VSTE
Littera Scripta

ISSN (online): 1805-9112

Open access

The Wage Impact on the Net Value-Added in the Agricultural Sector

Lukáš Frýd1*, Veronika Machová2, Jakub Horák2, Pedro Pardal3

  1. University of Economics, Department of Econometrics, Prague, Czech Republic
  2. Institute of Technology and Business, School of Expertness and Valuation, České Budějovice, Czechia
  3. Polytechnic Institute of Setubal, Department of Accounting and Finance, Setubal, Portugal

* Corresponding author: xfryl00@vse.cz

Abstract

Entrepreneurship in the area of agriculture is subject to various factors, such as nature risks (hydrologic, pests and diseases) or recently notable economic hazards with disastrous consequences for a lot of enterprises. A grave issue is, for example, a vast wage rise to which the agricultural sector fails to respond, thus threatening the existence of these companies.  The article aims at analysing the way wages influence the net value-added (NVA) in the agricultural sector of V4 and identifies components with a significant impact on NVA thereof. The data set from 2004-2017 of V4 provides the relevant data, using statistical methods – Maddala-Wu Test, Akaike Information Criterion and Engle and Granger two-step method to detect the relation between the average and minimum wage of NVA. The average wage increase positively influences NVA, as contrasted with minimum wages harming MVA.

Keywords:market value-added (MVA)average and minimum wageagricultureperformance and productivity of the enterprise

References

  1. Abbritti, M., & Fahr, S. (2013). Downward wage rigidity and business cycle asymmetries. Journal of Monetary Economics, 60(7), 871–886.
  2. Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. In Handbook of labor economics (Vol. 4B, pp. 1043–1171).
  3. Alekneviciene, V., Stareviciute, B., & Alekneviciute, E. (2018). Evaluation of the efficiency of European Union farms: A risk-adjusted return approach. Agricultural Economics, 64(6), 241–255.
  4. Arellano, M. (1987). Computing robust standard errors for within-groups estimators. Oxford Bulletin of Economics and Statistics, 431–434.
  5. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3–30.
  6. Bachmann, R., Bechara, P., & Vonnahme, C. (2020). Occupational mobility in Europe: Extent, determinants and consequences. Ruhr Economic Papers, 168(1), 79–108.
  7. Bachmann, R., Cim, M., & Green, C. (2019). Long-run patterns of labour market polarization: Evidence from German micro data. British Journal of Industrial Relations, 57(2), 350–376.
  8. Batyra, A., et al. (2019). Changes in the labor market and the rise of early retirement in France and Germany. German Economic Review, 20(4), 38–69.
  9. Beckers, V., et al. (2018). Modelling farm growth and its impact on agricultural land use: A country scale application of an agent-based model. LAND, 7(3).
  10. Bernués, A., et al. (2011). Sustainability of pasture-based livestock farming systems in the European Mediterranean context: Synergies and trade-offs. Livestock Science, 139(1–2), 44–57.
  11. Bojnec, S., & Fertő, I. (2020). The growth of farms: A Hungarian-Slovenian comparison. Post-Communist Economies.
  12. Braun, H., et al. (2019). Macroeconomic long-run effects of the German minimum wage when labor markets are frictional. Jahrbücher für Nationalökonomie und Statistik [Journal of Economics and Statistics], 240(2–3), 351–386.
  13. Cassidy, A., & McGrath, B. (2015). Farm, place and identity construction among Irish farm youth who migrate. Journal of Rural Studies, 37, 20–28.
  14. Davis, J. P., Caskie, P., & Wallace, M. (2013). Promoting structural adjustment in agriculture: The economics of New Entrant Schemes for farmers. Food Policy, 40, 90–96.
  15. Duesberg, S., Bogue, P., & Renwick, A. (2017). Retirement farming or sustainable growth—Land transfer choices for farmers without a successor. Land Use Policy, 61, 526–535.
  16. Duetsch, M., Himmelreicher, R., & Hohmeyer, K. (2020). Characteristics contributing to low- and minimum-wage labour in Germany. Jahrbücher für Nationalökonomie und Statistik [Journal of Economics and Statistics], 240(2–3), 161–200.
  17. Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.
  18. Hostiou, N., et al. (2020). Employment and farmers’ work in European ruminant livestock farms: A review. Journal of Rural Studies, 74, 223–224.
  19. Iyer, P., et al. (2018). Measuring farmer risk preferences in Europe: A systematic review. Journal of Agricultural Economics, 71(1), 3–26.
  20. Kühl, S., Flach, L., & Gauly, M. (2019). Economic assessment of small-scale mountain dairy farms in South Tyrol depending on feed intake and breed. Italian Journal of Animal Science, 19(1), 41–50.
  21. Mackinnon, J. G. (1991). Critical values for cointegration tests. In Long-run economic relationships: Readings in cointegration.
  22. Maddala, G. S., & Wu, S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 631–652.
  23. McKillop, J., Heanue, K., & Kinsella, J. (2018). Are all young farmers the same? An exploratory analysis of on-farm innovation on dairy and drystock farms in the Republic of Ireland. The Journal of Agricultural Education and Extension, 24(2), 137–151.
  24. Spicka, J., et al. (2019). Approaches to estimation the farm-level economic viability and sustainability in agriculture: A literature review. Agricultural Economics, 65(6), 289–297.
  25. Stehel, V., Horak, J., & Vochozka, M. (2019). Prediction of institutional sector development and analysis of enterprises active in agriculture. E&M Ekonomie a Management, 22(4), 103–118.
  26. Vrbka, J., & Rowland, Z. (2020). Using artificial intelligence in company management. Lecture Notes in Networks and Systems, 84, 422–429.
  27. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.