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1 INTRODUCTION AND CONCEPTUAL FRAMEWORK

Foreign direct investment (FDI) by banks into Central and Eastern Europe (CEE) and more recently in

South-Eastern Europe (SEE) and transition economies further East has risen considerably (Breuss, Fink

and Haiss, 2004; Fink, Haiss and von Varendorff, 2007). In 2007 total banking assets in general continued

to grow at a high pace and majority foreign owned banks are the dominant players: In the Czech

Republic, Slovakia, Croatia and Bosnia and Herzegovina, for example, their market shares exceed the

90% mark, while in Albania and Estonia nearly 100% of the banking assets are owned by foreigninstitutes (RZB Group, 2007).


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CHART 2Asset share of foreign banks with foreign ownership above 50% for selected transition countries, Source:

own calculations, data from RCB Group 2004, 2005, 2006, 2007.

Foreign currency risk taking, however, entails a number of additional direct and indirect risk factors to the

lender, the borrower and the financial stability as a whole: exchange rate risk, interest rate risk,

performance risk of repayment vehicle, and economic risks, among others (Waschiczek, 2002, 91f; Bokor


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2007; IMF, 2008) speculate about the role of Austrian banks in the diffusion process of fx-loans: Since

Austrian institutes can draw on considerable experience concerning fx-lending from their home market,

they may have built up specific competencies to market fx-loans also abroad, thus fostering the fx-creditboom.

Apart from foreign bank entry, the literature suggests several other factors which might drive fx-credit

growth, located on the demand side as well as on the supply side of the market: Luca and Petrova (2007)

argue that the level of fx-deposits has an influence on the banks willingness to issue fx-denominated

loans. The rationale behind this consideration is that financial institutions may prefer holding currency-matched portfolios in order to be hedged against the currency risk. According to Guidotti and Rodriguez

(1992, quoted in Uzun 2005, 7), foreign currencies tend to be used as a unit of account in countries with

high inflation rates. Accordingly Delgado et al. (2002, quoted in Uzun 2005, 17) claim that also the

uncertainty regarding the future inflation rate encourages banks to issue loans denominated in a foreign

currency. In line with that, Ize and Parrado (2002, quoted in Uzun 2005, 17) point out that even

historically high inflation rates may increase fx-lending over a longer time period due to persistence and

hysterisis effects.

Within the dollarization literature (see e.g. Levy Yeyati, 2006, for a recent overview), other authors

argue that the surge in fx-credit is mainly demand driven: Got and Ross (2006) detect three principal


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development in the host counties (see e.g. Eller, Haiss and Steiner, 2006, for recent empirical evidence).

With regard to foreign banks or other factors determining the level of fx-denominated loans, little

empirical research has been conducted so far, with Basso et al (2007) and Luca and Petrova (2007) asrecent exceptions. This paper is meant to fill this gap by presenting the results of several regression

analyses, which investigate the impact of the various determinants of foreign exchange lending suggested

in the literature. Our contribution lies in combining the analysis of foreign banks and foreign bank origin

with demand-side parameters (interest rate differential, exchange rate, exchange rate regime, foreign

trade) and supply-side parameters (foreign currency deposits, inflation differential, and bank

concentration) to shed more light on the cause of fx-lending in transition economies. As can be seen in

chart 2, the research framework represents a two-step approach. In a first step, the question of whether

foreign bank entry has a significant influence is examined. Subsequently, the impacts of further

environmental factors, which are located on the demand side as well as on the supply side, are tested as

well.

FSFDI

FURTHER VARIABLES:

DEMAND SIDE:

INTEREST RATE GAP

EXCHANGE RATE

E


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assumptions:

For those models investigating the influence of the market share of foreign banks we ran the regression

for two scenarios: In the original analysis we processed the data from all the countries under investigation

whereas in the second regression model we introduced a 20% threshold regarding market share.

Therefore, only those countries where foreign banks held a market share exceeding this 20% hurdle rate

were included in the calculations. The rationale behind this adaptation is the following: Waschiczek

(2002, 89) argues, pointing to the example of Austria, that herding behavior contributes to the growing

popularity of fx-credits. In the case of fx-lending this theory suggests that a potential borrower imitatesother borrowers, assuming that they have additional pieces of information. Similarly, Tzanninis (2005, 7)

attributes high importance to herding and spreading the practice through word of mouth. As a certain

threshold might be required in order to trigger these effects, the 20% hurdle was introduced in the second

regression model.

Another modification concerns the selection of the countries according to their currency regime. Theintroduction of three different categories (Float, Peg/hard peg/currency board and Exchange Rate

Mechanism II) is meant to clarify possible differences between the various types of currency regimes. The

countries have been classified according to the IMF classification framework (IMF, 2006). In line with

gert (2007) and Herzberg and Watson (2007) we include Croatia as a quasi peg in the Peg-group.


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3 DATA

Our sample includes data on 16 countries from Central and Eastern Europe (i.e. the New EU Member

States from CEE), South-Eastern Europe (SEE), as well as the former CIS region 2, covering the period

from 1999 to 20063. The data have been obtained from the database of the Economist Intelligence Unit,

the various editions of the RZB Groups CEE Banking Sector Reports, the Erste Bank (2007) Sector

Report on SEE Banks and the EBRD Banking database. For descriptive statistics for the variables used in

the regressions see Table 13 in the appendix.

4 EMPIRICAL RESULTS

This section presents the empirical results of our regression analyses. In different scenarios we test

several explanatory variables to their impact on the level of fx-lending.

4.1 MARKET SHARE OF FOREIGN BANKS

As outlined above, several recent papers discuss the impact of foreign banks on the level of fx-lending in

transition economies. Basso et al (2007) argue that better access by foreign banks to foreign funds leads

to a higher level of foreign-currency denominated loans. As subsidiaries of foreign banks, they have

l t f i f th i t b k T k th b k t h d th


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Dependent Variable FX-Loans (% of total loans)Independent Variable Asset share of foreign banks (% of total assets)

Countries Threshold onAsset Share Adjusted R Beta Significance

All countries no 0.012 -0.147 0.127FLOAT no 0.031 -0.321 0.131PEG no 0.348 -0.610 0.000ERM II no 0.050 0.276 0.098PEG & ERM II no -0.008 -0.085 0.498All countries yes (20%) 0.031 -0.204 0.053FLOAT yes (20%) 0.041 -0.263 0.127PEG yes (20%) 0.505 -0.728 0.000ERM II yes (20%) -0.150 0.126 0.478PEG & REM II yes (20%) 0.011 -0.171 0.212

TABLE 2Market share of foreign banks as explanatory variable

Our empirical results show that contrary to earlier findings by Basso et al (2007) and Luca and Petrova

(2007) in broader samples, foreign banks do not increase the risk of foreign currency lending in Centraland Eastern Europe. In fact, the asset share of foreign banks is not significantly correlated with the level

of fx-lending (the Beta of -0.147 even points to a negative relationship, although not statistically

significant on a 95% confidence interval). Introducing a 20% threshold on the asset share of foreign

banks improves the significance of the general regression model. The group of countries with a fixed peg


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of fx-lending. The results point to a stabilizing influence of foreign banks in CEE, possibly an effect of

foreign banks superior risk management techniques. This is in line with the findings of Eller et al (2006)

of a rather positive impact of financial sector FDI on economic development in CEE.

4.2 MARKET SHARE OF AUSTRIAN BANKS

The second variable we tested was the asset share of Austrian banks in CEE. The OenB (2007) and the

IMF attribute Austrian banks a special role in the context of the rapid credit expansion in CEE, arguing

that Austrian banks account for a disproportionately high share of foreign currency lending in some of the

countries (Oenb, 2007, 43; Tieman, 2007).

Dependent Variable FX-Loans (% of total loans)Independent Variable Asset Share of Austrian Banks (% of total assets)

CountriesThreshold onAsset Share Adjusted R Beta Significance

All countries no 0.029 -0.217 0.109FLOAT no 0.181 0.465 0.022PEG no 0.846 -0.924 0.000ERM II no 0.281 -0.589 0.044PEG & ERM II no 0.713 -0.850 0.000


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therefore expect a positive relationship between the level of fx-deposits and the level of fx-lending.

With regards to the impact of the exchange rate regime on currency mismatches, most economists agree

that a fixed exchange rate regime encourages currency mismatches as banks and firms do not hedge their

foreign currency liabilities (Arteta, 2002, 1). Consequently, we expect a stronger relationship between fx-

deposits and fx-lending in those countries with a floating exchange rate regime, as banks and firms seek

to limit their exposure to currency fluctuations.

Dependent Variable FX-Loans (% of GDP)Independent Variable FX-Deposits (% of GDP)

Countries Adjusted R Beta Significance

All countries 0.090 0.314 0.001FLOAT 0.466 0.691 0.000PEG 0.043 0.305 0.191

ERM II 0.105 0.359 0.027PEG & ERM II 0.005 0.151 0.259

TABLE 4FX-Deposits as explanatory variable

At a 95% confidence interval the regression analysis covering the data from all the countries under

examination confirms that there is a positive relationship between the level of fx deposits and fx loans


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TABLE 5Total Exports as % of GDP as explanatory variable

Our results should be treated carefully, as we do not have data distinguishing household from corporate

fx-lending. This distinction, however, would be very important, as the export orientation intuitively

should be linked with corporate fx-lending, but not household fx-lending. Basso/Calvo-Gonzalez/Jurgilas

(2007) find that a country's openness to the international economy is contributing to corporate but not to

household financial dollarization.

4.5 INTEREST RATE DIFFERENTIAL

Theoretically, uncovered interest parity suggests that any interest differential between two countries can

be explained by an expected depreciation or appreciation of the currency (Basso/Calvo-Gonzalez/Jurgilas,

2007, 53). Therefore, if uncovered interest parity holds, it should not be possible to take advantage of

lower interest rates on foreign currency loans. However, the decisions of households or firms to finance in

foreign currency might not be based on interest parity considerations. As outlined above, the attraction of

lower nominal interest rates, coupled with herd-behavior, might have a considerable influence on fx-

lending decisions.

Dependent Variable FX-Loans (% of GDP)Independent Variable Interest Differential Germany


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while in the second regression we observed the gap between the local rate and a Eurozone Proxy lending

rate.4

Still, the outcomes of both approaches are similar: The calculations covering all the countries do not point

at a statistically significant nexus applying the 95% confidence interval. By contrast, the examinations of

the Peg-countries and the ERM II countries exclusively revealed significant relationships. It is

interesting to note that the existence of an interest rate differential in the Peg -subset appears to be

positively correlated with the level of fx-credit (beta = 0.422 and 0.396 respectively) whereas the

calculation for the ERM II countries results in a negative correlation (beta = -0.561 and -0.570respectively).

4.6 INFLATION DIFFERENTIAL

As high levels of inflation typically imply high expected interest rates, it might seem rational in such a

scenario to switch to foreign currency loans, to benefit from lower interest rates. Accordingly, we wouldexpect to find a positive relationship between the inflation differential and the level of fx-loans.5

Dependent Variable FX-Loans (% of GDP)Independent Variable Inflation Differential Eurozone


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consistent with the underlying theory. By contrast, the relationship detected in the ERM II countries is

significantly negative with a beta of -0.632 and an adjusted R of 0.385.

4.7 REAL EXCHANGE RATE

The impact of changes in the real exchange rate on foreign currency borrowing is ambiguous. On the one

hand, an appreciation of a currency reduces the interest and redemption payments agents have to make on

loans denominated in foreign currencies. Hence, we would expect firms and households to increase their

fx-lendings in times of local currency appreciations. On the other hand, however, Catao and Terrones

(2000) argue that banks which seek to maximize their profits in dollars (or any other foreign currency for

that matter) will decrease the foreign currency part of their loan portfolio in the wake of an expected

appreciation of the local currency. Following Luca and Petrova (2003), who show that bank specific

factors, as opposed to firm specific factors, are the main driving force of credit dollarization, we would

expect the relationship between the real exchange rate and the level of fx-loans to be negative.

Dependent Variable FX-Loans (% of GDP)Independent Variable Real Effective Exchange Rate

Change (%)

C t i Adj t d R B t Si ifi


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Dependent Variable FX-Loans (% of GDP)Independent Variable Asset share of 5 largest banks (%)

Countries Adjusted R Beta Significance

All countries 0.171 0.422 0.000FLOAT 0.256 0.522 0.000PEG 0.147 -0.418 0.017ERM II 0.651 0.812 0.000PEG & ERM II 0.136 0.384 0.001

TABLE 10 Market share of the 5 largest banks as explanatory variable

In this regression model the asset share of the five largest banks in every country was used as a proxy for

market concentration. In general, we find support of the theory that fx-lending is a function of the size of

banks. The general model reveals a strong positive relationship with a beta of 0.422 and an adjusted R of

0.171. The analysis of ERM II countries exclusively shows the strongest nexus: beta is 0.812 and also the

adjusted R of 0.651 is comparatively high. By contrast, the impact of market concentration in thosecountries with a currency peg appears to be negative the adjusted R value of 0.147 is rather low

though.


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4.9 EXCHANGE RATE REGIME

Boissay, Calvo-Gonzalez and Kozluk (2006) suggest that the existence of exchange rate regimes might be

a driver for the rising level of fx-credit. They assume that due to exchange rate regimes the exchange rate

risk is perceived as very low, which might in turn encourage foreign currency lending (Boissay / Calvo-

Gonzalez / Kozluk, 2006, 16-17). In order to investigate whether there is a link between the type of

exchange rate regime and the level of fx-credit we conduct an ANOVA. The observed countries are

assigned either the variable 0, which indicates that their currency is floating freely, the variable 1,

meaning that the countrys currency is pegged against another currency, or 2, which stands for all the

countries participating in ERM II.

The calculation shows that on average the level of fx-denominated loans is the lowest in the Peg subset

(34.96%), while the ERM II countries exhibit the highest average share (44.86%). In those countries with

a floating exchange rate the mean equals 39.93%. According to the ANOVA, however, the type of

exchange rate regime is not the reason for these differences even if considering a 90% confidence

interval, the influence is statistically insignificant.


17/24CHART 6 Error bar showing the means of fx-loans for the three groups of exchange rate regime countries (95%


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explaining the rise of fx-loans in Austria is important for herding to work.

Our analyses of the market share of Austrian banks, the interest rate differential and the inflation

differential led to ambiguous results. With regards to export orientation, there seems to be even a negative

correlation with the level of fx-credit. According to our analysis, the investigation of fx-deposits and their

impact on foreign currency lending deserve particular attention: In line with the theoretical assumption

there seems to be positive relationship in countries with floating exchange rate regimes and ERM II

countries. Hence, banks could indeed try to avoid currency mismatches and shift the exchange rate risk to

the borrowers. In countries with a currency peg this relationship could not be confirmed, though. Thisdeviation could indicate that exchange rate regimes promote currency mismatches.

To a certain degree the regression models also support the theoretical nexus between the real exchange

rate development and the level of fx-credit. Whereas the link is rather weak in the aggregate model, it is

considerably negative in those countries with a floating exchange rate. This correlation could point at the

banks ability to successfully market higher shares of fx-credits in times of local currency depreciation,thus increasing their income.

With regard to the impact of market concentration, in general, the regression model meets the initial

expectations, as a concentrated market appears to foster the issuance of fx-loans. This finding is important


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6 LITERATURE

Arteta, Carlos scar (2002), Exchange Rate Regimes and Financial Dollarization: Does FlexibilityReduce Bank Currency Mismatches? CIDER Working Paper No. C02-123.

Back, Peter, Ritzberger-Grnwald, Doris, and Stix, Helmut (2007), The Euro on the Road East,

Monetary Policy and the Economy Q1/07, 114-127, OeNB.

Basso, Henrique, Calvo-Gonzalez, Oscar and Jugilas, Marius (2007), Financial Dollarization: The Roleof Banks and Interest Rates, ECB Working Paper No. 748, May 2007,

http://www.ecb.eu/pub/pdf/scpwps/ecbwp748.pdf(6.4.2008).

Boissay, Frederic, Calvo-Gonzalez, Oscar and Kozluk, Tomasz (2006), Is Lending in Central and Eastern

Europe developing too fast? Preliminary draft, paper presented on the Conference on European

Economic Integration, 14-15th November 2005, Vienna, http://www.eu-financial-system.org/fileadmin/content/Dokumente_Events/second_conference/Boissay_Calvo-

Gonzales_Kozluk.pdf (9.4.2008).

Bokor, Lszl and Pellnyi, Gbor (2005), Foreign Currency Denominated Borrowing in Central
http://www.ecb.eu/pub/pdf/scpwps/ecbwp748.pdfhttp://www.ecb.eu/pub/pdf/scpwps/ecbwp748.pdf


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ECB (2006b), EU Banking Sector Stability, November 2006,

www.ecb.int/pub/pdf/other/eubankingsectorstability2006en.pdf (9.4.2008).

EIU Economist Intelligence Unit (2008) EIU Country Data, https://eiu.bvdep.com/version-

2008226/cgi/template.dll?product=101 (1.3.2008).

Eller, Markus, Haiss, Peter, and Steiner, Katharina (2006), Foreign direct investment in the financial

sector and economic growth in Central and Eastern Europe: The crucial role of the efficiency

channel, Emerging Markets Review 7, 300-319.

gert, Balzs (2007), Central Bank Interventions, Communication and Interest Rate Policy in Emerging

European Economies, OeNB Working Paper Nr. 134, http://www.oenb.at/de/img/wp134_tcm14-

51029.pdf (6.4.2008).

Erste Bank (2008), SEE Banks Boom or bust? CEE Equity Research Sector Report, February 19, 2008.

European Commission (2004), Financial Market Integration and the Use of the Euro in the New Member

States, Quarterly Note on the Euro-Denominated Bond Markets, No. 75, September 2004,

http://ec.europa.eu/economy_finance/publications/bond_markets/2004/bondq0304_en.pdf

(7.12.2007).
http://www.oenb.at/de/img/wp134_tcm14-51029.pdfhttp://www.oenb.at/de/img/wp134_tcm14-51029.pdfhttp://www.oenb.at/de/img/wp134_tcm14-51029.pdfhttp://www.oenb.at/de/img/wp134_tcm14-51029.pdfhttp://www.oenb.at/de/img/wp134_tcm14-51029.pdf


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IMF (2008), Austria-2008 Article IV Consultation, Preliminary Conclusions of the Mission, March 17,

2008, http://www.imf.org/external/np/ms/2008031708.htm (18.3.2008).

Ize, A. and E. Parrado (2002) Dollarization, Monetary Policy and the Pass-Through, IMF Working Paper,

No.188.

Karmin, Craig and Perry, Jiellen (2007), Homeowners Abroad Take Currency Gamble in Loans, The

Wall Street Journal, May 29, 2007

Levy Yeyati, Eduardo (2006), Financial dollarization: evaluating the consequences, Economic Policy,Jan. 2006, 61-118.

Luca, Alina and Petrova, Iva (2007), What drives credit dollarization in transition economies? Journal of

Banking and Finance, doi: 10.1016/j.jbankfin.2007.06.003.

Tieman, Alexander (2007), Cross-Border Banking Issues for the Austrian Banks and Their Supervisors,in IMF (ed.): Austria: Selected Issues, IMF Country Report No. 07/143, April 2007, 30-43.

Martens, Jul (2003), Statistische Datenanalyse mit SPSS fr Windows, 2. Auflage, Mnchen und Wien,

Oldenbourg.


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OeNB, Financial Stability Report 4, Wien, 83-99, http://www.oenb.at/en/img/fsr_04_tcm16-

8061.pdf (9.4.2008).

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http://siteresources.worldbank.org/INTECA/Resources/EU8+2_SpecialTopic.pdf(30.3.2008).
http://siteresources.worldbank.org/INTECA/Resources/EU8+2_SpecialTopic.pdfhttp://siteresources.worldbank.org/INTECA/Resources/EU8+2_SpecialTopic.pdf


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7 APPENDIX

Detailed statistics on all 55 regression analyses have been omitted for space considerations, but are available

from the authors upon request (please contact the corresponding author).

Variable name N Minimum Maximum Mean Std. Deviation

FX_Loans_pc_of_total_loans 128 10.10 82.10 40.3041 20.63653

FX_Loans_pc_of_GDP 117 .14 27.85 4.7999 4.40740

FX_Deposits_ps_of_GDP 131 .78 25.10 7.0447 5.81661Asset_Share_Foreign_Banks 123 .30 100.00 56.5033 32.09626

Asset_Share_Austrian_Banks 56 1.10 62.60 24.3393 17.67158

Asset_Share_5_largest_banks 144 35.32 99.45 65.8949 15.68209

Exports_in_pc_of_GDP 158 2.24 53.16 17.6214 11.06771

Interest_Differential_Germany 161 -4.17 68.30 6.0658 12.68509

Interest_Differential_Eurozone 161 -3.60 71.56 8.7616 12.29795

Inflation_Differential 162 -1.17 293.73 12.0663 28.94528Real_Exchange_Rate_Change 153 -50.13 24.09 2.0734 8.14839


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Correlations

FX_Loans_pc_of_total_loans

FX_Loans_pc_of_GDP

FX_Deposits_ps_of_GDP

Asset_Share_Foreign_Banks

Asset_Share_Austrian_Banks

Asset_Share_5_largest_banks

Exports_in_pc_of_GDP

Interest_Differential_Germany

Interest_Differential_Eurozone

Inflation_Differential

Real_Exchange_Rate_Chang

e

FX_Loans_pc_of_total_loans

Pearson Correlation1 .336(**) -.175 -.147 -.217 .422(**) -.199(*) .064 .054 .137 -.196(*)

Sig. (2-tailed) .000 .060 .127 .109 .000 .025 .475 .548 .122 .030

N 128 115 116 110 56 124 127 128 128 128 122

FX_Loans_pc_of_GDP Pearson Correlation .336(**) 1 .314(**) .141 -.078 .292(**) .525(**) -.394(**) -.384(**) -.264(**) -.001

Sig. (2-tailed) .000 .001 .149 .580 .002 .000 .000 .000 .004 .989

N 115 117 106 106 52 115 115 117 117 117 111

FX_Deposits_ps_of_GDP Pearson Correlation -.175 .314(**) 1 .220(*) .586(**) .178(*) .097 -.287(**) -.248(**) -.223(*) .002

Sig. (2-tailed) .060 .001 .020 .000 .041 .272 .001 .004 .010 .978

N 116 106 131 111 52 131 130 130 130 131 123

Asset_Share_Foreign_Banks

Pearson Correlation-.147 .141 .220(*) 1 .697(**) .152 .305(**) -.463(**) -.405(**) -.356(**) .149

Sig. (2-tailed) .127 .149 .020 .000 .095 .001 .000 .000 .000 .110

N 110 106 111 123 56 121 121 122 122 123 116

Asset_Share_Austrian_Banks

Pearson Correlation-.217 -.078 .586(**) .697(**) 1 .543(**) .072 -.279(*) -.160 -.399(**) -.024

Sig. (2-tailed) .109 .580 .000 .000 .000 .599 .038 .238 .002 .868

N 56 52 52 56 56 56 56 56 56 56 52

Asset_Share_5_largest_banks

Pearson Correlation.422(**) .292(**) .178(*) .152 .543(**) 1 .100 -.116 -.094 .029 .028

Sig. (2-tailed) .000 .002 .041 .095 .000 .237 .169 .263 .731 .743

N 124 115 131 121 56 144 141 143 143 144 136

Exports_in_pc_of_GDP Pearson Correlation -.199(*) .525(**) .097 .305(**) .072 .100 1 -.483(**) -.468(**) -.222(**) .147

Sig. (2-tailed).025 .000 .272 .001 .599 .237 .000 .000 .005 .074

N 127 115 130 121 56 141 158 157 157 158 149

Interest_Differential_Germany

Pearson Correlation.064 -.394(**) -.287(**) -.463(**) -.279(*) -.116 -.483(**) 1 .994(**) .620(**) -.084

Sig. (2-tailed) .475 .000 .001 .000 .038 .169 .000 .000 .000 .301

N 128 117 130 122 56 143 157 161 161 161 152

Interest_Differential_Eurozone

Pearson Correlation.054 -.384(**) -.248(**) -.405(**) -.160 -.094 -.468(**) .994(**) 1 .603(**) -.077

Sig. (2-tailed) .548 .000 .004 .000 .238 .263 .000 .000 .000 .345

N 128 117 130 122 56 143 157 161 161 161 152

Inflation_Differential Pearson Correlation .137 -.264(**) -.223(*) -.356(**) -.399(**) .029 -.222(**) .620(**) .603(**) 1 -.150

Sig. (2-tailed) .122 .004 .010 .000 .002 .731 .005 .000 .000 .065

N 128 117 131 123 56 144 158 161 161 162 153

Real_Exchange_Rate_Change

Pearson Correlation-.196(*) -.001 .002 .149 -.024 .028 .147 -.084 -.077 -.150 1

Sig. (2-tailed) .030 .989 .978 .110 .868 .743 .074 .301 .345 .065

N 122 111 123 116 52 136 149 152 152 153 153

TABLE 14Cross-correlations of the variables used in the regression analyses

** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).

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