The impact of SMS-based agricultural information on farmers: Insights from a randomized control trial

Marcel Fafchamps and Bart Minten

I. IntroductionMobile phones are rapidly spreading:

global penetration rate at about 76 per 100 inhabitants in 2010

Poor developing countries increasingly part of this widespread use of mobile phones

Rapid spread offers new possibilities for poor rural and agricultural households in developing countries: can overcome important barriers of physical distance and improve access to information

I. IntroductionMobile phones increasingly used in rural areas in

Africa and Asia to disseminate daily prices of commodities, possibly leading to improved welfare:

1. Better information would lead to better allocation of production factors;

2. Information can improve bargaining position of small farmers and competition between traders;

3. Farmers can use information to switch between end markets;

4. Famers can use information to make choices around timing of marketing;

Mobile phones should thus reduce erratic price variations as better arbitrage over time and space

I. IntroductionPurpose of this study is to ascertain whether

the distribution of agricultural information through mobile phones generates important economic effects in rural/agricultural areas

Randomized control trial of a commercial service (Reuters Market Light - RML), the largest and best established private provider of agricultural price information via SMS

Offered a one-year free subscription to RML to a random sample and analyze impacts on different indicators

I. Introduction

RML one of many initiatives in this area;

In India: e.g. IFFCO Kisan Sanhar, Qualcomm/Tata Teleservices;

In Africa: e.g. Manobi, Eseko.

II. Description of the intervention Thomson-Reuters: transnational

corporation specialized in information services, its core business

Thomson-Reuters started up an innovative service to distribute agricultural information to farmers in India, called RML;

Subscribers get SMS messages, in total 75 or 100 messages a month, at a price of $1.5/month;

At the time of survey, they had 25,000 RML subscribers

II. Description of the intervention Content offering of RML:1. Market information: subscriber gets regular

price updates for three markets of his choice for a chosen crop (crops covered at time of survey: soybean, cotton, pigeon pea, wheat, green gram, chick pea, maize, onion, pomegranates, grapes, oranges);

2. Weather forecast: temperature, humidity, rainfall, and probability of rainfall at tehsil (district) level

3. Crop advisory tips: given for the chosen crop4. Commodity news: news about agriculture and

price trends of commodities at national/international level

III. Experimental designRandomized control trial, in close

collaboration with Thomson ReutersFive crops selected in the state of

Maharashtra (India): tomato (Pune); pomegranate (Nashik); onions (Ahmadnagar); wheat (Dhule); soybean (Latur).

Assumption that information might have different value for different crops (e.g. perishable versus non-perishable)

100 villages selected: 20 villages in each district not previously targeted by RML; 10 farmers per village

III. Experimental designTreatment 1: All 10 farmers are offered the

RML serviceTreatment 2: 3 farmers randomly selected

(to test if treatment of some farmers benefit others as well)

Control: No farmers treatedStratification improves efficiency in RCT

(Bruhn/McKenzie): triplets of similar villages selected based on longitude, latitude, size population, distance to road, road availability index, distance markets, distance cell phone tower, number of extension visits

III. Experimental designExtension agents: randomly half of

treatment 1 and 2 villages (to test if information given to them makes individual information unnecessary)

No point of estimating effect on people who are unlikely to sign up or benefit from RML; Sample of selected farmers limited to:

1. Farmers that grow selected crop for sale;2. Farmers with a cellphone (all 100 villages

had cellphone coverage/electricity; half of farmers in state had cellphone).

III. Experimental designEach farmer interviewed twice:- Baseline survey: June-July 2009;- Impact survey: June-July 2010Survey done after main harvest; survey

covers one complete agricultural season and two cropping cycles (rabi/karif); Same survey instrument used in the two rounds;

Questionnaire focus: agricultural information; use of sms information; price and time at which farmer sold crop; total revenue from sales of crop; crop yields; input use.

III. Experimental designNames and phone numbers collected

for everybody in first round; those ones selected for treatment were sent to RML which then tried to hook up selected farmers;

Farmers were free to refuse service and some did;

To ascertain extent of contamination and non-compliance: additional phone interview after impact survey (to see if difference between target and actual user).

IV. Conceptual frameworkTwo models in paperChannel 1: Timely access to market

price information at the time of harvest helps farmers to arbitrage across markets; treated farmers should receive a better price than uninformed farmers;

Channel 2: Farmers that are better informed about prices can obtain higher prices from traders because of better negotiation power (especially when selling at the farm-gate)

IV. Conceptual frameworkOther effects not modeled: 1. Better knowledge of quality price

differentials would induce farmers to upgrade quality of output;

2. Weather information helps with farm operations (e.g. timing of use of pesticides);

3. Crop advisory would lead to adoption of more appropriate technologies.

V. Testing strategy Intent-to-treat estimates (W=1 if offered free

subscription) because of self-selection issues (Y is outcome variable):

Yi = θ + βWi + ei

IV-LATE (Instrumental variables – Local Average Treatment Effect) where S (=1 if farmer signed up for RML) is instrumented by W:

Yi = θ + αSi + ei

Heterogeneous effects (X is characteristics of farmer that are thought to affect the effect of treatment on the outcome variable, e.g. bigger farmers or less experienced ones):

V. Testing strategy Caveats:1. Value of information changes with

circumstances; for information to be useful, it must be provided in a timely matter; information can be useful in one year but not in another;

2. Information circulates through other channels than RML; assumption that circulation not so rapid that control villages benefit as well; however, cannot completely rule out spillovers across villages;

3. Price information possibly leads to increased bargaining power; as traders and commission agents can not distinguish RML and non-RML users and if they change behavior, all farmers might benefit; in this case, we would underestimate benefits from RML

VI. The context and data

Take-up of RML is one possible measure to measure benefits:

Table 1. Number of agricultural holdings and RML subscribers in the five districts studied in Maharashtra District: Crop followed Number of Number of RML subscribers** in survey agricultural holdings* 2007 2008 2009 2010 Ahmadnagar onion 916,724 711 1,377 3,763 1,637 Dhule wheat 230,216 108 1,296 1,028 840 Latur soya 305,706 163 914 1,048 826 Nashik pomegranate 591,763 2,176 1,561 3,934 6,514 Pune tomato 667,365 392 653 3,495 781 Total 2,711,774 3,550 5,801 13,268 10,598 *: Government of India, Agricultural Census, 2000/01

**: Thompson-Reuters

VI. The context and data Attrition: - 1000 farmers in baseline; - 933 in impact survey; - To test if there is anything specific about

attrition, we run regression of attrition dummy on household characteristics;

- Only onion dummy is significant dummy (more likely to drop out); is controlled for by triplet dummies.

VI. The context and data Compliance: Only 59% of farmers that were

offered RML actually used it; Reasons: - Refusal (suspicion that they had to pay later;

illiterate households);- Never activated RML (had to chose

crops/markets they wished information on);- Changes in phone numbers/migration;- Chinese phones could not display the Marathi

script.Reasons given an indication of some lack of

interest in price information.

VI. The context and data Contamination: Low; 10 farmers or about

3.7% of the interviewed farmers in control group villages;

Balancedness: Good balance on all variables included, area planted to target crop, marketing, transaction costs, past weather, and technological innovation;

Follow Deaton (2009) to not include unnecessary control variables as to not artificially inflate t-values.

VII. Empirical analysis Intent-to-treat analysis. Average Treatment

effect on the Treated (ATT) is calculated using nearest neighbor matching methodology (matching performed by triplet dummy);

IV-LATE regression with instrumented actual RML use with random assignment to treatment;

Standard errors clustered by village triplet;In presentation: results of whole sample (in

paper also treatment 1 versus control only; heterogeneous effects).

Sources of information (baseline survey; % of farmers) Own

observations/experimentation main source of information

RML Usage Treated farmers are significantly

more likely to mention RML as a source of information (but much below 100%);

IV-LATE effects > ATT.

Knowledge/information sharing

Treated farmers appear more knowledgeable about crop prices

Treated farmers share more information with other farmers

Share

one one one at informationWhole sample day week month planting farmingNumber of observations 722 723 722 722 922Nearest neighbor matching (a)ATT Coeff 0.084 0.095 0.097 0.078 0.063

z-value 3.100 2.830 3.090 2.540 4.050Regression results (b)1. IV-LATETreatment Coeff 0.130 0.158 0.181 0.146 0.119 t-value 2.180 1.980 3.280 1.960 4.080Intercept Coeff 0.717 0.304 -0.034 0.010 0.676 t-value 64.780 20.640 -3.280 0.720 116.330

Know price before sale:

Realized pricesDifferent crops different variation (CV

wheat=0.07; CV soya=014; CV tomato=0.22; CV onions=0.44; CV pomegranate=0.45); RML expected to be more beneficial for perishable crops;

Dependent variable: log(unit price in sales transactions over last 12 months of the target crop)

Different methods: (a) ATT comparing treated/control; (b) ATT using dif-in-dif; (c) IV-LATE; (d) OLS long model to control for omitted variables; (e) Heterogeneous effects

Realized prices ATT (a) ATT (b) IV-LATE OLS

long-model(c) OLS IVFor whole sample(e) No obs. 1480 688 1480 1425 1464 1457Treatment Coeff -0.031 -0.043 -0.062 -0.028 -0.034 -0.026 t-value -2.000 -0.520 -1.670 -1.510 -1.860 -1.430Intercept Coeff 2.260 2.159 2.248 2.249

t-value 309.620 21.250 93.64 99.080Dummy young head of hh Coeff 0.021 -0.013 -0.013

t-value 0.990 -0.500 -0.530Total crop area cultivated Coeff 0.005 0.001 0.001

t-value 5.720 1.900 1.550Dummy if sold to a trader Coeff -0.011 -0.006 -0.008

t-value -0.250 -0.190 -0.290Treatment extension agent Coeff -0.013

t-value -0.500Interaction with treatmentDummy young head of hh Coeff 0.057 0.059

t-value 1.750 1.850Total crop area cultivated Coeff -0.001 -0.000

t-value -0.590 -0.240Dummy if sold to a trader Coeff 0.085 0.091

t-value 1.750 1.830

Heterogeneous effects(d)

Realized pricesOverall no significant effect on prices received

for treated households;Farmers that grow more get better prices;Younger treated farmers obtain a significantly

higher price (6%);Treated farmers that sell to traders get

significantly higher prices (9% higher);Results consistent when we do regressions at

the crop level; also no effect of treatment on prices;

Treatment does not decrease price variance in treated villages (in contrast with e.g. Aker, 2008).

ProfitabilityFour measures:1/ Transactions costs: transportation;

loading/unloading; rates paid to commission agents

2/ Net price: Gross price minus transaction costs

3/ Total sales revenues of the crop: quantity sold times prices

4/ Value added: total sales minus monetary cost of inputs

Profitability No effect of RML on profitability

measuresTransaction Net Sale Value

cost price revenues added

For whole sampleNumber of observations 713 713 713 713Nearest neighbor matching (a)ATT Coeff 0.078 -0.760 48,247 46,352

z-value 1.420 -1.480 0.580 0.580Regression results (b)IV-LATETreatment Coeff 0.146 -1.450 87,074 84,530 t-value 1.050 -1.730 0.880 0.910Intercept Coeff 1.576 8.906 66,545 59,235 t-value 59.060 55.350 3.500 3.320

Spatial arbitrage and market changesMost sales take place at the market (except

for pomegranate, farm-sales account only for 2% of sales)

Market diversification varies from crop to crop; sales of perishable crops geographically concentrated (98% and 81% of tomato and pomegranate sold at one market)

Overlap index of markets:1=same location for the two years (100%

overlap);0=nothing sold at the same location between

the two years (0% overlap).

Spatial arbitrage and market changes Treated households do more

spatial arbitrage and change markets

Overlapadded dropped index (c)

For whole sampleNumber of observations 691 691 691Nearest neighbor matching (a)ATT Coeff 0.099 0.087 -0.095

z-value 2.980 2.680 -3.030Regression results (b)IV-LATETreatment Coeff 0.208 0.194 -0.197 t-value 2.120 2.080 -2.090Intercept Coeff 0.575 0.463 0.493 t-value 30.430 25.850 27.290

Number of markets

Other marketing characteristics Small effects on other marketing

indicatorsSold if whole- Sold Sold Crop was

in whole- sale through to graded/sale market, a trader sorted

market chosen commis- beforebecause sion saleclosest agent

For whole sampleNumber of observations 1477 1352 1482 1470 1478Nearest neighbor matchingATT Coeff 0.030 -0.078 0.006 0.046 0.033

z-value 2.540 -3.220 0.230 1.740 2.260IV-LATETreatment Coeff 0.063 -0.131 0.539 0.084 0.055 t-value 1.750 -0.940 0.844 1.050 1.120Intercept Coeff 0.923 0.199 0.933 0.450 0.925 t-value 132.800 6.940 89.080 28.350 98.140

Effect on marketing RML helped farmers realize that

they could obtain a higher price at a more distant market and some tried it out;

However, treated farmers are not emboldened to sell more at farmgate or at local markets;

Treated farmers pay more attention to grading and sorting.

Weather information No effect of weather information

on treated farmersDid not

incur output output outputstorm/ loss loss increase

For whole sample heavy rain before harvest at harvest Number of observations 915 529 529 529Nearest neighbor matchingATT Coeff -0.031 0.029 -0.042 0.046

z-value -1.000 0.720 -1.240 1.620IV-LATETreatment Coeff -0.108 -0.034 -0.025 0.095 t-value -2.080 -0.450 -0.310 1.390Intercept Coeff 0.288 0.690 0.369 0.024 t-value 27.740 39.630 19.860 1.540

Because of storm, heavy rainfall,

Crop varieties grown and cultivation practices 31% of the farmers changed

varieties between the two survey years;

Of those who changed variety, 65% stated that they did so to improve profitability;

22% of farmers stated that they changed cultivation practices.

Crop varieties grown and cultivation practices Influence of RML on changes, but

no differences in practices for treated and control

Change If yes, If profita- Change Ifof crop reason bility, in change, variety is because cultivation because since profita- of practices of

For whole sample last year bil ity RML last year RMLNumber of observations 895 240 156 911 203Nearest neighbor matchingATT Coeff 0.029 0.020 0.155 -0.027 0.211

z-value 1.100 0.460 2.830 -1.110 3.990Regression resultsIV-LATETreatment Coeff 0.043 0.006 0.200 -0.045 0.410 t-value 0.970 0.090 2.060 -1.240 5.170Intercept Coeff 0.525 0.374 -0.033 0.476 -0.016 t-value 59.350 30.950 -2.060 65.780 -0.950

VIII. Conclusions RCT on effect of better information. Three possible effects:1. The price information is expected to improve farmers ability to negotiate with buyers and to enable them to arbitrage better across space; 2. Weather information is expected to help farmers reduce crop losses due to extreme weather events such as storms; 3. Crop advisory information is expected to induce farmers to adopt new crop varieties and improve their cultivation practices.

VIII. Conclusions -Treated farmers make use of the information that RML provides. However, no statistically significant average effect of treatment on:1. the price received by farmers,2. crop losses resulting from rainstorms, 3. the likelihood of changing crop varieties and cultivation practices.- Disappointing results but in line with the take-up rate of RML services

VIII. Conclusions 1. Some effect of treatment:- Less likely to sell at the farmgate and

more likely to sell at different, more distant markets;

- Small increase in the likelihood of grading/sorting;

2. Arbitrage does not results in higher prices, possibly because of two reasons:

- Very few farmers sold at farmgate- Limited range of alternative market

destinations (spatial concentrations of crop sales)

VIII. Conclusions Effect of food inflation during the study

period:- Would expect information to be valuable in

high-inflationary environments- Conceivable that magnitude of the change

blunts our capacity to identify a significant price difference, given how variable prices are;

One year experiment; maybe more time needed.

VIII. Conclusions Possible external validity considerations: 1. Price information could help if spatial arbitrage

across agricultural markets does not hold (segmented; thin; disorganized);

2. Price information could help for those farmers that sell at the farmgate (little the case in our survey);

3. Effect on crop quality obtained if price information detailed by variety and grade and farmers are provided with complementary information on how to produce this quality products.

Need for similar experiments in other settings