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 raises cross-county SME loan volumes by roughly 6-9% and mortgage loan volumes by a similar margin, 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, lower 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, an effect confirmed 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, leaving little room for further omitted-variable attenuation
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 predicts lower delinquency and default rates	Table 7, cols. 3-4, p. 2787	Delinquency coefficient -0.004*** (SE 0.000); default coefficient -0.002** (SE 0.000); 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 experience 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); confirmed with shale-boom IV (Table 13, col. 3)
R8	Employment also rises 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; shale-boom IV confirms effect

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 bank profitability result suggests that on average information reduction rather than favoritism is the dominant mechanism.

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 rule out omitted-variable bias. 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

Social connectedness measure. The SCI is introduced by Bailey et al. (2018b) and defined at the county-pair level as (equation 1, p. 2766):

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

It quantifies the relative probability that a person in county $i$ is acquainted with a person in county $j$. The log of this variable is the main regressor throughout.

Baseline loan-volume specification. County-pair-level Poisson pseudo-maximum-likelihood (PPML) regression (equation 2, p. 2770):

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

where $\alpha_i$ and $\alpha_j$ are bank-county and borrower-county fixed effects, $M_{i,j}$ is a vector of county-pair controls (same-state FE, common-border FE, percentile FE for commuting, migration, trade, industry-share differentials, GDP and unemployment differentials, highway travel costs, and flight-and-drive time). Standard errors are clustered at the bank-county and borrower-county levels. PPML is used because loan volumes are non-negative and often zero; it accommodates heteroskedasticity and yields consistent elasticity estimates. The coefficient $\beta$ is the elasticity of loan volume with respect to social connectedness.

Loan-type heterogeneity. For the screening-incentive tests, the estimation adds county-pair-by-loan-type observations (equation 3, p. 2781):

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

where $k$ indexes loan types (guaranteed, securitized, low-LTV, fintech), and county-pair fixed effects $\alpha_{i,j}$ absorb all time-invariant pair characteristics.

Portfolio social connectedness (bank-profitability analysis). Each bank’s portfolio social connectedness is defined as the loan-weighted average of cross-county connectedness (equation 5, p. 2790):

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

Bank profitability is then regressed on this measure, extending the Loutskina and Strahan (2011) empirical model of bank performance (equation 6, p. 2791):

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

Social proximity to banks (real-effects analysis). Equation 7 (p. 2794) weights total bank assets in county $i$ by social connectedness:

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

Real outcomes in county $j$ are regressed on lagged log(social proximity to banks), controlling for physical and cultural proximity to banks, county and year fixed effects, and industry-share controls (equation 8, p. 2796).

Shale-boom IV. Bank exposure to shale-boom liquidity windfalls (equations 9-11, pp. 2799-2800) instruments lending potential. County $i$‘s boom exposure is the share-weighted average of its banks’ shale-boom exposures; county $j$‘s social proximity to shocked banks replaces social proximity to all banks in the IV regressions (Table 13).

Empirical specifications

Section 2 (loan volumes, county pairs): PPML on the full 9.1-million-pair cross-section (2017 lending data, 2016 SCI). Main estimates: Table 2 (baseline, columns 1-8), Table 3 Panel A (progressive controls, columns 1-11 for SME loans; columns 12 for mortgages). Heterogeneity by county type: Table 4 (five county-type interactions, PPML, N = 7,022,043). Heterogeneity by loan type: Table 5 (fintech vs. traditional banks, guaranteed, securitized, low-LTV; PPML, N = 11,870,270 for mortgage-by-type pairs). All columns include bank-county and borrower-county FE; columns with controls add same-state, common-border, and percentile FE.

Section 3 (loan terms and performance, loan level): OLS on 1,268,200 Fannie Mae/Freddie Mac 30-year fixed-rate mortgages originated 2000-2008, observed until 2018 (equation 4, p. 2785). Outcomes: LTV (col. 1), interest rate in bp (col. 2), delinquency indicator (col. 3), default indicator (col. 4). Controls include FICO score, DTI, first-time buyer indicator, log(loan amount), LTV (for performance columns), bank origination-year and borrower-state origination-year FE, and physical/cultural distance percentile FE. Standard errors clustered at bank-county and borrower-county.

Interest rate dispersion (Section 3.3): OLS at the county-pair level, dependent variable is the within-pair SD of interest rates (Table 8); controls include same-state, common-border, and percentile FE for physical and cultural distance. N = 6,999 county pairs with at least four loans.

Section 4 (bank profitability, bank-quarter level): OLS on 18,914 bank-quarter observations (844 banks, 2009-2017). Outcomes: ROA, ROE, % NPL (Table 10 Panels A and B). Controls: bank-state and year FE, log(portfolio physical distance), portfolio cultural distance, loan concentration, and further bank-level controls (log total assets, securities/assets, real estate loans/assets, C&I loans/assets, unused commitments/assets, letters of credit/assets, deposits/assets, interest expenses/deposits).

Section 5 (real effects, county-year level): OLS on 3,021 counties, 2009-2017 (N = 24,152-24,161). Outcomes: log(loan volume), real GDP growth, log(employment) (Table 12). Shale-boom IV regressions replace social proximity to all banks with social proximity to shocked banks (Table 13, N = 22,047-22,053). Controls: county and year FE, log(physical and cultural proximity to banks), industry-share controls, commuting/migration controls for employment. Standard errors clustered at the county level.

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 IV construction	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 designing a shale-boom IV for 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.