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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 22 items for :

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Cover Staff Discussion Notes
Broadening the Gains from Generative AI: The Role of Fiscal Policies
Fernanda Brollo
,
Era Dabla-Norris
,
Ruud de Mooij
,
Daniel Garcia-Macia
,
Tibor Hanappi
,
Li Liu
, and
Anh D. M. Nguyen
Generative artificial intelligence (gen AI) holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality. This note discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains. Given the vast uncertainty about the nature, impact, and speed of developments in gen AI, governments should take an agile approach that prepares them for both business as usual and highly disruptive scenarios.
Keywords:
Artificial intelligence; AI; technological change; labor market; fiscal policy; social protection systems; tax systems; IMF Staff Discussion Note SDN2024/002; gen AI; advance public service delivery; role of fiscal policies; replacement ratio; job displacement; income support; tax treatment; market power; Labor markets; Corporate income tax; Automation; Capital income; Global
Cover Regional Economic Outlook, Europe, April 2024
Regional Economic Outlook, Europe, April 2024
Soft Landing in Crosswinds for a Lasting Recovery
International Monetary Fund. European Dept.

Abstract

A soft landing for Europe’s economies is within reach. Securing the baseline of growth with price stability will require careful monetary policy calibration. Faster fiscal consolidation would ensure buffers are adequate to tackle future shocks, while structural fiscal reforms would help address mounting long-term expenditure pressures. Beyond the near-term recovery, raising potential growth prospects calls for efforts at both the domestic and European levels. Measures should aim to raise labor force participation, prepare the workforce for looming structural shifts, set an enabling environment for private investment, and promote innovation on a level European playing field—especially when it comes to the green transition, including through a strong commitment to carbon pricing. Greater European integration would amplify the effect of these reforms. Formulating an ambitious set of growth-enhancing reforms should be a key priority of a new EU commission.

Keywords:
Inflation; Growth; Monetary Policy; Fiscal Policy; Productivity; single market; soft landing; CESEE economy; inflation rate; IMF staff calculation; EU accession path; EU country; per capita income; Labor markets; Artificial intelligence; Europe; Global
Cover Staff Climate Notes
Key Challenges Faced by Fossil Fuel Exporters during the Energy Transition
Diego Mesa Puyo
,
Augustus J Panton
,
Tarun Sridhar
,
Martin Stuermer
,
Christoph Ungerer
, and
Alice Tianbo Zhang
The global energy transition is affecting fossil fuel exporters from multiple angles. It is adding to longstanding uncertainties on relative movements of fossil fuel demand and supply—which impact fossil fuel-related exports, fiscal flows, investment and subsequently external and fiscal accounts, economic growth, and employment. While policymakers are very familiar with these challenges, they now also face expectations of a permanent decline in the long-run global demand for fossil fuels. Key factors that could determine country-level impacts include (i) the type of fossil fuel a country exports (ii) extraction costs and (iii) country characteristics. The monitoring and mitigation of fiscal risks will need to be stepped up. Fiscal policy also has a role in reducing domestic emissions, encouraging adoption of low-carbon technologies, and helping those most vulnerable to changes from the transition. Broader macroeconomic risks can be reduced by accelerating ongoing structural reforms that support alternative engines of growth. Low- or zero-carbon emission energy industries could offer new avenues that build on existing fossil fuel knowledge and infrastructure. Concurrently, improved financial regulation and supervision could reduce financial sector exposures. Finally, international coordination on the design and implementation of climate policy as well as international transfer schemes (financing and capacity development) could reduce uncertainties surrounding the transition path and associated adverse economic consequences.
Keywords:
Macroeconomic policy; climate change; energy transition; fossil fuel exporters; commodity market; climate mitigation; economic diversification; international coordination; IMF staff Climate note; IMF staff Climate Note 2024/001; fossil fuel exporter; building financial asset buffer; cost exporter; Non-renewable resources; Exports; Oil; Renewable energy; Global
Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
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.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover Staff Discussion Notes
Gen-AI: Artificial Intelligence and the Future of Work
Mauro Cazzaniga
,
Florence Jaumotte
,
Longji Li
,
Giovanni Melina
,
Augustus J Panton
,
Carlo Pizzinelli
,
Emma J Rockall
, and
Marina Mendes Tavares
Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills
Keywords:
Artificial Intelligence; Labor Market; Job Displacement; Income Inequality; Advanced Economies; Emerging Market Economies; Low-Income Developing Countries; AI preparedness index; AI benefit; AI exposure; ICT employment share; AI adoption; Emerging and frontier financial markets; Income; Global; Africa
Cover FinTech Notes
Generative Artificial Intelligence in Finance: Risk Considerations
Mr. Ghiath Shabsigh
and
El Bachir Boukherouaa
In recent years, technological advances and competitive pressures have fueled rapid adoption of artificial intelligence (AI) in the financial sector, and this adoption is set to accelerate with the recent emergence of generative AI (GenAI). GenAI is a significant leap forward in AI technology that enhances its utility for financial institutions that have been quick at adapting it to a broad range of applications. However, there are risks inherent in the AI technology and its application in the financial sector, including embedded bias, privacy concerns, outcome opaqueness, performance robustness, unique cyberthreats, and the potential for creating new sources and transmission channels of systemic risks. GenAI could aggravate some of these risks and bring about new types or risks as well, including for financial sector stability. This paper provides early insights into GenAI’s inherent risks and their potential impact on the financial sector.
Keywords:
artificial intelligence; embedded bias; cybersecurity; fintech; financial risks; financial sector policy and analysis; financial stability; generative; hallucination; machine learning; privacy; synthetic data; sample generative AI application; AI-machine learning; IMF Library; IMF Fintech Note 2023/006; generative artificial intelligence in finance; Financial sector; Global
Cover IMF Working Papers
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
Mr. Jorge A Chan-Lau
,
Ruofei Hu
,
Maksym Ivanyna
,
Ritong Qu
, and
Cheng Zhong
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Keywords:
Crisis prediction; machine learning; surrogates; explainable models; IMF ML crisis; ML crisis prediction; Annex I. IMF ML; surrogate data models; model interpretability; Early warning systems; Yield curve; Deposit rates; Global
Cover FinTech Notes
Blockchain Consensus Mechanisms: A Primer for Supervisors
Parma Bains
Technology plays an increasingly important role in financial services. With the pace of technological inno-vation moving ever faster, the role new technology plays in the provision of financial services is becoming increasingly fundamental. New technology can generate efficiencies for firms, lowering costs that can be passed on to end users. It can increase access to financial services and products for consumers, particularly the most vulnerable; however, new technology can also create new risks and unintended consequences that can harm financial stability, consumer protection, and market integrity. This primer is designed for financial supervisors at central banks, regulatory authorities, and government departments. It adds to existing literature by summarizing key aspects of popular consensus mechanisms at a high level, with a specific focus on how such mechanisms may impact the mandates of supervisors and policymakers when deployed in financial services markets. It could also help inform IMF staff on policy development and technical assistance related to crypto assets, stablecoins, and blockchains.
Keywords:
fintech, blockchain, distributed ledger technology, proof-of-stake, proof-of-work, crypto assets, stablecoins, central bank digital currencies, Bitcoin, BigTech; Blockchain consensus mechanism; bitcoin mining; FINTECH note; A. consensus mechanism; B. consensus mechanism; Blockchain and DLT; Virtual currencies; Technological innovation; Global
Cover Staff Discussion Notes
Toward a Global Approach to Data in the Digital Age
Mr. Vikram Haksar
,
Mr. Yan Carriere-Swallow
,
Emran Islam
,
Andrew Giddings
,
Kathleen Kao
,
Emanuel Kopp
, and
Gabriel Quiros
The ongoing economic and financial digitalization is making individual data a key input and source of value for companies across sectors, from bigtechs and pharmaceuticals to manufacturers and financial services providers. Data on human behavior and choices—our “likes,” purchase patterns, locations, social activities, biometrics, and financing choices—are being generated, collected, stored, and processed at an unprecedented scale.
Keywords:
Data, finance, bigtech, competition, privacy, trade, policy coordination, global principles; data portability; DATA policies country; privacy trade-off; policy divergence; data availability divide; data economy; Digital economy; Competition; Financial sector stability; Artificial intelligence; Global
Cover IMF Working Papers
License to Spill: How Do We Discuss Spillovers in Article IV Staff Reports
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.
Keywords:
spillover discussion; model performance; discussion of spillover; General spillover pattern; spillover event; IMF staff calculation; Spillovers; Probit models; Machine learning; Capital account; Inflation; Global