Policy changes that should happen but probably won’t

Here are some local policy changes that I think could make a substantial impact on quality of life:

User fees for roads, prices set to vary by level of congestion

User fees for roads / congestion pricing. The present system of collective financing (subsidies) of public roads is badly mistaken in my view, both from a perspective of fairness and functionality. Currently, people who drive more are subsidized by people who drive less. To be sure, taxes on gasoline are a kind of user fee, but they don’t cover the costs of the roads. In addition, gas taxes don’t differentiate between travel in congested areas with travel in uncongested areas. Choosing to drive on a busy road imposes higher costs on others than choosing to drive on a less busy road. Thus, usage fees for busy roads (or at times of day when roads are busier) should be higher than user fees for roads with comparatively little traffic. As the economists would say, such fees internalize the externalities.

Usage fees make the costs of roads transparent to users of those roads. We now have the technology to track usage of roads very closely. Fees for roads should be instituted in many high traffic locations, if not everywhere, and should fluctuate with the amount of other traffic on the road. Such fees would keep traffic flowing freely, and encourage people to drive less, walk more, live closer to work, and take more economical means of transit like buses.

User fees need not be tax increases, as these taxes be offset by reductions in other taxes that currently fund transit maintenance.

Revision / relaxation of regulations on the density of housing

The cost of housing has risen dramatically in many highly productive parts of the country, especially in many places along the East and West Coast. Increased demand to live in these places has outpaced supply, in part due to increasing restrictions on density instituted by local governments since the 1970s. Economist Edward Glaeser has documented how much more difficult it is to build high in NYC today than fifty years ago. Ryan Avent wrote a book arguing that restrictions on housing supply in coastal cities has reduced productivity by making it difficult for more people to move to the most productive places in the country. Avent’s research is based on the finding that productivity tends to be higher in higher density places.

Regulations on housing density are partly driven in part by (1) the self-interest of existing property owners naturally wishing to restrict competition from new housing; (2) negative externalities generated by new development such as more traffic, blocked views, etc., and (3) simple fear of change. Only concern #2 can play a valid role in public policy. And agreements can be reached between developers and existing property owners in order to mitigate the negative externalities and allow denser buildings to get built. The alternative is that desirable places to live will become prohibitively expensive for most people as population grows.

There’s no reason why large parts of Queens, Brooklyn, or Northern New Jersey should remain at lower densities given the demand for living in these areas. Preservation of character is nice, but it unfairly benefits existing property owners at the expense of first time home buyers.

Increasing density often overburdens transportation infrastructure, but this need not be the case.  (See policy change #1.)


Who tends to work more overall — men or women?

It’s well known that men on average engage in more paid work and women engage in more household work.  But is there a gender difference in total hours worked (paid work + household work)?

This is the sort of question that can be addressed through time-use diary data, based on people recording what they do during a 24 hour period.


A paper by economists Burda, Hamermesh and Weil (2007) addressed this, among other issues related to gender differences in work hours cross-nationally.

The table below is copied from that paper, Burda et al. (2007).  For now, I had a parochial interest in the left half of the table showing average minutes spent in both market work and home work by marital status and gender.

In the US, among married people, women reported 12 fewer minutes of total work per day than men on average.  The difference is less than 3%.   Apparently there is little or no gender difference in the various developed they examine except for Italy, where women reported substantially more time on housework, driving up their total work hours above that of men.

Good example of spurious association — national PISA rankings

A while back, international test scores in reading, math and science were released. As usual, the US did not score well among developed nations, and there was the usual commentary about our poor schools, etc. I recall listening to something on NPR about it, in which a commentator mentioned that the scores for top-scoring China were concentrated in one of the wealthiest cities in that nation, Shanghai. I recall the commentator acknowledging the issue of non-representative sample in China, but then saying that issue still couldn’t explain why the US was so low.

Before considering the quality of schools in the US relative to that of European countries, consider the following:

  • Immigrant kids tend not to do as well on these tests as do native-born kids.
  • A larger percentage of the US population is made up of immigrants than any other developed nation.

Putting aside the question of why there is a difference between native-born and non-native born, the two facts mentioned above suggest we need to control for immigrant status before comparing the school systems of various nations.

A post-doc at the University of Chicago presents the results of such an analysis here. These results may be well-known to scholars who study education cross-nationally, but virtually unknown to most people.

Sociologists should engage with behavior genetics

Over the last thirty years, behavior geneticists have examined the influence of genetics on dozens of outcomes of interest to social scientists. A fundamental conclusion of this research is that genes have non-negligible (and often quite substantial) effects on nearly every behavior (Turkheimer, 2000). For some outcomes, the effect of genes appears strong enough to account for the bulk of (measured) similarity between parents and their children (Rowe, 1994; Harris, 1999).

These conclusions would seem to warrant a paradigm shift in the field of sociology, a discipline that has long treated biological influences as irrelevant or negligible. Yet the core curriculum of sociology still makes little mention of biological influences. Biological theories are typically given relatively little attention in commonly used textbooks, and when such theories are considered, they are often portrayed as remnants of racist crack pot theories that lack an empirical basis.

But the foundation on which behavior genetic evidence relies is no less tenable — and no more tenable — than the evidence on which sociologists rely for many of their conclusions. In other words, twin studies are no more or less scientific than regression analysis. For this reason, I think sociologists should take behavior genetics more seriously. Seriously doesn’t mean “adopt it whole,” but instead to engage with it, present it as a viable theory in textbooks, attempt rigorous critiques of its assumptions, etc.

Studies of twins reared apart

In an earlier blog post I mentioned that until very recently there were essentially three types of studies researchers use to differentiate between the effects of genes and the effects of parental socialization. I have mentioned two of those study designs – adoption studies and twin studies. There are various methodological variants on the twin study which I will not get into.

However I have not mentioned the third study design used to estimate the effects of genes. This is the so-called combination adoption twins study. This sort of study design requires that the researcher finds identical (MZ) and/or fraternal (DZ) twins who have been raised in different families since birth for at least since very early in their life. Obviously, it’s very difficult to find these kinds of people and so there are very few studies of twins raised apart.

Perhaps the most famous study of this kind is the Minnesota study of twins reared apart (aka MISTRA). Over perhaps a decade or more researchers at the University of Minnesota recruited MZ and DZ twins who reported that they had been separated at birth or a bit later. The Minnesota group recruited twins from all over the country and in some cases from outside of the United States.  Twin pairs were brought to Minnesota to answer dozens of questions gauging their beliefs, skills and personality skills. The results were stunning.

The Minnesota team reported many of their most important findings in a classic 1990 article in Science magazine. MZ twins who had been raised in separate families nonetheless were quite similar to each other in a variety of ways.  The Minnesota group also compared monozygotic twins reared apart with monozygotic twins reared together.  For example consider IQ.    Correlations between the IQs of MZ twins reared apart were quite high, albeit smaller than the IQs of MZ twins reared together.    Among the 48 pairs of MZ twins reared apart, WAIS full test scores correlated at 0.69.  Among  the 40 pairs of MZ twins reared together, WAIS full test scores correlated at 0.88.  This result provides persuasive evidence that IQ has a very large heritable component.

Limitations with studies of twins reared apart?

Theoretically, the adopted twins studies provide the most robust test one can imagine. Think about what these studies have potentially found. The idea is that you take two genetic clones, put them in different families and then 40 years later see how similar they are. If the twins are very similar then you can say with a very level of confidence that genes are making a big difference. On the other hand if the twins aren’t that similar you can say that genetic influence is weak. Of course the reality is somewhere in between but the upshot of the Minnesota study of twins reared apart as well as other similar studies such as those in Sweden is that genetic influence is substantial.

This is not to say that there are not limitations or at least questions about these sorts of studies. For example, some researchers questioned whether the twins in the Minnesota study had been raised in households that were entirely distinct from one another. You could have for example two twins whose parents had died adopted by different members of the same extended family.  Such twins adopted in different nuclear families but the same extended family may have remained in close contact for much of their lives.  In defense of the Minnesota study, researchers did collect data on the amount of time that the twins spent together and the age at which they had been separated. Generally the researchers found that age of separation and the amount of time that the twins had spent together made little or no difference on similarity in how they turned out.

The Minnesota study of twins reared apart may also suffer from selection bias.  When doing a study of twins reared apart, you don’t have the luxury of randomly choosing your sample from your population of interest. Instead when you’re looking for such a rare population as identical twins separated at birth or in childhood, you’re going to take anybody you can get. The problem with this is that the twins who end up volunteering for your study may in fact be twins who tend to be more similar to each other. Twins who are distant or dislike each other may be less likely to volunteer for such a study.   For this reason, correlations between the twins in the study on any given trait may be artificially inflated relative to what you would find if you had a truly representative sample. Nevertheless the findings of Minnesota study cannot be easily dismissed. Even if we allow for the fact that some of the twins in the Minnesota sample did in fact spend a good deal of time together prior to the study, the conclusion and the results are still striking.

The equal environments assumption

Over the last 30 or 40 years, there has been a considerable amount of research — perhaps two dozen studies — examining whether environmental similarity biases the results of twin studies. The vast majority of this research finds little to no evidence that twin studies are biased in this regard.  Most twin studies have cited this research in support of what is known as the equal environment assumption or EEA for short.   The equal environment assumption is perhaps a misnomer because it doesn’t mean that we assume that MZ twins share environments that are as similar as DZ twins. Instead, the equal environments assumption simply states that environmental similarity between twins does not have much of an impact on trait similarity.

There are however some gaps in the research investigating the EEA and that’s where my dissertation and forthcoming book came in. In my dissertation, I attempted to test the EEA in a more comprehensive fashion then had been done previously. Using a nationally representative sample of twins, I tested the impact of a wider array of environmental similarity measures. I also looked at the impact of all of these environmental similarity measures on genetic estimates for a wide variety of outcomes that social scientists are interested in.  So what did I find? When controlling for a wide variety of measures of environmental similarity I found that the bias affecting twins studies is perhaps larger than many behavior geneticists like to believe but also smaller than many critics of behavior genetics like to believe.

Let me try to explain what I did in my dissertation in layman’s terms. What I essentially did was to find MZ twins and DZ twins whose parents and peers treated them with the same level of similarity at least in so far as could be measured.  Consider a concrete example. Most MZ twins are in good contact with each other. Meanwhile DZ twins are less likely to remain in good contact. Part of what I did in my dissertation was to find the DZ twins who do remain in close contact like many of the MZ twins and then to compare that subset of the DZ group with the MZ group. The idea here again is to eliminate any effects of environmental similarity. In some cases when I controlled for environmental similarity, the apparent effects of genes declined dramatically. But in many cases the effects of genes were robust to the inclusion of controls for environmental similarity. This means that when I compared the DZ twins who spend a lot of time together and had very similar experiences with the MZ twins, I still found the same genetic effects as would have been found in the standard twin study that didn’t control for environmental similarity.

One thing that we can conclude is that whatever bias introduced by environmental similarity is not large enough to invalidate the central conclusion of twin studies. The overall conclusion reached by behavior geneticists from twin studies is that nearly every trait you can imagine is heritable. The general idea is that the effects of genes pervade our life and explain a lot of the differences between us. My dissertation added a caveat to that conclusion: perhaps genetic effects are not quite as large as some of the twin studies lead us to believe.  Nonetheless, genetic effects are pervasive and often substantial.


Potential limitations / problems with adoption & twin studies

I have explained how you can estimate the effects of genes on traits that vary with data from adopted kids or with data from MZ and DZ twins.  Both of these study designs have limitations, which, depending on your point of view, either have little impact on the results, or render the results meaningless.

I’ll briefly mention several potential problems with each study design before introducing a third study design hat potentially avoids all of these problems.

Potential problems with adoption studies

  • Data from the biological parents of adopted children are hard to come by.  An adoption study is still doable provided one has data from at least one adoptive parent as well as from the adopted kid herself.  The researcher will have to assume that the assignment of children to parents occurred essentially at random.  This assumption is sometimes tenable, sometimes not.
  • Adoptive parents are sometimes biologically related to their adopted children in some way (e.g. an aunt or uncle adopts his/her niece when the niece’s parent dies or becomes unable to care for the child.)  In this case, adopted kids may behave more similarly to their adoptive parents simply because they share some of their genetic make-up.
  • Adoptive parents may treat their adopted kids differently than they treat their biological kids.  Evolutionary psychology would predict this, although there’s evidence against it (http://asanet.ba0.biz/images/press/docs/pdf/Feb07ASRAdoption.pdf).  If parents invest less in their adopted kids than they invest in their biological kids, then we might expect any effect of parental socialization on adopted kids to be muted to some degree.

Potential problems with the classic twin study

Since MZ twins are genetic clones, they tend to:

  • Experience very similar treatment from everyone who stands to influence them
  • Spend a great deal of time together
  • Experience a powerful bond, perhaps unlike the bonds experienced by any other pair of human beings

Meanwhile, DZ twins share no more genes than non-twin full siblings, so they will tend to experience relatively less similar treatment, spend less time together, and experience less powerful bonds than MZ’s.