Integrated Household Living Conditions Survey 2000-2001
Rwanda, 1999 - 2001
Created on
Apr 25, 2019
Last modified
Apr 25, 2019
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Identification
Survey ID Number
RWA_2000_EICV-I_v01_M
Title
Integrated Household Living Conditions Survey 2000-2001
Translated Title
Enquête Intégrale sur les Conditions de Vie des Ménages 2000-2001
Country
| Name | Country code |
|---|---|
| Rwanda | RWA |
Study type
Income/Expenditure/Household Survey [hh/ies]
Series Information
The Rwanda EICV is a standardized income and expenditure survey with a periodicity of 5 years. The surveys in the series are as follows:
EICV I 2000-2001
EICV II 2005
EICV III 2010-2011
EICV I 2000-2001
EICV II 2005
EICV III 2010-2011
Abstract
The Household Living Conditions Survey, also known as Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) in French, was conducted by the Statistics Department of the Ministry of Finance and Economic Planning. The survey was primarily intended to provide policy planners and decision-makers with basic data on household living standards in Rwanda.
In addition, the survey was to be used to:
- calculate weights for the Consumer Price Index and estimate final household consumption,
- measure the effect of macro-economic policies and projects on the conditions and living standards of the population,
- produce key indicators of household welfare in order to assist policy-makers and development partners to improve the design of their development strategy,
- identify policy target groups with a view to ensuring that state interventions are better targeted.
- provide information on the socio-economic characteristics of households with a view to setting up a socio-economic data base.
- carry out in-depth studies, for example on poverty, nutrition, housing conditions, etc,
- improve the national capability to conduct statistical surveys, however complex they may be.
In addition, the survey was to be used to:
- calculate weights for the Consumer Price Index and estimate final household consumption,
- measure the effect of macro-economic policies and projects on the conditions and living standards of the population,
- produce key indicators of household welfare in order to assist policy-makers and development partners to improve the design of their development strategy,
- identify policy target groups with a view to ensuring that state interventions are better targeted.
- provide information on the socio-economic characteristics of households with a view to setting up a socio-economic data base.
- carry out in-depth studies, for example on poverty, nutrition, housing conditions, etc,
- improve the national capability to conduct statistical surveys, however complex they may be.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
-Household
-Individual
-Commodity (for GDP computation)
-Individual
-Commodity (for GDP computation)
Version
Version Description
- v01: Edited, anonymous datasets
Version Date
2001-06-07
Version Notes
Note: An original version of the DDI was done after the survey and hosted on a NESSTAR server. Over the course of the evolution of the NISR the server license was dropped and the survey metadata stored on a CDROM. The original version of the documentation has since been distributed. This version was recreated.
Scope
Notes
The scope of the Integrated Household Living Conditions Survey inculdes:
- demographic and migration characteristics,
- education and health,
- employment and housing,
- agro-pastoral activities and own-produce consumption,
- household expenditure,
- non-agricultural economic activities,
- transfers,
- durable goods, access to credit and savings.
- demographic and migration characteristics,
- education and health,
- employment and housing,
- agro-pastoral activities and own-produce consumption,
- household expenditure,
- non-agricultural economic activities,
- transfers,
- durable goods, access to credit and savings.
Coverage
Geographic Coverage
National coverage with all 11 former provinces (now 5 major provinces) and the City of Kigali.
Geographic Unit
cell level
Universe
Household members (institutional and itinerant populations excluded)
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| National Institute of Statistics of Rwanda (NISR) | Government of Rwanda |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Department for Intenational Development | DIFD | Bilateral funding assistance |
| World Bank | WB | Financial assistance |
| United Nations for the Children | UNICEF | Financial assistance |
| United Nations for Development Program | UNDP | Financial assistance |
| ADB | ADB | Financial assistance |
Other Identifications/Acknowledgments
| Name | Affiliation | Role |
|---|---|---|
| Oxford Policy Management | DFID | International Technical Assistance |
| MINECOFIN | Government of Rwanda | Primary user of data (EDPRS) |
Sampling
Sampling Procedure
The sampling plan was drawn up with the technical support of the late Christopher Scott, Survey Consultant, during his mission in July 1997.
Constraints
The two main factors considered in designing the sampling plan were:
- the objectives of the survey,
- the fieldwork methodology given the available logistical resources.
For the survey one objective was determinant: the Government wanted statistically reliable results at the level of each province, Kigali city and the "other urban sector". Thus, the objective called for 13 domain of analysis. Experience of conducting this type of survey shows that a minimum sample of 500 households per domain of study is required for sound analyses.
Sample size
The sample size was therefore 6,450 households, with 1,170 households for urban areas and 5,280 households for rural areas.
Two stage sampling
A two stage stratified sample was used: sampling at area level and at household level.
Sampling base
*At the area level, the chosen sampling base ( or at the enumeration district) was the "cellule"in the rural areas and the zone in urban areas, since they are usually fairly homogeneous in size and are well demarcated.
Knowledge of the size of each cellule enabled the use of the classical method of sampling with probability proportional to size at the first stage. A list of all cellules including estimates of the number of households in each was compiled from information provided by the local authorities.
*For sampling at the household level, an up-dated list of households was prepared for each of the selected first stage cellule by carrying out a listing in each sampled cellule simultaneously but with a lag in data collection before or while collecting the data. Part of this operation was carried out in collaboration with the National Population Office (ONAPO) and the Food Security Research Project (FSRP) of MINAGRI.
Constraints
The two main factors considered in designing the sampling plan were:
- the objectives of the survey,
- the fieldwork methodology given the available logistical resources.
For the survey one objective was determinant: the Government wanted statistically reliable results at the level of each province, Kigali city and the "other urban sector". Thus, the objective called for 13 domain of analysis. Experience of conducting this type of survey shows that a minimum sample of 500 households per domain of study is required for sound analyses.
Sample size
The sample size was therefore 6,450 households, with 1,170 households for urban areas and 5,280 households for rural areas.
Two stage sampling
A two stage stratified sample was used: sampling at area level and at household level.
Sampling base
*At the area level, the chosen sampling base ( or at the enumeration district) was the "cellule"in the rural areas and the zone in urban areas, since they are usually fairly homogeneous in size and are well demarcated.
Knowledge of the size of each cellule enabled the use of the classical method of sampling with probability proportional to size at the first stage. A list of all cellules including estimates of the number of households in each was compiled from information provided by the local authorities.
*For sampling at the household level, an up-dated list of households was prepared for each of the selected first stage cellule by carrying out a listing in each sampled cellule simultaneously but with a lag in data collection before or while collecting the data. Part of this operation was carried out in collaboration with the National Population Office (ONAPO) and the Food Security Research Project (FSRP) of MINAGRI.
Response Rate
In the course of the survey, some households did not respond, for one reason or the other. Of 6,450 households 6,431 responded, giving a response rate of 99.7%. In the course of processing the data, an additional 11 questionnaires were rejected because they did not contain useable information, in particular in respect to expenditure and consumption. Hence, the analysis was based on 6,420 households, giving a coverage rate of 99.5% of the sample households.
Weighting
In order for the estimates from each survey to be representative at the national level, it is necessary to apply sampling weights to the survey data. The weights for the sample households were calculated as the inverse of the overall probability of selection, taking into account each sampling stage. Given the nature of the sample design and the new listing of households, the weights vary by sample ZD. An Excel spreadsheet with all the sampling frame information for the sample ZDs was used for calculating the weights, which were then attached to the corresponding records in the survey data files.
WEIGHTING
There are two kinds of weighting: spatial weighting and temporal weighting. Use of these methods enabled annual estimates to be obtained for the whole of the Rwandan population.
* Spatial weighting
Spatial weighting enables results relating to the sample to be extrapolated for the whole of the population for the same period. It was calculated using the inverse of the overall probability of selection of a particular household. The details of the theory for calculating the various probabilities are shown in Annex I.Starting from the overall probability formula Fhi=p1hi x p2hi where p1hi is the probability proportional to size of drawing cellule i in stratum h and p2hi is the conditional probability of drawing a household knowing that unit i of stratum h has been selected. The numbers 1 and 2 indicate the stage or level of sampling.Spatial weighting is given by the formula Whi=1/Fhi=Mhi/ahbhi where Mhi is the total number of households in unit i of stratum h and ah is the number of sample units in stratum h and bhi is the number of households surveyed in unit i of stratum h .
*Temporal weighting
Temporal weighting is intended to produce annual estimates of values relating to the survey period.Thus, the temporal weighting coefficient depends on the length of the collection period.By using CPTmj to designate the coefficient of temporal weighting of the variable ymj for household m, and Jmj to designate the number of collection days
Ymj=CPTmj x ymj or CPTmj=365/Jmj
Ymi being the annual value of the variable ymj for household m.
WEIGHTING
There are two kinds of weighting: spatial weighting and temporal weighting. Use of these methods enabled annual estimates to be obtained for the whole of the Rwandan population.
* Spatial weighting
Spatial weighting enables results relating to the sample to be extrapolated for the whole of the population for the same period. It was calculated using the inverse of the overall probability of selection of a particular household. The details of the theory for calculating the various probabilities are shown in Annex I.Starting from the overall probability formula Fhi=p1hi x p2hi where p1hi is the probability proportional to size of drawing cellule i in stratum h and p2hi is the conditional probability of drawing a household knowing that unit i of stratum h has been selected. The numbers 1 and 2 indicate the stage or level of sampling.Spatial weighting is given by the formula Whi=1/Fhi=Mhi/ahbhi where Mhi is the total number of households in unit i of stratum h and ah is the number of sample units in stratum h and bhi is the number of households surveyed in unit i of stratum h .
*Temporal weighting
Temporal weighting is intended to produce annual estimates of values relating to the survey period.Thus, the temporal weighting coefficient depends on the length of the collection period.By using CPTmj to designate the coefficient of temporal weighting of the variable ymj for household m, and Jmj to designate the number of collection days
Ymj=CPTmj x ymj or CPTmj=365/Jmj
Ymi being the annual value of the variable ymj for household m.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 1999-10-24 | 2000-12-24 | urban |
| 2000-07-19 | 2001-07-10 | rural |
Data Collection Mode
Face-to-face [f2f]
Supervision
Collection teams
Thirteen teams were assigned to the various provinces and, of those, three teams were assigned to urban areas. Each team was composed of:
- 1 area supervisor
- 1 controller
- 5 interviewers.
Training
Training of approximately 5 weeks was organised for all staff. It comprised a theoretical component delivered in the classroom and a practical component in the field in order to practise how to conduct interviews.
Thirteen teams were assigned to the various provinces and, of those, three teams were assigned to urban areas. Each team was composed of:
- 1 area supervisor
- 1 controller
- 5 interviewers.
Training
Training of approximately 5 weeks was organised for all staff. It comprised a theoretical component delivered in the classroom and a practical component in the field in order to practise how to conduct interviews.
Data Collection Notes
Reference period
The long and complex nature of the questionnaire was a determining factor in distributing the work over time. In effect, two of the modules comprise a long list of questions on products purchased and consumed. For frequently-consumed products, those answering the survey may have difficulty in remembering activities that took place more than three days previously.For the reference period, a period of 30 days was preferred in urban areas, in order to ensure that payday effect was included for each wage earner.
In rural areas, where wage earners are rare, it is less important to maintain the 30-day reference period. Thus, the reference period was brought down to 16 days.
Field interviews
The calendar year was divided into ten cycles and interviews were conducted all through the year.
In urban areas, the first collection cycle began on 24 October 1999 and the last collection cycle ended on 24 December 2000.In rural areas, collection began on 19 July 2000 and ended on 10 July 2001.
Visits to households
Within each cycle, data collection was organised into a number of visits to households:
- in urban areas, 11 visits at 3-day intervals,
- in rural areas, 8 visits at 2-day intervals.
At each visit, certain modules of the questionnaire had to be completed.
The long and complex nature of the questionnaire was a determining factor in distributing the work over time. In effect, two of the modules comprise a long list of questions on products purchased and consumed. For frequently-consumed products, those answering the survey may have difficulty in remembering activities that took place more than three days previously.For the reference period, a period of 30 days was preferred in urban areas, in order to ensure that payday effect was included for each wage earner.
In rural areas, where wage earners are rare, it is less important to maintain the 30-day reference period. Thus, the reference period was brought down to 16 days.
Field interviews
The calendar year was divided into ten cycles and interviews were conducted all through the year.
In urban areas, the first collection cycle began on 24 October 1999 and the last collection cycle ended on 24 December 2000.In rural areas, collection began on 19 July 2000 and ended on 10 July 2001.
Visits to households
Within each cycle, data collection was organised into a number of visits to households:
- in urban areas, 11 visits at 3-day intervals,
- in rural areas, 8 visits at 2-day intervals.
At each visit, certain modules of the questionnaire had to be completed.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| National Institute of Statistics, Rwanda | NISR | Government of Rwanda |
Questionnaires
Questionnaires
The questionnaires are published in French.
Three types of questionnaire were used in the field for data collection:
- the household questionnaire comprising of 12 modules divided in two parts, A and B.
- the community questionnaire for collecting data on economic and social infrastructures in the sample units in rural areas and
- a conversion form for non-standard units used by households.
Household questionnaires
Part A collects data on each member of the household. It covered the following areas:
- demographic and migration characteristics,
- education and health,
- employment and housing.
Part B deals with the economic activity of the household. It comprises of the following five modules:
- agro-pastoral activities and own-produce consumption,
- household expenditure,
- non-agricultural economic activities,
- transfers,
- durable goods, access to credit and savings.
Three types of questionnaire were used in the field for data collection:
- the household questionnaire comprising of 12 modules divided in two parts, A and B.
- the community questionnaire for collecting data on economic and social infrastructures in the sample units in rural areas and
- a conversion form for non-standard units used by households.
Household questionnaires
Part A collects data on each member of the household. It covered the following areas:
- demographic and migration characteristics,
- education and health,
- employment and housing.
Part B deals with the economic activity of the household. It comprises of the following five modules:
- agro-pastoral activities and own-produce consumption,
- household expenditure,
- non-agricultural economic activities,
- transfers,
- durable goods, access to credit and savings.
Data Processing
Data Editing
Data Editing (see external resource entilted: Final Data Processing Report)
Questionnaires were reviewd by the controller in the field before they were dispatched for data entry. A control sheet was provided to the contollers to assist in the process of manually editing the questionnaires. Questionnaire structures were verified when the questionnaires were checked in prior to data entry. Three contracted persons reviewed the questionnaire and filled in a form that served as a primary data control sheet. Automated data editing was largely done during the data entry phase (see "Other Data Processing" for details). Some batch edit programs were used to identify inconsistent data.
Data Imputation
Data iimputation was largely done during the analysis phase by analysts. However, a "structural" imputation on the microdata was required for the own consumption data. This was done to adjust for erroneous pricing when the unit for measuring own consumption was buckets. For more information, please refer to the SPSS su=yntax files orthe data processing report.
Primary Data Issues
Coding of products was based on sequential codes for each section.
Questionnaires were reviewd by the controller in the field before they were dispatched for data entry. A control sheet was provided to the contollers to assist in the process of manually editing the questionnaires. Questionnaire structures were verified when the questionnaires were checked in prior to data entry. Three contracted persons reviewed the questionnaire and filled in a form that served as a primary data control sheet. Automated data editing was largely done during the data entry phase (see "Other Data Processing" for details). Some batch edit programs were used to identify inconsistent data.
Data Imputation
Data iimputation was largely done during the analysis phase by analysts. However, a "structural" imputation on the microdata was required for the own consumption data. This was done to adjust for erroneous pricing when the unit for measuring own consumption was buckets. For more information, please refer to the SPSS su=yntax files orthe data processing report.
Primary Data Issues
Coding of products was based on sequential codes for each section.
Other Processing
Data processing
In the process of filling in the questionnaires and data entry, various types of error slipped into the data. Controls were carried out on a number of levels: in the field by the controllers and supervisors and at the Statistics Department after data entry.
More detailed checks and controls were carried out after data entry, since the process can itself introduce errors.
Data processing is a very important stage in a survey. This often-neglected phase is the cause of delays in the publication of the results.
In addition to corrections made at the time of data entry, data processing goes through the following 6 main stages:
- Exhaustivity control
This involved checking the use of identical geographical codes in various data files and verification that questionnaires had not been entred more than once or omitted.
- Consistency between variables
With the aid of absolute frequency tables, verification is made whether eligible respondents for all the questions replied and whether those not eligible did not in effect reply.
- Standardisation
Some quantitative variables were aggregated over the year before validation. Variables arising from local measurements were converted to the conventional measurement system.
- Re-coding
Certain continuous quantitative variables were divided into classes:
- Creation of derived variables
This involved variables (which are derived from other variables.) not in the questionnaire or the data dictionary
- Imputation of values
During processing, extreme values were encountered for some variables. These were confined to values that deviated more than three standard deviations from the mean. After verification, they were replaced by the mean value of the variable.
-IT programmes
A number of programming software and languages were used from capturing the data to preparing tables of results, inter alia IMPS, CS PRO, MS ACCESS, Visual Basic and COBOL, SPSS.
In the process of filling in the questionnaires and data entry, various types of error slipped into the data. Controls were carried out on a number of levels: in the field by the controllers and supervisors and at the Statistics Department after data entry.
More detailed checks and controls were carried out after data entry, since the process can itself introduce errors.
Data processing is a very important stage in a survey. This often-neglected phase is the cause of delays in the publication of the results.
In addition to corrections made at the time of data entry, data processing goes through the following 6 main stages:
- Exhaustivity control
This involved checking the use of identical geographical codes in various data files and verification that questionnaires had not been entred more than once or omitted.
- Consistency between variables
With the aid of absolute frequency tables, verification is made whether eligible respondents for all the questions replied and whether those not eligible did not in effect reply.
- Standardisation
Some quantitative variables were aggregated over the year before validation. Variables arising from local measurements were converted to the conventional measurement system.
- Re-coding
Certain continuous quantitative variables were divided into classes:
- Creation of derived variables
This involved variables (which are derived from other variables.) not in the questionnaire or the data dictionary
- Imputation of values
During processing, extreme values were encountered for some variables. These were confined to values that deviated more than three standard deviations from the mean. After verification, they were replaced by the mean value of the variable.
-IT programmes
A number of programming software and languages were used from capturing the data to preparing tables of results, inter alia IMPS, CS PRO, MS ACCESS, Visual Basic and COBOL, SPSS.
Data Appraisal
Estimates of Sampling Error
Given that the survey estimates are subject to sampling variability, it is important to calculate the sampling errors for the most important estimates from each survey. The sampling error is measured by the standard error, or square root of the variance of the estimate. The CENVAR software, a component of the Integrated Microcomputer Processing System (IMPS) developed by the U.S. Census Bureau, was used for tabulating the standard errors and other measures of precision, taking into account the stratification and clustering in the sample design. The CENVAR output tables show the value of the estimates, standard errors, coefficients of variation, 95 percent confidence intervals, design effects and number of observations. Given that the confidence intervals provide a user-friendly interpretation of the sampling variability, an annex was produced with tables showing the 95 percent confidence intervals for the most important estimates from the EICV1 and EICV2 data appearing in the preliminary report. These tables provide a quick conservative test to determine whether any difference between the EICV1 and EICV2 estimates is statistically significant.
The INSR was also provided with tables showing the full CENVAR results. The design effect is defined as the variance of an estimate based on the actual sample design divided by the corresponding variance based on a simple random sample of the same size; it is a measure of the relative efficiency of the sample design. In comparing the CENVAR results from EICV1 and EICV2, it was found that the design effects are generally lower for EICV2, indicating that the stratification used for this survey was very effective. Given that the EICV1 was based on an older sampling frame from the 1991 Rwanda Census, this also contributed to the higher design effects for the EICV1 estimates.
The INSR was also provided with tables showing the full CENVAR results. The design effect is defined as the variance of an estimate based on the actual sample design divided by the corresponding variance based on a simple random sample of the same size; it is a measure of the relative efficiency of the sample design. In comparing the CENVAR results from EICV1 and EICV2, it was found that the design effects are generally lower for EICV2, indicating that the stratification used for this survey was very effective. Given that the EICV1 was based on an older sampling frame from the 1991 Rwanda Census, this also contributed to the higher design effects for the EICV1 estimates.
Access policy
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economics Planning | info@statistics.gov.rw | www.statistics.gov.rw |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| National Institute of Statistics of Rwanda | Ministry of Finance and Economics Planning | info@statistics.gov.rw | www.statistics.gov.rw |
Confidentiality
Individual confidentiality and responses are secured by law. The current data set has provided only relevant levels of geographic disaggregation to the old provincial level. A district code is provided (new district) since there is a demand to examine results at this level. The identifying key for the household in not a geographic key.It is based on a sequential cluster number and sequential household number.
Citation requirements
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
National Institute of Statistics of Rwanda. Rwanda Integrated Household Living Conditions Survey (EICV) 2000-2001. Ref. RWA_2000_EICV-I_v01_M. Dataset downloaded from http://www.measuredhs.com on [date].
- the Identification of the Primary Investigator
- the title of the survey (including acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
National Institute of Statistics of Rwanda. Rwanda Integrated Household Living Conditions Survey (EICV) 2000-2001. Ref. RWA_2000_EICV-I_v01_M. Dataset downloaded from http://www.measuredhs.com 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.
Metadata production
DDI Document ID
DDI_RWA_2000_EICV-I_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| National Institute of Statistics of Rwanda | NISR | Ministry of Finance | Data and metadata producer and deposit |
| Department of International Development | DFID | British Government | Provided technical assistance for archiving the data set |
| Development Data Group | DECDG | The World Bank | Modification of the DDI |
Date of Metadata Production
2012-06-17
DDI Document version
Version 02 (May 2013): Adopted from "RWA-NISR-EICV-2001-v1.0" DDI, which was done by the National Institute of Statistics of Rwanda (NISR) and the Department of International Development (DFID). The second version is modified by the World Bank.