# 6.1.3 Valuing or Weighting the Factors<sup>20</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% 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>21</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-monetized forms. In these cases, non-economic methods need to be used – including multiattribute and multi-criteria decision theory, Delphi approach, and analytical hierarchy process (AHP).

**All methods and techniques for priority setting in decision making come with caveats.** Monetizing across the board has the advantage of converting factors in a common unit of measurement, but often generates important moral dilemmas. Alternatively, solely using expert opinion to direct decision making may result in skewed investments, as experts usually have their own bias toward their expertise. Given the long-term nature of most infrastructure investments and their lock-in characteristic, the time dimension and how to discount the future play a particularly important role. Yet, this further complicates decision making since decisions made today generate political pressures and future rentseeking. For example, implicit and explicit subsidies to urban sprawl in the United States generated a pattern of private vehicle dependency and expectations of a “rural” living combined with an urban experience and income generation. This in turn has tremendous external consequences that are difficult to address often because of political reasons. The process advanced mostly in the 1950s but continues today given vested interests. It is also well known that the way in which the future is discounted in decision making economic methods like CBA penalize the future with respect to the present. The economic logic of the currently used discount factor remains and enables comparisons of “apples and apples.” Yet, if long-term discrete or continuous phenomena that may potentially cause severe harm to societies – like pandemics, and climate change – are to be effectively addressed by decision makers, better methodologies need to be designed to enable comparisons for prioritizing investments across long time horizons under scarce resources.

6.2 Infrastructure Gap and Prioritization

> 20 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).

> 21 This last factor becomes particularly important under uncertainty and over a long term as argued in Hallegatte et al. (2012).

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