Factors Influencing the Economic Behavior of the Food, Beverages and Tobacco Industry: A Case Study for Portuguese Enterprises

Main Article Content

Kelly Patricia Murillo
https://orcid.org/0000-0003-1479-8757
Eugénio Rocha
https://orcid.org/0000-0003-3628-6795

Abstract

In today's world, it is increasingly important to conduct economic and financial analyzes of enterprises in all sectors to determine strengths, identify weaknesses and adopt strategies that allow them to be at the highest competitive level. In particular, the food sector plays an essential role in the economy of any country, representing a significant contribution to gross domestic product, total employment, and disposable income of households. In this work, we adopt a methodology for measuring efficiency based on the multidirectional efficiency analysis and other mathematical techniques (the calculation of the normal distribution intersection coefficient (NC value), analysis of clusters and principal components, and model fitting) in order to examine the factors that influence the performance of Portuguese enterprises in the food, beverages and tobacco industry for the period of 2006-2013. The results show a characterization of the financial structure of the sector and diagnosis through indexes that identify the strategic positioning of the enterprises in terms of efficiency scores. In addition, we also show that an analysis of the variables that must be approached differently to obtain better results regarding economic performance. Although there is an increase in credit with the acquisition of long-term debts, there is no evidence that this implies the ability of enterprises to grow faster, which affects profitability.

Downloads

Download data is not yet available.

Article Details

How to Cite
Murillo, K., & Rocha, E. (2020). Factors Influencing the Economic Behavior of the Food, Beverages and Tobacco Industry: A Case Study for Portuguese Enterprises. World Journal of Applied Economics, 6(2), 99-121. https://doi.org/10.22440/wjae.6.2.1
Section
Research Articles

References

Abdi, H., & Williams, L. J. (2010). Principal Component Analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433–59. doi:10.1002/wics.101

Asmild, M., Holvad, T., Hougaard, J. L., & Kronborg, D. (2009). Railway Reforms: Do They Influence Operating Efficiency? Transportation, 36, 617–38. doi:10.1007/s11116-009-9216-x

Asmild, M., Paradi, J. C., & Kulkarni, A. (2006). Using Data Envelopment Analysis in Software Development Productivity Measurement. Software Process Improvement and Practice, 11, 561–72. doi:10.1002/spip.298

Banker, R. D., Charnes, A., & Cooper, W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30, 1078–92. doi:10.1287/mnsc.30.9.1078

Bergman, L. R., & Magnusson, D. (2001). Person-centered Research. In T. Cook, & C. Ragin (Eds.), Logic of Inquiry and Research Design (Vol. 8 of the International Encyclopedia of the Social and Behavioral Sciences ed., pp. 11333–39). Oxford: Elsevier.

Bhat, Z. U., Sultana, D., & Dar, Q. F. (2019). A Comprehensive Review of Data Envelopment Analysis (DEA) in Sports. Journal of Sports Economics & Management, 9, 82–109.

Bogetoft, P., & Hougaard, J. L. (1999). Efficiency Evaluations Based on Potential (Non-proportional) Improvements. Journal of Productivity Analysis, 12, 233–47. doi:10.1023/A:1007848222681

Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R. Springer-Verlag New York. doi:10.1007/978-1-4419-7961-2

Central Bank of Portugal. (2011). Sectoral Analysis of Manufacture of Food Products. Central Balance-Sheet Studies.

Chen, X., Chen, C., & Jin, L. (2011). Principal Component Analysis in Anthropological Genetics. Advances in Anthropology, 1, 9–14. doi:10.4236/aa.2011.12002

Dray, S. (2008). On the Number of Principal Components: A Test of Dimensionality Based on Measurements of Similarity Between Matrices. Computational Statistics & Data Analysis, 52, 2228–37. doi:10.1016/j.csda.2007.07.015

Escoufier, Y. (1973). Le Traitement Des Variables Vectorielles. Biometrics, 29, 751–60.

EU-MERCI. (2016). Analysis of Food and Beverage sector in Different Countries. Horizon 2020 Project Nr. 693845.

Ferré, L. (1995). Selection of Components in Principal Component Analysis: A Comparison of Methods. Computational Statistics & Data Analysis, 19, 669–82. doi:10.1016/0167-9473(94)00020-J

Figueiredo Filho, D. B. & Silva Junior, J. A. da. (2010). Visão Além do Alcance: Uma Introdução à Análise Fatorial. Opinião Pública, 16, 160-185. doi:10.1590/S0104-62762010000100007

Gongbing, B., Pingchun, W., Feng, Y., & Liang, L. (2014). Energy and Environmental Efficiency of China's Transportation Sector: A Multidirectional Analysis Approach. Mathematical Problems in Engineering, 2014, 1–12. doi:10.1155/2014/539596

Good, I. (1969). Some Applications of the Singular Decomposition of a Matrix. Technometrics, 11, 823–31. doi:10.1080/00401706.1969.10490741

Gordon, A. D. (1981). Classification. Chapman and Hall, London.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. (2006). Multivariate Data Analysis. Pearson International Edition, New Jersey.

Hauke, J., & Kossowski, T. (2011). Comparison of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data. Quaestiones Geographicae, 30, 87–93. doi:10.2478/v10117-011-0021-1

Hirschberg, J. G., & Lye, J. N. (2001). Clustering in a Data Envelopment Analysis Using Bootstrapped Efficiency Scores. Department of Economics - Working Papers Series, The University of Melbourne.

Hotelling, H. (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal Educational Psych, 24, 417–41. doi:10.1037/h0071325

Inman, H. F., & Bradley, E. L. (1989). The Overlapping Coefficient as a Measure of Agreement between Probability Distributions and Point Estimation of the Overlap of Two Normal Densities. Communications in Statistics - Theory and Methods, 18, 3851–74. doi:10.1080/03610928908830127

Interreg Central Europe. (2017). Food Sector Related Knowledge Integration: Food Sector Global Market Trend Analysis. I-CON project.

Jackson, D. (1993). Stopping Rules in Principal Components Analysis: A Comparison of Heuristical and Statistical Approaches. Ecology, 74, 2204–14. doi:10.2307/1939574

Jolliffe, I. (2002). Principal Component Analysis. Springer, Berlin.

Kaffash, S., & Marra, M. (2017). Data Envelopment Analysis in Financial Services: A Citations Network Analysis of Banks, Insurance Companies and Money Market Funds. Annals of Operations Research, 253, 307–44. doi:10.1007/s10479-016-2294-1

Karun, K., & Isaac, E. (2013). Cogitative Analysis on k-means Clustering Algorithm and its Variants. International Journal of Advanced Research in Computer and Communication Engineering, 2, 1875–80.

Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by Means of Medoids. In Y. Dodge (Ed.), Statistical Data Analysis Based on the L_1–Norm and Related Methods (pp. 405–16). North-Holland.

Kim, J. H., Choi, J. H., Yoo, K. H., Loh, W. K., & Nasridinov, A. (2019). A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph. Electronics, 8, 1–23. doi:10.3390/electronics8101094

Machado, D. M. (2017). Portugal Food Processing Sector. Gain Report, PT1102, Global Agricultural Information Network.

Morey, L. C., Blashfield, R. K., & Skinner, H. A. (1983). A Comparison of Cluster Analysis Techniques Within a Sequential Validation Framework. Multivariate Behavioral Research, 18, 309–29. doi:10.1207/s15327906mbr1803_4

Murillo, K. P., & Rocha, E. M. (2018). The Portuguese Manufacturing Sector During 2013-2016 After the Troika Austerity Measures. World Journal of Applied Economics, 4, 21–38. doi:10.22440/wjae.4.1.2

Murillo, K. P., Rocha, E. M., & Pardo, C. I. (2018). Energy Production and C0_2 Emission Efficiency of Eight European Countries in the Manufacturing Area. Journal of Management and Information Technology, 13, 1–17. doi:10.24297/ijmit.v13i1.7427

Murillo, K. P., Rocha, E. M., & Ramalho, J. S. (2018). About the Efficiency Behavior of the Portuguese Manufacturing Firms During the Financial Crisis. Libertas Mathematica (new series), 38, 1–27.

Pearson, K. (1901). On Lines and Planes of Closest Fit to Systems of Points in Space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, 559–72. doi:10.1080/14786440109462720

Peres-Neto, P. R., Jackson, D. A., & Somers, K. M. (2005). How Many Principal Components? Stopping Rules for Determining the Number of Non-trivial Axes Revisited. Computational Statistics & Data Analysis, 49, 974–97. doi:10.1016/j.csda.2004.06.015

Ramalho, J. J., & da Silva, J. V. (2009). A Two-part Fractional Regression Model for the Financial Leverage Decisions of Micro, Small, Medium and Large Firms. Quantitative Finance, 9, 621–36. doi:10.1080/14697680802448777

Walesiak, M. (1993). Multivariate Statistical Analysis in Marketing Research. Research Papers, Wroclaw University of Economics.

Wang, K., Wei, Y. M., & Zhang, X. (2013). Energy and Emissions Efficiency Patterns of Chinese Regions: A Multi-directional Efficiency Analysis. Applied Energy, 104, 105–16. doi:10.1016/j.apenergy.2012.11.039

Wen, H. J., Lim, B., & Lisa Huang, H. (2003). Measuring E‐commerce Efficiency: A Data Envelopment Analysis (DEA) Approach. Industrial Management & Data Systems, 103, 703–10. doi:10.1108/02635570310506124