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When facing threats from automation, a worker residing in a large Chinese city might not be as lucky as a worker in a large U.S. city, depending on the type of large city in which one resides. Empirical studies found that large U.S. cities exhibit resilience to automation impacts because of the increased occupational and skill specialization. However, in this study, we observe polarized responses in large Chinese cities to automation impacts. The polarization might be attributed to the elaborate master planning of the central government, through which cities are assigned with different industrial goals to achieve globally optimal economic success and, thus, a fast-growing economy. By dividing Chinese cities into two groups based on their administrative levels and premium resources allocated by the central government, we find that Chinese cities follow two distinct industrial development trajectories, one trajectory owning government support leads to a diversified industrial structure and, thus, a diversified job market, and the other leads to specialty cities and, thus, a specialized job market. By revisiting the automation impacts on a polarized job market, we observe a Simpsons paradox through which a larger city of a diversified job market results in greater resilience, whereas larger cities of specialized job markets are more susceptible. These findings inform policy makers to deploy appropriate policies to mitigate the polarized automation impacts.
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China is the worlds second largest economy. After four decades of economic miracles, Chinas economy is transitioning into an advanced, knowledge-based economy. Yet, we still lack a detailed understanding of the skills that underly the Chinese labor force, and the development and spatial distribution of these skills. For example, the US standardized skill taxonomy O*NET played an important role in understanding the dynamics of manufacturing and knowledge-based work, as well as potential risks from automation and outsourcing. Here, we use Machine Learning techniques to bridge this gap, creating Chinas first workforce skill taxonomy, and map it to O*NET. This enables us to reveal workforce skill polarization into social-cognitive skills and sensory-physical skills, and to explore the Chinas regional inequality in light of workforce skills, and compare it to traditional metrics such as education. We build an online tool for the public and policy makers to explore the skill taxonomy: skills.sysu.edu.cn. We will also make the taxonomy dataset publicly available for other researchers upon publication.
We investigate structural change in the PR China during a period of particularly rapid growth 1998-2014. For this, we utilize sectoral data from the World Input-Output Database and firm-level data from the Chinese Industrial Enterprise Database. Starting with correlation laws known from the literature (Fabricants laws), we investigate which empirical regularities hold at the sectoral level and show that many of these correlations cannot be recovered at the firm level. For a more detailed analysis, we propose a multi-level framework, which is validated with empirically. For this, we perform a robust regression, since various input variables at the firm-level as well as the residuals of exploratory OLS regressions are found to be heavy-tailed. We conclude that Fabricants laws and other regularities are primarily characteristics of the sectoral level which rely on aspects like infrastructure, technology level, innovation capabilities, and the knowledge base of the relevant labor force. We illustrate our analysis by showing the development of some of the larger sectors in detail and offer some policy implications in the context of development economics, evolutionary economics, and industrial organization.
Chinas pledge to reach carbon neutrality before 2060 is an ambitious goal and could provide the world with much-needed leadership on how to limit warming to +1.5C warming above pre-industrial levels by the end of the century. But the pathways that would achieve net zero by 2060 are still unclear, including the role of negative emissions technologies. We use the Global Change Analysis Model to simulate how negative emissions technologies, in general, and direct air capture (DAC) in particular, could contribute to Chinas meeting this target. Our results show that negative emissions could play a large role, offsetting on the order of 3 GtCO2 per year from difficult-to-mitigate sectors such as freight transportation and heavy industry. This includes up to a 1.6 GtCO2 per year contribution from DAC, constituting up to 60% of total projected negative emissions in China. But DAC, like bioenergy with carbon capture and storage and afforestation, has not yet been demonstrated at anywhere approaching the scales required to meaningfully contribute to climate mitigation. Deploying NETs at these scales will have widespread impacts on financial systems and natural resources such as water, land, and energy in China.
We use geospatial data to examine the unprecedented national program currentlyunderway in the United States to distribute and administer vaccines against COVID-19. We quantify the impact of the proposed federal partnership with the companyDollar General to serve as vaccination sites and compare vaccine access with DollarGeneral to the current Federal Retail Pharmacy Partnership Program. Although dollarstores have been viewed with skepticism and controversy in the policy sector, we showthat, relative to the locations of the current federal program, Dollar General stores aredisproportionately likely to be located in Census tracts with high social vulnerability;using these stores as vaccination sites would greatly decrease the distance to vaccinesfor both low-income and minority households. We consider a hypothetical alternativepartnership with Dollar Tree and show that adding these stores to the vaccinationprogram would be similarly valuable, but impact different geographic areas than theDollar General partnership. Adding Dollar General to the current pharmacy partnersgreatly surpasses the goal set by the Biden administration of having 90% of the popu-lation within 5 miles of a vaccine site. We discuss the potential benefits of leveragingthese partnerships for other vaccinations, including against influenza.
Which and how many attributes are relevant for the sorting of agents in a matching market? This paper addresses these questions by constructing indices of mutual attractiveness that aggregate information about agents attributes. The first k indices for agents on each side of the market provide the best approximation of the matching surplus by a k-dimensional model. The methodology is applied on a unique Dutch households survey containing information about education, height, BMI, health, attitude toward risk and personality traits of spouses.
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