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Income Inequality and Frontend Innovation: Evidence from Frontier Markets

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Abstract

The lack of innovation hurts small firm access to resources for expansion in frontier markets. This incidence has implications for economic growth and therefore income inequality. Despite an emerging view that front-end innovation (FEI) is critical for small firm’s access to credit, the relationship between FEI and inequality especially in the context of frontier markets is little studied in the empirical literature. To close this gap, this paper contributes to the literature on income inequality, by extending existing models to examine the effect of FEI on income inequality. We use a fixed effect panel regression on annual country-level data for thirty-one frontier markets, over a 15-year (2003–2018) period, and find an insignificant correlation between income inequality and FEI. The instrumental variable estimates, however, show a significant association between both measures of FEI and income inequality. Our instrumentation strategy and robustness checks suggest that this correlation partly reflects a causality from FEI to Inequality: for instance, when measured by education expenditure, a 1% change in FEI increases inequality by 0.234%. When measured by gross value added, a 1% change in FEI reduces income inequality on average by 0.105%. Overall, our findings confirm that FEI is a significant determinant of increases in entrepreneurial income share.

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Data Availability Statement

The dataset generated (including codes) during the current study are available from the corresponding author at reasonable request.

Notes

  1. The reason for this study and the gap in the literature is based on real-world problems and not solely academic or theoretical considerations. Most small firms in developing regions cannot access credit and yet given the importance of technological progress to credit access, there is little or no inquiry into the role FEI plays in this process. Moreover, the inability to access credit by most small firms exacerbates income inequality in frontier markets. Hence, there is a need to quantify the relationship.

  2. FEI is experimental and not linear; it is fraught with risks and uncertainty; information asymmetry is a critical component of this phase. Moreover, the firms organizational and managerial competencies are evolving during FEI and the firm usually can not access finance. Because of its experimental nature, the risks and uncertainty associated with an invention/product are cleared during FEI prior to entering the innovation phase. The innovation phase is a continuation of FEI but with less risk and uncertainty, with access to credit, fully evolved organizational competencies and information, but on a much larger scale (Jovanovic, 1982; Kazanjian,1988; Koen et al., 2014; Pereira et al., 2017).

  3. In the context of FEI in frontier markets, a company registration date/new venture creation is equivalent to a patent application, while access to credit is comparable to a patent grant in developed countries. The entrepreneur experiences a significant rise in income when they access credit. The majority of small firms in frontier markets will not access credit due to the inability to engage in FEI. Majority will collapse in the first 5 years, and out of the few who survive, only a minority will go on to scale and access credit (Wennekers et al., 2005).

  4. Although there is insufficient data, on government expenditure on education, total (% of GDP), the authors assert that the adjusted savings: education expenditure (% of GNI) mirrors the pattern of the former and, thus, is a reliable measure for education spending in these countries.

  5. Each country (Argentina, Kenya, Vietnam, and Romania) was selected from a different continent (Africa, South America, Asia, and Eastern Europe) in order to understand the underlying trends in FEI and inequality data regardless of location. The data show a similar trend in FEI in frontier markets.

  6. The stagnation, and/or slight increase in the Gini index, is because frontier markets have pursued policies of economic liberalization, which has increased the income disparity between owners of capital and labor as well as between the skilled and unskilled (Zhuang et al., 2014;IMF, 2016; Jaumotte et al., 2013).

  7. The government size increases inequality; however, it was dropped from the analysis because it was statistically insignificant (t-values of 0.95 and 1.13) in both equations.

  8. There are at least thirty frontier markets globally. https://www.bloomberg.com/news/articles/2019-05-25/what-are-frontier-markets-and-why-invest-in-them-quicktake.

  9. The study time for Permana et al. (2018) is 2003–2014 and for Wlodarczyk (2017) is 2005–2014 and both studies focused on Europe. Frontier markets have extremely limited data.

  10. The entrepreneurs’ income increases significantly after the firm has reached economies of scale and is, thus, able to access institutional finance—the FEI process. Specifically, significant increases in value addition and education expenditure will precede the innovators rise in income (Gama & Parida, 2017; Herrera & Minetti, 2007).

  11. On the whole, the correlation coefficient between both instruments is insignificant (−0.31).

  12. The statistical capacity score is mostly funded by external development partners and is, thus, exogenous to a country’s economic growth. It is a metric which improves with economic development and, thus, will have a delayed impact on FEI. https://statisticalcapacitymonitor.org/pdf/Statistical%20Capacity%20Development%20Outlook%202019.pdf. NPISHs are institutions which provide goods and services to households for free or below market prices. They mainly derive their income from grants and donations and are not controlled by the government. FDI determines changes in household consumption in developing countries; thus, changes in household consumption are exogenously driven. FDI spurs host country entrepreneurial activity which boosts incomes and, thus, consumption via jobs (Jaumotte et al., 2013).

  13. Radical technological change is captured by new firms entering the market rather than intellectual property like patents. Moreover, intellectual property development is discouraged in frontier markets due to institutional factors like poor legal rights (Vivarelli & Quatraro, 2015; Srholec, 2011; Kraemer-Mbula et al., 2019). Developing countries with low entrepreneurial human capital and a poor business environment will not benefit significantly from spillover effects (Gorg & Greenaway, 2004; Durham, 2004).

  14. This data is not available for Nigeria, Ghana, and Benin among others.

  15. Unlike innovation, FEI is an experimental and expensive process, it is not linear, and it is fraught with risks and uncertainty. Moreover, competition and failure among others can elongate the FEI journey. Furthermore, due to institutional factors, most entrepreneurs in developing markets lack the skill to conduct FEI (Amoros et al., 2011; Ayyagari et al., 2008; Pereira et al., 2017).

  16. The panel fixed effects results capture the impact of entrepreneurs who engage in FEI, and those who do not. The NDEs are larger than the entrepreneurs who engage in FEI, hence the negative and statistically insignificant result (Amoros et al., 2011; Wennekers et al., 2005). Likewise, the activity of NDEs only temporarily decreases income inequality.

  17. The Gini index is a broad measure of income inequality; it captures the activities of a wider range of innovators, when compared to the top 1% and 10%. Therefore, FEI will impact inequality negatively.

  18. Results from the first stage regression (Table 8) show that both instruments have a significant and positive impact on the GVA. A one unit rise in the statistical capacity score will increase GVA by 3% while a 1% increase in household expenditure will raise GVA by 2.5%. The statistical capacity score tracks the activity of the most innovative (top 1%) segment of entrepreneurs hence, its higher magnitude (3%) of effect on FEI, over the household expenditure (2.5%) which appropriates the activity of the less innovative segment (top 10%, 20% etc.). The finding proves that the practice of FEI advances with a nation’s statistical capacity (Herrera and Minetti, 2007; Peng, 2017).

  19. Gross value added is a broader measure for FEI compared to education expenditure; it captures the impact of a wider range of innovative entrepreneurs. A number of empirical studies confirm a negative relationship between innovation, and inequality, especially when the innovators are not in the top 1%, or when broader measures of inequality like the Gini index is used (Aghion et al., 2015; Wlodarczyk, 2017).

  20. Education is not a primary engine of upward mobility in developing countries, rather it increases inequality, especially in the developing countries of Asia and Africa which make up 25 countries in our sample ( Batuo et al., 2010 and Jaumotte et al., 2013). Aghion et al. (2015) also reveal a positive correlation between innovation and the share of income owned by the top 1% in the USA. The literature on skill biased technical change (Permana et al., 2018) confirms that the dominant reason for wage inequality is education. Highly educated people can better use new technologies and therefore will receive higher incomes.

  21. The results for IV estimation in advanced countries (Aghion et al., 2015 and 2019; Benos & Tsiachtsiras, 2019) usually reinforce panel OLS results, but not so in developing countries due to the ubiquity of NDEs.

  22. Most entrepreneurs in developing countries are motivated by necessity rather than innovation; hence, their impact on inequality is temporary (Amoros et al., 2011; Wennekers et al., 2005).

  23. Profiles of innovative entrepreneurs in frontier markets—https://www.entrepreneur.com/article/283272—https://www.smallstarter.com/get-inspired/10-super-success-african-entrepreneurs-and-how-they-raised-money-to-start-their-businesses/.

  24. A 1% rise in the household’s NPISH’s consumption expenditure decreases education expenditure by 1.12%, while a unit rise in the statistical capacity score reduces education expenditure by 1%. The fact that the magnitude of the statistical capacity score is smaller (the statistical capacity score reduces FEI by a smaller amount when compared to the household expenditure) than that for the household expenditure, is further proof that the statistical capacity score captures the impact of the most creative (top 1%) segment of entrepreneurs. Similarly, in the reduced form equation the weight of the statistical capacity score (1%) is more serious (or reduces income inequality) than the household expenditure (0.16%). This outcome is further evidence that the statistical capacity score captures the impact of the most creative sector of innovators.

  25. Environmental factors in developing countries such as the rule of law and property rights, poor infrastructure, political instability, corruption, and other institutional factors which dampen FEI (Banerjee & Newman, 1993; Beck et al., 2008; Cenni et al., 2015).The activity of NDEs also dampens FEI and increases inequality.

  26. It takes at least 3 years for an entrepreneur to reach economies of scale and access finance (Kazanjian, 1988). The results from the education expenditure model are also similar and available upon request from the authors.

  27. The decline in magnitude is due to creative destruction, uncertainty, and risk which is typical of FEI (Antonelli and Gehringer, 2017; Chu & Cozzi, 2018; Jones & Kim, 2018; Pereira et al., 2017).

  28. Kenya, Nigeria, Morocco, Niger, Mali, Nepal, Bhutan, Pakistan, Sri Lanka, Jordan, Lebanon, Senegal, Tunisia, Ethiopia, and Bangladesh.

  29. Twenty-five countries in our sample have at least doubled their GDP per capita during the study period.

  30. Papers (Wennekers et al., 2005, Van Stel et al., 2005, Sternberg & Wennekers, 2005; Amoros & Cristi, 2008 and Amoros et al., 2009) that have researched the impact of entrepreneurship in developing countries.

  31. WBES captures firm-level innovation data only.

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Acknowledgements

We would like to thank Lauri Elliott, the founder of Conceptualee Resources, USA. Lauri dedicated over 15 years, using the methodology for FEI she designed, to help small firms access credit in frontier markets. Her experiences provided the motivation for this paper. The authors also want to thank Nathan Lupton, Julia Wlodarcyzk, and Abiodun Egbetokun for their insightful reviews.

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  1. Department of Economics, Sahiwal University, Sahiwal, Pakistan

    Asma Kanwal

  2. Quaid-I- Azam University, Islamabad, Pakistan

    Asma Kanwal

  3. Department of Economics, University of Nigeria, Nsukka, Nigeria

    Nwakego Eyisi

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  1. Asma Kanwal
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  2. Nwakego Eyisi
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Correspondence to Asma Kanwal.

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Table 8 FEI first stage IV and reduced form results
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Kanwal, A., Eyisi, N. Income Inequality and Frontend Innovation: Evidence from Frontier Markets. J Knowl Econ 14, 255–286 (2023). https://doi.org/10.1007/s13132-021-00861-3

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