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Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang
This paper describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
Mr. Romain A Duval, Yi Ji, Mr. Chris Papageorgiou, Mr. Ippei Shibata, and Mr. Antonio Spilimbergo
Are preferences for reforms driven by individuals’ own endowments or beliefs? To address this question, we conducted a cross-country survey on people’s opinions on employment protection legislation—an area where reform has proven to be difficult and personal interests are at stake. We find that individuals’ beliefs matter more than their own endowments and personal pay-offs. A randomized information treatment confirms that beliefs explain views about reform, but beliefs can change with new information. Our results are robust to several robustness tests, including to alternative estimation techniques and samples.
Antonio Bassanetti
Capacity development is one of the IMF’s core activities. Its impact is monitored through a Results-Based Management framework. Using for the first time the resulting dataset, the paper investigates how the likelihood of achieving targeted outcomes correlates with macroeconomic conditions and project-specific characteristics. Results indicate a positive correlation with per capita GDP growth and the involvement of resident advisors and regional centers. Results also confirm lower chances of achieving targeted outcomes for fragile, conflict-affected, and small states as well as in complex projects. These findings inform Fund CD strategy, prioritization and delivery to help member countries achieve better outcomes.
Carlo Pizzinelli, Kotaro Ishi, and Tariq Khan
To complement the early warning signals literature, we study the determinants of banking and currency crises for small states and currency boards. Building on the crisis dataset by Laeven and Valencia (2020), we estimate a binominal logit model to identify the determinants of crises, and as a case study, we apply our models to the Eastern Caribbean Currency Union (ECCU). Our findings largely confirm past studies’ results that both external and domestic fundamentals matter in predicting crisis likelihood, but we find that small states and fixed exchange rate regimes are more sensitive to these fundamentals, compared to larger economies. Our empirical results also suggest that for currency board economies, keeping a high level of the foreign reserve cover—the “backing ratio” defined as official foreign reserves as a share of central bank demand liabilities—is critical to reduce the likelihood of both banking and currency crises. The backing ratio is particularly important during years of global economic downturn.
Rasmané Ouedraogo and Mr. David Stenzel
The COVID-19 pandemic and lockdowns have led to a rise in gender-based violence. In this paper, we explore the economic consequences of violence against women in sub-Saharan Africa using large demographic and health survey data collected pre-pandemic. Relying on a two-stage least square method to address endogeneity, we find that an increase in the share of women subject to violence by 1 percentage point can reduce economic activities (as proxied by nightlights) by up to 8 percent. This economic cost results from a significant drop in female employment. Our results also show that violence against women is more detrimental to economic development in countries without protective laws against domestic violence, in natural resource rich countries, in countries where women are deprived of decision-making power and during economic downturns. Beyond the moral imperative, the findings highlight the importance of combating violence against women from an economic standpoint, particularly by reinforcing laws against domestic violence and strengthening women’s decision-making power.
Metodij Hadzi-Vaskov, Samuel Pienknagura, and Mr. Luca A Ricci
This paper explores the macroeconomic impact of social unrest, using a novel index based on news reports. The findings are threefold. First, unrest has an adverse effect on economic activity, with GDP remaining on average 0.2 percentage points below the pre-shock baseline six quarters after a one-standard deviation increase in the unrest index. This is driven by sharp contractions in manufacturing and services (sectoral dimension), and consumption (demand dimension). Second, unrest lowers confidence and raises uncertainty; however, its adverse effect on GDP can be mitigated by strong institutions and by a country’s policy space. Third, an unrest “event”, which is captured by a large change in the unrest index, is associated with a 1 percentage point reduction in GDP six quarters after the event. Impacts differ by type of event: episodes motivated by socio-economic reasons result in sharper GDP contractions compared to those associated with politics/elections, and events triggered by a combination of both factors lead to sharpest contractions. Results are not driven by countries with adverse growth trajectories prior to unrest events or by fiscal consolidations, and are robust to instrumenting via regional unrest.
Jelle Barkema, Mr. Mico Mrkaic, and Yuanchen Yang
This paper dives into the Fund’s historical coverage of cross-border spillovers in its surveillance. We use a state-of-the-art deep learning model to analyze the discussion of spillovers in all IMF Article IV staff reports between 2010 and 2019. We find that overall, while the discussion of spillovers decreased over time, it was pronounced in the staff reports of some systemically important economies and during periods of global spillover events. Spillover discussions were more prominent in staff reports covering advanced and emerging market economies, possibly reflecting their role as sources of global spillovers. The coverage of spillovers was higher in the context of the real, financial, and external sectors. Also, countries with larger economies, higher trade and capital account openess and lower inflation are more likely to discuss spillovers in their Article IV staff reports.
Olusegun Ayodele Akanbi, Nikolay Gueorguiev, Mr. Jiro Honda, Paulomi Mehta, Mr. Kenji Moriyama, Keyra Primus, and Mouhamadou Sy
High persistence of state fragility (a fragility trap) suggests the presence of substantial benefits from avoiding a fall into fragility and considerable hurdles to successful exit from fragility. This paper empirically examines the factors that affect the turning points of entering and exiting from state fragility by employing three different approaches: an event study, the synthetic control method, and a logit model. We find that avoiding economic contraction is critical to prevent a country on the brink of fragility from falling into fragility (e.g., among near fragile countries, the probability of entering fragility would rise by 40 percentage points should real GDP per capita growth decline from +2.5 percent to -2.5 percent). Also, strengthening government effectiveness together with increasing political inclusion and maintaining robust economic activity should help make exit from fragility more successful and sustainable. In the current environment (the COVID-19 crisis and its aftermath), the findings suggest the importance of providing well-directed fiscal stimulus with sufficient financing, (subject to appropriate governance safeguards and well-designed policies), and protecting critical socio-economic spending to keep vulnerable countries away from being caught in a fragility trap.
Brandon Buell, Reda Cherif, Carissa Chen, Jiawen Tang, and Nils Wendt
The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.