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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 37 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 IMF Working Papers
Reinforcement Learning from Experience Feedback: Application to Economic Policy
Tohid Atashbar
Learning from the past is critical for shaping the future, especially when it comes to economic policymaking. Building upon the current methods in the application of Reinforcement Learning (RL) to the large language models (LLMs), this paper introduces Reinforcement Learning from Experience Feedback (RLXF), a procedure that tunes LLMs based on lessons from past experiences. RLXF integrates historical experiences into LLM training in two key ways - by training reward models on historical data, and by using that knowledge to fine-tune the LLMs. As a case study, we applied RLXF to tune an LLM using the IMF's MONA database to generate historically-grounded policy suggestions. The results demonstrate RLXF's potential to equip generative AI with a nuanced perspective informed by previous experiences. Overall, it seems RLXF could enable more informed applications of LLMs for economic policy, but this approach is not without the potential risks and limitations of relying heavily on historical data, as it may perpetuate biases and outdated assumptions.
Keywords:
LLMs; GAI; RLHF; RLAIF; RLXF; experience feedback; RL model; applying RLXF; Reinforcement Learning from AI feedback; Reinforcement Learning method; Artificial intelligence; Economic classification
Cover IMF Working Papers
Exposure to Artificial Intelligence and Occupational Mobility: A Cross-Country Analysis
Mauro Cazzaniga
,
Carlo Pizzinelli
,
Emma J Rockall
, and
Marina Mendes Tavares
We document historical patterns of workers' transitions across occupations and over the life-cycle for different levels of exposure and complementarity to Artificial Intelligence (AI) in Brazil and the UK. In both countries, college-educated workers frequently move from high-exposure, low-complementarity occupations (those more likely to be negatively affected by AI) to high-exposure, high-complementarity ones (those more likely to be positively affected by AI). This transition is especially common for young college-educated workers and is associated with an increase in average salaries. Young highly educated workers thus represent the demographic group for which AI-driven structural change could most expand opportunities for career progression but also highly disrupt entry into the labor market by removing stepping-stone jobs. These patterns of “upward” labor market transitions for college-educated workers look broadly alike in the UK and Brazil, suggesting that the impact of AI adoption on the highly educated labor force could be similar across advanced economies and emerging markets. Meanwhile, non-college workers in Brazil face markedly higher chances of moving from better-paid high-exposure and low-complementarity occupations to low-exposure ones, suggesting a higher risk of income loss if AI were to reduce labor demand for the former type of jobs.
Keywords:
Artificial intelligence; Employment; Occupations; Emerging Markets; AI adoption; college-educated worker; structural change; stepping-stone job; workers in Brazil; Labor markets; Wages; Labor force
Cover IMF Working Papers
Harnessing Satellite Data to Improve Social Assistance Targeting in the Eastern Caribbean
Sophia Chen
,
Ryu Matsuura
,
Flavien Moreau
, and
Joana Pereira
Prioritizing populations most in need of social assistance is an important policy decision. In the Eastern Caribbean, social assistance targeting is constrained by limited data and the need for rapid support in times of large economic and natural disaster shocks. We leverage recent advances in machine learning and satellite imagery processing to propose an implementable strategy in the face of these constraints. We show that local well-being can be predicted with high accuracy in the Eastern Caribbean region using satellite data and that such predictions can be used to improve targeting by reducing aggregation bias, better allocating resources across areas, and proxying for information difficult to verify.
Keywords:
Social assistance targeting; satellite data; machine learning; Eastern Caribbean; Small Island Developing States.; aggregation bias; hard-to-verify information; satellite imagery processing; Income; Natural disasters; Caribbean
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
The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions
Mariarosaria Comunale
and
Andrea Manera
We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs.
Keywords:
Artificial Intelligence (AI); labor market; task exposure; productivity; regulation; governance.; productivity effect; regulation of AI; AI system; AI model; AI technology; Artificial intelligence; Machine learning; Automation; 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 IMF Working Papers
Political Fragility: Coups d’État and Their Drivers
Aliona Cebotari
,
Enrique Chueca-Montuenga
,
Yoro Diallo
,
Yunsheng Ma
,
Rima A Turk
,
Weining Xin
, and
Harold Zavarce
The paper explores the drivers of political fragility by focusing on coups d’état as symptomatic of such fragility. It uses event studies to identify factors that exhibit significantly different dynamics in the runup to coups, and machine learning to identify these stressors and more structural determinants of fragility—as well as their nonlinear interactions—that create an environment propitious to coups. The paper finds that the destabilization of a country’s economic, political or security environment—such as low growth, high inflation, weak external positions, political instability and conflict—set the stage for a higher likelihood of coups, with overlapping stressors amplifying each other. These stressors are more likely to lead to breakdowns in political systems when demographic pressures and underlying structural weaknesses (especially poverty, exclusion, and weak governance) are present or when policies are weaker, through complex interactions. Conversely, strengthened fundamentals and macropolicies have higher returns in structurally fragile environments in terms of staving off political breakdowns, suggesting that continued engagement by multilateral institutions and donors in fragile situations is likely to yield particularly high dividends. The model performs well in predicting coups out of sample, having predicted a high probability of most 2020-23 coups, including in the Sahel region.
Keywords:
Fragility; Drivers of Fragility; Coup d’État; Machine Learning; coup dynamics; event study result; coup probability; probability of coup; IMF working paper 24/34; Fiscal stance; Income; Sub-Saharan Africa; Middle East and Central Asia; Caribbean
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 IMF Working Papers
Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
Khaled AlAjmi
,
Jose Deodoro
,
Mr. Ashraf Khan
, and
Kei Moriya
Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research.
Keywords:
central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics; ML approach; IMF central bank transparency code; IMF Central Bank Legislation Database; capital markets Department; approaches to analyzing pattern; CBLD data flow; Central bank autonomy; Central bank transparency; Global