Graph and download economic data for Life Expectancy at Birth, Total for Developing Countries in Sub-Saharan Africa (SPDYNLE00INSSA) from 1960 to 2023 about Sub-Saharan Africa, 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...

Context

Life expectancy at birth is defined as the average number of years that a newborn could expect to live if he or she were to pass through life subject to the age-specific mortality rates of a given period. The years are from 1950 to 2018.

Content

For regional- and global-level data pre-1950, data from a study by Riley was used, which draws from over 700 sources to estimate life expectancy at birth from 1800 to 2001.

Riley estimated life expectancy before 1800, which he calls "the pre-health transition period". "Health transitions began in different countries in different periods, as early as the 1770s in Denmark and as late as the 1970s in some countries of sub-Saharan Africa". As such, for the sake of consistency, we have assigned the period before the health transition to the year 1770. "The life expectancy values employed are averages of estimates for the period before the beginning of the transitions for countries within that region. ... This period has presumably the weakest basis, the largest margin of error, and the simplest method of deriving an estimate."

For country-level data pre-1950, Clio Infra's dataset was used, compiled by Zijdeman and Ribeira da Silva (2015).

For country-, regional- and global-level data post-1950, data published by the United Nations Population Division was used, since they are updated every year. This is possible because Riley writes that "for 1950-2001, I have drawn life expectancy estimates chiefly from various sources provided by the United Nations, the World Bank’s World Development Indicators, and the Human Mortality Database".

For the Americas from 1950-2015, the population-weighted average of Northern America and Latin America and the Caribbean was taken, using UN Population Division estimates of population size.

Acknowledgements

Life expectancy:

Data publisher's source: https://www.lifetable.de/RileyBib.pdf Data published by: James C. Riley (2005) – Estimates of Regional and Global Life Expectancy, 1800–2001. Issue Population and Development Review. Population and Development Review. Volume 31, Issue 3, pages 537–543, September 2005., Zijdeman, Richard; Ribeira da Silva, Filipa, 2015, "Life Expectancy at Birth (Total)", http://hdl.handle.net/10622/LKYT53, IISH Dataverse, V1, and UN Population Division (2019) Link: https://datasets.socialhistory.org/dataset.xhtml?persistentId=hdl:10622/LKYT53, http://onlinelibrary.wiley.com/doi/10.1111/j.1728-4457.2005.00083.x/epdf, https://population.un.org/wpp/Download/Standard/Population/ Dataset: https://ourworldindata.org/life-expectancy

GDP per capita:

Data publisher's source: The Maddison Project Database is based on the work of many researchers that have produced estimates of economic growth for individual countries. Data published by: Bolt, Jutta and Jan Luiten van Zanden (2020), “Maddison style estimates of the evolution of the world economy. A new 2020 update”. Link: https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020 Dataset: https://ourworldindata.org/life-expectancy

Inspiration

The life expectancy vs GDP per capita analysis.

The World Bank Economic & Social Indicators (2010-2020) dataset is a comprehensive collection of global economic and social data sourced directly from the World Bank API. It spans a decade (2010 to 2020) and includes key indicators for countries worldwide. The dataset is enriched with country metadata to provide additional context, making it ideal for exploratory data analysis, time series forecasting, regression modeling, and policy research.

This dataset combines several critical indicators:

Economic Indicators: Such as GDP (current US$) and Unemployment Rate (%) Social Indicators: Including Population and Life Expectancy at Birth Country Metadata: Such as region, income level, capital city, longitude, and latitude Each record represents a country-year entry, allowing for analysis over time and across different regions and economic groups.

Column Descriptions

country_id: A unique identifier for each country, typically following the ISO code standard used by the World Bank.

country_name: The full name of the country.

year: The calendar year for the data record, ranging from 2010 to 2020.

GDP (current US$): The Gross Domestic Product of the country in current US dollars. This measures the total economic output and is a key indicator of economic performance.

Population: The total population of the country for the given year.

Life Expectancy: The average number of years a newborn is expected to live, based on current mortality rates.

Unemployment Rate (%): The percentage of the labor force that is unemployed, as modeled by the ILO estimates.

region: The geographical region of the country as classified by the World Bank (e.g., Sub-Saharan Africa, East Asia & Pacific).

income_level: The income classification of the country (e.g., low income, lower-middle income, upper-middle income, high income) based on World Bank criteria.

capital_city: The capital city of the country, providing a reference point for geographic and administrative context.

longitude: The longitude coordinate of the country’s capital city.

latitude: The latitude coordinate of the country’s capital city.

The “gender gap index” describes the degree of difference between sexual inequality to access to political role and education and health resources in 2010. Countries where part of population does not have access to such resources is more sensitive to climate change consequences, because sacrifice part of its potential. The index results from the third cluster of the Principal Component Analysis preformed among 14 potential variables. The analysis identify three dominant variables, namely “literacy gender ratio”, “women political participation” and “life expectancy gender ratio”, assigning a weight of 0.40 to the first one and 0.3 to the others two variables. Before to perform the analysis the variables were score-standardized (converted to distribution with average of 0 and standard deviation of 1; all variables with inverse method) in order to be comparable. The country base data for “literacy gender ratio” (average from 2008 to 2012) and “women political participation” (i.e. proportion of seats held by women in national parliament in the last election) were gathered from World Bank, whereas the “life expectancy gender ratio” (average from 2008 to 2012) data were collected from the medium fertility scenario of UNPD World Population Prospects, the 2012 Revision. Tabular data were linked by country to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). Women’s representation in parliaments is one aspect of women’s opportunities in political and public life, and it is therefore linked to women’s empowerment. This indicator gives an idea of the progress of women participation in the highest levels of society, such as the decision making process, and becoming a leader and voice of the community. Gender parity in literacy and thus in education, is an indicator for female participation and can hence be seen as a general measure for gender equality. The equality of educational opportunities is a basic state to increase the status and capabilities of women. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.