This dataset is about books. It has 1 row and is filtered where the book is As time goes by in Argentina : economic opportunities and challenges of the demographic transition. It features 7 columns including author, publication date, language, and book publisher.

• The dataset is part of Eurostat's collection of population and employment statistics. The code "NAMQ_10_PE" specifically refers to data related to employment and population trends in European countries and likely spans a range of years from 1980 to 2024.

Eurostat provides statistical data on various aspects of the labor market across Europe, including:

• Total Population – The total number of people residing in a particular country or region.
• Labor Force – The portion of the population that is either employed or actively looking for work.
• Employment Rate – The percentage of the working-age population that is employed.
• Unemployment Rate – The percentage of the labor force that is unemployed.
• Youth Employment Rate – The employment rate among young people (typically aged 15-24).

• Sectoral Employment – Employment distribution across various sectors like agriculture, industry, and services.

• **Details of the Dataset **

This dataset would typically cover European Union countries and potentially other European countries (depending on the specific version). The data likely spans multiple years (1980-2024) and provides insights into the demographic and economic changes in these countries over time.

-**Some example insights you might explore:**

Trends in Employment: Analyzing the employment and unemployment rates over time to see how they correlate with major economic events, such as the global financial crisis. Sectoral Shifts: Investigating how the structure of employment has shifted from agriculture and industry to services over the decades. Impact of Population Growth: Exploring how changes in population size relate to changes in employment, labor force participation, and unemployment.

• Link to Eurostat’s Dataset

You can access the Eurostat dataset directly using the following link:

• Eurostat – NAMQ_10_PE Dataset

This link takes you to Eurostat's Labor Force Survey (LFS) data, which includes datasets related to employment, unemployment, and other labor force indicators across EU countries. You can navigate and search for NAMQ_10_PE by using Eurostat’s filtering and search tools. Here, you can download data in various formats such as CSV, Excel, or TSV.

The analysis of the world's population is a complex and multifaceted endeavor, encompassing a wide range of demographic, economic, social, and environmental factors. Understanding these trends and dynamics is crucial for policymakers, researchers, and organizations to make informed decisions and plan for the future. This article delves into a comprehensive analysis of the world's population, examining its growth patterns, demographic shifts, challenges, and opportunities.

Population Growth. The world's population has experienced remarkable growth over the past century. In 1927, the global population reached its first billion, and since then, it has surged exponentially. As of the latest available data in 2021, the world's population stands at approximately 7.8 billion. Projections indicate that this figure will continue to rise, with estimates suggesting a population of over 9 billion by 2050.

Factors Driving Population Growth. 1. Fertility Rates: High birth rates, particularly in developing countries, have been a significant driver of population growth. Access to healthcare, education, and family planning services plays a crucial role in reducing fertility rates. 2. Increased Life Expectancy: Improvements in healthcare, nutrition, and sanitation have led to longer life expectancy worldwide. This has contributed to population growth, as people are living longer and healthier lives. 3. Demographic Shifts: Demographic shifts are shaping our world in significant ways. In developed countries, an aging population with a higher median age is reshaping healthcare systems, retirement policies, and workforce dynamics. Simultaneously, urbanization is accelerating, with over half of the global population now living in cities, presenting challenges and opportunities for infrastructure, resource management, and social development.

Challenges. 1. Overpopulation: Rapid population growth in certain regions can strain resources, leading to issues such as food scarcity, water shortages, and overcrowding. 2. Aging Workforce: As the global population ages, there may be a shortage of skilled workers, affecting economic productivity and social support systems. 3. Environmental Impact: Population growth is closely linked to increased resource consumption and environmental degradation. Sustainable development and conservation efforts are essential to mitigate these effects.

Opportunities. 1. Demographic Dividend: Countries with youthful populations can benefit from a demographic dividend, where a large working-age population can drive economic growth and innovation. 2. Cultural Diversity: A diverse global population can lead to cultural exchange, creativity, and a richer societal tapestry. 3. Innovation and Technology: Addressing the challenges posed by population growth can drive innovation in areas such as healthcare, agriculture, and energy production.

Analysing the world's population is a complex task that involves understanding its growth patterns, demographic shifts, challenges, and opportunities. As the global population continues to rise, it is essential to address the associated challenges while harnessing the potential benefits of a diverse and dynamic world population. Policymakers, researchers, and organizations must work collaboratively to create sustainable solutions that ensure a prosperous future for all.

This project is integrating scientific research in the Arctic with education and outreach, with a strong central focus on engaging undergraduate students and visiting faculty from groups that have had little involvement in Arctic science to date. Science and society in the United States will be stronger in the long-term if the scientific workforce more closely reflects the racial, ethnic, and cultural diversity of its residents. The Arctic research community currently does not. Of the Principal Investigators funded by NSF's Arctic programs in the past five years, only 1% were African American, Hispanic, Native American, or Alaska Native. This project is catalyzing change in these demographics by engaging faculty from Minority Serving Institutions (MSIs) and a diverse group of undergraduate students in cutting-edge Arctic research and providing them encouragement, mentoring, and opportunities to continue pursuing Arctic studies in subsequent years. The central element of the project is a month-long research expedition to the Yukon River Delta in Alaska. The expedition provides a deep intellectual and cultural immersion in the context of an authentic research experience that is paramount for "hooking" students and keeping them moving along the pipeline to careers as Arctic scientists. The overarching scientific issue that drives the research is the vulnerability and fate of ancient carbon stored in Arctic permafrost (permanently frozen ground). Widespread permafrost thaw is expected to occur this century, but large uncertainties remain in estimating the timing, magnitude, and form of carbon that will be released when thawed. Project participants are working in collaborative research groups to make fundamental scientific discoveries related to the vulnerability of permafrost carbon in the Yukon River Delta and the potential implications of permafrost thaw in this region for the global climate system.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project in Burkina Faso focuses on advancing women's empowerment to spur demographic transition and mitigate gender disparities. This project seeks to empower young women by promoting entrepreneurship through business skills training and grants, and by enhancing access to reproductive health information and contraception, thereby aiming to lower fertility rates.

The World Bank Africa Gender Innovation Lab, along with its partners, is conducting detailed impact evaluations of the SWEDD program’s key initiatives to gauge their effects on child marriage, fertility, and the empowerment of adolescent girls and young women.

This data represents the first round of data collection (baseline) for the impact evaluation and include a household and community level surveys. The household level sample comprises 9857 households, 70,169 individuals and 9382 adolescent girls and young wives aged 24 living in the Boucle du Mouhoun and the East regions of Burkina Faso. The community level sample includes 175 villages.

The insights derived from this survey could help policymakers develop strategies to: - Reduce fertility and child marriage by enhancing access to contraceptives and broadening reproductive health education. - Promote women’s empowerment by increasing their participation in economic activities

This data is valuable for planners who focus on improving living standards, particularly for women. The Ministry of Women, National Solidarity, Family, and Humanitarian Action of Burkina Faso, along with District Authorities, Research Institutions, NGOs, and the general public, stand to benefit from this survey data.

Geographic coverage

Burkina Faso, Regions of Boucle du Mouhoun and East

Analysis unit

The unit of analysis is adolescent girls for the adolescent survey and households for the household survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

We randomly selected 200 villages from the 11 provinces in the two regions of the Boucle du Mouhoun and the East. The 200 villages were selected proportionally, based on the formula (Np/N)*200, where Np represents the number of eligible villages in the province and N the total number of eligible villages. 25 villages were later dropped because of lack of safety.

A census was first administered in each village to identify eligible girls and young wives, as well as households with these eligible individuals. All households with at least one eligible person then constituted the universe from which the survey sample was drawn. In total 9857 households and 9382 girls and young wives were sampled. A village-level questionnaire was also administered.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to: 1. List of household members 2. Education of household members 3. Occupations of household members 4. Characteristics of housing and durable goods 5. Food security 6. Household head's aspirations, as well as those of a boy aged 12 to 24 7. Opinions on women's empowerment and gender equality

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The questionnaire administered at the village-level contains the following sections: 1. Social norms (marriage norms) 2. Ethnic and religious compositions 3. Economic infrastructures (markets and roads) 4. Social services a. Health b. Education

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to each eligible pre-selected individual within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The village-level questionnaire was administered to a group of three to five village leaders with enough knowledge of the village. The enumerators were instructed to include women in this group whenever possible. The questionnaires were written in French, translated into the local languages, and programmed on tablets in French using the CAPI program.

Cleaning operations

Data was anonymized through decoding and local suppression.

The use of genetic data for identifying species-level lineages across the tree of life has received increasing attention in the field of systematics over the past decade. The multispecies coalescent model provides a framework for understanding the process of lineage divergence, and has become widely adopted for delimiting species. However, because these studies lack an explicit assessment of model fit, in many cases, the accuracy of the inferred species boundaries are unknown. This is concerning given the large amount of empirical data and theory that highlight the complexity of the speciation process. Here, we seek to fill this gap by using simulation to characterize the sensitivity of inference under the multispecies coalescent to several violations of model assumptions thought to be common in empirical data. We also assess the fit of the multispecies coalescent model to empirical data in the context of species delimitation. Our results show substantial variation in model fit across datasets. Posterior predictive tests find the poorest model performance in datasets that were hypothesized to be impacted by model violations. We also show that while the inferences assuming the multispecies coalescent are robust to minor model violations, such inferences can be biased under some biologically plausible scenarios. Taken together, these results suggest that researchers can identify individual datasets in which species delimitation under the multispecies coalescent is likely to be problematic, thereby highlighting the cases where additional lines of evidence to identify species boundaries are particularly important to collect. Our study supports a growing body of work highlighting the importance of model checking in phylogenetics, and the usefulness of tailoring tests of model fit to assess the reliability of particular inferences.

Complete Mobi-Twin Dataset

In a nutshell

The Complete Mobi-Twin dataset is created in the Mobi-Twin (Twin transition and changing patterns of spatial mobility: a regional approach) project funded by the European Union’s Horizon Europe Research and Innovation Programme (Grant Agreement no. 101094402).

This dataset combines existing European-level survey and register datasets with publicly available open and big data sources to produce a data product containing information on mobility flows and regional characteristics from Europe. The dataset is at NUTS 2 (Nomenclature of territorial units for statistics) regional level and covers data from 2005 to 2023. The dataset is an outcome of Mobi-Twin research project.

The complete dataset includes five interlinked sections:

1) Regional characteristics,

2) Mobility data,

3) Microsimulation data for five pilot regions,

4) The Mobi-Twin Survey data,

5) The NUTS 2 spatial layers.

The regional characteristics dataset provides essential information on the NUTS 2 regions in Europe for understanding and redefining regional attractiveness in the twin transition. This data consists of seven themes ranging from variables describing digitalization and environmental characteristics of the regions to socio-economic, demographic, and typological information.

The mobility dataset provides information on mobility flows of the three identified mobility forms – long-term, short-term and circular mobility. The long-term mobility form contains permanent migration and long-term student mobility. The short-term mobility form contains short-term student mobility and seasonal work mobility. The circular mobility form contains long distance commuting, cross-border commuting and multilocal living. The mobility data is extracted from Labour Force Survey data, Twitter data, and Eramus+ mobility data.

Microsimulation dataset provides essential socio-economic and demographic input data for agent-based modelling in the five case study regions of the Mobi-Twin project. The Mobi-Twin Survey data is a final and clean dataset from the survey conducted during the project, including additional geographical and mobility type profiling variables derived from the initial survey questions. The NUTS 2 spatial layer dataset includes all official versions of the NUTS 2 territorial division.

This documentation describes in detail the creation and structure of the dataset. The complete dataset can be updated with newer or corrected data during the project. The complete Mobi-Twin dataset will be made openly available after the project ends.

General Description

The Mobi-Twin dataset is a curated collection of five datasets: regional characteristics, mobility flow data, the Mobi-Twin survey data, input data for agent-based modelling in the five pilot regions of the project, and spatial layers for official versions of NUTS 2 division over time.

The datasets are interconnected to each other based on the unique NUTS 2 identifier code (ID) and mappable via the spatial layer of NUTS 2 regions in Europe (Figure 1). These data have been collected by the Mobi-Twin partners from various sources and are presented in tabular and spatial formats. The dataset is the main output from data collection performed in the beginning of the project and will provide the input data for analyses done later in the project.

Figure 1. The relation between the five sections of the complete Mobi-Twin dataset. Author: Tuomas Väisänen. Full-sized figure HERE.

The mobility data section covers seven types of mobility, each of which belongs to one of the three main mobility forms – long-term, short-term, and circular mobility (Section 2). The long-term mobility form covers permanent migration and long-term student mobility types. This mobility form refers to mobility where the individual is staying in the destination region for longer than 12 months. The short-term mobility form covers short-term student mobility and seasonal work mobility types. This mobility form refers to mobility where the individual is staying in the region for a duration between three and 11 months. Finally, the circular mobility form covers mobility where the mobility between origin and destination region is habitual, frequent, and implies a return trip to the origin region. This form contains the following mobility types: long-distance commuting, cross-border commuting, and multilocal living.

The regional characteristics data section of the complete Mobi-Twin dataset provides information on the regions in Europe from 2005 until 2023. These characteristics have been further sectioned into seven themes, that capture different characteristics of these regions. These themes include social fabric, living conditions, economy and labour market, access and connectivity, digitalization, landscape and environment, and finally regional typologies. Each theme includes several variables describing each region throughout the years in the context of the theme. For instance, the social fabric files include variables describing gender balance, population at risk of poverty, income levels and median age of population. The information in the regional characteristics section of the dataset provides essential background information for understanding the mobilities through differences between the receiving and sending regions. To exemplify in the context of the twin transition, student mobility might be better explained by a large difference in the penetration rates of affordable high-speed broadband and mobile internet connections between the regions than climate differences.

The microsimulation data section provides a basic regional information on demographics and employment in the five pilot regions for agent-based modelling (Section 4). using the NUTS on its third level (NUTS 3). This data is used to model mobility patterns within the five focus NUTS 2 regions of the project:

• Spain: Castilla-La Mancha (ES42)

• The Netherlands: Groningen (NL11)

• Italy: Lombardy (ITC4)

• Greece: Central Macedonia (EL52)

• Finland: Northern and Eastern Finland (FI1D).

This information consists of tabular data on age/sex structure, marital status, education levels, employment status, and household sizes per NUTS 3 regions that make up the above-mentioned NUTS 2 regions. This data covers the years 2001, 2011, and 2021 where data is available, and some of the years in between depending on data availability per country. Any annual gaps in the data will be filled with interpolation in WP3.

The Mobi-Twin survey data section provides survey data on the populations in the five pilot countries regarding their mobility patterns relating to the twin transition (Section 5). The survey data was collected from the Netherlands, Spain, Italy, Greece, and Finland, but also on a more general level from all over Europe. It contains questions on past, current, and future regions of residence, but also on demographics, employment, and attitudes towards the twin transition. The survey data has been processed by the partners to extract mobilities between NUTS 2 regions, to classify respondents as digital nomads, return migrants and retirement migrants, and to provide weights for the respondents.

The NUTS 2 spatial layer dataset ensures the interoperability of the four Mobi-Twin datasets with each other (Figure 1), and potential outside sources of the data (Section 6). Here, each data record of the dataset is associated with a NUTS unique code. Regional NUTS 2 regional codes enable enriching mobility flow information with regional characteristics of the origin and destination regions, which is essential for modelling the effects of the twin transition on inter-regional mobilities. Spatial NUTS divisions are available on three levels for statistical analyses of different scopes. Mobi-Twin project focuses on the NUTS 2 level, as that is the level where regional policies are applied, and the availability of the data is better compared to the NUTS 3 or LAU (local area unit) levels.

Interested to collaborate and use the dataset?

Feel free to reach out to Olle Järv (olle.jarv@helsinki.fi).

How to cite the Complete Mobi-Twin Dataset?

Väisänen, T., Malekzadeh, M., Havusela, M., Inkeröinen, O. & Järv, O. (2024) The Complete Mobi-Twin Dataset. DOI: 10.5281/zenodo.14228376

For more than three decades UCSUR has documented the status of older adults in the County along multiple life domains. Every decade we issue a comprehensive report on aging in Allegheny County and this report represents our most recent effort. It documents important shifts in the demographic profile of the population in the last three decades, characterizes the current status of the elderly in multiple life domains, and looks ahead to the future of aging in the County. This report is unique in that we examine not only those aged 65 and older, but also the next generation old persons, the Baby Boomers. Collaborators on this project include the Allegheny County Area Agency on Aging, the United Way of Allegheny County, and the Aging Institute of UPMC Senior Services and the University of Pittsburgh. The purpose of this report is to provide a comprehensive analysis of aging in Allegheny County. To this end, we integrate survey data collected from a representative sample of older county residents with secondary data available from Federal, State, and County agencies to characterize older individuals on multiple dimensions, including demographic change and population projections, income, work and retirement, neighborhoods and housing, health, senior service use, transportation, volunteering, happiness and life satisfaction, among others. Since baby boomers represent the future of aging in the County we include data for those aged 55-64 as well as those aged 65 and older.

Please, if you use this dataset or do you like my work please UPVOTE 👁️

This dataset comes from World Bank

This dataset contains historical population data from the World Bank, spanning from 1960 to 2023, for various countries across the globe. The data is organized by country, income group, and region, offering a comprehensive view of population trends worldwide. It includes information on the total population for each country from 1960 onwards, as well as regional and income group classifications, providing insights into demographic changes, economic development, and social progress.

Key Features:

country_code: A unique identifier for each country.

country_name:The official name of the country.

region: The region to which the country belongs (e.g., Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, etc.).

income_group: The income classification of the country (e.g., Low income, Lower middle income, Upper middle income, High income).

Years(1960-2023): The dataset includes population figures for each year from 1960 through 2023 for each country.

GitHub repository:

https://github.com/fredericksalazar/world_population_dataset

Eurostat’s annual data collections on demographic statistics are structured as follows:

NOWCAST: Annual data collection on provisional monthly data on live births and deaths covering at least six months of the reference year (Article 4.3 of the Commission implementing regulation (EU) No 205/2014).

DEMOBAL (Demographic balance): Annual data collection on provisional data on population, total live births and total deaths at national level (Article 4.1 of the Commission implementing regulation (EU) No 205/2014).

POPSTAT (Population Statistics): The most in-depth annual national and regional demographic and migration data collection. The data relate to populations, births, deaths, immigrants, emigrants, marriages and divorces, and is broken down into several categories (Article 3 of Regulation (EU) No 1260/2013 and Article 3 of Regulation (EC) No 862/2007).

The aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate - legal acts and other agreements’.

The completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes. For more information on mandatory/voluntary data collection, see 6.1. Institutional mandate - legal acts and other agreements’.

The following statistics on deaths are collected from the National Statistical Institutes:

• Deaths by month of occurrence
• Deaths by age, year of birth, sex and by:
  • Region (NUTS 2) of residence;
  • Region (NUTS 3) of residence;
  • Country of birth;
  • Country of citizenship;
  • Legal marital status;
  • Educational attainment (ISCED 2011).
• Infant deaths by age and sex;
• Infant deaths by parents' level of educational attainment (ISCED);
• Late foetal deaths by mother's age.

Statistics on mortality: based on the different breakdowns of data on deaths received, Eurostat produces the following:

• Statistics available in the online table Population change - Demographic balance and crude rates at national level (demo_gind):
  • Natural change of population, crude death rate.
• Statistics available in the online table Infant mortality rates (demo_find):
  • Infant mortality rate;
  • Neonatal mortality rate;
  • Early neonatal mortality rate;
  • Late foetal mortality rate;
  • Perinatal mortality rate.
• Life table (demo_mlifetable);
• Life expectancy by age and sex (demo_mlexpec);
• Life expectancy by age, sex and educational attainment (ISCED 2011) (demo_mlexpecedu).

https://ec.europa.eu/eurostat/cache/metadata/en/demo_r_gind3_esms.htm" target="_self">Information about statistics on deaths by NUTS regions.

By the middle of the 1990s, Indonesia had enjoyed over three decades of remarkable social, economic, and demographic change and was on the cusp of joining the middle-income countries. Per capita income had risen more than fifteenfold since the early 1960s, from around US$50 to more than US$800. Increases in educational attainment and decreases in fertility and infant mortality over the same period reflected impressive investments in infrastructure. In the late 1990s the economic outlook began to change as Indonesia was gripped by the economic crisis that affected much of Asia. In 1998 the rupiah collapsed, the economy went into a tailspin, and gross domestic product contracted by an estimated 12-15%-a decline rivaling the magnitude of the Great Depression. The general trend of several decades of economic progress followed by a few years of economic downturn masks considerable variation across the archipelago in the degree both of economic development and of economic setbacks related to the crisis. In part this heterogeneity reflects the great cultural and ethnic diversity of Indonesia, which in turn makes it a rich laboratory for research on a number of individual- and household-level behaviors and outcomes that interest social scientists. The Indonesia Family Life Survey is designed to provide data for studying behaviors and outcomes. The survey contains a wealth of information collected at the individual and household levels, including multiple indicators of economic and non-economic well-being: consumption, income, assets, education, migration, labor market outcomes, marriage, fertility, contraceptive use, health status, use of health care and health insurance, relationships among co-resident and non- resident family members, processes underlying household decision-making, transfers among family members and participation in community activities. In addition to individual- and household-level information, the IFLS provides detailed information from the communities in which IFLS households are located and from the facilities that serve residents of those communities. These data cover aspects of the physical and social environment, infrastructure, employment opportunities, food prices, access to health and educational facilities, and the quality and prices of services available at those facilities. By linking data from IFLS households to data from their communities, users can address many important questions regarding the impact of policies on the lives of the respondents, as well as document the effects of social, economic, and environmental change on the population. The Indonesia Family Life Survey complements and extends the existing survey data available for Indonesia, and for developing countries in general, in a number of ways. First, relatively few large-scale longitudinal surveys are available for developing countries. IFLS is the only large-scale longitudinal survey available for Indonesia. Because data are available for the same individuals from multiple points in time, IFLS affords an opportunity to understand the dynamics of behavior, at the individual, household and family and community levels. In IFLS1 7,224 households were interviewed, and detailed individual-level data were collected from over 22,000 individuals. In IFLS2, 94.4% of IFLS1 households were re-contacted (interviewed or died). In IFLS3 the re-contact rate was 95.3% of IFLS1 households. Indeed nearly 91% of IFLS1 households are complete panel households in that they were interviewed in all three waves, IFLS1, 2 and 3. These re-contact rates are as high as or higher than most longitudinal surveys in the United States and Europe. High re-interview rates were obtained in part because we were committed to tracking and interviewing individuals who had moved or split off from the origin IFLS1 households. High re-interview rates contribute significantly to data quality in a longitudinal survey because they lessen the risk of bias due to nonrandom attrition in studies using the data. Second, the multipurpose nature of IFLS instruments means that the data support analyses of interrelated issues not possible with single-purpose surveys. For example, the availability of data on household consumption together with detailed individual data on labor market outcomes, health outcomes and on health program availability and quality at the community level means that one can examine the impact of income on health outcomes, but also whether health in turn affects incomes. Third, IFLS collected both current and retrospective information on most topics. With data from multiple points of time on current status and an extensive array of retrospective information about the lives of respondents, analysts can relate dynamics to events that occurred in the past. For example, changes in labor outcomes in recent years can be explored as a function of earlier decisions about schooling and work. Fourth, IFLS collected extensive measures of health status, including self-reported measures of general health status, morbidity experience, and physical assessments conducted by a nurse (height, weight, head circumference, blood pressure, pulse, waist and hip circumference, hemoglobin level, lung capacity, and time required to repeatedly rise from a sitting position). These data provide a much richer picture of health status than is typically available in household surveys. For example, the data can be used to explore relationships between socioeconomic status and an array of health outcomes. Fifth, in all waves of the survey, detailed data were collected about respondents¹ communities and public and private facilities available for their health care and schooling. The facility data can be combined with household and individual data to examine the relationship between, for example, access to health services (or changes in access) and various aspects of health care use and health status. Sixth, because the waves of IFLS span the period from several years before the economic crisis hit Indonesia, to just prior to it hitting, to one year and then three years after, extensive research can be carried out regarding the living conditions of Indonesian households during this very tumultuous period. In sum, the breadth and depth of the longitudinal information on individuals, households, communities, and facilities make IFLS data a unique resource for scholars and policymakers interested in the processes of economic development.

This package has the final datasets compiled as the parameters of the earnings dynamics model with mean income growth, wage volatility and employment transitions for all demographic groups as panel time series for Chile. I also publish all the codes required to replicate the empirical analysis of the paper.

The work estimates a labor earnings dynamic model, using employment-unemployment transition probabilities and continuous income shocks. Workers during unemployment benefit from a replacement ratio of labor income during their unemployment spell.