Some quick plots pertaining to various “Asian” ethnic groups

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, 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

boxplot_ela_g2

Math Grade 2

boxplot_math_g2

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What “blank slate”?

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

Google Chrome (6)

Higher level cognitive function (general intelligence)

Google Chrome (3)

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Small update to my prior post on concentrated poverty

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

bw_ela_g2

 

Grade 2 Math

bw_math_g2

 

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On concentrated poverty and its effects on academic outcomes

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:

prog_model_1

prog_model_2

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:


reality_model_1

reality_2

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.

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A bit of data on the income stagnation and related arguments

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:

  • Women constitute a larger proportion of the workforce than they once did
  • Minorities, especially latinos, comprise a larger share of the population (households, families, tax units, etc)
  • Families (and thus households) are substantially smaller than before because younger generations are less likely to get and stay married and because they have fewer children when they do.
  • There has been a marked increase in education credential attainment.  Comparing a HS (only) grad from 1960 to 2015 doesn’t make much sense.
  • Some subgroups have changed their workforce participation behavior dramatically over the past few decades

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.

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On school quality, test scores, and SES

I am going to share a little analysis I’ve done by combining Pennsylvania’s PSSA test scores, Census ACS data,  and Department of Education statistics to refute a few popular progressive notions about education, namely, that:

1: The SAT/ACT only “measures family income”:

SAT_scores_by_income

2: This is somehow being caused by more and better test prep efforts amongst the more affluent.

3: Higher income school districts are actually better because they spend more money.

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Understanding the academic achievement gaps

Warning: This is long somewhat meandering post and a work-in-progress

My intent here was to compile the evidence in a narrative fashion.  There are more detailed and more technical sources for much of the information I presented here, but much of it is scattered and much of it is targeted at people that are both knowledgable and willing to invest the time.  My approach here was to present the information in a relatively accessible, top-down fashion, i.e., first identify the magnitude of problem, then characterize it, then present evidence that the favored environmental explanations do not add up, and then (briefly) touch upon some more controversial hypotheses….

One of the first things that clued me into the fact that school systems and socioeconomic status cannot explain the black-white (B-W) academic achievement gaps was seeing SAT data like this:

sat race income 2003

sat race education 1995

sat race income 1995

satracialgapfigure

The obvious pattern here is that high socioeconomic status (SES) blacks do no better (and often worse) than low SES whites, whether measured by their parents’ income or their parents’ educational credentials.   This is really hard to explain away as being mainly a product of poverty, bad schools, and things of that sort either.

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