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Social Science > Demography

You are looking at 1 - 10 of 39 items for :

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Cover IMF Working Papers
Satellite-Based Census of Residential Buildings: Application for Climate Risk Assessment
Andinet Woldemichael
and
Iyke Maduako
Housing represents the largest asset and liability, in the form of mortgages, on most national balance sheet. For most households it is their largest investment, and when mortgages are required also represents the largest component of household debt. It is also directly tied to financial markets, both the mortgage market and insurance sector. Although many countries have a rich set of housing censuses and statistics, others have large data gap in this area and therefore struggle to formulate effective policies. This paper proposes an approach to construct a global census of residential buildings using opensource satellite data. Such a layer can be used to assess the extent these buildings are exposed to climate hazards and how their production and consumption, in turn, affect the climate. The approach we propose could be scaled globally, combining existing layers of building footprints, climate and socioeconomic data. It adds to the ongoing effort of compiling spatially explicit and granular climate indicators to better inform policies. As a case study, we compute selected indicators and estimate the extent of residential properties exposure to riverine flood risk for Kenya.
Keywords:
Census of buildings; Climate Exposure; Building Footprint; flood risk; property indicator; aggregate dwellings Capital Stock value; descriptive statistics of the data; flood hazard map; return period; Housing; Climate change; Stocks; Global
Cover Technical Assistance Reports
United Kingdom-Montserrat-British Overseas Territory: Technical Assistance Report-Report on External Sector Statistics Mission (March 18–28, 2024)
International Monetary Fund. Statistics Dept.
and
International Monetary Fund. Western Hemisphere Dept.
A technical assistance (TA) mission on external sector statistics (ESS) was conducted for the Statistics Department of Montserrat (SDM), during March 18–28, 2024. The mission was undertaken as part of the Caribbean Regional Technical Assistance Centre (CARTAC) work program on ESS. The main purpose of this mission was to assist the SDM in extrapolating the results of the Visitor Expenditure Survey (VES) conducted by the SDM and Montserrat Tourism Division (MTD). The purpose of the VES was to enhance estimates of travel services credits recorded in the current account of the balance of payments (BOP).
Keywords:
Montserrat; balance of payments statistics; travel exports; visitor expenditure survey.; excursionist visitor; IMF's Statistics Department; travel credit; cruise visitor; visitor expenditure survey; Credit; External sector statistics; 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
The Macroeconomic Effects of Large Immigration Waves
Philipp Engler
,
Ms. Margaux MacDonald
,
Mr. Roberto Piazza
, and
Galen Sher
We propose a novel approach to measure the dynamic macroeconomic effects of immigration on the destination country, combining the analysis of episodes of large immigration waves with instrumental variables techniques. We distinguish the impact of immigration shocks in OECD countries from that of refugee immigration in emerging and developing economies. In OECD, large immigration waves raise domestic output and productivity in both the short and the medium term, pointing to significant dynamic gains for the host economy. We find no evidence of negative effects on aggregate employment of the native-born population. In contrast, our analysis of large refugee flows into emerging and developing countries does not find clear evidence of macroeconomic effects on the host country, a conclusion in line with a growing body of evidence that refugee immigrants are at disadvantage compared to other type of immigrants.
Keywords:
Immigration; productivity; dynamic gains; immigration wave; immigration shock; immigration episode; OECD migration flow data; migration shock; Migration; Employment; Total factor productivity; Estimation techniques; Middle East
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
Commercial Real Estate in Crisis: Evidence from Transaction-Level Data
Mr. Adrian Alter
,
Elizabeth M. Mahoney
, and
Cristian Badarinza
During the past two decades, the commercial real estate (CRE) market has been impacted by major disruptions, including the global financial crisis and the Covid-19 pandemic. Using granular data from the U.S., we document how these crises have unfolded and elaborate on the role of heterogeneity and underlying shocks. Both a set of reduced-form approaches and a structural framework suggest a prominent role for demand-side local factors in the short run, along with significant shifts in preferences during crisis episodes. However, valuations become more closely linked to macro-financial factors over the long term. A one-standard deviation tightening in financial conditions is associated with a drop of about 3% in CRE prices in the following quarter, with a stronger impact on the retail sector and milder effects in states where household indebtedness is lower.
Keywords:
Non-residential Real Estate; Financial Conditions; Liquidity; Big Data; level data; CRE price; household indebtedness; CRE market; CRE transaction; COVID-19; Real estate prices; Global financial crisis of 2008-2009; Global; Middle East and Central Asia
Cover IMF Working Papers
Private Savings and COVID-19 in Sub-Saharan Africa
Mr. Boileau Loko
,
Nelie Nembot
, and
Mr. Marcos Poplawski Ribeiro
The paper reexamines the main private savings determinants in Sub-Saharan Africa (SSA), followed by an analysis of the COVID-19 pandemic impact on private savings in SSA and other country groupings. Using an unbalanced panel data from 1983−2021 for 31 SSA economies, the paper finds that real per capita economic growth remains a key historical determinant of private savings in the region. In contrast with other regions, private saving rates have not increased during COVID-19 in SSA. Instead, COVID-19 deaths in our estimations are significantly associated with a decline in private savings in SSA. Robustness checks and a descriptive analysis of household surveys during the pandemic corroborate those results.
Keywords:
Private Savings; Sub-Saharan Africa; COVID-19; Economic Growth; savings determinants in SSA; pandemic impact; preventive measure; SSA economy; robustness check; Income; Estimation techniques; Middle East; North Africa
Cover IMF Working Papers
Measuring Quarterly Economic Growth from Outer Space
Robert C. M. Beyer
,
Yingyao Hu
, and
Jiaxiong Yao
This paper presents a novel framework to estimate the elasticity between nighttime lights and quarterly economic activity. The relationship is identified by accounting for varying degrees of measurement errors in nighttime light data across countries. The estimated elasticity is 1.55 for emerging markets and developing economies, ranging from 1.36 to 1.81 across country groups and robust to different model specifications. The paper uses a light-adjusted measure of quarterly economic activity to show that higher levels of development, statistical capacity, and voice and accountability are associated with more precise national accounts data. The elasticity allows quantification of subnational economic impacts. During the COVID-19 pandemic, regions with higher levels of development and population density experienced larger declines in economic activity.
Keywords:
Nighttime lights; economic measurement; quarterly GDP; national accounts; COVID-19.; GDP growth; Summary statistics; light growth; growth measure; GDP measure; COVID-19; GDP measurement; Vector autoregression; Global
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
Cover IMF Working Papers
Measuring Digital Financial Inclusion in Emerging and Developing Economies: A New Index
Purva Khera
,
Miss Stephanie Y Ng
,
Ms. Sumiko Ogawa
, and
Ms. Ratna Sahay
Adoption of technology in the financial services industry (i.e. fintech) has been accelerating in recent years. To systematically and comprehensively assess the extent and progress over time in financial inclusion enabled by technology, we develop a novel digital financial inclusion index. This index is based on payments data covering 52 developing countries for 2014 and 2017, taking into account both access and usage dimentions of digital financial services (DFSs). This index is then combined with the traditional measures of financial inclusion in the literature and aggregated into an overall index of financial inlusion. There are two key findings: first, the adoption of fintech has been a key driver of financial inclusion. Second, there is wide variation across countries and regions, with the greatest progress recorded in Africa and Asia and the Pacific regions. This index should offer a useful analytical tool for researchers and policy makers.
Keywords:
Fintech; digital financial services; financial inclusion; financial services industry; adoption of technology; capital markets department; services play; account ownership; Mobile banking; Caribbean; Asia and Pacific; Global; Africa