What do egalitarians care about?

I dunno, but if its “capabilities” or equality of opportunity then tracking income inequality between various income percentiles is the wrong measure to concern themselves with.

Income is a flow. Its tenuous. Its dynamic. It does not determine the size of your budget set; it does not determine your capabilities or opportunities.

If you doubt this, talk to Dell about the new computer I just bought on credit ((I know, I know… Cash flows are very irregular for grad students and I got a good price on the computer with zero interest)). Talk to the Bank where I took out my student loans.

If you care about the size of budget sets, then you care about lifetime income (and access to the credit market, but I don’t think this is a problem in the U.S.). This is because if someone has earning potential (let’s say they’re a college student or they’re just starting their career) then lenders will loan them money, even if they have low income today, because the lenders know their earnings, and thus their ability to pay the lenders back, will increase over their lifetime.

Is current income at least a good measure for lifetime income? Nope. Early in careers, there’s little correlation between current earnings and total lifetime earnings ((see this paper for a nice discussion of the issues involved in measuring lifetime income. They use some really, very cool Swedish data to estimate the relationship between current income and lifetime income.)). This picture shows the ratio between current and permanent income (annualized):

So, why do egalitarians seems to care so much about (current) income inequality dynamics?

9 thoughts on “What do egalitarians care about?”

  1. Do you have a better metric in mind?

    The college example is interesting. Did your lenders consider your chosen field before the loan was approved? It seems like a drama major could (should, would) face much higher interest rates than a law major.

  2. Alternative measure? I dunno. Lifetime income?

    Also, you’re right about college loans, their prices should be more rational. I wonder why they’re not… maybe because they’re subsidized by the government?

  3. I’m not sure that I’d interpret that paper correctly. Were they able to predict, with any degree of accuracy, the lifetime income of any individuals from that data set? It’s kind of like that old bit about climate models. With a strong model, you should be able to input last year’s data and predict yesterday’s climate. Did the researchers pull that off?

    Are there any white market loans that aren’t somehow subsidized (backed, insured, etc) by the government? Are subsidies for students bad? You can’t have lots of happy positive externalities flowing out of a university without pleb^H^H^H^H undergrads. (Unless grad students spring fully formed out of the heads of tenured professors. I’m a little shaky on the subject.)

  4. Of course they can’t predict the weather. They’re just saying cold days are usually followed by hot days and reporting an average temperature. On cold days one has to increase the recorded temperature by a certain amount to get the average and on hot days you have to decrease the recorded temperature to get the average. Egalitarians care about the average temperature, btw, because its the indicator of global warming… err inequality of opportunity… I really don’t know what to do with this metaphor.

    Anyway, I’m not convinced higher education provides much “productive” value. It may be that it provides other things like “well-roundedness”. If people care about that non-productive value, then they’ll pay for it. Its not clear why it would need to be subsidized.

    I like learning stuff, that’s why I’m a grad student… learning stuff sometimes results in productive output (new technologies or whatever) but that’s not why I like it or participate in it. In that sense, research creates positive externalities and maybe for that reason it should be subsidized. Some fields provide more externalities than others, but measuring them is extremely hard. Example: Brown discovered “Brownian motion” in the early 19th century, Einstein explained it via the atomic theory around 1900, and a dude in Japan developed the math to describe it. It wasn’t until the 70’s, about 150 years after its discovery, that the concept was used productively (in the pricing of financial options). Certainly, Brown, Einstein and Ito had no idea there was a productive use of their respect research. So biology, physics and mathematical statistics all had tremendous positive externalities… with lifetime frequency latency.

    I’m not quite as convinced the humanities (or even much of the social sciences… although Black and Scholes were economists) provide such externalities. Although, maybe Brown was inspired to look at pollen by some literature he read or some painting he looked at.

  5. Predicting lifetime income seems a little like predicting the climate. You take factors and trends that you know today and extrapolate their future course. Good models and methods tighten up the probability gradient around your predictions.

    Of course, you can’t perfectly predict the lifetime income of any one person any more than you can predict the climate over a given square meter of land. You make an educated guess about your level of uncertainty, and set your prices accordingly. Statisticians use a special technical term for this guess: “risk.” Maybe the lenders should hook up with the people who devise actuary tables for life insurance firms.

    I can’t speak for the humanities in general, but the arts certainly have positive externalities. Like science, all art begins with observation, although the arts are focused on humans. Different eras in the arts can be marked by new insights into human perception; how we interpret both symbols and the natural world. Isn’t this the very essence of innovation? Looking at phenomena from new angles?

    Consider the contributions of Islamic mosaic art to geometry. Music to math. Weaving to computer science. Figure studies to anatomy, and later medicine. Impressionist painting to digital imaging techniques (they use the same trick). Cubism and other modern art forms to machine perception. Photography as a potent tool for scientific investigation. These are all direct contributions, beyond “This sonnet inspired so-and-so to invent…”

  6. Based on the best guess at the best long-term return on that investment, of course. I’m just wary of saying “all in!” on the obvious stuff like math and engineering. The US has been ahead over the past century or so, not because we pumped out more mathematicians or engineers, but because we made wild, risky investments that paid off handsomely. If your subsidy strategy is too conservative, you miss opportunities. It’s not like you have to sink 50% of your budget into interpretive dance programs and mime schools.

  7. Whoa, whoa, whoa. This is all pretty wrong. First, Brownian motion was actually discovered by some French dude in the 19th century who was studying the incidence of “death by mule kicks” in the French army (not a trivial topic, lemme assure you). The point was to reduce the number of deaths by, um, “friendly fire”. Brown himself was studying the motion of particles in water. I’m not sure what you think constitutes practical applications but his stuff had a pretty immediate impact on his particular area. My sense of it is that people – mathers and scientists – had a pretty good idea early on that this stuff was important. Ito’s contributions were largely unknown for awhile mostly because western scholars do, or did at that time, tend to ignore non-western contributions. His work basically linked it up with a lot of other phenomenon that people were studying though.

    I think what you mean is that it wasn’t until the 1970’s that the concept was first used productively in ECONOMICS. As an aside, there were people who were doing “Chaos Theory” way back, it’s just nobody had written a book about flapping butterflies in China at that point and it was still called “borin’ ol Probability Theory and Functional/Real Analysis”.

    If you want examples of mathematical ideas that at their genesis had no obvious practical value but proved to be tremendously important then you probably gotta reach into something like Topology or Number Theory. And look around. Probability pretty much from the outset was all about practical applications. People came up with it in order to win at cards.

  8. Where were stochastic differential equations used “productively” (i.e. in the creation of a product bought and sold) before B&S? I suppose they’re used in electrical engineering, but at the most that reduces the span between Brown’s proving pollen isn’t alive (queue Frankenstein music) and practical application to 130 years.

    That french dude may have invented statistics — it’s all practical and stuff — but the thread that connects Brown to option pricing doesn’t go through main line statistics. B&S’s 1973 paper cited Ito (well, actually a textbook about Ito’s Calculus written by one of Ito’s co-authors). Ito was inspired by Weiner’s work and Weiner by Einstein (and some Polish dude named Smoluchowski) and Einstein by Brown (although I can’t read German so I don’t know for a fact he cites Brown in his 1905 paper). None of these folks (except Weiner) where particularly given to pragmatism.

    And like everything else, Brownian motion was originally discovered by the ancient Greeks…

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