Learning by (virtually) doing: experimentation and belief updating - PowerPoint PPT Presentation

Learning by (virtually) doing: experimentation and belief updating in smallholder agriculture Emilia Tjernström ∗ , Travis Lybbert ∗∗ , Rachel Frattarola Hernández ∗∗∗ , Juan Sebastian Correa ∗ ∗ University of Wisconsin - Madison, ∗∗ UC Davis ∗∗∗ OMB October 24, 2019

Motivation We think modern inputs could boost smallholder yields For a household to adopt a new technology, they want to know that it exists (and how to use it) something about profitability Other constraints matter for adoption; our design allows us to ignore many of them Introduction MahindiMaster Data & Experimental design Results 2 / 25

Motivation Our setting is particularly challenging for learning Substantial heterogeneity in soil quality in Kenya (Tittonell 2008) → information diffusion by central agencies difficult and inaccurate for many → learning from others harder/less beneficial (Munshi 2004; Tjernström, 201?) We still know relatively little about how farmers form and update beliefs Introduction MahindiMaster Data & Experimental design Results 3 / 25

Motivation Some organizations have begun experimenting with tailored input recommendations But many issues / open questions remain, including: making test results accessible real-life experimentation is risky at what level to test and recommend? Introduction MahindiMaster Data & Experimental design Results 4 / 25

Underlying questions Practical questions: Can we convey soil test results to smallholders in an accessible way? Can we enable farmers to learn from this important information? Introduction MahindiMaster Data & Experimental design Results 5 / 25

Underlying questions Practical questions: Can we convey soil test results to smallholders in an accessible way? Can we enable farmers to learn from this important information? → We created MahindiMaster Introduction MahindiMaster Data & Experimental design Results 5 / 25

Underlying questions Practical questions: Fundamental research question: Can we convey soil test results to How does MM change farmers’ smallholders in an accessible way? understanding of production conditions & optimal inputs? Can we enable farmers to learn from How does MM change (short-term) this important information? behavior? → We find evidence of learning → We created MahindiMaster consistent with enhanced productivity Introduction MahindiMaster Data & Experimental design Results 5 / 25

Research questions: details Formation and evolution of beliefs do farmers update their beliefs? is this effect stronger for unfamiliar inputs? Changes in behavior do farmers change behavior (i.e., does belief updating reflect learning?) Experimentation within the app do farmers experiment more with unknown inputs now that cost/risk is lower? does past experience correlate with experimentation? Introduction MahindiMaster Data & Experimental design Results 6 / 25

Research questions: details Formation and evolution of beliefs do farmers update their beliefs? Yes, farmers revise beliefs about returns ↑ is this effect stronger for unfamiliar inputs? Yes (suggestive) Changes in behavior do farmers change behavior (i.e., does belief updating reflect learning?) Effects are concentrated among those with high ex ante returns Experimentation within the app do farmers experiment more with unknown inputs now that cost/risk is lower? does past experience correlate with experimentation? More experimentation with unfamiliar input among those with high expected returns; not much action on knowledge or confidence, etc. Introduction MahindiMaster Data & Experimental design Results 6 / 25

Aside on pH and lime pH affects plant growth – maize likes it slightly acidic but... if pH is too low, fertilizer will have little to no effect over half of soils in Kenya are low-responsive to nitrogen (Kihara 2016) → Can farmers discover this fact by (virtually) experimenting in MM? Introduction MahindiMaster Data & Experimental design Results 7 / 25

Information interventions Do information interventions work? Null effects for many information-only interventions: migration (Bryan et al., 2014) , college decisions (Bettinger et al., 2012) , water purification (Ashraf et al., 2013) , student loan take-up (Booij et al., 2012) But some effective interventions: HIV risk info (Dupas, 2011) and earnings info for college majors (Wiswall and Zafar, 2015) What do ineffective info interventions get wrong? is the info not useful/specific enough? lack of updating? (behavior? over-confidence?) (Dessà and Zhao (2018) already had the information? Introduction MahindiMaster Data & Experimental design Results 8 / 25

Meet MahindiMaster: behind the scenes 1 Use DSSAT to simulate maize growth 2 Input soil samples from each farmer’s field and construct 3 weather scenarios based on historical weather in the area 3 Three different fertilizer choices (decreasing order of familiarity): DAP, CAN, lime 4 Discretize fertilizer application rates → a menu of options Introduction MahindiMaster Data & Experimental design Results 9 / 25

Meet MahindiMaster: UI Wanted to let farmers “query” the model in an interactive, experiential fun way Introduction MahindiMaster Data & Experimental design Results 10 / 25

Meet MahindiMaster: UI Wanted to let farmers “query” the model in an interactive, experiential fun way Introduction MahindiMaster Data & Experimental design Results 10 / 25

Meet MahindiMaster: UI Wanted to let farmers “query” the model in an interactive, experiential fun way Introduction MahindiMaster Data & Experimental design Results 10 / 25

Meet MahindiMaster: UI Wanted to let farmers “query” the model in an interactive, experiential fun way Introduction MahindiMaster Data & Experimental design Results 10 / 25

Meet MahindiMaster: UI Farmers play in seasons/rounds, with limited choice at first: only DAP (first 3 rounds) [25kg intervals ∈ [0 , 125]] then CAN introduced [25kg intervals ∈ [0 , 125]] finally lime available after 5 rounds [250kg intervals from ∈ [0 , 2000]] Introduction MahindiMaster Data & Experimental design Results 11 / 25

Sample: background Introduction MahindiMaster Data & Experimental design Results 12 / 25

Sample: background Introduction MahindiMaster Data & Experimental design Results 13 / 25

Sample: MM pilot Introduction MahindiMaster Data & Experimental design Results 14 / 25

Data sources (I) 1 3 rounds of panel data from earlier RCT (2013, 2015, 2016) prior experience with fertilizer input use & yields (soil samples collected in 2013) 2 New soil samples collected in Oct 2016 Introduction MahindiMaster Data & Experimental design Results 15 / 25

Data sources (II) 3 Pre- and post-game data subjective yield beliefs (no fertilizer, with "typical" fertilizer, with fertilizer + lime) allocate a budget between DAP, CAN and lime farming knowledge quiz ( → confidence) [only pre-] Get moments of distribution of beliefs by fitting lognormal CDF using nonlinear least squares Confidence: guesses about how many questions they got correct 4 Interactions with MahindiMaster # of rounds # of rounds they experiment with different fertilizers Introduction MahindiMaster Data & Experimental design Results 16 / 25

Estimation Post i = α + β Pre i + γ Trait i + ǫ i (1) Post i = α + β Pre i + γ 1 pH i + γ 2 pH 2 i + u i (2) 5 � pH k Post i = α + β Pre i + φ k i + u i (3) k =1 pH k i : dummy variables indicating farmer i ’s pH is in one of five ranges of pH (pH < 5 . 5, pH ∈ (5 . 5 , 6), pH ∈ (6 , 6 . 5), pH ∈ (6 . 5 , 7), &r pH > 7) Introduction MahindiMaster Data & Experimental design Results 17 / 25

Descriptive statistics: farmer characteristics Table: Farmer Characteristics Mean Std Deviation Min Max No. of seasons used fertilizer (long rains) 2.06 2.29 0 5 No. of seasons used hybrids (long rains) 2.10 2.15 0 5 Uses any DAP in a “normal” year 0.49 0.50 0 1 Uses any CAN in a “normal” year 0.38 0.49 0 1 Uses any lime in a “normal” year 0 0 0 0 pH of sampled plot 6.45 0.70 4.93 8.65 CEC of sampled plot 25.3 16.3 5.91 68.6 Sampled plot size (acres) 1.15 0.92 0.12 5 No. farming quiz questions correct 2.93 1.12 0 6 No. farming quiz questions=don’t know 4.87 2.04 0 9 Overconfident (0/1) 0.59 0.49 0 1 Introduction MahindiMaster Data & Experimental design Results 18 / 25

Descriptive statistics: game play Table: Game play Mean Std Deviation Min Max Amount of DAP (kg) across rounds 31.8 17.1 7.14 95 Amount of CAN (kg) across rounds 23.8 14.2 0 85 Amount of Lime (kg) across rounds 118.2 95.1 0 611.1 Yields (kg/acre) obtained in game 1343.8 498.4 419.3 2765.3 Share of rounds with DAP 0.78 0.23 0.20 1 Share of rounds with CAN (conditional on avail.) 0.81 0.25 0 1 Share of rounds with lime (conditional on avail.) 0.60 0.31 0 1 Share of rounds with no fertilizer 0.046 0.062 0 0.25 Random rainfall scenario (1=poor, 2=normal, 3=good) 2.01 0.30 1.25 2.80 Chosen rainfall scenario (1=poor, 2=normal, 3=good) 2.70 0.44 1 3 Rounds played 10.6 2.93 1 19 N 175 Introduction MahindiMaster Data & Experimental design Results 19 / 25