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Dimitris Drakopoulos
,
Yibin Mu
,
Dmitry Vasilyev
, and
Mauricio Villafuerte
Cross-border payment inefficiencies are a significant barrier to trade both within Latin America and the Caribbean (LAC) and between LAC and other regions. This paper provides a comprehensive review of historical efforts undertaken by various countries within the LAC region to address these challenges. We also explore the potential of recent financial innovations, such as digital currencies and blockchain technology, to enhance cross-border payments. While new technologies do not substitute for prudent and credible macroeconomic policies, leveraging these technologies can help LAC countries reduce transaction costs and times, thus enhancing economic efficiency and fostering deeper regional and global trade relationships.
Eugenio M Cerutti
,
Jiaqian Chen
, and
Martina Hengge
The rapid growth of crypto assets raises important questions about their cross-border usage. To gain a better understanding of cross-border Bitcoin flows, we use raw data covering both on-chain (on the Bitcoin blockchain) and off-chain (outside the Bitcoin blockchain) transactions globally. We provide a detailed description of available methodologies and datasets, and discuss the crucial assumptions behind the quantification of cross-border flows. We then present novel stylized facts about Bitcoin cross-border flows and study their global and domestic drivers. Bitcoin cross-border flows respond differently than capital flows to traditional drivers of capital flows, and differences appear between on-chain and off-chain Bitcoin cross-border flows. Off-chain cross-border flows seem correlated with incentives to avoid capital flow restrictions.
Daniel Garcia-Macia
,
Waikei R Lam
, and
Anh D. M. Nguyen
Managing the climate transition presents policymakers with a tradeoff between achieving climate goals, fiscal sustainability, and political feasibility, which calls for a fiscal balancing act with the right mix of policies. This paper develops a tractable dynamic general equilibrium model to quantify the fiscal impacts of various climate policy packages aimed at reaching net zero emissions by mid-century. Our simulations show that relying primarily on spending measures to deliver on climate ambitions will be costly, possibly raising debt by 45-50 percent of GDP by 2050. However, a balanced mix of carbon-pricing and spending-based policies can deliver on net zero with a much smaller fiscal cost, limiting the increase in public debt to 10-15 percent of GDP by 2050. Carbon pricing is central not only as an effective tool for emissions reduction but also as a revenue source. Delaying carbon pricing action could increase costs, especially if less effective measures are scaled up to meet climate targets. Technology spillovers can reduce the costs but bottlenecks in green investment could unwind the gains and slow the transition.
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
Flora Lutz
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Florian Schuster
,
Marwa Alnasaa
,
Lahcen Bounader
,
Il Jung
,
Jeta Menkulasi
, and
Joana da Mota
Many countries find themselves with elevated debt levels, increased debt vulnerabilities, and tight financing conditions, while also facing increased spending needs for development and transition to a greener economy. This paper aims to place the current debt landscape in a historical context and investigate the drivers of debt surges, to what degree they result in a crisis as well as examine post-surge debt trajectories and under what conditions debt follows a non-declining path. We find that fiscal policy and stock-flow adjustments play important roles in debt dynamics with the valuation effects arising from currency depreciation explaining more than half of stock flow adjustments in LICs. Debt surges are estimated to result in a financial crisis with a probability of 11–20 percent and spending-driven fiscal expansions during debt surges tend to result in a high probability of non-declining debt path.
Mr. Cian Allen
,
Deepali Gautam
, and
Luciana Juvenal
This paper assembles a comprehensive dataset of the currency composition of countries’ external balance sheets for 50 economies over the period 1990–2020. We document the following findings: (i) the US dollar and the euro still dominate global external balance sheets; (ii) there were striking changes in the currency composition across countries since the 1990s, with many emerging markets having moved from short to long positions in foreign currency, thus moving away from the so-called “original sin”; (iii) financial and tradeweighted exchange rates are weakly correlated, suggesting the commonly used trade indices do not adequately reflect the wealth effects of currency movements, and (iv) the large wealth transfers across countries during COVID-19 and the global financial crises increased global imbalances in the former, and reduced them in the latter.
Hites Ahir
,
Mr. Giovanni Dell'Ariccia
,
Davide Furceri
,
Mr. Chris Papageorgiou
, and
Hanbo Qi
This paper uses text analysis to construct a continuous financial stress index (FSI) for 110 countries over each quarter during the period 1967-2018. It relies on a computer algorithm along with human expert oversight and is thus easy to update. The new indicator has a larger country and time coverage and higher frequency than similar measures focusing on advanced economies. And it complements existing binary chronologies in that it can assess the severity of financial crises. We use the indicator to assess the impact of financial stress on the economy using both country- and firm-level data. Our main findings are fivefold: i) consistent with existing literature, we show an economically significant and persistent relationship between financial stress and output; ii) the effect is larger in emerging markets and developing economies and (iii) for higher levels of financial stress; iv) we deal with simultaneous causality by constructing a novel instrument—financial stress originating from other countries—using information from the text analysis, and show that, while there is clear evidence that financial stress harms economic activities, OLS estimates tend to overestimate the magnitude of this effect; (iv) we confirm the presence of an exogenous effect of financial stress through a difference-in-differences exercise and show that effects are larger for firms that are more financially constrained and less profitable.
Jing Xie
Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
Maximiliano Appendino
,
Olga Bespalova
,
Ms. Rina Bhattacharya
,
Jean François Clevy
,
Ms. Nan Geng
,
Mr. Takuji Komatsuzaki
,
Justin Lesniak
,
Weicheng Lian
,
Ms. Sandra Marcelino
,
Mr. Mauricio Villafuerte
, and
Mr. Yorbol Yakhshilikov
After providing a general overview of the nature, pros, and cons of crypto assets and CBDCs, this paper focuses on documenting their recent experience in LAC. The region records a high interest in unbacked crypto assets and stablecoins and its authorities’ policy responses have varied substantially, ranging from the introduction of Bitcoin as legal tender in El Salvador to their prohibition in many other countries worried about their impact on financial stability, currency/asset substitution, tax evasion, corruption, and money laundering. This paper also describes briefly the results of a survey on CBDCs’ introduction plans and crypto assets regulation. Finally, this paper presents some general lessons and policy recommendations for the region on the regulation of cypto assets, digital currencies and cross-border payments, and on the potential introduction of CBDCs.
Sebastian Beer
and
Ruud A. de Mooij
This paper develops a simple model to explore whether a higher detection probability for offshore tax evaders—e.g. because of improved exchange of information between countries and/or due to digitalization of tax administrations—renders it optimal for governments to introduce a voluntary disclosure program (VDP) and, if so, under what terms. We find that if the VDP is unanticipated, it is likely to be optimal for a revenue-maximizing government to introduce a VDP with relatively generous terms, i.e. a low or even negative penalty. When anticipated, however, the VDP is neither incentive compatible nor optimal, as it induces otherwise compliant taxpayers to evade tax. A VDP can then only be beneficial if tax evasion induces an external social cost beyond the direct revenue foregone, e.g., due to adverse effects on overall tax morale. In contrast to the common view that VDPs should come along with additional enforcement effort, we find that governments should relax enforcement if the VDP itself provides more powerful incentives to come clean.