CÓDIGO ORGÁNICO DE LA PRODUCCIÓN COMERCIO E INVERSIONES

Most empirical studies attempting to measure the level of liquidity created in traditional financial intermediaries have surfaced in the past 5 years. An earlier attempt to measure levels of liquidity was introduced by Deep and Schaefer, in 2004. The authors used loan maturity data from the largest US banks in the time period 1997 to 2001, to calculate the ‘liquidity gap’ between illiquid liabilities and working capital. In the study, Deep and Schaefer propose measuring the distribution of liquidity in financial intermediaries, by calculating the ‘liquidity gap’ between total liquid liabilities and assets, scaled by total assets. The values are from -1 to 1, with higher values denoting a higher level of liquidity creation. A positive value indicates that the financial intermediary finances illiquid assets with liquid liabilities, and through it, creates liquidity. They classify liabilities and assets solely based on the lengths of their maturity, and specifically exclude off-balance sheet activities such as loan commitments, citing their contingent nature (Schaefer and Deep, 2004).

The authors find that the liquidity gap on average is small, approximately 20% for large US banks. They conclude that the low level of liquidity created is not due to the presence of deposit insurance, since the deposit insurance tends to replace uninsured liabilities instead of expanding the existing loans or deposit base. Instead, the authors argue that the dampening of liquidity transformation should be found in credit risk (Schaefer and Deep, 2004).

A more recent attempt at calculating liquidity produced for the market, is done in a study by Berger and Bouwman (2009), who use data from US banks with assets over $1 million, in the time period 1993 to 2003. The authors find that as much as 80% of all liquidity created in the US market, is created by banks with assets over $1 billion, lending credence to the notion ‘too big to fail’.

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Berger and Bouwman propose a way to measure liquidity creation, based on four distinct calculation measures: cat fat, cat non-fat, mat fat and mat-non-fat. Mat denotes a classification based on maturity, where the cat methodology classifies assets and liabilities by product. Fat and non-fat defines whether or not the methodology includes off-balance sheet activities. The preferred measure for Berger and Bouwman is category-based classification including off-balance sheet activities (cat fat). Their mat non-fat measurement, in which the classification of liabilities and assets is based entirely on maturity and off- balance sheet activities are not included, is conceptually identical to Deep and Schaefer’s approach. Unlike Deep and Schaefer, Berger and Bouwman include most commercial banks, as opposed to focusing strictly on the largest intermediaries. They do, however, exclude banks that do not take deposits, as well as banks with negative equity.

Berger and Bouwman specifically include off-balance sheet activities, arguing that findings by Holmström and Tirole (1998) and Rajan et al. (2002) suggest that liquidity creation also can be accomplished through off-balance sheet activities such as loan commitments and other claims to liquid funds (Berger and Bouwman, 2009).

The study shows how the liquidity created by the US banks almost doubled between 1993 and 2003, increasing incrementally every year. The banks created €2843 trillion liquidity in 2003, which was 4.56 times the level of overall bank capital, and 39% of their total assets (Berger and Bouwman 2009). Looking specifically at the relationship between capital and liquidity creation, Berger and Bouwman find that the relationship is positive for large and medium banks, while negative for smaller banks (Berger and Bouwman 2009).

A number of empirical studies have since used the Berger and Bouwman measurement of liquidity creation, including the work by Fidrmuc, et al. (2015). Here, the authors look at the impact of deposit insurance on bank capital and liquidity creation in Russian banks, where deposit insurance was introduced in 2004. The study finds a negative correlation between bank capital and the creation of liquidity, both before and after the introduction of deposit insurance. Additionally, they find that the strength of the impact varies with size and ownership: they observe no significant correlation for large, foreign or state-owned banks, while they find a significant negative relationship for private, small and medium domestic banks (Fidrmuc et al., 2015).

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Another recent study using the Berger and Bouwman measurement of liquidity creation, is a 2014 study of Czech banks from 2002 to 2010, by Horvath, Seidler and Weill (Horvath et al., 2013). The authors study the causal relationship between capital and liquidity creation, by running a series of Granger-causality tests. Like Berger and Bouwman, they find a consistent increase of liquidity creation through the observed period, and note negative impact on liquidity creation by capital in the Czech banks, which fall within the ‘small’ category of Berger and Bouwman. (Horvath et al., 2013).

While Fidrmuc et al. (2015) and Horvath et al. (2013) found a negative relationship between bank capital and liquidity creation for small financial intermediaries, Rauch et al. find no relationship between the size and liquidity creation in German savings banks (Rauch et al., 2009).35 Employing data from 457 savings banks between 1997 and 2006, the authors use Deep and Schaefer’s LT Gap, as well as the Berger-Bouwman measurement of absolute amounts of liquidity created for the market. They find a significant positive correlation between liquidity creation and the general health of the economy, as well as a positive and significant effect of the yield curve spread, suggesting that a lower ECB refinancing rate positively impacts liquidity creation (Rauch et al., 2009:21). The authors argue that, due to a strict regulatory framework, the German savings banks do not have to hold large buffers of liquid capital for risk management reasons, but instead that the use of relationship lending increases the quality and scope of their monitoring abilities, and enables the intermediaries to better anticipate loan defaults (Rauch et al., 2009:22).

As can be seen from the discussion above, previous studies of liquidity creation have focused entirely on the banking sector in developed and emerging markets. In the field of microfinance, there exists very literature discussing liquidity at all, and to my knowledge, no previous research has attempted to evaluate the ability of MFIs to function as traditional liquidity creators. Further insight into this will help policymakers and practitioners determine the best use of microfinance from a macroeconomic point of view, and help

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Most empirical studies of liquidity (e.g. Berger and Bouwman, 2009; Fungacova et al. 2017 and Horvath et al., 2013) find a negative correlation between small banks and LC, and use assets as their determinant of size. However, Rauch et al. use the number of borrowers, which might explain the conflicting results.

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expand the understanding of the liquidity transformation processes in microfinance, as well as its drivers.36