IHSN Survey Catalog
  • Home
  • Microdata Catalog
  • Citations
  • Login
    Login
    Home / Central Data Catalog / CIV_1988_EPAM_V01_M
central

Enquête Permanente Auprès des Ménages 1988-1989 (Wave 4 Panel)

Côte d'Ivoire, 1988 - 1989
Get Microdata
Reference ID
CIV_1988_EPAM_v01_M
Producer(s)
Direction de la Statistique
Metadata
DDI/XML JSON
Study website
Created on
Sep 29, 2011
Last modified
Mar 29, 2019
Page views
184429
Downloads
3961
  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • Related Publications
  • Data files
  • COMM88
  • COTERAIN
  • HHEXP88
  • HHINC88
  • HLTHADMIN
  • INSPECT
  • PRIMARY
  • SEC00A
  • SEC00B
  • SEC00C
  • SEC01A
  • SEC01B
  • SEC02A
  • SEC02B
  • SEC03A1
  • SEC03A2
  • SEC03B
  • SEC04
  • SEC05A
  • SEC05B1
  • SEC05B2
  • SEC05B3
  • SEC05B4
  • SEC05C1
  • SEC05C2
  • SEC05D
  • SEC05E1
  • SEC05E2
  • SEC05E3
  • SEC05E4
  • SEC05F
  • SEC05G1
  • SEC05G2
  • SEC05H
  • SEC06
  • SEC07
  • SEC08
  • SEC09A1
  • SEC09A2
  • SEC09B
  • SEC09C
  • SEC09D1A
  • SEC09D1B
  • SEC09D1C
  • SEC09D2A
  • SEC09D2B
  • SEC09D2C
  • SEC09D3A
  • SEC09D3B
  • SEC09D4A
  • SEC09D4B
  • SEC09D4C
  • SEC09D5
  • SEC09E
  • SEC09F
  • SEC09G
  • SEC09H
  • SEC09I
  • SEC09J
  • SEC09K
  • SEC10A
  • SEC10B
  • SEC10C
  • SEC11A
  • SEC11B
  • SEC11C
  • SEC11D
  • SEC12A
  • SEC12B
  • SEC13A
  • SEC13B
  • SEC13C
  • SEC14A
  • SEC14B
  • SEC15A
  • SEC15B
  • SEC15C
  • SEC16A
  • SEC16B
  • SEC17
  • SECOND
  • SET01
  • SET01IND
  • SET02
  • SET02IND
  • SET03
  • SET03IND
  • SET04
  • SET04IND
  • SET05
  • SET05IND
  • SET06
  • SET06IND
  • SET07
  • SET07IND
  • SET08
  • SET08IND
  • SET09
  • SET09IND
  • SET10
  • SET10IND
  • SET11
  • SET11IND
  • SET12
  • SET12IND
  • SET13
  • SET13IND
  • SET14
  • SET14IND
  • WEIGHT88

Data Dictionary

Data file Cases Variables
COMM88
Community-level data
29 213
COTERAIN
Rainfall data associated with CILSS clusters.

Rainfall data are available for the years 1974-1988 by weather `station'. Each weather station can be linked to the CILSS Clusters. Most CILSS clusters are not located in exactly the same place as the stations with which they are associated. In such cases, the CILSS cluster is linked with the nearest station. Rainfall measurements are millimeters.
522 15
HHEXP88
Household Expenditure Aggregates

The survey data contain all necessary information for the construction of a complete set of current accounts for each household. Since income and expenditure data are available in great detail throughout the questionnaire permitting the calculation of detailed income and expenditure aggregates, this enables, theoretically, the derivation of savings as a residual.

Given the complexity and detail involved in the different income and expenditure modules, it is possible to build household income and expenditure aggregates in different ways, each of which are legitimate but which may provide considerably different results. Thus, various researchers have constructed their own Income and Expenditure Aggregates using CILSS data. However, only one set of researchers constructed a complete set of income and expenditure aggregates for all four years of the CILSS (85-88), along with their sub-aggregate components, namely the research project "Poverty and the Social Dimensions of Structural Adjustment in Côte d'Ivoire" (RPO 675-26). Oh and Venkataraman (1992) document in detail all of those income and expenditure aggregates and sub-aggregates. The documentation includes data set names, documentation of procedures used to `clean' the data, clear the data of outliers (including information on the percentage of observations classified as outliers), and the summation procedures used to build up variables in the questionnaire into sub-aggregate level variables and finally into aggregates.

Since this set of aggregates is also the only one which is accompanied by documentation, it is the only dataset of aggregates formally available for public use. However, users should be cautioned that these data are cleared of outliers and therefore, researchers who want the presence of outliers in their data in the belief that they are meaningful, may not find this set of aggregates suitable.

Total Household Expenditure = Food Expenditure + Consumption of Home-Produced Food + Consumption of Home-Produced Non-Food Products + Other Expenditures + Paid Remittances + Wage Income in Kind
1600 11
HHINC88
Household Income Aggregates

The survey data contain all necessary information for the construction of a complete set of current accounts for each household. Since income and expenditure data are available in great detail throughout the questionnaire permitting the calculation of detailed income and expenditure aggregates, this enables, theoretically, the derivation of savings as a residual.

Given the complexity and detail involved in the different income and expenditure modules, it is possible to build household income and expenditure aggregates in different ways, each of which are legitimate but which may provide considerably different results. Thus, various researchers have constructed their own Income and Expenditure Aggregates using CILSS data. However, only one set of researchers constructed a complete set of income and expenditure aggregates for all four years of the CILSS (85-88), along with their sub-aggregate components, namely the research project "Poverty and the Social Dimensions of Structural Adjustment in Côte d'Ivoire" (RPO 675-26). Oh and Venkataraman (1992) document in detail all of those income and expenditure aggregates and sub-aggregates. The documentation includes data set names, documentation of procedures used to `clean' the data, clear the data of outliers (including information on the percentage of observations classified as outliers), and the summation procedures used to build up variables in the questionnaire into sub-aggregate level variables and finally into aggregates.

Since this set of aggregates is also the only one which is accompanied by documentation, it is the only dataset of aggregates formally available for public use. However, users should be cautioned that these data are cleared of outliers and therefore, researchers who want the presence of outliers in their data in the belief that they are meaningful, may not find this set of aggregates suitable.

Total Household Income = Wage Income + Farm Income - Depreciation of Farm Equipment + Non-Farm Income + Non-Farm Capital Asset Depreciation + Rental Income + Income from Scholarships + Income from Remittances + Other Income.
1599 15
HLTHADMIN
Health Facility Data from Administrative Sources. These data contain summary statistics on 311 (out of 329) health facilities located within the 200 clusters interviewed during the 4 years of the CILSS Household Survey. The data set contains information about every health facility in the same urban "commune" as a CILSS cluster as well as about each one located within a CILSS rural cluster. Facilities near a rural cluster but not located within them, are not included. In cases where no health facility data are available for a rural cluster, information about the nearest health facility to rura l clusters can be obtained from the CILSS community data or, for 1987 only, from the health facility questionnaires completed for that year.

The data for each facility were extracted from a publication of the Direction de la Planification et des Statistiques Sanitaires in the Ministry of Public Health and Population, entitled "Annales de la Santé, 1989", as part of the research project on "The Economic and Policy Determinants of Fertility in Sub-Saharan Africa."

The Health Facility dataset includes the following information: owners hip, number of beds, number of staff of different types (doctors, paramedics, etc) and types of services offered (maternity, pharmacy, radiology, pediatrics etc). The amount of information for each facility is limited to the amount of information available from the publication. The full range of information is available for hospitals, urban hospital centers, rural hospital centers and some private facilities in Abidjan. For dispensaries and maternities, little more is offered than the name and type of facility.

The variables for the types of services available are based on staffing lists, some of which disaggregate personnel by type of service. It is possible that some smaller facilities offered a service but did not have staffing lists that were disaggregated to reflect this. Thus, a "yes" answer to a service implies that the service was definitely provided; a "no" means that the service may or may not have been provided.
311 27
INSPECT
PRIMARY SCHOOL INSPECTORATE (Administrative) DATA
Data collected for each primary school inspectorate linked to a CILSS cluster include the number of schools, classrooms, teachers, students and female students in the inspectorate. The data are organized by cluster and year (1985-1988). A particular cluster may have more than one primary school inspectorate associated with it, depending on the year to which the data pertain. For example, the cluster of Arrah (031) belonged to the inspectorate of Bongouanou in 1985 and 1986; but in 1987, Arrah became a new inspectorate. Therefore, cluster 031 now belongs to this new inspectorate (Arrah). Note also that if a particular cluster does not belong to a commune, then the variable NAMCOMMU (name of the commune) will be missing and NUMCOMMU (number of the commune) will be set equal to 98.
515 18
PRIMARY
School Data from Administrative Sources. Primary school data contains information about the characteristics of primary schools nearest to or within each urban cluster; secondary school data contain similar information for secondary schools; and the inspectorate data contain information about the primary school inspectorate that covers each of the 200 clusters. These data were extracted from documents at the Côte d'Ivoire Ministry of Education and linked to each CILSS cluster as part of the data collected for the research project on "Economic and Policy Determinants of Fertility in Sub-Saharan Africa".

Data collected for the primary schools include the following information: ownership; whether the school has a library; whether there is housing available for the teachers; number of grades; number of classrooms; total number of students enrolled; and number of girls enrolled.

Only urban clusters are covered by the Primary and Secondary School Data Sets. Recall that for rural clusters, information about schools can be obtained from the CILSS Community Surveys. The Primary School Dataset contains 9055 observations and the Secondary School Dataset contains 1129 observations. The reasons for the larger than expected number of observations are primarily two-fold. First, data is gathered for up to three years (1986-88) for primary school and up to four years (1985-88) for secondary schools, per school. Secondly, all schools associated with a cluster — those located both within the cluster and nearby — are listed in the database.
9055 14
SEC00A
1666 48
SEC00B
24264 15
SEC00C
1602 35
SEC01A
Household Roster
10563 20
SEC01B
Household Roster
10126 20
SEC02A
Housing
1599 13
SEC02B
Housing
1616 44
SEC03A1
Education
8302 26
SEC03A2
Education
8239 20
SEC03B
Education
3414 13
SEC04
Health
10125 38
SEC05A
Employment
7608 22
SEC05B1
Employment
3802 20
SEC05B2
Employment
576 16
SEC05B3
Employment
576 19
SEC05B4
Employment
576 16
SEC05C1
Employment
209 20
SEC05C2
Employment
9 14
SEC05D
Employment
3803 16
SEC05E1
Employment
4175 19
SEC05E2
Employment
53 16
SEC05E3
Employment
53 19
SEC05E4
Employment
53 15
SEC05F
Employment
4176 11
SEC05G1
Employment
346 18
SEC05G2
Employment
347 14
SEC05H
Employment
7616 11
SEC06
Migration
8288 15
SEC07
ID of Round 2 Respondents
1599 24
SEC08
Housing Characteristics
1598 9
SEC09A1
Agriculture
936 15
SEC09A2
Agriculture
474 17
SEC09B
Agriculture
6252 17
SEC09C
Agriculture
2054 10
SEC09D1A
Agriculture
267 9
SEC09D1B
Agriculture
169 9
SEC09D1C
Agriculture
6 8
SEC09D2A
Agriculture
219 9
SEC09D2B
Agriculture
73 7
SEC09D2C
Agriculture
82 9
SEC09D3A
Agriculture
1 7
SEC09D3B
Agriculture
929 9
SEC09D4A
Agriculture
40 8
SEC09D4B
Agriculture
235 8
SEC09D4C
Agriculture
1624 6
SEC09D5
Agriculture
164 7
SEC09E
Agriculture
175 10
SEC09F
Agriculture
860 16
SEC09G
Agriculture
6 5
SEC09H
Agriculture
506 7
SEC09I
Agriculture
84 6
SEC09J
Agriculture
936 9
SEC09K
Agriculture
188 14
SEC10A
Non-Farm Self-Employment
664 39
SEC10B
Non-Farm Self-Employment
1620 9
SEC10C
Non-Farm Self-Employment
920 6
SEC11A
Expenditures and Inventory of Durable Goods
5469 5
SEC11B
Expenditures and Inventory of Durable Goods
19768 7
SEC11C
Expenditures and Inventory of Durable Goods
3479 8
SEC11D
Expenditures and Inventory of Durable Goods
711 9
SEC12A
Food Expenses and Consumption of Home Production
22648 10
SEC12B
Food Expenses and Consumption of Home Production
5613 8
SEC13A
Fertility
1353 7
SEC13B
Fertility
5021 13
SEC13C
Fertility
1354 24
SEC14A
Other Income
876 5
SEC14B
Other Income
495 9
SEC15A
Savings
1597 7
SEC15B
Savings
637 21
SEC15C
Savings
1579 15
SEC16A
Anthropometrics
10203 11
SEC16B
Anthropometrics
4259 12
SEC17
ID of Panel Households
5211 12
SECOND
School Data from Administrative Sources. Data collected for the secondary schools include the following information: ownership; whether the school has a library; whether there is housing available for the teachers; number of grades; number of classrooms; total number of students enrolled; and number of girls enrolled.

Only urban clusters are covered by the Primary and Secondary School Data Sets. Recall that for rural clusters, information about schools can be obtained from the CILSS Community Surveys. The Primary School Dataset contains 9055 observations and the Secondary School Dataset contains 1129 observations. The reasons for the larger than expected number of observations are primarily two-fold. First, data is gathered for up to three years (1986-88) for primary school and up to four years (1985-88) for secondary schools, per school. Secondly, all schools associated with a cluster - those located both within the cluster and nearby - are listed in the database.
1129 20
SET01
795 3
SET01IND
6537 3
SET02
79 3
SET02IND
448 3
SET03
714 3
SET03IND
5762 3
SET04
714 3
SET04IND
5736 3
SET05
86 3
SET05IND
495 3
SET06
107 3
SET06IND
769 3
SET07
693 3
SET07IND
5399 3
SET08
693 3
SET08IND
5099 3
SET09
107 3
SET09IND
671 3
SET10
99 3
SET10IND
573 3
SET11
701 3
SET11IND
4570 3
SET12
701 3
SET12IND
4577 3
SET13
99 3
SET13IND
523 3
SET14
801 3
SET14IND
4768 3
WEIGHT88
CILSS Corrective Weights Dataset
1600 20
Back to Catalog
IHSN Survey Catalog

© IHSN Survey Catalog, All Rights Reserved.