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.     

Linear quantile mixed models

Dependent data arise in many studies. Frequently adopted sampling designs, such as cluster, multilevel, spatial, and repeated measures, may induce this dependence, which the analysis of the data needs to take into due account. In a previous publication (Geraci and Bottai in Biostatistics 8:140–154, 2007), we proposed a conditional quantile regression model for continuous responses where subject-specific random intercepts were included to account for within-subject dependence in the context of longitudinal data analysis. The approach hinged upon the link existing between the minimization of weighted absolute deviations, typically used in quantile regression, and the maximization of a Laplace likelihood. Here, we consider an extension of those models to more complex dependence structures in the data, which are modeled by including multiple random effects in the linear conditional quantile functions. We also discuss estimation strategies to reduce the computational burden and inefficiency associated with the Monte Carlo EM algorithm we have proposed previously. In particular, the estimation of the fixed regression coefficients and of the random effects’ covariance matrix is based on a combination of Gaussian quadrature approximations and non-smooth optimization algorithms. Finally, a simulation study and a number of applications of our models are presented.     

A progressive shift alternative to evaluate nonparametric tests for skewed data

ABSTRACT

When the power of different nonparametric tests is evaluated by simulation, the alternative hypothesis should be carefully designed to ensure validity of the results in the specific research field. In the article, we propose a probit-based progressive shift alternative that is more realistic than the simple shift alternative for skewed non-negative data that occur in many research areas. Our motivation comes from parasitology. The progressive shift alternative is used to compare the power of six location-scale tests and seven commonly used location tests for several skewed theoretical and empirical parasite distributions. It is shown that location-scale tests are more powerful than location tests. Programs for applying the methods studied in the article are freely available for download.     

Cluster detection and clustering with random start forward searches

The forward search is a method of robust data analysis in which outlier free subsets of the data of increasing size are used in model fitting; the data are then ordered by closeness to the model. Here the forward search, with many random starts, is used to cluster multivariate data. These random starts lead to the diagnostic identification of tentative clusters. Application of the forward search to the proposed individual clusters leads to the establishment of cluster membership through the identification of non-cluster members as outlying. The method requires no prior information on the number of clusters and does not seek to classify all observations. These properties are illustrated by the analysis of 200 six-dimensional observations on Swiss banknotes. The importance of linked plots and brushing in elucidating data structures is illustrated. We also provide an automatic method for determining cluster centres and compare the behaviour of our method with model-based clustering. In a simulated example with eight clusters our method provides more stable and accurate solutions than model-based clustering. We consider the computational requirements of both procedures.     

Fitting spatial regressions to large datasets using unilateral approximations

Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacity, even in relatively small samples, and becomes unfeasible in very large datasets. The unilateral approximation approach to spatial model estimation (suggested in Besag 1974 Besag, J. E. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B (Methodological) 36 (2):192–236.[Crossref], [Web of Science ®] , [Google Scholar]) provides a viable alternative to maximum likelihood estimation that reduces substantially the computing time and the storage required. In this article, we extend the method, originally proposed for conditionally specified processes, to simultaneous and to general bilateral spatial processes over rectangular lattices. We prove the estimators’ consistency and study their finite-sample properties via Monte Carlo simulations.     

HERD BEHAVIOR IN FINANCIAL MARKETS: AN EXPERIMENT WITH FINANCIAL MARKET PROFESSIONALS

We study herd behavior in a laboratory financial market with financial market professionals. An important novelty of the experimental design is the use of a strategy‐like method. This allows us to detect herd behavior directly by observing subjects' decisions for all realizations of their private signal. In the paper, we compare two treatments: one in which the price adjusts to the order flow in such a way that herding should never occur, and one inwhich the presence of event uncertainty makes herding possible. In the first treatment, subjects seldom herd, in accordance with both the theory and previous experimental evidence on student subjects. A proportion of subjects, however, engage in contrarianism, something not accounted for by the theory. In the second treatment, the proportion of herding decisions increases, but not as much as the theory would suggest. Moreover, contrarianism disappears altogether. In both treatments, in contrast with what theory predicts, subjects sometimes prefer to abstain from trading, which affects the process of price discovery negatively. (JEL: C92, D82, G14)     

A Game–Theoretic View of the Fiscal Theory of the Price Level

The goal of this paper is to probe the validity of the fiscal theory of the price level by modelling explicitly the market structure in which households and the government make their decisions. I describe the economy as a game, and I am thus able to state precisely the consequences of actions that are out of the equilibrium path. I show that there exist government strategies that lead to a version of the fiscal theory, in which the price level is determined by fiscal variables alone. These strategies are however more complex than the simple budgetary rules usually associated with the fiscal theory, and the government budget constraint cannot be merely viewed as an equilibrium condition.