metadata: authors: 0: author: Gregora,Jiri
1: author: Melecky,Ales
2: author: Melecky,Martin
count: World
docna: 0: docna: Interest Rate Pass-Through : A Meta-Analysis
            of the Literature
docty: Policy Research Working Paper
subtopic: Macroeconomic Management,Inflation,Financial Structures,Financial Crisis Management & Restructuring,International Trade and Trade Rules
teratopic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,International Economics and Trade
abstracts: cdata!: The interest rate pass-through describes
            how changes in a reference rate (the monetary policy, money
            market, or T-bill rate) transmit to bank lending rates. This
            paper reviews the empirical literature on the interest rate
            pass-through and systematizes it by means of meta-analysis
            and meta-regressions. The paper finds systematically lower
            estimated pass-through coefficients in studies that focus on
            transmission to long-term lending rates, consumer lending
            rates, and average lending rates. The interest rate
            pass-through is significantly influenced by country
            macro-financial and institutional factors. The estimated
            pass-through tends to be stronger for economies with deeper
            capital markets (measured by market capitalization).
            Interestingly, central bank independence rising from lower
            levels can reduce interest rate pass-through, while central
            bank independence rising from already high levels can boost
            the pass-through.
repnme: repnme: Interest Rate Pass-Through : A
            Meta-Analysis of the Literature
display_title: Interest Rate Pass-Through : A
            Meta-Analysis of the Literature
listing_relative_url: /research/2019/01/30770302/interest-rate-pass-through-meta-analysis-literature
topicv3: Inflation m1326709 0870F4727ACED1CF8525815C00471347,Trade m1326924 698,Central Banks m1327453 1235,Monetary Policy m1327609 444,Credit Risk m1505083 3885
publishtoextweb_dt: 2019-01-18T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,International Economics and Trade
no_of_pages: 53
author_keys: 0: author_key: 915923
model_extractions: WPS8713 Policy Research Working Paper 8713 Interest Rate Pass-Through A Meta-Analysis of the Literature _Jiří Gregor Aleš Melecký Martin Melecký_ Finance, Competitiveness and Innovation Global Practice January 2019
Policy Research Working Paper 8713

# **Abstract**

The interest rate pass-through describes how changes in a reference rate (the monetary policy, money market, or T-bill rate) transmit to bank lending rates. This paper reviews the empirical literature on the interest rate pass-through and systematizes it by means of meta-analysis and meta-regressions. The paper finds systematically lower estimated pass-through coefficients in studies that focus on transmission to long-term lending rates, consumer lending rates, and average lending rates. The interest rate pass-through is significantly influenced by country macro-financial and institutional factors. The estimated pass-through tends to be stronger for economies with deeper capital markets (measured by market capitalization). Interestingly, central bank independence rising from lower levels can reduce interest rate pass-through, while central bank independence rising from already high levels can boost the pass-through..

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at mmelecky@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Interest Rate Pass-Through:**

# **_A Meta-Analysis of the Literature_**<sup>**_*_**</sup>

Jiří Gregor<sup>a</sup> , Aleš Melecký<sup>a</sup> , Martin Melecký<sup>b</sup>

**Keywords:** Interest Rate Pass-Through, Literature Survey, Monetary Policy Rates, Money Market Rates, Bank Lending Rates, Meta-Analysis.

**JEL Classification:** E4, E5, C23

> * We thank Martin Cihak, Erik Feyen, Alfonso Garcia Mora, Mario Guadamillas, and the participants of the World Bank Finance, Competitiveness and Innovation Seminar for useful comments and suggestions on earlier drafts of this paper. Financial support from the Czech Science Foundation “16-13784S – Financial sector policy and institutions: Current challenges in balancing financial development and stability” is gratefully acknowledged.

> a Department of Economics, VSB-Technical University of Ostrava, Czech Republic.

> b Finance, Competitiveness, and Innovation Global Practice, World Bank, Washington DC, USA.
# **1. Introduction**

The interest rate channel of monetary policy is one important transmission channel through which monetary policy affects the real economy. The strength of the pass-through from the monetary policy rate to bank lending rates reflects the effectiveness of monetary policy and how the policy can help manage economic activity and stabilize prices. Central bankers are thus greatly interested in understanding the extent to which changes in the monetary policy rate transmit to lending rates and the factors that affect this transmission.

The interest rate pass-through is a keenly studied economic phenomenon. Because of imperfect information, competition, and other frictions, the pass-through from the monetary policy rate to various lending rates is, in practice, typically incomplete. The broader economic environment and various macro-financial factors may play an important role in different country contexts. Several recent studies summarize the empirical research on the interest rate pass-through. For instance, Sorensen and Werner (2006) survey 22 papers, covering aspects such as the type of data, econometric approach, reference rate, and time period. Andries and Billon (2016) review the empirical literature by comparing methodological approaches across studies and their implications for the estimated pass-through. Several papers also provide insights into the pass-through process from specific perspectives. For instance, Marotta (2009) reviews the literature on short-term business lending rates. Aziakpono and Wilson (2013) focus on the possible asymmetry in the interest rate pass-through, and Grigoli and Mota (2017) survey the pass-through studies for the Dominican Republic. Overall, these literature reviews confirm the heterogeneity in the interest rate pass-through across country contexts and lending rates. They suggest that the transmission of changes in the monetary policy rate to corporate lending rates may be the strongest, while the one to consumer lending rates may be the weakest.<sup>1</sup> Our paper contributes to the literature by conducting, to our knowledge, the first quantitative survey (a meta-analysis) of the empirical literature on the interest rate pass-through. In our meta-analysis, we

> 1 The literature also studies the transmission of changes in the monetary policy rate to deposit rates, which is documented to be weak on average. To ensure greater homogeneity of our meta-analysis, we focus our survey only on the transmission to lending rates.

2
cover both publications in peer-reviewed journals and the gray literature to address possible issues of a publication bias and consider the latest empirical research. Our dataset includes 52 studies and 1,040 extracted coefficients of the estimated interest rate pass-through. To summarize the empirical estimates from the literature, we start by describing the typical (most frequent) paper on the interest rate passthrough and highlight several relevant factors that might have been under-researched. The meta-analysis considers three types of factors affecting the pass-through estimates. First, we consider the individual characteristics of a study such as the functional form applied in the estimation, the type of data, and the impact factor of the journal/paper. Such consideration allows us to control for possible estimation biases due to different methodological approaches, data, and journal selectivity. Second, we consider the importance of the macro-financial environment. To this end, we use control variables for the level of economic development, openness of the economy, and financial depth, among others. Third, we consider monetary policy characteristics such as central bank independence, monetary policy framework, and exchange rate regime. The macro-financial and monetary policy factors can help identify the economic and institutional environment that boosts or reduces the transmission of the monetary policy rate to bank lending rates.

More than a thousand collected estimates from over 50 studies suggest the average interest rate passthrough to be around 0.8—disregarding research methodologies, modes of publication, and country macro-financial and institutional context. When controlling for these confounding factors in a metaregression, the average conditional pass-through is estimated to be lower, at about 0.6. The pass-through estimates for consumer lending rates, longer lending rates, and average lending rates tend to be significantly lower. Importantly, country macro-financial and institutional factors markedly influence the transmission process for interest rates. Countries with deeper capital markets experience a significantly higher interest rate pass-through. Furthermore, countries with more independent central banks could experience, on average, a lower interest rate pass-through. This average effect is likely driven by study estimates for countries with lower central bank independence and study estimates for the period before 1998. In contrast, studies for countries with a history of high central bank independence show a strongly positive effect of the independence on the pass-through. Alternative estimations confirm 3
the results, offering yet another possible explanation: Greater central bank transparency may encourage the market to react more through expectations. The pass-through may also decline because commercial banks respond to changes in the monetary policy rate even before they happen (Woodford, 2005; and Blinder et al., 2008).

The rest of the paper is organized as follows. Section 2 describes the theoretical underpinnings for the estimation of the interest rate pass-through and relates them to existing research. Section 3 describes the selection of studies for the meta-analysis. Section 4 shows descriptive statistics of the overall metasample and reviews the typical study estimating the interest rate pass-through. Section 5 describes the meta-regression analysis and discusses the estimation results. Section 6 performs robustness tests. Section 7 concludes.

# **2. Theoretical Underpinnings of the Empirical Literature**

The literature uses three reference rates to study the interest rate pass-through: the monetary policy rate, the money market rate, and the capital market rate. The effect of changes in the monetary policy rate—as a policy instrument—on the bank lending rates could be of interest to policymakers. Finance professionals interested in understanding bank lending behavior may be more interested in the effect of the money market rate on bank lending rates because the money market rate could better reflect the marginal cost of funds for bank loans (Borio and Fritz, 1995).

The empirical model of interest rate pass-through used in the literature can be broadly described as: where coefficient a represents a constant markup (the interest rate spread) over the reference rate and _b_ represents the pass-through coefficient. In conditions of a perfectly competitive market and perfect information, the pass-through coefficient, _b_ , would equal one. e� is the disturbance term. Although equation 1 might be the most common specification, alternative specifications do appear in the literature.

Some researchers expand equation 1 by including a vector X� to control for macro-financial factors such as bank competition, credit risk, liquidity premium, or some other variables that can influence the 4
<!-- Start of picture text --> t=—atb-reference rate, +c-X_+ e<br>t<br>interest rate spread(a); =a’ +c:X, +e<br>t=—at+b-refernce rate, +c-X;<br>+d-referencerate, ,+<br>b+d-X 7<br>i i<br><!-- End of picture text -->
For the meta-analysis, we collect the b coefficients from empirical studies of interest rate passthrough. In doing so, we focus on studies that estimate the interest rate pass-through using equation 1 or its extended specification as in equations 2 and 4.

The vector _X_ appearing in equations 2-5 typically includes variables capturing the overall macroeconomic environment and bank-level characteristics. For instance, Holton and d’Acri (2015) include into vector _X_ macroeconomic variables such as the government bond yield, unemployment rate, and inflation rate. Van Leuvensteijn et al. (2013) and Leroy and Luccote (2015) control for bank competition using the aggregate Boone and the Lerner indices, respectively, both as a stand-alone control and in an interaction with the money market rate (see equation 4). Studies also control for market expectation of future interest rates, including the spread between the government bond yield and the money market rate (Cifarelli and Paladino, 2016; Gregor and Melecký, 2018), the forecast of the money market rate (Banerjee et al., 2013), and the Volatility Index (VIX), which reflects the expectation of the market’s volatility over the next 30 days (Grigoli and Mota, 2017).

As for bank-level characteristics, researchers mostly control for credit risk using the ratio of nonperforming loans (Gambacorta et al., 2015; Grigolli and Mota, 2017; Gregor and Melecký, 2018) or determinants of credit risk such as the business climate indicators (Mihaylov, 2016) or the price of credit default swaps (Gambacorta et al., 2015). Other typical bank characteristics include bank assets, bank capital, the leverage ratio, the loans to deposits ratio, and the liquidity ratio (Horváth and Podpiera, 2012; Holton and d’Acri, 2015; Havránek et al., 2016).

The empirical literature, however, does not typically control for broader country context, which is understandable for bank-level studies for a single country. For instance, overall financial depth, capital market development, and penetration of financial services among firms and households may influence the intensity of the interest rate pass-through (Visco, 2007; Khan, 2011; Saborowski and Weber, 2013; Mehrotra and Yetman, 2015; IMF, 2016). Moreover, the intensity of the interest rate pass-through may vary depending on the monetary policy framework and institutions, such as central bank independence, in a given country (Frenkel, 1999; Kharroubi and Zampolli, 2016). The approach of meta-regression 6
analysis employed in this paper could shed some light on the role of a broader country context in the interest rate pass-through.

The global financial crisis marked a significant shift in the pass-through (Hristov et al., 2014; Aristei and Gallo, 2014; Gambacorta et al., 2015). The interest rate transmission lost much of its strength and the pass-through became more uniform across different lending rates. One explanation for the weakened transmission could be a greater sensitivity to risks and uncertainty in the post-crisis era. However, some studies such as von Borstel et al. (2016) argue that the interest rate pass-through remained strong and mostly complete even after the global financial crisis. They see the difference in the post-crisis transmission in the composition of the pass-through, but not in its overall strength. In particular, they decompose the pass-through from the monetary policy rate to bank lending rates into four individual components. Accordingly, they find that while the overall pass-through has remained unchanged, the individual components such as the term spread, sovereign risk, banks risk, and credit risk have seen significant but offsetting shifts.

# **3. Method of Paper Selection**

To select papers for our meta-analysis, we used the following search approach: First, we created a database of all studies focusing on the interest rate transmission. For the search, we used Google Scholar, Scopus, and the Web of Science. The key words entered in each database were “Interest Rate PassThrough” and “Interest Rate Transmission.” After the primary search, we also performed an alternative search for any other relevant studies cited in the papers retrieved in the first round. The search was completed by mid-2017. Therefore, studies published after July 2017 are not considered. The conducted search produced a data set of over 100 studies.

Not all the 100 studies, however, met our three selection criteria. First, we selected only studies published in peer-reviewed journals, and working, discussion, and research paper series registered in the REPEC database. Further, we selected only studies that estimated the coefficient of long-run interest rate pass-through. This step substantially reduced our data set. Third, we selected papers that publish 7
the estimated standard errors, standard deviations, t-statistics, or z-statistics. These measures of statistical inference are used in the meta-regression analysis to control for the precision of estimation.

The three co-authors of this paper independently evaluated each retrieved study in the original data set, compared their results, and reconciled their classification to arrive at the final sample of studies for the meta-analysis. The final sample of studies included 52 papers fulfilling all selection criteria. Table A1 in the Appendix lists all selected papers for the meta-analysis. Thirty studies were published in peerreviewed journals and the remaining 22 were classified as gray literature (working, discussion, and research papers). Figure 1 shows the breakdown of studies in our dataset by type.

## [Figure 1 about here]

As for the country focus of the studies, figure 2 shows that they focus mostly on the European Union (EU) countries. Some studies, such as Sander and Kleimeier (2003), among others, analyze the interest rate pass-through in more than one country. Thus, the total sum of the pass-through studies per country exceeds the total number of studies in our dataset. The interest rate transmission is most intensely examined in the old EU member states, particularly in Italy, Germany, France, and Spain.

[Figure 2 about here]

# **4. Meta-Analysis of Collected Pass-Through Coefficients**

We first extract 1,040 long-run pass-through coefficients ( _b_ ) from the 52 selected studies to analyze the estimated long-run pass-through coefficients and the factors influencing the intensity of the passthrough. Second, we collect or compute the estimate standard deviations of each estimated pass-through to control for the precision of the estimates. Figures A1 and A2 in the Appendix show the number of extracted pass-through coefficients by country and study.

Table 1 shows the descriptive statistics of collected pass-through coefficients and associated standard errors and t-statistics. The mean value of the pass-through across all studies is 0.8, which indicates an incomplete pass-through in general. In addition, the median value suggests a mild skewness 8
in the distribution of the collected coefficients. The maximum and minimum values show a wide range from 5.32 (Horváth and Podpiera, 2012, Table A4, for the Czech Republic and lending rates on large corporate loans with floating rate) to -27.1 (Horváth and Podpiera, 2012, Table A4, for the Czech Republic and consumer lending rate). Both extreme values are insignificant in the referenced study and in our estimation weighted down toward zero.

# [Table 1 about here]

Figure 3 plots the extracted interest rate pass-through coefficients against their reverse standard errors. The left panel displays all extracted coefficients, while the right panel omits extreme values,<sup>2</sup> thus offering a more detailed look at the structure of the sample. The full vertical line represents a complete interest rate pass-through (b�1). The dashed vertical line in the plot on the right-hand side shows the mean value of the extracted coefficients (see also Table 1). The histogram on the right-hand side shows that the empirical literature, on average, estimates an incomplete interest rate pass-through rather than an over-adjusting pass-through. The histogram also shows that values close to the mean are more precise (higher value of the inverse standard error) than values that are more distant from the mean.

# [Figure 3 about here]

# **4.1 The Typical Paper in the Literature**

The pass-through literature typically examines the transmission from the money market rate to shortterm lending rates (see figure 4). From 1,040 estimated pass-through coefficients, 888 are estimated using the money market rate as the reference rate. The rest of the estimates use as the reference rate either the monetary policy rate (71) or the capital market rate (81), represented by the yield on government bonds of different maturities (typically 2, 5, and 10 years). The literature examines the passthrough to short-term lending rates (576) slightly more often than to long-term lending rates (464).

> 2 We omit the extreme values only for visual effect in the graph. In the analysis, we keep those observations in the sample to cover as many observations as possible. Since we weight all coefficients by their standards errors, the extreme values have no power to bias the result of the estimation (for more detail see Methodology section).

9
# [Figure 4 about here]

Figure 5 shows that more than half of all estimates focus on the interest rate pass-through to corporate lending rates (541 estimates). The greater focus on corporate lending rates may originate from greater allocations of bank loan books to this market segment.<sup>3</sup> If we compare the amount of loans outstanding in individual segments across countries, we find that corporate loans usually occupy a leading position among credit segments, followed by mortgage and consumer loans. Researchers also focus on the pass-through to the average lending rate across all categories. Other lending rates include rates on loans such as business, debt consolidation, and education.

# [Figure 5 about here]

As for the estimation methodology, the typical paper estimates a single-equation model in an error correction form (ECM) for a single country using aggregate data in monthly frequency (see figure 6). Specifically, the approach for estimating the interest rate pass-through is primarily based on a singleequation model (948 estimates). Simple reduced-form models are thus most popular for examining the relationship between the reference rate and bank lending rates. These models are mostly based on the error correction form (790 estimates) using estimation methodologies such as autoregressive distributed lag (ARDL), mean group (MG), and pooled mean group (PMG) estimators.

As for the data, the studies use mostly aggregate (macro level) data for the whole banking system in a single country. More recent studies have been increasingly using micro (bank level) data for better identification. For instance, Holmes et al. (2015) examine the interest rate pass-through in Colombia using data for 15 banking institutions. Holton and d’Acri (2015) analyze the pass-through for 188 Euro Area banks, and Havránek et al. (2016) test the pass-through based on data from 52 Czech banks. Typically, the studies use data for a single country (967 estimates). Only a few studies estimate the passthrough using cross-country panel data (Bernhofer and van Treeck, 2013; Leroy and Lucotte, 2015). One reason may be that the economic, social, and political environment is specific to each country and researchers try to avoid this source of heterogeneity. To increase power of the estimates and help

> 3 The ECB Statistics Bulletin: <u>http://sdw.ecb.europa.eu/servlet/desis?node=1000004045.</u>

10
identification, the studies use micro panel data, higher frequency (monthly) data, or both. In our sample, the most studies use time series data in monthly frequency (940 estimates).

# [Figure 6 about here]

Typically, papers do not control for any confounding factors and estimate a bivariate relationship between the reference rate and a specific lending rate. However, some factors may indeed bias the passthrough coefficient (see Section 2). Therefore, we distinguish between literature that controls for confounding factors such as bank credit risk, bank competition, bank concentration, and the effect of the global financial crisis, and literature estimating only a bivariate relationship (see Figure 7). Studies control for credit risk and bank competition only rarely (59 and 71 estimates). This is rather surprising, considering that most studies are motivated by the Rousseas (1985) framework, which assigns a significant role to bank competition. Overall, 122 pass-through estimates control for a possible GFC effect using a crisis dummy. Somewhat less than half of these estimates consider the dummy also in an interaction with the reference rate (60).

# [Figure 7 about here]

Next, we review the quality of authors and the publication outlet in which the papers selected for our meta-analysis were published.

To measure the quality of authors, we use the individual author’s _h_ -index from the ResearchGate and Scopus databases. Because the _h_ -index of some co-authors is not available, we use the _h_ -index value for the co-author with the highest available _h_ -index. We construct three conditioning variables for the meta-regression based on the highest _h_ -index from among the co-authors. The first variable is constructed using the Scopus database. The second and third variables are constructed using the ResearchGate database, distinguishing between the overall _h_ -index and the _h_ -index that excludes selfcitations. Due to better data availability, we use the overall _h_ -index from the ResearchGate database in our baseline meta-regression. Figure 8 (left panel) shows the histogram of the highest _h_ -index from among the co-authors per study. The figure indicates that pass-through coefficients are most often published by authors with _h_ -index between 17.5 and 20.

11
To measure the quality of a paper, we use variables from the RePEc database. The database assesses both journal publications and the gray literature (working papers). In our baseline meta-regression, we use the simple impact factor. Figure 8 (right panel) displays the histogram of the simple impact factor. The figure shows that the pass-through coefficients in our sample are most often collected from studies with a simple impact factor of between five and six.

# [Figure 8 about here]

Table 2 shows the expanded descriptive statistics for author and study quality. While both _h_ -indices from ResearchGate, with and without self-citations, differ only slightly, the _h_ -index from the Scopus database shows substantially lower values. Nevertheless, the variation of the measures is comparable and the correlation coefficient between the _h_ -index from the ResearchGate and Scopus databases is 0.94. This high correlation confirms a similar assessment of author quality across the databases. Looking at the ratings of paper quality, one may notice greater differences among the various ratings, owing also to the different construction and scaling of the indices. Interestingly, for all measures of study quality the median value lies bellow the mean value, indicating similar skewness of the ratings’ distribution.

# [Table 2 about here]

To control for the difference between journal publications and the gray literature, we create a dummy variable ( _work_paper_ ) taking a value of one if the estimated coefficients are collected from working, discussion, or research papers, and zero otherwise. Figure 9 shows the distribution of the estimated coefficients between those collected from gray literature and those collected from journal publications. It reveals that our sample contains slightly more coefficients from journal publications. Using estimates from both the gray literature and journals helps us control for a possible publication bias. The problem of publication bias lies in the review process. Reviewers, especially if they are somehow involved in the topic of study, might force authors to re-estimate their models until the results match the generally known and desired ‘truth’ for a given area of research. Hence, some original research results may stay unpublished.

[Figure 9 about here] 12
Next, we review the country context in which the collected coefficients were estimated. We focus on macro-financial factors potentially relevant for the interest rate pass-through: GDP per capita ( _gdppc_ ), trade openness measured as exports over GDP ( _openness_ ), net foreign assets ( _nfa_ ), financial depth measured as credit to GDP ratio ( _credit_gdp_ ), market capitalization ( _capitalization_ ), turnover ratio for stocks ( _stock_turnover_ ), and claims on central government divided by GDP ( _claims_gov_gdp_ ). Each macro-financial variable is measured at the median year of the sample used in the original study. For example, if a research study uses a sample from 2006 to 2010, then the median year for this research is 2008. Table 3 displays descriptive statistics for all macro-financial variables.

# [Table 3 about here]

The third group of control variables covers monetary policy characteristics: a measure of central bank independence ( _cbi_G_ ), a dummy for inflation targeting framework ( _inf_target_ ), and a dummy for floating exchange rate arrangement ( _floating_exrate_ ). For the measure of central bank independence, we use the same approach as for macro-financial variables. That is, we choose the median year of the study’s sample and match it with the corresponding assessment of central bank independence from the Garriga (2016) data set.<sup>4</sup> Similarly, the median year of the sample determines the setting of the dummies for inflation targeting and floating exchange rate regime. Table A4 in the Appendix presents the descriptive statistics of the central bank independence variable.

The two dummy variables for the type of monetary policy framework and exchange rate arrangement are based on the Annual Reports on Exchange Arrangements and Exchange Restrictions from the IMF.<sup>5</sup> We differentiate inflation targeting from other monetary policy regimes. The corresponding dummy ( _inf_target_ ) takes a value of one if the country targets inflation, and zero otherwise. The dummy _floating_exrate_ then takes a value of one for a _de facto_ floating exchange rate arrangement as classified by the IMF, and zero otherwise. Figure 10 shows that a floating exchange rate

> 4 Garriga, Ana Carolina (2016). Central Bank Independence in the World: A New Data Set. _International Interactions_ , 42(5), pp. 849-868.

> 5 The reports are available online at the IMF website: https://www.imf.org/en/Publications/Annual-Report-on-Exchange-Arrangements-and- <u>Exchange-Restrictions/Issues/2017/01/25/Annual-Report-on-Exchange-Arrangements-and-Exchange-Restrictions-2016-43741.</u>

13
arrangement characterizes the country context for a little more than half of the collected coefficients. In contrast, an inflation targeting framework applies to only one quarter of the collected coefficients.

## [Figure 10 about here]

# **5. Meta-Regression Analysis**

The meta-regression analysis (MRA) is a method of quantitative literature review. It summarizes and explains the variation of different results across the same research phenomenon (Stanley et al., 2013). In the field of economics, this quantitative tool finds use in several areas. For instance, CrespoCuaresma et al. (2014) examine determinants of foreign currency loans in CESEE countries. Havránek et al. (2015) use meta-analysis to test the social cost of carbon, and Arnold et al. (2014) examine the determinants of corporate hedging. The diverse use of meta-regression analysis shows a strong interest and support for the use of this quantitative tool.

In the literature, meta-analyses mostly apply the estimation methodology formulated by Stanley and Jarrell (1989): where b� is the variable capturing the effect size of the research phenomenon—in our case the interest rate pass-through—α is a constant reflecting the true size of the effect, Z�� is a vector of control variables, and ε� is the error term. The subscript j stands for the number of studies in the sample, and _k_ represents the number of conditioning variables. According to Stanley and Jarrell (1989), Z�� might include: (i) dummy variables showing whether the original study includes or omits independent variables possibly relevant to the research, (ii) specification variables that capture the differences in methodology and data definitions or sources, (iii) variables reflecting the sample size, (iv) variables that capture specific authors’ characteristics, and (v) variables measuring the research or data quality.

We expand this model to add external factors such as macro-financial variables and monetary-policy characteristics to control for specific country context that could affect the interest rate pass-through.

14
K L k=1 l=1 R r=1 J J jk ir jl jk jk jk J jk jk jk jk K L R j k ik l jl j j j ~~La~~ j a ~~(—)~~ j +p+¥k=1 ~~4+~~ j y1=1 ~~4~~ j ~~y~~ yrar ~~2 yt~~ j
K L R ij ~~_~~ ij, «( ~~-)~~ +B . Z 1 , ij ~~=~~ ; <u>ij| ijl</u> ) ; ijur:

<u>eij</u> ij

|
pass-through (Table 5, column 1). Further, we sequentially add groups of variables into the model. First, we add the study-specific variables (Table 5, column 2), then the study-specific variables and the dummy variables for all lending rates except the corporate rate (Table 5, column 3), and lastly, we estimate the model based on the entire set of variables (Table 5, column 4).<sup>6</sup> To verify the results, we use General to Specific modeling approach (GETS) and display the results in the last column of Table 5.<sup>7</sup>

# [Table 5 about here]

The estimation results slightly vary across the meta-regression models. The variable measuring the precision of the interest rate pass-through ranges from 0.69 to 0.95. The lower precision is reported for models that include the macro-financial and policy variables. Therefore, the external environment largely influences the true size of the interest rate pass-through. Figure 11 plots extracted pass-through coefficients against their reverse standard errors. The graph on the left side shows the relationship for our estimated sample of 1,037 observations, but for clearer analysis, we also display a graph that excludes extreme values, showing the relationship based on 1,032 observations (Figure 11, graph on the right side). The right dashed vertical line represents a complete pass-through (equal to one), the left dashed vertical line shows the precise (true) pass-through estimated by the GETS model (0.69; Table 5, column 5), and the middle solid vertical line shows the unconditional mean of the interest rate passthrough measured as a simple mean of all collected pass-through coefficients (0.8; Table 1, first column). Both the unconditional mean and the precise pass-through estimate, α, indicate that the pass-through is overall incomplete. In addition, the discrepancy between the two estimates of 0.11 (0.80 minus 0.69)

> 6 Due to high correlation, GDP per capita, claims on central government, and credit to GDP ratio were excluded from the estimated regression.

7 The General to Specific variable selection approach (GETS) is a data driven methodology that allows selecting from the set of all variables potentially relevant to the specific research only the most important ones, using series of statistical tests. In this paper, we use the GETS modeling approach based on the algorithm defined by Clarke (2014). He applies the stepwise regression procedure, during which in each step the variable that shows the lowest t-statistic is removed (this is the first type of the search path; the second type uses second lowest t-statistics, the third type uses third lowest, and so on). This process continues until the final model is specified. When obtaining more than one final model (depending on the number of search paths), the algorithm chooses the final model using the encompassing procedure (for more information see Clarke, 2014). Employing Monte Carlo simulation, Clarke (2014) showed that the GETS modeling approach performs well and the results of the final model are approximately equal to an accurately specified model.

17
reveals that omitting relevant conditional variables can lead to an upward bias of the estimated passthrough.

The estimated constant, _β_ , capturing the asymmetry of the pass-through (Table 5, second row), indicates some asymmetry toward pass-through estimates smaller than one. This asymmetry is also apparent from Figure 11, which shows the histogram slightly skewed toward the left side. Therefore, estimated pass-through coefficients take values of less than one more often than value greater than one. Doucouliagos and Stanley (2013) infer that a statistically significant coefficient of asymmetry, _β_ , may reflect a publication selectivity. How large the selectivity is depends on the size of the coefficient. When the coefficient falls between one and two (in absolute terms), as is our case (Table 5, column 1), Doucouliagos and Stanley (2013) rate the publication selectivity as substantial. However, when we add controlling variables into the estimation, the size of the asymmetry drops and becomes statistically insignificant, showing only modest selectivity ( _β_ lower than one). In any case, possible publication selectivity may be attributed to missing competing theories—perhaps a demand-based theory of the interest rate pass-through. Based on the current, supply-side theory, researchers might be tempted to publish results that correspond to a complete or nearly complete interest rate pass-through, and less so results with an estimated pass-through greater than one.

# [Figure 11 about here]

Next, we discuss the effect of the three groups of factors (explanatory variables) reported in Table 4: study-specific, macro-financial, and monetary policy factors.

For the study-specific factors, we find a statistically significant effect of the maturity of loans on the size of the pass-through estimated in empirical studies. In general, lending rates on long-term loans respond less to changes in the reference rate than lending rates on short-term loans. All models concur on this result. One explanation could be that the lower responsiveness of rates at the long end of the yield curve stems from their dependence on additional and more uncertain factors, such as the risk premium, term premium, and long-term expectations. Further, we find a significant effect of two other variables. Namely, studies that control for credit risk and use a system of equations for their estimation 18
report higher pass-through on average. This shows that the estimation procedure could affect the identification and estimates of the interest rate pass-through. However, only fully specified models confirm the significance of those two variables (Table 5, column 4 and 5 column).

In addition, we test whether the pass-through differs with the type of lending rate. To do so we include in the meta-regression model the dummy variables for all lending rate types used in the passthrough literature, except the corporate rates that represent the reference category. The results suggest that the pass-through is significantly weaker for average lending rates and for lending rates on loans for other purposes compared with corporate rates. Hence, in the loan market, the corporate segment shows the highest elasticity to changes in monetary or money market rates, while the average rates and rates on loans for other purposes show the lowest elasticity.

For the macro-financial factors, we find only one statistically significant effect. Market capitalization positively influences the estimated size of the pass-through. Thus, in countries with a deeper banking (financial) system, the interest rate transmission seems to be stronger. This result concurs with the findings of Mehrotra and Yetman (2015) that the interest rate as a policy instrument tends to have a larger impact in countries with a higher share of financially included households. We also confirm the results by Singh et al. (2008) who report that developed financial markets aid a faster and stronger interest rate pass-through. The significance of market capitalization holds throughout all estimated models, suggesting a robust effect. In addition, the GETS procedure also selected openness as a variable that adds significant information to the regression as a whole. However, individually the estimated coefficient is not significant at the common levels. The estimated negative sign could stem from the increasing role of trade finance with higher levels of trade openness (Demirguc-Kunt and Maksimovic, 2001; Casey and O'Toole, 2014; and Carbó-Valverde et al., 2016).

For the monetary-policy factors, we find that higher central bank independence may reduce the magnitude of the interest rate pass-through. This negative average effect of central bank independence on the strength of the pass-through is rather puzzling. Although the estimated coefficient on central bank independence is significant only at the 10 percent level, we investigate possible explanations of this negative average effect next.

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# **5.2 Central Bank Independence and the Interest Rate Pass-Through**

To get deeper insight into the relationship between the central bank independence (CBI) and the strength of the interest rate pass-through, we first test whether the negative effect of CBI could vary at different CBI levels or occur only at some levels. To this end, we construct three 0/1 dummies for low, medium, and high levels of independence. The primary intention was to split the CBI into three equallysized groups. The CBI, however, contains numerous repeating values due to the inclusion of developed countries in the sample that have reported the same level of CBI over several years (mostly European countries). Thus, the creation of equally-sized groups was not possible. As a result, we create three unequally-sized groups based on the level of CBI. The group of the least independent central banks contains 390 observations that cover CBI in the range of 0.15 to 0.83. The group with medium CBI levels includes 555 observations covering CBIs in the range of 0.84 to 0.86, and the group with high CBI levels includes 92 observations covering CBIs in the range of 0.87 to 0.90. We rerun the GETS regressions sequentially including the three dummies for low, medium, and high levels of CBI. The results are reported in Table 6, columns 6-8.

# [Table 6 about here]

The estimation results using GETS suggest that low levels of CBI can negatively affect the passthrough. However, the estimated coefficient is not statistically significant at the common levels. The medium-level dummy was dropped by the GETS selection procedure, suggesting that the dummy turns out to be insignificant as well. In contrast, high levels of CBI could have overall a highly positive effect on the size of the pass-through (Table 6, column 8). Thus, the relationship between CBI and the interest rate pass-through appears to be negative only at lower levels of CBI, but as central bank independence increases, the effect of CBI on the pass-through becomes significantly positive.<sup>8</sup>

> 8 The negative effect of CBI on the pass-through might be associated with more discretionary (less predictive) monetary policy implementation in newly-independent central banks. Over time, as central bank core policy functions develop, their systematic component increases (discretionary component decreases). For instance, after adopting the inflation targeting regime the monetary policy rate typically adjusts in response to expected inflation and the output gap in a manner more predictable for the markets. This may help further strengthen central bank independence, policy predictability, and the interest rate pass-through.

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To further examine the relationship between central bank independence (CBI) and the strength of the interest rate pass-through, we replace our baseline measure of independence by Garriga (2016)— which enables us to maximize the estimation sample—with that of Dincer and Eichengreen (D&E; 2014) that can be complemented with a commensurate metric of central bank transparency. However, using the variables from D&E reduces our sample size by about 22 percent, eliminating all estimates with the median year before 1998 (175 estimations). Countries such as Chile, Ghana, Nigeria, and the Slovak Republic drop out of the sample as a result. The estimation results for this subsample using the alternative measure of CBI and the additional variable measuring central bank transparency (CBT) are reported in Table A5 in the Appendix (column 1). Due to increased collinearity among variables, only the GETS results are reported—that is, results after the general to specific variable selection procedure has been applied.

The results show a higher estimated precision of the pass-through compared with the full sample. One explanation might be lower credibility and transparency of monetary policy before 1998 (Crowe and Meade 2008; Blattner et al., 2008), which might have weakened the transmission of interest rates. Hence, the meta-analysis without study estimates from before 1998 shows a higher precision, implying a complete interest rate pass-through. This supplementary meta-regression supports our results of the baseline meta-regression. Namely, we confirm the statistical significance of the credit risk dummy, average lending rates, rates on loans for other purposes, and market capitalization. In addition, other factors turn out to be statistically significant. From the study-specific factors, we find that studies using panel data estimate a lower pass-through than studies using single dimensional data. This result highlights the importance of data structures for estimating the pass-through. From the macro-financial factors, we also find a statistically significant effect of trade openness—similar to our baseline model, in which the estimated negative coefficient did not reach the common significance levels. The removal of about 200 observations with the median year before 1998 leaves us with a sample that includes the pass-through estimates with median year ranging from 1998 to 2011. During those years, global trade, trade openness, and development of global value chains were on the rise (World Bank, 2017). The latter 21
brought about more supply-chain (intra-company) trade finance, which is less sensitive to changes in interest rates. This lower sensitivity implies a lower overall pass-through.

Interestingly, when we use the alternative measure of CBI by D&E (2014), we find a positive effect of CBI on the pass-through. We see two possible explanations. One, the negative effect prevails in the years before 1998, and for developing countries that dropped out of the alternative estimation sample such as Ghana, Nigeria, and Chile. Two, the negative effect could be largely driven by central bank transparency (CBT), the data on which was not available for our larger baseline sample. Here, one explanation could be that the more transparent the central bank, the higher the probability that the market will react through expectations (Swanson, 2006; Ehrmann et al., 2012). Therefore, commercial banks may respond to changes in the monetary policy rate before they actually happen, which would result in a lower observed pass-through.<sup>9</sup> In addition, commercial banks in a highly transparent monetary-policy environment may exhibit a smoothing behavior, which would result in lower pass-through as well (Kleimeier and Sander, 2006; Frappa et al., 2008; and Kwapil and Scharler, 2010).<sup>10</sup> Finally, the dummy variables for the inflation-targeting framework and floating exchange regime both turn out to be significant and positive in the alternative estimation. Assuming that, before 1998, central banks in general lacked experience with the “new” policy framework of inflation targeting, markets could have reacted less unanimously to interest rate changes during that period. Once central banks fully adopted inflation targeting, this framework increased the systematic response of central banks to market development, the predictability of interest rate changes, and the size of the pass-through (Davis et al., 2014), which, as a result, becomes statistically significant in our alternative estimation. Also, a greater number of exchange rate regimes have become more flexible after 1998, granting the

> 9 Woodford (2005) argues that market players are in general forward-looking and thus central bank communication could help drive market behavior alongside traditional policy tools. Blinder et al. (2008) concur, claiming that central banks affect the economy predominantly through the guidance of the future path of policy rates, rather than through the actual changes of policy rates that may follow.

> 10 Anticipating the future steps of the central bank, commercial banks adjust their lending rates only partly not following all hikes, which in general result in an incomplete pass-through. For instance, when bankers anticipate that the central bank will decrease the policy rate, they may

> decrease their lending rates smoothly and less drastically to avoid to sharp drops in loan pricing.

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<u>ijc</u> . ~~—~~ =a ~~(—)~~ + KLR ~~Ye~~ <u>kijck</u> ~~Ew~~ l ijcl ~~Eh~~ r ijcr 486 + Gye k=1 ye l=1 He rsa
where ξ�� � ��� ~~,~~ _i_ stands for the individual pass-through estimate, _j_ stands for studies, and _c_ stands for ���� countries. Hence, ω� represents the study-specific effects and θ� is the country-specific effects.

The estimation results of the mixed effects model in equation (9) are reported in Table 7. The results concur with our study fixed effect estimates. In addition, we identify five other significant factors. Namely, we find that researchers using panel data estimate a significantly greater strength of the interest rate pass-through, and those using cross-country data estimate a significantly lower strength. The results also suggest that while higher GDP per capita and stock turnover ratio weaken the transmission process, a higher inflow of foreign assets strengthens the pass-through of interest rates. Applying the GETS selection approach reduces the number of significant factors, however. We find a robust effect for only four study-specific factors (cross-country estimates, rates on loans with long-term maturity, consumer lending rates, and averaged lending rates), one macro-financial factor (market capitalization), and one monetary policy factor (central bank independence). Thus, the results from the GETS mixed effects model confirm the results from the GETS study fixed effects model. Two minor differences can still be identified. First, the coefficient of asymmetry (β) turns out to be insignificant. Recall however that the asymmetry in the GETS study fixed effects model equals -0.8523, which according to Doucouliagos and Stanley (2013) suggests a very modest or even no asymmetry. Second, the mixed effects model identifies the dummy for cross-country estimates as a significant study-based factor. It suggests that researchers estimating the pass-through using cross-country data typically find a lower pass-through strength than those with data for a single country.

# [Table 7 about here]

In addition, a large part of the sample covers Euro Area countries with ostensibly much greater institutional, legal, and structural homogeneity. We test the possibility that the results for our entire sample are driven solely by the Euro Area subsample, and do not apply to non-Euro Area countries. The estimation results for such split samples are reported in Table A6 in the Appendix (columns 2 and 3). Due to increased collinearity among variables, we report only the GETS results after the variable selection procedure has been applied.

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The estimation results for the Euro Area show a greater precision of the interest rate pass-through that the one estimated for the entire sample. The precision approaches one—that is, an almost complete pass-through. Interestingly, the Euro Area results reveal a substantial asymmetry of the pass-through. Therefore, for the Euro Area countries the study estimates may suffer from a significant publication selectivity bias. Regarding study-specific, macro-financial, and monetary policy factors, the results show a statistical significance of only three factors: market capitalization, the stock turnover ratio, and the dummy for rates on loans for other purposes. The significance of market capitalization and the dummy appear in all previous estimations regardless of the sample size and the estimation method. As for the stock turnover ratio, the effect seems to be rather small and statistically significant only at the 10% level. Nevertheless, it suggests that more actively-trading (liquid) stock markets can reduce the size of the pass-through.

For the non-EA countries, the estimation sample covers only about one-quarter of our original sample. Therefore, the regression estimation faces the problem of increased collinearity among variables, which could bias the results even after applying the GETS selection procedure. Nevertheless, the model still largely confirms the results of our previous estimations. We confirm significant effects of the dummy for rates on long-term loans, consumer loan rates, average rates, market capitalization, central bank independence (using the Garriga (2016) data series), and the inflation targeting regime. Several additional factors appear significant in this case. Namely, for the non-Euro Area countries, we find that using data in quarterly frequency reduces the size of the estimated pass-through. Further, using the monetary policy rate rather than the money market rate as the reference rate increases the size of the estimated pass-through. Looking at the macro-financial factors, two additional variables appear significant. One, the stock turnover ratio lowers the size of the pass-through—much more so than for the Euro Area subsample. This could suggest that active stock markets may serve as competing source of capital to finance both firms and banks alike. For this reason, changes in the central bank rate and money market rate might have a lower impact on funding costs of banks and firms—resulting in a lower interest rate pass-through. Two, increasing net foreign asset balances strengthen the interest rate passthrough. This estimated effect may imply that as more domestic savings are exported abroad (relative 25
to imported savings), the competition for the remaining savings in the domestic funding market increases, and so does the market’s sensitivity to changes in domestic interest rates. As a result, the interest rate pass-through rises as well.

Overall, the main results could be driven by the EA subsample, but they largely apply to non-EA countries as well. In fact, some estimates for the non-EA country subsample could also drive the overall significance of some variables in the baseline estimations.

# **7. Conclusion**

This paper reviewed the empirical literature on the pass-through of monetary policy, money market, and T-bill rates to bank lending rates. This review covered 52 studies, of which 30 were published in journals and the remaining 22 appeared in the gray literature (working and discussion papers). Overall, we observe that researchers typically focus on the transmission from the money market rate to shortterm lending rates for non-financial companies. To examine such a transmission, they typically employ single-equation models in the error correction form using aggregate data for a single country in monthly frequency. The collected estimates from the reviewed studies suggest the average interest rate passthrough could be about 0.8—disregarding research methodologies, modes of publication, as well as country macro-financial and institutional context.

Using meta-data for the reviewed studies and collected estimates, this paper also conducted a quantitative review of the literature by running meta-regressions to explain the pass-through estimates across studies. First, the paper attempted to identify any research and methodological biases that could exist in the literature and affect the size of researchers’ pass-through estimates. To that end, the metaregressions controlled for study-specific characteristics such as the functional form, type of data, possibly omitted variables, sample size, authors’ characteristics, quality of research, type of reference rate, and type of examined lending rate. We found that studies using cross-country data report lower estimates of the pass-through than studies using single-country data. Controlling for the type of lending rate, we find a weaker pass-through to consumer rates, to average rates, and to rates on loans with long26
term maturity. This has important implications for central bankers, who might be advised not to expect to affect pricing of consumer loans as effectively as shorter-term corporate loans.

Second, the paper tried to explain the variation in pass-through estimates across studies by controlling for macro-financial and institutional developments. The considered macro-financial factors included credit to GDP, openness, GDP per capita, market capitalization, stock-turnover ratio, net foreign assets, and claims on central government. The relevant institutional development reflected central bank independence, inflation targeting, and floating exchange rate regime. Absent the supporting macro-financial and institutional environment, the average size of the pass-through falls to about 0.6— much lower than the unconditional mean estimate across studies of 0.8. Namely, one robust finding is that countries with deeper capital markets experience a higher interest rate pass-through. Encouraging further capital market deepening could thus help policymakers improve interest rate transmission and potentially also better manage the credit and business cycles. For example, increasing the depth of the capital market (market capitalization to GDP) from 30% to 90% can boost the pass-through from 0.59 to 0.68.

Furthermore, we found that greater central bank independence may lower the interest rate passthrough on average. Further examining this average effect showed that study estimates for countries with lower central bank independence and study estimates for the period before 1998 could drive this average negative estimate. In contrast, countries with high central bank independence experience a strong positive effect of this variable on the pass-through. Also, robustness checks using an alternative measure of central bank independence confirmed the results. In addition, they offered the alternative explanation that greater central bank transparency might encourage the market to react more through expectations. In such cases, the pass-through declines because commercial banks may respond to changes in the monetary policy rate before they actually happen (Woodford, 2005; and Blinder et al., 2008).

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