A Duplication and Replication of Two Econometric Demand Models Explaining the Effects of Promotion on Mill-Level Demand of U.S. Upland Cotton
Type of DegreeThesis
DepartmentAgricultural Economics and Rural Sociology
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Many studies have been conducted to determine the effects of generic promotion on the demand for a certain commodity. Two studies examining the effects that cotton promotion expenditures by Cotton Incorporated has on the mill-level demand for U.S. upland cotton are examined in this paper. Researchers at the Research Triangle Institute conducted a study in collaboration with researchers from North Carolina State University (hereafter Murray et al.). They found long-run elasticity estimates of 0.02 for promotion and 0.35 for nonagricultural research. In addition, they found the long-run elasticity estimate for the own-price of cotton to be –0.4. With these estimates, they suggested that the U.S. Cotton Research and Promotion Program was effective in increasing the mill-level demand for U.S. Upland Cotton. Ding and Kinnucan conducted a study that found the long-run elasticity estimate to be 0.06 for promotion. This estimate was somewhat larger than the Murray et al. estimation. They, furthermore, found the long-run elasticity estimate for the own-price of cotton to be –0.3. They, too, suggested that promotion expenditures expanded the mill-level demand for upland cotton. Following William Tomek’s guidelines for duplication and replication of research results, an attempt was made to duplicate the Murray et al. results and then, to replicate Ding and Kinnucan’s results with Murray et al. data. The Murray et al. OLS and GLS results were confirmed by duplication. The duplicated results exhibited only slight differences in parameter estimates and t-ratios. Problems did arise from the 2SLS results because of identification problems caused by collinear variables. However, after deletion of two variables (justified by a variable selection method utilized in SAS), regression analysis was continued (without correction for first-order autocorrelation because of unclear methods) and reasonable results were attained although exact duplication of Murray et al. results could not be accomplished. One of the major problems that researchers run into in replication studies is locating missing data. This was the main problem that was incurred during this study. Replication was not perfect in that monthly, rather than quarterly, data were used and two proxy variables had to be used due to some missing data. This coupled with the fact that it was not known whether the researchers used unadjusted or seasonally adjusted advertising data, may have caused different regression results. It was first suggested, using unadjusted advertising data with Model D (the model that compared directly with Ding and Kinnucan’s), that the inferences made by the researchers were negated and their results were suggested to be very fragile. However, it was later suggested, using seasonally adjusted advertising data with Model D, that Ding and Kinnucan’s inferences (advertising expands the demand for cotton) were robust, although many conclusions were altered and the model’s fit was not ideal. When an interaction term was included in the model without the monthly dummy variables (Model C with seasonally adjusted advertising data), it became significant. This implied that advertising played the role of a “taste shifter” by rotating the demand curve and therefore changed Ding and Kinnucan’s findings of no curve rotation. With the regression results being severely altered using seasonally adjusted advertising data, it is suggested that the use of such data causes the inferences from these studies to be conditional upon whether advertising data are seasonally adjusted and on the particular model specification. Furthermore, questions about the robustness of the results are brought up when such dramatic changes occur from the use of modified data.