Venture capital investment, financial development, and economic growth: the case of European single market countries

Abstract

Venture capital (VC) is a key catalyst for nurturing start-up firms with high-growth potential to undertake innovative endeavors that contribute to national wealth. Existing literature concentrates on the impact of venture capital on firm-level performance. Unlike much of the earlier work, we conduct a macro study examining short-term and long-term relationships between VC investment, the state of the financial sector, and economic growth in 20 European single market countries between 1989 and 2015. We show that major transformations (political, economic, financial and institutional) over the sample period in the Eurozone region have resulted in the data series used in the study (economic growth, financial sector development, and VC investment) to be non-stationary. As such, the vector error-correction model (VECM) and Granger-Causality test are used to examine short-term and long-term relationships between VC investment, financial development, and economic growth for the sample countries. The findings suggest that economic growth strategies and financial sector reforms are critical for a vibrant venture capital industry. In the short term, there are bi-directional relationships between some of the variables. These results suggest that plans to create a vibrant venture capital industry will reinforce financial sector development and economic development, creating a more sustainable economic development model for countries in the Eurozone region.

Keywords:

• Financial development
• venture capital investment
• economic growth
• European single market countries

Acknowledgment

The authors are grateful for the comments from the anonymous referees and the editor of this journal.

Notes

1. Florida and Smith (1990) note that VC firms adopt co-investment strategies within a particular region to diversify their risk.

2. Venture capital is defined as a professionally managed capital fund used to finance private companies at various stages of their development where the providers of the capital influence the decisions made by their portfolio companies (Barry 1994; Cannice, Allen, and Tarrazo 2016; Sahlman 1990).

3. See, for example, Popov and Roosenboom (2013); Geronikolaou and Papachristou (2012); Faria and Barbosa (2014); Barry and Mihov (2015); Li, Vertinsky, and Li (2014); Liao, Lu, and Wang (2014); and Samila and Sorenson (2011).

4. While there is a substantial literature on the relationship between economic growth and VC investment, there is very little on the relationship between financial market development and VC investment. Additionally, we consider the direction of causality for these relationships.

5. We have an unbalanced panel which was dictated by data availability on the countries selected. Both the time period and selection of countries were driven by data availability.

6. The countries are: Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, Switzerland, and the United Kingdom.

7. These proxies for venture capital investment have previously been used by Faria and Barbosa (2014).

8. The procedural details are shown in Pradhan et al. (2014).

9. We also use the same model to obtain the short-term Granger-causal relationships among per capita economic growth, VC investment, and financial development – and to explain the variance of one variable (say VC) by the other two variables (financial development and per capita economic growth).

10. The test is meant to determine the presence or absence of unit root (non-stationary) of the time series variables. If a macroeconomic variable is characterized by unit root, it implies that any random shock (arising due to any other factors) will produce significant and long-lived effects on the level of series. The shock persistence feature imparts a tendency for the variable not to return on its long-run trend and instead drift apart over time (Elliott, Rothenberg, and Stock 1996; Enders 1995). This test is conducted here for three cases and they are the model with (i) no trend and no intercept; (ii) with intercept only; and, (iii) with a constant and deterministic trend.

11. Cointegration entails a long-run equilibrium relationship that ties these three time series variables even though short-term departures from equilibrium may exist (Engle and Granger 1987; Füss and Schweizer 2012).

12. Defined as VCE, VCL, or VCT.

13. Defined as BAD, STD, or FID.

14. This finding is congruent with Cumming (2011a,b) and Da Rin et al. (2011).

15. This is true for Case 2 and Case 3 under all the three scenarios. This result supports the findings of supports the findings of Pradhan et al. (2014, 2016) and Hou and Cheng (2010).

16. This is true for Case 3 under all the three scenarios. This results support the findings of Füss and Schweizer (2012) who studied the United States economy during the period from 1991 to 2006.

17. FMOLS is fully modified ordinary least squares (OLS), a non-parametric estimation approach, taking into account the possible correlation between the error term and the first differences of the regressor as well as the presence of a constant term to deal with corrections for serial correlation (Maeso-Fernandez, Osbat, and Schnatz 2006; Pedroni 2000).

18. DOLS is dynamic OLS, a parametric estimation approach that adjusts the errors by augmenting the static regression with leads, lags, and contemporaneous values of the regressor in first differences (Kao and Chiang 2000).

19. This results support the findings of Füss and Schweizer (2012), Gompers and Lerner (1998), Jeng and Wells (2000), and Ning, Wang, and Yu (2015).