Gender, social networks,
technological change and learning
Evidence from a field experiment in Uttar Pradesh, India
Nichol...
Background: CSISA
Objective: Increase food, nutrition, and income security in South Asia
through sustainable intensificati...
Background: Gender and CSISA
 Initially, poor articulation of gender dimensions of
sustainable intensification in CSISA
...
Our research question
 Do gendered dimensions of information acquisition play a
role in household decision-making on tech...
Study site
 Eastern Uttar Pradesh (EUP): poorest part of UP
 Highly agrarian; intensive rice-wheat farming system
 Samp...
Study design
1. Info session on LLL
2. LLL auction and lottery: Divides sample into 3 groups
3. Lottery-winning farmers pa...
2011 2012
Mar-Jun July-Sept Oct-Dec Jan-Mar Apr-June
-LLL info session
-Auction and lottery
-HH baseline survey
-HH networ...
Eliciting social network data
Units of analysis
Individuals
• “HH”: Household head (N = 478)
• “MHH”: Head of male-headed HH (N = 392)
• “FHH”: Head of ...
Work, talk, and influence
Indicator Overall Poor Wealthy
Womensay
Works on farm 0.55 0.68 0.44 ***
Percent of time spent o...
Exchanges of agricultural information
Unidirectional link Possible links Actual links %
HH to HH (either sex) 9,306 317 3....
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-...
Network size among sub-groups
Subgroup Contact
type
All ag info
contacts
Would be
adopters
All
(N=366)
MHH to MHH 0.78 0.5...
Learning and demand effects
Variable
Probability of believing that LLL use is…
Beneficial
Water
saving
Labor
saving WTP
Ad...
Conclusions
 Women and men in same households have very little overlap in their
agricultural information networks
 Women...
Thank you
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
CSISA GAAP presentation
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CSISA GAAP presentation

  1. 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. 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. 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. 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. 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
  6. 6. 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
  7. 7. 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
  8. 8. Eliciting social network data
  9. 9. 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
  10. 10. 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 *
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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***
  15. 15. 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
  16. 16. Thank you

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