In late August 2019, I move to Johns Hopkins University to become the director of the 21st Century Cities Initiative.     After having served as the Chair of the Department of Economics at USC for the last two years,  I now have a strong understanding of how to be an effective administrator and how to work with Deans and other university leaders.

JHU doesn't want me to be a "little Dean".  JHU wants me to continue my research on urban and environmental economics. It has created an opportunity for me to play to my strength as an interdisciplinary scholar who has broad interests concerning many urban issues.   The rebooted 21CC is expected to connect the university's great scholars and students.  JHU wants my Center to engage with alumni, and urban stakeholders.   This plays to main my intellectual strength. I see how various pieces of the puzzle connect together and I love to talk about ideas.

On a daily basis, my team will be using urban "Big Data" to study the causes and consequences of urban economic growth. We will also be studying the causes and consequences of urban quality of life in U.S cities and cities around the world.   Similar to Raj Chetty at Harvard, I seek to teach a big general education class at JHU to teach students about how to use "data crunching" for understanding our cities.

To gain a  better appreciation of what we will do, please read our new newsletter.  The Center is hiring undergraduates at JHU as research assistants, we are hiring pre-docs and welcome Visiting Scholars.

Our center will be working closely with  Beth Blauer's impressive Centers.  

Follow us on Twitter at @JHU_Cities

The end goal should be like Columbia's Earth Institute but with a focus on social science and its synergies with other core strengths at Hopkins 

Each Sunday morning, my wife and I shop at the Beverly Hills Farmers Market.  As we tried to drive out of the parking lot, we were stuck in a huge traffic jam.  This has never happened before.  I exited the car and walked up to the traffic kiosk to find out why we were stuck in traffic.  At this kiosk, you are supposed to pay for parking or hand over your parking validation (because each driver receives 2 free hours of parking).  At the front of the line was a SUV with a bumper sticker that said ("UCLA Mom"). This driver had lost her parking ticket and she refused to pay the $16 fee.  She was the cause of the growing line.

What happened next?  Another woman who was stuck on the line (she drives a nice Mercedes) was getting angry.  She walked up to the Kiosk and paid the first woman's $16 fee and all us stuck on this line were released.

This is a great example of the Coase theorem.  The person driving the Mercedes realized that she did not have property rights and she paid for the right to be released from this line.  The rest of us stuck on line were free riding on her willingness to pay to liberate herself from this situation.  This is an example of Coase's lighthouse at work.  She provided a local public good for all of us because her private benefits from being released > $16.

The NY Times has published a great opinion piece making the sensible argument that more local governments need to upzone.  One NY reader posted the following comment.

"IN THEORY Increasing the density of homes sounds good. My wife and I, before starting our family, lived in a beautiful old building in a time-warp-of-the-60s neighborhood that's still a time warp. The apartment was huge, luxurious and the rent cheap. It was also plagued by neighbors who felt entitled to blast their 800 watt stereos all night long. When I called them to plead for quiet, they invited me downstairs to join their party. Fortunately, we now live in a time when earbuds are ubiquitous and cheap, along with the universality of cell phone use. Requiring residents in dense housing to use earbuds to keep noise down will be a preventive to toxic, damaging levels of noise."

Coasians will find this to be quite amusing. He/she is saying that new technologies help to muffle the cross-neighbor noise externality and this means that people can now live in harmony in close physical proximity. He/she appears to believe that in the old days that the "solution to pollution is dilution". Now that we can cheaply muffle the cross-neighbor externality, we can coexist close by each other.

Back in 2006, James Heckman and co-authors published an important paper on essential heterogeneity in empirical economics.  A key idea they convey is to consider a case where people opt in to take a treatment such as attending college.  In their model,  people know what they as individuals will gain from going to college.  The econometrician seeks to estimate the empirical distribution of these gains.  For example, what is the average gain in earnings from attending college?  For those in the 90th percentile of the gains distribution, how much do they gain from attending college?   A fundamental self selection issue arises. Those who gain the most from college will be the most likely to attend.   Rational agents are sorting on the expected gain from taking the treatment.  As usual, a missing data issue arises. the econometrician does not observe what your earnings would have been if you had chosen the education level that you did not choose!

To appreciate this point, consider the following simple example.

Assume that the interest rate = 0%.  Assume that people live for two years. If you go to college for one year,  you only work the 2nd year. If you don't go to college, you work both years.

Person        Earnings if go to college              Earnings if do not go to college

Matthew        86000                                      41000
Jill                  99000                                      43000
Billy              108000                                     42000

Suppose that the tuition cost = $10,000

If each of these 3 different people is a lifetime net income  maximizer, then we see that;

Matthew does not go to college because 86000 -10000 <  2*41000
Jill does go to college     because 99000-10000 > 2*43000
Billy goes to college  because  108000-10000>2*42000

In this case, in the face of essential heterogeneity --- the researcher using these observational data would conclude that the average treatment effect of going to college =

E(earnings|go to college) -  E(earnings| not go to college) =  103500-41000*2

Note that the 103500 is the average lifetime earnings of Jill and Billy as college graduates and the 45000 is observed because this is Matt's earnings as he chooses not to go to college.

A key point in the Heckman paper is that there can be instrumental variables that would nudge Matt to go to college.  From the participation equation above, note that if Matt is offered free tuition to go to college (a cost shifter) he will choose to go to college because 86000>2*41000.  Matt is "at the margin" in this 3 person economy. If he is offered a tuition break of $500, he will still choose not to go to college.

Facing this zero tuition, all 3 would go to college and a researcher would not be able to estimate the average returns to going to college because there is no control group!  Nobody chose to not go to college.

Now suppose that we continue with this 3 types of person economy but we make the new assumption that there are 1000s of each of these 3 types.   These 3 types are identical except for 1 new assumption.

Suppose there are 5000 Matthews, 5000 Jills and 5000 Billys.  In this economy of 15,000 people let each of these decision makers  choose whether to go to college at random.  This may be a personality trait such as being over-confident on being impatient about bearing upfront costs (paying for education) versus later benefits.   So, I am assuming that this personality trait is iid mean zero for each of the 15,000 people.  Essentially, these people are flipping coins to see if they go to college or not.

Under the assumption that these behavioral personality traits are independent of one's skill level and that these behavioral traits determine whether one attends college or not, what does the econometrician observe?

1/2 of the Billys go to college and 1/2 do not.
1/2 of the Matthews go to college and 1/2 do not
1/2 of the Jills go to college and 1/2 do not.

The Econometrician recalculates;

E(earnings|go to college) -  E(earnings| not go to college) =

(1/6)*88000 + (1/6)*99000  + (1/6)*108000 - (1/6)*(41000+42000+43000)

and this is an accurate average college treatment effect for the entire population.

What just happened?  When economic agents sort on the gain, OLS overestimate the population average treatment effect. When economics agents sort into treatment at random, OLS performs much better.  Behavioral agents can be thought of as "randomly sorting".

In reality, only a subset of decision makers sort by such random factors but this means that the presence of an unknown subset of behavioral agents shrink the OLS treatment estimator back to "to the truth".

So, the point of this blog post is to highlight that behavioral agents improve the quality of life of the reduced form econometrician. 

Tack Yun,   , Professor Seoul National University


I'm posting this because I think that the University of Chicago will soon move this group picture into storage because the department has accumulated a lot of these photos.  My wife's class of 1986 photo is already in storage.  Given the strength of our class, I think our group portrait should continue to be proudly left out for all to see!