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The specification of the propensity score in multilevel studies
No full textPropensity Score Matching (PSM) has become a popular approach to estimation of causal
effects. It relies on the assumption that selection into a treatment can be explained purely in
terms of observable characteristics (the “unconfoundedness assumption”) and on the property
that balancing on the propensity score is equivalent to balancing on the observed covariates.
Several applications in social sciences are characterized by a hierarchical structure of data:
units at the first level (e.g., individuals) clustered into groups (e.g., provinces). In this paper
we explore the use of multilevel models for the estimation of the propensity score for such
hierarchical data when one or more relevant cluster-level variables is unobserved. We
compare this approach with alternative ones, like a single level model with cluster dummies.
By using Monte Carlo evidence we show that multilevel specifications usually achieve
reasonably good balancing in cluster level unobserved covariates and consequently reduce the
omitted variable bias. This is also the case for the dummy model
The specification of the propensity score in multilevel observational studies
Full text linkPropensity Score Matching (PSM) has become a popular approach to estimation of causal
effects. It relies on the assumption that selection into a treatment can be explained purely in
terms of observable characteristics (the “unconfoundedness assumption”) and on the property
that balancing on the propensity score is equivalent to balancing on the observed covariates.
Several applications in social sciences are characterized by a hierarchical structure of data:
units at the first level (e.g., individuals) clustered into groups (e.g., provinces). In this paper
we explore the use of multilevel models for the estimation of the propensity score for such
hierarchical data when one or more relevant cluster-level variables is unobserved. We
compare this approach with alternative ones, like a single level model with cluster dummies.
By using Monte Carlo evidence we show that multilevel specifications usually achieve
reasonably good balancing in cluster level unobserved covariates and consequently reduce the
omitted variable bias. This is also the case for the dummy model
Atypical Work and Employment Continuity
Full text linkAtypical employment arrangements such as agency temporary work and contracting have long been criticized as offering more precarious and unstable work than regular employment. Using data from two datasets – the CAEAS and the NLSY79 – we determine whether workers who take such jobs rather than regular employment, or the alternative of continued job search, subsequently experience greater or lesser employment continuity. Observed differences between the various working arrangements are starkest when we do not account for unobserved individual heterogeneity. Controlling for the latter, we report that the advantage of regular work over atypical work and atypical work over continued joblessness dissipates.employment continuity, open-ended work, atypical work, unemployment, inactivity
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The Effects of R&D Subsidies to Small and Medium-Sized Enterprises. Evidence from a Regional Program
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