Learning Through Noticing: Theory and Experimental Evidence in Farming

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

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

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

Tangible Information and Citizen Empowerment: Identification Cards and Food Subsidy Programs in Indonesia

Redistribution programs in developing countries often “leak” because local officials do not implement programs as the central government intends. We study one approach to reducing leakage. In an experi- ment in over 550 villages, we test whether mailing cards with program information to targeted beneficiaries increases the subsidy they re- ceive from a subsidized rice program. On net, beneficiaries received 26 percent more subsidy in card villages. Ineligible households re- ceived no less, so this represents substantially lower leakage

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

We use unique data from over 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 network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting

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

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries