In 2024, around ** percent of the population in Kenya lived in extreme poverty, the majority in rural areas. Those living on less than **** U.S. dollars a day in rural regions added up to around **** million, while around *** 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.

In 2025, *** percent of Kenya’s population live below **** U.S. dollars per day. This meant that over 8.9 million Kenyans were in extreme poverty, most of whom were in rural areas. Over *** million Kenyans in rural communities lived on less than **** U.S. dollars daily, an amount *** times higher than that recorded in urban regions. Nevertheless, the poverty incidence has declined compared to 2020. That year, businesses closed, unemployment increased, and food prices soared due to the coronavirus (COVID-19) pandemic. Consequently, the country witnessed higher levels of impoverishment, although improvements were already visible in 2021. Overall, the poverty rate in Kenya is expected to decline to ** percent by 2025. Poverty triggers food insecurity Reducing poverty in Kenya puts the country on the way to enhancing food security. As of November 2021, *** million Kenyans lacked sufficient food for consumption. That corresponded to **** percent of the country's population. Also, in 2021, over one-quarter of Kenyan children under five years suffered from chronic malnutrition, a growth failure resulting from a lack of adequate nutrients over a long period. Another *** percent of the children were affected by acute malnutrition, which concerns a rapid deterioration in the nutritional status over a short period. A country where prosperity and poverty walk side by side The poverty incidence in Kenya contrasts with the country's economic development. In 2021, Kenya ranked among the ten highest GDPs in Africa, at almost *** billion U.S. dollars. Moreover, its gross national income per capita has increased to ***** U.S. dollars over the last 10 years, a growth of above**** percent. Generally, while poverty decreased in the country during the same period, Kenya still seems to be far from reaching the United Nation's Sustainable Development Goals (SDGs) to eliminate extreme poverty by 2030.

As of 2022, over nine million people in Kenya are living in extreme poverty, with the poverty threshold at 1.90 U.S. dollars a day. This accounts for 18.1 percent of the total population. The number of poor people in the country fluctuated in recent years, following an overall descending trend. In 2016, 20.7 percent of the Kenyan population were in extreme poverty. The number is expected to decline to under seven million people in 2030. Increases in the number of poor inhabitants in 2020 and 2021 are possibly related to the coronavirus (COVID-19) pandemic.

Kenya KE: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 36.100 % in 2015. This records a decrease from the previous number of 46.800 % for 2005. Kenya KE: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 41.450 % from Dec 2005 (Median) to 2015, with 2 observations. The data reached an all-time high of 46.800 % in 2005 and a record low of 36.100 % in 2015. Kenya KE: 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 Kenya – Table KE.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.

As of 2021, over 7.8 million people in Kenya lived in extreme poverty, with the poverty threshold at 1.90 U.S. dollars a day. The number of poor people in the country fluctuated in recent years, following an overall descending trend. In 2016, there were nearly nine million Kenyans in extreme poverty. The number is expected to decline to some six million people in 2025. Increases in the number of poor inhabitants in 2020 and 2021 are possibly related to the coronavirus (COVID-19) pandemic.

Kenya poverty rate for 2021 was 91.30%, a 0.3% increase from 2020.

<ul style='margin-top:20px;'>

<li>Kenya poverty rate for 2020 was <strong>91.00%</strong>, a <strong>5.3% increase</strong> from 2015.</li>
<li>Kenya poverty rate for 2015 was <strong>85.70%</strong>, a <strong>1.7% decline</strong> from 2005.</li>
<li>Kenya poverty rate for 2005 was <strong>87.40%</strong>, a <strong>5.3% increase</strong> from 1997.</li>
</ul>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.

Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.900 % in 2015. This records a decrease from the previous number of 16.800 % for 2005. Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 16.800 % from Dec 1992 (Median) to 2015, with 5 observations. The data reached an all-time high of 21.600 % in 1992 and a record low of 13.900 % in 2015. Kenya KE: Proportion of People Living Below 50 Percent Of Median Income: % 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 percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. 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.;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 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).

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. 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. 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).

Nearly 20 million poor people were estimated to be living in Kenya in 2020. This represented some six million more individuals than the estimates for 2019. According to the source, the increase in poverty was related to the coronavirus (COVID-19) pandemic. Before the health crisis, the number of poor Kenyans was decreasing.

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.

Kenya Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data was reported at 1.250 % in 2015. This records a decrease from the previous number of 1.420 % for 2005. Kenya Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % data is updated yearly, averaging 1.335 % from Dec 2005 (Median) to 2015, with 2 observations. The data reached an all-time high of 1.420 % in 2005 and a record low of 1.250 % in 2015. Kenya Proportion of Population Pushed Below the 60% Median Consumption Poverty Line By Out-of-Pocket Health Expenditure: % 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. This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).;Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection);Weighted average;This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

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'.

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.

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.

Kenya Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 38.500 % in 2021. This records a decrease from the previous number of 45.400 % for 2015. Kenya Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 41.950 % from Dec 2015 (Median) to 2021, with 2 observations. The data reached an all-time high of 45.400 % in 2015 and a record low of 38.500 % in 2021. Kenya Multidimensional Poverty Headcount Ratio: World Bank: % of total 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 multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;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).

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,

The objective of this research was to elicit data required for a quantitative assessment of the contribution of ecosystem services to wellbeing of the poor through a large standardized household survey administered to individuals.The survey was conducted in Kenya and Mozambique. The household survey aimed to sample a total of 1,200 household (600 household per country) and interview up to three people per household. The three respondents included the household head, spouse and a third randomly selected person in order to collect data on individual (i.e.) sub household access to ES.

This project aims to better understand the links between ecosystem services (ES) and wellbeing in order to design and implement more effective interventions for poverty alleviation. We do this in the context of coastal, social-ecological systems in two poor African countries; Kenya and Mozambique. Despite recent policy and scientific interest in ES, there remain important knowledge gaps regarding how ecosystems actually contribute to wellbeing, and thus poverty alleviation. Following the ESPA framework, distinguishing ecological processes, 'final ES', 'capital inputs', 'goods' and 'values', this project is concerned with how these elements are interrelated to produce ES benefits, and focuses specifically on how these benefits are distributed to (potentially) benefit the poor, enhancing their wellbeing. We thus address the ESPA goal of understanding and promoting ways in which benefits to the poorest can be increased and more people can meet their basic needs, but we also identify conflicted tradeoffs, i.e. those which result in serious harm to either the ecosystem or poor people and which need urgent attention. Several fundamental questions are currently debated in international scientific and policy fora, relating to four major global trends which are likely to affect abilities of poor people to access ES benefits: (1) devolution of governance power and its impacts on local governance of ecosystems and production of ES, (2) unprecedented rates and scales of environmental change, particularly climate change, which are creating new vulnerabilities, opportunities and constraints, 'shifting baselines', and demanding radical changes in behaviour to cope, (3) market integration now reaches the most remote corners of the developing world, changing relationships between people and resources and motivations for natural resource management, (4) societal changes, including demographic, population, urbanisation and globalisation of culture, forge new relationships with ES and further decouple people from direct dependency on particular resources. Study sites have been chosen so as to gather empirical evidence to help answer key questions about how these four drivers of change affect abilities of poor people to benefit from ES. We aim for direct impact on the wellbeing of poor inhabitants of the rapidly transforming coastal areas in Mozambique and Kenya, where research will take place, while also providing indirect impact to coastal poor in other developing countries through our international impact strategy. Benefits from research findings will also accrue to multiple stakeholders at various levels.

Local government, NGOs and civil society groups - through engagement with project activities, e.g. participation in workshops and exposure to new types of analysis and systems thinking.

Donor organizations and development agencies - through research providing evidence to inform strategies to support sector development (e.g. fisheries, coastal planning and tourism development) and methods to understand and evaluate impacts of different development interventions - e.g. through tradeoff analysis and evaluation of the elasticities between ecosystem services and wellbeing.

International scientific community - through dissemination of findings via conferences, scientific publications (open access), and from conceptual and theoretical development and new understandings of the multiple linkages between ecosystem services and wellbeing. Regional African scientists will benefit specifically through open courses offered within the scope of the project, and through dissemination of results at regional venues. Our strategies to deliver impact and benefits include (1) identifying 'windows of opportunity' within the context of ongoing coastal development processes to improve flows of benefits from ecosystems services to poor people, and (2) identifying and seeking to actively mitigate 'conflicted' tradeoffs in Kenya and Mozambique.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National coverage.

Analysis unit

Individuals

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world’s population (see table A.1 of the Global Findex Database 2017 Report for a list of the economies included). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents are randomly selected within the selected households. Each eligible household member is listed and the handheld survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer’s gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size was 1000.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

As of January 2025,13.6 million people in Kenya lacked sufficient food for consumption. That corresponded to around 26 percent of the country's population. Compared to the year prior, the number of inhabitants in food insecurity decreased by 4.2 percent.