SECTORAL GROWTH DYNAMICS OF COUNTRY GROUPS: A COUNTRY GROUPING SUGGESTION

Year 2023, Volume: 12 Issue: 1, 25 - 44, 31.03.2023

Selcuk Alp Elcin Aykac Alp Tugba Kiral Ozkan Mefule Findikci Erdogan

https://doi.org/10.17261/Pressacademia.2023.1724

Abstract

Purpose- In the study, the effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. These findings may help policymakers see sector impacts that help countries in their growth targets. The study aims to contribute to the literature in two ways. The first of these analyses are based on long-term economic growth and primary sector analysis. The second contribution is to propose an alternative empirical methodology with clustering analysis which is not used to obtain the basic assumption of homogeneity in the application of panel data analysis.
Methodology- The effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. In the second step, countries were divided into groups using K-means clustering analysis according to these impact values. With the help of these weights, the growth dynamics of similar countries and the contributions of sectors to this dynamic were obtained.
Findings- Countries analyzed in terms of the contribution of sectoral growth rates to the growth rate of the country were divided into groups by cluster analysis. It is determined that the countries grouped in terms of the contribution of sectors to growth are divided into 5 groups. The first group has 10 member countries. The second group has 12 countries and the third group it has 7 countries, the fourth group has 4 countries and only 1 country belongs to the fifth group. The countries in group 1 are Estonia, Turkey, Greece, Italy, Poland, Portugal, Lithuania, Latvia, Slovakia, and Slovenia. The countries in group 2 are Australia, Belgium, Czech Republic, Germany, Denmark, Hungary, Ireland, Mexico, Netherlands, Norway, Sweden, and New Zealand. The countries in group 3 are Austria, Spain, Finland, France, the Republic of Korea, Luxembourg, Switzerland, the USA, Israel, Costa Rica, the United Kingdom, and Japan.
Conclusion- Countries that have similar sectoral structures can analyze growth with panel data analysis, but it is important to form homogeneous groups while doing this analysis. For this reason, another critical suggestion it is offered based on the study is the use of FGP methodology in the analysis method.

Keywords

Economic growth, sectoral growth, Fuzzy Goal Programming, Cluster Analysis, Panel VAR

References

• Bai, J. and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70, 191-221.
• Bai, J. and Ng, S. (2004). A panic attack on unit roots and cointegration. Econometrica, 72, 1127-1177.
• Bhattacharya, B. B. and Mitra, A. (1989). Agriculture-industry growth rates: widening disparity: an explanation. Economic and Political Weekly, August 26.
• Bhattacharya, B. B. and Mitra, A. (1990). Excess growth of the tertiary sector: issues and implications. Economic and Political Weekly, November 3.
• Breitung, J. and Das, S. (2005). Panel unit root tests under cross-sectional dependence. Statistica Neerlandica, 59(4), 414-433.
• Canova, F. and Ciccarelli, M., (2013). Panel Vector Autoregressive models: A survey. European Central Bank Working Paper, 1507-1518.
• Chakravarty, S., and Mitra, A. (2009). Is industry still the engine of growth? An econometric study of the organized sector employment in India. Journal of Policy Modelling, 31, 22–35.
• Charnes A., and Cooper, W.W. (1961). Management Models and Industrial Applications of Linear Programming. New York, John Wiley.
• Charnes A., Cooper, W.W. and Ijiri, Y. (1963). Breakeven budgeting and programming to goals. Journal of Accounting Research, 1, 16-43.
• Charnes A., Cooper, W.W., Learner, D.B. and Snow, E.F. (1968) Application of goal programming model for media plannig. Management Science, 14, 431-436.
• Choi, I. (2002) Combination unit root tests for cross sectionally correlated panels. Mimeo, Hong Kong University of Science and Technology, 1-26.
• Clark C. (1940) The Conditions of Economic Progress. London: McMillan & Co.s.
• Courtney, J.F., Klastorin, T.D. and Ruefly, W. (1972) A Goal Programming Approach to Urban-Suburban Location Preferences. Management Science, 18(6), B258-B268.
• Dickey, D.A. and Fuller, W.A. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431.
• Forsyth, J.D. (1969). Utilization of goal programming in production and capital expenditure planning. CORS Journal, 7(2), 136-140.
• Gershuny J.J. and I.D. Miles (1983). The New Service Economy: The Transformation of Employment in Industrial Societies. New York: Praeger Publishers.
• Guris, S. (2018). Uygulamalı Panel Veri Ekonometrisi, Istanbul, Der Yay.
• Fisher A.G.B. (1939). Primary, Secondary and Tertiary Production. Economic Record 15: 24–38.
• Han, J. and Kamber, M. (2006). Data Mining Concepts and Techniques, San Francisco: Morgan Kauffmann Publishers Inc.
• Hannan E.L. (1981). On fuzzy goal programming. Decision Sciences, 12(3), 522-531.
• Haraguchi, N., Cheng, C. F. C. and Smeets, E. (2017). The importance of manufacturing in economic development: Has this changed? World Development, 93, 293–315.
• Holtz-Eakin, D., Newey, W. and Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica: Journal of the Econometric Society, 1371-1395.
• Hurlin, C. and Mignon, V. (2007). Second generation panel unit root tests. Working Papers/halshs-00159842, HAL.
• Ignizio, J.P. (1976). Goal Programming and Extensions. Lexington: D. C. Heath and Company.
• Im, K. S., Pesaran, M. H. and Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1), 53-74.
• Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys, 31(3), 264-323.
• Kaldor, N. (1966). Marginal productivity and the macro-economic theories of distribution: comment on Samuelson and Modigliani. The Review of Economic Studies, 33(4), 309-319.
• Karami, M., Elahinia, N. and Karami, S. (2019). The effect of manufacturing value added on economic growth: empirical evidence from Europe. Journal of Business Economics and Finance, 8(2), 133-147.
• Kuznets S. (1966). Modern Economic Growth, New Haven: Yale University Press.
• Lee, S.M. (1972). Goal Programming for Decision Analysis. Philadelphia: Auerbach.
• Lee, S.M. and Clayton, E.R. (1972). A Goal Programming Model for Academic Resources Allocation. Management Science, 18(8), B395-B408.
• Levin, A., Lin, C. F. and Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-sample properties. Journal of Econometrics, 108(1), 1-24.
• MacQueen, J. B., (1967). Some Methods for Classification and Analysis of Multivariate Observations, Proc. Symp. Math. Statist. and Probability (5th), 281–297.
• Moon, H.R. and Perron, B. (2004). Testing for a unit root in panels with dynamic factors. Journal of Econometrics, 122(1), 81-126.
• OECD (2018). Value added by activity (indicator). doi: 10.1787/a8b2bd2b-en (Accessed on 04 July 2018)
• Pang-Ning Tan, P. N., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Pearson Boston: Addison-Wesley.
• Pesaran, M.H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265-312.
• Phillips, P.C.B. and Sul, D. (2003). Dynamic panel estimation and homogeneity testing under cross section dependence. The Econometrics Journal, 6(1), 217-259.
• Schniederjans, M. J. (1984). Linear goal programming. Princeton, New Jersey: Petrocelli Books.
• Steuer, R.R. (1986). Multiple criteria optimization: Theory, computation, and application. New York: John Wiley.
• Szirmai, A. and Verspagen, B. (2015). Manufacturing and economic growth in developing countries, 1950–2005. Structural Change and Economic Dynamics, 34, 46-59.
• Su, D. and Yao, Y. (2017). Manufacturing as the key engine of economic growth for middle income economies. Journal of the Asia Pacific Economy, 22(1), 47-70.
• Venkatasubbaiah K, Acharyulu S.G. and. Chandra Mouli K.V.V. (2011). Fuzzy goal programming method for solving multi-objective transportation problems. Global Journal of Research in Engineering, 11(3), 4-10.
• Xu, R. and Wunsch II, D. (2005). Survey of clustering algorithms. IEEE Transaction on Neural Networks, 16, 645-678.
• Zeira, J. and Zoabi, H. (2015). Economic growth and sector dynamics. European Economic Review, 79, 1-15