• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Copyright
 Foreword
 Table of Contents
 List of Tables
 Introduction
 Chapter 1: Subject areas for women...
 Chapter 2: Review of national...
 Chapter 3: Sub-national source...
 Chapter 4: Improving data...
 Chapter 5: Existing data bases
 Recommendations
 Tables
 References
 Other documents of interest
 Back Cover














Group Title: A Gender-disaggregated data base on human resources in agriculture : data requirement and availability
Title: A Gender-disaggregated data base on human resources in agriculture
CITATION THUMBNAILS PAGE IMAGE ZOOMABLE
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STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00084631/00001
 Material Information
Title: A Gender-disaggregated data base on human resources in agriculture data requirement and availability
Physical Description: 77 p. : ; 30 cm.
Language: English
Creator: Food and Agriculture Organization of the United Nations
Publisher: Food and Agriculture Organization of the United Nations
Place of Publication: Rome
Publication Date: 1993
 Subjects
Subject: Statistics, Agricultural -- Directories   ( ltcsh )
Women in development -- Statistics   ( lcsh )
Statistics, Agricultural -- Women   ( ltcsh )
Agricultural information systems -- Women   ( ltcsh )
Women in agriculture -- Statistics   ( lcsh )
Agriculture -- Women   ( lcsh )
Statistical theory and methods   ( ltcsh )
Genre: international intergovernmental publication   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 71-77).
 Record Information
Bibliographic ID: UF00084631
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 31272076

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Title Page
    Copyright
        Copyright
    Foreword
        Foreword
    Table of Contents
        Table of Contents 1
        Table of Contents 2
        Table of Contents 3
    List of Tables
        List of Tables
    Introduction
        Page 1
        Page 2
    Chapter 1: Subject areas for women in agriculture: data requirements and availability
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
    Chapter 2: Review of national sources
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
    Chapter 3: Sub-national sources
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
    Chapter 4: Improving data availability
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
    Chapter 5: Existing data bases
        Page 41
        Page 42
        Page 43
        Page 44
    Recommendations
        Page 45
        Page 46
        Page 47
        Page 48
    Tables
        Page 49
        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
        Page 70
    References
        Page 71
        Page 72
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
    Other documents of interest
        Page 78
    Back Cover
        Page 79
Full Text




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FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS
Rome, 1993






























































All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means, electronic, mechani-
cal, photocopying or otherwise, withoutthe prior permission of the copyright owner.
Applications for such permission, with a statement of the purpose and extent of the
reproduction, should be addressed to the Director, Publications Division, Food and
Agriculture Organization of the United Nations, Viale delle Terme di Caracalla,
00100 Rome, Italy.


FAO 1993


The designations employed and the presentation of material in this
publication do not imply the expression of any opinion whatsoever on
the part of the Food and Agriculture Organization of the United
Nations concerning the legal status of any country, territory, city or
area or of its authorities, or concerning the delimitation of its frontiers
or boundaries.










FOREWORD


For many years, the Food and Agriculture Organization of the United Nations
(FAO) has been undertaking activities to promote the role of women in agriculture and
rural development. The 1989 FAO Conference adopted the FAO Plan of Action for the
Integration of Women in Development to further intensify its efforts in such an important
field. One of the eight priorities indicated under the Plan of Action concerns the need to
improve and compile statistical data on the roles of women in agriculture and rural
development.

Under the guidance of the Women in Agricultural Production and Rural
Development Service (ESHW) and the Statistical Development Service (ESSS), a study
was carried out to determine data requirements, identify existing data and indicate the
strategy to collect those that are not currently available. The study also reviewed existing
data bases and addressed the technical and organizational aspects involved in the collection
of genderized data and the eventual development of gender-disaggregated data bases.

The publication represents one effort in the implementation of the Plan of Action,
which is intended to enhance the design of policies and programmes so that these will
address all aspects of women's roles in rural activities. Equally important is the
improvement of the formulation of agricultural development projects and programmes on
the basis of concrete and updated genderized statistical data in agriculture.







W.D. Maalouf J. Ay
Officer-in-Charge Director
Human Resources, Statistics Division
Institutions and Agrarian
Reform Division






Contents



Page

INTRODUCTION 1


Chapter 1
SUBJECT AREAS FOR WOMEN IN AGRICULTURE:
DATA REQUIREMENTS AND AVAILABILITY 3

INTRODUCTION 3

SUBJECT AREAS 3

DATA REQUIREMENTS AND AVAILABILITY 7



Chapter 2
REVIEW OF NATIONAL SOURCES 9

INTRODUCTION 9

FAO STATISTICAL SOURCES 9
Agricultural Census 10
Farm Management Surveys 12
Food Consumption Surveys 12
Special Compilations 13
FAO Administrative Records 14
WCARRD 14
Global Consultation on Extension 15
Estimates of the Agricultural Population 15

ILO STATISTICAL SOURCES 16
Labour 16
Household Income and Expenditure Surveys 19

UN STATISTICAL SOURCES 19
Population and Housing Censuses 19
Household Surveys 20
Special Compilations 21

UNESCO STATISTICAL SOURCES 21

WORLD BANK STATISTICAL SOURCES 22
Social Indicators 22
Social Dimensions of Adjustment 22









UNITED STATES BUREAU OF THE CENSUS STATISTICAL SOURCES 22

SELECTED REGIONAL SOURCES 23
Economic and Social Commission for Asia and the Pacific 23
FAO Regional Office for Asia and the Pacific 23
South Pacific Commission 23





Chapter 3
SUB-NATIONAL SOURCES 25

INTRODUCTION 25

DATA AVAILABILITY 25
Employment 26
Access to Resources 26
Productivity 26
Time-Use 27
Decision-Making 27
Nutrition 27
General 28

FARMING SYSTEMS 28

PEOPLE'S PARTICIPATION PROGRAMME 29





Chapter 4
IMPROVING DATA AVAILABILITY 31

INTRODUCTION 31

NATIONAL DATA 31
Available Data 31
Partially Available Data 33
Not Available Data 36

IMPROVEMENT OF SUB-NATIONAL DATA 37

OBTAINING EXISTING DATA FROM COUNTRIES 37

TRAINING FOR IMPROVED DATA COLLECTION 38
Training for National Statisticians 38
National Producer-User Collaboration 39






Chapter 5
EXISTING DATA BASES 41

INTRODUCTION 41

REVIEW OF EXISTING DATA BASES 41
AGROSTAT 41
WISTAT 42

THE RELATIONSHIP BETWEEN DATA BASES 42




RECOMMENDATIONS 45
GENERAL DATA COLLECTION 45
GENDER-DISAGGREGATION 45
RURAL/URBAN-DISAGGREGATION 46
OTHER DISAGGREGATION 46
OMISSIONS 47
COOPERATION 47
TRAINING 48



TABLES 49


REFERENCES 71


OTHER DOCUMENTS OF INTEREST







Tables



Table 1

General Areas Relevant to Women in Agriculture
Identified in the FAO Plan of Action on
Integration of Women in Development and WCAARD


1. Agricultural Employment

1.1 Women's participation in agricultural labour force
1.2 Women's participation in market-oriented agriculture
1.3 Women's participation in fisheries
1.4 Women's participation in forestry
1.5 Women's participation in subsistence agriculture
1.6 Women's participation as occasional, temporary and permanent labour


2. Access to Means of Production

2.1 Women's access to land
2.2 Women's access to water/irrigation
2.3 Women's access to drainage
2.4 Women's access to credit and revolving funds
2.5 Women's access to agricultural inputs (seeds, pesticides, fertilizers)
2.6 Women's access to technology (tools and machinery)
2.7 Women's access to extension services
2.8 Women's access to transport


3. Membership of Agricultural/Rural Organizations

3.1 Women's membership of cooperatives
3.2 Women's membership of credit unions
3.3 Women's membership of marketing organizations
3.4 Women's membership of labour unions
3.5 Women's membership of marketing organizations
3.6 Women's membership of community organizations


4. Productivity

4.1 Women's productivity


5. Time-Use


5.1 Gender division of labour






Introduction


International concern about the need for adequate data on the situation of women was first
expressed in 1975 in the World Plan of Action adopted by the World Conference of International
Women's Year. The need for data on women is grounded in the UN goal of equality between
women and men: appropriate data are needed in the measurement of the situation of women, in
the examination of the effects of economic growth on women and in the formulation, monitoring
and evaluation of policies and development programmes aimed at achieving equality. Since 1975,
various resolutions and recommendations have led to significant improvements in the availability
of relevant data on women and to the development of gender-disaggregated data bases. Where data
on women in agriculture are concerned, however, the availability and accessibility of data remains
rather poor.

The work reported here specifically concerns gender-disaggregated data on human resources
in agriculture. Its basis and justification is laid out in the FAO Plan of Action for Integration of
Women in Development (FAO, 1988a). The Plan points to the need for statistical data on the role
of women in agriculture, and proposes the establishment of a global data base on women in
agriculture and rural development. The first stage of the establishment of this data base was
undertaken in 1989 and comprised a feasibility study (Scott, 1989). The present study takes this
task a step further by determining data requirements, identifying which data are available, and by
indicating how to obtain those data that are not currently available. The study also reviews
existing data bases and addresses the technical and organizational aspects involved in the
establishment of the data base.

Chapter 1 defines the subject areas of relevance to women in agriculture and details data
requirements. A list of required data is compiled, based on the FAO Plan of Action for Integration
of Women in Development and WCARRD. This required list is assessed in terms of data
availability, based on a review of national and sub-national data sources, presented in Chapters
2 and 3 respectively. The data reviewed, both national and sub-national, are essentially those that
are available in FAO. (Many seemingly potential sources are not covered because, in fact, they
totally lack data relevant to women in agriculture: these include the 1989 State of Food and
Agriculture, the World Health Statistics Annual and the FAO Production Yearbook.)

The review of national data sources in Chapter 2 concentrates on the gender issues involved
in the collection of data on women in agriculture. Thus, definitions and concepts have been
examined, as have guidelines on methodological and technical issues. This has enabled the
identification of practices that could be changed in order to improve coverage of women in
agriculture. This review therefore forms an important basis for determining how to obtain data
that are not currently available (discussed in Chapter 4). The findings of this review are
documented in detail in Chapter 2 and are intended as a practical reference for further work on
establishing a gender-disaggregated data base on human resources in agriculture. The review of
sub-national data sources in Chapter 3 is less detailed than that of national sources, since the
volume of available sub-national data is much smaller and more difficult to locate.

The distinction between national and sub-national data sources was used by Scott (1989) and
is followed here. By 'national' data is meant data produced at the national level, that is for the
entire country; 'sub-national' data are those produced for a part of a country, be it a region or a
village. The distinction derives principally from the different data uses, collection methods and
institutions involved. National data are obtained through censuses or sample surveys conducted
by national statistical offices, while sub-national data are usually obtained through projects and








research, the main purpose of which is to carry out certain studies and activities, with data
collection as a secondary activity. National data have the advantage of being replicable over time,
allowing for the monitoring of trends, and are useful for the identification of areas of need, but
sub-national data are appropriate for studying such needs in detail. It should be noted that in some
cases, sub-national data are not strictly statistically representative of the area that they cover,
though where they cover an entire community, they are representative of that community. On the
other hand, national sources are sometimes less likely to represent women in agriculture
adequately than are sub-national sources, because of methodological and conceptual problems in
national data collection. To give two examples, very small holdings (often operated by women)
are omitted from agricultural censuses, and women's work is not generally regarded as economic.
Both national and sub-national sources of data are, of course, important for understanding the role
and contribution of women in agriculture, and the two should be regarded as complementary.
Some data are best produced at the national level, whilst others are only possible or meaningful
at the sub-national level. Many data can, of course, be obtained at both levels.

Chapter 4 discusses how to improve data availability, including obtaining data from countries
and training. The need for improvement applies even to those data that are regarded as available,
since many national data are available in theory but suffer in practice from low country coverage
and methodological and conceptual problems often leading to poor reliability. In addition, some
data (notably national data on employment) are not reliable, again leading in practice to poor
availability. Two major areas for the improvement of national data on women in agriculture are
gender-disaggregation and rural/urban-disaggregation or disaggregation according to
agricultural/non-agricultural or farm/non-farm households or by agro-ecological zone. These refer
to situations where the necessary data are collected but not tabulated. Other desirable
disaggregation includes disaggregation by age and socio-economic group and the detailed
classification of employment variables. Further areas for improvement are data on the landless and
on access to resources and services.

Where data are not generally available from national sources, it is often the case that they are
best obtained from sub-national sources. These sources, however, are relatively rare and cover
specific locations, project activities and cultures so that, whilst being valuable in themselves, they
may not easily be generalized or applied to other situations. There is thus a need to increase the
volume of sub-national data in general and to emphasize the importance of collecting data in all
possible situations. For FAO, the most efficient way to achieve this is through project activities.

Chapter 5 reviews existing data bases, including the FAO data base, AGROSTAT, and the
UN Statistical Office data base on women, WISTAT, and their relationship to the data base on
women in agriculture.

The Consultant, Ms. H. Booth, was hired by the Human Resources, Institutions and Agrarian
Reform Division (ESH) and Statistics Division (ESS) for the preparation of this document. She
is a free-lance consultant with specialization in social statistics, population and development
planning. The opinions expressed in this document are those of the author and do not necessarily
reflect the position of the Organization.






Chapter 1


Subject areas for women in agriculture:
data requirements and availability


INTRODUCTION

This chapter identifies the broad subject areas for which data on women in agriculture are
required and discusses each in terms of data requirements and their availability in FAO. Details
of the sources of data are given in Chapter 3 for national data and Chapter 4 for sub-national data.

Data on women are required for effective project formulation, development planning, and in
monitoring and evaluating the status of women. The lack of relevant data has been identified as
a major obstacle to the integration of women in development planning and to effective project
formulation. This is especially true for women in agriculture.


SUBJECT AREAS

The basic subject areas presented below are based on the FAO Plan of Action for Integration
of Women in Development (FAO, 1988a) and on the list of indicators developed for the World
Conference on Agrarian Reform and Rural Development, WCARRD (FAO, 1988b). These
documents detail the main concerns of FAO with respect to women in agriculture, covering more
than 50 items for which data are required in order to address these concerns. These items have
been grouped into 13 broad subject areas as shown in Table 1. It should be noted that these are
not data requirements as such, but only the areas and items for which data are required.


Employment

Data on employment or labour are crucial to an understanding of women's role in agricultural
production. Despite this, few meaningful data are available. Required data cover the agricultural
labour force, with a distinction between market-oriented and subsistence agriculture since women
are principally engaged in the latter. Further detail is required on fisheries and forestry separately.
These data requirements are covered to some extent at the national level by data from population
censuses and labour force surveys, based on ILO standards. However, these concentrate on the
formal sector (waged, urban) rather than on the informal sector (subsistence, agricultural, rural).
Rather than disaggregation by rural/urban areas, these data are disaggregated by industry or
occupation, with one major group. distinguishing agricultural activities (including fisheries and
forestry). Data published by ILO are not disaggregated beyond this major group level, though
those published by countries generally are. However, standard classifications do not involve
detailed disaggregation of agriculture. These data are gender-disaggregated, but seriously
under-represent women in agriculture because women are often not considered economically
active.

Some national data on agricultural labour are also available from agricultural censuses, but
these are based on the holder and on paid permanent employees and omit unpaid family members.
They do not therefore provide the required distinction between permanent and temporary labour.
Neither are data on holders necessarily indicative of labour since some holders are absent from
the holding and many women are not recognized as holders. Agricultural census data also omit








very small holdings, often operated by women, and only briefly cover fisheries and forestry.
Other data are available from farm management surveys and from sub-national sources.


Access to Means of Production

Data on access to means of production are necessary to determine whether women receive a
fair share of the various inputs necessary for agricultural production. These inputs include land,
water and irrigation, drainage facilities, agricultural credit, agricultural inputs such as seeds,
fertilizer and pesticides, new technologies and techniques, extension services and transport. Data
on extension services are available from the FAO Global Consultation on Extension. Data on
access to many of the remaining items will be available from the 1990 round of agricultural
censuses, but this source is deficient in that smallholdings are omitted and many women are not
recognized as holders. To cover certain items, such as access to credit, and to obtain the level of
detail required, especially at the intra-household level, sub-national sources are appropriate.


Membership of Agricultural/Rural Organizations

Data on women's membership of agricultural/rural organizations are required to monitor the
general status of women and also their access to resources, since this is often controlled by
membership of organizations. Access to credit, for example, may be through membership of credit
unions For project work, the only avenue through which women may initially be reached is often
community organizations, usually of a social nature. Membership of other organizations, such as
cooperatives, credit unions, marketing organizations, labour unions and tenants associations, is
often gained as a result of project activities. Data on women's membership of such
agricultural/rural organizations are not generally available in FAO, though they may be found at
the national level in administrative records and at the sub-national level through FAO projects.


Productivity

Improved access to the means of production will result in the increased productivity of
women's labour Data are required to monitor this process. Such data are usually available only
at the sub-national level since productivity is a composite measure requiring detailed data, usually
available only at this level. The data that do exist are often subjective due to arbitrary valuations
of women's output per time unit.


Time-Use

Time-use data are related to data on employment in that they provide much of the detail
required on women's economic activity in agriculture. These data also cover non-economic
activities, such as child-minding. Time-use data are required for effective project formulation and
targeting, involving the identification of the different responsibilities of members of the household.
Such data should ideally be disaggregated by both gender and age since children undertake certain
tasks and since the role of younger women in the household is different from that of older women.
In this respect, time-use data are also useful in determining whether women, particularly younger
women, are likely to have sufficient time to participate in project activities.








At the national level, time-use data are not available for developing countries though they have
been collected for some developed countries. At the sub-national level, time-use studies are
relatively frequent.


Decision-Making

Women's access to resources and their role in agricultural production and rural development
are often determined by whether they are involved in the decision-making process. It is thus
necessary to have data on women's participation in decision-making, including decisions about the
use of own and household income, other household decisions and community decisions. Such data
are usually obtained at the sub-national level.

Data on decision-making at the project and institutional level are measured by the extent to
which women hold positions in the agricultural professions, particularly in FAO as project
managers and in other field positions.


Education

Education is an important determinant of the status of women. Required data include basic
literacy and access to primary and secondary education. Data on literacy and educational
attainment are available at the national level from population censuses, but data on school
attendance, obtained from school records and published by UNESCO, are not disaggregated by
rural/urban area. Data on distance to primary school are not generally available at FAO, but might
be found at the national level in the reports of individual population censuses and surveys.

For women to be involved in decision-making in projects, they must be educated at the
tertiary level. Data on access to agricultural education, which includes fisheries and forestry, are
available in UNESCO publications. Access to FAO training activities, fellowships and study tours
are also available from FAO administrative records.


Housing and Facilities

This group of variables describes the housing and living conditions of rural populations,
including dwelling condition, type of water supply system, existence of electricity supply, type
of sanitation, persons per room and dwelling tenure. Data on these are required mainly as
background variables. Many are available from population and housing censuses and household
surveys, disaggregated by rural/urban area but not by gender of head of household. Data on the
homeless are available by gender.

This group of required background variables also includes access to communications,
including distance to public transport and radio receiver ownership. Distance to fuelwood, potable
water, and health and social services are also covered. At the national level, these data are not
generally available, even without gender-disaggregation of head of household (which for
community variables involving distance is not strictly necessary). At the sub-national level, data
on distance to fuelwood and potable water are available in terms of hours walked through time-use
studies.








Income

The disadvantaged position of women in agriculture is reflected in both their rates of pay and
total income. Data are thus needed on individual wage rates, as well as on income. At the national
level, data on agricultural wage rates are compiled by the ILO, but country coverage is poor. Data
on the income of individuals are not generally available at the national level, but data on
household income are available from income and expenditure surveys, though few are
disaggregated by gender of head of household. Sub-national data on income are occasionally
produced in connection with income-generating projects.


Marketing

In order to sell their excess produce and to earn income, women need access to markets.
Variables describing access to markets include distance to market, type of market (local, district,
regional, national) and availability of transport. Required data on distance to market can be at the
household level and need not necessarily be gender-disaggregated. Data on type of market permit
a distinction to be made between small-scale marketing at the local market and larger-scale
marketing at, for example, the regional level. Data on distance to market, type of market,
availability of transport and access to surfaced roads are not generally available.


Nutrition

Data on nutritional status are required in order to monitor the situation of women, especially
pregnant and lactating women. Two basic sources of data are available, namely food consumption
and direct measures of nutritional status. Food consumption data are available at the household
level from income and expenditure surveys, giving household expenditure on food by household
size. However, these data are not disaggregated by gender of head of household and do not
provide data on individuals.

On an individual level, nutritional status is assessed through anthropometric measurements and
detailed studies of food intake. The former include height, weight and skinfold thickness and are
available by gender at the national level as well as at the sub-national level. Data on food intake,
in terms of calorie and protein intake, are only available at the sub-national level and are rare.


Household Composition

Household composition variables are important as background variables. Required data include
gender of head of household, absent male members of the household, household size and
household members' relationship to head of household. The identification of female heads of
households is particularly important, especially in relation to marital status (an important
determinant of the economic situation of women) and absence of male household members. Data
are available for most of these variables from population censuses, but not for absent spouse.
However, in most cases these data are not effectively used in cross-tabulation with other variables,
as is ideally required.







Demographic

Demographic variables are also important background variables and include population
structure (in terms of age, gender and marital status), fertility, mortality (especially infant and
maternal mortality), migration and rural population growth rates. Most of these data are available
from population censuses and analyses, but rural/urban disaggregation is needed for marital status
and maternal and general mortality. Data on migration are not generally available.


DATA REQUIREMENTS AND AVAILABILITY

The data requirements and availability for each subject are presented in Table 2 in greater
detail than the above discussion allows. For each subject area, and item within subject area,
specific data requirements are detailed. For each of these, availability at the national level is
noted, indicated by (A) available, (P) partially available, or (N) not available.

Where data are available at the national level (A), the source is given. (It should be recalled
that availability refers to availability at FAO.) In the case of data from agricultural censuses, some
data will only become available from the 1990 round of censuses, and are thus not strictly
available at present but will become available over the coming years. Where available data are
already contained in an existing data base, this data base is given as the source, rather than giving
the original source; this is done with data entry in mind since machine-readable data are easier
to incorporate into the data base on women in agriculture.

Where data are partially available at the national level (P), a code is given in the column
headed (P) indicating: (G) that gender-disaggregation is needed, (R) that rural/urban
disaggregation is needed, or (D) that some other disaggregation is needed. In all cases of partially
available data, the data have been collected (by most countries), but have not been tabulated to
give the required data on women in agriculture. The source of the collected data is thus given,
as is the type of detail required.

Where data are not available at the national level (N), the proposed national source is given.
In many cases, however, national sources are unsuitable for the type of data required, and SN is
marked, sometimes in conjunction with a sub-national source, indicating that sub-national sources
(or the specific source noted) are appropriate.









Chapter 2


Review of national sources




INTRODUCTION

The data reviewed in this chapter are from national sources covering documents available in
FAO. The review is organized according to the available sources of data, which include data
published by UN agencies, the World Bank, the United States Bureau of the Census and the South
Pacific Commission. For most national data collection exercises, UN guidelines are available and
these are reviewed as appropriate in association with published sources. Suggestions as to how to
improve data on women in agriculture through these sources are made in Chapter 4.

A distinction may be made between two basic types of information available from UN
publications. These are estimates made from various sets of data and data obtained from national
tabulations. In the former, the estimates refer to specific standardized dates (for example,
mid-year) and are adjusted for quantifiable inaccuracies. Data for the latter refer to the time point
of data collection (for example, date of census) and are not modified or adjusted in any way (even
though known biases and inaccuracies may exist). These data are roughly comparable across
countries, since they are collected following UN guidelines, but national variations occur in
concepts and coverage. For women in agriculture, this is particularly important concerning the
definition of work or employment.

In addition to these data published in international volumes, national statistical reports of
agricultural and population censuses and household surveys contain varying amounts of extra data
or detail depending on national data collection and tabulation practices. This is particularly true
of detailed disaggregation of industry and occupation, and of information on migration. However,
it is noted that many developing countries do not yet provide the full range of data needed to
complete UN published tables, so that extra data as opposed to detail for developing countries are
not commonly available.

National data sources also include annual statistical reports, yearbooks and abstracts containing
data from administrative records. These cover a variety of subjects including education,
employment and unemployment, membership of organizations and others. Time has not permitted
a review of these sources, but the amount and nature of the material that they contain, over and
above that already included in international compilations, is expected to vary considerably between
countries.


FAO STATISTICAL SOURCES

Agricultural censuses, including agricultural surveys, provide the main source of national data
in which FAO has an advisory role. In most countries, agricultural censuses include a livestock
census, but fisheries and forestry are covered only minimally. In addition to agricultural censuses,
many developed countries and perhaps a quarter of developing countries conduct annual
agricultural surveys as recommended by FAO, but these surveys do not usually cover human
resources.









Agricultural Census

The primary concern of an agricultural census is to describe holdings, crops, livestock and
agricultural inputs. The statistical unit is thus the holding but many censuses do not publish
enough data on holders and household members.


Available Data on Women in Agriculture

The 1970 round of censuses did not record gender of holder, though gender of labour and
household members was sometimes collected (FAO, 1977, 1981a). By 1980, gender of holder was
often recorded but was not used in tabulations except to tabulate age and gender of holders,
sometimes by area of holding. Age and gender, and on occasion economic activity of household
members and gender of permanent workers was also recorded by some countries and tabulated,
occasionally by area of land holding. In some countries, gender of temporary/occasional workers
was also recorded. FAO publishes Census Bulletins giving the main 1980 results including, where
available, gender and age of holder and household members and gender of permanent workers
(FAO, 1979-1989). Coverage of data on these items is detailed in Table 3. Additional data are
available in the individual census reports for some countries.


Current Recommendations on Gender

FAO recommendations for the 1990 round of censuses (covering 1986 to 1995) include human
resources by gender so that more detailed information on the economic activity of women in
agriculture and women's access to the means of production should become available during the
next decade. These recommendations include gender and age of holder and of holder's household
members and gender of permanent agricultural workers. In tabulating gender and age of holder,
FAO recommends that these two variables are taken separately rather than in conjunction. (It is
noted from Table 3 that in 1980 only one country (Fiji) gave number of holders by gender by
age.) For women in agriculture, gender by age of holder would be useful especially in conjunction
with household composition, or with composition of labour used on the holding. Some countries
already tabulate gender of household members, about half in conjunction with age (see Table 3).

Emphasis is being placed in 1990 on gender of holder as one of ten key items for
cross-tabulation such that many of the recommended tabulations are by gender of holder,
including: holding size, cropland, livestock, number of permanent agricultural workers, tenure,
holder's household size, cultivation intensity, activity status of household members, gender of
permanent agricultural workers, irrigation, drainage, area harvested, plantation features, use of
fertilizers, pesticides and seeds, crop stand, livestock system, use of machinery and equipment,
ownership of non-residential buildings and activities in forestry and fishing. A tabulation of
interest to women in agriculture but not recommended by FAO is number of holdings by size of
holding by gender of holder by legal status of holder (holdings operated by an individual, a
household, a cooperative, etc.). This would provide information on how women organize
themselves, crucial for project planning.

Of course, many women are involved in agriculture in roles other than that of holder.
Permanent agricultural workers are enumerated as a labour input and their gender recorded, but
no other information on permanent labour is collected. Neither is information on occasional
agricultural workers recommended by FAO for collection. As a source of data on human







resources in agriculture, therefore, agricultural censuses are deficient because they omit those who
are both landless and temporary labour, thus under-representing women in agriculture in
particular. Similarly, data on economic activity of household members will be obtained in 1990,
but recommended tabulations do not include activity status or work on holding by gender (and
age) of household members. Important information for women in agriculture, most of whom are
unpaid family members, will thus be lost.


Holdings and Households

Further bias against women's participation in agriculture probably arises in practice from
measures taken to avoid duplication of households in the data. In cases where there is more than
one holder in a household, the holdings are often regarded as a single holding for practical
purposes in data collection (FAO, 1986, p. 23). A woman operating a holding separate to that of
her spouse or other male member of the same household may thus lose her holder status in
enumeration. Neither is joint holder status likely to increase the representation of women in the
data, since for tabulation purposes (by gender and age of holder) one of the joint holders must "be
identified as the senior holder" (FAO, 1986, p. 25). In any event, the need to avoid duplication
of household data would discourage the listing of joint holders in the same household, since as
a general rule data on the household members of all joint holders are to be collected. In cases
where an extended household operates multiple holdings, FAO recommends the identification of
several holders, equivalent to splitting the household into smaller households with one holding per
household (FAO, 1986, p. 23). This might increase coverage of female holders, but bias on the
part of enumerators (unless very well-trained) would probably lead to their being incorporated
statistically into male-headed households.


Minimum Size of Holding

It is noted that livestock and poultry kept by a household for home consumption (with no
agricultural land) are not usually considered holdings (though livestock holdings without land are
usually included). Neither does the kitchen garden constitute a holding. Both may be important,
if not the main, source of food for the household. Women, being mainly responsible for such food
production, are under-represented. FAO recommends that the minimum size of holding be set as
low as possible because of the importance of smallholdings in contributing to food production, but
does not set a recommended minimum. Countries thus set their own minimum, resulting in
considerable variation from country to country so that data are not comparable in this respect.
FAO suggests that holdings below the minimum be investigated through special inquiries (FAO,
1976). Unfortunately, such special inquiries are rarely, if ever, held on a national basis, though
small-scale studies exist.


Supplementary Data

Supplementary guidelines are being made available to regions by FAO (for example, FAO,
1990a, 1990b) suggesting that separate studies be conducted (in Asia and the Pacific at least) to
obtain data on the landless and their households, main and secondary occupations of the rural
population and unemployment and underemployment in agriculture. Such studies would be a
useful means of providing data on women. It is also suggested that agricultural censuses be
increased in scope to cover time worked on holding by holder and household, total time worked
per year by occasional-worker household members and by non-members, and gender of both








permanent and occasional agricultural workers (FAO, 1990b, pp. 19-20). Attention is also drawn
to the need to identify adequately share-cropped land and squatter and tribal forms of tenure
(FAO, 1990b, p.21), giving possibilities for the improvement of data on women. These
developments are important for data on women in agriculture, but as supplementary guidelines
they may not be readily adopted by countries unless due emphasis is given. It is disappointing to
find that these supplementary guidelines refer to the holder as male and use such measures as
man-months.


Farm Management Surveys

Farm management surveys used to be carried out with FAO guidance, based on the holding
as the statistical unit and covering large establishments. Some countries, especially developed, still
conduct such surveys, but those in developing countries are usually based on small samples, held
on an irregular basis and cover selected crops, livestock or types of farming areas.

In the FAO guidelines for conducting farm management surveys (Friedrich, 1971), farm
labour is included as one of 11 areas of investigation. Recommended detail includes gender of
farmer and household members, age of farmer and indication of age of household members,
activity status and occupation of farmer and household members, and for those household
members working off-farm, time spent in off-farm employment and income received. Data are
also obtained on number of days worked on certain activities for hired and family adult (by
gender) and child labour. It has not been possible in the time available to assess the coverage of
farm management surveys.

More recently, the farm management survey approach has been replaced within FAO by the
farming systems approach which generates sub-national data.


Food Consumption Surveys

Data on food consumption are compiled by FAO at roughly four year intervals from
household income and expenditure surveys. The latest compilation (FAO, 1988c) contains data
on 32 countries, only 8 of which provide data for rural areas. The 1985 review contains data for
39 countries, 12 of which are repeated in 1988 (FAO, 1986c). These data refer to household food
consumption, and are thus not directly relevant to women in agriculture.

An annotated bibliography of surveys taking place between 1976 and 1988 provides further
information (FAO, 1990c). These surveys are available in FAO. A bibliographical listing of 491
references to food consumption surveys held in the FAO Food Policy and Nutrition Division
(ESN) Library is available, though this may also include some sub-national studies. Several data
tapes of national samples of food consumption surveys are held in ESN: these include Brazil,
China, C6te d'Ivoire, Madagascar, Morocco, Togo, Tunisia and others.

Details and guidelines for food consumption surveys are contained in FAO (1981b). The list
of recommended data items includes both gender and farm/non-farm status. Thus, food
consumption for farm households should be made available, as should household structure of farm
households.

Data on food consumption of individuals are not easily obtainable since households purchase
and cook collectively and some eat from the same pot. Individual data are important to obtain







since average consumption per head obtained on a household basis would in most cases seriously
underestimate the food consumption of women, especially in cultures where women eat last. Sub-
national data are the more appropriate source for such data.


Special Compilations

FAO has encouraged the compilation of data to facilitate their use in various activities:


Workshop on Improving Statistics on Women in Agriculture, Rome, 21-31 October 1985

The Report of this workshop (FAO, 1985) contains country statements for 10 countries,
including mostly national and some sub-national data, referring mainly to the early 1980s.
Countries covered are Bangladesh, Brazil, Egypt, Iraq, Jamaica, Korea, Lesotho, Philippines,
Zambia and Zimbabwe. Four case studies were also undertaken for this workshop covering Korea,
India, C6te d'Ivoire and Bangladesh.


Country Pilot Studies on Socio-Economic Indicators for Monitoring and Evaluating WCARRD

These studies were conducted from 1981 to 1983 and cover 26 countries, only 13 of which
contain data on women in agriculture, namely Bangladesh, Barbados, Cameroon, Colombia, Costa
Rica, Jordan, Kenya, Korea, Peru, Philippines, Suriname, Thailand, and Yemen Arab Republic.
The amount of data on women in agriculture varies from a single table (e.g., Jordan and
Cameroon) to a number of tables (e.g., for Korea and Costa Rica). The data are now somewhat
dated, many referring to the 1960s and 1970s. Data for 1980 or 1981 are available for
Bangladesh, Barbados, Costa Rica, Korea, Peru and Suriname. Most data are national and
available elsewhere. Reports of these studies are available in ESS, as is a collection of copies of
tables relevant to women.

In addition, summary tables of indicators are available, some of which are related to women
in agriculture, mostly as background indicators. African countries covered are Benin, Cameroon,
Kenya, Sierra Leone, Toga and Zambia; Asian countries are Bangladesh, Korea, Nepal,
Philippines, Sri Lanka and Thailand; Latin American countries are Barbados, Colombia, Costa
Rica, Peru, Suriname and Venezuela; and Near Eastern countries are Jordan, Syria, Tunisia and
Yemen Arab Republic (FAO, 1983b, 1987a).


Special Data Collection Programme 1984-1985

This programme involved FAO Headquarters staff obtaining, or attempting to obtain, specific
data from certain countries while on duty travel (FAO, 1984). One of the priority subjects for this
study on rural poverty was women in agriculture, and relevant data were obtained for three
countries (Korea, St. Lucia and India). Documents for these and other countries are available in
ESS. These collections cover national and sub-national data.








Expert Consultation on Women in Food Production, Rome, 7-14 December 1983

One of the papers for this consultation addresses "The State of Statistics on Women in
Agriculture in the Third World", and contains summary tables of labour force statistics on women
in agriculture for the 1970s.


FAO Administrative Records

Review of FAO Training Activities

This review is conducted annually by the Inter-Departmental Working Group on Training
(IDWGT), (e.g., FAO, 1987b). Gender of participants in training activities is included in only
one table giving number of trainees completing the training activity by region. National data are
not tabulated. These data could be analyzed for gender issues. Field officers could also be asked
to tabulate all data by gender. Further information is available from service/division reports to
IDWGT.


Yearly Analysis of Fellowships and Study Tours

These data are made available internally through office memorandum by AGO. Data available
cover fellows by region of origin by type of training by gender. Other tables are not
gender-disaggregated, but could easily be disaggregated since gender of fellow is available. It is
particularly important to obtain Table 4 (of 1989 analysis) by gender, covering subject matter and
type of training.


FAO Personnel

All personnel data are kept centrally in PERSYS, a computerized information system. This
includes field and HQ personnel. Available variables include gender, age, field/HQ location, FAO
Service, grade, length of service, entitlement category (general service, professional, consultant,
etc.), and date of last promotion. Tables may be requested from Personnel Division. Monthly
reports are also made containing information on gender by grade, location and type of
appointment. Data on gender of field staff and external candidates of AG and ES divisions are
available in AGOMIS.

FAO provides data to the UN Administrative Committee on Coordination which publishes
UN-wide statistics on personnel (UN, 1989g). This gives limited information on the gender
distribution of FAO employees.


WCARRD

The Programme of Action of the World Conference on Agrarian Reform and Rural
Development (WCARRD) recommends the collection on a regular basis of quantitative data and
the development of appropriate indicators. Reports containing summary data were made in 1983
(FAO, 1983a) and 1987 (FAO, 1988d). Data from WCARRD 1987 are available in AGROSTAT.
Data are also available in FAO (n.d) and FAO (1987c), and five case studies of developed








countries are available in FAO (1988e). WCARRD is not a primary source and, to date, contains
very little of direct interest to women in agriculture, since gender-disaggregation is not common.

The statistics available in WCARRD consist of a series of primary indicators, some of which
are also "core" indicators, and a list of supplementary indicators (FAO, 1988b). More recently,
guidelines issued for the preparation of country reports on WCARRD for the 1991 Conference
gave a list of selected socio-economic indicators to be used in reporting. These selected indicators
are drawn from the list of primary and supplementary indicators.


Global Consultation on Extension

This consultation requested countries to provide data and other information on extension.
These include data on extension personnel by gender by type. Though educational qualifications
of personnel are included, these are not available by gender. To date, 113 countries are covered
and the information will be published by FAO in directory form. A previous directory also exists
(Swanson, 1981).

Case studies also exist for 24 countries and the raw data are available in the Agricultural
Education and Extension Service, ESHE. These data have been analyzed in conjunction with data
for 89 of the 113 countries providing directory data, focusing on farm women. This analysis will
be updated to include all 113 countries (Wilde, forthcoming). This is an excellent example of how
existing data can be reanalysed to include gender issues.

Raw data for five African country case studies are also available. These are national sources
and focus on agricultural extension for women. Countries covered are: Kenya, Malawi, Sierra
Leone, Zambia and Zimbabwe. Further studies are planned for the Near East.

In general, while data on women employed in extension are available, data on the number and
characteristics of women reached by agricultural extension are not. Gender-disaggregation in this
area is in its infancy.


Estimates of the Agricultural Population

Estimates of the agricultural population are currently made by FAO using UN and ILO
estimates of population and labour (FAO, 1986d). The steps of the entire procedure for deriving
the agricultural population are as follows:


1. Total population estimates and projections, detailed by age by gender, are prepared by the UN
using standard procedures based on population census enumerations and estimates of fertility,
mortality and international migration.

2. Age and gender specific economic activity rates are applied to the total population to give the
economically active population by age by gender (see ILO (1990b).

3. Gender-specific proportions of the economically active population in agriculture, industry and
services are applied to gender-specific economically active population totals to give absolute
numbers of males and females engaged in agriculture, industry and services.








4. Activity rates are applied to the economically active population in agriculture and in
non-agriculture to obtain the total population dependent on agriculture and on non-agriculture,
i.e., the agricultural population and the non-agricultural population. See FAO (1986d) for
details.

5. The projections of the agricultural population are based on UN demographic assumptions, ILO
assumptions on economic activity and on FAO assumptions about future levels of the ratio of
the economically active population in agriculture to the total economically active population.
The latter assumptions are gender-specific and embody a decreasing trend in the ratio of the
economically active in agriculture to total economically active.


These estimates are subject to biases, especially regarding women in agriculture, because of
the inaccuracies in the data on which they are based. In particular, economic activity rates for
women (ILO assessments) under-represent the work done by women. This is especially true of
women in agriculture.

In addition, if the agricultural activity rate is known, the agricultural population will be more
accurate than the economically active in agriculture population, because where women are not
classified as active, they are included as dependents. In practice, however, the activity rate in
agriculture is often assumed equal to the overall activity rate. This leads to probable bias in
estimates of the agricultural population. If women are more likely to be recorded as economically
active in the non-agricultural sector (because of more formal employment), the proportion of
economically active of the total population in the non-agricultural sector (and hence in total) will
be too high for the agricultural sector leading to an underestimate of the agricultural population.

It is noted that data from agricultural censuses are not used to estimate the agricultural
population, because of the poor coverage of human factors. It is also noted that very little, if any,
real use is made of estimates of the size of the agricultural population, partly because of
incomparability with agricultural census data. The agricultural population includes forestry and
fishing whereas data obtained in agricultural censuses do not, and agricultural censuses omit the
landless and smallholders, who would be included in the agricultural population.


ILO STATISTICAL SOURCES

Labour

Data on agricultural labour are available in the ILO Year Book (ILO, 1988b) and cover the
economically active population, employment, unemployment and wages in agriculture. These are
all gender-disaggregated, and coverage by gender is shown in Table 4. For women in agriculture,
however, these data are widely acknowledged as unreliable, as discussed below.

Data on economic activity are obtained mainly from population censuses and labour force
surveys, but some are obtained from establishment surveys which do not include informal
agriculture. The economically active population includes by definition those involved in the
production of primary products whether for the market, barter or own consumption for full
definition). Women in agriculture should thus be included, but are often not considered
economically active because of the subjectivity involved in classifying women as unpaid family
workers (economically active) or as housewives (not economically active). (See Scott, 1989:17
and Dixon-Mueller, 1985:94 for examples of the variability resulting from this subjectivity.)







Questionnaire wording is an important factor in this respect, as are gender and class of
interviewers (Anker, 1983). On the whole, data on economic activity seriously under-represent
women in agriculture.

Future data should better represent women in agriculture as a result of new definitions. Until
recently, activity status was classified into employer, own-account worker, employee, unpaid
family worker, member of producers' cooperative (not tabulated), and not classifiable. Women
in agriculture are concentrated in the unpaid family worker category. New ILO recommendations
will result in unpaid family workers (and members of producers' cooperatives) being classified
as self-employed and therefore economically active. These recommendations also reduce the
minimum time spent in economic activity from one-third of a 'normal' working day to one hour
(ILO, 1988a), so that many more women will be classed as economically active.

The definition of unemployment has also been broadened under these new recommendations
to include the informal sector. In situations where the conventional means of seeking work are of
limited relevance, the condition of seeking work is no longer required to define unemployment,
and the two conditions of being without work and being currently available for work suffice. Even
so, the notion of unemployment is inappropriate for subsistence agriculture so that unemployment
data are of very little, if any, use to women in agriculture.

The above-mentioned reference to minimum time spent in economic activity refers to measures
of the currently active population where the reference period is one day or one week. An
alternative measure is the usually active population, where the reference period is much longer,
such as one year. Where work is seasonal, this usually active approach will include a greater
number of people, especially women, than the currently active approach, unless of course the
census is conducted during the working season. For women in agriculture, therefore, data on the
usually active population are preferred.

The data published by ILO are disaggregated only by major industrial division and
occupational group. Even fully disaggregated data, available in country sources, do not provide
very detailed information in the area of agriculture. The International Standard Industrial
Classification of all Economic Activities (ISIC-1968) is:


Major Division 1 Agriculture, hunting, forestry and fishing

11 Agriculture and hunting
111 Agricultural and livestock production
112 Agricultural services
113 Hunting, trapping and game propagation
12 Forestry and logging
121 Forestry
122 Logging
13 Fishing
130 Fishing


Detail at the fourth digit level is only available for fishing. Where they are regarded as
economically active, women in agriculture are almost all under ISIC code 111. It is impossible
under this classification to account for mixed agricultural and fishing activities, which many
women do.








For occupation, the International Standard Classification of Occupations (ISCO) is used. Until
recently ISCO-1968 was in use:


Major Group 6 Agricultural, animal husbandry and forestry workers,
fishermen and hunters.

6-0 Farm managers and supervisors
6-1 Farmers
6-2 Agriculture and animal husbandry workers
6-3 Forestry workers
6-4 Fishermen, hunters and related workers


Recognizing the need to classify better the subsistence sector, this has now been replaced by
ISCO-88, which makes a clear distinction between market-oriented and subsistence agriculture:


Major Group 6 Skilled agricultural and fishery workers

61 Market-oriented skilled agricultural and fishery workers
62 Subsistence agricultural and fishery workers.



This distinction does not allow for mixed cash and subsistence agriculture in which many
women are involved. It will thus be necessary at the fieldwork stage to determine whether crops
are grown mainly for subsistence or mainly for cash. That this division is subjective and may be
seasonal or dependent on external factors will lead to differing national practices and hence the
incomparability of data.

In addition to Major Group 6, sub-major group 92 covers agricultural, fishery and related
labourers, with the following detail:


921 Agricultural, fishery and related labourers
9211 Farmhands and labourers
9212 Forestry labourers
9213 Fishery, hunting and trapping labourers


There is thus a distinction between skilled labour (including all subsistence labour) and
farmhands, though subsistence labour and farmhand labour may cover the same activities. It is
assumed that women in agriculture will be classified as 6210 (subsistence, agricultural and fishery
workers) in as far as they work for own consumption, and as 9211 (farmhands and labourers) in
as far as they work as labour for cash. It is noted in UN (1989i) that recommended tables combine
62 and 92 into one group for tabulation purposes. It remains to be seen how well this new
classification covers women in agriculture.

It is also noted that general managers in agriculture are coded 1311 as part of the General
Managers sub-major group (13); and that agronomists and related professionals are coded 2213








under sub-major group 22, Life Science and Health Professionals. Motorized farm and forestry
machinery operators are classed under 8331, part of 83, Drivers and Mobile Machine Operators.
Extension workers are also classed separately, possibly under 3340 (other teaching associate
professions). Appropriate detail for this aspect of women in agriculture may be available from
individual census and survey reports, if tabulated.

The above classifications and definitions of economic activity provide a framework for
countries to work within, and are not strictly adhered to by all. This means that comparability
between countries is limited. Details of the practices of each country can be found in the ILO
series, Statistical Sources and Methods, particularly Volumes 3 and 5 (ILO, 1986, 1990a).


Household Income and Expenditure Surveys

Statistics on household income and expenditure are compiled by ILO, the most recent covering
1968-1976 (ILO, 1979). Coverage extends to 87 countries, 55 of which provide data for rural
areas. Tables and examples refer to households and are thus not directly relevant to women in
agriculture.

Tables are presented by geographical area, social.or occupational group (of household head)
and household size. An extra dimension, gender of head of household, could be added, since
gender of household members is collected. For women in agriculture, gender of head of
household, social or occupational group and household size are preferred in conjunction with
geographical area, at least for a rural/urban or agricultural/non-agricultural breakdown. It is noted
that some surveys explicitly omit rural areas or households engaged in agriculture/fishing/forestry.


UN STATISTICAL SOURCES

Population and Housing Censuses

Demographic data obtained from population and housing censuses are published in the
Demographic Yearbook (UN, 1990a). These include population data by gender by age for rural
areas and demographic rates for rural areas, including birth rates, foetal death rates, infant
mortality rates, death rates and marriage rates. Each volume of the Demographic Yearbook also
covers a special topic; recent topics relevant to women in agriculture are population census
statistics (1988) and household composition (1987). Census data on economic activity are
published in the ILO Yearbook; those on educational attainment and literacy in the UNESCO
Statistical Yearbook; and those on housing and facilities are published by the UN in the
Compendium of Human Settlement Statistics.

UN guidelines are produced for each census round (UN, 1980a). For almost all tabulations,
it is recommended to distinguish between urban and rural areas, though many countries do not
do so even for 'priority' tabulations. In fact, coverage of countries by rural/urban area by gender
is poor, due more to a lack of disaggregation by rural/urban area than by gender. Table 5 shows,
for tables in the Demographic Yearbook that are theoretically disaggregated by gender and
rural/urban area, the number of countries actually providing data by gender for the total country
and for rural/urban areas. It is seen that coverage of women in agriculture could be improved
significantly by increasing the extent of disaggregation by rural/urban area.








These guidelines also recommend that household characteristics be tabulated by gender of head
of household and include a single tabulation detailing activity status and status in employment of
heads of households by gender as well as activity status of adult household members and their
relationship to the household head. Unfortunately, this latter tabulation is not recommended by
the UN 'for early release', which in practice means that it is seldom produced. In fact, judging
from the data available in the 1987 Demographic Yearbook (UN, 1989d), few of the
recommended tabulations on household characteristics are made available. Furthermore, it is seen
in Table 5 that coverage at the national level is not very good, especially where data on household
members are required, and that coverage at the rural level is very poor. The reality is thus far
from the ideal situation of having rural/urban-disaggregated tabulations relating gender of head
of household and household composition to other non-household variables such as employment
and education, of which the above-mentioned UN-recommended tabulation is a part.


Household Surveys

Household surveys include income, consumption and expenditure surveys, demographic
surveys, labour force surveys, food consumption and nutrition surveys, agricultural surveys and
others (health, cultural, education). Guidelines for the conduct of surveys are produced by the UN
(1984a). The National Household Survey Capability Programme (NHSCP) of the UN fosters the
improvement of household surveys (see, for example, UN (1989a)).

Survey data are published nationally and compiled by the relevant UN agency. ILO compiles
data from income, consumption and expenditure and labour force surveys; FAO compiles data on
food consumption and nutrition, including relevant data from incomeand expenditure surveys and
agricultural surveys; and UNSO compiles demographic surveys in the Demographic Yearbook.
Details of sample surveys, usually household surveys, conducted by national statistical offices are
published by the UN (1982).

Definitions of the household vary from common budget to common cooking pot. More
important for women in agriculture is the definition of household head and in this respect the
choice between a defacto or dejure approach. In the dejure approach, the number of women
heads of households will be far lower than in the de facto approach, especially in areas where men
migrate for work. For women in agriculture, the de facto approach is preferred to maximize
coverage of women heads of households.

A problem for data on women in agriculture, common to all household surveys, is the lack
of data on individuals. Details of age and gender and often other characteristics are obtained, but
they are not generally related to other variables. For women in agriculture, the household
approach needs to be adapted to provide more data on individuals' contributions to the household
total.

Surveys on household production, consumption, income and expenditure are mainly carried
out for national accounting purposes, in which subsistence production is theoretically included (see
UN, 1986). Though household, rather than individual, production and consumption are used, the
tendency in household surveys to underestimate the economic contribution of women, especially
in the informal sector including women in agriculture, results in underestimates of production and
GDP. The fact that this has proved convenient for countries attempting to gain least developed
country status in order to receive additional aid has not encouraged the recognition of women's
economic contribution in statistical data.







Special Compilations

Compendium of Statistics and Indicators on the Situation of Women 1986

This volume (UN, 1989b) is compiled from data in the UN Data base on Women, WISTAT,
and includes some additional rates of change in various indicators. The data are from population
censuses. Regional volumes, of which that for Africa is already available (UN, 1989c), are in the
same format and also contain useful information on the dates and titles of existing censuses and
surveys, and on planned censuses.


Compendium of Human Settlement Statistics

This compendium contains data on population and education, also available in the
Demographic Yearbook, as well as data on housing, infrastructure and services (UN, 1985a). The
latter are not directly related to women in agriculture, but they provide information on the general
living conditions of rural populations.


Selected Statistics and Indicators on the Status of Women

This report (UN, 1985c) contains indicators and statistics on women for 115 countries for the
years 1970, 1980 and 2000. The data repeat those available in the Demographic and ILO
Yearbooks and contain very little on rural women or women in agriculture.


Statistical Indicators Relating to the Implementation of the Forward-looking Strategies

This report (UN, 1989h) contains data included in the Demographic Yearbook and ILO
Yearbook, as well as data not so readily available. For women in agriculture, an important
background item not generally available elsewhere is the rural population growth rate.


UNESCO STATISTICAL SOURCES

The UNESCO Statistical Yearbook (UNESCO, 1989) contains data from population censuses
on literacy and educational attainment for rural areas by gender. However, data on education
obtained from Ministries or Departments of Education are not tabulated for rural/urban areas
separately. This leads to a lack of data on educational access for women in agriculture at primary
and secondary levels. At tertiary level, agriculture, forestry and fisheries is distinguished as a
single field of study, giving enrolment and graduation data. Coverage of countries is shown in
Table 6.








WORLD BANK STATISTICAL SOURCES

Social Indicators

The World Bank produces social indicators (World Bank, 1989), several of which are directly
relevant to women in agriculture. The following are available for rural areas:


females per 100 males
rural/urban differential in population growth rate (and urban rate)
child/woman ratio
females in labour force per 100 males in labour force
access to safe water
absolute poverty income
population in absolute poverty
households with electricity
primary school enrolment ratios by gender
secondary school enrolment ratios by gender.

As well as the most recent estimates, estimates for 15-20 and 25-30 years ago are made
available. Comparisons are also made by region, relevant income group and next higher income
group. These indicators are based mainly on data from UN agencies, supplemented by national
sources not covered by UN agencies and by bilateral sources. A total of 173 countries is covered.


Social Dimensions of Adjustment

The World Bank is the executing agency for the Social Dimensions of Adjustment (SDA)
project launched by the UNDP Regional Programme for Africa, the African Development Bank
and the World Bank in collaboration with other multilateral and bilateral agencies, including FAO.
Its objective is to strengthen the capacity of governments in Sub-Saharan Africa to integrate social
dimensions in the design of their structural adjustment programmes. One of the priorities is to
strengthen the capacity of participating governments to develop and maintain statistical data bases
on the social dimensions of adjustment.

To date, roughly 30 countries have requested participation in the project. The Living
Standards Surveys undertaken in participating countries are concerned with intra-household
income, consumption and decision-making as well as the measurement of the impact of adjustment
on the household. A special analysis plan on women has been prepared.


UNITED STATES BUREAU OF THE CENSUS STATISTICAL SOURCES

USBoC has published a series, Women of the World, covering four major regions of the world
(Latin America and the Caribbean, Sub-Saharan Africa, the Near East and North Africa, Asia and
the Pacific). Contents are similar but not identical in each volume. The data are mostly for the
1970s, also available from UN sources and are drawn from the USBoC WID Data base.








SELECTED REGIONAL SOURCES

Economic and Social Commission for Asia and the Pacific

Compendium of Social Development Indicators in the ESCAP Region

Contains data already available in the ILO Year Book of Labour Statistics, the Demographic
Year Book or similar UN publications (ESCAP, 1989).


FAO Regional Office for Asia and the Pacific

Selected Indicators of Food and Agriculture Development in Asia
and the Pacific Region, 1977-87

Contains nothing on people except the total population, the agricultural population and the
agricultural labour force, already available in global UN sources (RAPA, 1988).


Regional Expert Consultation on Data Base for Women in Agriculture and Rural Development,
6-10 August 1990

Documents produced for this expert consultation for Asia and the Pacific contain data on
women in agriculture for Bangladesh, India, Iran, Pakistan and the Philippines.


South Pacific Commission

South Pacific Economies Statistical Summary

Contains no data of relevance to women in agriculture, except for an indication of economic
activity rates gained from comparing total and cash economy rates (SPC, 1989).


Socio-Economic Statistics on Women: Five Case Studies in the Pacific

Contains information on all socio-economic data on women available in the five case study
countries: Fiji, Kiribati, Marshall Islands, Solomon Islands and Tonga (Fleming, 1987). Since
these are rural economies, most data are relevant to women in agriculture. Also lists questionnaire
topics covered in 20 Pacific population and housing censuses.









Chapter 3


Sub-national sources




INTRODUCTION

This chapter discusses the availability of data at the sub-national level for the main broad
subject areas of relevance to women in agriculture defined in Chapter 1 (see Table 1) for which
sub-national data are generally available. The farming systems approach, including the FAO
Farming Systems Development and the FAO People's Participation Programme, are also briefly
reviewed as sources of data.

The prevalence of women in subsistence and small-scale enterprises and the complexity of
intra-household dynamics means that sub-national data are a very important source for women in
agriculture. While it is the case that all data can be obtained at the sub-national level, some data
such as household composition, education, housing and facilities and demographic variables are
restricted at this level almost entirely to background variables. At the sub-national level, the more
important variables are those related to employment and time-use, access to the means of
production, productivity, decision-making and individual nutrition. Data on income, marketing
and membership of agricultural/rural organizations might also be obtained at the sub-national level
but specific sources have not been located at FAO. The methods of data collection in these are
areas are lengthy, often involving observation, and therefore costly, and are thus restricted to the
sub-national level.


DATA AVAILABILITY

It has not been possible in the time available to review fully the extent and content of data
available at the sub-national level. Such a review will effectively be carried out at the data search
and entry stage of establishing the data bases. Sub-national studies and data are not systematically
compiled and published in the way that UN agencies provide national data. There is, of course,
no standardization, either in publication or in the concepts and definitions employed. International,
or even intra-national, comparison is thus difficult. In addition, the concentration on women as
an area of study has often resulted in sub-national data being available for women only, allowing
no comparison with men.

The scarcity of data at the sub-national level means that data that are 10 or 20 years old are
often all that are available. However, since the way of life in many localities may not have
changed appreciably, these data may not be as inaccurate as their age might suggest. Nevertheless,
the collection of current data is to be encouraged wherever possible, especially where projects will
bring about change. In such situations it will be necessary to collect both before and after data,
in order to monitor and evaluate the impact of the project.

An indication of where sub-national data can be found is given below for the main subject
areas for which sub-national data are available. Only sources actually seen during this brief review
are cited, though these may be secondary; primary sources can be followed up from references.
Very few references are in fact available in the FAO library as indicated by the following selected
key-word combinations:









Marketing + women 6
Agricultural credit + women 0
Rural cooperatives + women 0
Producer cooperatives + women 1
Farming Systems + women 5
Labour productivity + female labour 4
Decision making + families 8


Employment

Sub-national data on employment are often the most reliable data available on the economic
activities of women in agriculture. Even where reliable national data exist, sub-national data are
useful in providing the more detailed information necessary for project planning. Sub-national data
on employment are often obtained through time-use studies, discussed in Chapter 3.

Sub-national data are particularly useful in the areas of fisheries and forestry where national
data are poor. Studies on women in fisheries are detailed in an annotated bibliography by
Merriken (1987) and for Asian countries in Zabala (1990). Fisheries Division (FI) has identified
individual reports containing data on women in fisheries. Very few data appear to be available on
women in forestry except data on time spent gathering fuelwood in time-use studies.


Access to Resources

Sub-national data on access to the means of production are important not only in themselves
but also because of the lack of national data on this subject. Until data become available from the
1990 round of agricultural censuses, sub-national sources form virtually the only data on this
subject, which covers access to land, water, irrigation, drainage, credit, seeds, pesticides,
fertilizers, new techniques, extension services and transport. Even though many of these will
eventually be covered at the level of the agricultural holding (see Table 2), sub-national sources
are required to provide data on intra-household access.

Very few data on access to resources have been found in this brief review. Those available
include data for Kenya, Burkina Faso and Botswana (Dixon-Mueller, 1985). The farming systems
approach, discussed in Chapter 3, is probably the best source of sub-national data on this subject.


Productivity

Productivity, or output per person-hour of labour, is not easily measured since it depends on
various factors including labour inputs (time and skill) and non-labour inputs (access to resources
such as fertilizer, seeds and appropriate technology). The amount of detail required of data for
this composite measure restricts its availability to the sub-national level. The measurement of
labour efficiency by gender, total productivity in terms of output per person-hour, and net
productivity in terms of the value of labour are discussed by Dixon-Mueller (1985). Relevant data
for Korea, Kenya and Papua New Guinea are given.

Many studies use arbitrary weightings for the output of children and women in relation to
men. These invariably represent women as less productive (usually two-thirds) than men.







Arbitrary weightings should not be mistaken for weightings based on data, and studies are
required to provide data for specific tasks and crops in order to develop realistic weightings.
Data on productivity can best be obtained through the farming systems approach, discussed in
Chapter 3.


Time-Use

A useful discussion of studies on time-use is given by Dixon-Mueller (1985) using examples
from Bangladesh, Burkina Faso, Egypt, India, Java, Nepal, Sierra Leone and Tanzania. All are
for females and males (often by age), except for the data for India which are for women only.
Tinker (1982) presents additional time-use data for Java, Nepal and Upper Volta. Lipton (1978)
provides detailed data for Botswana; while data for Java (females only) and India are reported in
Holmboe-Ottesen, Mascarenhas and Wandel (1986). Further data are found in McGuire and
Popkin (1988) covering 8 countries (some already covered above), and in Longhurst (1980) on
Nigeria.

Time-use data are also available from surveys conducted in C6te d'Ivoire, Togo, Brazil and
Rwanda in association with the FAO Food Policy and Nutrition Division (ESN). Raw data from
these surveys are held in ESNA, and could be analyzed on a gender basis. It is noted that time-use
data are collected by many developed countries in national household surveys. The UN
recommends their collection in developing countries every three to five years (UN, 1989i), but
in practice, this can only reliably be done for small samples.


Decision-Making

Data on decision-making are rare, but in a discussion of agricultural decision-making,
Dixon-Mueller (1985) cites data for seven countries, plus a comparison of ideal and actual
practices in Kenya. Methodologies for obtaining data on decisions concerning control over own
income are also discussed by Dixon-Mueller (1985). Data on decision-making in agriculture are
obtained through the farming systems approach. FAO's Farming Systems Development (FSD)
identifies the farm-household as the decision-making unit (FAO, 1990d).


Nutrition

Data on the nutrition and food consumption of individuals are obtained through detailed
studies of very small samples, as methods involve weighing all food consumed. Such studies are
relatively rare, but provide a means of disaggregating household food consumption. Measures of
nutritional status include calorie and protein intake, both absolute and as a proportion of WHO
recommended daily allowances. FAO and the Institute of Nutrition have done some studies of food
consumption within the family and percentage of daily requirements. Anthropometric
measurements are also made (sometimes on a national basis), but studies suffer from difficulties
in obtaining accurate ages of small children. Other studies are concerned with the seasonality of
bodyweight.

Nutrition profiles, based on small samples, are being prepared for each country by FAO Food
Policy and Nutrition Division (ESN) and are located in the divisional library. Data from studies
conducted by ESN and national bodies are available for C6te d'Ivoire, Togo, Brazil and Rwanda.
The raw data from these studies constitutes the ESNA data bases. These include some








anthropometric data (from Brazil, for example). These raw data are being used to develop
summary indicators of malnutrition through multivariate analyses. The data could also be analyzed
on a gender basis.

The ESN divisional library also lists 81 references to nutrition surveys, including some
national studies. A useful source of data on dietary intake (protein and calorie) of women is
McGuire and Popkin (1988), covering 27 developing countries. This paper also gives data on
anthropometric measurements of women in 16 developing countries and regional data on
haemoglobin concentrations of women.


General

Useful references to original and other sources can be found in each of the publications cited
above as well as in the bibliographies of Wandel and Holmboe-Ottesen (1986), Dixon-Mueller
(1985) and Engberg (1990). Further references are contained in UN (1989f, Chapter 3) and in
Spring (1987).

Goldschmidt-Clermont (1982, 1987) gives data on time-use, wages, output and labour returns
as part of her economic evaluations of unpaid work in the household. These studies also include
an extensive bibliography related to developing countries. A listing of references to 282 documents
on female labour in agriculture/rural areas in FAO libraries has been produced for this review.
Almost all referenced documents contain tables, as indicated in the listing. A collection of data
on "Women's Work", consisting of photocopies of tables in articles and other publications, is
available in the ESH library, though much of it is rather dated. A further article is Devadas (1978)
available in ESS.


FARMING SYSTEMS

At the sub-national level, the Farming Systems approach provides the most fruitful potential
source of data on women in agriculture. The FAO Farming Systems Development (FSD) adopts
the farm-household as the statistical unit but, unlike most household surveys at the national level,
goes beyond the farm-household to include intra-household dynamics, especially in the area of
decision-making and farm management. The farm-household is also viewed as part of the wider
physical, socio-cultural and policy/institutional environment (FAO, 1989b).

Data collection in FSD is always linked to an application. At the first stage, existing data
bases are reviewed and data/ information gaps identified. Key informants are then interviewed
with women being recommended as having knowledge on gender issues, decision-making and
family members' roles (FAO, 1990d, p. 89). Groups of farmers are also consulted. In order to
plan a farm-household system, data are collected through interviews of individual farmers, but
sample sizes are small. Depending on the particular application, FSD can be a source of
sub-national information on any aspect of women in agriculture, including access to resources and
credit, membership of organizations, decision-making, time-use and the gender division of labour,
marketing, productivity, and use of extension services. A preliminary examination of materials
in AGS library, however, shows that gender issues have not been adequately addressed and could
be better integrated into Farming Systems Development.






29

Data sources from the farming systems approach include extensive work by researchers (see
Poats, Schmink and Spring, 1988), and the publications of agricultural research centres such as
the International Centre for Agricultural Research in Dry Areas (ICARDA) and the International
Rice Research Institute (IRRI) are also useful sources (ICARDA, 1986; IRRI, 1985). A review
the gender-related impact of these centres has been undertaken by Jiggins (1986).


PEOPLE'S PARTICIPATION PROGRAMME

A further avenue for the collection of sub-national data is the FAO People's Participation
Programme (PPP). This programme is ideally suited to obtaining gender-disaggregated data on
membership of agricultural/rural organizations and on income-generating activities. Since PPP's
focus is on small farmers and small-scale organizations and activities, in which women are
engaged, all data produced by the programme should ideally be gender-disaggregated. Limited
data already exist for 10 projects operating under PPP, for example, gender of group promoters
and beneficiaries (FAO, 1990e).









Chapter 4


Improving data availability




INTRODUCTION

It is evident from the review of national and sub-national data sources in Chapters 2 and 3
that considerable improvements are needed in the availability and relevance of data on women in
agriculture if data requirements are to be met. These improvements include greater country
coverage of existing data series, increased gender-disaggregation and rural/urban- disaggregation,
relevant disaggregation of other variables, improved concepts and methodologies, and the
collection of data not currently available. The discussion below addresses these issues for national
and sub-national data separately.


NATIONAL DATA

The improvement of national data on women in agriculture is an ongoing process. Recent
notable initiatives in this process are the recommendations for the 1990 round of agricultural
censuses and the new ILO definitions of economic activity and classification of occupations. The
process is lengthy, and results from these two initiatives have yet to be seen in terms of the
improvement of data on women in agriculture.

Other possibilities for the improvement of data have been identified during the course of the
review of national data sources. The more general of these are discussed below, following the
distinction made in Chapter 1 between the availability, partial availability and non-availability of
data as summarized in Table 2. More specific points (such as the omission of a particular
tabulation) have already been mentioned as part of the review in Chapter 2. A complete summary
of all recommendations for the improvement of data on women in agriculture is given in
Chapter 7.


Available Data

Though data are noted in Table 2 as available, the review of national data sources in Chapter
2 has shown that coverage is often poor and that many data are of poor reliability (especially data
on employment from ILO sources). There is thus a need to address issues of actual availability
as opposed to theoretical availability.

In that these are general issues, responsibility lies with the relevant UN agency and national
institutions for encouraging the conduct of censuses and surveys and for improving concepts and
methods. This should be done through existing channels, including advisory services and relevant
materials. The latter includes UN guidelines for each population and housing census round (e.g.,
UN, 1980a) and the National Household Survey Capability Programme (NHSCP) (e.g., UN,
1984a, 1989a); ILO recommendations for the production of labour statistics (e.g., ILO, 1988a);
and FAO guidelines for the production of agricultural statistics (e.g., FAO, 1986b).

Within FAO, greater collaboration is needed between ESH, ESS, AGO, Forestry and
Fisheries to ensure the production of relevant data on human resources in general and on women








in agriculture in particular. This collaboration might concentrate on agricultural censuses as the
main source of data on agriculture. One way in which the coverage of women in agriculture could
be improved in agricultural censuses would be the recommendation by FAO of criteria for use in
setting a minimum size of holding, stressing the need for data on smallholders. A further
suggestion is that countries conducting agricultural sample surveys rather than true censuses might
oversample (with appropriate weighting for totals) certain sizes of holdings or in certain areas to
improve coverage of women holders.

It has been seen in Chapter 2 that the inclusion of the concept of the household in agricultural
censuses complicates data collection and leads to bias against women in agriculture. It has been
suggested in the past (e.g., FAO, 1986e) that agricultural censuses should adopt the household
as the statistical unit, but this has been rejected. Nevertheless, the two concepts (holding and
household) need to be separate if women are to be adequately represented as holders. (It is noted
that correspondence between the holding and the household unit operating the holding is said to
be good, but this does not take the landless into consideration.) It is recommended here that, if
the statistical unit is to be the holding, agricultural censuses should obtain information on the
labour used by the holding, by status holder, family member, hired labour (permanent, temporary,
occasional), age, gender and possibly rate paid. It is not necessary to obtain information on the
holder's household members who are not engaged in agricultural activities. Indeed, given the poor
coverage of data on household composition, it may well be the case that coverage of human
resources as labour would be somewhat higher because of the more obvious relevance of labour
to agricultural production.

In relation to this, care should be taken to define holdings correctly. Where spouses operate
separate holdings, these should be recorded as such. The elimination of both the household
concept and the enumeration of household members avoids problems previously encountered in
double enumeration of households with multiple holdings. In order to identify properly holdings
that would normally be grouped together under one holder (and household), a question can be
asked at the beginning about whether any part of the initial holding is in fact operated by a person
(or persons) other than the interviewee. If so, two (or more) holdings and holders must be
defined. The fact that one holder may occasionally provide labour for the holding of the other is
not a problem, and should be recorded as labour in the usual way.

Similarly, with joint holdings, the need to identify a senior holder would not be necessary,
since there is no need to identify the household. All holders could be recorded as joint holders
under the legal status of holder variable. In tabulations, if joint holdings are a significant
proportion of holdings, they could be identified: in other words, gender of holder could be
combined with legal status to provide a classification with more than two categories (for example,
female single-holder, male single-holder, joint holding, with more detail if appropriate on gender
of holders).

This approach would not detract from the data already obtained from agricultural censuses,
and would indeed provide more information on women and on the landless than is currently
provided. Neither would it obscure estimation of the agricultural population, since data from
agricultural censuses are not used for this purpose. Furthermore, information on households can
be more reliably obtained from surveys based on the household as the statistical unit. While it is
argued that some holdings operate in conjunction as one household economic unit (e.g., where
husband and wife are separate holders), this does not justify their combination: the statistical unit
is the holding, not the household.








It is recognized that any new recommendations to countries regarding agricultural censuses
will not apply until the 2000 round covering 1996-2005, unless supplementary recommendations
can be issued for the current 1990 round. The latter would be feasible regarding the tabulation
of data already being collected.


Partially Available Data

Data referred to as partially available in Table 2 are data that are collected but not tabulated.
The central issue is thus the disaggregation of data. For women in agriculture, disaggregation by
gender and rural/urban area is essential, but other disaggregation is also required for meaningful
data to be obtained.


Gender-Disaggregation

Gender-disaggregation could be improved in almost all the data sources reviewed in Chapter
2. Though in population censuses and household surveys, gender of individuals is collected almost
without exception, these data are not always used effectively in tabulation. It has been seen in
Table 5 that data from population censuses are gender-disaggregated for most data series
appearing regularly in the Demographic Yearbook, but that coverage is much reduced for special
topics. Thus data on education, economic activity and household head are not well-covered by
gender, though gender-disaggregation is recommended by the UN.

A similar situation is commonly found in household surveys: gender-disaggregated data exist
but are not used to examine gender issues. It is not widely appreciated that tabulation of the age
and gender structure of the household alone does not constitute meaningful tabulation by gender.
There is a widespread need for training of data producers in the uses of gender- disaggregated
data.

The specific data items noted in Table 2 as requiring gender-disaggregation of population
census and household survey data are almost all related to gender of head of household. The data
listed under items 8.1, 8.2, 9.1 and 11.1 all require that gender of head of household be used in
tabulations that go beyond household characteristics. A first step towards achieving this, would
be for UN agencies to recommend such tabulations.

For data currently available from agricultural censuses (i.e., from the 1980 round), the
inclusion of the gender variable is much less common, and gender-disaggregation confined to
holders and household members, as seen in Table 3. Though recommendations for the 1990 round
include extensive tabulation by gender of holder, few are yet available. Neither do they include
gender of household members working on the holding, noted under item 1.1 in Table 2 for
gender-disaggregation. This will result in a lack of data on women who are not holders, who in
many countries are the majority of women. It is recommended that FAO advise countries to
produce this tabulation, especially since very few countries have yet conducted and tabulated their
agricultural census for the current round.


Rural/urban-Disaggregation

For women in agriculture, it is often the lack of rural/urban-disaggregation that results in data
not being available. This applies to data from population and housing censuses and household








surveys. It has been shown in Table 5, for example, that the volume of tabulated data available
from population censuses is reduced substantially when the rural/urban dimension is required.

One of the reasons for this lack of rural/urban- disaggregation is that there are no UN
guidelines for the definition of 'rural' and 'urban'. Each country defines urban and rural as
appropriate for its situation, such that an area classed as rural in one country would be classed as
urban in another. In addition, the definition of rural is often a non-definition in that rural areas
are defined as those that are not recognized urban centres. This lack of a standardized definition
leads to non-comparability of data at the international level. It also leads to some countries
tabulating data for areas without explicitly labelling them urban or rural, though this may be
understood nationally, such that the user unfamiliar with the country cannot easily locate data for
rural areas, and omissions occur in international statistical publications.

Though data for rural areas (including gender-disaggregated data) are published by the UN,
in the Demographic Yearbook for example, there are no plans to make standardized population
estimates (which are by gender) by rural/urban breakdown. This has been considered by the UN
Population Division, but abandoned as too difficult to accomplish (personal communication with
ESS staff).

A second reason for the lack of rural/urban-disaggregation may perhaps be found in the
apologetic way in which UN guidelines for population and housing censuses discuss rural/urban-
and other geographical disaggregation (UN, 1980a, p. 101; UN, 1990b). Rural/urban-
disaggregation is in fact recommended equally for the total country, for each major civil division,
and for each minor civil division. More emphasis could be placed on tabulating data by
rural/urban area of the total country.

Specific data items noted in Table 2 as requiring rural/urban-disaggregation include marital
status, maternal mortality and life-expectancy. These are regarded as background variables for
women in agriculture. Marital status is, however, an important variable in that it is a determinant
of household headship. The availability of rural/urban-disaggregated data on maternal mortality
and life expectancy is a lower priority, especially in countries where reduced statistical reliability
would result from subdividing the population size.

Rural/urban-disaggregation is also specifically noted in Table 2 as being required under data
items 7.2 and 7.3, dealing with school enrolment at the primary and secondary levels. It has been
seen in Chapter 2 that UNESCO does not tabulate data from the administrative records of
ministries of education by rural/urban area. One reason for this, at the secondary level at least,
is the fact that rural children are often educated in urban schools. At the primary level, however,
schools are local so that rural/urban-disaggregated data would be meaningful. Since data are
obtained from individual schools, there would seem to be no reason why UNESCO should not
request countries to tabulate data for rural and urban schools separately.

Though geographical disaggregation has been addressed in terms of rural/urban location,
which is by far the more common criteria available, alternatives for such disaggregation exist.
These include disaggregation by farm/non-farm household, farming/non-farming household and
agricultural/non-agricultural household. Similar disaggregation can also be done at the population
level. Of the three possibilities, agricultural/non-agricultural is preferred since the other two omit
significant parts of the population of interest. Farm households include only those resident on the
holding, thus excluding both holders and the landless resident off-holding, while farming
households include only those with a household member operating a holding, thus excluding the
landless. FAO recommended definition of an agricultural household is based on a combination of







two criteria of economic activity: a household is considered to be an agricultural household when
at least one member of the household is operating a holding or when the household head,
reference person or main income earner is economically active mainly in agriculture" (FAO,
1978:31). This would imply the combination of data from agricultural and population censuses.
More recently, attempts are being made through supplementary recommendations for the 1990
round of population censuses to obtain an agricultural/non-agricultural dichotomy based on
existing data from population censuses. This would define the agricultural population as all
individuals in households whose heads or reference persons have been identified as principally
engaged in agriculture. This will be biased to the extent that some excluded households contain
persons engaged in (or dependent on income from) agriculture, and to the extent that some
included households may contain persons engaged in (or dependent on income from)
non-agricultural activities.

A further alternative for geographical disaggregation is agro-ecological zone, but its definition
is relatively complex. This variable is being used in the analyses of the Living Standards Surveys
of the SDA Programme of the World Bank.


Other Disaggregation

Gender- and rural/urban-disaggregation are not enough to define meaningful data on women
in agriculture and other specific disaggregation is often required. This includes the appropriate
disaggregation of occupational and industrial classifications and the disaggregation of subjects of
study.

The detail noted in Table 2 almost all relates to the need for tabulation of ISCO-88 codes at
the four-digit level. Such tabulations would be made available in national reports (of population
censuses and labour force surveys) rather than in the ILO Year Book. Recommendations for such
detail are, however, needed from the UN.

Other detail is required of agricultural censuses, and involves the extension of
gender-disaggregation to cover occasional workers (data item 1.6). In fact, this has been
recommended in supplementary guidelines (FAO, 1990b, 1990c), but it's questionable as to how
many countries will implement these without further emphasis.

In addition to these specific needs for disaggregition, other disaggregation of a general nature
may be significant for describing the situation of women in agriculture. Age-disaggregation, for
example, provides an added dimension that may be important. This has already been specified in
Table 2 for certain required data items (for example, in time-use studies where younger women
are expected to undertake a more onerous workload than older women) and it may be usefully
obtained more generally.

A further area for disaggregation is socio-economic group (based usually on economic
activity). However, requests for data for WCARRD have already shown that few countries
produce data on women in agriculture also disaggregated by socio-economic group (FAO, 1988a,
p. 2). This area should be given priority once the data bases on women in agriculture as a whole
has been established. Before refinement and detail are added, however, it would be useful to
undertake a survey of the data needs of users of the data bases.

For household data, one of the main problems for women in agriculture is the lack of
individual data. This problem can be partly overcome by appropriate disaggregation of household








data. Thus tabulation by such variables as household income levels by source of income,
socio-economic group, number of earners within the household, number of earners absent from
the household, and household composition would provide indicative information on the concerns
relevant to women in agriculture.


Not Available Data

Table 2 shows that many of the required data items are not currently available at FAO at the
national level. Some of these could be obtained at the national level, as indicated in the table, but
many more would be better obtained at the sub-national level. Some of the non-available data may
in fact exist within countries; the means of obtaining such data are addressed in Chapter 4.

One of the major omissions in the available national data on women in agriculture is data on
the landless (data item 2.1 in Table 2). These are by definition not included in holding-based
agricultural censuses, except as permanent labour. Though the landless are included in population
and housing censuses and household surveys as part of the total population, these sources do not
usually ask about land ownership or other forms of land tenure. The enumeration of the landless
has been discussed by ESS and ESH following the Expert Consultation on Landlessness (FAO,
1986e). The three approaches recommended are to cover landless labourers in agriculture (i) in
a population census, (ii) at the household listing stage of developing a frame of holdings for an
agricultural census or (iii) by obtaining a frame of landless households while developing a frame
of holdings for an agricultural census and then conducting a special survey of the landless. It
might also be possible to introduce a question on land tenure into existing rural household
surveys. In all cases, tabulations should involve individual land ownership by gender or household
land ownership by gender of head of household cross-tabulated with other variables of interest.

Many of the required data listed in Table 2 as not available might be obtained from
population and housing censuses and household surveys. These include household data by gender
of head of household on ownership or access to private transport (data item 2.9) and on dwelling
condition (item 8.1). Dwelling condition is partially addressed in the UN-recommended tabulations
for housing censuses through dwelling construction, which is available in national census reports.
Community variables such as distances to primary school (item 7.2), public transport and surfaced
roads (item 8.2), fuelwood (item 8.3), potable water (item 8.4), health facilities (item 8.5), social
services (item 8.6), and markets (item 10.1) could also be obtained from population and housing
censuses and household surveys. Some censuses obtain data on community variables, and
individual reports should be consulted. In general, however, these data are not well-covered nor
standardized.

Other data that could be made available from population censuses are data on migration. Both
gross and net data are required. Migration data are collected and tabulated in national reports, but
are not compiled internationally. In general, published data would require some manipulation in
order to obtain the gender-disaggregated adult migration rates required under item 13.4. National
census reports, both of tabulations and migration analysis, should be consulted.

Migration data relevant to women'in'agriculture also include absence of male spouse and
absence of adults by gender from the household (item 8.2). Absent spouses can be identified by
including a 'married with absent spouse' category in marital status, to be carefully distinguished
from separated couples (referring to marital breakdown). This should ideally be further
disaggregated by length of absence or distance involved since many males maintain control over
household decisions through weekend visits or messages. This would enable better identification







of female and male household heads in-households with an absent male. Data on absent spouse
might be obtained from population cersuses and household surveys, but further detail including
that on other absent household members is best restricted to household surveys where more time
can be devoted to identifying defacto and dejure household members.

Data on international out-migration can only provide background data for women in
agriculture, and are obtained from statistics on international travel. Many countries do not
disaggregate these data by gender, and very few would include rural/urban area of origin of the
out-migrant.

The remaining data items in Table 2 that are not available are those dealing with membership
of agricultural or rural development organizations (items 3.1 to 3.6) and education in agriculture
and rural development (items 7.4 and 7.5). National data may be available from administrative
records, some of which may be published in annual statistical reports.


IMPROVEMENT OF SUB-NATIONAL DATA

The brief review of sub-national data in Chapter 3 does not permit a thorough evaluation of
where and how improvements could be implemented. It is clear, however, that the immediate need
is to increase the volume of data. The creation of the data bases will serve to organize the
available data so that areas and topics may be targeted for data collection as part of FAO
activities, and as part of the activities of other agencies. The data bases will hopefully encourage
research on women in agriculture, both on the data made available and in those areas where data
are not available thus increasing the overall volume and coverage.

One potential, source of data on human resources is FAO's own projects. This includes
baseline studies, especially those undertaken by women in agriculture projects (see, for example,
Mayona et al., forthcoming). The encouragement of the production of gender-disaggregated data
is already a part of thi WID training programme for all FAO professional staff. This training
might be usefully supplemented by a set of guidelines or a manual on gender issues in data
production to be used by project personnel as reference material.

The farming systems approach and the People's Participation Programme are well-suited to
the collection of data on human resources, and would appear to be the most appropriate avenues
through which data production on women in agriculture can be encouraged. Greater collaboration
between ESHW and both AGSP and ESHA is thus recommended to ensure that the
gender-disaggregated data are produced.


OBTAINING EXISTING DATA FROM COUNTRIES

At both the national and sub-national level, data undoubtedly exist which are not available
in FAO. The problem is to locate such data within countries and to arrange for their transmittal
to FAO. This might be accomplished through the field offices of FAO (or other UN agencies).
Data bases staff at FAO Headquatters would compile a list of sources for each country, based on
the reviews in Chapters 2 and 3, UN listings (e.g., UN, 1982 and updates;), library holdings and
other references. These sources would be marked according to their availability in FAO, and field
staff asked to try to locate those not available (in FAO) and to send the document to Headquarters.
Field staff would be asked to add any additional sources that they find and also to send these to








Headquarters. This system is designed to involve a minimum of work for field staff and to ensure
a response. Where field staff are not available, national statisticians might be approached.

As a second stage, if deemed necessary and resources permit, special data searches in-country
could be made by field staff, data bases staff or consultants in order to locate data not covered at
the first stage. This process would be similar to the FAO special data collection programme
undertaken in 1984-5, which was successful in obtaining data on women in agriculture from
several countries. This programme tended to produce special country documents containing a
collection of data from various sources, but this is not considered necessary for the data bases.
Indeed, it is considered preferable to have complete individual data sources transmitted to FAO,
rather than copies of relevant tables or a collection of tables, because of the need to consult data
collection practices. These documents would also enrich FAO's existing library holdings.

Once published material has been assembled, this process might be expanded to cover
unpublished material as suggested by Scott (1989). In addition to this, governments should be
encouraged to improve the production of data on women in agriculture following existing UN
recommendations and the suggestions made in this report.


TRAINING FOR IMPROVED DATA COLLECTION

The above discussion on the improvement of data on women in agriculture addresses ways
in which UN agencies might assist in this process. Their efforts, however, are not assisted at the
national level by prevailing attitudes towards women in general and the production of data on
women. There is thus a need for training of statistical personnel in the uses of data, and for
greater producer-user collaboration in the production of data.


Training for National Statisticians

One of the main obstacles to the production of relevant data on women in agriculture is a lack
of recognition on the part of statisticians of the need for and uses of such data. Coupled with this
is a blindness to the ways in which methodologies and practices omit or under-represent women.
These obstacles can be addressed through appropriate training.

The UN and INSTRAW have accomplished much in this area, including the publication of
three reports dealing with the effective use of existing data (UN, 1984b), the improvement of
concepts and methods (UN, 1984c) and the development of statistics and indicators through
household data (UN, 1988). In addition, several regional training workshops have been held, for
example in Harare, Zimbabwe (UN, 1987). These workshops involve both the producers and
users of statistics. A similar workshop for the Pacific was held in Noumea in 1987 by the South
Pacific Commission (SPC, 1987).

While such training is undoubtedly beneficial to the improvement of data on women in
agriculture, it does not usually address the underlying obstacle of negative attitudes towards
women and might be much more effective if it were to be preceded by WID training. There is
a need for WID training for statisticians on a global scale. For agricultural statisticians, FAO
might be involved.






39
National Producer-User Collaboration

The production of statistics by national statistical offices is essentially a service provided for
government departments and other users, including women's leaders. As users, women's leaders
should define the data on women that they require, preferably at an early stage so that adequate
data are not only collected but appropriately tabulated. INSTRAW and the UN have initiated
training in statistics for women's leaders through the regional workshops already mentioned (e.g.,
UN, 1987). Again, FAO might be involved where agricultural statistics are involved.









Chapter 5


Existing Data Bases




INTRODUCTION

This chapter reviews existing data bases of relevance to women in agriculture and examines
the relationship between these and the data bases on women in agriculture currently being
developed. The two main existing data bases are AGROSTAT (including WCARRD) and
WISTAT, though other data bases may also contain additional data of interest.


REVIEW OF EXISTING DATA BASES

AGROSTAT

AGROSTAT is the FAO data bases containing data from FAO, ILO and the World Bank. It
is made available to FAO (Headquarters), ILO and the World Bank. AGROSTAT consists of 12
FAO and 8 external domains as follows:


FAO:

CLF Crops, Livestock and Fishery Products
SUA Supply/Utilization Accounts
CBD Commodity Balances Demand
CBS Commodity Balances Supply
PIN Production Index Numbers
TIN Trade Index Numbers
LUI Land Use, Inputs and Production
EAA Economic Accounts for Agriculture
DEA Demographic Estimates Annual Series
DEL Demographic Estimates Long-term Series
FOR Forestry Products
ARD WCARRD


External:

GDP National Accounts
GET UN External Trade Statistics
IFS International Financial Statistics
GFS Government Financial Statistics
WCP Wages, Consumer Price Indices
WNA World Bank National Accounts
WSI World Bank Socio-Economic Indicators
WBD World Bank Debt.








The data contained in AGROSTAT are only those that are comparable across countries. Very
few cover people; even fewer cover women and men separately. Data from agricultural censuses
are not included in AGROSTAT.

Among the demographic estimates, DEA (Annual Series) and DEL (Long-term Series) of the
FAO domains, only one item is of direct interest to women in agriculture: economically active
population in agriculture by gender. This is available annually for 1961-1988 and every five years
for 1950-2025. The annual and five-yearly values for 1950-1985 are estimates, whereas the
five-year values for 1990-2025 are projections. The population estimates are UN population
estimates (UN, 1989e) and labour force estimates are obtained using ILO assessments of age- and
gender-specific economic activity rates (see FAO, 1986d). DEA and DEL also contain data on
the agricultural population. DEL includes a list of 17 demographic indicators, none of which refer
to both women and agriculture.

Among the external domains, Wages, Consumer Price Indices (WCP) contains gender-
disaggregated data on wages and earnings in agriculture. These are made available by the ILO and
cover hourly, daily, weekly and monthly incomes.

World Bank Socio-Economic Indicators are contained in WSI, as are data from World Bank
(1988-89). None of the socio-economic indicators are directly relevant to women in agriculture
since either rural/urban- or gender-disaggregation is missing. The remaining data are not of
interest to women in agriculture: most are economic and the nine social indicators do not address
women in agriculture.


WISTAT

WISTAT is the United Nations Statistical Office microcomputer data bases on women (UN,
1988). It contains data published by several UN agencies, the United States Bureau of the Census
and the Population Council. The data are obtained mainly from population censuses, and both the
1970 and 1980 census rounds are reported. Projections, where relevant are available for 1990,
2000 and 2025. Table 7 details coverage of data series relevant to women in agriculture: coverage
of women in agriculture in population and economic activity data is good, but literacy, educational
attainment and fertility data are less commonly available.


THE RELATIONSHIP BETWEEN DATA BASES

The relationship between the data bases on women in agriculture and the two main existing
data bases, AGROSTAT and WISTAT, is essentially complementary. The three data bases serve
different functions with different groups of users. It has been seen above that AGROSTAT is
concerned mainly with agriculture at the macro level and contains little on human resources,
whereas WISTAT contains data on women. Neither covers women in agriculture adequately, and
both are restricted to data at the national level. The data bases on women in agriculture will bridge
these two existing data bases, forming a separate entity containing some of the same data (the
relevant women in agriculture subsets of AGROSTAT and WISTAT data) as well as other data,
both national and sub-national.

This duplication of content in the data bases is not seen as a problem. Indeed, it is envisaged
that some of the natioiial data compiled for the data bases on women in agriculture might be
incorporated into AGROSTAT. This would make the data available to a wider range of users and







might encourage gender issues to be taken into account more frequently. In incorporating such
data, it is not intended that a separate gender file be created, but rather that the data be
incorporated in appropriate files in the same way that existing AGROSTAT files contain UN and
ILO data. Such provision of data on women in agriculture might be of particular interest to the
WCARRD data bases, which is also created as a separate entity and incorporated as appropriate
into AGROSTAT. In relation to WCARRD, there is a need for coordination in order to avoid
duplication of effort, though many of the currently required indicators for WCARRD do not
include gender.

Coordination is also required in relation to WISTAT..In the longer term, it is envisaged that
national data on women in agriculture will be improved as a result of efforts made by FAO and
other agencies. Such data will undoubtedly be incorporated into WISTAT as well as into the FAO
data bases on women in agriculture, and coordination in their compilation is required.

Regarding other data bases, these should be examined for relevant data that are not already
available in AGROSTAT or WISTAT, for incorporation into the data bases on women in
agriculture. Coordination and collaboration with these data bases should be encouraged.









Recommendations


GENERAL DATA RECOMMENDATIONS

In some cases, data on women in agriculture are not available because of the general paucity
of data in a country. The basic need in such cases is for data collection per se. UN agencies are
recommended through their data collection guidelines and advisory role.to:


a) improve country coverage of agricultural censuses, population censuses, household
surveys and the collection of statistics in general;

b) improve country coverage of specific items in data collection sources, e.g., human
resources in agricultural censuses, literacy in population censuses;



GENDER-DISAGGREGATION

Gender-disaggregation is more common in strictly demographic tabulations than in data
dealing with other areas such as employment. Equally for households, where gender of head of
household is identified, it is related only to household composition rather than to the economic
responsibilities of the head. To achieve greater recognition of female household heads in data
collection, UN agencies are recommended to:


a) adopt the defacto approach to household membership;

b) distinguish 'married with absent spouse' from 'married with present spouse' and from
'separated due to marital dissolution';

c) obtain length of absence for absent spouses and other adult male members of the
household.


In tabulation, UN agencies are recommended to:


d) place greater emphasis on the importance of gender-disaggregation in tabulations of
socio-economic characteristics, such as employment;

e) recommend gender-disaggregation of head of household in tabulations from income,
consumption and expenditure surveys and other household surveys;

f) recommend the tabulation of gender-disaggregated intra-household data by economic
variables such as economic activity, education and contribution to household income.

Many data exist that could be retabulated for gender issues as a quick and cost-effective way
of obtaining data on women in agriculture. These include:








g) Review of FAO training activities;

h) FAO yearly analysis of fellowships and study tours;

i) FAO personnel;

j) raw data from studies conducted by ESN in various countries.



RURAL/URBAN-DISAGGREGATION

The absence of rural/urban disaggregation is one of the reasons for the poor coverage of data
on women in agriculture. In order to increase rural/urban disaggregation in population censuses
and household surveys, it is recommended that:


a) greater emphasis be placed on rural/urban disaggregation of the total country in UN
guidelines;

b) greater efforts be made to include rural and agricultural households in income and
expenditure surveys;

c) marital status be tabulated by rural/urban area;

d) statistics on school enrolment be disaggregated by rural/ urban area, at least at the
primary level.


It is also recommended that:


e) other forms of geographical disaggregation such as farm/non-farm and
agricultural/non-agricultural households and populations and agro-ecological zone be
adopted as alternatives to rural/urban disaggregation where appropriate and feasible.



OTHER DISAGGREGATION

The following recommendations have been identified involving other disaggregation of data:


a) tabulate holdings by gender of holder by legal status of holder;

b) collect and tabulate gender of occasional labour;

c) tabulate activity status and work on holding of household members by gender by age;


d) tabulate age by gender of holder against other variables;







e) UN guidelines to recommend detailed tabulation of ISCO-88 codes covering major
group 6 and sub-major group 92 (by gender);

f) UN agencies to monitor coverage of women in agriculture under ISCO-88 and new
definitions of employment;

g) disaggregate by age where feasible;

h) disaggregate by socio-economic group where possible.



OMISSIONS

Certain data items are not available or are deficient for methodological reasons. It is
recommended that:


a) FAO define criteria for use in setting a minimum size of holding for inclusion in
agricultural censuses;

b) for agricultural surveys, oversampling to be considered as a means of improving
coverage of women holders if they are a relatively small proportion of the total;

c) labour approach to human resources be adopted rather than the household approach,
including better identification of holder;

d) data on the population usually active in agriculture to be made available;

e) data on the landless be obtained through population censuses and household surveys;

f) population and housing censuses and/or household surveys include questions on
community variables, such as distance to schools and potable water, and access to
amenities.



COOPERATION

Cooperation between FAO Divisions is needed to produce the required data on women in
agriculture. It is recommended that:


a) ESS and ESH collaborate with AGO, Fisheries and Forestry to increase the production
of data on human resources by gender. in project activities;

b) in particular, ESHW collaborate with AGSP and ESHA in order to increase the volume
of gender-disaggregated data available from Farming Systems Development and the
People's Participation Programme respectively.








Cooperation is also needed between existing data bases, and it recommended that:


c) the women in agriculture data bases collaborate with AGROSTAT and WCARRD in
the compilation of data;

d) the women in agriculture data bases coordinate data bases development with WISTAT;

e) the women in agriculture data bases coordinate and collaborate with other data bases
as appropriate.



TRAINING

Attitudes are the main obstacle to the disaggregation of data by gender. Training is thus
required in the needs and uses of gender-disaggregated data. Better user-producer communication
is also desirable. The following recommendations should be addressed by international agencies:


a) conduct WID training for statisticians including agricultural statisticians;

b) conduct training workshops for the users and producers of statistics on women,
including women in agriculture;

c) development of a manual or guidelines on gender issues in data production in projects.






Tables



Table 1

General Areas Relevant to Women in Agriculture
Identified in the FAO Plan of Action on
Integration of Women in Development and WCAARD


1. Agricultural Employment

1.1 Women's participation in agricultural labour force
1.2 Women's participation in market-oriented agriculture
1.3 Women's participation in fisheries
1.4 Women's participation in forestry
1.5 Women's participation in subsistence
1.6 Women's participation as occasional, temporary and permanent labour


2. Access to Means of Production

2.1 Women's access to land
2.2 Women's access to water/irrigation
2.3 Women's access to drainage
2.4 Women's access to credit and revolving funds
2.5 Women's access to agricultural inputs (seeds, pesticides, fertilizers)
2.6 Women's access to technology (tools and machinery)
2.7 Women's access to extension services
2.8 Women's access to transport


3. Membership of Agricultural/Rural Organizations

3.1 Women's membership of cooperatives
3.2 Women's membership of credit unions
3.3 Women's membership of marketing organizations
3.4 Women's membership of labour unions
3.5 Women's membership of marketing organizations
3.6 Women's membership of community organizations


4. Productivity

4.1 Women's productivity


5. Time-Use


5.1 Gender division of labour







6. Decision-Making

6.1 Women's control of own income
6.2 Gender division of control of household income
6.3 Gender division of household decision-making
6.4 Women's participation in community decision-making
6.5 Women's participation in project-level decision-making and above through
professional positions


7. Education

7.1 Women's literacy
7.2 Women's access to primary education
7.3 Women's access to secondary education
7.4 Women's access to agricultural education
7.5 Women's access to rural development education
7.6 Women's access in FAO training


8. Housing and Facilities

8.1 Housing conditions (including water, electricity, sanitation)
8.2 Access to communications (radio, transport, roads)
8.3 Access to fuelwood
8.4 Access to potable water
8.5 Access to health facilities
8.6 Access to social services


9. Income and Expenditure

9.1 Income of female-headed households
9.2 Gender differentials in wages in agriculture


10. Marketing

10.1 Access to markets


11. Nutrition and Food Consumption

11.1 Expenditure on food of female-headed households
11.2 Gender differentials in wages in agriculture
11.3 Food consumption of women and female children


12. Household Composition

12.1 Women's household headship rates
12.2 Female-headed households with absent male(s)
12.3 Average household size of female-headed households
12.4 Composition of female-headed households





51




13. Demography

13.1 Population characteristics
13.2 Fertility
13.3 Mortality
13.4 Migration
13.5 Population Growth



* Note that all data are to be gender-specific (except community variables).








Table 2
Women in Agriculture: Data Requirements and Availability


Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

1. EMPLOYMENT persons economically active in agriculture by gender A FAO: AGROSTAT, DEA


1.1 Labour force persons economically active in agriculture (industry) by A ILO: YB, T2A
employment status by gender

persons economically active in agriculture (occupation) by A ILO: YB, T2B
employment status by gender

persons economically active in agriculture (industry and A ILO: YB, 2C
occupation) by gender

holdings by work of holder's household members on G FAO: AC
holding by gender

holdings by number of permanent workers by gender A FAO: AC

1.2 Market-oriented persons economically active in market-oriented agriculture D PC/LFS: Codes 611 to
agriculture by gender 613 of ISCO-88 to be
tabulated at 4 digit level

1.3 Fisheries persons economically active in market-oriented fisheries by D PC/LFS: Codes 6151 and
gender 6152 of ISCO-88 to be
tabulated

1.4 Forestry persons economically active in market-oriented forestry by D PC/LFS: Codes 6141 and
gender 6142 of ISCO-88 to be
tabulated







Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

1.5 Subsistence persons economically active in subsistence agriculture and D PC/LFS: Code 62 of
fishing by gender ISCO-88 to be tabulated

1.6 Permanent/ temporary holdings by number of persons employed in agriculture by D FAO: AC: occasional to
labour gender by occasional/permanent status be tabulated

2. ACCESS TO MEANS OF PRODUCTION

2.1 Land holdings by gender of holder by tenure by size of holding A FAO: AC (90)

persons in landless rural households by gender by age N PC: RHS; SN: PPP

rural households by gender of head of household by N PC: RHS; SN: PPP
landownership/ rent/landlessness

2.2 Water holdings by gender of holder by whether any land is A FAO: AC (90)
irrigated

plots by gender of holder by distance to water N SN: FSD


2.3 Drainage holdings by gender of holder by whether holding has N FAO: AC (90)
drainage facilities

2.4 Credit farm household by gender of farmer by whether agricultural N SN: FSD
credit received during specified period








Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

2.5 Agricultural inputs holdings by gender of holder by use of high yield variety A FAO: AC (90)
seeds

holdings by gender of holder by use of pesticides and A FAO: AC (90)
herbicides

holdings by gender of holder by use of fertilizers A FAO: AC (90)


2.6 Technology holdings by gender of holder by use of appropriate A FAO: AC (90); see
technology (specified) Annex 57 for list of tools
and machinery

2.7 Research holdings by gender of holder by use of new N SN: FSD
techniques (specified)

2.8 Extension services holdings by gender of holder by use of agricultural N SN: FSD
extension services

number of agricultural extension workers by gender by type A FAO: directory of
extension
number of recipients of extension services by gender by N FAO: global consultation
type of service on extension


2.9 Transport holdings by gender of holder by holder's use of private N FAO: AC: need holder's
transport use of private transport

rural households by gender of head of household by
availability of private transport N PC; RHS; SN







Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source
3. MEMBERSHIP OF AGRICULTURAL/RURAL ORGANIZATIONS

3.1 Cooperatives members of cooperatives by gender by type of cooperative N AR; SN: FSD, PPP

3.2 Credit unions members of credit unions by gender N AR; SN: FSD, PPP

3.3 Marketing organizations members of marketing organizations by gender N AR; SN: FSD, PPP

3.4 Labour unions members of labour unions by gender N AR; SN: FSD, PPP

3.5 Tenants associations members of tenant's associations by gender N AR; SN: FSD, PPP

3.6 Community organizations members of community organizations by gender N AR; SN, FSD, PPP

4. PRODUCTIVITY

4.1 Productivity productivity of labour by gender N AR; SN: FSD, PPP

5. TIME-USE

5.1 Division of labour average time per day spent on specified domestic activities N SN: FSD
by gender by age

average time per day spent on specified activities involved N SN: FSD
in food production by gender by age

average time per day spent on specified activities in cash N SN: FSD
crop production by gender by age
6. DECISION-MAKING

6.1 Control own income persons with cash income by control over own income by N SN: FSD
gender








Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

6.2 Control over household households with cash income by gender of head of N SN: FSD
income household by head's control over household income

6.3 Household decisions households by gender of head of household by head's N SN: FSD
control over household decisions

6.4 Community decisions office bearers in agricultural/ rural organizations by gender N SN: FSD, PPP

6.5 Project-level decisions and persons economically active in agricultural professions by D PC/LFS: detailed
above gender tabulations of ISCO-88


persons employed by FAO in professional/managerial A FAO: Personnel Data
grades by grade by gender by category by length of service (monthly)
and age

7. EDUCATION

7.1 Literacy rural population by gender by literacy by age A UN: DYB, 1988, T32,33
UNESCO: YB, T1.3

7.2 Access to primary rural population enrolled in primary school by gender R UNESCO: YB, T3.4
education
rural population attending primary school by gender A UN: DYB, 1988, T35

rural population by gender by age by educational attainment A UN: DYB, 1988, T34

rural households by distance to primary school N PC: village/
household level







Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

7.3 Access to secondary rural population enrolled in secondary school by gender by R UNESCO: YB, T3.7, 3.8
education grade
UN: DYB, 1988, T35
rural population attending secondary school by gender by A
grade

see also 7.2 (Educational Attainment)

7.4 Access to agricultural persons enrolled in agricultural further education by gender A UNESCO:YB,T3.123.13;
education by level Directory (Annex 58)

persons completing agricultural further education by gender N AR

pass rates for agricultural courses by gender
N AR

7.5 Access to rural persons enrolled in rural development further education by N AR
development education gender

persons completing rural development further education by N AR
gender

pass rates for rural development courses by gender N AR

7.6 Access to FAO training participants in FAO training activities by gender A FAO: Review of Training
Activities, T3
fellows by gender by region by origin by type of training A FAO: Yearly Analysis of
Fellowships and Study
Tours, TI









Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

8. HOUSING AND FACILITIES


8.1 Housing


8.2 Access to communications rural households by gender of household by radio ownership

rural households by distance to public transport

rural households by distance to surfaced roads


* rural households by gender of head of household by
dwelling condition

* rural households by gender of head of household by water
supply system

* rural households by gender of head of household by
electricity system

* rural households by gender of head of household by
sanitation

* rural households by gender of head of household by persons
per room

* rural households by gender of head of household by persons
by age

* rural households by gender of head of household by
dwelling tenure


PC


UN: CHSS, T18


UN: CHSS, T19


UN: CHSS, T18


UN: CHSS, T17


UN: CHSS, T21


UN: CHSS, T20


UN: CHSS, T28

PC/RHS: village/
household level
PC/RHS: village/
household level








Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source
8.3 Access to fuelwood rural households by distance to fuelwood N PC/RHS: village/
household level
see also 5 (Time-Use)
8.4 Access to potable water rural households by distance to potable water N PC/RHS: village/
household level
see also 5 (Time-Use)

see also 8.1 (Housing Conditions)
8.5 Access to health facilities rural households by distance to health facilities by type of N PC/RHS: village/
facility household level
8.6 Access to social services rural households by distance to social services N PC/RHS: village/
household level
9. INCOME

9.1 Household income rural households by gender of head of household by G IES; SN
household income by household size
9.2 Wage rates average wage rates of agricultural labour by gender A ILO: YB,T21
FAO: AGROSTAT, WCP
10. MARKETING

10.1 Access to markets rural households by distance to market by type of market N PC/RHS: village/
household level; SN: FSD
see also 2.9 (Transport)

see also 8.2 (Public transport and surfaced roads)








Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

11. NUTRITION

11.1 Expenditure on food expenditure on food of rural households by gender of head G FAO: Review of Food
of household by household size by food item Consumption Surveys

11.2 Nutritional status anthropometric measurements by gender by age A RHS

11.3 Food consumption calorie and protein intake by gender by age N SN

12. HOUSEHOLD COMPOSITION

12.1 Head of households rural households by gender of head of household A UN: DYB, 1987, T33,
T34
rural households by gender, age and marital status of head A UN: DYB, 1987, T35
of household

12.2 Absent male (s) rural households with female head of household by marital N PC/RHS: absent spouse
status of head (including category for absent spouse) category needed

rural households by gender of head of household by absence N PC:RHS:de facto and de
of household members by gender and age of absent jure comparison needed
members by length of absence

12.3 Household size rural households by gender of head of household by A UN: DYB, 1987, T33
household size

rural population in households and number of households by A UN: DYB, 1987, T36,
household size T37







Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

12.4 Composition rural households and population by gender and age of head A UN: DYB, 1987, T36,
of household by household size and members' relationship T37
to head

rural households by gender and age of head of household by A
household size and type of household UN: DYB, 1987, T38

13. DEMOGRAPHIC DATA/INDICATORS

13.1 Population rural households by gender by age A UN: DYB, T7
characteristics
rural households by gender by age by marital status R UN: WISTAT, T4.1


13.2 Fertility age-specific fertility rates for rural women A UN: WISTAT, T4.4


13.3 Mortality maternal mortality rate for rural women R UN: DYB, T17

infant mortality rate by gender for rural population A UN: DYB, T16

child mortality rate by gender for rural population A UN: DYB, T20

life expectancy at birth by gender for rural population R UN: DYB, T22

13.4 Migration gender specific rural-to-urban adult migration rates N PC

gender-specific rural-to-rural adult migration rates N PC

gender-specific adult international out-migration rates N International Migration
statistics























2. Data sources (national):


Availability refers to national data sources held in FAO
A Available
B Partially available collected but further disaggregation needed:
G Gender
disaggregation
R Rural/urban
disaggregation
D Other


AR Administrative Records
AC Agricultural Censuses
AC(90) Agricultural Census: 1990 round only
CHSS Compendium on Human Settlement Statistics
DEA AGROSTAT filename
DYB Demographic Yearbook
IES Income and Expenditure Surveys
LFS Labour Force Surveys
PC Population and Housing Censuses and Surveys
RHS Rural Household Surveys
WCP AGROSTAT filename
YB Yearbook


disaggregation


N Note available


SN Sub-national sources:
FSD Farming System Development
PPP People's Participation Programme
T Table


Availability: Source/
Subject/Item Data Required National level Proposed
A P N Source

13.5 Growth growth rate of rural population A UN: See UN, 1989h


Notes:
1.








Table 3
Data on Women in Agriculture Published by FAO from 1980
Round of Agricultural Censuses


Holder Holder and Household Permanent
Workers
COUNTRY
Sex Age Sex Persons Persons House- Persons by sex
by by sex by sex hold
Age by age size

American Somoa X -

Antigua & Barbuda -

Australia X X X

Austria XH XH XHE XH

Bahamas XH XH XH XHE XH XH

Bahrain XH -

Bangladesh X XC

Belgium XH -- XEH XH

Belize XH XH XH

Botswana

Brazil -XHE --XH

Cape Verde -

Canada X -

Central African Republic X X X

Congo X X X X X

Cyprus X XH XE -X X

Czechoslavakia X

Denmark X XE X

Ethiopia X X X XE X XA

Fiji X X X XE XE

Finland XH XH XH -

France X X X X

French Guyana X X -

Germany (Federal XH XHE XH
Republic of)

Grenada X X

Guadeloupe X X X

Guam X

Guatemala -

Hungary -XH










Holder Holder and Household Permanent
Workers
COUNTRY
Sex Age Sex Perspns Persons House- Persons by sex
by by sex by sex hold
Age by age size

Ireland --

Israel X -

Italy X X --

Jamaica XH XH -

Japan XH XEH XH -

Jordan XH XH XEH XEH XH XH

Kenya X X X X X

Korea XH XH XH XH

Luxembourg XH XH XH XH -

Madagascar X -

Malawi X -

Malta- XE XE X

Martinique X X X -

Mauritania X

Nepal XH XH X XC -

Netherlands -XH XHE XH

New Zeland -XH

Niger XH XHE XH XH

Northern Mariana Is. X -

Norway XH -

Oman X X X X

Pakistan XE XC X

Panama X XE X -

Paraguay X X X

Philippines XH -

Poland -

Portugal XH XH XH XH XH

Puerto Rico -

Reunion X X -

Rwanda X X XH XCH X

Saudi Arabia XH XCH XCH XH

Sierra Leone XH XH XH XH XH XH









Holder Holder and Household Permanent
Workers
COUNTRY
Sex Age Sex Persons Persons House- Persons by sex
by by sex by sex hold
Age by age size
Spain XH XH XH XH

Sri Lanka X X X X -

Suriname -

Sweden XH XHCE XHCE -XH

Switzerland XH -- XH


Thailand XH XH XH XH XH

Togo X XH XH

Tonga X X X X

Trinidad and Tobago -

Turkey X X -

United Kingdom XH -

U.S.A. X X -

Uruguay X X XE

Virgin Is. (U.S.A.) X -

Yemen Arab Republic X XE -

Yugoslavia XHE XH XH

TOTAL: 78 33 52 1 46 27 16 24


Source: FAO, Report on the 1980 World Census of Agriculture.
Census Bulletin Nos. 3-28.

Notes: X Data available
H By size of holding
E By economic activity
C Age distinguishes only children, adults, elderly
A By age also













Table 4

ILO Yearbook: Number of Countries Giving Data by Gender




Table Number Table content Coverage



2A Economically active/industry/status 92
2B Economically active/occupation/status 73
2C Economically active/industry/occupation 48
3B Employment/industry 78
3C Employment/occupation 43
10B Unemployment/industry 37
10C Unemployment/occupation 48
21 Wages in agriculture 21






67

Table 5

Demographic Yearbook: Number of Countries Giving Data by Gender,
by Rural/Urban Area and for Total Country


Table Table Content2 Rural/Urban Total'
Number Coverage3 Coverage

Regular Tables (1988)

7 Population/Age 92 167
9 Live births/crude rate 41 192
10 Live births/age of mother 45 122
11 Live births/age-specific rates 30 104
12 Late foetal deaths/ratios 24 80
15 Infant deaths/rates 40 185
16 Infant deaths/gender-specific 4 102
19 Deaths/gender-age-specific 51 129
20 Deaths/gender-age-specific rates 33 86
23 Marriages/rates 29 73


Special Topic (1988)

31 Localities/size -- 51
32 Literacy/age 15 58
33 Illiterate/total population 15+ 15 56
34 Educational attainment/age 11 72
35 School attendance/age 13 56
36 Economically active/rates/age 16 64
37 Not economically active/category 13 57
38 Economically active/industry/age 7 59
39 Economically active/occupation/age 3 62
40 Economically active/status/age 4 57
41 Economically active/status/industry 4 39
42 Economically active/status/occupation 3 36
43 Economically active/marital status/age 4 27


Special Topic (1988)

31 Population/age/living arrangements 9 71
32 Population/institutional 10 56
33 Households/head/age/size 9 72
34 Headship/rates/age 9 69
35 Households/head/age/marital status 7 62
36 Households/head/age/size/members 3 25
37 Population/household head/size/members 3 20
38 Households/head/age/size/type 2 31


Notes: 1. Tables 9, 10, 11, 12 and 15 are not gender-specific, but refere to women.
2. Refer to Annexes 24 to 28 for exact published titles.
3. Frequencies given here are maxima in that some detail, such as age, is not always complete for every
country. Note that countries with data disaggregated by rural/urban area are mostly included in the
frequency for countries with data for the total country.

















Table 6

UNESCO Yearbook: Number of Countries Giving Data by Gender,
by Rural/Urban Area or Agricultural Field of Study


Table Number Table content Coverage

1.3 Literacy 58
1.4 Educational attainment 32
3.12 Enrolment/agriculture 97
3.13 Enrolment/agriculture/level 75
3.14 Graduates/agriculture/level 72








Table 7

Coverage of WISTAT Data Series Relevant to Women in Agriculture


3 Some countries provide data for periods prior to 1970. Those numbered below provide no data at all.

' Estimates and projections available for 1970 to 2025.

2 Including 10 countries for which recent non-census data are available instead of 1980 round of population
censuses.


1980 1970 No
Series Table Content Round Round Data3
only

1.2 Population/age/rural 151' 14 13
1.3 Population/rural 24 118 36

2.1 Illiteracy/age/rural 30 27 121

2.2 Educational attainment/age/rural 22 23 33

3.2 Total, economically active and not economically 29 15 34
active/rural

3.5 Economically active/agricultural occupation 1122 18 58

3.6 Economically active/agriculture 151 27

3.7 Economically active/agriculture/ 109 18 61
employment status

4.4 Age-specific fertility rates/rural 27 8 43









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FAO (1978) Social Indicators and Social Statistics in the Context of FAO's Concerns.

FAO (1981) Preparation of Baseline Studies on Women in Rural Households. FAO: Rome.

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ILO/INSTRAW Women in Economic Activity: A Global Statistical Survey (1950-2000).
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UN (1980) Sex-Based Stereotypes, Sex Biases and National Data Systems. ST/ESA/STAT/99.
UN: New York.








































































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