The Unassuming Economist

Source: World Bank Working Paper (2022)

“This paper develops a methodology to identify countries that are at risk of debt default based on four elements of debt vulnerability. These elements capture the different ways in which risks associated with high debt are assessed, namely: (i) the fundamental, (ii) the subjective, (iii) the judgmental, and (iv) the theoretical. The fundamental element considers the liquidity, solvency, and institutional risk elements of debt vulnerability. The subjective element captures the investors’ perceptions of debt default, while the judgmental element is based on the debt thresholds as defined by Debt Sustainability Frameworks. Finally, the theoretical element is normative and captures what ought to be. The methodology constructs an index for each of these four elements and uses them as predictors in a model of public debt default. The methodology flags countries that are at risk of default by means of machine learning techniques and delivers outputs that point to underlying causes of vulnerability.”

Also Read:

Keeping Public Debt Sustainable in an Equitable Way

From a new report by Steven A. Altman and Caroline R. Bastian:

“The DHL Global Connectedness Index measures globalization based on international flows of trade, capital, information, and people. This update highlights key developments in these four areas for the world as a whole, with a focus on the Covid-19 crisis. Overall, globalization is emerging from the pandemic far stronger than many expected. The DHL Global Connectedness Index declined modestly in 2020, and there is clear evidence of a recovery underway in 2021. Nonetheless, the pandemic has also highlighted vulnerabilities that should be addressed in order to fortify and expand the benefits of global connectedness.”

From a new paper by Ning Jia, Raven Molloy, Christopher Smith, and Abigail Wozniak:

“Internal migration patterns in the US have drawn growing attention among researchers, policy analysts, and others. This interest has been driven by two trends. First, internal migration in the US has fallen for more than three decades (Molloy et al. 2011; Frey 2009; Cooke 2011, 2013). This decline raises questions about whether it stems from desirable factors, like improved location or job matching, or undesirable factors, like employer monopsony power or other barriers to job mobility (Kaplan and Schulhofer-Wohl 2017; Molloy et al. 2016). Relatedly, highly educated Americans have become increasingly concentrated in larger cities (Diamond 2016). Thus, both the level of migration in the US and the types of destinations chosen by different types of people have changed in important ways over the last several decades.

(…)

Dao et al. (2017) revisit the key ideas from BK and show more directly that the nature of local labor market adjustment to demand shocks has changed in the last few decades—and that the diminished responsiveness of net migration is a key reason for the change in how local labor markets adjust. The authors take a similar approach to BK by estimating adjustment margins at the state level’s response to demand shocks. However, they extend the BK sample with an additional 20 years of data and make other methodological innovations, including using administrative data on migration flows instead of inferring population adjustment from CPS-based measures. Among the many useful contributions of this analysis is a demonstration that after 1990, the net migration response to a state-level demand shock has been smaller on average than in earlier periods, and the response of the unemployment and labor force participation rates is larger. Hence, one way to reconcile the BK findings with the more recent conflicting evidence on local labor market adjustment and regional divergence is that migration was more important as an equilibrating mechanism from the 1970s through the early 1990s (the period in the BK sample) and has recently become less important.”

From a VoxEU post by Yoto Yotov:

“This year marks the 60th anniversary of the workhorse model of trade – the gravity equation. This column celebrates the anniversary by addressing some misconceptions about gravity and by tracing its evolution from an intuitive a-theoretical application to an estimating computable general equilibrium model that can be nested in more complex frameworks.

This year marks the 60th anniversary of the workhorse model of trade – the gravity equation (Tinbergen1962). Gravity is a ‘celebrity’ among economic models; it has been applied and extended in thousands of papers by trade economists, colleagues from other fields, and policy practitioners. Moreover, as noted by the brilliant late Peter Neary, the gravity equation is probably the only econometric model that has been featured on the front page of the Financial Times (on 19 April 2016). 

Unfortunately, and as sometimes happens to celebrities, the gravity model is misspecified (misunderstood) by the press. More worrisome, we often see gravity applications in academic papers and policy reports that are not consistent with theory and/or do not take into account major developments in the empirical gravity literature. As a result, the estimates in such papers could be severely biased and their policy recommendations could be misleading. Moreover, while it is well understood that trade theory and trade-policy analysis should be set in general equilibrium (GE), there is still a division and scepticism among academics and trade-policy practitioners about the usefulness of the gravity as a Computable GE (CGE) framework for counterfactual projections. A prominent example, which motivated the inclusion of the gravity equation in the Financial Times, is the debate among UK economists over gravity-based projections of the Brexit effects.

To celebrate gravity’s anniversary and address some misconceptions about the gravity model, in a new paper (Yotov 2022) I trace its evolution, as depicted in Figure 1, from a naive application to an ‘estimating CGE’ (E-CGE) model that can be nested in more complex frameworks.”

Continue reading here.

From a NBER paper by Efraim Benmelech and Michal Zator:

“Automation technologies, and robots in particular, are thought to be massively displacing workers and transforming the future of work. We study firm investment in automation using cross-country data on robotization as well as administrative data from Germany with information on firm-level automation decisions. Our findings suggest that the impact of robots on firms has been limited. First, investment in robots is small and highly concentrated in a few industries, accounting for less than 0.30% of aggregate expenditures on equipment. Second, recent increases in robotization do not resemble the explosive growth observed for IT technologies in the past, and are driven mostly by catching-up of developing countries. Third, robot adoption by firms endogenously responds to labor scarcity, alleviating potential displacement of existing workers. Fourth, firms that invest in robots increase employment, while total employment effect in exposed industries and regions is negative, but modest in magnitude. We contrast robots with other digital technologies that are more widespread. Their importance in firms’ investment is significantly higher, and their link with labor markets, while sharing some similarities with robots, appears markedly different.”