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The author is a Professor of Economics at the University of Wisconsin-Milwaukee. She completed this paper while a Visiting Scholar at the IMF Institute. She would like to thank Samir E1-Khouri, Robert Flood, Mohsin Khan, and Peter Montiel for a number of helpful comments. She is also grateful to Asmahan Bedri, Simone Randrianarivelo and Christian Wabritz for their assistance.
Examples are Kormendi and Meguire (1984); Ahmed (1987); Bils (1987); Ball, Mankiw, and Romer (1988); Kandil (1991a), (1991b); Gray, Kandil, and Spencer (1992); Kandil and Woods (1995), (1997); and Kandil (1995a), (1995b), (1996), (1997), (1999), and (2000).
The theoretical model assumes complementary labor and energy in the production process.
The theoretical model does not incorporate an explicit indexing parameter. The implications of this modification remain consistent concerning the effects of demand uncertainty in determining wage rigidity and subsequent effects. Demand uncertainty increases the optimal degree of wage indexation, increasing nominal wage flexibility while moderating fluctuations in the real wage (see, e.g., Gray (1978)).
This sample comprises all industrial countries according to the United Nations’ classification, except for Iceland, which is excluded due to data limitation.
Quarterly data are not available for all series in various countries. Description and sources of data are described in Appendix 2.
The empirical literature on the cyclical behavior of wages includes aggregate and disaggregated studies. Examples of aggregate studies are Wachter (1976), Sachs (1980), and Allen (1992). Disaggregated studies include Chirinko (1980) and Kim (1989). Some of the ad hoc measures of wage flexibility, e.g., unionization rates, are not available for all countries under investigation.
Nominal GNP or GDP approximates aggregate demand. It is possible, however, that this proxy is affected by supply variables in the economy. Accordingly, the empirical model accounts for two major sources of supply-side shifts: changes in the energy price and in the output produced per worker. By accounting for major supply-side shifts, shocks to nominal GNP/GDP capture the effects of demand-side shocks. Assuming rational expectations, each demand and supply variable is decomposed into anticipated and unanticipated components. This decomposition is also intended to separate the permanent component of the process, anticipated, from the transitory component, unanticipated.
Detailed results are available upon request.
One lag has proven to be adequate to capture all the dynamics in output. Additional lags are statistically insignificant.
This is a broad measure that accounts for a variety of shocks that underlie aggregate demand: domestic shocks to consumption, investment, government spending, velocity and the money supply as well as foreign shocks to net exports and capital mobility. As explained in Appendix 1, the estimation technique accounts for the endogeneity of aggregate demand. In addition, the equation of aggregate demand growth accounts for variables that determine its endogeneity. Accordingly, aggregate demand shocks (the difference between the endogenous proxy and the fitted value from the forecast equation) are exogenous, by construction, i.e., purely random and uncorrelated with the right-hand side variables in the estimated equations.
Based on data availability, productivity is measured by the ratio of total output to employment. This is a broad measure that captures the change in the output produced per worker in response to changes in the marginal product of labor or other factors in the production function that relate to advances in capital and technological innovations. By construction, productivity changes are endogenous. As explained in Appendix 1, the estimation technique accounts for this endogeneity in two ways: (i) First, instrumental variables are used to proxy the endogenous productivity variable. (ii) Second, the equation for agents’ forecast of productivity accounts for variables that are likely to determine its endogeneity. Accordingly, productivity shocks (the difference between the endogenous proxy and the fitted value from the forecast equation) are exogenous, i.e., purely random and uncorrelated with right-hand side variables in the estimated equations.
Anticipated demand shifts are fully perceived by agents and, in turn, they are not likely to have any positive effect on real output. For contracts longer than one year, nominal wages may not adjust fully to anticipated demand shifts at time t-1. Accordingly, anticipated demand shifts may prove non-neutral. Testing the validity of this hypothesis is beyond the scope of this paper. Further, anticipated demand shifts are orthogonal, by construction, to unanticipated shifts. Accounting for anticipated demand shifts in the output equation does not determine, therefore, the response of output to aggregate demand shocks, which is the primary focus of this paper.
The validity of this prediction is dependent, however, on the institutional structure that determines the speed of wage adjustment to anticipated demand shifts. For example, contracts of duration longer than one year may prevent the wage adjustment despite the fact that demand shifts are anticipated.
The investigation focuses on the contemporaneous response of variables to demand and supply shocks within a year period. These parameters approximate wage flexibility and accompanying fluctuations in the short-run. The average economy-wide contract length and the stipulation of wage indexation determine wage flexibility.
This is a broad measure of uncertainty that captures the variability of a variety of aggregate demand shocks and, to a lesser extent, supply-side shocks. Uncertainty attributed to energy price shocks and productivity shocks are also embedded in this measure. This measure of uncertainty also captures uncertainty attributed to higher inflation, which is likely to guide negotiations for wage flexibility. It is likely that other institutional and structural factors also determine the difference in cyclical fluctuations across countries. However, by focusing on aggregate uncertainty, the cross-section analysis seeks to verify the significance of this factor in endogenizing wage flexibility and accompanying cyclical fluctuations across countries.
Alternatively, higher uncertainty increases the optimal degree of wage indexation and, in turn, increases nominal wage flexibility.
Observations with a relatively low standard error are weighted more heavily and hence play a greater role in the estimation process compared to observations with a relatively high standard error.
It is worth noting that the proxy for aggregate uncertainty, the variability of nominal GNP/GDP shocks is highly correlated with inflation variability with a coefficient, 0.82, and with average inflation with a coefficient, 0.55, across countries. Indeed, average inflation is highly correlated with inflation variability with a coefficient, 0.78, across countries. Given these correlations, I experimented with the effects of inflation variability and average inflation on the coefficient estimates across countries. The qualitative evidence in Table 2 remains robust in these experiments. Furthermore, I purge the shock to nominal GNP/GDP growth from any correlation with the shock to price inflation. The variability of the residual represents the component of aggregate uncertainty that varies independently of inflation variability. This proxy is not significant in differentiating the coefficient estimates across countries, as evident in Table 2. Hence, the effects of aggregate uncertainty on wage flexibility and its accompanying effects are highly dependent on inflation variability.
It is possible that technology shifts, monetary and fiscal policy changes, the exchange rate system and financial innovations may have led to parameter shifts over the sample period. In addition, institutional changes that determine wage and price dynamics may have also varied over time. For example, the shortening of the average contract length over the sample period and the use of wage and price controls in a number of countries for part of the sample may also have caused shifts in the parameters of the model.
The first dummy variable equals one in 1970-79 and zero otherwise. The second dummy equals one in the 1980-98 period and zero otherwise. The two dummies interact with each shock to measure possible change in its effect on economic variables in the 1970-79 and 1980-98 sub-samples. Detailed results are available upon request.
The cross-section evidence is based on estimates from the time-series model that are weighted by the inverse of their standard error to correct for the two-step procedure.
Aggregate uncertainty has a negative effect that is statistically significant at the ten percent level on the nominal and real wage adjustments to demand shocks across countries in the fifties and sixties. This indicates rigidity in the wage adjustment to demand shocks in response to variation in uncertainty across countries within this sub-sample.
This evidence challenges the implications of the sticky-price explanation of business cycles. In these models, price flexibility is expected to determine the real wage response to demand shocks negatively. Higher price flexibility moderates the pro-cyclical response of the real wage to demand shocks. Moreover, the time-series evidence in Table 1, in contrast to the predictions of sticky-price models, is not consistent with a pro-cyclical adjustment of the real wage to demand shocks in general.