In assessing the viability of a project, corporations generally use measures related to the future expected cash flow or profitability resulting from the investment. In contrast, usage and benefits accruing to the general population are often more important in the not-for-profit sector. However, these factors can be even more difficult to assess than ‘hard’ financial numbers due to the inability to measure them directly. This was the case for one initiative related to a proposed multimillion dollar large city park infrastructure investment.
This initiative was going to rely on a combination of donations from philanthropic organizations and a bond referendum. In order to demonstrate the value of the investment, its champions needed some way to assess the potential additional visitors to the park and their economic impact on the city as a whole.
A scan of academic and consulting literature provided little in the way of standard models for assessing the economic impact of infrastructure projects. Rather, the most common approach seemed to be based on rough parallels to similar projects or rules of thumb. We did, however, find a consensus regarding the elements to be included in an economic impact model and we developed our own model around these.
Our first step was to identify the drivers of economic impact. These fell into five categories:
- The population within the target geography
- The likelihood of individuals within that population making additional visits to the park as a result of the investment
- The frequency with which individuals would make additional visits to the park
- The incremental spending patterns of visitors to the park
- The multiplier effect of spending
Assessment of these economic drivers required different considerations:
- The population within the target geography was the easiest of the elements to estimate. We identified potential visitor groups based on their proximity to the park, ranging from those people who lived within a 15-minute drive to those who lived more than 3 hours away. Then we converted the times into distances, and used census data to identify the corresponding populations.
- An online survey was used to assess the popularity of alternative attractions in the park. We used email subscription listings provided by the city and local organizations to solicit input and received 700 responses. We asked respondents not only how likely they were to visit each attraction, but how far they would be willing to travel in order to visit this attraction if it existed in another city. Assuming that people in neighboring cities would respond in a similar fashion, we were able to combine this information with the census data to estimate the number of people within each concentric ring who were likely to visit the attraction were it to exist. In the survey, we also asked for demographic data (age, gender, marital and family status, distance from park) to understand how it impacted responses.
- The team debated how to estimate the frequency with which individuals would make incremental visits to the park. We considered asking for this information in the survey but decided that it would be difficult to answer and could generate unreliable data. With no studies available to guide our assumptions, we built conservative estimates based on a range of scenarios. This meant that someone who lived close to the park and said that they would visit the attraction would probably do so more often than someone who lived 2-3 hours away.
- We surveyed people about their likely expenses if they were to visit the park. The resulting numbers weren’t used directly but were used to validate our initial assumption, which was that locals would not incur incremental expense but those who traveled more than an hour would likely pay for food and gas locally.
- We were able to leverage academic literature to develop a range of plausible multiplication factors for the knock on effect of local spending.
As a result of the survey and the economic model, we were able to provide our clients with a recommendation on the infrastructure investment alternatives that were likely to have the largest impact on the local economy. We were also able to demonstrate the impact that demographics had on the likelihood of someone being interested in a specific type of attraction. Given the patterns that we saw in the data, we were able to determine which attractions were complementary and which were independent of the others.
Although the overall economic impact estimate came with caveats, we were able to provide our clients with a level of analytical rigor that they had not previously been able to generate in their planning process. The city is currently considering a $5 million bond referendum to realize the vision of the parks district.
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