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08.02.2012 - Shawn Cole
 

08.02.2012 - Shawn Cole

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The Value of Advice: Evidence from Agricultural Production Practices

The Value of Advice: Evidence from Agricultural Production Practices

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    08.02.2012 - Shawn Cole 08.02.2012 - Shawn Cole Presentation Transcript

    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion The Value of Advice: Evidence from Agricultural Production Practices Shawn Cole (Harvard) and Nilesh Fernando (Harvard) IFPRI August 2, 2012
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Agricultural Extension Services Widely credited for speeding the green revolution But... “often fail due to inadequate consultation of farmers about their information needs” (Babu et al., 2012) are costly, reach few, and su¤er from limited accountability (Anderson and Feder, 2007) Arrival of low-cost ICT may provide improved way to convey information
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Today Evaluate a mobile-phone, voice-based agricultural advice and information service “Avaaj Otalo,” or AO, a for-pro…t startup in Gujarat RCT with 1,200 cotton farmers in 40 villages, randomized at individual level 400 get AO & Physical Agricultural Extension 400 get AO only 400 serve as pure controls [No control group of Extension only] Impact on: Sources of information Agricultural knowledge Real outcomes Peer e¤ects and learning: Information sharing Learning by observation Peer agricultural behavior
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Motivation Modern growth theory attempts to explain productivity di¤erences within and across countries through varying technology use Large productivity di¤erences in crop yields exist within and across countries. To what extent are these di¤erences explained by ine¢ cient agricultural practices? Does a lack of awareness and technical know how explain the limited adoption of pro…table agricultural investments in the context of Gujarat? Through what mechanisms do such ’informational ine¢ ciencies’limit technology adoption? How do farmers share information? Does informing some farmers lead to positive spillovers, or less observation and information exchange? What factors serve to promote or limit the di¤usion of agricultural technologies?
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Contributions Rigorous evaluation of agricultural extension E¢ cacy of training (Financial literacy, management consulting) Test of validity of rural surveys via mobile phone
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Speci…c Open Questions Impact: Can e¤ective agricultural extension be delivered via mobile phone? Inform general debate on delivery of public services via ICTs. Is ICT education a substitute or complement to traditional (in-person) extension? Demand-Driven Extension: What is the importance of ’top-down’information provided by experts versus ’bottom-up’ information generated by users? Predictors of Adoption: What demographic factors explain di¤erences in technology use? Technology Di¤usion: Is valuable agricultural information shared among peers?
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Location in Literature Identifying the Impact of Agricultural Extension : Bardhan & Mookherjee (2011), Gandhi et al. (2009), Evanson et al. (1990) Explaining Technology Adoption in Agriculture : Du‡o, Kremer and Robinson (2011), Suri (2011), Udry & Conley (2010) Markets for Advice : Anagol & Kim (2012), others???? Productivity and Technology (Banerjee-Du‡o 2005, Hsieh-Klenow 2009) Entire …eld of agricultural economics
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Feedback sought What are the most pressing questions in agricultural extension? Particular mechanisms worth testing Alternative applications in our setting
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Avaaj Otalo Avaaj Otalo (“Voice Stoop”) Based on open source software (hence scalable) Mobile and voice-based touch-tone platform Good for low literacy environments Facilitates consistent delivery and reception of information Easy to monitor information delivery, evaluate services Enables farmers to receive, solicit, and share agricultural information Bottom-up and top-down agricultural information Gujarat-based startup, founded by Stanford and Berkeley computer scientists About 8 implementations in 6 states of India (information network for sex workers, ag info, etc.)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Motivation for AO Training & Visit method of agricultural extension considered unsustainable (IFPRI 2010) Up to 97 percent of Ag Extension budgets pay salaries, leaving little resources for …eld visits Caste and gender limits use of e-Choupal kiosks (Kumar, 2004) Provides information on information needs quickly and cheaply AO provides ongoing ‡ow of information rather than one-shot training Agri-input dealers provide advice, but may have incentives to recommend incorrect quantities or even products Local NGO, DSC, has provided radio program with farming practices for 5 years in study area
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Avaaj Otalo Service: Main Features Push calls Question and answer service Experience Sharing Farmers can volunteer agricultural practice information, perspectives, etc.; respond to others Radio Program Normal implementation: farmer pays airtime Our implementation: computer provides free callbacks in response to a missed call
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Push Calls Delivered every Wednesday to all 800 treatment respondents Average Length: ~5 minutes Each message is based on: Weekly calls to ten randomly selected farmers about their information needs for the following week Questions from incoming calls the week before Weather information from Indian Meteorological Dept. Agronomists’knowledge of crop phase, and agricultural best practices
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Incoming Call Features QnA: Farmers can record their own questions as well as listen to or respond to existing questions and answers Typical response time ~24 hours Announcements: This forum contains all the push messages that are sent out weekly by DSC and CMF Radio: Many episodes on agriculture are available from a radio show run by DSC over the previous …ve years. Experience-sharing: This forum encourages farmers to share their own innovative practices. Personal Inbox: Gives users access to their own questions and messages.
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Value Proposition Provides customized, timely, regular and relevant agricultural information Mitigates failures of traditional extension systems Addresses spatial failures by providing geography-speci…c information, and mobile-based delivery decreases cost of delivery and thus has higher reach Addresses temporal failures by delivering information that is sensitive to local weather conditions, and is available 24/7 Addresses institutional failures by delivering information that is demand-driven, and the web-based moderation platform allows for centralized monitoring of extension delivery Demand-driven Builds on trust and expertise established by DSC, a local NGO
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Physical Extension Two hours long; one session in Kharif (monsoon), one in Rabi (winter) Run by NGO (AKRSP), not government Invited 400 people from treatment group, 168 came Provided free transportation and a meal, no other compensation At NGO site, ca. 10-50 km from respondents households Rabi session focuses on: Wheat and cumin variety selection Cotton pesticide usage Based on time in season and informed by AO questions 20-30 people per session
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion AO vs. Physical Extension Cost AO: Assume 20 minutes of push calls and 18 minutes of incoming usage each month Per Farmer Cost Monthly Cost (USD) Monthly Cost (USD) (N=800) (N=2000) Airtime .60 .60 Agronomist .90 .36 AO System .40 .17 Total Monthly 1.90 1.13 One agronomist can handle 2,000 farmers on a regular basis Physical Extension Cost: $8.50 per farmer
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Sample Selection Focus on cotton farmers Important crop for millions of farmers Similar varieties and irrigation methods over large area Well-settled science, but uncertainty about practice remains Identify 40 villages in which DSC has a good presence Identify all cotton farmers in village (NGO workers created lists) Selected 30 from these lists at random, strati…ed by subcaste Assigned 10 to Control, 10 to AO, and 10 to AO & Physical Extension
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Project Timeline Surveying and Intervention Timeline Date Event May/June 2011 Cotton planting decisions begin May 2011 Listing for Baseline Survey July 2011 Baseline (Paper) Survey August 2011 AO training for treatment respondents August 2011 AO missed call service activated, first treatment message delivered October 2011 Encouragement reminder calls November 1-6, 2011 Physical Extension November 10, 2011 Round 1 of phone survey November 21, 2011 Peer / General Reminder Calls Begin November 2011 Cumin planting decisions for Rabi 2012 December, 2011 Round 2 of phone survey March, 2012 Peer survey July, 2012 Midline survey July, 2012 2nd AO training session starts for treatment respondents
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Avaaz Otalo Administrative Data All calls, duration of call, features access Whether individual listens to push call or not Whether control group calls in to non toll-free line Linked by mobile phone number
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Results Map Randomization Check and Summary Statistics Impact Evaluation Randomization Check Sources of Information Knowledge Index Pesticide Purchase and Usage Sowing Decisions Peer E¤ects and Learning Information Sharing Behavior Spillovers within study sample Spillovers outside of study sample Attrition and other concerns
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Empirical Approach Standard simple di¤erence estimator, village …xed-e¤ects, robust standard errors Comparing AO and AO+Extension group to controls yi ,v = αv + β (AO or AO&Extension ) + ε i Sample size with paper survey 1,200=398 control + 399 AO + 403 AO&E Sample size with phone survey 737=369+184+184 Comparing AO to control yi ,v = αv + β (AO ) + ε i Sample size with paper survey: 797=398+399 Sample size with phone survey: 553=369+184
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Sample Characteristics and Randomization Check Balance Information Sources (Paper) Cotton Fertilizer Cotton Pesticide Cumin Planting Control (AO+AOE)-C Control (AO+AOE)-C Control (AO+AOE)-C Cell contents: Mean ITT Mean ITT Mean ITT (3) (4) (5) (6) (9) (10) Asked for or received advice 0.265 -0.005 0.594 0.035 0.131 -0.022 (0.442) (0.027) (0.492) (0.030) (0.337) (0.020) N 392 1180 392 1180 398 1200 Importance of source consulted Past experience 0.048 -0.014 0.030 -0.016 - 0.023 (0.215) (0.025) (0.171) (0.012) (0.016) Govt extension 0.010 -0.010 0.004 0.002 0.019 -0.019 (0.098) (0.010) (0.066) (0.006) (0.139) (0.019) NGO 0.019 -0.014 0.004 0.002 - 0.011 (0.138) (0.014) (0.066) (0.006) (0.012) Other farmers 0.731 -0.009 0.421 -0.005 0.635 0.101 (0.446) (0.054) (0.495) (0.039) (0.486) (0.082) Input shops 0.144 0.027 0.515 0.019 0.269 -0.062 (0.353) (0.043) (0.501) (0.040) (0.448) (0.076) N for Mean 104 233 52 N for ITT Regression 309 729 139 No di¤erence for Cotton and Wheat Planting, or by treatment
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Sample Characteristics and Randomization Check Randomization mostly successful, except Cotton 2010 Balance Planting (Paper) Control Group AO Only AO+Extension AO-C AOE-C (AO+AOE)-C Cell contents: Mean Mean Mean ITT ITT ITT (1) (2) (3) (4) (5) (6) A. Sample Size Entire Sample 398 399 403 797 801 1200 B. Planting in Kharif 10 Planted Cotton 0.98 0.98 0.99 -0.01 0.00 0.00 (0.12) (0.15) (0.11) (0.01) (0.01) (0.01) Area Cotton Planted 4.45 5.01 4.74 0.57** 0.29 0.43* (3.62) (4.05) (4.43) (0.27) (0.29) (0.24) Planted Wheat 0.78 0.72 0.72 -0.05* -0.05 -0.05** (0.42) (0.45) (0.45) (0.03) (0.03) (0.03) Area Wheat Planted 1.17 1.35 1.07 0.18 -0.10 0.04 (1.35) (2.30) (1.25) (0.13) (0.09) (0.09) Planted Cumin 0.42 0.40 0.41 -0.02 -0.01 -0.02 (0.49) (0.49) (0.49) (0.03) (0.03) (0.03) Area Cumin Planted 0.76 0.79 0.70 0.03 -0.06 -0.02 (1.41) (1.50) (1.34) (0.10) (0.10) (0.09) Imbalance in reported area cotton planted in Kharif 2010 (No cotton imbalance in Kharif 2011)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Usage (First Stage) Initially, some concern about usage / take-up AO service …rst provided free by IBM/DSC, high usage Subsequently required farmers to pay own airtime, usage dropped Maximize research power with free service for treatment group DSC started to charge farmers nominal fee (ca. $4/year) Could be …nancially self-sustaining even without subscription (airtime charges)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Usage (First Stage) First Stage: Effect of Assignment on AO Usage and Physical Extension Visits Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT (1) (2) (3) (4) Listened to more than 50% of push calls 0 0.755*** 0.745*** 0.766*** (0.017) (0.024) (0.024) Called AO 0 0.603*** 0.555*** 0.650*** (0.017) (0.023) (0.025) Duration of usage 0 72.548*** 53.866*** 90.828*** (10.644) (8.902) (18.589) Winsorized duration of usage (90%) 0 45.341*** 40.106*** 50.540*** (4.140) (4.534) (5.783) Total no. of questions asked 0 1.441*** 1.22*** 1.656*** (0.201) (0.205) (0.272) Total no. of times Q&A accessed 0 3.416*** 2.28*** 4.496*** (0.571) (0.338) (0.957) Attended Physical Extension 0.005 0.214*** 0.013* 0.413*** (0.071) (0.017) (0.007) (0.033)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Question Topics Thematic breakdown from Start until Round 1 Phone Survey: Topic Percent of Calls Pest Management 59 Fertilizers 8 Seeds 1 Crop Planning 5 Others 24
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Sources of Information: Planting Decisions AO may complement or substitute information collection Impact: Information Sources Cotton Planting Wheat Planting Cumin Planting Control (AO+AOE)-C Control (AO+AOE)-C Control (AO+AOE)-C Cell contents: Mean ITT Mean ITT Mean ITT (1) (2) (7) (8) (9) (10) Importance of source consulted Past experience 0.612 0.078** 0.138 -0.040* 0.179 -0.035 (0.488) (0.035) (0.346) (0.024) (0.384) (0.027) Govt extension 0.008 -0.003 0.000 0 0.005 -0.005 (0.090) (0.006) (0.074) (0.004) NGO 0.043 -0.008 0.014 -0.003 0.005 0.005 (0.204) (0.014) (0.116) (0.008) (0.074) (0.007) Mobile phone-based 0.003 0.087*** 0 0.052*** 0 0.125*** (0.052) (0.015) (0.012) (0.017) Other farmers 0.230 -0.127*** 0.033 -0.019* 0.070 -0.046*** (0.422) (0.027) (0.178) (0.011) (0.256) (0.016) Input shops 0.070 -0.016 0.000 0.005 0.011 0.000 (0.256) (0.018) (0.000) (0.004) (0.104) (0.008) 35
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Sources of Information: Agricultural Inputs Impact: Information Sources Cotton Fertilizer Cotton Pesticide Control (AO+AOE)-C Control (AO+AOE)-C Cell contents: Mean ITT Mean ITT (3) (4) (5) (6) Importance of source consulted Past experience 0.496 0.029 0.291 -0.018 (0.501) (0.037) (0.455) (0.033) Govt extension 0.011 0.005 0.008 0.003 (0.104) (0.009) (0.091) (0.007) NGO 0.051 -0.013 0.044 -0.011 (0.221) (0.015) (0.206) (0.014) Mobile phone-based 0.003 0.223*** 0.006 0.297*** (0.052) (0.022) (0.074) (0.024) Other farmers 0.252 -0.149*** 0.177 -0.073*** (0.435) (0.028) (0.382) (0.026) Input shops 0.146 -0.081*** 0.446 -0.190*** (0.354) (0.022) 35 (0.498) (0.035)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Impact on Agricultural Knowledge Measure agricultural knowledge with a series of ten questions related to agricultural practices Asked once at baseline paper survey, once in round 1 phone survey Example questions “If money were not a constraint, what is the best pesticide for cotton white‡y?” “Which fungicide should be applied to control wilt in cotton?” “Which variety of cumin is recommended as wilt-resistant?”
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Impact on Agricultural Knowledge Impact: Agricultural Knowledge Baseline Survey Round 1 Phone Survey Control (AO+AOE)-C Control (AO+AOE)-C Cell contents: Mean ITT Mean ITT (1) (2) (3) (4) Total 0.289 -0.001 0.350 0.008 (0.212) (0.014) (0.173) (0.011) Cotton-related 0.585 0.024 0.576 0.025 0.493 0.034 (0.380) (0.022) Fertilizer-related (0.162) -(0.004) 0.321 -0.015 0.279 0.016 (0.200) (0.014) Pesticide-related 0.284 0.003 0.202 -0.008 (0.451) (0.028) (0.257) (0.014) Cumin-related 0.254 -0.024 0.340 0.123*** (0.436) (0.025) (0.474) (0.035) Limited e¤ect, only on cumin-related question
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Cumin Adoption Cash crop with potential for high-return, but risky Important risks: wilt, frost AO provides timely weather forecasting and planting suggestions Nearly half of push messages (20) discuss Cumin, and physical extension covers cumin cultivation
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Impact on Adoption of Cumin Impact: Cumin Adoption Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT (1) (2) (3) (4) Planted cumin in R11 but not in R12 0.183 -0.016 0.007 -0.036 (0.387) (0.030) (0.039) (0.033) Planted cumin in R12 but not in R11 0.138 0.064** 0.054 0.064* (0.345) (0.028) (0.041) (0.035) Planted cumin both in R11 and in R12 0.233 -0.032 -0.016 -0.045 (0.423) (0.029) (0.040) (0.034)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Cumin: Acreage Planted (No Control) Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT Baseline (N=1200) (1) (2) (3) (4) Did you plant cumin in Rabi 2011? 0.425 -0.017 -0.023 -0.012 (0.495) (0.030) (0.034) (0.035) Total area of cumin planted in Rabi 2011? 0.762 -0.019 0.018 -0.055 (1.406) (0.078) (0.085) (0.106) Baseline Phone Respondents (N=798) Did you plant cumin in Rabi 2011? 0.425 -0.018 -0.035 -0.001 (0.495) (0.032) (0.039) (0.039) Total area of cumin planted in Rabi 2011? 0.762 0.000 0.036 -0.037 (1.406) (0.081) (0.110) (0.119) Round 1 Phone Survey (N=737) Did you plant cumin this Rabi 2012? 0.274 0.046 0.070* 0.022 (0.446) (0.033) (0.042) (0.039) Total area of cumin planted in Rabi 2012? 0.522 0.243** 0.240* 0.255* (1.174) (0.112) (0.127) (0.154)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Cumin: Acreage Planted (Control for Baseline Cotton Area) Cumin Sowing Decisions Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT Baseline (N=1200) (1) (2) (3) (4) Did you plant cumin in Rabi 2011? 0.425 -0.025 -0.032 -0.019 (0.495) (0.030) (0.035) (0.035) Total area of cumin planted in Rabi 2011? 0.762 -0.057 -0.036 -0.080 (1.406) (0.079) (0.085) (0.105) Baseline Phone Respondents (N=798) Did you plant cumin in Rabi 2011? 0.425 -0.034 -0.052 -0.014 (0.495) (0.033) (0.039) (0.038) Total area of cumin planted in Rabi 2011? 0.762 -0.086 -0.058 -0.099 (1.406) (0.084) (0.102) (0.115) Round 1 Phone Survey (N=737) Did you plant cumin this Rabi 2012? 0.274 0.038 0.051 0.019 (0.446) (0.035) (0.045) (0.040) Total area of cumin planted in Rabi 2012? 0.522 0.225 * 0.202 0.246 (1.174) (0.117) (0.138) (0.156)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Why Pest Management? Indian cotton yields are one third of Chinese yields (NCC, 2012) Cotton production accounts of 54% of all pesticide usage in India Pesticide accounts for roughly 15% of input costs in cotton production Pilot studies of AO in rural Gujarat suggest large demand for pest management information (Patel at al. 2010) Types of pests a- icting cotton vary by season and develop resistance to pesticides and varieties of cotton putting a premium on learning and timely information
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Pesticide Choice for Cotton Monocrotophos Imidachlororprid Price per Liter $8 $20 Dose for 1 acre 1.5L 300 ml Cost/acre $12 $6 Introduced 1980 2000 Kills Bollworm Yes No Many sucking pests Kills sucking pests have developed Yes immunity to this Toxicity High Medium-Low Key fact: 95% of farmers grow BT Cotton, which is resistant to bollworm
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Control Actions on Monocrotophos Indonesia: Banned for use in rice in 1986 Kuwait: Severely Restricted Germany: May not be handled by adolescents, pregnant and nursing women Malaysia: Registered for speci…c use Philippines: Severely restricted Sri Lanka: Severely restricted. Banned since 1995. US: Use is prohibited
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Use of Cotton Pesticides: More of the Good Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT Baseline (N=1200) (1) (2) (3) (4) Purchased imidacloprid in K10 0.428 0.028 -0.018 0.074 (0.496) (0.038) (0.041) (0.050) Amount of imidacloprid used in K10 0.435 0.034 0.066 0.019 (0.837) (0.068) (0.093) (0.083) Round 1 Phone Survey (N=737) Purchased imidacloprid in K11 0.388 0.128 *** 0.136 *** 0.122 (0.488) (0.037) (0.049) (0.043) Used imidacloprid in K11 0.388 0.125 *** 0.136 *** 0.116 (0.488) (0.036) (0.049) (0.044) Amount of imidacloprid used in K11 0.492 0.105 0.149 0.050 (1.254) (0.082) (0.128) (0.071) Intensity of imidacloprid used in K11 0.110 0.037 ** 0.039 0.032 (0.214) (0.018) (0.027) (0.019)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Use of Cotton Pesticides: Less of the Bad Usage of Monocrotophos Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT Baseline (N=1200) (1) (2) (3) (4) Purchased monocrotophos in K10 0.962 0.000 -0.001 -0.001 (0.191) (0.012) (0.010) (0.016) Amount of monocrotophos used in K10 2.328 0.254* 0.346* 0.219 (1.866) (0.137) (0.189) (0.169) Baseline Phone Respondents (N=798) Purchased monocrotophos in K10 0.962 0.002 -0.011 0.013 (0.191) (0.013) (0.015) (0.017) Amount of monocrotophos used in K10 2.328 0.306* 0.412 0.226 (1.866) (0.180) (0.287) (0.213) Round 1 Phone Survey (N=737) Purchased monocrotophos in K11 0.945 -0.013 -0.024 -0.002 (0.229) (0.019) (0.020) (0.026) Used monocrotophos in K11 0.942 -0.010 -0.021 0.001 (0.234) (0.019) (0.021) (0.026) Amount of monocrotophos used in K11 3.870 -0.486** -0.307 -0.684** (4.005) (0.210) (0.251) (0.278)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion At baseline, each farmer was asked to identify top agricultural contacts with whom they exchange information Can imagine two models for e¤ect of AO AO dramatically increases sharing of knowledge, because quality of knowledge increases, so returns to sharing are higher AO reduces sharing of knowledge, because farmers avail of AO
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion How does AO a¤ect farmers propensity to share information? Impact: Information Sharing Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT (1) (3) (5) (7) Shared agricultural information with top contacts 0.693 -0.019 0.003 -0.046 (0.462) (0.034) (0.038) (0.043) Topics of information shared: Crop decision 0.122 -0.037* -0.033 -0.041 (0.328) (0.020) (0.025) (0.027) Fertilizers 0.313 -0.053 -0.042 -0.067* (0.464) (0.033) (0.042) (0.040) Pests and diseases 0.043 0.027* 0.010 0.040* (0.204) (0.016) (0.016) (0.024) Pesticides and fungicides 0.454 -0.005 0.025 -0.038 0.499 0.036 0.040 0.046 Harvesting 0.014 -0.014** -0.014** -0.014** (0.116) (0.006) (0.006) (0.006)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion E¤ect on Information Collection Less likely to receive information from top contacts Impact: Information Reception and Observation Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT (1) (2) (3) (4) Received information from top contacts 0.563 -0.076 *** -0.086 ** -0.066 (0.497) (0.029) (0.040) (0.037) Topic of information received: Crop decision 0.114 -0.054 *** -0.046 ** -0.061 (0.318) (0.019) (0.022) (0.021) Field Preparation 0.038 -0.020 * -0.016 -0.026 (0.192) (0.011) (0.015) (0.013) Seeds 0.179 -0.045 * -0.057 * -0.033 (0.384) (0.026) (0.029) (0.030) Fertilizers 0.204 -0.052 ** -0.049 -0.058 (0.403) (0.025) (0.032) (0.033) Pests and diseases 0.035 -0.002 -0.003 -0.002 (0.185) (0.012) (0.016) (0.015)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Less Learning from Neighbors Control (AO+AOE)-C AO-C AOE-C Mean ITT ITT ITT (1) (2) (3) (4) Learned information from observing top 0.239 -0.107 *** -0.115 *** -0.102 contacts fields (0.427) (0.028) (0.035) (0.029) Topic of information learned: Crop decision 0.049 -0.040 *** -0.034 *** -0.043 (0.216) (0.013) (0.012) (0.013) Field Preparation 0.041 -0.024 ** -0.025 * -0.023 (0.198) (0.011) (0.015) (0.012) Seeds 0.035 -0.021 ** -0.034 *** -0.008 (0.185) (0.010) (0.010) (0.012) Fertilizers 0.041 -0.020 -0.037 ** -0.004 (0.198) (0.015) (0.016) (0.018) Pests and diseases 0.014 -0.011 -0.008 -0.014 (0.116) (0.006) (0.008) (0.006)
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Peer E¤ects: Spillovers on Imida Use, Control Group Peer Effects on Imidacloprid Adoption within Study Group 1=Purchased Imidacloprid in K11 (1) (2) (3) (4) (5) At least one top contact 0.123 0.304* received treatment (0.118) (0.160) 0.165 0.185 0.283** Proportion of top contacts (0.130) (0.133) (0.138) who received treatment Controls Age -0.008 -0.006 -0.003 (0.006) (0.006) (0.006) Years of education -0.003 0.004 0.012 (0.017) (0.015) (0.014) Land holdings (ac) 0.018 0.034 0.0419* (0.022) (0.021) (0.024) Area Cotton Planted (K10) -0.016 -0.0495* -0.048 (0.031) (0.029) (0.034) Top contacts average land holdings 0.003 0.010 0.010 (0.012) (0.013) (0.017) Number of references received -(0.048) (0.033) (0.039) (0.043) Village Fixed Effects Yes Yes Yes Yes Yes N 120 120 120 102 90 No. of villages 38 38 38 38 36
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Spillovers, Imida Use Among Peers 1=Purchased Imidacloprid in K11 (1) (2) (3) (4) (5) At least one top contact 0.033 0.033 received treatment (0.046) (0.051) Proportion of top 0.029 0.026 0.065 contacts who received (0.051) (0.051) (0.190) treatment Controls Age -0.001 -0.002 0.001 (0.002) (0.003) (0.003) Years of education 0.006 0.007 0.007 (0.005) (0.007) (0.005) Land holdings (ac) 0.005** 0.005** 0.0051** (0.003) (0.002) (0.002) Top contacts average land holdings 0.002** 0.001*** 0.002*** (0.001) (0.000) (0.001) Number of references received 0.003 0.005 (0.028) (0.051) Village Fixed Effects Yes Yes Yes Yes Yes N 687 687 651 360 528 No. of villages 40 40 40 40 40
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Caveats Attrition Number of attritors equal in treatment and control group Imbalance in attrition in round 1 phone: treatment group attritors more likely to have planted cumin in ’10 Demand e¤ects 55% of treatment group reports having called into AO to ask question Administrative data indicates 53% actually did so Continue knowledge tests
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Proof of Concept Take-up of AO is high, among randomly selected sample of poor farmers 75% listen to more than half of push calls 60% call into system, Each person asks 1.4 questions on average Young, technophiles more likely to use Telephone surveys appear to work well Midline: Section ‘ Z’randomly assigned phone or paper administration
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Impact Evaluation: Information Between 10-30% report AO as main information source on various topics Reduction in reliance on agro-dealers for pesticide (from 45% to 25%) and fertilizer (from 15% to 7%) No dramatic change in measured agricultural knowledge
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Impact Evaluation: Agricultural Practices Cumin Approximately 6 percentage point increase in cumin adoption (from base of 12 pp) Average area planted in cumin increase .22 acres, o¤ base of .52 acres Pesticide Dramatic increase in use of imidacloprid (from 40% to 55%) Modest reduction in intensity of monocrotophos (from 3.9 to 3.5 L) E¤ect on Yields Collecting data currently
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Conclusion Farmers will listen to advice Treatment has important e¤ects: pesticide choice cumin sewing Behavior may change without generic change in knowledge New technology may a¤ect information-sharing behavior Less reported sharing, but likely better quality information
    • Intro Context Usage Impact: Info Impact: Practices Peer E¤ects Discussion Future work Audit study of agri-dealers Peer survey Peer_Outcome=a+β Peer _Treated + ε i Health outcomes Measuring willingness to pay Role of trust in learning (DSC well-known) Education vs. persuasion