Research

Study Overview

On May 15, 2018, the European Union adopted the General Data Protection Regulation (GDPR) to protect the privacy and security of EU citizens’ data. This regulation lays out the requirements that data holders — such as researchers, humanitarian organizations, and financial institutions — must abide by when collecting, storing, and sharing data. In particular, the GDPR specifies three types of privacy risks that must be ameliorated prior to the sharing of personal information: singling-out attacks, linkage attacks, and attribute inference attacks. While differential privacy — the gold standard for privacy preserving data analysis — can provably prevent the first two attacks, it is not guaranteed to protect against attribute inference attacks.

Study Results

This project will develop new methods to protect personal data from attribute inference attacks, while also facilitating key downstream use cases.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This research explores the welfare consequences of data-based targeted pricing in the consumer loan market. We develop a general model of optimal loan pricing. Intuitively, loans should be more expensive for consumers with high demands, high default risk, and with demand and default that do not significantly vary with interest rates. We first apply our model to the pricing of credit scores. We use experimental data from a large European bank to estimate how loan take-up and repayment vary across borrowers with different credit scores. This analysis allows us to better understand the shortcomings of risk-based pricing models used in the industry. We then combine machine learning and experimental data to determine the important consumer characteristics that lenders should consider when pricing loans. Our algorithm partition consumers based on characteristics estimated to maximize either firm profits or consumer welfare under a zero-profit condition. This analysis allows us to better understand which consumer characteristics matter most for predicting both demand elasticities (how demand responds to variations in interest rates) and cost elasticities (how expected repayments respond to these shocks – a measure of adverse selection). Our quantitative analysis reveals how moving from a standard risk-based pricing scheme to personalized pricing can affect lender’s profit and consumer welfare.

Study Results

Pending

Intervention: Randomized variations in interest rates for consumer loans

Populations: Middle income households

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

We use a randomized controlled trial to examine the impact of a government-backed loan program for small businesses in Chile during the COVID pandemic.

Study Results

Exploiting neighborhood-level variation in lockdowns over time—which we show have significant effects on firms’ cash flows—combined with randomized variation in whether firms were offered loans and employer-employee linked administrative data, we find large effects of loans on employment for firms experiencing cash flow shocks. Firms not experiencing a cash flow shock due to their neighborhood not being in lockdown typically did not lay off workers regardless of whether they received an offer for a government-backed loan. However, firms experiencing a cash flow shock in the control group (i.e., received no loan offer) laid off workers. In contrast, firms in the treatment group did not lay off workers when experiencing a cash flow shock. Aggregating our RCT results to the macroeconomy, we estimate that Chile’s government-backed small business loan program reduced unemployment by 1.2 percentage points as of February 2021 when the unemployment rate was 10.3%. We also estimate that the cost to the government per job saved was US$488.5.

Populations: Small and medium enterprises (SME)

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

In growth markets, where an estimated 1.4 billion individuals remain unbanked, the reliable and consistent delivery of last-mile banking services remains a critical challenge despite multi-sector technological advancements. In emerging markets, neighborhood mom-and-pop store owners acting as micro-fintech agents are crucial basic banking service providers in underserved communities. However, efforts to improve access to banking services through these agents often face a significant challenge known as 'merchant dormancy'. Empowering last-mile micro-fintech agents with meaningful, actionable information on financial products and how to manage and maintain them could reduce dormancy, potentially catalyzing an estimated USD 380 billion in annual economic value and fostering local economic development. For micro-fintech agents serving the underbanked in emerging markets, the AI revolution offers an opportunity to jumpstart their productivity supported by access to personalized and timely advice. This research leverages Large Language Models (LLMs) in two ways a) Personalized recommendation (curation) with an AI virtual secretary and b) Low-cost and fast content creation through Generative AI models aimed to reduce merchant dormancy, increase access to and improve quality of financial services offered by micro-fintech agents in rural and peri-urban India.

Study Results

Pending

Intervention: AI-generated personal support for micro-fintech agents

Research Partner: Center for Growth Markets

Intervention Partner: FINO

Populations: Small, micro enterprises, unbanked households and individuals

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

News & media

Revolutionizing Financial Inclusion: AI-Powered Personalized Support for Last-Mile Banking

September 1, 2024

We’re pioneering an innovative approach that leverages the power of Large Language Models (LLMs) to democratize content personalization for small fintech merchant support, bridging the data gap that has long hindered effective service delivery.

Empowering financial inclusion in India through reliable micro-fintech agents

September 1, 2024

We have developed an AI-powered solution that offers personalized support to micro-FinTech agents in data-scarce environments. By leveraging LLMs and Generative AI, we deliver timely and relevant information that empowers agents to thrive in their roles.

Study Overview

Small and medium enterprises (SMEs) are critical to job creation and global economic development. SMEs form the backbone of the African economy, representing more than 90% of businesses and employing between 60% and 70% of workers, many of whom are women and youth. The World Economic Forum estimates that Africa’s workforce will increase by a staggering 910 million people by 2050, of which 830 million will be in Sub-Saharan Africa, creating enormous pressure for jobs on SMEs, which typically account for approximately 80% of new jobs. The UC Berkeley research team developed Skills for Effective Entrepreneurship Development (SEED), a 3-week mini-MBA modeled after western business curricula adapted to the Ugandan context. In collaboration with Educate!, we are poised to launch a large field experiment featuring 10,000 Ugandan youth to foster a new and dynamic generation of entrepreneurs. Among others, the study will assess the value added of teaching digital business skills to encourage tech adoption for business management (inventory, payroll, digital payments) and leverage online market opportunities (e/s-commerce, branding, online presence etc.) 

Study Results

Pending

Intervention Partner: Educate!

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The mobile phone revolution in low-and-middle income countries has transformed the way humanitarian organizations distribute relief. In the past four years alone, personal data from mobile phones have been used to target economic relief in to individuals experiencing poverty in Togo and Afghanistan. The key insight underlying these approaches is that poorer individuals tend to use their phones differently than richer individuals; hence, when combined with survey data on poverty, techniques from supervised learning can be used to generate a machine learning model that predicts an unknown individual’s poverty status. However, conducting these surveys can be expensive, time-consuming, and oftentimes impossible during a humanitarian crisis. A promising avenue of inquiry would be to collaboratively build a machine learning model using existing data from multiple organizations in unison. However, the use of personal data from economically disadvantaged individuals — even for humanitarian applications — raises privacy concerns.

Study Results

This project will develop tools to foster provably private collaborative machine learning between organizations to support anti-poverty initiatives, and will enable the responsible use of personal data, which in turn can unlock new innovation and facilitate collaborative machine learning between humanitarian organizations.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

In this review, we discuss changes resulting from both big data and non-traditional data in the financial industry. We explore ways these innovations are shaping markets and companies, but put greater focus on the consequences - both positive and negative - for end consumers. We aim to cast a wide net over offerings for both developed and emerging markets, as well as both products aimed at low-income, low-literacy individuals and those intended for more educated, wealthy, and financially savvy consumers.

Study Results

Analysis suggests there are many promises and potential risks - such as discrimination caused by bias in data used for credit scoring or consumers' behavior to game systems - related to data-driven fintech innovation. In many cases, better oversight and further research can help to address the concerns and ensure benefits are more widely distributed. As these technologies become more ubiquitous, the challenge will be for regulation and rigorous evaluation of impacts to keep pace with rising innovation.

Populations: General

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

We study the effects of the two most common slum policy interventions: in-situ upgrading and population relocation on (i) slum area physical characteristics, (ii) socioeconomic attributes of slum dwellers, and (iii) spillovers to nearby formal neighborhoods. To conduct our analysis we create a 20+ year novel panel dataset for the universe of slums in Chile using satellite images, census data, administrative records, construction permits, crime reports, and property tax records. Descriptively, slums tend to form on the periphery of cities, close to low-skilled labor centers. City level slum growth is linked to higher housing rental prices and improved labor markets for low-skilled workers.

Study Results

We find that both in-situ upgrading and slum relocation reduce a slum’s building footprint and the share land used for residences. However, in-situ upgrading generates long-lasting positive impacts on housing quality in slum areas and attracts higher SES (socioeconomic status) residents. In terms of spillovers to surrounding neighborhoods, in-situ upgrading dominates population relocation. Neighborhoods near in-situ upgraded slums experience a reduction in criminal activity, more housing investment, and attraction of more high SES residents. These findings are all the more impressive given that the average fiscal cost per beneficiary household for in-situ upgrading is only two-thirds of the cost of population relocation.

Research Partner: Universidad de Chile

Populations: low income households

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This research examines how the prolonged regulatory uncertainty surrounding blockchain and digital assets relates to entrepreneurial activity. Exploiting variation in U.S. congressional voting patterns, close-call elections of crypto-friendly politicians, and spatial distribution of crypto PAC contributors, we analyze startup formation, failure rates, capital raising, and strategic pivots inferred from hiring data.

Study Results

We find that regulatory uncertainty significantly influences entrepreneurial outcomes, with increased legislative support associated with positive startup performance. Paradoxically, while the industry advocates for regulation, startups’ hiring patterns suggest reluctance to prioritize regulatory compliance. Overall, our study helps to quantify the magnitude of regulatory uncertainty’s impact on emerging technology and offer insights for policymakers seeking to balance oversight with innovation.

Intervention: Regulation

Research Partner: Emory University

Populations: Blockchain start-ups, Government regulators

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Celsius was not a bank, but the now-bankrupt cryptocurrency network advertised and provided de facto banking services. We investigate the consequences of this collapse for Celsius's depositors. First, we summarize deposits by demographic and institutional affiliation. Then, we examine the intensive and extensive margin differences in digital assets stranded in bankruptcy.

Study Results

We find significant disparities across demographic groups, with institutions, males, and ethnic groups culturally distant from Celsius's founders doing better. Mechanism tests are consistent with more effective monitoring of risk by these groups. Finally, using a difference-in-differences approach, we analyze the influence of stranded assets on user's subsequent digital asset balances and transactions. We find significant differences in behavior, with those most impacted by the bankruptcy moving away from DeFi and NFTs toward more basic transactions (e.g., buy and hold) but also exhibiting tendencies to gamble for resurrection by holding riskier cryptocurrencies (e.g., meme coins). We conclude with a discussion of the implications for policymakers and industry participants, aiming to enhance the stability and resilience of digital asset markets.

Intervention: Cryptocurrency

Research Partner: Emory University

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Haraka leverages blockchain, local stablecoins, and community trust to reimagine micro-finance and expand financial inclusion and health of underserved populations. Savings circles are a crucial segment of last-mile financial services for the estimated 510 million individuals worldwide who have turned to informal and self-organized village savings and loan associations (VSLAs). Despite the crucial role that VSLAs play in providing services to underbanked people, especially women, savings groups remain excluded from access to formal credit. The goal of this research is to understand how access to credit for savings groups and their members might be expanded by coupling machine learning methods with data on VSLA transactions, digital records, and information on the groups’ social capital, reputation and cohesion when assessing creditworthiness. Technology is rapidly digitizing many aspects of saving groups’ functioning, including group interactions, access to information, digital record-keeping, and electronic transactions creating invaluable digital footprints. This information is then combined to build  a self-sovereign AI/ML credit score for individuals, enabling them to access financial opportunities beyond their immediate communities. Beyond traditional measures of access to credit and creditworthiness, we are also interested in possible impacts on members’ economic activities and their performance, as well as downstream impacts on wellbeing and gender empowerment.

Study Results

Pending

Intervention: Innovative credit scoring to expand access to financial services leveraging blockchain technology

Intervention Partner: Haraka

Populations: Rural, underbanked in community savings and loan associations

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This research explores different concepts of data rights, their current effectiveness, and privacy-enhancing technologies that can increase their effectiveness. In particular, the team evaluates various rights-based privacy approaches by collecting and analyzing numerous empirical pieces of evidence. The goal is also to assess how various data rights can affect underground data markets where customer data is traded without discretion and whether the data rights can intrinsically solve the issue regarding the underground data market.

Study Results

Pending

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This research project aims to analyze discrimination dynamics faced by international migrants when applying for loans in Colombia's informal economy. Working in collaboration with Galgo, a Fintech company operating in Latin America, the project examines how innovative scoring methods can assess credit risks for both native and immigrant populations. The project then seeks to evaluate the impact of Galgo's loan program on various outcomes, including financial behavior, repayment rates, labor market participation, income, and overall wellbeing. This research intends to contribute to the development economics literature by addressing topics such as discrimination in credit markets, economic impacts of migration, productivity of microcredit loans, and financial inclusion of underserved populations.

Study Results

Pending

Intervention: Innovative credit scoring

Research Partner: La Universidad Adolfo Ibáñez (UAI)

Intervention Partner: Galgo

Populations: Individuals, households, immigrant populations

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The global financial system remains fragmented by jurisdictional boundaries, with institutions repeatedly performing identical compliance checks on the same entities across different markets. This redundancy creates significant friction in cross-border financial flows, with estimates suggesting compliance costs account for a non-trivial portion of financial institutions' operating expenses. Blockchain technology offers a potential solution through portable, programmable compliance - where regulatory checks, once performed and cryptographically verified, could be re-used and “trusted’’ across jurisdictions. However, this vision of “verify once, use many times”' faces substantial regulatory skepticism due to concerns about systemic risk, data privacy, and national sovereignty. The tradeoff between efficiency and risk is particularly acute since policymakers often hesitate to support blockchain innovation, despite its potential efficiency gains, due to concerns about sanctions enforcement and illicit activities. Therefore, empirically examining the economic trade-offs that automated compliance systems generate will provide policymakers with an evidence-based approach to modernizing regulatory frameworks while maintaining robust security standards.Our study seeks to examine automated compliance systems by analyzing the recent initiatives that central banks and financial institutions have promoted, as they provide a unique laboratory for answering these questions. By examining the staggered adoption of automated compliance systems or initiatives aimed at such automation, we can assess portable compliance infrastructure's promised benefits and potential risks. Specifically, we ask three questions: (1) Does automated compliance technology increase financial inclusion through reduced costs and improved access? (2) Do early adopters of compliance initiatives gain competitive advantages that create barriers to entry? (3) How do different jurisdictions' approaches to automated compliance influence local business development?

Study Results

Pending

Research Partner: Emory University

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Technologies have expanded the set of financial contracts that financial firms can use in the market for consumer loans. A notable instance is digital collateral: consumers take a loan and lenders can remotely de-activate certain goods valued by consumers when they are late on their payments. For instance, smartphones or solar home systems (SHS) can be used as such digital collateral. The typical contract currently used in practice is a PayGo contract: a payment activates the good for a period; a missed payment shuts it off for a period. One pitfall of PayGo contracts is that borrowers can be strategic: they may repay only when they need their phone or SHS and otherwise miss payments. This behavior can extend loan durations and reduce lending profitability for lenders (and thus restricting loan supply). In this project, we explore how different contract designs can help spur repayment, consumer loan take-up and consumer welfare. One particular contract we will study features automatic catch-up where payments increase as consumers miss payments.

Study Results

Pending

Intervention: Randomized variation in contract types offered (PayGo vs. Automated Catch-up)

Intervention Partner: ENGIE

Populations: low income households

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This research project examines the economic efficiency of blockchain-based public goods funding mechanisms, specifically focusing on capital allocation to blockchain infrastructure and ecosystem development. While traditional institutions rely on financial professionals to make investment decisions through ROI and cost-benefit analyses, blockchain projects often distribute funds through decentralized autonomous organizations (DAOs) using token-holder voting and prediction markets. DAOs represent a novel organizational structure that replaces hierarchical decision-making with direct participant voting via blockchain mechanisms. Proponents argue this approach leverages collective wisdom and mitigates behavioral biases in capital allocation, while blockchain transparency reduces agency costs and information asymmetries. We have partnered with Funding the Commons, a consortium connected to major blockchain organizations, including the Ethereum Foundation, Gitcoin, and Arbitrum, to analyze their historical funding decisions and conduct field experiments. During the 2021 crypto bull market, these organizations distributed over $500 million to developers. This creates an opportunity to evaluate which funding mechanisms prove most effective and inclusive. The study will examine whether this decentralized approach democratizes capital access, particularly for underserved communities and emerging market developers, while distinguishing between luck and skill in allocation decisions. However, this raises important security considerations, as blockchain's borderless nature could enable funds to flow to sanctioned entities or hostile state actors. We will analyze how DAOs navigate these compliance challenges compared to traditional institutions' due diligence processes. Finally, these insights will be benchmarked against established corporate, venture capital, and government grant-making approaches to help provide actionable insights for policymakers and similar blockchain-based organizations.

Study Results

Pending

Intervention: Observational study using historical data, but based on initial findings, there will be opportunities for field experiments.

Research Partner: Funding the Commons

Populations: Various DAOs and Foundations token holders as well as the developers allocated capital.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The digital economy presents a significant opportunity for youth to engage in meaningful work and microwork. Despite its rapid expansion, little research to date documents its impacts or the gendered experiences of gig work. This study seeks to understand how digital financial inclusion, training and skill development opportunities, and the gig economy impact the labor market outcomes of youth and women, and financial inclusion.

Intervention Partner: Mercy Corps

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

News & media

The Potential of Cryptocurrency for Kenya’s Youth

Feburary 16, 2022

Mercy Corps Venture's pilot uncovered clear insights into the positive impact of digital stablecoins and mobile wallets on easing frictions and reducing costs in cross-border payments for un/underemployed youth microworkers in Kenya. This is only one of innumerable use cases for stablecoins’ transformative potential. For example, Kenya hosts one of the largest refugee populations in Africa, estimated at around 520,000 refugees and asylum seekers

Study Overview

This research proposes a novel decentralized platform enabling privacy-preserving data collaboration and analytics across organizations with private data sources. The platform integrates advanced cryptographic techniques like secure multi-party computation and federated learning to facilitate secure cross-entity data analysis and machine learning for accurate financial auditing, fraud/laundering detection while ensuring data privacy. Its decentralized approach breaks data silos, empowering collaborative insights extraction without compromising privacy and security. The platform simplifies development of privacy-preserving applications, reduces costs, and promotes regulatory compliance. It has broader applications beyond finance for secure cross-organizational analytics through privacy-preserving decentralized computing.

Study Results

Pending

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Firms frequently fail to adopt profitable business opportunities even when they do not face informational or liquidity constraints. We explore three behavioral frictions that explain inertia among individuals—present bias, limited memory, and distrust—in a managerial setting. In partnership with a FinTech payments company in Mexico, we randomly offer 33,978 f irms the opportunity to pay a lower merchant fee. We vary whether the offer has a deadline, reminder, pre-announced reminder, and the size of the fee reduction.

Study Results

Reminders increase take-up by 15%, suggesting a role of memory. Announced reminders increase take-up by an additional 7%. Survey data reveal the likely mechanism: When the FinTech company follows through with the pre-announced reminder, firms’ trust in the offer increases. The deadline does not affect larger firms, implying limited or no present bias, but does increase take-up by 8% for smaller firms. Overall, behavioral frictions contribute significantly to explaining profit-reducing firm behavior.

Intervention: Random variation in contract terms

Populations: Small businesses

Working Paper: Gertler, Paul, Sean Higgins, Ulrike Malmendier, Waldo Ojeda. 2025. Why Small Firms Fail to Adopt Profitable Opportunities.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

News & media

Weighing the Benefits and Drawbacks of e-Payments: Insights from Small Businesses in Mexico

June 28, 2018

For the merchants we spoke with, the benefits to accepting card payments seem to outweigh the drawbacks. Even those who we categorized as “inactive” users acknowledged that urban customers increasingly want to pay by card. Ultimately, while micro businesses may be frustrated with the commission rates and increase in registered transactions, these factors aren’t enough of a deterrent to abandon e-payment technology entirely.

Study Overview

How do inaccurate beliefs about the distribution of interest rates affect search and outcomes in consumer credit markets? In collaboration with Chile’s financial regulator, we conducted a randomized controlled trial with 112,063 loan seekers where we showed treated participants a price comparison tool that we built using administrative data on the universe of consumer loans merged with borrower characteristics. The tool shows loan seekers a conditional distribution of interest rates based on similar loans obtained recently by similar borrowers.

Study Results

We find that consumers thought interest rates were lower than they actually were, and the price comparison tool caused them to increase their expectations about the interest rate they would obtain by 56%. Consumers also underestimated price dispersion, and our price comparison tool caused them to increase their estimates of dispersion by 69%. The price comparison tool did not cause people to search or apply at more institutions, but it did cause them to be 39% more likely to negotiate with their lender, to receive 13% more offers and 11% lower interest rates, and to be 5% more likely to take out a loan. We also cross-randomized whether we asked participants their beliefs about the distribution of interest rates, and find that merely asking these questions led them to search at 4% more institutions and obtain 10% lower interest rates.

Intervention: Interest rate comparison tool

Populations: Consumer loan seekers

Working Paper: Higgins, Sean & Berwart, Erik & Kulkarni, Sheisha & Truffa, Santiago. (2025). Search and Negotiation with Biased Beliefs in Consumer Credit Markets.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

A form of secured lending using “digital collateral” has recently emerged, most prominently in low- and middle-income countries. Digital collateral relies on lockout technology which allows the lender to temporarily disable the flow value of the collateral to the borrower without physically repossessing it. This research explores this form of credit in a model and a field experiment using school-fee loans digitally secured with a solar home system.

Study Results

Securing a loan with digital collateral drastically reduced default rates (by 19 percentage points) and increased the lender’s rate of return (by 49 percentage points). Using a variant of the Karlan and Zinman (2009) methodology, we decompose the total effect on repayment and find that roughly two-thirds is attributable to moral hazard, and one-third to adverse selection. In addition, access to digitally secured school-fee loans significantly increased school enrollment and school-related expenditures without detrimental effects on households’ balance sheets.

Intervention: Household loans relying on digital collateral using PayGo technology

Intervention Partner: Fenix International (now ENGIE)

Populations: low income households

Working Paper: Paul Gertler & Brett Green & Catherine Wolfram, 2021. "Digital Collateral," NBER Working Papers 28724, National Bureau of Economic Research, Inc.

Journal Publication: Paul Gertler, Brett Green, Catherine Wolfram, Digital Collateral, The Quarterly Journal of Economics, Volume 139, Issue 3, August 2024, Pages 1713–1766, https://doi.org/10.1093/qje/qjae003

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

News & media

Testing financial innovations: Increasing loan repayment using digital collateral

June 18, 2021

We argue that collateral need not be physically repossessed in order to serve a useful role in access to credit. Recent technological innovations have facilitated the use of digital collateral without the need for costly and inefficient physical repossession, where our findings help validate how securing a loan with digital collateral can lead to positive benefits for both borrower and lender.

Study Overview

Using a nationally-representative panel of consumer credit records for the US from 1999 to 2021, we document a positive correlation between child and parent homeownership. We propose a new causal mechanism behind this relationship: parents extract home equity to help finance their child’s home purchase. To identify the mechanism, we use fixed effect, event study, local projection and matching methods.

Study Results

We find that children whose parents extract equity: (i) are 60-80% more likely to become homeowners; (ii) have lower leverage at origination; and (iii) buy higher-valued homes and at a younger age. The effects are stronger when housing affordability is worse and children’s financial constraints are more likely to bind. Using a simple structural model, we find that in a counterfactual economy with no role for parental equity, intergenerational homeownership mobility increases.

Research Partner: San Francisco Fed

Populations: households

Working Paper: Benetton, Matteo and Kudlyak, Marianna and Mondragon, John, Dynastic Home Equity (December 18, 2024). Available at SSRN: https://ssrn.com/abstract=4158773 or http://dx.doi.org/10.2139/ssrn.4158773

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The project addresses the global issue of rising household debt, focusing on Argentina, where inflation and interest rates are exceptionally high. In collaboration with Banco Galicia, the project aims to alleviate debt distress among low-income households through various financial interventions. The research objectives can be summarized as follows: (i) determining the most effective way for the bank to communicate new loan terms to clients; (2) exploring the factors that influence whether clients prefer fixed or variable rate contracts; (3) developing strategies for the bank to set contract terms in the face of uncertain demand.

Study Results

Within the context of this study, the team ran an experiment with the largest private bank in Argentina to enhance the marketing of their debt-refinancing program. This initiative took place while Argentina was experiencing a surge in household debt, exacerbated by high inflation and interest rates. The bank aimed to engage over 300,000 delinquent clients, primarily from lower-income households, through targeted email campaigns. The study tested if displaying monthly payments instead of the high-interest rates in emails would increase engagement. Contrary to expectations, clients were more likely to click on the link when the interest rate was displayed, with response rates varying significantly across different regions of Argentina. As interest rates started to decline, the bank launched a second, adaptive experiment. The results showed that a multi-prior approach proved effective as fewer emails were needed to achieve significant results compared to traditional randomized controlled trials. In the capital city of Buenos Aires, for instance, the experiment was stopped after 294 emails, far fewer than would be needed in traditional methods.

Intervention: Financial services

Intervention Partner: Banco Galicia

Populations: Loan recipients

Working Paper: Finan, Frederico & Pouzo, Demian. (2024). Reinforcing Rcts with Multiple Priors While Learning About External Validity. arXiv:2112.09170

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The proliferation of mobile phones in low- and middle-income countries has suddenly and dramatically increased the extent to which the world’s poorest and most vulnerable populations can be observed and tracked by governments and corporations. Millions of historically “off the grid” individuals are now passively generating digital data; these data, in turn, are being used to make life-altering decisions about those individuals – including whether or not they receive government benefits, and if they can qualify for a consumer loan. This paper develops and tests a novel approach to implementing decisions based on private personal data, which provides formal privacy guarantees while also enabling important downstream applications. The approach adapts differential privacy to applications that require decisions about individuals, and gives decision-makers granular control over the level of privacy guaranteed to data subjects.

Study Results

We show that stronger privacy guarantees typically come at some cost, and use data from two real-world applications – an anti-poverty program in Togo and a consumer lending platform in Nigeria – to illustrate those costs. Our empirical results quantify the tradeoff between privacy and predictive accuracy, and characterize how different privacy guarantees impact overall program effectiveness. More broadly, our results demonstrate a way for humanitarian programs to responsibly use personal data, and better equip program designers to make informed decisions about data privacy.

Intervention: Development of privacy-enhancing technology to generate "provably private data" for use in humanitarian targeting applications

Working Paper: Kohli, Nitin & Blumenstock, Joshua. (2024). Enabling Humanitarian Applications with Targeted Differential Privacy. 10.48550/arXiv.2408.13424.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Small businesses in the US rely on business credit cards to meet their financing needs. Using a large dataset from a credit reporting agency we document new facts on firms borrowing via business credit cards: average utilization is almost 30%, is significantly higher for smaller and riskier firms, and is correlated with delinquencies. Simultaneously, interest rates on card balances are twice as high as those on term loans. We develop a structural equilibrium model of firms' demand for credit cards, their utilization, and their default choice, accounting for correlation between ex-post utilization and default, as well as bank competition with non-banks. Our model helps rationalize firms' demand for card borrowing as a hedge against cash flow volatility, and enables us to evaluate whether the high rates charged on cards reflect a high cost of lending due to the correlation of utilization and delinquency or high markups. 

Study Results

Our estimation suggests high rates primarily reflect the latter. In counterfactual analyses, we explore the provision of business credit cards under stress scenarios featuring concurrent increases in firms credit card utilization and lenders costs. We find that absent large shocks to funding costs, lender profits increase as increased revenue through higher utilization more than offsets the accompanying increases in delinquency and lending costs. Finally, we use the model to explore the equilibrium impact of proposed bank capital rules that add a portion of undrawn credit card balances to bank risk-weighted assets. Such rules tend to reduce bank credit provision, push lending outside the regulated banking sector, while modestly decreasing firm surplus, especially among the smallest, most card-dependent firms.

Research Partner: Stanford University Graduate School of Business

Populations: Small businesses

Working Paper: Benetton, Matteo, and Greg Buchak. "Revolving Credit to SMEs: The Role of Business Credit Cards." Available at SSRN 4997456 (2024).

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Despite the promise of FinTech lending to expand access to credit to populations without a formal credit history, FinTech lenders primarily lend to applicants with a formal credit history and rely on conventional credit bureau scores as an input to their algorithms.

Study Results

Using data from a large FinTech lender in Mexico, we show that alternative data from digital transactions through a delivery app are effective at predicting creditworthiness for borrowers with no credit history. We also show that segmenting our machine learning model by gender can improve credit allocation fairness without a substantive effect on the model’s predictive performance.

Intervention: AI model that differentiates creditworthiness between men and women

Intervention Partner: RappiCard

Populations: unbanked and underserved

Working Paper: Laura Chioda & Paul Gertler & Sean Higgins & Paolina C. Medina, 2024. "FinTech Lending to Borrowers with No Credit History," NBER Working Papers 33208, National Bureau of Economic Research, Inc.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

News & media

There’s an easy way to make lending fairer for women. Trouble is, it’s illegal.

November 15, 2019

Preliminary results from an ongoing study funded by the UN Foundation and the World Bank are once again challenging the fairness of gender-blind credit lending. The study found that creating entirely separate creditworthiness models for men and women granted the majority of women more credit.

Gender-Differentiated Credit Scoring: A Potential Game-Changer for Women

February 27, 2020

The Alliance spoke to Sean about this research and the significant impact the model potentially could have on women’s ability to access credit.

Study Overview

Passively collected “big” data sources are increasingly used to inform critical development policy decisions in low- and middle-income countries. While prior work highlights how such approaches may reveal sensitive information, enable surveillance, and centralize power, less is known about the corresponding privacy concerns, hopes, and fears of the people directly impacted by these policies — people sometimes referred to as experiential experts. To understand the perspectives of experiential experts, we conducted semi-structured interviews with people living in rural villages in Togo, shortly after an entirely digital cash transfer program was launched that used machine learning and mobile phone metadata to determine program eligibility.

Study Results

This paper documents participants’ privacy concerns surrounding the introduction of big data approaches in development policy. We find that the privacy concerns of our experiential experts differ from those raised by privacy and development domain experts. To facilitate a more robust and constructive account of privacy, we discuss implications for policies and designs that take seriously the privacy concerns raised by both experiential experts and domain experts.

Populations: Adult population

Working Paper: Zoe Kahn & Meyebinesso Farida Carelle Pere & Emily Aiken & Nitin Kohli & Joshua E. Blumenstock. 2024. "Expanding Perspectives on Data Privacy: Insights from Rural Togo". arXiv:2409.17578

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

The data revolution in low- and middle-income countries is quickly transforming how companies approach emerging markets. As mobile phones and mobile money proliferate, they generate new streams of data that enable innovation in consumer finance, credit, and insurance. Already, this new generation of products are being used by hundreds of millions of consumers, often to use financial services for the first time. However, the collection, analysis, and use of these data, particularly from economically disadvantaged populations, raises serious privacy concerns. This white paper describes a research agenda to advance our understanding of the problem and solution space of data privacy in emerging market fintech and financial services.

Study Results

We highlight five priority areas for research: conducting comprehensive landscape analyses; understanding local definitions of "data privacy''; documenting key sources of risk, and potential technical solutions (such as differential privacy and homomorphic encryption); improving non-technical approaches to data privacy (such as policies and practices); and understanding the tradeoffs involved in deploying privacy-enhancing solutions. Taken together, we hope this research agenda will focus attention on the multi-faceted nature of privacy in emerging markets, and catalyze efforts to develop responsible and consumer-oriented approaches to data-intensive applications.

Intervention: Privacy-enhancing technologies, policies, and practices

Working Paper: Blumenstock, Joshua E.; Kohli, Nitin (2023): Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda. arXiv:2310.04970

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Pay-as-you-go (PAYGo) financing is a novel financial contract that has recently become a popular form of credit, especially in low- and middle-income countries (LMICs). PAYGo financing relies on technology that enables the lender to cheaply and remotely disable the flow benefits of collateral when the borrower misses payments. This paper quantifies the welfare implications of PAYGo financing. We develop a dynamic structural model of consumers and estimate the model using a multi-arm, large scale pricing experiment conducted by a fintech lender that offers PAYGo financing for smartphones.

Study Results

We find that the welfare gain from access to PAYGo financing is equivalent to a 5.8% increase in income while remaining highly profitable for the lender. The welfare gains are larger for low-risk and intermediate-income consumers. Under reasonable assumptions about the repossession technology, PAYGo financing consistently outperforms traditional secured loans. For riskier consumers, an intermediate degree of lockout can be welfare maximizing.

Intervention: Randomized variations in interest rates and minimum downpayment requirement

Populations: Low and Middle Income households

Working Paper: Gertler, Paul & Green, Brett & Li, Renping & Sraer, David. (2023). The Welfare Benefits of Pay-As-You-Go Financing. SSRN Electronic Journal. 10.2139/ssrn.4641559.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

This paper provides recommendations on regulatory approaches to reduce the barriers of entry Fintech entities face in order to benefit from emerging digital finance innovations in a sustainable environment. The paper suggests topics for further research to analyze the effect of public policies for closing financial inclusion gaps in the Latin America region.

Intervention: Public policy for open data and open access

Research Partner: La Universidad Adolfo Ibáñez (UAI)

Populations: Financial technology firms

Working Paper: Finanzas abiertas y Acceso a Infraestructura financiera: Políticas públicas para promover la innovación, competencia e inclusión financiera en América Latina

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Digital loans have exploded in popularity across low- and middle-income countries, providing short term, high interest credit via mobile phones. This paper reports the results of a randomized evaluation of a digital loan product in Nigeria. Being randomly approved for digital credit (irrespective of credit score) substantially increases subjective well-being after an average of three months. For those who are approved, being randomly offered larger loans has an insignificant effect. Neither treatment significantly impacts other measures of welfare. We rule out large short-term impacts– either positive or negative– on income and expenditures, resilience, and women’s economic empowerment.

Study Results

This project demonstrates evidence that increasing access to digital loans can improve subjective well-being among applicants, measured after an average of three months. The magnitude of this effect is similar to that of costly anti-poverty interventions, even though the digital loans do not consume government or donor resources. This result highlights how even small relaxations of constraints can substantially improve mental health. At the same time, offering applicants larger loans does not have a significant effect. Randomly allocated digital credit is not associated with large positive– and negative– effects on other dimensions of welfare, including income and expenditures, resilience, and women’s economic empowerment.

Intervention: Randomized digital loan product

Populations: New customers on a popular digital credit platform

Working Paper: Björkegren, Daniel & Blumenstock, Joshua & Folajimi-Senjobi, Omowunmi & Mauro, Jacqueline & Nair, Suraj. (2022). Instant Loans Can Lift Subjective Well-Being: A Randomized Evaluation of Digital Credit in Nigeria. 10.48550/arXiv.2202.13540.

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

For decades, US housing policies have aimed to increase homeownership and reduce racial disparities, but their success has been limited. We argue that the endogenous sorting of residents in response to place-based policies and deteriorating place-based factors help explain the lack of positive outcomes.

Study Results

In the context of the 1992 GSE Act, we show that, after the introduction of targeted support for mortgage financing in specific neighborhoods, Black homeownership mildly increased in those census tracts, but white homeownership strongly decreased. The sorting effect is most prevalent in tracts where, during the same time period, mortgage financing became more accessible in nearby census tracts. Children from low-income families who remain in the targeted areas display significantly lower upward mobility. We identify declining house prices, reduced education spending, and lower school quality as plausible channels.

Intervention: 1992 Federal Housing Enterprise Financial Safety and Soundness Act

Populations: low income households

Working Paper: Kulkarni, Nirupama & Ulrike Malmendier. 2022. "Mortgage Policies and their Effects on Racial Segregation and Upward Mobility"

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)

Study Overview

Research on regulating emerging financial technologies ("FinTech") has been siloed to individual branches. Instead, this paper present a high-level view of various FinTech branches and analyzes the economic incentives of each. By focusing on the dynamics and parallels between the branches, the paper offers new insights for optimal regulation that balances the costs and benefits as use cases expand.

Study Results

Decentralized Finance (DeFi) which combines advances from the AI and blockchain branches, reduces the cost of coordinating complex financial services. Yet the efficiency gains intertwine with potential legal risks associated with liability, financial crime, dispute resolution, jurisdiction, and taxes. To ensure financial stability, effective regulatory solutions include adapted definitions and safe harbors, regulatory sandboxes, self-regulatory organizations, and/or policing misleading characterizations (e.g., regarding the extent of decentralization or agreed to data uses).

Intervention: Regulation

Working Paper: Grennan, Jillian, FinTech Regulation in the United States: Past, Present, and Future (August 31, 2022). Available at SSRN: https://ssrn.com/abstract=4045057

IBSI Funding Acknowledgement: Lab for Inclusive FinTech (LIFT)