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

4

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

5

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

6

Research Questions

How do gender roles affect efficient

allocation of labor?

hired labor – male vs female

family labor – male vs female

7

= 0−𝑧𝛾

The Neo-Classical Farm

𝑃𝑦𝛻𝑓 − 𝑃𝑥

Profit:

𝜋 = 𝑃𝑦𝑓 𝑥 − 𝑃𝑥𝑥

What about:

Transaction costs?

Capacity?

Utility?

?

8

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

10

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

11

12

Province: West Java

Districts:

Garut, Tasik, Ciamis

13

Province: West Java

Districts:

Garut, Tasik, Ciamis

Sub-Districts:

Garut 8, Tasik 3, Ciamis 3

Villages:

3 villages each

14

15

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)

16

17

38%

63%

71%

73%

59%

62%

37%

29%

27%

41%

LAND PREP

PLANTING

GROWOUT

HARVEST

TOTAL

LABOR SHARESFemale Male

Gender Perception Module

18

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

19

(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

20

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

21

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

22

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

24

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

25

Female - Hired

Over Under

26

Female - Hired Male - Hired

Male - FamilyFemale - Family

27

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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