The relationship between different types
of market economies and CVC
investments and its effect on market
concentration
T.R. Winters – S2535769 Tel: +31 6 150 417 53 E-mail: t.r.winters@student.rug.nl
Supervisors: dr. Robbert Maseland & dr. Padma Rao Sahib University of Groningen
Faculty of Economics and Business Duisenberg Building – Nettelbosje 2
Abstract
Introduction
In 2017 the Chinese tech giant Huawei spent almost 14$ billion on R&D, ranking it amongst the top ten spenders worldwide, and the company has projected its budget to rise to 20$ billion the coming years (Lucas, 2018). Huawei is one of the many multinationals that spends billions of dollars on R&D in order to improve existing products, services or processes, or to invent completely new ones. According to PwC (2018), a trend of rising investments in R&D can be observed; during the last six years the CAGR of R&D investments in world's 1000 largest publicly listed corporate R&D spenders was 8,37 percent. This indicates a severe rise in the importance of innovation for firms.
A trend that might be related to the former is mean firm lifespan: while the average company lifespan on the S&P 500 index used to be 60 years in the 1960’s, this decreased to 25 years in 2000 and is expected to decrease even further to merely 12 years in 2027. (Scott D. Anthony, 2018). The report also predicts that nearly 50 percent of the current S&P 500 companies will be replaced over the next ten years. This increasingly shorter ruling of established names emphasizes the increasing importance of innovation.
The current digital disruption in every field requires them to transform their existing business models and strategy to fit the digital economy. And continuous renewal of their products or services is necessary in order to stay ahead.
Research shows that commitment to R&D is one of the key characteristics of successful, long lasting and visionary companies (Collins & Porras, 2000).
While conventional R&D was a process that was done internally, this yielded stagnant or diminishing returns for many firms. Hence a growing numbers of corporations are turning to sources of external sources of innovation such as Corporate venture capital (CVC). (Bielesch F., 2012). By combining in-house (‘make’) and external (‘buy’) innovation activities, firms are able to produce more and better innovations and thereby create competitive advantages that contributes to improving a company’s overall economic performance (Zahra & George, 2002). In 2017 CVC investments worldwide increased to a total value of more than 38 billion dollars, indicating alignment with the before mentioned fast growing worldwide R&D spending. The switch to corporate venture capital as an external source of innovation makes sense in the current digital economy where the costs for starting a venture has dropped significantly and the amount of startups has been rising strongly (Business Formation Statistics, 2018).
While CVC has been around for almost a century, interest in the investment type has been recently rising along with the amount of funding in CVC itself. The angles of research are various but an often encountered subject is the relationship between CVC investments and innovation strategies, both from the venture and the investor perspective (Chemmanur, Loutskina, & Tian, 2014) (Wadhwa, Phelps, & Kotha, 2016). Moreover, research also specifies the venture side by comparing corporate and independent venture capital in search of the optimal organization and financing structures (Fulghieri & Sevilir, 2009). Next to that, the geographical location of the venture compared to the investor and its effect on knowledge transfer is also extensively researched (Belderbos, Jacob, & Lokshin, 2018), and moreover what actual value it creates to investing firms (Dushnitsky & Lenox, 2006).
While current research has written extensively about the optimal structures between investor and investee, very little research has been done into one of the main antecedents of CVC; the ventures themselves and the climate in which there are created. Yet, given the increasing importance of innovation in today’s economy, understanding what drives the structure of innovation is key to comprehending economic dynamism. A deeper analysis is needed to uncover the influence of the local environment on the creation of ventures and the following CVC investments. Hall and Soskice (2001) developed a framework in which they analyze market environments and divide market economies into two types: Liberal market economies (LME)’s and Coordinated market economies (CME’s). Each have their own characteristics, one of them being innovation. CME’s focus on incremental innovation while the LME’s focus on radical innovation and in the latter ventures usually operate. Hence, their framework suits an analysis of contextual environment on CVC investments perfectly and will be used here. This is all the more relevant because of the potential implications of a shift towards CVC for market structures; the goal of CVC investments is often the development of new technologies, products or services for the investor, in this case the corporation. If ventures are developing innovations for their investors, competitiveness is eroded instead of stimulated. Market forces would become weaker in this case
Given the above this research focusses on the contextual environment in which startups are constructed and what influence this has on CVC investments, and in turn how this impacts market power. Hence the research question central to this study is: How does the type of market economy affect CVC investments and do these investments affect the concentration of market power?
The research question will be addressed by combining an analysis of existing literature and applying the found framework of Hall and Soskice (2001) to a dataset of CVC investments in 41 countries that range from 1995 until 2018. Although the focus of this research is on CME’s and LME’s, the dataset is not limited to solely those countries. For the influence of market power, Lerner Index will be used for the dataset of 41 countries to analyze if CVC investments correlate with market concentration.
This indicates that market concentration is influenced by CVC investments although visibility of changes in market concentration by CVC investments could be questioned.
Literature review
Innovation
Innovation has been defined in various ways but it is best grasped by the definition of Edquist (1997, p. 1) as: ‘ innovations are new creations of economic significance’, where a new creation can either be on the product or on the process side. New creations are developed through research and development, which is the process in which firms try to create new innovations (Times, 2019). More specifically, this is the ability of a firm to recombine existing technologies to generate new (technological innovations), and these recombination capabilities are a key driver of innovative performance (Yayavaram & Ahuja, 2008).
Reasons why firms engage in innovation are wide but mostly due to sustaining or creating a competitive advantage, which are critical to organizational performance and longevity (Wadhwa et al., 2016).
The classical theory of a firm is one where it tries to perform every activity in house, as this is the most economically viable option. It loses no knowledge (and its competitive advantage) due to outsourcing to other players in the value chain, thereby minimizing transaction costs. With high transaction costs, outsourcing is less viable and economies of scale can best be achieved by incorporating as much of the value chain as possible.
Globalization and the technological revolution of the 21st century caused serious disruption to the traditional firm, digitalization significantly lowered transaction costs, substantial reductions in product life cycles and outsourcing parts of the value chain became feasible due to enhanced governance possibilities (Fenwick & Vermeulen, 2015).
With these developments it also became possible for ventures to establish themselves quicker but mainly to grow at a much higher pace, creating actual competition for established firms in short amounts of time. With this higher pace of development and increased competition, innovation became more important for existing firms to maintain their competitive advantage. This is causing firms to search for innovation possibilities outside of traditional internal R&D and shifting to a more open R&D structure where a structure of in house innovation (make) is combined with external sources of innovation (buy) such as: licensing, alliances and technology agreements (Hagedoorn, 1993). The decrease in transaction cost and thereby increasing governance possibilities have made this possible and result in a higher innovation performance for companies combining make and buy innovation possibilities (Berchicci, 2013). A corporate venture capital program is a ‘hybrid’ model that combines features of corporate R&D and venture backed startups, which could be the best way to motivate innovation (J. Lerner, 2012). Corporate Venture Capital
Firms see the establishment of CVC subsidiaries as a very effective way of organizing R&D externally thereby exposing their management to new technologies and bringing an entrepreneurial way of thinking into the company (Chesbrough, 2002) (MacMillan et al., 2008). The result of this is that firms, which have CVC subsidiaries, enjoy higher innovation productivity and higher firm value overall (Dushnitsky & Lenox, 2006). On the other end of the spectrum it creates value as well; Chemmanur et al. (2014) prove that CVC-backed firms are more innovative compared to independent venture capital backed firms. Moreover, recent research by Anokhin, Wincent, and Oghazi (2016) shows that CVC investments which constitute strategic benefits for the corporate parent and in which the target venture has access to the technological resources of the parent firm, result in the possibility to take advantage of the increased innovation resources and improve scale efficiency yields.
Institutional theory and Market economies
Institutional theory emphasizes the power that systems have in shaping social and organizational behavior of the actors in the environment (Scott, 1995). Firms seek legitimacy in the institutional environment because this helps ensure organizational survival and success (DiMaggio & Powell, 1983). Therefore, being aware of the type of institutional environment a firm is in and its accompanying characteristics can be very beneficial for success.
A lot of research has been done into typology of the institutional environment and the result of that are many conflicting variations. Amable (2003) for example, determined five types which are partially based on geography: market-based, Asian, Continental European, social- democratic, and Mediterranean. A classification based on business systems was created by Whitley (1999): fragmented, coordinated industrial district, compartmentalized, state-organized, collaborative, and highly coordinated. Also Hall and Soskice (2001) produced a two-type capitalistic framework: Liberal Market Economies (LME’s) and Coordinated Market Economies (CME’s).
While all three typology models incorporate differences in innovation in their distinction of markets, this is only a simple divergence of incremental versus radical innovation. Research linking institutional differences to specific types of innovation has remained surprisingly underdeveloped. Both Amable (2003) and Whitley (1999) do not use external innovation methods in their models and although this is to some extent logic as external methods of innovation were less common at the time of writing, current developments do require research to take this into account. Hall and Soskice (2001) however, have included the presence of external innovation in their model in the form of venture capital. It is briefly touched upon several times but does not specify Corporate Venture Capital, a type that requires further analysis with an angle of institutional theory.
Despite the missing of CVC, the model of Hall and Soskice (2001) does provide the best basis for further analysis of CVC investments, its antecedents and its effects. A further explanation of the model can be read below.
They developed a firm centered framework where they try to explain the institutional differences and similarities across capitalist economies.
The firms interact with each other and other actors but the type of interaction is the main distinguishment in the type of market economy. All these interactions can be seen as relationships in which the firm encounters coordination problems which will hinder it from exploiting core competences or dynamic capabilities. This means that its success depends on the ability to overcome the coordination problems with all other actors. However, some firms with specific characteristics will perform better in either a liberal or coordinated environment due to complementarities with other actors.
While Hall and Soskice (2001) do recognize the value of existing theories of geographic distribution of production like Krugman (1991) describes, they argue that this does not specify enough which type of production should be concentrated in what nation.
That is why the framework incorporates coherence in institutions and institutional complementarities and with that, institutional comparative advantages;
Coherence in the sense that different institutional actors in a market type follow the same market logic, either coordinated or liberal. The institutional structure of that market type will provide it with advantages for specific kinds of activities. With institutional coherence present, complementarities will arise; a synergy for different institutional actors due to them all following the same market type and thereby reinforcing each other and generating increasing returns. The coherence in market logic and complementarities in market type will give countries comparative advantages over others as they will be able to perform certain activities more efficiently than others.
The framework divides two types of market economies based on degree of strategic or market coordination; Liberal Market Economies (LME’s) and Coordinated Market Economies (CME’s). A firm’s success depends on the ability to coordinate its relationships with both with internal and external actors.
Liberal Market Economies (LME’s)
Firms coordinate with other actors primarily through competitive markets, they coordinate business transactions through formal contracting. Relationships between firms are short-term focused and characterized by low trust. The financial system is market driven, equity-based and based on short term success. Labor markets are flexible, there is low trust between employers and employee; it relies on numerical flexibility. There are few networks and alliances among firms and the decision making inside firms is management driven and top-down.
Coordinated Market Economies (CME’s)
Firms coordinate with other actors through processes of (informal) strategic interaction, they coordinate business transactions through non-market relationships. These relationships between firms are long-term focused and characterized by high trust. The financial system is bank driven, credit-based and based on long term success. Labor markets are strong and based on employment protection; there is high trust between employers and employee.
Above institutional differences encourage differences in investment and utilization of assets. While LME’s focus more on transferable assets, which are not bound to long-term commitments, CME’s focus on relational assets, of which the value is dependent on that specific relationship.
Varieties of capitalism and innovation
Following the above logic, it makes sense that different types of market economies will result in and stimulate, different types of innovation. Radical innovation is characterized by completely new products or product lines or substantial changes in products or product lines. Incremental innovation on the other hand, is characterized by small scale improvements to existing products or product lines.
LME’s are more short-term oriented and are characterized by low trust between employer and employee, this results in radical innovation where the focus is more on developing something new than improving existing products or processes, considering this results in short-term high yields. Flexible employment markets complement this process by giving the employer the possibility to hire people when developing new products or processes and lay them off when the process has been completed.
CME’s on the contrary are focused on the long term and characterized by high trust between employer and employee, this results in incremental innovation where the focus is on improving existing products or services, considering this will result in a long-lasting success for the company. Competitive advantages from companies in these economies come from fine-tuning and continuously improving existing products or processes. Well-protected labor markets complement this process by ensuring security of labor for employees and thereby promoting a sense of working for the advancement of the company instead of one’s personal career. Given the fact that different types of market economies produce different types of innovation, LME’s are more likely to produce ventures because these are often focused on radical innovation and feature (highly) innovative technologies and/or business models (Kollmann, Stöckmann, Linstaedt, & Kensbock, 2015 ). Research has even proven that a startup will perform best when it implements radical innovation in its entire business model while still in the nascent phase (Hui & Qing-xi, 2006).
Varieties of capitalism and CVC
sector by promoting it through public programs and the creation of a special stock exchange. This would indicate a shift in paradigm which makes sense as more radical innovation in some sectors can be beneficial but the effects were rather more marginal than transformative (Vitols, 2004). As do ‘common’ venture capital investments confirm, where investments in the US are more than eleven times higher than in Germany (OECD, 2017). So despite the (unintended) marginal steps away from their base market type, the countries still seem to fall into the typology of LME and CME.
Concluding from the above; the general higher presence of ventures in LME’s gives corporates a larger pool to invest in. Next to that, LME’s have a focus on radical and disruptive innovation due to institutional alignment, both from the corporate and venture perspective, this will result in complementarities and synergies for both sides. Combining the higher presence of ventures and the focus on radical innovation, it is logic to assume that in an LME, there will be more Corporate Venture Capital investments. Hence my first hypothesis is:
H1: LME’s will have seen a relatively higher increase in Corporate Venture Capital investments than CME’s
Market Concentration
In perfect competition, there would be not entry barriers for new entrants, all firms would have an equal market share and hence market power would be evenly distributed among all firms, giving them no power to influence prices. However, in reality, perfect competition does not exist and market share is not divided equally among all firms. Imperfect competition provides firms with different amounts of market share and gives them unequal power to influence prices. Hence the statistic of market share, which is a function of the number of firms in an industry and their share of total production, is important to measure competition in that sector (Bain, 1954).
Even though perfect competition is only theoretically possible, we try to get as close as possible by imposing laws and regulations that foster competition. Antitrust laws are created to prevent alliances, acquisitions or any other price agreements that substantially lessen competition or will promote a monopoly (Stillman, 1983). On the other hand, entry barriers are lowered to create a level playing field and give new entrants a fair chance of gaining market share, thereby increasing competition. The resulting competition highly fosters efficiency which is beneficial for prices and thus consumers (Casu & Girardone, 2006).
Any form of alliance which in this case will result in more market power for the firms, involved relies on long-term relationships and high trust (Aulakh, Kotabe, & Sahay, 1996). Hence it is logic to assume that CME’s, which are characterized by these types of relationships, will have a higher amount of market concentration.
Contrasting, LME’s are characterized by short-term relationships and low trust, here exchange of goods and services are characterized by a context of competition and formal contracting (Hall & Soskice, 2001). The consequence is a relatively lower amounts of market concentration. This is reinforced by the higher amounts of ventures in those economies, which result in more competition. However, as hypothesis one predicts, LME’s have more ventures and consequently there will also be more CVC investments.
As CVC investments are often made with a strategic intention, these new ventures are no longer competing with large corporations but instead are operating in line with the investor’s goals. The result is that market power becomes more concentrated towards these large corporations instead of defused, due to more intense competition. Hence hypothesis 2a is:
H2a: CVC will lead to an increase in market concentration
But moreover, because CVC is more prominent in LME’s, these countries are losing their high degree of competitiveness due to a relatively stronger rise in market concentration compared to CME’s, in which CVC investments are less prominent. Hence the second hypothesis is:
H2b: LME’s have seen a relative increase in market concentration due to CVC investments.
Data
Sample and Data
To explore the relationship between the type of market economy, CVC investments and market concentration I constructed a large panel of 41 countries, for which I collected financial data over the period of 1995 until 2018, or the maximum time range available. The panel was constructed in the following way: all countries from the OECD were selected, given that data on these is widely available and as the ‘Organization for Economic Cooperation and Development’ mostly involves high income countries, or at least countries which are trying to develop themselves in the economic sense by collaboration. As by logic, economic prosperity (higher GDP per capita) results in a better ability to start (technology intensive) ventures which will again result in higher amounts of CVC investments, while lower GDP will result in the opposite. Hence, to prevent omitting the influence of GDP on CVC investments, it makes sense to select high income countries as much as possible.
To extent the sample and enlarge the possibility of significance, I added five more countries which were all selected from the top 50 GDP per capita countries in 2018, as are most OECD countries. This way the influence of GDP on CVC investments was controlled for as much as possible. Other countries in the top 50 did not have any CVC investments in the selected time period and were hence, not incorporated.
The final selected countries were: Australia, Austria, Belgium, Canada, Chile, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, Iceland, Ireland, Israel, Italy, Japan, Kuwait, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Saudi Arabia, Singapore, Slovak Republic, Slovenia, South Korea, Spain, Sweden, Switzerland, Turkey, UK, United Arab Emirates, United States. Of these countries the following were selected as Liberal Market Economies and Coordinated Market Economies, as stated by Hall and Soskice (2001):
Liberal Market Economies: Australia, Canada, Ireland, New Zealand, United Kingdom,
United States
Coordinated Market Economies: Austria, Belgium, Denmark, Finland, Germany, Iceland,
Japan, Luxembourg, Netherlands, Norway, Sweden and Switzerland.
Belgium and Luxembourg were classified as CME’s but were not included in the framework due to their small size. However, because they are actually CME’s, I have included them in the selection.
Their selection was based on the OECD countries at that time and only market economies were included. Meaning that all the OECD countries, which are not included in either the LME or CME type, did not fit either one of the descriptions and have different characteristics.
France, Italy, Spain, Portugal, Greece, and Turkey for example do not fall under one of the market types but are could be characterized as ‘Mediterranean’ capitalistic type. Those countries have large agrarian sectors and have a history of strong government intervention which left the corporate finance sector controlled by more non-market coordination while labor relations have a more liberal approach.
Post socialist countries in Eastern Europe such as the Czech Republic, Hungary, Poland, and the Slovak Republic have introduced a different form of capitalism which is called ‘Dependent Market Economy’. They are characterized by the importance of foreign capital for the socioeconomic setup and comparative advantages in the assembly and production of relatively complex and durable consumer goods (Nölke & Vliegenthart, 2009).
Due to the vast differences in countries in this sample, not all countries can be categorized in a type which is related to the Varieties of Capitalism model. Other models do characterize them but since I’m researching the original model, I will only be specifying the ‘original’ LME’s and CME’s. Other countries were included in the sample to enlarge it and its possible significance, but are not specifically researched.
Dependent variable
The main dependent variable in this research is market concentration, or monopoly power. To measure this, I adopted the Lerner index as a measure for market concentration. The Lerner index measures ‘the degree of monopoly’ in a given market or country with the difference between a firm’s price and its marginal cost at the profit-maximizing rate of output (A. Lerner, 1934). The index is defined by the formula 𝐿 =#$%&' , where P is the market price set by the firm and MC is the firm’s marginal costs. The index ranges from 0 to 1 where a higher score implies more market concentration.
Although the Lerner index is not the only indicator which calculates and describes market concentration, and it is less commonly used than for example the Herfindahl-Hirschman-index, it is proven to be a good indicator of monopoly power (Feinberg, 1980).
Independent variable
In this research the variable CVC investments acts as both a dependent (in hypothesis 1) and an independent variable (in hypothesis 2a and 2b), however for clarification I will extent on it in this section. A prominent source of CVC data is the Thomson Financial's VentureXpert database, which combines information from industry associations and the investment banking community and is used by research such as Wadhwa et al. (2016) and Anokhin et al. (2016). However, for a country comparison this proves less useful as it provides firm data, which would mean unnecessary complicated calculations and possibilities for errors.
Following the venture reports of KPMG (Fortnum, Hughes, & Speier, 2016), I selected the only database which provides country data on CVC investments: CBinsights. The strength of this database is first of all that it provides a comprehensive overview of all CVC deals in a certain country during time period X, but moreover, it is backed by both the National Science Foundation and venture capital investors. This gives it the advantage of having a public and private angle and with that resources, thereby creating the most complete CVC database. The platform collects information from patents, venture capital financings, M&A transactions, market sizings, startup and investor websites, news sentiment, etc. and uses machine learning and AI to create its database.
The time period for CVC investments ranges from 01/01/1995 until 31/12/2018 for all countries in the sample.
Robustness
To check for the robustness of the model and by that I mainly mean the correctness of the LME and CME indicators, I incorporated several indicators for both market types. The indicators used are a labor protection indicator and a long vs short orientation indicator.
The labor protection variable is built op from multiple data items concerning regulations for individual and collective dismissals. Together they form a score that ranges from 0 to 5 on which a higher score indicates a more protected labor market. The dataset was drawn from the OECD database and includes 35 countries that have a maximum time range from 1990 until 2013 (the data availability differs per country).
Results
In table 1a-c I present the descriptions, summary statistics and the pairwise correlation matrix for all the variables. As we see in the first row of table 1b, where the average for CVC investments is shown, the average investment per country per year is 388 million dollars and the standard deviation is 2690 million dollars. This extremely large SD is caused by CVC investments in the US, in 2018 their CVC investments were almost a hundred times the average investments of all other countries in the sample. The extreme values in CVC investments in the US indicate the presence of outliers, hence for increasing the significance of the model, the US was dropped in some parts of the analysis. The minimal value is 0; for several countries CVC investments were not present in certain years and for all countries, except the US, CVC investments did not start in 1995 but some years later.
The long vs short variable has 912 observations, for all years the same value was entered for that country. Three countries did not have data on that variable, hence they were excluded.
Variable Description
CVC The amount of CVC investments (M$) - Range: infinite Marketcon Index for market concentration - Range: 0-1
CVCGDP The amount of CVC investments as a percentage of GDP LogCVC Log of CVC investments (M$)
LogMarketcon Log of Market concentration
Laborprotec Index for Labor protection - Range: 0-5
Longvsshort The index for Long or Short orientation - Range: 0-100
LME When LME = 1
CME When CME = 1
LMECME When LME =0, when CME=1
Table 1a: Variable descriptions
Variable Observations Mean Std. Deviation Min Max
CVC 984 388,7024 2690,148 0 42500 Marketcon 748 0,2499 0,1491 0 0,94 CVCGDP 943 0,0001136 0,000476 0 0,0091 LogCVC 410 8,571897 2,245416 2,772589 15,26 LogMarketcon 744 3,045996 0,6377 0 4,54 Laborprotec 573 2,153069 0,7595897 0,25666 4,58 Longvsshort 912 51,73684 20,5394 21 100 LME 984 0,1463 0,35628 0 1 CME 984 0,29268 0,455 0 1 LMECME 432 0,66666 0,4719 0 1
Pairwise correlation 1 2 3 4 5 6 7 8 9 10 1. CVC 1 2. Marketcon 0,0085 1 3. CVCGDP 0,3739* 0,0962* 1 4. LogCVC 0,5316* -0,0101 0,4567* 1 5. LogMarketcon 0,0378 0,8989* 0,0908 0,0439 1 6. Laborprotec -0,3538 * -0,122* -0,3077 * -0,5515 * -0,1293 * 1 7. Longvsshort -0,1429 * -0,0047 -0,009 -0,878 0,0432 0,2085* 1 8. LME 0,2956* -0,1134 * 0,0829 0,3508* -0,0841 -0,6902 * -0,4198* 1 9. CME -0,0751 -0,1891 * -0,241 0,0489 -0,191* 0,0788 0,2376* -0,2663 * 1 10. LMECME -0,2627 * -0,0052 -0,1361 * -0,3507 * -0,0395 0,7843* 0,5916* -1* 1* 1
Table 1c: Pairwise correlation
Country AAGR Australia 47,93% Canada 89,87% Ireland 251,51% New Zealand 931,88% United Kingdom 57,45% United States 206,42% Austria 275,48% Belgium 72,28% Denmark 74,81% Finland 169,60% Germany 59,75% Iceland -90,70% Japan 135,09% Luxembourg 476,38% Netherlands 254,95% Norway 147,07% Sweden 159,95% Switzerland 103,12% LME 169,42% CME 146,36%
Group Observations Mean Std. Deviation LME 87 1,694177 6,346688 CME 141 1,46358 4,661085 t statistic 0,2936 Degrees of freedom 143,035 Pr(T||>|t|) 0,7695 * p<0,1 **p<0,05 ***p<0,01
Table 2b: t-test AAGR
In table 2a, I estimated Annual Average Growth Rate of CVC investments in both LME and CME countries. The formula used for AAGR is 𝐴𝐴𝐺𝑅 = (,-./0,-.140⋯0,-3) where 𝐺𝑅6 is the
growth rate in each year and 𝑁 is the number of years. Growth was calculated for each year and each country and then the mean was taken for the LME and CME grouping.
The AAGR during the time period 1995-2018 for LME’s is about 34 percent point higher than the growth for CME’s. This shows a stronger growth in CVC investments in LME’s compared to CME’s and would indicate support for hypothesis 1.
However, to establish the significance of the growth rates, I performed a t-test to compare the means, as can be seen in table 2b. The result show that the difference between the two means is not significantly different from zero. Hence we cannot interpret the growth rates of both LME’s and CME’s and cannot accept hypothesis 1.
Variance Std. Deviation Marketcon 0,022445 0,149146 e 0,0095454 0,0977007 u 0,0151976 0,1232784 Wald X2 1855,36*** * p<0,1 **p<0,05 ***p<0,01
(b) fixed (B) random (b-B) Difference
CVC -1,16E-07 -8,12E-08 -3,48E-08
Wald X2 0,00
* p<0,1 **p<0,05 ***p<0,01
Table 3b: Hausman test (Random or fixed)
In table 3a it is tested whether the panel structure of the dataset should be used or a pooled regression would be optimal here. To test this, the Breuch Pagan test was performed, its result is highly significant, thereby showing significant differences across the groups in the data and indicating the use of a panel structure.
In table 3b it is tested whether to use fixed or random effects by performing the Hausman test. The results show that the differences in coefficients is not systematic and hence the tests should be performed with random effects.
Model 1 2 3 4
Specification RE RE, Winsor RE, Trim RE Sample Full Full Full Full
CVC investment -8,12E-08 5,26E-06 0,000012*
CVC Investments/GDP 68,9501*** Observations 748 748 748 748 Countries 41 41 41 Overall R2 0,001 0,004 0,001 0,0093 Wald X2 0,00 0,90 2,72* 13,76*** * p<0,1 **p<0,05 ***p<0,01
Model 5 6 7 8 Specification RE RE RE RE Sample Without US Log LME CME
CVC investment 0,0001546*** 0,0150684*** 0,0000722 0,0002029*** CVC Investments/GDP Observations 728 261 86 226 Countries 40 34 5 12 Overall R2 0,0017 0,0001 0,062 0,0053 Wald X2 15,92*** 11,78*** 1,88 10,69*** * p<0,1 **p<0,05 ***p<0,01
Table 4b: CVC impact on market concentration (models 5-8)
In tables 4a and 4b I estimated several models using my main independent variable; CVC investments and my main dependent variable, market concentration. To search for the best fitting model, both the sample and variables were adapted in ways that would increase significance but would not affect usability and interpretability of the results. In model 1 the full sample was used with the unaltered variable CVC, this yielded an insignificant model.
As mentioned before, the US is a severe outlier in this sample with much larger CVC investments than any other country, this could result in sever disturbance in the model. Therefore, in models 2-6, various transformations were made to remove outliers or to minimize their influence and increase the fit of the coefficients and models.
To try and overcome these the sample was ‘winsorized’ in model 2. This means I transformed the largest outliers; the data below the 1st and above the 99th percentile was changed into the 1st and 99th percentile. However, this also resulted in an insignificant model and coefficient. In model 3 the sample was trimmed in order to exclude outliers; data below the 1st percentile and above the 99th percentile was removed. This resulted in a significant coefficient and model and is the first sign of an influence of CVC investments on market concentration. The data was of course trimmed so data is missing. Also the coefficient and model are only significant at the lowest significance level (90%) and the total fit of the model is very low. It is a small sign but not ideal.
In model 4 the amount of CVC investments was taken as a percentage of total GDP in that country, giving each country a more relative score and significantly reducing outliers. The results are highly significant: both the coefficient and the model are significant at the highest significance level. Also the fit of the model is high compared to the other models.
Model 6 shows the usage of the log version of CVC investments, thereby also overcoming the problem with outliers. This does however, significantly reduce the sample size as log can only be created from numbers above 0. The model did prove to be significant as well. But the fit of the model is relatively low.
In model 7 and 8 only LME and CME countries were included respectively. Model 7 was insignificant and can hence be discarded. Model 8 was significant, indicating that in the CME countries, a correlation can be found from CVC investments on market concentration. When comparing model 7 and 8, it makes sense that model 8 yields significant results: the sample size is almost three times the size of model 7, enlarging the chances of finding results.
Various models yielded significant results, indicating a correlation between market concentration and CVC investments. Transformations of the sample and variables do however, need to be taken into account when interpreting the results, that is why the preferred model is model 4. It used the full sample size and did not alter it in any way, while still accounting for outliers by using relative numbers for CVC investments. The coefficient and model are significant at the highest significance level, hence we can accept hypothesis 2a and conclude that CVC investments have some influence on market concentration. But with caution, it needs to be taken into account that the explanatory power of the model was relatively low (R2=0,0093), meaning that only a percent of the variance in market concentration is caused by the model. This would indicate the influence of CVC investments on market concentration is very small and other factors might influence it as well.
Variable Model 1 LME or CME -2233,569 constant 2308,447 Observations 432 Countries 18 Overall R2 0,069 Wald X2 1,23 * p<0,1 **p<0,05 ***p<0,01
Table 5: Impact of LME or CME on CVC
Robustness
The result of the robustness checks of the LME/CME model is shown in table 6, with the dependent variable being CVC investments. Both the independent variables labor protection and long vs short term orientation are significant at 95 and 99 percent significance level respectively. The two variables are negatively correlated with CVC investments, indicating that a country with lower labor protection and more short-term orientation, as is an LME, will have more CVC investments. This follows the theory about the type of market economy and CVC investments.
Variable Model 1 Model 2
Laborprotec -622,206 Longvsshort -19,432 constant 1611,047** 1423,523*** Observations 573 912 R2 0,1254** 0,2*** * p<0,1 **p<0,05 ***p<0,01
Discussion and Conclusion
This study researched the influence of the market economy type on corporate venture capital investments and how these investments affect the concentration of market power.
My research cannot conclude that LME’s are associated with higher growth in CVC investments than their counterpart, CME’s. This is a relatively surprising result as theory would suggest otherwise and also specific indicators which did point in the expected direction. Labor market protection is the first researched indicator, a lower amount of labor protection will result in higher CVC investments and thereby it follows the framework of Hall and Soskice (2001). LME’s are characterized by low protection of labor markets and short-term relationships, which brings us to the second indicator; long vs short term orientation. Also on this indicator, the results follow the theory by proving that a more short-term orientation, result in higher CVC investments.
The contradiction in the significant results of the individual indicators but the insignificant results in the total growth of the LME and CME countries, raises questions as to why the market type does not seem to follow its characterizing indicators. This however, could be connected to the age of the varieties of capitalism framework: it dates back to the beginning of this century. And while the typology did fit at that time, it might not be time insensitive. Due to globalization countries are changing faster than even and with that, their previously ‘set characteristics’. This means that despite a good basis for understanding the different type of market economies, the framework requires adaptation over time as the world changes and it is may not be optimal in explain time series data. Research into the validation of LME and CME indicators and which countries are currently categorized under which type, would be an interesting avenue for future research.
When addressing the second part of the research question by analyzing the influence of CVC investments on market concentration, various significant results were found in multiple models. The mediating relationship of LME’s through CVC on market concentration was not made evident but a general influence of CVC investments on market concentration was proven, which is even more widely applicable. It should be noted that the explanatory power of the model was very low, indicating that there could be other variables that explain market concentration (even more). But this makes sense as the type of investment is relatively new and (until now) makes accounts for a very small percentage of GDP in a country. But considering its potential, the results are noteworthy.
acquisition of a firm. The investments, in this case, result in the strategic alignment of the investment target with that of the investor (Chemmanur et al., 2014).
This causes a shift in market power toward the investors (in this case the corporates), which is only slightly measured and explained by the model. The fact that only a small influence was found of CVC investments on market concentration is not entirely surprising and could be much larger than measured through traditional measures such as the Lerner index.
CVC investments can result in a majority or minority share in the investments target, but as this is often not an actual merger or acquisition, the amount of firms in a market remain the same and so do the marginal costs, making the change in measurable market concentration very small of not even present.
With growing CVC investments worldwide, the result of this research becomes more important as well. The slight effect of CVC investments on market concentration and its accompanying model might very well be connected to the very small role it plays in comparison to the total economy of a given country. But as these investments grow faster every year, so does their role in the economy and with that influence in market concentration. Possibly, without being measurable.
The theoretical relevance of this research lies mainly in the expansion of research into CVC by using an existing framework. The expansion is actually two sided, in the first place the framework itself is tested on a relatively new type of investment, creating the opportunity to check if the framework holds its relevance with trends that should be visible according to its theory. In this case it didn’t, as LME’s did not have significantly more CVC investments, even though it was expected. The contradiction of its individual indicators against the full framework show that the framework needs adjustments to cover current trends.
The other side finds itself in the novelty of applying an existing framework to CVC investments and thereby attempting to find the best indicators for CVC investments. Given that this framework and its indicators were not fitting in predicting CVC investments, more research would be beneficial in trying to apply other frameworks to find the best set of indicators in predicting CVC investments.
The novelty of CVC investments and consequent short samples are impossible to overcome but it is therefore important that research should keep a focus on analyzing this type in the future when more data becomes available over time.
Besides that, future research should focus on finding other sources of market concentration data which cover different sectors, preferably ones that are less regulated and involve many new entrants. This should give a more dynamic and objective market concentration index.
References
Amable, B. (2003). The diversity of modern capitalism
Anokhin, S., Wincent, J., & Oghazi, P. (2016). Strategic effects of corporate venture capital investments. Journal of Business Venturing Insights, 5, 63-69.
Aulakh, P. S., Kotabe, M., & Sahay, A. (1996). Trust and Performance in Cross-Border Marketing Partnerships: A Behavioral Approach. Journal of International Business Studies, 27(4), 1005-1032.
Bain, J. S. (1954). Economies of Scale, Concentration, and the Condition of Entry in Twenty Manufacturing Industries. The American Economic Review, 44(1), 15-39.
Belderbos, R., Jacob, J., & Lokshin, B. (2018). Corporate venture capital (CVC) investments and technological performance: Geographic diversity and the interplay with
technology alliances. Journal of Business Venturing, 33(1), 20-34.
Berchicci, L. (2013). Towards an open R&D system: Internal R&D investment, external knowledge acquisition and innovative performance. Research Policy, 42(1), 117-127. Bielesch F., M. B., D. Khanna, A. Roos, F. Schmieg. (2012). Corporate Venture Capital,
Avoid the Risk, Miss the Rewards. Business Formation Statistics. (2018).
Casu, B., & Girardone, C. (2006). Bank competition, concentration and efficiency in the single European market. The Manchester School, 74(4), 441-468.
Chemmanur, T. J., Loutskina, E., & Tian, X. (2014). Corporate Venture Capital, Value Creation, and Innovation. The Review of Financial Studies, 27(8), 2434-2473. Chesbrough, H. W. (2002). Making sense of corporate venture capital. Harvard business
review, 80(3), 90-99.
Collins, J. C., & Porras, J. I. (2000). Built to last : successful habits of visionary companies (3rd ed. ed.). London: Random House Business.
Demirguc-Kunt, A., & Martinez Peria, M. (2010). A Framework for Analyzing Competition in the Banking Sector: An Application to the Case of Jordan.
DiMaggio, P. J., & Powell, W. W. (1983). The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48(2), 147.
Duffy, S., Dushnitsky, G., Rogan, J., & Easterby, T. (2017). Venture capital in the UK. Dushnitsky, G., & Lenox, M. J. (2006). When does corporate venture capital investment
create firm value? Journal of Business Venturing, 21(6), 753-772.
Edquist, C. (1997). Systems of Innovation: Technologies, Institutions and Organizations. (P. Press Ed.).
Feinberg, R. M. (1980). The Lerner Index, Concentration, and the Measurement of Market Power. Southern Economic Journal, 46(4), 1180-1186.
Fenwick, M., & Vermeulen, E. P. M. (2015). The New Firm: Staying Relevant, Unique and Competitive. European Business Organization Law Review, 16(4), 595-623.
Fortnum, D., Hughes, B., & Speier, A. (2016). Venture Pulse Q3 2016.
Hagedoorn, J. (1993). Understanding the Rationale of Strategic Technology Partnering: Interorganizational Modes of Cooperation and Sectoral Differences. Strategic Management Journal, 14(5), 371.
Hall, P. A., & Soskice, D. W. (2001). Varieties of capitalism : the institutional foundations of comparative advantage
Hui, Q., & Qing-xi, W. (2006, 17-20 Sept. 2006). Radical Innovation or Incremental Innovation: Strategic Decision of Technology-intensive Firms in the PRC. Kollmann, T., Stöckmann, C., Linstaedt, J., & Kensbock, J. (2015 ). European Startup
Monitor
Krugman, P. (1991). Increasing returns and economic geography. Journal of political economy, 99(3), 483-499.
Lazonick, W. (2010). Innovative Business Models and Varieties of Capitalism:
Financialization of the U.S. Corporation. The Business History Review, 84(4), 675-702.
Lerner, A. (1934). The Concept of Monopoly and the Measurement of Monopoly Power. Review of Economic Studies, 75.
Lerner, J. (2012). The architecture of innovation : the economics of creative organizations. Oxford: Oxford University Press.
Lucas, L. (2018). Huawei’s R&D budget hits $14bn as next-generation networks arrive. Financial Times.
MacMillan, I. C., Roberts, E. B., Livada, V., Wang, A. Y., National Institute of, S., & Technology. (2008). Corporate venture capital (CVC) seeking innovation and strategic growth : recent patterns in CVC mission, structure, and investment.: National Institute of Standards and Technology, U.S. Department of Commerce. Nölke, A., & Vliegenthart, A. (2009). Enlarging the Varieties of Capitalism: The Emergence
of Dependent Market Economies in East Central Europe. World Politics, 61(4), 670-702.
OECD. (2017). Entrepreneurship at a Glance
PwC. (2018). The 2018 Global Innovation 1000 study.
Scott D. Anthony, S. P. V., Evan I. Schwartz, and John Van Landeghem. (2018). 2018 Corporate Longevity Forecast: Creative Destruction is Accelerating.
Scott, W. R. (1995). Institutions and organizations. Thousand Oaks, Calif.: Sage. Stillman, R. (1983). Examining antitrust policy towards horizontal mergers. Journal of
Financial Economics, 11(1), 225-240.
Times, F. (Ed.) (2019) Finanial Times Lexicon. Financial Times.
Vitols, S. (2004). Changes in Germany's bank-based financial system: A varieties of
capitalism perspective. WZB, Markets and Political Economy Working Paper No. SP II, 3.
Wadhwa, A., Phelps, C., & Kotha, S. (2016). Corporate venture capital portfolios and firm innovation. Journal of Business Venturing, 31(1), 95-112.
Yayavaram, S., & Ahuja, G. (2008). Decomposability in Knowledge Structures and Its Impact on the Usefulness of Inventions and Knowledge-base Malleability. Administrative Science Quarterly, 53(2), 333-362.
