Presentation by Jennifer Twyman and Jacqueline Ashby at the Closing the gender gap in farming under climate change event on 19 March 2015 in Paris. More about the event: http://ccafs.cgiar.org/closing-gender-gap

1. At
the
cu(ng
edge:
current
knowledge
on
closing
the
gender
gap
in
farming
under
climate
change
Jennifer
Twyman
Jacqueline
Ashby

2. Gender
Inequali?es
in
Agriculture
• Farming
under
climate
change?
– Current
Climate
Variability
&
Future
Climate
Changes
– Increased
Risk
&
Uncertainty
• What
does
gender
have
to
do
with
this?
– Exis?ng
gender
inequali?es
in
agriculture,
– Affect
women’s
and
men’s
ability
to
adapt.

3. Gender
&
Agriculture
Myths

4. Policy
built
on
myths
….
Perpetuates
gender–differen?ated
low
produc?vity
traps
Lower
resources
and
rights
Lower
access
to
CSA
inputs,
services
and
markets
Lower
produc?vity,
and
degrada?on
of
the
natural
resource
base
Drudgery
and
labor
constraints
Loss
of
control
over
products,
sales
and
income
Lower
Incen?ves
to
invest
in
CSA

5. Danger
of
Relying
on
Myths
• Unreliable
evidence
is
not
ques?oned:
zombie
sta?s?cs
• Vague
concep?ons
of
gender
lead
to
imprecise
targe?ng:
zombie
stereotypes
• Root
causes
of
major
problems
are
disguised
Myths
are
a
bad
basis
for
policy
decision-­‐making
but
there
is
oYen
a
par?al
truth
behind
the
myth.

6. Gender,
Agriculture
and
Climate
Change
Myths
§ Women
produce
60
–
80%
of
our
food.
§ Women
are
the
most
vulnerable
to
climate
change
because
they
are
among
the
poorest.
§ Women
make
be>er
stewards
of
natural
resources
than
men.
§ New
CSA
technology
can
close
the
gender
gap.
§ The
feminiza?on
of
agriculture
means
women
are
increasingly
responsible
for
CSA.
§ Women
own
less
than
1%
of
the
land.
§ Inclusion
means
increasing
women’s
par?cipa?on
in
poli?cal
processes
for
CSA.

7. Objec?ves
• Reconsider
some
myths
related
to
gender,
agriculture
and
climate
change.
• Examine
related
data
and
iden?fy
the
truths
behind
the
myths.
• Explore
their
relevance
for
policy.

8. MYTH:
WOMEN
PRODUCE
60
–
80%
OF
OUR
FOOD.
Photocredit:
Neil
Palmer
(CIAT)

9. Source:
FAOSTAT
in
Doss,
2011
Report
agricultur
e
as
their
primary
ac?vity
79%
Report
a
different
occupa?o
n
as
their
primary
ac?vity
21%
Report
agricultur
e
as
their
primary
ac?vity
48%
Report
different
occupa?o
n
as
their
primary
ac?vity
52%
Ra?onale:
Economically
Ac?ve
Women
Developing
Countries
Worldwide

10. Source:
FAOSTAT
in
Doss,
2011
male
58%
female
42%
%
of
men
versus
women
reporPng
agriculture
as
their
primary
acPvity
in
developing
countries
male
female
Economically
ac?ve
men
and
women
in
agriculture

11. Women’s
contribu?on
to
farm
output
• There
is
a
large
range
in
contribu?ons
of
women
to
agricultural
produc?on.
3.1
25
33
38
2.1
13.2
12
17
13
16
15
17.6
0
5
10
15
20
25
30
35
40
China
BosniaHerzogovenia
Ghana
Nicaragua
%
of
households
headed
by
women
%
of
total
value
of
food
produced
by
FHHs
%
of
total
value
of
food
produced
plots
owned
or
managed
by
omen
Source:
Doss,
2011

12. Can
we
measure
women’s
contribu?on
to
farm
output?
• Women
and
men
produce
crops
together;
it’s
impossible
to
alribute
produc?on
to
women
or
men.

13. Contribu?on
to
farm
labor
• Women
contribute
a
significant
amount
of
?me
to
agricultural
produc?on.
– Time
use
studies
from
across
Africa
and
Asia
show
that
in
some
cases
women’s
share
is
60%
of
the
total
?me
spent
in
agricultural
ac?vi?es
(Doss
2011,
review
of
8
studies).
• Importance
of
gender
division
of
labor:
– Case
Study
from
Mexico
(Bee
2014);
– Gender
division
of
labor:
women
do
the
weeding.
– Collect
edible
plants
for
household
consump?on.

14. Contribu?on
to
farm
labor
• Women
contribute
a
significant
amount
of
?me
to
agricultural
produc?on.
– Time
use
studies
from
across
Africa
and
Asia
show
that
in
some
cases
women’s
share
is
60%
of
the
total
?me
spent
in
agricultural
ac?vi?es
(Doss
2011,
review
of
8
studies).
• Importance
of
gender
division
of
labor:
– Case
Study
from
Mexico
(Bee
2014);
– Gender
division
of
labor:
women
do
the
weeding.
– Collect
edible
plants
for
household
consump?on.
Photocredit:
Caitlin
Corner-­‐Dollhoff
(CIAT)

15. Implica?ons
• Policy
and
interven?ons
must
recognize
&
engage
women
as
farmers.
– Example
from
Ambo
District,
Ethiopia:
Unequal
access
to
services
0
10
20
30
40
50
60
70
80
90
100
Irriga?on
Extension
Land
redistribu?on
Microfinance
Group
associa?ons
Female
(%)
Male
(%)
Source:
Ogato,
Boon
&
Subramani
2009)

16. MYTH:
THE
POOR
ARE
THE
MOST
VULNERABLE
TO
CLIMATE
CHANGE
AND
SINCE
WOMEN
MAKE
UP
70%
OF
THE
POOR,
THEY
ARE
THE
MOST
VULNERABLE.
Photocredit:
Manon
Koningstein
(CIAT)

17. Ra?onale
• The
poor
have
fewer
resources
to
draw
on
for
coping
with
climate
change.
• Rural
households
oYen
rely
on
natural
resources
(e.g.
water
&
firewood).
– OYen
related
to
women’s
household
work.
• But,
it
draws
on
a
zombie
sta?s?c
and
a
zombie
stereotype...

18. Are
women
the
poorest
and
most
vulnerable?
• Several
studies
have
interrogated
the
claim
that
women
are
the
poorest
(Chant,
Jackson,
Arora-­‐Jonsson,
etc.).
• Portrays
women
as
vic?ms.
• Arora-­‐Jonsson:
dichotomy
women
as
vic?ms
or
saviors.
• Reality:
men
and
women
have
different
vulnerabili?es
to
climate
change,
dependent
on
gender
norms
that
prescribe
– Gender
division
of
labor
– Access
to
and
control
over
resources
– Decision-­‐making
power

19. Examples:
Hurricanes
&
Flooding
Bangladesh
• More
women
than
men
died.
– Women
do
not
generally
learn
to
swim,
they
have
limited
mobility,
felt
shelters
were
unsafe,
care
for
elderly
who
could
not
move
to
shelter.
Nicaragua
• More
men
than
women
died.
– Men
more
likely
to
par?cipate
in
risky
ac?vi?es
such
as
search
and
rescue.
Sources:
CCC
2013;
Delaney
and
Shrader
2000

20. Example:
Agriculture
• Mahajan
(2014):
Blame
it
on
the
rain?
• Overall,
changes
in
rain
palerns
did
not
impact
the
wage
gap.
• However,
it
did
in
rain-­‐fed
rice
producing
regions.
– Women
play
greater
role
in
crops,
such
as
rice,
that
depend
heavily
on
rainfall.
– Low
rainfall
years
widen
the
gender
wage
gap;
women
suffered
a
greater
loss.

21. Implica?ons
• Need
beler,
less-­‐fragmented
data
to
understand
the
palerns
behind
the
inconsistencies.
• Vulnerability
not
related
to
sex
but
rather
gender
norms,
which
are
an
expression
of
power
differen?als.

22. MYTH:
WOMEN
MAKE
BETTER
STEWARDS
OF
NATURAL
RESOURCES.
Photocredit:
Neil
Palmer
(CIAT)

23. Ra?onale
• Women
rely
more
than
men
on
natural
resources
for
food,
firewood
and
water
and
so
seek
more
ac?vely
to
conserve
these.
• Women
are
more
disposed
to
collaborate
in
collec?ve
ac?on
required
for
mi?ga?on
or
adapta?on
more
likely
to
succeed.
Photocredit:
Manon
Koningstein
(CIAT)

24.
Conflic?ng
evidence
• “
Women
are
less
likely
to
adopt
CSA
technologies:
of
13
empirical
studies,
8
found
men
were
more
likely
to
adopt
the
improved
prac?ce
.”
• “
In
East
Africa,
women…
were
more
likely
than
or
just
as
likely
as
men
to
adopt
CSA
prac?ces”
Why?
“Women”
don’t
have
a
single,
unified
interest.
Because
social
class
intervenes:
-­‐-­‐poor
women
may
have
more
in
common
with
poor
men
than
with
other
women
from
wealthier
social
strata
• Source:
Peters
et
al,
2010;

25. Does
women’s
par?cipa?on
improve
stewardship?
• Par?cipa?on
in
groups
can
be
beneficial
for
poor
women
and
men
as
well
• But
there’s
no
consistent
evidence
that
women’s
par?cipa?on
in
groups
for
NRM
or
CSA
purposes
improves
resource
management
outcomes
Photocredit:
Gian
Betancourt
(CIAT)
Photocredit:
Manon
Koningstein
Photocredit:
Manon
Koningstein

26. Implica?ons
• More
precise
socioeconomic
targe?ng
based
on
gender
and
social
class
is
required
• Inclusion
and
par?cipa?on
is
desirable
but
not
sufficient
if
underlying
gender
and
class
inequali?es
are
not
addressed

27. MYTH:
TECHNOLOGIES
CAN
CLOSE
THE
GENDER
GAP
Photocredit:
Manon
Koningstein
(CIAT)
Photocredit:
Gian
Betancourt
(CIAT)

28. Ra?onale
• Gender-­‐equal
access
to
technology,
training,
extension
and
credit
will
enable
women
to
benefit
equally
•
‘Women
friendly”
technologies
(e.g.
solar
ovens,
home
gardens,
small
irriga?on
pumps
etc.)
will
overcome
the
gender
gap

29. Unequal
decision-­‐making
power
0
10
20
30
40
50
60
70
80
90
100
When
husband
owns
plot
When
wife
owns
plot
Female
household
head
Data
for
Zambia
Zambia:
Woman
(%)
makes
decisions
on
income
from
sales
(N=439
individuals)
–
for
different
types
of
plot
ownership

30. Control
malers:
irriga?on
pumps
Tanzania
• New
technology:
few
women
(10%)
owned
or
controlled
small
irriga?on
pump
• Women’s
labor
input
increased
under
irriga?on
• Men
moved
into
high-­‐value,
high-­‐volume
irrigated
vegetables
(women
cul?vate
minor
vegetables)
• Men
exclusively
made
the
sales
and
spent
the
cash
autonomously
• Control
over
crop
choice
determined
the
how
benefits
of
the
irriga?on
technology
were
distributed:
more
work
for
women
to
generate
income
they
didn’t
control
Source:Njuki
et
al
2014

31. Implica?ons
• Gender
differences
in
adop?on
of
technology
are
a
symptom
of
other,
deeper
rooted
problems
• Policy
should
seek
equal
access
to
assets,
inputs
and
services
when
promo?ng
CSA
technology
• AND
complement
improved
access
with
ensuring
women
get
control
over
a
share
of
resources,
product
and
income

32. CONCLUSIONS
Photocredit:
Neil
Palmer
(CIAT)

33. WHAT
DOES
SUCCESSFUL
GENDER
RESPONSIVE
FARMING
UNDER
CLIMATE
CHANGE
LOOK
LIKE?
Photocredit:
Manon
Koningstein
(CIAT)

34. What
does
success
look
like?
Addressing
gender
gaps
in
farming
under
climate
change
means
policy
must:
• Recognize
women
as
farmers.
• Target
more
precisely
-­‐
dis?nct
socioeconomic
types
or
classes
of
women
with
quite
different
constraints
and
interests
• Accept
the
limits
of
new
technology
• Target
men
(not
uniquely
women)
to
address
issues
of
control
over
resources
and
benefits

35. Break
out
of
gender-­‐differen?ated
low
produc?vity
traps
Equal
access
to
inputs,
services
and
markets
Equal
produc?vity,
and
conserva?on
of
the
natural
resource
base
Secure
control
over
a
share
of
products,
sales
and
income
Gendered
Incen?ves
to
invest
in
CSA
Reduced
Drudgery
More
equal
resources
and
rights

36. What
next?
• Elevate
the
game
in
terms
of
the
scope
and
scale
of
research
investment.
– Research
and
evidence
in
this
field
are
fragmented
and
piecemeal,
making
cumula?ve
learning
difficult.
• Gender
norms
are
dynamic,
flexible
and
can
change—we’re
not
stuck…
• Large
scale
policy
experiments
are
needed
to
test
approaches
and
generate
clear
guidelines.