IN the area of international trade, there have been a number of studies designed to explain imports and exports disaggregated by commodities.' These studies, however, all concentrate on the trading patterns of industrial countries and there has been very little in the way of a systematic analysis of the imports and exports by types of commodities for developing countries. There is, of course, an obvious reason for ignoring these countries, and that has to do with the availability, or rather paucity, of adequate data; this data problem is most pronounced in the area of prices of disaggregated imports and exports. Although it is generally believed that the trading patterns of developing countries differ from those of the developed, an empirical examination would be necessary to determine the extent of such a difference. The purpose of this paper is to study the behavior of the imports of Venezuela at both the aggregated and disaggregated levels during the period 1953-1972. Data on imports at a disaggregated level have recently been made available for Venezuela for a nine-commodity breakdown;2 the data include the value and price for each of these categories on an annual basis, as well as the price of the domestic competing product. These nine categories cover some 80% of the total Venezuelan imports. The results of this study should provide information on the behavior of different types of import goods and on the nature and extent of the bias involved in estimates of elasticities based only on aggregate imports.

provide a criteria for rejecting the importance of substitution in ERP calculations. In addition, it is no less restrictive to assume a priori that all industries have the same value for a, whether it be 0.5, 2.0 or the traditional value of zero. It has been pointed out elsewhere that the realistic case to consider is what happens to the rankings when different industries have different values for a'.3 A meaningful approach would involve answering the question of what is the highest and lowest ranking an industry could obtain under any combination of different hypothetical values for o-. The data for the 135 industries in the investigation indicate that applying these criteria creates scope for the rank correlations to be somewhat less than the observed figures of 0.99, but the author's contention of a high correlation still stands. Nevertheless this result, while being interesting and useful, can however be misleading if applied as a general case. There is in fact considerable scope for variation in industry rankings when there is a wide disparity in the magnitude of the calculated ERP across the industries. This is the experience of some countries4 and it is also the case when, for a variety of practical purposes, industries are classified in smaller groups according to the intensity of their protection. For the group of highly protected industries, for example, there is traditionally a wide spread in the magnitude of the ERP and therefore considerable scope for variation in industry rankings. The result being that in the study under discussion for the 10 most highly protected industries there are 9 industries that could fill the top 4 positions and 7 industries that could fill the bottom 2 positions in the industry rankings.5