Skip to main content
Log in

Efficiency evaluation and influencing factors analysis of fiscal and taxation policies: A method combining DEA-AHP and CD function

  • Original Research
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

This paper combines the Cobb-Douglas (CD)with the method of Data Envelopment Analysis and Analytic Hierarchy Process(DEA-AHP) and applies it to the input efficiency evaluation of fiscal and taxation policies. Based on the input of the number of fiscal policy items and the output model design of transformation and upgrading, the overall efficiency of fiscal policy is calculated by the method of Data Envelopment Analysis and Analytic Hierarchy Process. It turns out that the overall efficiency of fiscal and taxation policies is 0.397, and the efficiency of pure policy factors is 0.616. There is a difference in input efficiency between preferential tax policies and fiscal subsidy policies. Tax preferential policy has insufficient investment, and financial subsidy has investment redundancy. Furthermore, the absolute value of tax incentives and financial subsidies are introduced into the CD Function to calculate the contribution rate, and the government and enterprise factors that are not effective in the efficiency of fiscal and taxation policies are attempted to be separated. As a result, it is found that the contribution rate of financial subsidies is weakly negatively correlated. It shows that the conversion rate of enterprises to the financial subsidy policy is low, and the tax preference policy has a better conversion rate. The results of the study reveal that the scope and intensity of tax optimization needs to be increased at the government level, and the utilization of fiscal subsidy policies needs to be improved at the enterprise level. The government should reduce financial subsidies and establish a universal tax preferential policy system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alexander, Whyte J. M. (1995). Output, income and employment multipliers for Scotland. Scottish Economic Bulletion, 50, 25–40.

    Google Scholar 

  • Andersen, P. , & Petersen, N. C. . (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. INFORMS.

  • Avkiran, N. K. (2009). Opening the black box of efficiency analysis: an illustration with uae banks. Omega, 37(4), 930–941.

    Article  Google Scholar 

  • Banker, R. D., Charnes, A., & W., & Cooper, W. W., (1984). Some models of estimating technical and scale inefficiencies in data envelopment analysis. Management ence, 30(9), 1078–1092.

  • Bin, Liu, & Yun, Wang. (2019). Research on the influence of fiscal and tax preferential policies on R & D performance of enterprises based on three stage DEA model-the case of industrial enterprises above Designated Size in Anhui Province. Marketing Industry, 52, 32–34.

    Google Scholar 

  • Castelli L., Pesenti R. (2014) Network, Shared Flow and Multi-level DEA Models: A Critical Review. In: Cook W., Zhu J. (eds) Data Envelopment Analysis. International Series in Operations Research & Management Science, vol208. Springer, Boston, MA

  • Chen, A., & Groenewold, N. . (2010). Reducing regional disparities in china: an evaluation of alternative policies. Economics Discussion / Working Papers,38(2), 189–198.

  • Cheng, Peng, & Changde, Zheng. (2014). Study on the Efficiency of Chinese Scientific and Technological Investments Based on Two-stage DEA with Decision Preference. Science & Technology Progress and Policy, 8, 125–129.

    Google Scholar 

  • Chien, H., Wang, Ram, D., & Gopal, et al. (1997). Use of data envelopment analysis in assessing information technology impact on firm performance. Annals of Operations Research, 73(1), 191–213.

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (dea)-thirty years on. European Journal of Operational Research, 192(1), 1–17.

    Article  Google Scholar 

  • FAN, Hui, & CHU, Rui. (2018). A Review about the Application of DEA Model in Network. Value Engineering,37(481(05)), 77–78.

  • Farrell.(1957).The measurement of productive efficiency.ournal of the Royal Statistical Society.1,253-290.

  • Fried, H. O., Lovell, C. A. K., Schmidt, S. S., & Yaisawarng, S. (2002). Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis, 17(1–2), 157–174.

    Article  Google Scholar 

  • Gong, X., Mi, J., Wei, C., & Yang, R. (2019). Measuring environmental and economic performance of air pollution control for province-level areas in china. International Journal of Environmental Research and Public Health, 16(8), 1378.

    Article  Google Scholar 

  • Gong, X., Mi, J., Yang, R., & Sun, R. (2018). Chinese national air protection policy development: a policy network theory analysis. International Journal of Environmental Research and Public Health, 15(10), 2257.

    Article  Google Scholar 

  • Guan, Li. (2001). Evaluation of Customer Satisfactory Degree with Fuzzy Theory and DEA Method. Journal of Shandong Inst.of Min,20(4), 76–78.

  • Guangqiang, Liu. (2016). An analysis of the incentive effect of tax preference and financial subsidy policy: An Empirical Study Based on the perspective of information asymmetry theory. Management World, 10, 62–71.

    Google Scholar 

  • Guoliang, Yang, & LiuWenbin, & Zheng Haijun., (2013). Review of data envelopment analysis. Journal of Systems Engineering, 6, 840–860.

  • Hongjun, Z. H. A. N. G., Youwei, X. U., Kai, C. H. E. N. G., et al. (2018). Review of data envelopment analysis hotspot. Computer Engineering and Applications, 54(10), 219–228.

    Google Scholar 

  • Jinbei., (2011). Transformation and Upgrading of China’s Industry. China Industrial Economics, 7, 14–25.

  • Kao, C., & Hwang, S. N. (2010). Efficiency measurement for network systems: it impact on firm performance. Decision Support Systems, 48(3), 437–446.

    Article  Google Scholar 

  • Li Chunhao,Liu Yuguo,Li Hui.(2003).A Modified CKS-DEA Model by Incorporating Assurance Regions on Qualitative Factors Weights.Chinese Journal of Management Science.11,33-37

  • Lin, T. Y., & Chiu, S. H. (2013). Using independent component analysis and network dea to improve bank performance evaluation. Economic Modelling, 32(may), 608–616.

    Article  Google Scholar 

  • Moreno, P. , & Lozano, S. . (2014). A network dea assessment of team efficiency in the nba. Annals of Operations Research, 214(mar), 99-124.

  • Pengyue, Wu. (2015). Research on evaluation indexes and influencing factors of transformation and upgrading of small and micro enterprises - Based on the survey evidence of 378 Enterprises. Statistical Theory and Practice, 12, 12–16.

    Google Scholar 

  • Quanling, Wei. (2006). Data Envelopment Analysis(dea). Chinese Science Bulletin, 45(17), 1793–1808.

    Google Scholar 

  • Rhodes, A. Charnesw, & Coopere, W. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 6(2), 429–444.

    Google Scholar 

  • Rouyendegh, B. D. .(2011). The dea and intuitionistic fuzzy topsis approach to departments’ performances: a pilot study. Journal of Applied Mathematics,2011,(2011-12-26), 2011(1110-757X).

  • Rouyendegh, Babak Daneshvar, Oztekin, Asil, Ekong, Joseph, & Dag, Ali. (2019). Measuring the efficiency of hospitals: a fully-ranking dea-fahp approach. Annals of Operations Research, 278, 361–378.

    Article  Google Scholar 

  • Seiford, L. . M., & Zhu, J. (1999). Profitability and marketability of the top 55 u.s. commercial banks. Management Science,45(9), 1270–1288.

  • Solow, R. M. . (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics(1), 65-94.

  • Suo Weilan, Lu, & Guichang & Chen Rui., (2015). A study of efficiency measurement of S&T resource allocation in universities based on the relational network DEA model with shared inputs. Science Research Management, 36(11), 157–163.

  • Tone, K., & Tsutsui, M. (2009). Network dea: a slacks-based measure approach. European Journal of Operational Research, 197(1), 243–252.

    Article  Google Scholar 

  • Wanliang, D. A. I., Jiaoping, Y. A. N. G., & Lihong, A. O. (2013). The Effect of Innovation Policy on the R&D Efficiency of Hi-tech Industries: Based on AHP and SE-DEA Model. Journal of Central University of Finance & Economics, 10, 70–74.

    Google Scholar 

  • Weijie, Kong. (2012). Research on the Influencing Factors of the Transformation and Upgrading of Manufacturing Enterprises. Management World, 9, 120–131.

    Google Scholar 

  • Wiberg, M. (2011). Political participation, regional policy and the location of industry. Regional ence and Urban Economics, 41(5), 465–475.

    Article  Google Scholar 

  • Wu,P.,Ma,J.,&Jiang,X.. A model of innovation diffusion based on policy incentives.Communications in Statistics-Simulation and Computation.https://doi.org/10.1080/03610918.2020.1758139

  • Xinyi, Lu, & Jian, Min. (2017). Empirical Analysis on performance evaluation of new energy vehicle industry policy based on DEA: a case of Hubei Province. Communication of Finance and Accounting, 752(008), 124–128.

    Google Scholar 

  • YAO Linxiang,LENG Nemin.(2018).An Analysis on the Incentive Effects of Fiscal Subsidies and Tax Incentives on the Innovation Efficiency of Strategic Emerging Industries. East China Economic Management,32(12),94-100.

  • YIN, Xiguo, & FENG, Xiao. (2012). High-Tech Industrial Policy Effects in China: Period Transition, Regional Convergence and Industry Differentiation. SCIENCE OF SCIENCE AND MANAGEMENT OF S. & T,33(4), 34–43.

  • Zhang Yongan, Lu, & Mingming., (2019). Rsearch on Enterprise Innovation Efficiency and Factor Input Difference From the Perspective of Innovation Drive-Based on Empirical Data of New Energy Vehicle Listed Companies. Journal of Industrial Technological Economics, 11, 86–93.

  • Zhi, Zhou, Guotai, Chi, & Sui, Zhang. (2014). Evaluation of scientific development for county and districts based on key factor judgment. Journal of Systems Engineering, 29(2), 257–268.

    Google Scholar 

  • Zhu, J. . (2020). Dea under big data: data enabled analytics and network data envelopment analysis. Annals of Operations Research(1).

Download references

Acknowledgements

This study is the fruit of social science planning project in Zhejiang Province which is supported by ”Philosophy and Social Science Planning Project of Zhejiang [19NDJC007Z]”, “Zhejiang Provincial Soft Science Research Program [2021C35047]” and the National Natural Science Foundation of China [grant numbers 71874088]. I am very grateful to the anonymous reviewers for their comments, which are very helpful in the innovation and writing methods of the article. I would also like to thank the editor for his tolerance and appreciation for giving me a valuable opportunity to revise. These will help me improve my research and writing level.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jing Ma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, P., Ma, J. & Guo, X. Efficiency evaluation and influencing factors analysis of fiscal and taxation policies: A method combining DEA-AHP and CD function. Ann Oper Res 309, 325–345 (2022). https://doi.org/10.1007/s10479-021-04194-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-021-04194-x

Keywords

Navigation