Identification
idno
ZWE_2011_PICES_v01_M_v01_A_SUF
Title
Poverty Income Consumption and Expenditure Survey 2011
Subtitle
Scientific Use File
Country
| Name | Country code |
|---|---|
| Zimbabwe | ZWE |
Abstract
The Poverty Income, Consumption, and Expenditure Survey (PICES) is the main data source for the compilation of national accounts aggregates and also provides information on living conditions, poverty levels and weights for the consumer price index (CPI).
The objectives of the survey are to provide data to enable:
· estimation of private consumption expenditure and disposable income of the household sector;
· compilation of weights for the Consumer Price Index (CPI);
· compilation of the production account of the agricultural sector for households in communal lands, small scale commercial farms, large scale commercial farms and resettlement areas;
· measurement of inequality and poverty;
· the study of income/expenditure disparities among socio-economic groups
· estimation of the contribution of the informal sector to GDP in Zimbabwe
· estimation of the size of household transfer incomes within and outside the country;
· the carrying out of market research by analysts
The objectives of the survey are to provide data to enable:
· estimation of private consumption expenditure and disposable income of the household sector;
· compilation of weights for the Consumer Price Index (CPI);
· compilation of the production account of the agricultural sector for households in communal lands, small scale commercial farms, large scale commercial farms and resettlement areas;
· measurement of inequality and poverty;
· the study of income/expenditure disparities among socio-economic groups
· estimation of the contribution of the informal sector to GDP in Zimbabwe
· estimation of the size of household transfer incomes within and outside the country;
· the carrying out of market research by analysts
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household
Version
Version number
v01 Edited, anonymous dataset for public distribution.
Anonymization details provided in anonymization report provided as Related Materials.
Anonymization details provided in anonymization report provided as Related Materials.
Version Date
2017-11-15
Coverage
Geographic Coverage
National Coverage
Geographic Unit
Province
District
Rural
Urban
District
Rural
Urban
Unit of Analysis
Household
Universe
Population can be enumerated using two methods: a "de jure" and a "de facto" population count. A " de jure" count is the enumeration of persons, who usually reside in a given place, whereas a "de facto" count is the enumeration of persons physically present at a specified place. For this survey we adopt the "de jure" concept. The usual members may be present or temporarily absent. We are thus concerned about the de jure household. A household is a single person or a group of people who usually live, cook and eat together, they may be related or not.
Producers and sponsors
Authoring entity/Primary investigators
| Agency Name | Affiliation |
|---|---|
| Zimbabwe National Statistical Agency | Ministry of Finance and Economic Development |
Producers
| Name | Role |
|---|---|
| United Nations Development Programme | Technical Support |
| United Nations Children’s Fund | Technical Support |
| African Capacity Building Foundation | Technical Support |
| African Development Bank | Technical Support |
| Department for International Development | Technical Support |
| United States Agency for International Development- Strategic Economic Research and Analysis | Technical Support |
| World Bank | Technical Support |
| Ministry of Labour and Social Services | Technical Support |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| United Nations Development Programme | UNDP |
| United Nations Children’s Fund | UNICEF |
| Department for International Development | DFID |
| African Capacity Building Foundation | ACBF |
| African Development Bank | AfDB |
| Government of Zimbabwe | GoZ |
Sampling
Sampling Procedure
This 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 and 31,248 households were selected with Probability Proportional to Size (PPS), the measure of size being the number of households enumerated in the 2002 Population Census. Additional details on sampling are available in chapter 1 the PICES report provided under the Related Materials section.
Response Rate
The number of EA's which were successfully interviewed in the 12 months of the study was 2,220 giving a coverage response rate of 99.5 percent. 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. Based on a total of 29,765 households with fully completed questionnaires the response rate calculated using the original sample is 95.3 percent.
Weighting
In order for the sample estimates from the PICES 2011/12 to be representative of the population, it was necessary to multiply the data by a sampling weight, or expansion factor. The basic weight for each sample household would be equal to the inverse of its probability of selection (calculated by multiplying the probabilities at each sampling stage). Additional details on weighting are provided in Section 1.7 of the survey report.
Data Collection
Dates of Data Collection (YYYY/MM/DD)
| Start date | End date |
|---|---|
| 2011-06-01 | 2012-05-31 |
Mode of data collection
Face-to-face [f2f]
Supervision
In order to ensure quality of data the Head Office and Provincial Supervisors put the following quality control measures in place:
Head Office Supervisors
· Carryout spot checks and ensure work is undertaken properly
· Ensure all project work is going according to schedule
· Listen to some selected interviews to check if questions were being asked correctly
· Resolve any challenges encountered in the province promptly
· Document challenges faced in the field and the corresponding solutions used
· Ensure appropriate and required quantities of materials are directed to; and reach the desired destination and are properly utilized
· Re-interview selected households as quality control
· Check that the households were completing the daily record books (DRBs) provided by enumerators
· Ensure that all field staff have received their monthly field allowances
· Write field reports which are used as monitoring tools
Provincial Supervisors
· See to it that enumerators are performing their work according to instructions
· Ensure that enumerators go to all selected households; and verify cases of non-contact
· Re-interview selected households to ensure complete enumeration
· Check each questionnaire for completeness and consistency before submitting the questionnaires to the Head Office
· Ensure that all questionnaires are submitted to Head Office on time
· Carry-out the day-to-day supervision of enumerators' work
· Liaise with Head Office to discuss any technical or logistics problems encountered in the field
· Ensure that enumerators are paid their field allowances
Head Office Supervisors
· Carryout spot checks and ensure work is undertaken properly
· Ensure all project work is going according to schedule
· Listen to some selected interviews to check if questions were being asked correctly
· Resolve any challenges encountered in the province promptly
· Document challenges faced in the field and the corresponding solutions used
· Ensure appropriate and required quantities of materials are directed to; and reach the desired destination and are properly utilized
· Re-interview selected households as quality control
· Check that the households were completing the daily record books (DRBs) provided by enumerators
· Ensure that all field staff have received their monthly field allowances
· Write field reports which are used as monitoring tools
Provincial Supervisors
· See to it that enumerators are performing their work according to instructions
· Ensure that enumerators go to all selected households; and verify cases of non-contact
· Re-interview selected households to ensure complete enumeration
· Check each questionnaire for completeness and consistency before submitting the questionnaires to the Head Office
· Ensure that all questionnaires are submitted to Head Office on time
· Carry-out the day-to-day supervision of enumerators' work
· Liaise with Head Office to discuss any technical or logistics problems encountered in the field
· Ensure that enumerators are paid their field allowances
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Zimbabwe National Statistical Agency | ZIMSTAT | Ministry of Finance and Economic Development |
Data Processing
Cleaning Operations
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 received 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 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 an errors or inconsistencies in a process known as “online editing”.
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 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 an errors or inconsistencies in a process known as “online editing”.
Data access
Contact
| Name | URI | |
|---|---|---|
| Zimbabwe National Statistical Agency | info@zimstat.co.zw | www.zimstat.co.zw |
Conditions
- Licensed datasets, accessible under conditions
Citation requirement
Use of the dataset must be acknowledged using the following citation :
Author's calculation based on the anonymized data set: Zimbabwe National Statistics Agency, Poverty Income Consumption and Expenditure Survey (PICES 2011).Scientific Use File Ref: ZWE_2011_PICES_v01_M._v01_A_SUF Data downloaded from ZIMSTAT Data Archive. www.zimstat.co.zw on [date].
Author's calculation based on the anonymized data set: Zimbabwe National Statistics Agency, Poverty Income Consumption and Expenditure Survey (PICES 2011).Scientific Use File Ref: ZWE_2011_PICES_v01_M._v01_A_SUF Data downloaded from ZIMSTAT Data Archive. www.zimstat.co.zw on [date].
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
Contact
| Name | URI | |
|---|---|---|
| Zimbabwe National Statistical Agency | info@zimstat.co.zw | www.zimstat.co.zw |
Metadata production
Document ID
DDI_ZWE_2011_PICES_v01_M_v01_A_SUF
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Zimbabwe National Statistics Agency | ZIMSTAT | Ministry of Finance and Economic Development | Documentation of the DDI |
Date of Production
2017-11-15
Document version
Version 1 (November, 2017)