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Empirical Economics

Erschienen in:

15.05.2021

Cross-country differences in the size of venture capital financing rounds: a machine learning approach

verfasst von: Marco Taboga

Erschienen in: Empirical Economics | Ausgabe 3/2022

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Abstract

We analyze the potential determinants of the size of venture capital financing rounds. We employ stacked generalization and boosted trees, two of the most powerful machine learning tools in terms of predictive power, to examine a large data set on start-ups, venture capital funds and financing transactions. We find that the size of financing rounds is mainly associated with the characteristics of the firms being financed and with the features of the countries in which the firms are headquartered. Cross-country differences in the degree of development of the venture capital industry, while highly correlated with the size of funding rounds, are not significant once we control for other country-level characteristics. We discuss how our findings contribute to the debate about policy interventions aimed at stimulating start-up financing.

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A Series A round is a start-up’s first significant round of VC financing. In this paper, we focus on Series A rounds so as to keep our sample as homogeneous as possible, but we find the large cross-country differences described here also in a larger sample including all types of funding rounds.

For analyses of the Italian VC market, see Generale et al. (2006), Generale and Sette (2008), Bentivogli et al. (2009), Magliocco and Ricotti (2013), Vacca (2013), Bronzini et al. (2017).

See also Sannino (2017) who builds a theoretical model in which entrepreneurs direct their search to VCs based on their projects’ quality.

We use the 0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95 and 0.99 quantiles.

We do not include country dummies in our models because including them would prevent us from analyzing the effects of country characteristics.

Missing values are included in \(A_{lm}\) or in its complement depending on which of the two sets is the majority class.

More precisely, the number of regressors added on each iteration is equal to the number of leaves in the tree that is estimated on that iteration. For the sake of simplicity and for developing intuition, in what follows we pretend that only one regressor is added on each iteration.

https://stats.stackexchange.com/questions/3893/can-cross-validation-be-used-for-causal-inference/4095.

To further improve the quality of the summary variables, we repeat the entire procedure 100 times with different random partitions \(I_{1},\ldots ,I_{K}\), and then take simple averages of the estimates obtained in the 100 runs.

The figures contain only a subset of 27 countries for which there are more than 20 funding rounds in the data set.

Year, year–month and month in which the funding round has been announced.

The three variables are two overall measures of the ease of doing business (“DTF global DB17-18 methodology” and “DTF global DB16 methodology”) and the ease of dealing with construction permits.

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Titel: Cross-country differences in the size of venture capital financing rounds: a machine learning approach
verfasst von: Marco Taboga
Publikationsdatum: 15.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 3/2022
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-021-02066-8

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