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Middle East and Central Asia > Qatar

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Cover Policy Papers
The Managing Director's Global Policy Agenda, Annual Meetings 2024: Secure A Soft Landing And Break From The Low Growth–High Debt Path
International Monetary Fund. Strategy, Policy, & Review Department
The global economy has proven resilient, and a soft landing is within reach. Inflation has moderated thanks to tight monetary policy and fading supply shocks, and growth is expected to remain steady. But uncertainty remains significant, with risks tilted to the downside; medium-term growth prospects are lackluster; public debt has reached record highs and is expected to approach 100 percent of GDP by 2030; and geoeconomic fragmentation threatens to undo decades of gains from cross-border economic integration. At the same time, transformative changes—the green transition, demographic shifts, and digitalization, including artificial intelligence—are poised to reshape the global economy, creating challenges but also opportunities. Against this background, the key policy priorities are to secure a soft landing and break from the low growth-high debt path, and address other medium-term challenges. Monetary policy should ensure inflation returns durably to the target, and fiscal policy needs to decisively pivot toward consolidation to rebuild buffers and safeguard debt sustainability. Growth-enhancing reforms are urgently needed to lift growth prospects by boosting investment, job creation, and productivity. Domestic policies must be complemented by multilateral efforts to support countries with debt vulnerabilities, protect gains from economic integration, accelerate climate action, and harness benefits of new technologies while mitigating the risks. As it has done since its founding 80 years ago, the IMF will continue to adapt to serve its members with tailored policy advice, financial lifelines when needed, and capacity development. The Fund will remain a strong advocate for multilateralism and economic integration as foundations on which to build a resilient and inclusive global economy.
Keywords:
Artificial intelligence; Capital flows; Climate change; Debt sustainability; Digitalization; Economic integration; Inflation; Growth; Reforms; Integrated Policy Framework; Poverty reduction strategy; Revenue mobilization; break from the Low Growth-High Debt Path; secure a soft landing; Policy agenda; IMF staff measure; lending toolkit; Global
Cover Global Financial Stability Report, October 2024
Global Financial Stability Report, October 2024
Steadying the Course: Uncertainty, Artificial Intelligence, and Financial Stability
International Monetary Fund. Monetary and Capital Markets Department

Abstract

Chapter 1 shows that although near-term financial stability risks have remained contained, mounting vulnerabilities could worsen future downside risks by amplifying shocks, which have become more probable because of the widening disconnect between elevated economic uncertainty and low financial volatility. Chapter 2 presents evidence that high macroeconomic uncertainty can threaten macrofinancial stability by exacerbating downside tail risks to markets, credit supply, and GDP growth. These relationships are stronger when debt vulnerabilities are elevated, or financial market volatility is low (during episodes of a macro-market disconnect). Chapter 3 assesses recent developments in AI and Generative AI and their implications for capital markets. It presents new analytical work and results from a global outreach to market participants and regulators, delineates potential benefits and risks that may arise from the widespread adoption of these new technologies, and makes suggestions for policy responses.

Keywords:
financial stability; vulnerabilities; uncertainty; risk; artificial intelligence; capital markets; tail risk; uncertainty index; monetary policy; mortgage rate; market liquidity risk; fiscal policy uncertainty; Emerging and frontier financial markets; Financial sector stability; Sovereign bonds; Credit; Global; Middle East; Europe; Central and Eastern Europe; South America
Cover IMF Working Papers
What Can Artificial Intelligence Do for Stagnant Productivity in Latin America and the Caribbean?
Bas B. Bakker
,
Sophia Chen
,
Dmitry Vasilyev
,
Olga Bespalova
,
Moya Chin
,
Daria Kolpakova
,
Archit Singhal
, and
Yuanchen Yang
Since 1980, income levels in Latin America and the Caribbean (LAC) have shown no convergence with those in the US, in stark contrast to emerging Asia and emerging Europe, which have seen rapid convergence. A key factor contributing to this divergence has been sluggish productivity growth in LAC. Low productivity growth has been broad-based across industries and firms in the formal sector, with limited diffusion of technology being an important contributing factor. Digital technologies and artificial intelligence (AI) hold significant potential to enhance productivity in the formal sector, foster its expansion, reduce informality, and facilitate LAC’s convergence with advanced economies. However, there is a risk that the region will fall behind advanced countries and frontier emerging markets in AI adoption. To capitalize on the benefits of AI, policies should aim to facilitate technological diffusion and job transition.
Keywords:
Artificial Intelligence (AI); Productivity Stagnation; Technological Innovation; Latin America; Caribbean; Economic Growth; Labor Productivity; Automation; Macroeconomic Impact; Digital Transformation; Cross-Country Analysis; Regional Development; Technology Adoption; Emerging Economies; Economic Policy.; difference AI; AI adoption; technology producer; AI's role; Artificial intelligence; Productivity; North America; Europe; Global; Asia and Pacific
Cover IMF Working Papers
Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning
Yang Liu
,
Ran Pan
, and
Rui Xu
Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap.
Keywords:
Core inflation; forecasting; machine learning models; LASSO; Japan; household inflation expectation; forecasting inflation; machine learning method; Annex I. machine learning; enhanced inflation; Inflation; Machine learning; Econometric models; Output gap
Cover IMF Working Papers
The Labor Market Impact of Artificial Intelligence: Evidence from US Regions
Yueling Huang
This paper empirically investigates the impact of Artificial Intelligence (AI) on employment. Exploiting variation in AI adoption across US commuting zones using a shift-share approach, I find that during 2010-2021, commuting zones with higher AI adoption have experienced a stronger decline in the employment-to-population ratio. Moreover, this negative employment effect is primarily borne by the manufacturing and lowskill services sectors, middle-skill workers, non-STEM occupations, and individuals at the two ends of the age distribution. The adverse impact is also more pronounced on men than women.
Keywords:
Artificial intelligence; technology; labor; local labor markets; shift share; middle-skill worker; labor market impact; impact of artificial intelligence; employment effect; low-skill services sectors; Employment; Employment rate; Labor markets; Global
Cover IMF Staff Country Reports
Kingdom of Lesotho: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper delves into few applications of machine learning (ML), with a particular application to economic forecasts in Lesotho. Amid delayed and often revised gross domestic product data, this paper explores the potential of ML to provide real-time insights into growth and inflation trends, crucial for informed policymaking. By leveraging nontraditional data and employing a variety of ML models, the paper presents a comprehensive analysis of current economic activity, evaluates the accuracy of standard statistical measures, and forecasts future inflation trends. The findings underscore the efficacy of ML in reducing prediction errors and highlight the significant role of alternative data in circumventing the limitations posed by traditional economic indicators. This paper contributes to the broader debate on the application of advanced computational techniques in economic forecasting, offering valuable insights for policymakers in Lesotho and similar countries grappling with data constraints and the need for timely economic analysis.
Keywords:
E. inflation forecasting; GDP Nowcasting result; D. GDP evaluation; C. GDP Nowcasting; ensemble Nowcasting model; Machine learning; Inflation; Africa; Global
Cover IMF Working Papers
Industrial Policies for Innovation: A Cost-Benefit Framework
Daniel Garcia-Macia
and
Alexandre Sollaci
When and how should governments use industrial policy to direct innovation to specific sectors? This paper develops a framework to analyze the costs and benefits of industrial policies for innovation. The framework is based on a model of endogenous innovation with a sectoral network of knowledge spillovers (Liu and Ma 2023), extended to capture implementation frictions and alternative policy goals. Simulations show that implementing sector-specific fiscal support is only preferable to sector-neutral support under restrictive conditions—when externalities are well measured (e.g., greenhouse gas emissions), domestic knowledge spillovers of targeted sectors are high (typically in larger economies), and administrative capacity is strong (including to avoid misallocation to politically connected sectors). If any of these conditions are not fully met, welfare impacts of industrial policy quickly become negative. The optimal allocation of support entails greater subsidies to greener sectors, but other factors such as cross-sector knowledge spillovers matter. For a sample of technologically advanced economies, existing industrial policies seem to be directing innovation to broadly the right sectors, but to an excessive degree in most economies, including China and the United States.
Keywords:
Industrial policy; innovation; knowledge spillovers; climate policy; AI; implementation friction; knowledge spillover; economy openness; R&D allocation; welfare impact; policy goal; Spillovers; Artificial intelligence; Global
Cover Selected Issues Papers
Impact of AI on Singapore's Labor Market: Singapore
Shujaat A Khan
Singapore is well-prepared for AI adoption but stands highly exposed to the increasing use of artificial intelligence (AI) technologies in the workplace, due to a large share of skilled workforce. While half of the highly exposed segment of the labor force stands to benefit from the appropriate use of AI to complement their tasks, potentially boosting their productivity, the other half may face greater vulnerability to AI’s disruptive effects due to lower levels of AI complementarity. Estimates suggest that women and younger workers are more exposed to the effects of AI, which, in the absence of appropriate policies, could worsen income inequality in Singapore. Targeted training policies, leveraging on the existing SkillsFuture program, can harness AI's potential. Additionally, focused upskilling can mitigate the disruptive impact of AI on vulnerable workers.
Keywords:
AI exposure; AI complementarity; AI adoption; disruptive effect; impact of AI; Artificial intelligence; Labor markets; Labor force; Global
Cover IMF Working Papers
Enhancing IMF Economics Training: AI-Powered Analysis of Qualitative Learner Feedback
Andras Komaromi
,
Xiaomin Wu
,
Ran Pan
,
Yang Liu
,
Pablo Cisneros
,
Anchal Manocha
, and
Hiba El Oirghi
The International Monetary Fund (IMF) has expanded its online learning program, offering over 100 Massive Open Online Courses (MOOCs) to support economic and financial policymaking worldwide. This paper explores the application of Artificial Intelligence (AI), specifically Large Language Models (LLMs), to analyze qualitative feedback from participants in these courses. By fine-tuning a pre-trained LLM on expert-annotated text data, we develop models that efficiently classify open-ended survey responses with accuracy comparable to human coders. The models’ robust performance across multiple languages, including English, French, and Spanish, demonstrates its versatility. Key insights from the analysis include a preference for shorter, modular content, with variations across genders, and the significant impact of language barriers on learning outcomes. These and other findings from unstructured learner feedback inform the continuous improvement of the IMF's online courses, aligning with its capacity development goals to enhance economic and financial expertise globally.
Keywords:
IMF Economics training; measuring training impact; learner feedback; application of artificial intelligence; learner satisfaction; Artificial intelligence; Machine learning; Information systems and platforms; Automation; Sub-Saharan Africa; Western Hemisphere; Middle East and Central Asia; Europe; Asia and Pacific
Cover IMF Staff Country Reports
Singapore: Selected Issues
International Monetary Fund. Asia and Pacific Dept
This Selected Issues paper estimates the exchange pass-through to inflation in Singapore with a particular focus on the role of labor market conditions. The paper first finds a strong exchange rate pass-through to inflation in Singapore, after accounting for the potential endogeneity of changes in the exchange rate. Further, it uncovers that labor market tightness dampens exchange rate pass-through and therefore could weaken monetary policy transmission. Overall, the results suggest that monetary policy should be more vigilant under a tight labor market condition. Under tight market conditions, the pass-through is found to be severely weakened and more so for the service components of the consumer price index basket. Overall, our findings suggest that the exchange rate-based monetary policy serves Singapore well, but it would need to be more vigilant when the labor market is tight. The paper then draws policy implications for taming inflation under tight labor market conditions. Further, policies designed to ease structural labor market tightness could help support monetary policy to ensure price stability in Singapore. This is consistent with a recent study on the US that suggests that dealing with the inflationary pressures originating from a tight labor market would require policy actions that bring labor demand and supply into a better balance.
Keywords:
AI exposure; IMF China growth projection; China growth shock; growth slowdown; growth shock; Artificial intelligence; Exchange rate pass-through; Inflation; Labor markets; Spillovers; Global; Southeast Asia; Asia and Pacific