The Unassuming Economist

New VoxEU.org post by Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini, Paulo Santos Monteiro published on 16 February 2022.

“For policymakers and healthcare providers, prediction of the evolution of an epidemic is extremely important (Manski 2020, Castle et al. 2020). Timely and reliable projections are required to assist health authorities, and the community in general, in coping with an infection surge and to inform public health interventions such as enforcing (or facilitating) local or national lockdowns (Heap et al. 2020). Weekly forecasts of the evolution of the COVID-19 pandemic generated by various independent institutions and research teams have been collected by the Centers for Disease Control and Prevention (CDC) in the US.¹ These forecasts are intended to inform the decision-making process for public health interventions by predicting the impact of the COVID-19 pandemic for up to four weeks. However, this wealth of forecasts also poses a problem: how to act when confronted with heterogeneous forecasts and, in particular, how to select the most reliable projections.

Forecasting teams, methods, and assumptions

The forecasting teams include data scientists, epidemiologists, and statisticians. They use different methodologies and approaches (e.g. the susceptible-exposed-infectious-recovered (SEIR), Bayesian, and deep learning models) and combine a range of data sources and assumptions concerning the impact of non-pharmaceutical interventions on the spread of the epidemic (such as social distancing and the use of face coverings). In Table 1, we report the eight teams that continuously submitted their predictions since the start of the pandemic, for the period 20 June 2020 to 20 March 2021. “

Read more here.

Source: VoxEU CEPR

Despite the commitments of the 2021 UN Climate Change Conference, countries’ climate mitigation policies are not enough to meet their ambitious emissions reduction targets. This column puts forward a framework for designing comprehensive decarbonisation strategies that promote growth and social inclusion. A policy mix based on three components is needed: (1) emission pricing, (2) standards and regulations, and (3) complementary policies that offset distributional effects. A robust and independent institutional framework and credible communications campaigns are key to managing policy constraints and enhancing public acceptance of mitigation policies.

Source: NBER Working Paper (2022)

Researchers Sergey Chernenko and David S. Scharfstein write about the significant racial disparities in borrowing through the Paycheck Protection Program (PPP) using data gathered from a large sample of restaurants in Florida and then investigate the causes of these disparities. They find that- “Black-owned restaurants are 25% less likely to receive PPP loans. Restaurant location explains 5 percentage points of this differential. Restaurant characteristics explain an additional 10 percentage points of the gap in PPP borrowing. On average, prior borrowing relationships do not explain disparities. The remaining 10% disparity is driven by a 17% disparity in PPP borrowing from banks, which is partially offset by greater borrowing from nonbanks, largely fintechs. Disparities in PPP borrowing cannot be attributed to lower awareness of PPP loans or lower demand for PPP loans by minority-owned restaurants. Black-owned restaurants are significantly less likely to receive bank PPP loans in counties with more racial bias. In these counties, Black-owned restaurants are more likely to substitute to nonbank PPP loans. This substitution, however, is not strong enough to eliminate racial disparities in PPP borrowing”.

From European Systemic Risk Board:

“In this report, the ESRB presents its medium-term assessment of vulnerabilities relating to the RRE sector across the EEA countries. In carrying out this assessment, the ESRB first performed an analysis of vulnerabilities across the EEA countries. For the 24 countries for which the vulnerabilities identified were more pronounced, an in-depth analysis was conducted. This analysis pointed also to the need to take into account or change other than macroprudential policies, for example by changing tax incentives or increasing the housing supply. A similar assessment was conducted by the ESRB in 2019, when 11 countries received either ESRB recommendations (Belgium, Denmark, Finland, Luxembourg, Netherlands and Sweden) or warnings (Czech Republic, Germany, France, Iceland and Norway).

The risk assessment concluded that, in five countries which received ESRB recommendations or warnings in 2019 (Denmark, Luxembourg, Netherlands, Norway and Sweden) the vulnerabilities relating to residential real estate markets remained high, while in six countries (Belgium, Czech Republic, Germany, Finland, France and Iceland) the vulnerabilities were assessed as medium. Among other EEA countries, 13 (Austria, Bulgaria, Estonia, Croatia, Hungary, Ireland, Liechtenstein, Lithuania, Malta, Poland, Portugal, Slovenia and Slovakia) were identified as facing medium risks.

The policy assessment found that in five countries which received ESRB recommendations or warnings in 2019 (Belgium, Czech Republic, France, Iceland and Norway), policies were assessed as appropriate and sufficient to mitigate the vulnerabilities identified. In two countries (the Netherlands and Sweden), policies were assessed as being appropriate but partially sufficient, while in four of the countries (Germany, Denmark, Finland and Luxembourg), policies were assessed as partially appropriate and partially sufficient. Among the rest of the EEA countries analysed in this report, in one country (Slovakia) policies were identified as appropriate and partially sufficient, while in five countries (Austria, Bulgaria, Hungary, Croatia and Liechtenstein) policies were found to be partially appropriate and partially sufficient.

In countries in which the policies were assessed as only partially sufficient to mitigate the identified vulnerabilities, the ESRB suggested various macroprudential measures to be considered by the national authorities. In particular, the ESRB pointed out that a number of countries should either introduce additional borrower-based measures or tighten existing ones to mitigate the existing vulnerabilities more effectively or prevent a further build-up of vulnerabilities. Countries with accumulated vulnerabilities should also ensure that capital is preserved until a possible materialisation of risks or consider (re)introducing capital-based measures once the economic recovery is on a firm footing. However, taking into account the economic uncertainty related to the pandemic, any policy actions should be carefully assessed to ensure that they contribute towards mitigating RRE vulnerabilities, while aiming to avoid procyclical effects on the real economy and the financial system. In the near term, it is particularly important for all countries that banks make adequate provision for expected losses. Finally, the analysis notes that, in some countries in which the systemic risk levels identified remain high, interventions in other policy areas may be required to complement macroprudential policy.”

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.”

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