Research Objectives

CGM facilitates research and evidence-based knowledge creation to foster economic development and improve lives in growth economies. The center actively encourages faculty and industry collaboration through cross-sector approaches, drawing from the Haas School of Business and related departments on the UC Berkeley campus – including economics, engineering, public health, and public policy. The center enables collaboration between UC Berkeley researchers and businesses, academic institutions, and policymakers. The center also provides a platform to disseminate research findings and knowledge to stakeholders.

Focus areas include digital commerce, financial inclusion, climate change, energy, healthcare, micro-entrepreneurship and inequality.

Corporate Research Partnerships

The center matches Berkeley faculty with emerging market businesses to answer sector-wide challenges. Examples include (a) using state-of-the art analytical techniques, including machine learning, to examine data sets from partner companies and (b) providing research insights for partner companies and policy makers based on impact evaluation and experimentation (for example, A/B testing).

Non-Governmental and Developmental Partnerships

CGM actively engages with key nongovernmental and developmental entities on testing social solutions through hands-on on-ground testing that feeds research, curriculum and policy initiatives.

Academic Partnerships

CGM collaborates with top universities on research, teaching and policy engagement. The Center also builds knowledge partnerships with leading universities in growth markets, engaging in joint research and teaching to build relevant curricula to train the local business community.

Research Support

CGM provides research services and support to faculty to conduct innovative experimental research in growth markets. It works to generate research funding for tenure-line and adjunct faculty, Ph.D. students, and postdoctoral scholars for research and curriculum development.


Ernesto Dal Bó

Phillips Girgich Professor of Business

Lucas W. Davis

Jeffrey A. Jacobs Distinguished Professor

Sunil Dutta

William D. Crawford Chair in Taxation and Accounting at the Haas School of Business

Paul Gertler

Li Ka Shing Professor of Economics

Ganesh Iyer

Faculty Director, Center for Growth Markets

Przemek Jeziorski

Associate Professor of Marketing

Jonathan Kolstad

Associate Professor of Economic Analysis and Policy

David Levine

Professor, Eugene E. and Catherine M. Trefethen Chair in Business Administration

Gustavo Manso

Professor, William A. and Betty H. Hasler Chair in New Enterprise Development Distinguished Teaching Fellow

Abhishek Nagaraj

Assistant Professor, Management and Organizations (MORS) group, Berkeley Haas

Catherine Wolfram

Cora Jane Flood Professor of Business Administration, & Deputy Assistant Secretary for Climate & Energy Economics in the U.S. Department of the Treasury

Guo Xu

Assistant Professor of Business and Public Policy at Berkeley Haas

Latest Research

Ongoing Research

Empowering Fin-tech Merchants: AI-Powered Interactive Information Journeys in Real-world Trials

Yixiang Xu, Rupa Ruchismita, Ganesh Iyer

In emerging markets, local merchants often act as financial agents, providing crucial access to banking services for underserved communities. By significantly reducing the cost of financial access, these fintech-enabled merchants offer a promising path for financial inclusion.

However, this channel suffers from poor merchant retention rates. A range of factors, including the high cost of sourcing both firm and market information impede merchant profitability. Indeed, of the 3,000 surveyed merchants, more than 50% found it difficult to acquire necessary know-how to provide reliable banking services. For the fintech firms, providing timely assistance to a large number of dispersed merchants has proven to be financially unsustainable, creating a demand for low-cost technologies for network support.

To mitigate these challenges, we leverage Generative AI to design personalized, interactive and digestible ‘information journeys’ covering a wide range of business-relevant content for merchants. To further enhance the information acquisition experience for merchants, we use Large Language Models (LLM) to create multimedia content with text, audio and video content journeys in the local language.

Discover more here. 

Impact of Video Know-Your-Customer (KYC) technology to improve financial inclusion in India

A thought leader, innovator and implementer of technology solutions. FINO enables end-to-end customer sourcing and servicing.

Segments of the population in low- and middle-income countries, such as daily wage earners or small business owners in rural areas, typically have limited access to formal financial services. Contactless digital payment technology could expand financial access for underbanked populations by reducing the need for physical banks and in-person services, which would also limit risky person-to-person interaction during the COVID-19 pandemic.

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Visual Know Your Customer (V-KYC) is a completely touchless, artificial intelligence-based digital banking technology that scans the video and audio of customers’ mobile calls and processes the data to provide instant and remote authorization and screening of potential customers. The V-KYC technology can potentially onboard and expand payments banking access to new merchants and end clients, increase transactions through eschewing the need for in person authentication, enable the uptake insurance services and money transfers. The research team aims to evaluate the impact of a novel V-KYC product on the expansion of and access to formal banking in India. In collaboration with FINO, one of the largest payment banks in India, the research team will develop and deploy the V-KYC technology and through a randomized control experiment study its impact on financial and business outcomes such as onboarding levels, volume and breadth of transactions, servicing of existing clients, and access to government subsidies and loan instruments. The team will also survey individuals and business owners to measure V-KYC’s effect on respondents’ ability to weather the economic shocks of the COVID-19 pandemic.

On My Own Time: Asynchronous Teamwork and Gender Differences in Performance

Aruna Ranganathan, Associate Professor

The future of work is seeing increased temporal restructuring, with less full-time work, less adherence to the five-day work week and greater scope for working from anywhere at any time (Moen, Kelly and Hill, 2011; Moen et al., 2011; Correll et al., 2014). Temporal restructuring at the workplace has manifested itself particularly in the form of asynchronous teamwork, where team members complete work at different times of the day, either from the same or different geographic location. However, there is little research on the effects of asynchronous teamwork on the performance of individuals within such teams.

Researchers work with Baul singers’ folk-music-ensemble recordings as the setting for their study. Bauls typically perform in ensembles consisting of one lead singer and a number of instrumentalists. While the lead singers can be men or women, the instrumentalists are almost always men. We focus on synchronous and asynchronous music recording experiences of men and women Baul lead singers to investigate differential performance effects by gender as a result of temporally restructuring work.

Can a multi-generational intervention improve daughters’-in-law sexual and reproductive health and mothers’-in-law psychological well-being? In Tamil Nadu, India

David Levine, Professor

Poor women (and their children) suffer from many sexual and reproductive health challenge, including unsafe menstrual supplies, lack of access to family planning, and domestic violence. The Tamil Nadu State Rural Livelihoods Mission (TNSRLM) runs a network of self-help groups (SHGs) for millions of rural women. This project involves a pilot test  of an intervention to improve sexual and reproductive health. Our intervention will both provide information and focus on behavior change. For example, we will offer safe and reusable menstrual supplies (that can improve women’s savings), a meditation audio that helps some women with menstrual cramps, and an app or calendar to track periods.

Reducing Tariff Evasion at the Ports of Paraguay using Machine Learning (ML)

Ernesto Dal Bó & Frederico Finan

Tariff evasion is a serious concern throughout the developing world. A  2014 study found that between 2003 and 2012, the developing world lost US$6.6 trillion in illicit outflows.
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This project involves developing and experimentally evaluating a new auditing algorithm for determining shipment inspections. Researchers collaborate with custom authorities to provide technical assistance in the redesign of their imports’ shipment auditing algorithm.The research will use recently developed machine learning (ML) techniques to provide Paraguay’s customs authority with a new data-driven algorithm for deciding which shipments to inspect. In particular, the algorithm uses the importing and audit history of importers in recent years combined with random audits to assign importers a “risk score.” 

Gender Representation, Identity, and Learning in Brazilian politics

Francesco Trebbi

A key issue in human capital development and entrepreneurship in low-income countries is the uneven representation of ethnic and gender minorities. This research project aims to increase the vote share of female politicians in high-stake elections in Brazil’s 2023 State elections – as a proof of concept through a new microtargeting campaign framework.


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This framework, which can be understood as a sophisticated advertising/persuasion architecture, designed to assess the mechanisms through which citizens make choices countering equal representation, and how to remedy this.

It operates through the combination of structural econometric methods and randomized control trial tools, facilitated via a collaboration with Instagram, a social media platform very popular in Brazil, and a Brazilian non-governmental organization in the field.

Developing an ML Decision Support System for Digital Finance in Emerging Markets

Ganesh Iyer, Yixiang Xu, Rupa Ruchismita

The rails for the ongoing digital payments revolution in emerging markets are dependent on Cash-in Cash-out merchant terminals at small mom and pop stores. These merchants play a crucial role in providing access to financial services in underserved regions. However, payment firms face high merchant churn rates and an even greater merchant dormancy challenge.

To tackle the challenge, we develop a two staged project and test a machine learning (ML)-driven decision support system (DSS) to augment merchant decision-making.

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The system serves two functionalities: it automates routines and liberates valuable merchant-time, otherwise deployed towards repetitive tasks, thus reducing their cognitive burden; and it complements merchants’ entrepreneurial ability by providing location and profile-specific personalized data-driven business insight to aid their entrepreneurial instincts. Through a series of field experiments, researchers work to demonstrate the efficacy of an ML-based decision support system to make small financial merchants thrive, thus deepening the financial inclusion ecosystem.

Improving online sales through a new model of gamification-Help and Haggle: China

Luyi Yang, Assistant Professor

This research is focussed on the fundamental question of firm growth through the lens of a novel approach – “Help-and-Haggle,” a social e-commerce scheme pioneered by Pinduoduo (PDD), one of China’s most popular e-commerce sites. “Help-and-Haggle” integrates online shopping with social media and effectively turns shopping into a game. A consumer initiates the help-and-haggle campaign by selecting her desired product. Then, a 24-hour timer is started. The consumer shares a custom link about the campaign and the product with friends in her social network, inviting them to “help” her “haggle” over the product price. Each friend who helps click on the link (no purchase required) triggers a tentative, random discount on the product price (i.e., a price cut) for the initiating consumer. If the consumer manages to gather enough clicks and cut the price down to zero within 24 hours, she gets the product for free. Otherwise, the campaign fails, and the product reverts to the original price.

The research will develop a dynamic game-theoretic model of “Help-and-Haggle ” to gain insight into how to manage such a program most effectively.

How trust and reputation factor into the investment in, adoption of, and pricing of emerging AI enabled technologies in healthcare and financial markets.

Matthew Grennan, Associate Professor

The recent pandemic accelerated pre-existing trends in healthcare markets aimed at utilizing emerging technologies such as artificial intelligence (AI) and blockchain for new medical applications. Yet markets for health technologies are rife with complex incentives and imperfect information that can affect whether consumers and businesses adopt these technologies; how insurers, governments, and companies price them; and ultimately how they deliver value for society.

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A major barrier to the adoption of these technologies – in healthcare and other sectors of the economy such as finance – is trust. Survey evidence suggests that consumers trust medical technologies less than human health care applications, despite evidence of better performance for technology-enabled products and services. Relatedly, adoption of technologies such as automated financial portfolio management has lagged expectations.  

To help identify points of inflection in the adoption of such technologies as well as to better understand counterfactual approaches, the researchers plan to make use of variation in the required interpretability and understandability of AI applications across jurisdictions and companies.

The Potential of Transport Platforms to Reduce Emissions in Developing Country Cities: Cairo, Egypt

Nick Tsivanidis, Assistant Professor

Transportation generates the largest share of greenhouse gas (GHG) emissions and is a primary driver of pollution in cities. In rapidly growing cities that lack formal mass transit systems, new app-based platforms like SWVL, Uber Bus, Treepz and ViaPool are introducing transit services across the Middle East, Africa and Asia and are rapidly growing.

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In this project, researchers partner with SWVL – the world’s largest provider of on demand bus-hailing – to run randomized controlled trials in Cairo, Egypt to a) Measure how demand for transit depends on attributes such as price, wait time, travel time and walk time. b). Measure how users substitute away from other modes of transport and change emissions when transit options improve. c) Estimate a model of the supply and demand for transit to solve for the optimal regulation of transit. Finally, d) How should cities price transit and what shape networks should they provide, given the network effects and impacts on emissions that may not be accounted for by individuals?

Human Centric Operations for the Future of Work

Park Sinchaisri, Assistant Professor

The rapid pace of technological change and the rise of the gig economy are leading to a computationally mediated future of work. While such a future promises increased productivity and flexibility, most work is characterized by tasks carried out in isolation and mostly devoid of learning and collaboration. For example, the independent nature of ride-hailing work means that drivers do not experience the benefits of learning from colleagues. At the same time, the decisions a worker faces have become more complex as platforms dynamically offer competing incentives.

To address these issues, researchers introduce and explore human-centric operations management for the future of work with the emphasis on the development of algorithmic tools that can automatically generate useful advice to human users. This research involves designing and evaluating human-AI interfaces to facilitate understanding and adoption of algorithmic advice in ways that upskill individuals, enhance their experience, and lead to improved collective outcomes.

Mobile Money in Tanzania

Przemyslaw Jeziorsk, Assistant Professor

In developing countries, mobile telecom networks have emerged as major providers of financial services, bypassing the sparse retail networks of traditional banks. Jeziorski and his coauthor analyze a large individual-level data set of mobile money transactions in Tanzania to provide evidence of the impact of mobile money on alleviating financial exclusion in developing countries.

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The authors identify three types of transactions:(i) money transfers to others; (ii) short distance money self-transportation; and (iii) money storage for short to medium periods of time. The study takes advantage of a “natural experiment” – an unanticipated increase in transaction fees makes it is possible to examine how user behavior changes. The authors find that the willingness to pay to avoid walking with cash an extra mile is 2% of an average transaction. The same figure is 0.8% for storing money at home for an extra day. An important implication is that mobile money ameliorates significant amounts of crime-related risk related to handling cash.