Christian Schluter

  • Faculty

Château Lafarge
Route des Milles
13290 Les Milles
Phone: +33 (0)4 13 55 25 72 / +33 (0) 413 94 20 27
Aix-Marseille University
Faculty of Economics and Management
Research themes:
Development economics
Labour economics
London School of Economics
Weak convergence to the Student and Laplace distributions, Christian Schluter and Mark Trede, Journal of Applied Probability, Volume 53, Issue 1, pp. 121-129, 2016

One often observed empirical regularity is a power-law behavior of the tails of some distribution of interest. We propose a limit law for normalized random means that exhibits such heavy tails irrespective of the distribution of the underlying sampling units: the limit is a t-distribution if the random variables have finite variances. The generative scheme is then extended to encompass classic limit theorems for random sums. The resulting unifying framework has wide empirical applicability which we illustrate by considering two empirical regularities in two different fields. First, we turn to urban geography and explain why city-size growth rates are approximately t-distributed, using a model of random sector growth based on the central place theory. Second, turning to an issue in finance, we show that high-frequency stock index returns can be modeled as a generalized asymmetric Laplace process. These empirical illustrations elucidate the situations in which heavy tails can arise.

The Composition of Wage Differentials between Migrants and Natives, Panagiotis Nanos and Christian Schluter, European Economic Review, Volume 65, Issue C, pp. 23-44, 2014

We consider the role of unobservables, such as differences in search frictions, reservation wages, and productivities for the explanation of wage differentials between migrants and natives. We disentangle these by estimating an empirical general equilibrium search model with on-the-job search due to Bontemps et al. (1999) on segments of the labour market defined by occupation, age, and nationality using a large scale German administrative dataset.

The Impact of Labor Market Dynamics on the Return Migration of Immigrants, Govert E. Bijwaard, Christian Schluter and Jackline Wahba, The Review of Economics and Statistics, Volume 96, Issue 3, pp. 483-494, 2014

Using administrative panel data on the entire population of new labor immigrants to the Netherlands, we estimate the effects of individual labor market spells on immigration durations using the timing-of-events method. The model allows for correlated unobserved heterogeneity across migration, unemployment, and employment processes. We find that unemployment spells increase return probabilities for all immigrant groups, while reemployment spells typically delay returns. © 2014 The President and Fellows of Harvard College and the Massachusetts Institute of Technology

Discussion of S.G. Donald et al. and R. Davidson on the econometrics of inequality measurement, Christian Schluter, Econometrics Journal, Volume 15, Issue 1, pp. C54-C57, 2012

This paper discusses aspects of the papers by S.G. Donald et al. and R. Davidson, which were presented at The Econometrics Journal sponsored special session on the econometrics of inequality measurement, held at the Royal Economics Society Meeting in Surrey in 2010.

On the problem of inference for inequality measures for heavy‐tailed distributions, Christian Schluter, Econometrics Journal, Volume 15, Issue 1, pp. 125-153, 2012

We consider the class of heavy-tailed income distributions and show that the shape of the income distribution has a strong effect on inference for inequality measures. In particular, we demonstrate how the severity of the inference problem responds to the exact nature of the right tail of the income distribution. It is shown that the density of the studentized inequality measure is heavily skewed to the left, and that the excessive coverage failures of the usual confidence intervals are associated with excessively low estimates of both the point measure and the variance. For further diagnostics, the coefficients of bias, skewness and kurtosis are derived and examined for both studentized and standardized inequality measures. These coefficients are also used to correct the size of confidence intervals. Exploiting the uncovered systematic relationship between the inequality estimate and its estimated variance, variance stabilizing transforms are proposed and shown to improve inference significantly.