In addition to the most pressing issues of the day, scholars at the Freeman Spogli Institute for International Studies focus their research on many regions of the world, from Beijing to Brazil.
Research Spotlight
The Ripple Effects of China’s College Expansion on American Universities
Researchers at SCCEI trace how China’s unprecedented expansion of higher education has impacted U.S. graduate education and local economies surrounding college towns.
While Nayib Bukele's style of authoritarianism may have some successes on paper, Beatriz Magaloni and Alberto Diaz-Cayeros argue that the regime is headed for a reckoning.
Time for Iran to Make a No-enrichment Nuclear Deal
The time has come for Iran’s leaders to reconsider their past intransigent, deceptive posture and instead pursue a nuclear power program that will benefit the Iranian people, write Abbas Milani and Siegfried Hecker.
American Political Science Review,
November 1, 2021
This paper stands at the intersection of two literatures—on partisan polarization and on democratic deliberation—that have not had much connection with one another. If readers find some of the results surprising, the authors have had the same reaction. In this paper we describe these results and our approach to explaining them.
Research in developed countries has found that paternal involvement has positive and significant effects on early childhood development (ECD). Less is known, however, about the state of paternal involvement and its influence on ECD in rural China. Using data collected in Southern China that included 1,460 children aged 6–42 months and their fathers (as well as their primary caregivers), this study examines the association between paternal involvement and ECD. Although the results demonstrate that the average level of paternal involvement is low in rural China, paternal involvement is related to a significant increase in three domains of ECD (cognition, language, and social-emotional skills). Older children benefit significantly more than do younger children from paternal involvement in all domains of ECD. The results also show that, if the mother is the primary caregiver, the mother’s higher educational level and the family’s higher socioeconomic status are positively associated with paternal involvement.
The Texas National Security Review,
October 1, 2021
Emerging and disruptive technologies spell an uncertain future for second-strike retaliatory forces. New sensors and big data analysis may render mobile missiles and submarines vulnerable to detection. I call this development the “standstill conundrum”: States will no longer be able to assure a nuclear response should they be hit by a nuclear first strike. If the nuclear weapons states can manage this vulnerability, however, they might be able to escape its worst effects. “Managing” could mean shoring up nuclear deterrence; it could mean focusing more on defenses; or it could mean negotiating to ensure continued viability of second-strike deterrent forces.
We present the results of a cluster-randomized controlled trial that evaluates the effects of a free, center-based parenting intervention on early cognitive development and parenting practices in 100 rural villages in China. We then compare these effects to a previous trial of a home-based intervention conducted in the same region, using the same parenting curriculum and public service system, accounting for potential differences between the studies. We find that the center-based intervention did not have a significant impact on child development outcomes, but did lead to increases in the material investments, time investments, and parenting skills of caregivers. The average impact of the center-based intervention on child skills and investments in children was significantly smaller than the home-visiting intervention. Analysis of the possible mechanisms suggests that the difference in effects was driven primarily by different patterns of selection into program participation.
A forward-thinking manifesto from three Stanford professors—experts who have worked at ground zero of the tech revolution for decades—which reveals how big tech’s obsession with optimization and efficiency has sacrificed fundamental human values and outlines steps we can take to change course, renew our democracy, and save ourselves.
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw input from which to create crop type maps. Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models. When training data is not available in one region, classifiers trained in similar regions can be transferred, but shifts in the distribution of crop types as well as transformations of the features between regions lead to reduced classification accuracy. We present a methodology that uses aggregate-level crop statistics to correct the classifier by accounting for these two types of shifts. To adjust for shifts in the crop type composition we present a scheme for properly reweighting the posterior probabilities of each class that are output by the classifier. To adjust for shifts in features we propose a method to estimate and remove linear shifts in the mean feature vector. We demonstrate that this methodology leads to substantial improvements in overall classification accuracy when using Linear Discriminant Analysis (LDA) to map crop types in Occitanie, France and in Western Province, Kenya. When using LDA as our base classifier, we found that in France our methodology led to percent reductions in misclassifications ranging from 2.8% to 42.2% (mean = 21.9%) over eleven different training departments, and in Kenya the percent reductions in misclassification were 6.6%, 28.4%, and 42.7% for three training regions. While our methodology was statistically motivated by the LDA classifier, it can be applied to any type of classifier. As an example, we demonstrate its successful application to improve a Random Forest classifier.
