Hacia un modelo de análisis de políticas públicas operativ : Un enfoque basado en los actores, sus recursos y las instituciones

Towards an operative analysis of public policies: An approach focused on actors, resources and institutions. This article develops an analytical model which is centred on the individual and collective behaviour of actors involved during different stages of public policy. We postulate that the content and institutional characteristics of public action (dependent variable) are the result of interactions between political-administrative authorities, on the one hand, and, on the other, social groups which cause or suffer the negative effects of a collective problem which public action attempts to resolve (independent variables). The 'game' of the actors depends not only on their particular interests, but also on their resources (money, time, consensus, organization, rights, infrastructure, information, personnel, strength, political support) which they are able to exploit to defend their positions, as well as on the institutional rules which frame these policy games.

Robust Estimates of the Negative Binomial Model

We consider robust parametric procedures for univariate discrete distributions, focusing on the negative binomial model. The procedures are based on three steps: ?First, a very robust, but possibly inefficient, estimate of the model parameters is computed. ?Second, this initial model is used to identify outliers, which are then removed from the sample. ?Third, a corrected maximum likelihood estimator is computed with the remaining observations. The final estimate inherits the breakdown point (bdp) of the initial one and its efficiency can be significantly higher. Analogous procedures were proposed in [1], [2], [5] for the continuous case. A comparison of the asymptotic bias of various estimates under point contamination points out the minimum Neyman's chi-squared disparity estimate as a good choice for the initial step. Various minimum disparity estimators were explored by Lindsay [4], who showed that the minimum Neyman's chi-squared estimate has a 50% bdp under point contamination; in addition, it is asymptotically fully efficient at the model. However, the finite sample efficiency of this estimate under the uncontaminated negative binomial model is usually much lower than 100% and the bias can be strong. We show that its performance can then be greatly improved using the three step procedure outlined above. In addition, we compare the final estimate with the procedure described in