The Unassuming Economist

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.”

From The News:

“The distinction between market-friendly and business-friendly economic policies is critical in shaping economic growth and inclusiveness. Our government must recognise this difference to ensure that policies benefit not just a select group of businesses but society at large.

Planners must understand the nuances between these policy approaches. Market-friendly policies focus on creating competitive markets with minimal government intervention, prioritising efficiency and resource allocation driven by market forces. However, this approach carries risks of concentrating benefits among established players, potentially fostering monopolies or oligopolies.

In contrast, business-friendly policies aim to support businesses of all sizes, including small and medium enterprises (SMEs) and startups. These policies encourage entrepreneurship and innovation while fostering a level playing field through regulations and incentives. The ultimate goal is broad-based economic growth that benefits all segments of society.

Pakistan’s existing economic policies pose significant challenges. These often favour large corporations or well-connected businesses, sidelining SMEs and participants in the informal sector. Regulatory inefficiencies stemming from governance flaws, inconsistent enforcement and lack of transparency create uncertainty that deters smaller businesses. Access to capital remains a critical issue for SMEs. High credit costs and limited financial access hinder inclusive growth. Furthermore, weak infrastructure, including inadequate transportation, energy and digital access, disproportionately affects smaller enterprises.”

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