Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs PDF Download
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Author: Luna Yue Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs.
Author: Luna Yue Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The rigorous evaluation of anti-poverty programs is key to the fight against global poverty. Traditional approaches rely heavily on repeated in-person field surveys to measure program effects. However, this is costly, time-consuming, and often logistically challenging. Here we provide the first evidence that we can conduct such program evaluations based solely on high-resolution satellite imagery and deep learning methods. Our application estimates changes in household welfare in a recent anti-poverty program in rural Kenya. Leveraging a large literature documenting a reliable relationship between housing quality and household wealth, we infer changes in household wealth based on satellite-derived changes in housing quality and obtain consistent results with the traditional field-survey based approach. Our approach generates inexpensive and timely insights on program effectiveness in international development programs.
Author: Luna Yue Huang
Publisher:
ISBN:
Category :
Languages : en
Pages : 40
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Book Description
Author: Martin Ravallion
Publisher: World Bank Publications
ISBN:
Category : Economic assistance, Domestic
Languages : en
Pages : 77
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Book Description
"The author critically reviews the methods available for the ex-post counterfactual analysis of programs that are assigned exclusively to individuals, households, or locations. The discussion covers both experimental and non-experimental methods (including propensity-score matching, discontinuity designs, double and triple differences, and instrumental variables). Two main lessons emerge. First, despite the claims of advocates, no single method dominates; rigorous, policy-relevant evaluations should be open-minded about methodology. Second, future efforts to draw more useful lessons from evaluations will call for more policy-relevant measures and deeper explanations of measured impacts than are possible from the classic ("black box") assessment of mean impact. " -- Cover verso.
Author: Felix Chan
Publisher: Springer Nature
ISBN: 3031151496
Category : Business & Economics
Languages : en
Pages : 385
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Book Description
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Author: Asian Development Bank
Publisher: Asian Development Bank
ISBN: 9292627694
Category : Business & Economics
Languages : en
Pages : 141
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Book Description
The “leave no one behind” principle of the 2030 Agenda for Sustainable Development requires appropriate indicators for different segments of a country’s population. This entails detailed, granular data on population groups that extend beyond national trends and averages. The Asian Development Bank (ADB), in collaboration with the National Statistical Office of Thailand and the Word Data Lab, conducted a feasibility study to enhance the granularity, cost-effectiveness, and compilation of high-quality poverty statistics in Thailand. This report documents the results of the study, providing insights on data collection requirements, advanced algorithmic techniques, and validation of poverty estimates using artificial intelligence to complement traditional data sources and conventional survey methods.
Author: Shaohua Chen
Publisher: World Bank Publications
ISBN:
Category : Economic assistance, Domestic
Languages : en
Pages : 40
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Book Description
By the widely used difference-in-difference method, the Southwest China Poverty Reduction Project had little impact on the proportion of people in beneficiary villages consuming less than $1 a day-despite a public outlay of $400 million. Is that right, or is the true impact being hidden somehow? The authors find that impact estimates are quite sensitive to the choice of outcome indicator, the poverty line, and the matching method. There are larger poverty impacts at lower poverty lines. And there are much larger impacts on incomes than consumptions. Uncertainty about the impact probably made it hard for participants to infer the gain in permanent income, so they saved a high proportion of the short-term gain.
Author: Letta, M.
Publisher: Food & Agriculture Org. [Author] [Author]
ISBN: 9251388237
Category : Technology & Engineering
Languages : en
Pages : 60
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Book Description
We leverage the multi-stressor nature of the COVID-19 generalized disruption as an opportunity to test the out-of-sample forecasting accuracy of both theory-based and data-driven vulnerability prediction models for the ex ante targeting of preventive interventions. [Author] Taking advantage of the World Bank multitopic surveys for Ethiopia and Nigeria, the two most populous African countries, our retrospective evaluation assesses the models’ ability to anticipate households and agrifood system actors experiencing food insecurity and income losses during the COVID-19 pandemic. [Author] The results are disappointing: we document that, despite considerable heterogeneity across data and methods, both models do not achieve satisfactory out-of-sample forecasting performances. [Author] Our findings are robust to the use of different data, estimation methods, and several heterogeneity analyses and sensitivity checks. [Author] This evidence calls for a refinement of current profiling methodologies and for interoperability efforts to close existing microdata gaps. [Author] Such efforts would enable policymakers to implement more effective early-warning systems of vulnerability hotspots and improve the cost-effectiveness of development interventions aimed at targeting groups vulnerable to future food crises. [Author]
Author: Martin Ravallion
Publisher:
ISBN:
Category :
Languages : en
Pages : 77
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Book Description
The author critically reviews the methods available for the ex-post counterfactual analysis of programs that are assigned exclusively to individuals, households, or locations. The discussion covers both experimental and non-experimental methods (including propensity-score matching, discontinuity designs, double and triple differences, and instrumental variables). Two main lessons emerge. First, despite the claims of advocates, no single method dominates; rigorous, policy-relevant evaluations should be open-minded about methodology. Second, future efforts to draw more useful lessons from evaluations will call for more policy-relevant measures and deeper explanations of measured impacts than are possible from the classic (quot;black boxquot;) assessment of mean impact.
Author: Ethan Brewer
Publisher:
ISBN:
Category : Data integrity
Languages : en
Pages : 0
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Book Description
Satellite imagery analysis using deep learning methods, specifically convolutional neural networks (CNNs), has grown in popularity since 2012, with uses extending into the estimation of population, wealth, poverty, conflict, migration, education, and infrastructure, among other applications. This dissertation contributes to this body of literature in three parts. First, I explore the use of deep learning to overcome the sparsity, or complete lack, of accurate information regarding existing road infrastructure across much of the world. Using a novel labeled dataset generated by a custom-coded Android application, I show that a transfer learning approach can estimate road quality based on high-resolution satellite imagery with an accuracy of up to 80%. In the second chapter, I illustrate the vulnerability of this and related models to cyber intrusions (data poisoning), and propose a new technique to mitigate these vulnerabilities. The third chapter applies the lessons learned to propose a novel model architecture for spatiotemporal monitoring of industrial sites in inaccessible regions around the world, integrating high-resolution satellite imagery, a segmentation algorithm, and a pretrained deep learning framework to automatically detect and monitor individual industrial sites within the People's Republic of China. These three chapters advance our understanding of many of the challenges unique to computer vision in the context of satellite data, and provide some guidance on fruitful future directions.
Author: Shaohua Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
By the widely used difference-in-difference method, the Southwest China Poverty Reduction Project had little impact on the proportion of people in beneficiary villages consuming less than $1 a day - despite a public outlay of $400 million. Is that right, or is the true impact being hidden somehow? Chen and Ravallion find that impact estimates are quite sensitive to the choice of outcome indicator, the poverty line, and the matching method. There are larger poverty impacts at lower poverty lines. And there are much larger impacts on incomes than consumptions. Uncertainty about the impact probably made it hard for participants to infer the gain in permanent income, so they saved a high proportion of the short-term gain. This paper - a product of the Poverty Team, Development Research Group - is part of a larger effort in the group to assess the impact on poverty of World Bank lending. The study was funded by the Bank's Research Support Budget under the research project Looking Beyond Averages: A Research Program on Poverty and Inequality (RPO 681-39).
