Tag Archives: Socioeconomic

Crime: Socio-Economic vs IQ – The Bell Curve

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It’s here, the last chapter comparison between the impact of the socioeconomic status of the family or the mother of the children and the IQ of the same.

I last posted on crime back in late July and then I mentioned:

When I picked up the book I was looking for books on “How to Raise Chickens” as a result of a post of mine some time back.  I saw the book on the shelves and was taken by the title.  I bought it and it was immediately relegated to my stack.  Some time later, Boortz was speaking about the author and I decided I better begin the book.  At this time I was still unaware of the controversy of the book.  Then I posted on it.  I was then made aware of the controversy.

As I mentioned then, I wasn’t aware of the massive controversy of the book.  I simply saw it on the shelf, bought it and then heard it referenced on the radio.  I started reading it and then posted on it.  Only later did I learn of that controversy.  And when I actually hit the big chapter, chapter 13, I understood why.

Now, as then, I won’t go further than chapter 12 [11 actually, I don't find 12 interesting] and for the same reasons.  However, the controversy that surrounds the later sections of the book shouldn’t diminish the value that the first chapters deliver.

Now.  Crime.

The authors look to quantify crime in two ways:

  1. Asking if the man ever was engaged in criminal activity.
  2. Reporting if the man was interviewed in a correctional facility.

As they point out, both have weaknesses.  The self-reporting may not be accurate but does have the upside of capturing uncaught criminal activity.  The other has valid crime involvement but doesn’t capture criminals who haven’t been caught.  The “smart” ones.

The results are below:

And then:

In both cases, when controlling for other factors, the SES status of the family fades and becomes meaningless.  In fact, as SES increases so to does the rate of self reported crime.  And in both cases, a man possessing a low IQ is at significant risk for each category.

 

Welfare: Socio-Economic vs IQ – The Bell Curve

We’re moving from “The Family” to “Welfare” in the latest installment on “The Bell Curve.”

The series has been focusing on various snapshots of impact that the wealth of an individual or family may have and then at the impact that IQ may have.  Long has the argument been made that much of the disparity in America is due to the fact that the rich get richer while the poor get poorer.

Perhaps it has less to do with the wealth of the family and more to do with the IQ of the family.

So, moving towards welfare.

Previously I posted on the probability of going on welfare, within one year, after the birth of the first child.  I posted on this probability using only the socioeconomic status of the family.  Here I show both the SES and the IQ of the mother:

The impact is significant.  Even more so after accounting for the fact that a woman with higher IQ would be able to avoid the condition that would result in welfare.  Yet, after accounting for age, poverty, marital status and SES, we see that IQ plays a massive role in the probability of welfare reliance.

Next, the topic of chronic welfare dependency.  The data suggests see below, that SES plays at least as important a role as IQ does.  However, the data is restricted to the point that makes it important to point out a note.  Of the women in the study that were long term recipients of welfare, none scored in the quintile of cognitive ability; only 5 were in the second quintile.

With that caveat, here is the data:

Both the economic background of the mother AND the IQ play a part.  As I mentioned in the original post, education may be the relevant influence on this topic.

The Family: Socioeconomics vs IQ – The Bell Curve

The election, work and a jammed family schedule have impacted not only the amount of recent blogging but the subject matter as well.  It’s been over a month since my last installment of the impact of IQ on the conditions of all of us.

The Impact of IQ on Family Matters

I’ve gone over subjects such as poverty, education and employment.  I’ve taken an approach that first shows the impact of the socioeconomic status of the folks involved; sometimes the parents or family and sometimes the individual himself.  Then I’ve come back to show what the circumstances look like when the population has been described in terms of IQ.  So far, to me, the differences are stunning.

Today I look to continue this by checking out the family.

Marriage

Because of the massive positive impact that being married has on society, it’s important to track who is getting married, when and why.  Earlier I showed a chart that described the state of folks at 30.  And because of the natural tendency of education to suppress marriage, both high school only and college graduates were displayed.  The chart below now includes taking IQ into account:

Most likely due to the fact that college is highly tracked to IQ, the differences don’t really matter.  But looking at the IQ of individuals without college education the impact i s large.  Of those with average IQs, 80% are married by 30.  Compare this with those of very low cognitive ability and their rate of 60%.

As I mentioned before, if marriage is important, then so is divorce.  And what does the comparison show?  Look:

The trends are interesting.  As individuals become wealthier, they are more likely to end their marriages in divorce.  But in a completely opposite way and manner, as the IQ of the folks increases, their likelihood of divorce decreases.

The subject of marriage is important because of the impact to the lives of the children of those families.  Because of this importance, it is required to look at the circumstances of birth.  Specifically illegitimate births.

The data are clear.  While the impact of the SES status is important, the top to bottom difference is 10 points, the impact of the IQ is even higher.  Where SES can show a 2 to 1 ratio, IQ shows a 7 to 1 ratio.

The last comparison is that of illegitimate births to white women already below the poverty line.  The data shows that as SES status increases so does the probability that a child is born out of wedlock.  If we include IQ?

Massive.

In perhaps the most glaring demonstration of the impact of IQ, this metric shows that women with higher cognitive ability avoid births out of wedlock at remarkable levels while women with lower cognitive ability are much less likely to avoid this condition.

In each of the 4 comparisons, IQ is shown to have a positive impact on the favorable conditions expected.  In some cases moderate, in others dramatic.

Next up – we look at the impact of IQ compared to SES and welfare.

Education: Socioeconomics vs IQ – The Bell Curve

The second installment of the comparison of socioeconomic status and IQ.  This post examines the impact of each on:

  1. Dropping out of school
  2. Obtaining a GED
  3. Graduating from college

In a previous post, I showed various charts.  Among them is the probability of cropping out of school based on the SES of the family:

The pattern is clear, kids from wealthier families have a better chance of obtaining a high school education.

The came the data showing the probability of a kid, who has dropped out, obtaining a GED:

This is a tale that is counter-intuitive.  We expect the narrative to be that rich kids do better than poor kids.  But this data shows the opposite for folks who obtain a GED after dropping out of school.

Finally we show data that speak to college degrees.  College is, arguably, a key factor to the success of an individual in today’s society.  Maybe.

The data suggests a massive SES impact.  Very few kids from the poorest families are graduating college while nearly 40% of the wealthiest kids are achieving that milestone.

The data is somewhat mixed.  High school and college graduation rates seem highly dependent on the SES of the parents while attainment of a GED is the exact opposite.

Now, what if we add in the predictive value of IQ?

First, dropping out of school:

The first thing that should be apparent is that dropping out of school is rare for kids of either average SES or intelligence.  But dropout rates escalate dramatically for those of below average intelligence.  IQ is more than a 3x predictor than SES of the school dropout.

How does GED look?

The data including IQ doesn’t change the fact that obtaining a GED goes against the commonly held belief that kids from poorer households do worse than the rich kids.  Even accounting for IQ, the folks from the poorer families obtain a GED at higher rates than do kids from wealthier households.

Our last look into education is the college graduation rate:

Again, a dramatic difference.  With one exception; the data shows very little difference between low SES and low IQ.  But when it comes to highly intelligent kids, it doesn’t matter if they come from poorer families or wealthier families; the kids are graduating college at a better than 75% clip.

As with poverty, IQ plays a dominant role in the educational attainment of our children.  All else being equal, the smarter the kid, the better they will achieve educationally.

 

Poverty: Socioeconomics vs IQ – The Bell Curve

About 6 weeks ago I started posting data from the book, “The Bell Curve.”  The first portion of the book deals with various conditions, poverty, education, crime and so on that take place in our society.  And more than just look and detail those conditions, the authors try and look at what might cause some of those conditions.  The point being that a vast majority of today’s commentators on such matters blame the socioeconomic conditions of families for the unfortunate plight many of our citizens find themselves in today.

Having problems graduating high school?  Check and see if the kid is from a poor family.

Mothers raising children in poverty?  Check and see of that mother herself came form a poor family.

Individuals in jail?  Check and see if those folks came from a poor family.

And the evidence is there that such an impact exists.  But is there another, stronger variable that impacts these conditions?  The author’s answer is, “Yes.  And that variable is IQ.”

Let’s review the first set of data I showed back then.  The first set of data shows the probability that an individual will be living below the poverty line in 1989, the data the study used:

The next set of data shows the probability that a child will be living below the poverty line in 1989 when her mother is married:

And the third set of data shows that same probability for that same child if her mother is single.

The data has an uncomfortable, but not surprising trend, to be born wealthy is better than being born in poverty.  However, here the authors, as I mentioned, looked for additional variables.  Specifically IQ.  Look at the data with the socioeconomic status AND the IQ included in the same graph.

Let’s go down the line starting with the probability of living in poverty:

The difference is dramatic.  Not only does having a very low IQ put you at significant risk of living in poverty compared to having a very low SES background, but being very intelligent is more important than being very wealthy.

Next we look at children of married mothers living in poverty and the impact that her SES and IQ have:

While the dramatic difference in the values isn’t the same, the pattern is.  A mother having everything else considered who is less intelligent has a higher probability of raising her children in poverty than an equally neutral mother of higher intelligence.

Finally, the probability of children of single mothers living in poverty and the impact that her SES and IQ have:

Right back to the dramatic difference.  What looked like an impacting variable before, SES clearly now has the appearance of having a minimal effect on raising children in poverty.  Rather IQ dominates this condition for children of single mothers.  Those children lucky enough to be born to the brightest of mothers have a 1/7th the chance of living in poverty compared to those children whose mothers score on the very lowest on IQ tests.

Clearly, as it relates to poverty and child poverty, IQ is the runaway variable when compared to SES.

 

Crime: SES Impact – The Bell Curve

This post speaks to the eleventh chapter in the book, “The Bell Curve.”  It is here that the authors take a look at the impact of SES on crime and criminality in the NLSY study.  Again, in the early chapters of the book, the authors only look at white members of the NLSY.  Data pulled for the criminality is further restricted to men.  The data is presented below.

Data on crime can be hard to obtain.  Many times an individual can be “successful” at crime and not be caught.  This would reduce the instance of crime and present a challenge in reporting group patterns.  In an attempt to overcome this shortcoming, the book uses two classifications:

  1. Self reported crime
  2. Being interviewed in a correctional facility

The impact of SES on the first, self-reported crime is shown here:

While the impact of the socioeconomic status of the parents of the men interviewed is low, less than 4 points, it’s interesting to note that the trend is reversed from what we might expect.  The lack of significant SES impact follows as demonstrated below.

Here it shows that SES plays an even smaller role in determining criminality.

As I mentioned, this is the 11th chapter in the book.  Chapter 12 deals with civility and citizenship.  After reading the chapter, I was left with the feeling that I was reading a filler, one more chapter to fill a book.  As with crime before it, the data presented was sparse and, in my opinion, unfulfilling compared to the earlier chapters and data.

Then comes Chapter 13.

When I picked up the book I was looking for books on “How to Raise Chickens” as a result of a post of mine some time back.  I saw the book on the shelves and was taken by the title.  I bought it and it was immediately relegated to my stack.  Some time later, Boortz was speaking about the author and I decided I better begin the book.  At this time I was still unaware of the controversy of the book.  Then I posted on it.  I was then made aware of the controversy.

It’s chapter 13 where that controversy begins.  It’s here that the authors go into the subject of racial disparities, if they exist, between races.  In the book, the  authors explicitly put pen to  paper and ask, rhetorically for us, why should we explore this topic, one so painful and emotional?  They gave an answer, however, I was struck by the question.

As a result, I won’t go there.

Much of the talk I talk here at TarHeel is, or may be, emotional.  But it’s easy political kinda stuff.  Shit we can all agree is fun or enjoyable to debate.  But race is different.  I have neither the expertise, the knowledge or the education to be able to talk about this subject with anything resembling expertise.  And while I think it’s important to talk about the painful experiences of race, I feel it’s CRITICAL to bring an expert’s touch to the subject of race and IQ.

I don’t have that expertise.

With that said, I understand the controversy surrounding the book.  It IS controversial.  However, I need to point out that the first 13 chapters didn’t discuss race even one time.  And as a I read those first 12 chapters, I was struck by the straight forward and powerful arguments presented.

The next series of posts on this book will go back to Chapter 1 and revisit each until we get to the 11th chapter again.  At that point, I’ll stop.

Welfare: SES Impact – The Bell Curve

I’m continuing my series on the chapters of “The Bell Curve”, by Herrnstein and Murray.  If you are interested in the posts so far, just go to the category selections on the right sidebar, I’ve grouped them together under The Bell Curve.

So far we’ve taken a look at the impacts that the socioeconomic status of the parents of white women in the NLSY have on various life outcomes.  Included in those outcomes so far is poverty, education, Employment and the family.  This post deals with welfare and the dependency on welfare.

The first look at the impact of SES has welfare is what the probablity is of a white woman going on welfare within a year of her first birth.  The data presented below shows that probability with poverty and marital status  taken into account:

As probability of going on welfare moved from the poorest, about 28%, to the wealthiest, about 19%, the trend is down.  However, the authors report that the results are not statistically significant.

But another picture arises altogether when we look at chronic welfare recipients:


Here the results are dramatic.    The probability of a white woman in the NLSy study is greatly influenced by the socioeconomic status of her parents.  The authors don’t explain what might cause the change in the mild predictive value of SES in welfare at all vs. the highly predictive value that it plays in chronic welfare dependency.  However, they do hint that education plays a role somehow.

The Family: SES Impact – The Bell Curve

I’m continuing my way through the book, “The Bell Curve” by Herrnstein and Murray.  I’ve posted already on several of the chapters describing the impact of the socioeconomic status of the families people come from.  Fascinating stuff.

The chapter next on the list deals with the family; specifically the family structure.  The chapter takes a look into what impacts how the family is formed and remains together, or not.

First, let’s take a look at marriage.  Specifically, marriage by the age of 30.  Marriage is very important in society and is critical in creating the building blocks that form successful family units.

So, how does the socioeconomic status of the parents impact the chance of marriage of the child?

The chart above shows data for white individuals in the study.

Because of the impact of education and its suppression on marriage, it’s useful to separate folks who have a high school diploma only from those who have a college diploma.  As you can see, socioeconomic status of the family of the individual has little impact on marriage.  Most people are married by 30 with an even higher percentage married by 40.

If marriage is important, then divorce is important as well.  After all, it’s the two parent home that’s critical to the success of ensuring kids gain a strong foothold in life.  And the data?

An interesting trend to be sure.  As family wealth increases, the rate of divorce increases as well.  Indeed, by the time we reach 2 standard deviations from the mean SES, the individuals are divorcing at 17 points higher than those on the lower SES end.  This represents a greater than 100% increase.

Here the conversation shifts from marriage and divorce without reference to children to those families formed outside of marriage.  And so enters the illegitimate child.  I tend to agree with the authors that the old-fashioned view of illegitimacy was that it occurred mostly at the lower ends of the socioeconomic scale.  It was “the poor girls” having babies out of wedlock, not the wealthy.

But does the data support that view?  The answer is kinda.

 

The women at the very end of the socioeconomic scale have illegitimate births at a 19% rate while the richest of women are giving birth about 8-9% before marriage.  The 10 points or so isn’t much, but again, does represent nearly a 100% increase in the rate comparing the very wealthy to the very poor.

Here the authors move into an interesting question.  Does poverty cause illegitimacy or does the welfare system cause illegitimacy?  The idea, or the argument, being is that the welfare system enables the single mom to refrain from taking precautions that she might otherwise take if she were to bear the cost of raising the child.

To tease out an answer to this, an interesting question is asked:

Among NLSY white mothers who were at or below the poverty line in the year prior to giving birth, what proportion of the babies were born out of wedlock?  The answer is 44%.  For women above the poverty line?  6%.

What does the data, shown in the usual format, show us?

A pretty compelling argument that the wealth of the mother’s family plays a role.

Employment: SES Impact – The Bell Curve

I’ve been posting data that comes from the book “The Bell Curve” in a rather chapter by chapter format.  I started with Poverty and then moved to Education.  This post deals with Employment.

I should mention that the data discussed comes from a study the authors use throughout their book.  They have decided to use this data because of the size, scope and amount of relevant data points gathered.  That study is The National Longitudinal Survey of Youth [NLSY].

From the book:

The NLSY is a very large [12,686 persons], nationally representative sample of American youths aged 14-22 in 1979, when the study began, and have been followed ever since.

In the beginning chapters of the book, the authors use the NLSY extensively.  However, the work that they have done and the results being shown in these early chapters are the result of including only non-Latino whites in the analysis.  I’ll explain the authors reasoning in following posts – or you can go ahead and read it for yourself ;-)

The next sets of data will show the impact that the socioeconomic status of the individual’s background has on employment and unemployment.  First, let’s take a look at the probability that an individual has of being out of the labor force for at least 1 month in 1989:

Interesting curve.  In all the data we’ve seen so far, the curve is to the advantage of the more wealthy households.  In this case, the probability of leaving the labor force goes up as a kid’s parent’s wealth grows. *

Now, let’s look at the same group of folks in the same year but instead of being out of the labor force, let’s measure unemployment:

Virtually straight.  It really doesn’t matter how wealthy your background is when predicting unemployment.

The impact of SES on the employment and/or unemployment of individuals is hard to gauge.  I’m guessing that with further context it’ll make more sense.

* The authors felt this was strange; I don’t.  Rich kids can afford not to work.

Update To Education

On Monday I posted on the impact of parental socioeconomic status as it pertains to their children’s educational outcomes.  In reviewing the post I failed to display 1 of 3 findings the authors made.  I think I did this because the data failed to demonstrate a point that I will be anxious to make in future posts regarding the book.

I will post now the data that speaks to kids who drop out of school only to later come back and earn their GED instead of obtaining a high school dipploma.  The graph is here:

As you can see, SES has a large impact on whether or not a child obtains a GED or stays in school to earn her high school diploma.  The wealthiest families generate graduates 9x more often than the poorest families of kids who drop out but come back to earn either their GED or diploma.