Actual value and historical data chart for Zimbabwe Poverty Headcount Ratio At National Poverty Line Percent Of Population

Historical dataset showing Zimbabwe poverty rate by year from 2011 to 2019.

Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data was reported at 46.500 % in 2011. Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data is updated yearly, averaging 46.500 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Urban: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Poverty. Urban poverty headcount ratio is the percentage of the urban population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

49.20 (%) in 2019. Population below $1.9 a day is the percentage of the population living on less than $1.9 a day at 2005 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.

Abstract

The Poverty, Income, Consumption, and Expenditure Survey 2017 is the main data source for the compilation of the informal sector, living conditions, poverty levels, and weights for the Consumer Price Index (CPI). The survey is based on a sample of 32,256 households, representative at Province and District Levels.

The objectives of the survey are to: - Estimate private consumption expenditure and disposable income of the household sector - Compile the production account of the agricultural sector - Study income/expenditure disparities among socio-economic groups - Estimate the contribution of the informal sector to GDP in Zimbabwe - Estimate the size of household transfer incomes within and outside the country - Calculate weights for the Consumer Price Index (CPI) - Calculate the poverty line, measure the poverty rate and inequality - Provide data useful to formulate national policies for social welfare programmes - Obtain data for poverty mapping - Obtain data useful in measuring the demographic dividend for Zimbabwe

Geographic coverage

• National Coverage: 62 administrative districts of Zimbabwe
• Rural and Urban areas
• Land-use sectors: Communal Lands (CL), Small Scale Commercial Farms (SSCF), Large Scale Commercial Farms (LSCF), Resettlement Areas (includes Old Resettlement Areas (ORA), A1 Farms and A2 Farms), Urban Council Areas (UCA), Administrative Centres (AC), and Growth Point (GP) and Other Urban Areas (OUA), e.g. Services Center and Mines.

Analysis unit

• Individuals
• Households

Universe

The sample is representative of the whole population of Zimbabwe living in private households. The population living in collective households or in institutions such as military barracks, prisons and hospitals are excluded from the sampling frame.

Kind of data

Sample survey data [ssd]

Sampling procedure

At the first sampling stage, the sample EAs for the PICES 2017 are selected within each stratum (administrative district) using random systematic sampling with Probability Proportional to Size (PPS) from the ordered list of EAs in the sampling frame. The measure of size for each EA are based on the total number of households identified in the 2012 Population Census sampling frame. The EAs within each district are ordered first by rural and urban codes, land-use sector, ward and EA number. This provides implicit land-use and geographic stratification of the sampling frame within each district, and ensures a proportional allocation of the sample to the urban and rural areas of each district.The Complex Samples module of the SAS software is used for selecting the sample EAs systematically with PPS within each stratum at the first stage. The module uses the “SURVEY SELECT” sampling procedure.

At the second sampling stage, a random systematic sample of 14 households are selected with equal probability from the listing of each sample EA. Reserve households are selected for replacements. The reason why the replacement of non interview households are considered was to maintain the effective sample size and enumerator workload in each sample EA. Four households are selected for possible replacement, and thus a total of 18 households are selected from each sample EA. A systematic subsample of 4 households are then selected from the 18 households, and the remaining 14 sample households are considered the original sample for the survey. A spreadsheet is developed for selecting the 14 sample households and 4 reserve households for possible replacement in each sample EA. This spreadsheet includes items for the identification of the sample EA, and formulas for the systematic selection of households once the total number of households listed has been entered.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The PICES 2017 data entry is conducted by the ZIMSTAT Data Entry Unit using the CSPro software to enter the data. Data entry was done from January 2018 to June 2018. Data is captured twice by different people for purposes of verification. Data from the daily record books (the household food consumption diaries) have been entered from July to November 2018. SAS and STATA software is used for data processing. Data cleaning is done at all stages i.e. during data entry and data processing to check for the consistency of the data. Tables are then generated for use in report writing.

Response rate

Out of a total of 32,256 sampled households, a total of 31,195 households successfully completed interviews. This gives a response rate of 96.7 percent of the sampled households.

Sampling error estimates

The standard error, or square root of the variance, is used to measure the sampling error, although it may also include a small variable part of the non-sampling error. The variance estimator should take into account the different aspects of the sample design, such as the stratification and clustering. Programs available for calculating the variances for survey data from stratified multi stage sample designs such as the PICES 2017 include STATA and the Complex Samples module of SPSS as well as SAS and Wesvar. All these software packages use an ultimate cluster (linearized Taylor series) variance estimator. The Complex Samples module of STATA is used with the PICES 2017 data to produce the sampling errors.

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.

Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: % of Population data was reported at 70.900 % in 2001. Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: % of Population data is updated yearly, averaging 70.900 % from Dec 2001 (Median) to 2001, with 1 observations. Zimbabwe ZW: 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 Zimbabwe – Table ZW.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.

Poverty gap at $3.2 a day of Zimbabwe shot up by 16.14% from 25.4 % in 2017 to 29.5 % in 2019. Since the 47.67% jump in 2017, poverty gap at $3.2 a day soared by 16.14% in 2019. Poverty gap at $3.20 a day (2011 PPP) is the mean shortfall in income or consumption from the poverty line $3.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

This dataset for Zimbabwe combines preprocessed data from two data sources to create a rich source of information that can be used to develop a detailed understanding of poverty in the country.

Content

Demographic & Health Surveys Preprocessed Data

The dataset contains preprocessed data from the DHS for Zimbabwe. There are five main data files:

1. Household data
2. Household Member data
3. Births data
4. Cluster information
5. Geographic information (shapefile)

The first three files contain all the features required for a complete calculation of the Multidimensional Poverty Index. The household member and births data both contain reference IDs that can be used to join them to a particular household in the household datafile. The cluster file contains information required to link each household to a particular cluster, which in turn can be associated with geographic location information.

For detailed descriptions of the features available, refer to the DHS Recode Manual.

For details on how the preprocessed data was obtained, refer to Part III of my submission for the Kiva Challenge https://www.kaggle.com/taniaj/kiva-crowdfunding-targeting-poverty-sub-nat .

Financial Inclusion Insights Survey Preprocessed Data

The dataset also contains preprocessed data from the FII Survey for Zimbabwe. It contains features relevant for developing a financial deprivation indicator, such as whether the respondent has a formal bank account, whether they have formal savings and whether they have access to formal borrowing services.

For detailed descriptions of the features available, refer to the documentation.

For details on how the preprocessed data was obtained, refer to Part IV of my submission for the Kiva Challenge https://www.kaggle.com/taniaj/kiva-crowdfunding-adding-a-financial-dimension .

Other data

In addition to the main datafiles, there are a number of "_sjoin" files, which are intermediate steps in my kernel, where a spatial join was run locally and saved to be read back in due partly to sjoin not working on Kaggle servers, partly to save time.

Terms of Use

Please refer to the following pages for the terms of use:

1. DHS Program Terms of Use
2. Intermedia Terms of Use

Acknowledgements

The original data was provided by:

1. The Demographic & Health Surveys Program, USAID
2. The Financial Inclusion Insights Program, Intermedia

Inspiration

This dataset was added for use in the Data Science for Good: Kiva Crowdfunding challenge

Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data was reported at 84.300 % in 2011. Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data is updated yearly, averaging 84.300 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: Poverty Headcount Ratio at National Poverty Lines: Rural: % of Rural Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zimbabwe – Table ZW.World Bank.WDI: Poverty. Rural poverty headcount ratio is the percentage of the rural population living below the national poverty lines.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.

49.20 (النسبة المئوية) in 2019. Population below $1.9 a day is the percentage of the population living on less than $1.9 a day at 2005 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.

Time series data for the statistic Poverty_Headcount_Ratio_at_1.90USD_a_Day and country Zimbabwe. Indicator Definition:Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 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.

Zimbabwe ZW: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data was reported at 74.000 % in 2011. Zimbabwe ZW: Poverty Headcount Ratio at $5.50 a Day: 2011 PPP: % of Population data is updated yearly, averaging 74.000 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: 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 Zimbabwe – Table ZW.World Bank.WDI: Poverty. Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. The aggregated numbers for low- and middle-income countries correspond to the totals of 6 regions in PovcalNet, which include low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia). See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

Abstract

The Income, Consumption and Expenditure Survey is the main data source for the compilation of national accounts aggregates. The main objectives of the 2011/2012 PICES were to provide data on: Poverty; Income distribution of the population; Consumption level of the population; Private consumption; Consumer Price Index (CPI) weights; Living conditions of the population; Production account of agriculture (Communal Lands Small Scale Commercial Farms, Resettlement Areas, A1 and A2 farms and Large Scale Commercial Farms).

Geographic coverage

National

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2002 Zimbabwe Population Census Master Sample frame (ZMS202) provided an area sampling frame for the 2011/12 PICES. The survey was based on a sample of 31,248 households which is representative at province and district levels. The sample design entailed two stages: selection of Enumeration Areas (EAs) as the first stage and selection of households in these EAs as the second stage. In total 2,232 EAs were selected with Probability Proportional to Size (PPS), the measure of size being the number of households enumerated in the 2002 Population Census. Finally the number of each of the EAs which were successfully interviewed in the 12 months of the study was 2,220 giving a covering response rate of 99.5 percent. The sample is representative of all the population in Zimbabwe residing in private households. The population living in institutions such as military barracks, prisons and hospitals was excluded from the sampling frame.

Stratification In order to increase the efficiency of the sample design for PICES 2010/11, it was important to divide the sample design for PICES 2011/12 it was important to divide the sampling frame of EAs into strata which are as homogeneous as possible. At the first sampling stage the sample EAs are selected independently within each explicit stratum. The nature of the stratification depended on the most important characteristics measured in the surveym as well as the domains of analysis. The strata was made consistent with the geographic disaggregation used in the survey tables.

The first level of stratification corresponded to the 60 administrative districts of Zimbabwe, which are the geographic domains of analysis defined for the PICES. The rural and urban areas are domains at the national level. Some of the administrative districts are completely rural or urban, while most districts have a combination of rural and urban EAs. Since many districts have relatively few urban sample EAs, it would not be effective to use explicit urban and rural stratification within each district. Instead, the sampling frame of EAs for each district was sorted first by the rural/urban code in order to provide implicit stratification. Given that the sample EAs were selected systematically with Probabilty Proportional to Size (PPS), this provided a proportional allocation of the sample within each district by rural and urban areas. The sampling frame includes codes for land-use sectors, which can also be used for implicit stratification. The following land-use sextors have been identified:

1- Communal land 2- Small scale commercial farming area 3- Large scale commercial farming area 4- Resettlement area 5- Urban council area 6- Administrative centres (districts) 7- Growth Point 8- Other Urban Area, e.g. Service Centres and Mines 9- State Land, e.g. National Parks, Safari Areas

Sections 1.4 - 1.6 of the survey report (provided as external resources) provide more information on Sample size and allocation, Sample selection and Systematic selection of EAs.

Sampling deviation

Out of a total of 30,838 households interviewed 29,765 questionnaires were fully completed. Partly completed questionnaires were excluded from the analysis as they would distort average incomes and expenditures.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

PICES 2011/2012 data was captured by the ZIMSTAT data entry unit and CSPro was used to develop data entry programmes. About 80 people were involved in data processing each month from December 2011 to the end of July 2012. These members of staff worked overtime on average for 20 days in a month. Data was captured twice by different people for purposes of verification. Statistical Analysis System (SAS) was used for data processing programmes. Data cleaning was done at all stages i.e. data entry and data processing to check for the consistency of the data.

Quality Control Measures Used During Data Processing

Data processing involved coding and editing of the questionnaires and data entry. The main reason why data processing was started early was to ensure that data processing is started whilst data collection was in progress. This enabled field staff to be informed of the quality of data collection whilst they were still in the field. It was also found necessary that any queries on the data could be resolved whilst the field staff remembered what transpired. This was also deemed necessary because the number of questionnaires reveived could be checked promptly and discrepancies on the questionnaires received and those expected would be investigated immediately and resolved.

During data processing one member of staff was given 4 batches to be completed in six days. About 80 ZIMSTAT staff members were requested to work outside normal business hours on workdays and on Saturdays. The first two days were for initial entry while the other two days were for verification entry. Two persons exchanged questionnaires during the verification stage. The third stage was to check for differences between the two entries and any errors in initial entry were corrected at that stage. A clean file was then set aside to be copied by programmers at the end of each data processing exercise.

Control sheets were used for monitoring the movement of questionnaires from one person to another during the editing and data processing stage. Any errors made during the data entry were corrected and all data capture operators were informed of these errors to avoid the same errors being repeated. Furthermore, as part of quality control, the data entry programme had inbuilt quality control programmes such as the skip patterns of the questionnaire and the automatic refusal if an unknown identification code (Geocode) or inconsistent code was entered. Data Entry Supervisors also made spot checks to see work being entered while a Statistical Officer was placed in each of the data entry pools to correct any errors or inconsistencies in a process known as "online editing".

In order to check the quality of data processing ZIMSTAT staff began to generate tables to do validity checks using Population Census data for 2002 and other surveys such as Zimbabwe Demographic and Health Survey (ZDHS 2010-11). The Finscope Zimbabwe 2011 Survey Results were also used in validating the data. The validation exercise was done for both the 6 months data and the 12 months data and any deviations from the norm were investigated. An audit of the questionnaires received and processed was also done and any discrepancies were investigated and resolved. ZIMSTAT also compared the geocodes sampled and the geocodes with processed data and any differences were also corrected. As a quality control measure, a Sampling Consultant was engaged to work with ZIMSTAT PICES team to check and review the PICES weights for the 6 months data and 12 months data respectively.

Response rate

Based on a total of 29,765 households with fully completed questionnaires the response rate calculated using the original sample is 95.3 percent.

Before analysis was done it was essential to know the total number of questionnaires that were returned by the provinces. A total of 30,838 interviews were conducted and these included partially completed questionnaires. After removing the partually completed questionnaires the number of households which were successfully interviewed in the study were 29,756, giving a response rateof 95.3 percent based on the initial sample of 31,248 households. The households with partially completed questionnaires were left out from the analysis as they would distort averages for variables such as income and expenditures. The response rates were highest in Manicaland Province which had 97.9 percent, followed by Masvingo 97.1 percent. Harare province and Bulawayo province had the lowest response rates of 82.8 percent and 85.6 percent respectively. The main reason for these low response rates in Harare and Bulawayo is a large number of households who are not found at home, refusals and relocation of households to other areas within the month of the survey. This was prevalent particularly in dwelling units occupied by lodgers. The number of partly completed questionnaires was also high in urban areas. In terms of enumeration area coverage, a total of 2,220 EAs were enumerated out of a sample total of 2,232 EAs and this represented a coverage response rate of 99.5 percent of the total number of EAs sampled.

Zimbabwe ZW: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data was reported at 47.200 % in 2011. Zimbabwe ZW: Poverty Headcount Ratio at $3.20 a Day: 2011 PPP: % of Population data is updated yearly, averaging 47.200 % from Dec 2011 (Median) to 2011, with 1 observations. Zimbabwe ZW: 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 Zimbabwe – Table ZW.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.

Zimbabwe Multidimensional Poverty Headcount Ratio: UNDP: % of total population data was reported at 25.800 % in 2019. Zimbabwe Multidimensional Poverty Headcount Ratio: UNDP: % of total population data is updated yearly, averaging 25.800 % from Dec 2019 (Median) to 2019, with 1 observations. The data reached an all-time high of 25.800 % in 2019 and a record low of 25.800 % in 2019. Zimbabwe 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 Zimbabwe – Table ZW.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/);;

Stata dataset used to explore relationship between socio-economic position and HIV status among young rural Zimbabwean women and whether different socio-economic domains (asset wealth; ability to afford essential items; and food sufficiency) are associated with HIV risk in different ways. See associated questionnaire.

Zimbabwe Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 42.400 % in 2019. This records an increase from the previous number of 39.900 % for 2017. Zimbabwe Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 41.150 % from Dec 2017 (Median) to 2019, with 2 observations. The data reached an all-time high of 42.400 % in 2019 and a record low of 39.900 % in 2017. Zimbabwe 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 Zimbabwe – Table ZW.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).

Income share held by third 20% of Zimbabwe slumped by 5.71% from 14.00 % in 2011 to 13.20 % in 2017. Since the 5.71% drop in 2017, income share held by third 20% remained constant by 0.00% in 2017. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

Abstract

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Zimbabwe or who observe activities related to social and economic development. The World Bank Group Country Opinion Survey will give the World Bank Group’s team that works in Zimbabwe, greater insight into how the Bank’s work is perceived. This is one tool the World Bank Group uses to assess the views of its stakeholders, and to develop more effective strategies that support development in Zimbabwe.

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

Geographic coverage

National coverage

Analysis unit

Stakeholders

Universe

Opinion leaders from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society.

Kind of data

Sample survey data [ssd]

Sampling procedure

In April-May 2014, 246 stakeholders of the World Bank Group in Zimbabwe were invited to provide their opinions on the World Bank Group's assistance to the country by participating in a country survey. Participants in the survey were drawn from the office of the President; the office of the Deputy President; government ministers; parliamentarians; government employees and civil servants; consultants/contractors working on World Bank Group-supported projects/ programs; project management units (PMUs) overseeing implementation of a project; local government officials; bilateral and multilateral agencies; private sector organizations; the financial sector/private banks; NGOs; community based organizations; the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; the judiciary branch; and other organizations. A total of 183 stakeholders participated in the survey.

Mode of data collection

Other [oth]

Research instrument

The questionnaire consists of 8 Sections:

A. General Issues Facing Zimbabwe: Respondents were asked to indicate whether Zimbabwe is headed in the right direction (socially and economically), what they thought were the top three most important development priorities in the country, which areas would contribute most to reducing poverty and generating economic growth in Zimbabwe, and how "shared prosperity" would be best achieved in Zimbabwe.

B. Overall Attitudes toward the World Bank Group (WBG): Respondents were asked to rate their familiarity with the WBG, the WBG's effectiveness in Zimbabwe, WBG staff preparedness to help Zimbabwe solve its development challenges, their agreement with various statements regarding the WBG's work, and the extent to which the WBG is an effective development partner. Respondents were asked to indicate the WBG's greatest values and weaknesses, the most effective instruments in helping reduce poverty in Zimbabwe, with which stakeholder groups the WBG should collaborate more, in which sectoral areas the WBG should focus most of its resources (financial and knowledge services), and to what reasons respondents attributed failed or slow reform efforts.

C. World Bank Group's Effectiveness and Results: Respondents were asked to rate the extent to which the WBG's work helps achieve development results in Zimbabwe, the extent to which the WBG meets Zimbabwe's needs for knowledge services and financial instruments, and the WBG's level of effectiveness across twenty-nine development areas, such as economic growth, private sector development, education, health, agriculture and rural development.

D. The World Bank Group's Knowledge Work and Activities: Respondents were asked to indicate how frequently they consult WBG's knowledge work and activities and to rate the effectiveness and quality of the WBG's knowledge, including how significant of a contribution it makes to development results and its technical quality.

E. Working with the World Bank Group: Respondents were asked to rate their level of agreement with a series of statements regarding working with the WBG, such as the WBG's "Safeguard Policy" requirements being reasonable, the WBG imposing reasonable conditions on its lending, disbursing funds promptly, and providing effective implementation support.

F. The Future Role of the World Bank Group in Zimbabwe: Respondents were asked to indicate what the WBG should do to make itself of greater value in Zimbabwe, which services the WBG should offer more of in the country, and which development areas would benefit most from WBG playing a leading role as compared to other donors.

G. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the WBG, and their usage and evaluation of the WBG's websites and social media channels. Respondents were also asked about their awareness of the WBG's Access to Information policy, past information requests from the WBG, and their level of agreement that they use more data from the WBG as a result of the WBG's Open Data policy.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the WBG, and their exposure to the WBG in Zimbabwe.

Response rate

74% response rate