Cost Benefit Analysis of Public Construction Projects under PPP Operation Mode

WU Ke xin

Article ID: 2592
Vol 3, Issue 1, 2023
DOI: https://doi.org/10.54517/vfc.v3i1.2592
VIEWS - 42 (Abstract)

Abstract

The public-private partnership (PPP) model that the state vigorously promotes is becoming a focus of stabilizing growth and adjusting structure. However, the application of the PPP model in public construction projects in China is still in its infancy. When the government makes project decisions, how to clarify the project risks, requirements and opportunities undertaken by all participants and weigh the external benefits brought by the PPP model has become an urgent problem to be solved to promote the reform of public service projects in China. Starting from practice, this paper reviews the relationship between school infrastructure investment and student performance involved in new school projects and the pros and cons of introducing the PPP model in the education field under the PPP framework. By comparing new PPP schools and non-PPP new schools in Victoria, Australia, the case is analyzed in multiple dimensions, it is concluded that there are indeed differences in operating expenditure and education performance between PPP schools and non –PPP schools. PPP schools have higher operating expenses than non-PPP schools and their educational achievements are also more prominent. The cumulative curve shows that the PoF of PPP school project operating expenditure is 48.37%, slightly higher than 48.05% of non-PPP school project. According to conclusion of this case data statistics, the introduction of the PPP model is explained to increase school infrastructure investment and promote better educational outcomes to a certain extent. School projects under the PPP framework can promote good educational achievements and provide theoretical support and countermeasures.


Keywords

PPP; School infrastructure investment; Education achievement; Hypothesis testing.

Full Text:

PDF



References

1. Ning Liang, Zhao Libo. Research on PPP Value Conflict and Coordination from the Perspective of Public Values: A Case Study of the New School Project in Australia [J] China Administrative Management, 2018 (010): 139-144.

2. Barrera Osorio, Felipe, Galbert D, et al. Impact of Public-Private Partnerships on Private School Performance: Evidence from a Randomized Controlled Trial in Uganda [J]. Social ence Electronic Publishing, 2016.

3. Cpppc.org. 2020. Project Management Database of National PPP Comprehensive Information Platform [(online)OL].

4. Hong K, Zimmer R. Does Investing in School Capital Infrastructure Improve Student Achievement? [J]. Economics of Education Review, 2016, 53:143-158.

5. Hanushek E A, Lindseth A A. Schoolhouses, Courthouses, and Statehouses: Solving the Funding -Achievement Puzzle in America's Public Schools [J]. Education Economics, 2009, 18 (4): 459-460.

6. Hanushek E A. The Economics of Schooling: Production and Efficiency in Public Schools [J]. Journal of Economic Literature, 1986, 24(3):1141-1177.

7. Hanushek E A. School resources and student performance[J].1996.

8. Hanushek E A. The Failure of Input -based Schooling Policies [J]. Economic Journal, 2010, 113.

9. Ferguson R F, Ladd H F. How and why money matters [J]. 1996.

10. Conlin M, Thompson P N. Impacts of new school facility construction: An analysis of a state -financed capital subsidy program in Ohio [J]. Economics of Education Review, 2017:13-28.

11. Lyons J B. Do School Facilities Really Impact a Child's Education? IssueTrak: A CEFPI Brief on Educational Facility Issues[J]. 2001.

12. Daisey J M, Angell W J, Apte M G. Indoor air quality, ventilation and health symptoms in schools: an analysis of existing information [J]. Indoor Air, 2010, 13(1):53-64.

13. Earthman G I. School facility conditions and student academic achievement [J]. 2002.

14. Schneider M. Do School Facilities Affect Academic Outcomes? [J]. 2002.

15. Krueger A B. Experimental estimates of education production functions [J]. The quarterly journal of economics, 1999, 114(2): 497-532.

16. Krueger A B, Whitmore D M. The effect of attending a small class in the early grades on college‐test taking and middle school test results: Evidence from Project STAR [J]. The Economic Journal, 2001, 111(468): 1-28.

17. Abott C, Kogan V, Lavertu S, et al. School district operational spending and student outcomes: Evidence from tax elections in seven states [J]. Journal of Public Economics, 2020, 183: 104142.

18. Verger A, Bonal X, Zancajo, Adrián. Featured Article: What Are the Role and Impact of Public -Private Partnerships in Education? A Realist Evaluation of the Chilean Education Quasi-Market [J]. Comparative Education Review, 2016, 60(2):000-000.

19. Wömann L. Public -private partnerships in schooling: Cross-country evidence on their effectiveness in providing cognitive skills [J]. Program on Education Policy and Governance, Research Paper PEPG, 2005: 05-09.

20. Patrinos H A, Barrera-Osorio F, Guáqueta J. The role and impact of public-private partnerships in education [M]. The World Bank, 2009.

21. LaRocque N. Public -private partnerships in basic education: An international review [M]. Reading: CfBT Education Trust, 2008.

22. Breakspear S. The Policy Impact of PISA: An Exploration of the Normative Effects of International Benchmarking in School System Performance [J]. OECD Education Working Papers, 2012:32.

23. Meng J, Xiu G, Qian F. Public-Private Partnership Project Risk Management in Education Industry [J]. Educational ences: Theory and Practice, 2018, 18(6).

24. Statistics A B O. Government finance statistics, education, Australia, 2016-17 [J]. Australian Bureau of Stats.

25. Wang M, Liu G. A Simple Two-Sample Bayesiant-Test for Hypothesis Testing [J]. The American Statistician, 2016.

26. De Land P N, Chase W W. Statistics notebook: entry IV.B: (3) two-sample pooled variance t-test and (4) two-sample separate variance t-test [J]. Optometry & Vision ence Official Publication of the American Academy of Optometry, 1993, 70(12):1065.

27. Lieberman M D, Cunningham W A. Type I and Type II error concerns in fMRI research: re-balancing the scale [J]. Social cognitive and affective neuroscience, 2009, 4(4): 423-428.

28. Freiman, Jennie, A, et al. The Importance of Beta, the Type II Error and Sample Size in the Design and Interpretation of the Randomized Control Trial [J]. New England Journal of Medicine, 1978.

29. Faul F, Erdfelder E, Lang A G, et al. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences [J]. Behav Res Methods, 2007, 39(2):175-191.

30. Kleijnen J P C. Sensitivity analysis and related analyses: A review of some statistical techniques [J]. Journal of Statal Computation and Simulation, 1997, 57(1-4):111-142.

31. Britain G. The Green Book: appraisal and evaluation in central government: Treasury guidance [M]. Stationery Office, 2003.

32. Grzybowski A Z. Monte Carlo Analysis of Risk Measures for Blackjack Type Optimal Stopping Problems [J]. Engineering Letters, 2011, 19(3).

33. Lai J, Zhang L, Duffield C, et al. Risk Appraisal in Engineering Infrastructure Projects: Examination of Project Risks Using Probabilistic Analysis[J]. 2014.

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 WU Ke xin

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.