INTRODUCTION
Introduction
The banking sector is a crucial indicator of a nation's economic health, serving as the backbone of the economy and heavily regulated in many countries Banks play a vital role in providing capital and fostering economic development According to Demirguc & Huizinga (2000), a developed economy necessitates robust support from a stable and sustainable national financial system.
Numerous studies highlight various factors influencing bank efficiency, categorized into external factors (such as macroeconomic and industrial elements) and internal factors specific to commercial banks (including size, losses, and liquidity) Research by Hasan and Marton (2003) explores the relationship between banking sector development and efficiency, while Hou, Wang, and Zhang (2014) investigate the connections between market structure, risk-taking, and commercial bank efficiency Given the financial sector's significance and its impact on the national economy, robust government regulation is essential However, in developing countries like Vietnam, regulatory frameworks often fall short, hindering sector stability and growth The Vietnamese banking system offers a diverse range of products, from retail banking services like savings accounts and loans to commercial banking options including business loans and risk management services.
In Vietnam, the evolution of commercial banks has preceded that of investment banks, making banking services particularly sensitive to both intrinsic economic challenges and external influences The stability of the currency and banking system is crucial for overall financial system stability Consequently, understanding the efficiency of the banking system and the factors influencing its operations is a significant focus for economists and scholars worldwide When assessing efficiency, the choice often lies between technical and economic efficiency, with the latter being more comprehensive This thesis utilizes the profit function as a means to measure economic efficiency.
Research objective
Despite the significant capital raised by commercial banks for the national economy, there is a lack of comprehensive research on their operational efficiency Understanding banking efficiency, along with various predictive determinants, is crucial for assessing how well commercial banks perform in competitive markets like Vietnam Enhancing individual bank performance can lead to a more efficient national banking system However, most existing studies have primarily focused on qualitative assessments of bank efficiency or large-scale economic factors influencing banks, without quantifying these elements for global rankings This thesis aims to explore the relationship between national financial growth and the efficiency of commercial banks in Vietnam.
In details, this thesis is to understand the connection between the economic efficiency and the economic growth of the banking system based on two following smaller objectives:
How efficient are commercial banks in Vietnam?
Which factors affect the economic efficiency of commercial banks?
This research on the efficiency of Vietnamese banks aims to help managers identify weaknesses and their underlying causes, enabling the development of effective strategies for optimal resource utilization Additionally, it serves as a valuable analysis of inefficiencies within the Vietnamese banking sector, promoting improvements in overall performance Furthermore, the findings provide insights into the current state of the Vietnamese banking industry, offering a practical guide for foreign investors interested in entering this dynamic market.
LITERATURE REVIEW
Theory of the efficiency
In the production economics, the definitions of efficiency and productivity are two concepts proxy for two different things Firstly, the definition of “productivity” and
“efficiency” in terms of firm production has to be differentiated Clearly speaking,
“productivity” considers the entire elements that decide the level of output achieved with the amount of input given Efficiency, however, have a different meaning compared to productivity.
Efficiency encompasses three main types: technical efficiency, allocative efficiency, and economic efficiency Economic efficiency is further divided into profit efficiency and cost efficiency This study focuses on profit efficiency as a key metric for assessing economic efficiency.
The production frontier illustrates the maximum output achievable with a given level of input, indicating efficiency for firms operating on this boundary Producing beyond this frontier is unrealistic due to inherent limitations in performance, while production below it signifies inefficiency The greater the distance from the frontier, the more inefficient the firm becomes.
Productivity and efficiency, while distinct concepts, are intricately linked; thus, enhancing productivity necessitates improved efficiency in production processes Factors influencing productivity levels include adjustments in the quantity and proportion of inputs, advancements in technology, and the strategic combination of these elements, as noted by Coelli et al (2005).
Efficiency is defined as the transformation of inputs into outputs, serving as a crucial competitive factor in various economic entities, including branches, industries, and entire systems (Forsund & Hjalmarrson, 1979) As society evolves, the concept of efficiency has become more nuanced, with Saha & Ravisankar (2000) emphasizing the importance of measuring output value relative to input from an engineering standpoint Koopmans (1951) highlighted that achieving efficiency requires balancing different outputs, indicating that maximum efficiency occurs when one output optimally utilizes a given input Building on this, Debreu (1951) and Sephard (1953) introduced quantitative methods for assessing efficiency, with Debreu measuring the gap between actual and potential output based on inputs, while Sephard focused on the difference between actual inputs and the minimal necessary inputs.
In 1957, Farrell advanced the concept of efficiency measurement by introducing distance functions that assess the gap between efficient production points and practical outputs, forming the basis of the Production Possibility Frontier (PPF) theory Building on this, Kablan (2010) quantified efficiency by identifying the optimal input combinations necessary to produce a single unit of output, represented by the PPF Line, and comparing it to actual production levels A firm is deemed efficient only when its output aligns with the PPF line The PPF theory can be analyzed through two perspectives: the Input-Oriented (IO) approach, which determines the minimal input required for a set of outputs, and the Output-Oriented (OO) approach, which forecasts the maximum output achievable from a specified input level.
In banking efficiency measurement, the Input-Output (IO) approach is often favored over the Output-Output (OO) approach, as it allows banks to concentrate on managing inputs, such as costs, rather than depending solely on outputs However, some studies have noted exceptions where both methodologies are utilized.
Farrell (1957) was a pioneering figure in defining efficiency as a measurable technical term, focusing on two key elements An isoquant, represented in Figure 1.1, illustrates the minimum combination of inputs X1 and X2 required to produce a specific output When a firm operates on this isoquant, it achieves technical efficiency in an input-oriented manner by minimizing input usage Additionally, the iso-cost line CC’ is established based on the input-price ratio, indicating the optimal input proportions needed to achieve the lowest possible cost.
Technical efficiency (TE) measures a bank's ability to maximize output with a fixed set of inputs, calculated as the ratio of output to resources (OR/OP) Allocative efficiency (AE) is determined by the ratio of output to optimal resources (OS/OR) The overall efficiency of a firm, known as economic efficiency (EE), is the product of AE and TE (EE = AE × TE) As illustrated in Figure 1.2, a bank operates on the frontier curve f(X), which represents the maximum output achievable with a given input level A bank is considered technically efficient when it operates on this frontier, where TE equals the ratio of BD to BE.
In the banking sector, a bank is considered efficient when it can achieve desired outcomes while utilizing minimal resources or effort.
Bank efficiency has been extensively analyzed in various studies, including those by Berger & DeYoung (1997), Berger and Humphrey (1997), Timothy J Coelli (1998), and Bonin, Hasan, & Wachte (2005) To assess the efficiency of commercial banks, it is essential to consider multiple measurement criteria such as scale efficiency, allocative efficiency, productive efficiency, and technical efficiency As research on bank efficiency has advanced, it has become clear that both internal and external factors play a significant role in determining efficiency, as highlighted by Fu et al (2014), Berger, Hasan, and Zhou (2009), and Tecles and Tabak (2010).
In conclusion, the theoretical framework surrounding efficiency and bank efficiency has been thoroughly established, garnering significant academic interest Researchers have not only focused on measuring efficiency but also on evaluating various determinants of bank efficiency within the context of the economic landscape.
Numerous studies have focused on measuring and analyzing technical efficiency (TE) using two primary methods: Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) The following section provides a concise overview of these two analytical approaches.
Reviews of empirical study on bank efficiency
Bank efficiency analysis dates back to the 19th century when Sherman and Gold (1985) used the DEA (CCR Model) to evaluate the performance of 14 branches of a US savings bank, revealing that six branches were inefficient Similarly, Milin Sathye (2002) assessed the efficiency of 94 Indian banks from 1997 to 1998, employing two independent models to rank efficiency based on input and output variations In his first model, he categorized inputs as both interest and non-interest expenses, while outputs were defined as net income The findings indicated that public sector banks in India exhibited higher average efficiency scores compared to private and foreign commercial banks.
In the second model, inputs were identified as deposits and staff numbers, while outputs included net loans and non-interest income This model revealed that private sector commercial banks exhibited a higher mean efficiency compared to their public sector counterparts.
Banking efficiency has garnered significant academic interest, as highlighted by studies from Liadaki & Gaganis (2010), Saha & Ravisankar (2000), and Yin et al (2013) Saha and Ravisankar (2000) emphasize that bank efficiency is a key indicator of competitiveness within the banking industry, asserting that commercial banks must maintain efficient operations to enhance their business sustainability Furthermore, Tecles and Tabak (2010) note that the efficiency of the banking sector is crucial for both financial development and economic growth A comprehensive review of financial institutions' efficiency can be found in the work of Berger and Humphrey (1997).
A study by Tecles and Tabak (2010) analyzed the efficiency of 156 commercial banks in Brazil from 2007 to 2010, identifying key characteristics such as size, ownership, market share, non-performing loans, and equity value The findings indicated that larger banks exhibited greater efficiency Similarly, the Vietnamese banking system has been evaluated for efficiency, particularly given its rapid economic growth during the transition economy Research by Nguyen (2011) and Lieu and Vo (2012) provides evidence of this trend, while Chao and Nguyen (2006) explored methodologies for assessing commercial bank efficiency in Vietnam, employing labor and various expenses to measure outputs like total loans against inputs such as total deposits, following the model established by Milin Sathye.
A study conducted in 2002 revealed that larger banks in the region demonstrated efficiency levels 11 times greater than their smaller counterparts, aligning with previous research findings Additionally, Ngo (2010) evaluated the performance of 22 Vietnamese commercial banks, contributing further insights into the banking sector's efficiency.
In 2008, a study evaluated the efficiency of banks in Vietnam by analyzing variables such as labor, capital, funds, and income The findings revealed that these banks attained average efficiency scores that were near optimal levels, indicating significant improvements in the banking sector in Vietnam.
Nguyen (2011) conducted a study assessing the efficiency of 20 commercial banks in Vietnam over three years (2007-2010), evaluating various efficiency determinants such as labor, fixed assets, and deposits as inputs, while interest and non-interest income served as outputs The results revealed that state-owned banks exhibited lower efficiency scores compared to their joint-stock commercial bank counterparts Subsequently, Lieu and Vo (2012) highlighted the intense competition among banks since 2006, exacerbated by the financial crisis and significant fluctuations in deposit values, which affected the operating efficiencies of joint-stock banks However, their research was limited due to the use of traditional methodologies in analyzing key financial ratios of commercial banks.
In recent years, Vietnam's stock market has emerged as one of the fastest-growing markets among developing nations, prompting an increase in commercial banks listing their stocks on the exchange Despite this growth, there is a lack of studies analyzing the efficiency of Vietnam's banking sector due to data limitations Therefore, establishing a standard measure of banking efficiency for Vietnamese commercial banks is crucial, as it will offer valuable insights for managers, customers, and investors.
Numerous studies have explored the connection between macroeconomic indicators and banking efficiency, revealing that various economic factors, including economic growth and stock market performance, significantly influence the efficiency of banks.
Avadi and Arbak (2013) explored the link between bank efficiency and economic growth in the southern Mediterranean region, revealing that profits from the banking sector significantly influence economic growth Similarly, Ferreira (2012) conducted a study in Europe, demonstrating that banks' cost efficiency plays a crucial role in driving economic growth, particularly in relation to GDP.
Research by Beccalli et al (2006) indicates that a bank's efficiency significantly influences stock market performance Their study, which assessed the efficiency of various European banks, demonstrated that fluctuations in cost efficiencies of these banks can lead to changes in bank share prices This highlights the interconnected relationship between bank efficiency and the stock market, suggesting that such dynamics can ultimately impact the broader economy.
Liadaki and Gaganis (2010) who indicate the changes in both cost and profit efficiency have affected stock prices of commercial banks operating in EU markets over the period from
Numerous factors influence bank performance within the banking industry, as highlighted in existing literature Key elements include industry development, competition, concentration, and growth These aspects have been examined in various studies, including those by Athanasoglou, Brissimis, and Delis (2008), Owoputi (2013), and Pulungan and Yustika (2014).
A study by Athanasoglou, Brissimis, and Delis (2008) examines the impacts of industry specific factors and bank profitability of Greek commercial banks over the period
1985 – 2001 Particularly, the authors use two determinants including ownership and concentration to measure the industry specific indicators; however, they find industry structure does not significantly affect bank profitability.
Naceur and Omran (2011) examine how banking system indicators, specifically regulation and competition, influence bank margins and cost efficiency across 11 countries from 1988 to 2005 They incorporate an industry development measure by analyzing the ratio of credit to the private sector as a percentage of GDP to assess the effects of bank financing on the economy Their findings indicate that while banking sector development proxies do not significantly impact the bank market, they exhibit negative correlations with cost efficiency The authors conclude that a well-developed banking sector can lead to reduced operating costs.
Several factors can be used to assess key characteristics of a bank, including financial leverage, size, equity, and risk Observational studies provide evidence of a strong relationship between these specific bank indicators and overall bank performance, particularly in terms of liquidity and profitability.
A study by Fu et al explores the relationship between return on equity and both cost and profit efficiency, utilizing various bank-specific factors The results reveal a significant impact of bank size, market risk, and credit risk on these efficiencies Additionally, Lensink, Meesters, and Naaborg incorporated equity to total assets and return on assets ratios in their research to analyze the effects of these factors on cost efficiency Their investigation focused on the efficiency of banks and the influence of foreign ownership in a large commercial bank.
DATA AND METHODOLOGY
Overview of the banking industry in Vietnam
The first independent bank in Vietnam was opened after the August Revolution in
Established in 1945, the Vietnam National Bank, now known as the State Bank of Vietnam, was the first commercial bank in Ho Chi Minh City, focusing on personal savings and business loans Since 1992, the Vietnamese banking system has been a pioneer in attracting foreign investment and integrating into the global economy Initially structured as a single/double-level banking system with four government-controlled commercial banks, the sector has evolved significantly Today, Vietnam's banking system is rapidly advancing, offering diverse services such as credit unions, credit cards, insurance, stock brokerage, and investment funds It features various types of banks, including state-owned, joint-stock, joint-venture, cooperative, private-limited, and foreign banks, each catering to different economic functions—ranging from local banking and retail services for individuals and small businesses to corporate and investment banking for larger entities.
The Vietnamese banking system, particularly in the joint-stock commercial sector, has undergone significant improvements in quality and diversity since the 1980s, driven by effective and customer-focused reorganization strategies Initially, commercial bank licenses were categorized based on minimum capital requirements, with urban joint-stock banks needing at least 50 billion Vietnam dong and suburban banks requiring 2 billion Over time, monetary policy has evolved, notably increasing the minimum capital for suburban banks from 2 to 5 billion Vietnam dong, establishing a threshold of 100 billion Vietnam dong for urban banks, and setting a new minimum of 3,000 billion Vietnam dong for all joint-stock commercial banks by the end of 2010.
As of December 31, 2009, Vietnam's banking system includes 37 joint-stock commercial banks, accounting for 42% of the total banking sector These banks have raised approximately 100,000 billion dong, which is double the amount raised by state-owned commercial banks and represents 60% of the nation's total capital The commercial banking network has experienced significant growth, expanding its reach from Lang Son to the southeastern provinces and even into the most remote areas of the country.
Over the past two decades, Vietnam's banking sector has undergone substantial transformation due to a comprehensive restructuring plan The State Bank of Vietnam has enhanced its supervisory role, distinctly separating it from the commercial operations of banks Concurrently, the structure of commercial banks has been fortified by the entry of robust financial institutions, aligning with the trends of liberalization and globalization in the global economy.
Vietnam's commercial banking sector is still viewed as underdeveloped compared to other Southeast Asian nations and the global market, contributing to economic and financial instability Since 1990, the sector has undergone significant reorganization aimed at reducing government dominance and promoting sustainable growth, leading to a surge in for-profit commercial joint stock banks However, challenges such as low public confidence and regulatory limitations hinder the banks' competitiveness in Asian and global financial markets The number of commercial banks skyrocketed from 4 in 1991 to 41 in 1993, reaching 51 by 1997, but the 1997 global financial crisis resulted in several bankruptcies and a decline in operational banks From 2002 to 2007, many banks focused on restructuring to enhance management and financial practices, including mergers and acquisitions of smaller, inefficient banks Additionally, the presence of international banks increased during this period, influenced by trade agreements, which led to a drop in local commercial banks' market share from 73% in 1993 to 40% in 2007.
Vietnam is home to approximately 40 commercial banks, which include state-owned institutions and urban joint-stock banks This count excludes government banks, equity funds, wholly foreign-owned banks, and representatives of foreign banks.
Analytical framework
To conduct a thorough analysis of the Vietnamese banking industry's profitability, we utilized the stochastic frontier analysis (SFA) method, initially introduced by Aigner, Lovell, and Schmidt (1977) and Meeusen and van der Broeck (1977) Specifically, we applied Green's (2005) SFA model to explore the factors influencing bank efficiency during the period from 2008 to 2013.
Research method
Researching the development of the banking industry involves various indicators, but the "banking industry development" and "bank concentration index" are the most crucial due to their availability of recent data The Stochastic Frontier Approach is utilized to assess profit efficiency and explore the correlation between bank efficiency and the development of the banking sector in Vietnam.
To analyze key factors influencing bank efficiency in Vietnam, particularly profitability, we utilize stochastic frontier analysis (SFA), a method introduced by Aigner, Lovell, and Schmidt (1977) and Meeusen and van der Broeck (1977) Our study specifically employs the SFA model proposed by Green (2005) to investigate the correlates of bank efficiency during the period from 2008 to 2013.
Theoretical Model
In the banking industry, analyzing input quantities as endogenous variables presents challenges, making the traditional production function approach to supply equations potentially ambiguous Consequently, a more effective method is to directly examine profit functions through a dual approach, allowing for a clearer evaluation of economic conditions This analysis is grounded in the assumption of profit maximization, which is essential for accurate results.
In particular, a profit function is determined as the given input prices (W) and output prices (P), the profit is got the maximization: π= π (W, P) (1)
The function should exhibit convexity with respect to output prices (P) and concavity concerning input prices (W) Additionally, the profit function must remain non-increasing with respect to input prices (W) and non-decreasing with respect to output prices (P).
Estimation Methodology
This study employs the Stochastic Frontier Approach (SFA) to evaluate banking efficiency, producing efficiency scores for individual banks and ranking them accordingly The SFA framework assesses inefficiencies within the banking system by comparing the performance of banks operating under similar conditions; a bank with lower profits and higher costs relative to its peers is deemed inefficient Additionally, due to potential firm inefficiencies and random noise, the observed total banking profits may deviate from the maximum profit efficiency frontier.
The Stochastic Frontier Approach (SFA) necessitates a defined functional form that incorporates appropriate input and output variables, accounting for random noise present in the data over time Additionally, SFA recognizes the variations among different banks, enabling the consideration of both random errors and environmental influences in the analysis.
The Stochastic Frontier Approach (SFA) utilizing panel data, as outlined by Berger and Mester (1997), presents the profit function in logarithmic form as ln(πit) = π (� �� � �� ) + � �� - � �� In this equation, the profit function incorporates a negative sign alongside an inefficiency term (� �,� ) Here, �� serves as a proxy for the total profit of the i-th bank (i = 1, 2, 3, N) in the t-th year (t = 1, 2,…), while Pit represents the vector of output prices and Wit denotes the vector of input prices within the banking sector.
Model specification
In the context of inefficiency terms, the distributional assumption plays a crucial role, with three common distributions being truncated normal, half normal, and exponential normal While both exponential normal and half normal distributions have their respective advantages and disadvantages for measuring bank efficiency, this study adopts the truncated normal distribution, denoted as N+(μ, σ²), as it offers the greatest flexibility Additionally, various determinants can be modeled for the parameter μ, which influences inefficiency.
The inefficiency model is represented by the vector of determinants, denoted as Z, and the corresponding parameters This model will be evaluated using the Stochastic Frontier Approach (SFA) Table 3.1 outlines the determinants of the inefficiency terms represented by Z The study assumes that the relationship of Z follows a linear functional form.
Various models have been developed to analyze how inefficiency changes over time when applying Stochastic Frontier Analysis (SFA) with panel data These models can be categorized into two types: time-varying and time-invariant Additionally, SFA with panel data can be classified based on estimation techniques into random effects and fixed effects models Fixed effects models, as noted by Greene (2005) and others, do not require the assumption that the inefficiency term is correlated with other variables in the model In contrast, random effects models, highlighted by Kumbhakar (1990) and others, necessitate the assumption of no correlation between the inefficiency term and other variables For this study, we have selected the model proposed by Cornwell et al.
In 1990, the author introduced a model that incorporates time-varying effects, where αit represents firm-specific effects that also change over time This model utilizes a quadratic function of time, with parameters that vary across different firms, allowing for a nuanced analysis of dynamic firm performance over time.
Functional form
This study utilizes the Translog functional form to describe the profit function, highlighting its flexibility while acknowledging its limitations in terms of tractability and parsimony.
This thesis will generate natural logarithm for all variables, with all variables are defined above, α is corresponding parameter, � � is the inefficiency term and � is idiosyncratic error.
Profit Efficiency
Banking inefficiency is measured by the ratio of actual costs or profits to the optimal costs or profits, which represents the ideal output when a bank operates at full efficiency, where the inefficiency value is zero.
This research highlights the endogenous relationship between profit efficiency and input variables, specifically the prices of labor (W1), capital (W2), and funds (W3) Endogenous phenomena refer to the correlation between a variable in the model and the error terms, which can lead to measurement errors, auto-regression with auto-correlated errors, omitted variables, and simultaneity issues To address these challenges, the study introduces lagged variables—lagW1, lagW2, and lagW3—to account for the endogeneity associated with the prices of labor, capital, and funds.
This research conducts regression analysis on endogenous variables and subsequently develops predictive functions to create new variables, namely Prlabor, Prcapital, and Prfund These three new variables will replace W1, W2, and W3 in the regression model.
We utilize a Stochastic Frontier Model with panel data regression to assess the impact of inputs and outputs on profit before tax, employing a fixed effects model as outlined by Schmidt and Sickles (1984) This approach allows us to calculate both time-invariant and time-varying technical efficiency for each bank.
Pitt and Lee (1981) with u half normal:
Battese and Coelli (1988) with u truncated normal:
Cornwell et al (1990) introduce time-varying effects in their analysis, emphasizing that these effects are influenced by a function of time They highlight the presence of time-varying firm effects, denoted as αit, which also follow a time-dependent function The authors describe these effects using a quadratic function of time, where the parameters differ across firms.
Kumbhakar (1990) with the time varying, e described as:
With � �� = �(�)� � where � � is fixed through time but varied across firms and � � follows a half normal distribution The suggested time function is:
The fact that �(�) ≥ 0 makes � �� always positive in the production.
Battese and Coelli (1992 and 1995) with a technical efficiency and truncated normal distribution at zero:
� �� = �(�)� � (Battese and Coelli 1992) With the form of �(�) = exp[−�(� − �)] and � � ��� |�(, 2 )|
(truncated normal distribution at zero)
Lee and Schmidt (1993) with a function with the time function is replaced by a set of dummy variables:
True” fixed effects model and “true” random effects model (Greene, 2005):
The “True” fixed model is described as:
The “true” random effects model can be written as:
With � � is a random constant term that varies across firms.
That is the way to have a result of banking efficiency.
This study aims to assess banking profit efficiency by analyzing various correlated variables To achieve this, a regression analysis will be conducted to explore the relationship between profit efficiency and factors such as banking concentration (CON), banking development (DEV), credit risk (CRER), liquidity (LIQD), bank size (SIZ), economic growth (GDP), and inflation rate (INF).
A comprehensive approach to assess profit efficiency and its determinants involves utilizing the Sfpanel and Emean methods together across various models This technique allows for simultaneous estimation of profit efficiency outcomes and the influence of key determinants.
Due to the limitations of the Stata system, which does not permit the use of the conditional mean model for the various regression models tested, this study will employ a two-step regression approach The first step involves identifying the determinants of banking profit and profit efficiency, while the second step focuses on examining the relationships between profit efficiency and its determinants.
Variables description
For many years, defining the inputs and outputs of banks has posed a significant challenge for researchers examining banking profit efficiency globally (Claudia Girardone et al., 2004) While the concepts of banking products are well-established, the input and output variables remain ambiguous Consequently, the measurement of banking indicators varies based on the specific objectives of each study, necessitating tailored sets of relevant input and output variables that align with these research goals.
In this study, we utilized the intermediary approach proposed by Sealey and Lindley (1977), which is widely regarded as the preferred method for measuring bank efficiency (Maudos et al., 2002; Koetter, 2006) This approach views banks as financial intermediaries that primarily facilitate the flow of funds from savers to investors Specifically, banks leverage labor, capital, deposits, and other borrowed resources to generate loans and other income-generating assets To achieve output (Yi) at specific output prices (Pi), banks optimize input quantities (Xi) at given prices (Wi) to maximize total profit.
In this study, we categorize inputs and outputs into three and two categories, respectively The input variables include the number of employees (X1), fixed assets (X2), and total funds (X3) The associated input prices are the wage rate (W1), calculated as the ratio of salaries and related expenses to the number of employees (X1); the price of physical capital (W2), defined as the ratio of rents and other administrative costs to fixed assets (X2); and the price of funds (W3), which is the ratio of total interest expenses to customer deposits (X3) The output variables consist of net loans (Y1) and other earning assets (Y2), with their respective prices being the per unit interest income (P1), determined by the ratio of total interest income from loans to total loans for customers (Y1), and non-interest operating income (P2), measured as the ratio of other non-interest income to total assets (Y2).
The table 3.1 below will summarize the detail of all variables will mention to the detailed inputs, outputs and profit in SFA efficiency model are specified as follow.
Table 3.1: Overview of variable in profit function
Variables Variable Name Variable Definition Unit of measurement
Total bank profit, also known as net profit before tax, is calculated by subtracting total operating expenses and provisions for credit losses from total operating incomes, which include both interest income and other interest income, measured in millions of VND.
X1 Labor Total number of employees person
X2 Capital Fixed asset million VND
X3 Fund Deposit from customer million VND
W1 Price of labor Ratio of salaries and related expenses for employees to labor (X1)
W2 Price of capital Ratio of rents, taxes, duties, fees, insurances and other administrative cost to fixed asset (X2)
W3 Price of fund Ratio of total interest expense to deposit from customer (X3)
Y1 Loan Total loan for customer million VND
Y2 Other earning asset Total asset million VND
P1 Price of loan Ratio of total interest income from loan to total loan for customer (Y1)
P2 Price of other earning asset
Ratio of other non-interest income to total asset (Y2)
Moreover, we will show the graph of scatter between dependent variables (Profit before tax Pbt) and its independent variables (such as Total number of staff X1, Fixed-assets
X2, Total funding X3, Net loans Y1, and other earning assets Y2), prices of input (consist of
W1 equals Total labor cost/X1, W2 equals other operating expenses/X2, W3 equals total
Ln(Total number of staf X1)
Ln(Total number of staf X1)
Ln(Total loan to customers Y1)
Ln(Total loan to customers Y1)
Ln(Price of labor W1) interest expenses/X3) and prices of output (including P1 equals total interest income/Y1, P2 equals noninterest operating income/Y2) as follows.
Figure 3.1 : Graph of scatter between Profit before tax (Pbt) and total number of staff (X1) from 2008 to 2013
Figure 3.2 : Graph of scatter between Profit before tax (Pbt) and total fixed- asset (X2) from 2008 to 2013
Ln(Total deposit from customer X3)
Figure 3.3 : Graph of scatter between Profit before tax (Pbt) and total deposit from customer (X3) from 2008 to 2013
Figure 3.4 : Graph of scatter between Profit before tax (Pbt) and total loan from customers (Y1) from 2008 to 2013
Figure 3.5 : Graph of scatter between Profit before tax (Pbt) and total asset
Figure 3.6 : Graph of scatter between Profit before tax (Pbt) and Price of labor
Profit before tax is a crucial financial metric that reflects a company's earnings prior to the deduction of tax expenses It serves as an indicator of a business's operational efficiency and profitability, allowing stakeholders to assess the company's financial health By analyzing profit before tax, investors and management can make informed decisions regarding future investments and strategies Understanding this figure is essential for evaluating overall business performance and making comparisons within the industry.
Ln(Price of other earning asset P2)
Ln(Price of other earning asset P2)
Figure 3.7 : Graph of scatter between Profit before tax (Pbt) and Price of capital (W2) from 2008 to 2013
Figure 3.8 : Graph of scatter between Profit before tax (Pbt) and Price of fund (W3) from 2008 to 2013
Figure 3.9 : Graph of scatter between Profit before tax (Pbt) and Price of loan
(P1) from 2008 to 2013 Figure 3.10: Graph of scatter between Profit before tax (Pbt) and Price of other earning asset (P2) from 2008 to 2013
The data illustrates the distribution of profit before tax alongside its independent variables, revealing a normal scatter as all values consistently fluctuate within a specific range Additionally, the profit efficiency models incorporate dependent variables, with total profit derived from the difference between net income.
(interest income and other interest income) and total costs.
This study examines the efficiency of Vietnamese banks by utilizing three groups of independent variables The first group focuses on bank-specific attributes, incorporating the ratio of loan loss provisions to total loans, net loans to total assets, and the logarithm of total assets as indicators for credit risk (CRER), liquidity (LID), and size (SIZ), respectively The second group consists of banking industry variables, including concentration (CON), represented by the ratio of total assets of the three largest banks to the total assets of the entire banking sector, and banking sector development (DEV), measured by the ratio of total assets of all banks in the sample to GDP.
Ln (P ro fit b ef or e ta x) Ln (P ro fit b ef or e ta x) Ln (P ro fit b ef or e ta x) Ln (P ro fit b ef or e ta x)
To investigate the connection between economic conditions and bank efficiency, this study utilizes inflation, measured by the Consumer Price Index (CPI), and economic growth, indicated by Gross Domestic Product (GDP) Table 3.2 outlines the independent variables, their expected impact on efficiency, and relevant notations.
Table 3.2 : Variables of potential correlates of profit efficiency function.
Profit efficiency PE Profit efficiency Calculated from SFA
Loan loss provisions/Loans Natural logarithm of total assets
Financial statement Financial statement Liquidity LIQD Ratio of net loan to total assets Financial statement
Concentration CON The total assets of largest three banks/total assets of the whole banking
Bank development DEV Banking sector development: total assets of the banking industry/GDP
Inflation CPI Consumer Prices, All items %
Change over Corresponding Period of Previous Year
GDP GDP Economic growth World bank
Data sources
This study aims to explore the relationship between banking profit efficiency and its determinants by analyzing panel data from 2008 to 2013, covering twenty-seven banks in Vietnam, which include four state-owned commercial banks and twenty-three private commercial and joint-venture banks.
Vietnam's banking sector comprises four types of banks: State-owned commercial banks, Private commercial banks, Joint-venture banks, and branches of foreign banks However, this study focuses exclusively on State-owned commercial banks, Private commercial banks, and Joint-venture banks due to the unavailability of data for foreign bank branches operating in Vietnam.
This thesis comprises 162 observations, with data collected from the financial statements of all banks available on their respective websites for the period from 2008 to 2013 The study utilizes a well-balanced dataset, and a table detailing the number of banks and observations categorized by bank type, as defined by the State Bank of Vietnam (SBV, 2014), will be presented below.
State-owned commercial banks are financial institutions owned by the government, commonly found in developing or transitioning countries In Vietnam, the four largest state-owned banks—Agribank, BIDV, Vietinbank, and Vietcombank—are predominantly government-owned, with over 90% of their shares held by the state.
Table 3.2 : Data sample of observations over period 2008-2013