The Unassuming Economist

From a paper by Oussama Ritahi, and Abdellah Echaoui:

“This study examines the impact of Brent oil price shocks on key economic variables—namely inflation, GDP, exchange rate, trade openness, and unemployment rate using annual data from 1990 to 2022. The results of the study show that the variables considered under this study are cointegrated in the long-run which means that there is a relationship between these variables in the long-run. By employing a vector error correction model (VECM), we analyze the impulse response functions to understand the short- and long-term effects of oil price fluctuations on these economic indicators. Our findings reveal that Brent oil price increases lead to higher inflation and depreciation of the exchange rate, with both effects persisting in the short and long run. Conversely, GDP experiences a consistent negative impact from oil price hikes, suggesting a detrimental effect on economic growth over time. Trade openness shows a positive response, indicating increased trade activity due to rising oil prices. Additionally, the unemployment rate decreases in response to higher oil prices, reflecting a potential reduction in joblessness.”

From a paper by Junyi Xiang, Dongmin Kong, and Fan Zhang:

“Labor cost has rapidly increased in the past decades. However, little is known about its effect on the firm-level robot adoption, and evidence about the consequences of robot adoption on firm production is limited. Based on a novel dataset of robot adoption at the firm-level, we use geographic discontinuity design to identify that labor costs significantly increase robot adoption and further improve product quality. Our findings are robust to alternative specifications and particularly pronounced for foreign firms, and firms with low financial constraints, and general trade, and firms more dependence on unskilled labor, and firms in higher position in the value chain. When adopting robots to substitute labor, firms tend to employ (layoff) skilled (unskilled) labors, which increases expenses on employee training.”

From a paper by Jinfang Pu, and Fangzhou Xia:

“As global climate change intensifies and urbanization accelerates, research on urban climate change has become a global concern. Urban decision-makers must determine optimal city sizes to achieve net-zero emissions. However, previous studies have mainly focused on average relationships between city size and carbon emissions, overlooking non-linear dynamics. This study used urban scaling laws to investigate relationships between city size and carbon emissions from population and land perspective across 294 Chinese cities. Results showed a sub-linear relationship between urban population size (UPS) and carbon emissions and a super-linear relationship between urban land size (ULS) and carbon emissions. Regionally, cities in central regions demonstrated higher carbon emission performance than those in western and eastern regions. The land perspective indicated lower carbon emission performance compared to the population perspective. Both perspectives revealed non-linear relationships between city size and urban scaling exponent for carbon emissions, characterized by multiple minima. Multiple city sizes can achieve optimal carbon emissions; however, only one ULS is ideal for a specific city size to ensure environmental sustainability. This study provides valuable policy insights for decision-makers in formulating reasonable low-carbon strategies.”