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It’s hard to quantify economic benefits of light-rail projects

A light rail rolls past the Salt Lake Temple in Salt Lake City. Started with the 2002 Winter Olympics in mind, Salt Lake City’s light-rail network was built to ease air pollution and traffic congestion. Some companies have cited the rail lines as one reason they located or added jobs nearby.
A light rail rolls past the Salt Lake Temple in Salt Lake City. Started with the 2002 Winter Olympics in mind, Salt Lake City’s light-rail network was built to ease air pollution and traffic congestion. Some companies have cited the rail lines as one reason they located or added jobs nearby. AP

One 11-mile light rail project does not quickly cause $4.2 billion in economic growth around itself.

That particular issue matters in my hometown of St Paul, Minn., but analogous questions arise everywhere. In Boise, Mayor Dave Bieter has long advocated a rail-based transit system in and around Downtown that could cost more than $1 billion. A fixed route would attract businesses and developers, Bieter says.

We frequently want to quantify the overall benefits of some particular government project. But we often simply cannot.

It is hard to measure economic outcomes. It is easy to distort such measurements when they are made. Practical difficulties mean that, looking backward in time, it is often hard to tell whether some decision, like building this particular transit line or that flood channel, was good or bad. Yet that difficulty pales in comparison to the uncertainties encountered when looking forward.

In St. Paul’s case, the regional transit agency simply added up the value of all new construction within a half-mile radius of the line since it started. The agency has funding for an additional line pending in the Legislature. So skeptics who question the political motivation, as well as the methodology, behind the $4.2 billion estimate have grounds. Skepticism always is warranted.

While this specific number is questionable, the rail line certainly has spurred economic activity that would not have occurred in its absence. It is a mistake to overestimate net beneficial outcomes of public investment, but equally erroneous to underestimate them. Unfortunately, coming up with really trustworthy numbers often is difficult and sometimes impossible.

Inferring that a rail line caused development is a “post hoc ergo propter hoc fallacy,” which students are strictly warned against. But such “A happened and then B happened, so A caused B” logic dominates public discourse, especially in election years. For many people, the sequence of two events is proof of cause and effect.

So how does one get beyond this?

Even transit’s strongest advocates acknowledge that while some development within an arbitrary radius was directly caused by the line, it was only one factor among many for other investment decisions. For some projects, such as a big addition to a county hospital, the line didn’t matter at all.

The key question therefore always is: How much activity would have taken place had some project not been built?

One approach would be to put things in historical context. What was the annual average of such investment, adjusted for inflation, in these zones before the project became a factor? What was the trend over time in this spending? How far are current levels or trends above those of the past?

One could also try to compare investment around the project with that in other areas where it is not a factor. Break the zone up into various categories and identify discrete neighborhoods or sectors — residential, industrial, retail, etc. Find similar zones elsewhere, pair them and compare levels of new investment. This is useful but still involves much subjectivity.

Which brings us to the larger problem of looking forward, using subjective cause-and-effect methodology to justify spending now to achieve returns later.

In many sciences, organized experiments to determine outcomes are a key method. But such organization is difficult in real world economic estimates. Comparing different approaches, historical trend versus cross-sectional, helps.

Uncertainty also is pervasive in broader questions, like Donald Trump’s assertion that our country pays much more than its fair share of NATO costs. The issue is an old one. Jimmy Carter tried to get our allies to boost defense spending some 40 years ago.

But again, just how does one measure whether Trump’s assertion is true?

As with the costs and benefits of infrastructure, economists would approach the NATO issue in terms of “opportunity cost.” How would U.S. Treasury outlays change if we no longer spent as much on NATO? That is a hard question to answer.

Critics often take the total costs of U.S. troops stationed in Europe or South Korea as a direct expense to U.S. taxpayers. During the Cold War, U.S. army divisions and air wings in Europe cost a significant percentage of defense outlays. But if we had not had those units overseas, would we have spent the same amounts maintaining them on U.S. bases? Or would we really have cut defense spending by that amount?

These are the dirty secrets of many economic studies. Often costs or benefits are real enough, but simply cannot be quantified. Economic theory and history do tell us the probable direction for the results of some choice. But assigning specific dollar values to those outcomes, after the fact or before, usually is maddeningly difficult and often impossible. We can camouflage uncertainty with financial mumbo-jumbo, but many real-world decisions —whether to buy a farm, build a transit line or leave a defense pact — ultimately get made on subjective bases.

St. Paul economist and writer Edward Lotterman can be reached at boise@edlotterman.com.

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