Etonogestrel The relevance of these resources can be gathere
The relevance of these resources can be gathered by comparing the percentage in revenue from the quota share vis-à -vis the tax revenue of the municipalities. On average, the resource transfers discussed are equivalent to around four times what the municipalities collect with their own tax authorities (341.25% in 2007 and 401.82% in 2009).
where “Quota” is the percentage which the municipality receives in the portion arbitrated by state legislation; “5th grade scores” is a 0–1 index, calculated using the average scores of 5th grade students in basic education achieved at the Portuguese and mathematics tests in the SPAECE (Sistema Permanente de Avaliação de Educação Básica do Ceará) – Permanent Evaluation System of Basic Education in the State of Ceará, or the Prova Brasil (literally “Brazil Test”), depending on the year; “4th to 5th grade school pass rate” is a 0–1 index of passing rates in the municipal education system; “2nd grade literacy” is a 0–1 index calculated based on the average scores of 2nd grade students in basic education in the municipal education system achieved on standard reading and comprehension tests in the SPAECE test; “TMI” is a 0–1 index for measuring advancements in the Etonogestrel of child mortality rates; and “Environment” is a binary variable, being 1 for municipalities certified by the state department of environment in terms of appropriate waste disposal.
For all the indicators that are part of Eq. (1), there were pre-established goals from SWAP Project. Thus, in the sequence of events previously described, the law became an incentive design mechanism, so that the municipalities could become partners of the state in accomplishing goals. This would occur through the increase in ICMS shares for those that would collaborate more profusely in the advancement of those indicators. The following section discusses the strategy for identifying the impact of rule (1) on those indicators.
Impact identification strategy The proposition for impact evaluation stems from the following question: what would have occurred to the municipalities in the state of Ceará, in terms of the discussed indicators, had the law change not come into effect? Thus, the definitions are, as usual: (i) two potential results for each indicator, (Y, Y), where the subscript 0 indicates the result without the law change, and the subscript 1 indicates the result otherwise, and i indicates the municipality; and (ii) a binary variable, where T=0 if the municipality has not been subjected to treatment (the law change), and T=1 otherwise. With those definitions in place, the answer to the original question would come from an analysis of for the n municipalities of the state of Ceará. However, as hydrophobic is impossible to observe , the counterfactual, such a list does not exist. The alternative for this analysis begins by defining a mean effect given by , which is known as average treatment on the treated (ATT). Given the absence of the counterfactuals, it is not possible to estimate . However, observing similar municipalities in another state where there was no law change (control group), it is possible to estimate . And under certain hypotheses the two measures can be interchanged without causing a bias in the ATT estimation (or mitigating the bias, as in Angrist and Pischke, 2008). As for the control group, given that it is expected that it will mimic the treatment group counterfactuals, it is natural to select the municipalities of another state in the Northeast region. In that sense, Pernambuco and Piauí can be discarded, as those states adopted similar laws to those present in Ceará. Therefore, analyzing the region\'s socioeconomic indicators cataloged by Bezerra and Carvalho (2011), the municipalities of the state of Bahia were chosen, as these show similar characteristics to the municipalities of Ceará. Having defined the control group, it can also be perceived that there are public policies which are common to both states, which can influence the indicators related to the law (e.g., the PAIC actions and the PSF). Thus, taking that into account, it is necessary to give more structure to the ATT estimation. In that sense, when one observes the indicators for the treatment and control groups in two moments in time, t=0 for before the treatment and t=1 for after the treatment, a conventional ATT estimator in situations such as these is the differences in differences estimator (ATTDD) described below: