This dataset contains information on County poverty rates estimates which is based on Kenya Integrated Household Budget Survey ( KIHBS) data for Constituencies in 2005/6

This dataset shows the Poverty rate, poverty gap, poverty density and the percentage of poor housing at Location level in Kenya in 1999. This dataset was originally collected by Kenya National Bureau of Statistics

Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging -1.180 % from Dec 2021 (Median) to 2021, with 1 observations. The data reached an all-time high of -1.180 % in 2021 and a record low of -1.180 % in 2021. Kenya Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The coverage and quality of the 2017 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2017 exercise of the International Comparison Program. See the Poverty and Inequality Platform for detailed explanations.;World Bank, Global Database of Shared Prosperity (GDSP) (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).;;The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

Kenya Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 45.700 % in 2021. This records an increase from the previous number of 44.800 % for 2020. Kenya Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 42.400 % from Dec 1992 (Median) to 2021, with 7 observations. The data reached an all-time high of 45.700 % in 2021 and a record low of 40.000 % in 1994. Kenya Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

In 2024, around 26 percent of the population in Kenya lived in extreme poverty, the majority in rural areas. Those living on less than 2.15 U.S. dollars a day in rural regions added up to around 12.1 million, while around 1.9 million extremely poor people resided in urban areas. During the period observed, the poverty incidence in Kenya peaked in 2022, likely due to the disruption to the country's economy caused by the coronavirus (COVID-19) pandemic.

Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data was reported at 36.100 % in 2021. This records an increase from the previous number of 35.000 % for 2020. Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data is updated yearly, averaging 29.400 % from Dec 1992 (Median) to 2021, with 7 observations. The data reached an all-time high of 36.700 % in 2005 and a record low of 25.400 % in 1997. Kenya KE: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 purchasing power adjusted 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, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Kenya’s population has nearly tripled in the last 35 years, from 16.3 million in 1980 to about 47 million today yet majority of the population are below the poverty line. poverty in Kenya is a widespread problem concentrated in the rural areas. This data set shows poverty rates within the Kenyan counties.

Poverty rate, poverty gap, poverty density and amount of Kenyan Shillings (per month per square kilometer) to close the poverty gap for Constituencies in eastern Kenya in 1999.

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.

The dataset contains the following features: Year, Industry Type, Contribution to GDP, Growth by GDP, Employment Types, and Total Employment of Kenya. This dataset was extracted from Statistical reports published by Kenya National Bureau of Statistics reports from 2011 to 2023. Researchers utilised advanced statistical techniques, machine and deep learning algorithms to predict the current extent of working poverty in Kenya, and assist policy makers in making informed decisions for future policy formulations.

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=14,055, var=11) is the first of two datasets needed to calculate the WEAI-related measures. It contains Module G data from the primary adult (18+) female decisionmaker within each household (for the sub-sample of households with a primary adult female decisionmaker).

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Abstract

Since 1992, Kenya has been a generous host of refugees and asylum seekers, a population which today exceeds 500,000 people. The Kakuma Refugee Camps have long been among the largest hosting sites (about 40% of the total refugees in Kenya), and have become even larger in recent years, with an estimated 67 percent of the current refugee population arriving in the past five years. In 2015, UNHCR, the Government of Kenya, and partners established Kalobeyei Settlement, located 40 kilometers north of Kakuma, to reduce the population burden on the other camps and facilitate a shift towards an area-based development model that addresses the longer term prospects of both refugees and the host community. The refugee population makes up a significant share of the local population (an estimated 40 percent at the district level) and economy, engendering both positive and negative impacts on local Kenyans. While Kenya has emerged as a leader in measuring the impacts of forced displacement, refugees are not systematically included in the national household surveys that serve as the primary tools for measuring and monitoring poverty, labor markets and other welfare indicators at a country-wide level. As a result, comparison of poverty and vulnerability between refugees, host communities and nationals remains difficult. Initiated jointly by UNHCR and the World Bank, this survey replicates the preceding Kalobeyei SES (2018), designed to address these shortcomings and support the wider global vision laid out by the Global Refugee Compact and the Sustainable Development Goals. Data was collected in October 2019 to December 2019, covering about 2,122 households.

Geographic coverage

Kakuma Refugee Camp, Kenya

Analysis unit

Household and individual

Universe

Sampled household survey, representative of all refugees living in Kakuma refugee camp.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kakuma SES utilized a two-stage sampling process where the first stage samples dwellings, stratified by subcamp, followed by second-stage households. Dwellings were drawn as the primary sampling unit (PSU) from an up-to-date list of all dwellings in the camp provided by UNHCR shelter unit, which serves as the sampling frame. The sample was drawn with explicit stratification for the four Kakuma subcamps, with uniform probability for Kakuma 1-3. For Kakuma 4, the selection probability was slightly increased because of higher expected nonresponse

The survey was designed to accurately estimate socioeconomic indicators such as the poverty rate for group sof the population that have at least a 50 percent representation in the population. A 3 percent margin of error at a confidence level of 95 percent is considered accurate, resulting in a sample size of 2,122. Considering a 10 percent nonresponse rate, the target sample size was 2,347.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following sections are included: household roster, education, employment, household characteristics, assets, access, vulnerabilities, social cohesion, coping mechanism, displacement and cunsumption and expenditure.

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, recoding and local suppression).

Response rate

The SES has a non-response rate of about 5%, mainly due to absence of respondent and refusal to participate in the survey

Kenya KE: Poverty Gap at $6.85 a Day: 2017 PPP: % data was reported at 48.000 % in 2015. This records a decrease from the previous number of 51.900 % for 2005. Kenya KE: Poverty Gap at $6.85 a Day: 2017 PPP: % data is updated yearly, averaging 44.900 % from Dec 1992 (Median) to 2015, with 5 observations. The data reached an all-time high of 51.900 % in 2005 and a record low of 41.100 % in 1992. Kenya KE: Poverty Gap at $6.85 a Day: 2017 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Social: Poverty and Inequality. Poverty gap at $6.85 a day (2017 PPP) is the mean shortfall in income or consumption from the poverty line $6.85 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, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Kenya Multi-dimensional poverty index per county

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

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

Feed the Future seeks to reduce poverty and undernutrition in 19 developing countries including Kenya by focusing on accelerating growth of the agricultural sector, addressing root causes of undernutrition, and reducing gender inequality. This dataset (n=14,055, var=11) is the first of two datasets needed to calculate the WEAI-related measures. It contains Module G data from the primary adult (18+) female decisionmaker within each household (for the sub-sample of households with a primary adult female decisionmaker).

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Coast, North Eastern, Nairobi, Rift Valley, , Eastern, Central, Nyanza, Market Average

Chart shows the percent of population and number of poor below the Kenya poverty line of Ksh 1,562 per month in rural areas; and Ksh 2,913 in urban areas per per person per month; based on estimated expenditures on minimum provisions of food and non-food items.

Abstract

The Hunger Safety Net Programme (HSNP) is a social protection project being conducted in the Arid and Semi-Arid Lands (ASALs) of northern Kenya. The ASALs are extremely food-insecure areas highly prone to drought, which have experienced recurrent food crises and food aid responses for decades. The HSNP is intended to reduce dependency on emergency food aid by sustainably strengthening livelihoods through cash transfers. The pilot phase ran from 2009 to 2013. The second phase has been launched in July 2013 and contracted to run until March 2018. Oxford Policy Management (OPM) was responsible for the monitoring and evaluation (M&E) of the programme under the pilot phase, as well as the second phase of implementation. Within the impact evaluation component for Phase 2, OPM used a range of analytical methods within an overarching mixed-method approach. The quantitative impact evaluation of HSNP Phase 2 compares the situation of HSNP2 beneficiaries and control households, relying on the Regression Discontinuity approach, integrated by a targeted Propensity Score Matching approach. In addition to the analysis at the household level, a Local Economy-Wide Impact Evaluation (LEWIE) was conducted to investigate the impact of the HSNP2 on the local economy, including on the production activities of both beneficiary and non-beneficiary households. A single round of data collection based on a household and business survey underpins the household quantitative impact evaluation and the LEWIE study. The objective of the survey is to collect household and business data to provide an assessment of the programme's impact on the local economy, as well as beneficiary households. The household survey is a survey of 5,979 people, carried out between 13 February and 29 June 2016 in 187 sub-locations across the four counties of Mandera, Marsabit, Turkana and Wajir. The survey covered modules on household demographic characteristics, livestock, assets, land, transfers, food and non-food consumption, food security, saving and borrowing, jobs, business, livestock trading and subjective poverty. In addition to the household survey, a business questionnaire was conducted in the three main commercial hubs of each county. Overall, 282 business questionnaires were administered in the four counties. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. The aim was to capture information on three main sectors of the local economy:

1. Retailing - shops that sell retail goods on which a price mark-up is applied
2. Services
3. Producers - businesses that transform inputs into outputs

Lastly, since livestock trading is a very important activity in the HSNP counties, livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews.

Geographic coverage

Regional

Analysis unit

Households

Universe

(a) At the household level, the study population consists of all the households in the four HSNP counties (i.e. Mandera, Marsabit, Turkana and Wajir). Within a household, the survey covered all de jure household members (usual residents).

(b) At the market level, the survey covered a random sample of businesses in the three main commercial hubs of each county. The following categories of businesses were excluded from the listing:

• Temporary stalls or mobile sellers located outside permanent kiosks
• Banks
• Education institutions (schools, universities etc.)
• Health facilities

(c) The livestock trader survey was conducted in the three main livestock markets of each county. To the extent possible, livestock traders have been sampled in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) HOUSEHOLD SURVEY The household survey used a two-stage sampling approach, for which the sample frame was defined by sub-locations and households in the HSNP Management Information System (MIS) data. The MIS data are data from a census of nearly all households in the four HSNP counties. The census contains the information that was gathered in respect of these households during the registration for the HSNP programme, their Proxy Means Test (PMT) score and their assignment to the HSNP cash transfers, as well as information about all payments received by all households since the start of Phase 2. The HSNP acknowledges that a small number of the population was recognised to be missed and was registered at a later date. The sampling procedure was intended to cover the different sample requirements of the impact evaluation approaches, including the Local Economy-Wide Impact Evaluation (LEWIE), the quantitative impact evaluation based on the Regression Discontinuity (RD) approach, and the Propensity Score Matching (PSM) back-up.

Drawing the sample consisted of two stages: 1. First stage: sampling of sub-locations 2. Second stage: sampling of households within a sub-location.

The sampling process yielded a sample of 187 sub-locations, including the 24 that were sampled with certainty. 11 sub-locations were sampled twice, and one sub-location was sampled three times. 44 sub-locations were selected in Mandera, 46 in Wajir, 48 in Marsabit and 49 in Turkana. In each sub-location 32 households were sampled. In a few sub-locations there were insufficient households to select the desired LEWIE sample, resulting in fewer than 32 households sampled. Overall, 6,384 households were sampled.

(b) BUSINESS SURVEY A business questionnaire was conducted in the three main commercial hubs of each county. The purpose of the survey was to learn more about local economic activities and livelihoods in the HSNP counties, and the data was used for the LEWIE analysis. In each sub-location, a sample of at least seven businesses from each category was targeted. Since no sampling frame for local businesses was available, the survey research teams in each county undertook a listing exercise of all businesses on the main commercial centre of the selected sub-locations. Once the listing was completed, the team leader sampled the required number of businesses using a step sampling approach. Overall, 282 business questionnaires were administered in the four counties. The business survey is not representative of any commercial hubs.

(c) LIVESTOCK TRADER SURVEY Since livestock trading is a very important activity in the HSNP counties, a number of livestock traders have been interviewed to understand better how the market works. In each county, three main livestock markets were targeted for interviews. Each enumerator team was asked to interview four traders in each of the sub-locations, leading to a total sample size of 12 livestock trader interviews per county. Sampling of livestock traders was mostly done purposively. To the extent possible, team leaders sampled livestock traders in order to achieve a balance between those trading large animals, those trading small or medium value animals, those trading only within the HSNP counties and those who also trade outside the HSNP counties. The livestock trader survey is not representative of any livestock markets.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

(a) QUALITY CHECKS

Given the data was electronically collected, it was continually checked, edited and processed throughout the survey cycle. A first stage of data checking was done by the survey team which involved: (i) checking of all IDs (ii) checking for missing observations (iii) checking for missing item responses where none should be missing (iv) first round of checks for inadmissible/out of range and inconsistent values.

(b) DATA PROCESSING Additional data processing activities were performed at the end of data collection in order to transform the collected cleaned data into a format that is ready for analysis. The aim of these activities was to produce reliable, consistent and fully-documented datasets that can be analysed throughout the survey and archived at the end in such a way that they can be used by other data users well into the future. Data processing activities involved:

• Computing and merging in the sampling weights
• Reshaping datasets in order to produce data files for each unit of observation (households, household members, and businesses)
• Anonymising data by removing all variables that identify respondents such as names, address, GPS coordinates, etc.
• Classifying non-response and coding them using a pre-determined classification scheme
• Properly naming and labelling the variables in each dataset

Response rate

Household survey response rate was 88.9 percent. For business survey and livestock trader survey, the response rate was 100 percent.

Data appraisal

The datasets were then sent to the analysis team where they were subjected to a second set of checking and cleaning activities. This included checking for out of range responses and inadmissible values not captured by the filters built into the CAPI software or the initial data checking process by the survey team. A comprehensive data checking and analysis system was created including a logical folder structure, the development of template syntax files (in Stata), to ensure data checking and cleaning activities were recorded, that all analysts used the same file and variable naming conventions, variable definitions,