1. Gender, social networks,
technological change and learning
Evidence from a field experiment in Uttar Pradesh, India
Nicholas Magnan, University of Georgia
Kajal Gulati, University of California, Davis
Travis J. Lybbert, University of California, Davis
David J. Spielman, International Food Policy Research Institute
A GAAP contribution to the Cereal Systems Initiative for South Asia (CSISA)
A CSISA contribution to the Gender, Agriculture and Assets (GAAP) Project

2. Background: CSISA
Objective: Increase food, nutrition, and income security in South Asia
through sustainable intensification of the region’s cereal-based systems
Coverage: Bangladesh, India (Bihar, Odisha, eastern UP), Nepal, Pakistan*
Duration: Phase I: 2009-12; Phase II: 2012-15
Focus: Technology development and delivery at scale
 New stress-tolerant rice, wheat varieties
 Sustainable management practices
 Laser land levelling
 Direct seeded rice
 Mechanized rice transplanters
 Zero tillage wheat
 Policy reforms in support of sustainable intensification
* Phase 1 only

3. Background: Gender and CSISA
 Initially, poor articulation of gender dimensions of
sustainable intensification in CSISA
 How does gender affect the development and adoption of CSISA
technologies?
 HH decision-making, asset ownership
 Machinery designs
 Community interactions
 Extension approaches
 How do CSISA technologies affect gender dynamics?
 Time allocation
 Effort/drudgery
 Household decision-making
 Income, asset accumulation, ownership, control

4. Our research question
 Do gendered dimensions of information acquisition play a
role in household decision-making on technology
adoption?
 Do women and men in the same household have different social
networks?
 If so, how these do these differences affect learning and adoption?

5. Study site
 Eastern Uttar Pradesh (EUP): poorest part of UP
 Highly agrarian; intensive rice-wheat farming system
 Sample site
 3 districts in EUP
 8 (randomly selected) villages per district
 20 (randomly selected) farmers per village
Intervention
 Custom-hired laser land leveling (LLL)
 Reduces water usage/pumping costs, improves yields
 More precise (±1-2cm) than traditional leveling (±4-5cm)
 Market rate where available: Rs. 500-600/hour
 1 ha. plot may cost Rs. 1,500-3,500, lasts 4-7 years
 5-10% of total annual production costs

17. Study design
1. Info session on LLL
2. LLL auction and lottery: Divides sample into 3 groups
3. Lottery-winning farmers paid for and received LLL
4. One-year later: Follow-up auction with no lottery
Random sample from
village v
Auction
(self-
selection)
Auction winners
Auction losers
Lottery
(random
selection)
Lottery losersLottery winners

18. 2011 2012
Mar-Jun July-Sept Oct-Dec Jan-Mar Apr-June
-LLL info session
-Auction and lottery
-HH baseline survey
-HH network survey
LLL service to
auction/lottery winners
-Kharif rice season
-Input use surveys (every 2-3 wks)
FCH network survey
Rabi wheat season
Input-use surveys (every 2-3 wks)
- HH endline survey
- FCH endline survey
- LLL auction 2
LLL service
to auction
winners
Implementation

19. Eliciting social network data

20. Units of analysis
Individuals
• “HH”: Household head (N = 478)
• “MHH”: Head of male-headed HH (N = 392)
• “FHH”: Head of female-headed HH (N = 86)
• “FCH”: Female co-heads (N = 335)
• Usually wife of MHHs (sometimes mother or daughter)
Network links (dyads)
• Each MHH and FHH identifies his/her links from among all farmers
in the sample
• Each FCH identifies her links from among all other FCHs in the
sample

21. Work, talk, and influence
Indicator Overall Poor Wealthy
Womensay
Works on farm 0.55 0.68 0.44 ***
Percent of time spent on farm 0.28 0.36 0.22 ***
Talk about ag with husband 0.47 0.57 0.40 ***
Talk about ag technology with husband 0.35 0.29 0.42 **
Talk about ag LLL with husband 0.67 0.71 0.63
Talk about LLL with other women 0.35 0.41 0.29 **
Present during discussion on 2012 bid 0.57 0.61 0.54
Tried to influence bid 0.61 0.65 0.57
Successfully influenced bid 0.60 0.65 0.56
Mensay
Discuss ag technology with wife 0.66 0.71 0.62 *
Wife’s opinion on ag tech and crop
choice “important” or “very important” 0.73 0.72 0.74
Discussed LLL with wife after auction 0.64 .68 0.60 *

22. Exchanges of agricultural information
Unidirectional link Possible links Actual links %
HH to HH (either sex) 9,306 317 3.5
MHH to MHH 6,338 289 4.5
MHH to MHH (with FCH data) 5,470 254 4.6
FHH to FHH 320 2 0.6
MHH to FHH 1,324 0 0
FHH to MHH 1,324 26 2.0
FCH to FCH 5,470 216 4.0
MHH to MHH & FCH to FCH 5,470 14 0.2
FCH to FCH | MHH to MHH 242 12 4.7
MHH to MHH | FCH to FCH 216 12 5.6

23. Agricultural info link {0.1} HH MHH FCH
Mean: 0.035 Mean: 0.045 Mean: 0.04
Both poor -0.020*** -0.026*** 0.019**
Both non-progressive -0.035*** -0.043*** -0.047***
Both lower caste 0.004 0.001 -0.001
Both female 0.008
Δ age|if young (10 years) 0.001 0.002 -0.002
Δ education|if low edu (years) 0.005*** 0.006*** -0.003
Both wealthy 0.005 0.004 -0.007
Both progressive 0.011** 0.010 0.062**
Both upper caste 0.011** 0.015** -0.004
Both male 0.027***
Δ age|if old (10 years) -0.003 -0.003 -0.009***
Δ education|if high edu (years) 0.002*** 0.003*** -0.009***
Household distance (km) -0.007 -0.007 0.011*
Observations 9,306 6,338 5,470
Pseudo-R2 0.111 0.0821 0.0591
Determinants of network formation

24. Network size among sub-groups
Subgroup Contact
type
All ag info
contacts
Would be
adopters
All
(N=366)
MHH to MHH 0.78 0.54
FCH to FCH 0.86 0.33a
Poor
(N=169)
MHH to MHH 0.76 0.52
FCH to FCH 1.09a 0.39
Wealthy
(N=197)
MHH to MHH 0.79 0.55
FCH to FCH 0.65b 0.27a,b
a pairwise t-test significance between MHH and FCH of same wealth subgroup
b pairwise t-test significance between wealth subgroups

25. Learning and demand effects
Variable
Probability of believing that LLL use is…
Beneficial
Water
saving
Labor
saving WTP
Adopter in FCH’s network {0,1} 0.14* 0.19** 0.21** 57.21
Adopter in MHH’s network {0,1} -0.19 0.14 0.14 163.97
Would-be adopters in FCH’s
network 0.02 -0.03 -0.02 0.20
Would-be adopters in MHH’s
network 0.13 -0.01 -0.09 19.98
FCH’s network size 0.01 0.01 -0.01 -6.42
MHH’s network size 0.05 0.04 0.09 -30.24
FCH’s education 0.03 -0.01 0.06 -8.55
FCH’s age 0.00 0.00 0.00 1.50
MHH’s education -0.01 0.00 -0.00 -2.03
MHH’s age -0.00 -0.01 -0.00 -3.41
Constant 0.91*** 0.95*** 0.04 432.20***

26. Conclusions
 Women and men in same households have very little overlap in their
agricultural information networks
 Women’s agricultural networks are as large as men’s and, in the case of
poor households, substantially larger
 Poor men tend to talk to wealthier ones about agriculture, whereas poor
women tend to talk to other poor women
 Poorer women’s networks might be sources of less information, despite
large networks
 Having adopters in networks help women learn about technology
Female social networks are likely more relevant to technology promotion and
extension efforts in many “male-dominated” cereal systems than previously
believed

27. Thank you