Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data was reported at 52.800 % in 2009. This records a decrease from the previous number of 56.600 % for 2003. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data is updated yearly, averaging 54.700 % from Dec 2003 (Median) to 2009, with 2 observations. The data reached an all-time high of 56.600 % in 2003 and a record low of 52.800 % in 2009. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population 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: Poverty. Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 46.000 % in 2009. This records a decrease from the previous number of 48.400 % for 2003. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 47.200 % from Dec 2003 (Median) to 2009, with 2 observations. The data reached an all-time high of 48.400 % in 2003 and a record low of 46.000 % in 2009. Nigeria NG: Poverty Headcount Ratio at National Poverty Lines: % of Population 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. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on population-weighted subgroup estimates from household surveys.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

Historical chart and dataset showing Nigeria poverty rate by year from 1985 to 2018.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Nigeria NG: Poverty Gap at National Poverty Lines: Rural: % data was reported at 20.100 % in 2009. This records a decrease from the previous number of 22.000 % for 2003. Nigeria NG: Poverty Gap at National Poverty Lines: Rural: % data is updated yearly, averaging 21.050 % from Dec 2003 (Median) to 2009, with 2 observations. The data reached an all-time high of 22.000 % in 2003 and a record low of 20.100 % in 2009. Nigeria NG: Poverty Gap at National Poverty Lines: Rural: % 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: Poverty. Rural poverty gap at national poverty lines is the rural population's mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across 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

Abstract

The national initiatives at poverty tracking started in Nigeria in the early 1990s between Federal Office of Statistics and the World Bank. At the inception, the National Consumer Surveys data set series for 1980-1996 were analysed which charted the profile of poverty in Nigeria. This culminated in a Poverty Profile for Nigeria Report (1980-1996) which has since served as bench-mark for monitoring and evaluation of various government anti-government poverty and policies. The Poverty Profile for Nigeria 2004 is the latest and a good follow-up to the previous one.

With the recognition by the Nigerian Government of the multi-sectoral and multi-dimensional nature of poverty, a number of coordinated programmes and policies had been formulated to combat poverty in all its ramifications. Among the programmes are National Poverty Eradication Programme (NAPEP), the National Economic Empowerment and Development Strategy (NEEDS) and the Millennium Development Goals of the government which are aimed basically at poverty reduction. These programmes require a framework for poverty statistics production, management and tracking.

The Nigeria Living Standard Survey institutionalised by the Federal Office of Statistics provided a major survey mechanism framework for regular production, management and tracking of poverty programmes and policies. The recent Profile of Poverty for Nigeria as elucidated in this report is a commendable effort in providing current, timely and highly relevant poverty statistics and indicators for monitoring and evaluation of anti-poverty programmes and policies. The findings of the report chronicled the magnitude, nature, character and dimensions of poverty in Nigeria in 2004.

Geographic coverage

National Zone State Lga

Analysis unit

Household and individual

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN The sampling designs for the NLSS was meant to give estimates at National, Zonal and State levels. The first stage was a duster of housing units called Enumeration Area (EA), while the second stage was the housing unit.

SAMPLE SIZE One hundred and twenty (120 EAs) were selected and sensitized in each state while sixty enumeration areas were selected at the Federal Capital Territory (FCT). Ten E.As with five housing units were studied per month. This meant that fifty housing units were canvassed per month in each state and twenty-five housing units in Abuja.

One hundred and twenty (120) EAs were selected in 12 replicates in each State from the NISH master sample frame in replicates (4-15). However, 60 EAs were selected in the Federal Capital Territory. Five (5) housing units (HUs) were scientifically selected in each of the selected EAs. One replicate consisting of 10 EAs in the State and 5 EAs in the Federal Capital Territory were covered every month. Fifty (50) HUs were covered in each State and 25 HUs in the Federal Capital Territory per month. This implied that the survey had an anticipated national sample size of twenty-one thousand and nine hundred (21,900) HUs for the country for the 12-month survey period. Each State had a sample size of 600 HUs, while the Federal Capital Territory had a sample size of 300. The sample size is robust enough to provide reasonable estimates at national and sub-national (State) levels. ESTIMATION PROCEDURE The following statistical notations were used: N = the number of EAs in each State ni = Size of replicates rth r = number of replicates in a State H = number of housing units listed in the ith selected EA. Xhj = number of housing units selected from ith selected EA.
Wrij = weight of the replicate =????????nhijNxH Yrij = total value of variable from the ith HU of ith selected EA.
Replicate Estimate (Monthly Estimate) ()??=yWyi Annual State Estimate ??? NOTE See page 91 and 92 of the report

Sampling deviation

Sampling Error (Variance) Estimate The Jacknife indefinite method of variance estimation was used for the survey because the method required replication and clustering. An estimate of State variance was first obtained. Cluster estimate is ()ywijiji???= Mean Estimate rnrz??= Therefore mean variance is ()rSnrV2=? where ()()221-?-?=?rnrSr
NOTE See page 93 of the report

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is a structured questionnaire developed as a joint effort of the National Bureau of Statistics, the World Bank and National Planning Commission. After series of meeting and two consultative workshops, seven survey instruments were developed: Household Diary Record Book. Questionnaire Part A: Household Questionnaire. Questionnaire Part B: Household Consumption Questionnaire. The interviewer's manuals . Supervisor's manuals. Occupation and Industry Code Booklets . Prices Questionnaire.

Cleaning operations

Headquarters Training of Trainers (T0T) The first level of training at the headquarter consisted of three categories of officers, namely, the trainers at the zonal level, fieldwork monitoring officers and data processing officers who were crucial to the successful implementation of the survey. The intensive and extensive training lasted for five days. Zonal Level Training The training took place in the six zonal FOS [now NBS] offices representing the six geo-political zones of the country. These are Ibadan (South West) Enugu (South East), Calabar (South South), Jos (North Central), Maiduguri (North East) and Kaduna (North West). The composition of the team from each State to the six different zones were the State officer, one scrutiny officer and two field officers, making four persons per state. Two resource persons from the headquarters did the training with the zonal controllers participating and contributing during the five-day regimented and intensive training. State Level Training The third level training was at the State level. A total of 40 officers were trained, comprising 20 enumerators, 10 editing staff and 10 supervisors. The State Statistical Agencies, as a matter policy, contributed 5-10 enumerators. The ten-day exercise was also regimented, intensive and extensive because the enumerators were also crucial for effective implementation of data collection.

Response rate

The response rate was very high

Abstract

The National Bureau of Statistics (NBS) has the statutory mandate to provide socio-economic data on a wide range of issues, including poverty reduction programs for informed decision making, policy formulation and implementation. Thus, the essence of adequate measurement and production of relevant evidence-based statistics on poverty and welfare of Nigerians cannot be overemphasized. The various laudable programs of government aimed at combating poverty such as NEEDS, 7-Point Agenda, NAPEP, NDE, MDG amongst many others required tracking, monitoring and evaluation. The Harmonized Nigeria Living Standard Survey (HNLSS) is an instrument for regular monitoring of welfare and social trends for different population groups of the society especially the poor.

Geographic coverage

National Zone State Local Government Sector (Urban/Rural)

Analysis unit

Household and individual

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design employed for HNLSS Survey 2008/09 is a 2-stage cluster sample design in which Enumeration Areas (EAs) or Primary Sampling Units (PSUs) constitutes the 1st stage sample while the Housing units (HUs) from the EAs make up the 2nd stage sample or the Ultimate Sampling Units (USUs)

Sampling Frame The enumeration areas (EAs) as demarcated by the National Population Commission (NPopC) for the 2006 population census served as the sampling frame for the HNLSS 2008/09.

Sample Size Sample sizes must meet some minimal requirement in order to obtain reliable estimate. Hence, for HNLSS Survey 2008/09, the sample size varies from state to state depending on the number of Local Government Areas (LGAs) in each state. Ten (10) EAs were selected in each LGA making a total of 7,774 EAs to be canvassed for throughout the federation from the 774 LGAs including the Federal Capital Territory (FCT) Abuja.

Selection Procedure The 7,740 EAs were selected directly from the population of the EAs in the NPopC with equal probability of selection. Prior to selection, all the contiguous EAs were arranged in serpentine order in each LGA of the state. This arrangement ensured that there was no overlapping

A total of 77,390 households were covered from a sample of 77,400 households giving the survey coverage rate of 99.9 percent. Of all the six zones, it was only SW zone that had the least response rate of 99.9 percent. The response rate in the remaining 5 zone was 100.0 percent each. Table 1.2 Status of Retrieval of Records by Zone and State attached to the report in External Resources

AS PER DATA SET At households level, out of the 77,390 retrieved, only 73,329 were scanable.

Estimation Procedure Let E be the number of EAs in the state e be the number of selected in the state For a given stratum or domain, the estimate of the variance of a rate, r is given by

Var(r) = (se)2 = 1 ?(ri - r)2 K(k -1)i=1 Where K is the number of clusters in the stratum or estimation domain r is the weighted estimate calculated from the entire sample of clusters in the stratum ri is equal to Kr - (K-1) r(i), where r(i) is re-weighted estimate calculated from the reduced sample of K-1 clusters

To obtain an estimate of the variance at a higher level, say, at the national level, the process is repeated over all strata, with K redefined to refer to the total number of clusters (as opposed to the number in the stratum)

Estimation of Mean Let N be the total number of Housing Units listed for the selected EA n be the number of selected Housing Units in the selected EA Yij be the value of element from selected HUs of the selected EA Y be the estimate of sample total

Therefore, for a proportion estimate, we have . yij .xi

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is a structured questionnaire (Scanable) developed as a joint effort of the National Bureau of Statistics, the World Bank and National Planning Commission. After series of meeting and two consultative workshops.

Section 1: Household Roster Section 2: Education - Part 2a: General Education Section 2: Education - Part 2b: Literacy and Apprenticeship Section 3: Health - Part 3a: Health Condition Section 3: Health-Part 3b: Malaria Section 3: Health - Part 3c: Disability and Activities of Daily Living Section 3: Health - Part 3d: Preventive Health and Vaccination Section 3: Health - Part 3e: Fertility, Prenatal Care and Contraceptive Use Section 3: Health - Part 3f: HIV/AIDS Section 3: Health - Part 3g: Gender-Based Violence Section 4: Employment and Time Use-Part A: Screening Questions & List of Occupations Section 4: Employment and Time Use-Part B: Characteristics of Main Wage Employment Section 4: Employment and Time Use-Part D: Unemployment & Employment Search in the Past Seven Days Section 4: Employment and Time Use-Part E: Household Chores Section 4: Employment and Time Use-Part F: Training/Program Participation Section 4: Employment and Time Use-Part G: Consolidated Desired Employment Section 5: Migration Section 6: Housing Part A: Type of Dwelling Section 6: Housing Part B: Occupancy Status of Dwelling Section 6: Housing Part C: Housing Expenditure (Rent) Section 6: Housing Part D: Physical Characteristics of Dwelling Section 6: Housing Part E: Energy Section 6: Housing Part F: Water and Sanitation Section 6: Housing Part G: Access to the Nearest Social Amenity Section 7: Ownership of Durable Assets Section 8: Crime and Security Section 9: Subjective Poverty

Cleaning operations

Headquarters Training of Trainers (T0T) The first level of training at the headquarter consisted of three categories of officers, namely, the trainers at the zonal level, fieldwork monitoring officers and data processing officers who were crucial to the successful implementation of the survey.

The intensive and extensive training lasted for five days. Zonal Level Training The training took place in the six zonal FOS [now NBS] offices representing the six geo-political zones of the country. These are Ibadan (South West) Enugu (South East), Calabar (South South), Jos (North Central), Maiduguri (North East) and Kaduna (North West).

The composition of the team from each State to the six different zones were the State officer, one scrutiny officer and two field officers, making four persons per state. Two resource persons from the headquarters did the training with the zonal controllers participating and contributing during the five-day regimented and intensive training.

State Level Training The third level training was at the State level. A total of 40 officers were trained, comprising 20 enumerators, 10 editing staff and 10 supervisors.

The State Statistical Agencies, as a matter policy, contributed 5-10 enumerators. The ten-day exercise was also regimented, intensive and extensive because the enumerators were also crucial for effective implementation of data collection.

Response rate

Total of 77,390 households were covered from a sample of 77,400 households giving the survey coverage rate of 99.9 percent

As per data set at households level, out of the 77,390 retrieved, only 73,329 were analysable giving 94.7 percent.

At sector level (Urban/Rural), 25.2% were recorded for Urban while Rural recorded 74.8%.

Data appraisal

The data processing of the HNLSS records was done at the 6 NBS Processing Centers which were located within the 6 Zonal Offices. The main activities include: The manual editing of records The scanning of completed questionnaires. Validation of data that was scanned. Computer editing of scanned records Data cleaning and table generation. A series of data quality tables and graphs are available.

Abstract

In this report, we present data from the emergency response survey conducted via telephone among households in three conflict affected regions of Nigeria, North East, North Central and South South between August-September 2017. This round is the second round of telephone data collected from a subsample of households in the Nigeria General Household Survey (GHS). The first round collected data on conflict exposure.

The purpose of this second round of data collection was to understand food insecurity in conflict affected regions. Armed conflict can have a detrimental effect on food security. This might be due to for example reduced agricultural production, or price increases due to malfunctioning markets. Food insecurity might be permanent, such that a household living below the poverty line has a constant struggle to acquire food from the market or produce food for their own use. In situations such as armed conflict, also better endowed households might be temporarily food insecure. In this report, we find that food insecurity is a major concern in all the three regions studied:

· The mean household in all the three regions is “highly food insecure” · North East of Nigeria is the most food insecure of the three regions · Reducing meals or portion size is the most important coping strategy in all three regions · Food prices are the most important source of food insecurity in all three regions · A large majority of households rely on the market as the main source of food in all regions. Price concerns should therefore be taken very seriously by policy makers. · Households in all three regions do not report there being an inadequate supply of food in the market.

Geographic coverage

Zones States Local Government Areas (LGAs) Households

Analysis unit

Individuals, Households and Communities

Universe

The Survey covered all household members. The questionnaire was administered to only one respondent per household - most often a male household head.

Kind of data

Sample survey data [ssd]

Sampling procedure

The food security survey was a telephone based survey conducted between August 15th and September 8th 2017. The interview was the second round of a telephone survey using a sub-set of the sample of GHS (General Household Survey) households. The first round of the telephone interview was administered during spring 2017 with 717 completed interviews with the following geographical distribution: 175 interviews in the North East, 276 in North Central and 266 in South South. The first round was focused on conflict exposure, while the second round discussed in this report focused on food insecurity in conflict affected regions.

In the three conflict affected geographical zones comprising of 16 states of Nigeria, households from LGS's that had high conflict exposure were oversampled chosen for a pilot sample, conducted before the telephone surveys. These LGS's were chosen based on the following criteria: The oversampled LGS's needed to have over 10 conflict events during 2012-14 recorded in the Armed Conflict Location & Event Data Project (ACLED) database.

The first round of the telephone survey (which took place after the pilot) first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 percent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews.

Conflict affected areas were oversampled in order to have a large enough sample of households that in fact experienced conflict events in order to shed light on the type of events that have happened. A random sample of the zones might have given too small sample of conflict affected households and therefore restricted the analysis of the various types of conflict events. Due to the oversampling however, the sample drawn was not representative at the level of the geographical zone, as is the case in the GHS. Therefore in the analysis we use sampling weights that adjust for the propensity of being in a conflict affected LGA in order to ensure that the sample is representative at the level of the geographical zone.

During the second round of the survey 582 of the 717 households were re-interviewed on food security related issues (only the 717 were attempted to be reached). Of the 582 households 147 in the North East, 219 in North Central, and 216 in South South were interviewed. The attrition rates in our sample from round one to round two are hence 16 percent, 21 percent, and 19 percent for North East, North Central and South South, respectively. The attrition from the conflict survey round was mostly due to not being able to reach the respondents possibly due to non-functioning phone numbers. Only 3 percent of respondents refused to answer.

Similar telephone-based surveys are being conducted in six countries in Sub-Saharan Africa under the World Bank project "Listening to Africa". As a comparison, a mobile phone survey in Tanzania (see Croke et al. 2012 for details), had a high drop-out rate between the very first rounds from 550 to 458 respondents, but very low attrition for the subsequent rounds for the 458 respondents, who could reliably be reached by a mobile phone. In light of this reference point and also considering the fact that the households interviewed live in conflict affected regions, our attrition rates seem to be within reasonable limits.

Sampling deviation

No Deviation

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire is divided into 9 sections including a household roster. Information on food insecurity (the coping strategy index, CSI), food and market access, water quality, employment, income, employment and assets was collected.

Cleaning operations

Data was analyzed using descriptive statistics in Stata 15. All data analysis was tracked using comprehensive do files to ensure reproducibility. All statistics presented in this report have been adjusted with probability weights, when possible, to be representative at the level of the geopolitical zone. Demographics for each geopolitical zone were analyzed based on the complete GHS 2016 dataset.

Response rate

The first round of the telephone survey (which took place after the pilot), first attempted to reach 742 households from the GHS panel, of which 529 could be reached and interviewed. The rest did not have phone numbers or functioning phone numbers (only 2.7 per cent refused to answer). In order to increase the sample size to a level that was considered adequate for the survey, an additional 288 replacement households were included in the sample also from the GHS panel. Out of these replacement households 188 could be interviewed. Therefore altogether 1030 households were attempted to be reached, with a final sample size of 717 completed interviews. The response rate is 96%

Sampling error estimates

No Sampling Error

Data appraisal

Limitations Recall Bias In the pilot data collection, respondents were asked to report on conflict events that had taken place in their family and their community over the last six years. This extremely long recall period must be considered when drawing inferences from the data. People are likely to under-report less severe (and therefore less memorable) events, particularly those that happened to community members in larger communities. Respondents are also more likely to recall events that happened to family members than those that happened to community members. Other biases may also be at play - for example, those who have been most highly affected by conflict over the last six years may have moved to another community. These factors demonstrate the importance of implementing a regular data collection schedule, which would allow far more accurate data to be collected. Sampling Bias The GHS is a panel survey taking place over multiple rounds through a period of time. Therefore, households that are more mobile or households that are nomadic are less likely to be represented in this sample. This may be particularly relevant in circumstances where nomadic groups are named as perpetrators of conflict events. Power Dynamics There are some disadvantages to the phone system, and for this reason it should be supplemented by additional types of data collection wherever possible. In a mobile phone survey, the respondent is the person who owns a mobile phone. In many areas, particularly those highly affected by poverty and those located in rural areas, only one family member owns a mobile phone. This is generally the household head, who is most likely male. Furthermore, in many of these communities, women are not allowed to have access to mobile phones and are forbidden from speaking to outsiders, which can prohibit mobile phone-based data collection. Gender Dynamics The questionnaire was administered to only one respondent per household - most often a male household head. This means that crimes that carry stigma, especially sexual violence, are less likely to be reported. In this dataset, no sexual assault was reported despite data collected elsewhere that indicate that rape was used as a weapon by Boko Haram

Abstract

The National Bureau of Statistics (NBS) has the statutory mandate to provide socio-economic data on a wide range of issues, including poverty reduction programmes for informed decision making, policy formulation and implementation. Thus, the essence of adequate measurement and production of relevant evidence-based statistics on poverty and welfare of Nigerians cannot be overemphasized. The various laudable programmes of government aimed at combating poverty such as NEEDS, 7-Point Agenda, NAPEP, NDE, MDG amongst many others required tracking, monitoring and evaluation.

The history of Nigeria Living Standard Survey (NLSS) dates back to three periods. The pre-1993 period, 1993-1999 period, and the 2000-2008 period. Each of these periods are unique in their own way. During the pre-1993 period, there were no national efforts at monitoring poverty and the National Consumer Survey (NCS) as NLSS was then known approached the measurement of poverty with different objectives. However, during the 1993-1999 periods, national effort started in May 1993 when the NBS (then FOS) collaborated with the World Bank to conduct several national consumer surveys. This period marked the beginning of a search for data.

The search further led the World Bank to collaborate with the NBS and National Planning Commission under the National Committee on poverty to produce the first ever poverty report in Nigeria. Using the NCS data of 1985-1992, three draft reports were produced leading to what is called "the evolution of poverty and welfare in Nigeria 1985-1992". This was followed by the "Poverty Profile for Nigeria 1980-1996" published in 1999 and was made possible through the World Bank support to NBS for the NCS of 1996 and the extended analysis to the NCS data of 1980/81.

With the search still on, the 2000-2008 periods, witnessed an era of influx of support from development partners for the measurement, monitoring and evaluation of welfare through NLSS and CWIQ in Nigeria. The NLSS used the expenditure approach to measure, monitor, and evaluate poverty. Thus, the NBS again through the support of the WB, DFID, EU and UNDP enlarged the implementation of the NCS of 2004, referred to as Nigeria Living Standard Survey. The support resulted to the emergence of two reports, a standalone poverty profile of Nigeria 2004 and the Nigeria Living Standard Survey 2004 report.

The Harmonized Nigeria Living Standard Survey (HNLSS) is an instrument for regular monitoring of welfare and social trends for different population groups of the society especially the poor. It is hoped that this report will be useful especially to the Federal Government of Nigeria, All states in Nigeria, Non-Governmental Organisation (NGO), and International Development Partners such as the World Bank, UNDP, UNICEF, and other institutions involved in monitoring welfare and poverty across the globe.

Geographic coverage

National Zone State Local Government Sector (Urban/Rural)

Analysis unit

Household and individual

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design employed for HNLSS Survey 2008/09 is a 2-stage cluster sample design in which Enumeration Areas (EAs) or Primary Sampling Units (PSUs) constitutes the 1st stage sample while the Housing units (HUs) from the EAs make up the 2nd stage sample or the Ultimate Sampling Units (USUs)

Sampling Frame The enumeration areas (EAs) as demarcated by the National Population Commission (NPopC) for the 2006 population census served as the sampling frame for the HNLSS 2008/09.

Sample Size Sample sizes must meet some minimal requirement in order to obtain reliable estimate. Hence, for HNLSS Survey 2008/09, the sample size varies from state to state depending on the number of Local Government Areas (LGAs) in each state. Ten (10) EAs were selected in each LGA making a total of 7,774 EAs to be canvassed for throughout the federation from the 774 LGAs including the Federal Capital Territory (FCT) Abuja.

Selection Procedure The 7,740 EAs were selected directly from the population of the EAs in the NPopC with equal probability of selection. Prior to selection, all the contiguous EAs were arranged in serpentine order in each LGA of the state. This arrangement ensured that there was no overlapping

A total of 77,390 households were covered from a sample of 77,400 households giving the survey coverage rate of 99.9 percent. Of all the six zones, it was only SW zone that had the least response rate of 99.9 percent. The response rate in the remaining 5 zone was 100.0 percent each. Table 1.2 Status of Retrieval of Records by Zone and State attached to the report in External Resources

AS PER DATA SET At households level, out of the 77,390 retrieved, only 73,329 were scanable.

Estimation Procedure Let E be the number of EAs in the state e be the number of selected in the state For a given stratum or domain, the estimate of the variance of a rate, r is given by

Var(r) = (se)2 = 1 ?(ri - r)2 K(k -1)i=1 Where K is the number of clusters in the stratum or estimation domain r is the weighted estimate calculated from the entire sample of clusters in the stratum ri is equal to Kr - (K-1) r(i), where r(i) is re-weighted estimate calculated from the reduced sample of K-1 clusters

To obtain an estimate of the variance at a higher level, say, at the national level, the process is repeated over all strata, with K redefined to refer to the total number of clusters (as opposed to the number in the stratum)

Estimation of Mean Let N be the total number of Housing Units listed for the selected EA n be the number of selected Housing Units in the selected EA Yij be the value of element from selected HUs of the selected EA Y be the estimate of sample total

Therefore, for a proportion estimate, we have . yij .xi

Sampling deviation

No deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is a structured questionnaire (Scanable) developed as a joint effort of the National Bureau of Statistics, the World Bank and National Planning Commission. After series of meeting and two consultative workshops.

SECTION 1: HOUSEHOLD ROSTER SECTION 2: EDUCATION - PART 2A: GENERAL EDUCATION SECTION 2: EDUCATION - PART 2B: LITERACY AND APPRENTICESHIP SECTION 3: HEALTH - PART 3A: HEALTH CONDITION SECTION 3: HEALTH-PART 3B: MALARIA SECTION 3: HEALTH - PART 3C: DISABILITY AND ACTIVITIES OF DAILY LIVING SECTION 3: HEALTH - PART 3D: PREVENTIVE HEALTH AND VACCINATION SECTION 3: HEALTH - PART 3E: FERTILITY, PRENATAL CARE AND CONTRACEPTIVE USE SECTION 3: HEALTH - PART 3F: HIV/AIDS SECTION 3: HEALTH - PART 3G: GENDER-BASED VIOLENCE SECTION 4: EMPLOYMENT AND TIME USE-PART A:SCREENING QUESTIONS & LIST OF OCCUPATIONS SECTION 4: EMPLOYMENT AND TIME USE-PART B:CHARACTERISTICS OF MAIN WAGE EMPLOYMENT SECTION 4: EMPLOYMENT AND TIME USE-PART D:UNEMPLOYMENT & EMPLOYMENT SEARCH IN THE PASS SEVEN DAYS SECTION 4: EMPLOYMENT AND TIME USE-PART E:HOUSEHOLD CHORES SECTION 4: EMPLOYMENT AND TIME USE-PART F:TRAINING/PROGRAM PARTICIPATION SECTION 4: EMPLOYMENT AND TIME USE-PART G:CONSOLIDATED DESIRED EMPLOYMENT SECTION 5: MIGRATION SECTION 6: HOUSING PART A: TYPE OF DWELLING SECTION 6: HOUSING PART B: OCCUPANCY STATUS OF DWELLING SECTION 6: HOUSING PART C: HOUSING EXPENDITURE (RENT) SECTION 6: HOUSING PART D: PHYSICAL CHARACTERISTICS OF DWELLING SECTION 6: HOUSING PART E: ENERGY SECTION 6: HOUSING PART F: WATER AND SANITATION SECTION 6: HOUSING PART G: ACCESS TO THE NEAREST SOCIAL AMENITY SECTION 7: OWNERSHIP OF DURABLE ASSETS SECTION 8: CRIME AND SECURITY SECTION 9: SUBJECTIVE POVERTY

Cleaning operations

Headquarters Training of Trainers (T0T) The first level of training at the headquarter consisted of three categories of officers, namely, the trainers at the zonal level, fieldwork monitoring officers and data processing officers who were crucial to the successful implementation of the survey.

The intensive and extensive training lasted for five days. Zonal Level Training The training took place in the six zonal FOS [now NBS] offices representing the six geo-political zones of the country. These are Ibadan (South West) Enugu (South East), Calabar (South South), Jos (North Central), Maiduguri (North East) and Kaduna (North West).

The composition of the team from each State to the six different zones were the State officer, one scrutiny officer and two field officers, making four persons per state. Two resource persons from the headquarters did the training with the zonal controllers participating and contributing during the five-day regimented and intensive training.

State Level Training The third level training was at the State level. A total of 40 officers were trained, comprising 20 enumerators, 10 editing staff and 10 supervisors.

The State Statistical Agencies, as a matter policy, contributed 5-10 enumerators. The ten-day exercise was also regimented, intensive and extensive because the enumerators were also crucial for effective implementation of data collection.

Response rate

Total of 77,390 households were covered from a sample of 77,400 households giving the survey coverage rate of 99.9 percent

As per data set at households level, out of the 77,390 retrieved, only 73,329 were analysable giving 94.7 percent.

At sector level (Urban/Rural), 25.2% were recorded for Urban while Rural recorded 74.8%.

Data appraisal

The data processing of the HNLSS records was done at

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

Abstract

In the past decades, Nigeria has experienced substantial gaps in producing adequate and timely data to inform policy making. In particular, the country is lagging behind in producing sufficient and accurate agricultural production statistics. The current set of household and farm surveys conducted by the NBS covers a wide range of sectors. Except for the Harmonized National Living Standard Survey (HNLSS) which covers multiple topics, these different sectors are usually covered in separate surveys none of which is conducted as a panel. As part of the efforts to continue to improve data collection and usability, the NBS has revised the content of the annual General household survey (GHS) and added a panel component. The GHS-Panel is conducted every 2 years covering multiple sectors with a focus to improve data from the agriculture sector.

The Nigeria General Hosehold Survey-Panel, is the result of a partnership that NBS has established with the Federal Ministry of Agriculture and Rural Development (FMA&RD), the National Food Reserve Agency (NFRA), the Bill and Melinda Gates Foundation (BMGF) and the World Bank (WB). Under this partnership, a method to collect agricultural and household data in such a way as to allow the study of agriculture's role in household welfare over time was developed. This GHS-Panel Survey responds to the needs of the country, given the dependence of a high percentage of households on agriculture activities in the country, for information on household agricultural activities along with other information on the households like human capital, other economic activities, access to services and resources. The ability to follow the same households over time, makes the GHS-Panel a new and powerful tool for studying and understanding the role of agriculture in household welfare over time as it allows analyses to be made of how households add to their human and physical capital, how education affects earnings and the role of government policies and programs on poverty, inter alia.

The objectives of the survey are as follows i Allowing welfare levels to be produced at the state level using small area estimation techniques resulting in state-level poverty figures ii With the integration of the longitudinal panel survey with GHS, it will be possible to conduct a more comprehensive analysis of poverty indicators and socio-economic characteristics iii Support the development and implementation of a Computer Assisted Personal Interview (CAPI) application for the paperless collection of GHS iv Developing an innovative model for collecting agricultural data v Capacity building and developing sustainable systems for the production of accurate and timely information on agricultural households in Nigeria. vi Active dissemination of agriculture statistics

The second wave consists of two visits to the household: the postplanting visit occurred directly after the planting season to collect information on preparation of plots, inputs used, labour used for planting and other issues related to the planting season. The post-harvest visit occurred after the harvest season and collected information on crops harvested, labour used for cultivating and harvest activities, and other issues related to the harvest cycle.

Geographic coverage

National Zone State Sector

Analysis unit

Agricultural Households.

Universe

Agricultural farming household members.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is designed to be representative at the national level as well as at the zonal (urban and rural) levels. The sample size of the GHS-Panel (unlike the full GHS) is not adequate for state-level estimates.

The sample is a two-stage probability sample:

First Stage: The Primary Sampling Units (PSUs) were the Enumeration Areas (EAs). These were selected based on probability proportional to size (PPS) of the total EAs in each state and FCT, Abuja and the total households listed in those EAs. A total of 500 EAs were selected using this method.

Second Stage: The second stage was the selection of households. Households were selected randomly using the systematic selection of ten (10) households per EA. This involved obtaining the total number of households listed in a particular EA, and then calculating a Sampling Interval (S.I) by dividing the total households listed by ten (10). The next step was to generate a random start 'r' from the table of random numbers which stands as the 1st selection. Consecutive selection of households was obtained by adding the sampling interval to the random start.

Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS, in which 10 households per EA are usually selected and give robust estimates.

In all, 500 clusters/EAs were canvassed and 5,000 households were interviewed. These samples were proportionally selected in the states such that different states had different samples sizes depending on the total number of EAs in each state.

Households were not selected using replacement. Thus the final number of household interviewed was slightly less than the 5,000 eligible for interviewing. The final number of households interviewed was 4,986 for a non-response rate of 0.3 percent. A total of 27,533 household members were interviewed. In the second, or Post Harvest Visit, some household had moved as had individuals, thus the final number of households with data in both points of time (post planting and post harvest) is 4,851, with 27,993 household members.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey consisted of three questionnaires for each of the visits; The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agriculture 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 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; agriculture 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 Household Questionnaire: The household questionnaire provides information on demographics; education; health (including anthropometric measurement for children and child immunization); labor and time use; food and non-food expenditure; household nonfarm incomegenerating 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 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.

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

Cleaning operations

Data Entry This survey used a concurrent data entry approach. In this method, the fieldwork and data entry were handled by each team assigned to the state. Each team consisted of a field supervisor, 2-4 interviewers and a data entry operator. Immediately after the data were collected in the field by the interviewers, the questionnaires were handed over to the supervisor to be checked and documented. At the end of each day of fieldwork, the questionnaires were then passed to the data entry operator for entry. After the questionnaires were entered, the data entry operator generated an error report which reported issues including out of range values and inconsistencies in the data. The supervisor then checked the report, determined what should be corrected, and decided if the field team needed to revisit the household to obtain additional information. The benefits of this method are that it allows one to: - Capture errors that might have been overlooked by a visual inspection only, - Identify errors early during the field work so that if any correction required a revisit to the household, it could be done while the team was still in the EA

The CSPro software was used to design the specialized data entry program that was used for the data entry of the questionnaires.

Data Cleaning The data cleaning process was done in a number of stages. The first step was to ensure proper quality control during the fieldwork. This was achieved in part by using the concurrent data entry system which was, as explained above, designed to highlight many of the errors that occurred during the fieldwork. Errors that are caught at the fieldwork stage are corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of

Abstract

Towards the goal of improving agricultural statistics, the World Bank, through funding from the Bill and Melinda Gates Foundation (BMGF), is supporting seven countries in Sub-Saharan Africa in strengthening the production of household-level data on agriculture.

The over-arching objective of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) program is to improve our understanding of agriculture in Sub-Saharan Africa - specifically, its role in household welfare and poverty reduction, and how innovation and efficiency can be fostered in the sector. This goal will be achieved by developing and implementing an innovative model for collecting agricultural data in the region.

Expected Benefits:

The specific outputs and outcomes of the revised GHS with panel component are: Development of an innovative model for collecting agricultural data in conjunction with household data; Development of a model of inter-institutional collaboration between NBS and the FMA&RD and NFRA, inter alia, to ensure the relevance and use of the new GHS; Building the capacity to generate a sustainable system for the production of accurate and timely information on agricultural households in Nigeria. Comprehensive analysis of poverty indictors and socio-economic characteristics.

Innovations

The revised GHS with panel component contains several innovative features. Integration of agricultural data at the plot level with household welfare data; Creation of a panel data set that can be used to study poverty dynamics, the role of agriculture in development and the changes over time in health, education and other labor activities, inter alia.
Use of small area estimation techniques (SAE) to generate state level poverty data by taking advantage of the integration of the panel households into the full GHS.
Collection of information on the network of buyers and sellers of goods that household interact with; Use of GPS units for measuring agricultural land areas; Involvement of multiple actors in government, academia and the donor community in the development of the survey and its contents as well as its implementation and analysis; Use of concurrent data entry in Wave 1. In later Waves the project will develop and implement a Computer Assisted Personal Interview (CAPI) application for the paperless collection of the GHS; Use of direct respondents for all sections of the questionnaires where individual level data or specific economic activity data are collected; Creation of a publicly available micro data sets for researchers and policy makers; Active dissemination of agriculture statistics.

Geographic coverage

National Zone State Local Government Sector (Urban/Rural)

Analysis unit

Household, individual, Farm, Plot and Crop

Universe

Household members

Kind of data

Sample survey data [ssd]

Sampling procedure

National Integrated Survey of Households (NISH)-2007/2012 Master Sample Frame (MSF) was adopted.

In order to select the NISH sub-sample of EAs in each state, the thirty (30) master sample EAs in each LGA for that state were pooled together such that the total number of the EAs in the LGA master sample for each state is equal to 30 times the number of the LGAs in the state except in FCT, Abuja where it is 40 times.

Thereafter, a systematic sample of 200 sample EAs were selected with equal probability across all LGAs within the state. Furthermore, the NISH EAs in each state were divided into 20 replicates of 10 EAs each, however, the sample EAs for most national household surveys such as the GHS are based on a sub-sample of the NISH master sample, selected as a combination of replicates from NISH frame in which the Household Panel was a subset of the GHS EAs 2010

The sample frame includes all thirty-six (36) states of the federation and Federal Capital Territory (FCT), Abuja. Both urban and rural areas were covered and in all, 500 clusters/EAs were canvassed and 5,000 households were interviewed. These samples were proportionally selected in the states such that different states have different samples. The distribution of the samples are shown in the table 3.1 below which shows the site of the sample in each state, allocation of EAs, households covered, field personnel used and the number of days for fieldwork by zone and state for the GHS Panel main survey 2010 (Post-Planting).

The Panel Survey used a two stage stratified sample selection process.

First Stage: The Primary Sampling Units (PSUs) were the Enumeration Areas (EAs). These were selected based on probability proportional to size (PPS) of the total EAs in each state and FCT, Abuja and the total households listed in those EAs.

Second Stage:

The second stage involved the systematic selection of ten (10) households per EA. This involved obtaining the total number of households listed in a particular EA, and then calculating a Sampling Interval (S.I) by dividing the total households listed by ten (10). The next step is to generate a random start 'r' from the table of random numbers which stands as the 1st selection. The second selection is obtained by adding the sampling interval to the random start. For each of the next selections, the sampling interval was added to the value of the previous selection until the 10th selection is obtained. Determination of the sample size at the household level was based on the experience gained from previous rounds of the GHS cross section, in which 10 HHs per EA are usually selected and give robust estimates.

Sampling deviation

No deviation from the sampling

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire is a structured questionnaire developed as a joint effort of the National Bureau of Statistics, the World Ban, Federal Ministry of Agriculture and Rural Development. Federal Ministry of Water Resources and National Food Reserve Agency during a series of meeting and two consultative workshops.
These are the Household Questionnaire and the Agricultural Questionnaire.

The household questionnaire consist of:

SECTION 1: HOUSEHOLD MEMBER ROSTER SECTION 2: EDUCATION SECTION 3: LABOUR SECTION 4: CREDIT AND SAVINGS SECTION 5: HOUSEHOLD ASSETS SECTION 6: NONFARM ENTERPRISES AND INCOME GENERATING ACTIVITIES SECTION 7A: MEALS AWAY FROM HOME EXPENDITURES SECTION 7B: FOOD EXPENDITURES SECTION 8: NON-FOOD EXPENDITURES SECTION 9: FOOD SECURITY SECTION 10: OTHER INCOME

Sections 7A, 7B and 8 are not included in the present data. These data sets will be given when the Post Hrvest data set is avaliable.

The Agricultural Questionnaire:

SECTIONS 11:
a PLOT ROSTER b LAND INVENTORY c INPUT COSTS d FERTILIZER ACQUISITION e SEED ACQUISITION f PLANTED FIELD CROPS g PLANTED TREE CROPS h MARKETING OF AGRICULTURAL SURPLUS i ANIMAL HOLDINGS j ANIMAL COSTS k AGRICULTURE BY-PRODUCT l EXTENSION SECTIONS 12: NETWORK ROSTER

Cleaning operations

The data cleaning process was done in a number of stages. The first step was to ensure proper quality control during the fieldwork. This was achieved in part by using the concurrent data entry system which was, as explained above, designed to highlight many of the errors that occurred during the fieldwork. At this stage errors that are caught at the fieldwork stage are corrected based on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then sent from the state to the head office of NBS where a second stage of data cleaning was undertaken.

During the second stage the data were examined for out of range values and outliers. The data were also examined for missing information for required variables, sections, questionnaires and EAs. This problem was then reported back to the state where the correction was then made. This was an ongoing process until all data were delivered to the head office.

After all the data were received by the head office, there was an overall review of the data to identify outliers and other errors on the complete set of data. Where problems were identified, this was reported to the state. There the questionnaires were checked and where necessary the relevant households were revisited and a report sent back to the head office with the corrections.

The final stage of the cleaning process was to ensure that the households and individuals were correctly merged across all sections of the household questionnaire. 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 plot identified in the main sections merged with the plot information identified in the other sections. This was also done for crop by plot information as well.

Response rate

The response rate 99.9% includeing replacements at household level. Replacement households represent 17.9% of the sample.

Data appraisal

VARIABLE NAMING SCHEME Generally, the variables are named to correspond with each of the questions. For example in the case of the cover dataset (SECTA) the variables names start with ‘SA’ which means section A of the household questionnaire. This is followed by ‘Q’ and a number e.g. ‘Q1’ which indicates the question number, so the first question in Section A is captured in the variable SAQ1.

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.

In 2014, the Federal Government of Nigeria (FGN) obtained financial assistance from the World Bank to support the implementation of the Youth Employment and Social Support Operation (YESSO). The support operation was in line with the World Bank and Nigeria’s commitment to poverty reduction by identifying and boosting capacity of poor households in Nigeria to access increased opportunities for youth employment and social service delivery. YESSO supported existing institutions, especially the National Directorate of Employment to help strengthen their capacities for poverty alleviation.

The ‘Skills for Job’ (S4J) is one of the four components of YESSO which is designed to pilot a new approach to skills training with emphasis on demand-driven skills training. It provided technical and soft skills training to young people that would enable trainees to find sustainable jobs, and promote institutional capacity building and public-private sector partnerships. To test the sustainability of the S4J component of YESSO, a baseline survey was carried out between October and December, 2018 to serve as benchmark for impact evaluation (IE) of the S4J interventions.