In 2022, an estimated population of 74 million people in Nigeria lived in extreme poverty, the majority in rural areas. The count of people living on less than 2.15 U.S. dollars a day in rural regions reached 65.7 million, while around 8.3 million extremely poor people were located in urban areas. Overall, throughout the period examined, the poverty incidence remained above 50 million in rural communities.

As of 2019, the population mostly affected by poverty in Nigeria was those living in large household in rural areas. Households in rural areas were generally much more impacted than those living in urban areas. For instance, almost 80 percent of people living in households with at least 20 individuals in rural areas lived below the poverty line. According to national standards, an individual with less than 137.4 thousand Nigerian Naira (roughly 361 U.S. dollars) per year is considered poor. Nationwide, 40.1 percent of population lived in poverty.

The Nigerian states of Sokoto and Taraba had the largest percentage of people living below the poverty line as of 2019. The lowest poverty rates were recorded in the South and South-Western states. In Lagos, this figure equaled 4.5 percent, the lowest rate in Nigeria.

A large population in poverty

In Nigeria, an individual is considered poor when they have an availability of less than 137.4 thousand Nigerian Naira (roughly 334 U.S. dollars) per year. Similarly, a person having under 87.8 thousand Naira (about 213 U.S. dollars) in a year available for food was living below the poverty line according to Nigerian national standards. In total, 40.1 percent of the population in Nigeria lived in poverty.

Food insecurity on the rise

On average, 21.4 percent of the population in Nigeria experienced hunger between 2018 and 2020. People in severe food insecurity would go for entire days without food due to lack of money or other resources. Over the last years, the prevalence with severe food among Nigerians has been increasing, as the demand for food is rising together with a fast-growing population.

Nigeria NG: Poverty Gap at $1.90 a Day: 2011 PPP: % data was reported at 21.800 % in 2009. This records a decrease from the previous number of 21.900 % for 2003. Nigeria NG: Poverty Gap at $1.90 a Day: 2011 PPP: % data is updated yearly, averaging 21.900 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 31.100 % in 1996 and a record low of 21.500 % in 1985. Nigeria NG: Poverty Gap at $1.90 a Day: 2011 PPP: % 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. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

In 2022, an estimated population of **** million people in Nigeria lived in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day. This stood as an increase from the previous year, when around **** million people lived in the said state of poverty. The headcount was expected to maintain the rising trend through to 2027.

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

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

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.

According to governmental data from 2020, 18 percent of urban population and 52.1 percent of rural population in Nigeria lived in poverty as of 2019. Based on national standards, an individual with less than 137.4 thousand Nigerian Naira (roughly 361 U.S. dollars) per year is considered poor. In absolute numbers, this means that almost 83 million people in Nigeria lived in poverty (40.1 percent of the population).

Nigeria NG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 77.600 % in 2009. This records a decrease from the previous number of 79.900 % for 2003. Nigeria NG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 78.500 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 82.000 % in 1996 and a record low of 77.100 % in 1992. Nigeria NG: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % 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: Poverty. Poverty headcount ratio at $3.20 a day is the percentage of the population living on less than $3.20 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

This dataset is created based on a Nigeria's 1km Poverty map provided by worldpop.org. Data have been aggregated to local government areas level.

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.

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 $5.50 a Day: 2011 PPP: % data was reported at 59.600 % in 2009. This records a decrease from the previous number of 60.700 % for 2003. Nigeria NG: Poverty Gap at $5.50 a Day: 2011 PPP: % data is updated yearly, averaging 60.700 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 65.100 % in 1996 and a record low of 59.600 % in 2009. Nigeria NG: Poverty Gap at $5.50 a Day: 2011 PPP: % 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. Poverty gap at $5.50 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $5.50 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Increasing poor people's access to financial services can help them weather personal financial crises and increase their chances of climbing out of poverty. The FSP interactive map tool plots financial service locations throughout Africa. This tool can be used to identify gaps in access to financial services, and to design policy and inform decision-making. Increasing access to humanitarian information is one of the key principles of the Open Humanitarian Initiative. The Open Humanitarian Data Repository contains a comprehensive repository of openly available data for the Ebola Outbreak in West Africa.

Abstract

The Nigeria IDP Survey was conducted in IDP camps and host communities in the Northeast regions of Nigeria. The survey was used to compile a socio-economic profile of IDPs and host communities to help inform durable solutions to internal displacement. The survey contains information on poverty, education, service access, employment and livelihoods, as well as perceptions. The data combines detailed household information with displacement-specific information including drivers of displacement, access to resettlement mechanisms, and return intentions. It also includes comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption methodology.

Geographic coverage

The survey covered IDPs in camps, IDPs in host communities, and host communities in six states in Northeast Nigeria. The six states are Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe. The survey thus represents IDPs (in camps and in host communities)) and host communities of Northeast Nigeria.

Analysis unit

Household, individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The Nigeria IDP Survey is a household survey with a multi-stage stratified random sample. Six Northeastern states were covered: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe. The sampling frame consisted of a list of wards with IDP household counts in the six states, provided by the International Organization for Migration (IOM) Displacement Tracking Matrix (DTM) 2017.

The sample had 6 strata with each state as a stratum. Each stratum was divided into grids of 150 by 150 meters using GIS technology defining the enumeration areas (EAs). The number of EAs to be selected from each stratum was obtained proportional to the IDP population sizes in each state. For the IDP sample, the EAs consisted of two types of IDP settlements, which were host community settings and camp settings. A total of 111 EAs across the 6 states were randomly selected proportional to size. Out of these, 77 EAs were host community type EAs and 48 were camp-like EAs. For the host community sample of 1,500 households, EAs were restricted to the 77 host community type EAs that were selected based on IDPs population numbers, in the absence of residential population estimates at this level. The survey is representative for IDPs and host communities, defining host communities as the non-displaced population living in the EAs with displaced populations.

All the households in the selected EAs were first listed, and 12 IDP households and 12 (or multiples of it) host community households were randomly selected and surveyed per EA, to reach the designated sample size.

Sampling deviation

Certain adaptations had to be made during the listing and survey period. Households with duplicate names and contact numbers and households outside the cluster boundaries were dropped. A few EAs also had to be dropped from the sampling due to insecurity or – in occasional cases – due to unreachability due to inclement weather over prolonged periods of time. While EAs were initially replaced at the beginning of the survey, new replacements were avoided at the end of the survey duration to avoid extended field work time due to cost implications.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire contains modules on Household Member Roster, Household Characteristics, Food Consumption, Non food consumption, Livestock, Durable Goods, Wellbeing and Opinions, and Displacement. The questionnaire is available for download with the dataset.

Cleaning operations

See accompanying Stata do-files, available under the related materials tab.

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

In Nigeria, malnutrition is widespread, particularly in the rural areas. This is partly due to inadequate food and low standard of living. Hence this study was aimed at ascertaining risk factors predisposing children to malnutrition in Okigwe Local Government of Imo State. Specifically, the study sought to examine the impact of poverty on the abilities of families to afford a balanced diet for their children, identify specific food preference among children and how they contribute to malnutrition, identify cultural practices and beliefs that affect child feeding and nutrition and examine how educational levels of parents correlate with their ability to make informed nutritional decision for their children. The study adopted a survey descriptive research design. A target population of 13, 325 women between the ages of 15-45 years (25% of estimated population) was used for the research. This study adopted Taro Yamen's formulae to determine the sample size from the population. Multi stage sampling technique was also used. Self-administered structured questionnaire with closed-ended questions was used for data collection for this study. Data collected wereanalyzed using frequencies and percentages. Findings from the study revealed that poverty has a negative impact 365 (97.3%) on abilities of families to afford a balanced diet for their children which in turn, predisposes children to malnutrition. The study also revealed that Specific food preferences that can contribute to malnutrition among children include, preference for sugary food 300 (80%) preference for high fat foods 285 (76%), preference for highly processed foods 305 (81.3%), preference for sugary drinks 297 (79.2%), preference for junk foods 325 (86.7%) and that preference for foods that are high in salt or fat 275 (73.3%) are the specific food preference that can contribute to malnutrition among children. Additionally, the study revealed that belief that children should be feed with certain types of food 325 (86.7%), forbidding children on eating during certain times of the day 297 (79.2%), belief that children should only eat certain amount of food 305 (81.3%), belief that children should not eat certain types of food like eggs 326 (86.9%) and belief of seeing certain food as unclean or impure 240 (64%) are some of the cultural practices and beliefs that can that affect child feeding and nutrition in Okigwe. Finally, the study revealed that there is a strong positive correlation 300 (80%) between educational level of parents and their ability to make informed nutritional decision. It is therefore recommended, that Governments in all parts of Nigeria especially in Okigwe L.G.A should prioritize reducing poverty and hunger, as this would have a significant impact on health and economic outcomes. It is also important to enlighten parents on those cultural belief and practices that are capable of exposing children to malnutrition so that they may put a stop to those practices especially in areas where children are affected by those cultural beliefs and practices

Nigeria NG: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 92.100 % in 2009. This records a decrease from the previous number of 94.100 % for 2003. Nigeria NG: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 92.800 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 94.100 % in 2003 and a record low of 92.100 % in 2009. Nigeria NG: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % 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. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; 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. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include 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). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

Despite the significant growth experienced in Nigeria in recent years, poverty has been on the rise, particularly in rural areas. The National Social Safety Nets Project (NASSP) was designed to provide poor and vulnerable household’s access to targeted transfers and to pilot scalable livelihoods interventions to support sustainable income generating activities and graduation out of poverty. To assess the impacts of the livelihood pilot activities, a sample of households was interviewed for the baseline data collection before the introduction of these interventions. These households will then be re-interviewed after the completed implementation of the livelihood package in a random subset of communities, to evaluate any resulting changes in household welfare and to gain insights about the most cost-effective approaches to deliver the livelihood package at scale.

Geographic coverage

8,035 NASSP beneficiary households from 460 communities in 12 local government areas (LGAs) in the six livelihood pilot states: Anambra, Bauchi, Cross River, Jigawa, Niger and Oyo.

Sampling procedure

The survey used a multistage sampling procedure. Stage 1: Six states were selected for the pilot study, one from each geopolitical zone, to ensure it represents national diversity. The states also satisfied the following criteria: availability of a State Beneficiary Register (SBR); participation in the Community and Social Development Project (CSDP) and/or Fadama; and adoption of the co-responsibility component of the NASSP project. Stage 2: Local Government Areas (LGAs) were selected from each state. Both LGAs were rural, to ensure the livelihood works in the most challenging of environments. Not more than one LGA was selected in each senatorial district to ensure widespread representation. Stage 3: Pilot communities were selected based on these factors: 12 to 56 households per community (for implementation feasibility); at least 3 communities per ward (to allow stratification of treatment arms within wards); at least 2 wards per LGA. Stage 4: Households were randomly sampled from each community, proportionate to the coverage of the NASSP project.

Research instrument

The baseline survey questionnaire was programmed into the SurveyCTO application on Android tablets and trained enumerators administered the survey to household respondents in face-to-face interviews conducted during household visits.

The survey questionnaire was developed in English language by the research team and translated into Hausa, Igbo, Yoruba, Nupe and Pidgin by professional translators. Heads of households and caregivers were the main target respondents and separate sections of the survey were targeted to each of them. If a household head was unavailable, the caregiver responded on their behalf (7.5% of household). If the caregiver was unavailable, the alternate responded (2.5% of households). Questions were also asked to economically active members in each household when available. The average interview duration was 3 hours.

The survey questionnaire was divided into the following sections: household composition, employment, agriculture, livestock, business activities, program participation, safety nets, other income, remittances, consumption, finances, shocks, food security, dwelling type, household assets, psychosocial wellbeing, household dynamics, and time use.