MSc thesis Small Business and Entrepreneurship
What affects SME survival in the Netherlands?
An empirical study about the impact of determinants on SME survival
Written by Tim Zwama S3713407 Friesestraatweg 332 9718 NT Groningen t.zwama@student.rug.nl University of Groningen Faculty of Economics and Business
22nd of June 2020
Supervisor: dr. Maria Kristalova Co-assessor: PD dr. Michael Wyrwich
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Abstract
The creation and existence of small- and medium sized enterprises (SMEs) is essential in the development of regional and national economies as these enterprises generate a significant amount of revenue and employment. Researchers increasingly paid attention to how SMEs survive and current study adds to this research field as a result of the gaps that arise. This paper examines how three proposed determinants (Solvency Ratio, Access To External Capital and Firm Size) affect the survival chances of SMEs. Using a Cox regression model on a large database containing Dutch manufacturing SMEs, I find that two (Access To External Capital and Firm Size) of the three determinants affect the survival chances of SMEs in a positive manner. The results on the third determinant (Solvency Ratio) aren’t significant and therefore not interpretable. These findings suggest that additional research is needed in order to help small business owners survive in the competitive environment.
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Table of contents
Abstract ... 2
1. Introduction ... 4
2. Literature review ... 7
2.1. Financial performance and SME survival ... 7
2.2. Access to external capital and SME survival ... 8
2.3. Firm size and SME survival ... 9
2.4. Conceptual model ... 11 3. Methodology ... 12 3.1. Data collection ... 12 3.2. Measures ... 17 3.2.1. Kaplan-Meier curves ... 20 3.3. Data analysis ... 20
3.3.1. The Cox proportional hazards model ... 21
4. Results ... 22
4.1. The Cox regression results ... 23
4.1.1. Sample size and fit of the model ... 23
4.1.2. Interpretation of hazard ratios ... 24
4.1.3. Testing the proportional-hazards assumption ... 26
4.1.4. Robustness checks ... 26
5. Summary and conclusion ... 27
5.1. Implications and limitations ... 28
5.2. Implications for future research ... 29
References ... 31
Planning of the thesis semester ... 34
Appendix ... 35
Appendix 1: Kaplan-Meier curves ... 35
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1. Introduction
The creation and existence of small- and medium sized enterprises (SMEs) is essential in the development of regional and national economies. The European Commission (2017) argues that SMEs are the backbone of Europe’s economy and Gupta, Barzotta and Khorasgani (2018) mention that SMEs are important in the creation of economic growth, development and poverty reduction of a country. This isn’t different in the Netherlands, the gross domestic product (GDP) of the Netherlands has been growing for the fifth time in a row (statistic from 2018) and small- and medium sized enterprises (SMEs) are responsible for 68% of the generated revenue and 71% of the employment (Het Nederlands Commité voor Ondermerschap, 2019).
Although the role of SMEs in the Netherlands and Europe is undisputed, more recent research noted some conflicting findings about SMEs. Fritsch and Weyh (2006) found that approximately half of the new enterprises exist less than five years and the other new
enterprises, which managed to survive, are mostly prone to existence-threatening performance declines (Rico et al, 2019; Blackburn and Kovalainen, 2009). These declines can be associated with insolvency and not being able to pay creditors in time or in the amounts which were arranged on forehand. As a result, most of the new enterprises remain rather small and only few are able to generate a significant amount of employees (Fritsch and Weyh, 2006).
Statistics about Dutch SMEs are slightly better (in comparison to the finding of Fritsch and Weyh), but it remains shocking that many of the newly found enterprises won’t survive in competitive environment for a substantial time. Roughly 30% of the newly found Dutch enterprises went bankrupt in the period 2010-2018 (Het Nederlands Commité voor Ondernemerschap, 2019).
Literature on firm survival was published for the first time over 40 years ago and is still growing today. In the late 70’s famous authors like Caves and Porter (1977) and later again Porter (1979) started investigating the economic function (in markets) and profitability of SMEs. These academics concluded that SMEs are able operate and experience good levels of profitability through avoiding direct competition and entering niche markets. Rawwas and Iyer (2013) added that this is the case due to the inaccessibility and not being lucrative for larger enterprises. These findings formed the basis of survival literature.
5 et al. concluded that standard finance indicators (Return on Assets, gross margin and solvency ratio) have a positive and weighty impact on the survival chances of enterprises. Additionally, Hechavarriá (2016) and Pajunen an Järvinen (2018) found that receiving external funding increases the survival chances and reduces the intendance of quitting over time. Besides these two determinants, firm size was found to be an important factor in acquiring legitimacy,
financing and increasing the probability of survival over time (Audretsch et al., 2000; Beck et al., 2006).
Although firm survival is a research field that increasingly received attention, current study aims to add to the understanding how specific determinants, that have been highlighted as highly relevant in the literature, could affect the survival chances of SMEs. In order to get a better understanding of determinants of SME survival, current study investigates how financial management, access to external capital and firm size (later on: the three determinants) affect the survival chances of these type of enterprises. Based on this demarcation and the focus on these determinants, the following research question is formulated:
‘What affects SME survival in the Netherlands?’
To achieve the goal of analyzing the three determinants, an extensive literature review about SME survival is conducted. The evidence of this literature review formed the input for my own empirical analysis that is carried out by using data on Dutch SMEs for the period from 2011 until 2018 (seven years). The data has been acquired through the Orbis database from Bureau van Dijk. After checking the data for completeness and adjusting the data to the format of survival analysis, the Cox proportional hazards model is used to acquire the results. These results show that two of the three determinants (Access to External Capital and Firm Size) have a small significant effect on the chances of SME survival. The findings on these two
determinants are interesting as they show that it is useful to search for external sources of financing and invest in employment to stimulate efficiency and professionalism. An enterprise can be granted legitimacy by stakeholders through investing in these determinants
Unfortunately, the Cox hazards model didn’t present me with all the results I would hope for. The results for the other determinant (Solvency Ratio) aren’t significant and therefore not interpretable. Solvency Ratio might be a determinant of survival after all, but should be included in a different study to prove this empirically.
6 classification (there are 6.645 of the 9.650 enterprises included with 20 or less than 20
employees) and the fact that there is little recent literature that uses the same business context to compare the results with. Only several studies made use of the same business context in the last 20 years, results are therefore hard to compare to previous work.
After finalizing the conclusions based on the results generated from running the survival analysis, it can be said that there are several contributions to the research field. Current study adds to the literature field by investigating whether the existing literature arguments hold for the Netherlands and more specifically SMEs in the Netherlands. I tried to accomplish this through assessing SME survival over a longer period of time (seven years) and check how survival chances develop over this period of time. Another contribution to the literature field is made by using SMEs in Dutch manufacturing industries as sample. Dutch manufacturing industries haven’t received attention lately which means that findings could have interesting implications when it comes enterprise survival in general, enterprise survival for SMEs,
enterprise survival in manufacturing industries and enterprise survival for manufacturing SMEs. Cefis and Marsili included Dutch manufacturing industries in several studies between 2009 and 2012, but rather focused on the link between innovation capabilities and firm exit instead of different (more diverse) determinants and the survival of enterprises. These studies particularly focused on how innovation capabilities (process and product innovations) affect the likelihood of mergers and acquisitions (M&As) or exit (Cefis et al, 2008; Cefis and Marsili, 2011; Cefis and Marsili, 2012).
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2. Literature review
2.1. Financial performance and SME survival
Financial performance of an enterprise interests many stakeholders, such as the management, lenders, potential investors and suppliers (Voulgaris et al., 2000). Being able to generate internal returns (Tatikonda et al, 2013) and making sure that the SME is able to pay back the creditors (Bruton et al., 2003) is necessary in guaranteeing continuity and acquiring legitimacy from the stakeholders in the competitive environment of SMEs. Stakeholders are constituents (either groups or individuals) who in addition have the power and ability to impact an enterprise (Carroll, 1993). Satisfying the needs of stakeholders is an important job as these stakeholders parties are the ones who grant legitimacy to a SME.
In previous work, Bruton, Ahlstrom and Wan (2003) stated that insolvency negatively affects the survival probability as enterprises become unable in predicting their capital
requirements (Hall, 1992). The insolvency results in a situation in which the enterprise can’t pay its debt in time and in the amounts arranged with the debtors which eventually could resolve in bankruptcy. Having a positive solvency ratio is helpful in acquiring legitimacy from for example lenders or suppliers as this signals the ability to engage long-term relationships with
stakeholders. This results in trust which in turn grants the SME legitimacy from a stakeholder perspective.
Besides this finding on solvency, a more recent study of Baumöhl, Iwasaki and Kočenda (2019) concluded that ROA (Return on Assets), gross margin and solvency positively affects the survival probability of enterprises. These findings were in line with the findings of Görg and Spaliara (2014).
Although most of these studies weren’t specifically focused on the Netherlands or SMEs, the solvency ratio was found to have a positive relation with enterprise survival in multiple occasions. For this reason, the assumption is made that the relationship will be a positive one. Thus, the following hypothesis is formulated:
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2.2. Access to external capital and SME survival
Creating revenue is important to every enterprise, but just revenue isn’t enough to survive. Cheng (2005) mentions that inadequate financial resources are the main reason that venture fail to grow, to innovate and to survive. In addition to revenue and internal funds, small firms often search for additional capital in order to operate more effectively and generate growth. Small firms that survive in the competitive environment are found to be more able to acquire
additional capital in comparison to small firms that go bankrupt (Carter and van Auken, 2006). Although there are several ways to attract external capital, actually receiving (additional) financing isn’t particularly easy for SMEs. For external funding, small firms are relying on a segmented and imperfect part of the debt and equity markets (Walker, 1989). The other part of these markets are not accessible to small firms.
The imperfections on the debt and equity markets are a result of (1) the demands about size and economic characteristics and (2) the regulatory and also financial entry barriers to more traditional corporate markets. In other words, banks are often hesitant in lending money to small firms due to limited possibilities in assessing the profitability and survivability of these type of firms (Cheng, 2005). Small firms don’t audit financial statements most of the time, which means that banks could face relatively high fixed costs and having to gather information about the firm by themselves. There arises a situation in which both parties have to cope with two agency problems, namely moral hazard and adverse selection.
Moral hazard arises when the entrepreneur of a small firms undertakes actions that might have a different value to the entrepreneur than to the investor and these actions are unobservable for the investor as well (Hechavarría, Matthews and Reynolds, 2016). Adverse selection arises when the entrepreneur of a small firm possesses more information about future prospects and the current operations than the investor does (information asymmetry) (Hechavarriá, Matthews and Reynolds, 2016).
9 Based on these findings of previous work, the following hypothesis is formulated for current paper: ‘Hypothesis 2: Having access to higher amounts of external funding has a positive effect on SME survival'.
2.3. Firm size and SME survival
Firm size is used repeatedly as an indicator that affects the survival chances of enterprises. Increasing the size of an enterprise is found to be important in remaining active in the
competitive environment (Bercovitz and Mitchell, 2007). In fact, previous research on survival shows that larger enterprises remain in business longer in comparison to their smaller
counterparts (Bercovits and Mitchell, 2017; Fackler et al., 2013) due to their ability to acquire larger amounts of resources (Watson, 2007). Watson (2007) adds that larger enterprises make use of the larger amounts of resources through seizing opportunities that increase the likelihood of survival. Making use of resources helps enterprises in becoming more specialized and
professionalized (Jones, 2012) as job descriptions become more focused on certain tasks in comparison to everybody doing all sorts of tasks. This is the situation in which hierarchy becomes more clear and business becomes more structured.
Gupta, Barzotto and Khorasgani (2018) argue that small enterprises encounter various problems due to their size. Problems such as competing for labor, meeting government requirements and raising capital are very common in small sized enterprises. The most common problems
originate from raising capital to develop the enterprise and acquiring legitimacy from the stakeholders and are referred to as the liability of newness (Fackler et al, 2013).
The problem with raising capital originates from the fact that investor parties are confronted with situations in which adverse selection and moral hazard could come about (Cheng, 2005). SMEs often possess limited information that can be shared with the investors which makes the company less transparent. As a result of the opaque or asymmetry in
10 capital become more accessible. Greater amounts of resources help enterprises to increase productivity and possibilities to reinvest to develop the enterprise.
These stated problems arise and are consequences of the liability of smallness (Kale and Arditi, 1998). The liability of smallness emerges from the lack of financial resources and lack of strong support from creditors (Kale and Arditi, 1998; Aldrich and Auster, 1986). Overcoming these problems has to do generating revenue, capital and generating growth (Audretsch et al, 2000). Audretsch et al. (2000) found that this notion about increasing firm size also holds in the Dutch manufacturing industries.
Although there previous studies developed a strong argument about increasing firm size in order to be granted legitimacy, there is also a growing literature stand that argues that small enterprises can successfully commercialize disruptive technologies at expense of larger
counterparts (Carayannopoulos, 2009). This argument is becoming more meaningful due to findings about greater exploring and learning capabilities from small firms in comparison to larger ones (learning advantages of newness) and the greater flexibility to adapt in volatile competitive environments (Autio et al., 2001; Fiegenbaum and Karnani, 1991).
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2.4. Conceptual model
Figure 1 presents the conceptual model of this study. The arrows show the relationships between the independent variables (Financial Performance, Access to External Capital, Firm Size) and the dependent variable (SME survival). The +represents the expected outcomes of the proposed relationships. The H1, H2 and H3 indicators show which hypothesis is focused on which relationship (see theoretical foundation). The control variables (Industry dummy
variables, Year dummy variables and Firm Age) that are included when running analysis are presented below the independent variable (Swaen, 2018).
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3. Methodology
3.1. Data collection
The data that is used is existing quantitative data that is acquired through Orbis.
Orbis is a database that contains company information from more than 360 million companies around the world and was founded by Bureau van Dijk. Bureau van Dijk is part of Moody Analytics since 2019, which is a
leading enterprise when it comes to financial tools and analytic
intelligence (Moody Analytics, 2020
).
The dataset that has been used comprises 9.650 SMEs from which all of them are or were active in the Netherlands. The included firms had to satisfy the following
conditions: they fulfill the staff headcount condition (i.e. SMEs are enterprises with a staff headcount between 10 and 250 employees) formulated by the European
Commission (2017), they are or were active in the Netherlands and the enterprises are active in manufacturing industries (i.e. all manufacturing industries, NACE Rev. 2: 9-17 and 19-32). Graph 1 (on the right) presents the
distribution based on size and shows us that most of the enterprises in the sample are small-sized enterprises (0 to 50 employees). 2161 enterprises are excluded from the sample since there wasn’t firm size data available from for these enterprises. For this reason, the sample is based on 7689
enterprises instead of 9650 enterprises. There are 20 corporates included in the sample as these enterprises managed to become an corporate between 2011 and 2017.
Graph 1: Distribution based on size classification
Graph 2: Distribution based on Enterprise Status
67% 11%0%
22%
Distribution based on firm
size
Small-sized Enterprises Medium-sized Enterprises Corporates No firm size available
95% 5%
Distribution based on status of
included enterprises
13 Since the database is used to run survival analysis, it seems useful to present the
distribution based on the current status. Graph 2 shows how the different type of statuses are distribute across the SMEs. Graph 2 and table 1 only present the status Active and status
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Status
NACE-code 2 digits Active Dissolved Frequency Percentage Cumulative
10 - Manufacture of food products 883 53 936 9,70 9,70
11 - Manufacture of beverages 51 5 56 0,58 10,28
12 - Manufacture of tabacco products 5 0 5 0,05 10,33
13 - Manufacture of textiles 193 9 202 2,09 12,42
14 - Manufacture of wearing apparel 90 10 100 1,04 13,46
15 - Manufacture of leather and related products 59 7 66 0,68 14,15
16 - Manufacture of wood and products of wood or
cork 406 15 421 4,36 18,51
17 - Manufacture of paper and paper products 147 12 159 1,65 20,16
19 - Manufacture of coke and refined petroleum
products 19 1 20 0,21 20,36
20 - Manufacture of chemicals and chemical products 361 16 377 3,91 24,27
21 - Manufacture of basic pharmaceutical products
and pharmaceutical preparations 103 3 106 1,10 25,37
22 - Manufacture of rubber and plastic products 513 31 544 5,64 31,01
23 - Manufacture of other non-metallic mineral
products 339 23 362 3,75 34,76
24- Manufacture of basic metals 154 9 163 1,69 36,45
25 - Manufacture of fabricated metal products, except
machinery and equipment 2.278 106 2.384 24,70 61,15
26 - Manufacture of computer, electronic and optical
products 415 27 442 4,58 65,73
27 - Manufacture of electrical equipment 308 14 322 3,34 69,07
28 - Manufacture of machinery and equipment nec 1.269 67 1.336 13,84 82,91
29 - Manufacture of motor vehicles, trailers and
semi-trailers 210 14 224 2,32 85,23
30 - Manufacture of other transport equipment 270 20 290 3,01 88,24
31 - Manufacture of furniture 494 32 526 5,45 93,69
32 - Other manufacturing 566 43 609 6,31 100,00
Total 9.133 517 9.650 100
15 The presented numbers in table 1 don’t correspond with the numbers of dissolved enterprises that are noted by CBS (CBS, 2020). CBS state that between 7.000 and 12.000 enterprises (number fluctuates over the years under observation) go out of business in the Dutch manufacturing industries. The numbers of table 1 are hard to compare with these industries numbers since there aren’t possibilities to filter on SMEs and many enterprises are excluded due to lack of data.
In order to obtain a sample, three steps were taken before the data analysis started. The first step was to create the sample itself, the second step was to determine which variables should be included into the database and the last step was used to check for data availability. Table 2 is used to provide the definitions for the independent variables, table 3 presents some basic descriptive statistics of the included variables and table 4 provides the Pearson’s correlations coefficients. The Pearson’s correlations coefficients are included to demonstrate that
multicollinearity among variables does not hinder our results and that the correlation between the independent variables is low in general.
Variable name: Measured through:
Definition
subpart(s) Formula(s):
Financial Management Solvency Ratio
Solvency Ratio is an indicator that determines
whether the cashflow is sufficient to meet both
short- and long-term liabilities
Solvency Ratio = Net Income +
Non-Cash Expenses / Short-term Liabilities +
Long-term Liabilities
Access to external
capital Long term debts
Form of immidiate capital that matures in more
than one year
n/a
Firm Size Number of
employees
The amount of employees that are working for an
enterprise
n/a
16 Table 3 is used to present some descriptive statistics on the independent variables. The median of the Access to External Capital- variable tells us that less than half of the sample has access to external capital. The corresponding standard deviation shows that there is a lot of variance which means that are probably some companies with access to large amounts of external capital.
Another interesting statistic from table 3 is about the firm size. The median of firm size is 11, but the mean is 23,58. This tells us that there is probably a small portion of large SMEs (SMEs with between 150 and 250 employees) that make the mean differ from the median. The mean and median are both quiet low which tells us that there are more small-sized enterprises (10- 50 employees) included compared to medium-sized enterprises (50- 250 employees).
Rectype SolvencyRatio AccessToCapital FirmSize
Rectype 1,0000 SolvencyRatio -0,9900 1,0000 0,3302 AccessToCapital -0,0030 -0,0156 1,0000 0,7694 0,1261 FirmSize -0,0277* 0,0487* 0,0371* 1,0000 0,0066 0,0000 0,0003
Table 4 presents the correlation coefficients on the linear relationships between the independent variables and dependent variable as well as the linear relationships between the independent variables. Most of the relationships (the negative ones) represent a weak downhill linear
Descriptive Statistics Variable name: Name of subpart(s): Mean Std. Dev. Median Financial Management Solvency Ratio 36,55 36,37 39,66 Access to External Capital Long term debts 2.586,28 80.972,04 0
Firm Size Number of employees 23,58 37,44 11
Table 4. Correlation matrix
17 relationship. The relationships between firm size and the other two independent variables show a weak uphill linear relationship (Dummies, 2020).
3.2. Measures
Financial performance. The measure for financial performance is obtained from the study of Baumöhl, Iwasaki and Kočenda (2019). In their study about the impact of institutional quality on firm survival the following three measures are used: ROA (Return on Assets),
solvency ratio and gross margin. Unfortunately, ROA and gross margin had to be excluded from current study due to limited data availability from Orbis. Therefore, solvency ratio is used to assess the financial performance of the enterprises in the database.
Measuring the financial performance is necessary to determine the soundness and positioning in comparison to other enterprises in the niche or market (Voulgaris et al., 2000). While there are financial indicators that can determine the financial performance of an
enterprise, current paper uses the solvency ratio to assess the financial situation of enterprises. The reasons why I chose for this measure instead of others has to do with the empirical
evidence and the manufacturing enterprises in the sample. Manufacturing industries are
associated with relatively high costs due to productive resources and processes (Sanz-Lobera et al., 2015) and the stated measure takes costs into account. Therefore, I found it useful to explain the financial performance through this particular measure.
The measure of financial performance has to be interpreted in the following manner:
- The higher the solvency ratio, the more easy it is for the enterprise to pay its debtors (Investopedia, 2019a).
Year N Mean Variance Std. Dev. Min. Max. p5 p95
2017 8.921 39,1 1.209,19 34,77 -99,41 100 -25 89 2016 9.636 36,67 1.319,77 36,33 -99,84 100 -33 88 2015 8.830 37,05 1.193,42 34,55 -99,88 100 -25 88 2014 8.482 35,82 1.204,69 34,71 -99 100 -25 87 2013 8.126 35,17 1.205,05 34,71 -99,66 100 -26 87 2012 7.806 35,31 1.164,98 34,13 -99,19 100 -25 87 2011 7.498 34,94 1.124,09 33,53 -98,39 100 -22 87
18 Table 5 presents the descriptive statistics for solvency ratio, looking at the different years under observation. The only remarkable findings in the table are the number of observations in 2016 and the mean of 2017. Both of these statistics turn out to be a bit higher in comparison to the other years under observation. The standard deviation is also changed for 2016, probably as a result of the higher amount of observations.
Another interesting statistic on solvency ratio is the minimum. A negative solvency ratio tells us that the top part of the formula (net income – tax income + non-cash expenses) which is used to calculate the solvency ratio is a negative number (Investopedia, 2019a). This
suggests that these enterprises are responsible for lacking financial performance.
Access to External Capital. Access to External Capital is measured through the amount of long term debt that a SME received over the last five years. By comparing these amounts of external capital to amounts of external capital that are received by SMEs that failed it might be possible to draw conclusions about whether Access to External Capital increases the SME survival chances.
The obvious statistics in the table above are the variance, maximum and 95th percentile statistic
for all the years. The 95th percentile shows that 95 percent of the database has access to less
than 1 million euro. The remaining five percent creates a lot of variance as the maximum of the different years is between the 3.7 and 6.55 billion euros. These enterprises are large SMEs or subsidiaries of corporates (from which the access to external capital is stated).
Year N Mean Variance Std. Dev. Min. Max. p5 p95
2017 9650 2.642,19 1,00E+10 100.193,50 -10,35 6.555.000 0 1.120,00 2016 9650 2.586,22 6,56E+09 80.972,04 -11,57 4.743.690 0 1.197,27 2015 9650 1.856,06 3,86E+09 62.106,58 -29,26 4.273.001 0 1.100,00 2014 9650 1.726,29 3,56E+09 59.626,60 -22,5 3.944.201 0 1.035,79 2013 9650 1.795,50 4,04E+09 63.568,79 -486,44 5.000.000 0 1.006,44 2012 9650 1.749,97 2,99E+09 54.648,97 -488,94 3.700.000 0 933,58 2011 9650 1.527,17 3,96E+09 62.951,86 -9,53 5.340.000 0 964,44
19 Firm Size. The measure for Firm Size is the amount of employees active at the
enterprise. Based on the amount of employees it’s possible to determine the size classification of the enterprise. The size classification of the European Commission (2017) is used to determine whether an enterprise is a micro (< 10 headcount), small (10 ≥ < 50 headcount) or medium-sized (50 ≥ < 250 headcount) enterprise (the three size classes that are included in the SME definition).
Table 7 above shows the descriptive statistics for the years when the enterprises have been under observation. The maximum of Firm Size can be seen as remarkable as the maximum statistic for all the years show a firm size above 250. The reason for this is that several enterprises included in current study managed to become a corporate is terms of firm size. Nevertheless, these enterprises had a firm size below 250 for three years or more during the time under observation. The mean of firm size is on the low side which tells that there are many small enterprises included in the dataset.
Control variable. Industry dummy variables, year dummy variables and firm age are used as control variables. This means that dummy variables are generated for all manufacturing industries and all years that are included when running the Cox proportional hazards model. In doing so, I try to include industry dynamics and take industry differences into account in order to obtain more validity in results. Year dummies are included to take the differences between years into account when running analysis.
In addition to the industry and year dummy variables, Firm Age is included when running analysis. It seemed logical to include this variable as Firm Age is used a as determinant of survival in previous work and could , therefore, possibly influence the results of the Cox proportional hazards model.
Year N Mean Variance Std. Dev. Min. Max. p5 p95
2017 7.442 26,62 1.511,68 38,88 1 483 1 107 2016 9.612 23,25 1.305,81 36,14 1 349 1 94 2015 8.100 24,5 1.314,85 36,26 1 347 1 97 2014 7.877 24,21 1.247,02 35,31 1 294 1 99 2013 7.625 23,99 1.251,09 35,37 1 474 1 97 2012 7.791 23,44 1.224,21 34,99 1 491 1 95 2011 7.426 23,56 1243,31 35,26 1 497 1 92
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3.2.1. Kaplan-Meier curves
In order to determine the probability of survival past time, the estimator of Kaplan and Meier (1958) has been used. This estimator is a form of nonparametric survival analysis as there aren’t any assumptions made about what the outcome of the analysis is going to be and it follows the philosophy of letting the dataset speak for itself (Cleves et al, 2010). When a dataset contains of observed failure times, the Kaplan-Meier estimate at any time t is given by
𝑆̂(𝑡) = ∏
(
𝑛
𝑗−𝑑
𝑗𝑛
𝑗)
𝑗|𝑡𝑗≤𝑡 (1)
In the formula, nj represents the number of cases at risk at time tj and dj represents the amount (in numbers) of failure at time tj. The Kaplan-Meier curves for each of the covariates (independent variables) are estimated and can be found in appendix 1. The curves are somewhat hard to interpret as the amount of failures on the low side, but it is possible to interpret them. The Kaplan-Meier curves show small positive effects in favor of our hypotheses. Based on these results, I expected that the results of the Cox proportional hazards model would be positive as well.
For most of the enterprises it can be said that censoring takes place. Censoring arises when the failure event takes place and there is no observation at that particular time (Cleves, 2012). In current study, the only type of censoring that takes place is right-censuring as current study used a prespecified period of time between 2011 and 2018 as the follow-up period. This means that I don’t know whether the enterprise failed afterwards (between 2018 and 2020) or is still active. Left-censoring and interval-censoring doesn’t take place as failure events before observation are excluded and the failure event is marked by the statusdate variable that is included in the database. This variable states the date at which an enterprise is declared to be out of business.
3.3. Data analysis
21 survival is not normally distributed across time and there are elements that could violate the robustness of the linear regression (Cleves et al., 2010).
The two kinds of survival analysis that are chosen for current paper are the Kaplan-Meier curves (nonparametric analysis) and the Cox proportional hazards model (semiparametric
model). The Kaplan-Meier curves have been introduced in the previous paragraph and can be found in appendix 1.
3.3.1. The Cox proportional hazards model
The Cox proportional hazards model (1972) or Cox model is a popular semiparametric survival model that estimates of various factors on SME survival. The model is commonly used as survival analysis and one of the main reasons for this is that baseline hazard can be remained unestimated. Unestimated means that the model doesn’t make assumptions about how the hazard shapes over time, which is particularly helpful when it this it is hard to make predictions for instance due to lack of prior research. This is also the case for current research as the Dutch manufacturing industries received little attention (only in the work of Cefis and Marsili) in the previous years.
The Cox proportional hazards model makes use of the following formula to assert the hazard rate:
ℎ(𝑡|𝑥
𝑗) = ℎ
0(𝑡) exp(𝑥
𝑗𝛽
𝑥)
(2)the formula is expressed for the jth subject from which the
ßx
should be estimated based on thedata. The xj contains of a set relevant covariates (the independent variables) which determine the hazard ratio of an enterprise (Baumöhl et al, 2019).
The parameters
ß
will be presented in the results chapter in the form of a hazard ratios.
22 In addition to this, the more the hazard ratio drops below 1 the higher the probability of survival and the other way around concerning enterprise exit (for instance, firm exit is more probable for an enterprise with a hazard ratio of 1.8 in comparison to the latter case with a hazard ratio of 1.2).
4. Results
Table 8 is used to describe the measurements of the dependent variable, independent variables and control variables in a qualitative manner. The table is used to explain which variables are inserted into the analysis and how these variables are operationalized.
Variable Definition Measure
Dependent SME survival 1(Failure), 0 (Survival)
Independent Solvency Ratio Percentage change in Solvency Ratio Independent Access To Capital Access to an additional 1000 euro
Independent Firm Size Number of employees
Control Industry Different NACEcodes of industries Control Year Measure for all failures in each year under observation Control Age incorporation is before 1960 and positive after 1960) Year of incorporation untill 2020 (negative if
Table 9 presents and compares the means of SMEs that survived in comparison to SMEs that failed. Based on the table it is possible to say that SMEs that survive have on average a higher solvency ratio (36,59% compared to 32,75%), have access to
larger amounts of external capital (2609,20 thousand euros compared to 37,97 thousand euros) and have a larger firm size (23,38 employees compared to 12,36 employees ) than SMEs that didn’t survive. Finally, failed SMEs are older (33,70 years compared to 28,21 years) than the enterprises that survived. These assumptions are only first intuitions and no conclusions. The Kaplan- Meier curves for the relationships between the independent variables and dependent variable are shown in appendix 1. These curves are indicators for the expected outcomes as they let the data speak for itself.
Variable Survival Failure Solvency Ratio 36,59 32,75 Access To Capital 2609,20 37,97 Firm Size 23,38 12,36
Age 28,21 33,70
Table 8. Qualitative description of included variables
23
4.1. The Cox regression results
Table 10 of the next subsection describes sample size, the amount of failures and assesses whether the chosen Cox regression fits the data. The baseline model presented in table 11 in the second subsection contains the three determinants (Solvency Ratio, Access to capital and Firm Size) and presents the hazard ratio and corresponding significance level for each of these covariates. The coefficients are included to allow calculations for hazard ratio’s based on the coefficients. Additionally, table 12 in subsection three is included in order to test the hazard assumption and check whether the hazards are proportional or not. Finally, subsection four is used to show which robustness checks are executed.
4.1.1. Sample size and fit of the model
Table 10 starts with a presentation of the sample that is used to run the Cox proportional hazard regression model (Cox, 1972). 86 of the 9.650 enterprises included in the sample failed to remain in business and went bankrupt or is already dissolved. Due to the large sample size in comparison to the amount of failures, the Breslow method for ties is used as this approximation works well in these circumstances (Cleves, 2013). This method is more often used when amount of failures is low in comparison to the sample size.
The LR chi2 and Prob > chi2 are included in table 10 to evaluate the fit of the model. The LR chi2 value of 41,43 with a significance of 0,0033 exhibits that the model including the determinants fits better to the data than a model without the determinants. Based on these indicators it is possible to conclude that the model fits the data and therefore it is possible to interpret the hazard ratios for the determinants of SME survival and the corresponding
significance levels. The corresponding significance levels are stated in the footnote of table 10, the stars represent which significance levels are met.
Cox regression- Breslow method for ties
No. of subjects = 9.650 No. of obs. = 9.650
No. of failures = 86
Time on risk = 77083 LR chi2 = 41,43***
Log likelihood = -768,213 Prob > chi2 = 0,0033
24
4.1.2. Interpretation of hazard ratios
Table 11 is used to present the results of the Cox regression analysis. The table presents the hazard ratios with significance levels, the control variables, the number of enterprises, the log pseudolikelihood and the Wald test for the four models.
Model 1 Model 2 Model 3 Model 4
Independent variables:
Solvency Ratio 0,997 0,996 0,996
-0,78 -1,44 -1,35
Access to External Capital 0,998** 0,998** 0,998**
-2,27 -2,11 -1,98
Firm Size 0,987** 0,987**
-1,98 -2,02
Control variables:
Industry dummies: Yes Yes Yes Yes
Year dummies: Yes Yes Yes Yes
Firm age: Yes Yes Yes Yes
N 9.650 9.650 9.650 9.650
Log pseudolikelihood -778,21 -771,04 -768,210 -769,09
Wald test (X²) 0,61 6.44** 10,16** 8,56**
Note: The presented coefficients are hazard ratios. The z statistics are reported in parentheses. The Wald test examines that all coefficients are zero. ***, ** and * represent statistical significance at the 0,01, 0,05 and 0,10 levels, respectively.
Table 11 comprises four models that describe hazard ratio’s for the different independent variables. In model two, three and four there are independent variables excluded to see what the effect on the other hazard ratio’s would be. Model three is the model in which all
independent variables and control variables are included. The Wald test (X²) shows that the last three models are significant when checking for all coefficient being zero. Finally, the word ‘Yes’ indicates that control variables are included when running the analysis.
Hypothesis 1 is focused on the relationship between Solvency Ratio and SME survival. Table 11 shows that the hazard ratio of solvency ratio is 0,997. Unfortunately, the hazard ratio
25 isn’t significant according to the evaluated significance levels. This means that the hazard ratio isn’t interpretable and it isn’t possible to draw any conclusions based on the Cox regression model.
The second hypothesis assesses the relationship between Firm Size and SME survival. Firm Size is a continuous variable which changes the way the hazard ratio is interpreted. Each extra employee (an increase in Firm Size) affects the hazard of enterprise failure based on the hazard ratio. The hazard ratio of Firm Size is 0,987, which means that every extra employee decreases the hazard of enterprise failure with 1,23%. The hazard ratio of Firm Size is
significant at the level of 0,048 (p < 0,05). This finding is in line with previous studies that found that increasing the Firm Size is associated with acquiring larger amounts of resources that can be used to become more specialized and professionalized in order to ensure continuity of the SME (Watson, 2017; Jones, 2012). The finding is also consistent work on the relationship between Firm Size and survival as several studies found this relationship to be a determinant of survival (Audretsch et al, 2000; Bercovits and Mitchell, 2017; Cassar, 2014)
The last hypothesis that has been tested assesses the relationship between that Access to External Capital. Access to External Capital is included as a continuous variable. The hazard ratio of Access to External Capital is 0,998, which means that enterprise every thousand euro increases the chances of SME survival by 0,2%. The hazard ratio of Access to ExternalCapital is significant at the level of 0,035 (p < 0,05). This seems logical as more capital means more possibilities to reinvest and increase the productivity. Additionally, this finding is found to
consistent with previous work of Hechavarriá et. (2016), Pajunen & Järvinen (2017) and Cassar (2004). These researchers argued that receiving external capital reduces the incidence of
quitting over time and is most commonly used by small business owners that want to generate growth.
Concluding, the results shows that two of the three hypothesis are significant and therefore the hazard ratios could be interpreted and the findings could be related to findings of previous working. These determinants affect the survival chances of a SME and should therefore be considered in resource allocation.
26
4.1.3. Testing the proportional-hazards assumption
The tests to check the proportional-hazards assumption are a way to verify whether the model is adequately parameterized, therefore these tests are also known as model specification tests (Cleves et al, 2013). In current paper, the test based on Schoenfeld residuals is used to check the model specifications and the outcomes are presented in the table below (table 12).
chi2 Df Prob>chi2
global test 4,85 3 0,183
The chi2 and Prob>chi2 from table 12 shows that the hazards aren’t proportional for the Cox regression model. For this reason, the null-hypothesis can’t be rejected which is a good outcome as this tells that the model is correctly specified.
4.1.4. Robustness checks
In order to verify the validity of the Cox regression results, two robustness checks have been executed. The results of these robustness checks can be found in appendix 2. The first robustness check compares the results of smaller with the results of larger enterprises (more than 15 employees) and the second part of the table presents a robustness check that compares younger firms (active for less than 25 years) with older firms.
The results of the robustness checks aren’t significant (and therefore not to be
interpreted) and bring to light the limitations of current study as the data shows several flaws. The distribution of the database is skewed in a way that close to 69% of the included
enterprises has 20 or less employees and more than half of the database was founded in the last 25 years. When the database is divided in two groups, the results aren’t significant due to the fact that number of failures becomes too small in comparison to the amount of survivals. This notion is important and helps in deriving to the second limitation of current study: data availability. More about the data availability can be found in the limitations part of next chapter.
27
5. Summary and conclusion
During current study I investigated the relationship between three determinants and firm survival. An extensive database of 9.650 Dutch manufacturing SMEs observed between 2011 and 2018 has been employed to investigate how the determinants affect SME survival and how these determinants differ between surviving and failing SMEs. I used the Cox proportional hazards model to obtain the results, used control variables (Industry, Year and Age) and performed robustness checks to verify the findings.
The importance of recognizing determinants of survival is apparent from the significant amount of revenue (68%) and employment (71%) that is generated by Dutch SMEs (Het Nederlands Commité voor Ondermerschap, 2019). These types of enterprises are crucial, being able to accomplish survival in the long run affects SMEs on the individual level and stimulates the growth of local and national economies. Research on determinants of SME survival is therefore necessary in order to create risk awareness for small business owners, know-how for educational purposes and input for consultants & local and national policymakers. Long-term survival for more manufacturing SMEs could potentially increase the contribution to the employment and generated revenue of the Dutch economy.
Based on a critical analysis of previous work concerning SME survival and failure, I developed three hypotheses consisting determinants of survival. The formulated hypotheses state that Dutch manufacturing SMEs with a positive solvency ratio, that SMEs with access to external capital and that SMEs that increase their firm size are more likely to accomplish SME survival. To be more precise, The dataset acquired through Orbis is used as the sample to run the Cox proportional hazards analysis on and to test these hypotheses.
Based on the findings and conclusions that have been drawn from these findings it is possible to answer the research question. Two (firm size and access to capital) of the three variables have a small significant effect on the chances of survival of a SME, which means that it’s good to be aware of these variables as they influence the continuity of an enterprise. Increasing the firm size in times of resource availability or finding external capital are
opportunities for SMEs to accomplish survival. Investing increasing the firm size or acquiring access to external capital will lead to more trust from stakeholder parties. This is important as these stakeholder parties grant legitimacy, which is needed as stakeholders are parties who in addition have the power and ability to impact an enterprise (Carroll, 1993)
28 should be included in a different study to prove this. This is an important issue as Solvency Ratio formed the measure of financial performance in current study. Financial performance is
considered to be an important factor when it comes to guaranteeing continuity and acquiring legitimacy from the stakeholders.
Nevertheless, the findings should be retested in a setting in which the group of failures is bigger than or equal to the group of survivors. In doing so, the groups will be more
homogeneous in terms of distribution and size of the groups. This helps in finding hazard ratio’s that might be relevant in different SME business contexts.
5.1. Implications and limitations
The conclusions of current study contain several implications that can be useful for SME owners, educational institutions, business consultants and policy makers.
For managers, the results can be used as input for making decisions about searching for external resources/capital or increasing the firm size when there are resources available. These activities help in acquiring legitimacy (Cassar, 2014) and becoming specialized and
professionalized (Jones, 2012). More stakeholders become familiar with the enterprise and the network of the SME expands which could result in successful relations with current stakeholders and future cooperation’s of any sorts.
Additionally, change of strategy might be needed in circumstances in which the SME has no Access to External Capital and/or the Firm Size has been stable for a while. Firm Size and Access to External Capital can then be indicators for lacking performance and might even affect the continuity. For this reason, it might be useful to take these variables into consideration.
The implication for educational institutions, business consultants and policy makers is that the findings about Firm Size and Access to External Capital might be useful in their business context after doing a retest. Educational institutions and business consultants can make
29 Although it is possible to formulate several implications based on the Cox regression results, current study has several limitations as well. These limitations have come about as result of the data availability and Cox regression results.
The first limitation originates from the low amount of failures in comparison to sample size. A higher amount of failures would result in hazard ratios that are more reliable and maybe even generalizable across different industries or manufacturing industries in different countries. Many of the enterprises in the database were filtered out due to missing financial performance measures or missing number of employees. Data availability is harder in SME context as many of the enterprises aren’t obligated to publish financial statements. Working with a database with a better distribution (between small and medium-sized enterprises) and a better data availability would also increase reliability and generalizability.
Besides having a low amount of failures to work with, it was hard to relate the results to previous studies that investigates the same context. The last study that used Dutch
manufacturing industries as the sample was published in 2012 (Cefis and Marsili, 2012). As the last article with the same sample was published 8 years ago and was focused on innovation capabilities and exit, it is hard to compare the results to the findings of previous work. The competitive environment has changed significantly due to for instance the rise of digitalization and the last financial crisis (2007-2011). None of the previous studies have taken these developments into account as these developments didn’t occur yet.
5.2. Implications for future research
The presented results and conclusions can be seen as interesting, but might be lacking reliability due to the distribution in terms of firm size and small amounts of failures in comparison to survivors. For research purposes I suggest to do a retest with data that’s filled in better with a more realistic sample of enterprises that failed in comparison to enterprises that are still active. In doing so, the results will become more applicable to enterprises, educational institutions, policy makers and business consultants.
Doing a retest also provides the opportunity to check whether the results hold across different industries in the Netherlands or in manufacturing industries in foreign countries. These types of studies would contribute to the research literature on SME survival and helps to better understand ways through which enterprises accomplish survival.
30 field, I suggest to continue investigating about different innovation capabilities and indicators of financial performance. As mentioned earlier, SMEs have an important role in local and national economies and finding more determinants of survival helps to increase the BBP and the
31
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34
Planning of the thesis semester
Week Activity
Week 1 (3rd till 7th of February) Processing of feedback proposal
Week 2 (10th till 14th of February) Adding to theoretical foundation
Week 3 (17th till 21st of February) Adding to theoretical foundation
Week 4 (24th till 28th of February) Adding to theoretical foundation
Week 5 (2nd till 6th of March) Adjustments to the methodology
Week 6 (9th till 13th of March) Completeness check database and analyzing
database
Week 7 (16th till 20th of March) Reading about statistical tests in Stata
Week 8 (23rd till 27th of March) Studying for the exams
Week 9 (30th of March till 3rd of April) Exams
Week 10 (6th till 10th of April) Executing statistical tests
Week 11 (13th till 17th of April) Making adjustments to improve
reproducibility, validity and reliability Week 12 (20th till 24th of April) Executing statistical tests
Week 13 (27th of April till 1st of May) Reading about documentation of results
Week 14 (4th till 8th of May) Writing the results section
Week 15 (11th till 15th of May) Writing the results section
Week 16 (18th till 22nd of May) Drawing the conclusions from the results
Week 17 (25th till 29th of May) Writing the conclusions and implications and
limitations
Week 18 (1st till 5th of June) Writing implications and limitations
Week 19 (8th till 12th of June) Writing the abstract, analyzing the thesis in a
critical manner and studying for exams Week 20 (15th till 19th of June) Margin for error and studying for exams
35
Appendix
Appendix 1: Kaplan-Meier curves
37
Appendix 2: Robustness checks
Model 1 Model 2 Model 3 Model 4
Smaller Larger Younger Older
Independent
variables:
Solvency Ratio 0,998 0,99 0,999 0,993
-0,97 -0,97 -0,40 -1,47
Access to External Capital 0,999 0,997 0,998 0,998
-1,37 -1,08 -1,59 -1,35
Firm Size 0,923*** 1.001 0,98 0,991
-3,03 0,49 -1,6 -1,09
Control variables:
Industry dummies: Yes Yes Yes Yes
Year dummies: Yes Yes Yes Yes
Firm age: Yes Yes Yes Yes
N 6.645 3.005 5.060 4.590
Log pseudolikelihood -652,1852 -69,039 -451,648 -246,960
Wald test (X²) 12,59*** 2,19 5,60 5,07
