forthcoming in -RXUQDO RI /DERU (FRQRPLFV, October 2000 $EVWUDFWThe effects of wage dispersion on productive efficiency is a topic rich in theoretical conjecture, a common object of Scandinavian polemical debate and at the same time an issue almost barren of systematic econometric evidence. The Swedish record of enormous compression of relative wages under the institutional regime of centralized solidarity bargaining, followed by substantial de-compression of wages after central bargaining broke down, supplies observations well suited for empirical testing of theories and assertions about the response of productive efficiency to shifts in wage distribution. Results presented in this paper obtained from regression experiments based on distribution-augmented production and labor productivity functions yield no support of 'fairness, morale and cohesiveness' theories implying that wage leveling within workplaces and industries may enhance productivity. We do find substantial evidence, however, that reduction of inter-industry wage differentials contributed positively to aggregate output and productivity growth, most likely for the structural reasons first emphasized by leading Swedish trade union economists almost a half century ago.

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This paper describes an alternative approach for testing for the existence of trend among time series. The test method has been constructed using wavelet analysis which has the ability of decomposing a time series into low frequencies (trend) and high-frequency (noise) components. Under the normality assumption, the test is distributed as F . However, using generated empirical critical values, the properties of the test statistic have been investigated under different conditions and different types of wavelet. The Harr wavelet has shown to exhibit the highest power among the other wavelet types.The methodology here has been applied to real temperature data in Sweden for the period 1850-1999. The results indicate a significant increasing trend which agrees with the 'global warming'hypothesis during the last 100 years.

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In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.