Striving for Standards

Social finance has focused on small scale projects and programs. In order to attract large investors like pension funds, thinking about social finance must be on a bigger scale.

With co-ordination of data inputs, the form of the output, and a transparent benefit-cost analysis and risk analysis, social finance can build trust and be used to aggregate, fund and develop more and bigger infrastructure projects in Canada.

The benefits of a standardization is that investors can compare projects. Small projects can be bundled together to meet investors’ objectives. And, projects can be designed to meet stakeholders’ objectives. The Canadian Impact Infrastructure Exchange (CIIX), a concept describing a proposed method of building capacity for large-scale public/private investment was chosen by the Canadian government from over 150 submissions received from across the country that address key priorities for Canada’s long-term prosperity (Harnessing the Power of Social Finance report). The CIIX can play a key role in standardizing the business case evaluation of infrastructure projects in Canada.

The government, as a stakeholder in an infrastructure project, may have the objective of providing clean, safe drinking water to a community. An investor may have the objective of adding inflation protected returns to its portfolio. And a community may want to get more green space while managing its storm water to improve its drinking water. How is a storm water project management team to juggle these objectives when designing a project?

How can a business case be made that satisfies government, investor, community needs? If everyone was using a standard business case methodology and common data that produced output that met all stakeholders’ needs, the problem would be solved. And if the design team could get feedback, in real time, as it makes design decisions, on financial, community and sustainability metrics they will be able to create a project that better meets the needs of all stakeholders.

Standardization to Lower Costs and Improve Transparency

The U.S. are experimenting with this standardization approach. An example of this can be found in the successive rounds, over 2009-2013, of stimulus funding in the U.S..

“The TIGER program enables DOT to use a rigorous process to select projects with exceptional benefits, explore ways to deliver projects faster and save on construction costs, and make investments in our Nation’s infrastructure that make communities more livable and sustainable.”1

The U.S. Department of Transportation (DOT) administered the Transportation Investment Generating Economic Recovery (TIGER) discretionary grant program included in the American Recovery and Reinvestment Act. The legislation provided $3.1 billion over 2009-2012 years awarded on a competitive basis.

“Each applicant should provide evidence that the expected benefits of the project justify the costs (recognizing that some costs and benefits are difficult to quantify). If it is clear that the benefits do not justify the costs, the Department will not award a TIGER Discretionary Grant to the project. Benefits include the extent to which residents of the United States as a whole are made better off as a result of the project.”2

Over the life of the TIGER program to date three significant innovations have been made. In order to compare often very different projects on a common footing: the DOT standardized the methodology, the expected output, and the data to be used.

Standard Methodology

The first DOT innovation was that the methodology to be used by grant applicants was defined3. The DOT used benefit-cost analysis, which has been around for over a hundred years and is the gold standard for decision making. The DOT published the representative benefit categories. They also released technical information for monetizing benefits and costs in their Benefit-Cost Analyses.4 For example, the re-iterated a key tenet of benefit-cost analysis:

Transfers are not benefits. Analysis should distinguish between real benefits and transfer payments. Benefits reflect real resource usage and overall benefits to society, while transfers represent payments by one group to another and do not represent a net increase in societal benefits.”

And to be concrete, they provided examples:

“In the case of job creation, for example, every job represents both a cost to the employer (paying a wage) and a benefit to the employee (receiving a wage), so it is a transfer payment, rather than a net benefit. While wages are a transfer payment, increases in the productivity of the labor force, measured by increases in how much workers produce per hour, can be included as a benefit of the project, but these benefits must be carefully measured and justified to be included. With respect to economic development, providing estimates of capital investments or property tax revenues are not legitimate benefits in a benefit-cost analysis. For example, while the tax is a benefit to the tax assessor it is a cost to the taxpayer. These transfers are commonly included in ‘‘economic impact analyses;’’ an economic impact analysis is not acceptable as a substitute for a benefit-cost analysis.”

Standard Data Inputs

The second DOT innovation was that the input data to be used was published. The example below shows an extract from a table that supplies the value to place on human life and the cost of carbon to cite two controversial examples:

“While the impacts may differ from place to place, the Department does recognize certain monetized values (and monetizing methodologies) as standard, such that various projects from across the country may be evaluated on a more equivalent “apples-to-apples” basis of comparison. The following table summarizes key values for various types of benefits and costs that the Department recommends that applicants use in their benefit-cost analyses.”5

Table 1. Recommended Monetized Values (extract)

Cost/Benefit Category

Recommended Monetized Value(s)

Reference and Notes

Value of Statistical Life (VSL)

$6,200,000 per fatality ($2011)

Treatment of the Economic Value of a Statistical Life in Departmental Analyses (2008 revised guidance and 2011 update) http://ostpxweb.dot.gov/policy/reports.htm

Social Cost of Carbon (3%)

3% SCC Year (2007$)

2010 21.40

2011 21.90

2012 22.40

2013 22.80

2014 23.30

2015 23.80

2016 24.30

3% SCC Year (2007$)

2031 33.40

2032 34.10

2033 34.70

2034 35.40

2035 36.00

2036 36.70

2037 37.30

Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 (February 2010), page 39, Table A-1 “Annual SCC Values 2010-2050 (in 2007 dollars)” http://www.epa.gov/oms/climate/regulations/scc-tsd.pdf

NOTE: – SCC values are per unit metric ton of carbon dioxide and already discounted forward to the reference year (in 2007 nominal dollars).

– See Part II, Section 1 (“Clarification on the Social Cost of Carbon (SCC) Guidance and the Annual SCC Values”), for methodology of how to use 3% SCC values in TIGER BCA.

And,

“Applicants should discount future benefits and costs to present values using a real discount rate (i.e., a discount rate that reflects the opportunity cost of money net of the rate of inflation) of 7 percent, following guidance provided by OMB in Circulars A–4 and A–94 (http://www.whitehouse.gov/omb/circulars_default/). Applicants may also provide an alternative analysis using a real discount rate of 3 percent. They should use the latter approach when the alternative use of funds currently dedicated to the project would be for other public expenditures, rather than private investment. In presenting these year-by-year streams, applicants should measure them in constant (or ‘‘real’’) dollars prior to discounting. Applicants should not add in the effects of inflation to the estimates of future benefits and costs prior to discounting.”6

Standard Output

The third DOT innovation was that good examples of outputs and benefit-cost analysis presentation were published so that applicants knew what they were aiming at. The output expected was defined using these examples. For the published examples a common feature was that all of these analyses were “transparent and reproducible.”7 Insisting on transparent and reproducible analysis also helped the DOT do its due diligence on the hundreds of projects they had to evaluate which should be funded.

“The best applications are often prepared by transportation agencies that have used in-house economic expertise and benefit-cost analysis (BCA) to influence the design of the project from the beginning.”

The ideal case then, is one where the design is influenced by the business case results throughout the planning process.

The proposed Canadian Impact Infrastructure Exchange (CIIX) could help build trust and provide a mechanism to achieve the scale required to interest large pools of impact investment capital by following the same three steps as the US DOT did with TIGER.

By standardizing the inputs, the outputs, and the methodology the CIIX can provide output that investors want to see, in a form that can be compared and aggregated across projects of differing scale, type and geography. It could therefore, through scale, lower the transaction costs of infrastructure deals.

Can Guidance Be Provided on Risky or Contentious Input Values?

When less tangible outcomes that may not have a market price are predicted from infrastructure projects, such as a more healthy society or cleaner environment, the risk associated with these measures needs to be explicitly estimated.

Sometimes, there may be markets that can provide prices. An example would be pollution emission trading that results in a price associated with the pollutant traded. Other times policy makers will impose a price based on society’s preferences. In some cases the inputs have no price that can be directly observed as the outcome of policy or market transactions.

When input data values are not explicit, or they vary because of geography or study method there is still the ability to put boundaries on the values and be transparent and consistent in their use.

Economics uses several methods to value these non-market “externalities”. The table below shows for how the reduced flooding, cleaner water and green-space value benefits from wetlands creation can be valued.

Table 2. Examples of Valuation Techniques for Wetland Services

Benefit Type

Valuation Method

Habitat for commercial species

Market prices for commercial species and productivity per acre

Habitat for wildlife & visual or cultural benefits

Prices paid by government agencies to protect wetlands

Wetland conservation

Opportunity costs; i.e., benefits of wetland conversion

Amenity or aesthetic value

Hedonic property price model; i.e. the change in nearby property values resulting from the wetland conversion

Recreation value

Travel cost method; i.e. how much people pay to get to a wetland for recreation. Participation model using unit-day values; i.e. how much people pay to access a wetland for recreation. Contingent valuation; i.e survey of people’s willingness to pay to access a wetland for recreation.

Flood control and shoreline anchoring

Damages avoided

Water purification

Reduced treatment costs by alternative methods

Non-use, bequest & option value

Contingent valuation; i.e survey of people’s willingness to pay to have option of access a wetland, the value to future generations or how much people value the existence of the wetland.

Table Source: Adapted from David W. Pearce and R. Kerry Turner. 1990. Economics of Natural Resources and the Environment. Baltimore: Johns Hopkins University Press. pp. 226-235.

While methodologies for valuation may not vary for similar projects, often the values themselves will vary by region of the country or by income or demographics of those affected. A type of research report called a meta-analysis is important in collecting these types results and the great resource of the world wide web means that these meta-analytical summaries are at researchers’ fingertips.

“In statistics, a meta-analysis refers to methods focused on contrasting and combining results from different studies, in the hope of identifying patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies. ”8

By using meta-analyses that synthesize many studies guidance can be provided on the most important variations in these values so that if, for example, the social cost of water is high in the an area due to scarcity, this can be captured in the analysis.

As shown in the table above, non-market valuation methods are used to value things that people may never use:

  • Revealed preference methods: Infer the value of a non-market good or service using other market transactions. For example, the price of a house may be used to determine the value of transit services. Hedonic pricing methods start from the premise that the price of a good is a function of the service’s characteristics. A regression model then determines the contribution of each characteristic to the market price.
  • Stated preference methods: Contingent valuation studies survey people on how much they are willing to pay to get access to a good or service or how much they would be willing to accept as compensation for a given harm or lack of access.

Market-based methods are used to measure value from the perspective of what you would have spent had you taken another approach:

  • Avoided cost analysis: This methodology looks at “the marginal cost of providing the equivalent service in another way. For example, rainfall retention and infiltration can offset a water utility’s cost to capture, transport, treat and return each additional gallon of runoff.”9 Rather than the avoided cost of not building facilities, it may be more appropriate to consider the converse, what the cost would be of damages be if the project does not go ahead.

A risk analysis approach can be used to, instead of assigning one number to these difficult to value inputs, assign a range of values while still discounting for the fact that some values maybe be more likely than others.

High, medium and low values from the literature can be collected from these meta-analyses to reflect the range of uncertainty about inputs as well as their most likely values. A three-point estimation technique can then be used to construct a probability distribution representing the outcome of future events, based on this guidance information. These distributions are then an input into a Monte Carlo risk analysis. The risk analysis is done for all inputs into the benefit-cost analysis.

The positive “externality” of this approach is that the outputs are also in terms of probability distributions or ranges. The risk-adjusted results provide yet another dimension of transparency for investors and governments. Portfolios, or packages, of infrastructure projects can now be constructed so that they meet specified risk characteristics.

Standard and Transparent Builds Trust and Infrastructure

A partnership that brings together well-designed, efficiently executed projects with private capital to meet government goals, is dependent on an exchange of money for risk and return – both social and financial. A transparent exchange, with established methodology and data for assessing projects will provide gains to trade to all participants. It will meet the needs of the Canadian society, provide an attractive use of funds for impact investors, and be a model for unleashing impact investment to meet social goals.

It is only through a transparent methodology as proposed by CIIX, and measurement of the costs, benefits and risks that social finance will get the support required to move from small-scale projects to the large infrastructure projects that are needed.

Even when input data is subject to large risks and can vary substantially, standardization can done on the most likely as well as the range of values so that even faced with an uncertain world, project sponsors and investors can understand and evaluate projects.

Social finance faces a credibility gap and a scale problem. To meet these challenges a national infrastructure exchange could provide the transparency, scale and credibility to build a prosperous Canada.

1 http://www.dot.gov/tiger

2 2013 Benefit-Cost Analyses Guidance for TIGER Grant Applicants http://www.dot.gov/sites/dot.dev/files/docs/TIGER%202013%20NOFA_BCA%20Guidance_0.pdf

3 Federal Register /Vol. 77, No. 20 /Tuesday, January 31, 2012 /Notices http://www.gpo.gov/fdsys/pkg/FR-2012-01-31/pdf/2012-1996.pdf

4 TIGER Benefit-Cost Analysis (BCA) Resource Guide http://www.dot.gov/sites/dot.dev/files/docs/TIGER_BCA_RESOURCE_GUIDE.pdf

5 TIGER Benefit-Cost Analysis (BCA) Resource Guide http://www.dot.gov/sites/dot.dev/files/docs/TIGER_BCA_RESOURCE_GUIDE.pdf

6 Federal Register /Vol. 77, No. 20 /Tuesday, January 31, 2012 /Notices http://www.gpo.gov/fdsys/pkg/FR-2012-01-31/pdf/2012-1996.pdf

7 Tiger Benefit-Cost Analysis (BCA) Examples from http://www.dot.gov/sites/dot.dev/files/docs/TIGER-bca-examples-03-06-12.pdf

8 “Meta-analysis” From Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Meta-analysis

9 The Value of Green Infrastructure – A Guide to Recognizing Its Economic, Environmental and Social Benefits, Center for Neighborhood Technology 2010, p. 14, downloaded from: http://www.cnt.org/repository/gi-values-guide.pdf January 22nd 2013. (referred to as CNT below)

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