Knowledge that Transforms

This paper is a review of an attempt by V. K. Chetty to nmeasure the relative amount of monetary services rendered by nearmoney assets such as time deposits at commercial banks, deposits at mutual savings banks, and savings and loan association shares. If such a measure could be found, then one could use it to construct a better money supply total; that is, one which would more accuratelv measure the total amount of monetary services available in the economy at any point of time. For example, if it could be established that a dollar of savings and loan shares (SL) rendered the same amount of monetary services as fifty cents of money (M) then by adding 50 percent of the value of outstanding SL to the conventional money supply, we would arrive at an adjusted money supply (Ma) which would take into account the monetary services of SL as well as M. The Ma would then measure the amount of M alone it would take to provide the same monetary services as the actual combination of M and SL in existence. Interest in constructing an Ma along these lines has been expressed by several writers.1 Their interest stems from the belief that there exists a stable relationship between the level of income, properly defined, and the desired level of monetary services. If this is the case, then a more accurate measure of the total amount of monetary services available in the economy would enable one to more accurately predict the level of income. Chetty's attempt to construct an Ma will be reviewed in two sections. In Section I, the theory that Chetty uses to construct Ma is examined, and it is shown that Chetty's theoretical presentation contains an error. Moreover, even if the theory is corrected along the lines suggested by Chetty in a footnote, it would still be impossible to estimate the monetary services of near-money assets since these cannot be separated from the other nonmonetary services rendered by these assets. In Section II, Chetty's empirical results are examined. Because Chetty cannot separate the monetary from the nonmonetary services of near-money assets, he is led to assign weights to near-money assets which are greater than one, implying that these assets yield a larger amount of monetary services per dollar than does money itself.

In their article in this Review, W. Lee Hansen, Burton Weisbrod, and William Scanlon (HWS) conclude that estimated payoff to more schooling is for their sample of men who were rejected for military service due to low scores on the Armed Forces Qualification Test (AFQT). It is shown in this comment that they obtained a downward biased estimate of the true profitability of schooling for their sample and that the correct rate of return may be at least 13 percent. While this is lower than the rate of return to males from high school, it is similar to the private return from college.' The HWS data are a sample taken in November 1963 of approximately 2,400 males, aged 17 to 25. The dependent variable (Y) is annual income in 1962 after deducting transfer payments. The explanatory variables include years of schooling (S), age (A), AFQT score (AFQT), and a dummy variable which takes the value of one if the individual received training outside of school (T). Their model is

In a recent paper published in this Review, V. K. Chettv developed an interesting method of estimating the substitution parameters between liquid assets and found that commercial bank time and savings deposits, mutual savings bank deposits, and savings and loan association shares (T,MS, and SL hereafter) rank in the descending order of imnportance as near-moneys. While Chetty suggested inclusion of these nearmoneys in the definition of money, he argued that since T are closer substitutes for money than are SL, Milton Friedman's definition of money including T but not SL is also justified. Indeed, Friedman and Anna Schwartz (1970, p. 188) subsequently claimed that his definition of money is confirmed by Chetty's results. Chetty's finding, however, depends critically on the incorporation in his analysis of pre-1951 observations which do not reflect an important institutional change that occured in 1951 affecting the substitution parameters. Omitting such observations but using the same method, this paper will show that SL are closer substitutes for money than are T. Although this result does not affect the basic methodological contribution made by Chetty, it reverses his empirical finding. While I will not argue for including SL in the definition of money for the reasons discussed later, the present finding has an important implication for rejecting Friedman's concept of money now widely accepted in monetary analyses. Chetty employed 1945-66 annual timeseries observations for his analysis, but the analvsis should have been confined to a period after 1950. In the fall of 1950, the insurance provision of the Federal Savings and Loan Insurance Corporation was made more liberal than before in the event of default of an insured savings and loan association. As noted by Friedman and Schwartz (1963, p. 669), this provision in fact became identical to that governing the Federal Deposit Insurance Corporation. It is, therefore, quite reasonable to expect that the nearness of SL to money has increased since 1951. Indeed, there is evidence (see G. K. Kardouche, Lee (1966)) showing that the substitutability of SL for money has shifted due to the institutional change cited here. In addition, the post-1950 data pertain to the period of revival of monetary policy since the 1951 Accord and the analysis of substitution effects in a policy context should be directed to such data. With the 1951-66 data, Chetty's estimating equations are recomputed. Since there was evidence of autocorrelation among the least squares residuals, the equations are reestimated to remove autocorrelation by using Phoebus DhrvNmes' method with the following results.: 1 (1) log T = .0180 38.81 log (.061) (2.94) 1 + rT

Contents: Measurement and meaning of income inequality; Some empirical evidence; Long term trends and their explanation.

There have been two major classes of urban area models: nonspatial models of income, employment, and structural change; and land use models usually oriented toward transportation planning. Recent efforts have become relatively complicated and have employed quite sophisticated techniques, with particular attention being paid to the housing market. Nevertheless, most of the work done so far appears somewhat deficient; convincing behavioral relations forming the basic structure are absent; and there have been inadequate efforts to test and validate the models. Further, relatively few efforts have specified the institutional framework necessary to introduce policy actions directly, although some recent efforts have been made in this direction. We propose to construct a model of the Boston metropolitan area that contains three major parts: a macroeconomic nonspatial model of output, employment, and income distribution; a model of long-term adjustments of population and capital stocks; and a model of spatial allocation. The equations of the model will be econometrically estimated and the main thrust of our efforts will be devoted to specification and testing of structural relationships reflecting actions of households, busi nesses, and governments interacting within both market and nonmarket institutions. The purpose of building the model is to permit systematic evaluation of a very wide range of policy alternatives considered at national, state, metropolitan, or local jurisdiction levels. If this is to be accomplished, there are three requisites. First, the model must endogenously generate those variables that enter evaluative (social welfare) functions. In this model we consider income, income distribution, availability of public services to particular population groups, and residential segregation of racial and income groups to be such variables. Second, the model must be designed so that policy alternatives can be modelled by varying the levels of particular exogenous variables. Finally, the model structure and parameter estimates must provide a model with a high degree of predictive power if the enterprise is to be of any value for policy evaluation. This paper contains a general guide to our thinking about how to construct and implement such a model. Many crucial questions of specification remain unresolved. To date we have collected most of the data that will be needed for preliminary versions of the model and some equations have been estimated. Undoubtedly many compromises will have to be made between our plans and what * Massachusetts Institute of Technology. This research was supported by a grant from the Ford Foundation.