Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted.
The Longevity Economy: How People Over 50 Are Driving Economic and Social Val...Longevity Network
The full 2016 Longevity Economy Report.
Background: By 2015, more than 1.6 billion people in the world were part of the 50-plus cohort. By 2050, this number is projected to nearly double to about 3.2 billion people. Throughout the world, the growth of this age group is having a transformative impact, economically and socially. The U.S. alone is home to 111 million in the 50-plus cohort; they represent a powerful force that is driving economic growth and value. This is the Longevity Economy, representing the sum of all economic activity driven by the needs of Americans age 50 and older, and includes both products and services they purchase directly and the further economic activity this spending generates. The difference it makes is substantial. In our first report released in 2013, the Longevity Economy fostered $7.1 trillion in annual economic activity. This figure has now been revised up to $7.6 trillion in our 2016 report. The outsized contribution reflects the changing demographics, wealth and spending patterns of the 50-plus population as the life span increases and the Longevity Economy becomes more pervasive and central to economic and social policies.
Find out how far OECD countries are from achieving the SDG targets. Based on the UN Global Indicator List, and using UN SDG and OECD data, the OECD has developed a unique methodology allowing comparison of progress across SDG goals and targets. Measuring Distance to the SDG Targets 2019 - An Assessment of Where OECD Countries Stand also includes innovative analyses on trends and on measuring the transboundary aspects of the 2030 Agenda as well as country notes showing performance at the target level and data gaps for individual OECD countries.
Για τρίτη συνεχή χρονιά, ο Κύκλος ιδεών για την Εθνική Ανασυγκρότηση,
σε συνεργασία με τη Συμεών Γ. Τσομώκος Α.Ε., πραγματοποιούν το ετήσιο διήμερο συνέδριο H ΕΛΛΑΔΑ ΜΕΤΑ
στις 19 και 20 Ιουνίου 2019
στο ξενοδοχείο Divani Caravel.
Κεντρικό θέμα στο φετινό συνέδριο είναι: Η ανασύσταση της μεσαίας τάξης
Κύκλος ΙΙ: Οι επιπτώσεις της περιόδου 2009- 2019 στη μεσαία τάξη
https://ekyklos.gr/19-20-iouniou-ellada-meta-iii-i-anasystasi-tis-mesaias-taksis.html
The Media Playbook: City and The Cities is a media concept sandbox prepared by the Future City Summit and Good City Foundation. The webinar and podcast series develops to sustain virtual dialogue among the policy planners, technologists, mayors and investors to continue learning about our post-pandemic world.
How's Life? 2020: Measuring Well-being charts whether life is getting better for people in 37 OECD countries and 4 partner countries. This fifth edition presents the latest evidence from an updated set of over 80 indicators, covering current well-being outcomes, inequalities, and resources for future well-being. Since 2010, people’s well-being has improved in many respects, but progress has been slow or deteriorated in others, including how people connect with each other and their government.
The UK in 2030 - An expert informed view on some key trendsFuture Agenda
At a time when there is much speculation on what the next twelve months may bring, some are also looking ahead to prepare for the longer term. What will the UK be like in 2030 when the nation is post-Covid, post-Brexit and post-Johnson? Now that vaccines are being rolled out and the initial outline hard Brexit deal has been done, how will the UK fair over the decade – economically, socially and demographically? What changes are already locked-in and what is open to future variation? Based on numerous discussions with a wide range of experts across the UK in late 2020, this document explores some of the key potential trends for the next decade and highlights where the UK may be heading.
Having a well-defined future view is never easy – particularly in times of uncertainty. However, if we can differentiate between the certain, the probable and the possible we can build a clearer picture of the future which may help to challenge assumptions. Since 2010, Future Agenda has been using open foresight to explore decade-long trends with a high degree of accuracy. The World in 2020, written in 2010 for example, accurately anticipated a range of developments such as a global pandemic, the challenges around data privacy, the scaling up of electric and autonomous vehicles, the widespread use of drones and the building impact of solar energy. All of these were anticipated through extensive expert dialogue across multiple disciplines to curate an integrated, informed perspectives which can be accessed by everyone.
We used a similar approach to explore the pivotal shifts ahead for the UK. Following multiple expert discussions including academics, regional and central government, social and business leaders, as well as the military, this document summarises eight areas of alignment about UK 2030 but also highlights three fields where there is substantial difference of opinion.
Our conversations identified eight core areas where we can have confidence that changes will take place. These trends are:
1. A Changing Demographic Mix
2. Accelerating to Zero Carbon
3. Improved Digital Connectivity
4. Declining Economic Influence
5. More Devolved Power
6. Rising Inequality
7. Emphasis on the Local
8. UK Leadership
COVID-19: Impact on Creative Jobs – Ekaterina TravkinaOECD CFE
Presented by Ekaterina Travkina during the meeting held in virtual format entitled, "Culture & jobs: rescue, support, unleash" from 27-28 January 2021 as part of the CULTURE, CREATIVE SECTORS AND LOCAL DEVELOPMENT Policy webinar series. Providing evidence and guidance to cities and regions on ways to maximize the economic and social impact of culture and support the creative economy.
Strategy of bangladesh to face coronavirusM S Siddiqui
If widespread social distancing must be pursue, then effective steps must be made to get food, fuel, and cash into the hands of the people most at risk of hunger and deprivation. This is especially challenging in countries without well-developed social protection infrastructure. The government has taken a right decision based on economic benefits and support system to the needy people. Let us hope for the best.
The report reveals that many governments are not paying enough attention to the social implications of their economic policies geared towards recovery from the economic crisis. According to the report, recovery has been uneven and continues to be fragile, with a wide range of negative social impacts lingering from the economic downturn. Poverty and unemployment have for instance been linked to crime, gender-based violence, substance abuse and mental illness.
The Report on the World Social Situation 2011: The Global Social Crisis, published by the UN Department of Economic and Social Affairs (UN-DESA), finds many governments did not pay sufficient attention to the social implications of the global economic crisis. The report says economic policies considered in isolation from their social consequences often create dire results for people’s nutrition, health and education, which, in turn, adversely affect long-term economic growth.
On 29 April 2019, ICTD's Wilson Prichard presented at the UN ECOSOC Meeting, discussing taxation and equity. Find out more about the research: https://www.ictd.ac/theme/tax-welfare-and-inequality/
Globalization has altered the way we live and earn a livelihood. Consequently, trade and travel have been recognized as significant determinants of the spread of disease. Additionally, the rise in urbanization and the closer integration of the world economy have facilitated global interconnectedness. Therefore, globalization has emerged as an essential mechanism of disease transmission. This paper aims to examine the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel, and countries most impacted.
The Longevity Economy: How People Over 50 Are Driving Economic and Social Val...Longevity Network
The full 2016 Longevity Economy Report.
Background: By 2015, more than 1.6 billion people in the world were part of the 50-plus cohort. By 2050, this number is projected to nearly double to about 3.2 billion people. Throughout the world, the growth of this age group is having a transformative impact, economically and socially. The U.S. alone is home to 111 million in the 50-plus cohort; they represent a powerful force that is driving economic growth and value. This is the Longevity Economy, representing the sum of all economic activity driven by the needs of Americans age 50 and older, and includes both products and services they purchase directly and the further economic activity this spending generates. The difference it makes is substantial. In our first report released in 2013, the Longevity Economy fostered $7.1 trillion in annual economic activity. This figure has now been revised up to $7.6 trillion in our 2016 report. The outsized contribution reflects the changing demographics, wealth and spending patterns of the 50-plus population as the life span increases and the Longevity Economy becomes more pervasive and central to economic and social policies.
Find out how far OECD countries are from achieving the SDG targets. Based on the UN Global Indicator List, and using UN SDG and OECD data, the OECD has developed a unique methodology allowing comparison of progress across SDG goals and targets. Measuring Distance to the SDG Targets 2019 - An Assessment of Where OECD Countries Stand also includes innovative analyses on trends and on measuring the transboundary aspects of the 2030 Agenda as well as country notes showing performance at the target level and data gaps for individual OECD countries.
Για τρίτη συνεχή χρονιά, ο Κύκλος ιδεών για την Εθνική Ανασυγκρότηση,
σε συνεργασία με τη Συμεών Γ. Τσομώκος Α.Ε., πραγματοποιούν το ετήσιο διήμερο συνέδριο H ΕΛΛΑΔΑ ΜΕΤΑ
στις 19 και 20 Ιουνίου 2019
στο ξενοδοχείο Divani Caravel.
Κεντρικό θέμα στο φετινό συνέδριο είναι: Η ανασύσταση της μεσαίας τάξης
Κύκλος ΙΙ: Οι επιπτώσεις της περιόδου 2009- 2019 στη μεσαία τάξη
https://ekyklos.gr/19-20-iouniou-ellada-meta-iii-i-anasystasi-tis-mesaias-taksis.html
The Media Playbook: City and The Cities is a media concept sandbox prepared by the Future City Summit and Good City Foundation. The webinar and podcast series develops to sustain virtual dialogue among the policy planners, technologists, mayors and investors to continue learning about our post-pandemic world.
How's Life? 2020: Measuring Well-being charts whether life is getting better for people in 37 OECD countries and 4 partner countries. This fifth edition presents the latest evidence from an updated set of over 80 indicators, covering current well-being outcomes, inequalities, and resources for future well-being. Since 2010, people’s well-being has improved in many respects, but progress has been slow or deteriorated in others, including how people connect with each other and their government.
The UK in 2030 - An expert informed view on some key trendsFuture Agenda
At a time when there is much speculation on what the next twelve months may bring, some are also looking ahead to prepare for the longer term. What will the UK be like in 2030 when the nation is post-Covid, post-Brexit and post-Johnson? Now that vaccines are being rolled out and the initial outline hard Brexit deal has been done, how will the UK fair over the decade – economically, socially and demographically? What changes are already locked-in and what is open to future variation? Based on numerous discussions with a wide range of experts across the UK in late 2020, this document explores some of the key potential trends for the next decade and highlights where the UK may be heading.
Having a well-defined future view is never easy – particularly in times of uncertainty. However, if we can differentiate between the certain, the probable and the possible we can build a clearer picture of the future which may help to challenge assumptions. Since 2010, Future Agenda has been using open foresight to explore decade-long trends with a high degree of accuracy. The World in 2020, written in 2010 for example, accurately anticipated a range of developments such as a global pandemic, the challenges around data privacy, the scaling up of electric and autonomous vehicles, the widespread use of drones and the building impact of solar energy. All of these were anticipated through extensive expert dialogue across multiple disciplines to curate an integrated, informed perspectives which can be accessed by everyone.
We used a similar approach to explore the pivotal shifts ahead for the UK. Following multiple expert discussions including academics, regional and central government, social and business leaders, as well as the military, this document summarises eight areas of alignment about UK 2030 but also highlights three fields where there is substantial difference of opinion.
Our conversations identified eight core areas where we can have confidence that changes will take place. These trends are:
1. A Changing Demographic Mix
2. Accelerating to Zero Carbon
3. Improved Digital Connectivity
4. Declining Economic Influence
5. More Devolved Power
6. Rising Inequality
7. Emphasis on the Local
8. UK Leadership
COVID-19: Impact on Creative Jobs – Ekaterina TravkinaOECD CFE
Presented by Ekaterina Travkina during the meeting held in virtual format entitled, "Culture & jobs: rescue, support, unleash" from 27-28 January 2021 as part of the CULTURE, CREATIVE SECTORS AND LOCAL DEVELOPMENT Policy webinar series. Providing evidence and guidance to cities and regions on ways to maximize the economic and social impact of culture and support the creative economy.
Strategy of bangladesh to face coronavirusM S Siddiqui
If widespread social distancing must be pursue, then effective steps must be made to get food, fuel, and cash into the hands of the people most at risk of hunger and deprivation. This is especially challenging in countries without well-developed social protection infrastructure. The government has taken a right decision based on economic benefits and support system to the needy people. Let us hope for the best.
The report reveals that many governments are not paying enough attention to the social implications of their economic policies geared towards recovery from the economic crisis. According to the report, recovery has been uneven and continues to be fragile, with a wide range of negative social impacts lingering from the economic downturn. Poverty and unemployment have for instance been linked to crime, gender-based violence, substance abuse and mental illness.
The Report on the World Social Situation 2011: The Global Social Crisis, published by the UN Department of Economic and Social Affairs (UN-DESA), finds many governments did not pay sufficient attention to the social implications of the global economic crisis. The report says economic policies considered in isolation from their social consequences often create dire results for people’s nutrition, health and education, which, in turn, adversely affect long-term economic growth.
On 29 April 2019, ICTD's Wilson Prichard presented at the UN ECOSOC Meeting, discussing taxation and equity. Find out more about the research: https://www.ictd.ac/theme/tax-welfare-and-inequality/
Social contacts are a key transmission channel of infectious diseases spread by the respiratory or close-contact route, such as COVID-19. There is no evidence, however, on the question of whether the nature and the organisation of work affect the spread of COVID-19 in different countries. I have developed a methodology to measure country-specific levels of occupational exposure to contagion driven by social contacts. I combined six indicators based on Occupation Information Network (O*NET) and the European Working Condition Survey (EWCS) data. I then applied them to 26 European countries, and found substantial cross-country differences in levels of exposure to contagion in comparable occupations. The resulting country-level measures of levels of exposure to contagion (excluding health professions) predict the growth in COVID-19 cases, and the number of deaths from COVID-19 in the early stage of pandemic (up to four weeks after the 100th case). The relationship between levels of occupational exposure to contagion and the spread of COVID-19 is particularly strong for workers aged 45-64. I found that 20-25% of the cross-country variance in numbers of COVID-19 cases and deaths can be attributed to cross-country differences in levels of occupational exposure to contagion in European countries. My findings are robust to controlling for the stringency of containment policies, such as lockdowns and school closures. They are also driven by country-specific patterns of social contacts at work, rather than by occupational structures. Thus, I conclude that measuring workplace interactions may help to predict the next waves of the COVID-19 pandemic.
The International Journal of Computational Science, Information Technology an...rinzindorjej
The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE) is an open access peer-reviewed journal that publishes quality articles which make innovative contributions in all areas of Computational Science, Mathematical Modeling, Information Technology, Networks, Computer Science, Control and Automation Engineering. IJCSITCE is an abstracted and indexed journal that focuses on all technical and practical aspects of Scientific Computing, Modeling and Simulation, Information Technology, Computer Science, Networks and Communication Engineering, Control Theory and Automation. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced techniques in computational science, information technology, computer science, chaos, control theory and automation, and establishing new collaborations in these areas.
The Role of Communication In COVID-19 Crisis Management: Findings about Infor...CSCJournals
Given the different levels of pandemic severity in Germany and Italy, the paper investigates the differences in information behavior, and its consequences, between German and Italian young adults during the COVID-19 pandemic, especially in the first and second waves of infections in the two countries. In particular, the issue of crisis management through institutional communication, the development of information behavior and information consumption throughout the year 2020 are analyzed through a questionnaire-based case study considering the constructs topic fatigue, trust in government sources and vaccination hesitancy. The paper reveals that there are considerable differences in time spent looking for information, with Italian respondents generally spending more time in these activities. Surprisingly, Italian respondents seem to place more trust in their government and institutions than Germans do. Vaccination hesitancy is estimated as higher in Germany than in Italy. People who feel less informed are also less likely to get a vaccination when offered the possibility, moreover, the perception of risk of infection plays a major role in vaccination acceptance. From the point of view of public management, findings of this work highlight the knowledge of citizens’ information behavior and habits as relevant to the design of communication campaigns to address health crises.
impact of metropolitanization on covid 19 cases in india using entropy weight...ijtsrd
The global pandemic COVID 19 which was started early this year spreading rapidly in developed countries as all well as developing countries of the world. The noticeable fact is that most of the metropolitan cities of the world are severely affected by Corona pandemic and toped in their respective country among the COVID cases. The impact of the metropolitan city on COVID 19 cases, example can be cited around every corner of the world, From Wuhan to New York, Mumbai to Sao Paulo and Moscow to Madrid the Metropolitan cities of the world’s come out as deep rooted hotspots of novel coronavirus pandemic. Mumbai, Delhi, Chennai, and Kolkata are the leading metropolis in India also leading in COVID 19 cases it can be explained by their connectivity to the rest of the world by the people and products. Therefore this article aims to summerise the impact of six metropolitan cities on the total COVID 19 cases of their states and try to find out which city have best suited in the concept of the Metropolitanization of COVID 19 cases. Entropy based TOPSIS methods are applied to compare the dataset of six metropolitan cities of India, and try to find out which city best fitted in the concept of metropolitanization of COVID 19 cases. Seven factors are chosen to analyze the impact of metropolitan cities on COVID 19 cases such as city population, percentage of slum population, number of COVID cases, airport traffic movement, relative humidity, and temperature. Entropy methods applied to weights the criterion for finding which criteria have maximum influence on COVID 19 cases. After that on the basis of Entropy weights, the TOPSIS method has been used to evaluate the dataset of six cities to track down the relative position of cities on the concentration of COVID 19 cases. After comparing the alternatives in TOPSIS method i.e those six cities , Delhi came in the first position, followed by Mumbai 2nd , Chennai 3rd , Kolkata 4th , Ahmedabad 5th , Hyderabad 6th based on the concentration of COVID 19 cases in the metropolitan cities. Sanu Dolui | Sayani Chakraborty "Impact of Metropolitanization on Covid-19 Cases in India using Entropy Weights Based Topsis Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32929.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/enviormental-science/32929/impact-of-metropolitanization-on-covid19-cases-in-india-using-entropy-weights-based-topsis-approach/sanu-dolui
Predictive analysis WHO's life expectancy dataset using Tableau data visualis...Tarun Swarup
Performed predictive analysis on global Life expectancy dataset (WHO) to analyze the vital factors affecting human health and other societal risks demographically.
Designed a visual dashboard to identify intrinsic patterns in different factors and extract valuable insights to predict life expectancy accordingly.
▪ Infant Death Rate almost reduced by 40% in the last two decades.
▪ Overall adult mortality rate turned down by almost 17% in the previous years.
CAMA: The global macroeconomic impacts of COVID-19: Seven scenarios (results)TatianaApostolovich
The research of Warwick McKibbin (Australian National University, The Brookings Institution, Centre of Excellence in Population Ageing Research) and Roshen Fernando (Australian National University, Centre of Excellence in Population Ageing Research (CEPAR))
This is the presentation of Matthias Braubach at the event "Enabling nature-based health and social care through Knowledge Alliances" of the 1st Decemeber 2021.
This event was jointly organized by Green4C and Connecting Nature. Learn more about the event here:
https://www.greenforcare.eu/news/green-care-knowledge-alliances/
Presentation from Tatsuyoshi Oba, Executive Manager of Group HR Division, Persol Holdings during the OECD WISE Centre & Persol Holdings Workshop on Advancing Employee Well-being in Business and Finance, 22 November 2023
Presentation from Amy Browne, Stewardship Lead, CCLA Investment Management, during the OECD WISE Centre & Persol Holdings Workshop on Advancing Employee Well-being in Business and Finance, 22 November 2023
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Spatial Inequalities during and after the COVID-19 Pandemic, Laura Khoury
1. OECD Seminar: Spatial Inequalities during
and after the COVID-19 Pandemic
A Poorly Understood Disease? The Impact of COVID-19 on the
Income Gradient in Mortality over the Course of the Pandemic
Paul Brandily (PSE), Clément Brébion (CBS), Simon Briole (JPAL-PSE),
Laura Khoury (NHH)
July 8th
, 2021
2. Introduction
I An unprecedented worldwide decline in mortality over the last
century but still a substantial income gradient within most countries
(Case et al., 2002; Cutler et al., 2006; Currie et al., 2020)
→ Chetty et al. (2016): since 2000 in the US, +3 years in the life
expectancy of the top 5% vs. 0 for the bottom 5%
I Explained by:
n
Individual factors: information and education to healthy behaviours,
economic and social stress, access to health care, etc.
n
Ecological factors: living environment, presence of health care
infrastructures, density, pollution, etc.
1/32
3. Introduction
I Pandemic = sudden exogenous shock (i) reveals latent inequalities
and (ii) potentially amplifies them by spreading differently across
living environments (Alfani, 2021; Beach et al., 2021)
n
Only a few studies in the case of pandemics
n
Papers focusing on COVID-19 essentially correlational and cover the
first months of the epidemic (Chen and Krieger, 2021; Abedi et al.,
2020; Ashraf, 2020; Jung et al., 2020; Decoster et al., forthcoming;
Glaeser et al., 2020; Almagro and Orane-Hutchinson, 2020)
What is the impact of COVID-19 on the pre-existing spatial
gradient in mortality and what are the underlying mechanisms?
2/32
4. Our paper (1/2)
I Analysis of municipality-level variations within urban areas
across the French territory
I Quantification of the COVID-19-specific income gradient by using an
excess mortality measure
→ +30% in excess mortality in the poorest municipalities on average in 2020
→ 1.3 excess mortality-income elasticity
I Persistence of the gradient over both waves: size of the gradient
increases with the size of the mortality shock (at the urban-area
level)
→ suggests that the “harvesting effect” cannot explain the whole gradient
3/32
5. Our paper (2/2)
I Distinction between the epidemic-induced vs. lockdown-induced
effect on the gradient
→ no independent contribution of the lockdown
I Exploration of housing condition and occupational exposure
mechanisms
→ Capture a substantial share of the gradient
4/32
6. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
5/32
7. Definitions - Data
I Definitions :
n
Unit of analysis = municipality within the 621 French urban
areas. 16,640 municipalities. Median ≈ 784 inhab., 11km2
n
Urban areas = poles gathering at least 1,500 jobs and their periphery
based on commuting patterns ≈ local labor markets. Account for
about 85% of the population.
n
Départements: administrative units usually bigger than urban areas
I Data :
n
Death registry provided by the National Statistical Institute (INSEE)
at the individual level: information on date of death (day),
municipality of residency, place of death (hospital, home, elderly
care home), gender, age
n
Matched with information on the labour market, housing conditions,
municipalities’ demographics and income from administrative payroll
data, survey, Census.
6/32
8. Measures - All-cause excess mortality
Dp
m =
Np,2020
m − [0.5 × (Np,2018
m + Np,2019
m )]
Population2014
m
(1)
Np,y
m : number of deaths of residents of municipality m during period p of
year y
Populationm,2014: total number of inhabitants (/10,000) of municipality
m, as recorded in 2014, the most recent available year in our data.
7/32
9. Measures
All-cause excess mortality avoids numerous biases present in
COVID-19 infection or mortality data:
I Limited testing at the beginning of the epidemic and not randomly
distributed
I All COVID-19 cases or deaths are not always attributed as such
I Indirect deaths
I Excess mortality accounts for the pre-existing gradient
Poverty: bottom 25% of the national weighted distribution of
municipalities’ median standard of living
Robust to alternative definitions
8/32
10. A municipality-level approach
Municipal vs. Individual data: effect of living in a poor environment vs.
being poor
I Ecological approach
I Pr(DeathCovid ) function of the transmission and lethality
probabilities
I Individuals at risk of transmission not necesarily the ones at risk of
dying
I Important spillovers in case of an epidemic
I Policymakers need to identify clusters
9/32
11. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
10/32
12. Figure 1: Monthly counts of excess deaths in French urban areas
NOTE: The figure represents the difference between the monthly number of deaths in 2020 and its
average over 2019 and 2018 in the relevant zone. The “red” zone corresponds to the areas that
were the most severely hit by the first wave, and that are located in the North-Eastern quarter of the
country. This zone covers about 44% of the urban population of (mainland) France. The “green” zone
encompasses the rest of the French territory.
11/32
13. Figure 2: Cumulative excess mortality rate per 10,000 inh. by poverty
status
NOTE: The graph plots the cumulative sum of all excess deaths per 10,000 inhabitants from January
2020 for poor and non-poor municipalities. Poor is defined as belonging to the bottom quartile of the
national distribution of municipal median income weighted by the municipality size.
12/32
14. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
13/32
15. Empirical analysis
Dp
[m,ua] = β.Q1[m,ua] + Xp
[m,ua].Λ + γua + νp
[m,ua] (2)
I γua allows us to only exploit differences between municipalities
located in a contiguous urban environment.
I Xp
[m,ua] includes the total population and the share of the population
above 65 years old.
I Standard errors are clustered at the urban-area level.
I Identification assumption: absent COVID-19 and the associated
public policies, the average difference in the evolution of mortality
over period p (2020 vs. before) between rich and poor municipalities
of the same urban area would have remained stable
14/32
17. Empirical analysis
Table 1: Excess mortality rate by municipality income
(1) (2) (3) (4)
2020 Wave 1 Wave 2 No wave
Q1 (poor) 2.627*** 1.178* 1.083*** 0.366
(0.996) (0.672) (0.359) (0.297)
Controls X X X X
Urban area FE. X X X X
Non poor average 8.668 3.661 4.825 0.182
Observations 16640 16640 16640 16640
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the urban-area level.
NOTE: This table reports the coefficients associated with equation 2. The independent variable, excess
mortality rate, is computed considering four different time periods: the whole year (column 1), wave 1
(March to April, column 2), wave 2 (October to December, column 3) and other months in 2020 outside
the two waves (January, February and from May to September, column 4). By construction, column 1
is the sum of column 2 to 4. The non-poor average line reports the mean of the dependant variable in
non-poor municipalities. Controls include total population size and the share of the population over 65
years old.
16/32
18. Robustness checks
I Monotonicity of the gradient Table
I Main specification with deciles or log(income): +1 excess deaths for
a 10% lower income Table
I Falsification test Figure
I Main specification excluding elderly care homes Figure
I Main specification by age group Figure
17/32
19. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
18/32
20. Epidemic and Lockdown-induced effects
I Epidemic-related sources of the income gradient: increase with the
level of infection
n
Related to COVID-19 infections, e.g. reported and unreported
infection-caused deaths
n
Related to COVID-19 spread, e.g. deaths due to altered access to
health services, anxiety due to the level of infection, etc.
I Policy-related sources of the income gradient
n
Related to COVID-19 lockdown, e.g. deaths of despair (Mulligan,
2021), car crashes (Brodeur et al., 2021), domestic violence
(Bullinger et al., 2020), etc.
19/32
21. Quasi-experiment
I First lockdown (March 17 - May 11) implemented uniformly over the
country while the epidemic was at very heterogeneous stages of
development across départements → triple-difference strategy
I Identification assumption: the evolution of the within urban-area
gradient in the absence of COVID-19 and associated policies would
have been similar on average in red and green zones
I Gradient in green zones = gradient in the independent effect of the
lockdown
I Gradient in red zones - gradient in green zones = net
epidemic-induced gradient
20/32
22. Identification strategy - Green vs Red
Figure 4: High- (red) and low-infection (green) Départements as of May 7,
2020
21/32
23. Identification strategy - Green vs Red
I Pre-lockdown indicators Table
n
Average occupancy rate of intensive care beds on March 18: 26.5%
in red départements; 7.0% in the green départements.
n
Likelihood to visit an emergency unit for suspicion of COVID-19 on
March, 18:12.0% in the red départements; 6.3% in the green
départements.
n
Le Bras (2020) and Fouillet et al. (2020) provide evidence on the
absence of a relationship between the location of the first epicentres
and socio-demographic characteristics at the département level.
22/32
24. Double-differences by zone
Figure 5: Excess mortality by income and zone
NOTE: The graph plots the β from equation 2 evaluated each month on each zone separately. It
accounts for the monthly difference in all-cause excess mortality between the poor municipalities
and the rest in each zone. The red zone corresponds to the areas that were the most severely hit
by the first wave, and that are located in the North-Eastern quarter of the country. The green
zone encompasses the rest of the French territory. 23/32
25. The triple-difference setting
Dp
[m,d] = β.Q1m + δ.Redd + ρ.Redd .Q1m + Xp
[m,d].Λ + γua + νp
m (3)
Figure 6: Income gradient in the direct effect of COVID-19 on mortality
NOTE: The graph plots ρ from equation 3 evaluated each month. It accounts for the
monthly difference in all-cause excess mortality between the poor municipalities and the
rest in the red and in green zones.
24/32
26. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
25/32
27. The choice of mechanisms
I Causal impact of COVID-19 on mortality inequalities but income
can cover many potential mechanisms
I Pre-existing conditions play a major role (Wiemers et al., 2020;
Raifman and Raifman, 2020): ignored here because of lack of data
but control for age
I Ecological approach: more interested in mechanisms affecting the
transmission probability
I Significant gradient in incidence rate → the gradient in mortality
cannot be fully explained by differences in lethality (more related to
individual factors) Figure
I Labour market and housing conditions pointed as potential
mechanisms very early on
26/32
28. Occupational exposure
I Measure of exposure in normal conditions: use of a pre-COVID
survey (DEFIS) that measures the "frequency of direct contact with
the public" for each 3-digit level occupation code
I List of essential workers from the Paris Region Health Observatory:
occupations and sectors which kept on going to their workplace
during lockdown
I Both measure mapped to exhaustive social security records (DADS)
from forms firms are compelled to fill in yearly
n
We observe occupation and sector of employment at a very detailed
level, municipality of work and residency
n
We compute (i) the worker-weighted average frequency of contact;
(ii) the share of essential workers in each French municipality
27/32
29. Housing conditions
I Share of overcrowded housing units based on Census data (based on
total size of the housing and household, and number of rooms)
I Share of multi-generational households = with at least 1 member
over 65yo and a younger one currently employed (only from Census
files in municipalities with more than 2,000 inhab.)
28/32
30. Relation with poverty
Table 2: Relationship between poverty status and
mechanism variables
Index of frequent contact Share of essential workers Share of over-crowded housing Multigenerational hh
Main coefficient 0.13598*** 0.15640*** 0.28968*** 0.06955
(0.04143) (0.01664) (0.04138) (0.05136)
Urban areas FE X X X X
Controls X X X X
Control outcome mean 0.251 0.251 0.251 0.272
Observations 16267 16267 16267 3971
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses are clustered at the département level. This table shows
the result of regressing the poverty dummy on mechanism variables measuring either housing conditions or occupational exposure
and the interaction of these variables. The poverty dummy is equal to one for the bottom 25% of the national distribution of
municipal median income weighted by population size. Each column reports the result of a separate regression examining one
mechanism. The main coefficient corresponds to the correlation between each of the mechanism variable and poverty. The
mechanism variables have been standardized such that coefficients can be interpreted in terms of the effect of a one standard-
deviation change, and can be compared with each other. All regressions include urban areas fixed-effects and control for total
population and for the share of inhabitants over 65 y.o. in the municipality.
29/32
31. Table 3: Horse-race between mechanisms Incidence
(1) (2) (3) (4) (5)
Excess mortality rate, wave 1
Poor 0.51309* 0.40407* 0.33580 0.01338 -0.09418
(0.29809) (0.22907) (0.27580) (0.13489) (0.14011)
Index of frequent contact 0.54991** -0.11774
(0.26948) (0.16746)
Share of essential workers 0.51327*** 0.42277***
(0.10279) (0.12855)
Share of over-crowded housing 1.50339*** 1.45786***
(0.28401) (0.30927)
Urban areas FE X X X X X
Controls X X X X X
Control outcome mean 3.689 3.709 3.687 3.795 3.786
Adjusted R2 0.1710 0.1720 0.1722 0.1762 0.1768
Observations 16267 16267 16267 16267 16267
Excess mortality rate, wave 2
Poor 0.45548*** 0.39177*** 0.17436 0.41355** 0.16927
(0.15906) (0.14304) (0.13468) (0.20919) (0.17754)
Index of frequent contact 0.32137* -0.36327*
(0.16477) (0.21621)
Share of essential workers 0.81386*** 0.98330***
(0.12239) (0.14665)
Share of over-crowded housing 0.12615 0.05592
(0.24226) (0.22109)
Urban areas FE X X X X X
Controls X X X X X
Control outcome mean 4.870 4.882 4.868 4.879 4.858
Adjusted R2 0.1274 0.1276 0.1298 0.1274 0.1299
Observations 16267 16267 16267 16267 16267
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the
urban-area level. 30/32
32. Table of Contents
1 Data and Measurement
2 Descriptive statistics
3 The impact of Covid-19 on the income gradient
4 Epidemic and Lockdown-induced effects
5 Mechanisms
6 Conclusion
31/32
33. Conclusion
I We provide clear evidence that COVID-19 contributes to increasing
spatial inequalities in mortality through an unequal impact across
municipalities
I The income gradient persists over waves and is the strongest in the
most affected urban areas
I Policy responses to COVID-19 do not play a significant role in the
income gradient
I Suggestive evidence on the mediating role of LM and housing
conditions: ecological factors are key determinants of the spread of
epidemics
I Housing mechanisms are more important during the 1st
wave, LM
mechanisms are more important during the 2nd
wave
32/32
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40. Table 4: Excess mortality rate by municipality income
Back
(1) (2) (3) (4)
2020 Wave 1 Wave 2 No wave
Q1 (poor) 4.216*** 1.986** 1.975*** 0.255
(1.157) (0.796) (0.416) (0.347)
Q2 2.280** 1.170** 1.476*** -0.365
(0.926) (0.484) (0.406) (0.380)
Q3 2.229*** 1.122*** 1.041** 0.0661
(0.585) (0.339) (0.440) (0.328)
Controls X X X X
Urban area FE. X X X X
Q4 average 6.584 2.674 3.682 0.228
Observations 16640 16640 16640 16640
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the
urban-area level.
NOTE: This table reports the coefficients associated with equation ??. The independent
variable, excess mortality rate, is computed considering four different time periods: the
whole year (column 1), wave 1 (March to April, column 2), wave 2 (October to December,
column 3) and other months in 2020 outside the two waves (January, February and from
May to September, column 4). By construction, column 1 is the sum of column 2 to 4.
Controls include total population size and the share of the population over 65 years old.
41. Table 5: Excess mortality rate by municipality income
Back
(1) (2) (3) (4) (5) (6) (7) (8)
2020 Wave 1 Wave 2 No wave 2020 Wave 1 Wave 2 No wave
Q1 (poor) 6.822*** 3.235** 3.109*** 0.478
(1.402) (1.283) (0.557) (0.444)
Q2 5.543*** 2.589*** 2.030*** 0.924
(1.036) (0.444) (0.520) (0.587)
Q3 3.629*** 1.960*** 1.932*** -0.263
(0.913) (0.397) (0.499) (0.551)
Q4 4.343*** 2.080*** 2.155*** 0.108
(1.096) (0.550) (0.516) (0.491)
Q5 3.439*** 1.964*** 1.354** 0.121
(1.119) (0.412) (0.552) (0.603)
Q6 4.052*** 1.994*** 1.714*** 0.344
(0.647) (0.272) (0.497) (0.428)
Q7 3.647*** 1.865*** 1.500** 0.281
(1.010) (0.361) (0.676) (0.565)
Q8 3.277*** 1.795*** 0.677* 0.805
(0.760) (0.350) (0.377) (0.510)
Q9 1.818*** 0.970*** 0.553* 0.294
(0.450) (0.314) (0.331) (0.323)
log Median Income -10.07*** -4.311*** -4.820*** -0.934
(1.993) (1.479) (0.751) (0.766)
Controls X X X X X X X X
Urban area FE. X X X X X X X X
Richest average 5.079 1.945 3.232 -0.0984
Observations 16640 16640 16640 16640 16640 16640 16640 16640
42. Figure 7: Falsification test Back
Note: This Figure plots the coefficient β as estimated from equation (2) using two alternative depen-
dent variables. The black solid line shows the relative evolution of excess mortality in 2019 for poorest
municipalities (Q1) as compared to others. For the sake of comparison, we also display the excess
mortality of 2020 as compared to the same, 2018 baseline.
43. Table 6: No elderly care homes Back
(1) (2) (3) (4)
2020 Wave 1 Wave 2 No wave
Q1 (poor) 2.524** 1.021 1.139*** 0.363
(1.004) (0.648) (0.310) (0.327)
Controls X X X X
Urban area FE. X X X X
Non poor average 6.486 2.784 3.831 -0.129
Observations 16640 16640 16640 16640
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the urban-area level.
NOTE: This table reports the coefficients associated to equation ??. The independent variable, excess
mortality rate, is computed considering four different time periods: the whole year (column 1), wave 1
(Mars to April, column 2), wave 2 (October to December, column 3) and other months in 2020 outside
the two waves (January, February and from May to September, column 4). By construction, column 1
is the sum of column 2 to 4.
44. Figure 8: Income gradient (LHS) and Excess mortality rate (RHS) over
2020 by age category Back
NOTE: This Figure plots on the left-hand side the coefficient β as estimated from equation (2)
run separately on excess mortality over 2020 for different age categories. The first point reports the
coefficient estimated on the whole population. 95% confidence intervals are reported. On the right-hand
side, we show the magnitude of excess mortality defined as the number of excess deaths per 10,000
inhabitants over 2020, for the same age categories.
45. Identification strategy - Green vs Red
Table 7: Mortality trends in red and green zones Back
(1) (2) (3)
Red zone Green zone Difference
2019 excess mortality rates (2019 vs 2018)
Annual excess mortality rate 0.239 1.473 -1.234
March-April -1.357 -0.685 -0.672
2020 excess mortality rates (2020 vs 2018-2019)
January (pre-COVID) -0.225 -0.457 0.232
February (pre-COVID) -0.355 -0.434 0.080
March 0.816 -0.087 0.904***
- first 2 weeks -0.252 -0.165 -0.088
- last 2 weeks 1.139 0.216 0.923***
April 2.696 0.333 2.364***
May 0.341 0.359 -0.018
Nb municipalities 7,358 9,947 1,7305
Total population 23,457,888 30,460,492 53,918,380
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the urban-area level.
NOTE: Mortality rates are expressed as number of deaths per 10,000 inhabitants.
46. Table 8: Triple-difference regression on the number of
deaths, wave 1 Back
Number of deaths, wave 1
2020=1 0.12952*** 0.18605***
(0.03773) (0.05675)
2020=1 × Red area=1 0.51121*** 0.75283***
(0.17458) (0.26704)
2020=1 × Poor=1 -0.05089 0.19666*
(0.03267) (0.11114)
2020=1 × Red area=1 × Poor=1 0.81664* 2.41010**
(0.41663) (1.08131)
Municipality FE X X
Sample
All Major urban centres
municipalities and their outskirts
Control outcome mean 1.736 2.290
Observations 188688 100686
* p<0.05, ** p<0.01, *** p<0.001. Standard errors in parentheses clustered at the département
level.
NOTE: This table reports coefficients from a triple-difference regression on the number of deaths in
wave 1 (March-April) over the years 2018-2020. The three dimensions are (i) time, with 2020 being the
post period; (ii) the level of infection in the département before the first wave; (iii) the poverty status
of the municipality, defined as the bottom quartile of the national distribution of municipal income
weighted by the municipal population size. We include municipality fixed-effects to account for time-
invariant factors at the municipal level, such as the population measured in 2014. The control outcome
mean is the mean number of deaths in March-April in the years 2018-2019 in non-poor municipalities
in green zones.
47. Figure 9: Weekly gradient in incidence rate Back
NOTE: The graph plots the point estimate and the 95% confidence intervals of the estimation of β
from equation 2 evaluated each week with the incidence rate as the dependent variable. It accounts
for the weekly difference in incidence rate between the poor municipalities and the rest, where poor is
defined as belonging to the bottom quartile of the national distribution of municipal median income
weighted by the municipality size.
48. Table 9: Horse-race between mechanism variables Back
(1) (2) (3) (4) (5)
Incidence rate (per 100k. inh.), wave 2
Poor (Q1) 5.92693*** 5.02211** 4.18254* 0.04053 -1.06828
(2.11590) (2.36330) (2.15177) (2.56955) (2.17981)
Index of frequent contact 4.55486* -3.46067
(2.45458) (3.40661)
Share of essential workers 5.04624* 4.89758
(2.98801) (4.02379)
Share of over-crowded housing 17.69906*** 18.00957***
(1.73201) (2.18879)
Urban areas FE X X X X X
Controls X X X X X
Control outcome mean 236.111 236.277 236.096 237.359 237.241
Adjusted R2 0.6922 0.6928 0.6934 0.6990 0.6997
Observations 16267 16267 16267 16267 16267
* p<0.1, ** p<0.05, *** p<0.01. Standard errors in parentheses clustered at the urban-area level.
This table shows the result of regressing municipalities’ incidence rate on a variable measuring either
poverty, housing conditions or occupational exposure. The table only shows results for the second wave
(October-December) - that is when test data are available - on municipalities in all urban areas. The
first column only examines the poverty channel. Columns (2) to (4) respectively include one additional
variable capturing either the occupation or housing mechanism. The last column includes both the
poverty dummy and all the mechanism variables. All regressions include urban-area fixed-effects and
control for total population and for the share of inhabitants over 65 y.o. in the municipality. The
mechanism variables have been normalized such that coefficients can be interpreted in terms of the
effect of a one standard-deviation change, and can be compared with each other. The outcome-mean
line reports the mean of the incidence rate per 100K inhabitants (conditional on controls and fixed
effects) in each wave.
49. Figure 10: Effect of the index of frequent contact on excess mortality
rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on
the index of frequent contact, including controls for total population, share of the population over 65
y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the
index of frequent contact have been normalized. Confidence intervals at the 95% level.
50. Figure 11: Effect of the share of essential workers on excess mortality
rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on
the share of essential workers, including controls for total population, share of the population over 65
y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the
share of essential workers have been normalized. Confidence intervals at the 95% level.
51. Figure 12: Effect of the share of over-crowded housing on excess
mortality rate over time
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on the
share of over-crowded housing, including controls for total population, share of the population over 65
y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate and the
share of over-crowded housing have been normalized. Confidence intervals at the 95% level.
52. Figure 13: Effect of the share of multigenerational households on excess
mortality rate over time Back
NOTE: The graph plots the coefficient of the regression of the excess mortality rate each month on the
share of multigenerational households, including controls for total population, share of the population
over 65 y.o., and urban-area fixed effects. Both red and green areas are included. Excess mortality rate
and the share of multigenerational households have been normalized. Confidence intervals at the 95%
level.