technique, with the weight given by the inverse of the number of banks for the country in each year. Finally, as an extension, the paper also estimates a model to explore potential variations in how bank, macroeconomic and structural, and institutional variables affect net interest margin at different levels of economic development: The sample is formed by an unbalanced panel of more than 14,000 banks in 160 countries for the period 2005-2014. Bank-level data are taken from the Bankscope database. We focus on depository financial institutions, including commercial banks, savings banks, and cooperative banks. To address issues of redundant financial statements in Bankscope, we adjust for mergers and acquisitions among banks and retain unconsolidated statements only for those with concurrent consolidated reports, using the procedure discussed in Duprey and Lé (2016). Structural, macroeconomic and institutional variables are drawn from the World Bank Global Financial Development Database, World Development Indicators, and Ease of Doing Business Indicators, respectively.

Our dependent variable, the net interest margin, is calculated as the ratio of total interest revenue minus total interest cost divided by average earning assets. In line with previous studies, this is our baseline measure of cost of financial intermediation. As a robustness check, however, we use the ratio of total interest revenue minus total interest cost divided by total assets.

We use a variety of control variables to explain variations in net interest margins. Specifically, we use the following set of bank-level variables:

- **Size of operations** : this variable is proxied by the log of gross loans. While ideally we would like to measure this variable by the average lending transaction size, data are not available for our sample so, in line with the previous studies, we resort to the whole loan book taken in log. The benchmark theoretical model posits a positive relationship between the size of bank operations and margins since, for a given credit and market risk, the larger the operation the larger the potential loss and hence the higher the margin the bank will demand. However, the presence of economies of scale stemming from size or diversification would suggest that the fixed costs associated with the transaction are spread over a larger base, enabling the bank to achieve a smaller margin. Therefore, we do not have a particular prior on the expected sign of this covariate.

- **Risk aversion** : as imperfect as it is, we proxy the bank’s degree of risk aversion by the ratio of equity to total assets, again, in line with similar studies. As equity is more costly than other sources of funding, a higher proportion of equity in the bank’s capital structure indicates greater risk aversion and is expected to be reflected in higher margins. Hence, the estimated coefficient is expected to be positive.

- **Opportunity cost of bank reserves** : this represents a regulatory and opportunity cost for the bank and is proxied by the ratio of cash and balances held at the central bank over total assets. The opportunity cost arises from the foregone interest the bank can earn from investing in higher-yielding assets the money it keeps in cash reserves. A positive association is therefore expected between this variable and net interest margins because banks must compensate for the missing interest.

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