Measuring… What? Notes on Some Globalization Indices

The article examines some of the instruments devised to measure globalization, in particular the CSGR Globalisation Index and the A.T. Kearney/Foreign Policy Globalization Index. The article begins by comparing the features and results of these instruments, and then highlights their strengths and weaknesses. The two most significant weaknesses are as follows. The first consists in what Beck has called ‘methodological nationalism’: the fact that attempts are made to study an essentially transnational phenomenon on a national basis. The second weakness resides in the problems—in terms of the completeness, adequacy, and timeliness of information—of the database used to calculated these indices. Also to be emphasized is that analysis of the strengths and weaknesses of these indices cannot be separated from the purpose for which they have been devised. In other words, there is no one ‘best’ instrument in absolute terms; rather there is an array of instruments with which to pursue particular knowledge goals. Finally, discussion of the techniques used to measure the phenomenon provides an occasion to raise considerations concerning the nature of globalization.     

Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

ABSTRACT

This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.     

Maximum likelihood estimation of bivariate circular hidden Markov models from incomplete data

In this paper, we propose a hidden Markov model for the analysis of the time series of bivariate circular observations, by assuming that the data are sampled from bivariate circular densities, whose parameters are driven by the evolution of a latent Markov chain. The model segments the data by accounting for redundancies due to correlations along time and across variables. A computationally feasible expectation maximization (EM) algorithm is provided for the maximum likelihood estimation of the model from incomplete data, by treating the missing values and the states of the latent chain as two different sources of incomplete information. Importance-sampling methods facilitate the computation of bootstrap standard errors of the estimates. The methodology is illustrated on a bivariate time series of wind and wave directions and compared with popular segmentation models for bivariate circular data, which ignore correlations across variables and/or along time.     

THE IMPACT OF IMMIGRATION ON THE STRUCTURE OF WAGES: THEORY AND EVIDENCE FROM BRITAIN

Immigration to the UK, particularly among more educated workers, has risen appreciably over the past 30 years and as such has raised labor supply. However studies of the impact of immigration have failed to find any significant effect on the wages of native‐born workers in the UK. This is potentially puzzling since there is evidence that changes in the supply of educated natives have had significant effects on their wages. Using a pooled time series of British cross‐sectional micro data on male wages and employment from the mid‐1970s to the mid‐2000s, this paper offers one possible resolution to this puzzle, namely that in the UK natives and foreign born workers are imperfect substitutes. We show that immigration has primarily reduced the wages of immigrants—and in particular of university educated immigrants—with little discernable effect on the wages of the native‐born.     

Disciplining Democracy: How the Upper and Middle Class in Manila Envision Democratic Order

Qualitative Sociology - I describe how the upper and middle class in Metro Manila see the democratic state and how they imagine reforming it. I argue that they are not turning away from democracy...     

Cointegration testing under structural change: reducing size distortions and improving power of residual based tests

This paper investigates how standard residual based tests for cointegration—under structural change in the long run relationship—can be modified in order to reduce size distortions and improve power, by following the same ideas used in the unit root context. This is a natural strategy given that these tests are unit root statistics applied to estimated residuals from a cointegrating regression. In order to assess the finite sample performance of the alternative tests, a Monte Carlo experiment will be implemented to analyze size and power. Critical values for the tests constructed with GLS detrended data, proposed by Elliot et al. (Econometrica 64:813–836, 1996), are obtained by simulation.     

The bipolar Choquet integral representation

Cumulative Prospect Theory is the modern version of Prospect Theory and it is nowadays considered a valid alternative to the classical Expected Utility Theory. Cumulative Prospect theory implies Gain-Loss Separability, i.e., the separate evaluation of losses and gains within a mixed gamble. Recently, some authors have questioned this assumption of the theory, proposing new paradoxes where the Gain-Loss Separability is violated. We present a generalization of Cumulative Prospect Theory which does not imply Gain-Loss Separability and is able to explain the cited paradoxes. On the other hand, the new model, which we call the bipolar Cumulative Prospect Theory, genuinely generalizes the original Prospect Theory of Kahneman and Tversky, preserving the main features of the theory. We present also a characterization of the bipolar Choquet Integral with respect to a bi-capacity in a discrete setting.