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Social Science > Demography

You are looking at 1 - 10 of 15 items for :

  • Type: Journal Issue x
  • Technological Change: Choices and Consequences; Diffusion Processes x
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Cover Selected Issues Papers
Transforming the Future: The Impact of Artificial Intelligence in Korea
Soo Jung Chang
,
Hamin Lee
,
Sumin Lee
,
Samil Oh
,
Zexi Sun
, and
Xin Cindy Xu
This paper examines the economic impact of Artificial Intelligence (AI) in Korea. Korea is among the global frontrunners in AI adoption, with higher adoption rates among larger, younger, and technologically advanced firms. AI holds the promise for boosting productivity and output, though the effects are more pronounced among larger and mature Korean firms. About half of jobs are exposed to AI, with higher exposures among female, younger, more educated, and higher income workers. Korea’s strong innovation and digital infrastructure highlights its AI readiness, while enhancing labor market flexibility and social safety nets are essential to fully harness AI’s potential.
Keywords:
Artificial Intelligence; AI; labor market; productivity; growth; E. AI preparedness; profitability regression; preparedness index; AI adoption; B. AI adoption; Aging; Labor market policy; Global
Cover IMF Working Papers
Prometheus Unbound: What Makes Fintech Grow?
Serhan Cevik
The rise of financial technologies—fintech—could have transformative effects on the financial landscape, expanding the reach of services beyond the confines of geography and creating new competitive sources of finance for households and firms. But what makes fintech grow? Why do some countries have more financial innovation than others? In this paper, I use a comprehensive dataset to investigate the emergence and spread of fintech in a diverse panel of 98 countries over the period 2012–2020. This empirical analysis helps ascertain economic, demographic, technological and institutional factors that enable the development of fintech. The magnitude and statistical significance of these factors vary according to the type of fintech instrument and the level of economic development (advanced economies vs. developing countries). Finally, these findings reveal that policies and structural reforms can help promote financial innovation and cultivate fintech ventures—particularly by strengthening technological and institutional infrastructures and reducing cybersecurity threats.
Keywords:
Fintech; financial innovation; technological change; cybersecurity threat; fintech venture; copyright page; government stability foster fintech growth; fintech transaction; Digital financial services; Financial sector development; Population growth; Europe
Cover IMF Working Papers
Government Debt and Growth: The Role of R&D
Can Sever
Economic growth in the advanced economies (AEs) has been slowing down since the early 2000s, while government debt ratios have been rising. The recent surge in debt at the onset of the Covid-19 pandemic has further intensified concerns about these phenomena. This paper aims to offer insight into the high-debt low-growth environment in AEs by exploring a causal link from government debt to future growth, specifically through the impact of debt on R&D activities. Using data from manufacturing industries since the 1980s, it shows that (i) government debt leads to a decline in growth, particularly in R&D-intensive industries; (ii) the differential effect of government debt on these industries is persistent; and (iii) more developed or open financial systems tend to mitigate this negative impact. These findings contribute to our understanding of the relationship between government debt and growth in AEs, given the role of technological progress and innovation in economic growth.
Keywords:
Government debt; fiscal policy; economic growth; R&D; innovation; financial development; economic growth in the advanced economies; R&D activity; impact of debt; debt level; affect tax dynamics; Financial sector development; Manufacturing; Productivity; Aging; Global
Cover IMF Staff Country Reports
Benin: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper presents stylized facts on Benin’s ongoing economic transformation, and analyzes the country’s new eco-system. A recent IMF paper explores conditions under which the country’s industrial policy could meet its intended goals while avoiding unintended economic distortions down the road. While economic diversification is found to be associated with higher economic growth, evidence on the causal impact of industrial policies is hard to establish. While empirical evidence suggests that Benin’s reliance on traditional sectors, notably the Port of Cotonou, is moderating, economic diversification remains limited. The government embarked on industrial policy with the transformation of local commodities as main engine, including via the launching of a Special Economic Zone (SEZ) in 2020. It is recommending that the authorities should pursue efforts to ensure transparency in the selection of SEZ-related incentives. Intra-regional trade integration holds significant potential for Benin and could support economic diversification. Ongoing post-electoral policy shifts in Nigeria and formalization underway of economic ties between both nations, if permanent, would curb rent-seeking in Benin.
Keywords:
demographics dividend; B. addressing Internet accessibility; infrastructure investment; Beninese government; completion rate; Digitalization; Exports; Global; Sub-Saharan Africa; West Africa
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 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 IMF Working Papers
How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan)
Tohid Atashbar
In this study we introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data. Our analysis of the fragility data collected by the OECD for its States of Fragility index showed that the use of such techniques could provide further insights into the non-linear relationships and diverse drivers of state fragility, highlighting the importance of a nuanced and context-specific approach to understanding and addressing this multi-aspect issue. We also applied the methodology used in this paper to South Sudan, one of the most fragile countries in the world to analyze the dynamics behind the different aspects of fragility over time. The results could be used to improve the Fund’s country engagement strategy (CES) and efforts at the country.
Keywords:
Fragile and Conflict-Affected States; Fragility Trap; Fragility Syndrome; Machine Learning; Artificial Intelligence; AI technique; fragility data; Bird's-Eye View; artificial intelligence technique; aggregate fragility; Artificial intelligence and machine learning; Global; Middle East; North Africa; South Asia; Sub-Saharan Africa
Cover IMF Working Papers
Estimating Digital Infrastructure Investment Needs to Achieve Universal Broadband
Edward Oughton
,
Mr. David Amaglobeli
, and
Mr. Mariano Moszoro
We develop a detailed model to evaluate the necessary investment requirements to achieve affordable universal broadband. The results indicate that approximately $418 billion needs to be mobilized to connect all unconnected citizens globally (targeting 40-50 GB/Month per user with 95 percent reliability). The bulk of additional investment is for emerging market economies (73 percent) and low-income developing countries (24 percent). We also find that if the data consumption level is lowered to 10-20 GB/Month per user, the total cost decreases by up to about half, whereas raising data consumption to 80-100 GB/Month per user leads to a cost increase of roughly 90 percent relative to the baseline. Moreover, a 40 percent cost decrease occurs when varying the peak hour quality of service level from the baseline 95 percent reliability, to only 50 percent reliability. To conclude, broadband policy assessments should be explicit about the quantity of data and the reliability of service provided to users. Failure to do so will lead to inaccurate estimates and, ultimately, to poor broadband policy decisions.
Keywords:
Broadband; Digital Infrastructure; Public Investment; Sustainable Development Goals; cost decrease; investment requirement; infrastructure investment; cost sensitivity; data consumption; policy assessment; estimation metrics; cost driver; data traffic; infrastructure cost modeling; Digitalization; Infrastructure; Emerging and frontier financial markets; Sub-Saharan Africa; Global; Caribbean; Central Asia and the Caucasus; Asia and Pacific
Cover IMF Working Papers
Forecasting Social Unrest: A Machine Learning Approach
Chris Redl
and
Sandile Hlatshwayo
We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial, socioeconomic, development and political variables. The prediction model correctly forecasts unrest in the following year approximately two-thirds of the time. Shapley values indicate that the key drivers of the predictions include high levels of unrest, food price inflation and mobile phone penetration, which accord with previous findings in the literature.
Keywords:
Social unrest; machine learning.; machine learning model; risk index; prediction model; machine learning approach; IMF working; Machine learning; Inflation; Food prices; Global; unrest event