What Policies Will Help At-Risk Adolescents?

The Glenn Show, Glenn Loury’s semi-monthly discussion show on blogging heads, is always outstanding and I watch most of them if I don’t discuss it here. Happily, a good chunk of his recent discussion* with Robert Cherry of Brooklyn College involved vocational education and at-risk student populations.

I’m going to criticize some points below, but the conversation is excellent. Cherry speaks passionately about his topic, and  Loury comes through every so often to summarize with an elegant clarity that’s one of his great strengths. If you don’t have the time to listen, here’s a transcript of the vocational education section, which I created to be sure I didn’t misrepresent anything.

One small point regarding the section on at-risk youth: Cherry goes on at some length about how at risk kids coming from weak, dysfunctional families experience violence, hunger, lack of love. This disruption and chaos profoundly affects their ability to perform academically and increases the likelihood they’ll act out, even strike out. He thinks high schools should spend resources and time understanding and assisting the stressed, traumatized youth come from, give them support, help them work through their trauma instead of merely disciplining them.

On behalf of Title I schools everywhere:  Um, dude, what the hell do you think we’re about? High schools spend as much time as they can understanding and getting help for their kids. We have psychologists at our school. Kids who feel stressed can go see their counsellors.  Teachers often know what’s going on with their kids, and we email key info to colleagues with the same students. Administrators do a lot of listening, a lot of bringing families in to discuss issues, a lot of calling in secondary support services.  Could we use more resources?  Sure. Would more resources improve outcomes?  I don’t know. But Cherry seems utterly clueless as to the vast array of substantial support high schools give now, which calls into question his certainty that such services would help.

Cherry then argues that at-risk students who struggle in school should be given short-term career training to immediately prepare them for jobs and income that will alleviate their stress. In this section he makes three points:

1. “High school jobs are a thing of the past.” Teenagers don’t work anymore: only one in seven black teens has a job, just 2 in 7 white teens do.
2. The reason teens don’t work anymore is because of the view that everyone must go to college.
3. Colleges are inundated with unqualified or remedial students, but they have thus far been more likely to lower standards than discourage people from going to college, thus further discouraging any other development paths.

The first is a fact. The third is also true,  as I wrote in my last piece. But the second point is way off, and in important ways.

Cherry doesn’t mention relevant research on teen unemployment, although he often supports his comments elsewhere in the discussion with studies or data. But the employment drop  has been discussed  at some length for a number of years, with debates on whether the primary cause is supply or demand. Supply: teens aren’t working because they are taking summer school enrichment classes, working at museum internships,  jaunting off to Europe or maybe just doing homework imposed by teachers trying to get them to college.  Demand: teens face competition from other workers. So Cherry’s only proffered reason is supply-related. He thinks teen employment is down because academic activities are becoming more important to high school students, thanks to societal demands and pressures to go to college.

I’m deeply skeptical. First, on a purely anecdotal basis, the teens I know are eager to work, whether it’s full-time over the summer or part-time during the year. But employment requires a work permit, and permits often require acceptable GPAs**. I have had more than one student beg me to boost their grade so they can keep a  job or get a permit for a job offer.

Of course, the same students ineligible to work during the school year are then stuck in  summer school, retaking courses they still don’t care about.  Summer employment is a particular challenge for the same students who can’t get work permits during the year, for the same reason.

As I wrote earlier, high school students are failing classes at epic rates, and graduate requirements have increased. In our district, I see a disproportionately black and Hispanic summer school population repeating geometry, algebra, US History, English–and every August, they have a summer school graduation ceremony for the seniors who couldn’t walk in June because they hadn’t passed all their required courses.(Remember Michael Brown of Ferguson had just graduated a day or two before he was shot in August? That’s why.)

Rich kids of all races might be going off to Haiti to build houses instead of working. Asian kids, particularly Chinese and Koreans, are almost certainly not working because their parents won’t allow it. The days of supporting mom and dad in the business are mostly over, at least where I live. Chinese and Korean parents, particularly those who just got here, go  into debt, borrow money from back home, and send their kids to hundreds of hours a year in private instruction. But it’s not schools pushing them into this activity. (Schools, if anything, try to discourage this obsessive devotion to academics.)

But rich kids and certain Asian demographics aside, the average teen, particularly those from disadvantaged families, cares considerably more about financial remuneration than academic enrichment.  If teen employment has decreased dramatically and academic activities are taking up any bit of that time, the first thought should not be “Oh, they’re just being encouraged to value academics so they can go to college” but “Oh, they aren’t being allowed to work because they’re failing required classes.”

Teen employment is not a “thing of the past” because teens have decided not to bother with it. They face significant, intentional policy barriers that preclude employment. Most students want jobs.  Cherry implied that teens considered employment passé. That’ s not my experience and the data doesn’t support that interpretation.

Surprisingly, Cherry doesn’t even mention the possibility of demand-related drops. If you could CTRL-F the conversation, as Steve Sailer says, “immigra” would return a “not found”.  Neither Loury or Cherry mention that constant increases in low-skilled immigration would present competition for teenage workers.***

Which is odd, because there’s all sorts of research on plummeting teen employment, and  immigration is often identified as the culprit.   Christopher Smith, on the Federal Reserve Board of Governers, has two papers precisely on point.

The first,  The Impact of Low-Skilled Immigration on the Youth Employment Market has this conclusion:

The second, written a year later, examines the degree to which the decline might be to other factors–was it immigration, or the displacement of adults from better paying jobs, or is it the push for college? From Polarization, immigration, education:

Notice it’s 3.5 or more for demand issues–immigration, increasing competition in low-skill market (which is just another way of saying increased  immigration)–and 3 at most for supply factors–things like summer school or other educational opportunities.

Remember, too, that if employers have a choice, they prefer adults devoted to working as many hours as possible with no parents or schools hovering in the background. So  teens  are competing against ever increasing supplies of low-skilled immigrants–and thus more adult low-skilled workers generally–and competing from the bottom of the desirability index, too.

Cherry talks about the “current push” to send everyone to college, suggesting the push is a recent development. As Kevin Carey pointed out a few years ago, people have been questioning the value of college since at least the seventies, when Richard Freeman wrote The Overeducated American. (If the Harvard Crimson isn’t pulling my chain, college journalists were complaining about wasted degrees back in 1883.)

But Freeman’s book didn’t have the impact of  A Nation at Risk. The 1983 education treatise didn’t list “Everyone must go to college” as a recommendation. It did suggest that if all high school kids didn’t take four years of English,  three years each of advanced math and science, and resolutely study a foreign language for two years, Japan would bomb us back into the Stone Age.

I’ve written before that Nation At Risk killed high school vocational education. In that same piece, I point out that  2001’s No Child Left Behind did much to redefine vocational ed as highly competitive career technical education (CTE). Both changes made non-college paths practically unreachable for the average schlub uninterested in college and belatedly trying to get some career options going.

Since the rise of education reform in the 1990s, low test scores have been the club used to beat up public schools in favor of charters using the  KIPP “no excuses” model.  Low test scores aren’t really important unless used as a club to argue that those scores keep students from college.

All of these things have increased the demands on high school. But it’s not new.  The first push to send everyone to college began back in the 70s, before escalating immigration and while teens were still working.  For many years, sending more students to college didn’t conflict with teenage employment. So I don’t see how it could suddenly be a big cause of the change now.

Cherry is dead on the money regarding public universities’ response to unqualified students. After decades of losing borderline or weaker students to the quagmire of remediation, colleges are simply ending the struggle by reducing already lowered standards even further.

Cherry: So CUNY is just dumbing down the assessment exam, the math assessment exam that has mostly arithmetic but some algebra. They’ve just decided they are taking out the algebra, make it just arithmetic. So at Brooklyn College we’re already seeing that, the provost has just sent out a notice that he’s worried, too many people are transfer students…that 500 people are going on probation, 200 are being expelled. He thinks it’s more tutoring, more support services, when we’re just taking in people who don’t have the skills….

Well, yeah.  That sounds familiar, as I just recently wrote that California’s largest university system, and the largest in the country  has gone even further, simply ending the remedial category altogether.

But  Cherry’s prescriptive tone has vanished. He certainly put the “everyone must go to college” rhetoric at high schools’ feet, and (wrongly) implied that high schools are more eager to discipline than support at risk students.  But here, when talking about colleges’ continual failure to enforce their own standards he merely sounds sad. Loury doesn’t follow up on the point, either.  The two men seem remarkably passive about post-secondary failings. I hope to say more about that in a subsequent piece.

My complaints notwithstanding, check out the conversation. I’m glad that our best intellectuals are seriously engaging with the problems presented by low-skilled students. But they still seem more likely to blame culture than look further afield–the culture not only of black families, but what they imagine to be the culture of high school education communities.

Our education policies certainly help to discourage low-achieving teens, making them feel like failures, taking up their spare time in joyless academics far beyond their capabilities and interests. I am certain we can do more to make education more accessible to this population, and believe the path involves more time to learn less demanding content. But ultimately, I continue to believe the most important factors affecting teen employment are demand-related. I hope Glenn Loury and Robert Cherry come down harder on this point in later discussions.

***************************************************
*Okay, a month ago. Hey, I have a day job.

**Work permits vary by state, but in most states the school, not the state, issues the permit. Age/Certification by State
*** Loury has previously acknowledged the impact of immigration on low-skilled employment.

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:

• Black Poverty as percent of Total Black Enrolled
• Black Poverty as percent of Total Enrolled
• Enrolled Student Poverty
• District Overall Black Poverty

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.