Violent Crime Rate
[Un-weighted correlation: -0.38]
[Un-weighted correlation: -0.40]
I have long been of the impression that most of the gender gap in academic achievement is skewed towards lower SES groups and URMs, that much of it has much to do with a comparative lack of male engagement in primary and secondary school, especially with respect to home work compliance, so I decided to exploit my prior efforts to try to assess the accuracy of this view. Of course this data isn’t broken out by race or SES per se, but the right side on the x-axis is generally higher SES and more white and asian (whereas the left side is generally more black and latino and generally lower SES).
English Grade 2
English Grade 4
In a prior post I compared the performance of various ethnic groups directly against each other within the same schools. That method has the advantage of reducing the effect of racial/ethnic composition from influencing the apparent relationship between each groups performance within school environments. However, there aren’t always enough of both groups to clear the minimum reporting requirements for student privacy and the school average is a better indicator of “school quality” in some peoples’ minds. Below I simply took the reported average for all students on the x-axis so that I could compare multiple groups at once.
I do not believe that this is a particularly good way to view the data, since it’s confounded by racial/ethnic composition and sorting by education levels (despite the obvious correlation), but if you do happen to think that the average score is a particularly good measure of “school quality” this approach might be eye opening.
I also plotted this same data for poor and non-poor blacks and whites and (all race/ethnic) scores by parental education level below. You’ll need to scroll down a ways to see it though.
English Grade 2
English Grade 4
English Grade 6
Since I have heard more than a few complaints about disparities in school suspension rates, I thought I’d extend my prior analysis of data from California public schools to better understand the patterns here.
UPDATE: I realized after the fact that I botched the asian rate data a bit because California reports Filipinos as a separate group for some strange reason, i.e., they’re not included in the reported suspensions/numerator, and my denominator uses the normal federal/OMB definition, which includes doesn’t put filipinos in a separate category. Filipinos are about 1/4th of the (federal) Asian category in CA schools and probably have a higher suspension rate than east asians (which is extremely low). I don’t think it’d alter the between group differences all that much, especially not in ordinal terms, but I don’t feel like re-doing these all plots right now, so keep that in mind.
Out-of-school suspension rate (“(OOOS”) by district (weighted)
OOSS by district (weighted), y-axis as log-10
Observation: There are clear racial/ethnic patterns here that cut across many different school districts.
To exploit some of my prior work with California’s test score data, I decided to extend this analysis to SAT, ACT, and AP scores in the state of California, i.e., to compare the relationships of these different tests within and between schools.
ACT vs SAT (r=0.97)
[It’s almost like they’re testing the same construct…. :-)]
SAT by English grade 11 (r=0.84)
Back in my earlier post on the effects of concentrated poverty, I mentioned that California provides test score data for various specific “Asian” ethnic groups (e.g., Chinese, Japanese, Korean, Hmong, Indians, etc) and that I might update my analysis with that information later.
To aid interpretability across multiple tests, I first converted all average scores into standard deviations above the non-hispanic white mean (both are weighted for the number of test takers in each school district to better approximate the actual individual distributions) and then plotted these as box plots for detailed ethnic groups, poverty status by race/ethnicity, and education levels. All of these box plots are also weighted by the number of test takers in each group.
X = 0 should correspond to the weighted white mean across school districts in California (which ought to be very close to the individual white average state wide), ergo groups or parts of groups (e.g., IQR) that are on the right hand side are generally outperforming the white mean and vice versa for groups on the left hand side.
English Grade 2
Math Grade 2
James Thompson of Psychological Comments blogged on a huge meta-analysis of 50 years worth of twin studies published in the journal Nature. (Full text copy here). Although the study covered a wide range of phenotypes, several of them have particular relevance to intelligence, academics, and occupational outcomes. The authors of the study also published an interactive website, MaTCH, where more detailed statistics can be browsed.
I screen captured traits some traits relevant to academic and occupational outcomes below:
Structure of Brain
Higher level cognitive function (general intelligence)
This is just quick update to my prior post on concentrated poverty. I re-ran the California test score data at a school-level to compare within school black-white differences in test scores and converted all of the scores data to standard deviation units relative to (above) the non-hispanic mean by school, weighted by the number of test takers. The pattern can be observed as early as 2nd grade and it is quite consistent for all major/mandatory tests.
Black vs White within school comparison
Grade 2 English
Grade 2 Math
According to a large and growing number of progressives, the achievement gap between “minorities” (especially blacks) and whites can be traced directly to the effects of “concentrated poverty”. This implies that we cannot compare the outcomes of individual “middle class” blacks to whites of similar income because they don’t have the same amount of wealth, which would allow them to escape their poor neighbors, bad schools, or something along those lines.
Presumably the relationship between the actual neighborhood-level SES, as measured by poverty rates, income levels, education levels, etc, and academic outcomes should look something like this:
In other words, this achievement gap is presumably only found in areas of concentrated poverty, but those few families that manage to “escape” these particular bad environments converge on white outcomes or even close the gap entirely.
Having actually studied this data, I can tell you that reality looks more like this:
Put simply, there is no evidence to support convergence. Broader outcome measures generally show a solidly linear relationship with these measures. There is also much more overlap in material condition than the picture that most progressives present (curiously they sing a very different tune when they want to talk about these differences in other contexts). Below I will present some evidence to this effect.
The main difficulties I have with the “falling incomes” argument is that the country has changed dramatically over the past few generations and people are often unclear about what they mean by this.
Here are just some of the key changes/issues:
Thus when we talk about directional changes in income it’s important to understand what we are actually concerned with. Is it more along the lines of “the same groups in the same job working the same number of hours are earning less in real dollars” (i.e., people are getting paid less for the same sorts of efforts) or is it a broader statement like “households have less income than they did generations ago” (regardless of work, household size, race/ethnicity, gender, etc)? The latter category is much easier to argue than the former.