Providing a Framework for Allocating Public R&D Budget to the Universities Using Robust Optimization

Document Type : Research Article

Authors

1 Assistant Professor, STI financing and economics research group, National Research Institute for Science Policy, Tehran, Iran

2 Researcher, STI financing and economics research group, National Research Institute for Science Policy, Tehran, Iran

Abstract

Government-funded universities play a central role in national research and development (R&D). However, due to the government's budget constraints, the optimal allocation of public resources has become very important. In this study, a decision support framework was developed for the optimal and robust allocation of the R&D budget to maximize the direct and uncertain outputs of R&D activities in universities. The experiments were designed in two phases. In the first phase, without considering the uncertainty of R&D outputs of universities, the optimal budget allocation was determined according to three scenarios for each university. By changing the model parameters (robustness cost control parameter and output uncertainty control parameter), the optimal budget allocation plan in uncertain and robust conditions was calculated for 12 scenarios. The analysis of data using descriptive statistics methods and the comparison of results showed that in the current situation, there was no clear correlation between R&D outputs and the R&D budget allocated to the universities, and the budget allocation process was very volatile and lacked a clear increasing or decreasing pattern. While the R&D budget that was calculated in a robust optimal way showed a logical and proportional trend to the R&D activities’ output. Also, the optimal model distributed the total available budget for the universities in such a way as to achieve the maximum performance in the total budget ceiling. Implementing the model for 5 universities in 6 consecutive years improved the output from a minimum of 2 to a maximum of 6% and saved the allocated budget from 0.1 to 1.1%.

Keywords

Main Subjects


- Abbassi, M.; Ashrafi, M. & Sharifi Tashnizi, E., (2014). “Selecting balanced portfolios of R&D projects with interdependencies: A Cross-Entropy based methodology”. Technovation, 34(1): 54-63. https://doi.org/10.1016/j.technovation.2013.09.001.
- Agasisti, T.; Catalano G.; Landoni, P. & Verganti, R., (2012). “Evaluating the performance of academic departments: an analysis of research-related output efficiency”. Research Evaluation, 21: 2–14, doi:10.1093/reseval/rvr001.
- Alizadeh, P. & Ghazinoori, S., (2015). “The foundations of measuring research and development costs with an emphasis on considerations and points of measurement in Iran”. National Research Institute for Scince Policy, Tehran, Iran.
- Alizadeh, P.; Fasihi, M. A.; Khormandania, S. & Shojaei, M. H., (2019). “A research plan to compile a framework for defining research and development credits in the annual budget of the whole country”. Report of Research Project, Institute of Technology Studies, Tehran, Iran.
- Alizadeh, P.; Ghazinoory, S.; Amiri, M. & Ghazinoori, S., (2018). “Designing a Policy Mix to Enhance the Business Expenditure on Research and Development (R&D) in Iran”. Journal of Improvement Management, 12 (3): 1-24. (In Persian).
- Alizadeh, P. & Manteghi, M., (2019). “Policies for Supporting R&D in the Business Sector”. Journal of Science and Technology Policy, 12(2): 363-378. (In Persian).
- Alizadeh, P.,  (1390). “Policies to promote research and development and innovation (2): research and technology organizations”. Majlis Research Center, Policy Report, serial number: 12207.
- Alizadeh, P., (2010). “Science and Technology Assessment (1): Science and Technology Assessment System in Iran”. Majlis Research Center, Policy Report, serial number: 10450.
- Azar, A.; Amini, M. R. & Ahmadi, P., (2013). “Robust Fuzzy Performance based budgeting model an approach to managing the budget allocation risk - Case Study: Tarbiat Modares University”. Management Research in Iran, 17(4): 65-95. (In Persian).
- Azar, A.; Amini, M. R. & Ahmadi, P., (2014). “Performance-Based Budgeting Model: A Robust Optimization Approach Case Study of Tarbiat Modares University”. Planning and Budgeting, 19(1): 53-84. (In Persian).
- Azar, A. & Najafi, S., (2011). “Mathematical model of budgeting in the public sector: robust optimization approach”. Public Administration Perspective, 2(2): 83-98 (In Persian).
- Bai, Y.; Song, S.; Jiao, J. & Yang, R., (2019). “The impacts of government R&D subsidies on green innovation: Evidence from Chinese energy-intensive firms”. Journal of Cleaner Production, 233: 819-829.
- Barrio-García, S. D. A.; Kamakura, W. & Luque-Martínez, T., (2019). “A Longitudinal Cross-product Analysis of Media-budget Allocations: How Economic and Technological Disruptions Affected Media Choices across Industries”. Journal of Interactive Marketing, 45: 1-15.
- Ben-Moshe, B.; Elkin, M.; Gottlieb, L. A. & Omri, E., (2016). “Optimizing budget allocation for center and median points”. Theoretical Computer Science, 627: 13-25. https://doi.org/10.1016/j.tcs.2016.02.013.
- Bertsimas D. & Sim M. (2014). “The Price of Robustness”. Operation Research, 52 (1): 35–53. Doi: 10.1287/opre.1030.0065.
- Boeing, PH., (2016). “The allocation and effectiveness of China’s R&D subsidies - Evidence from listed firms”. Research Policy, 45(9): 1774-1789.
- Bozeman, B. & Rogers, J., (2001). “Strategic Management of Government-Sponsored R&D Portfolios”. Environment and Planning C: Government and Policy, 19(3): 413-442. https://doi.org/10.1068/c1v.
- Brantley, M. W.; Lee, L. H.; Chen, CH. H. & Xu, J., (2014). “An efficient simulation budget allocation method incorporating regression for partitioned domains”. Automatica, 50(5): 1391-1400. doi: 10.1016/j.automatica.2014.03.011.
- Çağlar, M. & Gürel. S., (2019). “Impact assessment based sectoral balancing in public R&D project portfolio selection”. Socio-Economic Planning Sciences, 66: 68-81. https://doi.org/10.1016/j.seps.2018.07.001.
- Chen, Y.; Wang, Y.; Hu, D. & Zhou, ZH., (2020). “Government R&D subsidies, information asymmetry, and the role of foreign investors: Evidence from a quasi-natural experiment on the shanghai-hong kong stock connect”. Technological Forecasting and Social Change, 158. https://doi.org/10.1016/j.techfore.2020.120162.
- Chun, D.; Hong, S.; Chung, Y.; Woo, CH. & Seo, H., (2016). “Influencing factors on hydrogen energy R&D projects: An ex-post performance evaluation”. Renewable and Sustainable Energy Reviews, 53, 1252-1258. https://doi.org/10.1016/j.rser.2015.09.074.
- Coldrick, S.; Longhurst, P.; Ivey, P. & Hannis, J., (2005). “An R&D options selection model for investment decisions”. Technovation, 25, 185–193. https://doi.org/10.1016/S0166-4972(03)00099-3
- Dai, X. & Cheng, L., (2015). “The effect of public subsidies on corporate R&D investment: An application of the generalized propensity score”. Technological Forecasting and Social Change, 90(B): 410-419.
- Dziallas, M. & Blind, K., (2018). “Innovation indicators throughout the nnovation process: An extensive literature analysis”. Technovation, 80-81: 3-29. DOI:10.1016/j.technovation.2018.05.005
- Edler, J. & Georghiou, L., (2007). “Public procurement and innovation—Resurrecting the demand side”. Research Policy, 36 (7): 949-963.
- Eilat, H.; Golany, B. & Shtub, A., (2008). „R&D project evaluation: an integrated DEA and balanced scorecard approach”. Omega, 36: 895–912. https://doi.org/10.1016/j.omega.2006.05.002.
- Endo E. & Tamura, Y., (2001). “Resource allocation model for planning R & D on solar cells”. Solar Energy Materials & Solar Cells, 67: 655-661
-Europa web. (2021). https://ec.europa.eu/eurostat/cache/metadata/en/gba_esms.htm#contact1616163588011, Accessed 15th July,.
Fisch, J. H., (2003). “Optimal dispersion of R&D activities in multinational corporations with a genetic algorithm”. Research Policy, 32: 1381–1396.
- Fu, Y.; Xiao, H.; Lee, L. H. & Huang, M., (2021). “Stochastic optimization using grey wolf optimization with optimal computing budget allocation”. Applied Soft Computing, 103:  https://doi.org/10.1016/j.asoc.2021.107154.
- Gao, S.; Xiao, H.; Zhou, E. & Chen, W., (2017). “Robust ranking and selection with optimal computing budget allocation”. Automatica, 81: 30-36. https://doi.org/10.1016/j.automatica.2017.03.019.
- Ge, J.; Fu, Y.; Xie, R.; Liu, Y. & Mo, W., (2018). “The effect of GVC embeddedness on productivity improvement: From the perspective of R&D and government subsidy”. Technological Forecasting and Social Change, 135 (C): 22-31. DOI: 10.1016/j.techfore.2018.07.057.
- Gerchak, Y., (1998). “On allocating R&D budgets among and within projects”. R and D Management, 28(4): 305–309. doi:10.1111/1467-9310.00107 
- Gharun, M., (2013). “Developing a model for estimation of public investment in science, research and technology in Iran”. IRPHE, 19 (1): 1-19
- Ghazi, A. & Hosseinzadeh Lotfi, F., (2019). “Assessment and budget allocation of Iranian natural gas distribution company- A CSW DEA based model”. Socio-Economic Planning Sciences, 66: 112-118.
- Gomez, J.; Rios Insua, D. & Alfaro, C., (2016). “A participatory budget model under uncertainty”. European Journal of Operational Research, 249(1): 351-358. https://doi.org/10.1016/j.ejor.2015.09.024.
- Hassanzadeh, F.; Nemati, H. & Sun, M., (2014a). “Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection”. European Journal of Operational Research, 238: 41–53. https://doi.org/10.1016/j.ejor.2014.03.023.
- Hassanzadeh, F.; Modarres, M.; Nemati, H. R. & Amoako-Gyampah, K., (2014b). “A robust R&D project portfolio optimization model for pharmaceutical contract research organizations”. International Journal of Production Economics 158: 18-27. https://doi.org/10.1016/j.ijpe.2014.07.001.
- Heidenberger, K. & Stummer, C., (1999). “Research and development project selection and resource allocation: a review of quantitative modelling approaches”. International Journal of Management Reviews, 1: 197–224.
- Jalalabadi, A.; Seyyed Nourani, S. M. R. & Sannei, M., (2005). ”The Effect the Improvement of Categorizing Country,s Budget Item on Universities Budgeting (Affiliated to Ministry of S.R.T.”. Journal of Research and planning in higher education, 11(1): 65-101 (In Persian).
- Jang, H., (2019). “A decision support framework for robust R&D budget allocation using machine learning and optimization”. Decision Support Systems,121: 1-12.
- Jang, H.; Woo, C. & Kim, T., (2018). “A Study for Designing Optimal R&D Portfolios”. Report from Science and Technology Policy Institute, Sejong, Republic of Korea.
- Jonkers, K., (2011). “A functionalist framework to compare research systems applied to an analysis of the transformation of the Chinese research system”. Research Policy, 40(9): 1295-1306. DOI: 10.1016/j.respol.2011.05.027.
- Jung, Uk. & Seo, D. W., (2010). “An ANP approach for R&D project evaluation based on interdependencies between research objectives and evaluation criteria”. Decision Support Systems, 49: 335–342. https://doi.org/10.1016/j.dss.2010.04.005.
- Khaleghi Souroush, F.; Abolghasemi, M.; Garaei Nejad, G. & Davalloo, M., (2017) “Designing a model for the allocation of higher education resources in Iran”. Financial Economics, 11: 147-170. (In Persian).
- Kim, J. H.; Bae, S. J. & Yang, J. S., (2014). “Government roles in evaluation and arrangement of R&D consortia. Technological Forecasting and Social Change, 88 (1): 202-215. DOI:10.1016/j.techfore.2014.06.022
- Kurth, M.; Keisler, J. M.; Bates, M. E.; Bridges T. S.; Summers, J. & Linkov, I., (2017). “A portfolio decision analysis approach to support energy research and development resource allocation”. Energy Policy, 105: 128-135. http://dx.doi.org/10.1016/j.enpol.2017.02.030
- Lee, H.; Choi, Y. & Seo, H., (2020). “Comparative analysis of the R&D investment performance of Korean local governments”. Technological Forecasting and Social Change, 157, https://doi.org/10.1016/j.techfore.2020.120073.
- Lee, J. & Yang, J. S., (2018). “Government R&D investment decision-making in the energy sector: LCOE foresight model reveals what regression analysis cannot”. Energy Strategy Reviews, 21, 1-15.
- Lee, J. & Yang, J. S., (2020). “Strategic R&D budget allocation to achieve national energy policy targets: the case of Korea”. Policy Studies, https://doi.org/10.1080/01442872.2020.1772216.
- Lee, S. & Lee, H., (2015). “Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach”. Journal of Informetrics, 9(4): 942-953.
- Lee, S. K.; Mogi, G. & Hui, K. S., (2013). “A fuzzy analytic hierarchy process (AHP)/data envelopment analysis (DEA) hybrid model for efficiently allocating energy R&D resources: In the case of energy technologies against high oil prices”. Renewable and Sustainable Energy Reviews, 21: 347-355. https://doi.org/10.1016/j.rser.2012.12.067
- Lee, Y. H. & Kim, Y. J. (2016). ”Analyzing interaction in R&D networks using the Triple Helix method: Evidence from industrial R&D programs in Korean government”. Technological Forecasting and Social Change, 110 (C): 93-105. DOI: 10.1016/j.techfore.2015.10.017.
- Lin, F. J.; Wu, Sh. H.; Hsu, M. Sh. & Perng, Ch., (2016). “The determinants of government-sponsored R&D alliances. Journal of Business Research, 69(11): 5192-5195. DOI: 10.1016/j.jbusres.2016.04.111.
- Litvinchev, I. S.; Lopez-Irarragorri, F.; Alvarez, A. & Fernández González E. R., (2010). “Large-scale public R&D portfolio selection by maximizing a biobjective impact measure”. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40: 572–582.  DOI:10.1109/TSMCA.2010.2041228
- Liu, C. C., (2011). “A study for allocating resources to research and development programs by integrated fuzzy DEA and fuzzy AHP”. Scientific Research and Essays, 6: 3973–3978. DOI:10.5897/SRE10.838.
- Luo, L. M., (2012). “Optimal diversification for R&D project portfolios”. Scientometrics 91: 219–229.
- Málek, J.; Hudečková, V. & Matějka, M., (2014). “System of Evaluation of Research Institutions in the Czech Republic”. Procedia Computer Science, 33: 315-320. https://doi.org/10.1016/j.procs.2014.06.050.
- Modarres M. & Hasanzadeh, F., (2009). “A Robust Optimization Approach to R&D Project Selection”. World Applied Sciences Journal, 7 (5): 582-592.
- Momeni, F. & Alizadeh, P., (2014). “Analysis of the barriers for innovation policy-making effectiveness in Iran: An Institutional Approach”. Journal of Applied Economics Studies in Iran2(8): 73-89.
- Mulyanto. (2016). “Productivity of R&D institution: The case of Indonesia”. Technology in Society, 44: 78-91. https://doi.org/10.1016/j.techsoc.2015.12.001.
- OECD, Frascati Manual, (2015). Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- Park, H.; Lee, J. & Kim, B., (2015). ”Project selection in NIH: a natural experiment from ARRA”. Research Policy, 44: 1145–1159. https://doi.org/10.1016/j.respol.2015.03.004.
- Perez-Sebastian, F., (2015). “Market failure, government inefficiency, and optimal R&D policy”. Economics Letters, 128: 43-47.
- Philpott, K.; Dooley, L.; O’Reilly C. & Lupton, G., (2011). “The entrepreneurial university: Examining the underlying academic tensions”. Technovation, 31: 161-170.
- Pourtalei, F. & Atashak, M., (2010). ”A Model for Research and Technology Institutes Budgeting Based on Science and Technology Outputs Cost”. Journal of Science and Technology Policy, 2(4): 53-65 (In Persian).
- Rahmani Fazli, H. & Arabmazar, A., (2016). “Optimal Provincial Budget Allocation: A Goal Programing Approach”. Applied Theories of Economics, 3(3): 133-152 (In Persian).
- Rahnema, G.; Motafaker azad, M. A. & Ranjpoor, R., (2015). “The Impact of Internal R&D Capital, Imported Capital Goods Stock and Human Capital on Iranian High-Tech Industries' Value Added”. Journal of Applied Economics Studies in Iran4(15): 21-54 (In Persian).
- Rajabi, A., (2012). “Goal Programming: An Effective Approach for Budgeting and Optimal Financial Resource Allocation (Case Study: Budget Allocation in Ministry of Health and Medical Education)”, Health Accounting, 1(2-3): 1-16 (In Persian).
- Sánchez-Barrioluengo, M., (2014). “Articulating the ‘three-missions’ in Spanish universities”. Research Policy, 43(10): 1760-1773. DOI: 10.1016/j.respol.2014.06.001.
- Seru, A., (2014). “Firm boundaries matter: Evidence from conglomerates and R&D activity”. Journal of Financial Economics, 111(2): 381-405. https://doi.org/10.1016/j.jfineco.2013.11.001.
- Sirin, S. M. & Erdogan, F. H., (2013). “R&D expenditures in liberalized electricity markets: The case of Turkey”. Renewable and Sustainable Energy Reviews, 24(C): 491-498. DOI: 10.1016/j.rser.2013.03.069.
- Sun, B.; Liu, Y. & Yang, G., (2017). ”A robust pharmaceutical R&D project portfolio optimization problem under cost and resource uncertainty”. Journal of Uncertain Systems, 11: 205–220.
- Talias, M., (2007). “Optimal decision indices for R&D project evaluation in the pharmaceutical industry: Pearson Index versus Gittins Index”. European Journal of Operational Research, 177: 1105–1112. https://doi.org/10.1016/j.ejor.2006.01.011.
- Tan, B.; Anderson Jr., E. G.; Dyer, J. S. & Parker, G. G., (2010). “Evaluating system dynamics models of risky projects using decision trees: alternative energy projects as an illustrative example”. System Dynamics Review, 26: 1–17. https://doi.org/10.1002/sdr.433.
- Tangian, A., (2004). “Redistribution of university budgets with respect to the status quo”. European Journal of Operational Research, 157: 409–428. doi:10.1016/S0377-2217(03)00271-6
- Tolga, AÇ., (2008). “Fuzzy multicriteria R&D project selection with a real options valuation model”. Journal of Intelligent & Fuzzy Systems, 19 (4-5): 359–371.
- Üçtuğ, F. G. & Yükseltan, E., (2012). “A linear programming approach to household energy conservation: Efficient allocation of budget”. Energy and Buildings, 49: 200-208.
- UNESCO-UIS. (2009). “Definitions of R&D, innovation and S&T activities”. Training Workshop on Science, Technology and Innovation Indicators, Cairo, Egypt, 28-30 September
- Vandaele, N. J. & Decouttere. C. J., (2013). “Sustainable R&D portfolio assessment”. Decision Support Systems, 54(4): 1521-1532. https://doi.org/10.1016/j.dss.2012.05.054.
- Wang, K.; Mao Y. & Chen, J. Sh. Yu., (2018). “The optimal research and development portfolio of low-carbon energy technologies: A study of China”. Journal of Cleaner Production, 176: 1065-1077. DOI: 10.1016/j.jclepro.2017.11.230.
- Wiesenthal, T.; Leduc, G.; Haegeman, K. & Schwarz, H., (2012). “Bottom-up estimation of industrial and public R&D investment by technology in support of policy-making: The case of selected low-carbon energy technologies”. Research Policy, 41(1): 116-131. DOI: 10.1016/j.respol.2011.08.007.
- Wu, A., (2017). “The signal effect of Government R&D Subsidies in China: Does ownership matter?”. Technological Forecasting and Social Change, 117: 339-345. https://doi.org/10.1016/j.techfore.2016.08.033.
- Wu, T.; Yang, SH. & Tan, J., (2020). “Impacts of government R&D subsidies on venture capital and renewable energy investment - an empirical study in China”. Resources Policy, 68, https://doi.org/10.1016/j.resourpol.2020.101715.
- Xiao, H.; Gao, S. & HayLee, L., (2017). “Simulation budget allocation for simultaneously selecting the best and worst subsets”. Automatica, 84: 117-127. https://doi.org/10.1016/j.automatica.2017.07.006.
- Yu, F.; Guo, Y.; Le-Nguyen, K.; Barnes, S. J. & Zhang, W., (2016). “The impact of government subsidies and enterprises’ R&D investment: A panel data study from renewable energy in China”. Energy Policy, 89: 106-113. https://doi.org/10.1016/j.enpol.2015.11.009.
- Zera’at Kish, Y.; Nasiri, H.; Davari, A. & Yousefi, H., (2019). “Examining the budget bill for the year 1400 of the whole country 24. Higher education, research and technology funding”. Majlis Research Center, Policy Report, serial number: 17337.
- Zhang, W., (2018). “Government R&D subsidy policy in China: An empirical examination of effect, priority, and specifics”. Technological Forecasting and Social Change, 135: 75-82.
- Zhao, B. & Ziedonis, R., (2020). „State governments as financiers of technology startups: Evidence from Michigan's R&D loan program”. Research Policy, 49(4): https://doi.org/10.1016/j.respol.2020.103926. Zhao, SH., Xu, B.,