1. Learning from leaders or peers? Experimental evidence on communication and incentives among Malawian farmers Ariel BenYishay U. of New South Wales Mushfiq Mobarak Yale University

2. Motivation Adoption of key ag technologies remains low in many African countries, despite demonstrated large gains Reproduced from Conley (2010)

3. Motivation Adoption of key ag technologies remains low in many African countries, despite demonstrated large gains Pit planting in southern Africa: returns of 50-100% in 1 st year (Haggblade and Tembo 2003) Compost application also has substantial returns for maize production (Nyirongo et al 1999) Limited adoption: In our Malawi sample, baseline PP adoption = 1% Baseline composting adoption = 19%

4. Why Don’t People Adopt? Apart from liquidity constraints, intra-household disagreements, risks associated with experimentation, there may be information failures. Do rural farmers know about the technology? Do they believe the official message about the benefits of the new technology?

5. Insufficient information? Baseline awareness: PP 25%, Compost 54% Baseline technical knowledge lacking: Knows correct depth of PP (+/-25%) 0.005 (0.064) Knows correct width of PP (+/-25%) 0.005 (0.064) Knows correct length of PP (+/-25%) 0.005 (0.064) Knows correct number of seeds for PP 0.038 (0.19) Knows correct quantity of manure for PP 0.009 (0.094) Knows how to use maize stovers for PP 0.045 (0.206)

6. Related Literature Social learning in agriculture: Foster and Rosenzwieg (1995), Munshi (2004), Conley and Udry (2010), Duflo, Kremer and Robinson (2010), Waddington et al (2011) Social learning in other contexts: Job information: Beaman (2009), Magruder (2009) Deworming: Miguel and Kremer (2007) Health behaviours: Godlonton and Thornton (2009), Oster and Thornton (2009) Social promoters: Kremer et al (2009)

7. Research Questions (1) Can social learning be used to cost-effectively increase the adoption of key technologies? (2) Does the extent of this learning vary when the information is initially disseminated to individuals of different social stature? (3) Can relatively small differences in incentives for dissemination of these technologies generate significant differences in social learning?

8. Historical policy response Public Departments of Agricultural Extension ubiquitous all over the developing world Data shows extension workers often lack technical knowledge, farming skills, and communication abilities

9. Insufficient extension Access to agricultural extension: In our sample, 56% of ag extension officer (AEDO) positions staffed, average of 2455 hh/AEDO Only 32% of households visited by AEDO How to extend the reach of extension? Use farmer-led training “Lead Farmer” model recently adopted in Malawi We experiment with different variations on selection of these lead farmers

10. Project Description Introduce two sustainable technologies to understand diffusion through social networks Two complementary projects: Extension Partner Project 1Project 2 Extension worker working directly Y Partner farmers Worker-selected (“Lead farmers”) Focus group-selected (“Peer farmers”) Social network- based: (“Simple contagion” & “Complex Contagion”) Geography-based Control YY

11. Project Design

12. Non-comm. households LFPFDifference between Actual"Shadow"Actual"Shadow" Non-comm. & LFs Non-comm. & PFs PFs & LFs Household has grass roof 79.1%64.0%67.6%73.6%75.6%12.63% ***4.18% **8.45% ** Respondent education > year 5 45.6%76.3%64.3%54.5%55.8%-22.8% ***-9.1% ***-13.1% *** Household size4.65.4895.5485.1745.153-0.93 ***-0.60 ***-0.37 * [2.123][2.376][2.149][2.145][1.974] Respondent age 41.541.242.240.241.4-0.40.5-0.9 [16.8][14.1][13.3][14.2][14]

13. Use Technology (Observed in OFM) (1)(2)(3)(4) Score on Knowledge of Relevant Technology, 0-1, All Included 0.325***0.286***0.268***0.277*** (0.0737)(0.0673)(0.0638)(0.0709) Treatment village0.240***0.00360 (0.0818)(0.0415) Incentive treatment0.216*** (0.0465) CF District-0.563***-0.512***-0.505***-0.499*** (0.0577)(0.0567)(0.0506)(0.0460) Observations718861858861 Marginal effects are shown. Standard errors in parentheses, clustered by village * p<0.1, ** p<0.05, *** p<0.01

14. (1)(2)(3)(4) (5)(6)(7)(8) Unincentivized communicatorsIncentivized communicators AEDO treatment0.165***0.166*** 0.053***0.056*** 0.055*** (0.0467)(0.0449)(0.0451)(0.0453)(0.0244)(0.0236) LF treatment0.080*** 0.137***0.136***0.065***0.066***0.064***0.063*** (0.0285)(0.0279)(0.0388)(0.0390)(0.0238)(0.0234)(0.0252) PF treatment0.02780.02410.02340.02380.118*** 0.095***0.094*** (0.0247)(0.0248)(0.0215) (0.0325)(0.0324)(0.0301)(0.0300) Male HH head respondent0.008660.006460.004590.0422 0.044*** (0.0267)(0.0268)(0.0279)(0.0263)(0.0258)(0.0253) Female HH head respondent-0.06*** -0.0198-0.0180-0.0112 (0.0283)(0.0281)(0.0295)(0.0274)(0.0265)(0.0270) Female Non-HH head respondent-0.0113-0.0133-0.01450.01490.01620.0231 (0.0257)(0.0261)(0.0302)(0.0274)(0.0267)(0.0283) Village assigned to Female LF-0.11***-0.1050.003790.0217 (0.0459)(0.0805)(0.0362)(0.0803) Village assigned to Female PF0.001030.002030.04140.0578 (0.0418)(0.0935)(0.0521)(0.0995) Female communicator * Female HH head respondent-0.0340-0.0318 (0.0808)(0.0786) Female communicator * Female Non-HH head0.00500-0.0316 respondent(0.0603)(0.0753) Female communicator * Male HH head respondent0.00847-0.0101 (0.0770)(0.0789) CF District0.193*** 0.190*** 0.210*** 0.211***0.210*** (0.0226)(0.0222)(0.0218)(0.0219)(0.0200)(0.0198)(0.0199)(0.0198) Constant-0.007800.003940.007420.00678-0.0163-0.0380-0.0389-0.0423 (0.0132)(0.0261)(0.0266)(0.0278)(0.0126)(0.0265)(0.0261)(0.0265) Observations2,601 2,664 R-squared0.1940.2070.2180.2190.2180.2300.2310.232 F-test AEDO = LF (p-value)0.09190.07940.6520.705 F-test AEDO = PF (p-value)0.007070.004180.06250.0717 F-test AEDO = LF Male0.5930.776 F-test AEDO = PF Male0.003650.229 F-test AEDO = LF Female0.008540.736 F-test AEDO = PF Female0.0164 0.107

15. Unincentivized CommunicatorsIncentivized Communicators [1][2][3][4][5][6][7][8] AEDO treatment0.17*** 0.06*** (0.045)(0.0448) (0.0475)(0.0235)(0.0236)(0.0235)(0.0228) LF treatment0.07*** 0.06180.07***0.09***0.08***0.09*** (0.0353)(0.0284)(0.0400)(0.0365)(0.0308)(0.0244)(0.0289)(0.0286) PF treatment0.173***0.02020.200***0.170***0.197***0.149***0.201***0.196*** (0.0788)(0.0335)(0.0775)(0.0847)(0.0592)(0.0356)(0.0618)(0.0598) Male HH head respondent0.009050.01010.01200.004210.04010.0432***0.04070.0258 (0.0259)(0.0262)(0.0251)(0.0257)(0.0253)(0.0257)(0.0250)(0.0253) Female HH head respondent-0.06*** -0.0226-0.0192-0.0221-0.0263 (0.0278) (0.0268)(0.0282)(0.0264)(0.0268)(0.0263)(0.0267) Female Non-HH head respondent-0.0133-0.00944-0.0102-0.02660.01220.01590.01330.00483 (0.0259)(0.0246) (0.0257)(0.0266)(0.0270)(0.0265)(0.0268) LF x Communicator has grass roof0.01370.02050.0235-0.0535-0.0482-0.0513 (0.0513)(0.0533)(0.0532)(0.0359)(0.0353)(0.0333) PF x Communicator has grass roof-0.189***-0.248***-0.2***-0.121-0.0950-0.114 (0.0877)(0.0976)(0.0950)(0.0810)(0.0933)(0.0817) LF x Communicator comp. 5 th grade or less0.08980.0949-0.080***-0.073*** (0.114)(0.111)(0.0293)(0.0404) PF x Communicator comp. 5 th grade or less0.01120.0546-0.0674-0.0448 (0.0348)(0.0372)(0.0552)(0.0610) Household has grass roof-0.0061-0.020*** (0.0138)(0.0116) Respondent completed 5th grade or less-0.03*** (0.0136)(0.0111) Village assigned to CF0.191***0.195***0.196***0.189***0.208***0.209***0.208*** (0.0223) (0.0229)(0.0193)(0.0192)(0.0190)(0.0189) Constant0.005090.000943-0.0005400.0290-0.0348-0.0386-0.03540.00707 (0.0262)(0.0255)(0.0250)(0.0285)(0.0258)(0.0259)(0.0255)(0.0263) Observations2,601 2,4172,664 2,466 R-squared0.2110.2090.2150.2160.2370.2360.2410.242 Robust standard errors in parentheses * p<0.1, ** p<0.05, *** p<0.01

16. Knowledge scores among communicators [1][2] [3][4] Unincentivized communicatorsIncentivized communicators Respondent is shadow LF0.05910.03940.0794***0.0903*** (0.0398)(0.0423)(0.0344)(0.0340) Respondent is LF assigned to0.04300.192***0.09370.152*** actual communication(0.0658)(0.0921)(0.0567)(0.0789) Respondent is PF assigned to0.0215-0.03550.155***0.132*** actual communication(0.0571)(0.0574)(0.0576)(0.0633) Village assigned to Female LF0.009390.133*** (0.0819)(0.0553) Village assigned to Female PF0.1140.115 (0.0936)(0.104) Respondent is actual LF * Female-0.256***-0.210*** (0.105)(0.0970) Respondent is actual PF * Female0.00747-0.00162 (0.0795)(0.0970) Village assigned to CF0.319***0.312***0.357***0.362*** (0.0470)(0.0481)(0.0419)(0.0413) Constant0.0775***0.0803***0.04800.0156 (0.0372)(0.0453)(0.0313)(0.0374) Observations450 444 R-squared0.2080.2250.2740.296 Omitted group is shadow PF. Standard errors clustered by village in parenthesis. * p<0.1, ** p<0.05, *** p<0.01

17. Pr(Respondent participated in communicator-led activity) [1][2][3] AEDO treatment0.142***0.147***0.149*** (0.0593)(0.0583)(0.0593) LF treatment0.05150.05530.0498 (0.0801)(0.0803)(0.0785) Incentives x AEDO0.06930.06800.0619 (0.0575)(0.0561)(0.0565) Incentives x LF0.149*** 0.154*** (0.0785)(0.0788)(0.0771) Incentives x PF0.283***0.287***0.294*** (0.0694)(0.0699)(0.0679) Male HH head respondent-0.00950-0.0422 (0.0551)(0.0561) Female HH head respondent-0.0840-0.0869 (0.0608)(0.0611) Female Non-HH head respondent0.03030.00949 (0.0617)(0.0611) Household has grass roof-0.0232 (0.0266) Respondent education > year 50.0808*** (0.0237) Village assigned to CF-0.0153-0.0167 (0.0461)(0.0462)(0.0451) Observations2,962 2,725 P-value AEDO = LF0.1880.1770.139 P-value AEDO = PF0.0163**0.0120**0.0119** P-value LF = PF0.5200.4910.526 P-value AEDO + Incent = LF0.0350**0.0364**0.0276** P-value AEDO +Incent = PF0.00153***0.00139***0.00118*** P-value AEDO + Incent = LF + Incent0.8760.8840.919 P-value AEDO + Incent = PF + Incent0.2940.2910.208 P-value LF = PF + Incent0.00208***0.00199***0.000745*** P-value LF + Incent = PF0.00491***0.00465***0.00416*** P-value LF + Incent = PF + Incent0.2470.2480.202 P-value AEDO = PF + Incent0.0170**0.0147**0.0127** P-value AEDO = LF + Incent0.341 0.377