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.

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).

On SAT, ACT, IQ, and other psychometric test correlations

Although I touched on this topic briefly before, I seem to have forgotten some relevant plots and data and don’t feel like editing the last one, so this post is somewhat redundant…

Both the SAT and ACT are quite well correlated with IQ tests, so much so that you can make good guesses about someone’s IQ if you know their SAT/ACT score and vice versa.

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.

 

 

 

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.

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.

NLSY97: Assessing systematic patterns in relationships between IQ and other test scores by SES, GPA, and more

*WARNING*: This is just a dump of plots (for sharing purposes) until I get the time to do some actual thoughtful analysis of this topic…. :-)

 

 

 

 

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.

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

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)

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

Comparing parental income to parental education as predictors of educational attainment

Just some plots without comment for now…..

 

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

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.

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

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).

Predicting economic mobility from 10th grade test scores

In my last post I briefly touched upon economic mobility vis-a-vis the link between test scores and subsequent adult incomes.  Because these individuals were still pretty young, just a few years out of college (if they graduated), the earnings correlations were weaker than one might have expected.  Since then I discovered an interesting continuous SES variable (F3SES) in the ELS:2002 data set that is probably a better measure of future earnings or mobility.

F3SES is the average of 3 inputs (2011 earnings from employment, the prestige score associated with the respondents current/most recent job, and educational attainment), each of which is standardized to a mean of 0 and a standard deviation of 1 prior to averaging.

Data users should note that, as of the third follow-up, socioeconomic status may be less-than fully stable for some third follow-up respondents, e.g., respondents with graduate-level education who are just beginning or have yet to begin their careers.  Users should also note that F3SES does not account for the income, occupation, or education of the respondents spouse/partner, and therefore may not be fully indicative of household socioeconomic status as of the third follow-up.

NOTE: While the two versions of the BY family SES composites (BYSES1 and BYSES2) were created by differential assignment of prestige scores based on the 16-category BY occupation variables, F3SES is created by assigning prestige scores based on the 2-digit ONET code associated with the respondents current/most recent job as of the third follow-up.

While I am sure I could derive my own formula to produce a similar composite score, I’ll just use theirs for the time being.

---

There is no statistically significance difference between blacks and whites here.

Asian SES is higher than white SES for most of the distribution, but that’s not statistically significant either.