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Middle East and Central Asia > Qatar

You are looking at 1 - 8 of 8 items for :

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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
AI and Macroeconomic Modeling: Deep Reinforcement Learning in an RBC model
Tohid Atashbar
and
Rui Aruhan Shi
This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, one of which is deterministic without the technological shock and the other is stochastic. The objective of the deterministic environment is to compare the learning agent's behavior to a deterministic steady-state scenario. We demonstrate that in both deterministic and stochastic scenarios, the agent's choices are close to their optimal value. We also present cases of unstable learning behaviours. This AI-macro model may be enhanced in future research by adding additional variables or sectors to the model or by incorporating different DRL algorithms.
Keywords:
Reinforcement learning; Deep reinforcement learning; Artificial intelligence; RL; DRL; Learning algorithms; Macro modeling; RBC; Real business cycles; DDPG; Deep deterministic policy gradient; Actor-critic algorithms; RBC model; AI-macroeconomic simulator; AI experiment; learning algorithm; Machine learning; Rational expectations; Debt relief; 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 IMF Research Bulletin
IMF Research Bulletin, Fall-Winter 2019
International Monetary Fund. Research Dept.
It has been two years since the trade tensions erupted and not only captured policymakers’ but also the research community’s attention. Research has quickly zoomed in on understanding trade war rhetoric, tariff implementation, and economic impacts. The first article in the December 2019 issue sheds light on the consequences of the recent trade barriers.
Keywords:
IRB; trade; debt measurement; exporter; policy session; firm; Mundell-Fleming Lecture; US-China trade tension; tariff factor; Exports; Tariffs; Infrastructure; Big data; Trade liberalization; Global; East Asia; North America; Sub-Saharan Africa; Europe; Africa; threshold ModelsHow; ship movement; TrajectoriesThe global financial crisis; Imports; Asia and Pacific
Cover IMF Working Papers
Nonlinearity Between the Shadow Economy and Level of Development
Dong Frank Wu
and
Mr. Friedrich Schneider
This paper is the first attempt to directly explore the long-run nonlinear relationship between the shadow economy and level of development. Using a dataset of 158 countries over the period from 1996 to 2015, our results reveal a robust U-shaped relationship between the shadow economy size and GDP per capita. Our results imply that the shadow economy tends to increase when economic development surpasses a given threshold or at least does not disappear. Our findings suggest that special attention should be given to the country’s level of development when designing policies to tackle issues related to the shadow economy.
Keywords:
WP; shadow economy size; size of the shadow economy; shadow economy data; labor market; Shadow Economy; Level of Development; Nonlinearity; GDP per capita; causes of the shadow economy; shadow economy driver; shadow economy's determinant; shadow economy while; Informal economy; Tax incidence; Inflation; Global
Cover Policy Papers
Overarching Strategy on Data and Statistics at the Fund in the Digital Age
International Monetary Fund. Strategy, Policy, &amp
and
Review Department
The first data and statistics strategy for the Fund comes at a critical time. A fast-changing data landscape, new data needs for evolving surveillance priorities, and persisting data weaknesses across the membership pose challenges and opportunities for the Fund and its members. The challenges emerging from the digital revolution include an unprecedented amount of new data and measurement questions on growth, productivity, inflation, and welfare. Newly available granular and high-frequency (big) data offer the potential for more timely detection of vulnerabilities. In the wake of the crisis, Fund surveillance requires greater cross-country data comparability; staff and authorities face the complexity of integrating new data sources and closing data gaps, while working to address the weaknesses noted by the IEO Report (Behind the Scenes with Data at the IMF) in 2016. The overarching strategy is to move toward an ecosystem of data and statistics that enables the Fund and its members to better meet the evolving data needs in a digital world. It integrates Fund-wide work streams on data provision to the Fund for surveillance purposes, international statistical standards, capacity development, and data management under a common institutional objective. It seeks seamless access and sharing of data within the Fund, enabling cloud-based data dissemination to support data provision by member countries (e.g., the “global data commons”), closing data gaps with new sources including Big Data, and improving assessments of data adequacy for surveillance to help better prioritize capacity development. The Fund also will work with policymakers to understand the implications of the digital economy and digital data for the macroeconomic statistics, including new measures of welfare beyond GDP.
Keywords:
PP; director; data landscape; consumer sentiment; Fund staff estimate; data commons; data weakness; big data experiment; Big data; Data dissemination; Special Data Dissemination Standard (SDDS); Global
Cover IMF Working Papers
Comparing the Employment-Output Elasticities of Expatriates and Nationals in the Gulf Cooperation Council
Mr. Alberto Behar
We estimate the elasticity of private-sector employment to non-oil GDP in the Gulf Cooperation Council (GCC) for GCC nationals and expatriates using a Seemingly Unrelated Error Correction (SUREC) model. Our results indicate that the employment response is lower for nationals, who have an estimated short-run elasticity of only 0.15 and a long-run response of 0.7 or less. The elasticity is almost unity for expatriates in the long run and 0.35 in the short run. We interpret low elasticities as indirect evidence of labor market adjustment costs, which could include hiring and firing rigidities, skills mismatches, and reluctance to accept private sector jobs. Forecasts suggest that, absent measures to reduce adjustment costs, the private sector will only be able to absorb a small portion of nationals entering the labor force.
Keywords:
WP; labor market; Employment Elasticities; Labor Market Adjustment Costs; GCC; Gulf Cooperation Council; Inclusive Growth; GCC country; employment growth; GCC employment; GCC labor market structure; employment response; growth forecast; employment elasticity; GCC elasticity; employment projection; employment-growth relationship; employment rigidity; GCC aggregate; Employment; Labor markets; Public employment; Labor force; Public sector; Middle East; Middle East and Central Asia; North Africa; East Africa; Global
Cover Summary Proceedings of the Twenty-Seventh Annual Meeting of the Board of Governors 1972
Summary Proceedings of the Twenty-Seventh Annual Meeting of the Board of Governors 1972
International Monetary Fund. Secretary's Department

Abstract

The speeches made by officials attending the IMF–World Bank Annual Meetings are published in this volume, along with the press communiqués issued by the International Monetary and Financial Committee and the Development Committee at the conclusion of the meetings.

Keywords:
AS; prefix; R. van S.; fund documents no; IBRD election; IMF election; van S.; International monetary system; Monetary systems; Currencies; International liquidity; Reserve assets; Global; Africa; Central America; Europe; Caribbean