Seychelles had the largest Gross Domestic Product (GDP) per capita in Africa as of 2024. The value amounted to 21,630 U.S. dollars. Mauritius followed with around 12,330 U.S. dollars, whereas Gabon registered 8,840 U.S. dollars. GDP per capita is calculated by dividing a country’s GDP by its population, meaning that some of the largest economies are not ranked within the leading ten. Impact of COVID-19 on North Africa’s GDP When looking at the GDP growth rate in Africa in 2024, Libya had the largest estimated growth in Northern Africa, a value of 7.8 percent compared to the previous year. Niger and Senegal were at the top of the list with rates of 10.4 percent and 8.3 percent, respectively. During the COVID-19 pandemic, the impact on the economy was severe. The growth of the North African real GDP was estimated at minus 1.1 percent in 2020. However, estimations for 2022 looked much brighter, as it was set that the region would see a GDP growth of six percent, compared to four percent in 2021.
Contribution of Tourism Various countries in Africa are dependent on tourism, contributing to the economy. In 2023, travel and tourism were estimated to contribute 182.6 billion U.S. dollars, a clear increase from 96.5 in 2020 following COVID-19. As of 2024, South Africa, Mauritius, and Egypt led tourism in the continent according to the Travel & Tourism Development Index.

This dataset provides values for GDP PER CAPITA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Seychelles' GDP per capita amounted to 22,000 U.S. dollars in 2025, the highest in East Africa. Mauritius ranked second, with a GDP per capita worth around 13,000 U.S. dollars. Burundi, on the other hand, had the lowest average income per person, at about 160 U.S. dollars.

As of April 2025, South Africa's GDP was estimated at over 410 billion U.S. dollars, the highest in Africa. Egypt followed, with a GDP worth around 347 billion U.S. dollars, and ranked as the second-highest on the continent. Algeria ranked third, with nearly 269 billion U.S. dollars. These African economies are among some of the fastest-growing economies worldwide. Dependency on oil For some African countries, the oil industry represents an enormous source of income. In Nigeria, oil generates over five percent of the country’s GDP in the third quarter of 2023. However, economies such as the Libyan, Algerian, or Angolan are even much more dependent on the oil sector. In Libya, for instance, oil rents account for over 40 percent of the GDP. Indeed, Libya is one of the economies most dependent on oil worldwide. Similarly, oil represents for some of Africa’s largest economies a substantial source of export value. The giants do not make the ranking Most of Africa’s largest economies do not appear in the leading ten African countries for GDP per capita. The GDP per capita is calculated by dividing a country’s GDP by its population. Therefore, a populated country with a low total GDP will have a low GDP per capita, while a small rich nation has a high GDP per capita. For instance, South Africa has Africa’s highest GDP, but also counts the sixth-largest population, so wealth has to be divided into its big population. The GDP per capita also indicates how a country’s wealth reaches each of its citizens. In Africa, Seychelles has the greatest GDP per capita.

The average for 2024 based on 52 countries was 6829 U.S. dollars. The highest value was in the Seychelles: 29242 U.S. dollars and the lowest value was in Burundi: 836 U.S. dollars. The indicator is available from 1990 to 2024. Below is a chart for all countries where data are available.

The GDP per capita in Libya was estimated at around 7,200 U.S. dollars in 2022. Libya had the highest GDP per capita in North Africa in that year. Algeria followed, with a GDP per capita of approximately 4,300 U.S. dollars. In total, Egypt was the largest economy in the region.

In 2025, Luxembourg was the country with the highest gross domestic product per capita in the world. Of the 20 listed countries, 13 are in Europe and five are in Asia, alongside the U.S. and Australia. There are no African or Latin American countries among the top 20. Correlation with high living standards While GDP is a useful indicator for measuring the size or strength of an economy, GDP per capita is much more reflective of living standards. For example, when compared to life expectancy or indices such as the Human Development Index or the World Happiness Report, there is a strong overlap - 14 of the 20 countries on this list are also ranked among the 20 happiest countries in 2024, and all 20 have "very high" HDIs. Misleading metrics? GDP per capita figures, however, can be misleading, and to paint a fuller picture of a country's living standards then one must look at multiple metrics. GDP per capita figures can be skewed by inequalities in wealth distribution, and in countries such as those in the Middle East, a relatively large share of the population lives in poverty while a smaller number live affluent lifestyles.

Seychelles recorded the highest Gross National Income (GNI) per capita in Africa as of 2023, at 16,940 U.S. dollars. The African island was, therefore, the only high-income country on the continent, according to the source's classification. Mauritius, Gabon, Botswana, Libya, South Africa, Equatorial Guinea, Algeria, and Namibia were defined as upper-middle-income economies, those with a GNI per capita between 4,516 U.S. dollars and 14,005 U.S. dollars. On the opposite, 20 African countries recorded a GNI per capita below 1,145 U.S. dollars, being thus classified as low-income economies. Among them, Burundi presented the lowest income per capita, some 230 U.S. dollars. Poverty and population growth in Africa Despite a few countries being in the high income and upper-middle countries classification, Africa had a significant number of people living under extreme poverty. However, this number is expected to decline gradually in the upcoming years, with experts forecasting that this number will decrease to almost 400 million individuals by 2030 from nearly 430 million in 2023, despite the continent currently having the highest population growth rate globally. African economic growth and prosperity In recent years, Africa showed significant growth in various industries, such as natural gas production, clean energy generation, and services exports. Furthermore, it is forecast that the GDP growth rate would reach 4.5 percent by 2027, keeping the overall positive trend of economic growth in the continent.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

19 of the 20 countries with the lowest estimated GDP per capita in the world in 2024 are located in Sub-Saharan Africa. South Sudan is believed to have a GDP per capita of just 351.02 U.S. dollars - for reference, Luxembourg has the highest GDP per capita in the world, at almost 130,000 U.S. dollars, which is around 400 times larger than that of Burundi (U.S. GDP per capita is over 250 times higher than Burundi's). Poverty in Sub-Saharan Africa Many parts of Sub-Saharan Africa have been among the most impoverished in the world for over a century, due to lacking nutritional and sanitation infrastructures, persistent conflict, and political instability. These issues are also being exacerbated by climate change, where African nations are some of the most vulnerable in the world, as well as the population boom that will place over the 21st century. Of course, the entire population of Sub-Saharan Africa does not live in poverty, and countries in the southern part of the continent, as well as oil-producing states around the Gulf of Guinea, do have some pockets of significant wealth (especially in urban areas). However, while GDP per capita may be higher in these countries, wealth distribution is often very skewed, and GDP per capita figures are not representative of average living standards across the population. Outside of Africa Yemen is the only country outside of Africa to feature on the list, due to decades of civil war and instability. Yemen lags very far behind some of its neighboring Arab states, some of whom rank among the richest in the world due to their much larger energy sectors. Additionally, the IMF does not make estimates for Afghanistan, which would also likely feature on this list.

Mauritius concentrated the highest private wealth per capita in Africa in 2021: ****** U.S. dollars. South Africa followed, with a wealth amount of ****** U.S. dollars per capital. Overall, total private wealth on the continent amounted to *** trillion U.S. dollars that year.

As of 2023, the GDP of Africa was estimated at roughly 3.1 trillion U.S. dollars. This was the highest value since 2010 when the continent's GDP amounted to approximately 2.1 trillion U.S. dollars. The GDP value in Africa generally followed an upward trend in recent years and was estimated to exceed 4.2 trillion U.S. dollars by 2027.

Leading the charge: the three leading African economies

Among the African countries, in 2021, Nigeria had the highest GDP with approximately 442 billion U.S. dollars. South Africa and Egypt followed. These three countries have the largest economies for various reasons. The most notable factors are their population size, natural resources, and level of economic development. Furthermore, Africa was projected to have a real GDP growth rate of 3.9 percent in 2023. Libya was the economy experiencing the highest growth rate in that year.

The Sub-Saharan African economy on the rise

A global comparison showed that Sub-Saharan Africa had the smallest GDP among all world regions in 2021, amounting to 1.87 trillion U.S. dollars. A closer look revealed that Sub-Saharan Africa had a GDP per capita of 1,626.3 U.S. dollars in 2021, again the lowest worldwide. However, the region's economy was forecast to experience continued growth in the following years, with the real GDP increasing by 3.7 percent in 2023.

The particularities of agriculture, as a sector which ensures food supply, result from many factors, including the multilateral interaction between the environment and human activity. The extent of human intervention in the food production process is usually measured with the amount of capital expenditure. Therefore, the food production potential and the resulting food security depend on both natural and economic factors. This paper identifies the current status of food security in different countries around the world, considering both aspects (physical and economic availability) combined together. The variables published by FAO were used together with a variable estimated based on the author’s own methodology to identify 8 groups of countries characterized by economic development level, net trade in agricultural products, and selected variables related to agriculture and food situation. As shown by this study, the degree to which food security is ensured with domestic supply varies strongly across the globe. Domestic production provides a foundation for food security in wealthy countries, usually located in areas with favorable conditions for agriculture (including North America, Australia, New Zealand, Kazakhstan) and in countries which, though characterized by a relatively small area of arable land per capita, demonstrate high production intensity (mainly European countries). International trade largely contributes to food security in Middle East and North African countries as well as in selected South American countries which are net importers of food products. The most problematic food situation continues to affect Sub-Saharan Africa and Central Asia.

In 2021, the BRICS countries with the highest estimated GDP per capita were Russia and China, with between 12,000 and 13,000 U.S. dollars per person. Brazil and South Africa's GDP per capita are thought to be closer to the 7,000 mark, while India's GDP per capita is just over 2,000 U.S. dollars. This a significant contrast to figures for overall GDP, where China has the largest economy by a significant margin, while India's is the second largest. The reason for this disparity is due to population size. For example, both China's population and overall GDP are roughly 10 times larger than those of Russia, which results in them having a comparable GDP per capita. Additionally, India's population is 23 times larger than South Africa's, but it's GDP is just seven times larger; this results in South Africa having a higher GDP per capita than India, despite it being the smallest of the BRICS economies.

TThe ERS International Macroeconomic Data Set provides historical and projected data for 181 countries that account for more than 99 percent of the world economy. These data and projections are assembled explicitly to serve as underlying assumptions for the annual USDA agricultural supply and demand projections, which provide a 10-year outlook on U.S. and global agriculture. The macroeconomic projections describe the long-term, 10-year scenario that is used as a benchmark for analyzing the impacts of alternative scenarios and macroeconomic shocks.

Explore the International Macroeconomic Data Set 2015 for annual growth rates, consumer price indices, real GDP per capita, exchange rates, and more. Get detailed projections and forecasts for countries worldwide.

Annual growth rates, Consumer price indices (CPI), Real GDP per capita, Real exchange rates, Population, GDP deflator, Real gross domestic product (GDP), Real GDP shares, GDP, projections, Forecast, Real Estate, Per capita, Deflator, share, Exchange Rates, CPI

Afghanistan, Albania, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burundi, Côte d'Ivoire, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Congo, Costa Rica, Croatia, Cuba, Cyprus, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Mauritania, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Samoa, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, South Africa, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, Yemen, Zambia, Zimbabwe, WORLD Follow data.kapsarc.org for timely data to advance energy economics research. Notes:

Developed countries/1 Australia, New Zealand, Japan, Other Western Europe, European Union 27, North America

Developed countries less USA/2 Australia, New Zealand, Japan, Other Western Europe, European Union 27, Canada

Developing countries/3 Africa, Middle East, Other Oceania, Asia less Japan, Latin America;

Low-income developing countries/4 Haiti, Afghanistan, Nepal, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Liberia, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe;

Emerging markets/5 Mexico, Brazil, Chile, Czech Republic, Hungary, Poland, Slovakia, Russia, China, India, Korea, Taiwan, Indonesia, Malaysia, Philippines, Thailand, Vietnam, Singapore

BRIICs/5 Brazil, Russia, India, Indonesia, China; Former Centrally Planned Economies

Former centrally planned economies/7 Cyprus, Malta, Recently acceded countries, Other Central Europe, Former Soviet Union

USMCA/8 Canada, Mexico, United States

Europe and Central Asia/9 Europe, Former Soviet Union

Middle East and North Africa/10 Middle East and North Africa

Other Southeast Asia outlook/11 Malaysia, Philippines, Thailand, Vietnam

Other South America outlook/12 Chile, Colombia, Peru, Bolivia, Paraguay, Uruguay

Indicator Source

Real gross domestic product (GDP) World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service all converted to a 2015 base year.

Real GDP per capita U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table and Population table.

GDP deflator World Bank World Development Indicators, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Real GDP shares U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, GDP table.

Real exchange rates U.S. Department of Agriculture, Economic Research Service, Macroeconomic Data Set, CPI table, and Nominal XR and Trade Weights tables developed by the Economic Research Service.

Consumer price indices (CPI) International Financial Statistics International Monetary Fund, IHS Global Insight, Oxford Economics Forecasting, as well as estimated and projected values developed by the Economic Research Service, all converted to a 2015 base year.

Population Department of Commerce, Bureau of the Census, U.S. Department of Agriculture, Economic Research Service, International Data Base.

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...

The statistic shows gross domestic product (GDP) per capita in the Maghreb countries from 2020 to 2023, with projections up until 2030. GDP is the total value of all goods and services produced in a country in a year. It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth. The Maghreb region in North Africa comprises Algeria, Libya, Mauritania, Morocco, and Tunisia. In 2022, GDP per capita in Algeria amounted to around 4,983.55 U.S. dollars.

Africa Poultry Feed Market size was valued at USD 8.5 Billion in 2024 and is projected to reach USD 13.2 Billion by 2031, growing at a CAGR of 5.4% from 2024 to 2031.

Africa Poultry Feed Market Drivers

Rising Population and Urbanization: The growing population and urbanization in Africa are driving the demand for protein-rich foods, including poultry. Increasing Per Capita Income: As incomes rise, consumers are demanding higher-quality and more diverse protein sources. Government Support: Government initiatives to promote livestock farming and food security are driving the growth of the poultry industry.

Africa Poultry Feed Market Restraints

Economic Challenges: Economic instability and poverty in many African countries can limit the adoption of high-quality feed. Infrastructure Constraints: Inadequate infrastructure, including transportation and storage facilities, can hinder the distribution of feed. Climate Change and Natural Disasters: Climate change and natural disasters can impact feed production and distribution.

This dataset comprises 348 files, each representing a unique economic indicator for the BRICS nations—Brazil, Russia, India, China, and South Africa—spanning from 1970 to 2020. The dataset includes a wide array of economic metrics such as government consumption expenditure, GDP growth, adjusted savings, and various other national accounts data. This comprehensive dataset is ideal for economic research, financial analysis, and policy evaluation, offering a robust foundation for exploring economic trends and making data-driven decisions.

Key Features: - Diversity of Indicators: Covers a wide range of economic indicators, including net national income, government expenditure, GDP, and more. - Historical Coverage: Provides data spanning five decades, enabling both historical trend analysis and long-term forecasting. - Country Focus: Specifically tailored to the BRICS nations, offering insights into some of the world’s most influential emerging economies.

Usage

This dataset can be utilized for various purposes, such as: - Economic Analysis: Researchers can use the dataset to study economic trends and performance in BRICS countries. - Machine Learning: Data scientists can train models to predict future economic indicators or identify patterns in the data. - Policy Development: Policymakers can analyze the data to develop informed strategies for economic development.

Example Use Case: Suppose you want to analyze the trend in GDP per capita growth across BRICS nations. You could load the relevant files, clean the data, and use statistical tools or machine learning models to study the trend and make predictions.

System

This dataset is self-contained and can be integrated into broader economic research systems. The data files are in CSV format, making them easy to load and manipulate with standard data analysis tools like Python, R, and Excel.

Integration: While the dataset is standalone, it can be combined with other datasets or models for more complex analyses, such as predicting future economic performance or simulating policy impacts.

Data Provenance

The dataset is sourced from the World Bank’s BRICS Economic Indicators, a trusted and comprehensive source of economic data. The data was compiled, cleaned, and structured to facilitate easy analysis and integration into various analytical workflows.

Source: Kaggle - BRICS World Bank Indicators Dataset Coverage: The dataset includes data from Brazil, Russia, India, China, and South Africa, from 1970 to 2020.

Data Preprocessing: Each file was cleaned to remove inconsistencies, and missing values were handled appropriately to ensure the quality and reliability of the data.

Data Overview

The dataset is organized into 348 CSV files, each focusing on a specific economic indicator. Examples include: - GDP per Capita (Constant 2010 US$): Tracks the GDP per capita adjusted for inflation. - Government Final Consumption Expenditure (% of GDP): Measures government spending as a percentage of GDP. - Adjusted Net Savings: Accounts for environmental depletion and degradation in national savings.

Each file contains the following columns: - SeriesName: Describes the economic indicator. - CountryName: The name of the BRICS country. - Year: The year the data was recorded. - Value: The numerical value of the indicator for that year.

This dataset provides a rich resource for anyone looking to delve into the economic history and performance of BRICS countries, offering the data necessary to explore past trends and project future developments.

Abstract

Nigeria was among the first few countries in Sub-Saharan Africa to identify cases of COVID-19. Reported cases and fatalities have been increasing since it was first identified. The government implemented strict measures to contain the spread of this virus (such as travel restrictions, school closures and home-based work). While the Government is implementing these containment measures, it is important to understand how households in the country are affected and responding to the evolving crises, so that policy responses can be designed well and targeted effectively to reduce the negative impacts on household welfare.

The objective of Nigeria COVID-19 NLPS is to monitor the socio-economic effects of this evolving COVID-19 pandemic in real time. These data will contribute to filling critical gaps in information that could be used by the Nigerian government and stakeholders to help design policies to mitigate the negative impacts on its population. The Nigeria COVID-19 NLPS is designed to accommodate the evolving nature of the crises, including revision of the questionnaire on a monthly basis.

The households were drawn from the sample of households interviewed in 2018/2019 for Wave 4 of the General Household Survey—Panel (GHS-Panel). The extensive information collected in the GHS-Panel just over a year prior to the pandemic provides a rich set of background information on the Nigeria COVID-19 NLPS households which can be leveraged to assess the differential impacts of the pandemic in the country.

Each month, the households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Food security, employment, access to basic services, coping strategies, and non-labour sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

Wave 4 of the GHS-Panel conducted in 2018/19 served as the frame for the Nigeria COVID-19 NLPS survey. The GHS-Panel sample includes 4,976 households that were interviewed in the post-harvest visit of the fourth wave in January/February 2019. This sample of households is representative nationally as well as across the 6 geopolitical Zones that divide up the country. In every visit of the GHS-Panel, phone numbers are collected from interviewed households for up to 4 household members and 2 reference persons who are in close contact with the household in order to assist in locating and interviewing households who may have moved in subsequent waves of the survey. This comprehensive set of phone numbers as well as the already well-established relationship between NBS and the GHS-Panel households made this an ideal frame from which to conduct the COVID-19 monitoring survey in Nigeria.

Among the 4,976 households interviewed in the post-harvest visit of the GHS-Panel in 2019, 4,934 (99.2%) provided at least one phone number. Around 90 percent of these households provided a phone number for at least one household member while the remaining 10 percent only provided a phone number for a reference person. Households with only the phone number of a reference person were expected to be more difficult to reach but were nonetheless included in the frame and deemed eligible for selection for the Nigeria COVID-19 NLPS.

To obtain a nationally representative sample for the Nigeria COVID-19 NLPS, a sample size of approximately 1,800 successfully interviewed households was targeted. However, to reach that target, a larger pool of households needed to be selected from the frame due to non-contact and non-response common for telephone surveys. Drawing from prior telephone surveys in Nigeria, a final contact plus response rate of 60% was assumed, implying that the required sample households to contact in order to reach the target is 3,000.

3,000 households were selected from the frame of 4,934 households with contact details. Given the large amount of auxiliary information available in the GHS-Panel for these households, a balanced sampling approach (using the cube method) was adopted. The balanced sampling approach enables selection of a random sample that still retains the properties of the frame across selected covariates. Balancing on these variables results in a reduction of the variance of the resulting estimates, assuming that the chosen covariates are correlated with the target variable. Calibration to the balancing variables after the data collection further reduces this variance (Tille, 2006). The sample was balanced across several important dimensions: state, sector (urban/rural), household size, per capita consumption expenditure, household head sex and education, and household ownership of a mobile phone.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behaviour and social distancing; access to basic services; employment; income loss; food security; concerns; coping/shocks; and social safety nets.

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; employment (including non-farm enterprise and agricultural activity); other income; food security; and social safety nets.

ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; housing; employment (including non-farm enterprise and agricultural activity); other income; coping/shocks; and social safety nets.

ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic goods and services; credit; employment (including non-farm enterprise, crop farming and livestock); food security; income changes; concerns; and social safety nets.

ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise and agricultural activity); and other income.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; employment (including non-farm enterprise); COVID testing and vaccination; and other income.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise); food security; concerns; and safety nets.

ROUND 8: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; employment (including non-farm enterprise and agriculture); and coping/shocks.

ROUND 9: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; education; early childhood development, access to basic services, employment (including non-farm enterprise and agriculture); and income changes.

ROUND 10: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; access to basic services; employment (including non-farm enterprise and agricultural activity); concerns and COVID testing and vaccination.

ROUND 11: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on demographics; credit; access to basic services; education; employment (including non-farm enterprise); safety nets; youth contact details; and phone signal.

ROUND 12: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on youth aspirations and employment; and COVID vaccination.

Cleaning operations

COMUPTER ASSISTED TELEPHONE INTERVIEW (CATI): The Nigeria COVID-19 NLPS exercise was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Data Analytics and Tools Unit within the Development Economics Data Group (DECDG) at the World Bank. Each interviewer was given two tablets, which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CATI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication