Showing papers by "Rema Hanna published in 2012"

Incentives Work: Getting Teachers to Come to School

Abstract: We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers' attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children's test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies. (JEL I21, J31, J45, O15)

Targeting the Poor: Evidence from a Field Experiment in Indonesia

Abstract: The brief summarizes the targeting the poor: evidence from a field experiment in Indonesia for the period December 2008 - January 2009. This paper reports an experiment in 640 Indonesian villages on three approaches to target the poor: proxy means tests (PMT), where assets are used to predict consumption; community targeting, where villagers rank everyone from richest to poorest; and a hybrid. Defining poverty based on PPP$2 per capita consumption, community targeting and the hybrid perform somewhat worse in identifying the poor than PMT, though not by enough to significantly affect poverty outcomes for a typical program. Elite capture does not explain these results. Instead, communities appear to apply a different concept of poverty.

Up in Smoke: The Influence of Household Behavior on the Long-Run Impact of Improved Cooking Stoves

Abstract: It is conventional wisdom that it is possible to reduce exposure to indoor air pollution, improve health outcomes, and decrease greenhouse gas emissions in the rural areas of developing countries through the adoption of improved cooking stoves This belief is largely supported by observational field studies and engineering or laboratory experiments However, a new evidence is provided, from a randomized control trial conducted in rural Orissa, India (one of the poorest places in India), on the benefits of a commonly used improved stove that laboratory tests showed to reduce indoor air pollution and require less fuel Households are tracked for up to four years after they received the stove [BREAD Working Paper No 338] URL:[http://iplecondukeedu/bread/papers/working/338pdf]

Does the Effect of Pollution on Infant Mortality Differ Between Developing and Developed Countries? Evidence from Mexico City

Abstract: Much of what we know about the marginal effect of pollution on infant mortality is derived from developed country data. However, given the lower levels of air pollution in developed countries, these estimates may not be externally valid to the developing country context if there is a nonlinear dose relationship between pollution and mortality or if the costs of avoidance behavior differs considerably between the two contexts. In this paper, we estimate the relationship between pollution and infant mortality using data from Mexico. We find that an increase of 1 parts per billion in carbon monoxide (CO) over the last week results in 0.0032 deaths per 100,000 births, while a 1 µg/m3 increase in particulate matter (PM10) results in 0.24 infant deaths per 100,000 births. Our estimates for PM10 tend to be similar (or even smaller) than the U.S. estimates, while our findings on CO tend to be larger than those derived from the U.S. context. We provide suggestive evidence that a non-linearity in the relationship between CO and health explains this difference.

Discrimination in Grading

Abstract: In this paper, we report the results of an experiment that was designed to test for the presence of discrimination in grading and to explore the mechanism through which such discrimination might operate. In India, we ran an exam competition in which children compete for a large financial prize, and then we recruited teachers to grade the exams. We randomly assigned child “characteristics” (age, gender, and caste) to the cover sheets of the exams to ensure that there is no systematic relationship between the characteristics observed by the teachers and the quality of the exams. We find that teachers give exams that are assigned to be lower caste scores that are about 0.03 to 0.09 standard deviations lower than those that are assigned to be high caste. The teachers’ behavior appears more consistent with statistical discrimination models than taste-based models. Finally, we find that discrimination against low caste students is driven, on average, by low caste teachers.

Does the Effect of Pollution on Infant Mortality Differ Between Developing and Developed Countries? Evidence from Mexico City

Abstract: Much of what we know about the marginal effect of pollution on infant mortality is derived from developed country data. However, given the lower levels of air pollution in developed countries, these estimates may not be externally valid to the developing country context if there is a nonlinear dose relationship between pollution and mortality or if the costs of avoidance behavior differs considerably between the two contexts. In this paper, we estimate the relationship between pollution and infant mortality using data from Mexico. We find that an increase of 1 parts per billion in carbon monoxide (CO) over the last week results in 0.0032 deaths per 100,000 births, while a 1 μg/m3 increase in particulate matter (PM10) results in 0.24 infant deaths per 100,000 births. Our estimates for PM10 tend to be similar (or even smaller) than the U.S. estimates, while our findings on CO tend to be larger than those derived from the U.S. context. We provide suggestive evidence that a non-linearity in the relationship between CO and health explains this difference.

Up in Smoke: The Influence of Household Behavior on the Long-Run Impact of Improved Cooking Stoves

Abstract: It is conventional wisdom that it is possible to reduce exposure to indoor air pollution, improve health outcomes, and decrease greenhouse gas emissions in the rural areas of developing countries through the adoption of improved cooking stoves. This belief is largely supported by observational field studies and engineering or laboratory experiments. However, we provide new evidence, from a randomized control trial conducted in rural Orissa, India (one of the poorest places in India), on the benefits of a commonly used improved stove that laboratory tests showed to reduce indoor air pollution and require less fuel. We track households for up to four years after they received the stove. While we find a meaningful reduction in smoke inhalation in the first year, there is no effect over longer time horizons. We find no evidence of improvements in lung functioning or health and there is no change in fuel consumption (and presumably greenhouse gas emissions). The difference between the laboratory and field findings appear to result from households' revealed low valuation of the stoves. Households failed to use the stoves regularly or appropriately, did not make the necessary investments to maintain them properly, and usage rates ultimately declined further over time. More broadly, this study underscores the need to test environmental and health technologies in real-world settings where behavior may temper impacts, and to test them over a long enough horizon to understand how this behavioral effect evolves over time.

Learning through Noticing: Theory and Experimental Evidence in Farming

Abstract: Existing learning models attribute failures to learn to a lack of data. We model a different barrier. Given the large number of dimensions one could focus on when using a technology, people may fail to learn because they failed to notice important features of the data they possess. We conduct a field experiment with seaweed farmers to test a model of "learning through noticing." We find evidence of a failure to notice: On some dimensions, farmers do not even know the value of their own input. Interestingly, trials show that these dimensions are the ones that farmers fail to optimize. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, farmers change behavior only when presented with summaries that highlight the overlooked dimensions. We also draw out the implications of learning through noticing for technology adoption, agricultural extension, and the meaning of human capital.

Incentives Work: Getting Teachers to Come to School

Abstract: We use a randomized experiment and a structural model to test whether monitoring and financial incentives can reduce teacher absence and increase learning in India. In treatment schools, teachers’ attendance was monitored daily using cameras, and their salaries were made a nonlinear function of attendance. Teacher absenteeism in the treatment group fell by 21 percentage points relative to the control group, and the children’s test scores increased by 0.17 standard deviations. We estimate a structural dynamic labor supply model and find that teachers respond strongly to financial incentives. Our model is used to compute cost-minimizing compensation policies. (JEL I21, J31, J45, O15)

Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

Abstract: We use unique data from 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to different network structures, which we show are borne out in the data. We apply our findings to a community-based targeting program, where villagers chose which households should receive aid, and show that networks the model predicts to be more diffusive differentially benefit from community targeting.

Learning Through Noticing: Theory and Experimental Evidence in Farming

Abstract: Existing learning models attribute failures to learn to a lack of data. We model a different barrier. Given the large number of dimensions one could focus on when using a technology, people may fail to learn because they failed to notice important features of the data they possess. We conduct a field experiment with seaweed farmers to test a model of “learning through noticing”. We find evidence of a failure to notice: On some dimensions, farmers do not even know the value of their own input. Interestingly, trials show that these dimensions are the ones that farmers fail to optimize. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, farmers change behavior only when presented with summaries that highlight the overlooked dimensions. We also draw out the implications of learning through noticing for technology adoption, agricultural extension, and the meaning of human capital.

Ordeal Mechanisms in Targeting: Theory and Evidence from a Field Experiment in Indonesia

Abstract: This paper explores whether ordeal mechanisms can improve the targeting of aid programs to the poor ("self-targeting"). We first show that theoretically the impact of increasing ordeals is ambiguous: for example, time spent applying imposes a higher monetary cost on the rich, but may impose a higher utility cost on the poor. We examine these issues by conducting a 400-village field experiment with Indonesia’s Conditional Cash Transfer program (PKH), where eligibility is determined through an asset test. Specifically, we compare targeting outcomes from self-targeting, where villagers came to a central site to apply and take the asset test, against the status quo, an automatic enrollment system among a pool of potential candidates that the village pre-identified. Within self-targeting villages, we find that the poor are more likely to apply, even conditional on whether they would pass the asset test. Exploiting the experimental variation, we find that self-targeting leads to a much poorer group of beneficiaries than the status quo. Selftargeting also outperforms a universal asset-based automatic enrollment system that we construct using our survey data. However, while experimentally increasing the distance to the application site reduces the number of applicants, it does not differentially improve targeting. Estimating our model structurally, we show that there are large unobserved shocks in the decision to apply, and therefore increasing waiting times to 9 hours or more would be required to induce detectable additional selection. In short, ordeal mechanisms can induce self-selection, but marginally increasing the ordeal can impose additional costs on applicants without necessarily improving targeting.

Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

Abstract: We use a unique data-set from Indonesia on what individuals know about the income distribution in their village to test theories such as Jackson and Rogers (2007) that link information aggregation in networks to the structure of the network. The observed patterns are consistent with a basic diffusion model: more central individuals are better informed, and individuals are able to better evaluate the poverty status of those to whom they are more socially proximate. To understand what the theory predicts for cross-village patterns, we estimate a simple diffusion model using within-village variation, simulate network-level diffusion under this model for the over 600 different networks in our data, and use this simulated data to gauge what the simple diffusion model predicts for the cross-village relationship between information diffusion and network characteristics (e.g. clustering, density). The coefficients in these simulated regressions are generally consistent with relationships suggested in previous theoretical work, even though in our setting formal analytical predictions have not been derived. We then show that the qualitative predictions from the simulated model largely match the actual data in the sense that we obtain similar results both when the dependent variable is an empirical measure of the accuracy of a village's aggregate information and when it is the simulation outcome. Finally, we consider a real-world application to community based targeting, where villagers chose which households should receive an anti-poverty program, and show that networks with better diffusive properties (as predicted by our model) differentially benefit from community based targeting policies.