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    Home / Food and Agriculture Microdata Catalogue / AGRICULTURAL-SURVEYS / CIV_1986-1987_LSS-W2_V01_EN_M_V01_A_OCS
agricultural-surveys

Living Standards Survey 1986-1987, Wave 2 Panel

Côte d'Ivoire, 1986 - 1987
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Reference ID
CIV_1986-1987_LSS-W2_v01_EN_M_v01_A_OCS
Producer(s)
Direction de la Statistique
Collections
Agricultural Surveys
Metadata
Documentation in PDF DDI/XML JSON
Created on
Oct 23, 2020
Last modified
Nov 08, 2022
Page views
67126
Downloads
397
  • Study Description
  • Data Dictionary
  • Downloads
  • Get Microdata
  • Data files
  • COMM86
  • COTERAIN
  • F00A
  • F00B
  • F00C
  • F01A
  • F01B
  • F02
  • F02A
  • F02B1
  • F02B2
  • F03A1
  • F03A2
  • F03B
  • F04
  • F05A
  • F05B1
  • F05B2
  • F05B3
  • F05B4
  • F05C1
  • F05C2
  • F05D
  • F05E1
  • F05E2
  • F05E3
  • F05E4
  • F05F
  • F05G1
  • F05G2
  • F05H
  • F06
  • F07
  • F08
  • F09A1
  • F09A2
  • F09B
  • F09C
  • F09D1A
  • F09D1B
  • F09D1C
  • F09D2A
  • F09D2B
  • F09D2C
  • F09D3A
  • F09D3B
  • F09D4A
  • F09D4B
  • F09D4C
  • F09D5
  • F09E
  • F09F
  • F09G
  • F09H
  • F09I
  • F09J
  • F09K
  • F10A
  • F10B
  • F10C
  • F11A
  • F11B
  • F11C
  • F11D
  • F12A
  • F12B
  • F13A
  • F13B
  • F13C
  • F14A
  • F14B
  • F15A
  • F15B
  • F15C
  • F16
  • F16A
  • F16B
  • F17
  • HHEXP86
  • HHINC86
  • HLTHADM
  • INSPECT
  • PRICE86
  • PRIMARY
  • 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
  • WEIGHT86

Data Dictionary

Data file Cases Variables
COMM86
Community-level data
57 241
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
F00A
Section 0
1722 50
F00B
Section 0
23942 16
F00C
Section 0
1601 36
F01A
Household Roster
13867 21
F01B
Household Roster
12911 21
F02
Housing
1601 52
F02A
Housing
1599 14
F02B1
Housing
1601 28
F02B2
Housing
1601 18
F03A1
Education
10729 22
F03A2
Education
2882 18
F03B
Education
3529 14
F04
Health
12910 24
F05A
Employment
9871 23
F05B1
Employment
4826 21
F05B2
Employment
610 17
F05B3
Employment
609 20
F05B4
Employment
609 17
F05C1
Employment
237 21
F05C2
Employment
7 15
F05D
Employment
4830 17
F05E1
Employment
5715 20
F05E2
Employment
91 17
F05E3
Employment
91 20
F05E4
Employment
91 16
F05F
Employment
5716 12
F05G1
Employment
431 19
F05G2
Employment
432 15
F05H
Employment
9872 13
F06
Migration
6699 16
F07
ID of Round 2 Respondents
1601 25
F08
Housing Characteristics
1597 10
F09A1
Agriculture
1019 16
F09A2
Agriculture
1018 18
F09B
Agriculture
7591 18
F09C
Agriculture
2887 11
F09D1A
Agriculture
502 10
F09D1B
Agriculture
228 10
F09D1C
Agriculture
24 9
F09D2A
Agriculture
381 10
F09D2B
Agriculture
152 8
F09D2C
Agriculture
292 10
F09D3A
Agriculture
5 8
F09D3B
Agriculture
1015 10
F09D4A
Agriculture
41 9
F09D4B
Agriculture
389 9
F09D4C
Agriculture
1372 7
F09D5
Agriculture
266 8
F09E
Agriculture
307 11
F09F
Agriculture
1191 17
F09G
Agriculture
21 6
F09H
Agriculture
676 8
F09I
Agriculture
126 7
F09J
Agriculture
1017 10
F09K
Agriculture
298 15
F10A
Non-Farm Self-Employment
744 38
F10B
Non-Farm Self-Employment
1808 10
F10C
Non-Farm Self-Employment
978 7
F11A
Expenditures and Inventory of Durable Goods
5827 6
F11B
Expenditures and Inventory of Durable Goods
22217 8
F11C
Expenditures and Inventory of Durable Goods
4519 9
F11D
Expenditures and Inventory of Durable Goods
1117 10
F12A
Food Expenses and Consumption of Home Production
26321 11
F12B
Food Expenses and Consumption of Home Production
6570 9
F13A
Fertility
1495 8
F13B
Fertility
6206 14
F13C
Fertility
1492 19
F14A
Other Income
1272 6
F14B
Other Income
575 10
F15A
Savings
1598 8
F15B
Savings
789 22
F15C
Savings
1558 16
F16
Anthropometrics
13154 21
F16A
Anthropometrics
13110 13
F16B
Anthropometrics
6042 13
F17
ID of Panel Households
6257 13
HHEXP86
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
HHINC86
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.
1598 15
HLTHADM
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
PRICE86
Price data
71 125
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
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
WEIGHT86
CILSS Corrective Weights Dataset
1600 20
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