Things you may have missed, #1

Inequality of opportunity

Inequality being something of the chef’s special at the moment, this paper from the World Bank argues that the notion of ‘inequality of opportunity’ (which, some argue, is the only inequality governments ought to eridicate; inequality deriving from different levels of effort being morally legitimate) rests on uneasy philosophical ground. For example, it takes issue with the notion that inequality that stems from people’s innate talents is ‘natural’ and hence acceptable:

Since endowed talents are by definition beyond an individual’s control, it is odd that de
Barros et al. (and others) are so quick to accept as just inequalities stemming from
inequalities in talents.

Calvin Trillin on corrections

As everyone knows, the New Yorker has opened its doors and is letting the air in: you can read through, as far as I understand it, all of their back catalogue. And while the 20s and 30s may have been the heyday of New Yorker casuals, this piece by Calvin Trillin spoofing NYT-style corrections is proof that the Americans are still as good at the short humour piece as they were in the days of Benchley and Parker.

Because of an editing error, an article in Friday’s theatre section transposed the identifications of two people involved in the production of “Waiting for Bruce,” a farce now in rehearsal at the Rivoli. Ralph W. Murtaugh, Jr., a New York attorney, is one of the play’s financial backers. Hilary Murtaugh plays the ingénue. The two Murtaughs are not related. At no time during the rehearsal visited by the reporter did Mr. Murtaugh “sashay across the stage.”

The academic urban legend of the decimal point and spinach

My friend Leah, in general a good source of vaguely horrifying stories about academic/research malpractice highlighted for our attention this story. It’s about how one paper claimed that the reason for the urban myth of spinach being iron rich resulted from a decimal point error–and how that unsubstantiated claim then itself became an urban legend. In the process, it reiterates a lot of things that most of us should have learnt in first year undergrad–but maybe didn’t. And some rather ominous warnings:

The digital revolution has certainly made it easier to expose and debunk myths, but it has also created opportunities for new and remarkably efficient academic shortcuts, highly attractive and tempting not just in milieus characterized by increasing publication pressure and more concerned with quantity than quality, but also for groups and individuals strongly involved in rhetorics of demarcation of science, but less concerned with following the scientific principles they claim to defend. Some academic urban legends may perish in the new digital academic environment, but others will thrive and have ideal conditions for explosive growth.

A little longer on the cross: interregional slave trading in the antebellum South

Most people who become interested in economic history end up having Engerman and Fogel’s book Time on The Cross thrust upon them, often by people who aren’t economic historians, as an example of how proper economic history and paying due attention to the data can clear away sentimental myths in historiography. Engerman and Fogel’s book is very old now, and the problems with it are well known. An interesting new paper (in draft) takes another look at one of the authors’ claims about the size of the export market for slaves in the South and concludes that it was much, much bigger than Fogel and Engerman allowed.

Perry Anderson on footnotes

In this gloriously peripatetic article of Perry Anderson’s on the history of the New Left Review, he has a swipe at the cultural barbarism that is the Harvard Referencing System:

A major change of the past epoch, often remarked upon, has been the widespread migration of intellectuals of the Left into institutions of higher learning. This development—a consequence not only of changes in occupational structure, but of the emptying-out of political organizations, the dumbing-down of publishing houses, the stunting of counter-cultures—is unlikely to be soon reversed. It has brought with it, notoriously, specific tares. Edward Said has recently drawn attention sharply to some of the worst of these—standards of writing that would have left Marx or Morris speechless. But academization has taken its toll in other ways too: needless apparatuses, more for credential than intellectual purposes, circular references to authorities, complaisant self-citations, and so on. Wherever appropriate, NLR aims to be a scholarly journal; but not an academic one. Unlike most academic—not to speak of other—journals today, it does not shove notes to the end of articles, or resort to sub-literate ‘Harvard’ references, but respects the classical courtesy of footnotes at the bottom of the page, as indicators of sources or tangents to the text, immediately available to the reader. Where they are necessary, authors can be as free with them as Moretti is in this issue. But mere proliferation for its own sake, a plague of too many submissions today, will not pass. It should be a matter of honour on the Left to write at least as well, without redundancy or clutter, as its adversaries.

Andrew Gelman and Guido Imbens say: don’t use higher order polynomials in regression discontinuity designs–use local regression

Regression discontinuity designs in causal impact studies (that is, investigating a variable of interest either side of a critical threshold) have become popular again recently: for example, see David Lee’s work on incumbency effects in Congressional races. In order to estimate the value of the variable at some small distance above and below the threshold in the forcing variable, you usually use either a higher-order polynomial or local regression (loess). On the few occasions I’ve used an RD design for something, I’ve always used a loess for essentially aesthetic reasons, and just because using an arbitrarily high-order polynomial felt kind of wrong. Andrew Gelman and Guido Imbens say my spidey senses were right. In particular:

Results based on high order polynomial regressions are sensitive to the order of the polynomial. Moreover, we do not have good methods for choosing that order in a way that is optimal for the objective of a good estimator for the causal effect of interest. Often researchers choose the order by optimizing some global goodness of fit measure, but that is not closely related to the research objective of causal inference.

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