Schmid Comparative Migration Studies (2020) 8:25 analysis. In addition, this pooled approach has already been used to construct immigration policy openness indices by applying standard PCA analysis to ordinal data (e.g. Peters 2017). The CATPCA is confirmatory rather than exploratory. Hence, rather than relying on extraction criteria such as component Eigenvalues greater than 1 – a criterion often used for exploratory principal component analyses – a solution with two dimensions is specified ex ante. The CATPCA therefore tests whether the various policy components can indeed be reduced to two distinct and internally consistent dimensions that describe IRO and CRI, respectively. Additional tests with more and fewer dimensions are provided in Additional file 1. The tests show that the two-dimensional model is the most adequate. For the subsequent analyses, I do not use the component scores from the CATPCA. This is not only because they are highly skewed, but also because the rotation used (VARIMAX; see below) assumes statistical independence, thus making correlation analyses futile. Instead, I use equally weighted average scores based on the six IRO policy components and the four CRI policy components, respectively. As the IRO values are still highly skewed, however, I apply a cubic transformation to better identify the important sources of variation. More details and justifications for this decision are elaborated in Additional file 1. For the configurational analysis, the typology is used as a heuristic tool to make simplified nominal regime classifications. Both theoretically and empirically there are no clear thresholds above which cases are open or inclusive in an absolute sense. At least when conceptualised and measured in a statistical rather than a set-theoretic the openness of borders and the inclusiveness of citizenship are matters of degree, not of kind (cf. Vink 2017, p. 226). However, I still categorise cases as instances of the four regime types, but only in a relative way by using the arithmetic means of IRO and CRI as the cut-off points. Dimensional analysis Do the components of IRO and CRI configure along two internally consistent and statistically distinct dimensions? A confirmatory CATPCA reveals that a two-dimensional model indeed provides an adequate description of the data. Both dimensions are relevant in terms of their Eigenvalue, and the model explains almost 60% of the variation in all items (Table 2; a correlation matrix of all variables can be found in Additional file 1). The first dimension describes Immigration Regime Openness (IRO), the second Citizenship Regime Inclusiveness (CRI). While the dimension describing CRI is not fully consistent as the Cronbach’s alpha score is below the standard threshold for internal consistency of 0.7, the model performs well. This indicated by the high value of the Cronbach’s alpha assessing the full model. This finding adds to the existing literature in several ways. First, previous studies have cautioned against the idea that there is a general tendency in immigration policies across its various dimensions in terms of degrees of restrictiveness (Beine et al. 2016; de Haas et al. 2016). The analysis presented here instead suggests that immigration regimes are rather coherent. It corroborates the idea that IRO – in terms of entry and stay policies regarding labour immigration, family reunification, and asylum seekers and refugees – can be reduced to the same dimension (for the same finding with the Page 12 of 20