B1. Context and initial action
B2. Methodology utilized
B3. Specific results
International meetings have put gender issues on the agenda in the last 20 years or so, insisting on the need for reliable statistics on men's and women's economic contributions. Increased quantification of women's participation in the productive process, especially in the agricultural sector, has enriched the development debate. The figures have attained sizeable proportions, and yet many planners and decision-makers have either ignored them on the grounds that the data are scientifically flawed, or else taken them into consideration only after concrete confirmation of their relevance has been received.
Pressure from gender advocates and a growing awareness of women's contributions have gradually induced the governements of various nations to take into account that other half of their human resources that they earlier overlooked. This success has been overshadowed, however, by the methodological impossibility of forming a clear picture and description of the feminine population from the existing data, using the available statistical tools and instruments. This has brought out the need for a complete revamping of data collection and processing methods, particularily for agricultural statistics. Indeed, a simple observation of the agricultural sector in developing countries sufficed to illustrate that no complete and accurate picture of agricultural production was possible without factoring in the women.
The ministries of agriculture in almost every country have statistical bureaux which carry out regular surveys in the sector every year or so, in addition to agricultural censuses. These surveys are a major source of raw data which can usually be tapped to evaluate the following items:
· characteristics of the holder and holding;In recent years, agricultural census data have brought to light three data categories concerning women:
· cropped areas and yields;
· inventories of poultry and other livestock, output and by-products;
· the farm population;
· agricultural practices, inputs, tools and equipment.
· the number and characteristics of women heads of holding;These data had many limitations, however. Chapter 3 of this publication examines the unsuitability of the head of holding concept, the blurring of economic and non-economic activities, the lack of awareness of women's productive activities, and other shortcomings.
· the economically active women erroneously classified as unpaid family workers;
· the economically inactive female members of the household.
In addition to these limitations, tabulation plans have often failed to correlate production characteristics, cropped areas, inputs, etc. with holdings by sex of holder.
This case study is about how Tunisia and Benin reoriented the tools and methodologies used in their national agricultural censuses.
In late 1995, Tunisia launched its ninth economic and social development plan. At that time the Ministry of Agriculture made a major planning decision on agricultural guidelines, programmes and action; gender issues were to be incorporated as an essential step towards gender equity for all farmers in the benefits of sectoral investments.
In that same year, the Ministry of Agriculture carried out a survey of agricultural structures to enumerate all large holdings and some medium-sized and smallholdings - a total of 45 000. The purpose of this survey, which had not been done since 1962, was to analyse the structural data of agricultural holdings. The questionnaire contained a dozen items concerning identification, holding type, mode of operation, personnel, use of parcels, tree-growing, the holding itself (analysis), employment, buildings and equipment, use of inputs and financing.
The full results of this far-reaching survey were not available in time for the planning exercise and an analysis of the tabulation plans and raw data revealed gaps in terms of gender-specificity. Despite this, the design of the structure survey did allow cross-tabulation of gender with other variables to obtain disaggregated data on the human resources and characteristics of the holdings.64
64 Data processing of the structure survey from a gender perspective is still in progress.The Ministry of Agriculture, aware that the in-depth survey would produce more data, investigated the possibility of obtaining gender-based socio-economic data on a regular basis. The Base Agricultural Survey, conducted every year since 1970, was selected as the best tool for this purpose. The objective of this survey was to monitor specific variables such as land use, farm employment and the use of livestock and inputs. It also supplied data on an ad hoc basis on gender-disaggregated human resources, differentiated by status (head of holding, household and holding members, salaried workers - permanent and/or temporary, family labour) and productive activities.
The Ministry of Agriculture then opted for a gradual procedure. The first stage was a pilot survey to test a module for evaluating ways of introducing gender considerations into the agricultural survey.
In Benin, a workshop, held in 1992 for users and producers of agricultural and food statistics noted the following:65 "There are very few statistical data for an analysis of the status of rural women and their contribution to agricultural production, despite their numerical and economic importance...". To fill this gap, The Ministry of Rural Development did a pilot survey on the collection of gender-disaggregated statistics as a back-up to the 1994/95 farm year agricultural statistics survey.
65 FAO. 1995. Test-survey on the collection of gender-disaggregated agricultural statistics in Benin. Analysis of the status of Mono and Zou rural women. Volume I: Data methodology and analysis, draft version, p. 1. FAO Regional Office for Africa.As in Tunisia, and for the same reasons, the annual farm survey were used as the principal tool for launching the incorporation of gender considerations within the statistical services, which are the responsibility of the Statistical Analysis, Projections and Analysis Division of the Ministry of Rural Development.
The short-term objective of the pilot survey was to establish an easily accessible database concerning the working conditions of women in the rural sector and their contribution to agricultural production, as well as replenishing the existing agricultural statistics database. The long-term objective was to incorporate the collection of gender-differentiated agricultural statistics into the frame of a national agricultural sample survey.
This pilot survey was evaluated in March 1997 for the purpose of making a gender-based comparison of agricultural statistics and verifying the results of the 1994/95 agricultural statistics survey.
Table 1 shows the working methodologies that were used to evaluate the Benin pilot survey66 and the Tunisian pilot survey.67
66 For Benin, the evaluation of the test-survey is examined, as this was the most recent exercise and the last stage before the incorporation of the statistical apparatus.67 The data are from the paper by Drira, M. 1996. "Enquête-pilote sur l'activités des femmes dans l'agriculture. FAO-Government of Tunisia Project TCP/TUN/4555.
B3.1 Evaluation of the time-use budget
Benin and Tunisia both looked at time-use, a gateway to ascertaining paid and unpaid farm and non-farm activities and, therefore, a way of estimating the value of economic contributions (see Chapter 4).
The statistical tools developed for both countries are listed in Tables 2 and 3. The activities listed were chosen to fit with the socio-economic contexts of each country.
The tools were complicated to use. The evaluation of the time spent on each activity is a fairly difficult exercise, especially when activities that took place in the past are being evaluated. An activity that took place in the past is harder to time. In addition, the enumerators were not wholly familiar with this relatively new tool. Particular caution is required with when dealing with eventual anomalies in the answers.
TABLE 1
Evaluation methodologies
Country |
Survey scope |
Benin |
· Two districts, Mono and Zou,
representative of the economic and agro-ecological zones of Benin, were chosen
as the subject of the test-survey. · |
Tunisia |
· A sample survey was done on a
sample of randomly selected agricultural holdings scattered throughout five
administrative districts - one for each major Tunisian economic and
agro-ecological zone. · |
Sampling plan |
|
Benin |
A two-tier sample survey was used: · |
Tunisia |
A three-tier sample survey was used: · |
Sampling base |
|
Benin: |
· First tier: list of villages
by agro-ecological zone, based on National Statistical Institute maps. |
Tunisia |
Universe is that of the 1995 agricultural structure survey. In
the first tier: |
Sampling |
|
Benin |
· Primary units: six villages
from each of two departments. |
Tunisia |
Sample of 300 agricultural holdings per district for a total
of 1 500; 1 282 of which were medium-sized and smallholdings and 282 of which
were large holdings: |
Country |
Variables observed/topics |
Benin |
· Access to land; |
Tunisia68 |
· Identification and
characteristics of the agricultural holding; |
Tools |
|
Benin |
· A questionnaire with the
following items: location, access to land, ownership of animals, ownership of
tools and equipment, access to extension services and credit, production
activities and distribution of earnings, distribution of waking hours and travel
times, and responsibilities of household members. |
Tunisia |
· Holder's/producer's
sheet, |
Concepts |
|
Benin |
· The usual agricultural census
concepts were not modified. |
Tunisia |
· The usual agricultural census
concepts were not modified. |
68 The new cross-tabulations of sex with variables in the structure survey, as pointed out at the beginning of the case study, will be based on the following: holder/producer profile; labour on the holding; active members of holder's household; relatives of the holder working regularly on the holding but not members of the household; permanent salaried workers on the holding; seasonal or occasional labour; characteristics of the holding -i.e. parcels, livestock, equipment, tools, buildings, inputs and agricultural financing.TABLE 2. BENIN: TIME DISTRIBUTION OF WAKING HOURS
Which of the following activities were carried out yesterday and how long did each take?
Activities (1) |
Code (2) |
Time in hours and minutes |
||
Man (3) |
Wife(4) |
Second wife(5) |
||
Farm work |
/_1_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Land preparation |
/_1_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Ploughing |
/_1_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Ridging |
/_1_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Sowing |
/_1_/_4_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Maintenance |
/_1_/_5_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Irrigation |
/_1_/_6_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Treatments |
/_1_/_7_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Harvesting |
/_1_/_8_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Drying |
/_1_/_9_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Fisheries |
/_2_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Fishing |
/_2_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Repairing gear |
/_2_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Other |
/_2_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Livestock |
/_3_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Pasturing |
/_3_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Care of animals |
/_3_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Other |
/_3_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Domestic chores |
/_4_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Cooking |
/_4_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Gathering fuelwood |
/_4_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Carrying water |
/_4_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Other |
/_4_/_4_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Processing |
/_5_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Gari |
/_5_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Oilseeds |
/_5_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Other |
/_5_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Commerce |
/_6_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Social obligations |
/_7_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Visiting a sick person · |
/_7_/_1_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Wake |
/_7_/_2_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Ceremonies |
/_7_/_3_/ |
/_/_/ |
/_/_/ |
/_/_/ |
· Other |
/_7_/_4_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Entertainment |
/_8_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Child care |
/_9_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Other |
/_0_/_0_/ |
/_/_/ |
/_/_/ |
/_/_/ |
Name:.......................................
Time |
Wake-up time!_!_!_!_! Yesterday....... |
Last week (hours) |
Last month (days) |
Last year (days) |
||||||||||||||||||
Activities |
Dawn |
Morning |
Noon |
After-noon |
Night |
Total |
2nd |
3rd |
4th. |
5th. |
6th. |
7th. |
Total |
2nd |
3rd |
4th |
Total |
Summer |
Spring |
Fall |
Winter |
Total |
A. Agricultural |
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2. Harvesting, picking |
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3. Semi-agri. activities (transport, harvest, marketing,
maintenance, storage, etc) |
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B. Livestock |
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2. Small stock (poultry, bees, rabbits, etc.) |
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C. Fishing |
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2. Molluscs |
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D. Unpaid domestic chores |
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3. Meal preparation and bread-making |
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4. Other domestic chores and care of family members |
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5. Transporting water |
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6. Transporting fuelwood |
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7. Marketing |
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E. Paid non-agri. Activities (commerce, admin.
services, industry) |
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2. Informal sector |
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F. Other |
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2. Travel to place of work |
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NB: Put the letter "a" after the number if the activity is carried out on another holding.B3.2 Collection of data on access to factors of production
The capacity to contribute to agriculture depends on access to the factors of production, of which land is the most important. Rarely, however, do agricultural censuses contain the tools that would allow such data to be captured. Table 4 lists the method used in Benin to ask questions concerning land access. Analytical tables can be produced from the data collected by this method to illustrate the distribution of parcels according to the following, and other, combinations of categories:
1. Sex of the person(s) responsible and means of acquisition;An in-depth exploration of production factors is crucial to an understanding of gender differentiation in labour status and working conditions, and for a comparison of each economic actor's potential participation in the productive process. All other production factors (labour, technology, etc.) and production supports (extension, credit, etc.) can and must be explored in the same way.
2. Number of years farmed, sex of the person(s) responsible and means of acquisition;
3. Sex of the person(s) responsible and type of crop;
4. Sex of the person(s) responsible and farming practice;
5. Sex of the person(s) responsible, means of acquisition and farming practice;
6. Sex of the person(s) responsible, means of acquisition and type of crops.
B3.3 Conclusions
Surprisingly, although the two countries are very different in terms of their social, economic and political contexts, they are quite similar with regard to several features:
History
Both countries became interested in gender-sensitive agricultural statistics in 1994/95, at the time of the fourth conference on the world's women.
Concepts and methodology
The agricultural statistics apparatus in both countries used the permanent or periodic agricultural survey as the tool for obtaining gender-sensitive statistics. Neither country has so far revised or redefined the basic concepts and categories habitually used for agricultural censuses.
Operations
Both countries opted for gradual implementation of a gender perspective into existing statistical systems, new treatments to maximize existing data, and gradual modification of procedures as and when new data became available.
Each country devised a similar statistical revision strategy that included the following features:
a) development of a module (pilot survey) based on the current conceptual framework of the permanent agricultural survey;· Both countries have reached stage c). A frame for stage d), an agricultural survey to meet the needs of gender-sensitive statistics, is not yet operational.b) testing of the module in representative agro-ecological areas;
c) evaluation of the capacity of data collection and processing tools and methods to adapt to a gender perspective;
d) integration of gender-differentiated data into the periodic agricultural survey once the results and conclusions are known.
· Both countries had one immediate objective, to establish a statistical database on women in agriculture, their working conditions and their contribution to the sector.
Other ways of obtaining gender-differentiated agricultural statistics could also be adopted. There is no magic formula, but the first condition is awareness of the importance of gender-specific statistics and the second is the willingness to initiate a process of review, reorientation and change.
TABLE 4. BENIN: ACCESS TO LAND
Field no. |
Parcel no. |
Responsible for parcel |
How acquired* |
Crop** |
Cropping practice |
Years farmed |
Worked by |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
(7) |
(8) |
/__/__/__/ |
/__/__/ |
/__/ |
/__/ |
/__/__/ |
/__/ |
/__/__/__/ |
/__/ |
/__/__/__/ |
/__/__/ |
/__/ |
/__/ |
/__/__/ |
/__/ |
/__/__/__/ |
/__/ |
/__/__/__/ |
/__/__/ |
/__/ |
/__/ |
/__/__/ |
/__/ |
/__/__/__/ |
/__/ |
/__/__/__/ |
/__/__/ |
/__/ |
/__/ |
/__/__/ |
/__/ |
/__/__/__/ |
/__/ |
/__/__/__/ |
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/__/ |
/__/ |
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/__/ |
/__/__/__/ |
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Notes:* How acquired:
1= inherited;
2= purchased;
3= customary attribution;
4= sharecropping;
5= loan;
6 = loan from head of holding;
7 = rented;
8 = as a pledge;
9 = other.** Crop
1= maize;
2= millet;
3= sorghum;
4= rice;
5= Digitaria exilis;
6 = yam;
7 = cassava;
8 = sweet potato;
9 = ground;
10 = earth pea;
11 = cowpea;
12 = soya;
13 = tomato;
14 = pepper;
15 = okra;
16 = cotton;
17 = oil-palm;
18 = pineapple;
19 = cashew.