Over the past 30 years, there has been an almost constant reduction in the poverty rate worldwide. Whereas nearly ** percent of the world's population lived on less than 2.15 U.S. dollars in terms of 2017 Purchasing Power Parity (PPP) in 1990, this had fallen to *** percent in 2022. This is even though the world's population was growing over the same period. However, there was a small increase in the poverty rate during the COVID-19 pandemic in 2020 and 2021, when thousands of people became unemployed overnight. Moreover, the rising cost of living in the aftermath of the pandemic and spurred by the Russian invasion of Ukraine in 2022 meant that many people were struggling to make ends meet. Poverty is a regional problem Poverty can be measured in relative and absolute terms. Absolute poverty concerns basic human needs such as food, clothing, shelter, and clean drinking water, whereas relative poverty looks at whether people in different countries can afford a certain living standard. Most countries that have a high percentage of their population living in absolute poverty, meaning that they are poor compared to international standards, are regionally concentrated. African countries are most represented among the countries in which poverty prevails the most. In terms of numbers, Sub-Saharan Africa and South Asia have the most people living in poverty worldwide. Inequality on the rise How wealth, or the lack thereof, is distributed within the global population and even within countries is very unequal. In 2022, the richest one percent of the world owned almost half of the global wealth, while the poorest 50 percent owned less than two percent in the same year. Within regions, Latin America had the most unequal distribution of wealth, but this phenomenon is present in all world regions.

Historical dataset showing World life expectancy by year from 1950 to 2025.

The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on earth, which far exceeds the world population of 7.2 billion from 2015. Our own estimate based on UN data shows the world's population surpassing 7.7 billion.

China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, the country of India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.

The next 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.

Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.

In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added each year.

This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by the year 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Global life expectancy has also improved in recent years, increasing the overall population life expectancy at birth to just over 70 years of age. The projected global life expectancy is only expected to continue to improve - reaching nearly 77 years of age by the year 2050. Significant factors impacting the data on life expectancy include the projections of the ability to reduce AIDS/HIV impact, as well as reducing the rates of infectious and non-communicable diseases.

Population aging has a massive impact on the ability of the population to maintain what is called a support ratio. One key finding from 2017 is that the majority of the world is going to face considerable growth in the 60 plus age bracket. This will put enormous strain on the younger age groups as the elderly population is becoming so vast without the number of births to maintain a healthy support ratio.

Although the number given above seems very precise, it is important to remember that it is just an estimate. It simply isn't possible to be sure exactly how many people there are on the earth at any one time, and there are conflicting estimates of the global population in 2016.

Some, including the UN, believe that a population of 7 billion was reached in October 2011. Others, including the US Census Bureau and World Bank, believe that the total population of the world reached 7 billion in 2012, around March or April.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Graph and download economic data for Life Expectancy at Birth, Total for the World (SPDYNLE00INWLD) from 1960 to 2023 about life expectancy, life, birth, and World.

Abstract

Young Lives: An International Study of Childhood Poverty is a collaborative project investigating the changing nature of childhood poverty in selected developing countries. The UK’s Department for International Development (DFID) is funding the first three-year phase of the project.

Young Lives involves collaboration between Non Governmental Organisations (NGOs) and the academic sector. In the UK, the project is being run by Save the Children-UK together with an academic consortium that comprises the University of Reading, London School of Hygiene and Tropical Medicine, South Bank University, the Institute of Development Studies at Sussex University and the South African Medical Research Council.

The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.

Objectives of the study The Young Lives study has three broad objectives: • producing good quality panel data about the changing nature of the lives of children in poverty. • trace linkages between key policy changes and child poverty • informing and responding to the needs of policy makers, planners and other stakeholders There will also be a strong education and media element, both in the countries where the project takes place, and in the UK.

The study takes a broad approach to child poverty, exploring not only household economic indicators such as assets and wealth, but also child centred poverty measures such as the child’s physical and mental health, growth, development and education. These child centred measures are age specific so the information collected by the study will change as the children get older.

Further information about the survey, including publications, can be downloaded from the Young Lives website.

Geographic coverage

Young Lives is an international study of childhood poverty, involving 12,000 children in 4 countries. - Ethiopia (20 communities in Addis Ababa, Amhara, Oromia, and Southern National, Nationalities and People's Regions) - India (20 sites across Andhra Pradesh and Telangana) - Peru (74 communities across Peru) - Vietnam (20 communities in the communes of Lao Cai in the north-west, Hung Yen province in the Red River Delta, the city of Danang on the coast, Phu Yen province from the South Central Coast and Ben Tre province on the Mekong River Delta)

Analysis unit

Individuals; Families/households

Universe

Location of Units of Observation: Cross-national; Subnational Population: Children aged approximately 1 year old and their households, and children aged 8 years old and their households, in Ethiopia, India (Andhra Pradesh), Peru and Vietnam, in 2002. See documentation for details of the exact regions covered in each country.

Kind of data

Sample survey data [ssd]

Sampling procedure

Purposive selection/case studies

A key need for the study's objectives was to obtain data at different levels - the children, their households, the community in which they resided, as well as at regional and national levels. This need thus determined that children should be selected in geographic clusters rather than randomly selected across the country. There was, however, a much more important reason for recruiting children in clusters - the sites are also intended to provide suitable settings for a range of complementary thematic studies. For example, one or a few sites may be used for a qualitative study designed to achieve a deeper level of understanding of some social issues, either because they are important in that particular place, or because the sites are appropriate locales to investigate a more general concern. The quantitative panel study is seen as the foundation upon which a coherent and interesting range of linked studies can be set up.

Thus the design was decided, in each country, comprising 20 geographic clusters with 100 children sampled in each cluster.

For details on sample design, see the methodological document which is available in the documentation.

Sampling deviation

Ethiopia: 1,999 (1-year-olds), 1,000 (8-year-olds); India: 2,011 (1-year-olds), 1,008 (8-year-olds); Peru: 2,052 (1-year-olds), 714 (8-year-olds); Vietnam: 2,000 (1-year-olds), 1,000 (8-year-olds).

Mode of data collection

Face-to-face interview

Research instrument

Every questionnaire used in the study consists of a 'core' element and a country-specific element, which focuses on issues important for that country.

The core element of the questionnaires consists of the following sections: Core 6-17.9 month old household questionnaire • Section 1: Locating information • Section 2: Household composition • Section 3: Pregnancy, delivery and breastfeeding • Section 4: Child care • Section 5: Child health • Section 6: Caregiver background • Section 7: Livelihoods and time allocation • Section 8: Economic changes • Section 9: Socio-economic status • Section 10: Caregiver psychosocial well-being • Section 11: Social capital • Section 12: Tracking details • Section 13: Anthropometry

Core 7.5-8.5 year old household questionnaire • Section 1: Locating information • Section 2: Household composition • Section 3: Births and deaths • Section 4: Child school • Section 5: Child health • Section 6: Caregiver background • Section 7: Livelihoods and time allocation • Section 8: Economic changes • Section 9: Socio-economic status • Section 10: Child mental health • Section 11: Social capital • Section 12: Tracking details • Section 13: Anthropometry

The communnity questionnaire consists of the following sections: • Section 1: Physical environment • Section 2: Social environment • Section 3: Infrastructure and access • Section 4: Economy • Section 5: Health and education

Graph and download economic data for Life Expectancy at Birth, Total for the Arab World (SPDYNLE00INARB) from 1960 to 2023 about Arab World, life expectancy, life, and birth.

Introduction

Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. It is a key metric for assessing population health.

Life expectancy has burgeoned since the advent of industrialization in the early 1900s and the world average has now more than doubled to 70 years. Yet, we still see inequality in life expectancy across and within countries. The study by Acemoglu and Johnson demonstrated the relationship between increased life expectancy and improvement in economic growth (GDP per capita), controlling for country-fixed effects [3]. In the table below, we have shown how life expectancy varies between high-income and low-income countries. However, further analysis is necessary to determine how the allocation of a country’s wealth through certain investments in healthcare, education, environmental management, and some socioeconomic factors have an overall effect in determining average life expectancy.

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The Sub-Saharan African region experiences the lowest life expectancy at birth compared to other regions over the past 3 decades. SSA countries have consistently ranked as the lowest-earning countries in terms of GDP per capita. Therefore, there is a huge scope for improvement in life expectancy in SSA countries and hence our research focuses on the 40 Sub-Saharan African (SSA) countries with the lowest GDP per capita

Research Questions

After reviewing the rich existing literature on Life Expectancy, we realized the lack of concrete research on understanding the impact of all-encompassing determinants that cover socio-economic and environmental factors for SSA countries using Panel Data techniques. Hence, we tried to address this inadequacy through our research. In this paper, we aim to have a better understanding of factors affecting life expectancy in the SSA region for an efficient policy-making process and better allocation of funds and resources in addressing the prevalence of low life expectancy in Sub-Saharan Africa. To achieve that we attempt to answer the following questions in this research:

1. What’s the Impact of Expenditure on Health and Education (% of GDP) on Life Expectancy?
2. How does the prevalence of undernourishment and communicable disease Affect Life Expectancy?
3. Do factors like corruption and unemployment rate impact life expectancy? If yes, quantify
4. Increase in CO2 emissions decrease life expectancy? Is it significant?

Data

Main sources of data - World Bank Open Data & Our World in Data

1. Country - 174 countries - list

2. Country Code - 3-letter code

3. Region - region of the world country is located in

4. IncomeGroup - country's income class

5. Year - 2000-2019 (both included)

6. Life expectancy - data

7. Prevalence of Undernourishment (% of the population) - Prevalence of undernourishment is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normally active and healthy life

8. Carbon dioxide emissions (kiloton) - Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during the consumption of solid, liquid, and gas fuels and gas flaring

9. Health Expenditure (% of GDP) - Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT, and stocks of vaccines for emergencies or outbreaks

10. Education Expenditure (% of GDP) - General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditures funded by transfers from international sources to the government. General government usually refers to local, regional, and central governments.

11. Unemployment (% total labor force) - Unemployment refers to the % share of the labor force that is without work but available for and seeking employment

12. Corruption (CPIA rating) - Transparency, accountability, and corruption in the public sector assets the extent to which the executive can be held accountable for its use of funds and for the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to...

2007 marked the first year where more of the world's population lived in an urban setting than a rural setting. In 1960, roughly a third of the world lived in an urban setting; it is expected that this figure will reach two thirds by 2050. Urbanization is a fairly new phenomenon; for the vast majority of human history, fewer than five percent of the world lived in urban areas, due to the dependency on subsistence agriculture. Advancements in agricultural practices and technology then coincided with the beginning of the industrial revolution in Europe in the late 19th century, which resulted in waves of urbanization to meet the demands of emerging manufacturing industries. This trend was replicated across the rest of the world as it industrialized over the following two centuries, and the most significant increase coincided with the industrialization of the most populous countries in Asia. In more developed economies, urbanization remains high even as economies de-industrialize, due to a variety of factors such as housing availability, labor demands in service industries, and social trends.

Abstract

The Life in Transition Survey, after the crisis (LiTS II), is the second round of LiTS surveys, previously conducted in 2006 (LiTS I). In late 2006, the EBRD and World Bank carried out the first comprehensive survey of individuals and households across virtually the whole transition region. The purpose was to gain a better understanding of how people's lives had been shaped and affected by the upheavals of the previous 15 years.

Four years later, the EBRD and World Bank commissioned a second round of the survey. The circumstances facing most people were significantly different between the first and second rounds. The Life in Transition Survey I (LiTS I) was carried out at a time when the region's economies were, with few exceptions, growing strongly. In contrast, LiTS II took place in late 2010, at a time when most countries were still facing the aftershocks of a severe global economic crisis.

LiTS II advances and improves on LiTS I in two important ways. First, the questionnaire was substantially revised. The new questionnaire includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes. Second, the coverage has been expanded to include five western European "comparator" countries - France, Germany, Italy, Sweden and the UK. This allows us to benchmark the transition region against some advanced market economies, thereby giving a clearer perspective on the remaining challenges facing transition countries.

Geographic coverage

The second Life in Transition Survey (LiTS II) was implemented in 30 transition countries (Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Former Yugoslav Republic of Macedonia (FYROM), Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Moldova, Mongolia, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan and Kosovo) as well as five comparator countries in western Europe (France, Germany, Italy, Sweden and the United Kingdom).

Analysis unit

• individuals
• households

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling methodology was designed to make the sample nationally representative. In order to achieve this, a two-stage clustered stratified sampling procedure was used to select the households to be included in the sample. In 25 transition countries, France, Germany, Italy and Sweden, the survey was conducted face-to-face in 1,000 randomly chosen households. In Russia, Ukraine, Uzbekistan, Serbia, Poland and the United Kingdom there were 1,500 household interviews in order to allow for a reasonably large sample for a follow-up telephone survey, which will be based on a shortened version of the current questionnaire and which will be conducted one year after the face-to-face survey, i.e., in autumn 2011.

In the first stage of the sampling, sample frame of Primary Sampling Units were established. In all countries, the most recent available sample frame of Primary Sampling Units (PSUs) was selected as the starting point. Local electoral territorial units were used as PSUs wherever it was possible, as they tend to carry the most up-to-date information about household addresses. The following sampling frames were used:

Electoral districts: Bulgaria, Hungary, Poland, Romania, Serbia. Polling station territories: Albania, Armenia, Belarus, Bosnia and Herzegovina, Moldova, Montenegro. Census Enumeration Districts: Slovak Republic, Sweden, Tajikistan, Turkey. Geo-administrative divisions: the remaining countries.

The second stage in sampling consisted of selecting households within each PSU. The aim was to make sure that each household was selected with an equal probability within any given PSU and hence all households in the country had the same probability of being selected. Two sampling procedures were used. In the majority of countries, a random walk fieldwork procedure was used: the fieldwork coordinator selected the first address to be sampled, and the interviewer was given clear instructions on how to select remaining addresses within the PSUs. For a small number of countries - Hungary, Lithuania, Slovenia and Sweden and the United Kingdom - the sample was pre-selected to ensure that the probability of any household's inclusion was always equivalent to the probability generated by random selection.

The sampling procedures are more fully described in "Life in Transition Survey 2010 - Final Report" pp.114-115.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire of LiST II includes sections on the impact of the crisis and on climate change issues, as well as improved and expanded questions in areas such as corporate governance, public service delivery, and economic and social attitudes.

There are 8 Sections in the questionnaire: Household Roster, Housing and Expenses, Attitudes and Values, Climate Change, Labour, Education and Entrepreneurial Activity, Governance, Miscellaneous Questions, and Impact of the Crisis.

The respondents of the questionnaire are the head of the households or other knowledgeable household members for section 1 and 8. For sections 3-7, the respondents are the people selected randomly by using selection grids.

Response rate

The standard interview method called for each selected household to be visited at least three times before being replaced. In the majority of cases (79 percent), however, the interviews were completed on the first visit. In 61 percent of cases, the head of the household and the principal respondent were the same person; in the remaining 39 percent, two different interviews were required to be carried out in the same household.

Abstract This paper discusses some of the factors associated with life satisfaction in Brazil using four waves of the World Values Survey (1991 to 2014). Some results already described in the literature were confirmed, as we found that individuals who were married, employed, more religious, in better health, with greater freedom/control over their lives and who had a better financial situation were more satisfied with life, regardless of the time period. The variables for age and cohort showed non-significant associations with life satisfaction when aspects that theoretically correlated with life satisfaction were controlled in the analysis. When the different cohorts were analyzed separately, the results suggest that life satisfaction might be related to the conjectural and historical factors represented by period effects.

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.

Graph and download economic data for Life Expectancy at Birth, Total for Developing Countries in Europe and Central Asia (SPDYNLE00INECA) from 1960 to 2023 about Central Asia, life expectancy, life, birth, and Europe.

Real-time world population counter with births, deaths, and demographic breakdowns

Graph and download economic data for Life Expectancy at Birth, Total for Italy (SPDYNLE00INITA) from 1960 to 2023 about life expectancy, life, birth, and Italy.

Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

Graph and download economic data for Life Expectancy at Birth, Total for High Income Countries (SPDYNLE00INHIC) from 1960 to 2023 about life expectancy, life, birth, and income.

More than two thirds of the world population were living in some form of autocracy in 2024. This high share is explained by the fact that around one third of the world population resides in India and China, classified as an electoral autocracy and closed autocracy, respectively. India's falling from an electoral democracy to an electoral autocracy explains why the share of people living in autocracies increased sharply in 2017.

Male life expectancy of Viet Nam went up by 0.17% from 69.8 years in 2022 to 69.9 years in 2023. Since the 1.24% downward trend in 2021, male life expectancy improved by 0.55% in 2023. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Mode of data collection

Face-to-face [f2f]