Luba Kurkalova and Sergey Rabotyagov

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Presentation transcript:

Estimation of a Discrete Choice Model when Individual Choices are Not Observable Luba Kurkalova and Sergey Rabotyagov Midwest Economics Association Meeting St. Louis, Missouri, March 29, 2003

Research Interest Economic analysis of policies that pay farmers for adoption of conservation tillage Basic data: National Resource Inventory (NRI), 1992 and 1997

Model of conservation tillage adoption

Probability of tillage choice Problem: in 1997, the are not available, But, grouped data on are available

Grouped data on conservation tillage choices CTIC (Purdue) collects expert opinion surveys on adoption of conservation tillage Reports percentage of area in conservation tillage by state, county, and crop, 1989- current Conversion of area percentage data into count data NRI: 92 corn points in Boone county, IA CTIC: 55% corn in County#15 is in CT Count data (CTIC+NRI): 92*0.55=51 NRI corn points from Boone county must be in CT

Linear versus nonlinear model Farm-level Aggregated Where

Proposed approach Express the likelihood of observing as a function of original (farm-level) model parameters, and Farm-level data on explanatory variables

Probability of observing 1 point in conservation tillage out of 3 points in a group

Application to 1992 NRI data Random sub-sample (1,339 observations) of Iowa 1992 NRI data (soil and tillage) supplemented with Census of Agriculture (farmer characteristics) and climate data of NCDC 63% of farmers use conservation till Grouped the observed individual choice data into 240 groups by county and crop Pretended that we do not observe individual choices

Results Variable Aggregated Discrete data model data model Corn dummy 44.7 41 (6.9) (11) Slope 0.56 0.22 (0.18) (0.12) Permeability 0.85 0.63 (0.37) (0.31) Water capacity 0.87 0.73 (0.32) (0.29) 5.69 6.0 (0.12) (1.6)

Problems Programming  Area percentage versus percent of points IA, County #5, corn, 1992 NRI 75% area in CT could be represented by minimum 5 and maximum 7 points TILL 1 Acres 19 2 20 22 32

Accuracy of aggregate data: IOWA, 1992 Cons. till acres Cons. till percent Total acres Corn NRI 8,668 65% 13,377 CTIC 4,474 35% 12,784 Soybeans 5,542 66% 8,424 4,687 57% 8,265

Conclusions Method allows recovery of individual-level model parameters with aggregated information on choices Worked very satisfactory on artificially aggregated data