The linkage between prices, wages, and labor productivity: a panel study of manufacturing industries.

• Author: Strauss, Jack

1. Introduction

   Policy makers and financial analysts cite wage pressures and productivity gains as leading factors in explaining inflation. This cost-push explanation of inflation, however, is questioned by Mehra (1991, 1993, 2000), who shows that prices explain wages, but that wages are not a causal factor in determining inflation. Studies by Hu and Trehan (1995) and Gordon (1988, 1998) report evidence indicating that wage growth has no predictive content for inflation, rejecting the cost-push view. Emery and Chang (1996) and Hess (1999) demonstrate that these findings are sensitive to the sample period examined. Ghali (1999), using Granger-causality tests, finds that wage growth does help to predict inflation, supporting the cost-push view. A related but also relatively unexplored relationship is that between real wages and productivity. Although this link is the building block of many macroeconomic models and is frequently cited in intermediate macroeconomic textbooks, few empirical works have tested this relationship. (1)

   Recently, a large number of studies have begun testing long-standing macroeconomic hypotheses using panel unit-root and/or panel cointegration tests. However, few studies have employed industry-level panel data sets. The current article contributes to filling this void. It employs manufacturing industry data to evaluate the long-run dynamics between wages, prices, and productivity rather than the traditional approach of examining macroeconomic aggregates. More specifically, using annual four-digit industry-level data from the manufacturing sector over the period 1958-1996, this article examines the relationship between prices and wage-adjusted productivity as well as the linkage between productivity and real wages, using panel unit-root and cointegration estimation methods. The increased power and precision of the panel methods are particularly valuable in this context because they allow the researcher to more accurately test for the existence of a one-for-one cointegrating equilibrium between labor market variables and industry output prices. An additional objective of this article is to show the advantages and disadvantages of employing panel unit-root and cointegration tests. We demonstrate that the considerable heterogeneity of the data imply that the practitioner must be cautious in making inferences about the linkage between variables when using either pooling estimation methods or aggregate-level data. Our methodology accommodates for heterogeneity, by averaging coefficients, as well as examines outlier effects through quartile analysis. Heterogeneity of the cointegrating vector and cross-correlations are accommodated through analysis by industry of the extent of cointegration across the panel and Monte Carlo simulations that calculate correctly sized critical values.

   Our results suggest that a stable, long-run relationship exists between prices and wage-adjusted productivity as well as between real wages and productivity for many, but not all, industries. Both relationships, however, have considerably varied estimates and in most cases differ from the one-for-one linkage found by Mehra and others in aggregate-level data. Furthermore, Granger-causality tests support one-way causation from prices to per-unit labor costs (ULC) in both the short and the long run. Hence, the industry-level data reject the standard cost-push explanation of wage pressures contributing to inflation, supporting the aggregate-level findings of Mehra (1991, 1993, 2000) and others. Our findings suggest that prices may be driven more by demand-side factors than supply-side factors. Results further support bidirectional Granger causality in the long run between real wages and labor productivity. This implies that changes in real wages lead to productivity changes and is not inconsistent with the efficiency wage hypothesis. At the same time, productivity movements affect real wages, which is consistent with efficient labor markets.

   The article is organized as follows. Section 2 provides a motivation for our study. Section 3 provides a simple theoretical foundation upon which our empirical results are based. Section 4 discusses the different panel testing methodologies employed. Section 5 discusses the data set employed and presents our empirical results. Section 6 presents a summary and conclusion.

2. Motivation

   Business periodicals such as the Wall Street Journal and Business Week regularly report per-unit labor costs and labor productivity growth, and claim that they are leading factors in explaining inflation. In the February 10, 2000, issue of the Wall Street Journal, one can find the following statement: "Economists have credited rising productivity--defined as output per hour worked--with allowing the longest economic expansion in U.S. history to continue without the kind of inflationary pressures normally associated with rapid growth" (p. C2). One implication of the expectations augmented Phillips curve is that prices are set as a mark up over productivity-adjusted wages, defined as nominal wage minus labor productivity. At the macrolevel, Mehra (1991) investigates the causal linkage between the aggregate price level and productivity-adjusted wages. He finds that inflation and the growth rate of per-unit labor costs are correlated in the long run. In addition, he also shows that inflation, surprisingly, Granger-causes growth in per-unit labor costs (ULC). Such findings are inconsistent with the price mark-up hypothesis. Using more modern cointegration and stationarity tests as well as alternative measures of the price level and different sample periods, Mehra (1993, 2000) examines the robustness of his earlier results, and demonstrates that aggregate ULC and the aggregate price level contain a common stochastic trend. He finds bidirectional Granger causality between these variables. Most recently, Mehra (2000) demonstrates that (GDP deflator) inflation always helps predict wage growth and this relationship is stable across all sample periods, but that wage growth predicts inflation only during the period 1966:I-1993:IV, a period during which inflation steadily accelerated. One objective of this article is to investigate whether the aggregate-level findings of Mehra (2000) hold up at the industry-level using panel estimation methods.

   A further contribution of this article is to highlight the advantages of panel cointegration testing in industry-level analysis while overcoming some of the problems associated with panel data testing procedures. Recent panel cointegration procedures yield considerably more precision by pooling the long-run relationships across the panel while allowing the associated short-run dynamics and fixed effects to be heterogeneous across different members of the panel. At the same time, however, panel unit-root tests suffer from five potentially severe drawbacks: (i) difficulty in interpreting the null hypothesis; (ii) the lack of formal stability tests; (iii) the possibility of incorrect standard errors occurring when mixing stationary and nonstationary data; (iv) possible heterogeneity of the first-order autoregressive coefficients; (v) contemporaneous correlations that may lead to a spurious rejection of the null. In addition to these limitations, panel cointegration tests suffer from the problem of normalization. This problem can potentially bias the cointegration estimation and testing procedures. (2)

   A key concern with panel unit-root procedures is that the alternative hypothesis states that at least one series is stationary (Im, Pesaran, and Shin 2003 [hereafter IPS 2003]) or that all the series are stationary (Levin, Lin, and Chu 2002). McKoskey and Kao (2001) and Pedroni (1995, 1999), for cointegrated series, have equivalent alternative hypotheses for the estimated residuals. In practice, the researcher does not want to conclude cointegration across the panel if it occurs infrequently. (3) To address this issue, Engle and Granger (1987) cointegration tests are conducted on our industry-level data in such a way that we can evaluate the frequency of a cointegrating equilibrium by examining the results from different quartiles. Furthermore, parameter-stability tests by industry are conducted to determine whether a structural change has occurred in the cointegrating relation. These tests also provide a robust method of testing for cointegration by industry.

   In practice, rejection of the null of nonstationary residuals does not imply that most, or even all, of the series are cointegrated. In this case, panel cointegration procedures or Granger-causality tests may yield spurious test statistics. To avoid the mixing of I(1) and I(0) series, we conduct our estimation using a subsample of our 459 industries that exhibit cointegrating relations using the univariate Engle-Granger test. We also compare the coefficients to results from the full sample to ascertain whether the estimates are robust to sample selection.

   As for the fourth potential hazard, several panel cointegration tests are adopted that allow for different restrictions on the cointegrating vectors, autoregressive coefficients, and patterns of serial correlation. Given heterogeneity among industries as well as different null/alternative hypotheses, it would not be surprising to find that different methods yield different findings. Hence, we examine the robustness of different panel unit-root and cointegration tests in the investigation of the linkage between industry output prices and ULC.

   Perhaps the most commonly cited problem with panel unit-root or cointegration procedures is that their increased power is derived from pooling or combining N independent regressions. In practice, however, regional and macroeconomic shocks across industries can lead to cross-correlated residuals, contributing to possible false inference in support of stationarity and cointegration. This article accounts for contemporaneous...