“It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so,” quipped American humorist Mark Twain. For infrastructure policy, it’s a weighty warning, since sectoral data and analysis inform the policies and strategies that determine who gets critical infrastructure services, and at what cost, reliability, and quality.

Because health, water, power, transport, and other infrastructure services are both complex to deliver and critical to economic growth, development, and quality of life, it is unsurprising that they are subject to strong views.

Staunch advocates and detractors often draw on anecdote to make claims about PPP that become part of the rhetorical fabric of the infrastructure space, recycled by those of us working in the field. Moreover, tremendous PPP successes and debacles are referred to frequently, since stark examples of success or failure are helpful to arguing a point. The claim, “PPPs lead to new investments that help extend services to the poor,” may be countered with an assertion that “PPPs are the privatization of services for the primary benefit of the private sector.” In the same vein of contrast, some maintain that PPPs help control corruption, whereas others argue that PPPs are just new opportunities for rent-seeking. These claims can all be correct in individual cases, but certainly don’t apply across the board.

Thirty years into the marked revival of private participation in infrastructure, we know that PPPs can capture managerial, technological, and innovational strengths of the private sector and help governments tap new sources of capital. As experience mounts, however, we have also witnessed waning ardor for PPP as the “it” solution to all infrastructure woes, giving way to a more realistic and productive appreciation of the risks and complexities associated with bringing good projects to market and keeping them on track. Multilaterals, governments, operators, and financiers alike are more aware that PPPs are complex and living organisms, whose creation and performance outcomes are contingent on a constellation of institutional, political, economic, and physical factors, including the fitness-for-purpose of each PPP for its context. Thus, it becomes important to understand how these factors affect the prospects for good PPPs in different sectors and markets.

Fortunately, we have amassed a vast set of data on infrastructure PPPs, encapsulated in case studies, reports, and several large-N (large number) data sets. The World Bank’s Private Participation in Infrastructure (PPI) Project Database, for one, includes information on over 6,000 projects from 1984 onwards, capturing data across 30 fields, including contractual form, project closure date, location, contract duration, private sector partners, and multilateral support. Moreover, universities and multilateral institutions collect and publish banks of case studies of long-term infrastruct-ure contacts.

The case studies are immensely useful for examining the jumble of factors that lead to difficulties and achievements over time (the whys and hows), and they deliver practically applicable lessons for practitioners working to create and manage successful PPPs. But because every project is different—every context and situation unique—it is difficult to draw generalizable conclusions from one case. How do we know, for example, how a factor affecting one project will impact others? If a factor is critical to one case, how comfortable can we be with assuming it will be to others, or even if it will be influential in a similar direction? If we perceive a problem in one, how can we know its degree of prevalence?

Large-N quantitative data can be analyzed via descriptive statistics to deliver insights on general patterns of PPP investment and performance—that is, “what is going on” in the data. With richer data sets, we can use statistical methods to determine the typical influence of factors such as contract design, political regime, or corruption on outcomes such as investment levels, service quality, or increased access. Economic models can tell us more about why certain patterns exist and how particular factors tend to affect the project. In other words, these large-N datasets show how extensively outcomes are experienced, under what conditions PPPs are likeliest to work given a particular context, and where we should focus efforts to improve viability across the board.

By drawing on multiple bases of evidence and large data sets, in particular, we can identify patterns to improve PPP implementation and be better prepared for potential challenges in different political-economic contexts. Large-N data can also confirm or debunk PPP myths rooted in popular commentary. A few examples follow, focusing particularly on quantitative research in the water sector.

 

Of course, not all large-N research on PPP yields major surprises—see, for example, work on the impacts of economic shock and government effectiveness on contract termination (House 2014) or the influence of risk ratings on FDI in infrastructure (Moszoro, et al, 2014). These confirmatory findings also have great value, as they offer comfort that the existing understanding is, indeed, aligned with evidence.

What is important is that we continue to build good data sets (on both private and public infrastructure services) and test common rhetoric empirically, to incorporate lessons of the past, generate finer and more extensive appreciation of the variables that matter to PPP performance and investment, and better focus our efforts to address the challenges that are most impactful and pervasive to PPP and infrastructure investment.

References

Banerjee, S., Oetzel, J., and Ranganathan, R. (2006). Private provision of infrastructure in emerging markets: Do institutions matter? Development Policy Review 24(2): 175-202.

Basılio, M. (2011). Infrastructure PPP investments in Emerging Markets.

Bergara, M., Henisz, W., & Spiller, P. (1998). Political institutions and electric utility investment: A cross-nation analysis. California Management Review, 40, 18-35.

Carrera, J., Checchi, D., & Florio, M. (2005). Privatization discontent and its determinants: evidence from Latin America. IZA.

Finger, M., Allouche, J., LuÌs-Manso, P. (2007). Water and Liberalisation: European Water Scenarios. International Water Association.

Hall, D., Lobina, E., & de la Motte, R. (2005). Public Resistance to Privatisation in Water and Energy. Development in Practice, 15(3), 286-301.

Hammami, M., Ruhashyankiko, J. and Yehoue, E. (2006). Determinants of Public-Private Partnerships in Infrastructure. IMF Working Paper, WP/06/99, Washington, DC.

House, R. Schuyler (2014). Public Utilities in the Age of Partnership: Lessons from Private Participation in Urban Water Supply.

Jandhyala, S. (2016). International organizations and political risk: The case of multilateral development banks in infrastructure projects. Working paper, ESSEC Business School.

Mandri-Perrot, X. and Stiggers, D. (2013). Dealing with imperfect data. Public-private partnerships in the water sector: Innovation and financial sustainability, 249-264. IWA.

Moszoro, M., Araya, G., Ruiz-Nuñez, F., and Schwartz, J. (2014). Institutional and political determinants of private participation in infrastructure, International Transport Forum Discussion Paper, No. 2014-15

Nose, M. (2014). Triggers of contract breach: contract design, shocks, or institutions?. World Bank Policy Research Working Paper, 6738.

World Bank PPP CSA (forthcoming). The impact of multilateral support on project cancellation.

Zhang, X. (2005). Critical success factors for public-private partnerships in infrastructure development. Journal of Construction Engineering and Management, 131, 3.