Information Transmission in Technology Diffusion: Social Learning, Extension Services and Spatial Effects in Irrigated Agriculture. Associate Prof. Phoebe Koundouri Director of RESEES [Research tEam on Socio-Economic Sustainability] Athens University of Economics & Business
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Information Transmission in Technology Diffusion: Social Learning, Extension Servicesand Spatial Effects in Irrigated Agriculture
Associate Prof. Phoebe Koundouri
Director of RESEES [Research tEam on Socio-Economic Sustainability]
Athens University of Economics & Business
Visiting Senior Research Fellow
Grantham Research Institute on Climate Change and the Environment
London School of Economics & Political Science
We acknowledge the financial support of the European Union FP6 financed project FOODIMA: Food Industry Dynamics and Methodological Advances (Contract No 044283). www.eng.auth.gr/mattas/foodima.htm.
- dynamic maximization behavior model
- econometric duration analysis model merged with:
- factor analysis for identification of info transmission paths and peers
- flexible method of moments for risk attitudes estimation
- applied to micro-dataset olive farms in Crete
e.g. Dinar, Campbell and Zilberman AJAE 1992
Dridi and Khanna AJAE 2005
Koundouri, Nauges and Tzouvelekas AJAE 2006, etc.
…matter in explaining TAD.
Sources of Information and Knowledge:
World Bank: Rivera & Alex 2003; World Bank 2006; Birkhaeuser et al. 1991
Usually ES target specific farmers who are recognized as peers.
Rogers 1995: via peers (homophilic or heterophilic neighbors).
Farmers may also follow or trust the opinion of those that they perceive as being successful in their farming operation, even though they occasionally share quite different characteristics.
Farmers exchange information and learn from individuals with whom they have close social ties and with whom they share common professional or/and personal characteristics (education, age, religious beliefs, farming activities etc.)
Measuring the extent of information transmission via different
channels& identifying its role in TAD is difficult:
Expected efficiency gains are uncertain at adoption decision time.Uncertainty can be reduced via accumulation of knowledge:- Extension Services, before and after adoption- Social Networks, before and after adoption- Learning-by-doing /using, after adoption
At each time period the farmer decides whether to adopt by comparing :
yj: crop production
xjv: vector of variable inputs (labor, pesticides, fertilizers, etc.)
xjw: irrigation water
xjw< , risk of low (or negative) profit in case of water shortage.
Adoption allows hedging against the risk of drought and consequent profit loss.
Aj: technology index: irrigation effectiveness index:
(water used by crop)/(total water applied in field)
Aj⁰ with conventional technology
Aj* with new technology farmer produces same y using same xv and lower xw.
Aj = Aj* : max irrigation effectiveness is reached
Aj* > Aj⁰ : max irrigation effectiveness cannot be reached with Aj⁰
May require time before the new technology is operated at A*.
: the expected, at time s, efficiency index for t, under the assumption the new technology is adopted at time τ.
c , : fixed cost of NIT known at period t.
Fixed time horizon
s + 2
Info gathering period
τ (adoption time)
for t until T
E(Profit) for t until T
For t until T
Adopt or Not
No Adoption during t:
Adoption at τ:
Farmer max over τ her temporal aggregate discounted profit:
Benefit: Delaying investment by one year allows the farmer to purchase the modern irrigation technology at a reduced cost.
Cost: Delaying adoption by one year results in producing with the conventional less efficient technology and bearing a higher risk of water shortage (thus a loss in expected profit).
Note: Farmer considers that technology efficiency index will remain constant after adoption because she does not have enough information to predict the evolution of the technology efficiency after adoption (which is a complex function of learning from others and learning-by-doing).
The model could be extended to allow for the farmers anticipating learning after adoption. Such an extension would need to incorporate assumptions about farmer-specific learning curves, which will differ between adopters based on initial adoption time and farmer-specific socio-economic characteristics.
Such an extension does not alter the learning processes of our model, neither before, nor after adoption, but it does make the first order conditions less clear.
Expected Discounted Equipment Cost:
The quantity represents approximately the expected excess discounted cost, between choosing to adopt the new technology at time s+1, namely, as soon as possible, and postponing the adoption for a very long period, namely, for a period where the rate of decrease of the equipment cost is practically zero.
extension services before and after adoption
social learning before and after adoption
learning by doing after adoption
socioeconomic characteristics (age, education, experience)
identification & behavior of influential peers
input & output prices
environmental conditions (defining min water crop requirements)
Incorporating Risk Attitudes in the Analysis
Endogenous Technology Adoption Under Production Risk: Theory and Application to Irrigation TechnologyKoundouri, Nauges, Tzouveleka2, AJAE 2006
Investigate the microeconomic foundations of technological adoption under production risk and heterogeneous risk preferences.
- Construct theoretical model of adoption by risk-averse agents under production risk
- Approximate it with flexible empirical model based on higher-order moments of profit.
- Derive risk preference from estimation results.
- Use risk preferences to explain adoption through a discrete choice model.
- Risk preferences affect the prob. of adoption: evidence that farmers invest in new technologies as a means to hedge against input related production risk.
- The option value (value of waiting to gather better information) of adoption, approximated by education, access to information & extension visits, affects the prob. of adoption.
Antle (1983, 1987): max E[U(π)] is equivalent to max a function of moments of the distribution of ε (=exogenous production risk), those moments having X as arguments. Agent's program becomes:
- time of adoption (drip or sprinklers)
- variables related to their farming operation on the same year (: production patterns, input use, gross revenues, water use and cost, structural and demographic characteristics).
Table 1: Definitions and Summary of the Variables (cont.)
The variable of interest in empirical application is the length of time between the year of drip irrigation technology introduction (1994) and the year of adoption. Mean adoption time is 4.68 years in our sample.
- olive-oil revenues
- variable inputs (labor, fertilizers, irrigation water, pesticides)
- fixed (land) input
The residuals have been used to estimate the kth central moments (k=1,…,4) of farm profit conditional on variable and fixed input use.
- number of extension visits on her farm prior to the year of adoption
- age and educational level of her peers (according to farmer)
-geographical distance between the farmer and extension agencies
- geographical distance between farmers and her peers
Duration Model of Adoption and Diffusion
Survival models in statistics relate the time that passes before some event occurs to one or more covariates that may be associated with that quantity of time.Duration model formulated in terms of conditional probability of adoption at a particular period, given that adoption has not occurred before and given farmer-specific information channels, socioeconomic (& risk attitudes), environmental &spatial characteristics.
Empirical Hazard Function
Some vary only across farmers (e.g. soil quality and altitude) other vary across farms and time (e.g. cost of acquiring the new technology)
Marginal Effects on Hazard Rate and Adoption Time
Peers are not JUST physical neighbors!
x : the vector of 12 observable indicators
: latent components, (4x1) random vector with zero mean and variance-covariance matrix I
μ : vector of constants corresponding to the mean of x
Γ : (12x4) matrix of constants
v: (12x1) random vector with zero mean and variance-covariance matrix
Factor analytic model estimated using principal components method with varimax rotation.
[From the perspective of individuals varimax seeks a basis that most economically represents each individual: each individual can be well described by a linear combination of only a few basis functions.]
Mathematical procedure: uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly uncorrelated variables called PCs. (no. PCs ≤ no. of original variables.
(a) any given variable has a high loading on a single factor but near-zero loadings on the remaining factors
(b) any given factor is constituted by only a few variables with very high loadings on this factor, while the remaining variables have near-zero loadings on this factor.
Table 3: Estimation Results of the Factor Analytic Model for Informational Variables
-ve coefficient implies a negative marginal effect on duration time before adoption: faster adoption.
Scale parameter of the Weibull hazard function is statistically significant and well above unity in both models.
Endogenous learning as a process of self-propagation of information about the new technology that grows with the spread of that technology:
The impact of social learning is comparable to the impact of information
provision by extension personnel (mean marginal effects on adoption times
are -0.293 and -0.306 for the stock of adopters and exposure to extension
Time before adoption of drip irrigation technologies decreases with age up to 60 years (experience effect) and then follows an increasing trend (planning horizon effect).
For more than 9 years of education, higher educational levels lead to faster adoption rates: only highly educated farmers are more likely to benefit from modern technologies.
PR1: Provision of extension services more effective speeding up the adoption process in areas where there is already a critical mass of adopters.
PR2: Spatial dispersion of extension outlets could also be designed away from market centers in a way that allows minimization of the average distance between outlets and peer farms in remote areas.
PR3: Nature of extension provision should be redesigned taking into account its complementarity with farmers' social networks.
PR4: Efficient pricing of agricultural inputs and outputs should become an explicit target of the reformed agricultural policy as it crucial affect adoption.
PR5: Farmer's characteristics (education, age) and environmental variables (aridity, altitude) are also found to be important drivers of farmers' technology adoption & diffusion and as such should be integrated in relevant policies.
PR6: Policy makers should take into account the level of farmers' risk-aversion, in order to correctly predict the technology adoption and diffusion effects, as well as the magnitude and direction of input responses.
Such policies are particularly relevant nowadays, given EU agricultural policy (CAP) reform & EU Directives and almost worldwide tight government budgets.
- Over 750 million olive trees are cultivated worldwide, 95% of which are in Mediterranean: Southern Europe, North Africa and the Near East. 93% of European production from Spain, Italy and Greece.
- Greek agriculture employs 528,000 farmers, 12% total labor force; produces 3.6% GDP ($16 billion annually).
- Greece devotes 60% of its cultivated land to olive growing; has more varieties of olives than any other country; holds 3rd place in world olive production with more than 132 million trees, 350,000 tons of olive oil annually, of which 82% is extra-virgin; half of annual Greek olive oil production is exported, but only 5% of exports reflects the origin of the bottled product. Greece exports mainly to European Union (EU), principally Italy, which receives 3/4 of total exports.