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The politics of burden-sharing in the field of asylum policy within the European Union is largely a story about numbers. This article presents a quantitative analysis of the development of asylum applications and recognition rates in EU Member States over the past twenty years. Starting from the assumptions, first, that asylum seekers tend to go those countries where there is a higher chance of being recognized as refugee, and second, that the EU has been instrumental in legitimating a curb on generosity in traditionally more lenient states, we hypothesize a converging "asylum burden" among EU Member States. The empirical data presented here provide initial support for this "implicit burden-sharing thesis". (Original abstract)

In this article a causal model is developed that explains the unequal level of economic development of Third World countries on the basis of structural variables. The model is tested using empirical data on 84 developing countries from the 1960s, 1970s and 1980s and applying a multi-sample LISREL analysis. The model appears to fit the data for the entire period rather well. In an attempt to analyse the changes of the world system over the last several decades, hypotheses were formulated concerning changes in the form of dependence, democratization in the Third World, and the tendency of Third World countries to diversify their production. The results of the analysis indicate that the expected change from `classical' to `modern' dependence is corroborated by the data. No such change was apparent where political repression was concerned. Finally, the causal chain of primary commodity concentration, exploitation, domestic capital formation and economic development appeared to exhibit significant changes over the 1965-92 period.

We discuss structural equation models for non‐normal variables. In this situation the maximum likelihood and the generalized least‐squares estimates of the model parameters can give incorrect estimates of the standard errors and the associated goodness‐of‐fit chi‐squared statistics. If the sample size is not large, for instance smaller than about 1000, asymptotic distribution‐free estimation methods are also not applicable. This paper assumes that the observed variables are transformed to normally distributed variables. The non‐normally distributed variables are transformed with a Box–Cox function. Estimation of the model parameters and the transformation parameters is done by the maximum likelihood method. Furthermore, the test statistics (i.e. standard deviations) of these parameters are derived. This makes it possible to show the importance of the transformations. Finally, an empirical example is presented.