DOI: 10.5817/FAI2014-2-2 No. 2/2014
26
Concentration of Banking Sector and Loan Markups
in Short Term: An Example from Poland
Krzysztof Drachal
Warsaw University of Technology
Faculty of Mathematics and Information Science
00 - 662 Warszawa, ul. Koszykowa 75, Poland
E-mail: k.drachal@mini.pw.edu.pl
Abstract: The aim of this paper is to present an analysis of the relationship
between concentration of the banking sector and banks' markups on offered
loans. The markup is understood as the difference between the rate offered by
banks and the reference rate fixed by the Monetary Policy Council. The period
between 2009 and 2013 was analyzed. Monthly data from the Polish banking
sector were considered. This paper also consists of the literature review, which
focuses on the mortgage market. The methodology used for the analysis is based
mainly on simple linear regression techniques. It is found that such methods are
not sufficient to give conclusive answers. Therefore additional future research is
proposed.
Keywords: concentration of banking sector, loan markup, Poland
JEL codes: E32, E40, E50, G21
Introduction
The process of consolidation of banks has both advantages and disadvantages.
Consolidated banks can use their assets more efficiently. They are also more
competitive, especially on the global market, where a player must be sufficiently
large in order to participate. This might provide some benefits for customers also.
Through mergers and takeovers, banks can become more innovative and they can
diversify their capital. Non-efficient branches are closed or taken over by
profitable and efficient banks, which leads to the optimization of the banking
sector as a whole.
On the other hand, concentration leads a market towards oligopoly. As a result,
banks gain the advantage over consumers. They can abuse the financial market
and put too much pressure on the whole economy. As a result, there is no
common opinion on the effect of concentration of the banking sector.
However, it has to be remembered that the Polish banking sector has been
dynamically evolving since 1989. Not only have the processes associated with
globalization intensified since then, but this time period was also marked by a
transition in economy. In order to switch towards a capitalist economy, banks
were privatized. There has also been a significant role of foreign capital in these
processes.
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27
Before 1993, there were many banks. Consolidation processes began mainly
because of the low level of capital held by these banks, as well as high
competition from foreign banks. Also, the number of banks was inadequate to suit
market needs. Moreover, economies of scale played an important role.
There were 43 mergers and takeovers in the banking sector between 1993 and
2004 in Poland. As a result, the number of banks controlled by domestic investors
declined from 48 to 8 in this period. On the other hand, the number of banks
controlled by foreign investors increased from 10 to 41 (Kraciuk, 2006;
Pawłowska, 2003; Stępień, 2004).
Therefore it is interesting to analyze some recent, short-term (noticed between
2010 and 2013) processes with a stress on costs paid by customers. In this paper
the increase of concentration ratio of the five biggest banks (measured by the
share in total banking assets), is analyzed with regards to whether it can
significantly influence the difference between interest rates offered by banks to
customers and the reference rate fixed by the Monetary Policy Council. Some
special attention is paid to the impact on housing loans rates.
1 Literature review
Banks consolidate due to the potential benefits. There are various explanations
for this process (Micek, 2002), but the most important ones are: corporate
synergy, geographic expansion, product diversification, cost reduction, new
market expansion, increase in the share of credit and deposit market, prestige
from being the leader, competitive advantage, reducing information asymmetry.
The paper by Kokoszczyński (2001) presents a concise historical review of
consolidation processes in the Polish banking sector after the fall of communism
in Poland. Currently the Polish banking sector is quite stable and concentration is
rather stabilized. Its profitability is good. It has increased especially after the EU
accession (Piocha, Radlińska, 2010).
1.1 Advantages and disadvantages of the concentration
Lakic (2013) argues that bank concentration leads to various pathological
situations: abuse of the credit system, mechanism of interest rate, derivative
market, etc. High banking concentration leads towards monopoly. In practice,
there is no real control over such huge institution by the official authorities. Big
banks, knowing that they are "too big to fail", can take exceedingly high risks
(Carletti, Hartmann, 2002).
Herring and Wachter (1998) argue that real estate cycles and banking crises are
significantly correlated. They warn that if banks hold too much of the country’s
assets, it might have severe consequences on the nations economy. Garmaise
and Moskowitz (2004) analyzed data from 11 states in U.S. between 1992 and
Financial Assets and Investing
28
1999. They focused on the social costs of bank mergers. They found that bank
mergers usually lead to worsening credit terms and therefore to less development
and investment. These may decrease real estate prices and harm poorer
households. In the longer term it increases the number of property crimes. In
fact, mergers reduce bank competition which has a positive impact on credit
terms.
Allen et al. (2012) have discusses that a merger has an impact on customers by
worsening the negotiations process. But the overall impact on average rates is
small. The market may still remain competitive. In the case of mortgage rates,
financial literacy of customers plays an important role.
Basing on the SCP (structure-conduct-perform) paradigm, Allen et al. (2001)
argue that there are negative social effects arising from high banking sector
concentration. Ferreira (2013) also found that an increase in bank concentration
decreases bank efficiency. This is because of small competition present in
concentrated markets.
On the other hand, bank concentration can expand access to long-term credit.
Bank concentration can play a positive role on the market, by influencing a firms'
debt structure (González, González, 2008). Levine (2004) has found that entry
restrictions on foreign banks can lead to an increase in loan rates. On the other
hand, such restriction on domestic banks do not increase loan rates.
Cipollini and Fiordelisi (2008) analyzed over 18000 banks (also cooperative ones)
from Austria, Belgium, France, Germany, Italy and Spain and their activity
between 1997 and 2005. They have found that there is a negative correlation
between bank concentration and financial distress. But this is mainly due to the
role of savings and cooperative banks. If the sample is restricted to commercial
and listed banks there is a positive correlation between bank concentration and
financial distress. Moreover, Uhde and Heimeshoff (2009) have found that the
increase in banking concentration lead to worsening of financial soundness of
banks. Their research is based on a sample of more than 2600 banks from the
EU-25 for the period between 1997 and 2005.
It is usually thought that a high ratio of state-owned banks results in less access
to credit and reduced financial system stability. On the other hand, foreign-owned
banks are perceived as the ones offering competitive prices and accessible credits
for customers (Berger et al., 2004; Santillán Salgado, 2011).
Based on the data from 70 countries for the period between 1980 and 1997, Beck
et al. (2003) has concluded that concentration in the banking sector reduces the
risk of crisis. Decreasing the regulations of restrictions on bank competition and
bank activities also has positive results for the economy. Moreover, deregulations
can intensify the competition of public institutions.
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29
A high concentration in the banking sector can lead to higher profitability of
various financial institutions. Also, supervision and control by the authorities is
easier. As a result, there is a smaller risk of crisis (Allen, Gale, 2000).
1.2 Impact on housing market
Renaud (2009) emphasizes that for the first time in the world history more people
live in urban areas than in rural areas. As a result, the financial system connected
with housing becomes an increasingly important part of the whole economy.
Housing loans will increase, because urban expansion intensifies. This cannot be
covered by government expenditures solely. On the other hand, D'Arista (2009)
has noticed that the traditional role of a bank as a lending institution declines.
Household savings are not invested in banking deposits, but rather in mutual and
pension funds.
Sørensen and Lichtenberger (2007) have found slight evidence that smaller
concentration in banking sector might lead to lower mortgage rates. Their
research is based on the Eurozone countries. Calza et al. (2009) have found that
the features of residential mortgage markets differ significantly across
industrialized countries. Moreover, the transmission of monetary policy shocks to
residential investment and house prices is stronger in countries with more flexible
and developed mortgage markets.
Favara and Imbs (2010) have found that branch banking deregulation can lead to
an increase in house prices, due to an increase in mortgages. They have
researched house prices in the U.S. between 1994 and 2005. The bank branching
deregulation was measured by methods of Rice and Strahan (2010). Bergstresser
(2008) has analyzed the period between 1980 and 1994 in the U.S. It has been
found that the increase in bank concentration reduced the flow of bank capital to
construction and land development loans. Iacoviello (2002) has found significant
impact of tight monetary policy on the decrease of real house prices in France,
Germany, Italy, Spain, Sweden and UK for the periods covering late 1970s till
1998.
The debate on links between housing and macroeconomics dates back to Fischer
(1933) and his debt deflation theory. Residential capital stock plays an important
role in the economy, housing expenses constitute an important part of household
expenditures, etc. (Chetty, Szeidl, 2004; Greenwood, Hercowitz, 1991; Skinner,
1994). The relationship of macroeconomics and the housing market has been
extensively studied for developed countries (for example: Davis, Heathcote,
2005; Hwang, Quigley, 2006; Seko, 2003; Wen, 2001).
Mortgage banks have a small share in the mortgage loans market in Poland.
Mortgages are dominated by universal banks, which hold over 98% of all housing
loans. The housing market cycle has a small impact on the Polish banking system.
Despite some disadvantages on the market, there has been no price bubble,
Financial Assets and Investing
30
because of permanent impediments on supply and demand. Also, supervisory
recommendations were implemented on time (Lissowska, Szufler, 2012).
The polish property market has not gone through the full housing cycle yet. In
fact, it is a young market. It is in its first periods of a cycle in comparison with
U.S. or some European markets. Nevertheless it is highly vulnerable to financial
market behavior and both fiscal and monetary policies (Łaszek et al., 2009).
The impact of interest rates on housing supply is significant, especially in the case
of long-term relationship (Augustyniak et al., 2013). It is expected (BrzozaBrzezina,
2005) that in the case of euro adoption there would be a significant
increase in loans. The impact of euro adoption on the housing market is an
interesting question by itself. Generally, they are low interest rates (required by
Maastricht criteria), which influence the credit boom (Eichengreen, Steiner, 2008).
On the other hand, interest rates are still quite low. This is due to the expectation
of euro adoption and the reaction to the recent financial crisis. For example, such
a situation has occurred in Slovakia (Hüfner, 2009). If long-term expenditures
exceed incomes, there must be a moment when severe reduction in expenditures
will happen. A recession may begin as a result. Moreover, even if wages would
increase, it would be naturalized by interest costs. In other words: credit can
boost both economic growth and recession (Rytelewska, Huszczonek, 2004).
Aggressive lending instruments magnify the cycle (Pavlov, Wachter, 2006;
Tsatsaronis, Zhu 2004).
2 Methodology and Data
In order to maintain clarity, abbreviations of all analyzed variables are presented
in Table 1.
Table 1 Explanation of variables
CONC assets of 5 biggest banks / total banking assets
AS log of total banking assets
NI log of net income of banking sector
NIC log of non-interest costs of banking sector
NIR log of non-interest revenues of banking sector
BR number of branches of banks
EMP number of people employed in banking sector
EMPH number of people employed in headquarters of banks
PCRED average credit rate - reference rate
PnCRE average rate for new credits - reference rate
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PCRED_H average rate for credits for households - reference rate
PnCRED_H average rate for new credits for households - reference rate
PCONS_H average rate for consumption credits for households - reference rate
PnCONS_H average rate for new consumption credits for households - reference rate
PDEP average rate for deposits - reference rate
PnDEP average rate for new deposits - reference rate
PMORT average housing loan rate - reference rate
PnMORT average rate for new housing loan - reference rate
CRED average credit rate
RR reference rate
Source: Own elaboration
Kozak (2008) has noticed that the difference between credit rate and reference
rate (see also Rousseas, 1985) increases slightly, if concentration increases. The
research has been done for U.S. market between 1994 and 2005. Also, some
other factors have been considered in the constructed model. Therefore in this
paper a similar (but not the same) methodology is adopted, but for the Polish
market. For example, in the case of the U.S., Berger and Hannan (1989) have
found that the increase in concentration decreases the deposit rate in comparison
with the reference rate. Similar conclusions have been stated by Hannan and
Prager (1998) and Rhoades (1996). On the other hand, Erel (2011) has argued
that the concentration allows banks to use their capital more efficiently and
therefore margins on loans can be smaller.
Various ratios can be applied in order to measure the bank concentration ratio.
The choice is usually based on the individual needs and data availability. There is
no privileged choice of the ratio. One possibility is to consider CR5. CR5 is called
the concentration ratio of the five biggest banks. The concentration itself is
measured for example by the share in total assets (Rogowski, 2001).
This research is based on monthly date from the period between 31.12.2009 and
30.11.2013. The date has been possessed from KNF (2013) and NBP (2013)
official statistics. Calculations and graphs have been done in GRETL.
3 Analysis
It can be observed that the concentration (CONC) has significantly increased in
the analyzed period. However, it has been fluctuating between 2010 and 2012,
but the real increase happened in 2012 and 2013. This increase is not great in
Financial Assets and Investing
32
0.43
0.435
0.44
0.445
0.45
0.455
0.46
0.465
0.47
2010 2011 2012 2013 2014
CONC
6400
6600
6800
7000
7200
7400
7600
2010 2011 2012 2013 2014
BR
173500
174000
174500
175000
175500
176000
176500
177000
177500
178000
178500
2010 2011 2012 2013 2014
EMP
67000
68000
69000
70000
71000
72000
73000
74000
2010 2011 2012 2013 2014
EMPH
absolute values. Moreover, it should be noticed that this value is rather small in
comparison to other European countries.
There is also a significant increase in the number of branches during the analyzed
period. It is worthwhile to mention that the two biggest market players have
relatively many branches, whereas other banks have rather few branches.
On the other hand, the number of people employed in the banking sector
increased in 2010 and in the first half of 2011, but then it started to decrease.
Currently, this number is similar to that from the beginning of 2010. However,
there is a continuous and significant trend in increasing employment in banks'
headquarters. Details for the general characteristics of the banking sector are
presented on Figure 1.
Figure 1 General characteristics for the analyzed period
Source: Author’s elaboration in GRETL, based on KBN (2013) and NBP (2013)
The average credit rate has fallen by almost 2.38 pp. in the analyzed period. This
is due to the Monetary Policy Council's reaction to the recent financial crisis. It
can also be observed that average credit rate behaves as the reference rate.
No. 2/2014
33
0.06
0.065
0.07
0.075
0.08
0.085
0.09
2010 2011 2012 2013 2014
CRED
0.025
0.03
0.035
0.04
0.045
0.05
2010 2011 2012 2013 2014
RR
0.038
0.04
0.042
0.044
0.046
0.048
0.05
0.052
2010 2011 2012 2013 2014
PCRED
0.035
0.04
0.045
0.05
0.055
0.06
0.065
0.07
2010 2011 2012 2013 2014
PnCRE
There seems to be no delay in reducing credit rates by commercial banks after
the Monetary Policy Council's decisions about the level of the reference rate.
Further, in this paper, it will be verified, whether the difference between the
interest rate for different types of credits and the reference rate is somehow
influenced by concentration ratio (and some other factors). However, it is
interesting that this difference declines with time. Therefore, even though, the
concentration increases, the markup of banks decreases. Details are presented on
Figure 2. For other interest rates, please see Figures 3 and 4 in Appendix.
Figure 2 Average credit rate, reference rate and selected markups
Source: Author’s elaboration in GRETL, based on NBP (2013)
Moreover, at a 5% significance level non-stationarity of CONC cannot be rejected
in the case of the ADF (augmented Dickey-Fuller) test (Adkins, 2010). Therefore
it is reasonable to state that the concentration increased and that this is not a
random effect, indeed. (In this analysis 12 lags have always been used in any
Financial Assets and Investing
34
performance test that requires some number of lags. This is because monthly
data are considered.)
However it is interesting that performing ADF tests at a 5% significance level
allows rejection of the null hypothesis of non-stationarity of PCRED, but not for
PnCRE. Repeating the ADF test for other markups leads to the conclusion that
PnCRED_H, PnCONS_H, PnMORT, PDEP and PnDEP are non-stationary, whereas
PCRED_H, PCONS_H and PMORT are stationary.
This is quite a reasonable result. Markups on average rates are stationary, so
there is no significant trend in their time evolution. As a result, they are suitable
variables for a regression model. On the other hand, markups for new loans are
non-stationary, i.e. some trends are significant. It might be, for example, due to
the recent financial crises and changes in credit policies. However, such variables,
if unmodified, cannot be used in a regression model. Using them can give
"spurious regression". In other words: a nicely looking model can be obtained
(with high R-squared), but there might be no causal connection between the
variables (Granger, Newbold, 1974; Parker, 2013; Nielsen 2005).
The only exception from the above regularity is deposit rate, for which both
average and recent rates are non-stationary.
Similarly as in the paper by Kozak (2008), it can be supposed that the considered
markups are influenced by concentration of the banking sector, total banking
assets, net income of banking sector, non-interest costs and non-interest
revenues of the banking sector, number of branches and number of people
employed in the whole sector and in headquarters. Such a relation can be
motivated by economics theory.
Therefore linear regression models have been constructed for all previously
considered markups. Stepwise regression with backward elimination (Cody, 2011)
has been performed. A ten percent significance level has been assumed for
significance of coefficients.
The results are presented in Table 2. For results of "spurious regression" please
see Tables 4 and 5 in the Appendix. It is interesting that in both cases (stationary
and non-stationary) the signs of coefficients are the same. The only exception are
coefficients for NIC and BR and one exception for AS.
The obtained models explain changes in the dependent variables quite well (high
R-squared). In three models out of four the concentration ratio has a significant
and positive impact on bank markups. On the other hand, an increase in total
banking assets or in net income has the opposite effect. The other variables are
significant only in half of the obtained models. EMPH is not significant for any
model.
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Table 2 Regression estimates
PCRED PCONS_H PCRED_H PMORT
const 0.419416 0.77408 1.01214 0.0970906
CONC 0.0616278 0.0990081 0.0635673 0
AS -0.0132696 -0.0262868 -0.0341119 0
NI -0.0136716 -0.0141661 -0.0160369 -0.0093258
NIR 0.0120637 0 0.0142652 0
NIC 0 0.0124601 0 0.00829045
BR 0 7.15246e-06 0 3.2849e-06
EMP 0 0 0 -4.49147e-07
EMPH 0 0 0 0
R-squared 0.849796 0.663869 0.929218 0.564381
Source: Author’s elaboration in GRETL, based on KNF (2013) and NBP (2013)
Unfortunately, it has to be remembered that CONC is non-stationary. Moreover,
the problem of stationarity is present for other independent variables. In fact,
using a simple linear regression model becomes questionable, because it is
desired that all variables included in a model are stationary. The discussed
relationship between selected variables should be checked with some other
methods for example, a cointegration property methods (Engle, Granger, 1987;
Granger, 1981; Adkins, 2010) can be applied. On the other hand, the constructed
models are still worth a short discussion. At least, variables have not been
selected without any economic reasoning. Therefore, as far as now, the obtained
models should be treated as "dangerous" rather than "completely wrong" (see
also Diebold, 2006). The specification of the models has been based on some
presumptions, indeed.
Jarque-Bery test for normality of distribution of residuals for a PCRED model, at
5% significance level, confirms that residuals have a normal distribution. The
same conclusion is valid for PCONS_H, PCRED_H and PMORT models. This fact is
a desired one for a regression model. Unfortunately, the RESET test for model
linear specification confirms that PCRED and PCONS_H are badly specified. On the
other hand, models for PCRED_H and PMORT can be assumed linear. The BreushPagan
test confirms that residuals in all models are homoscedastic. This is a nice
property for a linear regression model. The Breusch-Godfrey test for
autocorrelation (up to 12th order) of residuals indicated some slight problems for
all models. In all tests 5% significance level has been assumed. Finally, all models
have small Durbin-Watson statistics, which together with high R-squared, can be
a sign of the mentioned "spurious regression" (Granger, Newbold, 1974).
Moreover, it can be concluded from the obtained models that an increase in the
concentration increases the markups. But analyzing Figures 1 and 2 leads to the
Financial Assets and Investing
36
opposite conclusion. Therefore, relying on the above discussion, the proposed
linear models are not suitable in the current form. In fact, they are a good
starting point for future research. Yet, it is known that a simple linear regression
for non-transformed variables will not work well.
The aim of this paper is to discuss the relationship between bank markups and
concentration ratio. Having in mind the discussed stationarity problem, the ADF
test has been performed for the first differences of CONC. This time series is still
non-stationarity. However, the second differences of CONC can be assumed
stationary at a 10% significance level. At the same significance level: the second
differences of PnCRE, the third differences of PnCONS_H, the third differences of
PnCRED_H, the third differences of PnMORT, the third differences PDEP, the fourth
differences of PnDEP have occurred to be the smallest order differences, which
are stationary.
Therefore a collection of linear regression models with one independent variable
(second differences of CONC) has been constructed. The results are presented in
Table 3.
Table 3 Regression estimates
PCRED
2nd diff.
PnCRE
PCRED_H
3rd diff.
PnCRED_H
const 0.0434021*** -8.5332e-05 0.052905*** 3.05535e-05
2nd diff. CONC 0.0174392 -0.101564 0.0232228 -0.113636
R-squared 0.002068 0.013386 0.001504 0.008238
PCONS_H
3rd diff.
PnCONS_H
PMORT
3rd diff.
PnMORT
const 0.106097*** -1.7897e-05 0.0230934*** 1.44846e-05
2nd diff. CONC 0.00521318 -0.321364 0.00911311 0.0962809*
R-squared 0.000148 0.038514 0.001607 0.067015
3rd diff.
PDEP
4th diff.
PnDEP
const 7.36908e-05 3.17044e-05
2nd diff. CONC -0.0104519 0.2388***
* 10%
significance
level
R-squared 0.001312 0.208785
*** 1%
significance
level
Source: Author’s elaboration in GRETL, based on KNF (2013) and NBP (2013)
No. 2/2014
37
The obtained models have very small R-squared values. However, this might be
mainly due to the use of just one independent variable. In most of the models,
the coefficient for the independent variable is statistically not significant at the
5% significance level. However, in the model for the 3rd differences of PnMORT
and 4th differences of PnDEP the independent variable is significant, indeed.
These models indicate that there is a positive relationship between bank markup
and concentration ratio. Unfortunately, none of the models is significant as a
whole.
Conclusions
The methods used in this paper do not indicate that the concentration of the
banking sector results in the increase of the difference between offered loan rates
and the reference rate. Although, from the constructed models there is a weak
indication that it might happen, but the models itself are questionable.
The problems that have emerged during the regression diagnostic are mainly
connected with non-stationarity of variables. Of course, such problems are
workable (Adkins, 2010; Cody, 2011; Engle, Granger, 1987; Granger, 1981;
Nielsen, 2005; Parker, 2013). However, they need quite more subtle methodology
than simple linear regression. Therefore it is not possible to give a quick and
simple answer for the problem analyzed in this paper. In fact, it has been proved
that more sophisticated methods are a must.
On the other hand, graphical analysis tends to indicate that there is a weak
negative relationship between the concentration of the banking sector and the
difference between offered loan rates and the reference rate. Therefore, as far as
now, it cannot be answered what is this relationship actually. Further research is
necessary.
It could be asked, why such a short period has been used for the analysis. The
Polish banking sector, understood as part of a capitalist economy, is a young one.
Poland has been a member of the European Union since 2004. Three years after
this transition, the global financial crisis started. Also, banking law (itself, but also
recommendations, etc.) has been changed quite often. These facts influenced the
banking system strongly. Therefore any analysis of a longer period should include
these factors. It does not seem to be possible in a reasonably short paper.
In consideration of the above facts, it should be mentioned that banks advantage
over consumers may be not solely from markups on rates. In the case of the
recent crisis and worsening of households' financial conditions, banks might lower
markups, but worsen some terms and conditions. Moreover, in the case of a loan,
the markup on the rate is not the only source of the bank's profits. There are
other costs as well.
Financial Assets and Investing
38
The data required to perform these additional analyses are not easily accessible.
Moreover, research focusing on terms and conditions should be more qualitative
rather than quantitative. This makes the whole problem more complex.
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Appendix
Figure 3 Selected markups
Source: Own elaboration in GRETL, based on NBP (2013)
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Figure 4 Selected markups
Source: Own elaboration in GRETL, based on NBP (2013)
Financial Assets and Investing
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Table 4 Regression estimates
PnCRE PnCONS_H PnCRED_H
const 0.537499 3.64188 0.951073
CONC 0 0 0
AS 0 -0.113996 0
NI -0.0301861 0 -0.0216027
NIR 0.0863886 0 0.118207
NIC -0.0601147 -0.00230188 -0.100298
BR 0 0 -1.10765e-05
EMP -2.18556e-06 -4.22918e-06 -3.87249e-06
EMPH 0 6.27072e-06 0
R-squared 0.779981 0.441775 0.833087
Source: Own elaboration in GRETL, based on KNF (2013) and NBP (2013)
Table 5 Regression estimates
PnMORT PDEP PnDEP
const 0.228244 1.41527 -0.496013
CONC 0.0565545 0 0
AS 0 -0.0361382 0.0192108
NI -0.0159656 0 0
NIR 0.040111 0 0.0003818
NIC -0.0257871 0
BR -3.22892e-06 4.58485e-06 0
EMP -9.09359e-07 -2.48771e-06 -7.07141e-07
EMPH 0 0 0
R-squared 0.908702 0.800919 0.513833
Source: Own elaboration in GRETL, based on KNF (2013) and NBP (2013)