Métricas que Marcan la Diferencia: Uso de los Indicadores de Bienestar y del Desarrollo Sostenible en América Latina y el Caribe/Metrics that Make a Difference: Policy Uses of Well-being and Sustainable Development Indicators in Latin America and the Caribbean, 23-24 October 2019, Bogotá, Colombia. More information at: www.oecd.org/statistics/lac-well-being-metrics.htm
The economic prosperity of a nation depends on the quality of its workforce. The present study attempts to describe the work force participation rates in India. This study illustrates the spatial and temporal change in the work force participation of persons (males and females) in India, highlighting important differences due to sex, age, place of residence. A striking feature has been a rising trend in the rural female work force participation rates after liberalization (1991) but declining trend in the last decade (2011). This work force distribution also presents data regarding number of main and marginal workers. The time series data on work force distribution by category of workers like cultivators, agricultural laborers, workers in rural Household industries, etc. also presents a picture of structural change occurring in the economy. The temporal analysis of total workers of India explains that the work participation rate has registered continuous increase in the last three decades.
The economic prosperity of a nation depends on the quality of its workforce. The present study attempts to describe the work force participation rates in India. This study illustrates the spatial and temporal change in the work force participation of persons (males and females) in India, highlighting important differences due to sex, age, place of residence. A striking feature has been a rising trend in the rural female work force participation rates after liberalization (1991) but declining trend in the last decade (2011). This work force distribution also presents data regarding number of main and marginal workers. The time series data on work force distribution by category of workers like cultivators, agricultural laborers, workers in rural Household industries, etc. also presents a picture of structural change occurring in the economy. The temporal analysis of total workers of India explains that the work participation rate has registered continuous increase in the last three decades.
Accountability and Public Sector Performance in the Third World Country A Cas...ijtsrd
"This study focuses on accountability and public sector performance in the third world country A case study of Nigeria. The study is a demonstration of simple random sampling techniques on the bases of which a survey administration of questionnaires was done. The data collected was analysed by using chi square statistical tool. The result revealed that there is relationship between appraisal of transparency public office holders and public sector performance using a case study of Ose Local Government Area Secretariat, Ose, Ondo State Nigeria. The findings revealed that there is relationship between appraisal of integrity of public office holders and performance output within short and long period their regime using a case study of Ose Local Government Area Secretariat, Ose, Ondo State Nigeria. The paper recommends that issue of immunity clause as treated in the 1999 Constitution as amended must be revisited to improve accountability of public officeholder in Nigeria public service. Public officeholders need be made to answer for any suspected acts of funds misappropriation or mismanagement irrespective of social status. Oloruntoba Sunday Rufus | Gbemigun Catherine O ""Accountability and Public Sector Performance in the Third World Country: A Case Study of Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21748.pdf
Paper URL: https://www.ijtsrd.com/management/accounting-and-finance/21748/accountability-and-public-sector-performance-in-the-third-world-country-a-case-study-of-nigeria/oloruntoba-sunday-rufus"
A presentation by Caroline Skinner (Researcher African Centre for Cities, / WIEGO Urban Policies Programme Director) at the Informal Trading Summit on March 20, 2013
HLEG thematic workshop on Measurement of Well Being and Development in Africa...StatsCommunications
HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress
HLEG thematic workshop on Measurement of Well Being and Development in Africa...StatsCommunications
HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress
South african welfare state and the demographic dividend's window of opportunityFabio Torreggiani
In this paper, I analysed the main characteristics of the South African Welfare State in terms of inputs and outputs of the key policies usually identified by the literature to be useful to exploit a demographic dividend. In particular, I focused on the state of the labour market, the social assistance policies and the education and healthcare systems. To do this I studied some quantitative indicators of both inputs and outputs and I reported the qualitative analysis of some other articles of these individuals sectors. The conclusion is that, despite some important progress made by the democratic governments, there are many improvements needed to create a consistent and inclusive growth.
Gender and Equity Implications of Indian Budget, 2015 Ranjani K.Murthy
This presentation argues the case for gender and equity analysis of budgets to look at how much is allocated for women and marginalised groups, as well as the gender and equity implications of the broader development paradigm, taxation policies, method of generating funds (plan vs non plan) and fund channeling mechanisms. It argues that the Indian budget 2015 is gender and equity blind in many ways, and few ways gender/equity specific. There are no gender transformative elemnts
Beyond GDP: Measuring well-being and progress of NationsKübra Bayram
Everyone aspires to a good life. But what does a "good" (or better) life mean? In recent years, concerns have emerged that standard macro-economic statistics, such as GDP, which for a long time had been used as proxies to measure well-being, failed to give a true account of people’s current and future living conditions. The ongoing financial and economic crisis has reinforced this perception and it is now widely recognized that data on GDP provide only a partial perspective on the broad range of factors that matter to people’s lives.
Human Resources and Economic DevelopmentAyesha Arshad
INTRODUCTION TO HUMAN RESOURCES & ECONOMIC DEVELOPMENT
INDICATORS OF HUMAN RESOURCES
IMPORTANCE OF HR DEVELOPMENT
COMPONENTS OF HUMAN RESOURCE DEVELOPMENT
SOCIAL/ NON-ECONOMIC FACTORS OF ECONOMIC GROWTH
Women's participation in the labour market is dependent on a number of factors. The policies and the budget has to be gender sensitive to create an enabling environment for the women workers. We need to shift from the gender neutral approach to the gender sensitive approach.by asking the right questions during budget preparation.
Accountability and Public Sector Performance in the Third World Country A Cas...ijtsrd
"This study focuses on accountability and public sector performance in the third world country A case study of Nigeria. The study is a demonstration of simple random sampling techniques on the bases of which a survey administration of questionnaires was done. The data collected was analysed by using chi square statistical tool. The result revealed that there is relationship between appraisal of transparency public office holders and public sector performance using a case study of Ose Local Government Area Secretariat, Ose, Ondo State Nigeria. The findings revealed that there is relationship between appraisal of integrity of public office holders and performance output within short and long period their regime using a case study of Ose Local Government Area Secretariat, Ose, Ondo State Nigeria. The paper recommends that issue of immunity clause as treated in the 1999 Constitution as amended must be revisited to improve accountability of public officeholder in Nigeria public service. Public officeholders need be made to answer for any suspected acts of funds misappropriation or mismanagement irrespective of social status. Oloruntoba Sunday Rufus | Gbemigun Catherine O ""Accountability and Public Sector Performance in the Third World Country: A Case Study of Nigeria"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21748.pdf
Paper URL: https://www.ijtsrd.com/management/accounting-and-finance/21748/accountability-and-public-sector-performance-in-the-third-world-country-a-case-study-of-nigeria/oloruntoba-sunday-rufus"
A presentation by Caroline Skinner (Researcher African Centre for Cities, / WIEGO Urban Policies Programme Director) at the Informal Trading Summit on March 20, 2013
HLEG thematic workshop on Measurement of Well Being and Development in Africa...StatsCommunications
HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress
HLEG thematic workshop on Measurement of Well Being and Development in Africa...StatsCommunications
HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress
South african welfare state and the demographic dividend's window of opportunityFabio Torreggiani
In this paper, I analysed the main characteristics of the South African Welfare State in terms of inputs and outputs of the key policies usually identified by the literature to be useful to exploit a demographic dividend. In particular, I focused on the state of the labour market, the social assistance policies and the education and healthcare systems. To do this I studied some quantitative indicators of both inputs and outputs and I reported the qualitative analysis of some other articles of these individuals sectors. The conclusion is that, despite some important progress made by the democratic governments, there are many improvements needed to create a consistent and inclusive growth.
Gender and Equity Implications of Indian Budget, 2015 Ranjani K.Murthy
This presentation argues the case for gender and equity analysis of budgets to look at how much is allocated for women and marginalised groups, as well as the gender and equity implications of the broader development paradigm, taxation policies, method of generating funds (plan vs non plan) and fund channeling mechanisms. It argues that the Indian budget 2015 is gender and equity blind in many ways, and few ways gender/equity specific. There are no gender transformative elemnts
Beyond GDP: Measuring well-being and progress of NationsKübra Bayram
Everyone aspires to a good life. But what does a "good" (or better) life mean? In recent years, concerns have emerged that standard macro-economic statistics, such as GDP, which for a long time had been used as proxies to measure well-being, failed to give a true account of people’s current and future living conditions. The ongoing financial and economic crisis has reinforced this perception and it is now widely recognized that data on GDP provide only a partial perspective on the broad range of factors that matter to people’s lives.
Human Resources and Economic DevelopmentAyesha Arshad
INTRODUCTION TO HUMAN RESOURCES & ECONOMIC DEVELOPMENT
INDICATORS OF HUMAN RESOURCES
IMPORTANCE OF HR DEVELOPMENT
COMPONENTS OF HUMAN RESOURCE DEVELOPMENT
SOCIAL/ NON-ECONOMIC FACTORS OF ECONOMIC GROWTH
Similar to Policy Uses of Well-being and Sustainable Development Indicators in Latin America and the Caribbean, Sebastian Nieto Parra & Juan Miguel Gallego
Women's participation in the labour market is dependent on a number of factors. The policies and the budget has to be gender sensitive to create an enabling environment for the women workers. We need to shift from the gender neutral approach to the gender sensitive approach.by asking the right questions during budget preparation.
How can haiti prepare for disruption in the future of workOnyl GEDEON
The nature of work is changing. People will need to adapt and readapt. The Haitian government must invest in early childhood education and health and build a lifelong learning system that will allow the Haitian youngs and adults to be reskilled and/or upskilled in many cases. Also, it must build a social protection system that will promote a renewed social contract. In order to do so, the government may conduct tax reforms that will allow the leaders to find the financial means they need.
Women Workers in Informal Sector in India: Understanding the Occupational Vul...Dr Lendy Spires
Unorganised or informal sector constitutes a pivotal part of the Indian economy. More than 90 per cent of workforce and about 50 per cent of the national product are accounted for by the informal economy. A high proportion of socially and economically underprivileged sections of society are concentrated in the informal economic activities [1]. Informal employment is generally a larger source of employment for women than for men in the developing world. Other than in North Africa where 43 per cent of women workers are in informal employment, 60 per cent or more of women workers in the developing world are in informal employment(outside agriculture).
In sub-Saharan Africa 84 per cent of women non-agricultural workers; in Latin America 58 per cent for women in comparison to 48 percent for men. In Asia, the proportion of women and men non-agricultural workers in informal employment is roughly equivalentto Women and Men in the Informal Economy [2].The informal economy in India employs about 86 per cent of the country’s work force and 91 per cent of its women workers [3]. Many of these women workers are primary earners for their families. Their earnings are necessary for sheer survival. Low income women workers, especially in the informal sectorform one of the most vulnerable groups in the Indian economy.
The reasons for their vulnerability are-(a) irregular work, (b) low economic status, (c) little or no bargaining power, (d) lack of control over earnings, (e) need to balance paid work with care for children and homework, (f) little or no access to institutional credit, training and information, and (g) lack of assets. Unequal gender relations play a very important role in defining their insecurities. Given their vulnerable status at home and at work, income generation alone may not improve the socio-economic status of women attached to the informal sector. Their economic empowerment needs to go along with political empowerment, which could improve their bargaining power both in household and at work.
This means that organizing women workers in the informl economy could have beneficial impacts on their work and their life if such organizationcombines voices representation along with access to resources such as credit and information- a holistic strategy that provides political empowerment allied with economic empowerment.The present study aims at understanding the degree of vulnerabilityof the women workers in informal sector in India.
Santiago Garganta & Leonardo Gasparini: The impact of a social programUNDP Policy Centre
This presentation is part of the programme of the International Seminar "Social Protection, Entrepreneurship and Labour Market Activation: Evidence for Better Policies", organized by the International Policy Centre for Inclusive Growth (IPC-IG/UNDP) together with Canada’s International Development Research Centre (IDRC) and the Colombian Think Tank Fedesarrollo held on September 10-11 at the Ipea Auditorium in Brasilia.
Need for wage policy and relationship between wages and employmenthemurathore1
Wage is paid to the assembly line workers or worker at operational level. It is paid hourly/daily/weekly.
The term “Wage Policy” refers to legislation of government action undertaken to regulate the level or structure of wages or both for the purpose of achieving specific objectives of social and economic policy.
There are two components of wages: financial and non-financial
How human resources affects the economic development of the country.
Similar to Policy Uses of Well-being and Sustainable Development Indicators in Latin America and the Caribbean, Sebastian Nieto Parra & Juan Miguel Gallego (20)
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
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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
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Sum with in-place strategies of CUDA mode (reduce)
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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Policy Uses of Well-being and Sustainable Development Indicators in Latin America and the Caribbean, Sebastian Nieto Parra & Juan Miguel Gallego
1. Wellbeing in the informal Economy
October 22-23 , 2019
Metrics that Make a Difference: The Use of
Indicators of Well-Being and Sustainable
Development in Policy
Sebastian Nieto Parra – Juan Miguel Gallego
Grupo de Trabajo de Digitalización
Córdoba-Argentina, 2 de julio de 2019
2. Informality affects wellbeing of
people and households
Explore the relations between informality and wellbeing using households as the unit
of analysis.
Capture aspects of life that matter to people and that help to shape the quality of
their lives and could be affected by the prevalence of informality of working
members in the household.
Informality hampers the consolidation of growing middle class in LAC countries.
It make households more vulnerable because its associated with less access to
public services and social security.
Study the distribution of outcomes across the population and disparities
associated with status of job.
3. Key questions
1. What are the different profiles of informal economy workers and their
households?
2. What are the links between the informal work and wellbeing?
• What are the risks and vulnerabilities in terms of lower wellbeing
for informal workers?
3. What can policy makers do?
4. Contributions
1. Update and make comparable informality measures for selected Latin American
Countries. Merging Informality indicators with wellbeing and poverty constructed
variables.
2. Assessing the availability of wellbeing indicators at the individual and household level
according to the OECD wellbeing framework
3. Focus on the household dimension into the profiling of informal workers using new
OECD indicators on informality to increase the policy relevance of the informality
diagnosis Key Indicators of Informality based on Individuals and their Households
database (KIIbIH).
5. Informality is a concern around the World
Source: OECD (2019). Tackling vulnerability in the informal economy. ILO data.
53.8%
More tan half of Latin
America and
Caribbean workers
are part of the
informal employment
6. Workers (Households)
• Less Access to social security
• Worse working conditions
• Less education and training opportunities
• Irregular and lower income (earnings)
• Longer working days.
Firms
• Less Access to credit
• Lower participation in regulated
markets (government related and
international markets)
• Associated to lower productivity
Informality is challenge for potential growth and
also for sustainable and inclusive development
Related to other
Wellbeing outcomes
7. ILO – Recommendation No. 204
• Achieving decent work for all and inclusive development:
• Integrated strategies to facilitate the transition to the formal economy
• Create new formal jobs
• Prevent further informalization
• It also recognizes the crucial role of statistics in the policy process.
Relevant SDG Targets related to Informal
Economy
8.3 Promote development-oriented policies that support
productive activities, decent job creation, entrepreneurship,
creativity and innovation, and encourage formalization and
growth of micro-, small- and medium-sized enterprises including
through access to financial services
8. How to compare data for LAC: The KIIbIH
Household surveys with labour force modules
13 LAC countries that represent 83% of the LAC population.
45 indicators
Socio-demographic status
Household characteristics
Economic status (poverty measures)
The Updated KIIbIH + Wellbeing.
Available year closest to 2017
Uses Household surveys – wellbeing and development indicators
Reviews the existence of variables to built indicators for the Wellbeing framework 11 Categories.
Housing, Income and Wealth, Job and earnings, Work life balance and Education and skills are
available for all countries.
Health status, Environmental quality, Personal Security, Subjective wellbeing and have indicators
available for few of the selected countries to be analysed at the individual and household level.
Social connections, and Civic Engagement and governance have not indicators available for selected
countries then it is not possible to link wellbeing in this dimension with informal employment status.
Source: The description of the Database is presented in detail in OECD-ILO (2019) Tackling vulnerability in the informal economy.
9. The KIIbIH methodology
Identification of countries with adequate survey data
• Labour force module or component with questions pertinent for the estimation of
informality
To enhance international comparability, we apply, as far as possible, a systematic approach to
measuring informal employment and employment in the informal sector when processing micro
data.
• Definition of informality follows the ILO (2019)* approach. Women and men in the informal
economy.
• Employed population aged 15 years old and over
• Country estimates might differ from national ones when they exist.
• Estimates calculated for 2017 - 2018 or the closest available year – Updating the KIIbIH
Source: The description of the Database is presented in detail in OECD-ILO (2019) Tackling vulnerability in the informal economy.
*To look for the definitions used for formal sector and formal employment see ILO (2019) Women and men in the informal economy
10. Benefits of the KIIbIH
• Complements existing international databases:
• ILO statistics focuses on individual-level data, based on labour force surveys.
• KIIbIH allows
• Household-level analysis
• Broader set of indicators
• Measuring risks and other vulnerabilities faced by informal workers
• Data to help design policy interventions targeted toward informal workers
• Informality-poverty -Wellbeing nexus is especially important, where
social protection has a large role to play
11. Key indicators in the KIIbIH database
Household informality composition
– Informal (100% workers employed in informal work)
– Formal (100% workers employed in formal work)
– Mixed (1+ informally employed and 1+ formally employed worker)
Gender and household dependents composition
– Gender of HHH
– Child, elderly (and potentially disabled) household dependency ratios
Location of households with informal workers
– Urban/rural
Magnitude of household income, and the share of informal workers income
– Informal workers by quintile of per capita or household income/consumption
Relative and absolute poverty status
– At national and international lines ($1.90/$3.10 per day)
Health insurance/eligibility for subsidized healthcare
Pension receipt/contributions
Source: The description of the Database is presented in detail in OECD-ILO (2019) Tackling vulnerability in the informal economy.
12. Informal Sector definition: (Economic units)
12
Registration
Bookkeeping
Destination
3 =
Private
househol
d
1 =
Government,
Corporations,
NGO, IO etc
2 = Farm or private
business (unincorporated)
Institutional sector
Only for own
final use
At least partly for the
market
Keeps accounts for
reporting to the
Government
Does not keep
accounts
Not registered at
national level
Registered at
national level
Other, DK,
NA, Not asked
Other, DK,
NA, Not asked
Other, DK,
NA, Not asked
Other, DK,
NA, Not asked
Status in employment
Social security contribution or tax
on wages
Employees Other
Yes No
Q6. Size
6 or more, other 5 or less
Place of work
Other Non-fixed premises
Employment in the informal sector
Households
(producing
exclusively for
own final use)
Employment in the formal sector
Key variables
*Based on ILO
definition used for
formal sector and
formal employment
see ILO (2019)
Women and men in
the informal
economy
13. Status in employment
Informal employmentFormal employment
2 = Employers; 3= own-account
workers; members of cooperatives
1 = Employees
4 = Contributing family
workers
Don’t know/
Others
Economic unit (enterprises)
2= Informal
sector
1 = Formal
sector
3 = Households
No production for
sales or batter
Paid annual leave (de facto)
Yes DK/NA or No
Paid sick leave (de facto)
Yes DK/NA or No
Informal employment definition: (Jobs) – More relevant
for wellbeing outcomes.
Note: To look for the definitions used for formal sector and formal employment see ILO (2019) Women and men in the informal economy. The description of
the variables used in each case could be found in OECD/ILO (2019) Tackling vulnerability in the informal economy
Yes
Social security (employment related social
security)
NoDK/NA
*Based on ILO
definition used for
formal sector and
formal employment
see ILO (2019)
Women and men in
the informal
economy
14. Adding the Wellbeing indicators at the individual level
1. Review the OECD Wellbeing framework and its 11 categories. Current Wellbeing
• The OECD framework for measuring well-being was introduced in How’s Life? 2011. It builds on a
variety of national and international initiatives for measuring the progress of societies, as well as on
academic and policy recommendations.
• The OECD well-being framework is an analytic and diagnostic tool to assess the conditions of people
and communities
• Conceptually, the framework reflects elements of the capabilities approach (Sen, 1985; Alkire and
Sarwar, 2009; Anand, Durand and Heckman, 2011), with many dimensions addressing the factors
that can expand people’s choices and opportunities to live the lives that they value – including
health, education and income (OECD, 2013).
2. Which of these indicators are relevant to be computed and analysed at the
individual/household level?
• Formulating adjusted indicators to measure the same aspects – considering available information
Limitations:
• Relevant and available for the LAC context
• Relevant for the individual household perspective
• Available in at least some of the Household Surveys (Importance of using not only Labour Market
modules/surveys)
15. Adding the Wellbeing indicators at the individual level
Source: OECD (2015), How’s Life? Measuring Well-Being, OECD Publishing, Paris, http://dx.doi.org/10.1787/how_life-2015-en
The OECD well-being framework – Current Wellbeing
Multidimensional country reviews in LAC
some adaptations have been made to:
• Capture people’s diverse experiences in all
dimensions that matter to them:
• Households’ material conditions (e.g.
income, jobs and housing),
• Broader quality of life (e.g. health,
education, environment, life
satisfaction)
• According to the Availability of regional
Data.
16. LAC Data: The KIIbIH + Wellbeing
* The final sample of countries will depend on data availability
Country Survey Year
Argentina Encuesta Permanente de Hogares 2018
Bolivia Encuesta de Hogares 2018
Brazil Pesquisa Nacional por Amostra de Domicilios 2015
Chile Encuesta de Caracterizacion Socioeconomica Nacional 2017
Colombia Encuesta Nacional de Calidad de Vida 2017
Costa Rica Encuesta Nacional de Hogares 2018
El Salvador Encuesta de Hogares de Propósitos Múltiples 2017
Honduras Encuesta Permanente de Hogares de Propositos Multiples 2014
Mexico Encuesta Nacional de los Hogares 2018
Nicaragua Encuesta Nacional de Hogares sobre Medición de Niveles de Vida 2014
Paraguay Encuesta Permanente de Hogares 2018
Peru Encuesta Nacional de Hogares 2018
Uruguay Encuesta Continua de Hogares 2018
18. “The informal economy is often considered a sign of underdevelopment that goes hand-inhand with poverty.”
Informality correlates negatively with higher levels of economic
and social development
Note: Vector of control variables has been adjusted for each
panel in order to reduce the effects of multicollinearity on
regression estimates. For all panels, controls include
geography, labour productivity, SIGI 2014, 2017 Ease of
doing business index, number of start-up procedures, share
of youth (aged 15-24) and KOF Economic Globalisation
Index. For HDI, they include composition of GDP and
exclude GDP per capita. For GDP per capita (2011 PPP), they
include infant mortality rate, life expectancy and education
and exclude HDI. Source: OECD/ILO (2019) Tackling
vulnerability in the informal economy
19. Informality is related with less productivity and less earnings. But beyond income, is informality affecting
affecting or shaping wellbeing outcomes?
• Using 10 of the 11 dimensions (That are also used for non OECD countries in the Multidimensional country
reviews)
19
Comparative performance on
informality (x-axis) and material
conditions (y-axis).
LAC selected countries, latest available data
Comparative performance on
informality (x-axis) and quality of life
(y-axis).
LAC selected countries, latest available data
Note: Material conditions encompasses 9 indicators across 3 dimensions: income and wealth, work (related with jobs and earnings), and housing. Quality of life is measured through 19 indicators spanning 7
dimensions: health status, education and skills, social connections, civic engagement and governance, environmental quality, personal safety and subjective well-being. For each indicator, countries are “scored”
according to their comparative performance (0 = bottom third of LAC, 5 = middle third of LAC, 10 = top third of LAC). Scores are then averaged within dimensions (applying equal weights to each indicator), before
then being averaged across dimensions (applying equal weights to each dimension in the material conditions and quality-of-life categories). Missing data points are excluded from each country’s score, and thus scores
may be under- or over-estimated in the case of large data gaps
ARG
BOL
BRA
CHL
COL
CRI DOM
ECU GTM
HND
MEX
PAN
PER
PRY
SLV
URY
y = -0.0208x + 6.1146
R² = 0.1273
0
1
2
3
4
5
6
7
8
9
10
20 30 40 50 60 70 80 90
ARG
BOL
BRA
CHL
COL
CRI
DOM ECU
GTM
HND
MEX
PAN
PER
PRY
SLV
URY
y = -0.0899x + 10.141
R² = 0.7322
0
1
2
3
4
5
6
7
8
9
10
20 30 40 50 60 70 80 90
20. 20
Material Conditions
Comparative performance on informality (x-axis) and dimensions of material conditions (y-axis).
LAC selected countries, latest available data
Note: Material conditions encompasses 9 indicators across 3 dimensions: income and wealth, work (related with jobs and earnings), and housing. Quality of life is measured through 19 indicators spanning 7
dimensions: health status, education and skills, social connections, civic engagement and governance, environmental quality, personal safety and subjective well-being. For each indicator, countries are “scored”
according to their comparative performance (0 = bottom third of LAC, 5 = middle third of LAC, 10 = top third of LAC). Scores are then averaged within dimensions (applying equal weights to each indicator), before
then being averaged across dimensions (applying equal weights to each dimension in the material conditions and quality-of-life categories). Missing data points are excluded from each country’s score, and thus scores
may be under- or over-estimated in the case of large data gaps
21. 21
Quality of Life
Comparative performance on informality (x-axis) and dimensions of Quality of life(y-axis).
LAC selected countries, latest available data
Note: Material conditions encompasses 9 indicators across 3 dimensions: income and wealth, work
(related with jobs and earnings), and housing. Quality of life is measured through 19 indicators
spanning 7 dimensions: health status, education and skills, social connections, civic engagement and
governance, environmental quality, personal safety and subjective well-being. For each indicator,
countries are “scored” according to their comparative performance (0 = bottom third of LAC, 5 =
middle third of LAC, 10 = top third of LAC). Scores are then averaged within dimensions (applying
equal weights to each indicator), before then being averaged across dimensions (applying equal
weights to each dimension in the material conditions and quality-of-life categories). Missing data
points are excluded from each country’s score, and thus scores may be under- or over-estimated in the
case of large data gaps
22. 22
Even after taking apart the part of some wellbeing outcomes that is explained by per capita GDP: higher
informality rate is correlated with poverty rates, unemployment rates, higher tan predicted by per capita
GDP. It is also related with lower growth in forest land and lower PISA Reading scores than predicted
according to per capita GDP levels.
GTM
SLV
BOL
BRACHL
COL
CRI
DOM
ECU
HND
MEX
PAN
PER
PRY
URY
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 10 20 30 40 50 60 70 80 90 100
Normalized error
Informal employment rate
ARG
BOL
BRA
CHL
COL
CRI
DOM
ECU
GTM
HND
MEX
PAN
PER
PRYSLV
URY
-1.5
-1
-0.5
0
0.5
1
0 10 20 30 40 50 60 70 80 90
Normalized error
Informal employment rate
BOLBRA
CHL
COL
CRI
DOM
ECU
GTM
HND
MEX
PAN
PER
PRY
SLV
URY
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
0 10 20 30 40 50 60 70 80 90 100
Normalized error
Informal employment rate
ARG
CHL
COL
CRI
DOM
MEX
PER
URY
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0 10 20 30 40 50 60 70 80
Normalized error
Informal employment rate
PISA Reading score (mean score)
Last available year
Annual deforestation (average annual change in forest land, 10 years)
Last available year
Unemployment, total (% of total labor force)
Last available year
Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)
Last available year
Note: Normalized errors are Actual values minus expected value for each country for the most recent available year. Expected well-being values (the black circle) are calculated using bivariate regressions of various
well-being outcomes on GDP per capita, using a cross-country dataset of around 150 countries with a population over a million. All indicators are normalised in terms of standard deviations across the panel.
Source: ILO (2019), ILOSTAT; OECD (2015), PISA Database; Transparency International (2019), Corruption Perceptions Index; Gallup (2018), Gallup World Poll; UNESCO (2019), UIS; World Bank (2019), World
Development Indicators; World Health Organisation (2019).
24. • LAC have a level of informality on average of 58% . there is significant variation across the region, ranging from 24% in Uruguay to
30-40% in Costa Rica and Chile, close to 80% in Honduras, Nicaragua, and Bolivia.
• Being informal is shaped by socioeconomic background.
• Poor are disproportionately informal (80%) compared with non poor (51%).
• Anti-poverty programmes could cover a large share of informal workers
Share of informal employment in total employment by poverty status
Vulnerable groups are disparately exposed to informality
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
0
10
20
30
40
50
60
70
80
90
100
Not Poor Poor
25. • LAC have a level of informality on average of 58% . there is significant variation across the region, ranging from 24% in Uruguay to
30-40% in Costa Rica and Chile, close to 80% in Honduras, Nicaragua, and Bolivia.
• Being informal is shaped by socioeconomic background.
• People in rural areas are almost 20% as likely as those in urban areas to be in informal employment: 74% vs. 53%
-19
-32
-8
-30
-15
-22 -20
-32
-24 -23 -26
-2
-21
-40
-20
0
20
40
60
80
100
Argentina Bolivia Brazil Chile Colombia Costa Rica El
Salvador
Honduras Mexico Nicaragua Paraguay Peru Uruguay LAC
Urban Rural GAP
Share of informal employment in total employment by location
Vulnerable groups are disparately exposed to informality - Location
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
26. • More than 2/3 of youth and older workers are in informal employment, compared with 55% of those aged 35-64 in
LAC.
Informality rate by age group
Vulnerable groups are disparately exposed to informality - Age
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
0
10
20
30
40
50
60
70
80
90
100
15-24 25-29 30-34 35-54 55-64 65-Plus
27. • There is an over-representation of
statuses at risk of informality at the
early and latest life stages, such as
higher proportions of contributing
family workers at younger ages and
own-account workers at age 65 and
over.
• Among employers and own-account
workers, there is a clear trend of
transition to formality with increasing
age and experience (at least until
retirement age) (Chacaltana, Bonnet
and Leung, forthcoming). How to
address informality for employers and
contributing family workers over life
cycle is key .
Distribution by employment status varies over the life cycle
Distribution of employment over the lifecycle, by status
Vulnerable groups are disparately exposed to informality - Age vs Job status
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
0
10
20
30
40
50
60
70
80
90
100
15-24 25-29 30-34 35-54 55-64 65-Plus
% of employment
Age group
Employees Employers Own-account workers Contributing family workers
28. Informal employment and highest level of educational attainment
Vulnerable groups are disparately exposed to informality - Education
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Formal
Informal
Argentina Bolivia Brazil Chile Colombia Costa Rica El Salvador Honduras Mexico Nicaragua Paraguay Peru Uruguay LAC
Percentage of total employment
No education Primary Secondary Tertiary
• Informal employment absorbs less-educated workers
• The positive effect of higher education levels on access to formal employment is evident among employees and employers but far
less clear among own-account workers (ILO/OECD, 2019)
29. Informality takes different forms at the household level
Source:The Key Indicators of Informality based on Individuals and their Households (KIIbIH) 2019. Regional and total sample averages are unweighted averages.
• Working household members may be informally or formally employed; households with more than one
employed member may therefore have varying levels of informality.
• Employees in formal employment are entitled to labour-based contributory social protection and/or
have the right to employment-based benefits, such as paid sick and annual leave.
• Independent workers in the formal sector are more likely to earn higher incomes and to contribute to
social security. (ILO/OECD, 2019)
• Social insurance benefits can protect dependents of formally employed household members (whether
employee or independent worker), entitling them to, for instance, medical insurance.
• it is critical to go beyond individual circumstances and propose new household-based monitoring
indicators of informality.
• The completely formal, completely informal or mixed households7 sampled for this analysis include only
those with workers. which represent between 85% and 100% in most of the 13 countries covered
30. Informality takes different forms at the household level
0 20 40 60 80 100
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
El Salvador
Honduras
Mexico
Nicaragua
Paraguay
Peru
Uruguay
LAC
Informal Mixed Formal
Distribution of overall population, by degree of
informality of households
Percentage of population
Latin America is roughly divided between
countries where a majority lives in
completely formal or mixed households
(e.g. Argentina, Bolivia, Chile, Costa Rica,
Uruguay) and those where a majority lives
in completely informal households (most in
Central America, as well as Colombia,
Paraguay and Peru).
• Incomplete form of informal-formal
segmentation at the household level.
Notes: Includes all sampled households with at least one worker; as compared to informal or formal households,
mixed households always have at least two workers. Source: OECD (2019), Key Indicators of Informality based on
Individuals and their Household (database)
31. Distribution at the household level also varies disparately by location
Degree of informality of households, by rural/urban location
(Households with 2 or more workers)
Percentage of population
• This should be taking into account for
social protection strategies.
• reduced public service
infrastructure can be an important
barrier to enrolment of rural
populations
• Urban households, however, exhibit
greater diversity and may be an
important entry point for the extension
of social protection to informal economy
workers.
Note: Argentina excluded from figure as survey data only representative for urban areas.
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database).
-60 -40 -20 0 20 40 60 80 100 120
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
El Salvador
Honduras
Mexico
Nicaragua
Paraguay
Peru
Uruguay
LAC
Informal Rural Mixed Rural Formal Rural Informal Urban Mixed Urban Formal Urban
32. Children and older individuals disproportionately live in informal households
Distribution of dependents (Households with 2 or more workers)
Percentage of population
• The distribution of dependents in
different types of households informs
the extent to which adverse well-
being implications of informality are
passed on to other segments of the
population
• In most countries, a majority of
children (below age 5 and aged 5-15)
and of individuals age 60 and over live
in completely informal households
Notes: Includes all sampled households with at least one worker;
mixed households have at least two workers. Regional averages
are unweighted average across sample countries.
Source: OECD (2019), Key Indicators of Informality based on
Individuals and their Household (database).
33. Risks and Vulnerabilities in terms of Wellbeing are higher for the informal
Informal households are poorer
and support higher number of
dependents.
• Informal work is associated
with larger occupational risks.
• They have less access to
traditional forms of social
security but also
• To other tax-financed
government programmes
• And credit markets and other
risk management instruments
Notes: Includes all sampled households with at least at least two
workers. Regional averages are unweighted average across
sample countries.
Source: OECD (2019), Key Indicators of Informality based on
Individuals and their Household (database).
The higher the degree of informality of households, the higher the
incidence of poverty and low income
Proportion of households falling below the national poverty line (at least 2 workers)
-80 -60 -40 -20 0 20 40 60 80 100 120
Argentina
Bolivia
Brazil
Chile
Colombia
Costa Rica
El Salvador
Honduras
Mexico
Nicaragua
Paraguay
Peru
Uruguay
LAC
Informal Non-poor Mixed Non-poor Formal Non-poor Informal Poor Mixed Poor Formal Poor
Non-poor
Poor
34. Across countries, a disproportionate share of the poorest households are
employed in the informal economy
Several clusters of countries:
1) countries with low and equal shares of
completely informal households in both
the poorest and richest quintiles (e.g.
Argentina and Chile);
2) countries with large differences in the
incidence of informal households, with
poorer households more likely to be
completely informal than those in the
richest.
3) In most of the countries being in a mixed
household change positively the odds for
being in the richest quintile compared to
the poorest quintile.
Notes: Includes all sampled households with at least one worker; mixed households have at least two workers. Regional averages
are unweighted average across sample countries. Quintiles based on overall consumption distribution of all households.
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database).
Share of households in the poorest and richest quintiles
depending the degree of informality
LAC
LAC
LAC
0
10
20
30
40
50
60
0 10 20 30 40 50 60
% of households in
richest quintile
% of households in poorest quintile
Informal Mixed Formal
35. There is a informal-formal wage gap
Several clusters of countries:
• Explain partially the higher incidence of
poverty among informal employees.
• On average, across the 13 countries for
which data are available, the ratio of
formal to informal hourly wages stands
at around 2, indicating a large wage
penalty associated with informality
• Lower education, lower productivity
and over-representation in occupations
or economic sectors with lower wages
are among features that differentiate
workers in informal and formal
employment.
Notes: Gross wage gap (as opposed to net wage gap) is reported, but varies depending on country data. Raw wage gap does not
remove some major “composition effects” arising from features that may differentiate informal and formal workers. Only wages
earned in the main occupation are considered. Averages based on median and mean wages are almost the same. Sample excludes
El Salvador due to missing information
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database).
Ratio of formal to informal hourly wages
0
1
2
3
4
5
Ratio
(formal/informal)
Mean Median
36. Using Well-being lenses for the situation
of informal workers in LAC
Wellbeing OECD Framework
We use a similar approach tan How’s life 2017, to measure Horizontal inequalities:
• between formal and informal workers performance,
• and general performance depending the level of informality of the household
37. Inequalities in Wellbeing: Informality as a key element
Notes: Per capita Household income comes from national household surveys. For most countries it is provided by statistical offices. It may be differences in the components of income that are including depending on the
country. Averages based on median and mean wages are almost the same. Sample excludes Nicaragua due to quality of information
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database).
• Governments worldwide have committed to act on inequality through multiple,
interconnected goals, requiring combined policy action in order to meet an overall
commitment to “leave no one behind” (Box 2.1; UN General Assembly, 2015).
• Measures of “horizontal” inequalities focus on the gap between population groups defined by
specific characteristics. In this case between people with formal and informal jobs, and
informal, mixed of formal households.
Measurement and data challenges
• The choice of the reference group. Depending on the logic of each indicator.
• Agreggation on inequality measures. Weighting.
• To be deloped
38. Material Conditions
Income and
Wealth
Notes: Per capita Household income comes from national household surveys. For most countries it is provided by statistical offices. It may be differences in the components of income that are including depending on the
country. Averages based on median and mean wages are almost the same. Sample excludes Nicaragua due to quality of information
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database).
Per capita household income
Per capita household income ratio (Formal to informal
workers)
Per Capita Household Income ratio ( individuals in households with
certain level of informality to total mean/median) – LAC Average
0
1
2
3
4
ARG BOL BRA CHL COL CRI SLV HND MEX PRY PER URY LAC
Mean Median
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Mean Median Mean Median Mean Median
Informal Mixed Formal
• Formal workers have a higher per capita income that can be
until 2 times higher in countries like Bolivia, Honduras and
Paraguay.
39. Material Conditions
Income and
Wealth
Motor Vehicle Ownership
Percentage of people that are part of household that owns a vehicle (including motorbikes)
Notes: Includes Motor Vehicles for household use.
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
LAC Average
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
BOL BRA CHL COL CRI SLV HND MEX NIC PRY PER URY
Informal Mixed Formal
0%
10%
20%
30%
40%
50%
60%
Formal Mixed Informal Total
• Used as a proxy of wealth Mixed households are more similar to
informal households that exhibit motor vehicle ownership in a
larger extend
40. Material Conditions
Job and Earnings /
Work-Life Balance
Workers with occupational risk insurance
Notes: Self reported having an occupational risk insurance. Reported number of answers of unpaid work. Each country have different questions with specific activities or in general. For comparability reasons we only look
at the ratio between formal and informal workers.
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Percentage of workers with occupational risk insurance
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Brazil Colombia Costa Rica Mexico
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
COL CRI HND
Formal Infomal Total
Unpaid work hours
Unpaid work hours ratio (informal to formal)
• Informal workers experience poor conditions at their work with less
occupational risk insurance.
• Informal workers spend more hours in unpaid work.
41. Material Conditions
Job and Earnings /
Work-Life Balance
Job satisfaction
From 1 to 10 How satisfied do you feel about your current job
Notes: Only available for Colombia in the household surveys used for the Key Indicators of Informality based on Individuals and their Household (database).
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
All occupied, by status in informal employment Colombia
0
1
2
3
4
5
6
7
8
9
10
Formal Infomal Ratio
8.3 8.4
7.3
7.8
-
0
1
2
3
4
5
6
7
8
9
10
Informal Mixed Formal
Formal Infomal
All occupied, depending on their informality status and the
level of informality of the household (at least 2 workers) -
Colombia
• Informal workers are less satisfied in their current job. However, if
they are part of a mixed household their satisfaction improves.
42. Material Conditions
Job and Earnings
/ Work-Life
Balance
Notes: Self reported having an occupational risk insurance. LAC average is the simple average between the countries included in the sample of the indicator average number of hours worked in a week depicted in the
figure.
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
0
10
20
30
40
50
60
Argentina Bolivia Brazil Colombia Costa
Rica
Honduras Mexico Paraguay Peru Uruguay LAC
Formal Infomal
Average number of hours worked in a
week
By status in informal employment
In LAC formal workers work on
average 15.5% more hours
(almost 6 hours ) during the
week tan informal workers
affecting their income.
43. Material Conditions
Housing
Notes: Basic Sanitation- Basic Sanitation: Definition This indicator refers to people living in a dwelling : the house have water and sewage system, with an indoor flushing toilet for the sole use of the household
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Households has a floor made form
different material than of dirt, sand or
dung.
Individuals by level of informality of the
household.
Basic Sanitation: Dwelling with water and
sewage system, indoor flushing toilet for the
sole use of the household
Individuals by level of informality of the household.
Mixed households have more similar levels of wellbeing to
completely formal households in the housing dimension
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Informal Mixed Formal
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Informal Mixed Formal
44. Material Conditions
Housing
Notes: For some countries is the number of total rooms and for some others just the number of sleeping rooms. For a better comparability between countries we are only considering the ratio between complete informal
to formall and mixed to formal. Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Number of rooms per person
(Ratio of informal/formal and
mixed/formal)
Individuals by level of informality of the
household (at least 2 workers).
Housing affordability
cost of rent (imputed) / household income
Individuals by level of informality of the household
(at least 2 workers).
Mixed households have more similar levels of wellbeing to
completely formal households in the housing dimension
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Ratio Informal/Formal Ratio Mixed/ formal
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
Informal Mixed Formal
45. Quality of life
Health
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Perceived satisfaction with
their own health
Percentage of people that answered
satisfied or very satisfied, by level of
informality of the household.
Percentage of people that
have a chronic disease
by level of informality of the
household.
• The perceived satisfaction with health condition is higher in completely formal
households
• There is a larger percentage of people with chronic disease in completely informal
households which put them in a vulnerable situation since it is associated with
lack of social protection or at least with a differentiated type of service in
healthcare.
Percentage of people that
reported being sick in the last 30
days (last 6 months for paraguay)
by level of informality of the household.
50%
55%
60%
65%
70%
75%
80%
85%
90%
95%
100%
Chile Colombia
Informal Mixed Formal
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Chile Colombia
Informal Mixed Formal
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Chile Colombia Mexico Paraguay
Informal Mixed Formal
46. Quality of life
Environment
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Percentage of people whose household have
been affected by air pollution in the last year
by level of informality of the household.
• Informality level is more associated with experience a natural disaster in the
dwelling.
• The relation with air pollution is less clear since this is a urban phenomenon
Percentage of people whose household have been
affected by a natural disaster in the last year
by level of informality of the household.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Chile Colombia
Informal Mixed Formal
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
Chile Colombia
Informal Mixed Formal
47. Quality of life
Safety
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
Percentage of people that feel safe or very
safe around their house
by level of informality of the household.
• The association of informality with perception of being safe is not completely clear
• Informality seems to be negatively associated with subjective wellbeing in general
From 0 to 10 how satisfied do you feel about your
life?
by level of informality of the household.
Quality of life
Subjective Wellbeing
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Bolivia Colombia
Informal Mixed Formal
7
7.5
8
8.5
9
9.5
10
Informal Mixed Formal
Colombia
48. Final remarks
Source: OECD (2019), Key Indicators of Informality based on Individuals and their Household (database). + Wellbeing indicators derived from Household Surveys
• Informal employment affect different dimensions of wellbeing, not only for workers but
also for their household members.
• Informality is especially related with lower levels of wellbeing, in the dimensions of
education, earnings and jobs, work life balance and subjective wellbeing
• Data in LAC is limiting wellbeing measures by not including some of the key dimensions in
their household surveys with also prevents public policy to build on clearer more specific
evidence.
• There‘ is still work to do in terms of formulating wellbeing indicators that can be analysed
in specific for the formal and informal employment sectors.
• Formalisation and labour market policy should take into account the way in which
informality levels is related with wellbeing and prioritize completely informal households.
49. Grupo de Trabajo de Digitalización
Córdoba-Argentina, 2 de julio de 2019
¡Muchas
gracias!Thank you!