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Hanna, Rema

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Hanna

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Rema

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Hanna, Rema
Author's Publications: search.filters.isAuthorOfPublication.53fa8156-eefb-4d28-bf8b-32b500afa471

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Now showing 1 - 10 of 21
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    Does Elite Capture Matter? Local Elites and Targeted Welfare Programs in Indonesia
    (Center for International Development at Harvard University, 2013-01) Hanna, Rema; Alatas, Vivi; Banerjee, Abhijit; Olken, Benjamin A.; Purnamasari, Ririn; Wai-Poi, Matthew
    This paper investigates the impact of elite capture on the allocation of targeted government welfare programs in Indonesia, using both a high-stakes field experiment that varied the extent of elite influence and non-experimental data on a variety of existing government transfer programs. Conditional on their consumption level, there is little evidence that village elites and their relatives are more likely to receive aid programs than non-elites. Looking more closely, however, we find that this overall result masks a difference between different types of elites: those holding formal leadership positions are more likely to receive benefits, while informal leaders are actually less likely to. We show that capture by formal elites occurs during the distribution of benefits under the programs, and not during the processes when the beneficiary lists are determined by the central government. However, while elite capture exists, the welfare losses it creates appear quite small: since formal elites and their relatives are only 9 percent richer than non-elites, are at most about 8 percentage points more likely to receive benefits than non-elites, and represent at most 15 percent of the population, eliminating elite capture entirely would improve the welfare gains from these programs by less than one percent.
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    Optimal Public Transportation Networks: Evidence from the World’s Largest Bus Rapid Transit System in Jakarta
    (Harvard Kennedy School, 2023-06) Kreindler, Gabriel; Gaduh, Arya; Graff, Tilman; Hanna, Rema; Olken, Benjamin A.
    Designing public transport networks involves tradeoffs between extensive geographic coverage, frequent service on each route, and relying on interconnections as opposed to direct service. These choices, in turn, depend on individual preferences for waiting times, travel times, and transfers. We study these tradeoffs by examining the world's largest bus rapid transit system, in Jakarta, Indonesia, leveraging a large network expansion between 2016-2020. Using detailed ridership data and aggregate travel flows from smartphone data, we analyze how new direct connections, changes in bus travel time, and wait time reductions increase ridership and overall trips. We set up and estimate a transit network demand model with multi-dimensional travel costs, idiosyncratic heterogeneity induced by random wait times, and inattention, matching event-study moments from the route launches. Commuters in Jakarta are 2-4 times more sensitive to wait time compared to time on the bus, and inattentive to long routes. To study the implications for network design, we introduce a new framework to describe the set of optimal networks. Our results suggest that a less concentrated network would increase ridership and commuter welfare.
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    Environmental Regulations, Air and Water Pollution, and Infant Mortality in India
    (Center for International Development at Harvard University, 2011-07) Greenstone, Michael; Hanna, Rema
    Using the most comprehensive data file ever compiled on air pollution, water pollution, environmental regulations, and infant mortality from a developing country, the paper examines the effectiveness of India’s environmental regulations. The air pollution regulations were effective at reducing ambient concentrations of particulate matter, sulfur dioxide, and nitrogen dioxide. The most successful air pollution regulation is associated with a modest and statistically insignificant decline in infant mortality. However, the water pollution regulations had no observable effect. Overall, these results contradict the conventional wisdom that environmental quality is a deterministic function of income and underscore the role of institutions and politics.
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    Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
    (Center for International Development at Harvard University, 2012-08) Hanna, Rema; Alatas, Vivi; Banerjee, Abhijit; Chandrasekhar, Arun G.; Olken, Benjamin A.
    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.
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    Learning Through Noticing: Theory and Experimental Evidence in Farming
    (Center for International Development at Harvard University, 2012-09) Hanna, Rema; Mullainathan, Sendhil; Schwartstein, Josh
    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.
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    The Marginal Disutility from Corruption in Social Programs: Evidence from Program Administrators and Beneficiaries
    (Harvard Kennedy School, 2023-01) Gaduh, Arya; Hanna, Rema; Olken, Benjamin
    Concerns about fraud in welfare programs are common arguments worldwide against such programs. We conducted a survey experiment with over 28,000 welfare program administrators and over 19,000 beneficiaries in Indonesia to elicit the ‘marginal disutility from corruption,’ i.e., the trade-between more generous social assistance and losses due to corruption and fraud. Merely mentioning corruption reduced perceived program success, equivalent to distributing more than 20 percent less aid. However, respondents were not sensitive to the amount of corruption—respondents were willing to trade off 2 dollars of additional losses for an additional 1 dollar distributed to beneficiaries. Program administrators and beneficiaries had similar assessments.
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    Corruption
    (John F. Kennedy School of Government, Harvard University, 2012) Banerjee, Abhijit; Hanna, Rema; Mullainathan, Sendhil
    In this paper, we provide a new framework for analyzing corruption in public bureaucracies. The standard way to model corruption is as an example of moral hazard, which then leads to a focus on better monitoring and stricter penalties with the eradication of corruption as the final goal. We propose an alternative approach which emphasizes why corruption arises in the first place. Corruption is modeled as a consequence of the interaction between the underlying task being performed by bureaucrat, the bureaucrat's private incentives and what the principal can observe and control. This allows us to study not just corruption but also other distortions that arise simultaneously with corruption, such as red-tape and ultimately, the quality and efficiency of the public services provided, and how these outcomes vary depending on the specific features of this task. We then review the growing empirical literature on corruption through this perspective and provide guidance for future empirical research.
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    Learning Through Noticing: Theory and Experimental Evidence in Farming
    (HKS Faculty Research Working Paper Series, 2012) Hanna, Rema; Mullainatha, Sendhil; Schwartzstein, Joshua
    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.
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    Publication
    Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia
    (John F. Kennedy School of Government, Harvard University, 2012) Hanna, Rema; Alatas, Vivi; Banerjee, Abhijit; Chandrasekhar, Arun G.; Olken, Benjamin A.
    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.
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    Does the Effect of Pollution on Infant Mortality Differ Between Developing and Developed Countries? Evidence from Mexico City
    (John F. Kennedy School of Government, Harvard University., 2012) Arceo, Eva; Hanna, Rema; Oliva, Paulina
    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.