Knowledge that Transforms

1. Absolute Cost Advantage: High levels of advertising may create costs for potential entrants greater than those faced by existing firms when they entered. 2. Scale Economies: If there exist scale economies in advertising, defined as a greater than proportional increase in quantity sold per given increase in units of advertising,' potential entrants must either incur higher advertising costs per unit of output than existing firms or increase output. But increasing output increases industry supply, thus lowering prices, increasing advertising competition from established firms, or both, depending upon the structure of the particular industry. 3. Pecuniary Economies of Scale: Lower prices per ad for large advertisers via quantity discounts, etc., may prevail.

AS consumers choose among cities, they may trade off higher earnings against differences in the consumption of environmental goods. Commuter travel time, crime and air quality, the quality of educational and health facilities, each may involve unpurchased environmental goods. Decisions about -the consumption of such goods are made simultaneously with the choice of city of residence. By examining the compensating earnings differentials relative to differences in environmental goods across cities, one can estimate hedonic prices for each environmental attribute. The hedonic prices may be useful in valuing the benefit of environmental improvement, and as weights in constructing an index of the quality of life, as Tobin and Nordhaus (1972) propose. By relating the index of the quality of life to city size, something may be learned about the effect of urban growth on the quality of life. This method is a substantial improvement over the index of Liu (1975). Previous efforts to estimate these hedonic prices by Izraeli (1974) and by Hoch and Drake (1974) have been unsatisfying for several reasons. First, they model only the consumer side, while ignoring the possibility that differences in productivity may influence wage determination. Thus, they do not indicate the conditions necessary for their equations to be identified. Second, they do not include variables representative of a wide range of environmental attributes. Exclusion of important categories of environmental attributes may unduly bias the estimates. Hoch and Drake (1974), for example, focus on climate while ignoring many other environmental attributes. Third, if one includes a wide array of environmental attributes, one is confronted with a serious multicollinearity problem. Kelley (1977) takes explicit account of the demand side of the labor market in estimating hedonic prices for amenities. His analysis, however, does not account for differences in the cost of living in different cities, apparently assuming that all goods are traded in national markets. In addition, Kelley does not account for differences in labor force quality in different cities. This essay considers the production activity of cities in identifying the supply of labor. A rich set of environmental attributes is then used to develop new estimates of the hedonic prices for urban amenities.

Douglas O. Stewart, The Effect of Differential Property Tax Rates on the Sales Prices of Single Family Residential Properties, The Review of Economics and Statistics, Vol. 60, No. 1 (Feb., 1978), pp. 150-153

Implicit here is the argument that the owners of the corporation and hence the economy as a whole gain from allowing management to reinvest past profits on their behalf. This must be, true if it is assumed that managers always act in the interest of the owners. If, however, managers act in their own interest, their retention and reinvestment of past profits can be a source of inefficiency. Mueller (1969) has observed that, although the investment opportunities of the stockholders may be better than those which management can internalize,

assumptions in each individual case, and often a modification of the classical model may become necessary. On the other hand, for any research in which the frontier function is important, the programming methodology is probably a more relevant tool. True, the sampling properties of the programming estimators are mostly unknown. But with the development of modern computer technology, large scale simulation studies can be expected to ascertain the sampling properties under various assumptions. It is the hope of this author that reports about results of such simulation studies will appear in the literature in the near future.

In recent issues of this REVIEW, Myrick Freeman and Kenneth Small have helped to clarify some confusion which grew out of a study of air pollution and property values by Ridker and Henning (1967). Ridker and Henning (hereafter referred to as R-H) performed a multiple regression of property values on air pollution, housing characteristics, accessibility, and neighborhood characteristics using cross-section data for the St. Louis metropolitan area. As Freeman (1974a) and Small (1975) point out, the early debate on the R-H study was largely sidetracked with the issue of whether one can predict the aggregate change in property values resulting from an improvement in urban air quality. A more appropriate focus for policy purposes is the question of whether property value data can be used to estimate households' willingness to pay for cleaner air. The R-H study calculated willingness to pay by first multiplying the air pollution coefficient obtained in their linear regression by the change in air pollution estimated for each household, and then summing over all St. Louis households. The air pollution coefficient was thus inteipreted as the average willingness to pay for air quality improvements for all St. Louis households. Freeman (1971) had correctly pointed out in an earlier note that the R-H procedure of calculating willingness to pay is inappropriate because a hedonic housing regression cannot in itself isolate demand and supply elements; and Freeman was not very sanguine about the possibilities of doing so. Without taking a position on whether the true willingness to pay for clean air can be determined empirically, both Freeman and Small put the earlier empirical work into perspective by showing that the pollution coefficient in the housing value regression does provide a correct measure of the 'mnarginal valuation placed by individual consumers on uniform changes in pollution levels (Small, 1975, p. 105). As a theoretical point, this result is reassuring, since it suggests that benefit estimates obtained from studies like that of Ridker and Henning may be reasonable if the air quality improvement is small. Unfortunately, however, the air quality improvements generated by the Clean Air Act Amendments of 1970 and 1977 are distinctly nonmarginal (pollution reductions are often predicted to be 50% or more). No empirical evidence has been available thus far to assess the magnitude of the bias associated with using marginal valuations to estimate the benefits from these nonmarginal pollution reductions.' This note attempts to fill this gap. Several authors have argued as a matter of principle that the willingness to pay for nonmarginal air quality improvements can be obtained from property value data if the proper conceptual procedure is used.2 Using the approach suggested by these papers and an empirical focus suggested by Rosen (1974), we have elsewhere provided estimates of households' willingness to pay for clean air as well as details concerning the nature of the data and possible specification biases (Harrison and Rubinfeld, 1978). In this paper we confine our discussion to the magnitude of the bias that results from using the air pollution coefficient in the hedonic housing price equation directly to estimate the willingness to pay of urban households for a nonmnarginal air quality improvement. Section II summarizes the model we use to estimate the demand for nonmarginal changes in air quality. In section III we distinguish three potential sources of bias in the R-H procedure, and assess the quantitative importance of each with data for the Boston Metropolitan area.

AS is well known, economic growth leads, not without a feedback, to changes in the industrial composition of the economy. The seminal work of Simon Kuznets (1966, 1971) has well quantified various aspects of this process. One such aspect, which is of immediate interest to this paper, is the continuous decline in the relative importance of agriculture, a phenomenon that has already been observed by Engel (1857).' A change in industrial composition takes place through a change in resource allocation, including labor, leading to what can be referred to as occupational migration. Part of this phenomenon involves actual change of occupation by workers and part is performed by new entrants into the labor force whose choice of occupation differs from that of veteran workers. The relative importance of these two components may depend to a large extent on the rate at which the economy changes its composition as compared with the natural rate of population (labor force) growth. Independently of the form, at any point in time, the existing opportunities presumably dictate the allocation, and the more attractive the new opportunities are, the more people will be attracted. It is this premise, almost axiomatic to economists, that this paper proposes to measure. It is clear that the question of sectoral migration is of prime importance and that it bears important policy implications as well as having an analytic role. However, in spite of the importance of the subject, there does not seem to be any empirical sectoral migration equation at the macro level.2 The reason is not clear. It is possible that aggregation blurs the data and makes it difficult to obtain acceptable estimates. Whether or not this is the reason, it is clear that if such an equation is important, the data should reveal it and there must be a way to estimate it. In such an undertaking, it helps to have data with a large spread in the important variables. Such data are provided by a cross-section of countries. The plan of the paper is as follows. Section II deals with the formulation of the problem. It relies on existing and known concepts and it therefore concentrates on bringing the concepts together for the purpose at hand. Section III presents the results. A few concluding remarks appear in section IV.

Retail gasoline prices in 22 cities were surveyed to identify the effects of competition on price during the 1964-1971 period, when gasoline supplies were relatively normal and price wars were common. The informational theory of oligarchy is used to derive regression results and determine their market relevance. The 1965 price restoration move led by Texaco effectively eliminated small marketers selling at supra-competitive levels. The findings support the conclusion that collusive pricing was practiced to some extent during the period until competitive pricing returned in 1970. 8 references.