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

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

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

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?

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

Study design1. 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 losers

Lottery winners

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 seasonInput-use surveys (every 2-3 wks)

- HH endline survey- FCH endline survey- LLL auction 2

LLL service to auction winners

Implementation

Eliciting social network data

Units of analysisIndividuals• “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

Work, talk, and influence 

 Indicator Overall

Poor

Wealthy 

Women say

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 0.63Talk about LLL with other women 0.35 0.41 0.29 **Present during discussion on 2012 bid 0.57 0.61 0.54Tried to influence bid 0.61 0.65 0.57Successfully influenced bid

0.60 0.65

0.56

  

     

Men say

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 0.74Discussed LLL with wife after auction

0.64 .68 0.60 *

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

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 -0.001Both female 0.008    Δ age|if young (10 years) 0.001 0.002 -0.002Δ education|if low edu (years)

0.005*** 0.006*** -0.003

Both wealthy 0.005 0.004 -0.007Both progressive 0.011** 0.010 0.062**Both upper caste 0.011** 0.015** -0.004Both 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 5,470Pseudo-R2 0.111 0.0821 0.0591

Determinants of network formation

Network size among sub-groupsSubgrou

pContact

typeAll 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 subgroupb pairwise t-test significance between wealth subgroups

Learning and demand effects

 Variable

Probability of believing that LLL use is…

BeneficialWater saving

Labor saving WTP

Adopter in FCH’s network {0,1} 0.14* 0.19** 0.21** 57.21Adopter in MHH’s network {0,1} -0.19 0.14 0.14 163.97Would-be adopters in FCH’s network 0.02 -0.03 -0.02 0.20Would-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***

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

Thank you