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This paper measured the variations in performance of small municipalities in the State of São Paulo, Brazil, regarding the technical efficiency in the use of public funds in public primary health care actions concerning the funding profile, in a scenario of fiscal federalism. Technical efficiency is one of the parameters of evaluation of public sector performance and was measured by means of Data Envelopment Analysis (DEA). The correlation analysis of DEA score was used to verify possible associations between technical efficiency and the funding profile of expenses with health care. The results showed that 6.41% of the municipalities were considered efficient. They also showed that the level of municipality dependence to inter-governmental general purpose grants and the national health funding specific purpose grants have negative correlation with efficiency scores.

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Our article explores an underused mathematical analytical methodology in the social sciences. In addition to describing the method and its advantages, we extend a previously reported application of mixed models in a well-known database about corruption in 149 countries. The dataset in the mentioned study included a reasonable amount of zeros (13.19%) in the outcome variable, which is typical of this type of research, as well as quite a bit of social sciences research. In our paper, present detailed guidelines regarding the estimation of models where the data for the outcome variable includes an excess number of zeros, and the dataset has a natural nested structure. We believe our research is not likely to reject the hypothesis favoring the adoption of mixed modeling and the inflation of zeros over the original simpler framework. Instead, our results demonstrate the importance of considering random effects at country levels and the zero-inflated nature of the outcome variable.

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