Mixed Methods
Using Mixed Methods to Improve Economic Analysis
Economic analysis is an undoubtedly significant component of the selection process for community development entities (CDEs) when determining how to allocate New Markets Tax Credits (NMTC). And it should be – in a highly competitive landscape with limited allocation available, it is imperative for CDEs to maximize “bang for the buck” as they leverage NMTCs toward community impact. Quality economic analysis can provide projections that help CDEs put resources toward the most impactful projects available to them.
Like all of the available tools designed to help leverage NMTCs for community development, economic analysis should be used to maximize impact in low-income communities (LIC). Because economic analysis is primarily quantitative and relies on a series of complex multipliers, it can easily be perceived as an “objective” measure of economic output, independent of subjective interpretations or human bias. But it is for precisely this reason that economic analysis, like all community development tools, must be viewed critically and with an eye toward continuous improvement.
Economic analysis conducted using input-output (I-O) modeling has numerous advantages. It can help an investor understand the likely indirect, or interindustry, economic outcomes of an investment, as well as the induced effect – the economic activity resulting from wages that are paid as a result of the investment. I-O modeling can help an investor understand the direct and indirect effects of job creation on regional employment and earnings. In certain cases, it can also estimate the tax impacts of an investment, an obviously important metric for understanding the potential benefits for communities.
Economic analysis reports are concise, numeric, and methodologically sound. For these reasons, they are easy to take at face value without critical interrogation. Economic analysis conducted using I-O modeling can be improved, however, in at least two specific ways.
First, although the projections produced using I-O modeling are methodologically sound, they remain opaque and inaccessible to the majority of people, even many of those in the community development sector. Questions of reliability can arise in any modeling scenario that relies on predetermined multiplier effects. Economic modeling in community development could improve in its capacity to explain its metrics and the real-world meanings of its outputs. This includes articulating exactly what the output model shows, and in what ways it is limited.
Second, and perhaps more significantly, quantitative economic analysis provides little in the way of place-based impact projections; that is, it lacks the community context that should make it so valuable. By producing results in the form of dollar amounts, the real-world impacts on particular communities cannot be easily understood. To put it simply: an annual interindustry effect of $54M does not mean the same thing in Atlanta as it does in Tifton, GA. Neither does the creation of 240 full-time equivalent (FTE) quality jobs that are accessible to the LIC residents.
Adopting a mixed methods approach to economic analysis can be a step toward demystifying the actual meaning of economic output metrics. Qualitative methods rarely appear in economic analysis, but they are uniquely suited to bring I-O results to life through direct engagement with representatives from the communities in which the investment will occur. The following action steps can help address the shortcomings of quantitative economic analysis mentioned above.
Engage directly with local representatives to understand community needs and goals. This engagement can take on a variety of forms – interviews and focus groups are two examples. Share I-O model findings with local stakeholders and talk through the ways in which those economic and employment outcomes are likely to impact their specific community. This will help contextualize the scale of impact the investment can create – that is, how significant is that number of dollars and that number of jobs in that community? It will also bring to light specific goals the community has set for itself that may be enabled by the investment. Perhaps a particular community has a goal of setting up a workforce training partnership between a particular industry and a local educational institution, and the new jobs created in a particular investment can help push that project toward fruition. This type of specific finding paints a picture of impact that cannot be achieved through solely quantitative outputs.
Clearly explain quantitative output metrics and what they represent in terms of real-world outcomes. It is likely not pragmatic to go into deep detail about how the multipliers in the I-O model were constructed. But that doesn’t mean that output metrics can’t be brought down to earth. If a $10M investment in a particular industry is projected to create an interindustry effect of $25M, explain what that means using real examples. What possible examples of downstream spending contribute to that interindustry effect? What local industries might benefit from the indirect effects of the investment? What are the limitations of the model in terms of understanding what that $25M represents? The answers to these questions will help paint a clearer picture of the output results.
It is easy to see large output metrics resulting from I-O modeling and assume major positive community impact. Generally speaking, this assumption is probably correct. But to truly understand what these metrics mean for particular communities, it is necessary to demystify the process using additional methodological tools that go beyond the typical quantitative metrics on which the industry frequently relies.