The Unassuming Economist

From a paper by Boyang Lia, Runze Chena, and Yuqin Dua:

“The power generation mix in China heavily relies on fossil energy sources, impeding
the advancement of clean power generation and emission reduction efforts. This paper
presents a macroeconomic model incorporating Emissions Trading Systems (ETS),
clean energy subsidies, and intertemporal learning behavior. It examines how carbon
pricing and clean subsidy policies influence the power generation sector and emission
reduction goals. The findings indicate that (1) pricing strategies based on total
emissions effectively drive emission reductions but may not adequately incentivize
cleaner energy transitions. (2) Increasing clean energy subsidies encourages a shift
towards cleaner technologies, although the impact on emission reductions is moderate.
(3) Combining both policies proves to be more effective than implementing either one
alone. (4) There exists a gap in understanding the clean power generation industry, with
both policies contributing to knowledge accumulation in this sector. The insights from
this study are valuable for countries employing ETS mechanisms.”

From a paper by Christos A. Makridis  and Saurabh Mishra:

“The share of artificial intelligence (AI) jobs in total job postings has increased from 0.20% to nearly 1% between 2010 and 2019, but there is significant heterogeneity across cities in the United States (US). Using new data on AI job postings across 343 US cities, combined with data on subjective well-being and economic activity, we uncover the central role that service-based cities play to translate the benefits of AI job growth to subjective well-being. We find that cities with higher growth in AI job postings witnessed higher economic growth. The relationship between AI job growth and economic growth is driven by cities that had a higher concentration of modern (or professional) services. AI job growth also leads to an increase in the state of well-being. The transmission channel of AI job growth to increased subjective well-being is explained by the positive relationship between AI jobs and economic growth. These results are consistent with models of structural transformation where technological change leads to improvements in well-being through improvements in economic activity. Our results suggest that AI-driven economic growth, while still in the early days, could also raise overall well-being and social welfare, especially when the pre-existing industrial structure had a higher concentration of modern (or professional) services.”

From a paper by Muhammad Ibrahim Shah, Matteo Foglia, Umer Shahzad, and Zeeshan Fareed:

“This paper investigates how oil price, COVID-19, and global energy innovation can affect carbon emissions under time- and frequency-varying perspectives. We contribute to the literature by being the first research to document the relationship between these variables in the short and long run (dynamically) at different frequencies in a multivariate context, thus providing a more detailed picture of the forces driving CO₂ emissions. For this purpose, we use a novel methodology, i.e., the wavelet local multiple correlation (WLMC) recently developed by Polanco-Martínez et al. (2020). The results provide fresh evidence of long-run asymmetric dynamic correlations, highlighting how the oil price plays a key role in the dynamics of CO₂ emissions. Moreover, we find that, during the long period, there is a strong negative co-movement between CO₂ and the global energy innovation index, i.e., more investment in clean energy induces less emission. Supported by our findings, this research suggests crucial policy implications and insights for the governments worldwide in their efforts to revive their economies amidst the pandemic and environmental uncertainties.”

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 Roy Cerqueti, Raffaele Mattera, and Saverio Storani:

“This paper develops a class of complex network-based models whose interconnected nodes are commodities. We assume that the considered commodities are linked on the ground of the similarities of risk profiles and correlations of their returns. In this framework, we explore the resilience of the networks — i.e., their ability to absorb exogenous microscopic shocks. To this aim, we assume that high levels of resilience are associated with small variations of the community structure of the network when an exogenous shock occurs — hence, assuming that the stability of the networked commodities is measured through the maintenance of their connection levels. Shocks are conceptualized as impulsive modifications of the links among the considered commodities. The employed methodological instrument is the clustering coefficient, which is a nodal centrality measure describing the way the adjacent of the nodes are mutually connected. The theoretical proposal is empirically tested over a large set of commodities of different nature.”