Demand and Supply Drivers of Foreign Currency Loans in CEECs: A Meta-Analysis
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Demand and Supply Drivers of Foreign Currency
Loans in CEECs: A Meta-Analysis
September 2013
Abstract
We present a meta-analysis of the determinants of foreign currency loans in
Central and Eastern Europe. We base our inferences on the results of 21
studies that provide around 800 estimated coefficients for seven
determinants of foreign currency loan demand. Our results indicate that, on
average, supply factors (foreign currency deposits and the minimum
variance portfolio ratio) appear to play a more significant role than demand
factors (interest rate differentials) of foreign currency loan. Moreover, we
show that the estimates reported in literature tend to be influenced by
selected study characteristics such as the econometric methodology and their
regional focus.
JEL: E51, F31, C11.
Keywords: Foreign currency loans, meta-analysis, publication bias, meta-regression, Bayesian
model averaging.1 Introduction
As part of their financial integration and catching-up process, several Central and Eastern
European Countries (CEECs) have built up high stocks of assets and liabilities denominated
in foreign currency (mainly in euro or Swiss franc). As a result, high shares of unhedged
foreign currency loans have fuelled fears concerning systemic risks with regard to financial
vulnerability (see for example Beckmann et al., 2012, EBRD Transition Report 2011).The
analysis of the exposure of households and firms to foreign currency risks in CEECs has been
gaining importance in the last few years and numerous studies have been published on this
topic. Despite a large number of publications on this issue, especially in the recent years, there
is no unique agreement in the literature concerning the main driving factors of foreign
currency loans in the region (Crespo Cuaresma et al., 2011, Zettelmeyer et al., 2010,). Thus,
this contribution presents a meta-analysis of foreign currency loan determinants in CEECs,
including publication bias analysis and the explicit assessment of model uncertainty in the
specification of meta-analytic regressions. To the best of our knowledge, these issues have
hardly been analyzed in the previous literature. In addition, besides applying conventional
estimation approaches for meta-analysis (e.g. the random effects model, random effects
maximum likelihood model, and weighted least squares), we use Bayesian model averaging
(BMA) methods to assess the degree of model uncertainty attached to the meta-regressions
for foreign currency loans.
The presence of foreign currency loans dominates the private sector indebtedness in some
CEECs, while foreign currency loans are basically nonexistent in others. For instance, in the
Czech Republic, at one side of the spectrum, the share of foreign currency-denominated
private debt has remained low and relatively constant over the past years, being only present
in the corporate sector while almost absent in the household sector (Fidrmuc et al., 2012). On
the contrary, the shares of foreign currency loans have increased rapidly to about two thirds of
total loan stocks in South East European countries (e.g. Croatia and Serbia). Moreover, the
phenomenon of foreign currency loans quickly extended from corporate lending to household
loans (especially in Hungary and Romania).
The previous empirical and theoretical literature has identified several determinants of foreign
currency loans in CEECs. The determinants which are most often cited in the studies
surveyed include the interest rate differential, the inflation rate, the exchange rate changes, the
volatilities of inflation and the exchange rate and their ratio (the so called minimum variance
2portfolio ratio), as well as the degree of bank funding in foreign currency.1 These
determinants reflect demand and supply factors, as well as the interaction of both of them.
Therefore, the impact of some determinants may be ambiguous, depending on whether supply
or demand motives dominate. While it is difficult to assess the actual importance of demand
and supply factors for the individual determinants, we can expect that demand factors
dominate the role of the interest rate differential. In contrast, the asset structure of banks can
be largely viewed as a supply factor. One interesting finding of our contribution is that the
banks’ deposits in foreign currency and the portfolio factors (MVP-ratio) are highly robust
determinants of differences in foreign currency loans, while the demand-driven factors yield a
much less clear-cut picture. This can be interpreted as an indication that supply factors have
actually played a more important role in foreign currency loans than generally argued in the
literature (see e.g. Brown and De Haas, 2012, Brown et al., 2010 and 2011, Fidrmuc et al.,
2013).
Additionally, empirical studies related to foreign currency loans in the CEECs are inherently
characterized by significant data problems, a characteristic which is common to many other
fields in the natural and social sciences. Meta-analyses of existing studies on a common topic
have been suggested as a potentially fruitful way of overcoming such a problem and gaining
more powerful results (see e.g. Lipsey and Willson, 2001) by extending the analysis beyond
standard literature surveys. In addition to providing a more precise aggregate view on the
subject, this approach allows for the analysis of possible factors which may influence the
results (e.g. methodology used, data definition, characteristics of the authors). In the past
decade, the use of meta-analysis has become a popular research tool in economics (see e.g.
Stanley, 2001, Stanley and Jarrell, 2005), in particular in monetary economics (see Knell and
Stix, 2005 and Rose and Stanley, 2005, ), and in international economics (see Havránek et al.,
2013, Iršová and Havránek, 2013, Hanousek et al., 2011). Transition economics, being
especially restricted by data problems, has also become a field of meta-applications (see
Campos and Coricelli, 2002, Fidrmuc and Korhonen, 2006).
Inference related to the determinants of foreign currency loans is a particularly appropriate
field for a meta-analysis. On the one hand, the recent financial crisis has fuelled a general
interest for the determinants of foreign currency loans, in particular with the aim of improving
the design of economic policy actions. Hence, the number of studies based both on aggregate
1
We do not cover some possible special aspects as for example the perspective of EU accession and euro
adoption (see for example Rosenberg and Tirpák, 2009) due to their seldom inclusion in the empirical studies
and thus the low number of coefficients.
3and microeconomic data has mushroomed in the recent years. On the other hand, the
robustness of results reported in the literature may be questionable because of the reasons
sketched out above. In fact, a central finding of our meta-analysis is that previous studies on
foreign currency loans succeed in explaining only a minor fraction of foreign currency
lending developments. The majority of coefficients reported for the most common
determinants of foreign currency loans are statistically insignificant even at the marginal level
of 10%. Does this mean that standard economic theory fails to explain the credit behavior in
euroized countries? We try to answer this question and show that only supply factors are
relatively robust determinants of foreign currency loans.
The paper is structured as follows. Section 2 presents a literature review on the determinants
of foreign currency loans in CEECs with the aim of identifying the most common explanatory
factors. Section 3 then describes the meta-analysis framework. Section 4 provides the first
descriptive evidence of the effects of the selected determinants of foreign currency loans and
section 5 continues with the analysis of the publication bias. Section 6 discusses the results of
the meta-regression and the robustness of the effects. This analysis is extended by a Bayesian
model averaging exercise in section 7. The last section concludes.
2 The Determinants of Foreign Currency Loans: A Review
While loan dollarization emerged initially in Latin American countries and early studies
analyzed the determinants of the borrowing and lending behavior in foreign currency in this
region (e.g. Barajas and Morales, 2003), the number of studies on private sector dollarization
in CEECs has substantially increased during the past few years. While an increasing number
of empirical studies in recent years are based on survey-based (micro) data, the majority of
studies use aggregate data. Therefore they mainly concentrate on the effects of macro-level
determinants such as inflation, exchange rate depreciation and their volatilities as well as
foreign currency deposits and the interest rate differential. The literature on loan dollarization
in the private sector discusses three major factors. The traditional demand side determinants
in particular include interest rate differential, while the supply side determinants tend to
include the degree of deposit dollarization. This indicator is often explained by the minimum
variance portfolio (MVP) approach following Ize and Levy-Yeyati (2003). Finally the last
major factor is the ambiguous effects which exchange rates and inflation can exert on foreign
currency loans, as these variables are able to reflect both demand and supply side factors
(Kapounek and Lacina, 2011).
4Turning first to the determinants on the demand side; the differential between domestic and
euro area loan interest rates is included in most of the empirical studies as an explanatory
variable. On the one hand, it reflects the relative price of foreign currency loans. Hence, a
higher interest rate differential would induce more borrowing in foreign currency. On the
other hand, the real interest rate differential is influenced by macroeconomic stability and its
significance could be a result of the trade-off between currency risk (in the case of a large
devaluation of the domestic currency) and real interest rate risk (in the case of a lower-than-
expected inflation rate). Such a positive effect has been documented for economies of Latin
America (e.g. Barajas and Morales, 2003), while the findings for the CEECs are rather mixed.
Égert et al. (2007) document that borrowers in countries with a credible peg or currency board
regime react more strongly to differentials between local and euro interest rates than
borrowers in countries with flexible exchange rate regimes. Similarly, Rosenberg and Tirpák
(2009) analyze the determinants of private sector loans in euro and Swiss francs in the new
EU Member States and in Croatia and find that the interest rate differential is a robust
determinant of foreign currency loans. In contrast, using bank-level data, Brown and De Haas
(2010) do not find a significant impact of the interest rate differential (as compared to the euro
area) on foreign currency loans.
In turn, the supply side factors are usually proxied by the banks’ funding in foreign currency.
Basso et al. (2011) argue that currency matching plays a major role in the choice of the
currency denomination by borrowers. Not only do banks try to match their open foreign
currency positions as a result of regulatory measures (see e.g. Luca and Petrova, 2008), the
borrowers also want to match their foreign currency deposits and lending (as shown by Brown
and De Haas, 2010). In addition, the large share of remittances in some CEECs (e.g. Bosnia
and Herzegovina, Albania, and Romania) also strengthens the matching willingness of the
borrowers.
Moreover, the MVP-ratio (Ize and Levy-Yeyati, 2003) can also be considered as a
predominant supply-side determinant of foreign currency loans. The MVP approach possibly
receives the most attention in the empirical analysis of foreign currency loans. The concept
derives the optimal share of foreign assets as the ratio of the inflation volatility and real
exchange rate volatility,2
2 s
MVP 2 , (1)
ss2 2 s
2
The importance of the link between inflation and exchange rate volatility for Central and Eastern European
economies has been shown for example by Kočenda and Poghosyan (2009).
5where σ is the variance or covariance of inflation, , and changes in the real exchange rate, s.
The MVP-ratio is based on expectations about future developments of the particular factors,
although the empirical literature usually substitutes realized data for the respective
expectations of inflation and real exchange rate volatility.
Basso et al. (2011) confirm that a higher MVP induces a statistically significant higher degree
of both deposit and loan dollarization in CEECs, although the effect is negligible in economic
terms. By contrast, Neanidis and Savva (2009) find no relationship between loan dollarization
and the MVP indicator in the short run. Fidrmuc et al. (2013) use survey data on households’
portfolio behavior, and find MVP to be a significant and robust determinant of households’
foreign currency loans.
Finally, the remaining determinants of foreign currency loans could simultaneously express
demand and as well as supply side factors. For instance, higher inflation volatility induces
more borrowing in foreign currency due to the fact that it can be associated with more stable
real interest rates than borrowing in local currency. Hence, the impact of this factor on foreign
currency loans depends on the trade-off from the borrower’s and lender’s perspective between
currency and real interest rate risks. The empirical literature has shown that the problem of
high inflation is less dominant in EU countries. Studies based on aggregate data and survey-
based studies generally show a positive effect on loan dollarization (e.g. Zettelmeyer et al.,
2010), although some studies also show a significant negative effect from inflation (e.g.
Steiner, 2011).
Several empirical studies on foreign currency lending in CEECs have also included the real
exchange rate depreciation in the domestic currency and its volatility, which can also be
determined by demand and supply side factors. The exchange rate is an important factor for
output dynamics (Kočenda et al., 2012), thus often fueling lending booms. Moreover, the
theoretical impact of exchange rate depreciation may also be ambiguous, as it can have a
different impact on the behavior of lenders and borrowers. Lenders may try to shift the
exchange rate risks to the borrowers, who in turn would try to avoid it. Empirical evidence
suggests that a negative impact reflects the credit default risk of unhedged loans and a
positive influence could emerge from the expected stability of repayment rates.3 Barajas and
Morales (2003) provide evidence for Latin American countries in which exchange rate
volatility tends to reduce credit dollarization in the short run. Luca and Petrova (2008)
confirm this result for a large set consisting of 21 transition countries. Rosenberg and Tirpák
(2009) find that exchange rate volatility has negative but small effects on the share of foreign
3
The exact effect depends on the elasticity of the euro loan default probability and the rate of depreciation.
6currency loans in the new EU Member States and Croatia. In contrast to this, past exchange
rate volatility is not found to play a significant role, which has been explained by the increase
of the perceived stability of the exchange rate due to the EU membership making the
economic agents more willing to accept currency risks.
3 A Meta-Analysis of the Determinants of Foreign Currency Loans
Empirical studies on the determinants of foreign currency loans tend to build upon linear
regression models of the following type,
FCLit X it it , (2)
where FCL stands for the corresponding share of foreign currency loans or the change in this
share, X is a vector of variables that explains the differences in the dependent variable and is
an error term assumed to fulfill the usual assumptions of the standard linear regression model.
Usually equation (2) is estimated for sectors in one or more countries, which are indexed by i,
while the time period is indexed by t.
Similarly, microeconomic (survey) studies, where the dependent variable is a dummy which
measures whether a given individual borrower (firm or household) has taken a foreign
currency loan, estimate usually the following limited dependent variable model
P(FCLit = 1 | X ) = F ( + X it ), (3)
where F(.) is a nonlinear function, usually the cumulative normal distribution function for
probit models or the logistic function for logit models. Similarly to the previous estimation,
equation (3) is estimated for individual borrowers (possibly in several countries) indexed by i
over time t. Although the comparability of micro econometric results with macroeconomic
studies is questionable, it is to be kept in mind that all the reviewed studies using
specifications such as those in equation (3) report marginal effects whose interpretation is
similar to the elasticities reported in a standard OLS regression. Moreover, the conditional
expectation of the dependent variable in equation (3), E( FCLit | X it ) , can be interpreted as the
share of foreign currency loans held by individuals (households) or a firms in the whole
population of individual firms or households.
Using the corresponding parameter estimates from the 21 studies (see Table A.1 in appendix)
that dealt with the determinants of foreign currency loans in CEECs, we estimate meta-
regression models which aim to explain the differences in the estimated coefficients. A typical
meta-regression is therefore given by
̂ lm Dlm ulm (4)
7where ˆ is the estimate corresponding to variable l in study m and D is a vector containing
variables reflecting various characteristics of the study. It is further assumed that u is the
regression error term, which may have a different distribution for each of the analyzed
studies. The vector D includes both continuous and dummy variables, which summarizes
information related to data definition, data structure, estimation method, publication and
included control variables.
As control variables in our meta-regressions we include information on the sample used and
the characteristics of the study (for the exact definitions of the control variables, see table A.2
in the appendix). The year of publication of the study shows whether there is a trend in the
overall analysis of foreign currency loans. This could correspond to actual structural changes
(e.g. the increasing role of foreign currency loans, changes in the strategies of foreign banks
acting in CEECs) or to generally accepted views on determinants of foreign currency loans.
Next, we differentiate between publications using aggregate data (base category) and micro-
econometric datasets. We define dummy variables for models with fixed effects (such as
country, region or firm fixed effects)4 and special factors that are not frequently included in
the estimation. Such factors are for example the foreign currency income of the borrowers
(from own export activities in the case of firms or remittances in the case of households), as
well as an EU enlargement variable, which indicates whether a study accounts for the EU
accession or the expectation of euro adoption. We also include a dummy variable for
publications after the beginning of the recent financial crisis. Further variables describe the
geographic focus of the paper, e.g. data including Latin American and former Soviet Union
countries, and the exclusion of currency board countries (Bulgaria, Bosnia-Herzegovina, and
the Baltic states) from the sample. With the exception of the publication year variable, which
is measured as the deviation from the mean publication year in our sample, all other variables
are binary covariates. Since not all regression models reported in the studies in our sample
include information concerning these covariates, our meta-regression specifications do not
include all these variables for each of the parameters of interest.
We present several estimates based on the alternative specifications of the equation (4). First,
we start with a random effects model that does not explicitly take into account the relative
precision of individual estimates (i.e. their significance). We consider possible
heteroscedasticity in the meta-regression and report standard errors clustered at the level of
individual studies. In addition, we employ a weighted least squares (WLS) estimation by
4
Particularly studies using survey-level data (e.g. Brown et al., 2009 and 2011) have underlined the importance
of including country fixed effects for the statistical significance of the coefficient estimates.
8using the precision of each parameter estimate (measured by the inverse of their standard
errors or standard deviation) as a weight in the regression. This approach is consistent, for
instance, with Knell and Stix (2005), although the controversy of the weighting approach has
been acknowledged by various authors (e.g. Krueger, 2003).
While the application of WLS can account for the relative precision of the estimates within
the specification given by (4), it cannot deal with the potential heterogeneity in estimates
across studies. In particular, if we assume that the true value of 1 can only be imperfectly
approximated by Dm , so that l Dlm l , where l is a normally distributed
random variable with zero mean and variance 2 equal to the standard error reported for ̂ lm
in individual studies, then (4) can be written as
̂ lm Dlm l ulm . (5)
Under the assumption that l and u lm are uncorrelated, the model in equation (5) can be
estimated by using random maximum likelihood methods (see e.g. Thompson and Sharp,
1999). This specification is thus able to account for the between-study variance, 2 , and the
individual variance of the estimate when accounting for the relative precision across the
observed values of ̂ lm .
4 Meta-Statistics
For our meta-analysis we employ estimates from 21 papers which conduct empirical work
with the use of data on foreign currency loans in CEECs.5 Together, they provide nearly 800
estimates for seven determinants, with the interest rate differential being most often included
in the studies. Most of these studies rely on aggregate data (estimating the share or the change
in the share of foreign currency loans in the private sector as the main variable of interest)
although a growing number of studies are being based on survey-level data of firms or
households. Actually, an advantage of our regional focus on CEECs as opposed to Latin
5
We searched EconLit Database using keywords “foreign currency” and “loans”, “lending” or “borrowing”,
which were reviewed to check whether they presented regression analysis of determinants of foreign currency
loans in CEECs. This search identified about 20 studies. Several papers were published first as working papers
and then as journal articles. Both versions were surveyed and included in the meta-regressions unless the journal
article was fully identical to the working paper version. We completed the meta-analysis by few studies (PhD
thesis and forthcoming papers) which were quoted by other sources. The exclusion of these studies has no
influence on the results. The literature search was carried out in September 2012.
9American countries is the availability of results from survey data (mostly data on firms; two
papers use bank-level data and two papers include household survey data).
Table 1 reports some descriptive statistics for the coefficients of the determinants in the
studies surveyed. The coefficients of the determinants of foreign currency loans seem to be
rather similar despite differences in data types or the dependent variable (aggregate share of
loans or share of respondents with a foreign currency loan). We can draw three important
conclusions from the descriptive evidence given in Table 1. First, the average estimated
coefficient for the interest rate differential over all available estimates is surprisingly low. The
corresponding t-tests fail to reject the null hypothesis that the reported coefficients are zero
not only for the interest rate differential, but also for exchange rate volatility and inflation.
Second, there is a substantial variation for all analyzed determinants of foreign currency
loans. Third, an intriguing result from the descriptive evidence is that the share of significant
coefficients in the studies surveyed is mostly around 50% or even lower for all determinants
(with the exception of foreign currency deposits, where 76% of the coefficients are
significant).
However, this descriptive discussion does not take the differences between studies explicitly
and systematically into consideration. As the variation across subsamples of coefficients
indicates, it may well be that the coefficients are influenced by certain specific characteristics
of the individual studies and that correcting these particularities would lead to a more
coherent picture.
5 Publication Bias Analysis
The analysis of economic policy issues may often be the subject of general expectations on
findings. This can result in an unintended publication bias if authors, reviewers and publishers
follow their preference for statistically significant and theoretically sound results. Publication
bias or publication selection is a term often used also for other types of selection biases that
lead to estimates which are asymmetrically distributed around the true effect. Such a
phenomenon can be detected by a so called funnel plot, which is a scatter diagram displaying
the precision (e.g. the inverse of the standard error) against the estimated effect. If publication
bias is insignificant, the funnel plot should look like an inverted funnel and the estimates
should vary symmetrically around the true effect. The estimates which are close to the true
effect should be characterized by the highest precision. Similarly, the least precise estimates
should be located in the lower part of the chart. In contrast, if publication selection leads to an
10overrepresentation of significant results in the sample, the funnel plot becomes hollow and
unduly wide.
However, we have to keep in mind that the funnel plot is a subjective tool for the detection of
publication bias. Stanley (2005) notes that modeling issues (for example omitted variables,
estimation techniques) can likewise be a source of misspecification bias that can be wrongly
attributed to the publication bias.
Bearing in mind these limitations, we examine the funnel plots for the parameter estimates
corresponding to our covariates of interest, which are displayed in Figure 1. The precision (y-
axis) is defined as the inverse of the standard error. Evidence for publication bias can be seen
for the parameters associated to several of the analyzed variables, especially for the MVP-
ratio and the interest rate differential. Other variables, particularly exchange rate depreciation
and volatility (and to a lesser degree inflation volatility) do not exhibit the expected funnel
form, underlining the heterogeneity of these results.
Nevertheless, a visual examination of the funnel plots is often not conclusive in the detection
of asymmetry. To test for symmetry in a more formal manner, we employ the funnel
asymmetry test (FAT). It is based on the simple meta-regression of available effects and the
corresponding standard errors (Card and Krueger, 1995; Ashenfelter et al., 1999)
ˆki = k SEki + k + ki (6)
where ̂ ki denotes the reported estimates of the particular kth coefficient, that is assumed to
vary around the so called “true” effect , while stands for the so called publication bias. If
the estimates are distributed symmetrically around the true effect, , then the coefficient
should not be significantly different from zero. If, however, there is a tendency to report
certain parameter values or significant results, would be non-zero and significant and the
estimate would be proportional to the standard error. Thus, the publication selection bias can
be detected through the correlation of reported effects and their standard errors.
To deal with the differences in quality across studies, it is recommended to use inverse
standard errors as weights, which gives more weight to precise than to imprecise effects. This
means that equation (6) is rewritten as
ˆ ki 1
t ki k k ki (7)
SEki SE ki
This equation puts the t-statistic for significance in relationship with the inverse standard
error. Following Egger et al. (1997), we test for 0 , using standard and weighted versions
of the FAT test, whose rejection confirms the presence of publication bias (presence of
11asymmetry). We use OLS with clustered standard errors and random effects for the individual
studies. Similar to the funnel plots, Table 2 shows that all test specifications confirm
publication bias for the MVP-ratio, and nearly all do so as well for the interest rate
differential. There is only weak evidence (see OLS results) that publication bias exists also for
foreign currency deposits and for both of the exchange rate determinants. Finally, no
publication bias is robustly revealed for the inflation variables.
6 Meta-Regression Analysis
Meta-regressions can identify the adjusted effect of the individual determinants of foreign
currency loans, which is shown by the corresponding intercept in the specification. In
addition, we also discuss the impact of the included control variables as described in the
appendix on the performance of the meta-regression. Tables 3 to 5 present the results of the
meta-regression model given by equation (4) for our seven most common determinants of
foreign currency loans using random effects (RE), weighted least squares (WLS), and random
effects maximum likelihood (REML). For each of the seven determinants, we perform
separate meta-regressions. We use robust standard errors clustered at the level of individual
studies. In the case of WLS, we apply the inverse standard errors of the coefficient estimates
as weights. Thus, more precise coefficients (i.e coefficients with smaller standard errors)
receive a larger weight in the regression (table 4). Even though the results are largely similar,
the REML estimates can be expected to provide the most reliable findings as they account for
the variance both within and between studies.
The interest rate differential is commonly viewed as the main explanatory factor of foreign
currency loans on the demand side and is included in almost all studies (apart from a few
microeconomic estimations). Due to the larger number of observations of this coefficient
(288), it can be expected that the results are more clear-cut than for other determinants of
foreign currency loans. However, the intercept through the meta-regression appears mostly
insignificant with the exception of the REML estimates. It is even negative for the RE
specification, although it is counteracted by the large and positive (but insignificant)
coefficients for the main results. Additionally, the interest rate differential appears to have
played a more important role in earlier years, as shown by a negative coefficient for the last
year of the sample period. In spite of the latter result, the dummy for the financial crisis is
positive and significant, hence implying that the effects of the financial crisis are rather
unclear, because the crisis dummy and time trend (expressed by the last year of observation)
have counteracting effects. In general, micro-econometric studies report lower interest rate
semi-elasticities as compared to estimates from macroeconomic analyses. This implies that,
12even though the variable has been widely emphasized in literature, interest rate differentials
do not appear to play a major role in the demand for foreign currency. Moreover, this result is
irrespective of the applied meta-approach. This finding casts some doubt on whether demand
factors are actually a main determinant of foreign currency loans in CEECs.
A large part of the reviewed studies likewise include the MVP-ratio according to Ize and
Levy-Yeyati (2003), as defined by equation (1). Our meta-analysis confirms that the intercept
is highly significant in the MVP estimations. Though interestingly, as the CIS dummy shows,
the MVP ratio might play a smaller role in former Soviet Union countries, corresponding to
the less developed financial markets of this region.
Several authors use inflation and exchange rate volatility separately (e.g. Brown and De Haas,
2010; Zettelmeyer et al., 2010). The estimations confirm the importance of inflation volatility,
which is in line with the lack of a monetary credibility argument as proposed by Fidrmuc et
al. (2013). In turn, exchange rate volatility exerts a less robust influence on the foreign
currency loans. Overall, the meta-regression results detect inflation volatility as the more
important driver of foreign currency loans.
Supply factors are often proxied for by the share of foreign currency deposits in total deposits.
Banks with high deposits in foreign currency have an interest in shifting currency risk towards
their customers. In fact, the MVP indicator is usually found to be highly correlated with the
degree of deposits in foreign currency. Our meta-analysis confirms that foreign currency
deposits are, on average, a significant determinant of foreign currency loans.
The meta-regression analysis shows that the determinants of foreign currency loans which are
related mainly to supply factors (foreign currency deposits and the MVP-ratio) keep on
average the expected signs, while demand side factors (the interest rate differential) show on
average rather ambiguous results. In addition to the meta-regressions based on the whole
sample of estimates in each study, we also used the subsamples of results which were reported
as “preferred” or “baseline” specifications in the analyzed studies. However, one has to be
aware of the smaller number of coefficients in this case. The results of the meta-regressions
for these subsamples of estimates confirm the important role of the MVP-ratio, exchange rate
volatility and foreign currency deposits, while the other determinants are insignificant.6
It is rather difficult to assess the overall effect of all explanatory variables given a best-
practice approach (Havránek and Iršová, 2011), because different authors may also have
different preferences regarding the choice of an appropriate specification. Nevertheless, there
is a general agreement that the estimations should use methods which address the endogeneity
6
Detailed results are available upon request.
13problems. Similarly, panel estimations (fixed effects) are likewise superior to cross-sections
and time-series analysis. Finally, micro econometric approaches have recently gained in
importance and popularity, but the impact of macroeconomic variables (e.g. interest rate
differentials) is often difficult to estimate using micro-econometric data. Correspondingly, it
may be more difficult to compare studies using macroeconomic and survey data. While an
overall evaluation should reflect the preferences of a reader, a comparison of several
approaches shows that by and large the effects remain similar to the base specification. In
general, variables related to methodology do not seem to have large and significant effects.
Moreover, they often work in different directions. More importantly, results based on post-
crisis samples strengthen the effects found with data prior to the crisis.
7 Bayesian model averaging
In addition to the estimation of the single specification in the previous section, we extend our
analysis using Bayesian model averaging of specifications in the model space spanned by
specifications of the type given by equation (4). Assuming that there are K different potential
explanatory covariates and that all (linear) combinations of them can form a model of the type
described in (4), 2K specifications can be considered in order to assess the role played by the
characteristics of the analysis on the estimates. The lack of theoretical guidance concerning
the choice of independent variables implies that the specification choice may be an important
source of uncertainty when it comes to quantifying how the methods and samples used in
empirical studies affect the elasticities of foreign currency loans in CEECs to the variables of
interest.
Some recent studies have paid attention to model uncertainty in the framework of meta-
regression analysis (Moeltner and Woodward, 2009). As in Iršová and Havránek (2012), we
use Bayesian model averaging (BMA) methods to assess the degree of model uncertainty
attached to the meta-regressions for foreign currency loans. In particular, following the
standard BMA literature (see Doppelhofer, 2008, for a review on model averaging methods in
economics) we obtain weighted averages of parameter estimates in meta-regressions in which
the weights are given by the posterior model probabilities of the corresponding specifications.
Let the posterior model probability of a model, Mi, be given by P(Mi|Y). Using Bayes
theorem, such a posterior probability is proportional to the product of the prior probability of
the model, P(Mi) and its marginal likelihood, P(Y| Mi). For a given choice of priors over the
model space and over the parameters of each individual model, the posterior model
probability can be obtained using standard Bayesian methods (see Koop, 2003). Following the
14recent literature on model averaging in econometrics, we use a g-prior over the parameters of
individual models (see Fernández et al., 2001a and 2001b) and a beta-binomial prior on the
inclusion of individual covariates (see Ley and Steel, 2009).
Using these prior settings, we obtain model-averaged estimates of the parameters in (4) and
compute the posterior inclusion probability of each covariate, which is defined as the sum of
posterior model probabilities of specifications including that variable. This statistic thus
summarizes the importance of the covariate when it comes to explaining differences in
estimates, ˆ , across studies. The results of our BMA exercise are presented in Table 6. We
obtained BMA estimates for specifications based on the coefficients of the interest rate
differential, exchange rate depreciation, exchange rate volatility, inflation, inflation volatility,
minimum variance portfolio and foreign currency deposits. As above, the set of covariates
used for the BMA analysis differs between coefficients depending on the number of available
observations of the covariates of the meta-regressions, as well as on the existence of perfect
multicollinearity across explanatory variables. Since all models estimated include a constant,
the posterior inclusion probability of the intercept in equation (4) is equal to one in all cases. 7
Table 6 indicates that the power of explanatory variables in terms of discriminating robustly
across the results of different studies is strongly dependent on the elasticity that we are trying
to explain. While none of the variables used in the analysis appears robust in explaining
differences in the coefficient of interest rates and inflation across empirical studies, some
characteristics of the analysis are able to discriminate across coefficient estimates for the rest
of the estimates analyzed. For the remaining determinants, the last year of the sample, the
dummy for the financial crisis and methodological variables appear to be robust covariates in
the meta-regressions.
Using the BMA results, we concentrate on the median probability model (proposed by
Barbieri and Berger, 2004), that is, the specification which includes those variables which
have a posterior inclusion probability above 0.5 as covariates for each dependent variable.
Through the use of the median probability model, we are able to obtain the predicted estimate
for each coefficient that corresponds with the median values of the explanatory variable. We
dub such an estimate the median probability meta-estimate and report it in the last row of
Table 6, together with the corresponding standard deviation. The high degree of uncertainty
7
We used Markov Chain Monte Carlo (MCMC) methods to explore the model space. The results presented are
based on 100.000 models visited by the chain. Computing the correlation between the analytical and simulated
posterior density over models for selected subsamples of the visited set of specifications indicates that the chain
converged.
15surrounding the estimates is supported by the fact that only a half of the median probability
meta-estimates is significantly different from zero. The median probability meta-estimates for
the supply side indicators, especially for the degree of deposit dollarization, are particularly
close to the mean values of the coefficients. This finding supports the existence of a
significant effect of these determinants on foreign currency loans.
8 Conclusions
Foreign currency loans have played an important role in the catching-up process of the
majority of the CEECs and have gained increasing attention from economic researchers in the
past few years. In particular, the financial crisis intensified the attention paid to the lending in
foreign currency due to its negative impact on the financial stability in the CEECs. Overall,
we were able to find 21 empirical studies on the determinants of foreign currency loans in the
CEECs. From these studies, we collected about 300 estimation equations that roughly
provided 800 coefficients on the seven most common determinants.
Furthermore, while the literature underlines the importance of foreign currency loans, it also
provides a highly ambiguous picture of their effective determinants. The behavior of
individuals and firms concerning foreign currency loans is determined not only by economic
and monetary policies, but also through more general political developments. Additionally,
once lenders get used to foreign currency, it may take a relatively long period of time to
change their behavior again, which in turn has an indirect impact on foreign currency
borrowing.
Although several papers have been published by high-ranking journals or working paper
series with a strong policy impact, we can see that only roughly half of the published
coefficients are actually statistically significant. Moreover, a similarly wide differentiation
can be perceived in the economic significance of the coefficients. However, these differences
are not so surprising when considering the heterogeneity of the analyzed data and the methods
applied. The literature analyzes developments in highly heterogeneous countries, including
the EU Member States and the Western Balkan countries or CIS states. Some studies also
choose a more general focus and additionally include developing countries, with a tendency to
Latin American countries. Similarly, the level of data aggregation is highly diversified. Some
papers look at the aggregate share of foreign currency loans, while others focus more on the
foreign currency loans of individual firms and households based on survey-level data.
Despite these differences, we try to identify common ground, by using meta-statistics and
meta-regressions. Our results show that especially supply side indicators and foreign currency
16deposits play a robust role. The Bayesian model averaging analysis shows these variables to
being robust and explanatory, therefore making them significant determinants of the foreign
currency loans.
Similarly, we also show that several standard determinants play only a minor role in loan
euroization. For example, foreign currency loans are often viewed as a result of high domestic
interest rates. From this perspective, borrowers take an excessive risk when taking up foreign
currency loans and underestimating the danger of exchange rate depreciation. We show that,
on average, interest rate differentials do not influence the currency selection for loans. Thus,
foreign currency loans do not seem to be only a result of the demand of borrowers in the
CEECs.
In addition, we document that exchange rate movements do not play a clear role in foreign
currency loans. On the one hand, the exchange rate depreciation (not its volatility) does not
robustly influence the demand for foreign currency loans. This finding corresponds to the
previously mentioned weak results for interest rate differentials. Yet on the other hand, it also
shows that borrowers in the CEECs, may well underestimate the potential losses from
depreciations, despite their experience with trend appreciation before the financial crisis.
Altogether, our findings confirm that supply factors play a more significant role than demand
factors for foreign currency loans in CEECs.
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20Figure 1: Funnel Plots for Selected Determinants
Interest Rate Differential MVP-Ratio Exchange Rate Depreciation
Interest Rate Minimum Variance Portfolio Exchange Rate Depreciation
100
200
50
80
40
150
60
30
Precision
Precision
Precision
100
40
20
50
20
10
0
0
0
-5 0 5 10 -5 0 5 10 -2 -1 0 1 2
Coefficient Coefficient Coefficient
Exchange Rate Volatility Inflation Volatility Inflation
Exchange Rate Volatility Inflation Volatility
30
Inflation
1.5
20
15
20
1
Precision
Precision
Precision
10
10
.5
5
0
0
0
-.5 0 .5 1 1.5 -10 -5 0 5 10 15
Coefficient Coefficient
-10 -5 0 5
Coefficient
Foreign Currency Deposits
Foreign Currency Deposits
40
30
Precision
20
10
0
-1 -.5 0 .5 1 1.5
Coefficient
21Table 1: Meta-Statistics
No of Share of
Variable Mean Std. Dev. t-test Min Max
observ. sign. coeff.
Interest Rate Differential 288 0.053 0.958 0.930 -2.988 9.300 49.7
Exchange Rate Volatility 76 -0.139 1.273 -0.953 -4.017 7.069 23.7
Exchange Rate Depr. 120 0.129 0.579 2.450** -2.411 1.310 51.7
Minimum var. portfolio 183 0.143 0.772 2.506** -4.898 7.511 54.1
Inflation 88 -0.095 1.611 -0.553 -9.700 5.700 33.0
Inflation Volatility 77 0.969 3.696 2.300** -10.010 12.540 50.6
Foreign Currency Deposits 88 0.272 0.408 6.250*** -1.047 1.329 72.7
Note: a – The t-test tests whether the mean of reported coefficients is significantly different from 0. **, and ***
stands for significance at the 5%, and 1% level, respectively.
22Table 2: Funnel Asymmetry Test
Estimation Interest MVP Exch. Rate Exch. Rate Inflation FC
Inflation
Method Rate Diff. Ratio Depr. Volatility Volatility Deposits
OLS 0.238*** 0.361*** 0.330* 0.847 0.264 -0.003 0.000***
(0.029) (0.014) (0.173) (1.004) (0.159) (0.007) (0.000)
RE 0.238*** 0.361*** 0.280*** 1.423** 0.264* -0.003 0.000***
(0.029) (0.014) (0.096) (0.599) (0.159) (0.007) (0.000)
WLS 3.407** 1.343*** 2.829 -0.274 0.889 0.387 18.742
(1.529) (0.135) (3.333) (0.660) (0.504) (0.289) (18.129)
WRE 8.963 1.451*** 13.899 -0.306 0.340 0.852 18.742
(6.403) (0.261) (13.628) (0.529) (0.494) (0.560) (18.129)
Observations 288 183 120 76 77 88 88
No of studies 20 9 11 9 8 8 10
Note: We report coefficients and their t-statistics according to equation (6) and (7) for the OLS and random
effect (RE) specifications, respectively. WLS and WRE denote the results for weighted tests (weighted by
inverse standard errors). Robust standard errors in parentheses. *, **, and *** stands for significance at the 10%,
5%, and 1% level, respectively.
23Table 3: Meta-Regression, Random Effects
Interest MVP Exch. rate Exch. rate Inflation FC
Inflation
rate diff. ratio depr. volatility volatility deposits
intercept -0.073 2.703** 0.499** 5.529*** 1.916 -10.198*** 0.685***
(0.339) (1.280) (0.198) (1.279) (2.284) (1.821) (0.258)
main results 0.229 -0.074 -0.044 0.072 -0.765 -1.064 0.086
(0.287) (0.099) (0.052) (0.161) (0.839) (1.301) (0.060)
last year -0.201 -0.461*** 0.073* 0.210 0.441* -2.919*** -0.153***
(0.170) (0.085) (0.043) (0.275) (0.247) (0.271) (0.005)
micro study -0.404** -0.012 5.234*** -1.045 9.202*** -0.016
(0.203) (0.370) (0.980) (1.433) (0.756) (0.025)
fixed effects 0.483** -1.481 -0.171* 0.415** -0.652 0.436 -0.233*
(0.213) (1.381) (0.098) (0.179) (0.406) (0.703) (0.136)
bias correction 0.071 -0.190 0.172 -1.092 -1.748 16.152*** -0.121**
(0.093) (0.190) (0.221) (0.970) (1.436) (0.895) (0.049)
mortgage -0.062 -1.539*** -0.048 6.411*** -1.168***
(0.123) (0.297) (0.358) (1.365) (0.240)
hedging -0.087 0.161* -0.610*** 1.826 1.100*** -0.308***
(0.101) (0.096) (0.131) (2.150) (0.256) (0.038)
EU enlargement 0.212 0.060 -6.502***
(0.142) (0.329) (1.631)
post-crisis period 0.351 1.852*** 0.086 -11.140*** -2.948 -0.209 -0.053
(0.829) (0.330) (0.447) (2.623) (2.034) (0.514) (0.039)
FSU dummy -0.395 -1.084*** 0.339 -5.402*** -1.708 -0.225
(0.300) (0.175) (0.211) (0.784) (2.043) (0.250)
no curr. boards 0.023 0.067 -1.064 0.293
(0.076) (0.161) (1.301) (0.252)
Latin America -0.163 -0.331 11.908*** 2.237
(0.863) (0.428) (2.465) (1.418)
Observations 288 164 97 71 69 68 77
No of studies 20 8 10 9 7 7 10
Note: *, **, and *** stands for significance at the 10%, 5%, and 1% level, respectively. Robust standard errors
in parentheses.
24Table 4: Meta-Regression, Weighted Least Squares
Interest MVP Exch. rate Exch. rate Inflation FC
Inflation
rate diff. ratio depr. volatility volatility deposits
intercept 0.070* 1.521*** 0.705* 2.116*** -0.103 12.429*** 0.459*
(0.035) (0.006) (0.384) (0.047) (0.148) (0.313) (0.219)
main results 0.000 0.014*** -0.016 0.011 -0.001 0.000 -0.172**
(0.000) (0.000) (0.017) (0.018) (0.003) (0.000) (0.064)
last year -0.004 -0.585*** 0.037 0.009 0.054 -2.946*** -0.083
(0.028) (0.000) (0.121) (0.015) (0.059) (0.074) (0.092)
micro study -0.005 1.163*** -0.101 2.625*** 0.333** -6.823*** 0.455
(0.028) (0.005) (0.448) (0.045) (0.128) (0.165) (0.332)
fixed effects 0.021 -0.074*** -0.049 0.010 -0.001 0.017** -0.190**
(0.028) (0.004) (0.042) (0.017) (0.003) (0.007) (0.083)
bias correction -0.024 0.034*** 0.087 0.520*** -0.052 -0.505
(0.051) (0.008) (0.397) (0.053) (0.116) (0.281)
mortgage -0.049 -1.706** -0.476
(0.075) (0.549) (0.334)
hedging 0.000 0.064*** -0.628* -0.503 0.321
(0.000) (0.000) (0.335) (0.388) (0.322)
EU enlargement -0.032 2.319*** 0.045 -2.547*** -0.898***
(0.038) (0.005) (0.240) (0.082) (0.001)
post-crisis period -0.017 0.019 -5.314*** 8.832*** -0.171**
(0.100) (0.954) (0.121) (0.222) (0.064)
FSU dummy -0.091* -1.246*** 0.052 -2.213*** 0.004 -5.869*** 0.018
(0.044) (0.003) (0.342) (0.027) (0.094) (0.147) (0.081)
no curr. boards 0.000 0.088*** -0.160*** 0.995***
(0.000) (0.000) (0.034) (0.269)
Latin America 0.071 -0.053 5.322*** 0.219 -8.815***
(0.090) (0.938) (0.117) (0.348) (0.224)
Observations 288 164 95 71 66 68 77
Adjusted R2 0.078 0.192 0.773 0.167 1.000 0.644 0.997
Note: *, **, and *** stands for significance at the 10%, 5%, and 1% level, respectively. Robust standard errors
in parentheses.
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