Changes in the Structure of Global Food Demand: Discussion

Cranfield et al. raised important issues in their article and made a valuable contribution to the literature on global food demand. They predicted future patterns of global food consumption based on results from fitting the AIDADS to 1985 Phase V International Comparison Project (ICP) data. The AIDADS allows marginal budget shares to vary with income and is a vast improvement over constant marginal share models, such as the linear expenditure system (LES), that predict higher income elasticities of demand for necessities in rich countries than in less affluent ones. I strongly agree with and do not discuss further the main predictions of Cranfield et al.: that food expenditures will increase through the year 2020, while food's share in expenditure will decrease; that more growth in food expenditures will occur in low-income countries than in affluent ones; and that the shares of food groupings will change. Instead, I briefly describe the ICP and discuss two important issues associated with the use of ICP data for global analysis: fixity and data reliability. Next, I compare predictions from AIDADS and the Working P.I. (Preference Independence) models (Theil, Chung, and Seale). Finally, I conclude the article with some simple observations and questions. The first three phases of the ICP were carried out at the University of Pennsylvania (Kravis, Heston, and Summers). Beginning with Phase IV, the fixity principle was imposed on the data at the insistence of the European Union (EU). The fixity principle constrained numerical values for purchasing power parities and real volume indexes, based solely on EU (and Austria) country data, to remain unchanged when the sample was enlarged to include other Organization for Economic Cooperation and Development or global countries. The cost of fixity is that subcategories do not sum to category heading for non-EU members (except for Austria), making the data unsuitable for global-demand analysis. Communication with Dr. Hertel suggested that his 1985 ICP data were not plagued with the fixity problem, but all users of ICP data should be aware of this potential problem. Another issue associated with ICP data is quality. Many users of ICP data believed that the values of low-income countries, particularly African countries, were unreliable.' For example, Thiel, Chung, and Seale using 1980 ICP data, labeled seven African (including Ethiopia but not Senegal) and two Central American countries as outliers (based on information inaccuracy measures) and deleted them from their global demand system. Why are these low-income African countries outliers? Theil, Chung, and Seale surmised that it was due to underreporting of food consumed at home. A major portion of the food consumed in poor African countries is home-produced, and, although statisticians guess at the quantity of home-produced food consumed at home, it is easy to believe that much of it is underreported.2 In fact, Theil, Chung, and Seale dramatically increased the fit of low-income country data by increasing food consumption while leaving expenditure for other goods unchanged. Cranfield et al. included all Phase V countries (including low-income African countries) and fit these data with the AIDADS, a rank-three demand model (Rimmer and Pow-