Knowledge that Transforms

INCE World War II, many developing countries have adopted a strategy of rapid industrialization to accelerate economic development and presumably to absorb unemployed or underemployed labor from the traditional sector. In spite of these efforts, the rate of economic growth has been far from satisfactory and unemployment has been persistent. Several authors 1 point to the adoption of more capital-intensive techniques of production as being responsible for the low rate of labor absorption. The objective of the present study is to examine the capital-intensity of the manufacturing sector for a cross section of developing countries, compared with a cross section of developed countries. We suggest that the capital-intensity of developing countries is relatively too high given factor endowments and market size. The hypothesis to be examined is as follows: The capital-intensity of output in the manufacturing sectors of developing countries behaves differently from that of developed countries. In general it tends to be higher than that of developed countries given respective levels of development.

M OST interindustry studies have hitherto been made on the assumption of the stability of production coefficients between product and factors. Theoretical justification for these studies may be found in the so-called theorem, which was developed by Professors Arrow [2], Georgescu-Roegen [8], Koopmans [11], and Samuelson [18]. Some empirical economists, however, have doubts about the conclusion that all relative prices as well as production coefficients should be kept constant irrespective of changes in final demand. On the other hand, Professor Klein's substitution theorem [10] claims, on the assumption of the Cobb-Douglas production function and the marginal productivity relationships, that the ratios between the value of output and that of input, or the input coefficients in 'value' terms, should be kept constant while the input coefficients in 'quantity' terms and relative prices are flexible in response to changes in final demands. In this study, I intend to make an empirical analysis of the interdependency of prices and outputs on the basis of the latter theorem. Thus our empirical model is essentially the Walras-Hicks type general equilibrium model in which the production technique of each industry is expressed by a Cobb-Douglas function. In section I general features of this model are explained. Section II gives the estimated results of the model, based on the Japanese economy, which were obtained by using the 1960 input-output table and 19531965 time-series data for Japan. Section III is devoted to a discussion of the implications of the estimates of the excess supply functions. Here I present the estimated effects of a unit change in final demand on price, output, employment, and GNP originating in each industry. In connection with the thoretical problem, it is found that the Jacobian matrix of the estimated excess supply functions approximately realizes the gross substitutability property. Section IV tests our approach. The 1961 values of price, output, employment, and consumption in each industry, which are calculated from our estimated equations based on the 1960 input-output table, are compared with their observed values. The results indicate that our estimated model reproduces, with a tolerable degree of accuracy, the 1960-1961 fluctuation of the whole economy.

T HE inflation associated with the Vietnam War has had a strikingly differential impact on the rates of economic expansion in different regions of the country and on the distribution of income among major groups in the economy. This experience undoubtedly is of interest to economists concerned with the welfare implications of gains and losses in the relative position of principal groups of income recipients. In addition, these differential effects have enlivened the debate over the appropriate stance of public policies with respect to inflation. Consequently, a comprehensive quantitative examination of the impact of inflation on income distribution during the last decade appears to be a worthwhile undertaking. This paper presents the results of such an inquiry. In section II, the origins of the current inflation (in the Vietnam War) and its subsequent progression are traced. The regional impact of inflation is examined in section III. Broad trends in the distribution of personal income and the main factors affecting this distribution are analyzed in sections IV and V, respectively. The experiences of particular groups of income recipients are assessed in sections VI through IX. Finally, some implications of the findings for public policy are discussed in section X. II The Progression of Inflation

Balvir Singh, Helmar Drost, An Alternative Econometric Approach to the Permanent Income Hypothesis: An International Comparison, The Review of Economics and Statistics, Vol. 53, No. 4 (Nov., 1971), pp. 326-334

W HILE there are many general equilibrium models of trade and protection in the literature on economic theory, most empirical analyses of protection use partial equilibrium models. However, partial equilibrium analysis is useful only if the general equilibrium effects can be ignored safely. It is the contention of this paper that the production effects of protection should be analysed in a dynamic general equilibrium framework with investment and the foreign exchange rate as endogenous variables.' In section II, a dynamic general equilibrium model of trade and protection for the systematic analysis of alternative trade policies in a market economy, and the important tactical decisions made for empirical implementation using Australian data are described. The estimates of effective protection obtained from the model are presented in section IIL'2 The differences between the partial and general equilibrium approach are discussed in section IV and the conclusions summarized in section V.

EMPIRICAL research in economics has seen the development in recent years of large simultaneous equation econometric models -large both in terms of detail and degree of disaggregation but also in their demands upon a limited of data. In the main these models have been models of macro-economic activity estimated from annual or quarterly data in the postwar period. Statistical methods for estimating simultaneous equation models were first developed by the researchers at Cowles Commission [8]. In recent years, the sheer size of such empirical models has brought a new problem to the fore, as the estimation methods previously developed cannot be used without modification. Most of those estimators -those of the kclass and three-stage-least-squares involve a first of regression or estimation using the predetermined variables of the model as regressors.1 But frequently in large models the is smaller than the number of predetermined variables, so that a meaningful first-stage regression is not possible.2 This is a sample problem of a different sort instead of needing more observations in order that the distribution of the estimates will be satisfactorily approximated by their asymptotic distributions, the is small relative to the size of the large model, to the extent that the standard simultaneous equation estimators either do not exist or are identical to ordinary least squares.3 A variety of solutions have been proposed to cope with the large-model problem, two of which are examined in detail in this paper. Each can be viewed as a modification of twostage-least-squares: 1) 2SPC (Two Stage Principal Components), originally proposed by Kloek and Mennes [11], in which a limited number of principal components of the predetermined variables are used in the first stage. 2) SOIV (Structurally Ordered Instrumental Variables), proposed by Fisher [5, 6], in which a limited number of predetermined variables are selected for the first stage by detailed use of the structure of the model. A complete assessment of the properties of these estimators in a large econometric model requires the knowledge of their small-sample distributions. These are in general unknown, although some progress has recently been made for small models by Amemiya [1], Basmann [2], Kadane [9], Mariano [12], Sawa [15], and Takeuchi [17]. Some information might be obtained by Monte Carlo techniques, except that the computational cost of systematically exploring the parameter space of a large model would be prohibitive. Still, a feasible project would be to employ a miniature model with a very few equations, but having more predetermined variables than observations. It is not clear, however, that the distributions would * This work was supported in part by National Science Foundation Grant GS-2635 and by the Brookings Institution. The initial research was undertaken during the tenure of fellowships from the Danforth Foundation and the National Science Foundation. Computations were done at the Massachusetts Institute of Technology and Stanford University computation centers. I am happy to acknowledge the numerous helpful suggestions of T. Amemiya, T. W. Anderson, P. J. Dhrymes, E. Kuh, F. M. Fisher and a referee. Much of the data was made available by G. Fromm. 1 The limited-information-maximum-likelihood method requires extraction of a characteristic root from a matrix of moments of the predetermined variables; the method can be interpreted as a first-stage regression of a synthetic endogenous variable on the predetermined variables. 2 Full information maximum likelihood estimators fail to exist when the number of parameters to be estimated in the model exceeds the size, another problem which occurs in large models. Because of computational complexity, FIML has not been a feasible estimator for models of even moderate size. 'In other cases the problem may occur in a less acute form -there may be more observations than predetermined variables, but the excess may be small, and in some sense better estimates may be obtained by using fewer variables in the first stage.

T HE equation developed by the Research Department of the Federal Reserve Bank of St. Louis to explain changes in GNP ' has had a considerable impact on the thinking of monetary economists. It has, in particular, reinforced the position of those who contend that monetary policy has a powerful impact on GNP. On the other hand, serious doubts arise according to the basic St. Louis equation as to the efficacy of the fiscal impact on GNP. There have been a, number of comments on the St. Louis equation which presented alternative specifications of the basic reduced-form equa,tion for GNP.2 In general, these formulations have revealed a more powerful role for fiscal policy while the strength of monetary policy remained unaltered. Most of these reformulations of the St. Louis equation have specified different monetary and/or fiscal variables than those used by St. Louis. This has given rise to disputes between researchers as to the appropriate monetary and fiscal variables 3 to use in these equations. In this paper the results of various experiments with the basic St. Louis equation using the St. Louis variables are presented. In other words, the question asked here is: given the use of the same exogenous policy variables as St. Louis are there any changes in the basic regressions which would alter the conclusion regarding the efficacy of monetary and fiscal policy? The results reported below suggest that alternative specifications of the basic St. Louis equation do produce significantly different implications for the efficacy of fiscal policy. In particular, a straightforward division of the sample period produces Republican and Democratic St. Louis equations with the associated monetary and fiscal multipliers.

T DEALLY, to determine the extent to which persons of a particular income group spend an increment to income, time series data on consumption and disposable income for individual households (panel data) are needed. Unfortunately, such data are not available. Available are time series aggregate data, which do not allow one to determine differential marginal propensities to consume for different income groups, and cross section data which do not allow one to trace over time the effects of changes in income on consumption. However, by utilizing both time series and cross section data, the hypothesis that the marginal propensity to consume decreases as income increases can be tested.