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CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
CSISA GAAP Presentation January 2013
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CSISA GAAP Presentation January 2013

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Presentation given by CSISA at GAAP final technical workshop in Addis Ababa, January 2013

Presentation given by CSISA at GAAP final technical workshop in Addis Ababa, January 2013

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  • Because these two groups were exogenously bifurcated we can compare between the two, and between the network contacts of these two groups.
  • And here we look at network effects on impressions of the technology. [Go through results]We can also compare how having adopters in ones network affects impressions compared to being a first generation adopter.
  • Transcript

    • 1. Female networks, social learning, anddemand 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
    • 2. Study site and Female decision Identification and Summary andIntroduction 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)
    • 3. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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)
    • 4. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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?
    • 5. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps Definitions • “HHH”: Household head – Selected at random – 80% male • “Wife”: Female head of male headed household – Almost always the HHH’s wife
    • 6. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 7. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 8. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 9. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 10. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 11. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 12. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps Social network surveys (HHH in March 2011, Wives in Oct 2011)
    • 13. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 14. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 15. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 16. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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)
    • 17. Study site and Female decision Identification and Summary and Introduction experiment making and networks network effects next steps Do HHHs value wives’ opinions?• Wives say… 100% 80% 60% 40% 20% 0% Crop choice Technology to Family labor Spending use allocation money
    • 18. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps Do HHHs value wives’ opinions?• Husbands say… 100% 80% 60% 40% 20% 0% Discuss crop and Wifes opinion Wifes opinion "very technology choice "important" or "very important" important"
    • 19. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps HHH’s and wives’ ag networks Only 1.5 % of all ag network links to a HH are shared by husband and wife
    • 20. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps HHH’s and wives’ networks by wealth
    • 21. Study site and Network Identification and Summary andIntroduction 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
    • 22. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Auction losers WTP < X Random sample from village v Auction Auction winners (self-selection) WTP ≥ X
    • 23. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Auction losers Random sample from village v Auction Auction winners (self-selection)
    • 24. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Auction losers Random sample from village v Auction Auction winners (self-selection) Lottery (random selection) Lottery winners Lottery losers
    • 25. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Auction losers Random sample from village v Auction Auction winners (self-selection) Lottery (random selection) Lottery winners Lottery losers
    • 26. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Farmer j Farmer k Farmer i
    • 27. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Network effects on perceptions of LLL Base regression:Controlling forHHH’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)
    • 28. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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.
    • 29. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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.
    • 30. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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
    • 31. Study site and Female decision Identification and Summary andIntroduction experiment making and networks network effects next steps 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
    • 32. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps Interaction term for wife’s influenceBase regression:Controlling forHHH’s network: Xi: Control variables (for precision): Total network size (wife and HHH), education (wife and HHH), whether identified as “progressive” (wife and HHH)
    • 33. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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.
    • 34. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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
    • 35. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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
    • 36. Study site and Network Identification and Summary andIntroduction experiment descriptives network effects next steps 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?
    • 37. Questions? nmagnan@uga.edud.spielman@cgiar.org

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