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:

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.

Race is not just a social construct

I have frequently heard people insist that “race is just a social construct”, that there is no genetic basis to it, that it has no statistical relevance, and so on and so forth.  This is clearly wrong, as others have pointed it out repeatedly, but people keep on repeating it for some reason.   To save myself and others time next time around, here is a compilation of the facts, evidence, expert opinion, and more that ought to settle the issue for most fair minded people that are not overly ideologically blinkered.

In no particular order….

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.

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.

The probable genetic explanation for interstate differences in mortality amongst non-hispanic whites

A couple months ago I stumbled across ancestry.com’s  “Genetic Census of America”.  Since I was researching the health outcomes question already I remembered that this data existed and I decided to bite the bullet and actually analyze this data systematically.  Lo and behold, I quickly discovered some very strong correlations between these genetic proportions (crudely without any particular techniques) and the life expectancy of non-hispanic whites in each state.  I refined this a bit and produced a toy model that can explain about 85% of the variance in life expectancy between states (not to mention other things)!

Before I get started, let me get some caveats out of the way:

• correlation does not necessarily imply causation
• these particular genetic groups may just be proxies in this country for particular ethnic or other genetic groups (at least in part)
• this could “cultural” (people with particular frequencies of SNPs are also more likely to have had particular cultural mores, values, and the like passed onto them through their ancestors/parents).
• most “whites” have some fraction of other continental groups, but it’s usually pretty small on average
• the DNA testers may not necessarily be representative of the larger “white” population, but I think it’s good enough to represent the white population (probably less so other groups).
• binning these together by states and other high levels of aggregation likely improves the “accuracy” of these methods since random accidents, stochastic variances in gene expression, or what have you get averaged out to large degree.  Likewise, to the extent these groups are just a crude proxy for actual groups, this level of aggregation likely further helps.
• Ancestry.com does not provide details by race/ethnic group and my procedures cannot perfectly remove any potential signature introduced by others.   Blacks and latinos, in particular, surely introduce some european genetic groups into this data, although they are obviously much under-represented in ancestry’s DNA analysis and I do not think it would skew the results that much.

This is a simple model that I produced to calculate non-hispanic white (NHW) life expectancy by state using a simple genetic calculation and smoking rates (weighted equally on standard deviations from the national non-hispanic mean amongst states) .

As for how I got here….

Some other issues with comparing US healthcare costs and so-called “outcomes”

Besides my previously mentioned objections with simplistic comparisons between healthcare systems, vis-a-vis naive economic comparisons and the effect of taxation on behaviors, it is very difficult to compare the actual performance of healthcare systems, in both financial and human terms (e.g., life expectancy, mortality rates, etc), without accounting for other differences in the populations (e.g., genetics, health/risk behaviors, lifestyle, etc).   These simplistic comparisons of national health care systems based on crude mortality rates and the like are very much like comparing performance of goalies in various sports based on who wins the game alone, i.e., without making any real attempt to control for the performance of the rest of the teams’ defense, the performance of the offense, and so on, when what we really want to know, at bare minimum, is the number of saves as compared the number of shots on goal (and even then that’s an imperfect metric).  Of course some goalies are likely to be somewhat more effective than others and, other things equal, goalies can have a pronounced impact on the outcomes, but you cannot simply assume that there are not any significant systematic differences between teams in general or on game day.

These are just a few relevant differences I can think of off the top of my head:

• The United States population is not a mirror image of Europe: genetically, culturally, or otherwise
• Much higher smoking rates historically
• Relatively high rates of obesity (although other countries are starting to catch up to us now)
• Much higher homicide rate.
• Higher rates of sexually transmitted diseases (see the AIDS crisis)
• More geographically distributed than most (as in, lower population density, significant populations living in rural locations, etc)
• Higher rates of serious automobile accidents per capita

….. (and probably more I’m forgetting)

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 misc. charts and data on wealth shifts