Diffuse support for democracy, as captured in mass surveys, tends to be treated as impervious to regime performance. Such a finding is often presented as confirmation of the basic distinction between ‘diffuse’ and ‘specific’ support as proposed by David Easton. This study argues that this line of argument stems from an incomplete reading of important aspects of Easton's theorisation about the relationship between system outputs and diffuse support. Using multilevel models, evidence from more than 100 surveys in close to 80 countries, and different measures of democratic support, it is shown that government effectiveness is the strongest macro‐level predictor of such support. In democratic regimes, government effectiveness, understood as the quality of policy‐making formulation and implementation, is linked to higher levels of support for democracy. Furthermore, in non‐democracies, effectiveness and support for democracy are, under some model specifications, negatively related.

BackgroundThe World Mental Health Survey Initiative was designed to evaluate the prevalence, the correlates, the impact and the treatment patterns of mental disorders. This paper describes the rationale and the methodological details regarding the implementation of the survey in Portugal, a country that still lacks representative epidemiological data about psychiatric disorders.MethodsThe World Mental Health Survey is a cross-sectional study with a representative sample of the Portuguese population, aged 18 or older, based on official census information. The WMH-Composite International Diagnostic Interview, adapted to the Portuguese language by a group of bilingual experts, was used to evaluate the mental health status, disorder severity, impairment, use of services and treatment. Interviews were administered face-to-face at respondent’s dwellings, which were selected from a nationally representative multi-stage clustered area probability sample of households. The survey was administered using computer-assisted personal interview methods by trained lay interviewers. Data quality was strictly controlled in order to ensure the reliability and validity of the collected information.ResultsA total of 3,849 people completed the main survey, with 2,060 completing the long interview, with a response rate of 57.3%. Data cleaning was conducted in collaboration with the WMHSI Data Analysis Coordination Centre at the Department of Health Care Policy, Harvard Medical School. Collected information will provide lifetime and 12-month mental disorders diagnoses, according to the International Classification of Diseases and to the Diagnostic and Statistical Manual of Mental Disorders.ConclusionsThe findings of this study could have a major influence in mental health care policy planning efforts over the next years, specially in a country that still has a significant level of unmet needs regarding mental health services organization, delivery of care and epidemiological research.

Although public support for political authorities, institutions, and even regimes is affected by the delivery of positive economic outcomes, we know that judgments on authorities are also made on the basis of several other aspects that fall into the general theme of “procedural fairness.” So far, most of the literature examining satisfaction with democracy has, from this point of view, focused on the direct effects of both economic and procedural fairness indicators or evaluations. This study takes as its starting point a large number of studies in social psychology showing that procedural fairness moderates the effects of outcome favorability in the explanation of citizens’ reactions to authorities. It expands those findings to the macro-political level, using representative samples of European populations in twenty-nine countries. It reveals that the general depiction of satisfaction with the way democracies work in practice as a fundamentally “performance-driven attitude” needs to qualified: economic evaluations matter, but they do not matter in the same way in all contexts and for all people, with procedural fairness playing a relevant moderating role in this respect.

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Po ol li it ti ic cs s: : W Wa av ve el le et t A An na al ly ys si is s o of f P Po ol li it ti ic ca al l T Ti im me e--S Se er ri ie es s" " L Lu uí ís s A Ag gu ui ia ar r--C Co on nr ra ar ri ia a P Pe ed dr ro o C C. . M Ma ag ga al lh hã ãe es s M Ma ar ri ia a J Jo oa an na a S So oa ar re es s NIPE WP 25/ 2011 " "C Cy yc cl le es s i in n P Po ol li it ti ic cs s: : W Wa av ve el le et t A An na al ly ys si is s o of f P Po ol li it ti ic ca al l T Ti im me e--S Se er ri ie es s" " L Lu uí ís s A Ag gu ui ia ar r--C Co on nr ra ar ri ia a P Pe ed dr ro o C C. . M Ma ag ga al lh hã ãe es s M Ma ar ri ia a J Jo oa an na a S So oa ar re es s Abstract Spectral analysis and ARMA models have been the most common weapons of choice for the detection of cycles in political time-series. Controversies about cycles, however, tend to revolve about an issue that both techniques are badly equipped to address: the possibility of irregular cycles without fixed periodicity throughout the entire time-series. This has led to two main consequences. On the one hand, proponents of cyclical theories have often dismissed established statistical techniques. On the other hand, proponents of established techniques have dismissed the possibility of cycles without fixed periodicity. Wavelets allow the detection of transient and coexisting cycles and structural breaks in periodicity. In this paper, we present the tools of wavelet analysis and apply them to the study to two lingering puzzles in the political science literature: the existence to cycles in election returns in the United States and in the severity of major power wars. 1 3 The paper proceeds as follows. The next section uses empirical and constructed numerical examples to illustrate the use of ARMA models and spectral analysis in the detection of cyclesand their shared inability to deal with irregular and transient cycles. We will show that this inability does not extend to wavelet analysis. Following that section, we present the three basic tools in wavelet analysis: the wavelet power spectrum, cross-wavelets, and phase-differences.Equiped with these tools, we will develop two applications to real world political time-series data. The first is the analysis of presidential and congressional election returns in the UnitedStates from 1854 to 2008. The second application of wavelet analysis concerns the severity of major power wars from 1495 to 1975, using well-established data in the literature (Levy 1983;Goldstein 1988). In the last section, we sketch a research agenda in political science where the use of wavelet analysis may shed light on important empirical and theoretical discussions. In the appendix, we describe how to computationaly implement the wavelet tools.

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