INTRODUCTION
Problem Statement
The stable growth of businesses is crucial for driving socioeconomic development, as their activities significantly impact various aspects of the economy and society This includes influencing unemployment rates, national budgets, trade activity, and other key macroeconomic indicators.
Recent studies highlight the significant role of small and medium enterprises (SMEs) in driving economic growth According to Joshua and Quartey (2010), SMEs are crucial for creating efficient jobs, serving as the foundation for larger corporations, and sustaining national economies In advanced economies, the SME sector comprises a larger number of firms compared to multinational corporations (Mullineux, 1997) Additionally, Feeney and Riding (1997) note that many governments have implemented policies to foster SME development The growth of SMEs facilitates the redistribution of resources both regionally and within firms, acting as a counterbalance to the dominance of large corporations In South Africa, approximately 91% of enterprises are classified as small, medium, and micro enterprises (SMMEs), which contribute to 61% of job creation and represent 52%-57% of the country's GDP (Hassbroeck 1996, Berry et al 2002).
90% of the private business sector and play a crucial role in contributing GDP in most African countries (UNIDO 1999).
Since the mid-1980s, Vietnam has transitioned from a centralized planned economy to a socialist-oriented market economy following the "Đổi Mới" reforms This shift has led to significant economic growth and development, with the private ownership sector playing a crucial role in driving Vietnam's economic progress According to the Vietnam General Statistics Office, the country achieved approximately 7% GDP growth from 2000 onwards.
Since 2005, the growth rate has consistently reached 7.01 percent, with stability maintained in the development process, particularly within the small and medium enterprises (SMEs) sector SMEs play a crucial role in driving economic growth, creating jobs, and aiding in poverty reduction efforts.
Registration of operations Stop business
From 2007 to 2015, small and medium-sized enterprises (SMEs) emerged as a crucial tax resource for Vietnam's annual budget The Vietnam Chamber of Commerce and Industry (VCCI) reported that in 2011, there were 543,963 enterprises, with nearly 513,000 by 2015, of which approximately 97 percent were small and medium-sized firms, primarily private businesses The significance of SMEs has grown as they contribute substantially to the country's GDP, aid in poverty reduction, enhance social security, and generate over one million new jobs each year.
Between 2007 and 2015, the Ministry of Science and Technology reported that Vietnam registered 692,000 enterprises However, many of these businesses struggle to thrive in a competitive global market, leading to a significant number exiting the market each year The General Statistics Office of Vietnam highlights a concerning trend, with the number of firms ceasing operations rising annually: 63,500 in 2012, 70,500 in 2013, 67,800 in 2014, and 80,900 in 2015 This data underscores the increasing challenges faced by enterprises in maintaining their operations.
Figure 1.1: Number of registered enterprises and stop business over period 2007-2015
The increasing number of businesses ceasing operations annually presents a significant challenge that necessitates urgent action from government officials and policymakers.
Recent research has highlighted the significant impact of the birth-death ratio of enterprises and firm survival in developed countries, drawing attention from scholars globally (Parker 2004, Strotmann 2006) Entrepreneurial activities, increasingly noted in empirical literature, play a crucial role in accelerating economic progress (Stel et al 2005) Many studies examine the relationship between firm characteristics, policies, environmental factors, and the effects of international trade liberalization on firm exit rates Most investigations into firm exit focus on the firm or industry level (Hannan and Carroll 1992, Sarkar et al 2006), particularly the determinants influencing the exit probability of small and medium enterprises (SMEs) Furthermore, research indicates that the resilience of small firms is vital for economic development, especially amid rising import competition from low-cost countries (Colantone et al 2014).
Most research on enterprise survival has focused on foreign firms in developed countries, with limited studies addressing this issue in developing nations, particularly in Vietnam (Parker 2004) There is a notable lack of empirical investigations into the exit strategies of businesses in these regions.
The findings of this research will enhance the understanding of the factors influencing firm exit in Vietnam, providing valuable insights for policymakers By prioritizing these determinants, stakeholders can shape discussions and implement strategies aimed at reducing firm exit rates.
Research objectives
This study aims to identify the factors influencing a firm's decision to exit the market Utilizing SME data from 2005 to 2011, a logit model is employed, with the decision to exit as the dependent variable Key explanatory variables include total assets, firm age, total gross profit, firm size, investment levels, and debt leverage.
Research questions
This study investigates the factors influencing the exit probability of Small and Medium Enterprises (SMEs) in Vietnam It aims to identify and clarify these determinants by addressing specific research questions.
What factors influence probability of firm exit?
Scope of research
This study investigates the relationship between firm exit and its determinants using panel data from 2005 to 2011, focusing on small and medium-sized enterprises (SMEs) in both rural and urban areas of Vietnam Conducted through a collaboration between the Institute of Labour Studies and Social Affairs (ILSSA) under the Ministry of Labour, Invalids and Social Affairs (MOLISA) and the Department of Economics at the University of Copenhagen, the research was funded by DANIDA Key questions were selected from the main questionnaire each year, enabling the use of logistic models to analyze the collected panel data.
LITERATURE REVIEW
Literature Review
Understanding exit behavior and the reasons behind market exits is crucial for managers, as it enables them to make informed decisions that enhance their survival and improve evaluation efficiency Insights into product failure are essential for developing strategies that mitigate risks and drive success in competitive environments.
1994, Dixit et al 2007) In addition, thanks to the exit process, the manager can learn about their actual essential productivity levels (Yang and Temple 2012).
Understanding the reasons behind a firm's exit decision is crucial, as it sheds light on the factors that compel a company to leave the market Analyzing these drivers can provide valuable insights into the strategic choices made by businesses and the circumstances that lead to their departure.
A firm's decision to survive or exit the market hinges on its productivity, which serves as a critical factor for managers when evaluating their options According to Jovanovic (1982), this assessment influences whether to initiate a new venture or withdraw from the market, highlighting the importance of performance expectations in determining a company's future.
A study from 2007 identifies key reasons for the exit of small firms in Japan, highlighting that 38% attribute their departure to a despairing perception of future business prospects, followed by 20% citing aging management and 15% due to manager illness The decline in sales, affecting 71% of firms, plays a significant role in this perception, while only 9% report deficits Additionally, owners may exit to pursue a simpler lifestyle or new ventures Research indicates that companies prioritize profit maximization, and when profits drop below a certain threshold, they are likely to exit the market A study by Liedholm et al (1994) across several African nations reveals that only half of rural firms close due to business failures; others exit for personal reasons like retirement or health issues, or due to better opportunities Hopenhayan (1992) further notes that firms often exit following unfavorable productivity shocks, with less productive companies more likely to leave due to weak competition, while efficient firms seek to expand to boost sales and profits.
Colantone and Sleuwaegen (2010) suggest that entrepreneurs evaluate risk-reward dynamics and external options, such as scrap value and market wages, to decide whether to stay in or exit the market According to Baggs (2005), a firm's profit prospects—shaped by both internal characteristics and the external business environment—are crucial factors influencing the decision to remain in or leave an industry Additionally, Frazer (2005) identifies personal reasons and government closure orders as significant factors leading to enterprise shutdowns.
Incumbent enterprises are more inclined to continue operations when their performance is strong, while they are more likely to exit the market when their prior performance has been poor, according to various theories (McCann and Vroom).
In their analysis, Yang and Temple (2012) suggest that firms are likely to exit the market when they anticipate sufficient future operating profits, viewing exit as the least costly option Research by Baggs (2005) indicates that efficient firms have a higher survival probability compared to their inefficient counterparts Kovenock et al (1997) further elaborate on the exit decision, categorizing it into three types: strategic reallocation, restructuring, and changes in corporate form and structure, all of which are influenced by the owner or manager's assessment of the firm's capital arrangements.
2.1.2 Determinants of the decision to exit
Recent studies have explored the factors influencing firm exit and survival in both developed and developing countries (Roberts et al 1996a, Das et al 1997, Frazer 2005) Researchers have shown a growing interest in the determinants affecting firm longevity and the duration of survival in developed nations The issue of firm exit remains a significant aspect of the business landscape (Geroski 1995).
Recent literature increasingly examines the key factors influencing business dissolution rates, focusing on structural motivations, exit barriers, and firm characteristics According to Baggs (2005), a firm's profit prospects play a crucial role in determining whether a company chooses to exit or stay in an industry Both the competitive environment and the specific attributes of the firm significantly impact these profit prospects Many studies consistently utilize explanatory variables previously established in the literature to analyze these dynamics.
Numerous studies have examined the factors influencing firm exit and survival, with Audretsch and Mahmood (1995) identifying key determinants such as firm age, size, capital intensity, industry innovation rates, and the establishment of new branches The relationship between firm size and industry dynamics has been further explored by Baldwin and Gorecki (1989), Mata and Machado (1996), and Fariñas and Moreno (2000) Additionally, Audretsch and Mahmood (1995) highlight the impact of size, innovation rate, and price-cost margins on exit decisions, while Mata et al (1995) emphasize size as a critical factor for survival chances Other empirical studies also indicate varying relationships between firm size and exit likelihood.
Yang and Temple (2012) categorize firms into small, medium, and large based on their production capacity and fixed assets, with size determined by the total number of employees each year (Dimara et al., 2008) Research by Liedholm and Mead (1999) focuses on micro firms, defined as those with fewer than 10 workers Additionally, studies indicate that large and small firms encounter distinct competitive conditions, stemming from their operation in different market scopes and the use of varied technologies (e.g., Audretsch et al., 1999).
The majority of researches show a negative relationship between firm size and ratio of firm exit such as Segarra and Callejón (2002), Mahmood (2000), Audretch et al (2000), Baldwin
Research has shown varying relationships between firm size and exit probabilities Lieberman (1990) indicates a positive correlation between small firm size and the likelihood of exit, with several studies supporting this notion Conversely, other researchers find a negative correlation between firm size and exit probabilities (Cohen and Klepper 1996, Yang and Temple 2012, Frazer 2005, Liedholm and Mead 1999) Additionally, some scholars argue that firms operating at the lowest production levels are more prone to exit, suggesting that market selection processes may drive inefficient firms to exit more frequently (Blanchard et al 2012).
This article explores how the size of a firm influences its exit behavior, highlighting that larger companies often invest more in Research and Development (R&D) to innovate and improve products, as noted by Cohen and Klepper (1996) These enterprises benefit from ample physical, financial, and human resources, which not only shield them from potential failures but also enhance their ability to capitalize on economies of scale and navigate external challenges Consequently, larger firms are more likely to endure during periods of market instability.
A negative correlation exists between firm size and the probability of exit, primarily because larger companies can more easily decide to close when operating at minimum efficient scales Additionally, larger firms have better access to capital markets and can attract qualified, skilled workers, enhancing their survival capabilities compared to smaller firms (Ferragina et al 2012).
Recent research highlights the significance of firm age as a determinant of business failure rates Studies by Dunne et al (1988) and Thompson (2005) indicate that older firms tend to have a lower probability of failure, with the exit rate of non-failing enterprises decreasing as they age Consistent findings from Agarwal (1997) and others support the notion that, all else being equal, older firms are less likely to exit the market However, some studies suggest an inverted U-shaped relationship between firm age and failure rates, indicating that while older firms generally fare better, there is a point at which increased age may lead to higher failure risks due to the liability of aging.
“adolescene” (Fichman and Levinthal, 1991; Bruderl and Schussler, 1990).
Review of empirical studies on firm survival/exit
2.2.1 Approaches of analyzing firm exit and survival
Researchers examine exit and survival issues from various perspectives, with many studies analyzing firm survival and exit at the macro level Other investigations focus on the impact of specific determinants on exit within particular industries These studies often gather data from manufacturing and service sectors or concentrate on key industries within specific countries or across multiple nations.
Colantone and Sleuwaegen (2010) analyzed the exit ratios of firms across twelve manufacturing sectors in eight European countries from 1997 to 2003 Meanwhile, Kneller and McGowan (2012) investigated the impact of tax policies on business entry and exit in 19 OECD countries between 1998 and 2005.
Many scholars examine firm or industry exit as a dependent variable at the country level Baggs (2005) analyzes firm survival and exit over a 15-year period (1984-1998) using data from Canadian manufacturing firms Acs and Audretsch (1989) find a positive correlation between industry-level return ratios and the likelihood of entry and exit Recent empirical studies, including those by Backer and Sleuwaegen (2003), Mata and Portugal (1995), Dunne et al (1988), and Siegfried and Evans (1994), demonstrate a positive relationship between exit ratios in year (t) and entry ratios in year (t-1) within the same industry Colantone and Sleuwaegen (2010) also emphasize the significance of exit and entry ratios at the industry-country level Additional research, such as that by Brian & Vroom (2014) and Blanchard et al., further explores the probability of enterprise exit at the industry level.
(2013), Dunne (1988), Ferraginaa et al (2011), Dimara et al., (2007).
Recent research has increasingly focused on evaluating enterprise operations through the lenses of survival and exit strategies Various estimation models are employed to assess the impact of different determinants on a firm's likelihood of survival or its decision to exit the market.
Many studies, including those by Cefis and Marsili (2005), Agarwal and Gort (2002), Pérez et al (2008), Musso and Schiavo (2008), and Gửrg and Strobl (2000), examine the impact of explanatory variables on firm survival rates using hazard models.
Numerous studies globally employ hazard models to analyze the impact of various factors on firm exit rates (Dimara et al 2008, Ferragina et al 2012, Yang and Temple 2012) In contrast, a significant number of recent papers have adopted probit regression for similar analyses (Blanchard et al 2012, Frazer 2005, Baggs 2005) Notably, Tsionas et al (2006) utilized both logit and probit models to investigate the relationship between technical inefficiency and firm exit.
2.2.2 2 Emprical analyses of firm survival
The significance of enterprises in driving social and economic development has led to extensive discussions regarding their survival These discussions often focus on various determinants and characteristics that extend beyond the firms themselves.
Cefis and Marsili (2005) conducted a study using a proportional hazards Cox model on data from the Central Bureau of Statistics in the Netherlands, focusing on company survival time as a key variable Their research identified independent variables such as innovation, firm size, firm age, firm growth, and industrial classification Consistent with previous literature, they found that company age, size, and growth rate are significant factors contributing to longer survival Additionally, after accounting for other variables like age, size, sales growth, and technology characteristics, the study revealed that innovation positively influences the probability of company survival.
Agarwal and Gort (2002) identify three key determinants that influence firm survival: net investment, the quality disparity of initial endowments, and the learning processes within firms aimed at reducing costs, increasing returns, and enhancing productivity Their analysis employs the overall hazard-rate function to examine the simultaneous effects of these independent variables on survival probabilities Utilizing the Cox proportional-hazards regression model, commonly applied in survival analysis, they reveal a positive correlation between technology intensity and the likelihood of survival, while noting an inverse relationship between high capital-labor ratios and firm survival.
Pérez et al (2008) examine the factors influencing firm survival using annual data from Spain's Ministry of Industry, employing a hazard model rooted in Resource-Based Theory Their findings reveal that larger, more productive firms engaged in R&D have a significantly higher likelihood of survival, achieving a 1% significance level Additionally, they note that firms operating in both high and low innovation environments face greater challenges compared to those in intermediate innovation settings This study aligns with previous research by Audretsch (1995) and Segarra and Callejon (2002), which highlights an unusual relationship between a firm's age and its survival chances, indicating an initial increase in exit ratios followed by a subsequent decline and later rise.
In 2008, Muss and Schiavo introduced a novel approach to examine how financial constraints affect the survival and growth of firms Building on previous research, they utilized a proportional hazards model with panel data spanning from 1996 to 2004, specifically targeting manufacturing firms in France Their findings revealed a positive correlation between financial constraints and the likelihood of firm exit.
A study by Gửrg and Strobl (2000) examines the impact of multinational companies (MNCs) on the survival of firms in the Irish manufacturing sector from 1973 to 1996, utilizing a Cox proportional hazard model The research reveals that MNCs positively affect the survival chances of firms through technology spillovers, but they also contribute to the crowding out of competitors Notably, the presence of MNCs does not influence domestic low-tech companies or foreign firms in high-tech industries; however, it negatively affects the survival of foreign firms operating in low-tech sectors.
Numerous studies utilizing panel data from various countries have explored factors influencing firm survival, including notable works by Agarwal and Audretsch (2001), Mata and Portugal (2002), and Dunne et al (1989) These studies primarily focus on firm-level data, revealing that variables such as labor numbers, factory ownership, competitive advantages, diversity levels, learning capabilities, and management experience negatively impact survival rates Conversely, research at the industrial level indicates that factors like industry growth, life cycle stages, and technological development positively correlate with firm survival.
2.2.3 Emprical analyses of firm Exit
Colantone and Sleuwaegen (2010) emphasize the importance of international trade in influencing firm entry and exit rates at the industry-country level, calculated as the ratio of firm shutdowns or births to the total surveyed firms during a specific period Their study, conducted across eight European countries, explores various factors affecting the annual firm exit rate in the manufacturing sector, including investment, capital intensity, total factor productivity (TFP), and changes in trade The authors focus on two key dependent variables: industry-level firm exit and entry rates, utilizing least squares estimation and incorporating lagged entry and exit rates in their regression model Their findings indicate that previous firm entries are associated with higher exit rates, supporting the positive relationship between entry and exit identified in earlier research by Mata & Portugal (1994), Siegfried & Evans (1994), Caves (1998), and Dunne et al (1988) Additionally, they find a negative correlation between exit rates and forward-changing comparative advantage, while capital sensitivity and multi-factor productivity show no significant relationship Notably, the first lag of trade openness positively correlates with firm exit rates, although the second lag proves insignificant, aligning with previous studies that highlight a negative impact of trade openness on firm survival (Biernard et al 2006a).
Huyghebaert et al (2004) utilized a hazard model to investigate how competition, debt leverage, and financial market characteristics influence the likelihood of startup firm exit Their study, which analyzed data from 235 entrepreneurial startups in Belgium between 1992 and 1999, highlighted a significant correlation between competitive ability and the exit rate of startups Additionally, the research found a notable negative impact of employee numbers on exit rates in the year following the startup's establishment, while firm size served as a reliable indicator of exit likelihood; however, debt leverage was determined to be insignificant in this context.
RESEARCH METHODOLOGY
Conceptual framework and the econometric model
Based on the literature review, a conceptual framework has been developed, as illustrated in Figure 3.1 Empirical research reveals numerous factors influencing firm exit, derived from a wide array of previous studies and scholarly papers globally.
In this study, I concentrate on key determinants such as firm size, firm age, investment, profit, leverage, and assets This focus is primarily due to the lack of comprehensive data on other potential determinants Furthermore, certain factors were not included in the main questionnaires utilized for surveying small and medium-sized enterprises (SMEs) in Vietnam.
According to Random Utility Theory by Marschak (1960), the exit behavior can be descripted through utility of enterprise which is determined by deterministic components and random components.
In this way, the total utility of a firm (i) exit is equal the sum of the both utility components as below:
The variable V1i can be modeled as a linear function based on the exit vector Xi, while the variable 1i can be represented as a linear function of the non-exit vector 𝑖, incorporating the population utility weights for each attribute in the vector βi Thus, the relationship can be expressed as 1𝑖 = 1𝑖 * 𝑖 1𝑖.
In additional, � 1� is a random utility component.
Similarly, the total utility of a firm i with non- exit:
Where, � 0𝑖 can be approximated by a linear function of non-exit in the vector of Xi and the population utility weights for each attribute in the vector βi: � 0𝑖 = 𝑖 0𝑖 𝑖 0� In additional,
The probability that a firm can be expressed as the probability that the utility associated with exit is higher than utility of non-exit:
Or Pr (exit) = Pr (� 1𝑖 + 𝑖 1𝑖 > � 0𝑖 + 𝑖 0𝑖 ) = Pr (𝑖 1𝑖 - 𝑖 0𝑖
We assume that the error terms of different alternatives are uncorrelated, share the same variance, and follow a logistic distribution Under these conditions, the probability of a firm opting to exit the market is represented as a log-probability.
A repeat enterprise is defined as a firm that is still operational in the current survey year and was also active in the previous survey These businesses are classified as type 1 enterprises (Code 0) If an enterprise is no longer operating and has no immediate plans to resume operations, or if it has been officially declared bankrupt, it is categorized as an "exit." In this scenario, the response is coded as "exit." Conversely, if the firm remains in operation, the response is coded as "not-exit." This situation is represented by a binary variable, assigning a value of 1 for "exit" and 0 for all other cases.
The baseline estimating equation is as follows:
+ € � + � � * �� � (with D1 is provinces - dummy variable – model 2) (2)
+ € � + � � * �� � + � � * �� � (with D1 is provinces and D2 is year – dummy variable – model 3) (3)
In my study, I analyze the factors influencing a firm's decision to exit or remain in the market Using a binary logit model, I explore the two options of exit or non-exit The key advantage of the logistic regression model is its ability to simultaneously assess multiple variables, allowing for a comprehensive examination of how these factors impact the likelihood of the firm's exit from the market.
Logit and probit models are commonly used for estimation in various studies, with the logit model often preferred due to its mathematical simplicity (Gujara, 2003) This thesis utilizes a logit model for its analysis.
The above expression is generalized to the case in which there are k independent variables (� 2 , � 3 , � � ) each firm i, and t equal year (2005, 2007, 2009, 2011).
Where � � � * is unobservable variable, but we have � 𝑖𝑖 = 0 if yit*= 0.
Where F is the cumulative distribution function of � � and we assume the probability density function of � � is symmetric.
In the logit model, ut has logistic distribution The probability density function of ut is given:
� (� � ) (� + � �� � ) � Maximum Likelihood Estimation (MLE) is using to estimate the model It can be verified the
� � � � cumulative distribution function (CDF) of � � is �
The probability of non- exit P (� � =0):
= 1−𝑖𝑖 𝑖 𝑖𝑖𝑖 = Ο is the Odds Ratio (Probability of firm exit over probability of firm non- exit), or the log of the odds Ratio that � � =1 is a linear function of the explanatory variables.
Data and variables
The article utilizes firm-level exit data derived from annual surveys conducted collaboratively by the Institute of Labour Studies and Social Affairs (ILSSA) under the Ministry of Labour, Invalids and Social Affairs (MOLISA) and the Department of Economics at the University of Copenhagen, with funding from DANIDA The primary data collection method involves a comprehensive questionnaire designed for firm interviews, focusing on the actual development of SMEs in both rural and urban areas Data from Vietnamese firms is accessible for the years 2005 to 2011, covering various cities and provinces as detailed in the accompanying table.
Table 3.1: List of surveyed province/city
No Province/city Code Freq Percent Cum.
The variables in this paper mention about the exit, total gross profit, debt leverage,investment, firm size, total asset, firm age and other factor is province Table 3.2.2 defines variables.
Table 3.2: Definitions of variables Variable/symbol in data Descriptive
The firm is currently inactive and has no immediate plans to resume operations A dummy variable is assigned a value of "1" when the firm is exited and "0" when it is not.
Total gorss profit (tgp) Total gross profit equal Value of production/ manufactured output minus
In the last year, the total indirect costs and the value of raw materials used amounted to 1,000,000 VND, after deducting the total wage bill, which includes allowances and other labor costs such as social and health insurance, training, and recruitment Additionally, the debt leverage (lvr) is calculated by dividing the total short-term debt by the total assets for the previous year (t-1).
Investment (i) Investments during the past two years, then add up all the investments made (1,000,000 VND) Total asset (aset) Total asset in last survey year (end-year) (1,000,000,000 VND)
Firm size (size) Total Number of regular full-time labor force in year t-1
Firm age (old) Number of year since establishment up for survey year (t)
Province (prv - D1) Province or city of the main production facility
Year (year - D2) Survey year include 2005,2007, 2009, 2011
Enterprise number (id) Enterprise number For “repeat” enterprises make sure that the number of the enterprise corresponds with the number given in the last survey
The firm is considered as exit out market when it is last surveyed enterprises no longer in existence
To determine the status of the enterprise since the last survey, we ask the question, “Has the enterprise been closed down for a year or more?” A “yes” response is coded as “1,” indicating that the enterprise has exited, while a “no” response is coded as “0,” signifying that the enterprise is still operational.
RESEARCH RESULT
Descriptive Statistic
Variable Obs Mean Std Dev Min Max
Table 4.1 presents the descriptive statistic of the panel data, including 10 provinces and cities in the 2005-2011 period.
Regarding descriptive analysis, we use six main explanatory variables to explore the exit issue, including total gross profit, firm size, investment, total asset, firm age and leverage.
As can be shown in the above table, the huge difference in the value of these variables exposes the large differences among enterprises.
The disparity between the largest and smallest total gross profits among enterprises is significant, with profits ranging from a low of nearly -451 million dongs to a high of 236.824 billion dongs per year Although these figures may not seem substantial, they have a direct impact on a business's financial position, which is crucial for maintaining solvency When enterprises operate effectively and achieve high profits, they bolster their ability to meet matured liabilities; conversely, ineffective operations can jeopardize financial stability.
The mean of the total asset is about 3.34 billion dong This figure reaches the maximum at
1044 billion dong and minimum at nearly one million dong.
The age of enterprises evaluated ranges from 1 to 77 years, highlighting the significant advantages that longer-established SMEs possess These businesses benefit from accumulated production experience, easier access to credit, proactive market trend awareness, and a stable customer base, all contributing to consistent revenue stability.
The minimum size for small and medium-sized enterprises (SMEs) is one employee, while the maximum can reach up to 1,929 employees This substantial range in firm size reflects the varying characteristics of different industries and the distinctions between established and new businesses.
The average leverage ratio stands at 4.87 percent, indicating that short-term debt from credit institutions constitutes 4.87 percent of total assets With a variance ranging from a minimum of 0 percent to a maximum of 7 percent, this highlights the varying capital utilization among firms Financial and commercial enterprises typically utilize higher leverage compared to manufacturing firms, primarily due to their need for larger capital cycles to invest in fixed assets for production and business operations These differences in financial leverage reflect the varying levels of risk among firms within the same industry.
Table 4.2: The number of surveyed firm in each city/province
Province/ city Freq Percent Cum.
The survey reveals that firms predominantly target major cities and provinces, with Ho Chi Minh City leading at 23.78%, followed by Ha Noi at 11%, Ha Tay at 14.82%, and Nghe An at 14.04% This focus is on regions with the highest concentration of operating businesses, highlighting Ho Chi Minh City's status as the economic hub, which has shown a consistent upward trend in the number of enterprises, starting from 37.9 percent.
From 2007 to 2015, the percentage of firms in Ha Noi city increased to 42.1%, with over 97,000 businesses operating in 2015, representing 22.3% of the total firms in Vietnam The survey focused on small and medium-sized enterprises (SMEs) from previous studies and included new businesses established in the same locations during the survey year This resulted in a maximum of 10,102 observations collected from the period of 2005 to 2011.
Table 4.3: overview of firm exit
Firm exit Freq Percent Cum.
From 2005 to 2011, 10.51% of the total 7,952 surveyed enterprises were dissolved Additionally, data from the Department for Management of Business Registration and the Ministry of Planning and Investment indicates that between 2007 and 2015, a significant number of businesses were shut down or dissolved.
Between 2005 and 2011, Vietnam experienced a significant number of business dissolutions, with 428,000 units reported, representing 45.5% of enterprises Of these, 117,000 firms, or 12.5%, were officially dissolved It's important to note that firms surveyed in the previous year but not in the current year are not included in the exit statistics.
Table 4.4: Firm exit in province/city
Province/ City No Yes Total
Between 2005 and 2011, a survey of 7,952 enterprises revealed that only 836, or 10.51%, ceased operations The majority of these closures were concentrated in Phu Tho, Nghe An, and Quang Nam provinces In contrast, Ho Chi Minh City and Hanoi, the country's economic centers, experienced significantly lower shutdown rates This discrepancy can be attributed to geographical advantages and supportive government financial policies that benefit businesses in these major cities.
Regression results
To compare the average values of variables between the stopped working group and the not stopped working group, a T-test was conducted This statistical test aims to determine whether significant differences exist between the two groups, particularly when the standard deviations are unequal.
Table 4.5: A comparison between Firm non-exit and Firm exit in term of variables
In terms of statistics, the average total gross profit of the operating enterprise is 277.43 million, which much higher than the decommissioned enterprise (7.88 million).
The study revealed that the average size of active businesses, with 16.42 employees, significantly exceeds that of inactive firms, which have an average of 9.51 employees This finding, confirmed by a T-test analysis, indicates that operating enterprises employ an average of 16 individuals, while deactivated firms typically have only 9 employees.
The T-test results indicate a significant difference in average investment levels between operational and shut-down firms, aligning with real-world observations Firms that are not profitable often lack sufficient cash flow from their production and business activities, leading them to prioritize daily operations over investing in fixed assets.
A T-test comparison of total assets between operating and shutdown firms revealed significant differences, with operating firms holding total assets of 3.72 billion VND compared to 2.33 billion VND for shutdown firms.
A total of 836 businesses have been shut down out of 7,952, categorized by their operational status and age criteria The T-test results indicate significant differences, showing that operating firms have an average lifespan of approximately 21 years, while decommissioned firms average only 14 years of operation.
Based on T- test there are differences in using debt leverage among active business and business who has been dissolved, but this difference is negligible.
Variable exit Total asset Firm size Firm age Investment Debt leverage Total gross profit Exit
***, ** and * denote significance at 99%,95% and 90% respectively
The covariance matrix reveals significant correlations among various variables Notably, there is a negative correlation of -0.02 between assets and exits, indicating that higher asset levels are associated with fewer exits Conversely, a positive correlation of 0.42 at the 1% significance level suggests that firm size may increase asset levels Additionally, the correlation coefficient between exit and size is -0.04, indicating that larger firms may experience a decrease in exits Furthermore, firm age shows a negative correlation with both asset and size, with coefficients of -0.01, suggesting that companies with longer operating histories tend to have lower total assets and fewer full-time employees.
The correlation coefficients of investment - exit, investment – age are also negative (-0.03, -
0.01 respectively) It can be explained that firm with lower ratio of exit and age have possibly greater investment The correlation coefficient between investment –asset and investment - size; however, are positive (0.34 and 0.33) showing that high investment levels are likely to contribute increasing total asset and use more employees.
Leverage shows a positive correlation with asset size and investment, with coefficients of 0.04, 0.22, and 0.12, respectively Conversely, it negatively correlates with exit and firm age, indicated by coefficients of -0.03 and -0.02 Specifically, a higher ratio of short-term debt to total assets tends to reduce exit opportunities, while increased short-term debt is associated with younger firms.
The analysis reveals that total gross profit shows an insignificant correlation with age In contrast, total assets exhibit significant positive correlations with both investment and leverage, while demonstrating a significant negative correlation with the exit variable.
Testing for multicollinearity is essential when using panel data in regression analysis, as failing to do so can lead to flawed models The presence of multicollinearity can introduce bias into the results, compromising the reliability of the findings.
Total gross profit (mil VND) 1.22 0.81
VIF indices from table 4.7 reveal that the problem of multicollinearity is not severe because the all figures are smaller than 10.
4.2.4 esults of random-effect logistic regressions
Table 4.8: Results of Random – effect logistic regressions
Independent variable Exit (model 1) Exit (model 2) Exit (model 3)
Exogenous variables: asset size age tgp I lvr 2005.year 2007.year 2009.year 20011.year 1.codeprv 25.codeprv 28.codeprv 31.codeprv 40.codeprv 49.codeprv 56.codeprv 68.codeprv 79.codeprv 80.codeprv
***, ** and * denote significance at 99%,95% and 90% respectively
In this study, we employed three models utilizing logistic regression, as detailed in Chapter III, specifically in Table 4.7 The first model applied random-effects logistic regression to analyze the relationship between exit and various independent variables, excluding dummy variables Key factors examined include assets, firm size, firm age, total gross profit, investment, and debt leverage ratio Our findings reveal distinct relationships between the dependent variable and the independent variables.
The first model reveals a negative relationship between labor numbers and the likelihood of exit, aligning with prior theoretical findings, although this result is statistically insignificant Similarly, the relationships between investment, debt leverage, and exit are also insignificant at the 10% level Consistent with earlier studies, firm age shows a negative correlation with exit, with a significant relationship observed at the 5% level Additionally, leverage and total assets exhibit positive effects on the exit variable Notably, total gross profit and investment correlate with firm exit, with the correlation between total gross profit and exit being statistically significant at the 1% level.
In our second model, which includes a province dummy variable, we observed a notable change in results compared to the first model Specifically, the correlation between the number of labor (size variable) and firm exit shifted to a positive relationship, although it remains statistically insignificant Contrary to our initial expectations that size, investment, and leverage would significantly influence firm exit, the results indicate that these factors do not exhibit a meaningful correlation, consistent with the first model Additionally, other variables such as firm age and total gross profit showed no changes Ultimately, the province variable has a minimal impact on the results of the first model, primarily affecting the relationship between exit and the number of labor variable.
In model 3, we incorporate a year dummy variable in the logistic regression, leading to significant changes in the relationships between explanatory variables and the dependent variable compared to the first two models Notably, the correlation between size, leverage, and firm exit is significant at the 1% and 5% levels, while total gross profit and total assets, despite showing correlation, are not statistically significant with the dependent variable The introduction of two dummy variables alters the relationships, transforming the significance of debt leverage and firm exit from non-significant in models 1 and 2 to significant in model 3 This indicates that firms with high debt leverage have a higher probability of exit, particularly in 2009 and 2011, years marked by the Vietnamese economy's struggle with the global recession and subsequent financial crisis Additionally, the investment variable remains insignificant across all models, indicating that investment does not influence firm exit, regardless of the province or year.
In the next step, we use average marginal effects to calculate the effect of dependent variables with the independent variable.
After an estimation, Using the average marginal effect helps to understand the detail affection between explanatory variables and the dependent variable as prediction function.
Marginal effects, when used with dummy variables, indicate the discrete change in the probability of an event occurring as an explanatory variable increases by one unit, holding all other variables constant In contrast, for continuous variables, marginal effects reflect the absolute change in the dependent variable when an independent variable is increased by one unit.
Total gross profit (mil VND) -0.0190 -0.019 -0.0006
CONCLUSION
Conclusion
The probability of firm exit has become a concerning issue recently Some famous authors mentioned in this paper such as (Colantone & Sleuwaegen, 2010); (Baggs; 2005); (Rahaman;2014); (Ferragina et al 2012); (Leroy et al, 2015).
This paper explores the factors influencing firm exit in the SME sector in Vietnam, highlighting insights from previous global research It identifies key elements that contribute to the success or failure of enterprises and evaluates their impact on the firm exit rate in Vietnam Through this analysis, the study establishes a clear relationship between various factors and the likelihood of business closures in the region.
The findings indicate a distinction between endogenous and exogenous variables, revealing that firm size, age, investment, and total gross profit negatively correlate with firm exit Conversely, asset and leverage exhibit a positive relationship with firm exit.
A firm's ability to generate profits for future growth reduces the likelihood of exit, particularly when compared to inefficient businesses However, various factors such as investment levels, firm size, leverage, and age significantly impact operational performance Ultimately, the decision to continue business operations hinges on the specific circumstances and financial health of the firm.
This study enhances the understanding of firm exit dynamics, particularly within the context of Vietnam, where small and medium enterprises (SMEs) dominate the business landscape The empirical findings provide valuable insights for business owners, helping them assess effective strategies and tools to foster their firm's growth and development.
Policy Implications
Every year, a significant number of small and medium-sized enterprises (SMEs) face bankruptcy, highlighting the impact of both their operational capabilities and governmental policy constraints on firm exit rates By analyzing the factors influencing these exits, we can propose public policies aimed at reducing the bankruptcy rate among SMEs.
Government policymakers should implement strategies to enhance the business and investment environment, as total gross profit and investment negatively affect firm exit rates By fostering opportunities for new and small businesses, these policies can contribute to a more supportive ecosystem for entrepreneurship.
Business owners and entrepreneurs must focus on enhancing productivity and profitability while effectively managing their firm's debt levels to reduce the risk of exit Additionally, they should prioritize investing in assets that demonstrate strong production capabilities.
To reduce the likelihood of firm exits in Vietnam, it is essential to implement policies that tackle existing challenges The government should restrict monopolies and foster a competitive market that allows firms to enter freely, encouraging innovation and improved products to enhance market share and reduce bankruptcy rates Given that small and medium enterprises constitute a significant portion of private firms in Vietnam, the government must introduce incentives and support for these businesses Additionally, accelerating the privatization of state-owned enterprises will enable diverse companies to access industries currently dominated by state monopolies.
Thesis limitations and suggestion for further researches
Due to limited data availability, my model utilizes only six determinants from the conceptual framework outlined in Chapter 3, despite numerous variables being considered in previous empirical theories Many of the potential determinants, such as firm’s sunk cost, total sales, trade policy, government policy, and industry growth, lack sufficient data for inclusion This limitation may affect the robustness of my findings; however, incorporating these additional factors could potentially enhance the model's explanatory power and yield more meaningful results.
Due to challenges in focusing on my thesis, I plan to further explore the topic of firm survival, which has garnered significant interest from researchers and policymakers After my baby is born, I will dedicate time to this subject, aiming to collect and analyze data to incorporate additional variables into my model, thereby broadening my research scope.
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Variable VIF 1/VIF intercept 3.44 0.290879 old 3.14 0.317980 aset 1.85 0.539434 size 1.79 0.558102 i 1.50 0.667644 lvr 1.28 0.780449 tgp 1.22 0.817761
Appendix 2: Logistic regression with Random effects
Random-effects logistic regression Number of obs = 7853
Group variable: id Number of groups = 3602
Random effects u_i ~ Gaussian Obs per group: min
Integration method: mvaghermite Integration points = 12
Wald chi2(6) = 147.03 Log likelihood = -2373.3276 Prob > chi2 = 0.0000 exit Coef Std Err z P>|z| [95% Conf
Interval] asset 0060736 0033084 1.84 0.066 -.0004107 0125578 size -.0001798 0021062 -0.09 0.932 -.0043078 0039483 old -.012539 0041219 -3.04 0.002 -.0206177 -.0044603 tgp -.0190204 0016164 -11.77 0.000 -.0221885 -.0158523 i -.0000558 0000592 -0.94 0.345 -.0001718 0000602 lvr 3541131 3226672 1.10 0.272 -.2783029 9865291 _cons -1.690653 0946135 -17.87 0.000 -1.876092 -1.505214 /lnsig2u -.8330653 4087775 -1.634255 -.0318761 sigma_u 659329 1347594 4416987 9841883 rho 116715 0421419 0559827 2274575
Likelihood-ratio test of rho=0: chibar2(01) = 7.59 Prob >= chibar2 = 0.003
Appendix 3: The marginal effects for Model 1
Average marginal effects Number of obs = 7853
Expression : Linear prediction, predict() dy/dx w.r.t : asset size old tgp i lvr dy/dx
Delta-method Std Err z P>|z| [95% Conf
Interval] asset 0060736 0033084 1.84 0.066 -.0004107 0125578 size -.0001798 0021062 -0.09 0.932 -.0043078 0039483 old -.012539 0041219 -3.04 0.002 -.0206177 -.0044603 tgp -.0190204 0016164 -11.77 0.000 -.0221885 -.0158523 i -.0000558 0000592 -0.94 0.345 -.0001718 0000602 lvr 3541131 3226672 1.10 0.272 -.2783029 9865291
Appendix 4: Logistic regression with Random effects of sub-provinces
Random-effects logistic regression Number of obs = 7853
Group variable: id Number of groups = 3602
Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 2.2 max = 4
Integration method: mvaghermite Integration points = 12
Wald chi2(15) = 195.70 Log likelihood = -2342.6751 Prob > chi2 = 0.0000 exit Coef Std Err z P>|z| [95% Conf Interval] asset size old tgp i lvr prv
Likelihood-ratio test of rho=0: chibar2(01) = 5.13 Prob >= chibar2 = 0.012
Appendix 5: The marginal effects for Model 2
Average marginal effects Number of obs = 7853
Expression : Linear prediction, predict() dy/dx w.r.t : asset size old tgp i lvr dy/dx
Delta-method Std Err z P>|z| [95% Conf Interval] asset 0071332 0032839 2.17 0.030 0006968 0135696 size 0008079 0020801 0.39 0.698 -.0032691 0048848 old -.009316 0042573 -2.19 0.029 -.0176602 -.0009718 tgp -.0191163 0016228 -11.78 0.000 -.0222969 -.0159357 i -.0000268 0000487 -0.55 0.582 -.0001221 0000686 lvr 2639067 3232753 0.82 0.414 -.3697013 8975147
Appendix 6: Logistic regression with Random effects of sub-provinces and time
Random-effects logistic regression Number of obs = 7853
Group variable: id Number of groups = 3602
Random effects u_i ~ Gaussian Obs per group: min = 1 avg = 2.2 max = 4
Integration method: mvaghermite Integration points = 12
Wald chi2(18) = 124.19 Log likelihood = -2055.0387 Prob > chi2 = 0.0000 exit Coef Std Err z P>|z| [95% Conf Interval] asset size old tgp i lvr prv
Likelihood-ratio test of rho=0: chibar2(01) = 28.46 Prob >= chibar2 = 0.000
Appendix 7: The marginal effects for Model 3
Average marginal effects Model VCE : OIM
Expression : Linear prediction, predict() dy/dx w.r.t : asset size old tgp i lvr dy/dx
Delta-method Std Err z P>|z| [95% Conf
Interval] asset 0037232 0035906 1.04 0.300 -.0033143 0107607 size -.0106204 0030984 -3.43 0.001 -.0166931 -.0045477 old -.0122664 0050264 -2.44 0.015 -.022118 -.0024148 tgp -.0006138 0010845 -0.57 0.571 -.0027394 0015119 i -.0000706 0000571 -1.24 0.216 -.0001825 0000413 lvr 9026934 4459556 2.02 0.043 0286365 1.77675
Appendix 8: SCATTER THE PREDICTED PROBABILITY OF EXIT ON CONTINUOUS VARIABLES
Label variable exit "Predicted probability of exit"