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Operating leverage and profitability of SMEs: agri-food industry in Europe

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Abstract

The objective of this paper is to analyse the effect that operating leverage exerts on the profitability of SMEs, both in isolation and indirectly through its other determinants. We use panel data from a total of 9652 European agri-food SMEs from 2009 to 2016. The work’s principal contribution is the study of the SMEs of a key sector of the European economy in terms of the cost structure, since this is an issue that directly affects the competitiveness of firms, but which has not garnered sufficient attention in the literature. It also considers the importance of the institutional and legal environment on the risk assumed by companies. The results obtained confirm that operating leverage or cost structure, in addition to affecting profitability, also affects the relationship between that profitability and other sources of risk that depend on the country in which the company operates. More specifically, indebtedness, size, innovation specificity and reputation all affect profitability to a greater or lesser extent, depending on the level of operating leverage of the company.

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Notes

  1. Díaz and Rodríguez (2006), Rodríguez (2010) and Torrent-Sellens and Díaz-Chao (2013) through the SEPI Foundation (Sociedad Estatal de Participaciones Industriales).

  2. See the complete report at: https://cordis.europa.eu/news/rcn/8257_en.html.

  3. The study: The European Innovation Monitoring System (EIMS). (https://cordis.europa.eu/news/rcn/10728_en.html).

  4. Horizon 2020 is the largest research and innovation program in the EU with almost 80 billion euros of funds available for 7 years (2014 to 2020) (https://ec.europa.eu/programmes/horizon2020/what-horizon-2020). This program is supplemented by the budget of the EUREKA/Eurostars Joint Program (2014–2020) (https://ec.europa.eu/programmes/horizon2020/en/h2020-section/eurostars-programme).

  5. These actions are materialised in tailored services and projects (supporting the capacity of innovation management, management of intellectual property rights, etc.), network actions and mobilisation of innovation service providers and policy makers.

  6. COSME is the EU program for competitiveness. It operates from 2014 to 2020 with a planned budget of 2.3 billion (https://ec.europa.eu/easme/en/cosme).

  7. See the full document of the “Annual Report of the European Commission on SMEs” at: https://ec.europa.eu/growth/smes/business-friendly-environment/performance-review_en.

  8. This is due to the fact that during the study period, most of the German companies were not obliged to provide their annual accounts.

  9. There are two reasons for the small number of companies in Poland, Sweden and the UK: (i) the lower requirements for the presentation of financial statements for this type of company, so that for many of them there is no information available; and (ii) they are countries with a smaller number of agri-food companies.

  10. In the annual accounts of large listed companies, a distinction is made between cost-of-goods-sold and selling (COGS) and general administration expenses (SG&A). Some authors, such as Chen et al. (2019), use the weight of SG&A in the assets because they prove by means of a regression that these costs vary to a lesser extent in the face of a variation in sales. On the other hand, Cao (2015) uses the weight in the assets of the sum of the two costs, COGS and SG&A.

  11. The other most widely used measure in the financial literature is the degree of operational leverage (DOL) which measures the change in earnings before interest and taxes (EBIT) in the face of a change in sales (Mandelker and Rhee 1984; Houmes et al. 2012; Harjoto 2017). But this variable has several drawbacks, most notably that it is not a static value. DOL is higher for sales close to the break-even point, so that a company with lower fixed costs may have a higher DOL than a company with high fixed costs because the first is close to the break-even point and the second is far away. Houmes et al. (2012) study the impact of operational leverage on systematic risk in companies listed under the two measures mentioned above. That is, they compare the results obtained by using the degree of operating leverage (DOL) with those obtained by using the ratio of net fixed assets to total assets (OLM) that we have used. They conclude that the second measure is the one that most significantly determines the beta of the assets.

  12. Neter et al. (1989) note that individual values for FIV greater than 10 indicate problems of multicollinearity, as well as an average value greater than 6.

  13. Arellano and Bond (1991) implemented this procedure and presented two levels of application depending on the nature of the random disturbance. If the residuals are homoscedastic, the GMM estimate in one stage will be the most appropriate. If, on the other hand, there is heteroscedasticity, the estimator of the instrumental variables in one stage will remain consistent, but the estimation in two stages increases the efficiency.

  14. As we have explained throughout the work, when it comes to the activity sector, operating leverage is a good indicator of operational risk.

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  1. Department of Corporate Finance, Faculty of Economics, University of Valencia, Avda. de los Naranjos, s/n, 46022, Valencia, Spain

    Alfredo Grau & Araceli Reig

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  1. Alfredo Grau
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Grau, A., Reig, A. Operating leverage and profitability of SMEs: agri-food industry in Europe. Small Bus Econ 57, 221–242 (2021). https://doi.org/10.1007/s11187-019-00294-y

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