Taxes, R&D Expenditures, and Open Innovation: Analyzing OECD Countries
Abstract
1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Descriptions and Sources
3.2. Data Analysis Methods
3.3. Model Specification
3.4. Data Analysis and Specification
4. Results
5. Discussion: Tax, R&D Expenditures, and Open Innovation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Rodrı, A.; Cataldo, M.D. Quality of government and innovative performance in the regions of Europe. J. Econ. Geogr. 2014, 15, 673–706. [Google Scholar] [CrossRef]
- Basova, A.V.; Nechaev, A.S. Taxation as an instrument of stimulation of innovation-active business entities. World Appl. Sci. J. 2013, 22, 1544–1549. [Google Scholar] [CrossRef]
- Mukherjee, A.; Singh, M.; Žaldokas, A. Do corporate taxes hinder innovation? J. Financ. Econ. 2017, 124, 195–221. [Google Scholar] [CrossRef]
- Pece, A.M.; Simona, O.E.O.; Salisteanu, F. Innovation and Economic Growth: An Empirical Analysis for CEE Countries. Procedia Econ. Financ. 2015, 26, 461–467. [Google Scholar] [CrossRef]
- Keuschnigg, C.; Ribi, E. Profit Taxation, Innovation and the Financing of Heterogeneous Firms. CEPR Discussion Paper No. DP7626. 2011. Available online: https://ideas.repec.org/p/cpr/ceprdp/7626.html (accessed on 31 December 2020).
- Wu, J.; Wu, Z.; Zhuo, S. The effects of institutional quality and diversity of foreign markets on exporting firms’ innovation. Int. Bus. Rev. 2015, 24, 1095–1106. [Google Scholar] [CrossRef]
- Afolabi, L.O.; Bakar, N.A.A. Journal of Economics Library. J. Econ. Libr. 2016, 3, 100–110. [Google Scholar]
- Oanh, N.T. The Relationship between Innovation Capability, Innovation Type and Innovation Performance in FDI Enterprises in Vietnam. Int. J. Econ. Financ. 2019, 11, 1–28. [Google Scholar] [CrossRef]
- Dimitrova, L.; Eswar, S.K. Capital Gains Tax and Innovation. SSRN Electron. J. 2017. [Google Scholar] [CrossRef]
- Atanassov, J.; Liu, X. Corporate Income Taxes, Tax Avoidance and Innovation. Working Paper. 2014; unpublished work. [Google Scholar]
- Balsalobre-Lorente, D.; Shahbaz, M.; Jabbour, C.J.C.; Driha, O.M. The role of energy innovation and corruption in carbon emissions: Evidence based on the EKC hypothesis. In Energy and Environmental Strategies in the Era of Globalization; Springer: New York, NY, USA, 2019; pp. 271–304. [Google Scholar]
- Jia, J.; Ma, G. Do R&D tax incentives work? Firm-level evidence from China. China Econ. Rev. 2017, 46, 50–66. [Google Scholar] [CrossRef]
- Forteza, A.; Noboa, C. Perceptions of institutional quality and justification of tax evasion. Const. Polit. Econ. 2019, 30, 367–382. [Google Scholar] [CrossRef]
- Fischer, B.; Tello-Gamarra, J. Institutional quality as a driver of efficiency in laggard innovation systems. J. Glob. Compet. Gov. 2017, 11, 129–144. [Google Scholar] [CrossRef]
- Yao, Q.; Huang, L.; Li, M. The effects of tech and non-tech innovation on brand equity in China: The role of institutional environments. PLoS ONE 2019, 14, e0215634. [Google Scholar] [CrossRef] [PubMed]
- Akcigit, U.; Baslandze, S.; Stantcheva, S. Taxation and the international mobility of inventors. Am. Econ. Rev. 2016, 106, 2930–2981. [Google Scholar] [CrossRef]
- Li, W.; Jia, Z. Carbon tax, emission trading, or the mixed policy: Which is the most effective strategy for climate change mitigation in China? Mitig. Adapt. Strateg. Glob. Chang. 2017, 22, 973–992. [Google Scholar] [CrossRef]
- Liesegang, C.; Runkel, M. Equalizing tax bases or tax revenues under tax competition? The role of formula apportionment. J. Public Econ. Theory 2018, 1–18. [Google Scholar] [CrossRef]
- Canh, N.P.; Schinckus, C.; Thanh, D.S. Do economic openness and institutional quality influence patents? Evidence from GMM systems estimates. Int. Econ. 2019, 157, 134–169. [Google Scholar] [CrossRef]
- Bekhet, H.A.; Latif, N.W.A. The impact of technological innovation and governance institution quality on Malaysia’s sustainable growth: Evidence from a dynamic relationship. Technol. Soc. 2018, 54, 27–40. [Google Scholar] [CrossRef]
- Tomizawa, A.; Zhao, L.; Bassellier, G.; Ahlstrom, D. Economic growth, innovation, institutions, and the Great Enrichment. Asia Pac. J. Manag. 2019, 35, 7–31. [Google Scholar] [CrossRef]
- Campodonico, L.A.B.; Bonfatti, R.; Pisano, L. Tax policy and the financing of innovation. J. Public Econ. 2016, 135, 32–46. [Google Scholar] [CrossRef]
- Jones, C.I.; Williams, J.C. Measuring the social return to R&D. Q. J. Econ. 1998, 113, 1119–1135. [Google Scholar] [CrossRef]
- Akcigit, U.; Grigsby, J.; Nicholas, T.; Stantcheva, S. Taxation and Innovation in the 20th Century. SSRN Electron. J. (Work. Pap. 24982) 2018. [Google Scholar] [CrossRef]
- Aghion, P.; Bloom, N.; Schankerman, M.; Van Reenen, J. Identifying Technology Spillovers and Product Market Rivalry. Econometrica 2013, 81, 1347–1393. [Google Scholar] [CrossRef]
- Akcigit, U.; Hanley, D.; Serrano-Velarde, N. Back to Basics: Basic Research Spillovers, Innovation Policy, and Growth; PIER Working Paper 13-051; National Bureau of Economic Research: New York, NY, USA, 2020. [Google Scholar] [CrossRef]
- Olivares Olivares, B.D. Technological innovation within the Spanish tax administration and data subjects’ right to access: An opportunity knocks. Comput. Law Secur. Rev. 2018, 34, 628–639. [Google Scholar] [CrossRef]
- Barry, J.M. Taxation and Innovation: The Sharing Economy as a Case Study. In Nestor Davidson, Michèle Finck and John Infranca, Cambridge Handbook on Law and Regulation of the Sharing Economy; Cambridge University Press: Cambridge, UK, 2018; Volume 18, pp. 381–394. [Google Scholar]
- Hall, B.H. Tax Policy for Innovation. In Innovation and Public Policy; NBER Work. Pap. Ser., no. 506; University of Chicago Press: Chicago, IL, USA, 2019; pp. 1–24. [Google Scholar] [CrossRef]
- Nguyen, H.M.; Bui, N.H.; Vo, D.H.; McAleer, M. Energy consumption and economic growth: Evidence from Vietnam. J. Rev. Glob. Econ. 2019, 8, 350–361. [Google Scholar] [CrossRef]
- Vacca, A.; Iazzi, A.; Maizza, A. 3.1. Corporate tax behaviors and firm value: The moderating role of audit characteristics. In Proceedings of the International Online Conference “Corporate Governance: Examining Key Challenges and Perspectives”, Lisbon, Portugal, 7–9 May 2020; p. 134. [Google Scholar]
- Arellano, M.; Bond, S. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Rev. Econ. Stud. 1991, 58, 277–297. [Google Scholar] [CrossRef]
- Blundell, R.; Bond, S. Initial conditions and moment restrictions in dynamic panel data models. J. Econom. 1998, 87, 116–117. [Google Scholar] [CrossRef]
- Apergis, N.; Ozturk, I. Testing environmental Kuznets curve hypothesis in Asian countries. Ecol. Indic. 2015, 52, 16–22. [Google Scholar] [CrossRef]
- Saliminezhad, A.; Bahramian, P. Clean energy consumption and economic growth nexus: Asymmetric time and frequency domain causality testing in China. Energy Sources Part B Econ. Plan. Policy 2020, 15, 1–12. [Google Scholar] [CrossRef]
- Arellano, M.; Bover, O. Another look at the instrumental variable estimation of error-components models. J. Econom. 1995, 68, 29–51. [Google Scholar] [CrossRef]
- Ganda, F. The environmental impacts of financial development in OECD countries: A panel GMM approach. Environ. Sci. Pollut. Res. 2019, 26, 6758–6772. [Google Scholar] [CrossRef]
- Oseni, I.O. Exchange rate volatility and private consumption in Sub-Saharan African countries: A system-GMM dynamic panel analysis. Future Bus. J. 2016, 2, 103–115. [Google Scholar] [CrossRef]
- Gould, L.A. Exploring Gender-Based Disparities in Legal Protection, Education, Health, Political Empowerment, and Employment in Failing and Fragile States. Women Crim. Justice 2014, 24, 279–305. [Google Scholar] [CrossRef]
- Twerefou, D.K.; Danso-Mensah, K.; Bokpin, G.A. The environmental effects of economic growth and globalization in Sub-Saharan Africa: A panel general method of moments approach. Res. Int. Bus. Financ. 2017, 42, 939–949. [Google Scholar] [CrossRef]
- Leitao C., N.; Balslaobre-Lorente, D.B. The Linkage between Economic Growth, Renewable Energy, Tourism, CO 2 Emissions, and International Trade: The Evidence for the European Union. Energies 2020, 13, 4838. [Google Scholar] [CrossRef]
- Shehzad, K.; Xioxing, L.; Sarfraz, M.; Zulfiqar, M. Signifying the imperative nexus between climate change and information and communication technology development: A case from Pakista. Environ. Sci. Pollut. Res. 2020, 27, 30502–30517. [Google Scholar] [CrossRef]
- Leitão, N.C.; Balogh, J.M. The impact of intra-industry trade on carbon dioxide emissions: The case of the european union. Agric. Econ. (Czech Repub.) 2020, 66, 203–214. [Google Scholar] [CrossRef]
- Kate, F.T.; Milionis, P. Is capital taxation always harmful for economic growth? Int. Tax Public Financ. 2019, 26, 758–805. [Google Scholar] [CrossRef]
- Cheng, S.; Fan, W.; Chen, J.; Meng, F.; Liu, G.; Song, M.; Yang, Z. The impact of fiscal decentralization on CO2 emissions in China. Energy 2020, 192, 116685. [Google Scholar] [CrossRef]
- Davis, C.; Hashimoto, K.I. Corporate Tax Policy and Industry Location With Fully Endogenous Productivity Growth. Econ. Inq. 2018, 56, 1136–1148. [Google Scholar] [CrossRef]
- Herman, K.S.; Xiang, J. Environmental regulatory spillovers, institutions, and clean technology innovation: A panel of 32 countries over 16 years. Energy Res. Soc. Sci. 2020, 62, 101363. [Google Scholar] [CrossRef]
- Chambers, D.; Munemo, J. The Impact of Regulations and Institutional Quality on Entrepreneurship. SSRN Electron. J. 2018. [Google Scholar] [CrossRef]
- Upreti, B.N.; Dhakal, S.; Bhattarai, T.N.; Adhikari, B.R.; Bajracharya, S.R.; Yoshida, M. Climate change impact on glacier retreat and local community in the Langtang Valley, Central Nepal. J. Dev. Innov. 2017, 1, 45–59. [Google Scholar]
- Dechezlepr, A.; Eini, E.; Martin, R.; Nguyen, K.; van Reenen, J. Do Tax Incentives Increase Firm Innovation? An RD Design for R & D. No. 134057. 2020. Available online: https://as.nyu.edu/content/dam/nyu-as/econ/misc/Do%20tax%20incentives%20increase%20firm%20innovation.pdf (accessed on 31 December 2020).
- Halkos, G.; Paizanos, E. Fiscal Policy and Economic Performance: A Review of the Theoretical and Empirical Literature. Munich Pers. RePEc Arch. Fisc. 2015, 1–36. [Google Scholar] [CrossRef]
- Laplante, S.K.; Skaife, H.A.; Swenson, L.A.; Wangerin, D.D. Limits of tax regulation: Evidence from strategic R&D classification and the R&D tax credi. J. Account. Public Policy 2019, 38, 89–105. [Google Scholar] [CrossRef]
- Atanassov, J.; Liu, X. Corporate Income Taxes, Financial Constraints and Innovation. SSRN Electron. J. 2015. [Google Scholar] [CrossRef]
Variables | Measuring | Symbols | Unit Adopted | Source |
---|---|---|---|---|
Innovation | Innovation Index | LogIn | Points | The Global Economy (2020) |
Government Expenditure | Research and Development Expenditure | LogRDE | Government spending as percent of GDP | World Bank (2020) |
Taxes | Corporate Tax Rate | LogCT | Tax rate, percent of commercial profits | The Global Economy (2020) |
Taxes | Number of Taxes Paid by Businesses | LogTP | Number of Taxes | The Global Economy (2020) |
Governance | Rule of Law Index | LogRL | Points | The Global Economy (2020) |
LogIn | LogRL | LogCT | LogTP | LogRDE | |
---|---|---|---|---|---|
Mean | 3.9261 | 0.1101 | 3.6666 | 2.3160 | 0.5006 |
Median | 3.9646 | 0.3646 | 3.7062 | 2.1972 | 0.5247 |
Maximum | 4.2239 | 0.7419 | 4.2669 | 3.4657 | 1.5151 |
Minimum | 3.5293 | −4.6051 | 1.5040 | 1.3862 | −1.0498 |
Std. Dev. | 0.1549 | 0.7575 | 0.3355 | 0.4167 | 0.5501 |
LogIn | LogRL | Log RDE | LogCT | LogTP | |
---|---|---|---|---|---|
LogIn | 1.0000 | ||||
LogRL | 0.7376 | 1.0000 | |||
LogRDE | 0.7257 | 0.4659 | 1.0000 | ||
LogCT | −0.1593 | −0.0417 | 0.1324 | 1.0000 | |
LgoTP | −0.0305 | −0.0820 | 0.0821 | −0.1111 | 1.0000 |
Levin, Lin and Chu t * | |||||
At Level | At first Difference | ||||
Statistic | Prob. | Statistic | Prob. | ||
LogIn | −5.6531 *** | (0.0000) | ∆LogIn | −15.8990 *** | (0.0000) |
LogRL | −3.1488 *** | (0.0008) | ∆LogRL | −1.72575 ** | (0.0422) |
LogTP | −1.6324 ** | (0.0513) | ∆LogTP | −0.95526 * | (0.0697) |
LogCT | −95.337 *** | (0.0000) | ∆LogCT | −227.028 *** | (0.0000) |
LogRDE | −9.1304 *** | (0.0000) | ∆LogRDE | −18.2259 *** | (0.0000) |
Im, Pesaran And Shin W-Stat | |||||
At Level | At first Difference | ||||
Statistic | Prob. | Statistic | Prob. | ||
LogIn | −1.04127 | (0.1489) | ∆LogIn | −4.61309 | (0.0000) |
LogRL | 1.94442 | (0.9741) | ∆LogRL | 1.47487 | (0.0299) |
LogTP | −59971.1 *** | (0.0000) | ∆LogTP | −29065.9 | (0.0000) |
LogCT | −1.8 × 1014 *** | (0.0000) | ∆LogCT | −46.6347 | (0.0000) |
LogRDE | −2.53716 *** | (0.0056) | ∆LogRDE | −4.55159 | (0.0000) |
Augment Dickey-Fuller | |||||
At Level | At first Difference | ||||
Statistic | Prob. | Statistic | Prob. | ||
LogIn | 87.3449 ** | (0.0405) | ∆LogIn | 134.542 *** | (0.0000) |
LogRL | 52.9124 | (0.8373) | ∆LogRL | 52.7310 * | (0.0417) |
LogTP | 10.1459 | (0.9270) | ∆LogTP | 15.6750 *** | (0.0152) |
LogCT | 156.536 *** | (0.0000) | ∆LogCT | 190.201 *** | (0.0000) |
LogRDE | 105.012 *** | (0.0016) | ∆LogRDE | 134.626 *** | (0.0000) |
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
---|---|---|---|---|---|---|
OLS | Random Effects | Fixed Effects | FMOLS | DOLS | GMM | |
LogIn | 0.8540 *** | |||||
LogRL | 0.0965 *** | 0.04159 *** | −0.0464 *** | 0.2866 *** | 0.1375 *** | 0.0138 *** |
LogRDE | 0.1518 *** | 0.1158 *** | 0.0062834 | 0.029168 | 0.130642 *** | 0.0197 *** |
LogCT | −0.1005 *** | −0.0501 *** | 0.0179516 | −0.803093 ** | −0.059249 ** | −0.0077 *** |
LogTP | −0.0223 * | −0.0132 | −0.027002 * | −0.414572 *** | −0.0008 | −0.0065 *** |
_cons | 4.2597 *** | 4.0824 *** | 3.9249 *** | 4.1860 *** | 4.0630 ** | 15.0497 *** |
year | −0.0071 *** | |||||
sigma_u | 0.0652 | 0.1876 | ||||
sigma_e | 0.0342 | 0.0342 | ||||
Rho | 0.9677 | |||||
AR(1) | 0.000 | |||||
AR(2) | 0.066 | |||||
R-squared | 0.7754 | 0.6824 | 0.7169 | 0.7730 | 0.7334 | 0.61712 |
Adjusted R-Squared | 0.7021 | 0.6172 | 0.6451 | 0.7682 | 0.6152 | 0.5978 |
S.E. of Regression | 0.0104 | 0.0784 | 0.0874 | 0.07477 | 0.0672 | 0.0148 |
Long-Run Variance | 0.0687 | 0.0321 | 0.0158 | 0.0145 | 0.0127 | 0.0548 |
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Balsalobre-Lorente, D.; Zeraibi, A.; Shehzad, K.; Cantos-Cantos, J.M. Taxes, R&D Expenditures, and Open Innovation: Analyzing OECD Countries. J. Open Innov. Technol. Mark. Complex. 2021, 7, 36. https://doi.org/10.3390/joitmc7010036
Balsalobre-Lorente D, Zeraibi A, Shehzad K, Cantos-Cantos JM. Taxes, R&D Expenditures, and Open Innovation: Analyzing OECD Countries. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7(1):36. https://doi.org/10.3390/joitmc7010036
Chicago/Turabian StyleBalsalobre-Lorente, Daniel, Ayoub Zeraibi, Khurram Shehzad, and José María Cantos-Cantos. 2021. "Taxes, R&D Expenditures, and Open Innovation: Analyzing OECD Countries" Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1: 36. https://doi.org/10.3390/joitmc7010036
APA StyleBalsalobre-Lorente, D., Zeraibi, A., Shehzad, K., & Cantos-Cantos, J. M. (2021). Taxes, R&D Expenditures, and Open Innovation: Analyzing OECD Countries. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 36. https://doi.org/10.3390/joitmc7010036