Category Archives: Other economic thoughts

What doesn’t fall into the other categories

A transparent municipal utility’s reserve target

Reserves chart

As one of my civic activities, I sat on the City of Davis Utility Rates Advisory Commission. In my final action with that commission, along with Elaine Roberts-Musser and Lorenzo Kristov, we prepared what might be a first-of-its kind enterprise fund reserve policy for the four utilities managed by the city. Up to this point, the URAC had been presented with rates development reports that appeared to use somewhat arbitrary, and inconsistent, methods of setting reserve targets. The city also appeared to be holding tens of millions of dollars in those funds that might be unneeded to meet expected reserve requirements.

With the City Council’s approval and support from the staff and the Finance and Budget Commission, we identified the elements that needed to be covered by reserves, including working capital, debt covenants, unanticipated capital replacements, and revenue-expense volatility. The first two elements were fairly straightforward to calculate, and unanticipated replacements didn’t appear to be significant. It was the analysis of the relationship of revenue and expense volatility where the report innovates. Previous studies had used some variation of a percentage of capital assets with no underlying explanation. Our solution was to derive an estimate of the outerbound of an annual revenue shortfall for a utility as buffer to allow rate or management adjustments.

In the end, the target reserves generally didn’t change much, but the City now has a transparent target that it can use to determine when it has excess funds that might be used in different fashions instead.

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Soda tax really works in Berkeley

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A just released study on the effects of the Berkeley, California soda tax of one cent per ounce found that soda consumption has fallen 52% over the last four years. That is a remarkable price elasticity. Assuming a 20-ounce bottle costs $1.99, with a tax of 20 cents, that implies a price elasticity of -5. In other words, for every 1% o price increase, demand falls 5%. The study relied on household surveys, which are not always reliable about consumption quantities, so it would be interesting to see actual sales data.

Mismatch in job openings and the unemployed

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Evidence of how job training is lagging behind job needs. The U.S. Labor Department reported 7.3 million openings, but only 6.3 million people were actively seeking jobs and unemployed. Employers are not able to find the technically-trained individuals that they need for the changing economy. Only a small portion of this shortfall can be met through training in our standard educational institutions. We should be looking for other retraining solutions such as those in Europe.

Who says economists aren’t funny…

Willingness Toupee

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David M. McEvoyO. Ashton Morgan and John C. Whitehead

No 19-01, Working Papers from Department of Economics, Appalachian State University

Abstract: In this paper we tackle the hairy problem of male pattern baldness. We survey balding men and elicit their willingness to pay to move from their current sad situation to a more plentiful one. Then we comb-over the results. What’s the average willingness to pay to move from a glistening cue ball to a luscious mane? About $30,000.

Key Words: mullet, skullet, comb-over, ducktail, Beatlemania, buzz cut, whiffle, pageboy, attribute non-attendance

The sole reference: Carilli, Anthony M., “Scarcity, Specialization, and Squishees,” Chapter 1 in Homer economicus: The Simpsons and economics. Joshua Hall, ed., Stanford University Press, 2014.

Some sample footnotes:

  1. As is standard in the discipline, author order is determined by reverse Norwood Baldness Scale.
  2. The “stone piece” was a block of dark slate tied around the head to achieve the appearance of a full head of hair. While there are no sources of any such thing actually taking place, the authors imagine that it must have happened.
  3. “In ‘Simpson and Delilah,’ Homer attempts to pursue an executive position in which he doesn’t have a comparative advantage. Mr. Burns confuses Homer with a young go-getter and promotes him to an executive position after Homer has managed to scam himself some Dimoxinil–a miracle cure for baldness–and grow some hair.” (Carilli 2014, p. 11)
  4. It is important to note that the authors did not even bother looking for other studies.

7. Both of these models can be found in the NLogit manual (www.limdep.com) or via Google Scholar. They’re legit but we really don’t want to add any references besides the Simpsons book.

9.Referee #2 may try to claim that you cannot estimate WTP from a mixed logit model with a price parameter distribution that includes negative values because these respondents’ WTP will be undefined. Since distributions that constrain WTP to the positive realm do not perform as well statistically as the normal (we didn’t really check this) and (likely) generate goofy WTP estimates, we choose to present WTP estimated with the mean coefficients. The gullible, er, reasonable, reader will just go along with it since the MXL WTP number is so close to the ECLC WTP estimate and this lends reliability to our data.

PG&E “buys dear, sells cheap”

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PG&E spends $275 million a year on energy efficiency investments that reduce demand by 100 MW. It also spends $65 million a year on demand response to reduce peak loads by 400 MW.  If we assume that energy efficiency investments are effective an average of 12 years the incremental cost of those investments is $66 per MWH (6.6 cents per kWh). For demand response the incremental cost, which should match the market value, is $163 per kilowatt-year (or $13.60 per kW-month). Both of these values are reasonable investments for long-term resources.

Yet, PG&E argues in the PCIA exit fee proceeding and its annual ERRA generation cost proceeding that the appropriate market valuation for its resources are the short-term fire sale values that it realizes in the daily markets. According to PG&E, customers do not realize any additional value from holding these resources beyond what those resources can be bought and sold for the CAISO markets and in bilateral short-term deals.

So we are left with the obvious question: Why is PG&E continuing to invest in energy efficiency and demand response if the utility states that it can meet all of its needs in the short-term markets? This hypocrisy is probably best explained by PG&E manipulating the regulatory process. PG&E’s proposed “market valuation” sets the exit fee for community choice aggregation (CCA) at a high level. Instead, that market valuation should reflect how much CCAs have saved bundled customers in avoided procurement, and what PG&E pays for adding new resources.

Will Amazon’s HQ2 pay off for New York?

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Even though I have conducted regional economic impact studies, I’m always a bit skeptical when a project is touted as a huge payoff for taxpayer investment. Amazon’s HQ2 is a case in point. New York is claiming a $24 billion net return over 25 years from the $3.6 billion in tax breaks, based on impact analysis done with the REMI economic model. I would be interested in a retrospective analysis on the impact of Amazon’s HQ1 in Seattle. The campus is fairly self contained and it should be fairly straightforward to track the growth of Amazon employment in Seattle since the last 1990s. Clearly, there would be uncertainty about how to attribute regional economic activity to Amazon activity, but we could see bounds on various factors such as jobs and tax revenues. We could then see a comparison against the estimates for New York City.

Fixing college admissions

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Harvard is being sued by a group of Asian-American students for discriminating against them through affirmative action admission standards. The Trump Administration is joining on their side. I have been troubled by the reliance on standardized tests and inflated GPAs as appropriate metrics for college admission. I propose two solutions to both of these problems:

  1. Colleges and universities should create mission statements about what the university wants to accomplish through educating students. These statements should go well beyond just saying “a well-rounded individual who is prepared for a career.” It should state that it wants a set of alumni that positively impact their communities in a number of dimensions (some not easily quantified) in a manner that improves social well being in a manner chosen by the university’s policy makers. This will require developing a consensus among those policy makers (e.g., the Board of Regents), but it will prompt a larger debate about the purpose of a college education that is badly needed right now.
  2. The colleges and universities should then conduct at least two statistical/econometric studies to determine the mix of traits that it wants in its student body to accomplish its objectives:
    • The first one would establish what preparation will lead to the most likely academic success in that college. This would completely eliminate the need for standardized tests. The key is that most colleges have an extensive set of data on the GPAs of their students, the high schools that they went to and their GPAs in high school (along with other academic data). The study (a regression)  would regress the college GPA on the high school GPA weighted by the high school (this adjusts for differences between high schools) plus a specific set of other academic / extracurricular data. This would be socio-economic blind. But most importantly, these results would NOT be the singular admission standard. It would be just a measure of likely academic success that could help identify how to deploy resources for those students once they enroll.
    • The second would evaluate how alumni impact their communities. This would rely on data about alumni on occupations, income, community activities, rate of return to certain communities based on socio-economic status, and community well-being. Alumni characteristics would include incoming socio-economics traits, study major, college GPA, extracurricular activities, and other factors thought to be important. This study (again, likely econometric) would measure the relative impact on community well-being (or other chosen objectives) of admitting students with certain characteristics and likelihood of certain majors and academic performance. For example, it might show admitting a student from Watts with a lower predicted GPA has a bigger community impact than admitting another from Beverly Hills with a higher GPA.
    • The desirable traits found in the second study would be used with the results of the first study to set admission policies in a fairly transparent way. A third study might assess what mix of student body traits is most likely to achieve the mission statement objectives. That could further help make the admission process more transparent.