Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T01:46:02.276Z Has data issue: false hasContentIssue false
  • English
  • Français

Success at scale: six suggestions from implementation and policy sciences

Published online by Cambridge University Press:  09 July 2020

TECK-HUA HO ,
CHING LEONG  and
CATHERINE YEUNG
TECK-HUA HO*
Affiliation:
National University of Singapore, 21 Lower Kent Ridge Road, Singapore119077
CHING LEONG
Affiliation:
National University of Singapore, 21 Lower Kent Ridge Road, Singapore119077
CATHERINE YEUNG
Affiliation:
Chinese University of Hong Kong, Shatin, Hong Kong
*
*Correspondence to: National University of Singapore, 21 Lower Kent Ridge Road, Singapore119077 E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Very often, significantly smaller benefits are observed in final policy outcomes than are indicated by initial research discoveries. Al-Ubaydli et al. have identified a poor understanding of the ‘science of scaling’ as the underlying cause of this discrepancy. They propose a framework to increase our understanding of the science of scaling. We build on this framework by making six specific suggestions capturing three key ideas. First, researchers need to move away from their preoccupation with general theoretical models and focus on subject-specific theories of intervention, leading to individualized treatments. Second, there should be greater collaboration between researchers and policymakers, as well as more transparency in reporting findings, to ensure that the research environment is more representative of the policy environment. Third, researchers should recognize that policymakers do not always maximize social welfare; policymakers may have their own short-term incentives. Therefore, researchers must consider policymakers’ short-term incentives in designing interventions in order to increase the chances of a research intervention becoming a policy.

Type
Articles
Information
Behavioural Public Policy , Volume 5 , Special Issue 1: Field Experiments and Public Policy , January 2021 , pp. 71 - 79
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

1

Authors are listed in alphabetical order. All authors contributed equally.

References

Allcott, H. (2015), ‘Site Selection Bias in Program Evaluation’, The Quarterly Journal of Economics, 130(3): 1117–1165.CrossRefGoogle Scholar
Banerjee, A., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., Shotland, M. X. and Walton, M. (2017), ‘From Proof of Concept to Scalable Policies: Challenges and Solutions, with an Application’, The Journal of Economic Perspectives, 31(4): 73–102.CrossRefGoogle Scholar
Camerer, C. (1998), ‘Prospect Theory in the Wild: Evidence from the Field. Reprinted’, in Bazerman, Max H. (ed), 2003. Negotiation, Decision Making and Conflict Management, UK: Edgar Elgar Publishing, Ltd.Google Scholar
Demircioglu, M. (2018), ‘The Effects of Empowerment Practices on Perceived Barriers to Innovation: Evidence from Public Organizations’, International Journal of Public Administration, 41(15): 1302–1313.CrossRefGoogle Scholar
Ho, T. H., Yeung, C., Lim, N., van Dam, R. M., Sato, R., Tham, K. W. and Tan, H. C. (2019), Cash Incentives for Weight Loss Work Only for Males. Working Paper.Google Scholar
Hood, C. (2011), The Blame Game: Spin, Bureaucracy, and Self-Preservation in Government, Princeton University Press.Google Scholar
Howlett, M. (2014), ‘Why are policy innovations rare and so often negative? Blame avoidance and problem denial in climate change policy-making’, Global Environmental Change 29, 395–403. https://doi.org/10.1016/j.gloenvcha.2013.12.009CrossRefGoogle Scholar
J-PAL Evidence Review. (2019), Will Technology Transform Education for the Better? Cambridge, MA, Abdul Latif Jameel Poverty Action Lab.Google Scholar
Jacobs, A.M., (2008), ‘The Politics of When: Redistribution, Investment and Policy Making for the Long Term’, British Journal of Political Science 38, 193–220. https://doi.org/10.1017/S0007123408000112CrossRefGoogle Scholar
Kang, M. and Reich, M.R., (2014), ‘Between credit claiming and blame avoidance: the changing politics of priority-setting for Korea's National Health Insurance System’, Health Policy 115, 9–17. https://doi.org/10.1016/j.healthpol.2013.09.015CrossRefGoogle ScholarPubMed
Kilbourne, A. M., Neumann, M. S., Pincus, H. A., Bauer, M. S. and Stall, R. (2007), ‘Implementing Evidence-Based Interventions in Health Care: Application of the Replicating Effective Programs Framework’, Implementation Science, 2(1): 42.CrossRefGoogle ScholarPubMed
Kulik, J. A. and Fletcher, J. D. (2016), ‘Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review’, Review of Educational Research, 86(1): 42–78.CrossRefGoogle Scholar
Lazarova, V., Levine, B., Sack, J., Cirelli, G., Jeffrey, P., Muntau, H., Salgot, M. and Brissaud, F. (2001), ‘Role of Water Reuse for Enhancing Integrated Water Management in Europe and Mediterranean Countries’, Water Science and Technology: A Journal of the International Association on Water Pollution Research, 43(10): 25–33.CrossRefGoogle ScholarPubMed
Leong, C. and Howlett, M. (2017), ‘On Credit and Blame: Disentangling the Motivations of Public Policy Decision-Making Behaviour’, Policy Sciences, 50(4): 599–618.CrossRefGoogle Scholar
List, J. A. (2003), ‘Does Market Experience Eliminate Market Anomalies? The Quarterly Journal of Economics, 118(1): 41–71.CrossRefGoogle Scholar
Liu, K. A. and Dipietro Mager, N. A. (2016), ‘Women's Involvement in Clinical Trials: Historical Perspective and Future Implications’, Pharmacy Practice, 14(1): 708.CrossRefGoogle ScholarPubMed
Marx, B. (2017), Elections as Incentives: Project Completion and Visibility in African Politics. Working Paper. https://www.tse-fr.eu/elections-incentives-project-completion-and-visibility-african-politics.Google Scholar
Peytchev, A., Baxter, R. K. and Carley-Baxter, L. R. (2009), ‘Not All Survey Effort Is Equal: Reduction of Nonresponse Bias and Nonresponse Error’, Public Opinion Quarterly, 73(4): 785–806.CrossRefGoogle Scholar
Po, M., Kaercher, J. D. and Nancarrow, B. E. (2003), Literature Review of Factors Influencing Public Perceptions of Water Reuse. CSIRO Land and Water.Google Scholar
Rainey, H. (2009), Understanding and Managing Public Organizations. San Francisco, CA: John Wiley & Sons.Google Scholar
US Supreme Court. (1932), New State Ice Co. v. Liebmann, 285 U.S. 262. Retrieved from https://supreme.justia.com/cases/federal/us/285/262/.Google Scholar
Vervloet, M., Linn, A. J., van Weert, J. C., De Bakker, D. H., Bouvy, M. L. and Van Dijk, L. (2012), ‘The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: A systematic review of the literature’, Journal of the American Medical Informatics Association, 19(5): 696–704.CrossRefGoogle ScholarPubMed
Weaver, R. K. (1986), ‘The Politics of Blame Avoidance’, Journal of Public Policy, 6(4): 371–398.CrossRefGoogle Scholar
World Bank. (2006), Implementation Completion and Results Report: Education Sector Strategy Program, Republic of Mozambique. Report No: ICR000029.Google Scholar