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High-growth firms and international trade: evidence from Ecuador

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Abstract

The aim of this article is to open a detailed discussion about the relationship between international trade activities (export, import and trade openness) and the probability of becoming a high-growth firm. International trade has been widely related to a variety of outcomes, but there is no evidence of the relationship to high-growth firms. We study this issue by estimating a two-step probit model to correct the problem of self-selection that usually appears in trade research. Additionally, we use matching techniques as a robustness check to address possible endogeneity problems. We find that international trade is a consistent and robust path for becoming a high-growth firm. Several policy implications are derived from our work.

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Data Availability Statements

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. At this point, we must acknowledge that there might be other sources of endogeneity that we are not able to control for in this paper. However, it is well known in the empirical literature that differences between traders and non-traders is one of the most important sources of endogeneity. We thank an anonymous reviewer comment on this point.

  2. For an extensive review of the literature on this topic, see Wagner (2012) and Cassiman and Golovko (2018).

  3. The modifications made are mainly in the employment variable because of various inconsistencies in the complete data base used to classify HGFs.

  4. There are at least three issues that need to be considered when characterizing firm performance: i) the indicator of growth; (ii) the measure of growth; and (iii) the period under study (Reyes 2017; Delmar et al. 2003). The most commonly used indicators in the literature on high-growth firms are sales and number of employees (Daunfeldt et al. 2014).

  5. Similar participation of HGFs in the manufacturing sector are found in Segarra and Teruel (2014), Du and Temouri (2015) and Reyes (2017), among other international empirical evidence.

  6. For this paper, we group the medium-high tech industry and high-tech industry into one, because there are few observations in this latter group, and because the high-tech industry in the OCDE is different from that in Ecuador.

  7. Du and Temouri (2015) argue that employment growth and size patterns have more to do with industrial characteristics than firm performance variations.

  8. Firm size is defined in the Organic Code of Production, Trade and Investment of Ecuador: microenterprises, between 1 and 9 workers or revenue less than $100,000; small firms, between 10 and 49 workers or revenue between $100,001 and $1,000,000; medium firms, between 50 and 199 workers or revenue between $1,000,001 and $5,000.000; large firms, more than 200 workers or revenue above $5,000,001. Revenue ranks higher than the number of workers.

  9. We use the command heckprobit implemented in STATA by Miranda and Rabe-Hesketh (2006).

  10. We perform our estimations using the prodest command of Stata, developed by Rovigatti and Mollisi (2018). As a robustness check, we also estimate the TFP using the (Wooldridge 2009) estimator. Results of these estimations are available upon request.

  11. For an extensive review of the literature about this topic, see Keller (2010).

  12. According to the Central Bank of Ecuador, the manufacturing sector has in recent years received around 14% of the total FDI, and the main countries that have invested in this sector are the USA, Peru, England, Uruguay, New Zealand, Panama, Chile, Colombia, Costa Rica and Mexico.

  13. In Table 11 in the Annexes, we show the correlation matrix between our variables of interest. Specifically, we show that FDI is not correlated with being an HGFs

  14. There is a large body of empirical evidence that suggests that firms engaged in international trade activities grow faster than domestic firms (see, for example, Bernard et al. 2007; Cassiman and Golovko 2018; Wagner 2012).

  15. This modification is implemented by Borgen (2016) in the Stata command xtrifreg for fixed effects panel data quantile regressions.

  16. Porter (2015) mentions two reasons to prefer (UQR) over (CQR). First, the transformed outcome variable (the RIF) is defined pre-regression. Thus, unlike CQR, including control variables does not change the definition of the quantile. Second, the transformed outcome variable (the RIF) depends heavily on the estimated density, fY(qτ).

  17. In general, HGFs are located in quantiles 0.90 and 0.95 (Segarra and Teruel 2014).

  18. Similar to Teruel (2010), we suggest that manufacturing firms are achieving a minimum efficient size.

  19. Also, we use these two methodologies to control for potential sample bias between traders and non-traders. However, the nature of the problem of the endogeneity between variables is indeed still not totally controlled, but considerably reduced.

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Funding

This research was funded by the Spanish Ministry of Economy and Competitiveness (project ECO2017-82445-R).

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Authors and Affiliations

  1. Universidad Complutense de Madrid, Campus Somosaguas, Madrid, Spain

    Segundo Camino-Mogro

  2. ESAI Business School, Universidad Espíritu Santo, Samborondon, Ecuador

    Segundo Camino-Mogro

  3. Superintendencia de Compañías Valores y Seguros, Guayaquil, Ecuador

    Segundo Camino-Mogro

  4. Universidad Ecotec, Km. 13.5 Samborondón, Samborondón, EC092302, Ecuador

    Mary Armijos

  5. ESPAE Graduate School of Management, ESPOL Polytechnic University Campus Las Peñas, Malecón 100, EC090103, Guayaquil, Ecuador

    Paul Vera-Gilces

  6. Trade Data Lab, Guayaquil, Ecuador

    Paul Vera-Gilces

Authors
  1. Segundo Camino-Mogro
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  2. Mary Armijos
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  3. Paul Vera-Gilces
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Correspondence to Segundo Camino-Mogro, Mary Armijos or Paul Vera-Gilces.

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The views expressed here are those of the authors. We thank Uwe Cantner, the Editor in Chief, Roberto Fontana, the Editor, and anonymous reviewer for useful comments, and Mercedes Teruel for sharing her Stata routine so that we could estimate the Heckman equation as in her paper. S. Camino-Mogro acknowledges funding from the Spanish Ministry of Economy and Competitiveness (project ECO2017-82445-R).

Appendix

Appendix

Table 10 Correspondence between ISIC codes and industries according to technological intensity
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Table 11 Matrix of Pearson correlations
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Camino-Mogro, S., Armijos, M. & Vera-Gilces, P. High-growth firms and international trade: evidence from Ecuador. J Evol Econ 32, 299–332 (2022). https://doi.org/10.1007/s00191-021-00756-1

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