Female networks drive adoption of agricultural technology in India

Female networks, social learning, and
demand for an agricultural technology
in eastern Uttar Pradesh India
Nick Magnan (University of Georgia)
David J. Spielman (IFPRI)
Kajal Gulati (UC-Davis)

GAAP Workshop
January 10, 2013
Addis Ababa

Study site and Female decision Identification and Summary and
Introduction
experiment making and networks network effects next steps

Networks and technology adoption
• Assumed to drive ag technology adoption
– Extension often relies on the “progressive farmer”
– Impossible to individually reach many isolated farmers
• A good social network is an important asset
– Farmers can access information about technologies,
or the technologies themselves (McNiven and Gilligan 2012)
• Network effects are difficult to measure
empirically due to the “reflection problem”
– In recent years the networks literature has grown
rapidly with new techniques and better data
(Bandiera and Rasul 2006, Cai 2012, Conley and Udry 2010, Duflo et al. 2006
Maertens 2012, Munshi, 2004, Munshi and Myaux, 2006)

Introduction

Female social networks
• Women deeply involved in agriculture, even if
they are not the “head of household” or “plot
manager”
– Have extensive knowledge about agriculture
– Time use and drudgery affected by technology choice
• Women talk about agriculture with each other
– Often lack access to formal information channels (, Doss and Morris
2000, Meinzen-Dick et al. 2012, Quisumbing and Pandolfelli 2012)
– Distinct information networks from their husbands’?

• Women discuss agriculture with their
husbands and can potentially influence
technology choice (Fisher 2000)

Introduction

Overview
• Laser land leveling (LLL) introduction
• Study site, experiment design, and data
collection
• Women’s involvement in agriculture male and
female social networks
• Learning about laser land leveling through
female networks?
• Female network effects on household
demand?

Introduction

Definitions
• “HHH”: Household head
– Selected at random
– 80% male
• “Wife”: Female head of male headed
household
– Almost always the HHH’s wife

Introduction

Laser land leveling (LLL)
• Conservation ag technique, often a necessary
precursor for zero-till
• Shown to reduce input requirements (Jat 2006):
– Water (major resource saved, ~ 30%)
– Fertilizer and chemicals
– Labor (irrigating, weeding)
• Shown to decrease weed pressure and
increase yields

Introduction

LLL in India
• Introduced in Indo-Gangetic Plains in 2001
• Machinery is expensive and operation requires
skill
– LLL acquired through custom hire services
• 200,000 ha under LLL in 2008
– Mostly used in relatively productive western UP
– Still unheard of in relatively unproductive study region
• In Western UP price is around Rs. 600 per hour

Introduction

Study site
• Uttar Pradesh (UP) is the most
populous state in India
– Population is ~200 million
– 70% poverty rate
– EUP is poorest part
• Highly agrarian with rice-wheat
systems dominant
• Sample includes three districts
in EUP
– 8 (random) villages per district
– 20 (random) farmers per village

Introduction

Information session (March 2011)
• Information session with HHH (80% male)
• Presentation by study team member
• Short video of LLL in action and interview
with operator
• Q&A with “progressive” adopting farmer
• Distribution of picture brochure and
explanation of auction
• Photos taken of HHHs for network surveys

Introduction

Auction (March 2011)
• Held 3-4 days after information session with HHH
• Farmers chose up to 3 plots to bid on for LLL
• Becker-deGroot-Marschak type auction
– Non-competitive
– Incremental bids from Rs. 0 – 800 per hour
– HHH wins if WTP ≥ drawn price, pays drawn price
• The only way for farmers in the sample to get LLL
is through the study

Introduction

Lottery (March 2011)
• Auction losers and winners are not likely
comparable
– HHHs self-select into winning the auction
• 50/50 lottery used to pick LLL adopters from a
pool of would-be adopters (auction winners)
• Lottery winners pay for and get LLL, lottery
losers pay nothing and get nothing

Introduction

Social network surveys
(HHH in March 2011, Wives in Oct 2011)

Introduction

Social network surveys
(HHH in March 2011, Wives in Oct 2011)
• Each HHH asked about links with all other sample
HHHs in the village
– Is _______ a progressive farmer?
– Do you ever speak with _____ about agricultural issues?
• Each wife asked about links with other women in
sample households
– Are any females in _____’s household progressive?
– Do you ever speak with any females in ______’s household
about agricultural issues?
• Only uni-directional links considered
– A claims B, not B claims A

Introduction

Midline survey (Oct 2011)
• Perceptions of LLL after 4 months
– After rice season only
• Contact with adopting farmers’ wives, field
visits, etc.
• Wives asked if their husbands value their
input over agricultural decisions
• Husbands asked if they value their wives
input over agricultural decisions

Introduction

Auction (March 2012)
• HHH participated in auction without wives
• Same auction mechanism as 2011
– Farmers could bid on any unlevelled plot
• No lottery after the auction
– All farmers with winning bids received LLL

Introduction

Endline survey (May 2012)
• Access, ownership, and control over assets
• Access to credit and extension
• Decision-making:
– General ag and non-ag production
– Non-ag economic decision
– Household and children’s issues
– LLL adoption and bid in 2012 auction
• Adopted and adapted questions from
WEAI (IFPRI 2012)

Introduction

Do HHHs value wives’ opinions?
• Wives say…

100%

80%

60%

40%

20%

0%
Crop choice Technology to Family labor Spending
use allocation money

Introduction

Do HHHs value wives’ opinions?
• Husbands say…

100%
80%
60%
40%
20%
0%
Discuss crop and Wife's opinion Wife's opinion "very
technology choice "important" or "very important"
important"

Introduction

HHH’s and wives’ ag networks

Only 1.5 % of all ag network links to a HH are shared by husband and wife

Introduction

HHH’s and wives’ networks by wealth

Study site and Network Identification and Summary and
Introduction
experiment descriptives network effects next steps

Identifying network effects
• Reflection problem (Manski, 1993) makes
identification of network effects difficult
• Difficult to tell if two HHs use same
technology due to network effects, or because
they are similar or face similar constraints
• Auction/lottery experimental design helps us
circumvent the reflection problem

Introduction

Auction losers
WTP < X

Random sample from
village v Auction Auction winners
(self-selection) WTP ≥ X

Introduction

Auction losers

Random sample from
(self-selection)

Introduction

Auction losers

Random sample from
(self-selection)

Lottery
(random selection)

Lottery winners Lottery losers

Introduction

Farmer j
Farmer k
Farmer i

Introduction

Network effects on perceptions of LLL
Base regression:

Controlling for
HHH’s network:

• yi can be perceptions of the technology, WTP in the auction, or other outcome
• Xi: Control variables (for precision): Total network size (wife and HHH), education
(wife and HHH), whether identified as “progressive” (wife and HHH)

Introduction

Perceptions (after 4 months)
LLL is… beneficial water saving fertilizer saving
Adopting HH in F network {0,1} 0.20+ 0.20+ 0.16+ 0.17* 0.16+ 0.17*
(0.11) (0.11) (0.08) (0.08) (0.08) (0.08)
Adopting HH in M network {0,1} 0.01 0.11 0.11
(0.10) (0.08) (0.08)
Observations 369 369 369 369 369 369
R-squared 0.07 0.10 0.08 0.10 0.04 0.07

LLL is… chemical saving labor saving Yield improving
Leveled plot in F network {0,1} 0.16** 0.16** 0.23** 0.23** 0.09 0.09
(0.05) (0.05) (0.07) (0.07) (0.11) (0.11)
Leveled plot in M network {0,1} 0.08 0.09 0.07
(0.05) (0.07) (0.10)
Observations 369 369 369 369 369 369
R-squared 0.02 0.03 0.04 0.05 0.09 0.09

Dependent vars are agreement {0,1} with statements about LLL. IV linear probabiliy model with having a contact win the
lottery instrumenting for having a contact getting LLL. Control variables (coefficients not shown): Would be adopters in wife
(and HHH’s) network, total number of wife’s (and HHH’s) ag contacts, HHH and wife’s education and status as “progressive”.
Standard errors in parentheses: **p<0.01, * p<0.05, + p<0.1.

Introduction

Perceptions (after 12 months)
LLL is… beneficial water saving fertilizer saving
Leveled plot in F network {0,1} 0.00 0.00 0.01 0.01 -0.15 -0.15
(0.09) (0.09) (0.08) (0.08) (0.12) (0.12)
Leveled plot in M network {0,1} 0.07 -0.03 -0.02
(0.09) (0.08) (0.12)
Observations 327 327 327 327 327 327
R-squared 0.02 0.02 0.02 0.03 0.01 0.03

LLL is… chemical saving labor saving Yield improving
Leveled plot in F network {0,1} -0.16 -0.16 -0.03 -0.03 -0.01 -0.00
(0.12) (0.12) (0.12) (0.12) (0.11) (0.11)
Leveled plot in M network {0,1} -0.04 0.08 0.12
(0.12) (0.11) (0.11)
Observations 327 327 327 327 327 327
R-squared 0.02 0.04 0.01 0.02 0.03 0.05
Dependent vars are agreement {0,1} with statements about LLL. IV linear probability model with having a contact win the
lottery instrumenting for having a contact getting LLL. Control variables (coefficients not shown): Would be adopters in wife
(and HHH’s) network, total number of wife’s (and HHH’s) ag contacts, HHH and wife’s education and status as “progressive”.
Standard errors in parentheses: **p<0.01, * p<0.05, + p<0.1.

Introduction

Demand for LLL (Auction bid)
WTP All HHs Wife influences bid
Leveled plot in F network {0,1} 49.67 54.72 -7.08 -10.90
(47.18) (47.28) (55.52) (55.56)
103.69+ 119.18*
(59.94) (60.15)
Leveled plot in M network {0,1} 88.30* 91.81*
(44.31) (44.22)
Observations 328 328 328 328
R-squared 0.01 0.03 0.01 0.04
Dependent var is WTP of HHH from 2012 auction. IV linear model with having a contact win the
lottery instrumenting for having a contact getting LLL. Control variables are same as before.
**p<0.01, * p<0.05, + p<0.1.

• Mean WTP = Rs. 328
• Adopter in HHH’s network increases WTP by Rs. 90
• Adopter in wife’s network has no significant effect on average

Introduction

Wives’ involvement in LLL decision
100%

80%

60%

40%

20%

0%
Husband informed Husband discussed Wife tried to infleunce Wife felt she did
wife of LLL auction auction with wife LLL decision influence LLL decision

Introduction

Interaction term for wife’s influence
Base regression:

Controlling for
HHH’s network:

Xi: Control variables (for precision): Total network size (wife and HHH), education
(wife and HHH), whether identified as “progressive” (wife and HHH)

Introduction

Demand for LLL (Auction bid)
WTP All HHs Wife influences bid
Leveled plot in F network {0,1} 49.67 54.72 -7.08 -10.90
(47.18) (47.28) (55.52) (55.56)
Leveled plot in F network {0,1} 103.69+ 119.18*
x wife tries to influence decision (59.94) (60.15)
Leveled plot in M network {0,1} 88.30* 91.81*
(44.31) (44.22)
Observations 328 328 328 328
R-squared 0.01 0.03 0.01 0.04
Dependent var is WTP of HHH from 2012 auction. IV linear model with having a contact win the
lottery instrumenting for having a contact getting LLL. Control variables are same as before.
**p<0.01, * p<0.05, + p<0.1.

• Mean WTP = Rs. 328
• Adopter in HHH’s network increases WTP by Rs. 90
• Adopter in wife’s network has no significant effect on average
• Adopter in network of wife who tried to influence LLL decision
increases WTP by over Rs. 100.

Introduction

Summary
• Around 60% of HHH’s consider their wives’
opinions about agricultural technology adoption
– Men and women report consistently
• HHHs and wives’ networks are about the same size
on average, but composed differently
– Women are not just friends of husbands’ friends’ wives
– Higher caste/wealthier women have smaller ag
networks than husbands, less likely to know adopter
– Lower caste/poorer women have larger ag networks
than husbands, equally likely to know adopter

Introduction

Summary
• Strong evidence farmers’ wives early perception
of LLL is influenced by their network contacts’
households using LLL
– Most wives’ have favorable opinion off LLL after one
year
– Effect wears off (as everyone’s perception converges?)
• Some evidence women with adopting HHs in
their social networks who try to influence
adoption decision increase HHH’s WTP for LLL

Introduction

Next steps
• Closer examination of empowerment variables
– Are empowered women more likely to try to influence husband’s
technology decision?
– How to define empowerment?
• Input savings analysis (companion paper)
– Leverages randomized design
– Gender disaggregated labor data
– May help us disentangle mimicry from learning
– Early results show high and statistical water savings (30%)
• Network formation (Maertens and Barett 2012)
– How does female network formation compare to male network
formation?
– How might female networks be targeted differently?

Questions?
nmagnan@uga.edu
d.spielman@cgiar.org

Female networks drive adoption of agricultural technology in India

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