Market power and bank profitability: Evidence from Montenegro and Serbia

Grubišić, Zoran; Kamenković, Sandra; Kaličanin, Tijana Article Market power and bank profitability: Evidence from Montenegro and Serbia Journal of Central Banking Theory and Practice Provided in Cooperation with: Central Bank of Montenegro, Podgorica Suggested Citation: Grubišić, Zoran; Kamenković, Sandra; Kaličanin, Tijana (2022) : Market power and bank profitability: Evidence from Montenegro and Serbia, Journal of Central Banking Theory and Practice, ISSN 2336-9205, Sciendo, Warsaw, Vol. 11, Iss. 1, pp. 5-22, https://doi.org/10.2478/jcbtp-2022-0001 This Version is available at: https://hdl.handle.net/10419/299029 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ 5 Market Power and Bank Profitability: Evidence from Montenegro and Serbia * Belgrade Banking Academy, Belgrade, Serbia E-mail: zoran.gr[email protected].rs ** Belgrade Banking Academy, Belgrade, Serbia E-mail: sandra.kamenkov[email protected] *** Belgrade Banking Academy, Belgrade, Serbia E-mail: tijana.k[email protected].rs Journal of Central Banking Theory and Practice, 2022, 1, pp. 5-22 Received: 30 August 2020; accepted: 12 October 2021 UDK: 336.71(497.11:497.16) DOI: 10.2478/jcbtp-2022-0001 Zoran Grubišić *, Sandra Kamenković **, Tijana Kaličanin ***, Market Power and Bank Profitability: Evidence from Montenegro and Serbia Abstract: This study investigates the relationship between profitability and market power in the banking sector using data from the financial reports of the banks that operated in Serbia and Montenegro, covering the period from the first quarter of 2010 to the last quarter of 2019. In order to investigate this relationship, determinants of bank profitability are split between internal and external. As the external determinants, selected ratios of concentration were calculated and used in order to measure market power. The total of sixteen panel regression models were applied, eight for each country. The results indicate that variations of return on assets and return on equity in Serbia can be explained by the variations of the ratios of concentration. On the other hand, results of the panel regression model applied for the banking sector of Montenegro does not give enough argument to support such explanation, and bank profitability can be explained by bank efficiency to some extent. Keywords: banking sector, bank profitability, market power, competition. JEL Code: G21, E58, L10 Introduction When analyzing bank profitability and market power, the most common hypotheses in studies are the SCP hypothesis (Bain, 1951), market power hypothesis, and efficient-structure hypothesis (Demsetz, 1973). Market power hypothesis ar- 6Journal of Central Banking Theory and Practice gues that collusion among firms with market power results in higher pricing and profitability. The SCP hypothesis posits that the structure of a market influences firms pricing conduct and ultimately performance (Alhassan, Tetteh & Brobbey, 2016). Efficient-structure hypothesis predicts that efficient firms come out ahead in competition and grow as a result (Homma, Tsutsui & Uchida, 2014). In order to measure market power, two common approaches are usually used: the structural approach, also known as the empirical industrial organization model, and the nonstructural approaches known as the new empirical industrial organization models. The structural approach refers to including structural measures of concentration as independent variables in models which are analyzing bank profitability function. Considering that, selected measures of concentration are used to measure market power. Market power is usually measured as a market share, using the market share of the leading bank known as a ratio of concentration (CR1) and cumulative market share or three or four banks that have the highest market share in the market (CR3 or CR4). Considering market share in the banking sector, the authors usually use the value of total balance sheet assets or the value of total loans. Using these ratios and including them in regression models is the first step in analyzing the relationship between bank profitability and market power. Literature review Determinants of bank profitability are usually split between internal and external. In most of the papers external determinants of profitability are usually split between industry-specific and macroeconomic variables. Speaking about industry-specific determinants, besides ownership, the authors use different measures of concentration to analyze relationship between profitability and level of concentration in the banking sector. Speaking about measures of concentration, the influence of market power measured by the ratio of concentration of leading bank or group of leading banks is also analyzed due to profitability. Short (1979) was one of the first authors to test the relationship between the profit rates of 60 banks in Canada, Western Europe and Japan, as well as the market share of each of them. As a measure of market concentration CR1, CR3 and CR5 were included in the model. The results of this research support the hypothesis that higher market concentration leads to higher profit rates. Demsetz (1973, 1974), Peltzman (1977) and McGee (1974) argue that concentration is not an accidental event, but the result of the superior efficiency of leading 7 Market Power and Bank Profitability: Evidence from Montenegro and Serbia companies. Companies that have a comparative advantage in production become large and thus gain a higher market share and, as a consequence, the market becomes more concentrated. This view, which the authors have defined as the efficient structure hypothesis, implies that market share implies greater efficiency of the company and therefore efficiency is in a positive correlation with profitability. Kasman, Kasman, and Turgutlu (2011) in their work give a comparative analysis between developed and developing countries through a study of the relationship between profits and structures in the banking sector in the period 1995 - 2006. The results indicate that testing the efficient structure hypothesis is crucial to explain the relationship between profit and structure in the European banking market. When efficiency measures are included as control variables, the market share and concentration ratio are below the significance level in all regression models. The results confirm the efficient structure hypothesis as opposed to the relative market power hypothesis and the SCP hypothesis. In his paper, Sufian (2011) analyzed the profitability of the banking sector in Korea by applying a panel regression model that includes both variables specific to the banking sector and macroeconomic indicators in the period 1992-2003. The sample includes a total of 251 banks, the dependent variables are return on assets and return on equity, while the independent variables are divided into two groups - internal and external. In the group of external factors, as a measure of concentration, he used the concentration ratio of the first three banks with the largest market share based on total balance sheet assets - CR3. The results indicate that the concentration of the banking market has a positive and significant impact on the profitability of banks. In a sample of a total of 23 banks in the Turkish banking sector, the authors Çelik i Kaplan (2016) tested the SCP paradigm by applying a regression model of the panel data of the Turkish banking sector in the period 2008-2013. Return on assets was used as the dependent variable, while CR5 - concentration ratio of the top five largest banks and the market share of each bank measured by total balance sheet assets was included in the group of independent variables. The results presented in the paper indicate that the variable that measures efficiency is the most significant determinant of bank profitability, but also that the concentration measure (CR5) is a significant factor in profitability. Bucevska and Hadzi Misheva (2017) test the SCP hypothesis and the efficient structure hypothesis. The aim of their work is to analyze the significance of the structure-conduct-performance hypothesis versus the efficient structure hypothesis in explaining the performance of banks. Their sample includes 127 com- 8Journal of Central Banking Theory and Practice mercial banks from six Balkan countries (Slovenia, Croatia, Serbia, Bosnia and Herzegovina, Montenegro, and Macedonia) during the period 2005-2009. In addition to control variables grouped within macroeconomic variables, sectorspecific variables and bank-specific variables use the Herfindal-Hirschman index and the market share of individual banks measured by total balance sheet assets. In conclusion, they state that empirical results indicate that efficiency is one of the main determinants of bank profitability, but not the only one. The profitability of banks is determined by a combination of variables that are specific to banks and the sector. In addition, the effects of concentration and market share are not significant, i.e. they cannot be considered as determinants of profitability. Shijaku (2017) studies the impact of bank concentration on the likelihood of a country suffering systemic bank fragility. The sample included the panel data with quarterly frequency for individual bank balance sheet and income statement items of 16 banks operating in Albania and some macroeconomic indicators for the period 2008Q04 – 2015Q03. That included a total panel balanced observations with 448 observations and 28 periods. First, results provide supportive evidence consistent with the concentration-fragility view. Second, macroeconomic variables seem to have a significant effect on bank stability, which is not found for the sovereignty primary risk. By contrast, the bank-specific variables have also a significant effect on bank stability conditions. Arif & Awwaliyah (2019) analyze the influence of market structure on profitability of the Islamic banking industry in Indonesia. They used panel regression with a random effect model. The result shows that market structure - proxies by market share (MS) and concentration ratio (CR4) does not affect profitability of the Indonesian Islamic banking industry. This result implies that the performance of the Islamic banking industry in Indonesia is not supported by the traditional hypothesis and the efficient structures hypothesis. Kamarudin, Sufian, Nassir, Anwar, and Hussain (2019) investigate the potential internal (bank specific) and external (macroeconomic) determinants that influence the revenue efficiency of Malaysian domestic Islamic banks. The data cover domestic and foreign Islamic banks operating in the Malaysian Islamic banking sector during the period of 2006 – 2015. The results indicate that the level of revenue efficiency of the Malaysian domestic Islamic banks is lower compared to their foreign Islamic bank counterparts. They found out that bank market power, liquidity, and management quality significantly influence the improvement in revenue efficiency of the Malaysian domestic Islamic banks during the period under study. 9 Market Power and Bank Profitability: Evidence from Montenegro and Serbia On the panel data in the period 1996 - 2017, which include banks from Austria, the Czech Republic, Germany, Hungary, Luxembourg, the Netherlands, Poland, Slovakia and Switzerland, the profitability of banks is explained by the function of 13 different variables (Uralov, 2020). In addition to the non-structural measure of concentration - Lerner's index, a structural measure of concentration is included in the model – CR3, concentration ratio of the three largest banks based on total balance sheet assets, while the dependent variables are ROA and ROE. Based on the obtained results, the author concludes that there is a significant impact of concentration ratio on return on equity (ROE). Le and Ngo (2020) investigates the determinants of bank profitability in 23 countries from 2002 to 2016 using the system generalized method of moments. The findings show the negative impact of market power on bank profitability, implying that competition improves bank profitability. Further, the positive relationship between capital market development and bank profitability suggests that they should be considered as complementary to one another. The Model and data In accordance with the aim of the paper, the empirical research is based on the evaluation of the panel regression models. The sample includes all banks that operated in the period 2010 – 2019, on quarterly basis, on the territories of the Republic of Serbia and Montenegro, which makes a total of 40 quarters. The sample of the banking sector of the Republic of Serbia includes a total number of 36 banks that operated in the observed period, while the sample of Montenegro covers 15 banks. A representative set of data is formed using the official financial statements published by the National Bank of Serbia and the Central Bank of Montenegro, i.e. quarterly balance sheets and income statements. Due to the fact that the number of observations in the panel differs from one bank to another, these are unbalanced panel data. The general model to be estimated is of the following linear form (Athanasoglou, Brissimis & Delis, 2008): (1.1) where 𝜋 is the profitability of bank 𝑖 at time 𝑡, (𝑖=1, …, 𝑁; 𝑡=1, …, 𝑇), 𝑐 is a constant term, 𝑋𝑖𝑡 are 𝐾 explanatory variables and 𝜀𝑖𝑡 is the disturbance with 𝑣𝑖 the unobserved bank-specific effect and 𝑢𝑖𝑡 idiosyncratic error. 10 Journal of Central Banking Theory and Practice The explanatory variables 𝑋𝑖𝑡 are grouped into bank-specific, industry-specific and macroeconomic variables. The general model (1.1), with the explanatory 𝑋𝑖𝑡 separated into these three groups, econometrically is specified as follows: (1.2) where the 𝑋𝑖𝑡s with superscripts j, l and m, denote bank-specific, industry-specific and macroeconomic determinants respectively. The general linear regression model that is used in this paper is specified as follows: (1.3) where 𝜋 is the profitability of bank 𝑖 at time 𝑡, (𝑖=1, …, 𝑁; 𝑡=1, …, 𝑇), 𝑐 is a constant term, 𝑋𝑖𝑡 are explanatory variables, j and l - bank-specific and industry-specific variables, respectively, while gdp represents a control macroeconomic variable, and 𝜀𝑖𝑡 is a random error that includes the effects of all other variables that are not directly included in the model, including individual effects. As dependent variables roa and roe were used, by comparing net profit and assets, i.e. net profit and the amount of total equity. According to the discussion above, the independent variables are divided into three groups: macroeconomic variables, bank-specific variables, and industry-specific variables. Within the group of bank-specific variables, the following variables are included in the model: capital, credit risk, operating cost management and size. The variable related to capital with the notation cap_ass was obtained by comparing the amounts of capital and assets and a positive impact of this variable on the dependent variables is expected. The increase in net profit results indicates an increase in total capital and is based on the assumption of a positive correlation between leverage and bank profitability. The next variable within this group is credit risk with the notation cr_risk, which is obtained by comparing the position of interest expenses and the total amount of loans where a negative impact is expected. Loans are the riskiest part of bank assets, their quality is one of the most important determinants of business stability and success (Žunić, Kozarić, and Dželihodžić, 2021). It is believed that greater exposure to credit risk may adversely affect the bank's operations, resulting in reduced profitability. As a measure of the bank's efficiency, the variable op_exp was used, which was obtained by comparing the cost of earnings and assets. Poor management of these costs results in the value of the stated ratio being higher, i.e. 11 Market Power and Bank Profitability: Evidence from Montenegro and Serbia a lower level of bank efficiency, which is expected to have a negative impact of this variable on the dependent profitability measures. Given that the effect of bank size growth has to some extent proved positive on the bank's profitability, the model includes the logarithmic function of total assets - size where a positive impact is expected. It is argued that the worldwide credit crunch continued for an extended period leading to low/negative growth, raised unemployment, business & consumer confidence fell, companies were not able to borrow the funds required for investments. This highlighted the challenges and discrepancies in the banking system (Kolluru, Hyams-Ssekasi, and Rao, 2021). When it comes to macroeconomic variables, only one variable is included in the model - gdp_growth where a positive correlation is expected between the specified variable and the dependent roa and roe. Industry-specific variables are variables that measure the market power of certain market-leading banks. A large number of authors have used concentration ratios to examine the relationship between profitability and market structure (Demirgüç-Kunt, Laeven & Levine, 2003; Staikouras & Wood, 2004; Rinkevičiūtė & Martinkute-Kauliene, 2014; Antoun, Coskun & Georgiezski, 2018). Table 1: Regression models and key explanatory variables Model Key explanatory variables cr1a cr1l cr4a cr4l cr1a cr1l cr4a cr4l Source: Authors In regression models, selected concentration ratios were used as key independent variables to examine the relationship between market power and profitability in the banking sector. In accordance with the above, cr1 and cr4 were used, which were calculated on the basis of assets (notation a) and the total amount of approved loans (notation c). Key independent variables were simultaneously included in the models, examining the impact on roa and roe. Thus, the research is based on the econometric evaluation of 8 panel regression models for each of 12 Journal of Central Banking Theory and Practice the countries whose banks are included in the sample. Regression models and key explanatory variables are presented in Table 1. The values of key independent variables are graphically presented in the figures further below. Hausman test is used to decide which estimation technique is more appropriate between the fixed effects model and the random effects model. The results of the Hausman test suggest that models roe1, roe2, roe3, and roe4 for the Montenegro are more appropriate with the random effects model because the chi square is not significant at 5% levels and the other models are more suitable with the fixed effects model as it is significant at 1% for the chi square. Results and discussion Figure 1 shows a comparative analysis of the CR1 ratio calculated on the basis of total balance sheet assets for the banking sectors of Serbia and Montenegro in the observed time period. It is noticeable that the CR1 ratio in the case of Montenegro has a downward trend since the beginning of the observed period. The highest value of this ratio was in the first quarter of 2010. This indicates that the bank that was the market leader - CKB bank AD Podgorica had a market share of about a quarter of the total market - almost 26%. Figure 1: Comparative analysis of CR1 ratio based on total balance sheet assets, 2006Q1-2019Q4 Having in mind the number of banks that operated in that quarter, i.e. 11 banks in total, it can be concluded that the remaining 10 banks shared 75% of the marSource: Author's calculations based on data from the Central Bank of Montenegro (https://www.cbcg.me/) and the National Bank of Serbia (http://www.nbs.rs) 19 Market Power and Bank Profitability: Evidence from Montenegro and Serbia References 1. Alhassan, A. L., Tetteh, M. L. & Brobbey, F. O. (2016). Market power, efficiency and bank profitability: evidence from Ghana.Economic Change and Restructuring,49(1), 71-93. 2. Antoun, R., Coskun, A. & Georgiezski, B. (2018). Determinants of financial performance of banks in Central and Eastern Europe.Business and Economic Horizons (BEH),14(1232-2019-853), 513-529. 3. Arif, M. N. R. A. & Awwaliyah, T. B. (2019). Market share, concentration ratio and profitability: evidence from Indonesian Islamic banking industry.Journal of Central Banking Theory and Practice,8(2), 189-201. 4. Athanasoglou, P. P., Brissimis, S. N. & Delis, M. D. (2008). Bankspecific, industry-specific and macroeconomic determinants of bank profitability.Journal of international financial Markets, Institutions and Money,18(2), 121-136. 5. Bain, J. S. (1951). Relation of profit rate to industry concentration: American manufacturing, 1936–1940.The Quarterly Journal of Economics,65(3), 293324. 6. Bucevska, V. & Hadzi Misheva, B. (2017). The determinants of profitability in the banking industry: Empirical research on selected Balkan countries.Eastern European Economics,55(2), 146-167. 7. Çelik, T. & Kaplan, M. (2016). Testing the Structure-Conduct-Performance Paradigm for the Turkish Banking Sector: 2008-2013.International Journal of Economics and Financial Issues,6(4). 8. Demirgüç -Kunt, A., Laeven, L. & Levine, R. (2003). Regulations, market structure, institutions, and the cost of financial intermediation, National Bureau of Economic Research, Working Paper 9890 9. Demsetz, H. (1973). Industry structure, market rivalry, and public policy.The Journal of Law and Economics,16(1), 1-9. 10. Demsetz, H. (1974). “Toward a theory of property rights”, InClassic papers in natural resource economics, pp. 163-177. London: Palgrave Macmillan. 11. Homma, T., Tsutsui, Y. & Uchida, H. (2014). Firm growth and efficiency in the banking industry: A new test of the efficient structure hypothesis.Journal of Banking & Finance,40, 143-153. 12. Kamarudin, F., Sufian, F., Nassir, A. M., Anwar, N. A. M. & Hussain, H. I. (2019). Bank efficiency in Malaysia a DEA approach.Journal of Central Banking Theory and Practice,8(1), 133-162. 13. Kasman, A., Kasman, S. & Turgutlu, E. (2011). Testing Profit and Structure Relationship in the European Banking Markets Using Efficiency Measures.The Developing Economies,49(4), 404-428. 20 Journal of Central Banking Theory and Practice 14. Kolluru, M., Hyams-Ssekasi, D. & Rao, K. C. M. S. (2021). A Study of Global Recession Recovery Strategies in Highly Ranked GDP EU Countries.Economics,9(1), 85-105. 15. Le, T. D. & Ngo, T. (2020). The determinants of bank profitability: A crosscountry analysis.Central Bank Review,20(2), 65-73. 16. Lončar, D., & Rajić, V. (2012). Koncentracija i konkurentnost bankarskog tržišta Srbije-postojeće stanje i moguće promene pod uticajem buduće tržišne konsolidacije.Ekonomika preduzeća,60(7-8), 372-385. 17. McGee, J. (1974). “Efficiency and Economies of Size”, In Industrial Concentration: The New Learning, ed. H. Goldschmid, H. M. Mann, and J. F. Weston, 55-97. Boston: Little, Brown, and Company. 18. Peltzman, S. (1977). The gains and losses from industrial concentration.The Journal of Law and Economics,20(2), 229-263. 19. Rinkevičiūtė, V. & Martinkute-Kauliene, R. (2014). Impact of market concentration on the profitability of Lithuanian banking sector.Business: Theory and Practice,15(3), 254-260. 20. Shijaku, G. (2017). Does concentration matter for bank stability? Evidence from the Albanian banking sector.Journal of Central Banking Theory and Practice,6(3), 67-94. 21. Short, B. K. (1979). The relation between commercial bank profit rates and banking concentration in Canada, Western Europe, and Japan.Journal of banking & Finance,3(3), 209-219. 22. Staikouras, C. K. & Wood, G. E. (2004). The determinants of European bank profitability.International Business & Economics Research Journal (IBER),3(6). 23. Sufian, F. (2011). Profitability of the Korean banking sector: Panel evidence on bank-specific and macroeconomic determinants.Journal of economics and management,7(1), 43-72. 24. Uralov, S. (2020). The Determinants of Bank Profitability: A Case of Central European Countries.Management,8(3), 8-16. 25. Žunić, A., Kozarić, K. & Dželihodžić, E. Ž. (2021). Non-Performing Loan Determinants and Impact of COVID-19: Case of Bosnia and Herzegovina.Journal of Central Banking Theory and Practice,10(3), 5-22. 21 Market Power and Bank Profitability: Evidence from Montenegro and Serbia Appendix Table 1.1: Influence of market power indicators on profitability of the banking sector of Montenegro Montenegro roa1 roa2 roa3 roa4 roe1 roe2 roe3 roe4 cr1a -0.0306 -5.7727* (0.0766) -31.910 cr4a -0.0216 -35.458 (0.0477) -20.533 cr1l -0.0320 -56.148 (0.0708) -41.349 cr4l -0.0241 -24.405 (0.0417) -20.654 cap_ass 0.0429 0.0432 0.0428 0.0430 -0.0089 0.0625 -0.0088 0.0658 (0.0437) (0.0438) (0.0435) (0.0440) -10.076 -10.019 (0.9965) -10.484 cr_risk -0.0096 -0.0096 -0.0103 -0.0101 -0.0097 -0.0356 -0.1541 -0.1901 (0.0342) (0.0340) (0.0344) (0.0340) (0.2983) (0.2913) (0.3084) (0.3460) op_exp -0.5422*** -0.5470*** -0.5541*** -0.5503*** -38.407 -47.389 -59.954 -50.597 (0.1790) (0.1795) (0.1818) (0.1796) -71.551 -70.284 -66.484 -69.142 size 0.0113*0.0110 0.0115*0.0113*-0.1862 -0.2184 -0.1573 -0.1242 (0.0063) (0.0066) (0.0059) (0.0058) (0.2052) (0.2106) (0.1733) (0.1712) gdp_growth -0.0000 -0.0001 -0.0000 -0.0000 -0.0154 -0.0147 -0.0103 -0.0039 (0.0003) (0.0003) (0.0003) (0.0003) (0.0250) (0.0257) (0.0255) (0.0234) _cons -0.1346 -0.1240 -0.1358 -0.1248 34.631 48.035 30.330 30.120 (0.0918) (0.1068) (0.0854) (0.0914) -29.103 -33.608 -23.575 -23.985 R-Squared 0.15 0.15 0.15 0.15 0.03 0.04 0.03 0.02 Source: Authors 22 Journal of Central Banking Theory and Practice Table 1.2: Influence of market power indicators on the profitability of the banking sector of Serbia Serbia roa1 roa2 roa3 roa4 roe1 roe2 roe3 roe4 cr1a -0.1607 -0.3895 (0.1200) (0.3504) cr4a -0.1000*** -0.2875** (0.0329) (0.1216) cr1l -0.7960** -3.3825** (0.3634) -13.393 cr4l -0.1446*** -0.4699** (0.0488) (0.1960) cap_ass 0.0062 0.0077 0.0064 0.0085 0.2787** 0.2836** 0.2819** 0.2873** (0.0295) (0.0295) (0.0305) (0.0299) (0.1135) (0.1169) (0.1151) (0.1193) cr_risk -0.8964*** -0.8972*** -0.8941*** -0.8968*** -1.4130** -1.4148** -1.4016** -1.4135** (0.2223) (0.2213) (0.2211) (0.2211) (0.5362) (0.5360) (0.5260) (0.5333) op_exp -0.9897*-0.9881*-0.9883*-0.9862*-4.3408** -4.3344** -4.3278** -4.3261** (0.5818) (0.5777) (0.5792) (0.5763) -17.640 -17.529 -17.526 -17.474 size 0.0032 0.0036 0.0026 0.0039 0.0545*** 0.0558*** 0.0526*** 0.0573*** (0.0043) (0.0043) (0.0042) (0.0043) (0.0172) (0.0172) (0.0171) (0.0172) gdp_growth -0.0036** -0.0033*-0.0032** -0.0032** -0.0087 -0.0077 -0.0071 -0.0075 (0.0016) (0.0016) (0.0015) (0.0015) (0.0058) (0.0056) (0.0055) (0.0055) _cons 0.0116 0.0228 0.1223 0.0362 -0.8458** -0.8055** -0.3428 - 0.7511** (0.0796) (0.0775) (0.0930) (0.0771) (0.3227) (0.3180) (0.3824) (0.3236) R-Squared 0.61 0.61 0.61 0.61 0.38 0.39 0.39 0.39 Source: Authors