Proposals that have been considered to stimulate the flow of recycled materials are discussed. The thrust of proposals is that recycling rates are too low and that the Federal government should offer incentives to aid the competitive position of secondary materials sector. This paper examines principal economic arguments that have been offered in support of a Federal program of recycling incentives and analyzes some of the recent legislative proposals in light of available information on the structure of the secondary materials industry. Arguments advanced in support of recycling incentives is that tax equity should be established between recyclers and primary material producers. (Depletion deductions were supported in H.R. 148). A second argument is based upon market failure attributable to external diseconomies in primary material production (air and water pollution and disruption of scenic natural environments). Because resource recovery would lessen these environmental damages and create few new ones of its own, one may wish to subsidize the secondary materials industry. The force of this argument has been reduced by statutes such as the National Environmental Policy Act, the Federal Water Pollution Control Act, and the Clean Air Act. The existing pattern of municipal subsidization of postconsumer waste disposal constitutes amore » deterrent to recycling. A final argument is that the existing structure of Federal regulation favors primary production over secondary material recovery and should be balanced with incentives for recycling. Specifically, the evaluation of recycling subsidies proposed in H.R. 148 and H.R. 10612 is made. H.R. 10612 would grant to purchasers of recyclable materials credits against income tax liabilities. Other approaches involve loan guarantees for recycling facilities, governmental stockpiling to stabilize supply and demand for secondary materials, and the creation of futures markets for secondary materials to reduce price uncertainty. (MCW)« less

Even if there is an agreed-upon, fixed social objective function and policymakers know the timing and magnitude of the effects of their actions, discretionary policy, namely, the selection of that decision which is best, given the current situation and a correct evaluation of the end-of-period position, does not result in the social objective function being maximized. The reason for this apparent paradox is that economic planning is not a game against nature but, rather, a game against rational economic agents. We conclude that there is no way control theory can be made applicable to economic planning when expectations are rational.

Even if there is an agreed-upon, fixed social objective function and policymakers know the timing and magnitude of the effects of their actions, discretionary policy, namely, the selection of that decision which is best, given the current situation and a correct evaluation of the end-of-period position, does not result in the social objective function being maximized. The reason for this apparent paradox is that economic planning is not a game against nature but, rather, a game against rational economic agents. We conclude that there is no way control theory can be made applicable to economic planning when expectations are rational.

This paper deals with the theoretical development of some aspects of the trend removal problem. The objective is to show the difference between the two most popular trend removal methods: first differences and linear least squares regression. On the one hand, we show that if first differences are used to eliminate a linear trend, the series of residuals would be stationary but would not be white noises as they contain a first lag autocorrelation of -0.50. Furthermore, the spectral density function (SDF) of these residuals relative to that of a white noise series would be exaggerated at the high frequency portion and attenuated at the low frequency portion. On the other hand, we show that the regression residuals from the linear detrending of a random walk series would contain large positive autocorrelations in the first few lags. Relative to that of white noises, the SDF of the regression residuals would be exaggerated at the low frequency portion and attenuated at the high frequency portion.