Knowledge that Transforms

This paper examines the roles played by innovations identified from a simple four-variable VAR characterized by cointegration. Using knowledge of cointegration rank and “textbook” relations that link macroeconomic aggregates, we identify distinct “real” and “nominal” innovations that dictate the long-run behavior of the model. We also examine the explanatory power of transitory innovations that are orthogonal to these permanent shocks. One of the permanent shocks displays all the characteristics of a technology or “supply” innovation, while one of the transitory innovations—identified by imposing short-run price rigid-ity—is interpretable as a “demand” side impulse. The permanent nominal shock bears the imprint of an innovation in aggregate inflation expectations. Historical decomposition and comparison with variables that are external to the model reveals the relative importance of the shocks at various episodes.

In this paper, we introduce three flexible consumer demand systems in which expenditures on goods are quadratic functions of income. We view these alternatives as to the demand systems used heretofore in the empirical modeling of rank-three demands, namely those in which expenditure shares are quadratic functions of the logarithm of income. Curvature conditions required by theory can be imposed locally during the estimation for each, and a semiflexible version can be estimated. For illustrative purposes, we estimate various forms of two of the systems using Canadian data on seven categories of goods for the period 1947 to 1995.

Using Canadian data on large, private-sector contract negotiations from January, 1967, to March, 1993, we find that strikes and wages are substantially influenced by labor policy. The data indicate that conciliation policies have largely been ineffective in reducing strike costs. In contrast, general contract reopener provisions appear to make both unions and employers better off by reducing negotiation costs without systematically affecting wage settlements. Legislation banning the use of replacement workers appears to lead to significantly higher negotiation costs and redistribution of quasi-rents from employers to unions.

This paper utilizes recent simulation techniques in a two-stage estimation method which is applicable for a wide range of statistical models in the presence of missing data. The first stage of the method provides a way to estimate (and simulate from) the joint distribution of missing variables when the missing variables are continuous, binary, or ordered discrete. The second stage uses the first-stage estimates to “integrate” out the effects of the missing variables and obtain model estimates. The implementation of the method in this paper allows theoretically important, partially missing wage and school characteristic variables-which are not necessarily independently determined-to be included in a proportional hazard model of teacher attrition.

We use a new independent survey of 4000 Russian households (the Russian Longitudinal Monitoring Survey or RLMS) to study their saving behavior. The RLMS household saving rate (12%) is less than half the official figure (29%). Despite the massive changes of the transition, the Russian household saving rate of 1994 cannot be shown to be different from that of 1976. The patterns of Russian household saving differ from international experience: Its paradoxical U-shaped saving-age relationship may be explained by the dramatic deterioration of life expectancies of middle-aged Russians.

The detection of nonlinearities could depend on the sampling frequency. Asymmetric monthly series may become symmetric when aggregated to quarterly or annual frequencies. We test against nonlinearity using the nonlinear autoregressive asymmetric moving average (ARasMA) model, which nests the linear ARMA model as a special case. Using monthly, quarterly, and annual Swedish unemployment series, we find support for symmetry/linearity in the annual series but not in the monthly and quarterly series.

This paper estimates economic models of the determinants of state benefit levels in the Aid to Families with Dependent Children (AFDC) program using 1969–1992 data. These models have been extensively researched; however, the existing literature has produced an unacceptably wide range of estimates. Using alternative econometric procedures, this paper systematically examines both the specification assumptions underlying previous analyses as well as several additional specification issues. It is, therefore, able to replicate and reconcile estimates from previous studies and to provide updated, consensus estimates of the demand for welfare generosity. It finds that changes in the average level of income within states have small but statistically significant positive effects on benefits with the confidence bounds on the elasticity extending from 0.11 to 0.82. Changes in the effective price of redistribution are found to have, at most, weak negative effects with elasticities in the range of -0.14 to 0.02. These results are used to evaluate the effects of block grant provisions in the recently enacted welfare reform legislation.

This paper explores the effect of research and development (R&D) and capital on factor intensity and skill bias in a sample of manufacturing plants. Firm and industry R&D as well as plant level capital increase the factor intensity of labor over materials. In contrast, skill bias originates in portions of capital and R&D. Equipment capital and firm R&D in the same product as a plant are consistently skill biased, while structures are biased against skill. Furthermore, general firm and industry R&D increase investment in equipment but not structures. This shows that the skill bias of R&D occurs through two distinct channels. First, firm R&D specific to the product increases the relative demand for skilled labor directly and in the short run through the cost function. Second, general firm and industry R&D exert an additional skill bias by favoring equipment over structures in the long run, demonstrating the broader compass of the skill bias of R&D over time.

Using data from professional baseball, basketball, and hockey, we estimate the parameters of a sequential game model of best-of-n championship series controlling for measured and unmeasured differences in team strength and bootstrapping the maximum-likelihood estimates to improve their small sample properties. We find negligible strategic effects in all three sports: teams play as well as possible in each game regardless of the game's importance in the series. We also estimate negligible unobserved heterogeneity after controlling for regular season records and past appearance in the championship series: Teams are estimated to be exactly as strong as they appear on paper.

Ameasure of the relevancy of instruments used to estimate the coefficients of a linear multiple regression model is discussed. A method for computing the measure using only standard results from ordinary least squares and two-stage least squares estimation is described. The method is illustrated using an empirical example.