IN recent years several research studies have used a data base consisting of a time series of cross-section samples. A primary reason is that panel data of this type are potentially richer in information than a single cross-section sample. To date, however, the question of how to best analyze data bases of this type has not been fully explored in any of the research. To illustrate, a number of prior research projects which have utilized this type of data base are briefly reviewed. Hoch (1962) used moving cross-section samples as the data base for estimating the parameters of a CobbDouglas production function by analysis of covariance. Specifically the data were collected on 63 Minnesota farms for the years 1946 to 1951. Hoch reported an observed difference between the least squares parameter estimates and covariance estimates and the elasticities developed from these estimates. In terms of method, the major conclusion was that the covariance model might produce less biased elasticities and marginal return estimates. Massy and Frank (1965) investigated the relationship between price changes and dealing activities on a -firm's market share for frequently purchased household and food products. Panel data covering a 101-week time period of family purchase history provided the data base for the study. However, the data were aggregated so no methodological insight could be inferred concerning the question of analyzing time series of cross-section data. Laughhunn and Lyon (1971) applied Bayesian regression in analyzing a time series of cross-section cigarette consumption data using the Tiao and Zellner (1964) approximation method. The primary methodological issue in this research was to observe differences that might exist between classical pooling and the Bayesian regression technique. Comparison of the two techniques revealed very little difference between either parameter estimates or standard errors. Schipper (1964) used covariance regression to analyze a series of cross-section samples (19541957) collected by the Survey Research Center, University of Michigan. The central focus of this study was to analyze consumer discretionary behavior particularly with respect to durable expenditures, short term debt, and discretionary saving. The major methodological finding was that several differences between the covariance regression model and individual cross-section regressions existed. Schipper suggested the individual cross-section analyses might be biased but could not prove this point since he did not use experimental data. Palda and Blair (1970) conducted an analysis of toothpaste demand by using multiple cross sections of data collected by MRCA during the period 1958-1962. One focus of their research was to investigate the potential cross-section specification bias, based on the rationale presented by Simon and Aigner (1970), that can exist because of omitted variables. An interpretation of the results led them to think that the covariance model may reduce the specification bias. This interpretation cannot be considered conclusive since the analysis was not conducted in an experimental framework. Since there is an interest on the part of economic and business researchers to use multiple cross-section sample data, this would appear to be a sufficient reason for evaluating the different methods available for combining and analyzing the samples. Earlier work in this area includes studies by Nerlove (1967, 1968). He assumed models of the form Yit = aYit-l + Uit and Yit = aYit-l + 1-Xit + Uit respectively with Uit = yi + Vit with yi and Vit uncorrelated where =o-2 = 2 +or2. The estimation methods used were OLS, generalized least squares utilizing known p (p = o-A2/o-X2) analysis-of-covariance estimates with cross-sectional effects only, two-round estimates based on an estimated value of p, and maximum likelihood estimates. Generally, Nerlove's findings indicated that generalized least squares (if p is known) produces good esti-

T HE micro-economic foundation for most analyses of price and wage decisions is based on the optimizing behavior of familiar objective functions; employers maximizing profit and workers optimizing utility. The functions typically contain variables that measure opportunity cost (the unemployment rate), real income, and price-deflated wages. But neither objective function contains any variable that measures the situation of other actors in the economic system. Behavior in the real world, however, frequently depends on relative wages, profit margins, and so forth. There is a body of literature on the relationship between relative wages and the macro-economic variables of inflation and unemployment. Wachter (1970), finds that low wage workers tend to get relatively larger wage increases in periods of low unemployment, and thus, the interindustry spread among manufacturing wage-rates diminishes with low unemployment and increases with inflation. Wachter deals with the influence of the macro-economic variables on the relative wage structure. number of studies (1962, 1967, 1968, 1969) have been directed to the effect of relative wages on the general wage or price level.1 It is the latter subject that is the primary focus of this paper. Our micro-economic hypothesis is that wage and price behavior is influenced by both general and relative factors. Therefore, we added a measure of the relative wage structure to the typical Phillips curve relationship. This formulation will reflect the idea that under a given set of overall demand conditions, an individual will attempt to obtain a larger wage increase if he feels relatively underpaid. Our second hypothesis is that rapid inflation is unanticipated and leads to the distortions in the relative wage structure. These two hypotheses lead to the following dynamics. given Phillips curve exists at any point in time. If the economy operates at a point of low unemployment and high inflation rates, then distortions are created which move the Phillips curve to the northeast, i.e., the tradeoff is worsened. If the economy operates at a point of high unemployment and low inflation rates then the distortions tend to diminish and the trade-off improves.2 Thus, while a Phillips curve exists, there is only one point on it (i.e., one unemployment and inflation rate) that is stable; moreover, the stable point is determined by the previous historical experience which has created the distortions in the economy. This series of stable points defines a long-run trade-off that is steeper than the shortrun curve but is still less steep than the vertical line hypothesized by the accelerationists. These dynamics suggest that the absence of unanticipated inflation in the early 1960's brought about the stable wage structure of the 1963-1965 period and, with it, the favorable trade-off. The unanticipated inflation of the late sixties, however, again distorted relative wages and produced the worsened trade-off of 1969-1971. The short-term relationships will then take the general form: DP = f (1/U, DST) T ( ) Received for publication May 12, 1972. Revision accepted for publication August 17, 1972. * This work was performed as part of the CED project entitled A Reconsideration of Policies for Economic Stabilization. The authors thank Frank Schiff for his suggestions in the initiation of this work and George Perry, Charles Schultze, Arthur Okun, William Branson, Gary Fromm and the referee for their comments on preliminary drafts. The errors and omissions remain the responsibility of the authors. The views expressed are our own and not necessarily those of the officers, trustees or other members of the Committee for Economic Development. ' See Eckstein and Wilson (1962), Perry (1967, 1968) and Throop (1968). 2 One of the factors that gives an inflationary bias to our economy is the asymmetrical nature of the distortion process; that is, larger than average wage increases cause the distortion and the return to the normal structure is also achieved by larger than average increases.

Many papers have explored the relationship between average tariff rates and economic growth when theory suggests that the structure of protection is what should matter. We therefore explore the relationship between economic growth and agricultural tariffs, industrial tariffs, and revenue tariffs for a sample of relatively well-developed countries between 1875 and 1913. Industrial tariffs were positively correlated with growth, and agricultural tariffs were generally negatively correlated with growth, although the results are not robust. Revenue tariffs were not related to growth at all.

This paper considers the impact of the minimum wage on both labor and product markets using detailed store-level scanner data. I provide empirical evidence that a 10% increase in the minimum wage raises grocery store prices by 0.6% to 0.8% and suggest that the minimum wage not only raises labor costs but also affects product demand, especially in poorer regions. This points to novel channels of heterogeneity in pass-through that have distributional consequences, with key implications for real wage inequality. I also find that price rigidity within retail chains ameliorates these effects, reducing the pass-through elasticity for retail prices by about 60%.

This is the first study that decomposes unemployment into its structural and cyclical components and investigates their impact on income distribution, controlling for the influence of inflation. Increases in structural unemployment have a substantial aggravating impact on income inequality. Inflation has a progressive impact, which is due to the unexpected component. The study demonstrates that previous work failed to take into account the stochastic trend behavior of the variables. Consequently, specifications used by previous research cannot predict the behavior of income shares after 1983, whereas the specification used by this paper generates accurate forecasts. The results also indicate that a sustained GNP growth is not necessarily associated with an improvement in income inequality, because sustained GNP growth can coexist with increased structural unemployment.