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Employment effects of R&D and process innovation: evidence from small and medium-sized firms in emerging markets

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Abstract

This paper studies the impact of research and development (R&D) and innovation on employment growth, focusing on small and medium-sized firms. Employment effects of R&D and process innovation are unclear a priori as process innovation may be labor-saving or labor might have complementarities with other inputs. Employing firm-level data from 125 nations, results show that both R&D and innovation increased employment growth, suggesting that innovation was either capital-saving or labor had strong complementarities with other inputs. Upon splitting the sample into growing and contracting firms showed that contracting firms benefit from innovation but not from R&D. In other findings, sole proprietorships, larger firms, firms with relatively more experienced managers, firms with females as top managers, and firms facing the threat of informal competition had lower employment growth, while foreign-owned and government-owned enterprises have positive influences on employment growth. Finally, employment growth in shrinking firms was boosted in nations with greater economic freedom, but this growth is undermined by informal sector competition.

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Notes

  1. Some other studies, however, have used firm-level data in different contexts – see Avenyo et al. (2019), Baffour et al. (2020), Barbieri et al. (2019), Goel and Nelson (2018).

  2. Insightful surveys of the literature on the innovation-employment nexus can be found in Calvino and Virgillito (2018) and Vivarelli (1995, 2012). Also, see a recent compilation of the literature on this topic by Dosi and Mohnen (2019).

  3. This work can be viewed as complementary to research that studies the causes of innovation (Goel and Nelson 2018).

  4. The importance of another dimension – organizational innovation – is slowly dawning on economists (Polder et al. 2010).

  5. Given appropriate data, some scholars have been able to consider both process and product innovation (Antonucci and Pianta 2002, Mantovani 2006). In preliminary analysis, we included firms of all size levels (available upon request) with generally similar results, although with lower overall model explanatory power because of the wide variation in firm size in the 100 + employee size category. Approximately 83% of the total number of firms in our data set had 100 or fewer full-time employees at the start of the period under analysis.

  6. Also see Li and Hou (2019) regarding the lags between and R&D and its payoffs.

  7. Roper (1997) provides related comparisons of firms in Germany, Ireland and the U.K.

  8. The employment growth period is constrained by the questions asked in the World Bank Enterprise Surveys instrument that is the primary source of the data used in this analysis.

  9. See Barkham et al. (1996) and Birley and Westhead (1990) for a more general discussion, while Vivarelli (2014) considers employment growth.

  10. Note that, as Table 1 details, the available quantitative measures of R&D and INNOVATION are dichotomous in our dataset.

  11. More generally, in a multi-input production function, the impact of R&D would also depend upon labor’s relation with other inputs. Goel (1990) showed that, for the United States, the relationship of R&D with other inputs (i.e., whether inputs are substitutes or complements) varies across industries. In our context, if R&D is complementary to labor, then employment growth-based performance would improve.

  12. Firms with 100% government/state ownership were not surveyed by the World Enterprise Surveys, the principal data set used in this analysis (Table 1).

  13. https://en.wikipedia.org/wiki/Gibrat%27s_law.

  14. A survey of the literature on the role of informal markets can be found in Schneider and Enste (2000); also see Goel et al. (2022) and Schneider (2012).

  15. The Enterprise Surveys Group states on their website that “[e]merging economies are the primary focus and a few developed economies have been surveyed for comparative purposes.” (https://www.enterprisesurveys.org/about-us/frequently-asked-questions.) In our data set (see Appendix A), 19% of nations are classified as “low income”, 32% as “low middle income”, 35% as “upper middle income”, and 13% as “high income”, using the 2019 World Bank classifications.

  16. http://www.enterprisesurveys.org/methodology.

  17. Specifically, based on the number of observations for each industry in the data set and our own judgment of industry groupings that made sense, we included fixed-effect variables for the following eight industry classifications in our models: food, beverages, and tobacco; textiles, weaving, and tanning; wood and paper; chemicals; rubber/plastics; basic metals and fabricated metals; other non-metallic; machinery and electrical machinery; motor vehicles and other transport; and furniture. “All other” industry classifications became the omitted category in our analysis.

  18. Recall firm size is measured in natural logs. The finding about the impact of firm size can be seen as consistent with Beck et al. (2005) where, using a sample of 54 nations, they find that small firms benefit the most from financial and institutional developments (also see Falk and Hagsten (2021)). The negative sign on the size variable is consistent with the notion of a lack of scale economies.

    As an alternative test of the firm size dimensions, we re-estimated the baseline models in Table 3 for all firms (large and small) and the main results about the positive employment effects of R&D and innovation remained robust. An abridged version of Table 3 is produced in Appendix B as Table 8. Additional details are available upon request.

  19. In a theoretical model, Goel (2004) has shown that the research spending by nonprofit enterprises exceeds the profit-maximizing levels.

  20. These countries include Argentina, Bolivia, Chile, Columbia, Dominican Republic, Ecuador, Egypt, El Salvador, Ethiopia, Guatemala, Honduras, Mexico, Myanmar, Panama, Paraguay, Peru, Uruguay, and Zimbabwe.

  21. This robustness of the impact of process innovation is noteworthy, given that studies using similar data in a more limited fashion did not find a significant impact of process innovation on employment (Cirera and Sabetti 2019).

  22. We also considered the influence of product innovations to their employment impacts (see Avenyo et al. 2019, Baffour et al. 2020). Employing product innovation (whether the firm introduced a new product/service to the market) as the main explanatory variable in place of process innovation, we reran the baseline Models 2.1–2.3 from Table 3. The resulting coefficient on the product innovation variable was positive but statistically insignificant in all cases. Focusing on firm age by considering young Italian firms, Pellegrino et al. (2012) study the determinants of product innovation. They found that own R&D increased the introduction of product innovation both in mature and young firms. Also see Piva and Vivarelli (2018).

    Furthermore, when both process and product innovations were considered together as explanatory variables, the impact of process innovation was quite similar to what is reported in Table 3. Product innovation remained insignificant. Further details are available upon request.

  23. One plausible explanation for the lack of significance of the GOVTown variable might be that profit-maximization is not the prime objective of firms with significant government ownership and hence they are relatively less responsive to market conditions, with the result that their employment does not significantly grow over time even when innovating or conducting R&D.

  24. We also tried a Huber M-estimator which yielded very similar results (Verardi and Croux 2009).

  25. An interesting related angle is studied recently by Dosi et al. (2021), where the authors examine the employment effects of technological change across sectors. The sectors, in the context of vertical integration, refer to upstream and downstream industries. Their results indicate that whether R&D is labor-friendly depends on the type of sector considered (i.e., upstream or downstream industries); also see Piva and Vivarelli (2018) for a related focus on tech sectors.

  26. Approximately one-third of the firms in our sample reported no change in employment over the period analyzed and hence are excluded from the sample used in this subsection.

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Authors and Affiliations

  1. Department of Economics, Illinois State University, Normal, IL, 61790, USA

    Rajeev K. Goel

  2. Innovation and International Competition, Kiel Institute for the World Economy, Kiel, Germany

    Rajeev K. Goel

  3. Department of Economics, University of Akron, Akron, OH, 44325, USA

    Michael A. Nelson

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Appendices

Appendix A

1.1 Countries in data set

Albania (2007, 2013), Angola (2010), Argentina (2006, 2010, 2017), Armenia (2009, 2013), Azerbaijan (2009, 2013), Bahamas (2010), Bangladesh (2013), Barbados (2010), Belarus (2008, 2013), Belize (2010), Benin (2009, 2016), Bhutan (2009, 2015), Bolivia (2006, 2010, 2017), Bosnia and Herzegovina (2009, 2013), Botswana (2010), Brazil (2009), Bulgaria (2007, 2009, 2013), Burkina Faso (2009), Burundi (2014), Cambodia (2016), Cameroon (2009, 2016), Cape Verde (2009), Central African Republic (2011), Chad (2009, 2018), Chile (2006, 2010), China (2012), Colombia (2006, 2010, 2017), Costa Rica (2010), Croatia (2007, 2013), Czech Republic (2009, 2013), Côte d'Ivoire (2009, 2016), Democratic Republic of Congo (2010, 2013), Dominica (2010), Dominican Republic (2010, 2016), Ecuador (2006, 2010, 2017), Egypt (2013, 2016), El Salvador (2006, 2010, 2016), Eritrea (2009), Estonia (2009, 2013), Eswatini (2016), Ethiopia (2011, 2015), Fiji (2009), FYR Macedonia (2009, 2013), Gambia (2018), Georgia (2008, 2013), Ghana (2007, 2013), Guatemala (2006, 2010, 2017), Guinea (2016), Guyana (2010), Honduras (2006, 2010, 2016), Hungary (2009, 2013), India (2014), Indonesia (2009, 2015), Israel (2013), Jamaica (2010), Jordan (2013), Kazakhstan (2009, 2013), Kenya (2013), Kyrgyz Republic (2009, 2013), Laos PDR (2009, 2012, 2016), Latvia (2009, 2013), Lebanon (2013), Lesotho (2016), Liberia (2017), Lithuania (2009, 2013), Madagascar (2009, 2013), Malawi (2009, 2014), Malaysia (2015), Mali (2007, 2010, 2016), Mauritania (2014), Mauritius (2009), Mexico (2006, 2010), Micronesia (2009), Moldova (2009, 2013), Mongolia (2009, 2013), Montenegro (2009, 2013), Morocco (2013), Mozambique (2007), Myanmar (2014, 2016), Namibia (2014), Nepal (2009, 2013), Nicaragua (2016), Niger (2009, 2017), Nigeria (2014), Pakistan (2013), Panama (2006, 2010), Papua New Guinea (2015), Paraguay (2006, 2010, 2017), Peru (2006, 2010, 2017), Philippines (2009, 2015), Poland (2009, 2013), Romania (2009, 2013), Russia (2009, 2012), Rwanda (2011), Samoa (2009), Senegal (2007, 2014), Serbia (2009, 2013), Sierra Leone (2017), Slovak Republic (2009, 2013), Slovenia (2009, 2013), Solomon Islands (2015), South Africa (2007), Sri Lanka (2011), St. Lucia (2010), St. Vincent and Grenadines (2010), Suriname (2010), Tajikistan (2008, 2013), Tanzania (2013), Thailand (2016), Timor-Leste (2009, 2015), Togo (2009, 2016), Tonga (2009), Trinidad and Tobago (2010), Tunisia (2013), Turkey (2008, 2013), Uganda (2013), Ukraine (2008, 2013), Uruguay (2006, 2010, 2017), Uzbekistan (2008, 2013), Vanuatu (2009), Venezuela (2010), Vietnam (2009, 2015), Yemen (2010, 2013), Zambia (2007, 2013), Zimbabwe (2011, 2016).

125 countries in the data set, 69 countries with multiple surveys (year of survey in parentheses).

Appendix B

See Table 8.

Table 8 Employment effects of R&D and process innovation: Baseline models with all firms, large and small (Dependent variable: EMPgr)
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Goel, R.K., Nelson, M.A. Employment effects of R&D and process innovation: evidence from small and medium-sized firms in emerging markets. Eurasian Bus Rev 12, 97–123 (2022). https://doi.org/10.1007/s40821-022-00203-6

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