This paper examines the effect of drug prohibition on the black market prices of cocaine and heroin. The paper examines the ratio of retail to farmgate price for cocaine, heroin, and several legal goods, and it compares legal versus black market prices for cocaine and heroin. The results suggest that cocaine and heroin are substantially more expensive than they would be in a legalized market, but to a lesser degree than suggested in previous research.

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at

This paper examines a new possible explanation for the recent rejections of the life cycle-permanent income model of consumption: the treatment of seasonal fluctuations. The paper shows that when the seasonal fluctuations in consumption purchases are included in an analysis of the life cycle-permanent income model, there is no evidence in the aggregate data against the model. The estimates of the parameters of agents' utility functions obtained with seasonally unadjusted data are plausible, and the unadjusted data do not reject the overidentifying restrictions on the model.

tag=1 data=Alcohol consumption during prohibition. by Jeffrey A. Miron and Jeffrey Zwiebel tag=2 data=Miron, Jeffrey A.%Zwiebel, Jeffrey tag=3 data=The American Economic Review, tag=4 data=81 tag=5 data=2 tag=6 data=May 1991 tag=7 data=242-247. tag=8 data=ALCOHOL%DRUGS tag=10 data=The burgeoning debate over drug legalisation in the United States has drawn renewed attention to the nation's experience with Prohibition. tag=11 data=1991/3/9 tag=12 data=91/0780 tag=13 data=CAB

A great deal of research on the empirical behavior of inventories examines some variant of the production smoothing model of finished goods inventories. The overall assessment of this model that exists in the literature is quite negative: there is little evidence that manufacturers hold inventories of finished goods in order to smooth production patterns. This paper examines whether this negative assessment of the model is due to one or both of two features: costs shocks and seasonal fluctuations. The reason for considering costs shocks is that if firms are buffeted more by cost shocks than demand shocks, production should optimally be more variable than sales. The reasons for considering seasonal fluctuations are that seasonal fluctuations account for a major portion of the variance in production and sales, that seasonal fluctuations are precisely the kinds of fluctuations that producers should most easily smooth, and that seasonally adjusted data is likely to produce spurious rejections of the production smoothing model even when it is correct. We integrate cost shocks and seasonal fluctuations into the analysis of the production smoothing model in three steps. First, we present a general production smoothing model of inventory investment that is consistent with both seasonal and non-seasonal fluctuations in production, sales, and inventories. The model allows for both observable and unobservable changes in marginal costs. Second, we estimate this model using both seasonally adjusted and seasonally unadjusted data plus seasonal dummies. The goal here is to determine whether the incorrect use of seasonally adjusted data has been responsible for the rejections of the production smoothing model reported in previous studies. The third part of our approach is to explicitly examine the seasonal movements in the data. We test whether the residual from an Euler equation is uncorrelated with the seasonal component of contemporaneous sales. Even if unobservable seasonal cost shocks make the seasonal variation in output greater than that in sales, the timing of the resulting seasonal movements in output should not necessarily match that of sales. The results of our empirical work provide a strong negative report on the production smoothing model, even when it includes cost shocks and seasonal fluctuations. At both seasonal and non-seasonal frequencies, there appears to be little evidence that firms hold inventories in order to smooth production. A striking piece of evidence is that in most industries the seasonal in production closely matches the seasonal in shipments, even after accounting for the movements in interest rates, input prices, and the weather.

Jeffrey A. Miron, J. Joseph Beaulieu, What Have Macroeconomists Learned about Business Cycles from the Study of Seasonal Cycles?, The Review of Economics and Statistics, Vol. 78, No. 1 (Feb., 1996), pp. 54-66

Almost all recent research on macroeconomic fluctuations has worked with seasonally adjusted or annual data. This paper takes a different approach by treating seasonal fluctuations as worthy of study in their own right. Our results show that seasonal fluctuations are an important source of variation in all macroeconomic quantity variables but are small or entirely absent in both real and nominal price variables. The timing of the seasonal fluctuations consists of increases in the second and fourth quarters, a large decrease in the first quarter, and a mild decrease in the third quarter. The paper demonstrates that, with respect to each of several major stylized facts about business cycles, the seasonal cycle displays the same characteristics as the business cycle, in some cases even more dramatically than the business cycle. That is, we find that at seasonal frequencies as well as at business cycle frequencies, output movements across broadly defined sectors move together, the timing of production and the timing of sales coincide closely, labor productivity is procyclical, nominal money and real output are highly correlated, and prices vary less than quantities. There is a "seasonal business cycle" in the U.S. economy, and its characteristics closely mirror those of the conventional business cycle.

There is a tendency to think of official government statistics as unambiguous measures of economic activity. In truth, however, nearly all of the available series are based heavily on assumptions, and are sensitive to the estimation techniques used. Furthermore, many of these assumptions and estimation techniques have been refined and improved over time. Whether these underlying assumptions are reasonable and whether the refinement of assumptions over time has been useful depends crucially on the questions one is trying to answer. For example, an estimate of consumption derived from data on retail sales may be perfectly adequate for planning future production, or setting government budgets, but may be disastrous for testing a subtle economic theory. Similarly, gathering more genuine consumption data might improve our current estimates of consumption, but a series that reflects retail sales for one era and genuine consumption for another could wreak havoc when used in estimating a time-series rela