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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 13 items for :

  • Type: Journal Issue x
  • Demand and Supply of Labor: General x
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Cover Policy Papers
Gulf Cooperation Council: Pursuing Visions Amid Geopolitical Turbulence: Economic Prospects and Policy Challenges for the GCC Countries
International Monetary Fund. Middle East and Central Asia Dept.
The Gulf Cooperation Council countries have successfully weathered recent turbulence in the Middle East, and their economic prospects remain favorable. Nonhydrocarbon activity has been strong amid reform implementation, although overall growth has decelerated due to cuts in oil production. The growth outlook is positive, as the envisaged easing of oil production cuts and natural gas expansion spur the recovery in the hydrocarbon sector, while the nonhydrocarbon economy continues to expand. External buffers remain comfortable despite current account balances having narrowed. Risks around the outlook are broadly balanced in the near term. More challenging medium-term risks, especially in the context of geoeconomic fragmentation and climate change, call for action on policy priorities to continue to strengthen the private sector and to diversify the economy.
Keywords:
economic diversification; financial stability; fiscal sustainability; GCC; productivity improvements; regional integration; GCC country; policy priority; B. GCC; GCC well; GCC public sector balance sheet; Labor markets; Fiscal rules; Total factor productivity; Global; Middle East; Middle East and Central Asia
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 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 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
Cover IMF Staff Country Reports
Singapore: 2024 Article IV Consultation-Press Release; Staff Report; and Statement by the Executive Director for Singapore
International Monetary Fund. Asia and Pacific Dept
The 2024 Article IV Consultation with Singapore highlights that following a slowdown in 2023, growth is projected to recover gradually to 2.1 percent in 2024. After reaching 6.1 percent in 2022, inflation has steadily declined to 2.7 percent in April 2024. The pace of disinflation has nonetheless been gradual, with signs of persistent price pressures including from a tight labor market. With risks to global growth now broadly balanced, downside risks to growth outlook have diminished relative to last year, but Singapore remains vulnerable to a deepening of geo-economic fragmentation. Inflation risks remain tilted to the upside. The broadly neutral fiscal stance relative to 2023 will complement the tight monetary policy stance in achieving price stability, while targeted support to vulnerable households and firms will provide temporary relief from high costs of living and business. Singapore’s financial sector remains resilient with solid capital and liquidity buffers, though vigilance against pockets of vulnerabilities is warranted, including from potential systemic risks arising from the housing market. In this context, the tight macroprudential policy stance remains appropriate.
Keywords:
article IV consultation discussion; FSAP update; Singapore economy; CPI-All Items inflation; disinflation trend; Labor markets; Inflation; Artificial intelligence; Liquidity requirements; Global; Asia and Pacific
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
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 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 IMF Working Papers
Labor Market Exposure to AI: Cross-country Differences and Distributional Implications
Carlo Pizzinelli
,
Augustus J Panton
,
Ms. Marina Mendes Tavares
,
Mauro Cazzaniga
, and
Longji Li
This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI's potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa), revealing substantial variations in unadjusted AI exposure across countries. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure across countries are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity.
Keywords:
Artificial intelligence; Employment; Occupations; Emerging Markets; impact of artificial intelligence; exposure to AI; labor market exposure; employment share; educated worker; Labor markets
Cover IMF Working Papers
Structural Reforms and Economic Growth: A Machine Learning Approach
Mr. Anil Ari
,
Gabor Pula
, and
Liyang Sun
The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of cross-country structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.
Keywords:
Structural reforms; institutions; economic growth; C. PLS estimation procedure; machine learning approach; Gabor pula; Liyang sun; labor market composite; Business environment; Labor markets; Machine learning; Labor market reforms; Global