Zambia poverty rate for 2022 was 94.00%, a 2% increase from 2015.

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

<li>Zambia poverty rate for 2015 was <strong>92.00%</strong>, a <strong>1.5% decline</strong> from 2010.</li>
<li>Zambia poverty rate for 2010 was <strong>93.50%</strong>, a <strong>0.2% increase</strong> from 2006.</li>
<li>Zambia poverty rate for 2006 was <strong>93.30%</strong>, a <strong>0.6% increase</strong> from 2004.</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.

Zambia ZM: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 87.200 % in 2015. This records a decrease from the previous number of 90.500 % for 2010. Zambia ZM: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 88.200 % from Dec 1991 (Median) to 2015, with 9 observations. The data reached an all-time high of 92.400 % in 2002 and a record low of 83.900 % in 1996. Zambia ZM: 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 Zambia – Table ZM.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.

Zambia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 64.300 % in 2022. This records an increase from the previous number of 60.800 % for 2015. Zambia Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 59.950 % from Dec 1991 (Median) to 2022, with 10 observations. The data reached an all-time high of 64.700 % in 2006 and a record low of 49.800 % in 1996. Zambia 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 Zambia – Table ZM.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).

Zambia ZM: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 54.400 % in 2015. This records a decrease from the previous number of 54.700 % for 2010. Zambia ZM: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 54.550 % from Dec 2010 (Median) to 2015, with 2 observations. The data reached an all-time high of 54.700 % in 2010 and a record low of 54.400 % in 2015. Zambia ZM: 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 Zambia – Table ZM.World Bank: 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.

Abstract

The Living Conditions Monitoring Survey V (or Indicator Monitoring Survey) was conducted in December 2006 covering the whole country. The major objective was to provide poverty estimates, and provides a platform for comparing with previous poverty estimates derived from cross-sectional survey data. Using similar survey design to that earlier conducted in 1998, the poverty estimates from the 2006 survey are comparable to the survey of 1998. It should be noted that, although the Central Statistical Office conducted another survey for 12 months during 2002/2003, the poverty results could not be compared to the 1998 Living Conditions Survey that was used to provide baseline poverty estimates for reports that include the Poverty Reduction Strategy Paper (PRSP) of 2002-4 and the Millennium Development Goals.

Specifically the main objectives of the LCMIV Survey are to: - Monitor the impact of Government policies, programmes and donor support on the well being of the Zambian population - Monitor and evaluate the implementation of some of the programmes envisaged in the Poverty Reduction Strategy Paper (PRSP) - Monitor poverty and its distribution in Zambia - Provide various users with a set of reliable indicators against which to monitor development - Identify vulnerable groups in society and enhance targeting in policy formulation and implementation

The Living Conditions Monitoring Survey 2006 collected data on the living conditions of households and persons in the areas of education, health, economic activities and employment, child nutrition, death in the households, income sources, income levels, food production, household consumption expenditure, access to clean and safe water and sanitation, housing and access to various socio-economic facilities and infrastructure such as schools, health facilities, transport, banks, credit facilities, markets, etc.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Commodities

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Coverage The LCMS V covered the entire nation on a sample basis. It covered both rural and urban areas in all the nine provinces. The survey was designed to provide data for each and every district in Zambia. A sample of 1,000 Standard Enumeration Areas (SEAs) was drawn to cover approximately 20,000 households.

Sample Stratification and Allocation The sampling frame used for the LCMS V was developed from the 2000 Census of Population and Housing. The country is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 150 constituencies, which are in turn divided into wards. For the purposes of conducting CSO surveys, Wards are further divided into Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the Primary Sampling Units (PSUs).

In order to have reasonable estimates at district level and at the same time take into account variation in the sizes of the districts, the survey adopted the Square Root sample allocation method, (Leslie Kish, 1987). This approach offers a compromise between equal and proportional allocation i.e. small sized strata (Districts) are at least allocated larger samples. The allocation of the sample points to rural and urban strata was done in such a way that it was proportional to their sizes in each district.

Note: Detailed sampling procedure is presented in the LCMS 2006 final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three types of questionnaires are used in the survey. These are:- 1. The Listing Booklet - to be used for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - to be used for collecting detailed information on all household members. 3. The Prices questionnaire:- to be used to collect data on unit prices of various commodities in the established trading places found in districts, provincial capitals and cities.This information is vital for the hamonising regional differences in prices.

Response rate

The household response rate was also very high with a national average of 97.8 percent of the originally selected households. At provincial level, all the provinces recorded a household response rate of above 97 percent. The highest proportion of responding households was recorded in Southern Province at 99. 2 percent and the lowest was on the Copperbelt and Northern provinces with 97.1 percent.

Analysis by Residence shows that almost all the urban SEAs were covered with a response rate of 98.5 percent. North Western Province recorded the lowest coverage rate of SEAs with only 91.7 percent of the SEAs covered. In rural areas almost all the selected SEAs were covered. However, in North Western Province, out of the 60 rural SEAs selected, only 53 SEAs were enumerated representing 88.3 percent coverage.

In general, households in rural areas had slightly higher response rates than households in urban areas. At national level, the household response rate in rural areas was 98.5 percent compared to 97.1 percent.

The non coverage in most cases was due to inaccessibility of some areas due to floods and washed away bridges especially in North Western Province. Post stratification adjustment to the weights was done in order to compensate for non coverage of SEAs. The household selection technique allows for systematic method of replacing non responding households.

In 2023, the international poverty (based on 2017 purchasing power parities (PPPs)) and the lower-income poverty rate (3.65 U.S. dollars in 2017 PPP), was highest for Mozambique within the Southern Africa region, with 74.7 percent and 88.7 percent, respectively. However, the upper middle-income poverty rate was highest for Zambia, at 93 percent.

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.

Zambia is simultaneously amongst the poorest and the most unequal countries in the world. In 2022, 64.3 percent of the population - about 12.6 million individuals - was living on less than US$2.15 a day. This level is not only the 6th highest in the world but it is also misaligned with the country’s Gross Domestic Product (GDP) per capita level. In four of the five poorer countries, GDP per capita is between one-quarter and one-half of Zambia’s GDP per capita. The remaining country is South Sudan, which is immersed in a protracted fragility and conflict situation. At the same time, consumption inequality is high, even when compared with the sub-group of highly unequal resource-rich countries. In 2022, the Gini index stood at 51.5 - significantly above the World Bank’s newly adopted high-inequality threshold of 40. This places Zambia as the country with the 4th highest inequality in the region and the 6th highest globally. Resource-rich countries with similar or higher inequality have substantially lower poverty levels.

Zambia is simultaneously amongst the poorest and the most unequal countries in the world. In 2022, 64.3 percent of the population - about 12.6 million individuals - was living on less than US$2.15 a day. This level is not only the 6th highest in the world but it is also misaligned with the country’s Gross Domestic Product (GDP) per capita level. In four of the five poorer countries, GDP per capita is between one-quarter and one-half of Zambia’s GDP per capita. The remaining country is South Sudan, which is immersed in a protracted fragility and conflict situation. At the same time, consumption inequality is high, even when compared with the sub-group of highly unequal resource-rich countries. In 2022, the Gini index stood at 51.5 - significantly above the World Bank’s newly adopted high-inequality threshold of 40. This places Zambia as the country with the 4th highest inequality in the region and the 6th highest globally. Resource-rich countries with similar or higher inequality have substantially lower poverty levels.

Zambia ZM: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 74.300 % in 2015. This records a decrease from the previous number of 79.700 % for 2010. Zambia ZM: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 74.300 % from Dec 1991 (Median) to 2015, with 9 observations. The data reached an all-time high of 79.700 % in 2010 and a record low of 64.700 % in 1996. Zambia ZM: 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 Zambia – Table ZM.World Bank.WDI: 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.

Zambia is simultaneously amongst the poorest and the most unequal countries in the world. In 2022, 64.3 percent of the population—about 12.6 million individuals—was living on less than US$2.15 a day. This level is not only the 6th highest in the world but it is also misaligned with the country’s Gross Domestic Product (GDP) per capita level. In four of the five poorer countries, GDP per capita is between one-quarter and one-half of Zambia’s GDP per capita. The remaining country is South Sudan, which is immersed in a protracted fragility and conflict situation. At the same time, consumption inequality is high, even when compared with the sub-group of highly unequal resource-rich countries. In 2022, the Gini index stood at 51.5—significantly above the World Bank’s newly adopted high-inequality threshold of 40. This places Zambia as the country with the 4th highest inequality in the region and the 6th highest globally. Resource-rich countries with similar or higher inequality have substantially lower poverty levels.

The gini index in Zambia was forecast to continuously increase between 2024 and 2029 by in total 0.01 points (+1.75 percent). The gini is estimated to amount to 0.58 points in 2029. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the gini index in countries like Ethiopia and Uganda.

Zambia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 47.900 % in 2018. Zambia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 47.900 % from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 47.900 % in 2018 and a record low of 47.900 % in 2018. Zambia Multidimensional Poverty Headcount Ratio: UNDP: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Social: Poverty and Inequality. The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.;Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/);;

Abstract

The main objective of the 2006 and 2010 LCMS surveys was to provide the basis for comparison of poverty estimates derived from cross-sectional survey data between 2006 and 2010.

In addition, the survey provides a basis on which to: - Monitor the impact of government policies on the well being of the Zambian population. - Monitor the level of poverty and its distribution in Zambia. - Provide various users with a set of reliable indicators against which to monitor - Identify vulnerable groups in society and enhance targeting in policy implementation.

Geographic coverage

In the LCMS 2010, all the 1000 sampled SEAs were enumerated representing 100 percent coverage at national level.

Analysis unit

• Households
• Individuals

Universe

The survey covered all de jure household members (usual residents) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample stratification and allocation The sampling frame used for the LCMS VI was developed from the 2000 Census of Population and Housing. The country is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 150 constituencies, which are in turn divided into wards. For the purposes of conducting CSO surveys, Wards are further divided into Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the Primary Sampling Units (PSUs). In order to have reasonable estimates at district level and at the same time take into account variation in the sizes of the districts, the survey adopted the Square Root sample allocation method, (Leslie Kish, 1987). This approach offers a compromise between equal and proportional allocation i.e. small sized strata (Districts) are allocated larger samples compared to proportional allocation. However, it should be pointed out that the sample size for the smallest districts is still fairly small, so it is important to examine the confidence intervals for the district-level estimates in order to determine whether the level of precision is adequate. The allocation of the sample points to rural and urban strata was done in such a way that it was proportional to their sizes in each district. Although this method was used, it was observed from the LCMS 2006 that the coefficient of variation (CV) of the poverty estimates was highest in districts which are predominantly urban and lowest in rural districts. This means that the sample size in some urban districts may have been inadequate to measure poverty with a good level of precision. That is, given the higher variability in the urban districts, a larger sample size would be required. Also some districts had very low CV estimates, indicating a higher level of precision for the poverty estimates. In order to try and improve the precision of the poverty estimates for the urban districts, the initial distribution of the sample was adjusted. It was necessary to increase the number of PSUs for some districts without increasing the budget and at the same time not compromising significantly the precision of the poverty estimates for rural areas. Rural districts which had the lowest CVs in the 2006 LCMS results had their sample size reduced, and these were in turn distributed to districts with the highest CVs. The distribution of the sample for the LCMS 2006 and LCMS 2010 were initially the same but changed after the later was adjusted. Table 2.1 in the Survey Report shows the allocation of PSUs in the survey.

Sample Selection The LCMS VI employed a two-stage stratified cluster sample design whereby during the first stage, 1000 SEAs were selected with Probability Proportional to Estimated Size (PPES) within the respective strata. The size measure was taken from the frame developed from the 2000 Census of Population and Housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at the district, provincial, rural/urban and national levels. However, the reliability for some indicators may be limited for the smaller districts, given the limited sample size. This will be determined by the tabulation of sampling errors and confidence intervals.

Selection of households Listing of all the households in the selected SEAs was done before a sample of households to be interviewed was drawn. In the case of rural SEAs, households were stratified and listed according to their agricultural activity status. Therefore, there were four explicit strata created at the second sampling stage in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMS VI, Seven, five and three households were selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were explicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas. From each rural and urban SEA, 15 and 25 households were selected, respectively. However, the number of rural households selected in some cases exceeded the prescribed sample size of 15 households depending on the availability of large scale farming households.The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies: Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three types of questionnaires will be used in the survey. These are:- 1. The Listing Booklet - to be used for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - to be used for collecting detailed information on all household members in the selected households 3. The Prices questionnaire:- to be used to collect unit prices of various commodities. This information is vital for harmonising regional differences in prices

Cleaning operations

The Living Conditions Monitoring Survey data were entered using CSPro version 4.0 software. The LCMS 2010 application used a double entry system unlike the LCMS 2006 application which used single entry. The 2010 data entry was done by two teams, one team in the Provinces and another one at CSO headquarters. The data were then compared and matched by a team of matchers. Errors identified by matchers were corrected as a way of completing data entry. The major advantage of double entry (verification) is that data entry errors generated by the data entry operator are greatly minimized. The data were then exported to SAS, SPSS and Stata formats for data cleaning bulation and analysis.

Response rate

The household response rate was calculated as the ratio of originally selected households with completed interviews over the total number of households selected. The household response rate was also generally very high with a national average of 98 percent of the originally selected households for both survey periods.

Zambia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 66.500 % in 2022. This records an increase from the previous number of 66.400 % for 2015. Zambia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 66.450 % from Dec 2015 (Median) to 2022, with 2 observations. The data reached an all-time high of 66.500 % in 2022 and a record low of 66.400 % in 2015. Zambia 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 Zambia – Table ZM.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).

ambia is simultaneously amongst the poorest and the most unequal countries in the world. In 2022, 64.3 percent of the population—about 12.6 million individuals—was living on less than US$2.15 a day. This level is not only the 6th highest in the world but it is also misaligned with the country’s Gross Domestic Product (GDP) per capita level. In four of the five poorer countries, GDP per capita is between one-quarter and one-half of Zambia’s GDP per capita. The remaining country is South Sudan, which is immersed in a protracted fragility and conflict situation. At the same time, consumption inequality is high, even when compared with the sub-group of highly unequal resource-rich countries. In 2022, the Gini index stood at 51.5—significantly above the World Bank’s newly adopted high-inequality threshold of 40. This places Zambia as the country with the 4th highest inequality in the region and the 6th highest globally. Resource-rich countries with similar or higher inequality have substantially lower poverty levels.

Abstract

The World Bank is interested in gauging the views of clients and partners who are either involved in development in ZAMBIA or who observe activities related to social and economic development. The World Bank Country Assessment Survey is meant to give the World Bank's team that works in ZAMBIA, greater insight into how the Bank's work is perceived. This is one tool the World Bank uses to assess the views of its critical stakeholders. With this understanding, the World Bank hopes to develop more effective strategies, outreach and programs that support development in ZAMBIA. The World Bank commissioned an independent firm to oversee the logistics of this effort in ZAMBIA.

The survey was designed to achieve the following objectives: - Assist the World Bank in gaining a better understanding of how stakeholders in Zambia perceive the Bank; - Obtain systematic feedback from stakeholders in Zambia regarding: · Their views regarding the general environment in Zambia; · Their overall attitudes toward the World Bank in Zambia; · Overall impressions of the World Bank's effectiveness and results, knowledge and research, and communication and information sharing in Zambia; and · Perceptions of the World Bank's future role in Zambia. - Use data to help inform the Zambia country team's strategy.

Geographic coverage

National

Analysis unit

Stakeholder

Universe

Stakeholders of the World Bank in Zambia

Kind of data

Sample survey data [ssd]

Sampling procedure

In May and June 2012, 553 stakeholders of the World Bank in Zambia were invited to provide their opinions on the Bank's assistance to the country by participating in a country survey. Participants in the survey were drawn from among the office of the President or Prime Minister, the office of a Minister, the office of a Parliamentarian; employees of a ministry, ministerial department, or implementation agency; consultants/contractors working on World Bank supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials or staff; bilateral agencies; multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community- based organizations; the media; independent government institutions; trade unions; faith-based groups; academia, research institutes or think tanks; the judiciary branch; and local citizens.

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The Questionnaire consists of 8 Sections:

A. General Issues facing Zambia: Respondents were asked to indicate whether Zambia is headed in the right direction economically, politically, and socially, what they thought were the top three most important development priorities, and which areas would contribute most to reducing poverty and generating economic growth in Zambia.

B. Overall Attitudes toward the World Bank: Respondents were asked to rate their familiarity with the World Bank, the Bank's effectiveness in Zambia, Bank staff preparedness, the extent to which the Bank should seek to influence the global development agenda, their agreement with various statements regarding the Bank's work in Zambia, and the extent to which the Bank is an effective development partner. Respondents were also asked to indicate the sectoral areas on which it would be most productive for the Bank to focus its resources, the Bank's greatest values and greatest weaknesses in its work, the most and least effective instruments in helping to reduce poverty in Zambia, with which groups the Bank should work more, and to what reasons respondents attributed failed or slow reform efforts.

C. World Bank Effectiveness and Results: Respondents were asked to rate the extent to which the Bank's work helps achieve sustainable development results in Zambia, the Bank's level of effectiveness across thirty-six development areas, such as economic growth, and the extent to which the Bank meets Zambia's need for knowledge services and financial instruments.

D. The World Bank's Knowledge: Respondents were asked to indicate how frequently they consult Bank knowledge/research, the areas on which the Bank should focus its research efforts, and to rate the effectiveness and quality of the Bank's knowledge/research, including how significant of a contribution the Bank's knowledge and research make to development results, the technical quality of the Bank's knowledge and research, the effectiveness of the Bank providing linkage to non-Bank expertise, and the extent to which Zambia received value for money from fee-for-service products/services.

E. Working with the World Bank: Respondents were asked to rate their level of agreement with a series of statements regarding working with the Bank, such as the World Bank's "Safeguard Policy" requirements being reasonable, working with the World Bank increasing Zambia's institutional capacity, and the Bank disbursing funds promptly.

F. The Future Role of the World Bank in Zambia: Respondents were asked to rate how significant a role the Bank should play in Zambia's development in the near future and to indicate what the Bank should do to make itself of greater value in Zambia.

G. Communication and Information Sharing: Respondents were asked to indicate where they get information about economic and social development issues, how they prefer to receive information from the Bank, their access to the Internet, and their usage and evaluation of the Bank's website and PICs. Respondents were asked about their awareness of the Bank's Access to Information policy, past information requests from the Bank, and their level of agreement that they use more data from the World Bank as a result of the Bank's Open Data policy. Respondents were also asked their level of agreement that they know how to find information from the Bank and that the Bank is responsive to information requests.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the World Bank, their exposure to the Bank in Zambia, and their geographic location.

Response rate

A total of 312 stakeholders participated in the country survey (56%).

Abstract

Since 1991, the country has been utilizing cross-sectional sample data to monitor the well-being of the Zambian population, as was the case with the 1996 and 1998 LCMS surveys. However, in 2002/2003 a different methodology was employed to collect and analyze data. The survey was designed to collect data for a period of 12 months.

The Living Conditions Monitoring Survey IV (LCMSIV) was intended to highlight and monitor the living conditions of the Zambian society. The survey included a set of priority indicators on poverty and living conditions to be repeated regularly.

The main objective of the Living Conditions Monitoring Survey IV (LCMSIV) is to provide the basis for comparison of poverty estimates derived from cross-sectional survey data. In addition, the survey provides a basis on which to: - - Monitor the impact of government policies and donor support on the well being of the Zambian population. - Monitor poverty and its distribution in Zambia. - Provide various users with a set of reliable indicators against which to monitor development. - Identify vulnerable groups in society and enhance targeting in policy implementation. - Develop new weights for the Consumer Price Indices and generate information that is required to produce National Accounts Statistics.

Geographic coverage

The Living Conditions Monitoring Survey IV had a nationwide coverage on a sample basis. It covered both rural and urban areas in all the nine provinces. The survey was designed to provide data for each and every district in Zambia.

Analysis unit

• Households
• Individuals

Universe

This survey was carried out under the provisions of the Census and Statistics Act, Chapter 425 of the Laws of Zambia. All persons residing in Zambia except for foreign diplomats accredited to embassies and high commissions at the time of the survey were required by this act to provide the necessary information.

Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Stratification and Allocation The sampling frame used for LCMSIV survey was developed from the 2000 census of population and housing. The country is administratively demarcated into 9 provinces, which are further divided into 72 districts. The districts are further subdivided into 155 constituencies, which are also divided into wards. Wards consist of Census Supervisory Areas (CSA), which are further subdivided into Standard Enumeration areas (SEAs). For the purposes of this survey, SEAs constituted the ultimate Primary Sampling Units (PSUs).In order to have equal precision in the estimates in all the districts and at the same time take into account variation in the sizes of the district, the survey adopted the Square Root sample allocation method, (Lesli Kish, 1987). This approach offers a better compromise between equal and proportional allocation methods in terms of reliability of both combined and separate estimates. The allocation of the sample points (PSUs) to rural and urban strata was almost proportional.A sample size of about 1,048 SEAs and approximately 20,000 households was drawn.

Sample Selection The LCMS IV employed a two-stage stratified cluster sample design whereby during the first stage, 1048 SEAs were selected with Probability Proportional to Estimated Size (PPES). The size measure was taken from the frame developed from the 2000 census of population and housing. During the second stage, households were systematically selected from an enumeration area listing. The survey was designed to provide reliable estimates at district, provincial, rural/urban and national levels. The LCMS IV survey commenced by listing all the households in the selected SEAs. In the case of rural SEAs, households were stratified according to their agricultural activity status. Therefore, there were four explicit strata created in each rural SEA namely, the Small Scale Stratum (SSS), the Medium Scale Stratum (MSS), the Large Scale Stratum (LSS) and the Non-agricultural Stratum (NAS). For the purposes of the LCMSIV survey, about 7, 5 and 3 households were supposed to be selected from the SSS, MSS and NAS, respectively. The large scale households were selected on a 100 percent basis. The urban SEAs were implicitly stratified into low cost, medium cost and high cost areas according to CSO's and local authority classification of residential areas. About 15 and 25 households were sampled from rural and urban SEAs, respectively.However, the number of rural households selected in some cases exceeded the desired sample size of 15 households due to the 100 percent sampling of large scale farming households.The formulae used in selecting SEAs is provided in section 2.3.3 of the Survey Report in External Resources.

Selection of Households The selection of households from various strata was preceded by assigning fully responding households sampling serial numbers. The circular systematic sampling method was used to select households. The method assumes that households are arranged in a circle (G. Kalton, 1983) and the following relationship applies:

Let N = nk, Where: N = Total number of households assigned sampling serial numbers in a stratum n = Total desired sample size to be drawn from a stratum in an SEA k = The sampling interval in a given SEA calculated as k=N/n.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey. These are:- 1. The Listing Booklet - for listing all the households residing in the selected Standard Enumeration Areas (SEAs) 2. The Main questionnaire - for collecting detailed information on all household members.

Cleaning operations

The data from the LCMSIV survey was processed and analysed using the CSPRO and the Statistical Analysis System (SAS) softwares respectively. Data entry was done from all the provincial offices with 100 percent verification, whilst data cleaning and analysis was undertaken at CSO’s headquarters

Zambia ZM: Poverty Gap at $1.90 a Day: 2011 PPP: % data was reported at 29.500 % in 2015. This records a decrease from the previous number of 31.600 % for 2010. Zambia ZM: Poverty Gap at $1.90 a Day: 2011 PPP: % data is updated yearly, averaging 27.700 % from Dec 1991 (Median) to 2015, with 9 observations. The data reached an all-time high of 34.700 % in 1991 and a record low of 15.900 % in 1996. Zambia ZM: Poverty Gap at $1.90 a Day: 2011 PPP: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank: Poverty. Poverty gap at $1.90 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $1.90 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The Zambia COVID-19 Household Monitoring Survey monitors the economic and social impacts of and responses to the COVID-19 pandemic on households in terms of such topics as access to food staples, access to educational activities during school closures, employment dynamics, household incomes and livelihoods, income losses and coping strategies, and external assistance. The Round 1 dataset covers around 1,600 households and is representative of households with access to a mobile phone nationally and of Lusaka, urban excluding Lusaka, and rural areas.

Geographic coverage

The survey is representative of the population of Zambia that lives in an area of cellphone reception and has access to a cellular phone.

Kind of data

Sample survey data [ssd]

Sampling procedure

The HMS sample consists is drawn from a masterlist of phone numbers that have been collected during previous nationally-representative surveys in Zambia. The subsample of interviewed households is representative of those households with access to a phone, covering Lusaka, other urban and rural areas in all provinces of Zambia. The HMS achieved a sample size of 1,602 households, covering all provinces (as in the figure above) with 31 percent in Lusaka, 16 percent in Copperbelt and 10 percent of fewer in the other eight provinces.

Mode of data collection

Computer Assisted Telephone Interview [cati]