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 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.

As of 2025, an estimated population of over ***** million in Nigeria lived in extreme poverty, with the poverty threshold at **** U.S. dollars a day. This stood as an increase from the headcount of about ***** million recorded for the previous year.

In 2022, an estimated population of 88.4 million people in Nigeria lived in extreme poverty. The number of men living on less than 1.90 U.S. dollars a day in the country reached around 44.7 million, while the count was at 43.7 million for women. Overall, 12.9 percent of the global population in extreme poverty were found in Nigeria as of 2022.

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.

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.

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.

Multilevel poisson regression of the association between the poverty level and the ideal number of children among men in Nigeria.

Nigeria NG: Poverty Gap at $3.20 a Day: 2011 PPP: % data was reported at 40.300 % in 2009. This records a decrease from the previous number of 40.800 % for 2003. Nigeria NG: Poverty Gap at $3.20 a Day: 2011 PPP: % data is updated yearly, averaging 40.800 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 48.400 % in 1996 and a record low of 40.300 % in 2009. Nigeria NG: Poverty Gap at $3.20 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: Poverty. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 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.

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 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.

I have to give special thanks to the key stakeholders who contributed immensely to the success of the survey and the report. The stakeholders are the European Union, World Bank, Department for International Development and National Planning Commission.

I have to commend the professional competence and commitment of Federal Office of Statistics, the National Statistical Agency and for the provision of survey mechanism, infrastructures and personnels to implement the survey successfully.

Professor Ode Ojowu Economic Adviser to the President of Federal Republic of Nigeria.

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

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

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 (FMARD), 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 1. Allowing welfare levels to be produced at the state level using small area estimation techniques resulting in state-level poverty figures 2. 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 3. Support the development and implementation of a Computer Assisted Personal Interview (CAPI) application for the paperless collection of GHS 4. Developing an innovative model for collecting agricultural data 5. Capacity building and developing sustainable systems for the production of accurate and timely information on agricultural households in Nigeria. 6. Active dissemination of agriculture statistics

The second wave consists of two visits to the household: the post-planting 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 Coverage

Analysis unit

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 paper [f2f]

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.

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 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. Any problems found were 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 household- and individual-level data sets 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 plots 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 was very high. Response rate after field work was calculated to be 93.9% while attrition rate was 6.1% for households. During the tracking period, 52.4% of the attrition was tracked while at the end of the whole exercise, the response rate was: Post Harvest: 97.1%

Sampling error estimates

No sampling error

Context

High-resolution satellite imagery is increasingly available at the global scale and contains an abundance of information about landscape features that could be correlated with economic activity. Unfortunately, such data are highly unstructured and thus challenging to extract meaningful insights from at scale, even with intensive manual analysis. Recent applications of deep learning techniques to large-scale image data sets have led to marked improvements in fundamental computer vision tasks such as object detection and classification, but these techniques are generally most effective in supervised learning regimes where labelled training data are abundant. A convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes.

This data set contains 3 zip folders having a total of 32823 images. Ethiopia, Malawi and Nigeria are the 3 countries. Each folder contains images of the respective countries.

Ethiopia https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5563010%2Fcd8b70635ab2a334809438460e695fff%2F4.752716945228008_39.22483343759402_4.76768886663_39.269749201799996.png?generation=1603882372912555&alt=media" alt="">

Malawi https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5563010%2F63655de156cd773c2dc5ab39828db5bd%2F-9.651637_33.82882592140199_-9.651637_33.813854.png?generation=1603882497548977&alt=media" alt="">

Nigeria https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5563010%2F504125a68f6eab3489c1618d1dd8c7c5%2F4.78376024056_7.035940588761993_4.78376024056_7.020968667360001.png?generation=1603882549779492&alt=media" alt="">

• The images are of the dimensions 256x256 pixels RGB.
• They are named 'image_lat_image_lon_cluster_lat_cluster_lon.png'.
• These images have been taken from the Planet Developer Resource Center.
• A maximum cloud filter of 5% is applied.
• Filtered all images having more than 50% clouds.

Apache 2.0 - Licensed

Acknowledgements

https://developers.planet.com/

Inspiration

Around 767 million people in the world surviving on less than $1.90 a day. I hope this data helps reduce this number in some way.

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

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

Feed the Future Nigeria Livelihoods Project (FNLP) is a multi-component development project based on the graduation model pioneered by Bangladesh Rural Advancement Committee (BRAC) that intends to help 42,000 very poor households across rural communities of northern Nigeria’s Sokoto and Kebbi states, and the Federal Capital Territory (FCT). FNLP is a 5-year program implemented by Catholic Relief Services (CRS). Both the program and the impact evaluation are funded by United States Agency for International Development (USAID).

This program approach is founded on an agriculture-led growth strategy that is expected to help vulnerable families diversify their income and grow assets while the community is strengthened by improving nutrition, water sanitation, and hygiene. The most vulnerable families receive cash transfers. A caseworker-led livelihood mentoring scheme also matches households with the resources they need to engage effectively in the local economy and break free from the cycle of poverty and malnutrition.

The impact evaluation, led by The World Bank’s Africa Gender Innovation Lab (GIL), is being conducted in Kebbi state in North-West Nigeria and will evaluate the impact of the overall program as well as two experiments that focus on the impact of the cash transfers and the caseworker mentoring scheme. Baseline data was collected for the FNLP starting in May 2015.

Geographic coverage

The impact evaluation was conducted in Kebbi state in two Local Government Authorities (LGAs) Birnin Kebbi and Danko Wasagu across eight wards: Ujariyo/Junju, Lagga/Randalli, Kardi, Makera/Maurida, Kanya, Ribah/Waje, Maga/Kyabu and Danko.

Analysis unit

• Households

Universe

Households in both FNLP villages and villages not receiving FNLP services but are part of the control group for the impact evaluation.

Kind of data

Sample survey data [ssd]

Sampling procedure

To determine which areas within Kebbi State would benefit from the FNLP program and to establish a sample of vulnerable households that will be part of the program and impact evaluation, CRS and GIL identified eligible communities and households in Kebbi using a number of steps. Detailed explanations of each stage in the process are provided in the baseline report (Attached in the Related Materials).

For the Impact Evaluation baseline survey, a sample of 2,400 EV households and 1,100 households equally divided between the VV and ML households was necessary based on power calculations. We sampled 2,074 of the ‘Class B’ households in FNLP treatment villages and 2,254 from FNLP control villages and sent this sample of 4,328 households to the survey firm to conduct a baseline survey.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

For the baseline survey, three instruments were used for data collection:

1. Household questionnaire: The household questionnaire was administered to all households in the sample and collected demographic characteristics for all household members, information on dwelling characteristics, household consumption expenditures, household asset holdings, aspirations, exposure to shocks, and level of participation in safety net programs. In addition, individual-level questions around food security, risk aversion, and time preferences were asked to both the male and female decision-makers in the households.

2. Women’ questionnaire: Women were also asked to respond to a separate Women’s Survey that had questions based on the Women’s Empowerment in Agriculture Index (WEAI).

3. Agricultural questionnaire: An agriculture questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. The instrument asked questions on land holdings, agriculture production, sales, agricultural income and level of participation in extension services programs. Plot-level information was collected from the male and female decision-makers in the households who were the target respondents for this questionnaire.

4. Community questionnaire: A community questionnaire was administered to each village to collect information on the socio-economic indicators of the village communities where the sampled households reside. The community questionnaire collected information on basic characteristics of the community such as location, size, distance to larger towns and markets, and availability of and distance to sources of health services and schools. Data was collected from 5-10 community members during the Household Targeting Committee meetings.

Cleaning operations

Data quality was ensured at several levels. At the tablet level, the questionnaire was programmed so that questions or sections could not be skipped by interviewers. Numerous quality checks were also built into the programming that identified inconsistencies and prevented interviewers from moving forward with the survey until errors were corrected. Logic checks and range checks were also included in the programming so that implausible entries were flagged to the interviewer at the time of surveying.

Monitoring of data collection activities was also conducted by several people. Supervisors monitored interviewer performance by observing interviews and conducting spot checks that consisted of assessing whether questions were being asked appropriately and providing immediate feedback to interviewers. The World Bank’s Project Manager and Field Coordinator also provided another layer of quality control, visiting each interviewer team at least twice each week to observe interviews and review household listings.

A final level of data quality control involved the use of quality control reports that were automatically generated using a quality-check file created by the research team at the World Bank. The file would scan the data for possible errors or large outliers as soon as data was downloaded from the server. The types of checks the file would make included the following: whether the household identifiers were unique within the dataset, whether interviews were being completed in their entirety, reviewing observations with duplicate values of a variable for which duplicates are uncommon, checking that no variables have only missing values, checking important skip patterns, range checks and interviewer comments. This helped with data accuracy as the report was reviewed at least every week by the research team throughout the data collection period and any errors could be sent back to the field team and rectified in real time while the data collection was still taking place.

Response rate

The number of household interviews completed was 3,976 for a household response rate of 92 percent.

Mean ideal number of children, according to community-level characteristics in Nigeria.

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.

Pay-As-You-Go Solar Market Outlook

According to our latest research, the Global Pay-As-You-Go Solar market size was valued at $1.6 billion in 2024 and is projected to reach $6.9 billion by 2033, expanding at a CAGR of 17.8% during 2024–2033. This robust growth trajectory is primarily driven by the increasing demand for affordable, decentralized energy solutions in off-grid and underserved regions, particularly across Africa and Asia. The Pay-As-You-Go (PAYG) solar model enables households and businesses to access clean energy without the high upfront costs, leveraging flexible payment systems and mobile technology. This innovative financing approach is not only expanding energy access but also catalyzing socioeconomic development and supporting global sustainability objectives.

Regional Outlook

Africa holds the largest share of the Pay-As-You-Go Solar market, accounting for over 55% of global revenues in 2024. The continent’s dominance is underpinned by a combination of high energy poverty rates, limited grid infrastructure, and a dynamic entrepreneurial ecosystem. Countries such as Kenya, Nigeria, Tanzania, and Uganda have emerged as frontrunners, driven by supportive government policies, international donor funding, and rapid mobile money adoption. The proliferation of mobile payment platforms like M-Pesa has been instrumental in enabling micro-payments for solar energy, making the PAYG model viable for millions of low-income households. Additionally, the presence of several established PAYG solar providers and a robust distribution network have further solidified Africa’s leadership in this sector.

The Asia Pacific region is the fastest-growing market for Pay-As-You-Go Solar, projected to register a CAGR of 20.4% from 2024 to 2033. This impressive growth is fueled by the vast off-grid populations in countries such as India, Bangladesh, Indonesia, and the Philippines, where electrification gaps persist despite significant economic progress. Government initiatives promoting rural electrification, coupled with increased investments from international development agencies and private investors, are accelerating PAYG solar adoption. Moreover, the rapid penetration of smartphones and mobile banking services in rural areas is facilitating seamless payment and customer engagement, thereby driving market expansion. The region is also witnessing the entry of new market players and collaborations between local entrepreneurs and global technology providers.

Emerging economies in Latin America and the Middle East & Africa are also witnessing growing interest in the Pay-As-You-Go Solar market, but face unique adoption challenges. In Latin America, regulatory complexities, currency volatility, and fragmented rural markets hinder rapid scaling, though countries like Haiti and Honduras are showing promise. In the Middle East & North Africa, the market is nascent but gaining traction as governments seek to diversify energy sources and address rural electrification. Localized demand is often shaped by specific climatic, economic, and cultural factors, requiring tailored business models and product offerings. Policy reforms, public-private partnerships, and targeted subsidies are gradually overcoming these barriers, setting the stage for future growth.

Report Scope