Knowledge that Transforms

A LTHOUGH much research has been done IA-t on the relationships between industrial structure and performance, important puzzles persist. Specifically, it remains unclear whether profits rise with industry concentration when other structural variables, such as market share, are appropriately held constant. Also, what economic phenomena underlie the observed positive profit-market share associations'? This paper seeks to clarify these relationships. Until recently, data limitations have restricted cross-sectional structure-performance analyses to either industry level variables or firm level variables which aggregate quite different activities within a single corporate financial statement.' These limitations are overcome by the Federal Trade Commission's Line of Business survey, which compiles financial statistics disaggregated to the of business (LB) level. A line of refers to a firm's operations in one of 261 manufacturing and 14 nonmanufacturing categories defined by the FTC. The number of LBs per company ranges from I to 47, with an average of 8 lines per company. For each LB, information on pretax profit, advertising, research and development, assets, market share, diversification and vertical integration is reported. When combined with census and input-output data, the FTC line of data allow the estimation of a structure-performance equation of unprecedented richness. A primary emphasis is placed on the theoretical and empirical differences between variables measured at the LB and industry level. To accomplish this task and to relate this paper to the previous literature, regressions are performed at both the LB and industry level.

R ISK transfer and price discovery are two of the major contributions of futures markets to the organization of economic activity (Working (1962), Evans (1978, p. 80), and Silber (1981)). Risk transfer refers to hedgers using futures contracts to shift price risk to others. Price discovery refers to the use of futures prices for pricing cash market transactions (Working (1948), Wiese (1978, p. 87), and Lake (1978, p. 161)). The significance of both contributions depends upon a close relationship between the prices of futures contracts and cash commodities. This paper examines the characteristics of price movements in cash (or spot) markets and futures markets for storable commodities. Section II presents an analytical model of simultaneous price dynamics which suggests that, over short intervals of time, the correlation of price changes is a function of the elasticity of arbitrage between the physical commodity and its counterpart futures contract. Greater elasticity fosters more highly correlated price changes, and thereby facilitates the risk transfer function. The elasticity of supply of arbitrage services is constrained by, among other things, storage and transaction costs. Thus, futures contracts will not, in general, provide perfect risk transfer facilities over short time horizons. The essence of the price discovery function of futures markets hinges on whether new information is reflected first in changed futures prices or in changed cash prices (Hoffman (1932, pp. 258259)). The model in section II provides a framework for analyzing whether one market is dominant in terms of information flows and price discovery. In section III we develop a model based on section II which is appropriate for estimating the lead-lag relationship between cash prices and futures prices. Section IV presents empirical estimates of the parameters of the model for seven different storable commodities: wheat, corn, oats, frozen orange juice concentrates, copper, gold, and silver. The cost of arbitrage between cash and futures differs across these commodities. For this reason we are not surprised to find inter-commodity differences in the correlation of short-run price changes and in the substitutability of futures contracts for cash market positions. With respect to the price discovery function of futures markets, we find that while futures markets dominate cash markets, cash prices do not merely echo futures prices; there are reverse information flows from cash markets to futures markets as well.

Dennis W. Carlton, The Location and Employment Choices of New Firms: An Econometric Model with Discrete and Continuous Endogenous Variables, The Review of Economics and Statistics, Vol. 65, No. 3 (Aug., 1983), pp. 440-449

H ISPANIC American men have lower average wage rates than white non-Hispanics. In 1975 the average white non-Hispanic male wage-earner in the United States earned $5.97 an hour. Mexican men earned $4.31, 72% as much as white non-Hispanics; Puerto Rican men earned $4.52, 76% as much; and Cuban men earned $5.33, 89% as much as white non-Hispanics. By way of comparison, black men's average wages in 1975 were $4.65, 78% of the white male wage.' Several possible reasons for the Hispanics' lower wages come to mind. Among them are age and education, geographic location, immigration, language difficulties, and discrimination. For example, as shown in table 1, Mexicans and Puerto Ricans are younger, on average, than the white non-Hispanic population, and earnings tend to rise with age. Hispanics have lower average levels of education than white non-Hispanics, and wages are positively associated with education. Many Mexican Americans live in the Southwest, where prices are relatively low. Moreover, Hispanics are more likely to be recent immigrants and to lack fluency in English than white non-Hispanics, and so to be at a disadvantage in the labor market. In addition, there is a widespread belief that Hispanics suffer from employment discrimination, and cannot obtain the wages that their human capital would command if they were non-Hispanic whites. How much of the wage differentials described above are due to each of these factors, and to other inter-group differences in wage-related characteristics? In particular, how much impact does labor market discrimination have on the average Hispanic man's wage and how does this compare with discrimination against blacks? This paper provides answers to these questions. A few other efforts have been made to analyze the relative earnings of Hispanic and white nonHispanic men, using 1960 and 1970 Census data (Fogel, 1966; Poston and Alvirez, 1973; Poston, Alvirez, and Tienda, 1976; Long, 1977; and Gwartney and Long, 1978). We use more recent data from the 1976 Survey of Income and Education. This data set enables us to measure wage rates more accurately and to specify the wage function more completely than does the Census. Unlike previous analysts, in estimating the wage function we take account of possible selectivity bias due to the distinction between average wage offers and average observed wages. Thus, we hope to obtain a more accurate and up-to-date measure of labor market discrimination against Hispanic men. Section II explains the method used to separate the minority-white non-Hispanic wage differential into the portions due to differences in average characteristics and the portion due to differences in unobserved factors and discrimination, taking into account the possibility of selectivity bias in the observed wage sample. Section III describes the data used in the study and the specification of the wage equation. The breakdown of the observed wage differentials into components due to differences in participation in the wage and salary sector, local price levels, average characteristics, and discrimination are presented in section IV. Section V summarizes our findings and discusses their implications for efforts to improve the economic situation of Hispanics in the United States.