Richmond's Search for n*

     The past two summers, I have interned at an economic development organization (EDO) in Richmond that has the goal of attracting businesses, investment, and new talent to the area. This organization, as well as the practice of economic development as a whole, immediately came to mind as I read Tiebout, specifically assumption #7. In general, Tiebout explains that consumer-voters reveal their preferences for public goods when they choose where to live, given seven necessary assumptions. Assumption #6 states that all communities have an optimal population (n*), and assumption #7 states that communities will actively attempt to populate towards this n*. So, communities that are above their optimal level of residents will work to drive consumer-voters out, and those below their optimal population will work to attract consumer-voters to the community. EDOs fulfill the latter part of this assumption, working to bring in new residents by marketing the region and supporting policies that will make it more attractive for investment.

    According to Tiebout, if Richmond has such a dedicated EDO working to attract consumer-voters, that means they believe the population of the area is currently below n*. Based on data, this appears to be a realistic assumption, as the population density of Richmond in 2020 was 40% less than it was in 1950. While there are no figures on what the appropriate n* actually is, this significant difference suggests there is certainly room to grow. In addition, the existence of EDOs in general highlights progress towards Tiebout’s listed policy implications. Tiebout states that practices that increase the knowledge of the consumer-voters, which EDOs do through marketing initiatives, will bring the government closer to the efficient allocation of public goods.

Just Ask the Students at Virginia Tech

    When I toured colleges, I had a spreadsheet with rows for each school, with headers including “rank,” “location,” “nearest airport,” “Greek life,” “tuition,” “school colors,” etc. After each tour I filled in my spreadsheet, but little did I know I was actively shopping in a market. Tiebout’s model illustrates a preference revelation concept that requires many strict assumptions, likely the most unrealistic of which is that consumer-voters are fully mobile. In his case, consumers vote for an area’s revenue-expenditure model (the public goods they provide using tax dollars) by choosing to move there. In the case of universities, consumer-voters are much more mobile (they do not yet attend any school), there is an abundance of schools to choose from, and universities publish tons of statistics, making school selection a very applicable case for Tiebout’s model. My spreadsheet served as a personal utility function, where I measured characteristics of the schools I cared about, then ultimately weighed them against each other. I was deciding which school would give me the most “bang for my buck,” and would then “vote” for its services by giving it my tuition money.

    However, different from the Tiebout model, consumer-voters cannot simply decide which institution gives them the highest utility and attend. A second decision-maker is involved in the transaction: the university’s admissions office, which can be interpreted as maximizing an entirely different utility function. The university wants to admit students who give it the most “bang for its buck,” in other words, the students who contribute to the institution's goals/reputation. So, the university’s anticipated utility from a given student is a function of their grades, extracurriculars, talents, likelihood of enrolling, etc. This is a stark difference from the housing market, where there are much tighter legal constraints on which characteristics can be considered for selection or exclusion. This two-sided matching market is an interesting limitation to the “vote with your feet” model, and seems to serve as an ultimate mobility constraint. A student may be willing to bear any moving cost to attend UVA, but if UVA says no, the preferences on the spreadsheet don’t matter – it’s no longer a feasible choice in the market. So, while students can craft a beautiful application spreadsheet with an vast number of schools to apply to, their final decision is limited by a second decision-maker (just ask the students at Virginia Tech).

Cheap Eradication, Costly Consequences

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Glyphosate is a powerful herbicide that Colombia used in its Aspersión Aérea program to fumigate coca plantations from 1994 to 2015, before the Constitutional Court suspended it over health concerns. Now, as new president Abelardo de la Espriella settles into office, the debate is back. Weeks into his term, he has decided to repeal the ban. It seems increasingly likely that Colombia will return to the use of glyphosate, considering how hard the Trump administration is pushing for it and how critically high cocaine production levels are. De la Espriella defends this stance by arguing that glyphosate is cheap, fast, and has no real alternative, as manual eradication is dangerous and has limited reach in remote coca-growing areas. 

However, this argument only holds up if you ignore who actually pays for it. The direct costs ( aircraft, chemicals, pilots) fall on the government, and by extension, taxpayers. Yet, the real cost falls on farmers whose legal crops get wiped out along with the coca, on rural communities dealing with higher rates of cancer and respiratory illness, and on ecosystems that get contaminated. Meanwhile, cartel leaders, the people actually profiting off coca cultivation, remain unaffected by this transaction.

None of this is reflected in the government’s “cheap” price tag for eradication. In economic terms, this is a negative production externality: the true social cost of an activity exceeds the private cost paid by the decision-maker. The relevant question, therefore, is not simply whether glyphosate is cheaper than manual eradication, but whether it remains cheaper once the costs imposed on farmers, communities, and the environment are included.