Previous models of public spending fail to explain why high inflation rates are distributed non-randomly across countries. In the switching-regime specification proposed here, governments tend to resort to inflationary debt monetarization when the spending level chosen by voters exceeds actual revenue capacity. The voter-choice and fiscal-capacity models of earlier studies are special cases of this framework. Using cross country data for 1970, 1975 and 1980, the authors find that the voter-choice specification applies to most industrialized countries, whereas fiscal capacity appears to constrain government spending in most developing countries. High inflation appears to result from shocks that alter a country's fiscal capacity relative to voters' expectations.

T EMPORAL aggregation problems in econometrics pose an important but relatively unexplored set of issues relevant for analyses of economic behavior and policy problems. When the behavior of individuals, firms or other economic entities is analyzed with temporally aggregated data, it is quite possible that a distorted view of parameters' values, lag structures and other aspects of economic behavior can be obtained. Since policy decisions usually depend critically on views regarding parameter values, lag structures, etc., decisions based on results marred by temporal aggregation effects can produce poor results. Further, as emphasized by Orcutt and others, aggregating data temporally or otherwise usually involves a loss of information. In the context of temporal aggregation, aggregation can lead to (a) lower precision of estimation and prediction, (b) lower power for tests, (c) inability to make short-run forecasts and (d) a reduction of the probability of discovering new hypo-theses about short-run behavior from data. It is generally appreciated that when annual data are employed in analyses, it is difficult to obtain satisfactory results pertaining to the intra-year behavior of economic units, for example seasonal effects that are often important in analyzing the variations of such variables as inventories, agricultural prices, agricultural output, etc. Previous work concerned with the theoretical analysis of the effects of temporal aggregation on estimation include Mundlak's [6] and Engle's [3] analyses of distributed lag schemes, Telser's [8] treatment of autoregressive processes and Zellner's results for stock adjustment models [11, 12]. In all these papers, it is shown that when econometric models are implemented with temporally aggregated data for flow variables or stock data pertaining to periods longer than that considered appropriate on a priori grounds, the results of analyses will usually be marred by temporal aggregation effects. Further, empirical analyses of several single equation models using temporally aggregated and disaggregated data have been reported which reveal sensitivity of inferences about lag structures to the level of data aggregation (see, e.g., Bryan [1], Laub [5] and Ranson [7]). While much previous work has concentrated attention on the adverse effects of temporal aggregation, there has not been much attention devoted to the problem of what can be done in analyses when we have to work with temporally aggregated data, perhaps because these are the only data available. The approach to be taken in this paper, also utilized in Zellner [11], is to formulate an economic relation in terms of the time unit, say a week or a month, thought to be appropriate on economic grounds and then to derive logically the implications of the model for explaining the variation of temporally aggregated data. With the implied model for the aggregated data explicitly set forth, the problem of using the aggregated data to make statistical inferences can then be approached. Below we present applications of this approach and make several theoretical and empirical comparisons of results obtained with aggregated data with those obtained from analyses based on disaggregated data. The plan of the paper is as follows: In section II we specify a simple model, derive the implied model, and examine its properties. Then inference procedures for the monthly and quarterly versions of the model are compared and some generalizations of the analysis are indicated. In section III numerical results pertaining to a moneymultiplier model are presented. Finally, in

We develop an econometric model where the determinants of working while in school, academic performance, and the decision to drop out are set in the context of two types of high school students: those who prefer schooling and those who are more likely to join the labor market. The likelihood function of this model with heterogeneous preferences for schooling is composed of 48 individual contributions of a standard quadrivariate normal function. Exploiting a unique Canadian microdata set of high school students and school dropouts, we show that being a female student, attending a private school, and living with educated parents are linked to having a strong preference for schooling over the labor market. We also find that working fewer than fifteen hours per week while in school is not necessarily detrimental to success in school. Our results indicate that the decision to drop out is affected by the legal age to access the labor market, high minimum wages, and low unemployment rates. Several policies that aim at reducing the number of high school dropouts are identified.

This paper outlines a framework for the estimation of the opportunity cost of workers who are permanently displaced from their previous place of employment. It will also enable us to evaluate both the private and social costs of adjustment arising from labor displacement. This framework will then be applied to an empirical estimation of both the private and social opportunity cost of labor and the adjustment costs associated with workers laid off by the aircraft industry in Canada. (This abstract was borrowed from another version of this item.) (This abstract was borrowed from another version of this item.)