}_{b,t} + \gamma_3 \cdot \text{loan concentration}_{b,t} + \gamma_4 \cdot \text{further bank controls}_{b,t} + \alpha_s + \alpha_t + \epsilon_{b,t}. \tag{6}$$

The social proximity to bank capital measure is (Equation 7, p. 2794):

$$\text{social proximity to banks}_{j,t} = \sum_i \text{social connectedness}_{i,j} \cdot \text{total bank assets}_{i,t}. \tag{7}$$

For shale-boom exposure, bank exposure is the branch-share-weighted well count (Equation 9, p. 2799):

$$\text{shale-boom exposure}_{b,t} = \frac{\sum_c \text{number of branches}_{b,c,t} \cdot \text{number of wells}_{c,t}}{\sum_c \text{number of branches}_{b,c,t}}. \tag{9}$$

County boom exposure averages the exposure of banks headquartered in county i (Equation 10, p. 2799):

$$\text{county boom exposure}_{i,t} = \sum_b \text{shale-boom exposure}_{b,t} \cdot \frac{I(\text{HQ in county }j)_{b,i,t}}{\sum_b I(\text{HQ in county }j)_{b,i,t}}. \tag{10}$$

Social proximity to shocked banks weights county boom exposure by county connectedness (Equation 11, p. 2799):

$$\text{social proximity to shocked banks}_{j,t} = \sum_i \text{social connectedness}_{i,j} \cdot \text{county boom exposure}_{i,t}. \tag{11}$$

Empirical specifications

The baseline county-pair specification is Equation 2 above. The headline analysis estimates it by PPML on county-pair lending observations, using 2017 CRA/HMDA outcomes and 2016 SCI. It includes bank-county and borrower-county fixed effects; expanded versions add same-state, common-border, distance percentile, and percentile fixed effects for commuting, migration, trade, industry-share, GDP, unemployment, travel-cost, and travel-time controls. Standard errors are clustered by bank county and borrower county. Table 2 uses 7,127,218 SME pairs and 5,935,135 mortgage pairs (p. 2773); Table 3’s full-control columns use 6,872,500 SME pairs and 5,761,073 mortgage pairs (p. 2775). County-type interactions are PPML with the same county fixed effects and distance-percentile-by-type effects (Table 4, p. 2780; N = 7,022,043, or 1,079,877 for the large-bank split). Loan-type interactions use Equation 3, PPML and bank-county/borrower-county clustered standard errors; Table 5 reports county-pair FE variants as well (pp. 2781-2782; N ranges from 11,870,270 to 35,788 by specification).

Loan-level mortgage outcomes use (Equation 4, p. 2785):

$$\text{outcome}_{l,b,i,j,s,t} = \beta \cdot \log(\text{social connectedness})_{i,j} + \gamma \cdot \text{loan characteristics}_l + \text{distance percentile FE}_{i,j} + M_{i,j} + \alpha_{b,t} + \alpha_{s,t} + \epsilon_{l,b,i,j,s,t}. \tag{4}$$

The equation is estimated by OLS for LTV and interest rates and by linear probability models for delinquency and default, on 1,268,200 Fannie Mae/Freddie Mac mortgages originated in 2000-2008 and observed through 2018. It includes bank-by-origination-year and borrower-state-by-origination-year fixed effects, same-state and common-border effects, and physical- and cultural-distance percentile effects. Loan controls vary by outcome: FICO for LTV; FICO, DTI, first-time buyer, loan amount, LTV for interest rate; and these controls plus interest rate for delinquency/default. Errors are clustered at bank-county and borrower-county levels (Table 7, p. 2787). The rate-dispersion mechanism test is a separate county-pair OLS regression of the standard deviation of interest rates on log connectedness; it uses 6,999 pairs with at least four loans, distance percentile, common-border, and same-state fixed effects, then adds dispersion in loan characteristics in the full column; errors use the same two-way county clustering (Table 8, p. 2789).

Bank outcomes use Equation 6 above, estimated by OLS on 18,914 bank-quarter observations for 844 banks from 2009-2017. The regressions include bank-state and year fixed effects, portfolio physical and cultural distance, loan concentration, and bank balance-sheet controls. Errors are clustered by bank. Table 10 also interacts connectedness and controls with the heavy-lender indicator (banks above the median SME-loans-to-assets ratio) (pp. 2791-2793).

The county-year real-outcome regression is (Equation 8, p. 2796):

$$\text{outcome}_{j,t} = \beta \cdot \log(\text{social proximity to banks})_{j,t-1} + \gamma_1 \cdot \log(\text{physical proximity to banks})_{j,t-1} + \gamma_2 \cdot \log(\text{cultural proximity to banks})_{j,t-1} + \gamma_3 \cdot \text{additional control variables}_{j,t-1} + \alpha_j + \alpha_t + \epsilon_{j,t}. \tag{8}$$

OLS estimates use 3,021 counties over 2009-2017 (N = 24,152-24,161), with county and year fixed effects, lagged proximity measures and industry-share controls; employment regressions add commuting and migration controls. Errors are clustered by county. For the identification check in Table 13, Equation 8 replaces each proximity measure with proximity to shocked banks constructed from Equations 9-11, excluding county-years in which new fracking wells are built; the corresponding sample has N = 22,047-22,053. Fixed effects, controls, and county-clustered errors follow the baseline (pp. 2799-2800).

Datasets used

Dataset	Role in paper	Wiki page
Facebook Social Connectedness Index (Bailey et al. 2018b)	Main explanatory variable; county-pair relative probability of Facebook friendship	Facebook SCI
Community Reinvestment Act (CRA) data, FFIEC	County-to-county SME loan volumes for 2017 (and 2004-2018 for time series); bank-level loan counts	CRA (FFIEC)
Home Mortgage Disclosure Act (HMDA) data, FFIEC	County-to-county mortgage loan volumes for 2017; loan-type classification	HMDA
Fannie Mae and Freddie Mac Single Family Loan-Level Datasets	Loan-level mortgage data (2000-2008 originations, observed through 2018): LTV, interest rate, FICO, DTI, delinquency, default	Fannie / Freddie loan-level
FDIC Call Reports	Bank profitability (ROA, ROE, % NPL) and bank characteristics (2009-2017); branch-location data for assigning loans to bank counties	Call Reports
NBER county distance database	Physical distance (as-the-crow-flies, miles) between county centroids	No page yet
Bureau of Economic Analysis	County-level real GDP growth and industry-share data	No page yet
Bureau of Labor Statistics	County-level employment; unemployment differentials	No page yet
U.S. Census Bureau	Commuting, migration, common-border, and county-level population data	No page yet
National Transportation Center / Oak Ridge	Highway travel costs and flight-and-drive time between county pairs	No page yet
Gilje, Loutskina & Strahan (2016) / Gilje (2019)	Shale-boom well counts and bank branch locations in boom counties for the shale-boom natural experiment	No page yet

Sample summary: cross-sectional county-pair analysis uses 2016 SCI and 2017 CRA/HMDA lending data, covering over 9 million county pairs. Loan-level analysis: 1,268,200 mortgages originated 2000-2008. Bank-profitability analysis: 844 banks, 2009-2017. Real-effects analysis: 3,021 counties, 2009-2017.

When to read the full paper

Read the original if you are: studying the geographic determinants of bank lending beyond physical distance; researching social networks and credit markets; applying the Facebook SCI to a new financial context; building a social-proximity-to-institutions measure analogous to Kuchler et al. (2022); evaluating the information-vs.-favoritism debate in relationship banking; or studying shale-boom liquidity shocks and bank lending. The replication archive at Harvard Dataverse (https://doi.org/10.7910/DVN/T3G5MD) covers all tables and figures.

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(9), September 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The paper is paywalled (Oxford University Press standard reuse rights; not CC); this page is extract-only.

Rehbein, Oliver, and Simon Rother. “Social Connectedness in Bank Lending.” The Review of Financial Studies 38, no. 9 (September 2025): 2759-2809. DOI: 10.1093/rfs/hhaf014.