Friday, December 21, 2018

Reflecting on Berkeley Data Science

During the past 18 months, I served as an academic officer ("curriculum coordinator") in what was then known as the UC Berkeley Division of Data Sciences, and which will be one of the elements of the new Division of Data Science and Information. I left the Division in early December to switch to a research role across the bay at UCSF.

My involvement with what became the Division stretches back to Fall 2015, when Cathryn Carson brought me on to teach one of the 6 inaugural Data Science Connector courses that term. Connector courses are usually small seminar-style, half-time offerings (2 units rather than 4) that aimed to connect concepts in the foundational course at Berkeley, Data 8, to topics in academic fields. I taught a connector entitled "Health, Human Behavior, and Data" during that first term of Data Science at Berkeley, and again in Spring 2016. During the academic year 2016-2017, I taught statistics and econometrics at Mills College in Oakland.

Looking back on my involvement with Data Science these past several years and over the arc of my own education and career, I feel a sense of great pride and awe at what the group at Berkeley has accomplished. As a sophomore in Fall 1993, I studied computer science in the introductory course COS 126 taught by Robert Sedgewick at Princeton 25 years ago, during the same term I studied econometrics led that fall term by Henry Farber. I had my hands full. There were many Sundays when I didn't get to sleep until very late, working on problem sets for both classes, sometimes via a dialup modem on a Mac SE from my dorm room.

In 1993, econometrics was its own thing, and computer science seemed like it was all about loops and "pointers" whatever in the world those were. But Stata lived on three UNIX mainframes that all of us had access to, and there was some synergy to be had in studying C++ and applied econometrics in Stata at the same time. I definitely learned plenty of UNIX along the way.

I wish that modern Data Science had been around back then, and it thrills me to see it here now, at last. No joke, I earned a C in COS 126 that term, although I got an A in econometrics. I suppose a C+ in COS 126 would have been somewhat more appropriate! Back then, it was tough to straddle both worlds without destroying one's GPA. Now, it is far more seamless.

Students in Berkeley Data Science certainly include many folks who are Comp Sci whiz kids. But they also include many who were a lot more like I was in 1993, along with folks even further toward the social side of social science, and people in the humanities. Berkeley has figured out a great way of planting poles so widely that the tent is truly big. It has been a great pleasure watching and helping it unfold.

Thursday, December 6, 2018

All (immigrant) kids are always expensive

Another inquiry came in from the media that went something like this:

"I'd like to know the impact on state and local finances of an arrival of a skilled immigrant, as opposed to an immigrant with less than a high school degree."

There is some variation in the costliness of kids to governments according to the parents' education and language skills, but a lot less than there is in the net costliness of adults (i.e., how much they pay in taxes vs. absorb in benefits).  Kids are kids, pretty much.  States and localities have to hire K-12 teachers to take care of them regardless of whether their parents have Ph.D.'s or haven't graduated high school.

Tuesday, December 4, 2018

A wall doesn't remove the unauthorized

There seems to be continuing interest, likely because of the looming fight in Congress over the wall funding, about a FAIR report pegging the annual budgetary net costs of unauthorized immigrants at $100 billion. Folks who are in favor of building a wall argue that because a wall costs less than $100 billion, the wall will "pay for itself."

Fact checkers have emailed and called me about this, and my heart goes out to them, because the problem here really isn’t the imprecision of the facts per se.  (They are indeed imprecise.) 

The real problem is that comparing one-time costs of constructing a border wall to annual costs of net government benefits paid in excess of taxes received is a really silly comparison.  That’s because building a wall doesn’t magically remove unauthorized immigrants already in the country.  Only deportations do.

Without expanding the funding of removal, all you get is an expensive wall and 11 million unauthorized immigrants still living in the country and still absorbing more in benefits than they pay in taxes, probably to the tune of about $50 billion per year, mostly paid by state and local governments in the form of K-12 education.

But don't get me wrong, I'm not in favor of displacing 11 million people. The human and economic costs would be very large.

It's just that it's silly to compare the costs of a wall with the costs of something that a wall would not reduce.