Araştırma Makalesi
BibTex RIS Kaynak Göster

OECD ÜLKELERİNDE AR-GE VE KATMA DEĞER İLİŞKİSİNE ALT-SEKTÖREL BİR YAKLAŞIM

Yıl 2020, Cilt: 16 Sayı: 1, 1 - 12, 02.05.2020

Öz

Bu çalışma OECD ülkelerinde Ar-Ge harcamalarının imalat sanayi alt sektörlerinin katma değerlerine etkilerini incelemeyi amaçlamaktadır. Veriler 1998-2015 dönemini kapsamaktadır. Eşbütünleşme testleri tüm modeller için uzun dönemli ilişkileri onaylamıştır. Uzun dönem katsayı tahminlerine göre Ar-Ge esneklikleri “yiyecek, içecek ve tütün” alt sektörü için 0.3 ve 0.4; “makine ve taşımacılık ekipmanları alt sektörü” için 0.7 ve 0.8; “orta ve yüksek teknoloji sanayi alt sektörü” için 0.5 ve 0.6; “tekstil ve giyim alt sektörü” için -0.30 ve -0.31; ve “kimyasallar alt sektörü” için 0.4 ve 0.35’tir. Son olarak nedensellik testleri Ar-Ge harcamalarının “yiyecek, içecek ve tütün” alt sektörünün katma değerinin Granger-nedeni olduğunu göstermiştir.

Kaynakça

  • Acosta, M., Coronado, D., & Romero, C. (2015). Linking public support, R&D, innovation and productivity: New evidence from the Spanish food industry. Food Policy, 57, 50-61.
  • Asteriou, D., & Hall, S. G. (2011). Applied Econometrics. Macmillan International Higher Education.
  • Bozkurt, C. (2015). R&D expenditures and economic growth relationship in Turkey. International Journal of Economics and Financial Issues, 5(1), 188-198.
  • Bravo-Ortega, C., & Marin, A. G. (2011). R&D and productivity: A two way avenue?. World Development, 39(7), 1090-1107.
  • Choi, C., & Yi, M. H. (2018). The Internet, R&D expenditure and economic growth. Applied Economics Letters, 25(4), 264-267.
  • Choi, I. (2001). Unit root tests for panel data. Journal of international money and Finance, 20(2), 249-272.
  • Comanor, W. S., & Scherer, F. M. (1969). Patent statistics as a measure of technical change. Journal of political economy, 77(3), 392-398.
  • Crépon, B., Duguet, E., & Mairesse, J. (1998). Research, Innovation and Productivity: An Econometric Analysis at the Firm Level. Economics of Innovation and new Technology, 7(2), 115-158.
  • Doyar, B. V. (2019). R&D expenditures by field of science and GDP: Which causes which in Canada?. Economics and Business Letters, 8(1), 31-40.
  • Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic modelling, 29(4), 1450-1460.
  • Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 55(2), 251-276.
  • Erdil, E., Cilasun, S. M., & Eruygur, A. (2013). Do R&D Expenditures Matter for Labor Productivity in OECD Countries? An Unresolved Question. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), 71-82.
  • Falk, M. (2007). R&D spending in the high-tech sector and economic growth. Research in economics, 61(3), 140-147.
  • Fu, X., Mohnen, P., & Zanello, G. (2018). Innovation and productivity in formal and informal firms in Ghana. Technological Forecasting and Social Change, 131, 315-325.
  • Griliches, Z. (1964). Research expenditures, education, and the aggregate agricultural production function. The American Economic Review, 54(6), 961-974.
  • Guellec, D., & De La Potterie, B. V. P. (2002). R&D and productivity growth. OECD Economic studies, 2001(2), 103-126.
  • Harhoff, D. (1998). R&D and productivity in German manufacturing firms. Economics of Innovation and New Technology, 6(1), 29-50.
  • Hu, A. G. (2001). Ownership, government R&D, private R&D, and productivity in Chinese industry. Journal of Comparative Economics, 29(1), 136-157.
  • Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1), 53-74.
  • Kancs, D. A., & Siliverstovs, B. (2016). R&D and non-linear productivity growth. Research policy, 45(3), 634-646.
  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics, 90(1), 1-44.
  • Kao, C., & Chiang, M. H. (2001). On the estimation and inference of a cointegrated regression in panel data. In B. H. Baltagi, T. B. Fomby, R. C. Hill (Eds), Nonstationary panels, panel cointegration, and dynamic panels (pp: 179-222). Emerald Group Publishing Limited.
  • Kleinknecht, A. (2000). Indicators of manufacturing and service innovation: their strengths and weaknesses. In J. S. Metcalfe, I. Miles (Eds), Innovation systems in the service economy, (pp. 169-186). Springer, Boston, MA.
  • Lokshin, B., Belderbos, R., & Carree, M. (2008). The productivity effects of internal and external R&D: Evidence from a dynamic panel data model. Oxford bulletin of Economics and Statistics, 70(3), 399-413.
  • Lopez, L., & Weber, S. (2018). Testing for Granger causality in panel data. The Stata Journal, 17(4), 972-984.
  • 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, 61(S1), 631-652.
  • O’Mahony, M., & Vecchi, M. (2009). R&D, knowledge spillovers and company productivity performance. Research Policy, 38(1), 35-44.
  • OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, DOI: https://doi.org/10.1787/9789264239012-en.
  • OECD/Eurostat (2005), Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd Edition, The Measurement of Scientific and Technological Activities, OECD Publishing, Paris, DOI: https://doi.org/10.1787/9789264013100-en.
  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
  • Pedroni, P. (2000). “Fully Modified OLS for Heterogeneous Cointegrated Panels”, In B. H. Baltagi, T. B. Fomby, R. C. Hill (Eds.) Nonstationary panels, panel cointegration, and dynamic panels (pp. 93-130). Emerald Group Publishing Limited.
  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83(4), 727-731.
  • Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric theory, 20(3), 597-625.
  • Peng, L. (2010) Study on Relationship between R&D Expenditure and Economic Growth ofChina, In Proceedings of the 7th International Conference on Innovation & Management,1725-1728
  • Sadraoui, T., Ali, T. B., and Deguachi, B. (2014) Economic Growth and International R&DCooperation: A Panel Granger Causality Analysis, International Journal of Econometrics and Financial Management, 2(1), 7-21. doi: 10.12691/ijefm-2-1-2. Sylwester, K. (2001). R&D and economic growth. Knowledge, Technology & Policy, 13(4), 71-84.
  • Tsang, E. W., Yip, P. S., & Toh, M. H. (2008). The impact of R&D on value added for domestic and foreign firms in a newly industrialized economy. International Business Review, 17(4), 423-441.
  • Verspagen, B. (1995). R&D and productivity: A broad cross-section cross-country look. Journal of Productivity Analysis, 6(2), 117-135.
  • World Bank (2019). World Development Indicators. Retrieved: May 18, 2019, from http://databank.worldbank.org/data/ reports.aspx?source=world-development-indicators
  • Wu, Y., Zhou, L. and Li, J. X. (2007) Cointegration and Causality Between R&D Expenditureand Economic Growth in China: 1953-2004, In International Conference on Public Administration, 76.
  • Yang, C. H. (2006) Is innovation the story of Taiwan's economic growth?, Journal of Asian Economics, 17(5), 867-878. doi: 10.1016/j.asieco.2006.08.007.

A SUB-SECTORAL APPROACH ON THE NEXUS BETWEEN R&D AND VALUE-ADDED IN OECD COUNTRIES

Yıl 2020, Cilt: 16 Sayı: 1, 1 - 12, 02.05.2020

Öz

Current study aims to investigate the impact of R&D expenditures on added values of manufacturing sub-sectors in OECD countries. Data employed spans 1998-2015. Cointegration tests validate long-run relationship for each model. Long-run coefficient estimates show R&D elasticities of added values are 0.3 and 0.4 for ‘food, beverages, and tobacco’; 0.7 and 0.8 for ‘machinery and transportation equipment’; 0.5 and 0.6 for ‘medium and high-tech industry’; -0.30 and -0.31 for ‘textiles and clothing’; and 0.4 and 0.35 for ‘chemicals’. Lastly, causality tests reveal a causality from R&D expenditures to value-added of ‘food, beverages, and tobacco’ sub-sector.

Kaynakça

  • Acosta, M., Coronado, D., & Romero, C. (2015). Linking public support, R&D, innovation and productivity: New evidence from the Spanish food industry. Food Policy, 57, 50-61.
  • Asteriou, D., & Hall, S. G. (2011). Applied Econometrics. Macmillan International Higher Education.
  • Bozkurt, C. (2015). R&D expenditures and economic growth relationship in Turkey. International Journal of Economics and Financial Issues, 5(1), 188-198.
  • Bravo-Ortega, C., & Marin, A. G. (2011). R&D and productivity: A two way avenue?. World Development, 39(7), 1090-1107.
  • Choi, C., & Yi, M. H. (2018). The Internet, R&D expenditure and economic growth. Applied Economics Letters, 25(4), 264-267.
  • Choi, I. (2001). Unit root tests for panel data. Journal of international money and Finance, 20(2), 249-272.
  • Comanor, W. S., & Scherer, F. M. (1969). Patent statistics as a measure of technical change. Journal of political economy, 77(3), 392-398.
  • Crépon, B., Duguet, E., & Mairesse, J. (1998). Research, Innovation and Productivity: An Econometric Analysis at the Firm Level. Economics of Innovation and new Technology, 7(2), 115-158.
  • Doyar, B. V. (2019). R&D expenditures by field of science and GDP: Which causes which in Canada?. Economics and Business Letters, 8(1), 31-40.
  • Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic modelling, 29(4), 1450-1460.
  • Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 55(2), 251-276.
  • Erdil, E., Cilasun, S. M., & Eruygur, A. (2013). Do R&D Expenditures Matter for Labor Productivity in OECD Countries? An Unresolved Question. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), 71-82.
  • Falk, M. (2007). R&D spending in the high-tech sector and economic growth. Research in economics, 61(3), 140-147.
  • Fu, X., Mohnen, P., & Zanello, G. (2018). Innovation and productivity in formal and informal firms in Ghana. Technological Forecasting and Social Change, 131, 315-325.
  • Griliches, Z. (1964). Research expenditures, education, and the aggregate agricultural production function. The American Economic Review, 54(6), 961-974.
  • Guellec, D., & De La Potterie, B. V. P. (2002). R&D and productivity growth. OECD Economic studies, 2001(2), 103-126.
  • Harhoff, D. (1998). R&D and productivity in German manufacturing firms. Economics of Innovation and New Technology, 6(1), 29-50.
  • Hu, A. G. (2001). Ownership, government R&D, private R&D, and productivity in Chinese industry. Journal of Comparative Economics, 29(1), 136-157.
  • Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of econometrics, 115(1), 53-74.
  • Kancs, D. A., & Siliverstovs, B. (2016). R&D and non-linear productivity growth. Research policy, 45(3), 634-646.
  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics, 90(1), 1-44.
  • Kao, C., & Chiang, M. H. (2001). On the estimation and inference of a cointegrated regression in panel data. In B. H. Baltagi, T. B. Fomby, R. C. Hill (Eds), Nonstationary panels, panel cointegration, and dynamic panels (pp: 179-222). Emerald Group Publishing Limited.
  • Kleinknecht, A. (2000). Indicators of manufacturing and service innovation: their strengths and weaknesses. In J. S. Metcalfe, I. Miles (Eds), Innovation systems in the service economy, (pp. 169-186). Springer, Boston, MA.
  • Lokshin, B., Belderbos, R., & Carree, M. (2008). The productivity effects of internal and external R&D: Evidence from a dynamic panel data model. Oxford bulletin of Economics and Statistics, 70(3), 399-413.
  • Lopez, L., & Weber, S. (2018). Testing for Granger causality in panel data. The Stata Journal, 17(4), 972-984.
  • 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, 61(S1), 631-652.
  • O’Mahony, M., & Vecchi, M. (2009). R&D, knowledge spillovers and company productivity performance. Research Policy, 38(1), 35-44.
  • OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, DOI: https://doi.org/10.1787/9789264239012-en.
  • OECD/Eurostat (2005), Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd Edition, The Measurement of Scientific and Technological Activities, OECD Publishing, Paris, DOI: https://doi.org/10.1787/9789264013100-en.
  • Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.
  • Pedroni, P. (2000). “Fully Modified OLS for Heterogeneous Cointegrated Panels”, In B. H. Baltagi, T. B. Fomby, R. C. Hill (Eds.) Nonstationary panels, panel cointegration, and dynamic panels (pp. 93-130). Emerald Group Publishing Limited.
  • Pedroni, P. (2001). Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics, 83(4), 727-731.
  • Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric theory, 20(3), 597-625.
  • Peng, L. (2010) Study on Relationship between R&D Expenditure and Economic Growth ofChina, In Proceedings of the 7th International Conference on Innovation & Management,1725-1728
  • Sadraoui, T., Ali, T. B., and Deguachi, B. (2014) Economic Growth and International R&DCooperation: A Panel Granger Causality Analysis, International Journal of Econometrics and Financial Management, 2(1), 7-21. doi: 10.12691/ijefm-2-1-2. Sylwester, K. (2001). R&D and economic growth. Knowledge, Technology & Policy, 13(4), 71-84.
  • Tsang, E. W., Yip, P. S., & Toh, M. H. (2008). The impact of R&D on value added for domestic and foreign firms in a newly industrialized economy. International Business Review, 17(4), 423-441.
  • Verspagen, B. (1995). R&D and productivity: A broad cross-section cross-country look. Journal of Productivity Analysis, 6(2), 117-135.
  • World Bank (2019). World Development Indicators. Retrieved: May 18, 2019, from http://databank.worldbank.org/data/ reports.aspx?source=world-development-indicators
  • Wu, Y., Zhou, L. and Li, J. X. (2007) Cointegration and Causality Between R&D Expenditureand Economic Growth in China: 1953-2004, In International Conference on Public Administration, 76.
  • Yang, C. H. (2006) Is innovation the story of Taiwan's economic growth?, Journal of Asian Economics, 17(5), 867-878. doi: 10.1016/j.asieco.2006.08.007.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ekonomi
Bölüm Makaleler
Yazarlar

Bayram Veli Doyar 0000-0002-4886-7709

Yayımlanma Tarihi 2 Mayıs 2020
Kabul Tarihi 16 Mart 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 16 Sayı: 1

Kaynak Göster

APA Doyar, B. V. (2020). A SUB-SECTORAL APPROACH ON THE NEXUS BETWEEN R&D AND VALUE-ADDED IN OECD COUNTRIES. Ekonomik Ve Sosyal Araştırmalar Dergisi, 16(1), 1-12.

İletişim Adresi: Bolu Abant İzzet Baysal Üniversitesi İktisadi ve İdari Bilimler Fakültesi Ekonomik ve Sosyal Araştırmalar Dergisi 14030 Gölköy-BOLU

Tel: 0 374 254 10 00 / 14 86 Faks: 0 374 253 45 21 E-posta: iibfdergi@ibu.edu.tr

ISSN (Basılı) : 1306-2174 ISSN (Elektronik) : 1306-3553