The Unassuming Economist

From a chapter by Salil Seth, Mrinal Kanti Mahato, and Parveen Yadav:

“Confronted with proliferating geopolitical risks emanating from energy producers, the energy security of the European Union (EU) is surrounded by plethora of challenges. Amid high volatility in energy prices, political disruptions and energy supply constraints, myriad hurdles are in the offing for energy security in EU. The current energy policy of the EU, which emphasizes competition, sustainability, and a secure supply chain, requires revitalization. A roadmap should be established to develop energy security strategies. This work performs systematic data mining for drawing literature in line with the mentioned road map-based objectives.The intention of the work is to facilitate EU with conceptualized framework strategies pertaining to energy security. This is anticipated to act as a trailblazer for energy protagonists like sustainability engineers, energy policy drafters, energy supply chain intermediaries, and strategy makers and incubate the concept with reference to the EU. This will enable the EU to overcome challenges in the energy security and at the same time set a benchmark for other competing nations.”

From a paper by Panagiotis Delis, and Georgios Kontogeorgos:

“Evaluating macroeconomic forecasts for their unbiasedness and efficiency is essential for policymakers, economists, and investors. The degree to which these stakeholders incorporate expectations into their decision-making processes depends heavily on how these forecasts have been formed. Existing methodologies do not explicitly address critical dimensions, such as the variability of bias across target events and forecast horizons, the forecast errors’ heteroscedasticity, and the potential state-dependence in bias. More importantly, they encounter difficulties during high-uncertainty periods, which can lead to inaccurate inference due to the presence of outliers. Apart from generalising the unbiasedness tests, this study contributes to the literature on both strong and weak efficiency by incorporating these aspects. Finally, the proposed methods are applied to the expectations of a crucial survey of the US economy, namely, the Survey of Primary Dealers (SPD). The findings from this application indicated that interested parties should investigate unbiasedness and efficiency in an outlier-robust way, while also allowing for greater flexibility in the methods regarding the variables and periods examined.”

From a paper by Antoine Gaudin, Brendan Harnoys-Vannier, and Martin Kessler:

“In the context of the ongoing review of the Debt Sustainability Analysis (DSA) for Low-Income
Countries (LICs), this paper seeks to help shed light on IMF and World Bank macroeconomic
projections. DSAs are central to the financial architecture of developing countries. Yet, the ways the
projections are performed are rarely accessible to outside researchers.

The first contribution of this paper is to provide a newly constructed database of 605 DSAs
conducted from 2013 to 2024. It contains all the information of all published DSAs for LICs in Tables 1
(macro-economic and fiscal) and 2 (external debt dynamics), as well as the shock scenarios. It will be
updated regularly.

The second contribution of the paper is to analyze forecast errors concerning public and external
debt, as well as the main macroeconomic components. It highlights results on large optimistic biases,
with a 10 percentage point underestimation of the trajectory of the debt-to-GDP ratio on average after
5 years. Decomposing this result, it finds that:

1. Larger countries tend to be more affected by significant positive biases. Small Island
   Developing States (SIDS) and vulnerable countries tend to be more accurately forecasted. We
   interpret this finding as showing the integration of past shocks in the baseline.
   
2. It’s mostly fiscal: The main driver of forecast errors is the underestimation of primary deficits, followed by overestimated GDP growth. In particular, forecasting errors on primary deficits stem from overestimated fiscal revenues.
   
3. Mixed results post-2017 reform: While the 2017 reform introduced tools aimed at enhancing forecast realism, biases have persisted. This is evidence of some limited (non-statistically significant) improvements by reducing the optimism bias. This pleads for further disclosure of assumptions. However, given that they were rolled out in 2018, and that COVID-19 made projections difficult, we also caution against too broad interpretation of those results.
   
4. DSAs designed in the context of programs perform better on public debt, but worse on deficits: This tends to show that the IMF tends to overestimate the political feasibility of a program. We find some support for the idea that in LICs, the multipliers are still underestimated.
   
5. Influence of country-specific factors: The study identifies institutional, structural, and cyclical factors influencing these biases, including governance quality, economic diversification, and global economic conditions. Countries reliant on commodity exports tend to have significant forecast biases, particularly optimistic projections for both public and external debt ratios. Countries with fragile governance or in conflict display more pessimistic forecasts for primary deficits and external debt, but overly optimistic growth projections. Countries that have had market access and have build-up debt stocks toward defending market access.
   
6. Recession conditions: DSAs conducted during recessions are associated with strong optimism in public and external debt ratios as well as real GDP growth. This suggests both a tendency to overplay rebound effects and a misconception of the way macroeconomic effects transmit over various phases of the business cycle.
   
   
   Optimism bias is very hard to control, but it can have large policy consequences on the IMF and its members. By publishing more information on its DSAs, the IMF and the World Bank have allowed outside scrutiny. The database we are publishing hopefully provides the tools to outside researchers to help this scrutiny, and we hope that this paper is a first example of such exploration.

From a paper by Óscar Peláez-Herreros:

“We develop the first disaggregation of Okun’s law that quantifies all of the information that is subsumed within its coefficients. The proposed method decomposes the coefficients into the sum of the direct effect of the change in output upon the unemployment rate, plus the indirect effects of the variations in the output per hour worked, the hours worked per employed person, the participation rate, and the size of the working-age population. With quarterly data for the United States from 1948 to 2024, we found that the value of the intercept in Okun’s relation is determined by the increases in working-age population and output per hour of work, along with the decrease in the number of hours worked per employed person, plus the growth of the participation rate until the 1990s and its subsequent decline. For its part, the slope, that is, the value of Okun’s coefficient, depends mainly upon the variations in output per hour of work and the hours per employed person. The other factors were scarcely relevant. Changes in these components caused the Okun’s relation to vary over time, showing a greater sensitivity of the unemployment rate to variations in production since the 2008 crisis.”