NAEP TUDA: Does Black Poverty Matter?

In my last post, I point out that it makes as much sense to compare black scores in Boston and Detroit as it does to compare white scores in Vermont and West Virginia (not that people don’t do that, too), given the substantial difference in black poverty rates.

There are all sorts of actual social scientists investigate race and poverty, and I’m not trying to reinvent the wheel. I don’t need to prove that poverty has a strong link to academic achievement. Apparently, though, some people in the education industry need to be reminded. So part 2 of my rationale for digging into the poverty rates (with the first being lord, they’re hard to find) is that I wanted to remind people that we need to look at both factors. Ultimately, it doesn’t matter if my data analysis here is correct or I screwed it up. If people start demanding to know how poverty affects outcomes controlled for race—whether my analysis is correct or not—then this project has been worthwhile. Even given the squishy data with various fudge factors, there appears to be a non-trivial relationship, as you’ll see.

But the third part of my rationale for taking this on is linked to my curiosity about the data. Would it support—or, more accurately, not conflict with—my own pet theory?

I expected that my results would show a link between poverty and test scores after controlling for race, although given the squishiness of both the data I was using, the small sample size and NAEP’s sampling (which would be by NSLP participation, not poverty), I didn’t expect it to explain all of the variance.

But I also think it likely that poverty saturation, for lack of a better word, would have an additional impact. So Detroit has lots of blacks, Fresno doesn’t. But they both have a high rate of overall poverty, and since poverty is correlates both with low ability and, alas, low incentive, the classes are brutally tough to teach with all sorts of distractors. Disperse the poor kids and far more of them will shrug and pay attention, with only a few dedicated troublemakers determined to screw things up no matter what the environment.

This is hardly groundbreaking; that belief is behind the whole push for economic integration, it’s how gentrifiers are rationalizing their charter schools, and so on. I don’t agree with the fixes, and of course I don’t think that poverty saturation explains the achievement gap, but I believe the problem’s real enough to singlehandedly account for the small and functionally insignificant increase in some charter school test scores. I have more thoughts on this, but it would distract from my main purpose here, so hold on to that point. For now, I was also digging into the data for my own purposes, to see if it didn’t contradict my own idea of poverty’s impact.

Poverty Variables

I thought these rates might be related, all for the districts (not the cities):

  • Percentage of enrolled black students in poverty (as a percentage of all black students)
  • Percentage of enrolled black students in poverty (as a percentage of all students)
  • Percentage of enrolled poor kids
  • Percentage of enrolled poor black kids (as a percentage of all poor kids)
  • Percentage of blacks in poverty (overall, adults and kids, from ACS)

In my last post, I discussed the difficulty of assigning the correct number of poor black students to the district. Should I assume the enrolled poverty rate is the same as the district poverty rate for black and poor children, or assume that the bulk of the poor children enrolled in district schools, thus raising the poverty rate? This makes a huge difference in schools that only enroll 50-60% of the district students. I decided to assign all the poor kids to the district schools, which will overstate the poverty levels, but nowhere near as much as the reverse would distort them. So all the above poverty levels involving enrolled students assume that all poor kids enroll in district schools–that is, I used the far right row of each of the three poverty measures shown in the table below.

(Notice that in a few cases, the ACS poverty level is higher than the assigned poverty rate, which is nutty. But I’m creating the black child poverty rate by adding up children in and children not in poverty, rather than using children in poverty and total black children, to be consistent.)

Boston was the only school district I could find that provided data on how many district kids weren’t enrolled, what percent by race, and where they were (parochial, private, charters, homeschooled). Thanks, Boston!


How likely was it that all these kids were evenly pulled from every level of the income spectrum?

I also don’t think it’s a coincidence that the weakest schools have the greatest discrepancies in the two calculations. Particularly of interest is DC, which has a low black poverty rate, a low enrollment rate (because half the kids are in charters), and one of the lowest performers using my test metric (see below) Given that no one has established breathtakingly different academic performances between charter and public schools, it doesn’t seem likely that DC’s lower than expected performance is caused by purely by crappy teaching of a mostly middle class crowd.

Plus, I’m a teacher in a public school, and like most teachers in public schools, I see charter-skimming in action. I see the top URM kids go off to charter schools from high poverty high schools, and I see the misbehavers get kicked back to the public schools. To hell with the protestations and denial, I see cherrypicking in action. And there you see emotions at play. But only after two logical arguments.

So all the bullet points except the last one use that same assumption. And I know it’s a fudge factor, but it’s the best I could do. Here’s hoping the feds will give us a better measure in the future.

Other Variables

  • Percent of district kids enrolled (using ACS data and school/census enrollment numbers)
  • Percent of enrolled kids who are black (from district websites)
  • Percent of black students scoring basic or higher in 8th grade math

I decided to go with basic or higher because seriously, NAEP proficiency is just a stupidly high marker. This is the value I used as the dependent variable in the regressions.

Analysis and Results

What I looked for: well, hell. I don’t do math, dammit, I teach it. I figured I’d look for the highest R squared I could find and p-values between 0 and .05. When I started, I’d have been thrilled with anything explaining over 50% of the variation, so I decided that I’d give the results if I got 40% or higher for any one variable, and over 60% for multiple regressions. I used the correlation table to give me pointers:


The red and black is just my own markings to see if I’d caught all the possibilities. Red means no value in multiple regressions, bold means there’s a strong correlation, italic and bold means it might be a good candidate for multiple regressions. As I mention below, I kind of run out of steam later, so I’m going to come back to this to see if I missed any possibilities.

I don’t usually do this sort of thing, and I don’t want the writing to drown in figures. So I’ll just link in the results.

Single % Poor Enrolled (Approx) poor blks/Tot kids % Black Enrollment (frm dist) % Poor Kids in District Dist Overall Blk Pov
% poor blacks enrolled (approx) 0.463 0.520 0.607 0.593 0.551 0.574
% Poor Enrolled (Approx) 0.398 0.527 0.700
poor blks/Tot kids 0.516 0.640
% Black Enrollment (frm dist) 0.217 0.612
% Poor Kids in District 0.160
% blk kids poor in dist (ACS) 0.319
Dist Overall Blk Pov 0.488
% of 5-17 kids enrolled 0.216
Poor blcks/Poor 0.161

I ran some of the other multiple regressions and am pretty sure I didn’t get any other strong results, but honestly, yesterday I just ran out of steam. I have a brother showing up to help me move on Saturday, and he’ll be pissed if I’m not packed up. Normally I’d just put this off, but I’ve got two or three other “put offs” and I’m close enough to “done” on this that I want it over.

Scatter plots for the single regressions:

Apparently you can’t do a scatter plot for multiple regressions. Here’s what I did just to see if it worked, using the winning multiple regression of Overall Black Poverty and Total Enrolled Poverty:


I calculated the predicted value for each district using the two slopes and the y-intercept. Then I graphed predicted versus actual scores on a scatter plot and added a trend line. Is it just a coincidence that the r square of the trendline is the same as the r square for the multiple regression? I have no idea. If this is totally wrong, I’ll kill it later, but I’m genuinely curious if this is right or wrong, or if Excel does this and I just don’t know how to tell it to graph multiple regressions.

Again, I’m not trying to prove anything. I believe it’s already well-established that poverty within race correlates with academic outcomes. I was just trying to collect the data to remind people who discuss NAEP scores in the vacuum of either race or poverty that both matter.

And here, I’m going to stop for now. I am deliberately leaving this open-ended. If I didn’t screw up and if I understand the stats behind this, it appears that certain black poverty and overall poverty factors explain anywhere from 40 to 60% of the variance in the NAEP TUDA scores. Overall district poverty and total enrolled poverty combine to explain 70%. In my fuzzy, don’t fuss me too much with facts world view, this doesn’t contradict my poverty saturation theory. But beyond that, I want more time to mull this. I’ve already noticed some patterns I want to write more about (like my doctored black poverty number wasn’t as good as overall district black poverty, but my doctored total poverty number worked well—huh), but I’m feeling done, and I’d really like to get feedback, if anyone’s interested. I’m fine with learning that I totally screwed this up, too. Unlike the last post, where I feel pretty solid on the data collection, I’m new at this. If you want to see the very messy google docs file with all the data, gmail me at this blog name.

Two posts in two days is some sort of record for me–and three posts in a week to boot.

I’ll have my retrospective post tomorrow, I hope, since I’ve posted on Jan 1 every year of my blog so far. Hope everyone has a great new year.


About educationrealist

24 responses to “NAEP TUDA: Does Black Poverty Matter?

  • LBK

    Correlation certainly? Causation seems less likely though.

  • Candide III

    You have two obvious groups on that last graph, DET-CLE-MIL-FRES and everything else. To judge by the graph, if you remove the first group, the correlation evaporates into thin air, which leads me to think there is something different about these four. Of course, the grouping might very well be an artifact of the limited data set, but I have no idea how to check for that.

    • educationrealist

      Those are the four with the highest poverty. I think they are a result of the limited data set, but likewise I have no way to check for that. For example, what happens if you throw in Camden and New Orleans?

      I tried removing various cities to see if the correlation went away and didn’t have luck. But I didn’t remove a bunch of cities. I’ll try that, thanks.

  • Dave Thomas

    The parents of these children know the answer best. Give them vouchers and see what happens. They will stay will good teachers and flee bad ones. Of course this means bad teachers will get exposed, and we can’t have that can we.

  • Vijay

    My issue is that charter schools are being touted about as a an answer; but the economic-based political issues (Gulen schools or Harmony in Houston, Leona Group) etc are not being addressed by anyone except as a general Teachers Union vs. chartered school or bad teachers vs good charter school issue. I urge you to look deeper at Harmony in Houston. There are a bunch of new issues that the semi-professional Charter groups are creating. I feel that there is a need to regulate the groups that run charter schools. It is not some small guys vs teacher unions. There is a variety of ways money is being laundered here.

  • Pete

    It isn’t poverty. The kids now in school who are considered in poverty have much more in food housing and other things than 90% of the kids I went to school with in the 1940s and 1950s. As a matter of fact most of the scientists, businessmen, teachers, medical personnel, etc, in the 30s, 40s, 50s and forward had no advantages of wealth. They DID HAVE parents that valued education, a safe place to go to school, and a work ethic for learning.

    • educationrealist

      Poverty has always mattered, after race. I very much doubt you spent a lot of time with poor black kids in the 30s, 40s, and 50s, so what exactly is your point?

      • vijay

        The earlier comment has a minor point (although vaguely written), namely, there was substantial poverty in the US in 1940s and 1950s as shown here:

        YEAR Census year
        1940 1950 1960 1970 1980 1990
        Nonhispanic white 38.6 30.3 16.5 10.3 8.7 9.1
        Nonhispanic black 75.4 72.0 54.9 35.7 29.2 31.3
        Latino 69.6 58.1 43.2 25.9 23.1 24.0
        Asian, Pacific Islander 36.8 40.3 14.3 12.0 13.2 14.8
        Other 88.8 85.0 47.4 32.9 27.2 30.7
        Total 42.5 34.9 21.3 13.6 12.2 13.3

        I think he is saying that in the 30s, 40s, and 50s, whites have poverty rates higher than the blacks in the 80s and 90s; so why, should poverty be considered a significant impactor on educational achievement.

        Nonetheless, it may be hard to compare educational achievement of 40s and 50s vs SES/poverty/race across decades.

        I agree that race matters first, and then poverty.

      • educationrealist

        I know what his point is. But it’s irrelevant, since we are talking about average *black* achievement. It’s idiotic to talk about whites.

      • vijay

        I see this “personal narrative” response to societal issues all the time in the US media, which I could never understand, and goes like this:

        1. I was poor in the 40s in Brooklyn inner city, but I was able to study and improve economically. Why could the blacks not do the same?

        2. I failed all my classes in freshman year in Cal because I did not cope up, but I recovered and became a lawyer. Kashawn, If I can do it, you can too.

        I wish someone would ban forever all “personal narratives”
        from serious discussion, forever.

      • educationrealist

        What? I LOVE personal narratives! But they must be used as illustration, not evidence or rebuttal.

  • Paul Tierney

    I found the black poverty rate in various cities at I regressed the Grade 8 Math Black proficiency rate on those poverty rates. There is a close connection. Adj. R squared is .45 and the very significant coefficient is -0.52. Interpretation: a 10% point increase in Black poverty in a city (say from 30% to 40%) is connected to a 5% point drop in Black 8th grade Math proficiency.

  • Paul Tierney

    I had to shorten my comment so it would fit. I have enjoyed your discussion. Thanks!

  • Paul Tierney

    Vijay: Here is the link

    Under the first article, you can choose cities (one at a time) and find the poverty data. Note that I did not include Miami because in Smarick’s bar chart, it was labelled “Miami-Dade,” but at governing, they just use “Miami.” So I wasn’t sure it was apples to apples.

    • vijay

      Thanks for the data. I redid your analysis for all TUDA cities for all 4 (4/8 reading/math). Unfortunately, I got a R.squared of 0.2. It appears cities simply clump into groups but I found no overall dependency on black poverty rate.

  • Paul Tierney

    Also, there was no poverty data for the 2 counties listed in the original Smarick bar graph.

  • Paul Tierney

    Vijay: I think it is a mistake to lump all 4 data sets together the way you did in your regression. For example, let’s say (for the sake of argument) that there is a “perfect” relationship between poverty and Math8 and another perfect (but different) relationship between poverty and Reading8. When you lump them all together, the solid relationship will be hidden or eliminated. I found that there is a strong relationship between poverty and 8th grade black math proficiency and (separately) between poverty and 8th grade reading proficiency. I think using only one clearly-defined dependent variable for each regression makes sense. Each type of test and each level is measuring something different. What do you think?

    • vijay

      Actually, I broke them up to Math8+math 4 vs Poverty and Readin8+Reading 4 vs poverty (yes, even I am aware that verbal skills are better indicators of cognitive ability, and future growth). My basis is that Reading 4 and Reading 8 will be roughly paralles, and so was Math 4 and math 8. However, I am not getting anywhere, except to conclude, “we need better data of income distribution of parents to plot against, rather than just poverty level”.

      A good graph that exists is a plot of SES level described by annual income of parents against SAT scores which shows an almost linear increase at each SES level; nonetheless parallel curves for White and black students.

  • 200 Posts | educationrealist

    […] TUDA Scores: Detroit Isn’t Boston and NAEP TUDA Scores: Does Black Poverty Matter?—I wish more people would read these, and not just because they were a lot of work. Big […]

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