Indicator 1.2.1 Proportion of population living below the national poverty line, by sex and age

1: End poverty in all its forms everywhere

1.2: By 2030, 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 definitions

1.2.1: Proportion of population living below the national poverty line, by sex and age

2024

1.1.1: Proportion of population below the international poverty line, by sex, age, employment status and geographical location (urban/rural)

Kenya National Bureau of Statistics

Kenya National Bureau of Statistics

Senior Manager, Research and Development

Research and Development Division

Production, compilation and dissemination of poverty and socio-economic statistics

+254-202-911-000

30266-00100, Nairobi- Kenya

dscm@knbs.or.ke

DEFINITION, CONCEPTS, AND CLASSIFICATIONS

Definition: The national poverty rate is the percentage of the total population living below the overall poverty line. The overall poverty line is disaggregated by rural/urban. The rural poverty rate is the percentage of the rural population living below the rural poverty line. Urban poverty rate is the percentage of the urban population living below the urban poverty line. There are two welfare measures usually reported in Kenya. These are: Food poverty-defined as the proportion of population who are not able to meet their recommended daily caloric intake; and Overall poverty-defined as the proportion of population who are not able to meet basic food needs plus some basic non food expenditures such as clothing shelter and personal care.

Concepts: In assessing poverty in Kenya, and how best to reduce poverty according to national definitions, the overall poverty line is used. Overall poverty line is considered as the food poverty line plus non food allowance. Since the cost of living is typically higher in urban areas than in rural areas, the poverty lines are reported separately for urban and rural areas. Thus, Kenya have separate urban and rural poverty lines to represent different purchasing powers.

Percent (%). The unit of measure is the proportion of the population.

Not applicable

DATA SOURCE, TYPE AND DATA COLLECTION METHOD

Kenya Integrated Household Budget Survey(KIHBS)(2015/16) and Kenya Continuous Household Surveys (KCHS)(2019-2022)

Household Surveys

Every five years for KIHBS and Annually for KCHS

Within an year after publication

Kenya National Bureau of Statistics

Kenya National Bureau of Statistics

According to the Statistics Act of 2006, Kenya National Bureau of Statistics is mandated to collect, compile, analyze, publish and disseminate official statistics for public use

Poverty estimates are used for evidence-based planning, monitoring living standards, and allocating national resources. They are also used in monitoring the progress made in achieving the Vision-2030 agenda

For reporting of this indicator, Consumption approach is preferred as opposed to income approach. Consumption is the preferred welfare indicator for a number of reasons. Income is generally more difficult to measure accurately. For example, the poor who work in the informal sector may not receive or report monetary wages; self-employed workers often experience irregular income flows; and many people in rural areas depend on idiosyncratic, agricultural incomes. Moreover, consumption accords better with the idea of the standard of living than income, which can vary over time even if the actual standard of living does not. Thus, whenever possible, consumption-based welfare indicators are used to estimate the poverty measures reported here. But consumption data are not always available.

Consumption is measured by using household survey questions on food and nonfood expenditures as well as food consumed from the household’s own production, which is particularly important in developing countries. This information is collected through recall questions using lists of consumption items. The use of recall or diary methods do not always provide equivalent information, and depending on the approach used, consumption can be underestimated or overestimated. Different surveys use different recall or reference periods. Depending on the true flow of expenditures, the rate of spending reported is sensitive to the length of reporting period. The longer the reference period, the more likely respondents will fail to recall certain expenses—especially food items—thus resulting in underestimation of true expenditure.

Best-practice surveys administer detailed lists of specific consumption items. These individual items collected through the questionnaires are aggregated afterwards. But many surveys use questionnaires in which respondents are asked to report expenditures for broad categories of goods. In other words, specific consumption items are implicitly aggregated by virtue of the questionnaire design. This shortens the interview, reducing the cost of the survey. A shorter questionnaire is also thought to reduce the likelihood of fatigue for both respondents and interviewers, which can lead to reporting errors. However, there is also evidence that less detailed coverage of specific items in the questionnaire can lead to underestimation of actual household consumption. The reuse of questionnaires may cause new consumption goods to be omitted, leading to further underreporting.

Invariably some sampled households do not participate in surveys because they refuse to do so or because nobody is at home. This is often referred to as “unit nonresponse” and is distinct from “item nonresponse,” which occurs when some of the sampled respondents participate but refuse to answer certain questions, such as those pertaining to consumption or income. To the extent that survey nonresponse is random, there is no concern regarding biases in survey-based inferences; the sample will still be representative of the population. However, households with different incomes are not equally likely to respond. Relatively rich households may be less likely to participate because of the high opportunity cost of their time or because of concerns about intrusion in their affairs. It is conceivable that the poorest can likewise be underrepresented; some are homeless and hard to reach in standard household survey designs, and some may be physically or socially isolated and thus less easily interviewed. If nonresponse systematically increases with income, surveys will tend to overestimate poverty. But if compliance tends to be lower for both the very poor and the very rich, there will be potentially offsetting effects on the measured incidence of poverty.

Even if survey data were entirely accurate and comprehensive, the measure of poverty obtained could still fail to capture important aspects of individual welfare. For example, using household consumption measures ignores potential inequalities within households. Thus, consumption- or income-based poverty measures are informative but should not be interpreted as a sufficient statistic for assessing the quality of people’s lives. The national poverty rate, a “headcount” measure, is one of the most commonly calculated measures of poverty. Yet it has the drawback that it does not capture income inequality among the poor or the depth of poverty. For instance, it fails to account for the fact that some people may be living just below the poverty line, while others experience far greater shortfalls. Policymakers seeking to make the largest possible impact on the headcount measure might be tempted to direct their poverty alleviation resources to those closest to the poverty line (and therefore least poor).

Issues may also arise when comparing poverty measures within countries when urban and rural poverty lines represent different purchasing powers. For example, the cost of living is typically higher in urban than in rural areas. One reason is that food staples tend to be more expensive in urban areas. So the urban monetary poverty line should be higher than the rural poverty line. But it is not always clear that the difference between urban and rural poverty lines found in practice reflects only differences in the cost of living. In some countries the urban poverty line in common use has a higher real value—meaning that it allows the purchase of more commodities for consumption—than does the rural poverty line. Sometimes the difference has been so large as to imply that the incidence of poverty is greater in urban than in rural areas, even though the reverse is found when adjustments are made only for differences in the cost of living. As with international comparisons, when the real value of the poverty line varies it is not clear how meaningful such urban-rural comparisons are.

Lastly, these income/consumption based poverty indicators do not fully reflect the other dimensions of poverty such as inequality, vulnerability, and lack of voice and power of the poor.

The formula for calculating the proportion of the total, urban and rural population living below the national poverty line, or headcount index, is as follows:

Where I(.) is an indicator function that takes on a value of 1 if the bracketed expression is true, and 0 otherwise. If individual consumption or income y_i is less than the national poverty line z (for example, in absolute terms the line could be the price of a consumption bundle or in relative terms a percentage of the income distribution), then I(.) is equal to 1 and the individual is counted as poor. N_p is the total, urban or rural number of poor. N is the total, urban or rural population.

Consumption or income data are gathered from nationally representative household surveys, which contain detailed responses to questions regarding spending habits and sources of income. Consumption, including consumption from own production, is calculated for the entire household. To adjust for different household compositions, an equivalence scale is used. For the Kenyan case, the scale used is called Bernard-Anzagi where an adult(>15yrs) is assigned a weight of 1, persons aged 5-14yrs are assigned a weight of 0.65 and children 0-4yrs are assigned a weight of 0.24. To ensure comparability, the poverty line was computed using the per adult equivalent consumption aggregate.

Since data collection took a whole year, different households in different places faced different prices for food items. To adjust for spatial and seasonal price variations, Paasche deflator was used.

National poverty rates use a Kenyan specific poverty line, reflecting economic and social circumstances. Typically the urban poverty line (for both food and overall) is set higher than the rural poverty line; reflecting the relatively higher costs of living in urban areas.

The validation of the poverty estimates is undertaken in collaboration with the World Bank

The construction of the consumption aggregate is guided by methods and guidelines provided by the World Bank. The latest guidelines are based on the report “On the Construction of a Consumption Aggregate for Inequality and Poverty Analysis”. Available at https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e

The Kenya National Bureau of Statistics is ISO certified based on 9001:2015 Standard requirements. The processes of compilation, production, publication and dissemination of data, including quality control, are carried out following the methodological framework and standards established by the KNBS, in compliance with the Internationally acceptable standards

The KNBS adheres to Kenya Statistical Quality Assurance Framework (KesQAF) that underlines principles to be assured in managing the statistical production processes and output.

Data consistency and quality checks are conducted through Technical Working Groups (TWGs) before publication and dissemination. A quality assurance report on poverty estimates is also produced.

The processes of compilation, production, publication and dissemination of data, including quality control are subjected to a set criteria and standards to ensure conformity. Before estimates are produced, they are assessed by the World Bank team for quality.

The reported poverty estimates are disaggregated by rural/urban and county level. Data is available both in aggregate and item level and can be accessed through https://statistics.knbs.or.ke/nada/index.php/home

None

https://www.knbs.or.ke/publications/

https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099225003092220001/p1694340e80f9a00a09b20042de5a9cd47e

https://statistics.knbs.or.ke/nada/index.php/home