Knowledge that Transforms

1. THE theory of public goods 1 is sometimes confused with the theory of joint production. This is in the nature of a pun, or a play on words: for, as I have insisted elsewhere, as we increase the number of persons on both sides of the market in the case of mutton and wool, we converge in the usual fashion to the conditions of perfect competition. But when we increase the number of persons in the case of a typical public good, we make the problem more indeterminate rather than less. To elucidate the difference, I shall fill in what appears to be a minor gap in the literature, namely a needed statement in terms of modern welfare economics of the various optimality conditions as they appear in the case of joint products. The analysis is straightforward; and after it is before us, we can clearly see the difference between it and the well-known optimality conditions for the case of public goods. 2. I begin with an examination question given recently at the Massachusetts Institute of Technology: Corn is produced by land and labor; and so are wool-bearing mutton-bearing sheep. Assume the totals of available land and labor to be fixed. Write down the various welfare optimality conditions in the case where all people happen always to consume wool and mutton in the same proportions that sheep bear these products. And then, by contrast, write down the conditions that would have to prevail if individuals' indifference contours for wool, mutton, and corn involve the usual variability of proportions. This proved a difficult question for first-year graduate students in economic theory. Still many perceived that in the first case they could essentially work with two rather than three goods, substituting sheep as a kind of composite good for wool and mutton, and thereby ending up with the standard welfare conditions for two ordinary (private) goods, corn and sheep.

IN a previous article with Professor Harold Watts, the authors demonstrated empirically the loss of information in the parameter estimators when data are aggregated prior to computing least-squares regressions [3]. These results came from simulations with a simple economic model containing identical microcomponents. Specifically, in addition to the error term, each component spent 0.9 of its previous income and 0.2 of its cash balance. The main point of our previous paper was that estimation prior to yielded substantially greater precision in the estimates of the parameters and their standard errors than did estimation of the same parameters after aggregation. The implications of this for hypothesis testing and the development of satisfactory policy response models seemed obvious. On the basis of a variety of evidence, including the paper with Watts and a paper by Orcutt [4], the case for seeking and frequently using disaggregated data seemed strong but one nagging concern remained. Suppose, as seems likely, the microcomponents exhibit different behaviors. In this case it might not be sensible to pool the data and treat it as a single sample from a single universe. However, if estimators from each micro equation are computed separately, would it still be desirable to use disaggregated data instead of data aggregated over all components? This turned out to be the case with identical components but would it be with nonidentical components in which something more than constant terms were different? This paper copes directly with this issue, and we demonstrate the importance of using disaggregated data even when microcomponents exhibit different behaviors. We do not deal with cases where microcomponents have nonlinear relations, but the need for disaggregated data in such cases seems fairly obvious without Monte Carlo experiments. If we wish to compare the accuracy of estimation at different levels of aggregation, we need a measure of merit different from the extent of bias and variance of parameter estimators, which we used in our previous study, because in an aggregate model whose components have different behaviors, the expected values of the estimators may be meaningless or nonstationary [Zellner, pp. 3-5]. Therefore, we use the accuracy of the out-of-sample forecasts to measure the merit of the estimated equations. In particular, we forecast the aggregate expenditure for the eight time periods following the last sample period. The rootmean-square forecast errors from models based on data at different levels of provide the yardstick for comparisons. Our results suggest that models estimated from micro data will give generally superior out-of-sample forecasts. This finding is at variance with the belief that one reaps an aggregation by aggregating the micro data prior to estimation. The concept of a possible gain was formalized in a 1960 article in this Review by Grunfeld and Griliches:

SOME of the highest profit rates appear in industries that advertise heavily. These high earnings have been attributed to barriers to entry associated with product differentiation [2, 6]. A possible alternative explanation is that the treatment of long-lived advertising as current expenses leads firms that invest heavily in such intangibles to overstate their rates of return since their equity is understated [1, p. 153; 15, p. 167]. The same practice may result in the understatement of their dollar profits so that they pay less tax than other firms whose investments are all tangible. The purpose of this paper is to work out more precisely the overor under-statement of profit and rate of return involved in the expensing of advertising and to evaluate the mis-statement in practice.' Part I develops conceptually the conditions under which overor under-statements can be expected. Part II recomputes dollar profits and rates of return for a variety of industries, estimates the tax avoidance that results, and examines the relationship between recomputed profit rates and advertising. Part III contains a proposal for policy change.

ECONOMISTS have shown considerable interest in the relationship between productmarket competition and wage rates. Most of the analysis has centered on manufacturing, where the less competitive industries often have larger firms and larger plants. This paper presents evidence that differences in plant size are at least as important as differences in market structure when we try to account for wage differentials among manufacturing industries.

A NUMBER of studies have yielded evidence of significant association between certain characteristics of industry structuresuch as high concentration and substantial entry barriers and variations in industry performance, particularly with respect to profitability.* In general, these studies tend to confirm the expectation that, other things being equal, profits will tend to be higher in industries in which structural conditions depart substantially from those of the competitive model. However, as Stigler [16, p. 145] has noted, the statistical associations found are usually weak, and a substantial amount of performance diversity is left unexplained. Thus, the typical strength and character of the structure-performance associations, and the importance of individual structural factors in the overall pattern, have remained open to question. Many hypotheses have been suggested; only a few are subject to serious empirical investigation; fewer still have actually been examined. This paper presents a summary report on our efforts to test a small number of fairly straightforward structure-performance hypotheses against the most comprehensive collection of relevant data available, the concentration statistics for 1958 and 1963 [18, 19]. These tests have focused on a single performance measure, the percentage price-cost margin, which we take as an indicator of the ability of firms in an industry to obtain prices in excess of direct costs. We have found a significant association between the price-cost margin and the level of four-firm concentration among fourdigit SIC (Standard International Trade Classification) industries; and this association is not eliminated when differences in capital-intensity among industries are taken into account. We have further found: (1) a tendency for the strength of the concentration-margins association to increase over the period 19581963, particularly in industries in which the level of four-firm concentration was stable or increasing; (2) a substantially stronger association between concentration and margins in consumer goods industries, as compared to producer goods industries; and (3) evidence that the principal component of the concentration-margins association in consumer goods industries is a correlation between concentration and margins of the four largest firms alone, in those industries in which these firms have higher margins than their smaller rivals. The first section of this paper establishes the background and framework of our analysis, and the following sections present the evidence of these findings in some detail.