Knowledge that Transforms

IN recent years there have been numerous investigations of the nature and effects of competition in gasoline retailing. Implicit in much of the discussion is the assumption that retail prices for the major brands are usually identical. This article tests this assumption. It also analyzes why prices in a community are as uniform as they are, and why there are such differences as do exist. It sheds light on how competition works in this important market, and on the role of price in a successful merchandising program. The conclusions are applicable in varying degree to other industries. Detailed studies were made of six metropolitan areas in the Midwest, with populations ranging from iIO,OOO to over five million, and with a wide range of conditions that might influence prices. On a particular day a nationally known research company read the prices off the pumps of each gasoline outlet in five of the cities. In the sixth the data were collected by a refiner-marketer during a particular week. In four cities it was possible to get rather complete information about each gasoline outlet.' Information was also compiled on other factors which might influence the price pattern, such as neighborhood characteristics and traffic densities.

T HE intent of this study is to ascertain that linear combination of financial characteristics which large industrial corporations with low ratios of earnings per share to common stock price from those with high ratios.' The linear transformation of several variables into a single variate (z) permits the categorization of firms on the basis of whether the z values are greater or less than a predetermined mean value. The proposition which underlies the division of firms into high and low ratio groups is that, if allowance is made for the historical nature of earnings and for market imperfections, the earnings-to-stock-price ratio reflects the composite market valuation of such factors as financial risk and dividend policy.2 With this in mind, it is interesting to inquire whether certain basic measures can be used to differentiate successfully between the two classes. Discriminating variables, that is, financial characteristics chosen to reflect individual elements of risk and other factors which affect e/p ratios, include the ratio of dividends to earnings, the ratio of current assets to current liabilities, the rate of return on additional investment, the relative change in sales, and the comparative stability of the common stock price. The construction of the problem is designed to parallel the thinking of investors and/or financial executives. Despite the apparent continuity of risk gradations, firms tend to be grouped on the basis of low, medium, and high risk; e/p ratios (or their reciprocals) are often used as the initial stratification variable; and attention is customarily directed to the extreme classes. The evaluation of common stock and other corporate securities tends in ddition -to be carried out in terms of certain conventional ratios. Discriminant analysis, as employed here, is not intended as a substitute for multiple regression analysis. Given little knowledge as to the appropriate form and complexity of the general regression function, this approach nonetheless serves as a useful device for observing directly those characteristics which distinguish lowand high-risk categories. The relevant information obtained is large relative to the sample size. The derived relationships may in turn facilitate the formulation of multiple regression functions. The potential utility of the analysis which follows is at least threefold. First, procedures for the selection of underand overvalued stocks may be improved by the introduction of discriminant analysis. If the discriminating index suggests that a firm clearly belongs to one group while its e/p ratio indicates otherwise, some reason exists for believing the company's stock to be underor overpriced. Second, partial conclusions may be drawn as to the influence of changing stock market levels upon the importance of different factors which condition e/p ratios. Distributions of e/p ratios, exhibited in Table i for samples of large industrial firms, reflect (for example) a greater central tendency for the I952-55 period than for I948-5I. If the discriminating function based upon the I952-55 data fails to predict well for the earlier period, there is some presumption that weights of the individual variables have shifted. The index characteristic of discriminant analysis affords certain advantages in this respect. The discriminant function is applicable whatever the level of stock prices, provided the * The helpful assistance of W. W. Cooper and Carl Hensley, Carnegie Institute of Technology, and Charles Christenson, Harvard University, is acknowledged. The computations were performed in the computer center at Carnegie Institute of Technology. I The method employed is described in G. Tintner, Econometrics (New York, 1952), 96-I02. See also M. G. Kendall, The Advanced Theory of Statistics (London, I946), Vol. ii, 34I-48. By best discriminates is meant that the chance of erroneous classification is approximately minimal. 2Earnings-to-stock-price ratios are hereafter referred to as e/p ratios.

Jaroslav Vanek, The Natural Resource Content of Foreign Trade, 1870-1955, and the Relative Abundance of Natural Resources in the United States, The Review of Economics and Statistics, Vol. 41, No. 2, Part 1 (May, 1959), pp. 146-153

I F a speculator makes profits consistently this implies that he possesses the ability to forecast prices or price changes with a fair degree of success. Whether he thereby stabilizes price is another matter. That is the central problem of this article. The proposed theory relating speculators' profits and the stability of prices is intended to be applicable to those commodities traded on organized exchanges by two kinds of people, cspeculators and non-speculators. What distinguishes speculators from other traders in the market is that their profits depend only on the price or price change of the commodity they trade. Non-speculators' profits are determined not only by the price of the commodity traded on the organized exchange but also by the prices of other related commodities. If the non-speculators are hedgers, they can make their profits almost independent of the price level itself. What enters the excess demand curve of the non-speculators is some weighted sum of the prices of the commodities they handle. How-ever, I shall single out only the price of the commodity traded on the organized exchange when I come to discuss their excess demand. Some examples may make my distinction between speculators and non-speculators clearer. In the cotton market a non-speculator may be a textile manufacturer whose profit is a function of the price of raw cotton, cotton textiles, and other inputs besides raw cotton. By hedging or other means he can avoid taking a price risk and can specialize in producing cotton textiles to maximize his profits. In the foreign exchange market, one kind of non-speculator is an importer whose profit depends not only on the exchange rate, but also on shipping costs, the price of the commodity in the two countries, and the like. He too by hedging can avoid bearing the risk of a change in the exchange rate and can specialize in providing a merchandizing service. However, the stock market is not one for which the theory to be described seems applicable, because no reasonable distinction between speculators and other traders can be made. Perhaps the only non-speculators in that market are those corporations engaged in a new stock issue. In what follows I show that for a fairly general model positive speculators' profits imply that they have stabilized the price. My conclusion is the opposite of that reached by Professor Baumol. In a brilliant article recently published, Baumol gives a counter-example to disprove the proposition that if speculators make profits they necessarily stabilize the price.' After presenting my own model I show why Professor Baumol's results are unsatisfactory.

A GREAT deal of interest in metropolitan area analysis centers around the economic impact of autonomous forces upon any of the area's sectors and upon its totality. Researchers have often felt that even approximate knowledge of the structural relationship of the local economy would greatly facilitate their work. An interindustry flow table has been prepared for the St. Louis area, one of the ten largest metropolitan areas in the United States. From such a table it is but a short step to technical coefficients and the inverse matrix. Furthermore, from these tables income and employment multipliers can be estimated. Both are powerful tools in assessing the impact of final demand changes upon the economic activity of a metropolitan area. Much serious criticism has been leveled in the past against area technical coefficients which are deduced from national ones pertaining to an outdated period. This study undertakes to estimate 1955 coefficients with the help of data obtained directly from a sample of private and public bodies which together constitute the St. Louis metropolitan area economy. Based upon this information some activity and impact projections are attempted.

The following sections are included:The Leontief Paradox?Computational Procedures and Statistical DataStatistical Results and Implications