This is Lecture 7 of the course ECON 85600, “Inequality, Economic Opportunity, and Public Policy,” that my class and I are now conducting online. You are welcome to participate, and can review all the course materials at https://milescorak.com/equality-of-opportunity/teaching/ .
Warning: this is likely to interest social scientists in sociology, economics, or other fields, interested in developing a specialized knowledge of the subject!
This lecture summarizes research on how we should think about and interpret changes in intergenerational income mobility over time, and across space.
The empirical literature is not clear on the degree to which, or even whether, intergenational income mobility has changed in the United States. The focus in this presentation is to interpret these conflicting findings with the aid of theory, which leads us to appreciate not only that multiple causes may be at work, but also that the dynamics of mobility may have very long lags and follow non monotonic patterns in adjusting to a new steady state.
The full reading list and access to other papers are on the page devoted to this lecture at https://milescorak.com/equality-of-opportunity/teaching/lecture-7/
View this lecture in conjunction with your reading and of the student presentation of the longer run impacts of the Moving to Opportunity experiment in:
Raj Chetty, Nathaniel Hendren, and Lawrence F. Katz (2016). “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” American Economic Review. 106 (4): 855-902.
This paper marks an important turning point in our readings of the empirical literature, as we move from description to causal analysis using clearly articulated identification strategies.
Here is the student presentation, which you should download and review as a complement to your readings : https://milescorak.files.wordpress.com/2020/04/student-presentation-moving-to-opportunity.pdf
Be certain to leave a comment, question, or concern in the “What do you think?” box at the very bottom of this post. Frame your feedback in a way that is of benefit to the learning environment for all students, and don’t hesitate to raise a question of clarification if you don’t understand an issue
Economics for public policy 
Hello Professor,
Time series analysis is the first thing that comes to my mind when I think about changes in the intergenerational mobility. Given that there has been quite a lot of events that might be considered as shocks and given the non-monotone behavior of shifts between steady-states described by theory, is it possible to come up with a sound empirical understanding of change in intergenerational mobility? As current available data seems to be limited to a small number of generations. Is there anything else that might be indicative of the change in intergenerational mobility other than a time series analysis? Currently, I cannot think of anything else.
Also, Chetty et al. findings are very interesting. The results of the paper suggest a strong policy option as well when applied in small scale but how much can we extend the scope of such a policy? It seems its limits are too narrow to have a significant impact on intergenerational mobility.
We are going to go on in our readings and discussions to refine our understanding of human capital investment, recognizing that there are many stages in a child’s development. So human capital at each in a successive series of transitions to adulthood can be measured. And since this involves a shorter span of time, we may be able to get better indicators, or at least early warning signs, of changes in intergenerational income mobility.
That is why in part we are also reading the World Bank report. Its focus on education outcomes is helpful in tracking time series movement that in the least complements income mobility statistics, and arguably does better. But this will also be refined to focus on the early years. So we will need to revisit our theory again, and prime ourselves for another empirical perspective.
Did you have anything specific in mind by raising these issues?
I think the scalability question depends on the policy lesson one takes from the updated MTO analysis. Viewed narrowly, the results suggest that giving vouchers to families with young children to move to low-poverty census tracts would be beneficial. In the American case, this could be scaled up nationally because the federal government funds and regulates Section 8 vouchers. If the lesson is more broad-neighborhoods have casual effects and we have to figure out how to alter those effects, then the course of action is less clear. The place-based solutions that have shown the most promise, such as the Harlem Children’s Zone, are very resource and coordination intensive and hard deploy everywhere they are potentially needed.
Hello Professor,
I am not sure I quite understood in Slide 18:
a more general model may lead to even slower movement between steady states
-income and wealth as opposed to earnings
So, if we use income and wealth comparisons instead of earnings, it is possible to see a slower adjustment process. Did I get it right?
And if it is the case, why could that be?
My regards.
Thanks for this.
If I understand the question correctly, what I was trying to say is that if we examine other outcomes, the lags in the process may be even longer. Earnings (and wage rates) are outcomes that are immediately observable in the market, but household income also relies on the marriage market and family formation may take more time. If our variable of interest is wealth, then even more time might be needed for us, as analysts, to observe the outcomes, financial wealth accumulation taking more years, and also being transferred across generations more slowly.
Does this address your concern, or have I missed something?
It seems to me that the notion of fluctuations in mobility over time and long adjustment periods towards equilibrium give some weight to the approach by Gregory Clark. If his data is able to track families over many generations, perhaps he is able to pick up more of the signal of long-term mobility trends than the noise that is in more limited time series? Though, I’m sure this advantage has to weighed against other measurement and sample issues with looking long into the past.
Speaking up for the sociologists in the “room,” I was very interested in Nybom and Stuhler’s inclusion of a variable that’s directly related to parental well-being (as you say, nepotism), as it challenges the notion of meritocracy. From a sociological standpoint, it is compelling to see this included in the equation, as it reflects the notion that at least some portion of inherited mobility comes directly from parents, and that having parents who are better off will indisputably contribute to your chances for success (or indeed, that having parents who are economically worse off will lessen your chances for economic success). While this certainly cannot explain all of intergenerational mobility, I find it a convincing addition to the previous models.
I find the results in Chetty, Hendren and Katz (2016) quite interesting to motivate a discussion on the neighborhood effects on social mobility and on the factors that run those effects.
Moving from the analysis of empirical regularities of social mobility to theories that explain those regularities, Nybom and Stuhler (2014) suggest that a host of factors affect mobility, and they do so with lags and often in a long-lasting manner. The literature on these neighborhood effects suggests that the “quality” of the neighborhood in which a given family lives is among these host of factors. In particular, Chetty, Hendren and Katz (2016) suggest that intergenerational persistence in income among families on the lower tail of income distribution was reduced among families with young children that were on the treatment group in the MTO experiment. Given that, they conclude that “efforts to integrate disadvantaged families into mixed-income communities are likely to reduce the persistence of poverty across generations”.
I find these results interesting, but would like to stress the importance of discussing what may drive these results. I mean, are “poorer” neighborhoods, and thus “poorer” communities less able to promote social mobility among their children per se, or are there other factors that cause these results? Some factors could have to do with increased policing and incarceration rates affecting communities from these neighborhoods, as discussed in Derenoncourt (2019), or the high supply of low skilled labor in local labor markets, as discussed by Boustan in her work on the Economic effects of living in an ethnic enclave.
Understanding the drivers through which neighborhoods affect social mobility is quite important for policy design. It seems to me that a Moving to Opportunity e experiment has as an underlying assumption that poorer neighborhoods hurt mobility per se, while programs of direct intervention, such as the Harlem Children’s Zone cited by Max, seem to suggest that the causality runs from other factors that are prevalent in these neighborhoods.
Finally, I want to suggest that it is important to incorporate in such research a qualitative appreciation of subjective results of displacing people from their neighborhoods. My concern is that belonging to a given neighborhood is often part of one’s identity and one’s self perception. Lower income neighborhoods often host immigrant communities that find in such neighborhood a safe geographical area to practice their culture and to build social relations among equals. While we can find positive results on mobility and children’s mental health of moving families from lower income to mixed-income neighborhoods, there may be important social results that will hardly be captured in a quantitative analysis of such experiments. Results that may have social and political implications that may conflict with the way we collectively think that societies should be organized and how pluriculturality should be addressed in urban environments.