Center for Effective Global Action

Refugees require assistance for basic needs like housing but local host communities may feel excluded from that assistance, potentially affecting community relations. This study experimentally evaluates the effect of a housing assistance program for Syrian refugees in Jordan on both the recipients and their neighbors. The program offered full rental subsidies and landlord incentives for housing improvements, but saw only moderate uptake, in part due to landlord reluctance. The program improved short-run housing quality and lowered housing expenditures, but did not yield sustained economic benefits, partly due to redistribution of aid. The program unexpectedly led to a deterioration in child socio-emotional well-being, and also strained relations between Jordanian neighbors and refugees. In all, housing subsidies had limited measurable benefits for refugee well-being while worsening social cohesion, highlighting the possible need for alternative forms of aid.

We study how information sources as a signal of service quality shape consumers’ beliefs about and demand for air quality forecast services. We provide day-ahead SMS forecasts in Lahore, Pakistan, randomiz- ing whether the forecast is attributed to the government or an NGO. Respondents do not have differential demand by the assigned source but believe the government’s forecasts are worse than the NGO’s. The results demonstrate that consumers expect lower accuracy from the gov- ernment, have a limited willingness to pay for accuracy, and prefer the assigned source as they learn about its service quality.

A long-standing economic problem is how to incentivize costly but unobservable effort. Contests and contracts have been used in various settings where output, rather than effort, is contractible. We conduct a field experiment to compare the effectiveness of contests and tiered contracts in promoting energy conservation among households. While both mechanisms achieve similar energy savings relative to a control group (7 to 9 percent reductions), contests reduce energy use at half the cost. We develop and structurally estimate a model of energy consumption based on our experimental data. For the same budget, we show that an optimal contest dominates optimal contracts. We calculate the marginal abatement cost at USD 59.45-76.72/Mt CO2 not accounting for utility savings or social value of avoided blackouts from peak demand reduction. Our findings contribute to the design of demand-side management policies in the residential electricity sector, particularly in low- and middle-income countries.

Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics,natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private informationabout individual movements to potentially malicious actors. This paper develops and tests an approach for releasing privatemobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices, and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response.

Scarce information and human capital may make it difficult for residents of developingcountries to produce accurate forecasts, limiting responses to uncertain future eventslike air pollution. We study two randomized interventions in Lahore, Pakistan: 1)provision of air pollution forecasts; 2) general training in forecasting. Both reducedsubjects’ own air pollution forecast errors; the training effect suggests that modesteducational interventions can durably improve forecasting skills. Forecast receipt increased demand for protective masks and increased the responsiveness of outdoor time to pollution. Forecast recipients were willing to pay 60 percent of the cost of mobile internet for continued access.

Slack – the underutilization of factors of production – varies systematically with economic development. Using novel and detailed measures of the utilization of labor and capital from a large representative sample of firms in rural and urban Kenya, we show that utilization is increasing in firm size, market access, and economic activity. We present a model of firm capacity choice where indivisibility in at least one input is a key driver of slack. We embed the model in spatial general equilibrium, with features characteristic of low-income settings – including many small firms and high transport costs – and show that it rationalizes both the endogenous emergence of slack in steady-state and elastic aggregate supply curves. We empirically validate model predictions using reduced-form estimates of the general equilibrium effects of cash transfers from a large-scale RCT in Kenya. The parsimonious model replicates much of the experimental evidence, predicting a large real multiplier of 1.5, driven by expansion in low-utilization sectors and firms, and limited average price inflation. Counterfactual analyses indicate that multipliers are likely to be meaningfully smaller in lower slack settings, such as urban areas. We use the model to revisit the estimation of spatial spillovers in clustered RCTs and uncover non-trivial ’missing intercept’ effects on income and inflation. Additionally, we innovate methodologically by pre registering key elements of model estimation and validation. The findings suggest that input indivisibilities and slack are key features of developing country settings, and are quantitatively important for macroeconomic dynamics and policies.

We measure the impact of two main signals of tertiary-level human capital accumulation, college quality and certification, on hiring in India. Using a correspondence experiment, we send 16,944 resumes to 1412 job postings for recent engineering graduates at small and medium firms. In precisely estimated results, we find that these employers do not respond to signals of tertiary education quality. Specifically, there is no impact on callbacks of having graduated from a mid-tier college ranked in the top 300 relative to an unranked college outside of the top 1000, despite significant government investment in college rankings. There is also no impact of scoring in the highest as opposed to the lowest quartile of a post-tertiary certification test that has been taken by millions of graduating students. There is evidence that women modestly benefit in the first stage of hiring in this market, with this effect concentrated in some regions.

Can increasing women’s use of digital financial services raise their empowerment? We test this hypothesis using a randomized control trial with 152 female microfinance groups in Tanzania, where treated groups were randomly switched to repay their loan using mobile money instead of cash. This exogenous shift in women’s use of mobile money for loan repayment substantially increases their use for other types of transactions. Women’s control over their finances increases, they have higher levels of empowerment in the household and expenditures shift towards goods plausibly aligned with their preferences. These findings highlight the benefits of greater use of digital technologies for women.

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 whether they qualify for a consumer loan. This paper develops an approach to implementing algorithmic decisions based on personal data, while also providing formal privacy guarantees to data subjects. 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. 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.

How do inaccurate priors about the distribution of interest rates affect search and outcomes in consumer credit markets? Consumer credit markets feature large amounts of within-borrower price dispersion in interest rates; if consumers are unaware of the extent of this price dispersion, they may shop less and take out loans at higher interest rates than they would otherwise. We conducted a randomized controlled trial with 112,063 loan seekers in Chile where we showed treated participants a price comparison tool that we built using administrative data from Chile’s financial regulator. The tool shows loan seekers a conditional distribution of interest rates based on similar loans obtained recently by similar borrowers, using data on the universe of consumer loans merged with borrower characteristics. We also cross-randomized whether we asked participants their priors about the distribution of interest rates. 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 receive 13% more offers and 11% lower interest rates, and to be 28% more likely to negotiate with their lender and 4.7% more likely to take out a loan. In contrast, merely asking participants their expectations about interest rates led them to search at 4% more institutions and obtain 9% lower interest rates.