Tracing gender effects among Tanzanian rural households

Type Report
Title Tracing gender effects among Tanzanian rural households
Author(s)
Publication (Day/Month/Year) 2010
Publisher Statistics Norway
Country/State Norway
URL http://www.ssb.no/a/english/publikasjoner/pdf/rapp_201026_en/rapp_201026_en.pdf
Abstract
Tanzania is in the process of preparing the next Poverty Reduction Strategy Paper
(MKUKUTA). The second phase of the poverty assessment focuses on constraints
for households and individuals to make profitable investments, and differences in
women’s and men’s opportunity structures. Given the importance of agriculture,
we chose to base the current gender analysis on data from the 2002/ 03 “National
Sample Census on Agriculture” (NSCA). The related 2007 “Volume IV; Gender
Profile of Smallholder Agriculture Population in Tanzania Mainland” documents
differences between male and female headed households along a range of
dimensions.
In Chapter 2 we document that male and female headed households differ
systematically also with respect to other factors than the sex of the Head.
Comparing households by the sex of the Head is as much a comparison of female
headed households that have faced negative marital shocks with male headed
households not marked by such shocks. Female headship seems to be as much an
outcome as a cause, and is associated with small family size, few other adult
members, and single parenthood. Female headship yields little information about
family gender roles, since many of these households do not have members fully
considered as “adult males”. One should rather investigate the intra-household
responsibilities and tasks in male-headed households, which almost always also
comprises adult females. An analysis of the differences in women’s and men’s
opportunity structures should thus not be based on a comparison of female and
male headed households. Female headship is, however, a good indicator for
targeting support to vulnerable households.
Chapter 3 shows that household level gender variables have little influence on
household livelihood categories, because these categories are too broad as to serve
a basis for analysing the separate situation of men and women. Regional variations
in livelihood categories are more important than household level gender factors.
Chapter 4 shows, however, that gender is important for the assignment of the
specific activities for each livelihood. The most important “male” activity is animal
husbandry. Males also dominate all activities related to monetary transactions. The
most important “female” activities are non-domestic household maintenance tasks,
such as collecting firewood and water. “Female” activities generally neither
involve monetary issues, nor have an entrepreneur dimension. Many time
consuming crop production activities, such as soil preparation, crop protection,
planting weeding and harvesting are “gender neutral”. Gender roles may change
under certain circumstances: While women are hardly ever responsible for “male”
activities in male headed households, they are responsible for these activities in
female headed households, most likely due to the absence of adult males. On the
other hand, men rarely become responsible for “female” tasks, regardless of the sex
of the Head. When there is a male Head, the household almost always has female
members to perform female tasks. Only those few men living alone become
responsible for female tasks.
Our analysis show that men very rarely take on traditionally “female” tasks, and
attitudes to gender roles may be very difficult to change. Policies designed at
reducing women’s work burden in domestic activities and in providing their
households with water and energy may thus be the best approach in the short run.
This will allow women to spend more time on growing their own crops, and
engage in innovative income-generating activities. However, policies aiming at
introducing new crops and new farming techniques also change gender roles in an
often unpredictable manner. A proper understanding of this dynamics requires both
data on individuals, on specific female crops, and preferably also panel data, such
as in the currently ongoing Tanzania Panel Data Survey.

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