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Policy Change and Learning.

On the technical side, QCA algorithms rely on Boolean algebra and are therefore based on mathematical logic rather than statistical inference (see Rihoux and Ragin 2009 for a complete account on the approach and the technique of QCA).10 Three major technical steps structure a QCA. First, all the conditions (or variables) need to be dichotomized into 0 (zero) or 1 (one) values. For example, if social mobilization is a condition that measures its level of activity, the researcher needs to operationalize it and find measures which allow her to attribute a specific value (0 or 1) to this factor for each case. This calls for a very transparent and robust justification of the dichotomization process, as the subsequent analysis is extremely case-sensitive. The truth table summarizes the data thus dichotomized. Second, the data are inputted into QCA software which then runs the Boolean minimization. Depending on the objective of the analysis, the researcher deals with contradictory configurations, logical remainders, or hypothesis testing functions. Third, the equations obtained from the minimization process of both positive cases (where the dependent variable has a 1 value) and negative cases (where the dependent variable has a 0 value) are either further processed through factorization or are interpreted through a return to the cases. One of the major strength of QCA is indeed its capacity to zoom back to the case as a whole after having broken it into variables for analytical purposes. The data used by Giugni (2004) consist of yearly time series measuring social movement mobilization, the structure of political alliances, trends in public opinion, and changes in public policy in Italy, Switzerland, and the United States, covering the period from 1975 to 1995.11 We use the same, albeit reoperationalized, dataset to conduct the QCA. However, we performed three separate analyses for each movement in order to assess the factors’ impact according to policy type. We then conducted three analyses for each country in order to assess the factors’ impact according to the country where the protests took place. We analyzed 30 cases, defined and described below. The number of cases included in our analysis raises a number of issues that deserve to be mentioned. To begin with, we are not aiming at statistical inference. Statistical significance is essential to evaluate any statistical analysis. This is because results from statistical analysis are inferred from estimates that deviate from actual values. Therefore, such an inference should be built on a reasonably high number of cases. QCA, in its basic variant such as the one employed here, does not infer results. The latter are based on actually observed cases. The aim is not to find the average effect of one variable over another, but rather to find patterns among all configurations with equal weight. Had we decided to include logical cases (instances of nonobserved, but plausible cases), we could start talking about inference, but this is still different from the statistical meaning. Based on these features of the method, a reasonable balance between the number of cases and the number of conditions is enough to conduct a robust QCA. In our analysis, only three conditions are analyzed, which does not require a high number of cases