The Unassuming Economist

From a paper by Joe Piacentini, Harley Frazis, Peter B. Meyer, Michael Schultz, and Leo Sveikauskas:

“This paper surveys economic literature largely from 2020 and 2021 on how the COVID-19 pandemic and responses to it affect U.S. income inequality. Established trends of growing inequality may continue roughly as before, involving new technologies, international trade, and the growth of “superstar” firms. Employment, earnings, and schooling were affected differently across demographic groups and occupations. The pandemic disrupted lower-paid, service sector employment most, disadvantaging women and lower income groups at least temporarily, and this may have scarring effects. Government policies implemented in response to the pandemic offset much of the effect on income. Higher-paid workers tend to gain more from continuing opportunities to telework. Less-advantaged students suffered greater educational setbacks from school closures. School and day care closures disrupted the work of many parents, particularly mothers. We conclude that the pandemic is likely to widen income inequality over the long run, because the lasting changes in work patterns, consumer demand, and production will benefit higher income groups and erode opportunities for some less advantaged groups. Telework has increased permanently. High-contact jobs and services may continue to face reduced demand and increased automation. School disruptions have been worse for lower-income students and are likely to have lingering negative effects, which may widen future inequality within more recent birth cohorts. The history of the 1918 flu shows that the effect of a pandemic on inequality in income, education, health, and wealth depends on the nature of the pandemic and on behavioral and policy responses.”

From a paper by Andrea Foschi, Christopher L. House, Christian Proebsting, and Linda L. Tesar:

“We examine the responsiveness of labor participation, unemployment and labor migration to
exogenous variations in labor demand. Our empirical approach considers four instruments
for regional labor demand commonly used in the literature. Empirically, we find that labor
migration is a significant margin of adjustment for all our instruments. Following an increase
in regional labor demand, the initial increase in employment is accounted for mainly through
a reduction in unemployment. Over time however, net labor in-migration becomes the dominant
factor contributing to increased regional employment. After 5 years, roughly 60 percent
of the increase in employment is explained by the change in population. Responses of labor
migration are strongest for individuals aged 20-35. Based on historical data back to the
1950s, we find no evidence of a decline in the elasticity of migration to changes in employment.”

From a paper by Xuecheng Fan, Zeshui Xu, Marinko Skare & Xinxin Wang:

“This study examines the impact of the COVID-19 pandemic on unemployment dynamics, focusing on youth unemployment and gender-specific disparities across 20 European countries from 2013 to 2022. Using a panel structural vector autoregressive (PSVAR) model, the study analyzes the effects of the pandemic on total unemployment, youth unemployment (ages 15–24), and gender differences in unemployment rates. The results reveal that youth unemployment was disproportionately affected by the COVID-19 shock, with significant increases observed in countries with high pre-pandemic unemployment rates, such as Greece and Spain. Additionally, gender inequality in unemployment rates was exacerbated, particularly in countries like Greece, where women consistently faced higher unemployment rates than men throughout the observed period. The study also highlights the significant role of excess mortality during the pandemic, with countries experiencing higher COVID-19 mortality rates, such as Italy and Spain, also showing higher unemployment rates. These findings contribute to the understanding of how economic shocks, such as the COVID-19 pandemic, exacerbate the existing labor market inequalities, particularly for vulnerable demographic groups such as youth and women. The study calls for targeted policy interventions to address these disparities and promote a more inclusive recovery.”

From a paper by Luca Agnello & Pietro Pizzuto:

“In this paper we investigate the role of EU Structural and Investment Funds in affecting the dynamic impact of regional fiscal consolidation on regional income inequality. Relying on a panel of 162 NUTS-2 regions of twelve European countries, we find that regional spending cuts increase regional inequality in the medium-term, with the effects surviving to a large battery of robustness checks. The uneven distributional impact of regional austerity measures is however cushioned by larger EU funds expenditures, especially through the European Regional Development Fund (ERDF), with the effect magnified during periods of recession and when the regional quality of government is higher.”

From a paper by Ge Zhai, Maoxin Zhang, Yihua Hu, Yuwei Chen, Cifang Wu, Youpeng Lu, Tingting He:

“Rapid urbanization in China has driven significant economic growth, but the urban-rural economic disparity (URED) continues to widen. This growing disparity impacts domestic productivity, consumer demand, rural stability, and the achievement of common prosperity. To address this issue and promote sustainable development, understanding the spatiotemporal patterns and drivers of URED is essential. This study employs nighttime lighting (NTL) data to analyse URED changes in 36 Chinese metropolitans at three scales. The study results show that: (1) From 2000 and 2022, both the economic volume and area of the study regions grew more than fivefold, but the economic gap between urban and rural areas significantly widened. (2) Economic growth in Predominantly Urban regions has escaped area constraints, while other regions still rely on spatial expansion. (3) URED has shown a steady increase, with higher values in the southeast and lower values in the northwest. (4) Differences in policy implementation and geographic location strongly influence URED. The study confirms the effectiveness of NTL data in capturing urban-rural economic dynamics and the reliability of spatial entropy in measuring URED. Future research will further explore NTL data patterns and integrate additional urban attributes to provide deeper insights into URED.”