Abstract
Certain theories suggest that the capacity of a region to generate new firms, called entrepreneurship capital, has positive spillover effects on the production of the firms in that region. Evidence generated with aggregated data at the regional level supports this prediction. This paper argues that, using aggregated data at the regional level, entrepreneurship capital could be correlated with regional production even if entrepreneurship capital has no spillover effects on firm production. This will not be the case when data at the firm level are used. This paper provides evidence from a sample of 11,276 Spanish firms during the 2004–2012 period. Positive spillovers are estimated in between effects models, but such spillovers are only found in technological firms when within effects models have been estimated. Thus, the regional entrepreneurship capital spillovers are unclear when data at the firm level are used. Plausible interpretations and implications are discussed.
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Notes
Other authors have used the annual average of new firms per 1000 workers created in a 3-year period, such as Audretsch and Keilbach (2004a, b, 2008). Mueller (2006, 2007) uses this indicator in addition to the number of new firms created in 1 year. Sutter and Stough (2009) use the average number of technological and innovative firms created in the last 5 years, while Böente et al. (2008), Salas-Fumás and Sanchez-Asin (2013a, b), and Stough et al. (2008) use the self-employment rate on a regional level. Erken et al. (2016) use the business ownership rate (number of business owners per workforce) corrected for the level of economic development (GDP per capita) to evaluate the relation between entrepreneurship and total factor productivity.
Normalizing P i , 0 = 1 and assuming that the population of the region remains constant along time, and P i , t = 1, we obtain the case analysed in the previous section in which the entrepreneurship capital in the region is equal to the number of firms, E i , t = n i , t .
According to Congregado et al. (2012) terminology, E i , 0 is the non-stationary natural rate component, and ∆ i , t is the stationary cyclical component.
The next considerations regarding entrepreneurship capital can be extended to the remainder of inputs in Eq. 2. As the focus of the paper is based on the entrepreneurship capital, we omit such analyses.
All the analyses that are cited but not provided in the text are available on request from the authors.
There is also information available about the economically active population. The estimations presented in the next section have been replicated using the ratio between the stock of the firms and the economically active population as the measure of entrepreneurship capital. The conclusions are very similar but, in this case, the models have a lower explanatory capacity, R square, than those of the estimations presented in the text.
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Acknowledgments
The authors received financial support from the Spanish Government, “Ministerio de Economía y Competitividad” ECO2013-48496-C4-4-R. We are grateful for the helpful comments provided by two anonymous referees.
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Appendices
Appendix 1. Firm location.
The firms are allocated to Autonomous Communities (Comunidades Autónomas—CCAAs) on the basis of where they perform their research activities. Specifically, for each firm, there is information available to calculate the percentage of the following expenditures: Total Expenditure on Innovation, Total Internal Personnel in R&D Activities and Total Internal Expenditure on R&D located in each of the 18 CCAA’s. Then, 8012 firms make 100% of each of the expenditures in the same Autonomous Community. The first column (Location of R&D Activities) in Table 7 shows their distribution among CCAAs.
The remaining 4826 firms were allocated to a CCAA if:
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(i)
The same Autonomous Community concentrates 100% of the expenditure on which we have information (one or two types of expenditure).
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(ii)
This Autonomous Community is the one with the highest expenditure level at least in two of the three types of expenditures considered.
After this process, 1020 firms cannot be allocated to a specific CCAA. The second column (Location of most R&D Activities) in Table 7 shows the distribution of the firms finally allocated after this process.
The PITEC database also contains information about where the headquarters of the firm are located. The problem is that they only recognize three Autonomous Communities: Madrid, Catalonia and Andalusia. To those Autonomous Communities, 4848 firms are allocated, while 5719 firms are allocated to the other CCAAs without identifying which. Furthermore, there are 1465 missing values. The third column in Table 7 summarizes the distribution of firms according to information about their headquarters. We use this information to check the robustness of the classification based on where the firms perform their research activities. We only find 590 divergences; therefore, we do not include such firms in the analyses. The last column (final firm location) in Table 7 summarizes the distribution of firms among CCAA in the sample finally used in this paper.
Appendix 2. Firms’ stock of capital.
The PITEC provides information about the annual investment on the physical capital of each firm, I j,t . In accordance with (Goya and Vayá 2011; Barge-Gil and López 2013; Ortega-Argilés et al. 2011), we use the perpetual inventory method to estimate the stock of capital of firm j in period t : K j , t = (1 − d)K j , t − 1 + I j , t , being K j , 0 = I j , 0/d.
The depreciation rate adopted was d = 0.1. Given that the investments are highly affected by economic fluctuations, in accordance with Ferreira et al. (2013), we use the average of all of the sample years’ investments instead of I j,0 . We checked that the basic results of this paper are insensitive to this decision.
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Massón-Guerra, J.L., Ortín-Ángel, P. Regional entrepreneurship capital and firm production. Small Bus Econ 49, 595–607 (2017). https://doi.org/10.1007/s11187-017-9851-0
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DOI: https://doi.org/10.1007/s11187-017-9851-0