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Middle East and Central Asia > Qatar

You are looking at 1 - 9 of 9 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
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 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
Winning the War? New Evidence on the Measurement and the Determinants of Poverty in the United States
Katharina Bergant
,
Miss Anke Weber
, and
Andrea Medici
Using micro-data from household expenditure surveys, we document the evolution of consumption poverty in the United States over the last four decades. Employing a price index that appears appropriate for low income households, we show that poverty has not declined materially since the 1980s and even increased for the young. We then analyze which social and economic factors help explain the extent of poverty in the U.S. using probit, tobit, and machine learning techniques. Our results are threefold. First, we identify the poor as more likely to be minorities, without a college education, never married, and living in the Midwest. Second, the importance of some factors, such as race and ethnicity, for determining poverty has declined over the last decades but they remain significant. Third, we find that social and economic factors can only partially capture the likelihood of being poor, pointing to the possibility that random factors (“bad luck”) could play a significant role.
Keywords:
Poverty; Inequality; Consumption; Provision and Effects of Welfare Programs; consumption poverty; determinants of poverty; poverty indicator; poverty threshold; CPI assumption; inflation inequality; adjusted CPI-U; Poverty measurement; Income; Machine learning; Global
Cover IMF Working Papers
Technological Progress, Artificial Intelligence, and Inclusive Growth
Mr. Anton Korinek
,
Mr. Martin Schindler
, and
Joseph Stiglitz
Advances in artificial intelligence and automation have the potential to be labor-saving and to increase inequality and poverty around the globe. They also give rise to winner-takes-all dynamics that advantage highly skilled individuals and countries that are at the forefront of technological progress. We analyze the economic forces behind these developments and delineate domestic economic policies to mitigate the adverse effects while leveraging the potential gains from technological advances. We also propose reforms to the global system of governance that make the benefits of advances in artificial intelligence more inclusive.
Keywords:
C. putting AI; competition Policy; employment trend; C. intellectual property right; export-led growth; Technological innovation; Artificial intelligence; Income; Global; East Asia
Cover Policy Papers
2021 Comprehensive Surveillance Review— Background Paper on Traction
International Monetary Fund. Strategy, Policy, & Review Department
This paper presents traction as a multidimensional concept and discusses a comprehensive and complementary set of approaches to attempt to measure it based on the Fund’s value added to policy dialogue and formulation and public debate in member countries.
Keywords:
article IV advice; fund policy advice; fund advice; policy dialogue; IMF's transparency policy; COVID-19; Income; Financial Sector Assessment Program; Financial sector stability; Machine learning; Global
Cover IMF Working Papers
Digital Connectivity in sub-Saharan Africa: A Comparative Perspective
Mr. Emre Alper
and
Michal Miktus
Higher digital connectivity is expected to bring opportunities to leapfrog development in sub-Saharan Africa (SSA). Experience within the region demonstrates that if there is an adequate digital infrastructure and a supportive business environment, new forms of business spring up and create jobs for the educated as well as the less educated. The paper first confirms the global digital divide through the unsupervised machine learning clustering K-means algorithm. Next, it derives a composite digital connectivity index, in the spirit of De Muro-Mazziotta-Pareto, for about 190 economies. Descriptive analysis shows that majority of SSA countries lag in digital connectivity, specifically in infrastructure, internet usage, and knowledge. Finally, using fractional logit regressions we document that better business enabling and regulatory environment, financial access, and urbanization are associated with higher digital connectivity.
Keywords:
WP; digital connectivity; EDAI SSA distribution; account ownership; ICT indicators database; SSA countries lag; Digitalization; unsupervised machine learning; fractional logit model; connectivity index; SSA economy; Information technology in revenue administration; Infrastructure; Machine learning; Income; Global; Sub-Saharan Africa; Africa
Cover IMF Working Papers
Today versus Tomorrow - The Sensitivity of the Non-Oil Current Account Balance to Permanent and Current Income
Mr. Alun H. Thomas
and
Mr. Tamim Bayoumi
This paper applies the Permanent Income Model to the non-oil current accounts of the major oil exporters to assess the extent to which national consumption decisions in these countries are made on the basis of permanent versus current income. A test of whether the return on oil wealth and oil balance coefficients sum to unity is accepted for all specifications that adjust the return on wealth for future population changes. For oil-exporting countries outside Africa, around half of the fluctuations in the private sector non-oil balance are driven by considerations of changes in permanent income (the return on oil wealth) rather than current income. By contrast, for the public sector and African countries permanent income has little or no effect.
Keywords:
WP; trade balance