toward projects easily valued, while one could argue that public funds should go to those projects that yield the most public goods (e.g. power distribution versus flood protection).

A final factor that needs to be accounted for is the strength or weakness of a country’s institutional system. In the best case scenario, politicians and policy makers account for the aforementioned factors and the information coming out from the respective methods when negotiating the prioritization of particular interventions. Independent or quasi‐independent regulatory institutions (including the judicial system and civil society) may also weigh in with checks and balances, blocking questionable infrastructure projects. However, the result of such a decision process is unlikely to be optimal when there is rent‐ seeking behavior – which refers to situations where individuals organized in special interest groups (or acting alone) are motivated by their particular interests rather than by the general interest of the society. Examples are industrialists wanting infrastructure investments that only (or mainly) benefit them and farmers seeking cheap or free power for irrigation. Rent‐seeking behavior leads some groups to attempt to capture key state institutions (such as public contracts agencies) where they can extract rents. Infrastructure projects are by nature highly politicized and a significant source of rents. Political economy analysis focuses on distributing power and wealth between different groups and individuals, and the processes that create, sustain, and transform these relationships. This type of analysis helps to identify potential winners and losers of particular infrastructure interventions. For example, Sanchez et al. (2013) investigate the winners and losers in the rural and urban spaces of an increase in total factor productivity (TFP) in Pakistan’s transport sector. Understanding the political, economic, and social processes that promote or block pro‐poor investments – and understanding the role of institutions, power, and the underlying context in countries – allow more effective and politically feasible investments.

# **3.2 Valuing or Weighting the Factors**<sup>**9**</sup>

The biggest challenge in prioritizing infrastructure projects is how to value or weigh different factors that influence investment decisions. Decisions require understanding a goal or a set of goals and alternative paths to achieving these goals. In the case of infrastructure investments, these goals usually fall under welfare improvements (“a principle of inclusion”), including those that come from public goods like a cleaner environment, or fostering economic growth (“a principle of connectivity”) or both (Biller and Nabi, 2013). If one considers that costs also imply forgone goals due to trade‐offs that need to be equated, minimizing costs may be viewed as a goal or as a constraint to achieve other goals. A simple decision making matrix would thus bring together different goals with different paths to achieving these goals, and each cell would be filled by indicators (e.g. increase economic growth by 0.5 percent in year 1). One of the goals may act as a numeraire and weights may be assigned to the other goals with respect to this numeraire. From such a matrix, a weighted score of benefits can be derived. Once divided by the costs of each alternative path, a cost‐effective indicator would be derived, indicating a preferred option. Both indicators and weights may have a high degree of subjectivity given the measurement issues. Weights may differ spatially (e.g. local versus global, across countries, rural versus urban), across economic sectors, across stakeholders, and across time (outcomes now versus future outcomes).<sup>10</sup> A number of non‐economic theories/techniques exist in the decision science literature to weight these factors. While desirable, not all factors affecting priority setting can be measured in monetized or quasi‐

> 9 It should be noted that the discussion here is not exclusive to infrastructure investments but encompasses decision making processes. Much of this discussion comes from OECD (2002).

> 10 This last factor becomes particularly important under uncertainty and over a long term as argued in Hallegatte et al. (2012).

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