This paper studies the impact of an increase in higher education tuition on intergenerational mobility in China. We develop a theoretical model for the parental decision about the investment on education of children to illustrate the impact from the perspective of borrowing constraint. We consider the Chinese college tuition and subsidy reform around 1986 as a quasi-natural experiment for identifying the policy effect of the reform on intergenerational educational mobility by using the data from the census of 2000 and the China Family Panel Studies (CFPS). We find that an increase in the education burden induced by the reform of college tuition has reduced intergenerational educational mobility, and it is more noticeable in regions with a relatively higher increment in the tuition fee. Our results are robust with consideration of the co-residence bias, government investment in elementary education, and the higher education expansion.
Through an examination of the case of the iPhone X, this paper demonstrates that Chinese companies involved in the production of the iPhone X have moved up along the global value chain. According to the bill of materials, those companies contributed 25.4% of the value added of the iPhone X. About 45% of the value added of the iPhone X originated in Japan, South Korea, and other economies. The iPhone trade remains a significant element of the statistical distortion of the China–US bilateral trade imbalance. In terms of gross value, the import of one iPhone X results in a USD332.75 trade deficit for the US; measured in terms of the value added, however the deficit is a mere USD104. The depreciation of the Chinese yuan (CNY) has very limited power to counterbalance the tariffs imposed by the Trump administration because the foreign value added embedded in Chinese exports is 33.9% on average. Simulation results show that to counterbalance a 25% tariff, the CNY would have to depreciate by 43.3% against the US dollar on average, and to fully compensate for a 25% tariff burden on the iPhone X, a 400% depreciation of the CNY would be necessary. Hedging the risk of the punitive U.S. tariffs by depreciating the CNY is impossible.
We study the impact of the COVID-19 pandemic shock on household consumption in China. Using household survey data, we find that the proportion of liquidity-constrained households increases quickly, but the constraint levels vary across distinct groups. We build a heterogeneous agent life cycle incomplete market model to analyze the long-run and short-run effects of the pandemic shock. The quantitative results reveal a slow recovery of consumption due to three reasons: hiking unemployment rate, declining labor productivity, and worsening income stability. The hiking unemployment rate plays the key role in households’ consumption reduction since it simultaneously leads to a negative income effect and upsurging precautionary saving motives. Our paper highlights the importance of maintaining a stable labor market for faster recovery.
The global COVID-19 pandemic caused various economic contraction in most countries, including all of China’s major trading partners. Using a difference-in-differences model, this study examines the impact of the COVID-19 pandemic on China’s monthly exports from January 2019 to May 2020. We find strong and robust evidence that China’s exports to countries at high risk from the pandemic experienced a larger decline than exports to low-risk countries after the onset of the pandemic, with the prices of exports increasing significantly. Furthermore, the results of a triple differences model show heterogeneous effects across different industries and goods. Chinese industries located upstream in the global value chain are more vulnerable than those located downstream. Industries with high labor and contract intensity (proxies for processing trade) experienced greater declines than other industries. Exports of goods with high import elasticity of substitution experienced higher prices and moderate volume losses due to the pandemic.
Using a newly built soft power index, we examine whether and how soft power affects Chinese firm-level export to the Belt and Road (B&R) countries from 2000 to 2016. We find that soft power has significantly positive effects on both export value and export product types for the B&R countries. These effects are more pronounced than those for non-B&R countries and differ not only between the "Belt" and the "Road" countries but also regional groups, firm ownerships, modes of trade, and sectors. Further analysis shows that soft power increases the intensive margin of exports by approximately three times that of the extensive margin. Thus, our findings provide a new perspective for understanding both the Belt and Road Initiative (BRI) and the contemporary economic evolution occurring in China.
Given the enormous impact that the COVID-19 pandemic had on China’s economy, helping companies to revitalize post-pandemic economic activities promptly is a priority for the whole society. This necessitates the smooth circulation of production-factors among different economic entities, departments, and regions. The pandemic’s huge impact on the economy is evident in the severely hampered flow of these factors, including labor, materials, and capital. Therefore, using data and digital technology, combined with a contact-free allocation of labor, capital, and materials, to accelerate the flow of production-factors is critical to the post-pandemic economy’s restoration. Such a policy can not only provide a short-term stimulus but also a momentum for China’s mid- and long-term sustainable economic development.
This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers’ fear of infection.
This study investigates the role of job characteristics on an individual’s decisions to follow social distancing policies, work, and apply for unemployment insurance in the United States during the COVID-19 pandemic. We use data that track millions of mobile devices and their daily movements across physical locations to measure whether the devices’ owners leave their homes, or work part-time or fulltime on a given day, and we also collect data on weekly unemployment insurance claims. We find that the presence of jobs with a high work-from-home capacity in a region increases the ability of people to follow social distancing policies and decreases their unemployment risk, whereas the presence of jobs with high physical proximity decreases the incidences of following social distancing policies and unemployment and increases the incidence of work during the pandemic. These heterogeneous responses based on local job characteristics persist even conditional on a broad set of demographic and socioeconomic variables.
Individuals’ risk attitudes play an important role in economic decision making and policy evaluation, particularly in the midst of unprecedented uncertainty caused by the COVID-19 pandemic. We adopt a multiple-price-list elicitation method with real money incentives to measure precisely individuals’ risk attitudes at different stake levels and the extent to which they are affected by personal and social shocks following the COVID-19 outbreak in China. We find that subjects who had previously experienced negative personal shocks are more risk-averse at medium and large stakes but more risk loving at very small stakes. For our sample, COVID-19 has no significant impact on risk attitudes, as it is more likely to be regarded as a social shock. The result indicates that the impact of COVID-19 on individual risk attitudes is not as influential as expected, unless the individual’s personal life is affected directly.
In this paper, following Blanchard and Fischer (1989), I investigate how the presence of the COVID-19 pandemic—the increase in the probability of death—may affect growth and welfare in a scale-invariant R&D-based Schumpeterian model. Without money, the increase in the probability of death has no effect on long-run growth and a negative effect on welfare. By contrast, when money is introduced via the cash-in-advance (CIA) constraint on consumption, the increase in the probability of death decreases long-run growth and welfare under elastic labor supply. Calibration shows that the quantitative effect of an increase in the probability of death on welfare is much larger compared to that on growth.
This study analyzes the relationship between the age of first migration and the probability of repeat migration focusing on rural to urban migrants in China. It is based on the data of the 2015 Migrant Dynamics Monitoring Survey (MDMS). The data shows that 52.64% of migrants had experienced repeat migration before 2015, the amount of which is huge. The empirical results indicate an inverted U-shaped connection between age of first migration and the probability of repeat migration. The probability of making repeat migration from rural to urban areas reaches its peak if an individual experienced his/her first migration at around 16 years old. The probability for repeat migration continues to increase before the age of 16 and keeps dropping afterward. Additionally, this study explores the reason for this inverse U-shaped relationship, and it finds that reasons for first migration have great impacts. Specifically, the probability of repeat migration goes up with age if an individual first migrates before age 16 and is accompanied by parents. This probability decreases with age, if an individual first migrates after or at age 16, because of work.