5. SOBRE LA DIDÁCTICA DE LA TRADUCCIÓN
5.1.2. El enfoque por competencias: la competencia traductora
One of the main possible weaknesses of some of these reviewed studies is related to their data sets used. Many of the studies are based on aggregate level data (see tables 2.1, 2.3, 2.4 and 2.5), that are composed as a simple sum or as a weighted average of the bank-level data. However, aggregating the data of the micro units, according to Theil (1957) and Zellner (1962), may lead to aggregation bias. The theoretical basis of the aggregation bias is that the individual (micro) units from which the aggregated data is composed may be individuals with different (heterogeneous) behaviour. Consequently, by estimating the economic relations with aggregated data, the individual behaviour of each unit is suppressed and thus, it may be hidden in the disturbances of the model based on aggregated data that may result in biased estimates. The derivation of the aggregation bias based on simple (bivariate) time series regression, according to Theil (1957), Zellner (1962) and Lee et al. (1990) is as follows:
The general disaggregated model for each unit may be presented as:
Yit = βiXit + ui ; i = 1, 2, 3 ...n (2.11)
where: Y is the dependent variable; X is an independent variable; β is a coefficient to be estimated; u are white noise residuals; i and t are unit and time specific subscripts. The same equation derived for the aggregated data would be:
Yit = βiXit + ui (2.12)
However, in the empirical research based on aggregated data, the economic relations are estimated as follows:
Yit = β Xit + vi (2.13)
Equations 2.12 and 2.13 would be equal if the residuals of both equations are equal ( ui = vi), for which the following condition (H0) must be satisfied:
n i1 n i1 n i1 n i1 n i1 n i1
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H0: βiXit – β Xit = 0 (2.14)
or in a simplified form (Zellner, 1962):
H0:β1 = β2 = β3 = . . . . = βi (2.15)
Condition (H0) actually indicates that the parameters β from equation 2.12 must be
equal between each individual unit, implying to homogeneous behaviour among the units from which the aggregated data are composed. Otherwise, if the condition (H0) is not satisfied, then it implies that the units have heterogeneous
behaviour that will be hidden in the error term of equation 2.13 and would result in biased estimates.
In the case of the banking sector, de Graeve et al. (2004) argues that estimating the pass-through multipliers with aggregate data may also lead to aggregation bias arising from the heterogeneous nature of the data. This argument is empirically supported by in their paper which presents estimates for Belgium where the pass-through estimates based on aggregate data were lower compared to the same estimates based on individual (bank-level) data.
Another possible drawback regarding the studies that use bank-level data and some of the studies that use aggregated data for the same group of economies (EMU and CSEE economies) may be related to the estimation method used. The studies based on time series methods like: ECM (Mojon, 2000 and Cottarelli et al., 1995); TAR (Sander and Kleimer, 2004a, b), Panel Cointegration (de Graeve et al., 2004) and panel ECM (Weth, 2002; Mueller-Spahn, 2008 and Chmielewski, 2004) may provide inefficient estimates because they do not control for the contemporaneous cross-sectional correlation among the units. This may be especially pronounced for the studies based on panel cointegration because the estimators employed in those studies are based on the assumption of no cross- sectional correlation among the units (see section 3.3). Moreover, majority of the studies based on static panel data models that use both aggregated data for similar group of economies and/or bank-level data, may again suffer from the cross-
n
i1
n
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107 sectional correlation among the units, e.g. Mishra et al. (2010), Crowley (2007); Corvoisier and Gropp (2002), Maudos and de Guevara (2004), Angbazo (1997), Demiguc-Kunt and Huizinga (1999), More and Nagy (2003), Doliente (2005) and Afanseiff et al. (2002). However, some studies have tackled this issue by either using the SUR model (Sorensen and Werner, 2006; Lago-Gonzalez and Salas- Fumas, 2005 and Boutillier et al., 2006) that has been specifically developed for that purpose (see section 3.3); or have corrected the estimator employed by controlling for the cross-sectional correlation among the units (Berger and Hannan, 1989; Cihak, 2004 and Vaskov et al., 2010).
A general weakness of the studies based on ECM in estimating the size of the pass-through (Mojon, 2000; Cottarelli et al., 1995; Sander and Kleimeier, 2004a, b; Sorensen and Werner, 2006; de Greave et al., 2004; Weth, 2002; Mueller-Spahn, 2008; Chmielewski 2004; Wrobel and Pawlowska, 2002; Betancourt et al. 2008; Velickovski, 2006 and 2010 and Jovanovski et al., 2005); is that these a priori expect to find a cointegrating relationship between the „cost of funds‟ rate and banks‟ retail rates. Consequently in estimating the ECM model, some of the studies (de Greave et al., 2004; Weth, 2002; Mueller-Spahn, 2008; Chmielewski 2004 and Wrobel and Pawlowska, 2002) do not test if the interest rate series employed are stationary or not. Furthermore, many of the studies such as Weth (2002), Mueller-Spahn (2008), Chmielewski (2004), Wrobel and Pawlowska (2002) and Betancourt et al. (2008) do not test for the existence of a cointegrating relationship among the interest rate series. They directly estimate an ECM based on the assumption that the interest rate series a cointegrated. This approach of estimating the size of the pass-through within an ECM may be inappropriate. The reason for this is that, as explained in section 2.2.5, the mark- up pricing model is not clear whether a priori we might expect a long-run equilibrium relationship among the „cost of funds‟ rate and banks retail rates. Moreover, the rest of the theories assessed in section 2 are more inclined to suggest that a priori we might not expect a long-run equilibrium between the two interest rate series (see section 2.2.5). This maybe a reason why in the studies by Sander and Kleimeier (2004a, b), de Graeve et al. (2004), Egert and al. (2007) and
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108 Velickovski (2006), the authors failed to find a cointegrating relationship for most of the interest rate series used. Thus, apart from Velickovski (2006), they proceed by estimating the size of the pass-through with model by using first differences of the variables or by employing a VAR model.
Another possible problem with the majority of the studies conducted for the developing and transition economies from CSEE (Cottarelli and Kourelis, 1994; Betancourt et al., 2008; Berstein and Fuentes, 2005; Crowley, 2007; Demirguc-Kunt and Huizinga, 1999; Sander and Kleimeier, 2004b; Chmielewski, 2004; Wrobel and Pawlowska, 2002; Mishra et al., 2010; Cihak, 2004 and Vaskov et al., 2010), arises from the interest rate series used. These authors use loan and/or deposit rates composed of a weighted average of all currency denominations, i.e. loans and/or deposits denominated in foreign as well as domestic currency, including the foreign currency indexed loans/deposits. In contrast, as the reference policy rate they use either the domestic policy interest rate and/or the domestic money market rate, both of which relate to transactions denominated only in domestic currency. Accordingly, the authors in attempting to investigate the determinants of interest rate pass-through between the bank retail rates and the „cost of funds‟ rate, indirectly disregard the impact of the currency substitution phenomenon. This phenomenon is present in the afore-mentioned group of economies through the relatively high share of foreign currency loans/deposits and foreign currency indexed loans/deposits to total stock of loans/deposits. Not controlling for this phenomenon in the models may bias the results. Namely, the degree of pass-through may be under- or over-estimated because part of the aggregated retail interest rates does not only react to changes in domestic referent rate, but also to changes in the respective foreign reference rate(s). For example, according to the empirical studies of the bank lending channel conducted for the transition economies (see section 4.5.2), it is estimated that in many CSEE economies banks‟ total loans are more responsive to changes in foreign reference rate than domestic rate. This may be more pronounced where the currency substitution is larger.
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2.4 Conclusions
The aims of this chapter were to critically assess various theories of how banks adjust their retail rates and the main factors that affect the size of adjustment of banks‟ retail rates to changes in the „cost of funds‟ rate. Additionally, this chapter has critically surveyed various empirical studies that explore the structural factors that affect banks‟ retail rate setting decisions, classified according to the conceptual framework of what is investigated and how the underlying theoretical mark-up pricing model has been developed and modified through time. This analysis provides the foundation for the conduct of our empirical research in chapter 3, investigating what factors affect the size of adjustment of lending rate among banks in Macedonia.
Regarding the theoretical background to how banks‟ set their retail rates, the „core‟ model is the mark-up pricing model designed for a non-perfect competitive pricing environment. This model implies that variations in banks‟ retail rates are determined by the variations in the „cost of funds‟ rate plus the mark-up margin. The mark-up margin, according to Ho and Saunders (1981), is inversely related to the interest-rate risks that banks face, or as Allen (1988) and Angbazo (1997) argue, it is also determined by the cross-product diversification of loans and deposits in respect to their maturity and banks‟ credit risk exposure respectively.
The main focus of the later developed theories is in investigating the factors that affect the size of banks‟ retail rates adjustment, i.e. the proportion by which variations in the „cost of funds‟ rate are transmitted in banks‟ retail rates. Those theories are: the theory for asymmetric information and lending rate stickiness by Stiglitz and Weiss (1981); switching cost and related to that, relationship lending theory and “menu costs” theory established by Hannan and Berger (1991). Although these theories have some weaknesses, they provide some explanations for the possible reasons for the incomplete (sluggish) adjustment of
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110 banks‟ retail rates to changes in the „cost of funds‟ rate, i.e. retail rate adjustment rigidity.
Considering the empirical studies, although they have some weaknesses and there is substantial heterogeneity among them in respect of what they are estimating, how they are estimating and the type of the data they use; overall their findings are broadly consistent with the theoretical predictions. Namely, they point to common macroeconomic and banks‟ financial characteristics as significant determinants of banks‟ retail rate setting decisions. Among the macroeconomic factors considered, the most important ones appear to be economic growth and inflation. Considering the indicators for the development of the financial sector, the generally significant ones are estimated to be: money market volatility and the concentration in the banking sector. Regarding the banks‟ financial characteristics, the significant factors are: asset size, interest risk and credit risk exposure, liquidity, capitalisation, banks‟ involvement in relationship lending activities, operational efficiency and their portfolio diversification. However, none of the assessed empirical studies has examined the size of adjustment coefficients (pass-through multipliers) in the Macedonian banking sector using bank-level data and what are their major determinants, which is the main challenge of the next chapter.
111
CHAPTER 3: INVESTIGATION OF THE DETERMINANTS
OF THE SIZE OF ADJUSTMENT OF
LENDING RATES IN
MACEDONIA
– A
SUR APPROACH
3.1 Introduction ... 112 3.2 The model ... 114 3.3 Estimation method ... 134 3.4 Data issues ... 143 3.5 Results ... 147 3.5.1 Interpretation of the results ... 151 3.5.2 Robustness check ... 159 3.6 Conclusions ... 162MACEDONIA – A SUR APPROACH
112
3.1 Introduction
After critically assessing the various theoretical and empirical approaches to how banks set their retail rates in the previous chapter, the aim of this chapter is to directly respond to the first and second research questions of the thesis. Hence, this chapter will empirically investigate the size of banks‟ lending rate adjustment to changes in the „cost of funds‟ rate and whether this is heterogeneous among banks. In doing this, this chapter also aims to identify what factors affect the pass- through multipliers in Macedonia. The rationale for exploring these issues in more depth is to provide a fuller picture of the effectiveness of the monetary transmission through the interest rate channel. From the monetary policy-makers‟ perspective, this is seen as important issue, having in mind the significance of the interest rate channel in the monetary transmission mechanism. Additionally, this chapter will also eventually enable us to compare whether the same factors affect both the interest rate and bank lending channels (see chapter 5). Hence, this research may provide some policy implications regarding the effectiveness of the interest rate channel and identify the factors that impede „smooth‟ transmission in Macedonia, which ultimately may help monetary policy makers to take more appropriate policy measures.
In order to conduct this research we primarily follow the mark-up pricing model of how banks‟ set their retail rates designed for a non-perfectly competitive environment, established by Rousseas (1985) and Ho and Saunders (1981), as well as the applications of this found in the empirical literature (see sections 2.2 and 2.3). The latter may give us an indication as to how the theoretical underpinnings can be investigated in our empirical work (see section 2.3). According to the existing theoretical and empirical literature, various macro and microeconomic factors are seen to affect banks‟ pricing policy such as the structure of the financial system, macroeconomic characteristics of the economy and banks‟ balance sheet items.
The empirical studies that investigate interest rate pass-through in the Macedonian banking sector suggest that it is incomplete in the short-run
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113 (Jovanovski et al. 2005 and Velickovski 2010) or in both the short- and long-run (Velickovski, 2006), see section 2.3.4. However, we have argued that an important possible drawback in these studies is that they may suffer from aggregation bias (see section 2.3.5). Thus, the core aim of this chapter is to examine the size of lending rate adjustment, whether it is heterogeneous among banks as well as to explore how and what factors considered in the previous paragraph affect the size of lending rate adjustment among Macedonian banks to changes in the „cost of funds‟ rate,
Accordingly, the value added of this chapter is as follows: First, it investigates the size of lending rate adjustment, whether it is heterogeneous among Macedonian banks and what factors may have a significant impact over it. Accordingly, this investigation is based on a disaggregated (bank-level) data set, which has not been previously used to study the size of the pass-through in Macedonia (see section 2.3.4). Indeed, the literature for other countries, especially for the CSEE, based on bank-level data is quite limited (see tables 2.1 and 2.2). This may be of importance since studies that use industry level data may suffer from aggregation bias (see section 2.3.5). Second, in order to investigate whether there is banks‟ heterogeneous size of lending rate adjustment to changes in the „cost of funds‟ rate and what factors may have a significant impact over it by using bank-level data, this research employs the different and arguably more appropriate estimation method of Seemingly Unrelated Regression (SUR). This technique has not been previously used in the Macedonian research and is rarely used in the empirical studies even for the developed economies (see sections 2.3.1, 2.3.2 and 2.3.3 and 2.3.4). Third, this study focuses only on lending rates of loans denominated in domestic currency unlike the rest of the studies for Macedonia as well as CSEE that use aggregated data set including domestic and foreign currency denominated series. The rationale for this is explained in section (2.3.5).
This chapter is structured as follows: section 3.2 explains the model in detail. Section 3.3 provides the estimation method and strategy. Section 3.4
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114 describes the data used. The estimation results are presented in section 3.5, while the final section concludes.