cifsrf caricom project interactive information ... · mr. brijesh singh, field supervisor mr....

CIFSRF CARICOM Project Interactive Information Dissemination Workshop

5th March 2012

The uptake of new technology in agriculture in developing countries is often lower than optimal (Feder et al, 1985; Duflo et al, 2008)

Attitudes towards risk and uncertainty,

learning by doing and learning from others affect the level of technology adoption (Engle-Warnick et al, 2011; Foster and Rosenzweig, 2010)

To better understand the relationship behind learning from others (social learning) and technology adoption by reframing technology adoption as the provision of a public good.

Farmers’ beliefs about the distribution of yields of new technologies are uncertain

A farmer that takes up the new technology is essentially providing a ‘public good’ to other farmers by providing a realisation of the outcome of the new technology.

By experimenting, the farmer will face a reduction in the uncertainty about the new technology, which will consequently benefit other farmers who can learn from him.

To test whether farmers’ choices are affected by the knowledge that their choices provide a public-good reduction of ambiguity

To test whether farmers update their beliefs about ambiguity as they observe more outcomes from the ambiguous distribution

To determine whether communication acts as a coordination device in farmers willingness to provide a public good

An economic experiment was run in the farming community of Parika, Region 3, Guyana to measure farmers’ willingness to provide a public good and how learning occurs from its provision.

Economic experiments can be used to:

Test and refine theories and behavioural models for decision making

Measure preferences

Suggest new theories to account for observed behaviour

Calibrate policy-oriented programs for development

144 Farmers were recruited from 4 communities in Region 3 in Guyana

136 participated in the sessions - approximately 30% of farmers from the 4 villages

6 sessions of 20-24 farmers each were held from 17th November to 20th November. The sessions were 2 – 2 ½ hrs long.

Participants were compensated in cash for their participation - show-up fee of GYD$1500 and average GYD$1,400 from their decisions

The farmers participated in an individual economics decision making experiment with three main tasks

TASK 1 - Choose between a gamble of two outcomes with known probability (6 $HIGH chips and 6 $LOW) and a gamble of the same two outcomes with unknown probability – ambiguity (? $HIGH chips and ? $LOW chips)

The choice between the same two gambles was then reframed such that there was a public good motive for choosing the unknown distribution. Participants then made a second choice between the two gambles [TASK 2]

Half of the participants then participated in a semi-structured chat while the other half listened to the chat but did not participate.

After the chat, participants could change their decision for TASK 2 if they wished to.

Each subject then selected an outcome from his/her chosen gamble using pre-prepared bags with the chips corresponding to the gambles.

After all participants had chosen a chip, the results of their choices were then anonymously revealed to all participants and finally they completed the final decision task.

After this reveal the subjects made a final choice between the two gambles [TASK 3]

Once the tasks were completed, the participants sat with a field assistant for a short exit survey.

Demographics – age, sex, education, household size

Farming practices – crops, adoption of new varieties, willingness to pay for farming advice

Irrigation practices

Climate change

Avg. Age: 38 Gender: 49% Male; 51% Female Ethnic Back ground: 80% East Indian Descent Education: 48% Primary; 47% Secondary Marital Status: 75% Married Avg. Household Size: 4 persons Avg. Years Farming: 17 years Avg Farm Size: 6.67 acres Land Ownership: 27% Own; 70% Rent/Lease

A large proportion of participants did not change their decision between Task 1 and Task 2.

More individuals switched from the unknown to the known distribution than switched from the known to the unknown distribution

Suggests free-riding – selecting the known distribution to avoid providing information about the unknown distribution to others

A large proportion of participants did not change their decision when given the opportunity to change it for Task 2.

The switchers were evenly distributed between subjects who participated in the chat and subjects who listened to the chat but did not participate.

This suggests that participation in chat had little impact on subjects’ decision making.

15.4% switched from the known distribution to the unknown distribution and 8.8% switched from the unknown distribution to the known distribution.

The former is positively correlated with the number of $HIGH outcomes revealed from the unknown distribution and the latter is negatively correlated with the number of $HIGH outcomes revealed from the unknown distribution.

Participants may have updated their beliefs about the ambiguous distribution as they observed outcomes from it – social learning

It gives a better understanding of the constraints to the low adoption of technologies in Guyana

Results suggest that farmers may be free-riding – waiting for others to adopt technology to see their outcomes

There is also evidence of social learning Implication is that there may be a role for

incentivizing the adoption of new technology and the impact can be amplified through social learning

This Experiment Code chats for content Correlate experiment results with participant

agriculture and immigration data

Nutrition Experiments with child caregivers

Sonia Laszlo, Department of Economics

Jim Engle-Warnick, Department of Economics

Franque Grimard, Department of Economics

Dr. Homenauth and staff at NAREI Mr. Brijesh Singh, Field Supervisor Mr. Deodatt Seodatt, Shamir Baksh, Shyam

Patel and Tracyia William, Field Workers Participant farmers Principal of Parika Back Primary School Local agriculture organisation

Camerer C. 2003. Behavioral Game Theory: Experiments on Strategic Interaction. Princeton: Princeton University Press.

Cardenas J. and J. Carpenter, 2005. "Experiments and Economic Development: Lessons from Field Labs in the Developing World" Middlebury College Working Paper Series 0505, Middlebury College, Department of Economics.

Chaudhuri A. 2009. Experiments in Economics: Playing Fair with Money. Routledge, London and New York

Croson, R and S. Gächter, 2010. "The Science of Experimental Economics" Journal of Economic Behavior & Organization Vol. 73(1): 122-131

Duflo E., M. Kremer and J. Robinson. 2008. "How High Are Rates of Return to Fertilizer? Evidence from Field Experiments in Kenya" American Economic Review Vol. 98(2): 482-88

Engle-Warnick J., J. Escobal and S. Laszlo. 2011. "Ambiguity Aversion and Portfolio Choice in Small-Scale Peruvian Farming" The B.E. Journal of Economic Analysis & Policy Vol. 11(1): 68

Engle-Warnick, J., and S. Laszlo. 2011. “Social Exchange and Risk and Ambiguity Preferences." McGill Working Paper

Feder G., R. E. Just and D. Zilberman, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey" Economic Development and Cultural Change Vol. 33(2): 255-98

Foster A. D. and M. R. Rosenzweig, 2010. "Microeconomics of Technology Adoption" Annual Review of Economics Vol. 2(1): 395-424