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 national gross income per capita in Nigeria decreased to ***** U.S. dollars compared to the previous year. This marks the lowest national gross income during the observed period. Gross national income (GNI) per capita is the total amount of money received by a country (regardless of whether it originates in the country or abroad) divided by the midyear population. The World Bank uses a conversion system known as the Atlas method, which uses a price adjusted, three year moving average, which smooths out exchange rate fluctuations.Find more statistics on other topics about Nigeria with key insights such as gross national income (GNI), value of personal remittances paid, and personal remittances received.

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.

According to governmental data from 2020, the Gini coefficient in Nigeria was 35.1 points as of 2019. The Gini index gives information on the distribution of income in a country. In an ideal situation in which incomes are perfectly distributed, the coefficient is equal to zero.

The first eight countries with the biggest inequality in income distribution in the world are located in Sub-Saharan Africa, with an index over 50 points.

Nigeria NG: Income Share Held by Lowest 10% data was reported at 2.000 % in 2009. This records a decrease from the previous number of 2.100 % for 2003. Nigeria NG: Income Share Held by Lowest 10% data is updated yearly, averaging 2.000 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 2.500 % in 1985 and a record low of 1.300 % in 1996. Nigeria NG: Income Share Held by Lowest 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.

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.

Disposable Personal Income in Nigeria increased to 21437390.24 NGN Million in the second quarter of 2024 from 20532203.99 NGN Million in the first quarter of 2024. This dataset provides - Nigeria Disposable Personal Income - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nigeria NG: Income Share Held by Third 20% data was reported at 14.400 % in 2009. This records a decrease from the previous number of 15.400 % for 2003. Nigeria NG: Income Share Held by Third 20% data is updated yearly, averaging 14.400 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 15.500 % in 1985 and a record low of 12.300 % in 1996. Nigeria NG: Income Share Held by Third 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.

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

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

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 which they used to

As of the second quarter of 2021, the highest rail transportation revenue was generated from passengers traveling by this means. Passengers contributed around 1.1 billion Nigerian naira (NGN), roughly 2.6 million U.S. dollars, to rail traffic revenue in the country. Moreover, in the same period, goods or cargo transported by rail accumulated an approximate income of 72 million NGN, around 173,000 U.S. dollars.

Nigeria NG: Income Share Held by Second 20% data was reported at 9.700 % in 2009. This records a decrease from the previous number of 10.400 % for 2003. Nigeria NG: Income Share Held by Second 20% data is updated yearly, averaging 9.700 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 10.400 % in 2003 and a record low of 7.700 % in 1996. Nigeria NG: Income Share Held by Second 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 2006 Nigeria SAM is a comprehensive, economy-wide data framework, representing the structure of the Nigerian economy; the links among production activities, income distribution, consumption of goods/services, savings and investment, and foreign trade of the economic agents in year 2006. This 2006 Nigeria SAM is a 61 sector square matrix table with the column and row beginning with activities account, followed by commodities account and thereafter accounts for the economic agent in the Nigerian economy. Each cell in the matrix represents the flow of economic activities in monetary terms from a column account (expenditure or outflow) to a row account (income or inflow). Also, each activity and commodity account begins with letter 'a ' and 'œc' respectively. This 2006 SAM was built for the dynamic CGE (DCGE) model that examined the growth and investment options available in the agricultural sector for reducing poverty in Nigeria, and was an integral part of the Agricultural Policy Support Facilites activities for strengthening evidence-based policymaking in Nigeria. Given the agricultural policy analysis focus of the SAM and DCGE model, 34 sector of the SAM are under agriculture and included key cash and food crops as well as livestock sub-sector. The 2006 Nigeria SAM also includes 12 manufacturing (such as beef, textiles, and wood products); 2 mining sector (including crude petroleum and natural gas); and 13 service sectors (such as building and construction, electricity and water, and hotels and restaurants). While the total number of sector for the SAM is 61, the commodities account is 62 as fertilizer was treated as commodity rather than activity. The 2006 SAM data files comprise two worksheets; one for the SAM data and the other containing legend to the SAM data. The value for each of the cell entries is reported in naira million (2006 prices). The data used for building this SAM were obtained from various sources including but not limited to publications of the National Bureau of Statistics (NBS), the Central Bank of Nigeria (CBN), and the Federal Ministry of Agriculture and Water Resources (FMAWR). Data from an earlier SAM of the country developed by United Nations Development Programme (UNDP), 1995 are also used, and was balanced using the cross entropy estimation method. The SAM was built following the International Food Policy Research Institute (IFPRI) standard format (Lofgren et al. 2001).

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: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2009. This records an increase from the previous number of 40.100 % for 2003. Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 43.000 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 51.900 % in 1996 and a record low of 38.700 % in 1985. Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate 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. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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 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 2018/19 is the fourth round of the survey with prior rounds conducted in 2010/11, 2012/13, and 2015/16. GHS-Panel households were visited twice: first after the planting season (post-planting) between July and September 2018 and second after the harvest season (post-harvest) between January and February 2019.

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 of 5,000 households across 500 enumeration areas (EAs) and was designed to be representative at the national level as well as at the zonal level. The complete sampling information for the GHS-Panel is described in the Basic Information Document for GHS-Panel 2010/2011. However, after a nearly a decade of visiting the same households, a partial refresh of the GHS-Panel sample was implemented in Wave 4.

For the partial refresh of the sample, a new set of 360 EAs were randomly selected which consisted of 60 EAs per zone. The refresh EAs were selected from the same sampling frame as the original GHS-Panel sample in 2010 (the “master frame”). A listing of all households was conducted in the 360 EAs and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximated 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 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 the 6 geopolitical Zones. The systematic selection ensured that the distribution of EAs across the 6 Zones (and urban and rural areas within) is proportional to the original GHS-Panel sample. Interviewers attempted to interview all households that originally resided in the 159 EAs and were successfully interviewed in the previous visit in 2016. This includes households that had moved away from their original location in 2010. In all, interviewers attempted to interview 1,507 households from the original panel sample.

The combined sample of refresh and long panel EAs consisted of 519 EAs. The total number of households that were successfully interviewed in both visits was 4,976.

Sampling deviation

While the combined sample generally maintains both national and Zonal representativeness of the original GHS-Panel sample, the security situation in the North East of Nigeria prevented full coverage of the Zone. Due to security concerns, rural areas of Borno state were fully excluded from the refresh sample and some inaccessible urban areas were also excluded. Security concerns also prevented interviewers from visiting some communities in other parts of the country where conflict events were occurring. Refresh EAs that could not be accessed were replaced with another randomly selected EA in the Zone so as not to compromise the sample size. As a result, the combined sample is representative of areas of Nigeria that were accessible during 2018/19. The sample will not reflect conditions in areas that were undergoing conflict during that period. This compromise was necessary to ensure the safety of interviewers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 4 consists 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 provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets.

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

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

The Household Questionnaire is 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 collects different information during each visit, but for the same plots and crops.

Cleaning operations

CAPI: For the first time in GHS-Panel, the Wave four 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 4 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Survey Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given tablets 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 4 was highly automated. Each field team was given a mobile modem allow for internet connectivity and daily synchronization of their tablet. This ensured that head office in Abuja has access to the data in real-time. Once the interview is completed and uploaded to the server, the data is first reviewed by the Data Editors. The data is 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 is generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files are communicated back to respective field interviewers for action by the interviewers. This action is 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

39,00 (%) in 2018. Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

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.

Nigeria NG: Income Share Held by Fourth 20% data was reported at 21.600 % in 2009. This records a decrease from the previous number of 22.500 % for 2003. Nigeria NG: Income Share Held by Fourth 20% data is updated yearly, averaging 22.500 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 23.400 % in 1992 and a record low of 19.800 % in 1996. Nigeria NG: Income Share Held by Fourth 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.

Abstract

The Lagos Household Survey 2006, also reffered to as service delivery assesment survey, was the first Baseline Survey under the World Bank assisted Lagos Metropolitan Development and Governance Projects (LMDGP) which started a year earlier in 2005. The household survey was a key component of the Baseline Projects which also consisted of five major economic surveys.

The survey's objectives was to provide reliable data on a timely basis for monitoring changes in the welfare status by local government areas in the State, provides estimates at local government level , assess the social and economic situation in the State and provides relevant data required to monitor growth and development in the state ,as well as building the socio-economic database.

Geographic coverage

State Local Government Wards

Analysis unit

Individuals, households, and communities.

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

A sample of 6000 respondents were selected using a combination of probability proportional to size and equal size sample i.e (mix Design) in appreciation of the spread and peculiarities of the state inhabitants and landscapes.

The entire twenty (20) Local government Areas (LGAs) in the state was divided into two Zones: ( North comprising 9 LGAs and South having 11 LGAs) with sample size of 3052 respondents and 2948 rspondents respectively.

The first level of stratification comprised the Local Government Areas, with each of them divided into Political Wards (between 10 and 25). These wards formed the second level of stratification.

Sampling deviation

No deviation from sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

The Questionaire was made up of fifteen (15) modules namely,

Section 1: Household charasteristics and household listing Section 2: Types of Housing
Section 3: Land and Tenure Section 4: Access to Infrastructure - Stormy Water Drainage Section 5: Access to Infrastructure - Sanitation Section 6: Access to Infrastructure - Water Section 7: Access to Infrastructure - Solid Waste Removal Section 8: Access to Infrastructure - Energy and Electricity Section 9: Access to Infrastructure - Telephone Section 10: Transportation & Local Roads Section 11: Education Section 12: Health Section 13: Emergency and policing Services Section 14: Community Preferences Section 15: Household income and expenditure.

The questionaire was published in English language and was based on the generic one prepared by the world bank but modified to suit the Nigeria/ Lagos state environment. The head of the household was the key respondent in the households , individual members and community level information are included in the instrument. The questionaires is provided as external resources.

Cleaning operations

The Perseus Software Solution 6 Software which was used to upload survey instruments into the palmtop had in built capacity to download the completed questionaire from the palmtop into the designated laptop for the survey throgh microsoft active synching.

No data entry involved. however the downloaded dataset are then edited for cleaning and quality checks.

The data set wasc later transfered/exported to SPSS for more robust analysis.

Response rate

At both levels of stratification ie Local government Areas (LGAs) and Political Wards, the response rates were 100 percent respectively.

Sampling error estimates

Not calculated

Data appraisal

Some indicators such as average Household size, room density, access to portable water.

Abstract

The objectives of the Smallholder Household Survey in Nigeria were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Nigeria according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Geographic coverage

National coverage

Analysis unit

Households and individual household members

Universe

The universe for the survey consists of smallholder households defined as households with the following criteria: 1) Household with up to 5 hectares OR farmers who have less than 50 heads of cattle, 100 goats/sheep/pigs, or 1,000 chickens; AND 2) Agriculture provides a meaningful contribution to the household livelihood, income, or consumption.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Procedure

The smallholder household survey in Nigeria is a nationally-representative survey with a target sample size of 3,000 smallholder households. In order to take nonresponse into account, the target sample size was increased to 3,225 households assuming a response rate of 93%. The sample was designed to produce national level estimates as well as estimates for each of the six geo-political zones of

Nigeria is comprised of the following states: - North Central: Benue, Federal Capital Territory (FCT), Kogi, Kwara, Nasarawa, Niger, and Plateau - North East: Adamawa, Bauci, Borno, Gombe, Taraba and Yobe - North West: Jigawa, Kaduna, Kano, katsina, Kebbi, Sokoto and Zamfara - South East: Abia, Anambra, Ebonyi, Enugu, and Imo - South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and River - South West: Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo

Sampling Frame

Nigeria is divided into 774 local governments (LGAs) and its last housing and population census took place in 2006. In preparation for this last census, the National Population Commission (NPopC) demarcated over 662,000 enumeration areas (EAs) for the country. From these EAs, two hierarchical master sample frames were developed by the Nigeria Bureau of Statistics (NBS): the LGA master frame and the National Integrated Survey of Households (NISH). The smallholder survey used the NISH as sampling frame but retained only the EAs containing agricultural households.

Sample allocation and selection

The total sample size was first allocated to the geo-political zones in proportion to their number of agricultural EAs in the sampling frame. Within each zone, the resulting sample was then further distributed to states proportionally to their number of agricultural EAs. Given that EAs were the primary sampling units and 15 households were selected in each EA, a total number of 215 EAs were selected The sample for the smallholder survey is a stratified multistage sample. A stratum corresponds to a state and the sample was selected independently in each stratum.

In the first stage, EAs were selected as primary sampling units with equal probability. A household listing operation was carried out in all selected EAs to identify smallholder households and to provide a frame for the selection of smallholder households to be included in the sample. In the second stage, 15 smallholders were selected in each EA with equal probability.

In each selected household, a household questionnaire was administered to the head of the household, the spouse or any knowledgeable adult household member to collect information about household characteristics. A multiple respondent questionnaire was administered to all adult members in each selected household to collect information on their agricultural activities, financial behaviors and mobile money usage. In addition, in each selected household only one household member was selected using the Kish grid and was administered the single respondent questionnaire.

The full description of the sample design can be found in the user guide for this data set.

Sampling deviation

The household listing operation identified fewer than 15 smallholder households in many sampled EAs. As a result, the sample take of 15 households per EA couldn’t be implemented in those EAs. To avoid a situation where a sample falls short, the sample take was increased to 17 smallholder households where possible while retaining in the sample all smallholder households in EAs with fewer than 17 smallholder households. This yielded 3,457 sampled households.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

To capture the complexity of smallholder households, the smallholder household survey was divided into three questionnaires: the Household questionnaire, the Multiple Respondent questionnaire, and the Single respondent questionnaire. It was designed in this way to capture the complete portrait of the smallholder household, as some members of the household may work on other agricultural activities independently and without the knowledge of others.

The household questionnaire collected information on the following: • Basic household members’ individual characteristics (age, gender, education attainment, schooling status, relationship with the household head). • Whether each household member contributes to the household income or participates in the household’s agricultural activities. This information was later used to identify all household members eligible for the other two questionnaires. • Household assets and dwelling characteristics.

Both the Multiple and Single Respondent questionnaires collected different information on the following: • Agricultural practices—farm information such as size, crop types, livestock, decision-making, farming association, and markets. • Household economics—employment, income, expenses, shocks, borrowing and saving habits, and investments.

The Single respondent questionnaire collected the following information: • Mobile phones—attitudes toward phones, use, access, ownership, desire, and importance. • Financial services—attitudes toward financial products and services such as banking and mobile money, including ownership, usage, access and importance.

The questionnaires were translated into Igbo, Hausa, Yoruba and Pidgin and were pretested on 5 -7 November 2016. After the pretest, debriefing sessions were held with the pretest field staff and the questionnaires were modified based on the observations from the pretest. After the questionnaires were finalized, a script was developed to support data collection on smartphones. The script was tested and validated before it was used in the field.

Cleaning operations

The data files were checked for completeness, inconsistencies and errors by InterMedia and corrections were made as necessary and where possible.

Response rate

The tables in the User Guide show household and household member response rates for the Nigeria smallholder household survey. A total of 3,457 households was selected for the survey, of which 3,310 were found to be occupied during data collection. Of these occupied households, 3,026 were successfully interviewed, yielding a household response rate of 91 percent.

In the interviewed households 6,643 eligible household members were identified for the Multiple Respondent questionnaire. Interviews were completed with 5,128 eligible household members, yielding a response rate of 77 percent for the Multiple Respondent questionnaire.

Among the 3,206 eligible household members selected for the Single Respondent questionnaire, 2,773 were successfully interviewed, yielding a response rate of 92 percent.

Sampling error estimates

The sample design for the smallholder household survey was a complex sample design featuring clustering, stratification and unequal probabilities of selection. For key survey estimates, sampling errors taking into account the design features can be produced using either the SPSS Complex Sample module or STATA based on the Taylor series approximation method.