An Econ Conference for Undergrads

In April in Washington, DC.

My Proposed (but not accepted) Bet with Paul Krugman on the Obama Forecast

Scott Sumner declares that I would have been the winner.

What worries Americans?

When Gallup recently asked Americans what the biggest problem facing the United States is, the four most common answers were dissatisfaction with government, the economy in general, unemployment, and healthcare.  Each was mentioned by more than 15 percent of those polled.  The gap between rich and poor was mentioned by only 4 percent.


If President Obama wants to make the 2014 electoral debate about income inequality, as he seems to, he has an uphill climb ahead of him.

How an Economist Helped Inspire the Movie Dr. Strangelove

Thomas Schelling, of course.

Does income inequality increase mortality?

In his recent Times column, Paul Krugman writes:

Rising inequality has obvious economic costs: stagnant wages despite rising productivity, rising debt that makes us more vulnerable to financial crisis. It also has big social and human costs. There is, for example, strong evidence that high inequality leads to worse health and higher mortality.

The links are from the online version of Paul's column.  I followed the second link to an interesting article by Angus Deaton.  Angus writes the following (emphasis added):

Darren Lubotsky and I ⁷ have investigated the relationship between income inequality, race, and mortality at both the state and metropolitan statistical area level. In both the state and the city data, mortality is positively and significantly correlated with almost any measure of income inequality. Because whites have higher incomes and lower mortality rates than blacks, places where the population has a large fraction of blacks are also places where both mortality and income inequality are relatively high. However, the relationship is robust to controlling for average income (or poverty rates) and also holds, albeit less strongly, for black and white mortality separately. Nevertheless, it turns out that race is indeed the crucial omitted variable. In states, cities, and counties with a higher fraction of African-Americans, white incomes are higher and black incomes are lower, so that income inequality (through its interracial component) is higher in places with a high fraction black. It is also true that both white and black mortality rates are higher in places with a higher fraction black and that, once we control for the fraction black, income inequality has no effect on mortality rates, a result that has been replicated by Victor Fuchs, Mark McClellan, and Jonathan Skinner⁹ using the Medicare records data. This result is consistent with the lack of any relationship between income inequality and mortality across Canadian or Australian provinces, where race does not have the same salience. Our finding is robust; it holds for a wide range of inequality measures; it holds for men and women separately; it holds when we control for average education; and it holds once we abandon age-adjusted mortality and look at mortality at specific ages. None of this tells us why the correlation exists, and what it is about cities with substantial black populations that causes both whites and blacks to die sooner.

In a review of the literature on inequality and health, I note that Wilkinson's original evidence, which was (and in many quarters is still) widely accepted showed a negative cross-country relationship between life expectancy and income inequality, not only in levels but also, and more impressively, in changes. But subsequent work has shown that these findings were the result of the use of unreliable and outdated information on income inequality, and that they do not appear if recent, high quality data are used. There are now also a large number of individual level studies exploring the health consequences of ambient income inequality and none of these provide any convincing evidence that inequality is a health hazard. Indeed, the only robust correlations appear to be those among U.S. cities and states (discussed above) which, as we have seen, vanish once we control for racial composition. I suggest that inequality may indeed be important for health, but that income inequality is less important than other dimensions, such as political or gender inequality.¹⁰

Is Angus's article really support for Paul's claim?  It seems to me that it is more the opposite.

On Assortative Mating

A new working paper concludes:

"Data from the United States Census Bureau suggests there has been a rise in assortative mating....[I]f matching in 2005 between husbands and wives had been random, instead of the pattern observed in the data, then the Gini coefficient would have fallen from the observed 0.43 to 0.34, so that income inequality would be smaller"

How much income inequality is explained by varying parental resources?

When people think about inequality of incomes, a key issue is inequality of opportunity. Some people are born to rich parents who can afford private schools, summer camp, SAT tutors, etc., while others have poorer parents who cannot easily afford such things. One might wonder how much of the income inequality we observe can be explained by differences in the resources that people get because of varying parental incomes.

Let me suggest a rough calculation that gives an approximate answer.

The recent paper by Chetty et al. finds that the regression of kids’ income rank on parents’ income rank has a coefficient of 0.3. (See Figure 1.) That implies an R2 for the regression of 0.09. In other words, 91 percent of the variance is unexplained by parents’ income.

I would be willing venture a guess, based on adoption studies, that a lot of that 9 percent is genetics rather than environment. That is, talented parents have talented kids partly because of good genes. Conservatively, let’s say half is genetics. That leaves only 4.5 percent of the variance attributed directly to parents’ income.

Now, if you let me play a bit fast and loose with the difference between income and income rank, these numbers suggest the following: If we had some perfect policy invention (such as universal super-duper pre-school) that completely neutralized the effect of parent’s income, we would reduce the variance of kids' income to .955 of what it now is. This implies that the standard deviation of income would fall to 0.977 of what it now is.

The bottom line: Even a highly successful policy intervention that neutralized the effects of differing parental incomes would reduce the gap between rich and poor by only about 2 percent.

This conclusion does not mean such a policy intervention is not worth doing. Evaluating the policy would require a cost-benefit analysis. But the calculations above do suggest that all the money the affluent spend on private schools, etc., explains only a tiny fraction of the income inequality that we observe.
----
Addendum: A few readers seem confused about how to infer an R2 from a coefficient.  The key is that the left and right hand side variables in the regression have the same variance.  In this case, the R2 is the square of the coefficient.  This conclusion is a standard result for AR(1) models, which is what we have here, as applied to generational data.  (Also, a few readers are confused when they look at the paper's Figure 1. The points plotted are not the raw data but binned averages, so you cannot see the R2 in the plot.)