Prison privatization, political economy, and interest group creation

NPR has a great ongoing series exploring how privatization can have extraordinary externalities, namely the role of private prisons in the drafting of Arizona's immigration law:
Thirty of the 36 co-sponsors received donations over the next six months, from prison lobbyists or prison companies — Corrections Corporation of America, Management and Training Corporation and The Geo Group.

By April, the bill was on Gov. Jan Brewer's desk.

Brewer has her own connections to private prison companies. State lobbying records show two of her top advisers — her spokesman Paul Senseman and her campaign manager Chuck Coughlin — are former lobbyists for private prison companies. Brewer signed the bill — with the name of the legislation Pearce, the Corrections Corporation of America and the others in the Hyatt conference room came up with — in four days.

Brewer and her spokesman did not respond to requests for comment.
This is an excellent example of why economics is both an extraordinary tool for analysis but also one that is easily abused. The primary argument for privatization almost always comes down to one of efficiency: the public sector is slow, it's bloated, taxpayers pay $1000 for toilet seat installation, etc. That is not a concern to be belittled, and removing all constraints and checks from public sector workers is clearly a horrible idea.

But the flip side is that there are deep political economy concerns any time you privatize the provision of a public good. The incentives society as a whole faces (i.e., let's *not* incarcerate everyone all the time) are the exact opposite of what a for-profit prison faces (from the article: "They talk [about] how positive this was going to be for the community," Nichols said, "the amount of money that we would realize from each prisoner on a daily rate."). Since politicians respond to incentives, too, and for-profit companies have money to spend on campaign donations, political ads, etc., an already difficult problem is made much more complex (and, I'd argue, welfare decreasing) by following a simple welfare-enhancing efficiency argument.

Now, the flip side of the flip side (since I doubt most people reading a "sustainable development" blog are hungering for privatization) is that the same argument applies for all organized interest groups, not just private ones. This is very similar to the classic argument against unions, for example: New York's widely disliked (even by a lot of teachers) United Federation of Teachers is able to exert a huge amount of political pressure to support policies that are almost certainly harmful to educational outcomes, e.g., making firing bad teachers extraordinarily hard. The problem is the same: once an interest group comes into existence it will do its best to influence policy in its favor.

So what do we do? Legitimately, I think the two solutions are the obvious, difficult ones: transparency in campaign finance and mobilization of counter-interest groups. The first is hard for all of the obvious reasons (including most organized interest groups being against it) and the second is hard because often the counter-interest faces not just asymmetric funding (there's no private interest in keeping people *not* in prisons) but also a fundamental public goods problem: damages tend to be dispersed and costs of abatement concentrated, so anyone joining the counter-interest is either going to be doing it altruistically (e.g., a non-profit), because they were one of the unlucky few who got hit with particularly concentrated damages (e.g., the family of someone unjustly imprisoned), or because they reap some metabenefit (e.g., the NPR reporters covering this story) .

Which is to say, surprise, it's a fairly intractable and complex problem. If there's a lesson to extract I think that it is, as it so often seems to be, to always think about how incentives align, especially before you make large, difficult-to-reverse decisions. In particular, I think it's important to remember that creating a monied interest group is one of the most difficult-to-reverse decisions there is.

Statistical inference isn't easy, either

I was just playing around with the citation management software / website Mendeley (recommended by Amir, and worth checking out for the auto-formatting of citations alone) when I trolled over to their "most read articles in all disciplines" section and saw that the 3rd most read article was a PLoS Medicine piece titled "Why most published research findings are false: author's reply to Goodman and Greenland," by Ioannidis. Ignoring the fact that it's the response and not the original paper (huh?) and led on by the rather provocative title, I poked around and discovered that Ioannidis' work just got written up in The Atlantic and was covered in pretty nice detail by Marginal Revolution back when it came out. So blogging about it does feel a bit like trying to review a restaurant that's already been covered by Frank Bruni and Food and Wine, but I'm going to go ahead and do so anyway since the point is so worthwhile.

The crux of the paper rests on a pretty simple idea: if you're running a huge number of one-off statistical tests (i.e., not testing the same hypothesis over and over) a fraction of your results proportional to the power of your test will be false positives (i.e., type I error). This is pretty straightforward a concept for anyone doing applied work: if you're checking to make sure you've got balance across treated and controlled populations in a randomized trial, for example, having an occasional statistically significant difference between the two populations isn't a huge deal as long as the percentage of variables that turn up that way is proportional to the significance level you're setting. Yes, you should follow through as a good little applied researcher and make sure something's not hiding there, but some portion of your results will always end up that way due to random variation.

The nice step that Ioannidis takes is to look at the entire field of medical research and apply the same logic, effectively viewing the suite of randomized trials as a game where we keep picking new potential tests for the same problems over and over again, some subset of which are guaranteed to be incorrectly not-rejected. To quote Alex Tabarrok's pithy wording of it in the Marginal Revolution post:
Want to avoid colon cancer? Let's see if an apple a day keeps the doctor away. No? What about a serving of bananas? Let's try vitamin C and don't forget red wine.
Moreover, since the number of things that actually, say, help avoid colon cancer is likely small, and the number of tests being run to find things which do is large, Ioannidis concludes that a large portion ("most") results are in fast false positives and thus meaningless. It's a pretty simple premise which leads to a pretty deep statement about how we think about learning about the world.

So the solutions to this are, of course, pretty intuitive: don't trust small sample size studies; insist on retesting hypotheses; be skeptical of results in any field where a large number of researchers are pursuing solutions to the same problem. In short, demand robustness checks on everything, and make sure that what's being shown is not just an artifact of your specific data set. Good lessons that all applied researchers should have tattooed across their proverbial chests already, but nonetheless a nice thing to be reminded of.


The structure of human knowledge

Following up on Jesse's post:

After dinner today I told Brenda that I wanted a network map of all papers ever written so we could see where the biggest gaps in human knowledge were. In moments she had us browsing the site well-formed.eigenfactor.org looking at a coarser approximation of my dream (see picture).

I highly recommend any academic or casual intellectual browse the highly interactive site, it is simply too interesting, beautiful and [maybe] important to ignore.

Perhaps the two most striking observations one can make from simple visual inspection are that (1) biologists write a lot of papers and (2) social sciences/mathematics/computer science are extremely insular (observe the big "hole" in the network picture).

I'll let the data speak for itself (please please look at the site); but the only thing I'll say is that if anyone wanted to create a new field, bridging the social and physical sciences looks like a conspicuously good place to start.


Interdisciplinarity isn't easy

I'm currently wrapping up edits on a paper on interdisciplinarity and research success and came across a pretty cool paper for my lit review with a couple of choice quotes:
"[T]he young scientist, who grows up in the midst of a competition between university departments and amidst competition within his department, who inherits the individualistic research tradition and graduates without having had an opportunity to develop skills in cooperative thinking and collaborative study, is poorly prepared to participate in the activities of a committee or a research team.
"Over and above this pressure from the outside, there are important scientific grounds why interdisciplinary (and interdepartmental) research should become a greater concern of the universities. The assertion that institutes of an interdisciplinary character will be associated more often with industrial enterprises than with universities may be correct in the statistical sense, but it should not imply that cooperative research is an industrial prerogative.
"For the research worker who has grown up in the traditional departmentalized university and who is anxious to take part in interdisciplinary work, the first step is to get a bird's-eye view of the neighboring fields and to obtain familiarity with the problems which are currently the foci of interest. However, text-book acquaintance is not enough; some contact with actual work methods is essential. "
I think these are all reasonably fair points. The problem is that this article is from the December 8th, 1944 issue of Science. Reading through it and noting how little change there's been in the language around "interdisciplinary research" is fairly shocking, and makes me appreciate not only how difficult working outside of one discipline is, but also the extent to which the road towards doing quality work combining the social and natural sciences (which is probably the best way to describe the specific flavor of interdisciplinarity that Sol and I are in) has been a long and arduous one.

Not that there hasn't been any progress, mind you. The flip side of interdisciplinary work is field creation. Climatology, neuropsychology, behavioral economics, and an untold number of additional academic disciplines were all, at one point, "inter-discipline." It's just nice to be reminded of the fact that establishing those fields isn't easy.


Cleanup cost from Haiti earthquake

I was struck by this NYT article stating that just the cost of debris removal in Haiti is estimated at $1B
By late summer, however, the need to tackle the earthquake damage directly became so glaring that some initial steps were taken. The government tendered its very first cleanup contract to Mr. Perkins’s Haiti Recovery Group. Worth $7.5 million to $13.5 million — nobody would be more precise — the contract represented a minuscule piece of a debris removal operation expected to cost $1.2 billion.
This is an incredible sum, when you consider that income for the entire country is about $7B

Data from Timetric.

To view this graph, please install Adobe Flash Player.

The cost of cleanup is just what is paid for the removal of damaged property and excludes the value of the lost assets and lost revenue due to the destruction of assets.

Sustainable Development PhD Research Symposium Oct 28th

Here is the Earth Institute announcement for an upcoming event at Columbia University on October 28th.

Sustainable Development Ph.D. Research Symposium

Date: Thursday, October 28th
Time: 4.00-6.30 PM
Location: Jed D. Satow Conference Room; 5th Floor, Lerner Hall; Columbia University

The first annual Sustainable Development Ph.D. Research Symposium has been scheduled for Thursday, October 28th, 4.00-6:30 PMin the Jed D. Satow Conference Room (5th Floor, Lerner Hall).
The purpose of the symposium is to showcase the pioneering research of the Ph.D. Program in Sustainable Development’s 5th and 6th year doctoral candidates to the wider Columbia University community and invited guests from the private sector, governments, and NGOs. It will be attended by: the Director of the Earth Institute, Prof. Jeffrey Sachs; the Dean of the School of International and Public Affairs, Prof. John Coatsworth; the program’s Academic Directors, Prof. John Mutter and Prof. Wolfram Schlenker; and many of the program’s core faculty.
The symposium will consist of a series of short presentations, followed by short question and answer sessions and a general discussion.  The topics of the presentations will cover many of the most pressing global sustainability issues, including the global economic losses to tropical cyclones, the future of India’s dwindling groundwater resources, drought and floods and poverty traps in rural Mexico, the effects of climate change on Indian agriculture and the connections between Malaria ecology and demography.

(1) Chandra Kiran Krishnamurthy: A Quantile Regression Approach to Estimating Climate Change Impacts on Crop Yields. [Link to Chandra's profile].
(2) Gordon McCord: Improving Empirical Estimation of Demographic Drivers: Fertility, Child Mortality & Malaria Ecology. [Link to Gordon's profile].
(3) Anisa Khadem Nwachuku: The Materialism Paradigm: Neither Sustainable, nor Development. [Link to Anisa's profile].
(4) Marta Vicarelli: Exogenous Income Shocks and Consumption Smoothing, Strategies Among Rural Households in Mexico. [Link to Marta's profile].
(5) Jesse Anttila-Hughes: The Long Term Fertility Impacts of Natural Disasters. [Link to Jesse's profile].
(6) Ram Fishman: How Low Will It Go?  The Future of Groundwater Tables and Irrigation in India. [Link to Ram's profile].
(7) Solomon Hsiang: Global Economic Losses to Tropical Cyclones. [Link to Solomon's profile].
(8) Aly Sanoh: Municipal Taxes, Income, and Rainfall Uncertainty. [Link to Aly's profile].


Political institutions evolve only incrementally

Image copyright Nature 2010
I really enjoyed this interesting and creative article in Nature this week:

Rise and fall of political complexity in island South-East Asia and the Pacific

Thomas E. Currie, Simon J. Greenhill, Russell D. Gray, Toshikazu Hasegawa & Ruth Mac

Abstract: There is disagreement about whether human political evolution has proceeded through a sequence of incremental increases in complexity, or whether larger, non-sequential increases have occurred. The extent to which societies have decreased in complexity is also unclear. These debates have continued largely in the absence of rigorous, quantitative tests. We evaluated six competing models of political evolution in Austronesian-speaking societies using phylogenetic methods. Here we show that in the best-fitting model political complexity rises and falls in a sequence of small steps. This is closely followed by another model in which increases are sequential but decreases can be either sequential or in bigger drops. The results indicate that large, non-sequential jumps in political complexity have not occurred during the evolutionary history of these societies. This suggests that, despite the numerous contingent pathways of human history, there are regularities in cultural evolution that can be detected using computational phylogenetic methods.

Probably the most fun aspect of the article is that they take some clever statistical techniques to evaluate a complex archeological/anthropological data set that is never analyzed with quantitative methods. Their main result is that it seems very unlikely that political institutions develop complexity with leaps and bounds. Rather, institutions appear to evolve very slowly with only incremental changes in complexity.  However, they find that the reverse is not true: sometimes it looks as though complex political institutions may collapse rapidly to much simpler institutions.

They basically use maximum-likelihood techniques to estimate the probability that political systems make specific transitions in a simple markov-chain model of political institutions.  Because the probability of certain transitions between nodes in the chain seem very unlikely, they are able to rule out certain models of political development (leaps and bounds) that otherwise seemed plausible.  Very interesting.


African maritime infrastructure

A recent article by Michael Lyon Baker in Foreign Affairs make an unconventional but interesting point: African maritime ports are in bad shape and this produces a bottleneck for trade and economic growth.
Africa has the least efficient ports in the world. Dwell times -- the amount of time a ship must stay in port -- for the loading and unloading of cargo exceed global averages by several days and are nearly quadruple those of Asian ports, thus driving up shipping costs through delays. No African port can be found on the list of the top 70 most productive in the world. As a result, shipping companies send smaller, older, and cheaper ships to Africa in an effort to reduce their losses.
A number of factors are to blame: poor harbor maintenance, bureaucratic red tape, inadequate maritime law enforcement, and lax security....
Baker points out that poor infrastructure in ports means that shipping companies reallocate their fleet in such a way that further slows trade:
Moreover, many African ports cannot handle ships of median size due to infrastructure limitations. Meanwhile, the global shipping industry has been modernizing its fleets, scrapping obsolete vessels for newer mega-carriers. This means that shipping companies will continue deploying their remaining smaller and slower ships for transport to and from Africa.... In this environment, companies producing goods in Africa cannot reliably or efficiently get their wares to market. This plays a large role in explaining why Africa garners only 2.7 percent of global trade despite its cheap labor force, cheap commodities, and proximity to major markets.
However, if governments really want to get shipping companies to use their bigger and better ships in Africa, it is probably the case that infrastructure and market opportunities would need to not only improve, but surpass opportunities elsewhere (such as China).  This may not be realistic.

Probably too much of the article focuses on piracy, but his mention of labor market opportunities seems to agree with many of the things we know about predation:
A crucial means of countering piracy, oil theft, narcotics trafficking, and terrorism along the African coast is creating better job opportunities. Development approaches usually focus on land-based projects such as agriculture. To be sure, African states need to attract investors for manufacturing companies, but they must also entice shipping companies to get those goods to market. The poor state of Africa’s maritime sector is the most important factor stifling the continent’s growth.
I think it is extremely interesting to think about African maritime infrastructure, because Baker is right that most of the policy focus in Africa is land-based. Although I'm not sure we have enough evidence that dilapidated ports are the "most important factor" for African development. It is notoriously difficult to pin down the effects of infrastructure on economic development since infrastructure is frequently developed in response to increasing demands, itself a result of development.

Two more data websites

Permanently added to Meta-Resources:

Michael Kremer's data set website (economics)

The EPA Data Finder for US environmental and pollution data.