As in Ben Nassar, Martinez and Pineda (2014), equation (1) is estimated using a single-stage estimation procedure that employs an OLS-based Panel-Corrected Standard Errors (PCSE) approach, which is appropriate for time series cross-section data where N < T.<sup>3</sup> In estimating equation (1), it is necessary to settle the complications associated with error terms. First, the error terms for each bank are interdependent over time (autocorrelation). Second, as banks operate in the same industry and country, there is the possibility that the error terms are correlated between banks (contemporaneous correlation). Third, the errors tend to have non-constant variances between banks (heteroskedasticity).<sup>4</sup> For these reasons, the model is estimated using an OLS-based PCSE procedure, which improves parameter efficiency and generates more accurate z-statistics by correcting residual correlation between equations and cross-sectional heteroskedasticity (Beck and Katz 1995).

# **Dependent variable**

Bank interest spreads can be measured using both ex-ante and ex-post spreads.<sup>5</sup> The ex-ante spread is the difference between contractual rates charged to borrowers and those paid to depositors. The ex-post spread is the difference between banks’ actual interest revenue and their actual interest expense, usually divided by loans and deposits, respectively, or earning assets. The ex-post spread differs from the ex-ante spread by the amount of loan defaults. Therefore, ex-post spreads allow for a broader examination of the costs of financial intermediation.

Moreover, ex-post spreads allow for an analysis of the driving factors of financial intermediation efficiency in a general equilibrium setting, taking into account all bank operations. For example, banks may compensate for the high taxation of a certain form of intermediation by charging lower rates on other forms. This effect would not be captured by ex-ante spreads.

For all these reasons, this paper uses ex-post spreads. Specifically, ex-post spreads are narrowly calculated as the difference between interest received on loans divided by (net) loans and interest paid on deposits divided by total deposits. The text figure depicts the behavior of our dependent variable during the sample period (quarterly data are weighted averages of bank-specific net interest spreads, with weights given by banks’ total assets).

<!-- Start of picture text --> Net Interest Spread (%)<br>7.00<br>6.00<br>5.00<br>4.00<br>3.00<br>2.00<br>1.00<br>0.00<br>Source: Author’s elaboration on NBT data.<br>2011q1 2011q3 2012q1 2012q3 2013q1 2013q3 2014q1 2014q3 2015q1 2015q3 2016q1 2016q3 2017q1 2017q3 2018q1 2018q3 2019q1 2019q3 2020q1 2020q3 2021q1 2021q3 2022q1 2022q3<br><!-- End of picture text -->

> 3 Given the structure of the panel dataset, an alternative procedure that could be used is the feasible generalized least squares (FGLS). When comparing the performance of both estimators, the rule of thumb is that the OLS-PCSE estimator is preferable to 1 1 its FGLS counterpart when 2 (N<sup>2</sup> −N) > T (Jönsson 2005). For this paper, 2 (19<sup>2</sup> −19) = 171 > T = 48. 4 Standard tests confirm the presence of autocorrelation, heteroskedasticity and contemporaneous correlation in our panel data. Results are available upon request.

> 5 See Brock and Rojas-Suarez (2000) for a detailed discussion of the definitions of interest rate spreads.

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