Nigeria NG: Income Share Held by Highest 20% data was reported at 49.000 % in 2009. This records an increase from the previous number of 46.000 % for 2003. Nigeria NG: Income Share Held by Highest 20% data is updated yearly, averaging 49.000 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 56.500 % in 1996 and a record low of 45.000 % in 1985. Nigeria NG: Income Share Held by Highest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The Gross Domestic Product per capita in Nigeria was last recorded at 2447.64 US dollars in 2024. The GDP per Capita in Nigeria is equivalent to 19 percent of the world's average. This dataset provides the latest reported value for - Nigeria GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Nigeria NG: Income Share Held by Lowest 20% data was reported at 5.400 % in 2009. This records a decrease from the previous number of 5.700 % for 2003. Nigeria NG: Income Share Held by Lowest 20% data is updated yearly, averaging 5.400 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 6.000 % in 1985 and a record low of 3.700 % in 1996. Nigeria NG: Income Share Held by Lowest 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

In 2023, the national gross income per capita in Nigeria stood at ***** U.S. dollars. Between 2008 and 2023, the figure dropped by ** U.S. dollars, though the decline followed an uneven course rather than a steady trajectory.

Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data was reported at 47.404 % in 2016. This records a decrease from the previous number of 48.782 % for 2015. Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data is updated yearly, averaging 3.783 % from Dec 1960 (Median) to 2016, with 56 observations. The data reached an all-time high of 48.782 % in 2015 and a record low of 0.078 % in 1974. Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Exports. Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

In 2023, agriculture contributed around 22.72 percent to Nigeria’s GDP, 32.58 percent came from industry, and 42.77 percent from the services sector. Economic sectors The most common breakdown of economic activity in a country is looking at three economic sectors: The primary sector, which involves agriculture, forestry, and fishing, the secondary sector, industry, that includes manufacturing, processing, or transforming goods, and finally, the tertiary sector, services, i.e. providing information or services to consumers, such as in IT, tourism, or banking. A country’s contribution to GDP, and thus its own economy, is easily visible when looking at the performance of these three sectors. Soaring services in NigeriaLike in most thriving economies nowadays, the services sector is gaining momentum in Nigeria, because more and more people are moving from the countryside to the cities to find jobs. Nigeria is a mixed economy which focuses mainly on telecommunications, financial services, and technology, a strategy that is likely to pay off in the future and will see its GDP soaring. Nigeria’s reliance on oil is also an important contributor to its economic success; between 2001 and 2010, it was one of the countries with the highest GDP growth worldwide. However, oil prices are also responsible for a GDP growth slump in 2016 and for the first trade deficit in over a decade.

The gini index in Nigeria was forecast to remain on a similar level in 2029 as compared to 2024 with **** points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Ivory Coast and Senegal.

Actual value and historical data chart for Nigeria Merchandise Exports To Developing Economies Within Region Percent Of Total Merchandise Exports

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2023/24 GHS-Panel is the fifth round of the survey with prior rounds conducted in 2010/11, 2012/13, 2015/16 and 2018/19. The GHS-Panel households were visited twice: during post-planting period (July - September 2023) and during post-harvest period (January - March 2024).

Geographic coverage

National

Analysis unit

• Households • Individuals • Agricultural plots • Communities

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

The original GHS‑Panel sample was fully integrated with the 2010 GHS sample. The GHS sample consisted of 60 Primary Sampling Units (PSUs) or Enumeration Areas (EAs), chosen from each of the 37 states in Nigeria. This resulted in a total of 2,220 EAs nationally. Each EA contributed 10 households to the GHS sample, resulting in a sample size of 22,200 households. Out of these 22,200 households, 5,000 households from 500 EAs were selected for the panel component, and 4,916 households completed their interviews in the first wave.

After nearly a decade of visiting the same households, a partial refresh of the GHS‑Panel sample was implemented in Wave 4 and maintained for Wave 5. The refresh was conducted to maintain the integrity and representativeness of the sample. The refresh EAs were selected from the same sampling frame as the original GHS‑Panel sample in 2010. A listing of households was conducted in the 360 EAs, and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximately 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS‑Panel households from 2010 were selected to be included in the new sample. This “long panel” sample of 1,590 households was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across Nigeria’s six geopolitical zones.

The combined sample of refresh and long panel EAs in Wave 5 that were eligible for inclusion consisted of 518 EAs based on the EAs selected in Wave 4. The combined sample generally maintains both the national and zonal representativeness of the original GHS‑Panel sample.

Sampling deviation

Although 518 EAs were identified for the post-planting visit, conflict events prevented interviewers from visiting eight EAs in the North West zone of the country. The EAs were located in the states of Zamfara, Katsina, Kebbi and Sokoto. Therefore, the final number of EAs visited both post-planting and post-harvest comprised 157 long panel EAs and 354 refresh EAs. The combined sample is also roughly equally distributed across the six geopolitical zones.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 5 consisted of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing, and other agricultural and related activities. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

GHS-Panel Household Questionnaire: The Household Questionnaire provided information on demographics; education; health; labour; childcare; early child development; food and non-food expenditure; household nonfarm enterprises; food security and shocks; safety nets; housing conditions; assets; information and communication technology; economic shocks; and other sources of household income. Household location was geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets (forthcoming).

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicited information on land ownership and use; farm labour; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; household fishing activities; and digital farming information. Some information is collected at the crop level to allow for detailed analysis for individual crops.

GHS-Panel Community Questionnaire: The Community Questionnaire solicited information on access to infrastructure and transportation; community organizations; resource management; changes in the community; key events; community needs, actions, and achievements; social norms; and local retail price information.

The Household Questionnaire was slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collected different information during each visit, but for the same plots and crops.

The Community Questionnaire collected prices during both visits, and different community level information during the two visits.

Cleaning operations

CAPI: Wave five exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires (household, agriculture, and community questionnaires) were implemented in both the post-planting and post-harvest visits of Wave 5 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Living Standards Measurement Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given a tablet which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 5 was highly automated. Each field team was given a mobile modem which allowed for internet connectivity and daily synchronization of their tablets. This ensured that head office in Abuja had access to the data in real-time. Once the interview was completed and uploaded to the server, the data was first reviewed by the Data Editors. The data was also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file was generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files were then communicated back to respective field interviewers for their action. This monitoring activity was done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following the first and second stage cleaning. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

Response

Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data was reported at 12.239 % in 2016. This records a decrease from the previous number of 12.485 % for 2015. Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data is updated yearly, averaging 6.409 % from Dec 1960 (Median) to 2016, with 56 observations. The data reached an all-time high of 12.485 % in 2015 and a record low of 0.693 % in 1960. Nigeria NG: Exports: Low- and Middle-Income Economies: % of Total Goods Exports: Within Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Exports. Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

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.

Actual value and historical data chart for Nigeria Merchandise Imports From Developing Economies Within Region Percent Of Total Merchandise Imports

From January 2022 and February 2023, the Nigerian government allocated the highest disbursements to Delta, Akwa Ibom, Rivers, Bayelsa, Lagos, Kano, and Enugu. These states received over 100 billion Naira each, with Delta receiving almost ***** billion Naira. The FAAC (Federal Account Allocation Committee) disbursements represent the funds given to states and regions. They are allocated in proportion to each state population and, consequently, to the number of local governments in the state.

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.

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.The data included is here for the Yobe state.

Nigeria NG: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data was reported at 31.004 % in 2016. This records a decrease from the previous number of 32.433 % for 2015. Nigeria NG: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data is updated yearly, averaging 9.814 % from Dec 1960 (Median) to 2016, with 56 observations. The data reached an all-time high of 32.433 % in 2015 and a record low of 1.941 % in 1960. Nigeria NG: Imports: Low- and Middle-Income Economies: % of Total Goods Imports: Outside Region data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Imports. Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.; ; World Bank staff estimates based data from International Monetary Fund's Direction of Trade database.; Weighted average;

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.

This data was collected by IFPRI as part of the World Bank-funded project (Fadama III–Additional Financing (AF II) phase II ) that was implemented in North-Eastern Nigeria. The Project was supporting the recovery of the agriculture sector in the North East (NE) of Nigeria in response to support the Government’s recovery and reconstruction initiative. The project sought to respond to the urgent food and livelihood needs of farming households who were affected by conflicts in the six North-East states in Nigeria—Borno, Yobe, Adamawa, Taraba, Bauchi, and Gombe. The North East States suffered huge losses and damage to property, economic infrastructure, and livelihoods because of the insurgency. Among the participating communities and households, the project was intended to improve nutritional security, food security, household incomes, boost job creation, improve infrastructure and increase access to market information as well as enhancing the managerial capacities of the local communities. The North-Eastern region of Nigeria was renowned for its large agricultural potential, with 80 percent of the population engaged in farming and contributing significantly to the regional and national GDP. Over the past two decades, however, the region had regressed with low education levels, limited access to healthcare and other basic amenities, and low GDP per capita. A once-promising zone now trails the other regions of Nigeria across all socio-economic indicators. The NE region in most recent times has also borne the brunt of human casualty, loss of properties, and diminished livelihoods emanating from the Boko Haram terrorist insurgency. Towards the end of the project activities in 2018, IFPRI was contracted by the National Fadama Coordination Office (NFCO) in Abuja Nigeria which was the project implementing agency on behalf of the Government of Nigeria and World Bank to conduct an endline survey to collect primary data that would be used in rigorous impact assessment hence this data set. The endline survey collected both the project endline data ( 2018 measurements) and the retrospective baseline data ( 2016 measurements). The sample household survey covered all the six states in North-Eastern Nigeria that received project financial support. A total of 1800 households were sampled in both project treatment communities and non-project control communities. The Survey data has information on insecurity conflicts and how these insecurity conflicts impacted on household migration and socio-economic conditions, humanitarian support received, value addition and agricultural processing, agricultural input aid received, demographic characteristics, crop production, livestock production, non-farm income, Fishing, and Aquaculture Income, beekeeping income, forestry and agroforestry income, wildlife income, food insecurity assessment, household dietary diversity, access to marketing infrastructure, productive assets, non-productive assets, access to credit, access to market information and extension.

Nigeria NG: Income Share Held by Highest 10% data was reported at 32.700 % in 2009. This records an increase from the previous number of 29.800 % for 2003. Nigeria NG: Income Share Held by Highest 10% data is updated yearly, averaging 31.400 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 40.700 % in 1996 and a record low of 28.200 % in 1985. Nigeria NG: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

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

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet