Read the article in the December 2023 issue of Stanford Magazine.
Beatriz Magaloni can tell you which criminal justice reforms make communities safer in Mexico and beyond.
The Neue Zürcher Zeitung recently published an interview with SCCEI's co-director Scott Rozelle. The article was originally publshed in German and later translated to English. You can read the full article in either language online:
Katrin Buchenbacher from Neue Zürcher Zeitung interviewed Scott Rozelle about his recent book on China's rural population.
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Ukraine’s 17th Prime Minister (August 2019 – March 2020). In just 5 months Mr. Honcharuk initiated important changes that other Ukrainian politicians had not dared to do for years (launched of large and small privatization processes, started of land market implementation, conducted Naftogaz unbundling, started combating shade markets –– illegal gambling houses and petrol stations were closed, launched of Anti-Raider (illegal seizure of business or property) Office that would react within just 24 hours to any cases of such illegal seizure, etc).
Before he served as a Deputy Head of the Presidential Office of Ukraine and was a member of the National Reforms Council under the President of Ukraine. Previously for more than ten years, Mr. Honcharuk has been working in the legal sphere. He has established a reputation as a strong professional and qualified specialist. Mr. Honcharuk is also known as a strong fighter for business community rights. 2005-2008, he worked as a lawyer at PRIOR-Invest investment company and later on headed its legal department. During 2008-2015, he worked as an arbitration manager and managing partner at Constructive Lawyers, a law firm he had founded, which provided legal services in the field of investment and financing real estate construction.
From 2015-2019, Oleksiy Honcharuk headed Better Regulation Delivery Office non-governmental organization (BRDO). Among his achievements as the head of the BRDO was the cancellation of around 1000 Government acts and adoption of more than 50 decisions, facilitating activity of business in Ukraine. Oleksiy Honcharuk also served as an external advisor to the First Deputy Prime Minister - Minister of Economic Development and Trade of Ukraine.
Oleksiy Honcharuk has a degree in law from Interregional Academy of Personal Management and in Public Administration from National Academy for Public Administration under the President of Ukraine. He was born on July 7, 1984, in Zhmerynka, Vinnytsia region.
Please join us in congratulating Beatriz Magaloni, professor of political science, FSI senior fellow, and faculty director of the Program on Poverty, Violence & Governance, winner of the 2021 Heinz I. Eulau Award for the best article published in American Political Science Review!
In this award-winning article entitled “Killing in the Slums: Social Order, Criminal Governance and Police Violence in Rio de Janeiro," Professor Magaloni, Edgar Franco-Vivanco, and Vanessa Melo explore the conditions that allow criminal organizations to establish local governance structures and the mechanisms that enable the police to regain territorial control and legitimacy.
The article finds that in territories reigned by criminal orders, police have to gain legitimacy in the eyes of the community and emerge as the sole embodiment of coercive force and emerge as the legitimate embodiment of physical force. But this is not always easy. For instance, when criminal rule effectively provides local security and public goods, the state will have a hard time gaining territorial control. Why? Because residents often feel safer under the authority of drug lords than with the police presence. However, where criminal orders cannot restrain their forces from fighting among themselves and victimizing residents, it is significantly easier for the government to regain territorial control and create a legitimate state order. To do so, the state has to constrain violence, monitor and sanction police officers’ abusive behaviors, and bring public justice systems to the poor. Otherwise, the state will likely fail to retake territorial control, allowing criminal orders to prevail.
To read more, check out the article here.
Congratulations, Professor Magaloni, on this high honor!
The award-winning article is entitled “Killing in the Slums: Social Order, Criminal Governance and Police Violence in Rio de Janeiro.” Professor Magaloni coauthored the article with Edgar Franco-Vivanco, who earned his Ph.D. from Stanford and is now at the University of Michigan; and with Vanessa Melo, a graduate student in Anthropology at UCLA.
Bruno is a Research Data Analyst at the Center on Food Security and The Environment where he supports David Lobell in tackling issues related to food security using satellite imagery along with applied data analytics. He is a past intern at the Monterey Bay Aquarium Research Institute where he used satellite imagery to analyze phytoplankton blooms in the Gulf of Alaska. Bruno graduated from the University of California, Santa Cruz in June 2020, with a B.S In Earth Science. Here he examined regions heavily affected by rising Sea Surface Temperature Extremes throughout the globe.
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Prashant Loyalka is an Associate Professor at the Graduate School of Education and a Senior Fellow at the Freeman Spogli Institute for International Studies at Stanford University. His research focuses on examining/addressing inequalities in the education of children and youth and on understanding/improving the quality of education received by children and youth in multiple countries including China, India, Russia, and the United States. He also conducts large-scale evaluations of educational programs and policies that seek to improve student outcomes.
In Taiwan, thousands of students from Yuanzhumin (aboriginal) families lag far behind their Han counterparts in academic achievement. When they fall behind, they often have no way to catch up. There is increased interest among both educators and policymakers in helping underperforming students catch up using computer-assisted learning (CAL). The objective of this paper is to examine the impact of an intervention aimed at raising the academic performance of students using an in-home CAL program. According to intention-to-treat estimates, in-home CAL improved the overall math scores of students in the treatment group relative to the control group by 0.08 to 0.20 standard deviations (depending on whether the treatment was for one or two semesters). Furthermore, Average Treatment Effect on the Treated analysis was used for solving the compliance problem in our experiment, showing that in-home CAL raised academic performance by 0.36 standard deviations among compliers. This study thus presents preliminary evidence that an in-home CAL program has the potential to boost the learning outcomes of disadvantaged students.
Purpose: Although China has instituted compulsory education through Grade 9, it is still unclear whether students are, in fact, staying in school. In this paper, the authors use a multi-year (2003–2011) longitudinal survey data set on rural households in 102–130 villages across 30 provinces in China to examine the extent to which students still drop out of school prior to finishing compulsory education.
Design/methodology/approach: To examine the correlates of dropping out, the study uses ordinary least squares and multivariate probit models.
Findings: Dropout rate from junior high school was still high (14%) in 2011, even though it fell across the study period. There was heterogeneity in the measured dropout rate. There was great variation among different regions, and especially among different villages. In all, 10% of the sample villages showed extremely high rates during the study period and actually rose over time. Household characteristics associated with poverty and the opportunity cost of staying in school were significantly and negatively correlated with the completion of nine years of schooling.
Research limitations/implications: The findings of this study suggest that China needs to take additional steps to overcome the barriers keeping children from completing nine years of schooling if they hope to either achieve their goal of having all children complete nine years of school or extend compulsory schooling to the end of twelfth grade.
Originality/value: The authors seek to measure the prevalence of both compulsory education rates of dropouts and rates of completion in China. The study examines the correlates of dropping out at the lower secondary schooling level as a way of understanding what types of students (from what types of villages) are not complying with national schooling regulations. To overcome the methodological shortcomings of previous research on dropout in China, the study uses a nationally representative, longitudinal data set based on household surveys collected between 2003 and 2011.
In combating poverty, like any fight, it’s good to know the locations of your targets.
That’s why Stanford scholars Marshall Burke, David Lobell and Stefano Ermon have spent the past five years leading a team of researchers to home in on an efficient way to find and track impoverished zones across Africa.
The powerful tool they’ve developed combines free, publicly accessible satellite imagery with artificial intelligence to estimate the level of poverty across African villages and changes in their development over time. By analyzing past and current data, the measurement tool could provide helpful information to organizations, government agencies and businesses that deliver services and necessities to the poor.
Details of their undertaking were unveiled in the May 22 issue of Nature Communications.
“Our big motivation is to better develop tools and technologies that allow us to make progress on really important economic issues. And progress is constrained by a lack of ability to measure outcomes,” said Burke, a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR) and an assistant professor of earth system science in the School of Earth, Energy & Environmental Sciences (Stanford Earth). “Here’s a tool that we think can help.”
Lobell, a senior fellow at SIEPR and a professor of Earth system science at Stanford Earth, says looking back is critical to identifying trends and factors to help people escape from poverty.
“Amazingly, there hasn’t really been any good way to understand how poverty is changing at a local level in Africa,” said Lobell, who is also the director of the Center on Food Security and the Environment and the William Wrigley Fellow at the Stanford Woods Institute for the Environment. “Censuses aren’t frequent enough, and door-to-door surveys rarely return to the same people. If satellites can help us reconstruct a history of poverty, it could open up a lot of room to better understand and alleviate poverty on the continent.”
The measurement tool uses satellite imagery both from the nighttime and daytime. At night, lights are an indicator of development, and during the day, images of human infrastructure such as roads, agriculture, roofing materials, housing structures and waterways, provide characteristics correlated with development.
Then the tool applies the technology of deep learning – computing algorithms that constantly train themselves to detect patterns – to create a model that analyzes the imagery data and forms an index for asset wealth, an economic component commonly used by surveyors to measure household wealth in developing nations.
The researchers tested the measuring tool’s accuracy for about 20,000 African villages that had existing asset wealth data from surveys, dating back to 2009. They found that it performed well in gauging the poverty levels of villages over different periods of time, according to their study.
Here, Burke – who is also a center fellow at the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies – discusses the making of the tool and its potential to help improve the well-being of the world’s poor.
Why are you excited about this new technological resource?
For the first time, this tool demonstrates that we can measure economic progress and understand poverty interventions at both a local level and a broad scale. It works across Africa, across a lot of different years. It works pretty darn well, and it works in a lot of very different types of countries.
Can you give examples of how this new tool would be used?
If we want to understand the effectiveness of an anti-poverty program, or if an NGO wants to target a specific product to specific types of individuals, or if a business wants to understand where a market’s growing – all of those require data on economic outcomes. In many parts of the world, we just don’t have those data. Now we’re using data from across sub-Saharan Africa and training these models to take in all the data to measure for specific outcomes.
How does this new study build upon your previous work?
Our initial poverty-mapping work, published in 2016, was on five countries using one year of data. It relied on costly, high-resolution imagery at a much smaller, pilot scale. Now this work covers about two dozen countries – about half of the countries in Africa – using many more years of high-dimensional data. This provided underlying training datasets to develop the measurement models and allowed us to validate whether the models are making good poverty estimates.
We’re confident we can apply this technology and this approach to get reliable estimates for all the countries in Africa.
A key difference compared to the earlier work is now we’re using completely publicly available satellite imagery that goes back in time – and it’s free, which I think democratizes this technology. And we’re doing it at a comprehensive, massive spatial scale.
How do you use satellite imagery to get poverty estimates?
We’re building on rapid developments in the field of computer science – of deep learning – that have happened in the last five years and that have really transformed how we extract information from images. We’re not telling the machine what to look for in images; instead, we’re just telling it, “Here’s a rich place. Here is a poor place. Figure it out.”
The computer is clearly picking out urban areas, agricultural areas, roads, waterways – features in the landscape that you might think would have some predictive power in being able to separate rich areas from poor areas. The computer says, ‘I found this pattern’ and we can then assign semantic meaning to it.
These broader characteristics, examined at the village level, turn out to be highly related to the average wealth of the households in that region.
What’s next?
Now that we have these data, we want to use them to try to learn something about economic development. This tool enables us to address questions we were unable to ask a year ago because now we have local-level measurements of key economic outcomes at broad, spatial scale and over time.
We can evaluate why some places are doing better than other places. We can ask: What do patterns of growth in livelihoods look like? Is most of the variation between countries or within countries? If there’s variation within a country, that already tells us something important about the determinants of growth. It’s probably something going on locally.
I’m an economist, so those are the sorts of questions that get me excited. The technological development is not an end in itself. It’s an enabler for the social science that we want to do.
Adam Gorlick, Stanford Institute for Economic Policy Research: (650) 724-0614, agorlick@stanford.edu
A new tool combines publicly accessible satellite imagery with AI to track poverty across African villages over time.
