WAGE DETERMINATION UNDER NON-LINEAR TAXES: ESTIMATION AND AN APPLICATION TO PANEL DATA

This paper studies wage determination under piecewise lineartaxes in a unionised labour market. The purposes of the paperare to model how piecewise linear taxation affects the choiceset of the union, and to take this information into accountin the estimation. The empirical application is based on paneldata. Piecewise linear taxes necessitates formal assumptionsabout the sources of randomness, and we find that both unobservedheterogeneity and measurement errors in the wage rate are importantto consider in the estimation. We also find that taxes are likelyto have a non-negligible impact on the (pre-tax) wage rate.     

Factors Related to Employees’ Desire to Join and Leave Unions

Regression analyses based on a sample of 3372 nonunionized and unionized employees showed that, while the desire to join a union is associated with a wide range of work attitudes, perceived company performance, and facets of satisfaction, one's desire to leave one's union is associated with a narrow range of economic concerns. Implications of the findings are discussed in light of the declining unionization rate in the United States.     

Deep Habits in the New Keynesian Phillips Curve

We derive and estimate a New Keynesian Phillips Curve (NKPC) in a model with deep habits. Habits are deep in that they apply to individual consumption goods instead of aggregate consumption. This alters the NKPC in a fundamental manner since it introduces consumption growth and future demand terms into the NKPC equation. We construct the driving process in the deep habits NKPC by using the model's optimality conditions to impute time series for unobservable variables. The resulting series is considerably more volatile than unit labor cost. Generalized methods of moments estimation shows an improved fit and a much lower degree of indexation compared to the standard NKPC.     

Why Do EPS Forecast Error and Dispersion Not Vary with Scale? Implications for Analyst and Managerial Behavior

We document a counter‐intuitive finding regarding analyst forecasts of quarterly earnings per share (EPS): magnitudes of deviations from benchmarks—individual forecasts versus consensus (dispersion) and consensus versus actual (forecast error)—do not vary with scale. Seasonally differenced EPS, or time‐series forecast error, also exhibits substantial lack of variation with scale, but only for firms followed by analysts. This lack of variation with scale is not observed for analyst and time‐series forecasts for (a) EPS for some countries, (b) sales and cash flows for all countries, and (c) stock splits. We propose and investigate different explanations for these puzzling regularities that have important implications for practice and research. We believe the cause is managerial smoothing of EPS designed to reduce across‐firm variation in EPS volatility.