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    • Afanasieff et al obtained results that show a relatively

    2018-10-23

    Afanasieff et al. (2002) obtained results that show a relatively more important role of the macroeconomic determinants of bank spread, although in the authors’ conclusion it was mentioned that these factors may have reached their “limit” of influence, being necessary to make alterations in the microeconomic scenario in order to reduce the level of banking spread in Brazil. Therefore, a model that takes into account both macroeconomic and microeconomic aspects may present a solution to the issue of the high level of banking spread in Brazil. In fact, the interest rate should be controlled as it determines the floor, since theoretically represents the minimum that the rate of spread can reach, but it should not be overlooked the role of BGB-324 in the banking sector to change the maximum levels of this rate charged by banks. Recently, the Brazilian government used the national public banks – Banco do Brasil (Brazil\'s Bank) and Caixa Econômica Federal (Brazilian Federal Savings Bank) – to stimulate competition and to put pressure on private banks to reduce their banking spreads and increase the Credit/GDP ratio. Programs like Banco do Brasil\'s “Bom Pra Todos” (“Good For All”) and Caixa Econômica Federal\'s “Caixa Melhor Crédito” (loosely translated as “Bank Better Credit”) – both started in April 2012 – seek to reverse the spread behavior practiced by the banking sector by encouraging competition. The measures adopted by the government met resistance in the Brazilian Federation of Banks (Febraban) who presented 20 proposals for improvements to reduce banking spreads that did not involve a reduction in interest rates, such as measures that affected the individual default or a reduction in reserve requirements and taxation. However, private banks relented and followed the movement of public banks, reducing their interest rates after the analysis of the measures implemented by the government. The goal of the Brazilian government is to improve the volume and conditions of bank loans in the economy. It is noteworthy that these measures adopted by the government were taken not only in order to reduce the banking spread in Brazil, but also to encourage investment and productive activity in the country. This question has always been among the priorities of economic policy, but after the 2008 crisis, has become greater.
    Methodology For the empirical analysis, the methodology used will be static and dynamic panel data. The “panel data suggest the existence of differential characteristics of individuals, understood as’ basic statistical unit” (Marques, 2000). A set of panel data, as explained by Wooldridge (2006), “consists of a time series for each member of the cross section data set”, and the same units of a cross section is observed over a particular period. One of the benefits of using panel data is that observation over time allows you to control certain unobservable characteristics of the chosen variable, solve the problem of omitted variables and also allows to “somehow fix the inconsistency of the estimated parameters of the models” (Silva and Martins, 2012, p. 17). In addition, the largest number of observations increases the degrees of freedom and efficiency of the estimated parameters, reducing the collinearity problem between explanatory variables (Silva and Cruz, 2004). The panel data static models can be subdivided into fixed effects models and random effects models, the explanatory variables being independent of the terms of disturbance. Of course, if one or more regressors are endogenous electrostatic attraction is necessary to apply the method of instrumental variables. In static models only contemporaneous explanatory variables affect the dependent variable (Wooldridge, 2006), in dynamic models is obtained the advantage of observing how the lagged variables – both the dependent variable as the explanatory – can help explain the dependent variable. This is a breakthrough of this model in relation to static models, given that many economic relationships are dynamic in nature. As discussed by Baltagi (2001), these dynamic relationships are characterized by the presence of the lagged dependent variable among the regressors. For dynamic models with panel data can be made the GMM (Generalized Method of Moments) estimate that seeks “to find a consistent estimator with a minimum of restrictions over the moments” (Marques, 2000, p. 41). A generic equation for this model can be presented aswhere δ is a scalar, x are the explanatory variables and u is the error term.