AR-GE TÜRLERİNİN KATMA DEĞER ÜZERİNE ETKİLERİ: TÜRKİYE ÖRNEĞİ

Year 2022, Issue: 1, 32 - 46, 31.01.2022

İpek Akad

https://doi.org/10.51551/verimlilik.815152

Abstract

Amaç: Bu çalışma, OECD tarafından Temel Araştırma, Uygulamalı Araştırma ve Deneysel Geliştirme olmak üzere üç Ar-Ge türü olarak sınıflandırılan Ar-Ge türlerinin katma değer üzerine etkilerini açıklama amacındadır.

Yöntem: Kullanılan veri setinin içsellik probleminden hareketle eş anlı bir denklem sisteminde İki Aşamalı En Küçük Kareler (2AEKK) kullanılarak araç değişken tahminlemesi yapılmıştır.

Bulgular: Bu çalışmada, Türkiye'nin 1994-2019 yılları arasındaki verileri kullanılarak, Ar-Ge türleri kamu ve özel sektör finansmanı altında sınıflandırılmıştır. Özel sektörün Ar-Ge'ye kamuya göre daha fazla harcama yaptığı, ancak söz konusu harcamaların kamu sektörüne göre etkili sonuçlar vermediği sonucuna varılmıştır. Ayrıca özel sektör tarafından finanse edilen Temel ve Uygulamalı Araştırmanın hem kamu sektörü hem de diğer Ar-Ge türlerine göre en yüksek katma değeri yarattığı bu çalışmanın en önemli bulgularından biridir.

Özgünlük: Kamu ve özel sektör tarafından finanse edilen Ar-Ge türlerinin katma değer üzerindeki etkisi, gelişmekte olan ülkeler için araştırılması gereken önemli bir konudur. Bu çalışma, Türkiye gibi gelişmekte olan bir ülkenin verilerine dayanan ilk çalışmadır.

Keywords

Katma Değer, Araştırma ve Geliştirme, Kamu Politikaları, Araç Değişken Tahminlemesi

EFFECTS OF THE TYPES OF R&D ON THE VALUE ADDED: THE CASE OF TURKEY

Abstract

Purpose: This study aims to explain the effects of R&D types, which are classified as three types of R&D, namely Basic Research, Applied Research and Experimental Development by OECD, on value-added.

Methodology: Based on the endogeneity problem of the data set used in the study, a simultaneous equation system has been used to estimate the instrument variable by using Two-Stage Least Squares (2AEKK).

Findings: In this study, using data from Turkey between the years 1994-2019, R&D types are classified under public and private sector financing. It has been concluded that the private sector spends more on R&D than the public sector, but these expenditures do not yield effective results compared to the public sector. In addition, it is one of the most important findings of this study that Basic and Applied Research, financed by the private sector, creates the highest added value compared to both the public sector and other types of R&D.

Originality: The impact of R&D types financed by the public and private sector on value-added is an important issue that needs to be investigated for developing countries. This study is the first study based on data from a developing country such as Turkey.

References

• Arltova, M. ve Fedorova, D. (2016). “Selection of Unit Root Test on the Basis of Length of the Time Series and Value of AR (1) Parameter”, Statistika-Statistics and Economy Journal, 96(3), 47-64.
• Baum, C.F. Schaffer, M.E., ve Stillman, S. (2002). “IVREG2: Stata Module for Extended Instrumental Variables/2SLS and GMM Estimation”, Boston College Department of Economics.
• Baum, C.F., Schaffer, M.E. ve venStillman, S. (2007). “Enhanced Routines for Instrumental Variables/Generalized Method of Moments Estimation and Testing”, The Stata Journal, 7(4), 465-506.
• Cadil, J., Mirosnik, K., Petkovova, L. ve Mirvald, M. (2018). “Public Support of Private R&D–Effects on Economic Sustainability”, Sustainability, 10(12), 4612.
• Cassiman, B., Perez-Castrillo, D. ve Veugelers, R. (2002). “Endogenizing Know-How Flows Through the Nature of R&D Investments”, International Journal of Industrial Organization, 20(6), 775-799.
• Cragg, J.G. ve Donald, S.G. (1993). “Testing Identifiability and Specification in Instrumental Variable Models”, Econometric Theory, 222-240.
• Czarnitzki, D. ve Thorwarth, S. (2012). “Productivity Effects of Basic Research in Low-Tech and High-Tech Industries”, Research Policy, 41(9), 1555-1564.
• Durbin, J. (1954). “Errors in Variables”, Revue de l'institut International de Statistique, 23-32.
• Ebel, R.L. (1973). “Some Limitations of Basic Research in Education”, The Phi Delta Kappan, 49(2), 81-84.
• Gersbach, H., Sorger, G. ve Amon, C. (2018). “Hierarchical Growth: Basic and Applied Research”, Journal of Economic Dynamics and Control, 90, 434-459.
• Griliches, Z. (1985). “Productivity, R&D, and Basic Research at the Firm Level in the 1970s”, National Bureau of Economic Research, (No. w1547).
• Griliches, Z. (1998). Introduction to" R&D and Productivity: The Econometric Evidence", R&D and Productivity: The Econometric Evidence, University of Chicago Press, 1-14.
• Guellec, D. ve De La Potterie, B.V.P. (2002). R&D and Productivity Growth: Panel Data Analysis of 16 OECD Countries, OECD Economic Studies, 2001(2), 103-126.
• Hausman, J.A. (1978). “Specification Tests in Econometrics”, Econometrica: Journal of the Econometric Society, 1251-1271.
• Henard, D.H. ve McFadyen, M.A. (2005). “The Complementary Roles of Applied and Basic Research: A Knowledge‐Based Perspective”, Journal of Product Innovation Management, 22(6), 503-514.
• Holý, V. ve Šafr, K. (2018). “Are Economically Advanced Countries More Efficient in Basic and Applied Research?”, Central European Journal of Operations Research, 26(4), 933-950.
• Kleibergen, F. ve Paap, R. (2006). “Generalized Reduced Rank Tests Using the Singular Value Decomposition”, Journal of Econometrics, 133(1), 97-126.
• Klevorick, A.K., Levin, R. C., Nelson, R.R. ve Winter, S.G. (1995). “On the Sources and Significance of Interindustry Differences in Technological Opportunities”, Research Policy, 24(2), 185-205.
• Liao, X. (2018). “Public Appeal, Environmental Regulation and Green Investment: Evidence from China”, Energy Policy, 119, 554-562.
• Lichtenberg, F.R. ve Siegel, D. (1991). “The Impact of R&D Investment on Productivity-New Evidence Using Linked R&D-Lrd Data”, Economic Inquiry, 29(2), 203-229.
• Link, A.N. (1981). “Basic Research and Productivity Increase in Manufacturing: Additional Evidence”, American Economic Review, 71(5), 1111-1112.
• Luintel, K.B. ve Khan, M. (2011). Basic, applied and experimental knowledge and productivity: Further evidence. Economics Letters, 111(1), 71-74.
• Mansfield, E. (1980). “Basic Research and Productivity Increase in Manufacturing”, The American Economic Review, 70(5), 863-873.
• March, J.G. (1991). “Exploration and Exploitation in Organizational Learning”, Organization Science, 2(1), 71-87. OECD (2002), “The Measurement of Scientific and Technological Activities Frascati Manual2002: Proposed Standard Practice for Surveys on Research and Experimental Development”, OECD Publications Service, Paris.
• Pagan, A.R. ve Hall, A.D. (1983). “Diagnostic Tests as Residual Analysis”, Econometric Reviews, 2(2), 159-218.
• Reiersøl, O. (1941). “Confluence Analysis by Means of Lag Moments and Other Methods of Confluence Analysis”, Econometrica: Journal of the Econometric Society, 1-24.
• Rosenberg, N. (1990). “Why do Firms do Basic Research (with Their Own Money)?”, Research Policy, 19(2), 165-174.
• Rosenberg, N. ve Nelson, R.R. (1994). “American Universities and Technical Advance in Industry”, Research Policy, 23(3), 323-348.
• Rossi, P.E. (2014). “Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications”, Marketing Science, 33(5), 655-672.
• Ryan, K.F. ve Giles, D.E. (1998). “Testing for Unit Roots in Economic Time-series with Missing Observations”, Department of Economics, University of Victoria, 1-40.
• Salter, A.J. ve Martin, B.R. (2001). “The Economic Benefits of Publicly Funded Basic Research: A Critical Review”, Research Policy, 30(3), 509-532.
• Sargan, J.D. (1958). “The Estimation of Economic Relationships Using Instrumental Variables”, Econometrica: Journal of the Econometric Society, 393-415.
• Semadeni, M., Withers, M.C. ve TrevisCerto, S. (2014). “The Perils of Endogeneity and Instrumental Variables in Strategy Research: Understanding Through Simulations”, Strategic Management Journal, 35(7), 1070-1079.
• Stock, J.H. ve Yogo, M. (2002). “Testing for Weak Instruments in Linear IV Regression”, National Bureau of Economic Research, (No. t0284).
• Tsang, E.W.K., Yip, P.S.L. ve Toh, M.H. (2008). “The Impact of R&D on Value Added for Domestic and Foreign Firms in a Newly Industrial Economy”, International Business Review, 17(4), 423-441.
• Ventura, M. (2018). “Testing the Validity of Instruments in an Exactly Identified Equation”, International Journal of Computational Economics and Econometrics, 8(2), 159-169.
• Verma, R. (2012). “Can Total Factor Productivity Explain Value Added Growth in Services?”, Journal of Development Economics, 99(1), 163-177.
• Wu, D.M. (1973). “Alternative Tests of Independence between Stochastic Regressors and Disturbances”, Econometrica: Journal of the Econometric Society, 733-750.