HO CHI MINH CITY, 03/2022 MINISTRY OF EDUCATION AND TRAINING STATE BANK OF VIETNAM HOCHIMINH UNIVERSITY OF BANKING VO THI THUY LINH FACTORS AFFECTING THE COMPETITIVENESS OF VIETNAMESE COMMERCIAL BANKS[.]
INTRODUCTION
Research objectives
- Find out the factors affecting the competitiveness among Vietnamese commercial banks.
- Measure the extent and direction of impact on competitiveness among Vietnamese commercial banks.
- Propose options and solutions that can help Vietnamese commercial banks increase their competitiveness.
Research questions
- What factors affect the competitiveness among Vietnamese commercial banks?
- What is the extent and direction of impact on competitiveness among Vietnamese commercial banks?
- What solutions can help Vietnamese commercial banks increase their competitiveness?
Research subjects and scope
- Research objects: factors affecting the competitiveness of commercial banks in Vietnam.
- Research scope: In the 10 years from 2011 to 2020, secondary data was collected from 28 Vietnamese commercial banks, with the data being completely and clearly published in each bank's financial statements.
Research methodology
- The major study method is quantitative research based on a regression model using balanced panel data.
- Synthetic method: In order to create the theoretical foundation and test the hypothesis framework for the research, the study provides a clearer theoretical basis as well as empirical data on the impact of factors on competitiveness among Vietnamese commercial banks.
LER NERí t A, + ỊĩcAAP it + P S SIZE it + p 3 LLP it + PiINF it + ^GD Pí t + s it
In which, LERNER: Banking competitiveness
CAP: Equity capital SIZE: Bank's size LLP: Credit loss provision ratio INF: Inflation rate
GDP: Gross domestic product growth From empirical results and the collected data, the study will explain and evaluate the factors that influence the competitiveness of Vietnamese commercial banks, which is particularly important The study collects data from mostly Vietnamese commercial banks between 2011 and 2020, then uses econometric models to examine the links and their relevance, which is fundamentally highly substantial The research will then examine and explain the influence of various variables on the competitiveness of mostly Vietnamese commercial banks, resulting in recommendations to increase competitiveness and orient the sector in the future.
Research empirical significance
This study adds to practice by illustrating the range of factors impacting commercial banks' competitiveness using secondary data on banking performance and an exhaustive research methodology The study gives data that may be used as a reference point, as well as policy suggestions to assist bank administrators and state management agencies in analyzing competitiveness and altering parameters accordingly, therefore defining acceptable courses of action.
Research layout
The dissertation is divided into 5 chapters:
This chapter explains why the topic was chosen, discusses some previous research, and explains research objectives, research questions, subjects, and scope, as well as research methodologies and contributions in practice and scientific studies.
This chapter gives an overview of determinant variables based on reference models from prior research with the goal of building an impact model It also presents the theoretical underpinning connected to the competitiveness of the bank.
The study model, research methodology, data gathering, and processing methods, and the development and testing of scales to quantify the effect variables on competitiveness are all covered in this chapter.
Chapter 4: Analysis And Research Results
This chapter presents the result from the estimation model and discusses the obtained result.
This chapter outlines the study's primary results, as well as the research's importance and contribution to the banking industry and the economy in general.Meanwhile, making recommendations to increase the bank's competitiveness This study should serve as a foundation for others to continue to investigate and improve, while also highlighting some of the study's shortcomings and suggesting new research areas.
LITERATURE REVIEW
Theory of the competitiveness of commercial banks
Commercial banks are one of the financial intermediaries that play a vital role in constructing a financial environment, which is defined by offering a range of monetary services with the core business of receiving deposits and lending Banks employ idle money to produce money for customers and other financial institutions through their fundamental operation of moving money from areas with excess capital to places with a shortage of capital A business that they will not be ready to earn, or a minimum of not for an extended time Furthermore, commercial banks offer a good range of other services so as to fulfill society's high demand for goods and services. Besides, banks also create the credit reputation of consumers by ensuring safe money in order that good money is merely permanent loans and not lost on bad loans In other words, banks connect individuals, businesses, and other institutions to assist keep the economy going So if the banks go bankrupt, it'll cause the collapse of the whole system of the economy, and since banks and money are essential to sustain not only the economy but the entire society, they're extremely strictly regulated and must operate under strict guidelines and procedures.
In the US, a commercial bank is a currency trading organization that specializes in financial services and operates in the financial services industry.
According to Article 4 of the Law on Credit Institutions (Law No. 47/2010/QH12), a commercial bank is a type of bank that is permitted by this Law to undertake all banking activities and other business activities for profit purposes.
A commercial bank is a business, but banking is a distinct industry that deals with money and related financial services As a result, based on the WEF's level division, the competitiveness of the commercial banking system is measured in terms of enterprise competitiveness Enterprises have the capacity to compete when they dominate the market, attract many consumers by delivering high-quality products and services, produce customer happiness, and have a strong reputation in the market, in addition to producing enough profit to sustain the business's long- term growth.
Banking competitiveness, according to Kazarenkova (2006), is the practical ability as well as the potential of the credit institution to create and develop service products in the face of high market power in order to build a positive image of a reliable modern bank in responding to customer demands.
As banks and rivals extend their product and service portfolios, competition in the financial services market is getting increasingly severe Local banks offer lending, savings programs, retirement plans, and financial advice to companies and individuals These are services that are directly competed with by other banks, credit unions, investment banks, and financial firms Competitive pressure functions as a motivator for future service improvement.
2.1.3 The criteria to measure the competitiveness of the bank
This research measures the competitiveness of commercial banks through the Lerner method.
Lerner's (1934) methodology is extensively utilized in empirical research on bank competitiveness since it estimates each year and for each form of ownership of each bank This is a way of determining a bank's competitiveness that uses the Lerner index This index measures a bank's market power by taking the ratio of marginal cost to pricing into account In a completely competitive market, the selling price equals the marginal cost, but in a monopolistic environment, the selling price exceeds the marginal cost As a result, the Lerner index is the most generally used approach in the world to quantify monopolistic power, taking into account the difference between the selling price and the marginal cost, as follows: p it - Mc it _
- P: output price which is measured by total revenue over total assets.
- MC: marginal cost which is not directly observable MC is estimated in a two-step procedure based on the total cost function, as follows:
Step 1: Take the natural logarithm of the total cost function. lnTC it = a0 + annQtt + -ô 2 ( InQ t t ỵ + a 3 lnỳ)t tt + a4 lnứ) 2 tt + a 5 lnỳ) 3 tt
+ a 6 ln Qtt 1™!it + a 7 lQ Q tt lna ) 2 it + a s lQ Qtt lnM 3 it + a 9 Inơ ti it lnú ) 2 it
+ a t olnứ ti it ỈOM 3 it + a t 1 lnú ) 2 it Ino ) 3 it+-a 1 2 ( Inứ tit ) 2
+ ^a t3( ln ° 2 2 it) +ĩ a t4( 1 n( 3 3 it) +a t T + -a t 6 T 2 +-a t 7T ln Q ịt
+ a 18 Tlnứ) 1 it + a 1 9T lnơ2 2it + ô2 oT1™ 3 it
- TC: the total cost (including interest and non-interest expenses)
- Three input prices include ơ)^s the cost of deposits, (ứ 2 is the cost of material and (ủ 3 is the price of labor.
- T: a variable reflecting the technological change.
Step 2: After estimating the total cost function, the marginal cost is determined by taking the first derivative from the above equation (2) and is estimated as: dTC it
Fungancova et al (2010) with research “Market power in the Russian banking industry” investigates bank competitiveness in Russia by analyzing the factors of the Lerner index of local banks from 2001 to 2006 Their key results are that competition has only marginally improved, despite the fact that the average Lerner index is around the same across industrialized nations, and that banks' market power
RESEARCH METHODOLOGY
Table data collection
In this thesis, secondary data will be gathered It comes from two separate places The first data source will be audited financial statements and annual reports from 28 commercial banks in Vietnam for the research period 2011–2020, which will be obtained through the websites of the commercial banks The second data source is acquired from the websites of international organizations such as the World Bank to corroborate the thesis's reliability GDP and inflation rate data are available from the World Bank The observation sample was collected between 2011 and 2020.
Table 3 1 List of banks used for the research data
1 An Binh Commercial Joint Stock Bank ABBank ABB
2 Asia Commercial Joint Stock Bank ACB ACB
3 BAC A Commercial Joint Stock Bank BacA Bank BAB
Joint Stock Commercial Bank for
Investment and Development of Vietnam BIDV BID
5 Bao Viet Joint Stock commercial Bank BaoViet Bank BVB
6 Vietnam Joint Stock Commercial Bank of
Industry and Trade Vietinbank CTG
Vietnam Export Import Commercial Joint
Ho Chi Minh city Development Joint Stock
Commercial Bank HD Bank HDB
9 Kien Long Commercial Joint Stock Bank Kienlongbank KLB
10 LienViet Commercial Joint Stock Bank LienVietPostBank LPB
11 Military Commercial Joint Stock Bank MB MBB
12 The Maritime Commercial Joint Stock Bank MSB MSB
13 Nam A Commercial Joint Stock Bank NamA Bank NAB
14 National Citizen bank NCB NVB
15 Orient Commercial Joint Stock Bank OCB OCB
Petrolimex Group Commercial Joint Stock
17 Sai Gon Commercial Joint Stock Bank SCB SCB
Southeast Asia Commercial Joint Stock
19 Saigon-Hanoi Commercial Joint Stock Bank SHB SHB
20 Saigon Bank for Industry & Trade Saigonbank SGB 21
Saigon Thuong Tin Commercial Joint Stock
22 TienPhong Commercial Joint Stock Bank TPBank TPB 23
Joint Stock Bank Techcombank TCB
Joint Stock Commercial Bank for Foreign
Trade of Vietnam Vietcombank VCB
25 Viet A Commercial Joint Stock Bank VietA Bank VAB 26
27 Public Vietnam Bank Public Bank VIDBank
Vietnam Commercial Joint Stock Bank for
Private Enterprise VP Bank VPB
Furthermore, the author planned to gather researched variables by evaluating available sources such as academic papers and articles Collecting the examined variables thorough examination of literature was a huge benefit to the author since it allowed them to create a theoretical framework and acquire a more thorough image of all the concerns associated with the study topic.
Research process
The study was carried out according to the approach given in model 1 with the purpose of determining the direction and amount of influence of variables on the competitiveness of 28 joint-stock commercial banks in Vietnam from 2011 to 2020:
• Step 1: A brief review of background theory and previous studies.
Review the theoretical basis and related previous studies in Vietnam and other countries, then discuss previous studies to identify research gaps and design orientations for the research model for the topic.
The study's data is provided in statistical form, with the minimum, maximum, mean, median, and standard deviation values Briefly summarize the data features of Vietnamese commercial banks from 2011 to 2020 in order to depict the overall position of banks during this period in accordance with research requirements.
• Step 3: Analysis of the impact of factors affecting the competitiveness of commercial banks in Vietnam
Create a correlation coefficient matrix between all of the independent factors and the explanatory variables to identify the nature of the correlation between these variables, which will serve as a foundation for analyzing the correlation between the variables that have an impact on the research model's quality.
• Step 4: Regress the variables based on the models and select the most appropriate model.
If one of the conventional linear regression's basic assumptions is broken (variable variance, auto-correlation, multi-collinearity) The derived estimations will thus be altered, and using them for analysis will be incorrect To estimate tabular data, fixed effects model (FEM) or random effects (REM) regression methods will be utilized in addition to the fundamental POLS approach To choose the best model, tests like the F-test, Hausman, and Breusch-Pagan are utilized.
• Step 5: Using the best model, analyze the results of the regression and discuss the results of the study.
The research will assess the model's defects, such as multi-collinearity, autocorrelation, and variable variance, based on the model that is chosen to be the most suitable If defects are discovered, they will be corrected using tools.
• Step 6: Suggest policy implications and restrictions.
This is the final step of the process, based on the results of the regression, the topic will discuss, draw conclusions and give relevant suggestions and recommendations to answer the research questions as well as solve the problem stated research objectives.
The Lerner index, established by Lerner, A.P., is used in this study to assess the competitiveness of commercial banks (1934) This index measures a bank's market power by taking into account the ratio of marginal cost to pricing In a completely competitive market, the selling price is equal to the marginal cost, but in a monopolistic environment, the selling price is larger than the marginal cost As a result, the Lerner index is the metric most often used in the world to quantify monopolistic power, taking into account the difference between the selling price and the marginal cost.
According to Ariss (2010), the more the value of the Lerner index, the greater the degree of rivalry between the weaker banks, and the greater the competitiveness of each bank.
Research models
As described in section 2.2, the Lerner index consists of five measures thought to influence commercial bank competitiveness The Lerner index is used as a proxy in the study to assess bank competitiveness.
The major study method is quantitative research based on a regression model using balanced panel data In order to create the theoretical foundation and test the hypothesis framework for the research, the study provides a clearer theoretical basis as well as empirical data on the impact of factors on competitiveness among Vietnamese commercial banks.
= Po + ^LER NER ị t _ 1 + P C A P Pị t + &SI z Eị t + p s L L Pị t
Where: i represents for bank, t represents for time.
)^0 : intercept of the model. £ it : error of the model.
Internal factors include bank size (SIZE), equity capital (CAP), and credit loss provision (LLP) Gross domestic product growth (GDP) and the inflation rate (INF) are external variables.
The other variables are measured by:
Table 3 2 The dependent and independent variables of the model
Signal Name of variable Measurement
P-MC p where P is the output price, MC is the bank’s marginal cost by Lerner, A.P (1934).
CAP Equity capital Total assets Equity
SIZE Bank's size ln(total assets)
LLP Credit loss provision ratio Bad debtprovision expense Total assets
CPIt-CPIt-Ị CPIt-1 where CPI is the consumer price index. GDP
Gross domestic product growth GDPt-GDPt-Ị GDPt-!
Panel data is used in the research model, which is a regression in three ways: PooledOLS regression model (Pooled Ordinary Least Squares), fixed effects model (FEM),and random effects model (REM) This method is appropriate for this research because the panel data has T small and N large (28 banks).
Variables explanation
In economics, the Lerner index is a measure of a firm's market dominance The Lerner index, which was formalized in 1934 by Russian-British economist Abba P. Lerner, is stated in the following formula: p- MC
=p where P represents the firm's established price for the item and MC represents the firm's marginal cost The index essentially calculates the percentage markup that a company may charge above its marginal cost The index has a low value of 0 and a high value of 1 The higher the Lerner index value, the more the corporation may charge above its marginal cost, and hence the stronger its monopolistic power.
Some independent variables are offered for usage in this thesis, and they are divided into two groups: bank-specific factors and macro-economic variables Bank size, equity capital, and credit loss provision are instances of bank-specific factors. GDP and inflation are macroeconomic variables Each variable is addressed in further detail below:
Equity is the bank's own capital source contributed and supplemented by the owner during its operation Variable CAP is assessed by the ratio of equity to total assets, which analyzes the bank's financial capability, according to Berger (1995) and Gaber (2018).
The higher this ratio, the greater the bank's capital, which encourages increased competitiveness by gaining customer trust and hedging business risks (Pasiouras and Kosmidou, 2008) As a result, it has the potential to boost bank competitiveness.
H1: Equity capital have a significant impact to Vietnamese commercial bank competitiveness.
Various studies have found that bank size is an internal factor influencing bank competitiveness Because of the unique characteristics of the monetary business, banks' total assets are very large According to Nicole Petria et al (2015), Syafri
(2012), and most other studies, total asset size is measured by taking the natural logarithm of total assets to reduce the difference in values between variables.
By raising the bank's capital, the larger the bank becomes, the greater the advantage of scale, and therefore the better the competitiveness and client confidence.
H2: Bank size affected positively and significantly the Vietnamese commercial bank competitiveness.
3.4.2.3 Credit loss provision ratio (LLP)
The credit loss provision ratio indicates how well a bank is safeguarded against prospective losses A higher ratio indicates that the bank is better able to sustain future losses, especially unexpected losses beyond the loan loss provision Credit loss provision ratio is calculated by the ratio of bad debt provision expense to total assets.
The greater the ratio, the higher the bank's bad debt ratio, decreasing its competitiveness.
H3: The credit loss provision ratio affected negatively the Vietnamese commercial bank competitiveness.
3.4.2.4 Gross domestic product growth (GDP)
Banking, like every other business, is inextricably linked to the economy and society One of the numerous consequences of the economy's expansion, as measured by GDP growth, is a rise in banking activity.
GDP =ℎe official rate of economic growtℎpublisℎed by tℎe government.The higher this measure, the faster the economy grows This encourages the bank to enhance its operations, attract new clients, and increase revenues As a result,banks' competitiveness in the sector will improve.
H4: Gross domestic product growth (GDP) affected negatively and significantly the banks’ competitiveness.
The rate of inflation is a macroeconomic element that has a positive association with bank performance The World Bank's yearly inflation rate was used.
A high rate of inflation will result in increased loan rates, resulting in a shortage of borrowers and, as a result, lowering bank competitiveness.
H5: The inflation rate (INF) has a negative impact on the competitiveness of Vietnamese commercial banks.
ANALYSIS AND RESEARCH RESULTS
Descriptive statistics
Description statistics will be collected in this section It provides the mean and standard deviation values for each variable in this research As indicated in the previous chapter, a research model is created with the dependent variable as the LERNER and the independent variables classified into two categories The first category includes bank-specific characteristics such as bank size (SIZE), equity capital (CAP), and credit loss provision (LLP) The second category represents macroeconomic data such as GDP and inflation rate (INF) There are a total of 271 observations in the dataset, which covers 28 commercial banks in Vietnam The following is the outcome of descriptive statistics:
Source: Processing through Stata 14.0 software0 According to Table 4.1, all variables in the study model are unbalanced table data from 28 joint-stock commercial banks throughout a decade from 2011 to 2020. The following are the descriptive statistics for each variable:
In terms of LERNER: According to the table 4.1 above, the LERNER mean value of Vietnam's joint-stock commercial banks is 6.64%, with a standard deviation of 0.09281 In which, SCB bank in 2018 had the highest LERNER index of
71.12%, and VPB bank in 2018 had the lowest LERNER index of -26.66%.
In terms of CAP: In the research data, bank capital represented as equity over total assets has a broad distribution, with an average of 9.23% and a standard deviation of 0.04361 VPB has the lowest capital in 2014, with a CAP ratio of 0.05%. The fundamental reason is that this bank's total assets have continuously expanded over the years, but growing capital (primarily charter capital) has been challenging. VID Bank has the greatest capital ratio in 2019, with a ratio of equity to total assets of 26.37%.
In terms of SIZE: The bank size runs from 4.9686 to 5.8259, with a mean of
5.39287 and a standard deviation of 0.19323 The data is fluctuating steadily, and the standard deviation is not more than the mean In general, bank size has risen through time, with BID having the highest value of more than VND 1.51 quadrillion in 2020 and VID bank having the lowest value of more than VND 8.8 trillion in 2014 The figure also illustrates a significant size disparity among Vietnam's joint- stock commercial banks In terms of scale, BIDV, CTG, and VCB are the three leading banks in 2020, with roughly 1.51 quadrillion, 1.34 quadrillion, and 1.32 quadrillion, respectively.
In terms of LLP: Through descriptive statistical results, it shows that the mean of credit provision expenses to total assets of the bank is 0.615%, and the standard deviation is 0.00559 The level ranges from the smallest value is -0.34% (in 2013, SGB), and the largest value is 3.63% (in 2019, VPB).
For indicators of economic growth (GDP) and inflation (INF): the mean values are 5.967% and 4.8%, respectively, with standard deviations of 0.01188 and 0.02836 Because Vietnam belongs to the group of developing countries, the average economic growth is always high and therefore inflation cannot be low Vietnam stands out for its impressive economic growth, albeit showing signs of slowing, and equally impressive inflation The average economic growth rate was highest in 2018 and lowest in 2020, similar to the highest inflation in 2019 and the lowest in 2014.
Correlation analysis
The correlation coefficient (r) is a statistical metric that measures the degree of linear association between two variables This coefficient ranges between +1 and -1. The correlation coefficient may be used to determine the precise correlation direction between the dependent and explanatory variables It also demonstrates multicollinearity in the regression model (if r > 8) If there is a discrepancy in the influence trend of the explanatory and dependent variables between the correlation coefficient findings and the regression model results At that point, the regression model may not fully meet the research model's hypotheses, causing the sign of the estimated coefficient to differ from the influence trend based on actual data. According to Hoang (2008), when the pairwise correlation between the explanatory variables is higher than 0.5, multicollinearity may occur The results are presented in the Table below:
LERNER CAP SIZE LLP GDP INF
Source: Processing through Stata 14.0 software
According to the above table, the independent factors include: the ratio of equity to total assets (CAP), bank size (SIZE), and the inflation index (INF) all have positive effects on LERNER, with all having weak linear links The remaining factors,such as the provision expense for bad debt to total assets (LLP) and the GDP index,exhibit an inverse effect and a weak linear link.
Multicollinearity test
Multicollinearity is a phenomenon in which the independent variables in the model are linearly correlated with each other The research model needs to ensure that no high multicollinearity occurs Severe multicollinearity can cause the standard error as well as the confidence interval of the estimate to be large, and may even cause the estimate to be erroneous Therefore, to make sure that the phenomenon of multicollinearity does not appear in the model, the study conducts a test of the Variance Inflation Factor (VIF).
The results are presented in the table below:
Source: Processing through Stata 14.0 software
The following table shows that the VIF (Variance Inflation Factor) of any explanatory variable does not exceed 10, and the average VIF of the entire commercial bank sample was 1.41 It also indicates that the above model contains multicollinearity, however, the magnitude is minimal enough to allow an estimation.
Estimating the regression model byPooled OLS, FEM, and REM
The author will run three different regression models in this section: Pooled ordinary least square (Pooled OLS), fixed effect model (FEM), and random effect model (REM) Each model's output is shown below:
4.4.1 Pooled ordinary least square (Pooled OLS)
R-squared = 0.1555; Adjusted R-squared = 0.1396; F-test = 9.76; Prob > F = 0.0000
(Note: ***, **, * are equivalent to significance level of 1%, 5% and 10%)
Source: Processing through Stata 14.0 software
The adjusted r-square is 0.1396, which means that bank-specific factors, industry-specific variables, and macroeconomic variables explain 13.96% of the variance in LERNER The F-test is 9.76, and the p-value is 0.0000, which is less than 0.05 As a result, the F-test is statistically significant at 95% confidence The influence of each independent variable on LERNER in the Pooled OLS model is discussed more below:
The coefficients of CAP, and SIZE are 0.8660435, and 0.1915526, respectively, indicating that one percent of CAP, and SIZE will have an effect on a change in LERNER in 0.8660435, and 0.1915526 The p-value of CAP = the p- value of SIZE 0.000 < a = 1% It is determined that CAP, and SIZE have a beneficial influence on LERNER, although their effect is not statistically significant at the 95% confidence level In other words, CAP, and SIZE have a positive relationship with LERNER and are statistically significant at the 99% confidence level, implying that the higher the CAP, or SIZE ratio, the more efficient the bank, leading to an increase in commercial bank competitiveness, and vice versa In comparison, the LLP coefficient is -4.63
8421, with a p-value of 0.000 < a = 1% It signifies that the credit loss provision ratio has a negative impact on LERNER and is statistically significant at a 95% confidence level The higher the credit loss provision ratio, the lower commercial banks' competitiveness, and vice versa.
The INF coefficient is 0.3793444, and the p-value is 0.048 Because the p- value of INF is greater than 1% but less than 5%, INF has a positive impact on LERNER, and the effect of this variable is statistically significant at the 95% confidence level The greater the INF ratio, the more competitive commercial banks are, and vice versa.
GDP has a coefficient of -0.1918492 The T-test value is -0.42, and the p- value is 0.674 > α = 10% Because the p-value of GDP is more than 10%, the influence of
GDP on LERNER is negative but insignificant.
Following the Pooled OLS model, the FEM is calculated as follows:
Table 4 5 Fixed effect model output
(Note: ***, **, * are equivalent to significance level of 1%, 5% and 10%)
Source: Processing through Stata 14.0 software
Overall r-square is determined to be 0.1446, or 14.46% of LERNER variance is explained by bank-specific and macroeconomic factors The F-test is 4.76, and the p- value is 0.0004, which is less than 1% As a result, the F-test is statistically significant at 99% confidence The following describes the influence of each independent variable on LERNER in the FEM:
The coefficients of SIZE and INF are 0.184382 and 0.4031681 respectively, indicating that one percent of SIZE and INF will have an effect to a change in LERNER in 0.184382 and 0.4031681 The p-value of SIZE = 0.012 < a = 5% and the p-value of INF = 0.027 < a = 5% It is determined that SIZE and INF have a positive influence on LERNER, are statistically significant at a 95% confidence level This implies that the higher the SIZE and INF ratio, the more efficient the bank is, leading to an increase in commercial bank competitiveness, and vice versa In comparison, theLLP coefficient is -3.523458, with a p-value of 0.011 < a = 5% It signifies that the credit loss provision ratio has a negative impact on LERNER and is statistically significant at a 95% confidence level The higher the credit loss provision ratio, the lower commercial banks' competitiveness, and vice versa.
The CAP coefficient is 0.6419211, and the p-value is 0.003 < a = 1%, CAP has a positive impact on LERNER, but the effect of this variable is not statistically significant at the 95% confidence level In other words, CAP has a favorable effect on LERNER that is statistically significant with a 99% confidence level The greater the CAP ratio, the more competitive commercial banks are, and vice versa.
GDP has a coefficient of -0.3033865, the T-test value is -0.69, and the p-value is 0.491 which is higher than 10% Because the p-value of GDP is much more than 10%, the influence of GDPon LERNER is negative but insignificant.
Following the Pooled OLS and FEMs, the REM is calculated as follows:
Table 4 6 Random effect model output
Overall R-square = 0.1552; Wald-test 8.02; P-value = 0.0000
(Note: ***, **, * are equivalent to significance level of 1%, 5% and 10%) Source: Processing through Stata 14.0 software Overall r-square is determined to be
0.1552, or 15.52% of LERNER variance is explained by bank-specific and macroeconomic factors The Wald test is 38.02, and the p-value is 0.0000, which is less than 5% As a result, the Wald test is statistically significant at 95% confidence. The following describes the influence of each independent variable on LERNER in the REM:
The coefficients of CAP, and SIZE are 0.7798614, and 0 179716, respectively, indicating that one percent of CAP, and SIZE will have an effect on a change in LERNER in are 0.7798614, and 0 179716 The p-value of CAP = the p- value of SIZE = 0.000 < a = 1% It is determined that CAP, and SIZE have a beneficial influence on LERNER, although their effect is not statistically significant at the 95% confidence level In other words, CAP, and SIZE have a positive relationship with LERNER and are statistically significant at the 99% confidence level, implying that the higher the CAP, or SIZE ratio, the more efficient the bank, leading to an increase in commercial bank competitiveness, and vice versa In comparison, the LLP coefficient is - 4.347455, with a p-value of 0.000 < a = 1% It signifies that the credit loss provision ratio has a negative impact on LERNER and is statistically significant at a 99% confidence level The higher the credit loss provision ratio, the lower commercial banks' competitiveness, and vice versa.
The INF coefficient is 0.3850421, and the p-value is 0.032 Because the p- value of INF is greater than 1% but nearly 5%, INF has a positive impact on LERNER, and the effect of this variable is statistically significant at the 95% confidence level The greater the INF ratio, the more competitive commercial banks are, and vice versa. GDP has a coefficient of -0.2370064 The T-test value is -0.55, and the p- value is 0.583 > a = 10% Because the p-value of GDP is more than 10%, the influence ofGDP on LERNER is negative but insignificant.
Selecting a regression model
With differences in regression findings, it is required to do model selection and
Constant -0.9524362 -3.61 0.000 model testing after selection to ensure that the regression model is stable, unbiased, and effective Perform tests to choose the best model from three options combined OLS, FEM, and REM The author specifically accomplishes the following:
The author utilizes the Hausman test to choose between the FEM and the REM The Hausman test proposes Hypothesis H0: there is no link between the random error of the cross units and the independent variables in the model In other words:
H0: The REM is more appropriate than the FEM.
H1: The FEM is more appropriate than the REM.
The Hausman test findings demonstrate that:
Table 4 7 Model choice between FEM and REM
Conclusion The REM is more appropriate.
Source: Processing through Stata 14.0 software
The Chi-test is valued at 3.18 in the table above, and the P-value is 0.6728, which is much more than 0.05 As a result, hypothesis H1 is rejected or the REM is more appropriate than the FEM.
4.5.2 Between Pooled OLS and REM
The author uses the Lagrangian multiplier test to decide between Pooled OLS model and REM.
Based on the following assumptions:
H0: P-value > 5% => Pooled OLS model is appropriate.
H1: P-value < 5% => The REM is appropriate.
The following are the test results:
Table 4 8 Model choice between pooled OLS and FEM
Conclusion The REM is appropriate.
Source: Processing through Stata 14.0 software
The Lagrangian multiplier test is valued at 12.35 in the table 4.8 above, and the P-value is 0.0002, which is less than 5% As a result, hypothesis H0 is rejected or the REM is preferable to the Pooled OLS model.
From the above two conclusions, the author concludes that the Random Effect Model is a suitable model for the dependent variable LERNER to evaluate the factors affecting the competitiveness of Vietnamese commercial banks.
Model diagnostics
Autocorrelation, and heteroskedasticity are all issues that arise when using regression algorithms In this part, the author gives some statistical tests to determine whether or not these problems exist.
Initially, autocorrelation is found using the Wooldridge test for autocorrelation in panel data, which tests the null hypothesis of no first-order autocorrelation The obtained result is as follows:
H0: No first-order autocorrelation exists.
Conclusion No first-order autocorrelation exists.
Source: Processing through Stata 14.0 software
The Wooldridge test is rated at 0.904 in the table 4.9 above, and the P-value is 0.3501, which is more than 0.05, indicating that hypothesis H1 is rejected As a result, there is no issue with autocorrelation in the dataset.
The Lagrange multiplier test was utilized by the author to check heteroskedasticity The final result is as follows:
H0: The error variances are equal.
H1: The error variances are not equal.
Conclusion The error variances are not equal.
Source: Processing through Stata 14.0 software
The table above reveals that p-value = 0.0002 < a = 0.05 As a result, REM is facing up heteroscedasticity issue.
Model fix
The author uses cross-sectional time-series FGLS regression to correct for heteroskedasticity The obtained result is shown below:
Table 4 11 Cross-sectional time-series FGLS regression
(Note: ***, **, * are equivalent to significance level of 1%, 5% and 10%)
Source: Processing through Stata 14.0 software
The effect of independent factors on dependent variables is statistically significant at the 95% confidence level, according to the table above The Wald-test score is 96.08, and the p-value is 0.0000 less than 0.05 CAP, SIZE, and INF are among the independent variables that have a positive and statistically significant influence on LERNER LLP, on the other hand, has a negative and significant effect on LERNER GDP, on the other hand, has no influence on LERNER The author derives the following model of factors influencing the competitiveness of Vietnamese commercial banks from the quantitative data of statistically significant variables using the FGLS method:
CONCLUSIONS AND RECOMMENDATIONS
Conclusion
The primary study goal of this thesis is to assess the impact of various factors on the competitiveness of Vietnamese commercial banks This research goal is met during the construction of the numerous chapters A commercial bank is characterized in the research literature as a financial organization that works by retaining deposits, paying interest to depositors, and extending credit to other banks. distinct economic sectors and establish conditions to enhance a country's economic development The banking business is increasingly vital to both the economy and our daily lives The banking industry contributes to the country's economic growth by mobilizing capital and allocating it to manufacturing and company activities As a result, the banking system is regarded as a critical sector of the economy.
To evaluate the impact of these variables on the competitiveness of commercial banks in Vietnam, the author gathered secondary data from annual reports of all commercial banks operating in the nation The author used four distinct regression approaches, namely Pooled OLS, FEM, REM, and FGLS, together with their corresponding statistical tests, to determine which regression methodology is superior to the others and to diagnose the predicted model.
This article investigates the variables influencing the competitiveness of Vietnamese commercial banks between 2011 and 2020 The comprehensive analysis contents are thoroughly described in chapters 3 and 4, respectively, and chapter 5 will present a summary of all study findings: The analysis relied on panel data from
28 joint stock commercial banks from 2011 to 2020 Following the testing phases, the study employs Random Effect Model for the LERNER model, and the author discovers that: bank size (SIZE), equity capital (CAP), and inflation (INF) of banks have a favorable influence on bank competitiveness Only the LLP variable has a negative influence on Vietnamese commercial banks' competitiveness.
This result demonstrates that, since the 2008 financial and economic crisis,banks have been constantly attempting to increase their competitiveness by increasing their scale, mobilizing more equity sources, controlling and improving their competitiveness, and adjusting regulations to minimize bad debt However,banks have failed to make reasonable improvements to boost their competitiveness and soar above other industry competitors.
Recommendations
From the research results obtained in Chapter 4, the author finds that in order to increase the competitiveness of banks, banks should increase their size, equity, and strictly control their credit activities In addition, banks need to devise policies to help proactively deal with macro impacts such as inflation.
Based on the available results, we conclude that equity capital enhances the competitiveness of Vietnamese commercial banks As a result, boosting the bank's equity capital is an effective way to increase competitiveness Currently, most joint stock commercial banks are rushing to expand equity to fulfill the Basel II criteria mandated by the State Bank There are various options for bank executives to raise equity capital:
- Raise equity capital by issuing publicly traded stocks and bonds This is the most basic kind of equity capital increase available to Vietnamese commercial banks.
- Furthermore, commercial banks can raise equity capital through mergers and acquisitions Banks not only build capital and become stronger in this way, but they also alleviate the load on small banks.
According to the available data, bank size has a positive impact on the competitiveness of Vietnamese commercial banks As a result, expanding the bank's size is a beneficial approach However, if the size is raised but the bank is unable to manage, resulting in numerous management challenges, the management is not tight and cannot cut costs while also reducing risk bank efficiency In today's competitive market, joint-stock commercial banks need to have ways for expanding their size:
- Joint stock commercial banks can extend their size by raising capital in order to boost financial capacity, expand loans, and strengthen bank competitiveness.
- Continuously enhance the management system and upgrade the use of powerful technology to assist the bank in maintaining a high level of competitiveness and considerably increasing the market share of joint stock commercial banks.
- Estimating the right economic size for the bank and implementing appropriate scaling procedures to assist guarantee that the bank does not exceed the limit.
According to the findings in Chapter 4, the credit provision expense to total assets ratio has a negative impact on the competitiveness of Vietnamese commercial banks In other words, the greater this ratio, the less competitive Vietnamese commercial banks are:
- In order to attract more consumers, the bank can upgrade machinery, methods, and current working equipment to satisfy their demands as accurately, quickly, and safely as feasible at the lowest possible cost more consumers and a higher profit for the bank
- Despite the necessity to decrease and rigorously control expenses, the bank must nonetheless spend money on marketing and pushing products to additional clients Since then, the bank's competitiveness with other banks has progressively risen.
5.2.4 Plan for avoiding and dealing with inflationary issues
According to the findings in Chapter 4, the inflation rate has a favorable influence on commercial banks' competitiveness Previous study has found that the lower the inflation rate, the more commercial banks compete.
- To manage inflation, major central banks are considering progressively pulling easing monetary policy in the direction of gradually lowering financial support packages, although it appears that they will retain the low interest rate policy for the foreseeable future.
- In order to keep average inflation around 4%, the SBV must continue to aggressively and flexibly conduct monetary policy, closely cooperate with fiscal policy, and implement other macroeconomic programs If inflation rises quicker than projected, closely monitor inflation, local and global markets to establish an operational strategy.
- Operating interest rates in accordance with macroeconomic balance, inflation, market movements, and monetary policy objectives, establishing circumstances for individuals, enterprises, and the economy to minimize capital costs.
- Proactively apply monetary and credit solutions to boost economic recovery,concentrate credit capital for ubiquitous projects and areas, and generate a driving force for economic development.
Limitations of the study
This research paper has generally fulfilled the research objective of the factors affecting the competitiveness of joint stock commercial banks in Vietnam However, the study has numerous drawbacks, including:
Despite the fact that this study methodology is based on credible international studies on banking and macro issues However, not all factors influence commercial banks' competitiveness As a result, future research should look at additional elements, such as those in the banking sector or societal issues, in order to improve the model's explanatory power and make it more relevant to the topic.
The author was unable to analyze all joint-stock commercial banks, but only 28 were investigated, and the other three banks were removed since they were unable to be collected all of the necessary data for the study, so it could not be done theoretically.
Chapter 5 completed the research based on the value acquired from Chapter 4,and gave recommendations to overcome the current restrictions as well as increase the inherent benefits to strengthen Vietnamese commercial banks' competitiveness.Finally, the author recognizes the study's shortcomings and provides recommendations for further research and progress.
1 Trịnh Thúy Hằng (2007), Tăng cường năng lực cạnh tranh của hệ thống ngân hàng thương mại Việt Nam trong quá trình hội nhập quốc tế.
2 Hoàng Ngọc Nhậm (2008), Giáo trình kinh tế lượng, Trường Đại học Ngân hàng TP HCM
3 Phạm Việt Hùng và Dương Thị Ánh Tiên (2020), Ảnh hưởng giữa năng lực cạnh tranh, hiệu quả và rủi ro: Trường hợp của các ngân hàng thương mại Đông Nam Á Tạp chí Khoa học và Công nghệ.
4 Võ Xuân Vinh và Đặng Bửu Kiếm (2016), Năng lực cạnh tranh, lợi nhuận và sự ổn định của các ngân hàng Việt Nam Tạp chí phát triển kinh tế, (JED, Vol 27 (12)), 25-46.
1 Aguilar, G., Portilla, J., 2020 Determinants of market power in the Peruvian regulated microfinance sector Journal of Industry, Competition and Trade, 20(4), 657-688
2 Allen, F., and D Gale (2004) Competition and financial stability Journal of Money, Credit, and Banking, Vol 36, pp 453-480.
3 Ariss, R.T., "On the implications of market power in banking: Evidence from developing countries", Journal of banking & Finance, 34 (2010) 4, 765-775
4 Ayadi, R., Llewellyn, D.T., Schmidt, R.H., Arbak, E., De Groen, W.P., 2010. Investigating Diversity in the Banking Sector in Europe Key Developments, Performance and Role of Cooperative Banks Brussels: Centre for European Policy Studies.
5 Beck, T., D Coyle, M Dewatripont, X Freixas, and P Seabright (2010) Bailing out the banks: Reconciling stability and competition An analysis of state- supported schemes for financial institutions CEPR Report London, U.K
6 Berger, A N., L Klapper, and R Turk-Ariss (2009) Banking competition and financial stability Journal of Financial Services Research, Vol 35, pp 99-118.
7 Berger, A N., A Demirgüc-Kunt, R Levine, and J Haubrich (2004) Bank concentration and competition: An evolution in the making Journal of Money, Credit, and Banking, Vol 36, pp 433-451.
8 Carbó, S., Humphrey, D., Maudos, J & Molyneux, P., "Cross-country comparisons of competition and pricing power in European banking", Journal of International Money and Finance, 28 (2009) 1, 115-134.
9 Coccorese, P., Girardone, C., & Shaffer, S (2021) What affects bank market power in the Euro area? A structural model approach.
10 Delis, M.D (2012) Bank Competition, Financial Reform, and Institutions: The Importance of Being Developed Journal of Development Economics, 97, 450-
11 Efthyvoulou, G., Yildirim, C., 2014 Market power in CEE banking sectors and the impact of the global financial crisis Journal of Banking and Finance, 40, 11- 27.
12 Fernandez de Guevara, J., Maudos, J & Perez, F., "Market power in European banking sectors", Journal of Financial Services Research, 27 (2005) 2, 109-137.
13 Fu, Y., Foden, J.A., Khayter, C., Maeder, M.L., Reyon, D., Joung, J.K and Sander, J.D (2013) High-Frequency Off-Target Mutagenesis Induced by CRISPR- Cas Nucleases in Human Cells Nature Biotechnology, 31, 822-826. https://doi.org/10.1038/nbt.2623
14 Fungacova, Z., Solanko, L., Weill, L., 2010 Market power in the Russian banking industry International Economics, 124, 127-146.
15 Hausman, J A (1978) Specification tests in econometrics Econometrica: Journal of the econometric society, 1251-1271.
16 Kazarenkova (2006) Methodical and Organizational Approaches to Management of Competitiveness of Bank in the Regional Market of Credit Services for the Population Finance and Credit, 29, 44-49.
17 Keeley, M C (1990) Deposit insurance, risk and market power in banking. American Economic Review, Vol 80, pp 1183–1200.
18 Kosmidou, K., Pasiouras, F & Tsaklanganos, A 2008 Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad Journal of Multinational Financial Management, 17, 1-15.
19 Lerner, A.P., "Economic theory and socialist economy", The Review of Economic Studies, 2 (1934) 1, 51-61.
20 Martinez-Miera, D., and R Repullo (2010) Does competition reduce the risk of bank failure? Review of Financial Studies, Vol 23, pp 3638-3664.
21 Mirzaei, A., Moore, T., 2014 What are the driving forces of bank competition across different income groups of countries? Journal of International Financial Markets, Institutions and Money, 32, 38-71.
22 Rakshit, B., Bardhan, S., 2019 Bank competition and its determinants: Evidence from Indian banking International Journal of the Economics of Business, 26(2), 283-313.
23 Schaeck, K., & Cihák, M (2014) Competition, efficiency, and stability in banking Financial management, 43(1), 215-241.
24 Wagner, W (2010) Loan market competition and bank risk-taking Journal ofFinancial Services Research, Vol 37, pp 71-81.
Appendix 1 – Research data
ank year lerner cap size llp gdp inf
ABB 2011 0.2159 0.1137 5.2263 0.0137 0.062 0.092ABB 2012 0.2032 0.1065 5.2433 0.0037 0.052 0.066ABB 2013 0.0530 0.0997 5.2808 0.0059 0.054 0.048ABB 2014 0.0125 0.0847 5.3071 0.0064 0.06 0.006ABB 2015 -0.0236 0.0899 5.2993 0.0103 0.067 0.019ABB 2016 -0.0076 0.0788 5.3229 0.0089 0.062 0.035ABB 2017 0.0325 0.0724 5.3446 0.0058 0.068 0.028ABB 2018 0.0230 0.0763 5.3551 0.0035 0.071 0.069ABB 2019 0.0311 0.0765 5.3769 0.0048 0.07 0.092ABB 2020 0.0086 0.0766 5.3980 0.0044 0.029 0.032ACB 2011 0.1556 0.0426 5.5449 0.0011 0.062 0.092ACB 2012 0.1139 0.0716 5.4672 0.0030 0.052 0.066ACB 2013 0.0268 0.0751 5.4578 0.0051 0.054 0.048ACB 2014 0.0289 0.0690 5.4703 0.0054 0.06 0.006ACB 2015 0.1104 0.0635 5.4894 0.0044 0.067 0.019ACB 2016 0.1058 0.0602 5.5142 0.0052 0.062 0.035ACB 2017 0.0305 0.0564 5.5469 0.0090 0.068 0.028ACB 2018 0.1711 0.0638 5.5713 0.0028 0.071 0.069ACB 2019 0.1672 0.0724 5.5967 0.0007 0.07 0.092ACB 2020 0.1670 0.0797 5.6213 0.0021 0.029 0.032BAB 2011 0.0609 0.1261 5.1465 0.0014 0.062 0.092BAB 2012 0.0048 0.0933 5.1916 0.0036 0.052 0.066BAB 2013 0.0309 0.0658 5.2582 0.0073 0.054 0.048BAB 2014 0.0523 0.0719 5.2795 0.0045 0.06 0.006BAB 2015 0.0681 0.0790 5.2969 0.0027 0.067 0.019BAB 2016 0.0839 0.0765 5.3268 0.0008 0.062 0.035BAB 2017 0.0824 0.0695 5.3584 0.0032 0.068 0.028
BAB 2018 0.0737 0.0730 5.3677 0.0028 0.071 0.069BAB 2019 0.0757 0.0724 5.3854 0.0014 0.07 0.092BAB 2020 0.0517 0.0714 5.3991 0.0022 0.029 0.032BID 2011 0.0567 0.0601 5.6061 0.0124 0.062 0.092BID 2012 0.0682 0.0547 5.6358 0.0073 0.052 0.066BID 2013 0.0517 0.0584 5.6563 0.0118 0.054 0.048BID 2014 0.0762 0.0512 5.6848 0.0107 0.06 0.006BID 2015 0.1077 0.0498 5.7295 0.0067 0.067 0.019BID 2016 0.0671 0.0439 5.7575 0.0091 0.062 0.035BID 2017 0.0156 0.0406 5.7872 0.0123 0.068 0.028BID 2018 0.0549 0.0415 5.8019 0.0144 0.071 0.069BID 2019 0.0542 0.0521 5.8229 0.0135 0.07 0.092BID 2020 0.0298 0.0525 5.8259 0.0154 0.029 0.032BVB 2011 0.1741 0.1945 5.0771 0.0006 0.062 0.092BVB 2012 0.0684 0.1580 5.1100 0.0020 0.052 0.066BVB 2013 0.0249 0.1394 5.1282 0.0024 0.054 0.048BVB 2014 0.0155 0.1285 5.1468 0.0015 0.06 0.006BVB 2015 -0.0382 0.1142 5.1665 0.0019 0.067 0.019BVB 2016 -0.0316 0.1022 5.1848 0.0022 0.062 0.035BVB 2017 -0.0179 0.0838 5.2196 0.0022 0.068 0.028BVB 2018 0.0055 0.0739 5.2453 0.0027 0.071 0.069BVB 2019 -0.0006 0.0721 5.2631 0.0031 0.07 0.092BVB 2020 -0.0089 0.0637 5.2906 0.0057 0.029 0.032CTG 2011 0.1076 0.0619 5.6272 0.0289 0.062 0.092CTG 2012 0.0955 0.0668 5.6421 0.0087 0.052 0.066CTG 2013 0.1124 0.0938 5.6646 0.0072 0.054 0.048CTG 2014 0.1119 0.0832 5.6875 0.0059 0.06 0.006CTG 2015 0.1119 0.0720 5.7149 0.0060 0.067 0.019CTG 2016 0.1002 0.0637 5.7477 0.0053 0.062 0.035CTG 2017 0.0883 0.0582 5.7716 0.0076 0.068 0.028CTG 2018 0.0506 0.0579 5.7818 0.0067 0.071 0.069
CTG 2019 0.0891 0.0623 5.7924 0.0105 0.07 0.092CTG 2020 0.1133 0.0637 5.8054 0.0091 0.029 0.032EIB 2011 0.1669 0.0888 5.4739 0.0015 0.062 0.092EIB 2012 0.1346 0.0929 5.4613 0.0014 0.052 0.066EIB 2013 0.0655 0.0864 5.4610 0.0018 0.054 0.048EIB 2014 -0.0123 0.0873 5.4522 0.0051 0.06 0.006EIB 2015 0.0012 0.1053 5.4097 0.0115 0.067 0.019EIB 2016 0.0027 0.1044 5.4149 0.0085 0.062 0.035EIB 2017 0.0521 0.0954 5.4396 0.0040 0.068 0.028EIB 2018 0.0443 0.0975 5.4432 0.0047 0.071 0.069EIB 2019 0.0213 0.0940 5.4587 0.0041 0.07 0.092EIB 2020 0.0511 0.1048 5.4515 0.0042 0.029 0.032HDB 2011 0.3248 0.0788 5.2397 0.0019 0.062 0.092HDB 2012 0.0023 0.1022 5.2662 0.0057 0.052 0.066HDB 2013 -0.0430 0.0996 5.3480 0.0022 0.054 0.048HDB 2014 -0.0170 0.0892 5.3719 0.0046 0.06 0.006HDB 2015 0.0633 0.0924 5.3832 0.0088 0.067 0.019HDB 2016 0.0403 0.0662 5.4406 0.0066 0.062 0.035HDB 2017 0.0875 0.0780 5.4791 0.0054 0.068 0.028HDB 2018 0.1355 0.0779 5.5011 0.0046 0.071 0.069HDB 2019 0.1657 0.0888 5.5111 0.0056 0.07 0.092HDB 2020 0.1486 0.0774 5.5661 0.0056 0.029 0.032KLB 2011 0.1709 0.1936 5.0855 0.0020 0.062 0.092KLB 2012 0.1358 0.1854 5.0922 0.0039 0.052 0.066KLB 2013 0.1494 0.1626 5.1155 0.0039 0.054 0.048KLB 2014 0.0828 0.1456 5.1285 0.0018 0.06 0.006KLB 2015 0.0778 0.1332 5.1438 0.0026 0.067 0.019KLB 2016 0.0203 0.1105 5.1745 0.0030 0.062 0.035KLB 2017 0.0669 0.0951 5.2085 0.0018 0.068 0.028KLB 2018 0.0309 0.0886 5.2293 0.0009 0.071 0.069KLB 2019 -0.0057 0.0742 5.2608 0.0015 0.07 0.092
KLB 2020 0.0046 0.0684 5.2798 0.0000 0.029 0.032LPB 2011 0.0912 0.1175 5.2765 0.0050 0.062 0.092LPB 2012 0.0342 0.1113 5.3045 0.0034 0.052 0.066LPB 2013 0.0912 0.0914 5.3347 0.0009 0.054 0.048LPB 2014 0.0817 0.0733 5.3740 0.0030 0.06 0.006LPB 2015 0.0495 0.0706 5.3849 0.0047 0.067 0.019LPB 2016 0.1264 0.0587 5.4310 0.0035 0.062 0.035LPB 2017 0.0834 0.0574 5.4546 0.0032 0.068 0.028LPB 2018 0.0672 0.0583 5.4661 0.0035 0.071 0.069LPB 2019 0.0936 0.0623 5.4899 0.0022 0.07 0.092LPB 2020 0.0858 0.0587 5.5202 0.0023 0.029 0.032MBB 2011 0.1798 0.0695 5.4274 0.0046 0.062 0.092MBB 2012 0.1164 0.0733 5.4665 0.0115 0.052 0.066MBB 2013 0.1436 0.0840 5.4710 0.0105 0.054 0.048MBB 2014 0.2838 0.0826 5.4886 0.0101 0.06 0.006MBB 2015 0.1407 0.1049 5.5049 0.0095 0.067 0.019MBB 2016 0.1485 0.1038 5.5295 0.0079 0.062 0.035MBB 2017 0.1268 0.0943 5.5633 0.0104 0.068 0.028MBB 2018 0.1507 0.0943 5.5873 0.0084 0.071 0.069MBB 2019 0.1686 0.0969 5.6085 0.0119 0.07 0.092MBB 2020 0.1565 0.1012 5.6393 0.0124 0.029 0.032MSB 2011 -0.0218 0.0831 5.3951 0.0010 0.062 0.092MSB 2012 -0.0568 0.0827 5.3885 0.0046 0.052 0.066MSB 2013 -0.0397 0.0879 5.3842 0.0000 0.054 0.048MSB 2014 -0.0445 0.0905 5.3798 0.0069 0.06 0.006MSB 2015 -0.0457 0.1305 5.3797 0.0051 0.067 0.019MSB 2016 -0.0614 0.1469 5.3599 0.0188 0.062 0.035MSB 2017 -0.1455 0.1223 5.3919 0.0091 0.068 0.028MSB 2018 -0.0368 0.1003 5.4261 0.0054 0.071 0.069MSB 2019 0.0708 0.0947 5.4479 0.0059 0.07 0.092MSB 2020 0.0984 0.0955 5.4676 0.0061 0.029 0.032
NAB 2011 0.0934 0.1669 5.0949 0.0001 0.062 0.092NAB 2012 0.0451 0.2047 5.0674 0.0051 0.052 0.066NAB 2013 0.0144 0.1132 5.1651 0.0027 0.054 0.048NAB 2014 0.0691 0.0893 5.2083 0.0021 0.06 0.006NAB 2015 0.0634 0.0963 5.2000 0.0069 0.067 0.019NAB 2016 -0.0136 0.0801 5.2315 0.0112 0.062 0.035NAB 2017 0.0152 0.0674 5.2714 0.0096 0.068 0.028NAB 2018 0.1107 0.0564 5.3249 -0.0013 0.071 0.069NAB 2019 0.0721 0.0524 5.3636 -0.0001 0.07 0.092NAB 2020 0.0573 0.0491 5.4219 0.0042 0.029 0.032NVB 2011 0.0957 0.1430 5.1241 0.0031 0.062 0.092NVB 2012 0.0091 0.1476 5.1172 0.0041 0.052 0.066NVB 2013 0.0038 0.1102 5.1668 0.0008 0.054 0.048NVB 2014 -0.0017 0.0872 5.2063 -0.0003 0.06 0.006NVB 2015 -0.0020 0.0667 5.2512 0.0023 0.067 0.019NVB 2016 0.0079 0.0468 5.3109 0.0031 0.062 0.035NVB 2017 0.0241 0.0448 5.3176 0.0009 0.068 0.028NVB 2018 0.0260 0.0446 5.3189 0.0009 0.071 0.069NVB 2019 0.0482 0.0536 5.3363 0.0009 0.07 0.092NVB 2020 0.1016 0.0476 5.3544 0.0005 0.029 0.032OCB 2011 0.1220 0.1476 5.1445 0.0030 0.062 0.092OCB 2012 0.1148 0.1393 5.1571 0.0092 0.052 0.066OCB 2013 0.1386 0.1209 5.1869 0.0091 0.054 0.048OCB 2014 0.0575 0.1028 5.2162 0.0077 0.06 0.006OCB 2015 -0.0009 0.0855 5.2553 0.0074 0.067 0.019OCB 2016 0.0893 0.0739 5.2978 0.0052 0.062 0.035OCB 2017 0.1184 0.0728 5.3442 0.0030 0.068 0.028OCB 2018 0.0850 0.0880 5.3726 0.0094 0.071 0.069OCB 2019 -0.1085 0.0974 5.4005 0.0079 0.07 0.092OCB 2020 -0.1643 0.1143 5.4431 0.0083 0.029 0.032PGB 2011 0.1563 0.1474 5.0830 0.0069 0.062 0.092
PGB 2012 0.0666 0.1658 5.0982 0.0147 0.052 0.066PGB 2013 -0.0265 0.1290 5.1408 0.0065 0.054 0.048PGB 2014 0.0593 0.1295 5.1468 0.0040 0.06 0.006PGB 2015 -0.0009 0.1366 5.1395 0.0085 0.067 0.019PGB 2016 0.0573 0.1408 5.1405 0.0082 0.062 0.035PGB 2017 0.0868 0.1215 5.1681 0.0157 0.068 0.028PGB 2018 0.1315 0.1233 5.1715 0.0174 0.071 0.069PGB 2019 0.0063 0.0166 5.1806 0.0147 0.07 0.092PGB 2020 0.0444 0.0178 5.2031 0.0078 0.029 0.032SCB 2012 0.0649 0.0762 5.4394 0.0059 0.052 0.066SCB 2013 -0.0231 0.0724 5.4716 0.0038 0.054 0.048SCB 2014 -0.0314 0.0544 5.5201 0.0055 0.06 0.006SCB 2015 -0.1097 0.0496 5.5621 0.0073 0.067 0.019SCB 2016 -0.0733 0.0427 5.5870 0.0041 0.062 0.035SCB 2017 -0.0176 0.0350 5.6212 0.0020 0.068 0.028SCB 2018 0.7112 0.0326 5.6439 0.0042 0.071 0.069SCB 2019 -0.2147 0.0293 5.6622 0.0042 0.07 0.092SCB 2020 -0.0137 0.0262 5.6805 0.0021 0.029 0.032SEA 2011 0.0307 0.0548 5.3745 0.0007 0.062 0.092SEA 2012 0.0084 0.0744 5.3249 0.0020 0.052 0.066SEA 2013 0.0268 0.0717 5.3352 0.0009 0.054 0.048SEA 2014 -0.0550 0.0709 5.3359 0.0025 0.06 0.006SEA 2015 0.0249 0.0681 5.3451 0.0011 0.067 0.019SEA 2016 0.0152 0.0569 5.3782 0.0063 0.062 0.035SEA 2017 0.0198 0.0494 5.4099 0.0044 0.068 0.028SEA 2018 0.0252 0.0591 5.4294 0.0040 0.071 0.069SEA 2019 0.0548 0.0694 5.4483 0.0116 0.07 0.092SEA 2020 0.0765 0.0759 5.4709 0.0038 0.029 0.032SGB 2011 0.0806 0.0691 5.3156 0.0024 0.062 0.092SGB 2012 0.1356 0.0769 5.3982 -0.0005 0.052 0.066SGB 2013 0.0844 0.0721 5.4330 -0.0034 0.054 0.048
SGB 2014 0.0659 0.0620 5.4602 0.0037 0.06 0.006SGB 2015 0.0668 0.0550 5.4921 0.0041 0.067 0.019SGB 2016 0.0516 0.0566 5.5144 0.0056 0.062 0.035SGB 2017 0.0740 0.0514 5.5478 0.0066 0.068 0.028SGB 2018 0.0562 0.0505 5.5683 0.0044 0.071 0.069SGB 2019 0.0621 0.0507 5.5886 0.0066 0.07 0.092SGB 2020 0.0366 0.0582 5.6089 0.0112 0.029 0.032SHB 2011 0.1034 0.2151 5.0605 0.0131 0.062 0.092SHB 2012 0.1137 0.2383 5.0549 0.0019 0.052 0.066SHB 2013 0.0941 0.2384 5.0530 0.0106 0.054 0.048SHB 2014 0.1034 0.2203 5.0654 0.0154 0.06 0.006SHB 2015 0.0133 0.1911 5.0846 0.0151 0.067 0.019SHB 2016 0.0809 0.1845 5.0963 0.0071 0.062 0.035SHB 2017 0.0221 0.1603 5.1151 0.0132 0.068 0.028SHB 2018 0.0098 0.1686 5.1075 0.0169 0.071 0.069SHB 2019 0.0739 0.1561 5.1264 0.0009 0.07 0.092SHB 2020 0.0322 0.1512 5.1344 0.0066 0.029 0.032STB 2011 0.1004 0.1028 5.4305 0.0028 0.062 0.092STB 2012 0.0643 0.0901 5.4426 0.0088 0.052 0.066STB 2013 0.1305 0.1057 5.4525 0.0027 0.054 0.048STB 2014 0.0982 0.0952 5.4795 0.0051 0.06 0.006STB 2015 0.0318 0.0756 5.5513 0.0077 0.067 0.019STB 2016 -0.0107 0.0668 5.5727 0.0021 0.062 0.035STB 2017 0.0264 0.0631 5.5901 0.0022 0.068 0.028STB 2018 0.0440 0.0607 5.6062 0.0039 0.071 0.069STB 2019 0.0482 0.0590 5.6247 0.0047 0.07 0.092STB 2020 0.0442 0.0588 5.6384 0.0062 0.029 0.032TCB 2011 -0.2187 0.0672 5.1409 0.0038 0.062 0.092TCB 2012 0.0638 0.2195 5.0578 0.0005 0.052 0.066TCB 2013 0.1703 0.1153 5.1833 0.0026 0.054 0.048TCB 2014 0.2044 0.0823 5.2620 -0.0010 0.06 0.006
TCB 2015 0.1451 0.0630 5.3274 0.0018 0.067 0.019TCB 2016 0.0866 0.0537 5.3821 0.0026 0.062 0.035TCB 2017 0.0955 0.0538 5.4087 0.0037 0.068 0.028TCB 2018 0.1321 0.0780 5.4242 0.0038 0.071 0.069TCB 2019 0.1491 0.0795 5.4556 0.0079 0.07 0.092TCB 2020 0.1369 0.0812 5.4934 0.0086 0.029 0.032TPB 2011 0.1574 0.0693 5.4712 0.0019 0.062 0.092TPB 2012 0.0557 0.0739 5.4706 0.0081 0.052 0.066TPB 2013 0.0339 0.0876 5.4499 0.0089 0.054 0.048TPB 2014 0.0567 0.0852 5.4668 0.0128 0.06 0.006TPB 2015 0.0947 0.0857 5.4814 0.0189 0.067 0.019TPB 2016 0.1154 0.0832 5.5154 0.0156 0.062 0.035TPB 2017 0.2011 0.1000 5.5379 0.0134 0.068 0.028TPB 2018 0.2468 0.1613 5.5671 0.0058 0.071 0.069TPB 2019 0.2472 0.1618 5.5968 0.0024 0.07 0.092TPB 2020 0.2657 0.1697 5.6195 0.0059 0.029 0.032VAB 2012 0.0818 0.0781 5.5893 0.0095 0.062 0.092VAB 2013 0.0756 0.1003 5.6097 0.0080 0.052 0.066VAB 2014 0.2380 0.0904 5.6303 0.0075 0.054 0.048VAB 2015 0.0855 0.0751 5.6648 0.0079 0.06 0.006VAB 2016 0.0427 0.0670 5.6908 0.0090 0.067 0.019VAB 2017 0.0630 0.0610 5.7167 0.0081 0.062 0.035VAB 2018 0.0866 0.0508 5.7622 0.0060 0.068 0.028VAB 2019 0.1367 0.0579 5.7684 0.0069 0.071 0.069VAB 2020 0.1875 0.0662 5.7900 0.0054 0.07 0.092VCB 2011 0.1759 0.0709 5.8035 0.0075 0.029 0.032VCB 2012 -0.0129 0.1436 5.1390 0.0003 0.052 0.066VCB 2013 0.0763 0.1327 5.1547 0.0011 0.054 0.048VCB 2014 0.0786 0.1022 5.2005 0.0000 0.06 0.006VCB 2015 0.1022 0.0936 5.2276 0.0077 0.067 0.019VCB 2016 0.0406 0.0654 5.2916 0.0059 0.062 0.035
VCB 2017 0.0446 0.0639 5.2994 0.0048 0.068 0.028VCB 2018 0.0265 0.0594 5.3163 0.0065 0.071 0.069VCB 2019 0.0374 0.0581 5.3279 0.0048 0.07 0.092VCB 2020 0.0446 0.0662 5.3486 0.0009 0.029 0.032VIB 2011 0.0530 0.0842 5.3675 0.0100 0.062 0.092VIB 2012 0.0548 0.1287 5.3010 0.0114 0.052 0.066VIB 2013 -0.0389 0.1038 5.3289 0.0113 0.054 0.048VIB 2014 0.2478 0.1054 5.3369 0.0015 0.06 0.006VIB 2015 -0.0143 0.1021 5.3443 0.0060 0.067 0.019VIB 2016 -0.0288 0.0836 5.3801 0.0058 0.062 0.035VIB 2017 0.0175 0.0714 5.4074 0.0028 0.068 0.028VIB 2018 0.1117 0.0767 5.4278 0.0047 0.071 0.069VIB 2019 0.2045 0.0728 5.4748 0.0034 0.07 0.092VIB 2020 0.2215 0.0735 5.5218 0.0039 0.029 0.032VIDBank 2014 0.1488 0.1817 4.9686 0.0035 0.06 0.006VIDBank 2015 0.1645 0.1798 4.9843 0.0037 0.067 0.019VIDBank 2016 0.2093 0.2622 5.0383 0.0015 0.062 0.035VIDBank 2017 0.2081 0.2328 5.0682 0.0019 0.068 0.028VIDBank 2018 0.1673 0.2015 5.1015 0.0014 0.071 0.069VIDBank 2019 0.1647 0.2637 5.1579 0.0019 0.07 0.092VPB 2014 0.0648 0.0005 5.4544 0.0060 0.06 0.006VPB 2015 -0.1750 0.0691 5.4830 0.0169 0.067 0.019VPB 2016 -0.2031 0.0751 5.5106 0.0232 0.062 0.035VPB 2017 -0.2475 0.1069 5.5430 0.0288 0.068 0.028VPB 2018 -0.2666 0.1075 5.5683 0.0348 0.071 0.069VPB 2019 0.1273 0.1119 5.5940 0.0363 0.07 0.092VPB 2020 0.1504 0.1260 5.6115 0.0349 0.029 0.032
Appendix 2 – Descriptive statistics
sum lerner cap size llp gdp inf
Variable Obs Mean std Dev Min Max lerne r ca 271 0663959 0928099 -.2666 7112 p 271 0923133 0436107 0005 2637 size 271 5.392873 1932328 4.9686 5.8259 ll p 271 0061542 0055865 -.0034 0363 gd p
Appendix 3 – Correlation analysis
corr lerner cap size llp gdp ỉnf (obs'1) lerner ca p siz e llp gdp inf lerne r 1.0000 cap 0.1533 1.000 size 0.0732 0 -0.6295 1.000 llp - 0 0.1657 0.041
Appendix 4 - Multicollinearity test
Variabl e VIF 1/VIF size 1.93 0.518056 cap 1.82 0.550041 ll p 1.16 0.864450 in f
Appendix 5 - Pooled OLS
< iap size llp gdp inf
Source ss df MS Number of obs = 271
Conf Interval ] cap 8660435 1619854 5.35 0.000 5471014 1.184986 size 1915526 0376701 5.09 0.000 1173818 265723 llp -4.638421 1.008681 -4.60 0.000 -6.62447 5 -
Appendix 6 – FEM
xtreg lerner cap size llp gdp inf, fe
Fixed-effects (within) regression Group variable: bankl
R-sq: within = 0.0909 between = 0.3613 overa11 = 0.1446 obs per group: min = 6 avg = 9.7 max = 10
Err t p>|t| [95% Conf Interval] cap 6419211 2102555 3.05 0.003 2277217 1.056121 size 184382 0724154 2.55 0.012 041725 327039 llp -3.523458 1.380718 -2.55 0.011 -6.243447 -.8034698 gdp -.3033865 4399972 -0.69 0.491 -1.170173 5633998 in f 4031681 1816123 2.22 0.027 0453952 7609409
_cons -.9667749 4049133 -2.39 0.018 -1.764446 -.1691032 sigma u 04252077 sigma e 08098761 rho 21608866 (fractio n of variance due to u_i)
Appendix 7 – REM
xtreg lerner cap size llp gdp inf, re
GLS regression : bankl Number of obs =
R-sq: within = 0.0889 obs per group: min = 6 between
0.0000 lerner Coef std Err z p>1z1 [95% Conf Interval] cap size llp gdp inf
03189232 08098761.13425356 (fraction of variance due to u_i)
Appendix 8 – Hausman test
018126 0250229 b = consistent under Ho and Ha; obtained from xtreg
B = inconsistent under Ha, efficient under Ho; obtained from xtreg
Test: Ho: difference in coefficients not systematic
Appendix 9 - Breusch and Pagan Lagrangian multiplier test
Breusch and Pagan Lagrangian multiplier test for random effects lerner[bankl,t] = Xb + u[bankl] + e[bankl,t]
Var sd = sqrt(Var) lerne r e 0086137 006559 0928099 0809876 u 0010171 0318923
Test: Var(u) = 0 chibar2(01) = 12.35 Prob > chibar2 = 0.0002
xtserial lerner cap size llp gdp inf
Wooldridge test for autocorrelation in panel data HO: no íirst-order autocorrelation