"Os bárbaros estão chegando": o processo de democratização do governo Richa no Paraná

O presente trabalho de pesquisa acadêmica abrange a análise da conjuntura política do processo de transição democrática no Brasil e no Paraná, ocorrida no final da década de 1970 e inícios dos anos 80. No capítulo introdutório examino os elementos teórico-metodológicos, que são as categorias de análise que utilizo no estudo da situação concreta brasileira, como contexto amplo, e da conjuntura das eleiçôes de 1982 e do governo de José Richa no Paraná. Era necessário examinar a concepçâo "ampliada" de Estado, o conceito de estrutura e de superestrutura do bloco histórico e a concepção da vertente da democracia liberal e da vertente da democracia popular, no enfoque da literatura do materialismo histórico-dialético, de autores clássicos e modernos. No segundo capítulo examino o projeto de transição democrática dos generais presidentes Geisel e Figueiredo: um plano dos militares e das elites brasileiras para salvar as elites no poder, na travessia pelo alto. O projeto não ia além da legitimação do regime e do modelo econômico pela passagem do governo militar aos civis, por um processo eleitoral duvidoso. No Paraná, objetivo principal da pesquisa, examino o projeto liberal do governo do PMDB de Richa, desde a organizaçâo partidária, a elaboraçâo das diretrizes de governo, a campanha eleitoral, a participaçâo de intelectuais de ponta na campanha, as alianças do PMDB com os setores populares da sociedade até a composição e a "direitização" do governo. "OS BÁRBAROS ESTÃO CHEGANDO", título principal da dissertação, era a denominação que a aristocracia (elite) curitibana atribuiu a José Richa, quando o mesmo ganhou as eleições ao governo do Paraná, pelo fato de ser do interior do Estado e não fazer parte do mundo civilizado da Curitiba cosmopolita. No terceiro capítulo examino a questão da democratização da escola pública do Estado pela instituição das eleições para diretor e vice-diretor da escola, pelos professores, servidores, alunos e pais de alunos. Digo que a democracia vai além do decreto governamental que estabelece as eleições. Digo ainda que o discurso na prática é outro, no caso do governo Richa e da educação. A questão básica da pesquisa é trabalhar a idéia de que a educação é um campo de disputas e confrontos de classes pela hegemonia do poder, em que a classe dominante luta para manter-se no poder como classe dirigente e as classes subalternas ora se mantém aliadas e como classes auxiliares à classe dominante, ora consentem e raras vezes rebelam-se contra a dominaçâo da classe dirigente.

Formation and destruction of coalition groups under economic growth

This paper introduces a model economy in which formation of coalition groups under technological progress is generated endogenously. The coalition formation depends crucially on the rate of arrival of new technologies. In the model, an agent working in the saroe technology for more than one period acquires skills, part of which is specific to this technology. These skills increase the agent productivity. In this case, if he has worked more than one period with the same technology he has incentives to construct a coalition to block the adoption of new technologies. Therefore, in every sector the workers have incentives to construct a coalition and to block the adoption of new technologies. They will block every time that a technology stay in use for more than one period.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).