Knowledge that Transforms

A nonlinear regional programming model was used to explore the impact sulfur emission taxes will have on the coal industry. Because of the proximity of eastern mines to eastern markets, the model results indicate a better competitive position for Appalachian surface and deep low-sulfur mines if the taxes are imposed. The impact of taxes is multi-dimensional because policies affect not only the area of coal production but the use of coal in other areas. The rate at which low-sulfur western coal enters the market will depend largely on the balance of transport costs against the emission taxes. Environmental benefits from improved air quality will be offset by increased surface mining in an area where land-use issues are highly controversial. However the environmental balance is resolved, coal prices will go up unless imported oil dependence is increased. 13 references. (DCK)

Barten, A. P., Composition, Prices and Expenditure Patterns, Colston Papers 16 (1964), 277-292. Forsyth, F. G., Relationship Between Family Size and Family Expenditure, Journal of the Royal Statistical Society, A, vol. 123 (2) (1960), 367-397. Klein, L. R., and H. Rubin, A Constant Utility Index of the Cost of Living, Review of Economic Studies 15 no. 1 (1947), 84-87. Lluch, C., Extended Expenditure System, European Economic Review 4 (1) (1973), 21-32. Muellbauer, John, Household Composition, Engel Curves and Welfare Comparisons Between Households, European Economic Review 5 (2) (1974), 103-122. Identification and Consumer Unit Scales, Econometrica 43 (July 1975), 807-809. Podder, N., and Nanak C. Kakwani, Distribution of Wealth in Australia, Review of Income and Wealth, series 22 (1) (1976), 75-91. Prais, S. J., and Hendrik S. Houthakker, The Analysis of Family Budgets (Cambridge: Cambridge University Press, 1955). Singh, Balvir, and A. L. Nagar, Determination of Consumer Unit Scales, Econometrica 41 (2) (1973), 347-356. Stone, R., Linear Expenditure Systems and Demand Analysis: An Application to the Pattern of British Demand, Economic Journal 64 (3) (1954), 511-527.

and are appropriate for testing the null hypothesis: ,8i = 8j (Bolch and Huang, 1974, p. 128). We have carried out this calculation for only the 90-day rates in order to save space. Tables 1 and 2 taken together make a strong case for seasonality. Not only are many coefficients in table 1 significantly different from the intercept for the 90-day rate, but also table 2 shows that they are significantly different from each other at the 0.05 level. A glance at the R2 coefficients in table 1, along with the other material presented, clearly indicates that seasonality is present, but that it is not powerful enough to be of much use in prediction.

Percival, J., and R. Teach, Spectral Analysis: An Application to the Funds Flow of a Savings Institution, State University of New York at Buffalo, School of Management, Working Paper no. 88 ( 1970). Sargent, T., Rates in the Nineteen-fifties, this REVIEW (May 1968), 164-172. Smith, V. K., and R. Marcis, A Time Series Analysis of Post-Accord Interest Rates, Journal of Finance (June 1972), 589-605.

HIS article presents some empirical evidence on the statistical significance of factors determining the pattern of price changes for Japanese manufacturing industries. We are particularly interested in the role played by market power, which is usually represented by the concentration ratio when demand and cost factors are taken into consideration, or the so-called administered prices inflation hypothesis (hereafter denoted as A.P.I.H.). The analytical framework for testing the A.P.I.H. was provided by Weiss (1966), and several studies applying his approach to different countries have been reported (Phlips, 1969; Sellekaerts and Lesage, 1973; Ripley and Segal, 1973; Dalton, 1973; Shinkai, 1974; Cagan, 1975; Lustgarten, 1975).' These prior studies, which produced mixed results for different countries, seem to indicate that pricing behavior of firms in concentrated industries is influenced by the level of capacity utilization or the business cycle phase, as emphasized by Sellekaerts and Lesage (1973). It will be one of our major findings that, in contrast to the negative results obtained by Shinkai (1974) and others (Nishikawa, 1973) for Japan in the past, highly significantly positive associations can be found between concentration and price changes if we focus on a particular phase of the business cycle. In other words, the A.P.I.H. is supported for the short-run price changes by our results, though not for the long-run. The present analysis also follows the Weiss approach, but in specifying a model we modified his formulation slightly to take account of the cross-industry aspect of the data, i.e., the fact that each industry has different production coefficients.2 The specification of our model is discussed in section II. After giving a preliminary investigation into the pattern of price changes in section III, we report the estimation results in sections IV and V. Some concluding remarks are given in section VI.

John S. Hodgson, Alexander B. Holmes, Structural Stability of International Capital Mobility: An Analysis of Short-Term U.S.-Canadian Bank Claims, The Review of Economics and Statistics, Vol. 59, No. 4 (Nov., 1977), pp. 465-473

f [HE Austrian approach to production _ theory' provides a framework for treating intertemporal production problems that is quite different from contemporary methods. In the Austrian view, time and timing were the crucial aspects of production. Recently, Hicks (1973) has proposed an approach, which he terms Neo-Austrian, integrating the general intertemporal production model with the Austrian approach of viewing production as a process taking place through time.2 Allais (1965), long a proponent of such a view, has developed a model of such a process. In this article we develop anintertemporal production model capable of being implemented econometrically, and undertake some initial testing of the model at the aggregate level on U.S. data. The rest of the paper is as follows. In section II we present an intertemporal theory of competitive production and contrast it with the contemporary approach. In section III we specify our functional forms and estimating equations. In section IV we discuss our data, estimation procedure and results.

whatever the alternative activities might be. Even more generally, protective behavior such as staying at home is widely used, carrying with it substantial opportunity costs. These findings then clearly suggest that the total social cost of crime is understated if only the direct costs of victimization and the expenditures for protection are considered. In this analysis of individual households' responses to crime it is reasonable to treat victimization rates as exogenous. But in a complete model of crime and protection it would be desirable to account for the collective effect of private protection (as well as public police) on the level of crime in the community. The net effect of private protection on the crime rate, however, is unclear. On the one hand, protective measures such as the use of alarms, locks, and watch dogs tend to reduce the crime rate by reducing the expected return to criminal activity. However, some of the measures that individual households adopt for their own protection may in fact increase the level of crime. For example, in the absence of protective behavior, individuals may routinely produce external benefits for their neighbors by keeping watch over the street or by challenging suspicious strangers in the area. If the adoption of protective measures-such as staying inside rather than going out or taking taxis rather than walking-reduces these formerly routine external benefits, there may be a net social loss resulting from private protection. In this case, policies of reducing nighttime fares on public transportation or subsidizing local neighborhood surveillance programs can be justified on efficiency grounds.7

John Whalley, A Simulation Experiment into the Numerical Properties of General Equilibrium Models of Factor Market Distortions, The Review of Economics and Statistics, Vol. 59, No. 2 (May, 1977), pp. 194-203