08.02.2012 - Shawn Cole

Intro Context Usage Impact: Info Impact: Practices Peer Effects Discussion

The Value of Advice: Evidence from AgriculturalProduction Practices

Shawn Cole (Harvard) and Nilesh Fernando (Harvard)

IFPRI

August 2, 2012


Agricultural Extension Services

• Widely credited for speeding the green revolution• But...

• “often fail due to inadequate consultation of farmers abouttheir information needs” (Babu et al., 2012)

• are costly, reach few, and suffer from limited accountability(Anderson and Feder, 2007)

• Arrival of low-cost ICT may provide improved way to conveyinformation


Today• Evaluate a mobile-phone, voice-based agricultural advice andinformation service• “Avaaj Otalo,” or AO, a for-profit startup in Gujarat

• RCT with 1,200 cotton farmers in 40 villages, randomized atindividual 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 effects and learning:• Information sharing• Learning by observation• Peer agricultural behavior


Motivation

• Modern growth theory attempts to explain productivitydifferences within and across countries through varyingtechnology use

• Large productivity differences in crop yields exist within andacross countries. To what extent are these differencesexplained by ineffi cient agricultural practices?

• Does a lack of awareness and technical know how explain thelimited adoption of profitable agricultural investments in thecontext of Gujarat? Through what mechanisms do such’informational ineffi ciencies’limit technology adoption?

• How do farmers share information? Does informing somefarmers lead to positive spillovers, or less observation andinformation exchange? What factors serve to promote or limitthe diffusion of agricultural technologies?


Contributions

• Rigorous evaluation of agricultural extension• Effi cacy of training (Financial literacy, managementconsulting)

• Test of validity of rural surveys via mobile phone


Specific Open Questions

• Impact: Can effective agricultural extension be delivered viamobile 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 explaindifferences in technology use?

• Technology Diffusion: Is valuable agricultural informationshared among peers?


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 : Duflo,Kremer and Robinson (2011), Suri (2011), Udry & Conley(2010)

• Markets for Advice : Anagol & Kim (2012), others????

• Productivity and Technology (Banerjee-Duflo 2005,Hsieh-Klenow 2009)

• Entire field of agricultural economics


Feedback sought

• What are the most pressing questions in agriculturalextension?

• Particular mechanisms worth testing• Alternative applications in our setting


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 agriculturalinformation

• Bottom-up and top-down agricultural information

• Gujarat-based startup, founded by Stanford and Berkeleycomputer scientists

• About 8 implementations in 6 states of India (informationnetwork for sex workers, ag info, etc.)


Motivation for AO

• Training & Visit method of agricultural extension consideredunsustainable (IFPRI 2010)

• Up to 97 percent of Ag Extension budgets pay salaries,leaving little resources for field visits

• Caste and gender limits use of e-Choupal kiosks (Kumar,2004)

• Provides information on information needs quickly and cheaply• AO provides ongoing flow of information rather than one-shottraining

• Agri-input dealers provide advice, but may have incentives torecommend incorrect quantities or even products

• Local NGO, DSC, has provided radio program with farmingpractices for 5 years in study area


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 inresponse to a missed call


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 theirinformation 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 bestpractices


Incoming Call Features

• QnA: Farmers can record their own questions as well as listento or respond to existing questions and answers

• Typical response time ~24 hours

• Announcements: This forum contains all the push messagesthat are sent out weekly by DSC and CMF

• Radio: Many episodes on agriculture are available from aradio show run by DSC over the previous five years.

• Experience-sharing: This forum encourages farmers to sharetheir own innovative practices.

• Personal Inbox: Gives users access to their own questions andmessages.


Value Proposition

• Provides customized, timely, regular and relevant agriculturalinformation

• Mitigates failures of traditional extension systems• Addresses spatial failures by providing geography-specificinformation, and mobile-based delivery decreases cost ofdelivery and thus has higher reach

• Addresses temporal failures by delivering information that issensitive to local weather conditions, and is available 24/7

• Addresses institutional failures by delivering information that isdemand-driven, and the web-based moderation platform allowsfor centralized monitoring of extension delivery

• Demand-driven• Builds on trust and expertise established by DSC, a local NGO


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 othercompensation

• 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


AO vs. Physical Extension Cost

• AO: Assume 20 minutes of push calls and 18 minutes ofincoming usage each month

Per Farmer Cost Monthly Cost (USD) Monthly Cost (USD)(N=800) (N=2000)

Airtime .60 .60Agronomist .90 .36AO System .40 .17Total Monthly 1.90 1.13

• One agronomist can handle 2,000 farmers on a regular basis• Physical Extension Cost: $8.50 per farmer


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 createdlists)

• Selected 30 from these lists at random, stratified by subcaste

• Assigned 10 to Control, 10 to AO, and 10 to AO & PhysicalExtension


Project Timeline

Date EventMay/June 2011 Cotton planting decisions beginMay 2011 Listing for Baseline SurveyJuly 2011 Baseline (Paper) SurveyAugust 2011 AO training for treatment respondentsAugust 2011 AO missed call service activated, first treatment message deliveredOctober 2011 Encouragement reminder callsNovember 16, 2011 Physical ExtensionNovember 10, 2011 Round 1 of phone surveyNovember 21, 2011 Peer / General Reminder Calls BeginNovember 2011 Cumin planting decisions for Rabi 2012December, 2011 Round 2 of phone surveyMarch, 2012 Peer surveyJuly, 2012 Midline surveyJuly, 2012 2nd AO training session starts for treatment respondents

Surveying and Intervention Timeline


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


Results Map

• Randomization Check and Summary Statistics• Impact Evaluation

• Randomization Check• Sources of Information• Knowledge Index• Pesticide Purchase and Usage• Sowing Decisions

• Peer Effects and Learning• Information Sharing Behavior• Spillovers within study sample• Spillovers outside of study sample

• Attrition and other concerns


Empirical Approach

• Standard simple difference estimator, village fixed-effects,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


Sample Characteristics and Randomization Check

Control (AO+AOE)C Control (AO+AOE)C Control (AO+AOE)CCell 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 consultedPast experience 0.048 0.014 0.030 0.016 0.023

(0.215) (0.025) (0.171) (0.012) (0.016)

Gov't 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 52N for ITT Regression 309 729 139

Balance Information Sources (Paper)

Cotton Fertilizer Cotton Pesticide Cumin Planting

• No difference for Cotton and Wheat Planting, or by treatmentgroup


Sample Characteristics and Randomization Check• Randomization mostly successful, except Cotton 2010

Control Group AO Only AO+Extension AOC AOEC (AO+AOE)CCell contents: Mean Mean Mean ITT ITT ITT

(1) (2) (3) (4) (5) (6)A. Sample SizeEntire Sample 398 399 403 797 801 1200

B. Planting in Kharif '10Planted 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)

Balance Planting (Paper)

• Imbalance in reported area cotton planted in Kharif 2010• (No cotton imbalance in Kharif 2011)


Usage (First Stage)

• Initially, some concern about usage / take-up• AO service first provided free by IBM/DSC, high usage• Subsequently required farmers to pay own airtime, usagedropped

• Maximize research power with free service for treatment group• DSC started to charge farmers nominal fee (ca. $4/year)• Could be financially self-sustaining even without subscription(airtime charges)


Usage (First Stage)

Control (AO+AOE)C AOC AOECMean 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)

First Stage: Effect of Assignment on AO Usage and Physical Extension Visits


Question Topics

• Thematic breakdown from Start until Round 1 Phone Survey:

Topic Percent of CallsPest Management 59Fertilizers 8Seeds 1Crop Planning 5Others 24


Sources of Information: Planting Decisions• AO may complement or substitute information collection

Control (AO+AOE)C Control (AO+AOE)C Control (AO+AOE)CCell contents: Mean ITT Mean ITT Mean ITT

(1) (2) (7) (8) (9) (10)

Importance of source consultedPast 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)

Gov't 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 phonebased 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)

Impact: Information Sources

Cotton Planting Wheat Planting Cumin Planting

35

• Mobile phones have some influence, particularly for cumin;crowd out other farmers and past experience as sources ofinformation


Sources of Information: Agricultural Inputs

Control (AO+AOE)C Control (AO+AOE)CCell 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)

Gov't 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 phonebased 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) (0.498) (0.035)

Impact: Information Sources

Cotton Fertilizer Cotton Pesticide

35


Impact on Agricultural Knowledge

• Measure agricultural knowledge with a series of ten questionsrelated to agricultural practices

• Asked once at baseline paper survey, once in round 1 phonesurvey

• Example questions• “If money were not a constraint, what is the best pesticide forcotton whitefly?”

• “Which fungicide should be applied to control wilt in cotton?”• “Which variety of cumin is recommended as wilt-resistant?”


Impact on Agricultural Knowledge

Control (AO+AOE)C Control (AO+AOE)CCell 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)

Cottonrelated 0.585 0.024 0.576 0.0250.493 0.034 (0.380) (0.022)

Fertilizerrelated (0.162) (0.004) 0.321 0.0150.279 0.016 (0.200) (0.014)

Pesticiderelated 0.284 0.003 0.202 0.008(0.451) (0.028) (0.257) (0.014)

Cuminrelated 0.254 0.024 0.340 0.123***(0.436) (0.025) (0.474) (0.035)

Impact: Agricultural Knowledge

Baseline Survey Round 1 Phone Survey

• Limited effect, only on cumin-related question


Cumin Adoption

• Cash crop with potential for high-return, but risky• Important risks: wilt, frost• AO provides timely weather forecasting and plantingsuggestions

• Nearly half of push messages (20) discuss Cumin, and physicalextension covers cumin cultivation


Impact on Adoption of Cumin


(1) (2) (3) (4)Planted cumin in R'11 but not in R'12 0.183 0.016 0.007 0.036

(0.387) (0.030) (0.039) (0.033)

Planted cumin in R'12 but not in R'11 0.138 0.064** 0.054 0.064*(0.345) (0.028) (0.041) (0.035)

Planted cumin both in R'11 and in R'12 0.233 0.032 0.016 0.045(0.423) (0.029) (0.040) (0.034)

Impact: Cumin Adoption


Cumin: Acreage Planted (No Control)


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)


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)


Cumin: Acreage Planted (Control for Baseline CottonArea)


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)


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)


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)

Cumin Sowing Decisions


Why Pest Management?

• Indian cotton yields are one third of Chinese yields (NCC,2012)

• Cotton production accounts of 54% of all pesticide usage inIndia

• Pesticide accounts for roughly 15% of input costs in cottonproduction

• Pilot studies of AO in rural Gujarat suggest large demand forpest management information (Patel at al. 2010)

• Types of pests affl icting cotton vary by season and developresistance to pesticides and varieties of cotton putting apremium on learning and timely information


Pesticide Choice for Cotton

Monocrotophos ImidachlororpridPrice per Liter $8 $20Dose for 1 acre 1.5L 300 mlCost/acre $12 $6Introduced 1980 2000Kills Bollworm Yes No

Kills sucking pestsMany sucking pestshave developedimmunity to this

Yes

Toxicity High Medium-Low

• Key fact: 95% of farmers grow BT Cotton, which is resistantto bollworm


Control Actions on Monocrotophos

• Indonesia: Banned for use in rice in 1986• Kuwait: Severely Restricted• Germany: May not be handled by adolescents, pregnant andnursing women

• Malaysia: Registered for specific use• Philippines: Severely restricted• Sri Lanka: Severely restricted. Banned since 1995.• US: Use is prohibited


Use of Cotton Pesticides: More of the Good


Baseline (N=1200) (1) (2) (3) (4)Purchased imidacloprid in K'10 0.428 0.028 0.018 0.074

(0.496) (0.038) (0.041) (0.050)

Amount of imidacloprid used in K'10 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 K'11 0.388 0.128 *** 0.136 *** 0.122

(0.488) (0.037) (0.049) (0.043)

Used imidacloprid in K'11 0.388 0.125 *** 0.136 *** 0.116(0.488) (0.036) (0.049) (0.044)

Amount of imidacloprid used in K'11 0.492 0.105 0.149 0.050(1.254) (0.082) (0.128) (0.071)

Intensity of imidacloprid used in K'11 0.110 0.037 ** 0.039 0.032(0.214) (0.018) (0.027) (0.019)

• Dramatic increase in reported use of imidacloprid


Use of Cotton Pesticides: Less of the Bad


Baseline (N=1200) (1) (2) (3) (4)Purchased monocrotophos in K'10 0.962 0.000 0.001 0.001

(0.191) (0.012) (0.010) (0.016)

Amount of monocrotophos used in K'10 2.328 0.254* 0.346* 0.219(1.866) (0.137) (0.189) (0.169)

Baseline Phone Respondents (N=798)Purchased monocrotophos in K'10 0.962 0.002 0.011 0.013

(0.191) (0.013) (0.015) (0.017)

Amount of monocrotophos used in K'10 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 K'11 0.945 0.013 0.024 0.002

(0.229) (0.019) (0.020) (0.026)

Used monocrotophos in K'11 0.942 0.010 0.021 0.001(0.234) (0.019) (0.021) (0.026)

Amount of monocrotophos used in K'11 3.870 0.486** 0.307 0.684**(4.005) (0.210) (0.251) (0.278)

Usage of Monocrotophos

• Modest reduction in amount of the monocrotophos


• At baseline, each farmer was asked to identify top agriculturalcontacts with whom they exchange information

• Can imagine two models for effect of AO• AO dramatically increases sharing of knowledge, becausequality of knowledge increases, so returns to sharing are higher

• AO reduces sharing of knowledge, because farmers avail of AO


How does AO affect farmers propensity to shareinformation?


(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 0.0380.499 0.036 0.040 0.046

Harvesting 0.014 0.014** 0.014** 0.014**(0.116) (0.006) (0.006) (0.006)

Impact: Information Sharing


Effect on Information Collection• Less likely to receive information from top contacts


(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)

Impact: Information Reception and Observation


Less Learning from Neighbors


(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)


Peer Effects: Spillovers on Imida Use, Control Group

(1) (2) (3) (4) (5)0.123 0.304*(0.118) (0.160)

0.165 0.185 0.283**(0.130) (0.133) (0.138)

ControlsAge 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 (K'10) 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 90No. of villages 38 38 38 38 36

Peer Effects on Imidacloprid Adoption within Study Group

1=Purchased Imidacloprid in K'11

At least one top contactreceived treatment

Proportion of top contactswho received treatment


Spillovers, Imida Use Among Peers

(1) (2) (3) (4) (5)0.033 0.033(0.046) (0.051)

0.029 0.026 0.065(0.051) (0.051) (0.190)

ControlsAge 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 528No. of villages 40 40 40 40 40

1=Purchased Imidacloprid in K'11

At least one top contactreceived treatment

Proportion of topcontacts who receivedtreatment


Caveats

• Attrition• Number of attritors equal in treatment and control group• Imbalance in attrition in round 1 phone: treatment groupattritors more likely to have planted cumin in ’10

• Demand effects• 55% of treatment group reports having called into AO to askquestion

• Administrative data indicates 53% actually did so• Continue knowledge tests


Proof of Concept

• Take-up of AO is high, among randomly selected sample ofpoor 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 paperadministration


Impact Evaluation: Information

• Between 10-30% report AO as main information source onvarious 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


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, off base of.52 acres

• Pesticide• Dramatic increase in use of imidacloprid (from 40% to 55%)• Modest reduction in intensity of monocrotophos (from 3.9 to3.5 L)

• Effect on Yields• Collecting data currently


Conclusion

• Farmers will listen to advice• Treatment has important effects:

• pesticide choice• cumin sewing

• Behavior may change without generic change in knowledge• New technology may affect information-sharing behavior

• Less reported sharing, but likely better quality information


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

Documents

08.02.2012 - Shawn Cole