My response to the NYTimes article on school districts, test scores, and income.

On April 29th the New York Times posted a nominally data driven article on school districts, test scores, and socioeconomic status.   Though it contained some useful data, the analysis was terribly misleading and it excluded a tremendous amount of pertinent information.  Many progressives took the article as proof that the system is “rigged”.

screenshot_450.png

The NYTimes did not help matters by conflating the measures of socioeconomic status (SES) with income.  Although every one of their plots used a composite SES measure on the x-axis, the article itself and various annotations give a strong impression that money/income/wealth are the primary drivers of this:

screenshot_449.png

The words income, economic, wealth, money, rich, poor, and other related words were littered liberally throughout the  article.  Not a single mention was made of other predictors or even of the composition of the SES index they used, save for an easy to miss footnote at the end of the article.

The SES measure they used was defined in the SEDA archive as:

the first principal component factor score of the following measures: median income, percent with a bachelor’s degree or higher, poverty rate, SNAP rate, single mother headed household rate, and unemployment rate

[emphasis mine]

These non-economic dimensions actually exert significant influence on the correlations, are not directly tied to income/wealth/etc, and show marked racial/ethnic differences even at the same income level (e.g., single motherhood rates are much higher in the black community at any given level of income)

Rather than focus too much on what is wrong with this specific article (this sort of article is practically a genre unto itself these days), I will instead systematically address misperceptions here and attempt to shed more light on the nature and underlying causes of these patterns.   I will argue that (1) these gaps are mostly genetic (2) they generally have little to do with systematic differences in parental economics (3) they have even less to do with the school systems themselves (4) these patterns are not unique to the US.

I will use some data from the Stanford Education Data Archive (SEDA),  the same used by the NYTimes article, to help make some of my points but, unlike some of my other blog posts, I will try to cover each point in just enough depth to convey the gist of it (linking out for more in-depth analysis for those that are interested in the particular point).  I will also bring together good deal of supporting evidence that is buried in different academic articles, government databases, think-tank research, and so on.

Read More »

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) :

Read More »

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

Read More »

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).

Read More »

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

Read More »

A (very) brief exploratory analysis of NLSY97 height data

The NLSY97 data provides height information for both biological parents and students at yearly intervals, so I thought I’d take a look at that data.

parent_height_avg_squares

It looks very much like heritability increases with age here (unsurprisingly).  Around age 13 the slope is relatively flat, especially for boys, but it slowly increases and by age 21 (give or take) there is a rather stark contrast.

Read More »

On educational attainment rates and income as a causative factor

As I mentioned to Robert VerBruggen in his latest piece on educational attainment and income, I do not believe that economic concerns are a major cause of differences in education attainment rates by income.

I previously analyzed this and related issues using the ELS:2002 data, but I decided to extend my analysis with NLSY97 and clarify my views, now that I have marshaled a fair amount of data to support my arguments.

First

Although the IQ (ASVAB) is an excellent predictor and generally mediates these differences fairly well, there are other systematic differences that are not fully accounted for when you control for IQ.   High SES people, whether measured by income or educational attainment, typically have higher GPAs even with the same test scores.

gpa_by_asvab_income_b5

hsgpa_by_asvab_fed

(these differences would likely to be larger still if I did this as a composite SES index using education, income, occupational prestige, etc)

Read More »

IQ, test scores, GPA, income, and related correlations from NLSY97

Diving into the NLSY97 dataset a bit right now and I thought I’d share some plots pertaining to IQ/achievement tests, income, GPA, educational attainment, and more.   Nothing here is particularly novel, if you’ve looked for this sort of data before, but sometimes it’s nice to have additional independent analysis or alternative presentations of the data.


High school GPA-test score relationships

HS GPA by ASVAB in percentiles (IQ test)

gpa_by_asvab_q25

HS GPA by PIAT (Peabody Individual Achievement Test, IQ test admin. in early childhood)

gpa_by_piat

Read More »

Understanding socioeconomic mobility

Although it may not seem like it at first blush, given the apparently modest correlations, the socioeconomic figures that I blogged about earlier largely agree with the published data on economic mobility.

They are measuring income (or earnings) whereas I am measuring with the ELS SES index (which is an equally weighted average of the respondents own earnings as of 2011, educational attainment, and occupational prestige), but the systematic income differences between classes (however measured) are almost certainly virtually fully mediated by this more comprehensive SES measure.

ELS data, students 2011 SES by 2002 SES of parents

ses_mobility_all_people

The correlation between parent SES and child SES is 0.35.  This may not sound like much, but if you bin child SES and parent SES into quintiles the mobility estimates are very similar to the widely publicized economic mobility estimates.

Microsoft Excel (1)

Microsoft Excel

The average person born at the top of the SES distribution has little chance of ending up at the bottom and vice versa, but there is clearly a great deal of mobility that happens amongst less extreme groupings.

[Note: I didn’t correct these figures for oversampling, so I won’t claim they’re a perfect representation of reality, but they are generally pretty close in practice and they still are good for illustrative purposes.]

Mobility delta (child SES – parent SES) by parent SES

ses_mobilty_all_people

On average, as compared to their parents, high SES people are downward mobile and low SES people are upward mobile.  This may seem counter-intuitive to some, but this makes sense because r < 1, i.e., there is non-trivial mobility, and this must be true for there to be meaningful relative mobility.  Of course, just because there is mobility doesn’t imply that all people have an equal chance of ending up in any place in the SES distribution.

As I mentioned in my last post, there are other differences between groups besides just propensity to end up in particular SES bins and many of these differences are highly predictive of mobility — see test scores, HS GPA, etc.  Indeed, they almost fully mediate outcomes, so talking in terms of “mobility” here can be a bit misleading because relative starting position (parent SES) tells you relatively little about what is likely once you have better information (e.g., test scores, HS GPA, etc).

Read More »