The Unassuming Economist

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 Pilu Chandra Das, and Deb Kusum Das:

“Services have been the driver of India’s overall growth since the onset of economic reforms in India and particularly beginning the 2000s. However, India’s manufacturing sector continues to draw attention despite several decades of reforms covering industrial policies and trade liberalization. The government through its several initiatives—National Manufacturing Policy as well as ‘Make in India’ program—continues to drive the sectors role in the overall growth and development. The sector is targeted to contribute around 25% of GDP by 2025 as against its current 16% share. In the recent past, Indian manufacturing has attained a sharp rise in growth and this augurs well for a sector that has seen stagnancy in its share of GDP in the last several decades. The lack of jobs in organized manufacturing has so far failed India’s industrial objectives and add to that is the large number of people employed in informal manufacturing activities as well has remained a perennial challenge to development needs. The productivity performance of manufacturing industries has been well documented and continues to exhibit low productivity growth. A recent study by Das et al. (The World Economy: Growth or Stagnation? Cambridge University Press, Cambridge, pp. 199–233, 2016) however finds labour-intensive manufacturing outperforming non-labour-intensive goods during the period 2000–15 and this is important when we have evidence of declining labour intensity even in labour-intensive manufacturing (Sen and Das in Economic and Political Weekly 50(23):108–115, 2015). Several challenges remain if productivity is to be improved. Most critics would point to the labour market rigidities for the inefficiency in the manufacturing sector, but there remains several issues beyond simple labour market reforms that need to be addressed—particularly those related to skill formation and its impact of labour quality. The present study would cover the manufacturing industries for the period 2000–2015 in an attempt to understand the productivity dynamics in manufacturing sector and its relation to employment. Using a neoclassical growth accounting technique and the India KLEMS dataset, we would examine the manufacturing performance both at the aggregate-level as well as 13 disaggregated industries and present an industry-level perspective on manufacturing performance. The period of study would also take into account the several phases of the Indian economy including pre-global slowdown, slowdown and recovery phase. The study would address some of the possible determinants of manufacturing performance which need attention if the stagnancy of manufacturing share in overall GDP is to be reversed.”

From a paper by Ernest Alang Wung, Joslanie Douanla Tameko, and Muhamadu Awal Kindzeka Wirajing:

“This study investigates the effect of external dependency on structural change in 54 African countries between 1990 and 2021. The Two-Step System Generalized Method of Moments strategy is adopted to control for potential endogeneity problems. Findings reveal that structural change in Africa is strongly impaired by the level of external dependency. This is since all proxies of external dependency are negatively and statistically significant with all structural change proxies. For instance, under agricultural productivity, external debts stocks (EDS) give an eigen value (β) of 0.879, standard coefficient (SC) = 0.162, and p = 0.000; for external debt services (DSED), β = 0.240, SC = −0.040, and p = 0.972; and for personal remittances received (PRR), we have β = 0.764, SC = −0.133, and p = 0.031. Depicting that, the more African countries rely on the external world for change, the less they realize this change. The results remain consistent after accounting for income differences by segmenting African countries into low- and middle-income groups. As suggestions to policymakers, for structural change to concretely take place in Africa, the rate of external dependence should be limited, and resources in Africa and local methods of growth should be used rather than copying from the Western world. Though the results are valid across income groups and Africa, the case of countries could be more significant.”

From a paper by Emiliano Brancaccio, Fabiana De Cristofaro, and Raffaele Giammetti:

“The so-called ‘IMF-OECD consensus’ suggests that labour market deregulations increase employment and reduce unemployment. This paper presents a meta-analysis of research on this topic based on MAER-NET guidelines. We examine the relation between Employment Protection Legislation indexes on one hand, and employment and unemployment on the other. Among 53 academic papers published between 1990 and 2019, only 28 per cent support the consensus view, while the remaining 72 per cent report results that are ambiguous (21 per cent) or contrary to the consensus (51 per cent). The decline in support for the consensus view is particularly evident in the last decade. Our results are independent of the citations of papers examined, the impact factor of journals and the techniques used. A FAT-PET meta-regression model confirms these outcomes.”

From a paper by Nora Lustig and Andrea Vigorito:

“Inequality measures based on household surveys may be biased because they typically fail to capture incomes of the wealthy properly. The “missing rich” problem stems from several factors, including sampling errors, item and unit nonresponse, underreporting of income, and data preprocessing techniques like top coding. This paper presents and compares prominent correction approaches to address issues concerning the upper tail of the income distribution in household surveys. Correction approaches are classified based on the data source, distinguishing between those that rely solely on within-survey information and those that combine household survey data with external sources. We categorize the correction methods into three types: replacing, reweighting, and combining reweighting and replacing. We identify twenty-two different approaches that have been applied in practice. We show that both levels and trends can be quite sensitive to the approach and provide broad guidelines on choosing a suitable correction approach.”