I was feeling guilty because I had not updated this blog in two
weeks. Then I reminded myself that the whole *point* is that I put in
entries here when I have time, and if I’ve delayed in doing so because
I have been Living My Best Life™, that’s a good thing!

I was in Cambridge last week, semi-covert, to work with my
collaborator. We submitted one paper and *finally* broke a log jam on
another that had stalled for several months. While the project is
unrelated to the ULP-diffusion project I’m concentrating on in this
blog, solving problems related to it also reminds me why I do
research, and I think it is worth talking about. Also, this project,
like the one on workplace segregation that was covered on
Vox, uses Theil’s
information statistic to measure segregation. I do enough work with
this thing, and have explained it enough times, that I might as well
put it down here in the blog as well…

An intuitive definition of segregation is uneven division of a unit’s population among its sub-units. Think of uneven division of a school district’s students among its schools, or uneven division of a city’s residents among its neighborhoods. You need a metric of how unevenly divided that population is. There are a lot of these, but we like Theil’s statistic for several reasons. The most important is that it handles multiple groups gracefully. A lot of measures, including the popular index of dissimilarity, were devised for two-group cases. This is fine if you care about white/black or white/non-white, but they can give wonky results as the number of relevant groups increases. In America today, you really should use a multi-group measure.

The other great advantage of Theil’s statistic is that it is
*fractally decomposable*. Fancy words, but it just means that you can,
say, separate segregation in the US into segregation *between* labor
markets plus segregation *within* labor markets; then separate the
latter into segregation *between* industries within labor markets,
plus segregation *within* industries in labor markets, and so on. If
your goal is to understand how changes at different levels might drive
changes in segregation (and that is ours!), then this is an incredibly
useful feature.

The statistic leverages the concept of entropy that Claude Shannon
developed in his seminal 1948 article “A Mathematical Theory of
Communication.”
A bit is informative to the extent that it is rare or
unexpected. Imagine I were to feed you a letter at a time, and you
were to try to guess the word that I was spelling. Before I speak,
your possibility space is the entire vocabulary of English–that’s the
baseline level of entropy in the system. If I say “e,” that reduces
the search space for you some. But if I say “z,” that reduces the
search space a lot more. Because *z* is a rare letter and words
starting with *z* even more so, knowing there’s a *z* prunes the
probability space a lot more than knowing there’s an *e* does. It’s
this reduction in entropy that we define as information.

In the context of employment segregation, imagine we have a workforce. I choose a worker at random and ask you to guess their race. How much uncertainty do you have? That depends on the baseline level of entropy. If the workforce is all of one race, there’s no entropy, and no uncertainty. As you add races, and as you distribute the workforce more evenly among however many races, your uncertainty increases. Thus if \( \phi_r \) is the share of race \( r \) in the workforce, entropy is defined as \( -\sum_r \phi_r \ln \phi_r \). (Play around with that, keeping in mind that we’ll define the log of a zero as zero. It works.)

OK, so you have a baseline level of entropy in the workforce, and that
specifies your uncertainty about the race of a randomly chosen
worker. *Now suppose I tell you where they work*. How does that
information affect your uncertainty?

If there is

*no*segregation, this is uninformative. Your uncertainty remains unchanged.If there is

*total*segregation, this is extremely informative. Your uncertainty is removed completely.For in-between levels of segregation, your uncertainty is reduced proportionately to the level of segregation.

Theil’s statistic leverages this idea. We calculate the entropy for the unit, \( E \). We calculate the entropy for each of the \( j \) sub-units in the unit, \( E_j \). Then the statistic averages the sub-unit’s deviations from the larger unit. It’s a weighted average, where the weight is the sub-unit’s size as a share of the unit, \( p_j / p \). And we scale the entropy deviation by the unit’s entropy, so that we can compare this measure across different types of units. That gives us this:

$$ H = \sum_j \frac{p_j}{p} \frac{E - E_j}{E} $$

With me so far? Good. Here’s where it gets fun.

Let’s imagine that our workplaces are nested within groups. This
happens all the time. In the US, for example, workplaces are scattered
all over the country, and racial populations are also
scattered–non-uniformly!–over the country. Thus there are a lot of
latino workers in Californian worklplaces and far fewer in Maine
workplaces. Does this difference represent segregation, though? It’s
a judgment call in each case, but in *this* case I would say no. No
one who lives in California is likely to work in Maine and vice
versa. Where people live of course isn’t random, and a ton of people
study that; but if you want to characterize and understand segregation
among workers, you want to start with units that could feasibly be
integrated. Another way to say this is, if all of the segregation
between worklpaces in America could be reduced to the uneven
geographic spread of different racial populations, then fretting about
and intervening at the level of the workplace doesn’t make much
sense. We want a way to separate out these two, and Theil’s \( H \)
gives us a way.

If the unit can be completely divided into \( G \) mutually
exclusive groups then Theil’s \( H \) can be decomposed into \( G +
1 \) components: one for the segregation *between* groups and \( G
\) for the segregation *within* each group:

$$ H = \overbrace{ \sum_g \frac{p_g}{p} \frac{E - E_g}{E} }^\text{Between-group} + \overbrace{ \sum_g \frac{p_g}{p} \frac{E_g}{E} \left( \sum_j \frac{p_{gj}}{p_g} \frac{E_g - E_{gj}}{E_g} \right) }^\text{Within groups} $$

Notice that each of the within-group bits is just the simpler version
of \( H \). That’s how the fractal nature of this statistic shows
up. Those lower-level statistics are then put into a weighted sum,
where the weights are the group’s relative size *and* the group’s
relative diversity (i.e., its relative entropy).

I don’t want to lose track of why this is useful. If you go back to my
example, the first term above–that “Between-group” bit–would account
for the different distribution of races between, say, California and
Maine. This makes interpreting the second terms easier. *Given* the
structure of the workforce in California, how segregated are workers
between workplaces there? *Given* the structure of the workforce in
Maine, how segregated are workers between workplaces there?

This also gives you a way to think about why the weights are what they
are. Californian workplaces might be less segregated than “Downeaster”
ones (or whatever the Hell people from Maine call themselves–*I can’t
even be bothered to Google it*). Yet California is a lot *bigger* than
Maine, so what segregation there is affects more people. And
California is a lot more *diverse* than Maine, so again the possible
impact of segregation is bigger. Hence those weights.

You can keep doing this. We’ve calculated three-level decompositions
that include segregation between occupations *within* workplaces, for
example. But eventually the code for doing so starts to break your
head. Or at least it does given how we’d written it! Solving *that*
little problem will be a matter for future posts.