I. A Taxonomic Approach International financial intermediation can be defined to include three main types of transactions. 1) Financial transactions of a bank (a term used henceforward to include all intermediaries since banks are by far the major participants in these activities) with nonresidents denominated in the currency of the country in which the bank is resident. This item includes, for example, U.S. dollar deposits at American banks by nonresidents of the United States and U.S. dollar loans by American banks to nonresidents of the United States. 2) Financial transactions of a bank with nonresidents denominated in currencies other than the currency of the country in which the bank is resident. This category includes, for example, Eurocurrency deposits and Eurocurrency loans made by Eurobanks with nonresidents of the country in which the Eurobank is resident. 1 3) Financial transactions of a bank with residents denominated in foreign currencies. This category includes, for example, U.S. dollar deposits of Canadian residents at Canadian banks and U.S. dollar loans to Canadian residents by Canadian banks. Making use of these distinctions, one can construct a balance sheet of an individual bank or some aggregate of banks involved in international financial intermediation as follows.
Economics, whether radical or bourgeois, is concerned to a large extent with quantitative matters, and hence, it is not surprising that economic analysis turns towards statistical description and verification of abstract theorizing. This propensity towards quantitative methods is by no means a recent phenomenon. Primitive quantitative methods were evident in the tableaux of the physiocratic school and in the Malthusian population and Paretian distribution formulas. However, economists as diverse as Lawrence Klein (p. 416) and Joan Robinson (p. 76) have noted that the major impetus to the recent quantification of neoclassical economics has been economics. Unlike bourgeois economics, radical economics has not undergone anything akin to a Keynesian revolution, so that comparatively speaking, it has remained largely unquantified. The main theme of this study is that there is nothing intrinsic in radical economics which precludes quantification and, hence, econometric analysis. The radical literature can be characterized in part by its paucity of empirical analysis, and while it might be argued that this has been at the expense of a wider acceptance of radical doctrine by bourgeois economists, it will be argued here that more importantly, it has been at the expense of a more sound scientific foundation for radical analysis. I. Econometrics: Positive or Normative? In this essay will be distinguished from what has been called empiricism. Martin Bronfenbrenner (p. 12) has defined immanent empiricism as the doctrine which professes that if one looks at enough facts or cases long and hard enough, general solutions (or acceptable compromises) will become clear, less, by formal logic than by 'insight,' by 'vision,' by analogy, or sometimes by 'compulsive comparisons.' In contrast empiricism will be used here to describe the doctrine which tempers immanent empiricism with inductive reasoning based on a body of theory. Empiricism involves the concurrent development of theory and observation, and in this sense, it is an intrinsic element of scientific inquiry. As Robert Heilbroner (p. 18) has said: 'essentially the claim to being a scientific procedure rests on nothing more than a subscription to orderly repeatable methods and to the willing submission of hypothesis to empirical testing. In economics the methodology by which theory and observation are related, using appropriate methods of inference, is know as econometrics. The important distinction between the statistician and the econometrician is that the latter employs her or his statistical tools to the analysis of economic models. These economic models are the products of economic paradigms, and these paradigms serve as the bases for which endogenous versus exogenous classification and identifying restrictions are made. When the researcher replaces the statistician's hat with the econometrician's hat, then these actions involve statements conceming the economic operation of the real world. These actions serve as a source for normative inputs into econometrics. For years the debate over whether economics is a positive or a normative science has padded the publication *Associate Professor, Department of Political Economy, University of Toronto. Gratitude is owed to numerous individuals whose comments at various stages of development aided in preparation of this final draft; however, the opinions expressed here are the sole responsibility of the author.
At least since the time of Irving Fisher, it has been clear that nominal rates of interest differ from real rates not because of current or past price level changes but because of expected future price level changes. Accordingly, while nominal rates of return on fully discounted notes are observable magnitudes, expected real returns on the same notes are nonobservable. Nevertheless, real rates of return and real rates of interest are important concepts in the development of contemporary macroeconomic theory, particularly so as that theory develops richer theoretical roles for real rate and inflationary expectations measures. Furthermore, questions such as whether real rates are constant over time or are subject to systematic fluctuation through time reflect attributes of financial behavior that have important implications for understanding macroeconomic adjustment mechanisms. Even so, little attention has been given to the problem of forming and evaluating empirical measures of temporal movements in real rates of interest.' The work done by Fisher and several contemporary followers has proceeded on the premise the expected real rates of interest are constant over time. This theory has usually been examined empirically by estimating a model of the form:2
This paper presents results from an unusual microdata set assembled by the authors and researchers at the Social Security Administration. The data set pools information from three sources: death certificates for residents of Washington, D.C. dying in 1967; Washington, D.C. estate tax returns; and Social Security earnings records. Under an arrangement worked out by Smith with the city of Washington and the National Center for Health Statistics, all (about 2,500) estate tax returns for 1967 decedents were matched with their death certificates. The match provided information on age, sex, race, place of birth, marital status, cause of death and assets and liabilities. Washington, D.C. has its own estate tax, which unlike the federal estate tax, starts at a very low ($1,000) filing level. A full description of this part of the data base and an estate multiplier estimate of the distribution of wealth in Washington, D.C. has been published elsewhere (Smith). This year, thanks to our colleagues, Frederick Scheuren and Wendy Alvey of the Social Security Administration, a procedure was worked out which permitted us to turn over to them our files and to obtain from them analytical results from matched records from our files and their records of covered earnings under the Social Security Act. The intended use of this data base is to estimate a lifetime savings model with earnings as a key determinant. We still may be able to do so, but the prospects look rather grim. In the spirit that science is advanced by knowing what doesn't work as well as wuiat does, we present below a few initial findings which show some promise and of a lot of statistical husbandry which bore little fruit. We shall proceed by first looking at differences in the levels of covered income reported by black and white workers, then at the wealth levels of blacks and whites, and finally at an attempt to predict the wealth of black and white workers using demographic variables and earnings records.