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Middle East and Central Asia > Qatar

You are looking at 1 - 7 of 7 items for :

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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 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
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
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
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 IMF Working Papers
Does Public-Sector Employment Fully Crowd Out Private-Sector Employment?
Mr. Alberto Behar
and
Mr. Junghwan Mok
We quantify the extent to which public-sector employment crowds out private-sector employment using specially assembled datasets for a large cross-section of developing and advanced countries, and discuss the implications for countries in the Middle East, North Africa, Caucasus and Central Asia. These countries simultaneously display high unemployment rates, low private-sector employment rates and high proportions of government-sector employment. Regressions of either private-sector employment rates or unemployment rates on two measures of public-sector employment point to full crowding out. This means that high rates of public employment, which incur substantial fiscal costs, have a large negative impact on private employment rates and do not reduce overall unemployment rates.
Keywords:
WP; private sector; private-sector employment; Employment; Crowding out; Middle East and Central Asia; employment rate; crowding-out effect; C. private-sector employment; employment dataset; MENAP country; outcomes in the MCD; MCD country; employment equation; MCD countries' employment data; CCA country; labor force; Public employment; Employment rate; Government debt management; Unemployment; Middle East