BANKING RELATIONSHIP AND FIRM PROFITABILITY IN VIETNAM45483

VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS BANKING RELATIONSHIP AND FIRM PROFITABILITY IN VIETNAM Dang Thi Thu Hang Banking Faculty - Banking Academy ABSTRACT This study examines the relationship between the number of banking relationships and firm profitability in Vietnam Our sample of firms from Vietnam includes non-financial firms, 78% of which belong to Information Technology, Industry, Oil and Gas, Pharmaceutical and Medical, Consumer Services In the sample, 20.7% of the firms have a single bank relationship and 79.1% of them have no more than six relationships We construct two different proxies (ROE and ROA) for firms’ profitability and find that return on equity and return on assets increase as the number of bank relationship rises We also find that firm size and firm age are associated with firm profitability Keywords: Banking relationships, Vietnamese listed firm, firm profitability INTRODUCTION The role of banking relationships which was perceived in banking and the financial industry quite a long time ago impacts on banks as well as firms Yet, Irwin Teich, president of Fleet Capital Corporation, expresses: “The marketing philosophy of customer satisfaction, based on long-term relationships, must permeate all of the banks’ functions Relationship-building is a specific process we go through with our customers” (Teich, 1997) From the firm perspective, many theoretical and empirical studies establish that banking relationships are indeed important to a firm So far, the scientific literature has found example that banking relationships improve contracting flexibility between the customers and banks (Boot and Thakor, (1994) and Von Thadden, (1995)); alleviate the difficulty with loan contracts which can be renegotiated ex post (Rajan, 1992); enable reputation-building (Diamond, 1991); and mitigate the likelihood of leakage information to competitors (Yosha, (1995)) Also, some researchers investigate further the importance of banking relationships to firms by examining the influence of these relationships on firms’ profitability like Degryse and Ongena (2001), Weinstein and Yafeh (1998), Yu, Pennathur and Hsieh (2007), Limpaphayom and Polwitoon (2004) and Castelli, Dwyer, and Hasan (2006) Empirical results on 257 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 this effect are inconsistent While Ongena and Degryse (2001), Castelli, Dwyer, and Hasan (2006), and Yu, Pennathur and Hsieh (2007) find a negative relationship between the number of banking relationships and firm profitability, Limpaphayom and Polwitoon (2004) show the opposite result Extracting a dataset of 371 Vietnamese listed firm collected from the Hanoi Stock Exchange (HNX) database, this paper examines the connection between firm profitability and banking relationships and studies the impact of the firm’s characteristics on firm profitability The main research question is set as follows How banking relationships affect firm profitability? Is a single bank relationship more related to profitability than multiple banks? The remaining of the paper is organized as followed Section reviews theoretical and empirical literature Section describes data and methodology Section analyzes empirical specification and presents the estimates Section is the conclusion OVERVIEW OF BANKING RELATIONSHIP AND THE NUMBER OF BANKING RELATIONSHIPS AND FIRM PROFITABILITY 2.1 Definition So far, there does not exist a clear definition for the term “banking relationships” in the scientific literature However, “in its most general form, we define a banking relationship as the connection between a bank and a customer that goes beyond the execution of simple, anonymous, and financial transactions” (Ongena and Smith, 1999) This definition stresses close ties between the bank and the customer, a nonfinancial firm It means that the bank will not only concentrate on providing financial services to customers, but also make use of the obtained information for future expansion of its business to produce opportunities for other benefits Intrinsically, creating and maintaining customer relationships require many efforts from the bank According to Suwannaporn (2003), for most German banks, branch managers become relationship managers to small business firms and participate in helping middle-sized companies overcome financial or business distress These branch managers make suggestions to each business customer on both private financial affairs and on general financial questions related to the enterprise As shown in the above discussion, banking relationships are essential for both the bank and the firm However, this paper will concentrate on the firm perspective in order to understand better the impact of banking relationships on firm profitability in theory as well as empirical studies Single bank relationship and firm profitability The firm which wants to contact to only one bank can benefit from the information monopoly Bhattacharya and Chiesa (1995) discover that bilateral financing is preferable for firms in high-tech sectors pursuing research and development (R&D) activities This is because R&D activities contain proprietary information about the firm which can reduce the expected profits of innovators if competitors can gather this information This result is at the core of the finding of Memmel, Schmieder, and 258 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS Stein (2006) who find that the number of a firm’s lending relationships decreases with its R&D intensity Additionally, firms also can benefit from the existence of a single bank relation due to the cost minimization Dealing with more than one bank is costly since both screening and monitoring costs are duplicated It is more expensive to market debt claims to multiple creditors This argument is consistent with the Diamond (1984) delegated monitoring model Diamond (1984) illustrates that a single relationship can reduce both costly information frictions and problems related to renegotiation because firms delegate a monitoring responsibility to banks As a result, he concludes that there is no gain in having multiple relationships compare to a single one Sharpe (1990) agrees with this argument and shows that firms kept single relationships although other banks want to stay in contact with them Moreover, a single bank avoids agency costs and free-riding problems by private investors Therefore, in all activities prior to and during the loan contract it would be cheaper to communicate with a single bank Prowse (1990) observes Japanese firms and proposes that an exclusive firmbank relationship significantly reduces agency costs of loans which allow them to lend more debt than US firms Taking data collected in 1987 from a sample of 1389 American small firms, Petersen and Rajan (1994) show that exclusive relationships reduce the cost of credit In a further study also based on U.S data, Petersen and Rajan (1995) find that small and young firms tend to be less credit constrained and seem to receive better lending rates when they borrow from exclusive bank Cole’s (1998) evidence indicates that there is an increased likelihood of extension of credit for small businesses in the U.S in relation to the existence of a single bank relationship 2.2 Multiple banking relationships and firm profitability From the firm perspective, theories of multiple relationships are constructed to mitigate the hold-up problem and the soft budget constraint problem The hold-up problem is also based on the monopoly power in capturing proprietary information about the firm that banks obtain as a part of the bank relationship This generates bargaining power for the bank allowing it to charge ex post higher interest rates from the firm Thus, firms are involved in multiple relationships to avoid being overcharged, which leads to ex post competition among creditors This is in line with empirical results about this conjecture Ongena and Smith (2000) show that multiple relationships indeed reduce the hold-up problem, but lessen the credit availability D’Auria, Foglia, and Marullo Reedtz (1997) conclude that non-exclusive lending relationships lead to lower interest rates and the impact of credit market competition on interest rates is very small Berglof (1990) shows that firms financed by few banks are charged lower interest rates Cosci and Meliciani (2002) expect a negative relationship between the cost of debt and the number of bank relationships, yet in fact they find that this relationship is not significant The soft-budget constraint problem allows firms to more easily renegotiate the debt contract when having only a single banking relationship which consequently 259 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 promotes the bank to continue financing firm’s unprofitable projects without proper monitoring Therefore, multiple banking helps to limit inefficient credit that banks provide to firms and avoids incentives for a firm manager for strategic default (Bolton and tein, 1996) Ongena and Smith (2000) find that in countries with inefficient judicial systems and poor enforcement of creditor’s rights, firms have contacted to a higher number of bank relationships The interpretation for this result is that in countries with inefficient judicial systems and poor law enforcement, the cost of strategic default is low and the incidence of soft-budget constraint problem might be greater As a result, firms engage in multiple bank relationships as a solution to the soft-budget problem Many authors explore that in a largely decentralized economy, banks cannot commit to make a loan for unprofitable long-term projects because dispersed banks with limited capital will find it costly to coordinate actions (Bolton and Scharfstein, 1996) However, Memmel, Schmieder, and Stein (2006) test the hypotheses of Bolton and Scharfstein on German firms that the optimal number of creditors depend on the renegotiation costs of debt and find that companies with low credit quality tend to have slightly more lending relationships than high quality firms This finding does not support this hypothesis either Besides, multiple relationships are detrimental to the firm success due to the leakage of proprietary information When the firm seeks financial resources, it has to disclose some of its private information to creditors in order to convince the bank of its credit quality and mitigate asymmetric information problems This information can be easily transferred to the firm’s competitors due to either accident or during a bank’s advising activity that can hurt the firm Conversely, revealing confidential information to banks because non-exclusive banking relationships can also benefit the firm since it enhances better evaluation and introduces competition in the credit market that lower the cost of credit for high quality borrowers Von Rheinbaben and Ruckes (2004) argue that for highly rated companies, providing private information cannot significantly upgrade their ratings; so disclosing little private information and having a substantial number of creditors to induce competition are good choices for them By contrast, it is necessary for a firm with a low credit rating to communicate private information substantially to signal its quality at the expense of severe information leakage As a result, it is optimal for such firm to have a relatively small number of creditors Moreover, alleviating the hold-up and soft-budget constraint problems, liquidity risk is also a reason why firms engage in a large number of relationships Liquidity risk is the risk that a solvent but illiquid borrower is unable to obtain refinancing When financing at the early phase of the project, banks capture proprietary information due to closing ties with firms so that they can get rid of adverse selection At the refinancing stage, firms may need to contact non-relationship lenders By doing so, they will create adverse selection problems because non-relationship banks may not be able to recognize whether the refinancing is due to the bank’s internal distress or since the project is a lemon Therefore, firms may be charged high interest rates by outside lenders Angelini, Di Salvo and Ferri (1998) find evidence that liquidity constraints are relatively less frequent among firms financed by a few banks, with a resulting positive impact on firms’ performance Detragiache, Garella and Guiso 260 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS (2000) suggest that multiple bank relationships can diversify liquidity risk Harhoff and Korting (1998) and Cole (1998) study the increase in limits to credit for firms borrowing from more than one bank Their empirical results obtained from a model of the optimal number of bank relationships shows that multiple bank relationships decrease the probability of an interruption of funding due to a lender’s internal problems In summary, both theoretical and empirical results indicate that the impact of banking relationship on firm profitability is mixed The reason is that this connection also depends on other factors such as firm’s characteristics, a degree of competition in banking industry and so on THE DATA AND METHODOLOGY 3.1 The data The data in this paper is derived from the Vietstocks company and the Hanoi Stock Exchange (HNX) Untill 2011, the total number of listed firms on the HNX was 371 Initially, we collect financial statements for all 371 firms from 2011 to 2015 However, when building a data field is consistent with the research model, many firms not meet the necessary data Therefore, several firms were eliminated; the sample size was only 187 enterprises Information on banking relationships is based on the firms’ annual reports The number of firms actually surveyed to collect information about banking relationship is 187 During the period considered, only one firm have more than banking relationship 119 firms have two to six banking relationships 31 firms have a single banking relationships 36 firms have not had any information Finally, only 151 Vietnamese listed firms gathered the necessary information in five consecutive years: 2011, 2012, 2013, 2014, and 2015 In another word, there are 755 firm-year observations in total These firms belong to nine sectors of the economy: Information Technology, Industry, Oil and Gas, Pharmaceutical and Medical, Consumer Services, Consumer Goods, Real Estate, and Materials The dataset presented in the form of a balanced panel enables the use of a panel framework later in the econometric analysis The profitability of firms is measured by the ROA and the ROE In table 1, we see that the fluctuation of the ROA reflects the changes of the stock market as well as the situation of the economy Table Profitability of listed firm in the period 2011 - 2015 Unit (%) 2011 2012 2013 2014 2015 ROA 5,23 3,35 5,10 5,15 5,56 ROE 6,89 7,30 7,48 12,72 13,20 Sources: Vietstock 261 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 Figure Profitability of listed firm in the period 2011 - 2015 Sources Vietstock The return on equity (ROE) is considered as an important indicator for investors Therefore, it is an important market signal, so the CEOs are always trying to increase this ratio to attract investors Although the ROA ratio plummeted in 2012, the ROE ratio still showed no signs of decline or even a slight increase From 2013 to 2015, when the ROA ratio reflected the recovery of the economy, the ROE ratio reached an impressive boost The ROE ratio of 2014 nearly doubled that of 2013, reaching 12.72% and continue the momentum of growth for 2015 Figure illustrates the lending relationship of the listed firms in the period of 20112015 Bank debt accounts for over 30% of total assets of the firms It is obvious to see that most loans of non-financial firms are short-term loans Sources Vietstock Figure Lending ratios of listed firms in HNX Figure shows that different industries have relatively different debt ratios The industries having the highest debt ratio of over 50% are Real Estate and Industry The rates of Raw Materials, Consumer Goods, and Oil and Gas using the financial leverage are above average, while Pharmaceutical and Medical, Consumer Services use the least Most of industries use short-term leverage apart from Industry, Oil and Gas and Real Estate 262 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS Figure Lending ratios by industry of listed firms in HNX Sources Vietstock 3.2 Methodology To take advantage of the panel nature of the data, a fixed-effects model is used to control unobserved variables that differ between firms but are constant over time By contrast, random-effects model is applied in the situation that unobserved variables may be constant over time but vary between firms, and others may be fixed between firms but vary over time To choose the suitable model between fixed-effects and random-effects models for the dataset, a Hausman test is run The Hausman test makes sure that the more efficient model still gives consistent results in comparison with the less efficient but consistent model It tests the null hypothesis that the coefficients estimated by the efficient random effects estimator are equal to the ones estimated by the consistent fixed effects estimator If the test generates an insignificant p-value or Prob>chi2 larger than 0.05, it is safe to use random effects over the fixed effects estimator, and the other way around The Hausman test is run on the four samples and all Prob>chi2 are smaller than 0.05 So it is safe to use fixed effect estimators Table Hausman test to choose REM or FEM Model Chi2 Pro>Chi2 REM or FEM ROA 162,57 0,0000 FEM ROE 185,52 0,0000 FEM Sources: StataAnalyses Table shows that FEM model is considered to be more optimal than REM model 3.3 Explanatory variables and testable hypotheses Our basic regression specification includes five variables: size, age, leverage, coverage and the number of banks The first explanatory variable is LN_SIZE, which equals the natural logarithm of the firm’s average assets There are several factors that measure firm size, such as total 263 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 sales, total asset, invested capital, capital issues… etc Due to the availability of the data and the fact that there is no ideal proxy, in this paper, the firm’s average assets is chosen to represent firm size Firm size impacts on the firm performance in two directions First, Eisenbeis and Kwan (1996) find that a smaller firm is intrinsically riskier from the market’s perspective, either because the information provided is less dependable or due to the lack of public information available about it Therefore, a positive relationship between firm size and firm profitability is expected Other researchers also observed that larger the firm size, higher is the profit rate (Hall and Weiss (1967) Moreover, Punnose (2008) also finds that firm size positively affect firm profitability Second, some studies report that larger firms experience lower profit rates owing to diminishing returns to the fixed factors of production ((Marshall,1961) and (Marcus, 1969)) Haines (1970) estimating for the large U.S firms shows a negative correlation between firm size and firm profitability; similarly, Evans (1987) also finds an inverse relationship between firm size and firm growth rate Audretsch, Klomp, Santarelli E and Thurik A R (2002) provide a detailed survey of empirical studies testing the ‘law of proportionate effect’ Researchers find that industry profits are higher when production and marketing processes display economies of scale Sutton (1997) argues that there are the discrepancies in conclusions about the validity of Gibrat’s Law stating that “the size of a firm and its growth rate are independent” In Vietnam, large-scale enterprises still take advantages of the access to capital and the ability to diversify their business, which can help them reduce risks and increase profits Therefore, we expect a positive relation between firm size and profitability in this study Hypothesis 1: Firm size has a positive effect on firm profitability The second explanatory variable is LN_AGE, which is the natural logarithm of firm age Age could actually help firms become more efficient The reason is that firms discover what they are good at and learn how to things better over time (Ericson and Pakes (1995)) Thus, firm profitability can be improved through specializing and finding ways to standardize, coordinate, and speed up their production processes However, Agarwal and Gort (1996, 2002) confirm that old age may make knowledge, abilities, and skills obsolete and induce organizational decay On balance, it is therefore unclear whether aging helps firms prosper or whether it dooms them In the case of Vietnam, the listed firms have undergone a rigorous screening process by the authorities, so the “old age” assumptions mentioned in the research of Agarwal and Gort (1996, 2002) may no longer be suitable Therefore, a positive relationship between the firm’s age and profitability is expected in this study Hypothesis 2: Firm age has a positive effect on firm profitability The third explanatory variable is LEVERAGE, which is measured by the ratio of bank debt of firms (the sum of long-term debt and short-term debt) over total assets Financial leverage is employed as a proxy of the firm’s observed riskiness; the more leveraged the firm is, the higher is the probability of default (Watts & Zimmerman, 264 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS 1990) Financial leverage affects greatly a firm’s ability to invest; therefore, firms exploit to promote their competitive position and profitability Bolton and Scharfstein (1990) add that a firm with less leverage can prey on a highly leveraged firm because the highly leveraged firm has less flexibility to respond to changes in market conditions Although there are conflicting views on the impact of financial leverage on the firm profitability, we still expect a positive impact of LEVERAGE and firm profitability because of the characteristics of the bank-based financial system of Vietnam Hypothesis 3: Leverage has a positive effect on firm profitability The fourth variable is the presence of banking relationship, which is reflected in two explanation variables: MULTI_RELA and RELATIONSHIPS MULTI_RELA is the binary variable that takes the value of one if the firm belongs to the multiple relationships and zero otherwise RELATIONSHIPS is an integer variable that is equal to the firm’s number of banking relationship In this sample, it ranges from one to five If the firm has more than one banking relationships, it is considered having multiple banking relationships In their empirical work, Ongena and Degryse (2001) suggest that sales profitability of Norwegian publicly listed firms with bilateral bank relationships is higher than the profitability of firms with multilateral relationships The result seems in the core at the implication of Yosha (1995) and von Rheinbaben and Ruckes (1998) that if firms disclose proprietary information to creditors, firms using bilateral financing achieve higher sales profitability than those using multilateral financing The estimated negative association between multiple bank relationships and firms’ performance is consistent with results of (Angelini, Di Salvo and Ferri, 1998; Fok, Chang and Lee, 2004; Petersen and Rajan, 1994) Using Japanese data, Weinstein and Yafeh (1998) find the opposite result, a positive relationship between the number of bank relationships and firm profitability that have fewer other credit sources Another finding of Limpaphayomo et al (2004) indicates that, in Thailand, relationships with commercial banks may not always have a positive impact on firm valuation From the empirical findings, it is clear that the impact of banking relationship on firm profitability is quite complicated In the period of years from 2011 to 2015, Vietnamese economy had faced many difficulties such as high inflation rate, plummeted stock market, frozen real estate market, high lending interest rates, fluctuated exchange rates Many enterprises had to cope with problems leading to bankruptcy when they cannot establish good relationships with banks Hypothesis 4: The MULTI_RELA or RELATIONSHIPS has a positive effect on the firm profitability The fifth explanatory variable is COVERAGE, measured by earnings before interest and taxes (EBIT) divided interest expenses 265 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 Coverage ratio is used as a proxy for firms’ repayment ability Harris and Raviv (1990) find that leverage is negatively correlated to the interest coverage ratio; hence the coverage ratio might affect the firm profitability positively Yu, Pennathur, and Hsieh (2007) show that profitability of Taiwanese publicly listed firms is positively related to the coverage ratio but insignificant Hypothesis 5: The coverage ratio has a positive effect on the firm profitability Table 3.3.1 presents the summarized description of each variable, and Table 3.3.2 presents their predictions of the hypotheses Table 3.3.3, presents descriptive statistics of firm characteristics and table 3.3.4 calculates the correlation coefficients between variables Overall, the results recommend that there is little correlation between explanatory variables Table Description of variables Variables Description Dependent variable Roa The ratio of returns on total assets Roe The ratio of returns on total equity Explanatory variables Ln_size Natural logarithm of firm’s size (measured by firm’s average assets) Ln_age Natural logarithm of firm’s age in years Leverage Firm’s debt (the sum of long-term and short-term debt) related to firm’s total assets Relationships Numerical variable equal to the number of banking relationships Multi_rela Dummy variable equal to if the number of banking relationships is greater than and otherwise Coverage The ratio of earnings before interest and taxes over interest expenses Table Predictions of the hypotheses This table summarizes the empirical predictions of the six main hypotheses tested in this thesis with regard to the firm profitability Firm profitability Ln_size + Ln_age + Leverage + Relationships + Multi_rela + Coverage + 266 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS Table Descriptive statistics of firm characteristics Mean St.Dev Minimum Maximum Observations Ln_size 5,54 0,83 2,48 6,75 755 Ln_age 2,61 0,85 0,89 3,94 755 Leverage 0,35 0,23 0,54 755 Coverage 60,163 251,450 0,1086 1,711 755 Multi_rela 0,418 0,494 755 Relationships 1,747 1,041 Variable 755 Sources: STATA analyses Table Correlation matrix of variables Roa Roe Ln_size Ln_age Leverage Coverage Multi_rela Relationships Roa Roe 0,223 Ln_size 0,168 0,056 Ln_age 0,139 0,145 0,295 Leverage 0,238 0,164 0,006 0,205 Coverage 0,149 0,125 0,047 0,099 0,291 Multi_rela 0,212 0,141 0,163 0,139 0,104 0,131 Relationships 0,084 0,052 0,046 0,245 0,164 0,186 0,398 1 Sources: STATA analyses THE EMPIRICAL RESULTS 4.1.1 The empirical results with ROA as the dependent variable Table shows the OLS regression for the test of the hypotheses for firm profitability with ROA as the dependent variable Firm size having a positive effect on firm profitability is statistically confirmed by the regression result This result is in line with the conjecture of many theorists such as Hall and Weiss (1967), and Punnose (2008) Its marginal effect ranges from 0.02% to 0.28%, which is significant at the 5% level throughout three models It seems that Vietnamese listed firms are more concerned with their market share and want to gain the advantages of economies of scale to improve profitability Older firms which are more profitable is confirmed by the empirical regression The magnitude of its coefficients is not so large (0.65% in model and 0.73% in model 3) For all three models, firm age is statistically significant Table also supports that leverage has a positive impact on the profitability of the firm This result implies that high-levered firms have taken advantage of capital to increase profits This result is consistent with the findings of James (1987) which indicates a positive relationship between bank loans and firm value Positive regression coefficients of leverage also confirm empirical evidences recorded by Gorton and Schimid (2000), Limpathayom and Polwitoon (2004) Unfortunately, the marginal influence of leverage is still modest, even though it is statistically significant at 10% 267 IN TERNATIONAL CONFERENCE ON - CIFBA 2020 Table Test of the hypotheses for the firm profitability Explanatory variables Constant Ln_age Ln_size Leverage Model Model Model 0,1411* 0,1199** 0,1466* (0,0564) (0,0470) (0,0569) 0,0065** 0,0072** 0,0073** (0,0069) (0,0069) (0,007) 0,0000** 0,0028** 0,0002** (0,0034) (0,0034) (0,0034) 0,0011** 0,0009** 0,0009** (0,0034) (0,0031) (0,0046) 0,0192* Relationships (0,0141) 0,0302* Multi_rela Coverage (0,0322) 0,0000 0,0000 0,0000 (0,0000) (0,0000) (0,0000) 755 755 755 0,175 0,197 0,209 Observations R-squared * p < 0.01, ** p < 0.05, *** p < 0.001 The table reports the marginal effects (%) and standard errors (in brackets) From model to model 3, when variables MULTI_RELA and RELATIONSHIPS are added, the performance of the regression as shown by the R-squared increasing from 17.5% to 19.7% and 20.9% suggests that the number of banks is an useful explanatory variable Also, the connections between the number of banks and firm profitability are consistent throughout model and model This result contradicts the findings of many scholars’ studies that point out the negative connection between the number of banking relationships and the firm profitability (Castelli et al (2006), Cosci and Meliciani (2002) and D’Auria et al (1999)) but are suitable for business practice in Vietnam where the financial system has not yet developed and commercial banking system still plays a key role to provide capital for the economy The number of banking relationships may depend on firm profitability; hence, I also estimate the regression model using two-stage least-squares I use the lagged number of banks as an instrumental variable The two-stage least squares regression results presented in Table indicate that banking relationships measuring in both MULTI_RELA and RELATIONSHIPS have a significantly positive impact on firm profitability (with a significance at 10% level) This finding is inconsistent with Ongena and Degryse (2001), Von Rheinbaben and Ruckes (1998) and many other authors who document a negative relation between banking relationships and firm 268 VIETNAM NATIONAL UNIVERSITY - UNIVERSITY OF ECONOMICS AND BUSINESS profitability However, this is can be explained as a manifestation of the bank-based financial market that Vietnam is an example Banking relationship, especially credit relationships is considered a necessary condition for Vietnamese firms not only in the start-up stage but also throughout the operation process In robustness testing, the firm size measured by the average assets has been replaced by market capitalization According to the results shown in Table 8, the sign of the marginal effects and the confidence level of the explanatory variables remain unchanged, which confirms the previous findings It is interesting to note that the R-square of the models increased sharply by about 10% This suggests that the quality of the models is improved by replacing market capitalization to average assets Table Test of the two-stage least-square and robustness methods Explanatory variables Constant Ln_age Ln_size Leverage 2SLS 0,1965** 0,1086 0,2053*** 0,1143*** 0,0633 (0,0455) (0,0056) (0,0273) 0,0074** 0,0035** 0,0039** 0,0029** 0,007 (0,0068) (0,0074) (0,0073) 0,0013** 0,0007** 0,0047** 0,0043** (0,0035) (0,0035) (0,0035) (0,0035) 0,0006* 0,0005* 0,0002* 0,0011* (0,003) (0,0032) (0,003) (0,0032) Relationships Multi_rela Coverage R-squared Robustness test 0,0869* 0,0762* (0,0405) (0,0404) 0,0659* 0,0707* (0,0385) (0,0378) 0,0116 0,0132 0,0124 0,0134 (0,000) (0,000) (0,000) (0,000) 755 755 755 755 0,398 0,264 0,497 0,348 p