Optimal transportation policies depend on demand elasticities that interact across modes and vary across the population, but understanding how and why these elasticities vary has been an empirical challenge. Using an experiment with Uber in Egypt, we randomly assign large price discounts for transport services over a 3-month period to examine: (1) the demand for ride-hailing services, (2) the demand for total mobility (km/week), and (3) its contributions to external costs (e.g. congestion). A 50% discount more than quadruples Uber usage and induces an increase of nearly 49% in total mobility. These effects are stronger for women, who are less mobile at baseline and perceive public transit as unsafe. Technology-induced reductions in the price of ride-hailing services could generate substantial benefits to users (6.1% of GDP) that would be accompanied by considerable increases in external costs (0.7% of GDP), with benefits accruing to the most affluent and costs being borne by the entire population.

We experimentally study the impact of relatively large enterprise loans in Egypt. Larger loans generate small average impacts, but machine learning using psychometric data reveals that "top-performers" (those with the highest predicted treatment effects) substantially increase profits, while profits drop for poor-performers. The large differences imply that lender credit allocation decisions matter for aggregate income, yet we find that existing practice leads to substantial misallocation. We argue that some entrepreneurs are over-optimistic and squander the opportunities presented by larger loans by taking on too much risk, and show the promise of allocations based on entrepreneurial type relative to firm characteristics.

We conduct a randomized experiment that generates exogenous variation in access to foreign markets for rug producers in Egypt. Combined with detailed survey data, we causally identify the impact of exporting on firm performance. Treatment firms report 16-26% higher profits and exhibit large improvements in quality alongside reductions in output per hour relative to control firms. These findings do not simply reflect firms being offered higher margins to manufacture high-quality products that take longer to produce. Instead, we find evidence of learning-by-exporting whereby exporting improves technical efficiency. First, treatment firms have higher productivity and quality after controlling for rug specifications. Second, when asked to produce an identical domestic rug using the same inputs and same capital equipment, treatment firms produce higher quality rugs despite no difference in production time. Third, treatment firms exhibit learning curves over time. Finally, we document knowledge transfers with quality increasing most along the specific dimensions that the knowledge pertained to.

Is the type of financial support provided to businesses more important than which businesses receive it. Loans and grants can lead to differences in optimal investments and in scope for moral hazard. We randomize 3,293 business-loan applicants into receiving a loan, cash grant, in-kind grant or nothing. All treatments equally increase income, yet there are large differences within a treatment group with impacts concentrated at the top of the distribution. Those who succeed with loans are observationally equivalent to those who succeed with grants, showcasing that owner heterogeneity is more important than the type of support received in microenterprise

We study how complementarities in skill may affect the returns to vocational training using a randomized controlled trial in Cairo, Egypt. Participants, who were college-educated, were either given a 4-week training in soft skills (e.g., grooming, time management), technical skills (e.g., Microsoft programs, English language), or a mix of the two (half of each). We find large differences in outcomes between the three treatments. The technical and mixed treatments do best in the short term, raising first-job income by about 15%, relative to both a control group and the soft-skill treatment. In the longer term, the mixed-skill treatment significantly outperforms the other two treatments, giving participants 20-27% higher income. The high returns for this group seem to come from climbing the job ladder to access jobs that require speaking English, which may be at higher-quality employers. Overall, the results suggest that curriculum details play an important role in the outcomes of vocational training programs and that leveraging skill complementarity can yield tangible benefits.

Hundreds of studies have examined the impacts of microfinance, finding mostly modest or disappointing results. In this chapter, instead of asking whether microfinance works on average, we study the varied impacts of microfinance. Using data from several prominent recent studies, we show that the heterogeneity in returns to microcredit, microsavings, and microinsurance is large. This means that even programs that are not effective on average could be transformational for some people. We call for researchers and policymakers to focus more on identifying those who will benefit from microfinance, and understanding why they do. Together this will improve the targeting of these interventions, increase their positive impact, and help improve the design of future products.

Aversion to "stigma" - disutility associated with a program or activity due to beliefs about how it is perceived - may affect labor market choices and utilization of social programs, but empirical evidence of its importance is scarce. Using two randomized field experiments, we show that stigma can affect consequential labor market decisions. Treatments designed to alleviate stigma concerns about taking entry-level jobs - such as how those jobs are perceived by society - had small average effects on take-up of job assistance programs. However, using compositional analysis and machine learning methods, we document large heterogeneity in the responses to our treatments. Stigma significantly affects the composition of who takes up a program: the treatments were successful in overcoming stigma for older, wealthier, and working respondents. For other people, we show that our treatments merely increased the salience of the stigma without dispelling it. We conclude that social image concerns affect labor market decisions and that messaging surrounding programs can have important effects on program take-up and composition.

The future of travel will be characterized by changes in weather patterns and changes in transportation technology. How will these forces interact? We explore this question by utilizing a unique randomized experiment with Uber riders in Cairo, Egypt. We consider how very hot days (>35°C/95°F) affect transportation choices, how a sizeable price decrease (simulating a future with autonomous vehicles and access to cheaper transportation) changes travel, and how extreme weather interacts with these choices. We find that while travel will increase significantly in response to the price decrease, extreme weather dampens this effect by 26%. Individuals receiving subsidies also shift away from public transportation modes and towards private transportation modes, except when the public transit option is air-conditioned. These results provide important insights for policymakers when considering optimal travel policy for the future.

We study discrimination in hiring and its associated outcomes for the discriminators using a unique survey of Egyptian businesses. Discrimination against women is widespread and overt: about half (51%) of establishments directly admit that they prefer to hire men. The share varies widely across industries, from 60% in retail to only 16% in IT. Using a list randomization technique, we can rule out that discrimination against women is heavily stigmatized in Egypt, meaning that establishments are willing to admit it openly. We then provide novel suggestive evidence showing that discriminating against women is associated with hiring lower-quality workers relative to non-discriminators. We also provide guidance on the use of the list randomization technique and how to interpret it in settings with limited stigma.

Understanding jobseeker preferences, including their reservation wages and how much they value different non-wage amenities, is difficult because they are not directly observable. We test four different methods for estimating these preference parameters using an experiment in a job-matching center. We find large and important differences between methods. We also estimate jobseekers' valuations of several job attributes, and explore how those valuations differ by gender. Using a follow-up survey for validation and comparing the consistency of estimates with prior literature, we find that discrete choice experiments perform best. We show how these methods can help academics better understand jobseeker choices and identify labor market mismatches that may arise from information frictions. Utilization of these methods can help policymakers and employers develop targeted policies and compensation bundles to address inequities in the labor market.

Network-based hiring is a common form of recruitment in businesses across the world. We administered a unique survey of Egyptian retail establishments to study the use of these hiring methods. We document important differences in establishments' use of ties to the owner ("connections") and to employees ("referrals") and their relationships with hiring outcomes. While all types of establishments use referrals at similar rates, use of owner connections varies widely and is mostcommonamongsmallinformalestablishments. Wedevelopamodelofhiring which predicts that connections and referrals should have heterogeneous effects on hiring outcomes depending on establishment type. Our empirical results are consistent with the model's predictions. When high-productivity establishments use connections, the practice is associated with lower-quality hires (nepotism), yet when low-productivity establishments use connections, they find more productive workers. By contrast, referrals benefit high-productivity establishments more due to network homophily. These findings indicate that policies designed to either limit or expand network-based hiring

Two for-profit Philippine banks, aiming to increasing microlending to the poor, incorporated a widely used poverty measurement t ool into their loan applications and test ed the tool using randomized training content. Treate d loan officers were provided an explanation of the tool's purpose; exhortation tying the tool to the organizations' social missions; and reassurance that these data, conditional on other characteristics, do not predic t default and thus should not jeopardize incentive pay based on portfolio performance. The control group training merely labeled the tool "additional household informa tion." The strategy backfired, leading to no additional poor applicants and potentially lower-performing loans. Descriptive evidence suggests the training exacerbated loan officer misperceptions about compensation incentives and multitasking problems. This cautionary tale is an example of why management may want include social outcomes directly into employee perfor mance evaluations, or silo corporate social responsibility efforts from core

Low utilization of household credit in developing countries may be partially due to religious considerations.In a randomized marketing experiment in Jordan,this paper estimates the effect of sharia-compliant loan features on demand for credit. To comply with Islamic law, the sharia-compliant product uses a bank fee rather than an interest payment structure, while keeping the rest of the product features very similar. Sharia-compliance increased the application rate for loans from 18 percent to 22 percent,an increase in demand that is equivalent to a 10 percent decrease in interest rates. This study also randomly varied the price of the sharia-compliant loan and finds that less religious individuals are twice as elastic with respect to price as the more religious. By comparing reasons for refusal across treatment groups, this paper estimates that survey measures that try to assess the importance of religious objections to conventional credit overestimate the importance of this type of objection by a

We use tailored surveys and benchmarking in the flat-weave rug industry to better understand the shortcomings of standard productivity measures. Quantity-based productivity (TFPQ) performs poorly because of variation in product specifications across firms. Controlling for specifications aligns TFPQ with lab benchmarks. We also collect quality metrics to construct quality productivity (the ability to produce quality given inputs) and find substantial dispersion across firms. This motivates interest in multidimensional productivity, or capability. As quality productivity is negatively correlated with TFPQ, revenue-based productivity (TFPR) may perform better at capturing capabilities in settings where better firms make products with more demanding specifications that have greater input

Measuring the impacts of liquidity shocks on spending is difficult methodologically but important for theory, practice, and policy. We compare three approaches for tackling this question: directly asking borrowers how they spend proceeds from a loan (direct elicitation); asking borrowers using a list randomization technique (indirect elicitation) that allows them to answer discretely in cases where loan uses are at odds with lender policies or social norms; and a counterfactual analysis in which we compare household and enterprise cash outflows for those in a treatment group, randomly assigned to receive credit, to a control group. The counterfactual analysis yields an estimate that about 100% of loan-financed spending is on business inventory. For the direct and indirect elicitations, we find evidence of both strategic misreporting and "following the cash": borrowers likely report what they physically did with cash proceeds, rather than counterfactual spending.

Microfinance institutions (MFIs) have continued to grow over the past few decades, both in numbers of clients and portfolio sizes. The growth of these MFIs has enabled greater access to credit in many of the world's less developed nations. However, recent studies have shown that many of the poor, especially Muslims, remain unbanked. Confounding this problem in many Muslim countries is the poor's propensity to reject microfinance, when available, on religious grounds. In this paper we develop an alternative microfinance model that establishes credit unions for the poor in which the bank plays the role of a guarantor in the familiar rotating savings and credit association (RoSCA). We test the performance of this model against a stylized sequential Grameen-style microcredit provision in a laboratory experiment in the field conducted in poor Egyptian villages. Our model of bank-insured RoSCAs is shown to solve coordination-failure problems that may otherwise prevent the spontaneous development of informal RoSCAs in practice. Empirically, our bank-insured RoSCA model generated significantly higher take-up and repayment rates than the Grameen model. This suggests that, by overcoming religious barriers to credit, this model can be a useful alternative to Grameen-style microfinance.

Poverty is persistent but the evidence regarding the existence of poverty traps is mixed. We provide the first experimental test of asset-threshold poverty traps, by randomizing households in Egypt into a standard asset transfer, a half transfer, or control. Both transfer groups show large, sustained gains in assets and consumption 40 months later. By comparing how assets grow after the transfer for households at different points in the baseline asset distribution, we find no support for the presence of a poverty trap. These results suggest that scarce anti-poverty resources need not be concentrated in large asset transfers to be effective.

(required) Digital marketing enables firms to overcome information barriers and other frictions that prevent firms and customers connecting across space. However, firms may not adopt this new technology because they lack knowledge, or because they are uncertain of the returns and cons ider it risky. Our key research questions are whether firms can be induced to adopt this technology, and whether they will benefit from doing so. A randomized experiment with 1,575 small firms will test the relative effectiveness of a standard input-based approach (training, assistance, and subsidies) against that of a pay-for-results approach in getting firms to adopt digital marketing, comparing each treatment to a control group and to a group that receives only information. Our main outcome is adoption of this technology as measured by firms setting up a business Facebook page and carrying out paid advertising. We will also assess how this technology affects firm growth, as measured by new customers, sales, and profits.

The canonical approach to corporate governance posits a tradeoff between maximizing shareholder profits and maximizing other aspects of shareholder welfare such as social impact. Advances in machine learning that enhance the firm's ability to target specific customers may exacerbate or ameliorate this tradeoff. We estimate these tradeoffs using data from randomized microcredit approvals in South Africa, the Philippines, and Bosnia. We examine social impact on two dimensions: credit access (i.e., reaching disadvantaged groups that typically have less access to financial markets) and impact (i.e., treatment effect on household income). Two of the three lenders could have increased average loan profit margin by 7-9 percentage points through machine learning-based targeting. Such targeting would, however, lower the social impact vis-à-vis credit access (specifically women and lower-income households would have been more excluded) but the change in impact on borrowers' income is too imprecisely estimated and thus inconclusive. To gauge the magnitude of the first tradeoff, we examine how profits would change if the bank altered who they lent to within each quintile of baseline borrower income, thus holding the distribution broadly similar but fine-tuning the targeting within income bands. Such a constraint would lower the profit gains of targeting by about half. These findings highlight the importance of quantifying tradeoffs and complementarities when deciding what to maximize.

Abstract text will be added here. The paper links above remain available in the meantime.

Credit and information constraints can affect not only participation levels in different occupations, but also the types of individuals found in those occupations. I develop a model of occupational choice which shows how new information alters expectations and thus occupational choice. Credit and information constraints also interact with each other: the same information can have opposing impacts on occupational choice depending on the presence of credit constraints. Using a survey and information experiment in seven vocational high schools in Egypt, I find support for the model's predictions, including a key compositional prediction: risk averse individuals respond more to changes in expectations of risk at both the intensive (variance of income) and extensive (probability of finding a job) margins. This differential response leads highly risk averse individuals to shift towards "safer" occupations on average, and out of using credit to start a small enterprise. Because those who are risk averse report lower returns to credit in general (by favoring lower risk/lower return investments), the average expected income for those