The Unassuming Economist

From The Tribune:

“New Delhi [India], January 19 (ANI): Union Minister Ashwini Vaishnaw on Sunday shared insights on his participation at World Economic Forum (WEF) 2025.

The Union Minister highlighted that there will be a detailed discussion on issues ranging from inclusive growth, invetments in social phisical and digital infrastructure to democratising technology.

He pointed out the global interest in India’s approach to economic policy, digital transformation, and the Digital India programme.

He stated, “The Prime Minister Narendra Modi ji has put huge focus on inclusive development, and a growth which brings a big transformative change in the lives of the people at the bottom of the pyramid. People who have been left out of development over many decades in the past, whether it is bank accounts, whether it is providing toilets, gas connections, whether it is having tap water connections, getting the basic infrastructure in the or villages done. Infrastructure in the urban areas done. This is something which the world wants to understand.”

“In the World Economic Forum at Davos, there is a lot of interest in understanding our thought process, PM’s economic policy about digital transformation and also the way technology has been democratised by our PM’s Digital India program… There will be detailed discussion in the World Economic Forum about inclusive growth, investment in social, physical and digital infrastructure and democratizing technology,” he added.”

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From a paper by Elena Bárcena-Martín, Natalia Martín-Fuentes, and Salvador Pérez-Moreno:

“This work provides evidence of the heterogeneous effects of the ECB’s monetary policy across income classes. In particular, this investigation focuses on the labor market channel. Based on EU-SILC data, we estimate country-specific structural vector autoregressions (SVAR) models to analyze the impact of the expansionary monetary policy shocks over the 2006–2019 period. The results suggest that monetary easing helped decrease unemployment rates for lower- and middle-income classes, to a larger extent for the former. This differential impact is accounted for a stronger improvement in job finding rates for classes located at the bottom of the income distribution. Conversely, the employment status of the upper class remained largely unaffected. The analysis identifies a positive impact of expansionary monetary policy on real labor income, which seems to have mostly benefitted the upper class. Overall, our results suggest that expansionary monetary policy helped decrease labor income inequality by exerting a stronger positive impact on lower-income households.”

From a paper by Silas Xuereb, Matthew Fisher-Post, François Delorme, and Camille Lajoie:

“In this article, we estimate the distribution of all net national income in Canada from 1982 to 2021. We apply distributional national accounts (DINA) methodology to tabulated data from the Longitudinal Administrative Databank, combined with national accounts and survey data. Our descriptive results contribute to a more thorough understanding of income inequality in Canada over the past 40 years. We find that top income shares published by Statistics Canada tend to be underestimated relative to top income shares calculated using DINA, because DINA account for people who do not file taxes and for undistributed capital income that is retained in corporations. In line with previous research, income inequality in Canada increased significantly from 1982 until the mid-2000s. Although labour income drove initial growth in top shares, toward the end of this period capital income contributed most to growth in top shares. Top shares based on tax data were especially underestimated during this period because retained earnings were at their highest. Since the mid-2000s, top shares have decreased slightly and the income share of the bottom 50 percent has increased, although they have not returned to the levels observed in the early 1980s. During the pandemic, post-tax income inequality fell because of the large temporary transfer programs that were introduced. However, pre-tax income inequality increased in 2020, and even more so in 2021 when record levels of corporate profits were reached.”

From a paper by Muhammad Waqas Khan, Mehmet Akif Destek, and Zeeshan Khan:

“In the recent times, the role of artificial intelligence in social, economic, and environmental decision-making is important. Artificial intelligence is considered a source of enabling countries to achieve sustainable development goals. The economic consequences of the introduction of artificial intelligence are mostly overlooked and yet to be explore empirically. This work aims to empirically determine the impact of artificial intelligence on income inequality in the pioneers of the field, i.e., the G7 economies. Also, it aims to explore the role of fiscal intervention in mediating the impact of artificial intelligence on income inequality in these economies. The panel data techniques such as the test for cross sectional dependence and the test for slope heterogeneity are used. Furthermore, CIPS is used to determine the level of integration of the variables in the model. Westerlund test for cointegration and granger causality test by Dumitrescu and Hurlin (2012) are also used in the study. Furthermore, CSARDL technique is used to find out the impact of artificial intelligence along with control variables on income inequality. The results show that artificial intelligence reduces income inequality in the G7 both in the short and the long run. The absolute value of the long-term coefficients is larger than those in the short run. Based on the empirical findings of the work, it is recommended that appropriate fiscal interventions are needed in the short run to sustain the income inequality reduction impact of artificial intelligence. However, in the long run such interventions can be counter-productive but the requisite skills to optimally utilize artificial intelligence should be imparted to individuals.”

From a paper by Monika Junicke, Jakub Mateju, Haroon Mumtaz, and Angeliki Theophilopoulou:

“This paper uses administrative labour market data from Czechia to investigate the heterogeneous effects of technology shocks. Using a FAVAR, the shock is identified using medium run restrictions `a la Uhlig (2004b). Workers on low wages reduce their hours in response to the shock, while the shock has a positive effect on hours for workers with wages at and above the median. Analysis of industrial and demographic groups indicates that the latter group is likely to consist of males, to be educated or to work in services.”