Huan Wang, Sarah-Eve Dill, Huan Zhou, Yue Ma, Hao Xue, Sean Sylvia, Kumi Smith, Matthew Boswell, Alexis Medina, Prashant Loyalka, Cody Abbey, Dimitris Friesen, Nathan Rose, Yian Guo, Scott Rozelle
Agricultural Economics ,
This study examines the effects of local and nationwide COVID‐19 disease control measures on the health and economy of China's rural population. We conducted phone surveys with 726 randomly selected village informants across seven rural Chinese provinces in February 2020. Four villages (0.55%) reported infections, and none reported deaths. Disease control measures had been universally implemented in all sample villages. About 74% of informants reported that villagers with wage‐earning jobs outside the village had stopped working due to workplace closures. A higher percentage of rural individuals could not work due to transportation, housing, and other constraints. Local governments had taken measures to reduce the impact of COVID‐19. Although schools in all surveyed villages were closed, 71% of village informants reported that students were attending classes online. Overall, measures to control COVID‐19 appear to have been successful in limiting disease transmission in rural communities outside the main epidemic area. Rural Chinese citizens, however, have experienced significant economic consequences from the disease control measures.
National Bureau of Economic Research (NBER) ,
The theoretical literature has long noted that talent can be used in both the entrepreneurial and non-entrepreneurial sectors, and its allocation depends on the reward structure. We test these hypotheses by linking administrative college admissions data for 1.8 million individuals with the universe of firm registration records in China. Within a college, we find that individuals with higher college entrance exam scores – the most important measure of talent in this context – are less likely to create firms, but, when they do, their firms are more successful than those of their lower-score counterparts. Additional survey data suggest that higher-score individuals enjoy higher wages and are more likely to join the state sector. Moreover, the score-to-firm creation relationship varies greatly across industry, according to the size of the state sector. These findings suggest that the score is positively associated with both entrepreneurial ability and wage-job ability but higher-score individuals are attracted away by wage jobs, particularly those of the state sector.
Lei Wang, Yifei Chen, Sean Sylvia, Sarah-Eve Dill, Scott Rozelle
BMC Pediatrics ,
Cognitive development after age three tends to be stable and can therefore predict cognitive skills in later childhood. However, there is evidence that cognitive development is less stable before age three. In rural China, research has found large shares of children under age three are developmentally delayed, yet little is known about the trajectories of cognitive development between 0 and 3 years of age or how developmental trajectories predict later cognitive skills. This study seeks to describe the trajectories of child cognitive development between the ages of 0–3 years and examine how different trajectories predict cognitive development at preschool age.
We collected three waves of longitudinal panel data from 1245 children in rural Western China. Child cognitive development was measured by the Bayley Scales of Infant Development when the child was 6–12 months and 22–30 months, and by the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition when the child was 49–65 months. We used the two measures of cognitive development before age three to determine the trajectories of child cognitive development.
Of the children, 39% were never cognitively delayed; 13% were persistently delayed; 7% experienced improving cognitive development; and 41% experienced deteriorating development before age 3. Compared to children who had never experienced cognitive delay, children with persistent cognitive delay and those with deteriorating development before age 3 had significantly lower cognitive scores at preschool age. Children with improving development before age 3 showed similar levels of cognition at preschool age as children who had never experienced cognitive delay.
Large shares of children under age 3 in rural Western China show deteriorating cognitive development from infancy to toddlerhood, which predict lower levels of cognition at preschool age. Policymakers should invest in improving cognitive development before age 3 to prevent long-term poor cognition among China’s rural children.
We highlight a growing concern in the economics profession that young scholars face incentives that are misaligned with conducting research that furthers knowledge and addresses pressing policy problems. The premium given to publication in top journals leads to an emphasis on exhaustive treatment of narrow questions. Detailed, robustly identified studies of novel questions are of undeniable value; however, the opportunity cost of producing such studies is large in terms of research quantity and policy relevance. For economists who aim to achieve what we view as the ultimate goals of academic research (enhancing understanding of the world, solving social problems, and building foundational knowledge to enable future breakthroughs), we offer some insights from publication philosophy in the field of public health. We discuss how public health has developed norms around publishing that are more successful in meeting these ultimate goals. We then offer thoughts on potential lessons for young economists in China and the economics discipline.
Stanford scholars are setting and expanding research agendas to analyze China’s economic development and its impact on the world. The newly launched Stanford Center on China’s Economy and Institutions — co-directed by SIEPR senior fellows Hongbin Li and Scott Rozelle — is supporting their work. In this SIEPR Policy Brief, Li and Rozelle outline the research underway by the new center's affiliates.
Recent years have witnessed rapid growth in satellite-based approaches to quantifying aspects of land use, especially those monitoring the outcomes of sustainable development programs. Burke et al. reviewed this recent progress with a particular focus on machine-learning approaches and artificial intelligence methods. Drawing on examples mostly from Africa, they conclude that satellite-based methods enhance rather than replace ground-based data collection, and progress depends on a combined approach.