Abstract

The 2015 Zimbabwe Demographic and Health Survey (2015 ZDHS) is the sixth in a series of Demographic and Health Surveys conducted in Zimbabwe. As with prior surveys, the main objective of the 2015 ZDHS is to provide up-to-date information on fertility and child mortality levels; maternal mortality; fertility preferences and contraceptive use; utilization of maternal and child health services; women’s and children’s nutrition status; knowledge, attitudes and behaviours related to HIV/AIDS and other sexually transmitted diseases; and domestic violence. All women age 15-49 and all men age 15-54 who are usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed and for anaemia and HIV testing. All children age 6-59 months were eligible for anaemia testing, and children age 0-14 for HIV testing. In all households, height and weight measurements were recorded for children age 0-59 months, women age 15-49, and men age 15-54. The domestic violence module was administered to one selected woman selected in each of surveyed households.

The 2015 ZDHS sample is designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces (Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matebeleland South, Midlands, Masvingo, Harare, and Bulawayo).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-54

Universe

The survey covered all de jure household members resident in the household, all women age 15-49 years, men age 15-54 years and their young children.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2015 ZDHS sample was designed to yield representative information for most indicators for the country as a whole, for urban and rural areas, and for each of Zimbabwe’s ten provinces: Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare, and Bulawayo. The 2012 Zimbabwe Population Census was used as the sampling frame for the 2015 ZDHS.

Administratively, each province in Zimbabwe is divided into districts, and each district is divided into smaller administrative units called wards. During the 2012 Zimbabwe Population Census, each ward was subdivided into convenient areas, which are called census enumeration areas (EAs). The 2015 ZDHS sample was selected with a stratified, two-stage cluster design, with EAs as the sampling units for the first stage. The 2015 ZDHS sample included 400 EAs-166 in urban areas and 234 in rural areas.

The second stage of sampling included the listing exercises for all households in the survey sample. A complete listing of households was conducted for each of the 400 selected EAs in March 2015. Maps were drawn for each of the clusters and all private households were listed. The listing excluded institutional living arrangements such as army barracks, hospitals, police camps, and boarding schools. A representative sample of 11,196 households was selected for the 2015 ZDHS.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2015 ZDHS: - Household Questionnaire, - Woman’s Questionnaire, - Man’s Questionnaire, and - Biomarker Questionnaire.

These questionnaires were adapted from model survey instruments developed for The DHS Program to reflect the population and health issues relevant to Zimbabwe. Issues were identified at a series of meetings with various stakeholders from government ministries and agencies, research and training institutions, non-governmental organisations (NGOs), and development partners. In addition to English, the questionnaires were translated into two major languages, Shona and Ndebele. All four questionnaires were programmed into tablet computers to facilitate computer assisted personal interviewing (CAPI) for data collection, with the option to choose English, Shona, or Ndebele for each questionnaire.

Cleaning operations

CSPro was used for data editing, weighting, cleaning, and tabulation. In ZIMSTAT’s central office, data received from the supervisor’s tablets were registered and checked for inconsistencies and outliers. Data editing and cleaning included structure and internal consistency checks to ensure the completeness of work in the field. Any anomalies were communicated to the respective team through the technical team and the team supervisor. The corrected results were then re-sent to the central office.

Response rate

A total of 11,196 households were selected for inclusion in the 2015 ZDHS and of these, 10,657 were found to be occupied. A total of 10,534 households were successfully interviewed, yielding a response rate of 99 percent.

In the interviewed households, 10,351 women were identified as eligible for the individual interview, and 96 percent of them were successfully interviewed. For men, 9,132 were identified as eligible for interview, with 92 percent successfully interviewed.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2015 Zimbabwe DHS (ZDHS) to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2015 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2015 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF International. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y x , where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Nutritional status of children based on the NCHS/CDC/WHO International Reference Population - Completeness of information on siblings - Sibship size and sex ratio of siblings

Note: See detailed data quality tables in APPENDIX C of the report.

Abstract

The Zimbabwe Multiple Indicator Cluster Survey (MICS) was conducted between February and April in 2014 by the Zimbabwe National Statistics Agency (ZIMSTAT). Technical and financial support for the survey was coordinated by the United Nations Children’s Fund (UNICEF).

The MICS is designed to provide statistically sound and internationally comparable data essential for developing evidence-based policies and programmes and for monitoring progress towards national goals and global commitments, to enhance the welfare of women and children. Among these global commitments are those emanating from the World Fit for Children Declaration and Plan of Action, the goals of the United Nations General Assembly Special Session on HIV/AIDS (UNGASS), the Education for All Declaration (EFA) and the Millennium Development Goals (MDGs). The Zimbabwe MICS 2014 results are critical for final MDG reporting in 2015, and are expected to form part of the baseline data for the post-2015 era. The MICS plays a critical role in informing national policies such as the Zimbabwe Agenda for Sustainable Socio-Economic Transformation (ZimASSET) October 2013 to December 2018. The study covers the following areas: sample and survey methodology, sample coverage and the characteristics of households and respondents, child mortality, child nutrition, child health, water and sanitation, reproductive health, early childhood development, literacy and education, child protection, HIV and sexual behaviour, mass media and information and communication technology, and tobacco and alcohol use.

The Zimbabwe MICS is a nationally representative survey of 17,047 households, comprising 14,408 women in the 15-49 years age group, 7,914 men age 15-54 years and 10,223 children under 5 years of age. The sample allows for the estimation of some key indicators at the national, provincial and urban/rural levels. A two stage, stratified cluster sampling approach was used for the selection of the survey sample.

Geographic coverage

National

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all men aged between 15-54 years and all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the 2014 Zimbabwe MICS was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the ten provinces of the country namely: Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare and Bulawayo. Urban and rural areas in each of the ten provinces were defined as the sampling strata.

A two-stage, stratified sampling approach was used for the selection of the survey sample.

The sample size for the 2014 Zimbabwe MICS was 17,068 households. For the calculation of the sample size the key indicator used was the birth registration.

The number of households selected per enumeration area/cluster for the 2014 Zimbabwe MICS was determined as 25 households, based on a number of considerations, including the design effect, the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of sample households per cluster, it was calculated that 683 sample clusters would need to be selected nationwide.

Power allocation of the total sample size to the ten provinces was used. In total, 683 clusters were allocated to the ten provinces, with the final sample size calculated as 17 075 households (683 cluster*25 sample households per cluster). In each province, the clusters (primary sampling units) were distributed to the urban and rural domains proportionally to the number of urban and rural households in that province. The table below shows the allocation of clusters to the sampling strata. Of the 683 clusters, one cluster in Masvingo Province could not be covered due to floods which affected the Tokwe Mukosi area. Effectively, (682) clusters were covered during data collection.

The 2012 population census frame was used for the selection of clusters. Census enumeration areas were defined as primary sampling units (PSUs), and were selected from each of the sampling strata by using systematic sampling with probability proportional to size (PPS) sampling procedures the measure of size being the number of households in each enumeration area from the 2012 Population Census frame.

The first stage of sampling was thus completed by selecting the required number of enumeration areas from each of the ten provinces by urban and rural strata.

Since the sampling frame (the 2012 population census) was not up-to-date, a new listing of households was conducted in all the sample enumeration areas prior to the selection of households. Enumerators visited all of the selected enumeration areas and listed all households in each enumeration area. Two hundred enumerators were engaged in the listing operation and each enumerator covered a minimum of three clusters during the listing operation. The household listing operation involves three main steps: locating each cluster, preparing the location and sketch maps of each cluster, and the listing of all households found in each cluster. In some cases, segmentation was required for clusters with 300 or more households. The complete listing of large EAs is not cost effective. For that reason, large EAs were subdivided into smaller segments of which only one was selected and listed. Upon arrival in a large EA that may need segmentation, the enumerator first toured the EA and did a quick count to get the estimated number of households in the EA. The MICS standard recommends that each EA with 300 or more households should be subdivided into 2 or 3 segments. Where possible, the segments were roughly of equal size. However, it was important to adopt segment boundaries that were easily identifiable.

The second stage sampling procedure involved the selection of households after the listing operation. Lists of households and sketch maps were prepared by the listers/mappers in the field for each enumeration area. The households were then sequentially numbered from 1 to n (the total number of households in each enumeration area) at the provincial offices, where the selection of 25 households in each enumeration area was carried out using a household selection template.

The survey also had a questionnaire for men that was administered in every third household in each sampled cluster for interviews with all eligible men.

The sampling procedures are more fully described in "Zimbabwe Multiple Indicator Cluster Survey 2014 - Final Report" pp.333-335.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS5 model questionnaire with some modifications and additions. Household questionnaires were administered in each household, which collected various information on household members including sex, age and relationship. The household questionnaire includes household information panel, listing of household members, education, child discipline for children 1-14 years of age, household characteristics, water and sanitation, handwashing, indoor residual spraying, use of Insect Treated Nets (ITNs), and salt iodisation.

In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49, men age 15-49 and children under age five. The questionnaire was administered to the mother or primary caretaker of the child.

The women's questionnaire includes woman's information panel, her background characteristics, fertility, birth history, desire for last birth, maternal and newborn health, maternal mortality, postnatal care, marriage/union, illness symptoms, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, contraception, unmet need, sexual behaviour, and knowledge on HIV and AIDS.

The men's questionnaire includes man's information panel, his background characteristics, fertility, marriage/union, attitudes towards domestic violence, access to mass media and use of information communication technology, tobacco and alcohol use, sexual behaviour, circumcision and knowledge on HIV and AIDS.

The children's questionnaire includes children's characteristics, birth registration, early childhood development, breastfeeding and dietary intake, care of illness, immunisation and anthropometry.

The questionnaires are based on the MICS5 model questionnaire. From the MICS5 model English version, the questionnaires were customised and translated into Chichewa and Tumbuka and were pre-tested in Kasungu district during October 2013. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, observed the place for handwashing, and measured the weights and heights of children age under 5 years. Details and findings of these observations and measurements are provided in the respective sections of the report.

Cleaning operations

Data were entered into the computers using the Census and Surveys Processing System (CSPro) software package, Version 5.0. The data were entered on 32 desktop computers by 42 data entry operators and nine data entry supervisors. For quality assurance purposes, all

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.

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.

Abstract

the Zimbabwe Multiple Indicator Monitoring Survey (MIMS), conducted by the Zimbabwe National Statistics Agency (ZIMSTAT), formerly the Central Statistical Office (CSO), in April and May 2009, with financial and technical assistance from the United Nations Children’s Fund (UNICEF). The MIMS 2009 is a customised version of the third Multiple Indicator Cluster Survey1 (MICS3), which collects a broad array of valuable information on the situation of children and women in Zimbabwe. The MICS has been harmonized with other data collection efforts so that it produces internationally comparable information, which is the cornerstone of evidence-based decision making and formulation of policies, strategies and interventions, aimed at the improvement of the lives of children, women and other vulnerable groups.

The MICS uses three modular questionnaires that can be customized to fit national data needs. It measures key indicators on the following topics: nutrition, child mortality, child health, reproductive health, child development, education, child protection, HIV and AIDS, sexual behaviour and Orphans and Vulnerable Children (OVC). In the process of customizing MICS3 to MIMS, additional non-MICS questions on household expenditure, migration, and environmental assessment were added and some modules such as child development and sexual behaviour were excluded. However, the MIMS data collection instruments remained mostly the same as the global MICS instruments to ensure comparability with national data sets such as the Zimbabwe Demographic and Health Survey (ZDHS) as well as data from other countries.

The MIMS was based on the need to monitor progress towards goals and targets emanating from recent international agreements such as the Millennium Declaration which enshrines the Millennium Development Goals (MDGs), adopted by all 191 United Nations Member States in September 2000; the Plan of Action of A World Fit For Children (WFFC), adopted by 189 Member States at the United Nations Special Session on Children in May 2002; the Convention on the Rights of the Child, 1989; and the Convention on the Elimination of All Forms of Discrimination against Women, 1979 and the United Nations General Assembly Special Session (UNGASS), 2001 on the human immuno-deficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS). All these commitments build upon promises made by the international community at the 1990 World Summit for Children. In signing these international agreements, governments committed themselves to improving conditions for women and children and to monitor progress towards that end. UNICEF was assigned a supporting role in this task as highlighted in Appendix Box A.

The MIMS, a customized version of the MICS3, is part of a worldwide survey program, originally developed to measure progress towards an internationally agreed set of goals that emerged from the 1990 World Summit for Children.

Specifically, the MIMS 2009 objectives were to: • collect socio-economic data that will bring out an array of information on health, human capital and well-being of the population that can be used as a baseline for development interventions; • provide decision makers with evidence on children’s and women’s rights and other vulnerable groups in Zimbabwe; • serve as a monitoring tool on almost half of all the 2015 Millennium Development Goal (MDG) indicators, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; and • build capacity of national partners in data collection, compilation, processing, analysis and reporting.

Geographic coverage

The MIMS 2009 was designed to estimate indicators at the national level, for urban and rural areas,

Analysis unit

Household Women Children

Universe

the survey covered: - All household's members - All Women aged 15-49 years - All children under five years

Kind of data

Sample survey data [ssd]

Sampling procedure

The MIMS 2009 was designed to provide estimates on a large number of indicators on the health status of women, children and other vulnerable populations at the national level, for urban and rural areas, as well as for the 10 administrative provinces in Zimbabwe namely; Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare, and Bulawayo. Harare and Bulawayo provinces are predominantly urban provinces whilst the rest are predominantly rural.

The sampling frame for the MIMS was based on the 2002 Zimbabwe Master Sample (ZMS02), developed by the ZIMSTAT, then the CSO after the 2002 Population Census. With the exception of Harare and Bulawayo, each of the other eight provinces was stratified into four groups according to land use: (i) communal lands, (ii) large scale commercial farming areas (LSCFA), (iii) urban and semi-urban areas, and (iv) small scale commercial farming areas (SSCFA) and resettlement areas. Only one urban stratum each was formed for Harare and Bulawayo. There were a total of 34 strata for the whole country.

A representative probability sample of 12 500 households was selected for the MIMS 2009. The sample was selected in two stages with enumeration areas (EAs) as the first stage and households as the second stage sampling units. Each EA was delineated for the 2002 Population Census operations with well-defined boundaries identified on sketch maps, and the EA size was based on the expected workload for one interviewer. The EAs had an average of 100 households each, which was ideal for the survey listing operation.

In total the ZMS02 consists of 1 200 EAs selected with probability proportional to size (PPS), the size being the number of households enumerated in the 2002 Population Census. The MIMS EA selection was a systematic, one-stage operation, carried out independently for each of the 34 strata. In the second stage, a complete listing of households was conducted in the 500 sample EAs for the MIMS 2009 from 23 to 28 February 2009 concurrently for the 10 provinces. The list of households obtained was used as the frame for the second stage random systematic selection of 25 households from each sample EA. Within these selected households, all women aged 15-49 years identified were eligible for individual interviews. In addition, children under five years in the selected households were also identified and either their mothers or caretakers were interviewed on their behalf and children's measurements of weight, height and Mid-Upper- Arm Circumference (MUAC) taken and oedema checked.

The sample was stratified by province and land use and is not self-weighting. For reporting national level results, sample weights are used.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the survey as follows: • A household questionnaire was used to collect information on all de-jure and defacto household members, dwelling units, household characteristics and to identify eligible individuals for the women and children questionnaire interviews; • A woman’s questionnaire was administered in each selected household to all women aged 15-49 years; and • A questionnaire for children under five years was administered to mothers or caretakers of all children under five years living in the household.

The questionnaires were based on the MICS model questionnaire with modifications and additions. Even though the questionnaires were in English, they were translated into the various vernacular languages during interviews. Copies of the Zimbabwe MIMS questionnaires are provided in Appendix H. In addition to the administration of questionnaires, fieldwork teams measured the weights, heights and Mid-Upper-Arm Circumference (MUAC) and checked oedema of children age under 5 years.

Cleaning operations

DATA PROCESSING Data was entered on 56 microcomputers by 56 data entry operators, four questionnaire administrators and four data entry supervisors using the Census and Survey Processing (CSPro) system. In order to ensure quality control, all questionnaires were double entered and Survey Management Team as secondary editors complemented the efforts of the data entry supervisors to perform internal consistency checks. Procedures and standard programs developed under the global MICS3 Project were adapted to the MIMS questionnaire and used throughout the processing. One week data entry training was organized for all data entry operators from 27 April to 1 May, 2009. Data entry began on 5 May two weeks after fieldwork had started and the two activities ran concurrently thereafter. Data entry was completed on 24 June, 2009 and the last ten days included secondary editing. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software and the model program syntax and tabulation plans were customized for the MIMS.

QUALITY CONTROL Various quality control measures were put in place to ensure collection and dissemination of high quality data. Some of the controls used included:

Training: All interviewers were trained at one central location and this ensured that the same information and understanding of the survey objectives, instruments and filed operations were shared amongst them resulting in consistency of definitions thus ensuring collection of reliable information.

Field teams supervision: Effective office backup at the ZIMSTAT, then the CSO, head office during the data collection period enabled swift decision making in terms of handling any field work errors. A massive field presence for monitoring was mounted during the first three weeks of the

Abstract

The MIMS 2009 is a customised version of the third Multiple Indicator Cluster Survey1 (MICS3), which collects a broad array of valuable information on the situation of children and women in Zimbabwe. The MICS has been harmonized with other data collection efforts so that it produces internationally comparable information, which is the cornerstone of evidence-based decision making and formulation of policies, strategies and interventions, aimed at the improvement of the lives of children, women and other vulnerable groups.

The MICS uses three modular questionnaires that can be customized to fit national data needs. It measures key indicators on the following topics: nutrition, child mortality, child health, reproductive health, child development, education, child protection, HIV and AIDS, sexual behaviour and Orphans and Vulnerable Children (OVC). In the process of customizing MICS3 to MIMS, additional non-MICS questions on household expenditure, migration, and environmental assessment were added and some modules such as child development and sexual behaviour were excluded. However, the MIMS data collection instruments remained mostly the same as the global MICS instruments to ensure comparability with national data sets such as the Zimbabwe Demographic and Health Survey (ZDHS) as well as data from other countries.

The MIMS was based on the need to monitor progress towards goals and targets emanating from recent international agreements such as the Millennium Declaration which enshrines the Millennium Development Goals (MDGs), adopted by all 191 United Nations Member States in September 2000; the Plan of Action of A World Fit For Children (WFFC), adopted by 189 Member States at the United Nations Special Session on Children in May 2002; the Convention on the Rights of the Child, 1989; and the Convention on the Elimination of All Forms of Discrimination against Women, 1979 and the United Nations General Assembly Special Session (UNGASS), 2001 on the human immuno-deficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS). All these commitments build upon promises made by the international community at the 1990 World Summit for Children. In signing these international agreements, governments committed themselves to improving conditions for women and children and to monitor progress towards that end.

Specifically, the MIMS 2009 objectives were to: • collect socio-economic data that will bring out an array of information on health, human capital and well-being of the population that can be used as a baseline for development interventions; • provide decision makers with evidence on children’s and women’s rights and other vulnerable groups in Zimbabwe; • serve as a monitoring tool on almost half of all the 2015 Millennium Development Goal (MDG) indicators, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; and • build capacity of national partners in data collection, compilation, processing, analysis and reporting.

Geographic coverage

National

Analysis unit

• Households
• Children under five years
• Women aged 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The MIMS 2009 was designed to provide estimates on a large number of indicators on the health status of women, children and other vulnerable populations at the national level, for urban and rural areas, as well as for the 10 administrative provinces in Zimbabwe namely; Manicaland, Mashonaland Central, Mashonaland East, Mashonaland West, Matabeleland North, Matabeleland South, Midlands, Masvingo, Harare, and Bulawayo. Harare and Bulawayo provinces are predominantly urban provinces whilst the rest are predominantly rural. The sampling frame for the MIMS was based on the 2002 Zimbabwe Master Sample (ZMS02),developed by the ZIMSTAT, then the CSO after the 2002 Population Census. With the exception of Harare and Bulawayo, each of the other eight provinces was stratified into four groups according to land use: (i) communal lands, (ii) large scale commercial farming areas (LSCFA), (iii) urban and semi-urban areas, and (iv) small scale commercial farming areas (SSCFA) and resettlement areas. Only one urban stratum each was formed for Harare and Bulawayo. There were a total of 34 strata for the whole country.

A representative probability sample of 12 500 households was selected for the MIMS 2009. The sample was selected in two stages with enumeration areas (EAs) as the first stage and households as the second stage sampling units. Each EA was delineated for the 2002 Population Census operations with well-defined boundaries identified on sketch maps, and the EA size was based on the expected workload for one interviewer. The EAs had an average of 100 households each, which was ideal for the survey listing operation.

In total the ZMS02 consists of 1 200 EAs selected with probability proportional to size (PPS), the size being the number of households enumerated in the 2002 Population Census. The MIMS EA selection was a systematic, one-stage operation, carried out independently for each of the 34 strata. In the second stage, a complete listing of households was conducted in the 500 sample EAs for the MIMS 2009 from 23 to 28 February 2009 concurrently for the 10 provinces. The list of households obtained was used as the frame for the second stage random systematic selection of 25 households from each sample EA. Within these selected households, all women aged 15-49 years identified were eligible for individual interviews. In addition, children under five years in the selected households were also identified and either their mothers or caretakers were interviewed on their behalf and children's measurements of weight, height and Mid-Upper-Arm Circumference (MUAC) taken and oedema checked.

The sample was stratified by province and land use and is not self-weighting. For reporting national level results, sample weights are used.

Note: Detailed sample design description can be found in Appendix B of the 2009 Zimbabwe MIMS final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the survey as follows: • A household questionnaire -- was used to collect information on all de-jure and defacto household members, dwelling units, household characteristics and to identify eligible individuals for the women and children questionnaire interviews; • A woman’s questionnaire -- was administered in each selected household to all women aged 15-49 years; and • A questionnaire for children under five years -- was administered to mothers or caretakers of all children under five years living in the household.

The questionnaires were based on the MICS model questionnaire with modifications and additions. Even though the questionnaires were in English, they were translated into the various vernacular languages during interviews.

PRE-TEST The MIMS questionnaires were pre-tested from 9 to 17 March, 2009. Ten (10) teams were formed, made up of a supervisor and five interviewers each for the pretest, after they were trained on the questionnaires. The pre-test training was conducted during the same period, for 92 participants, with 7 participants coming from each of Zimbabwe’s 10 provinces (including the provincial supervisor). The remainder were from the ZIMSTAT, then the CSO, Survey Management Team (SMT), UNICEF and the Steering and Technical Committee members who facilitated the training sessions. A pre-test was conducted in three selected localities (2 urban and 1 rural) in Harare and Mashonaland East provinces to test the entirety of survey procedures. Based on the results of the pre-test, further modifications were made to the wording and flow of the questionnaires.

Cleaning operations

Data was entered on 56 microcomputers by 56 data entry operators, four questionnaire administrators and four data entry supervisors using the Census and Survey Processing (CSPro) system. In order to ensure quality control, all questionnaires were double entered and Survey Management Team as secondary editors complemented the efforts of the data entry supervisors to perform internal consistency checks. Procedures and standard programs developed under the global MICS3 Project were adapted to the MIMS questionnaire and used throughout the processing. One week data entry training was organized for all data entry operators from 27 April to 1 May, 2009. Data entry began on 5 May two weeks after fieldwork had started and the two activities ran concurrently thereafter. Data entry was completed on 24 June, 2009 and the last ten days included secondary editing. Data were analyzed using the Statistical Package for Social Sciences (SPSS) software and the model program syntax and tabulation plans were customized for the MIMS.

Sampling error estimates

The sample of respondents selected in the Zimbabwe Multiple Indicator Monitoring Survey is only one of the samples that could have been selected from the same population, using the same design and size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey results.

Calculation of Sampling Errors The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc).

Consumer Price Index CPI in Zimbabwe increased to 187.94 points in June from 187.42 points in May of 2025. This dataset provides - Zimbabwe Consumer Price Index Cpi- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Consumer Price Index in Zimbabwe increased 0.30 percent in June of 2025 over the previous month. This dataset provides - Zimbabwe Inflation Rate MoM- actual values, historical data, forecast, chart, statistics, economic calendar and news.