How Costly Are Cultural Biases: D'Acunto, Ghosh & Rossi (2026)

What this is. The paper’s core results, the competing hypotheses it tests, and the regression specifications with their defining equations: enough to know what it found and how, without reading all 26 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using lender-level panel data from Faircent, a peer-to-peer lending platform in India, D’Acunto, Ghosh, and Rossi compare the choices the same lenders make when unassisted and after observing suggestions from an automated robo-advising tool (Auto Invest). Unassisted Hindu lenders are 5.8 percentage points less likely to fund Muslim borrowers than Muslim lenders are, and lenders of all castes systematically under-lend to Shudra (lower-caste) borrowers relative to the platform population. These biases are costly: the disfavored borrowers default less and earn higher standardized returns on average. After adopting Auto Invest, both biases shrink substantially and lender-level total returns improve by 4.5 pp to 7.3 pp. Lenders rarely override robo-advised suggestions to previously-disfavored groups, which supports inaccurate statistical discrimination (biased beliefs) rather than taste-based discrimination, as in Becker (1957), as the dominant mechanism.

Core results

Magnitudes and significance are as reported; \*\*/\*\*\* = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Hindu lenders are 5.8 pp less likely to fund Muslim borrowers than Muslim lenders before robo-advising	Table 2, col 1, p.11	Hindu Lender = -0.058***, t=-3.52; baseline Muslim-borrower share is 18% for Muslim lenders and 12% for Hindu lenders
R2	Auto Invest reduces Hindu lenders’ out-group bias by 4.5 pp	Table 2, col 1, p.11	Hindu Lender x Auto Invest = 0.045**, t=2.51; after adoption, Hindu and Muslim lenders fund Muslim borrowers at the same rate as the platform population
R3	Discrimination is costly: Muslim borrowers in Hindu lender portfolios default 2.4 pp less than Hindu borrowers	Table 3, col 1, p.17	Muslim Borrower = -0.024**, t=-2.02; in-group bias selects worse borrowers from the preferred group
R4	After Auto Invest, Hindu (in-group) borrowers’ default drops 11.2 pp; Muslim (out-group) drops 7.3 pp; overall improvement driven by eliminating low-quality in-group loans	Table 3, col 2, p.17	Hindu Borrower x Auto Invest = -0.112***, t=-5.21; Muslim Borrower x Auto Invest = -0.073**, t=-2.49
R5	Before robo-advising, Muslim borrowers deliver higher standardized returns for Hindu lenders	Table 5, col 1, p.21	Muslim Borrower = 0.282***, t=6.25 (standardized return); confirms the financial cost of out-group bias
R6	After Auto Invest, return improvement concentrated entirely in Hindu (in-group) borrowers: +0.222 SD; Muslim borrowers unchanged	Table 5, col 2, p.21	Hindu Borrower x Auto Invest = 0.222***, t=3.07; Muslim Borrower x Auto Invest = -0.012, t=-0.18
R7	Lender-level total returns improve by 4.5 pp (in-group vs. out-group) and 7.3 pp (stereotypical discrimination) after Auto Invest	Fig. 11, p.23	Average lender-level return increase, value-weighted across all loans before and after Auto Invest adoption
R8	Shudra borrowers default 3.8 pp less than other borrowers before Auto Invest, confirming stereotypical discrimination is costly	Table 3, col 4, p.17	Shudra Borrower = -0.038***, t=-3.03; all-lender sample; lending to Shudra borrowers increases with robo-advice adoption

Overall (paper’s conclusion). Cultural biases lead lenders to select borrowers from preferred social groups who systematically underperform. The evidence is most consistent with inaccurate statistical discrimination: biased ex-ante beliefs about borrower quality correlated with ethnicity and caste, not a conscious taste for discrimination. Robo-advising reduces both types of bias and generates returns improvements of approximately 6% to 12% of the average capital invested, concentrated in loans to previously-favored in-group borrowers who were over-selected relative to their quality.

Theory / model

The paper has no formal economic model. It tests three competing hypotheses about the nature of the discrimination it documents, each with distinct predictions for lenders’ behavior when assisted vs. unassisted:

Hypothesis 1 (taste-based discrimination, Becker (1957)): lenders are willing to pay a utility cost to avoid transacting with disfavored groups. Prediction: lenders should frequently override robo-advised matches to disfavored-group borrowers, even when their economic incentives are aligned with the tool.

Hypothesis 2 (accurate statistical discrimination): disfavored groups are truly riskier on average, so the favored-group lending pattern reflects correct Bayesian updating on observable signals. Prediction: in-group borrowers should outperform (lower default, higher returns) relative to out-group borrowers matched by the platform’s unbiased screening.

Hypothesis 3 (inaccurate statistical discrimination, biased beliefs): lenders hold systematically incorrect ex-ante beliefs about out-group borrower quality, even when identical objective risk information is available. Predictions: (a) disfavored borrowers should outperform when lenders are unassisted; (b) performance should converge after robo-advising; (c) lenders should not override robo-advice, because their financial incentives do not justify doing so.

The platform design eliminates channels that could rationalize statistical discrimination: interest rates are set by the platform algorithm, borrower risk profiles are directly observable, lenders and borrowers never interact, and Faircent’s screening ensures no unbanked borrowers enter the pool. Heterogeneity tests rule out monitoring advantages, social collateral, and peer effects as alternative explanations (Fisman et al. (2017), Fisman et al. (2020)). By contrast, Hjort (2014) detects strong taste-based discrimination in a setting that removes scope for inaccurate statistical discrimination; the paper argues that Faircent’s FinTech context is closer in spirit to that ideal but that inaccurate beliefs nonetheless dominate. The evidence consistently supports Hypothesis 3: disfavored borrowers outperform before robo-advising, performance converges after, and lenders rarely override robo-advised matches. The paper further shows that biases are stronger for lenders in areas with higher Hindu-Muslim inter-ethnic conflict, consistent with culturally shaped priors rather than rational updating.

Method

The primary estimator is OLS on a lender-borrower-loan triad panel with lender fixed effects and year fixed effects, clustering standard errors at the lender level throughout (Table 2 caption, p.10). The within-lender before-after variation in tool adoption identifies the de-biasing effect; the approach builds on D’Acunto and Rossi (2020) who study robo-advising effects in a savings context.

For loan returns the paper additionally estimates quantile regressions (Eq. 5, p.22) to identify which part of the return distribution drives the improvement:

$$Q_\tau(\text{Returns}_{i,j}) = a(\tau) + \beta(\tau)\,\text{Auto Invest}_j + \mathbf{X}'_{i,j}\,\zeta(\tau) + \varepsilon_{i,j} \tag{5}$$

where $Q_\tau$ is the $\tau$-th quantile of standardized loan returns for loan $i$ of lender $j$, and $\mathbf{X}_{i,j}$ are loan risk controls. Coefficient $\hat{\beta}(\tau)$ measures how the $\tau$-th quantile shifts after the lender adopts Auto Invest.

The lender-level total return is computed as a value-weighted average across loans originated before and after Auto Invest adoption (Eq. 6, p.23):

$$\text{Lender Tot Ret}_{i,t} = 100 \times \frac{\sum_j \text{Amount Disbursed}_{i,j,t} \times \text{Loan Return}_{j,t}}{\sum_j \text{Amount Disbursed}_{i,j,t}} \tag{6}$$

and the lender-level change is POST minus PRE (Eq. 7, p.23). A purged measure (Eq. 8, p.23-24) removes compositional effects by holding the pre-period disbursed amounts fixed and applying post-period returns, isolating the cultural-debiasing channel from other effects of Auto Invest on portfolio composition.

Empirical specifications

Primary specification for in-group vs. out-group discrimination (Eq. 1, p.10):

$$\text{Muslim Borrower}_{i,j,t} = \alpha + \beta\,\text{Auto Invest}_{j,t} + \gamma\,\text{Hindu Lender}_j + \delta\,(\text{Hindu Lender}_j \times \text{Auto Invest}_{j,t}) + \zeta\,\mathbf{x}_{i,t} + \eta_j + \eta_t + \varepsilon_{i,j,t} \tag{1}$$

where $\text{Muslim Borrower}_{i,j,t} = 1$ if borrower $i$ funded by lender $j$ in year $t$ is Muslim; $\text{Auto Invest}_{j,t} = 1$ if the lender has adopted the tool by year $t$; $\text{Hindu Lender}_j = 1$ if lender $j$ is Hindu; $\mathbf{x}_{i,t}$ are loan characteristics assigned by the platform (maturity, amount, interest rate); $\eta_j$ are lender fixed effects; $\eta_t$ are year fixed effects. Coefficient $\hat{\gamma}$ (R1) measures the in-group bias before adoption; $\hat{\delta}$ (R2) measures the de-biasing effect of robo-advising.

Stereotypical discrimination specification (Eq. 2, p.14):

$$\text{Shudra Borrower}_{i,j,t} = \alpha + \beta\,\text{Auto Invest}_{j,t} + \zeta\,\mathbf{x}_{i,t} + \eta_j + \eta_t + \varepsilon_{i,j,t} \tag{2}$$

This drops the Hindu Lender interaction because all castes, including Shudra lenders, discriminate against Shudra borrowers (stereotypical discrimination is not in-group favoritism but group-wide negative stereotyping). Caste recognizability (continuous probability from the Bhagavatula et al. (2017, 2018) matrimonial-registry algorithm) is used to test whether the $\hat{\beta}$ coefficient grows with how easily a borrower is identifiable as Shudra (Fig. 5-6, pp.14-15).

Performance specifications (Eqs. 3-4, p.16 and p.19):

$$\text{Delinquent Loan}_{i,j,t} = \alpha + \gamma\,\text{Muslim Borrower}_j + \delta\,(\text{Muslim Borrower}_j \times \text{Auto Invest}_{j,t}) + \theta\,(\text{Hindu Borrower}_{i,j} \times \text{Auto Invest}_{j,t}) + \zeta\,\mathbf{x}_{i,t} + \eta_j + \eta_t + \varepsilon_{i,j,t} \tag{3}$$ $$\text{Loan Return}_{i,j,t} = \alpha + \gamma\,\text{Muslim Borrower}_j + \delta\,(\text{Muslim Borrower}_j \times \text{Auto Invest}_{j,t}) + \theta\,(\text{Hindu Borrower}_{i,j} \times \text{Auto Invest}_{j,t}) + \zeta\,\mathbf{x}_{i,t} + \eta_j + \eta_t + \varepsilon_{i,j,t} \tag{4}$$

where $\text{Delinquent Loan}_{i,j,t} = 1$ if the loan is closed delinquent (more than 90 days past due at closure) and $\text{Loan Return}_{i,j,t}$ is the standardized return. Coefficient $\hat{\gamma}$ tests whether disfavored borrowers outperformed before the tool was adopted (the cost of bias, R3 and R5); $\hat{\delta}$ and $\hat{\theta}$ capture the differential change in performance across borrower groups after adoption (R4 and R6). The falsification column (col 3 and col 6 in Table 3, p.17) adds loan risk controls to show that default changes are not driven by compositional shifts in borrower riskiness.

Heterogeneity tests use cross-sectional proxies for the salience of cultural stereotypes: state-level Hindu-Muslim riots (Ticku (2015)), BJP vote shares (Bhavnani (2014)), and birth-cohort exposure to the rise of Hindu-Muslim conflict (Fig. 4, p.13). The bias is about twice as large for lenders residing in high-riot states (6.4 pp vs. small and insignificant elsewhere), and the de-biasing effect is correspondingly stronger in these states.

Sample: lender-borrower-loan triads from the Faircent platform, January 2018 to March 2020. Main sample: 113,283 triads involving 2,818 unique Hindu and Muslim lenders and borrowers. Caste sub-sample: 62,831 triads for which Hindu varna (caste category) of the borrower can be inferred from the matrimonial registry. Loan maturity averages 22 months; median maturity 24 months; average loan amount is approximately Rs.130,000 (~$1,770); average annual interest rate is 24%. Standard errors are clustered at the lender level.

Datasets used

Dataset	Role in paper	Wiki page
Faircent P2P lending platform	Main panel: lender-borrower-loan triads; loan amounts, interest rates, maturity, delinquency; Auto Invest adoption and fund-allocation share; lender and borrower demographic characteristics (name, state, date of birth, occupation)	No page yet
Marriage registry (Bhagavatula et al. 2017, 2018)	Religion and caste inference for lenders and borrowers; 2.5 million individuals from online matrimonial agencies; used to assign religion and varna probabilities from surname, date of birth, and location	No page yet
Ticku (2015) Hindu-Muslim riots data	State-level count of large-scale riots between Hindus and Muslims (1980-2000); proxy for inter-ethnic conflict salience in heterogeneity tests	No page yet
Bhavnani (2014) BJP election data	Average BJP candidate vote shares across national and state elections (1977-2015) per Indian state; proxy for ideological salience of Hindu nationalism	No page yet
National Crime Records Bureau (NCRB 2019)	Crimes against Scheduled Castes per 100,000 inhabitants per Indian state (2018); proxy for salience of caste discrimination in stereotypical bias heterogeneity tests	No page yet

Sample: January 2018 to March 2020. Roughly 60% of loans issued in 2019 and 19% in the first three months of 2020. Median lender disburses funds to borrowers across 13 different Indian states; 90% of lenders serve borrowers in at least 5 different states.

When to read the full paper

Use the original if you are: studying the mechanism of discrimination (taste-based vs. inaccurate statistical) in financial markets; examining how robo-advising corrects culturally biased lending choices; studying in-group vs. out-group or stereotypical discrimination in a high-stakes economic setting; or extending the Faircent platform setting to other FinTech platforms or demographic groups. Table 2 (p.10) gives the main lending-bias results; Table 3 (p.17) the default-performance results; Tables 5-6 (pp.21-22) the return results; and Figs. 4, 6, and 8 the heterogeneity and mechanism tests.

Attribution and rights

Source: peer-reviewed, Journal of Financial Economics 175 (2026), article 104202. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). D’Acunto, Francesco, Pulak Ghosh, and Alberto G. Rossi. “How costly are cultural biases? Evidence from FinTech.” Journal of Financial Economics 175 (2026) 104202. DOI: 10.1016/j.jfineco.2025.104202. © 2025 The Authors. Published by Elsevier B.V. Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.