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:



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.

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.

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”:


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.

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


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.

On the popularly reported black implicit association test (IAT) results

Recently the media and various friends and family have been asserting that implicit association tests (IAT) “prove” that whites are biased against blacks and that this presumably substantially explains the racial disparities in police shootings.


Since I am skeptical about the racial angle in police shooting, the validity of measures like IATs, and of received wisdom in general, I thought I would take a look at “Project Implicit” to better understand it.  The raw data for these results is available in SPSS format on OSF.io (albeit at >2GB) so I downloaded the data and performed some analysis in R.

A brief post on racial disparities in officer involved shootings

I have recently heard it said that the reason the police shoot blacks, especially young black men, at such a disproportionate rate is because they have an irrational fear of them because they are black.   Presumably the proponents of this view believe that shootings, “justifiable” or otherwise, should happen in roughly equal proportion to their share of population.  Although I do not believe the police are incapable of excessive force, racial discrimination, negligence, or what have you, the presumption that such disparities must be explained by presumed irrational fear of blacks strikes me as terribly naive on several levels.

Robert VerBruggen of RealClearPolicy did an interesting post on “Race, Age, and Police Killings” a few weeks back that compared nation-wide homicide rates by age group and race to the police shooting statistics.

rcp_white_black_homicide_offenders rcp_whites_blacks_killed_by_age


I thought this was a good and fair way to better illuminate the “fairness” issues here, since groups (e.g., sex, age, race, ethnicity, education, etc) that commit more murder (and other violent crimes) nationally can be reasonably assumed to be more likely to have confrontations with police and more violent confrontations when they do.

I found some data to take this point further by looking more granularly at the demographics of offenders that have actually killed law enforcement and offenders that have assaulted and seriously injured the police (as in with guns, knives, etc).  This data gives us a much better sense for the risks posed by each groups to the police and which groups are relatively more likely to be be confrontational, disobey, or even resort to violence, i.e., it speaks much more directly to the dynamics of police encounters with particular demographics (to the extent that one can argue that, say, national homicide rates are only black-on-black, gang-on-gang, or some such).  Most police encounters do not result in death of either party or even an exchange of gun fire, but groups that kill, injure, or assault the police at (much) higher rates can be reasonably presumed to be at (much) higher risk of getting killed by the police, “justifiable” or otherwise.

More silliness related to corporate profits

I was pointed to this work by Hussman through Business Insider.

The implication here is that total dissaving is not only strongly correlated with corporate profits, but is directly causative.

Although he doesn’t fully specify this methods, it’s obvious that Corporate Profits is after-tax corporate profits (including foreign profits) and I was able to approximate his results using this FRED2 link.

Update: I re-charted this using the NIPA corporate profits inventory & capital adjusted data that he clearly used (CPROFIT).  It doesn’t really change the outcome here, but it matches his chart more precisely.

Corporate profits is, in other words, after-tax and including foreign profits.

Savings is approximately personal savings (PSAVE) + the Federal deficit/surplus (FGRECPT-FGEXPND) (multiplied by -1 to match to shape of the profit line)

There are many issues with this analysis

Some figures relating US household income inequality

The changing distribution of household income inequality is real, but it’s often overstated since it does not account for household size or the number of earners per household.

Put bluntly, actual work is far from evenly distributed and far moreso than most people realize or admit.

Below are some statistics from the most recent US census [very similar numbers are found pre-recession too]


Trouble with progressive estimates of the impact of consumption taxes

The Citizens for Tax Justice (CTJ)  and Institute on Taxation and Economic Policy (ITEP), amongst other progressive organizations, have put forth the claim that our tax code is nearly flat on the basis that “regressive” consumption taxes in state/local tax codes offset federal and (to a lesser extent) state income tax progressivity.

The problem with almost all of these analysis is that they all invariably hinge on the fact that their reported ratios between consumption and income is greater than 1 at lower income levels.  In other words, they are counting consumption taxes in the numerator that are not included in the income base (the denominator).

ITEP claims, in their description, that they’re correlating consumption patterns in the BLS’s Consumer Expenditure Survey to reported income.  The problem with this approach is that the BLS CEX consistently indicates that the ratio between consumption to “income” exceeds 1 just shy of the 50th percentile on down (the bottom is >2x)

see here for quick summary

ITEP further papers over these flaws by effectively hiding non-linearity at negative numbers in their models (e.g., consumption 20x negative income).

Some money quotes on their model:

Our procedure for imputing consumption onto individual tax records can be thought of as involving two distinct steps: (i) econometrically estimating the necessary relationships for each of the desired consumption items from the Consumer Expenditure Survey (CES); and (ii) using the resulting regression coefficients to simulate consumption on the merged data file for non-dependents. Implicit in this approach is reliance on the strong separability of a utility function over different categories of consumption; i.e., we used a “utility tree” approach to estimate several systems of share equations.

Next, total non-durable consumption expenditures were imputed in a similar manner: separate ordinary least squares (OLS) regressions were estimated from the CES on both samples with a similar set of predictor variables. Coefficients from these equations were then used to impute mean (non-durable) consumption expenditures to each household and a normally distributed error term with a mean of zero and a standard deviation equal to the standard error of the regression was added to each imputed amount. Two sets of adjustments were then made to the imputed amounts.

First, the particular functional form used was unstable at very low levels of income resulting in extraordinary amounts of imputed consumption for several records. For nondurable consumption, our OLS specification included two terms, 1/Y and 1/Y2, where Y is total family income, that presented problems at both ends of the income distribution. For very low incomes, the nonlinearity introduced by 1/Y and 1/Y2 caused estimates of mean consumption to approach infinity. This was handled by constraining consumption for these records to be no more than 1.5 times income. This limit was based on analysis of the CES data independent of the imputation process.

Second, the tax return data that formed the basis of the income information for filers contained income amounts far outside the range observed on the CES and caused problems when our regression coefficients were used. Our approach was to assume that the estimated equation was valid for incomes within the range of the CES and to fit a spline function for the portion of income in excess of this amount for those households (about 2.5%) with reported incomes outside the range of that reported in the CES.

Without getting into the weeds with respect to how their model works (they don’t disclose nearly enough information or data to do this), I can reproduce their consumption tax numbers very closely by simply using the BLS CEX consumption to pre-tax income ratios and a flat consumption tax.  In other words, you don’t need to assume that the poor are paying higher effective rates as a proportion of their consumption (e.g., on “sin” taxed goods) to get higher effective taxes as a percentage of this very limited definition of “income” on the poor.  It’s quite clearly almost entirely a byproduct of methodological flaws that vastly overstate spending-to-income at low incomes and vastly understate spending-to-income at upper incomes.