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Open Access Publications from the University of California

The CEGA Working Paper Series showcases ongoing and completed research by CEGA staff, affiliates, visiting fellows, and CEGA-supported project publications authors. CEGA Working Papers employ rigorous evaluation techniques to measure the impact of large-scale social and economic development programs, among other research designs, and are intended to encourage discussion and feedback from the global development community.

Cover page of SMS Training and Micro-Entrepreneurship Performance

SMS Training and Micro-Entrepreneurship Performance


The paper provides evidence of the effectiveness of massively scalable automated SMS business training. The training has very low marginal costs and requires only basic cell phone coverage for enrollment. We conducted a large-scale Randomized Control Trial in which we randomly manipulated the timing of delivery of the training to small shopkeepers in Kenya. In particular, the treatment group obtained financial resilience training during the December 2020 holidays, while the control group obtained the same module in November 2020. This timing manipulation successfully led to a higher intensity of received training in the treatment group. We find that the treatment group reports larger monthly revenue, greater financial resilience, more extensive usage of formal book-keeping, and a better self-reported understanding of financial concepts.

Cover page of Corruption Dynamics in International Trade: Evidence on Bribery and Tax Evasion from Tunisian Customs Transactions

Corruption Dynamics in International Trade: Evidence on Bribery and Tax Evasion from Tunisian Customs Transactions


Every year low- and middle-income countries import goods worth more than $7 trillion, and in many states these shipments must first pass through the hands of corrupt customs officials. With such high stakes, policymakers require a deep understanding of both the causes and the effects of customs fraud. In addition, researchers have the opportunity to use trade corruption as a laboratory to discover new insights about corruption as a whole. One previously unexplored complexity is that bribe payers and bribe receivers often have repeated interactions; given corruption’s characteristic contracting frictions, counterparty risks, and information asymmetries, these long-running relationships likely matter for a wide variety of outcomes across a wide variety of contexts. To pursue these learning objectives, we overcome the data and identification challenges inherent to investigating bribery: we build an original dataset on Tunisian customs transactions using an audit study to directly observe bribes, and we leverage a natural experiment in which a computer algorithm randomly assigns customs officials to import shipments. There are three sets of results. First, we show that bribery and tax evasion are widespread, that bribery is collusive (not coercive), and that age (but not gender) predicts officials’ corruptibility. Second, in line with a straightforward Nash bargaining model, we show that the length of official/trader relationships increases tax evasion but decreases bribe amounts. Third, we zoom out to consider the larger macroeconomic implications and show that, in terms of lost tax revenue, bribery costs the Tunisian government 0.7% of GDP or $80 per citizen.

Cover page of The competitive effects of entry in the deregulated Mexican gasoline market

The competitive effects of entry in the deregulated Mexican gasoline market


The success of market deregulation in low- and middle-income countries depends on the strength of price and non-price competition between firms. In this paper, we study the recently deregulated retail gasoline market in Mexico. During our sample period, nearly 650 new gasoline stations entered the market. We estimate the causal effect of entry on the prices and quality of incumbent firms. We find that the entry of a nearby station decreases markups by nearly 4% for regular gasoline and about 2% for premium gasoline and diesel. We validate these results using the structure of ownership in the market, showing near zero impacts when the incumbent and entrant have the same owner. In addition, we show that the effect of competition on markups attenuates with distance and driving time. We find no evidence that entry affects the quality of existing stations, as measured by online ratings and regulatory inspections. 

Cover page of Estimating Preschool Impacts when Counterfactual Enrollment Varies: Bounds, Conditional LATE and Machine Learning

Estimating Preschool Impacts when Counterfactual Enrollment Varies: Bounds, Conditional LATE and Machine Learning


We study the impact of preschools and the issue of close substitutes in a Cambodian context where newly built formalized preschools are competing with existing alternative early childcare arrangements. In addition to estimating the reduced-form impact of a vast preschool construction program using a random assignment, we implement several empirical techniques to isolate the impact on children who would have stayed at home if they had not been enrolled in the newly built preschools. We argue that this parameter is both critical for the preschool literature and, because it does not depend on the quality of alternative preschool, is often the only parameter that can be comparable across studies and contexts. Our results show that after one year of experiment, the average intention-to-treat impact on cognitive and socioemotional development measures is significant but small in magnitude (0.05 SD). Our analysis, however, suggests that the impact on the children who would have stayed at home will likely be high and significant, and can be bounded, under a set of reasonable assumptions, between 0.13 SD and 0.45 SD. Under heavier assumptions, we have evidence that the impact on the children who would have stayed at home is around 0.2 SD, closer to our low bound. In a context where infrastructures are improving in low-income countries, our analysis suggests that accounting for close substitutes is crucial to produce more external valid statements on programs’ performance and make appropriate policy recommendations.

Targeting impact versus deprivation


Targeting is a core element of anti-poverty program design, with benefits typically targeted to those most “deprived” in some sense (e.g., consumption, wealth). A large literature in economics examines how to best identify these households feasibly at scale, usually via proxy means tests (PMTs). We ask a different question, namely, whether targeting the most deprived has the greatest social welfare benefit: in particular, are the most deprived those with the largest treatment effects or do the “poorest of the poor” sometimes lack the circumstances and complementary inputs or skills to take full advantage of assistance? We explore this potential trade-off in the context of an NGO cash transfer program in Kenya, utilizing recent advances in machine learning (ML) methods (specifically, generalized random forests) to learn PMTs that target both a) deprivation and b) high conditional average treatment effects across several policy-relevant outcomes. We find that targeting solely on the basis of deprivation is generally not attractive in a social welfare sense, even when the social planner’s preferences are highly redistributive. We show that a planner using simpler prediction models, based on OLS or less sophisticated ML approaches, could reach divergent conclusions. We discuss implications for the design of real-world anti-poverty programs at scale. 

Cover page of Panel Data Evidence on the Effects of the COVID-19 Pandemic on Livelihoods in Urban Côte d'Ivoire

Panel Data Evidence on the Effects of the COVID-19 Pandemic on Livelihoods in Urban Côte d'Ivoire


In early March 2020, a few cases of COVID-19 were diagnosed in Abidjan, the capital city of Côte d’Ivoire. To combat the spread of the disease, large restrictions to mobility and gatherings were introduced between mid-March and late May 2020. We collected panel survey data on over 2,500 individuals from the Greater Abidjan area over the period immediately before and after the start of the pandemic. We document striking drops in employment, hours worked, income, and food consumption in the first months after the onset of COVID-19, when lockdown was in place. We also find that, in response, survey respondents received more private transfers from other parts of the country, at a time when remittances from abroad fell – and that some respondents moved either temporarily or permanently. In terms of recovery, we find that subjective well-being was lower on average in December 2020 than it was at baseline. Yet, despite schools being closed between mid-March and July 2020, school enrollment suffered little: by December 2020, enrollment rates had bounced back to their baseline level. Our results finally indicate that government policies aimed at alleviating the worst effects of lockdown only reached a few people, and not necessarily those most in need.

Cover page of Instant Loans Can Lift Subjective Well-Being: A Randomized Evaluation of Digital Credit in Nigeria

Instant Loans Can Lift Subjective Well-Being: A Randomized Evaluation of Digital Credit in Nigeria


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. 

Cover page of Can Competition Reduce Conflict?

Can Competition Reduce Conflict?


We examine the effect of inter-group economic competition on within-group violent conflict in the context of Indonesia’s signature Community Driven Development (CDD) program. Using a triple difference design, we exploit exogenous variation in the degree to which villages in sub-districts compete for public funds. We find that higher competition between villages reduces conflict but only up to moderate levels of competition. The conflict-reducing effects of competition are largest in the most ethnically fractionalized and segregated villages and exist regardless of the eventual outcome of the competition. Our results are consistent with external competition favouring coordination within otherwise divided communities and boosting village identity relative to ethnic identity. We find no evidence that competition increases inter-group violence. Our results suggest that economic incentives to compete with out-groups can be beneficial policy mechanisms to favour cooperation and reduce violence within communities.

Cover page of The Impact of Digital Credit in Developing Economies: A Review of Recent Evidence

The Impact of Digital Credit in Developing Economies: A Review of Recent Evidence


In recent years, a new generation of “digital credit” products have transformed the consumer lending landscape in many low- and middle-income countries. Offering short- term, high-interest loans via mobile phones or other digital platforms, these products have become wildly popular. This article reviews the small but emerging evidence on the welfare impacts of digital credit. These studies document very high rates of takeup – well in excess of traditional microcredit – despite the fact that customers often do not understand the terms of their loans. Overall, there is little evidence that access to credit has consistent positive impacts on borrower welfare, though two impact evaluations document positive effects on resilience and subjective well-being, respectively. No study finds statistically significant negative impacts of digital credit.

Cover page of The Syrian Refugee Life Study: First Glance

The Syrian Refugee Life Study: First Glance


This paper presents descriptive statistics from the first wave of the Syrian Refugee Life Study (S-RLS), which was launched in 2020. S-RLS is a longitudinal study that tracks a representative sample of 2,500 registered Syrian refugee households in Jordan. It collects comprehensive data on socio-demographic variables as well as information on health and well-being, preferences, social capital, attitudes, and safety and crime perceptions. This study uses these novel data to document the socio-demographic characteristics of Syrian refugees in Jordan, and compare them to those of the representative Jordanian and non-Jordanian populations interviewed in the 2016 Jordan Labor Market Panel Survey. The findings point to lags in basic service access, housing quality, and educational attainment for the Syrian refugee population, relative to the non-refugee population. The impacts of the pandemic may serve to partially explain these documented disparities. The data also illustrate that most Syrian refugees have not recovered economically from the shock of COVID-19 and that this population has larger gender disparities in terms of income, employment, prevalence of child marriage, and gender attitudes than their non-refugee counterparts. Finally, mental health problems are common for Syrian refugees in 2020, with depression indicated among over 61 percent of the population.