Gender & labor allocation in smallholder farms · Review of Gender Research in Agriculture...
Transcript of Gender & labor allocation in smallholder farms · Review of Gender Research in Agriculture...
Gender & labor
allocation in
smallholder farmsThe case of chili producers in West Java
Review of Gender Research in Agriculture
Women produce 60-80% of food in
developing countries (FAO, 2011)
“Women face discrimination in
access to key productive assets,
inputs and services” (FAO, 2011)
“Assume that men are the only
producers in the household and
the sole decision-makers regarding
farming activities” (ADB, 2013)
2(Quisumbing, 2014)
Review of Gender Research in Agriculture
Misunderstanding gender’s role in Ag
o Misunderstanding 60-80% of labor
force
o Misunderstanding household
constraint in accessing technologies
& markets
o Misunderstanding how farm
households make decisions
3(Quisumbing, 2014)
Book chapter General studies
Country and region specific studies
Total Africa South Asia
Southeast
Asia
Latin
America Other
5. Gender asset gap 21 51 25 11 6 7 2
6. Gender equity and land 26 55 32 13 3 7 0
7. Nonland agricultural inputs, technology and services
20 66 50 7 2 5 2
8. Access to financial services 37 64 33 14 5 11 1
9. Livestock 32 86 64 16 1 4 1
10. Gender and social capital 21 49 15 22 6 6 2 11. Nutrition and health 35 38 25 6 3 2 2
Geographical spread 59% 22% 6% 10% 2%
601 studies in total
409 country case studies
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Review of Gender Research in Agriculture
Source: (Rutsaert et al., IRRI)
(Van de Fliert et al., 2001) - Indonesia: Female sweet potato farm π
(Rola et al., 2002) - Philippines: Excess female labor: lower OC
Heavily African Context (50/66)
Inputs access – women constrained
Productivity|x – mixed results (mostly null effect)
Review of Gender Research in Agriculture
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Gender systems are diverse
o Community
o Country
o Region
Generalizing results may be an issue
o Vast majority of research in SSA
o Very little in Asia particularly SE Asia
Labor
For smallholders, labor is critical
Family labor
Hired labor
Inefficient labor allocation constrains development
Under-allocation
Over-allocation
Research on household labor decisions
Large body of work on Labor supply & demand technology & income
The few studies on labor allocation (Barret et al., 2008; Andrews et al., 2015)
African context
Focus on allocation of family labor
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Research Questions
How do gender roles affect efficient
allocation of labor?
hired labor – male vs female
family labor – male vs female
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= 0−𝑧𝛾
The Neo-Classical Farm
𝑃𝑦𝛻𝑓 − 𝑃𝑥
Profit:
𝜋 = 𝑃𝑦𝑓 𝑥 − 𝑃𝑥𝑥
What about:
Transaction costs?
Capacity?
Utility?
?
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FOC – Allocative Efficiency*:
𝑃𝑦𝑓′
$
𝑋
Px
𝑋∗
𝐴𝐸 ≡AE>0
AE<0
𝑥
𝑧𝛾
Under Allocated Over Allocated
𝑃𝑦𝑓′
𝑃
𝑋
Px
𝑋∗
Under Allocated Over Allocated
AE>0
AE<0
Interpreting Gamma
Conventional Way*:
𝐴𝐸 = 𝑧𝛾 + 𝜀
Problems:
-Bad fit
-Interpretation?
*Henderson, 2015; Barret et al., 2008
𝑧𝛾
Over Under
AE
$
𝛾 𝑂𝐿𝑆quantile regression time?
Estimation Strategy Stage 1 – Estimate the value of marginal product
Production Function
Function: Cobb-Douglas
Technical Efficiency (TE)
Stage2 – Quantile regress on AE
Allocative Efficiency
for input k
𝐴𝐸𝑘 ≡ 𝑃𝑦 𝑓𝑘(•) − 𝑃𝑥𝑘
Z ~ HH chars; Market chars; Gender
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𝑓𝑘(•) =𝛽𝑘 ∗ 𝑒
ln 𝑦
𝑥𝑘
ln 𝑦 = 𝛽0 +σ𝑗 ln 𝑥𝑗 𝛽𝑗 + 𝑣Equation 1
𝐴𝐸𝑘 = 𝑧𝛾𝑘 + 𝜀kEquation 2
Why Chili?
Commercial production Female participation
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Province: West Java
Districts:
Garut, Tasik, Ciamis
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Province: West Java
Districts:
Garut, Tasik, Ciamis
Sub-Districts:
Garut 8, Tasik 3, Ciamis 3
Villages:
3 villages each
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Chili Data June 2016
N=213
Input Module (Most recently completed cycle)
Fertilizers
Chemicals
Land
Fixed inputs
Labor Module
Male vs Female
Hired vs Family
Gender Module
Perception of responsibility in farming (male vs female)
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38%
63%
71%
73%
59%
62%
37%
29%
27%
41%
LAND PREP
PLANTING
GROWOUT
HARVEST
TOTAL
LABOR SHARESFemale Male
Gender Perception Module
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1. Husband 3. Both
2. Wife
P16
1 Preparing the land
2 Buying farm equipment
3 Buying inputs
4 Spreading seed
Always follows through on their commitments to buy my product 5 Mulching
6 Planting
7 Installing stakes
8 Fertilizing
9 Spraying chemicals
10 Weeding
11 Watering
12 Harvesting
13 Transporting chilli to point sale
14 Sorting and grading
15 Negotiating with buyer
16 Preparing meal
For each of the
following activities in
chili production,
please indicate who
has the main
responsibility between
the husband and wife?
𝑆𝑐𝑜𝑟𝑒𝑀𝑎𝑙𝑒𝐹𝑒𝑚𝑎𝑙𝑒
= 012
𝑖𝑓 𝐻𝑢𝑠𝑏𝑎𝑛𝑑𝑖𝑓 𝐵𝑜𝑡ℎ𝑖𝑓 𝑊𝑖𝑓𝑒
(𝜌 = .52)
Scores
Range set [0 to 10]
Perception (M): 2.07
Perception (F): 2.33
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(1) (2) (3) (4)
VARIABLES
Female
Hired
Male
Hired
Female
Family
Male
Family
Male Perception -0.0356 -10.30 1.513 6.862**
(2.106) (5.551) (1.488) (0.684)
Female Perception 1.351 0.78 -4.140** -0.616
(1.358) (3.581) (0.96) (0.441)
Constant 27.18** 104.0** 65.62** 4.450*
(5.782) (15.24) (4.085) (1.879)
Observations 236 236 236 236
R-squared 0.004 0.015 0.073 0.3
Estimation
Stage 1 – Estimate the value of marginal product
Plot-Production Function
Function: Cobb-Douglas
Technical Efficiency (TE)
Stage2 – Quantile regress on AE
Allocative Efficiency
for input k
𝐴𝐸𝑘 ≡ 𝑃𝑦 𝑓𝑘(•) − 𝑃𝑥𝑘
Z ~ HH chars; Market chars; Gender
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𝑓𝑘(•) =𝛽𝑘 ∗ 𝑒
ln 𝑦
𝑥𝑘
ln 𝑦 = 𝛽0 +σ𝑗 ln 𝑥𝑗 𝛽𝑗 + 𝑣 + 𝑢Equation 1
𝐴𝐸𝑘 = 𝑧𝛾𝑘 + 𝜀kEquation 2
ln 𝑦 = 𝛽0 +σ𝑗 ln 𝑥𝑗 𝛽𝑗 + 𝜌𝜎 ∗ 𝜆 + 𝜀
Labor Parameter Estimates
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Other inputs in model:
N, P, K, Mg, Ca, S, Insect/herb/fung-icides, Plot Area, Seeds, Altitude, Animal Traction, Water pump, Chili-Variety,
IMR ~ Not significant
TE ~ Gender Perception Scores: Insignificant (p~0.2)
(1) (2)
EQUATION VARIABLES OLS SF
SINGLE Labor-hired female (days) 0.004 0.004
(0.022) (0.021)
Labor-hired male (days) 0.053 0.047
(0.031) (0.028)
Labor-own female (days) 0.121 0.123
(0.083) (0.075)
Labor-own male (days) 0.002 0.002
(0.019) (0.017)
N 192 192
Kernel Densities - AE
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𝑃𝑦𝛻𝑓 − 𝑃𝑥
Gamma
Over Under
AE
P
𝛾 𝑂𝐿𝑆
Z:
Gender Perception HH/Farm characteristics
HH head & spouse characteristics
Age; education
HH characteristics
# of male adults
# of female adults
# of dependents
Assets
Land
Mobile phones
Motorcycles
Water pumps
Market characteristics
Distance to market from plot
Fertilizer price
Seed price
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0
10
20
30
40
50
60
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95%
Quantiles
Pseudo R2 by Quantiles
Female - Hired Male - Hired Female - Family Male - Family
Over Under
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Female - Hired
Over Under
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Female - Hired Male - Hired
Male - FamilyFemale - Family
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Female - Hired Male - Hired
Male - FamilyFemale - Family
Conclusions
Method
OLS – not the best
The important variation happens at the tail of the distribution
Relationship to AE is not consistent across distribution
Quantiles can help to identify
Where constraints start to bind
The effect of variables at important parts of the distribution
Empirical
Generally corroborate “gender” findings
Hired Male, female, and Female family labor
Exception: Male Family labor more efficiently allocated by women
Future work?
Robustness to functional forms, different IMR calculations
Improving “Gender Score” measure28
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