Knowledge that Transforms

Two seminal budget studies by David Davies (1795) and Frederick Eden (1797) are employed below to investigate place of bread in diets of English rural laborers at end of eighteenth century.' Because of considerable geographic and temporal dispersion in prices of foodstuffs found in these budgets, they afford a unique opportunity to study influences of both prices and income on individual household consumption decisions. In particular a test is made of famous hypothesis, attributed by Marshall to Robert Giffen,2 that a rise in price of bread, ceteris paribus, increases its consumption among lower classes. Wheaten bread was, in Middle Ages, a luxury food of landed classes in Europe. Its gradual introduction into laboring class diets in modern period prompted David Landes (1969, p. 47) to conclude, ... one of best signs of comfort in Europe is consumption of white bread. The transition in England to wheat as the almost universal bread corn of whole people took place, according to Sir William Ashley (1928, pp. 1-2) primarily in eighteenth century and was virtually complete by 1795. The rise of wheat occurred most rapidly in southern and eastern counties and was favored by a more capitalistic agriculture since it often required special liming, other fertilization, and tilling. To contemporaries, and some modern historians, this change to wheaten bread seemed, for purely psychological reasons, to be irreversible. Radcliffe Salaman (1949, pp. 480-481) writes:

T HE economic rewards of college training, which have been substantial for decades, began to fall at the outset of the 1970s. The once sizeable premium to the college graduate diminished; attainment of professional and managerial job status became less frequent; and the rate of return to the college investment dropped after having risen in the 1960s (Freeman, 1975, 1976a). In this paper I use information on individuals from the Current Population Survey (CPS) March consumer income tapes for 1969 and 1974 to examine the dimensions and routes of the decline in the economic value of college training. The 1969 tape contains income from 1968, when the college market was still strong; the 1974 tape contains income for 1973 when the market was weakening. The CPS tape data are supplemented with other statistics, which permit some evaluation of developments through 1975. The paper focuses on white men and women, as the market for college-educated blacks has been dealt with in detail elsewhere (Freeman, 1977). Particular attention is given to young persons on the hypothesis that, for reasons to be elucidated later, the market for their services is likely to be especially active, and thus that declines in the relative position of graduates will be concentrated among them. The paper is divided into three sections. The first presents reasons for expecting a decline in the economic rewards to college in the 1970s and for expecting the decline to be most severe among the young. Section II contains an analysis of the CPS data regarding change in the early 1970s and supplementary evidence through 1975. Section III examines the possibility that the developments under study represent normal cyclic changes and then investigates the impact of the changes on potential future income streams and rates of return to the college investment. There are four principal findings: (1) The economic return to college measured by the income or occupational differences between college and high school graduates fell markedly for young men in the 1970s, a sharp break with past stability that is not readily explained by cyclic developments. (2) Among women there was a noticeable fall in the occupational position of graduates but an unclear pattern of change in relative incomes. (3) Because of the concentration of the drop in relative incomes among young men, the cross-section age-earnings profiles for college graduates twisted in favor of older workers in the 1970s. (4) As a result of the decline in relative incomes and continued increases in the direct cost of college, rates of return to the investment dropped noticeably in the early 1970s.

T HE measurement of firm size plays a crucial role in applied microeconomics and industrial organization. Firm size has figured prominently in numerous studies of economies of scale in production, advertising, capital market, and cash balances, and in studies of concentration, diversification, profitability, regulation, technological change, and research and development. Even when firm size was not their main concern, many studies often found that size emerged as a robust empirical variable.' All these studies have based their findings on different alternative measures of firm size, often implying that great care in choosing between them is unnecessary since the measures are highly intercorrelated. In a note in this REVIEW, Smyth et al. (hereafter SBP) were the first to recognize that alternative measures of firm size are not interchangeable unless stricter conditions than correlation are met. They have further shown that empirical findings regarding economies of scale are not invariant with the size measure chosen, and that often different conclusions can be reached depending on the particular size measure used. The purpose of this paper is threefold: (I) to offer a general stochastic model that rigorously spells out the conditions for interchangeability among alternative measures of firm size, and of which SBP's deterministic model is a special case; (2) to conduct a statistical test of the interchangeability conditionis using a larger number of size measures, and a far larger sample than the one employed in SBP's empirical test; and (3) to empirically analyze the statistical properties of the most commonly used measures in order to help future investigators in selecting appropriate size measures suitable for their purposes. Section I reviews SBP's work, section II discusses the measurement problem, section III presents our theoretical model, and section IV concludes with some empirical evidence.

Clark W. Bullard III, Anthony V. Sebald, Effects of Parametric Uncertainty and Technological Change on Input-Output Models, The Review of Economics and Statistics, Vol. 59, No. 1 (Feb., 1977), pp. 75-81

N theoretical discussions industrial organization specialists often indicate a preference for comprehensive or summary concentration indices over the more readily available concentration ratios. Such indices consider the entire size distribution of sellers in a market, and they give weight to both fewness of sellers and inequality of market shares. The most popular such measure is probably the H index of Hirschman (1945) and Herfindahl (1950). (See Hart (1975) for a discussion of alternatives and a list of references.) Let P1 be the share of the largest firm in an industry, measured in terms of sales, employment, or whatever scale variable is considered most relevant, let P2 be the share of the second largest firm, and so on, in a market with N sellers. Then H is defined by

T HIS study examines the direction and level of aggregate bilateral flows in a multi-country network. We are not so much concerned with an explanation of a country's total imports as with the geographical pattern of its imports from among its trading partners. We therefore incorporate economic variables for both the importing and exporting countries, including demand and supply conditions and trade resistance factors, in particular, the costs of transportation. The latter are incorporated into the analysis through an errorsin-variables specification. Our study begins on familiar ground. Similar models have been proposed and tested by Tinbergen (1962), Poyhonen (1963a, b), Pulliainen (1963), Linnemann (1966) and others. The key difference between our study and previous ones lies in the treatment of the costs of transportation. Previous studies have used distance as a proxy for transport costs, but that has some serious limitations. First, the cost of transportation is influenced by other factors such as the value of the commodity being transported.1 For example, Moneta (1959) has pointed out that the effect of distance is small for high-valued commodities. Second, the use of distance imposes the assumption that the cost of transportation is the same in either direction between any pair of trading countries. This is restrictive when analyzing aggregate flows, since the commodity composition of differs by direction. Third, estimated relationships (e.g., elasticities) between flows and static variables such as distance are not very helpful in predicting future levels and thus are not very helpful in policy analysis. Finger and Yeats (1976) have shown for the United States that effective protection due to international transport costs is at least as high as tllat due to tariffs. Moreover, they indicate that the importance of transport costs has been increasing rapidly in recent years (even before full consideration of the recent petroleum price increases).2 The need to move beyond the distance specification of transport costs is clear. The fact that reliable data on transport costs are unavailable has been the primary reason for the use of distance as a proxy in the previous studies. In principle, the difference between c.i.f. and f.o.b. values represents the costs of freight and insurance.3 However, due to notorious measurement errors, these figures cannot be used in traditional econometric procedures. Consequently, most studies dealing with this subject have not utilized the differences between c.i.f. and f.o.b. values.4 Though these differences are indeed highly inaccurate measures of transport costs, they are included in our empirical analysis by applying an errorsin-variables approach. This allows the estimation of the elasticity of bilateral flows with respect to transport costs, which is the key product of this study. The theoretical background is introduced in section II and the empirical model is specified

SHORELINE development is a growing public policy issue in many urban areas. While there is some published research on a related topic, the importance of ambient water quality, economists have not yet turned their attention to the economic significance of the existence and width of the undeveloped apron offering public use and access to bodies of water in urban areas. There are several important issues to be considered here. May we expect the urban land market to provide a solution which is Pareto efficient? Have public agencies through zoning and other building restrictions acted in a socially optimal way? What contribution can studies of the determinants of property values make to our understanding of these issues? We begin an exploration of these issues and present some empirical results that enhance our knowledge of the economics of water-related open space. More generally. this paper extends recent economic work that has produced quantitative measures of value for phenomena hitherto restricted to qualitative expression. The process of transforming qualitative into quantitative knowledge, so essential to an empirical science as to be one of its distinguishing features, is well illustrated by the work of Cobb and Douglas on production (1928); Griliches (1961) on the qualitative characteristics of automobiles; and Fogel and Engerinan's (1974) controversial piece on slavery.' With respect to hotusing, implicit prices of each attribute contained in the bundle of housing services have been estimated by hedonic price regressions in the spirit of the work by Lancaster (1971) and others.2 In the following sections we first examine the choice of housing attributes, including waterrelated open space and proximity to bodies of water, faced by a household in a metropolitan area. Next, the process of implicit price formation is examined, and, employing data on individual dwelling units in a metropolitan area with numerous bodies of water, these implicit prices are estimated. We then turn to the question of what can and cannot be inferred from these results about the demand for open space and the welfare gains or losses resulting from possible changes in the amount of water-related open space.

The distribution of income in the urban context has received relatively little attention from economists. Limited information has been employed to assert an inverse relationship between city size and income inequality (Duncan and Reiss, 1956; Richardson, 1973). This relationship can be rationalized by the fact that both city size and inequality are related to the level of income. Kuznets (1955) hypothesized a negative relationship between income and inequality, and he is supported by the findings of Aigner and Heins (1967), Conlisk (1967), and Al-Samarrie and Miller (1967) using state data and by Frech and Burns (1971) using SMSA data. Sveikauskas (1975) has documented that incomes are higher in larger cities. In this paper we analyze the relationship between city size and income inequality. After controlling for other factors that influence income inequality by using regression analysis on cross section data for 79 U.S. metropolitan areas, we find that income inequality appears to increase with city size.

TN following the mean-variance analysis developed by Markowitz (1952) and Tobin (1958), Sharpe (1964), Lintner (1965a, b) and Treynor (1961) have developed the theory for determination of asset prices under conditions of uncertainty. The equilibrium asset pricing model, and its implication for measuring ex post performance of individual securities, have been empirically tested by Lintner,1 Jensen (1968, 1972), Miller and Scholes (1972), Douglas (1969), Roll (1969) and others. The empirical results obtained by both Douglas and Lintner deviated from the theory of the model. Moreover, Miller and Scholes have run empirical tests similar to those of Douglas and Lintner and found a significant disparity between the theoretical model and the empirical evidence. They maintain that part of this discrepancy can be explained by possible statistical biases, measurement errors, and consideration of the skewness of the distribution of returns. Black, Jensen, and Scholes (1972) (hereafter B-J-S) confirm the systematic bias. Using monthly data covering a 35 year period, they discovered that, on average, high risk securities earned less than the amount predicted by the model. Similarly, earnings on low risk securities exceeded the amount predicted.2 Although these disparities clearly suggest some systematic empirical bias, we are not examining a possible statistical bias but a mathematical bias stemming from one of the assumptions underlying the capital asset pricing model (CAPM). To be more specific, the model assumes that all investors are single period, expected utility of terminal wealth maximizers. There is no particular restriction on the length of this period as long as it is identical for all investors. Clearly, the length of the true investment horizon affects asset prices under conditions of uncertainty. We claim that the disparities noted above may result from using data calculated for an investment horizon that differs from the true investment horizon. In the various empirical tests, the investment horizon has been selected arbitrarily. For example, Lintner and Miller and Scholes use annual data (i.e., they implicitly assume a one-year horizon), Douglas uses quarterly and annual data; Black, Jensen and Scholes, as well as Friend and Blume (1970), use monthly rates of return in their empirical tests, while Roll uses weekly data. It has been shown elsewhere by Levy (1972) that the Reward to Variability index (developed by Sharpe, 1966) is a function of the investment horizon assumed. Hence, there exists a systematic mathematical bias that is a function of the horizon assumed. The above theoretical findings are related to the theory of pricing capital assets, but deal only with efficient portfolios and not individual stocks. In this paper we illustrate that the assumed horizon plays a crucial role in empirical testing. Any deviation from the true horizon causes a systematic bias in the regression coefficient (i.e., in the security systematic risk). This in turn causes a systematic bias in the performance measures of each security, and hence the deviation between the theoretical model and the empirical evidence. The results of this paper are not limited to the theory of pricing capital assets; they are applicable to any econometric study in which the variables have multiplicative rather than additive properties. In such a case the regression coefficients will have a matheReceived for publication February 27, 1975. Revision accepted for publication March 8, 1976. The authors acknowledge the technical assistance of Moshe Smith and two anonymous referees. The first author has been partially financed by the Maurice Falk Foundation, and the second author has been financed by the Ford Foundation. 1 Lintner's paper Security Prices and Risk: The Theory and a Comparative Analysis of A.T.&T. and Leading Industrials was presented at the Conference on Economics of Regulated Public Utilities, June 24, 1965, Chicago. 2 Miller and Scholes show that the presence of certain biases could have accounted for the Douglas and Lintner findings. BJ-S maintain that the assumption of borrowing at riskless interest rates could have accounted for these deviations.

N OTWITHSTANDING this early statement by Ravenstein (1885, p. 196), the separate study of geographic mobility among women has been virtually ignored by students of migration.' The reason is obvious: women are assumed to migrate because their husbands do.2 While it is undoubtedly true that most migration involves family units (the migration of husband and wife occurring jointly), the possibility that the wife's welfare is considered in the family's decision to migrate should not be ruled out. It is at least desirable to test the hypothesis that the wife's employment is considered in the migration decision and to examine the effect of that decision on women's earnings. In this paper an economic model is developed to explain the family's decision to migrate and the effect of migration on the labor market earnings of men and women. It is based on the tenet that family utility, defined operationally as the husband's and wife's labor market earnings and leisure, is maximized. The model suggests that the wife's labor market involvement is a significant consideration in a (husband-wife) family's decision to migrate. The data from the National Longitudinal Surveys (NLS) are well suited for empirical testing of this model.3 The surveys provide the opportunity to examine the change in labor market earnings of families and individuals over a five-year period. Availability of data on migratory status as well as on other personal characteristics of women and their families permits the direct testing of the model. In section I a family utility maximization model is used to derive implications with regard to the probability of migration by the family and the effect of migration on individual and family earnings. These implications are tested in section II using multiple regression analysis and the NLS data for white women who were 35 to 49 years of age in 1972. The implications of the empirical estimates for the economic welfare of women and for interpreting the observed earnings distribution are discussed in section III.