Agroforestry Adoption:Keynes’ Animal Spirits are Alive and

Well in the Yucatan

James F. CaseyWashington & Lee University

Prepared for SAPPCREE Session

Southern Economic Association

Annual Meeting in New Orleans, LA

November 21, 1999

CONTENTS OF THIS TALK• Background / Motivation for the body of

work

• Objective of this paper

• Keynes’ Theory of Investment

• Model for Agroforestry Adoption

• The Data

• Model Specification

• Results and Implications

BACKGROUND / MOTIVATION

• Agroforestry: emerging as a scientific discipline, emphasis on biophysical

• In general: lack of socioeconomic research– (Mercer and Miller 1998)

• Adoption rates are low

Low Rates of Adoption??

• Natural Science is well understood

• Insufficient Economic data– models for behavior (Ghadim and Pannell 1999)

– risk and uncertainty (Negatu and Parikh 1999)

– non-market benefits– market failure (Casey, Caviglia, Kahn and Rivas 1999)

OBJECTIVE

• Develop a model based on Keynes’ notion of profit expectation and “weight” in order to more accurately predict farmer interest in agroforestry

KEYNES’ THEORY OF INVESTMENT

• Profit expectations and confidence– (Anderson & Goldsmith 1997)

• Notions of “weight”

• Precarious nature of Investment

Keynes and Agroforestry

• Uncertainty

• Asking the farmer to invest

• Profit forecast and “Weight”

• Reducing Uncertainty

• Investment in Human Capital

Model for Agroforestry Adoption• Max U (A(ta), C(tc), M(*, taB))

• s.t. T - tc - ta = 0

– U(A(ta),C(tc),M(*, taB))> U(0,C(tc),M(*))

• Comparative Statics for B• dta/dB = (+)

• dtc/dB = (-)

– Investment = f(profit forecast, weight)– Weight = f(Human Capital Investment)– HC Investment = f(Ed, Exp, Trees, Part)– Invest AF = f(Human Capital Investment)

Human Capital Investment

• Education

• Farming Experience

• Experience with Trees

• Comfort with Participation

• Previous Success

THE DATA

• Calakmul, Campeche, Mexico, the CBR

• 176 farmers interviewed

• 15 communities

• 142 expressed interest in Agroforestry

Farmer Characteristics Characteristic Total

N=176YesN=143

NoN=33

Age 38 37.7 40.7No. of kids 4 3.9 4.5Schooling .506 .552 .303Total area 49 ha 49.8 46Income 11363 10787 13859Hectares in AF 0.38 .43 .13Farm experience .17 .19 .09Forested area 28 ha 28.7 25.4Ha in Milpa 4.8 4.8 4.9Participation in programs 46% .50 .31

Model SpecificationLogit

• Y = a + bSi + bHi + bR + e• S: Vector of socioeconomic variables

• H: Human Capital variables

• R: Rainfall strata

MODEL RESULTS

– Table 4. LOGIT MODEL: participation (1) or not (0)

• Variables Coefficient Z – stat

• TREES 1.29 2.08**

• ED .634 1.81*

• SURV .988 1.76*

• EXP 1.26 1.6

• PART .378 0.82

• INC -.000029 1.9*

• STRAT .661 1.43

• FARM .011 1.21

• Constant .104 .15

• Psuedo R2 = 0.156 Chi2 (8) = 25.98 N = 175 * significant at .1 Correctly Classified 84.00% ** significant at .05

HC variables

• Education (positive and significant)

• Farming Experience(positive)

• Experience with Trees (positive and significant)

• Comfort with Participation (positive)

• Previous Success (positive and significant)

Conclusion and Policy Implications

• Farmers with more human capital are more likely to participate.

• Weight is important. Investment is precarious.

• Education and Extension

• Listen to farmers.