On a recent Brookings blog post hyping economic inequality as a predictor of well-being

I recently came across a Brookings Institution blog post regarding the relationship between economic inequality and various measures of well-being.

Setting aside the lack of statistical controls and probable confounds, the practical significance implied by their own plots was actually rather trifling.

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Small update to prior post on school suspension rates, comparing racial/ethnic differences with national aggregates

In my last post I displayed a plot showing a striking correlation for single-motherhood rates and out-of-school suspension rates between racial/ethnic groups using national averages.

nat_by_sm

I am well aware that aggregating linearly correlated variables will tend to produce (much) stronger correlations than you’d see with more granular data (e.g., state, county, family, individual, etc).  On the other hand, I am familiar enough with these statistics to know that you will see substantially weaker correlations here with other common predictors.  Hispanics/latinos, for instance, tend to be worse off than than blacks by many economic measures, rarely appreciably better off, and yet their discipline problems are much less (even, interestingly, less than whites in California controlling for median family income).  Likewise, the distance between asians and non-hispanic whites tends to be modest on economic dimensions, but their suspension rates are roughly half the non-hispanic white average.

For the benefit of others, I decided to generate some plots of predictors aggregated at a national level for comparison’s sake (note: I reversed the x-axis to keep the graphic relationship the same where necessary).

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On the relationship between school suspensions, race, single-motherhood, and more.

As a follow up to my prior post on single-motherhood and mobility and in response to various assertions of discrimination against blacks in the school system, I decided to take a data-driven look into the relationship between race and school suspension rates.


There is, of course, ample evidence that discipline rates vary dramatically between racial/ethnic groups.

Microsoft Excel

Blacks get suspended at vastly disproportionate rates whereas “asians” (census/OMB definition), on the other hand, are about half as likely as whites are to get suspended.  Contrary to conventional wisdom, though, this pattern tends to be pretty consistent nation wide and the south is not notably “worse” with respect to disparities here.

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On Philip Cohen’s knee-jerk response to Chetty’s “causal mobility” data and its association with single-motherhood

Philip Cohen, a sociologist that blogs at Family Inequality, recently argued, in response to the proposition that single-motherhood is strongly associated with economic mobility, that the single-motherhood effect is “entirely in the % black effect”.

While I do not necessarily disagree with the notion that racial demographics are strong predictors (albeit probably for different reasons than he does) and I do not necessarily believe that the single-motherhood association is (mostly) causal, his strong language is clearly at odds with the data.  In fact, his statements are not even well supported by his own stats.

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On the effects of wealth on the B-W gaps, a response to questions posed by a commenter

Max, a commenter, asked:

Could you do an analysis on racial differences in educational outcomes after controlling for parental education, parental occupation, household wealth, neighborhood wealth, neighborhood education, single parent status, native language etc.? I’ve seen you control for family income and parental education (and occasionally both), but I’ve never seen you control for more beyond that (perhaps I’ve missed something!). In Chapter 16 of Affirmative Action for the RichThe Future of Affirmative Action, Dalton Conley of New York University used the Panel Study of Income Dynamics to show that parental wealth (not income) and parental education are the best predictors of college completion, which means that they may also be good predictors of other educational outcomes. He also discussed the data showing that racial wealth gaps are much larger than racial income gaps, which implies that wealth could account for a larger portion of the achievement gap than income. Could you do a similar analysis for IQ? The reason I’m asking for all this is that Carnevale and Strohl control for all of these factors and are left with a very small race effect: http://www.tcf.org/assets/downloads/tcf-CarnevaleStrivers.pdf

I have heard this bit about wealth before.  I am deeply skeptical that wealth can mediate much, if any, of the B-W gap.

Before I dig into this though, let’s take a very brief look at some of the studies Max cited (see here and here) :

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County level homicide rates by race/ethnicity of victim

In my last post, I plotted the overall US county-level homicide rate (all groups combined) by racial/ethnic demographics.  Much of that correlation is being driven by high black-on-black homicide rates throughout the country.

If I plot the rate by the race/ethnicity of the victim this pattern becomes clear and the group-level correlation weaken somewhat (especially asians and whites), but it’s still there…

hom_by_pct_black_grp_by_re

hom_by_pct_not_white_not_asian_grp_by_re

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A quick post on gun related homicides

While I am personally ambivalent about gun ownership and suspect it plays an incremental role in relative differences between countries/regions/etc (holding other things roughly equal), I thought I’d add some perspective into this argument about the presumed causality of gun ownership on homicide rates.

Mother Jones analysis includes gun suicides and makes no attempt to correct for even course-grain racial/ethnic confounds.


I downloaded the 2009-2013 data for gun-related homicides by race/ethnicity from the CDC’s WONDER database  and compared it to the gun ownership rate data via wikipedia.

gundeaths_by_gunownership

I do not feel like doing a lengthy analysis here and now, but suffice it to say that once you remove suicides and race/ethnicity from the equation the case gets much weaker.

There is no evidence of a positive correlation here for blacks and hispanics (if anything somewhat negative).

There is a positive correlation for non-hispanic whites (r=0.45), but it pales in comparison to the racial/ethnic differences here.  To put this into perspective, amongst non-hispanic whites (the bulk of the gun owners in most states), states with the highest gun ownership rates have just 1 death per 100,000 more than states with the lowest rates (on average).

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On sex differences within California public schools

A year or two ago I read an article which demonstrated that sex differences in math and reading achievement are inversely related within and across countries on the PISA tests.  The smaller the male-female math gap, the larger the verbal gap and vice versa.  This tends to support the view that there are innate underlying differences in average abilities and interests between the sexes that strongly influence these patterns.

 

Google Chrome

 

 

Google Chrome

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