Structural transformation in China and India: A note on macroeconomic policies

This paper explores macroeconomic policies that can sustain structural change in China and India. A two-sector open-economy model with endogenous productivity growth, demand driven output and income distribution as an important determinant of economic activity is calibrated to a 2000 SAM for China and a 1999/2000 SAM for India. Short-run analysis concerns temporary equilibria for output, productivity and employment growth rates in the formal sector. In the long-run, the model allows for multiple equilibria which can describe cases of (a) underdevelopment and structural heterogeneity or (b) sustained growth and development. Several simulation exercises are conducted. Specifically, we consider how changes in investment, wages, labor productivity trend and a depreciation of currency affect the macroeconomy and job creation in the formal sector.     

On MLEs in an extended multivariate linear growth curve model

In this paper the extended growth curve model is considered. The literature comprises two versions of the model. These models can be connected by one-to-one reparameterizations but since estimators are non-linear it is not obvious how to transmit properties of estimators from one model to another. Since it is only for one of the models where detailed knowledge concerning estimators is available (Kollo and von Rosen, Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005) the object in this paper is therefore to present uniqueness properties and moment relations for the estimators of the second model. One aim of the paper is also to complete the results for the model presented in Kollo and von Rosen (Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005). The presented proofs of uniqueness for linear combinations of estimators are valid for both models and are simplifications of proofs given in Kollo and von Rosen (Advanced multivariate statistics with matrices. Springer, Dordrecht, 2005).     

Leadership in Organizational Knowledge Creation: A Review and Framework

Organizational knowledge creation integrates context, knowledge assets, and knowledge creation processes throughout the organization. Using organizational knowledge creation theory as an organizing framework, we conduct a literature review that shows prior work has focused on the role of central, upper‐echelon, leadership in knowledge creation processes, without devoting much attention to context and knowledge assets. To remedy these weaknesses, we develop a new framework for situational leadership in organizational knowledge creation. The framework is based on a continuum that ranges from centralized to distributed leadership at three layers of activity: a core layer of local knowledge creation; a conditional layer that provides the resources and context for knowledge creation; and a structural layer that forms the overall frame and direction for knowledge creation in the organization. We discuss the implications of this framework for theory and practice.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.     

Does truth win when teams reason strategically?

We study behavior in the race game with the aim of assessing whether teams can create synergies. The race game has the advantage that the optimal strategy depends neither on beliefs about other players nor on distributional or efficiency concerns. Our results reveal that teams not only outperform individuals but that they can also beat the “truth-wins” benchmark. In particular, varying the length of the race game we find that the team advantage increases with the complexity of the game.     

Spousal Complementarity in Home Production

A bstract .   Economists have increasingly recognized the growing role of married women in the labor market by treating the labor supply decisions of married couples as joint decisions. However, they have yet to apply the same reasoning to home production. We build a more complete model of household time allocation that consists of a system of simultaneous equations estimating hours of labor supply and home production. Using data on white couples from Wave XXV of the Panel Study of Income Dynamics, we find that working wives act as if their husbands are substitutes for home production while other wives do not. Husbands' responses to their wives' behavior depends upon whether children are present.