Addis taxi economics

A is in his early 20s, and he's my go-to taxi driver. He speaks good conversational English, which he picked up in part through being befriended by a Canadian couple who lived in Ethiopia for a while. Addis traffic is crazy but a bit more forgiving than some cities I've seen -- there don't seem to be many real traffic rules, but there's more deference to other drivers. "A, you drive like a pro," my friend says. "How long have you been driving?" "Oh, just six months!" (We gulp.) In Addis "taxi" is used to refer to both ancient minibuses that drive set routes throughout the city and to traditional blue-and-white cars -- often ancient-er -- that will take you wherever you want to go. (Google Images of Addis taxis here.) A's car is the latter type, an old model that breaks down often and has one window handle you have to pass around to roll down each window.

Minibuses charge a flat rate on pre-specified routes, usually just a few Birr (ie, less than $0.20 US), but the personal taxis can charge much more. So having a few reliable drivers' cell numbers is helpful because the prospect of your continued business helps ensure that you'll get a better price for each ride.

Regarding taxis more generally: always negotiate a fare before you get in. Depending on the mood of the driver, current traffic and road construction, and the evident wealth, race, or nationality of the prospective passenger, the prices quoted will vary widely. I was once quoted 60 Birr and 150 Birr as starting prices ($3.50 and $8.80 US) by two drivers standing right next to each other!

Almost all of the taxi business seems to come from internationals and upper-class Ethiopians. Thus, taxis often congregate around the neighborhoods, hotels, and restaurants frequented by these groups. You'll also get quoted a higher starting price if you're seen coming out of a nice hotel than if you pick a cab just around the corner.

Starting prices definitely differ by race as well. (Here I cite conversations with Chinese-American and Bengali-American friends living in Addis.) Drivers will generally assume you're from America (if you're Caucasian), China (if you're East Asian), and India (if you're South Asian) and charge accordingly. White people get the highest starting prices, whereas if they assume you're Chinese or Indian the starting price will be about 70% of the white price. This is, of course, entirely anecdotal, so econ PhD students take note: there's some fascinating research to be done on differential pricing of initial and final fares for internationals living in Addis. In economics this differential pricing is called price discrimination (which can actually be good for consumers as it allows producers to provide services to a broader range of people, who often have different preferences and ability to pay).

A doesn't own his taxi, and says that most drivers don't either. Instead, he rents/leases his from a man who owns many taxis. That guy made enough money ("he is rich now!") that he now goes to Dubai to buy other cars to import into Ethiopia. (Dubai is the go-to place for importing many things here.) A pays the owner a flat rate to have the taxi for a 10-day period, with more or less automatic renewals as long as he's doing well enough to keep paying the fee. If he gets sick or wants to take a day off he has to pay that day's rental fee out of earnings from another day, so A gets up at 6 am and drives until after midnight. Seven days a week.

A is only six months into the job, but he's already looking for the next gig. He aspires to work as a tour guide -- better pay and better hours, he says. And, I think, less risk of injury: almost all the taxis in Addis are from an era before airbags and seatbelts became commonplace. I think A would be a great tour guide -- I hope it works out.

Stats lingo in econometrics and epidemiology

Last week I came across an article I wish I'd found a year or two ago: "Glossary for econometrics and epidemiology" (PDF from JSTOR, ungated version here) by Gunasekara, Carter, and Blakely. Statistics is to some extent a common language for the social sciences, but there are also big variations in language that can cause problems when students and scholars try to read literature from outside their fields. I first learned epidemiology and biostatistics at a school of public health, and now this year I'm taking econometrics from an economist, as well as other classes that draw heavily on the economics literature.

Friends in my economics-centered program have asked me "what's biostatistics?" Likewise, public health friends have asked "what's econometrics?" (or just commented that it's a silly name). In reality both fields use many of the same techniques with different language and emphases. The Gunasekara, Carter, and Blakely glossary linked above covers the following terms, amongst others:

  • confounding
  • endogeneity and endogenous variables
  • exogenous variables
  • simultaneity, social drift, social selection, and reverse causality
  • instrumental variables
  • intermediate or mediating variables
  • multicollinearity
  • omitted variable bias
  • unobserved heterogeneity

If you've only studied econometrics or biostatistics, chances are at least some of these terms will be new to you, even though most have roughly equivalent forms in the other field.

Outside of differing language, another difference is in the frequency with which techniques are used. For instance, instrumental variables seem (to me) to be under-used in public health / epidemiology applications. I took four terms of biostatistics at Johns Hopkins and don't recall instrumental variables being mentioned even once! On the other hand, economists just recently discovered randomized trials. (Now they're more widely used) .

But even within a given statistical technique there are important differences. You might think that all social scientists doing, say, multiple linear regression to analyze observational data or critiquing the results of randomized controlled trials would use the same language. In my experience they not only use different vocabulary for the same things, they also emphasize different things. About a third to half of my epidemiology coursework involved establishing causal models (often with directed acyclic graphs)  in order to understand which confounding variables to control for in a regression, whereas in econometrics we (very!) briefly discussed how to decide which covariates might cause omitted variable bias. These discussions were basically about the same thing, but they differed in terms of language and in terms of emphasis.

I think an understanding of how and why researchers from different fields talk about things differently helps you to understand the sociology and motivations of each field.  This is all related to what Marc Bellemare calls the ongoing "methodological convergence in the social sciences." As research becomes more interdisciplinary -- and as any applications of research are much more likely to require interdisciplinary knowledge -- understanding how researchers trained in different academic schools think and talk will become increasingly important.

Group vs. individual uses of data

Andrew Gelman notes that, on the subject of value-added assessments of teachers, "a skeptical consensus seems to have arisen..." How did we get here? Value-added assessments grew out of the push for more emphasis on measuring success through standardized tests in education -- simply looking at test scores isn't OK because some teachers are teaching in better schools or are teaching better-prepared students. The solution was to look at how teachers' students improve in comparison to other teachers' students. Wikipedia has a fairly good summary here.

Back in February New York City released (over the opposition of teachers' unions) the value-added scores of some 18,000 teachers. Here's coverage from the Times on the release and reactions.

Gary Rubinstein, an education blogger, has done some analysis of the data contained in the reports and published five posts so far: part 1, part 2, part 3, part 4, and part 5. He writes:

For sure the 'reformers' have won a battle and have unfairly humiliated thousands of teachers who got inaccurate poor ratings. But I am optimistic that this will be be looked at as one of the turning points in this fight. Up until now, independent researchers like me were unable to support all our claims about how crude a tool value-added metrics still are, though they have been around for nearly 20 years. But with the release of the data, I have been able to test many of my suspicions about value-added.

I suggest reading his analysis in full, or at least the first two parts.

For me one early take-away from this -- building off comments from Gelman and others -- is that an assessment might be a useful tool for improving education quality overall, while simultaneously being a very poor metric for individual performance. When you're looking at 18,000 teachers you might be able to learn what factors lead to test score improvement on average, and use that information to improve policies for teacher education, recruitment, training, and retention. But that doesn't mean one can necessarily use the same data to make high-stakes decisions about individual teachers.

What happened?

What happened during the 2007-8 financial crisis? Here's a reading from my classes that I think may be of interest to a broader audience: "Getting up to Speed on the Financial Crisis: A One-Weekend-Reader's Guide" by Gary B. Gorton and Andrew Metrick, writing in January 2012 (PDF from NBER). Covering 16 sources (academic papers, a few reports by institutions, and Congressional testimony by Bernanke) Gorton and Metrick provide a timeline of the crisis, some historical perspective on past banking crises, the build-up to this crisis, phases of the crisis itself, and government responses.

It's just 34 pages and interesting throughout -- the only shortcoming is that the PDF is rendered in Calibri.

A related article is Andrew Lo's "Reading About the Financial Crisis: A 21-Book Review" (PDF), which includes this:

No single narrative emerges from this broad and often contradictory collection of interpretations, but the sheer variety of conclusions is informative, and underscores the desperate need for the economics profession to establish a single set of facts from which more accurate inferences and narratives can be constructed.

Discussions of causes are difficult when you don't agree on the simpler matters of what actually happened -- which speaks to the importance of trying to simply get at (as Gorton and Metrick are trying to do) an account of what happened.

More on microfoundations

Last month I wrote a long-ish post describing the history of the "microfounded" approaches to macroeconomics. For a while I was updating that post with links to recent blog posts as the debate continued, but I stopped after the list grew too long. Now Simon Wren-Lewis has written two more posts that I think are worth highlighting because they come from someone who is generally supportive of the microfoundations approach (I've found his defense of the general approach quite helpful), but who still has some specific critiques. The end of his latest post puts these critiques in context:

One way of reading these two posts is a way of exploring Krugman’s Mistaking Beauty for Truth essay. I know the reactions of colleagues, and bloggers, to this piece have been quite extreme: some endorsing it totally, while others taking strong exception to its perceived targets. My own reaction is very similar to Karl Smith here. I regard what has happened as a result of the scramble for austerity in 2010 to be in part a failure of academic macroeconomics. It would be easy to suggest that this was only the result of unfortunate technical errors, or political interference, and that otherwise the way we do macro is basically fine. I think Krugman was right to suggest otherwise. Given the conservative tendency in any group, an essay that said maybe there might just be an underlying problem here would have been ignored. The discipline needed a wake-up call from someone with authority who knew what they were talking about. Identifying exactly what those problems are, and what to do about them, seems to me an important endeavour that has only just begun.

Here are his two posts:

  1. The street light problem: "I do think microfoundations methodology is progressive. The concern is that, as a project, it may tend to progress in directions of least resistance rather than in the areas that really matter – until perhaps a crisis occurs."
  2. Ideological bias: "In RBC [Real Business Cycle] models, all changes in unemployment are voluntary. If unemployment is rising, it is because more workers are choosing leisure rather than work. As a result, high unemployment in a recession is not a problem at all.... If anyone is reading this who is not familiar with macroeconomics, you might guess that this rather counterintuitive theory is some very marginal and long forgotten macroeconomic idea. You would be very wrong."

Name that quote

I'm reading Evolving Economics, a highly-regarded history of economic thought by Agnar Sandmo. I thought one tidbit early on was quite interesting: it comes in the course of a discussion of a once-common method of charging tolls based on the weight of carriages. Sandmo quotes an economist who recommended different rates for luxury versus other transport.

Thus, "...the indolence and vanity of the rich is made to contribute in a very easy manner to the relief of the poor, by rendering cheaper the transportation of heavy goods to all the different parts of the country."

Who said that? Answer below the fold...

Adam Smith, the patron saint of laissez-faire economists everywhere, in The Wealth of Nations no less. Sandmo comments, "This formulation is notable both for its substantial content and for the tone of its language, which leaves one with no doubt as to the author's sympathy and social concerns."

Up to speed: microfoundations

[Admin note: this is the first of a new series of "Up to speed" posts which will draw together information on a subject that's either new to me or has been getting a lot of play lately in the press or in some corner of the blogosphere. The idea here is that folks who are experts on this particular subject might not find anything new; I'm synthesizing things for those who want to get up to speed.]

Microfoundations (Wikipedia) are quite important in modern macroeconomics. Modern macroeconomics really started with Keynes. His landmark General Theory of Employment, Interest and Money (published in 1936) set the stage for pretty much everything that has come since. Basically everything that came before Keynes couldn't explain the Great Depression -- or worse yet how the world might get out of it -- and Keynes' theories (rightly or wrongly) became popular because they addressed that central failing.

One major criticism was that modern macroeconomic models like Keynes' were top-down, only looking at aggregate totals of measures like output and investment. That may not seem too bad, but when you tried to break things down to the underlying individual behaviors that would add up to those aggregates, wacky stuff happens. At that point microeconomic models were much better fleshed out, and the micro models all started with individual rational actors maximizing their utility, assumptions that macroeconomists just couldn't get from breaking down their aggregate models.

The most influential criticism came from Robert Lucas, in what became known as the Lucas Critique (here's a PDF of his 1976 paper). Lucas basically argued that aggregate models weren't that helpful because they were only looking at surface-level parameters without understanding the underlying mechanisms. If something -- like the policy environment -- changes drastically then the old relationships that were observed in the aggregate data may no longer apply. An example from Wikipedia:

One important application of the critique is its implication that the historical negative correlation between inflation and unemployment, known as the Phillips Curve, could break down if the monetary authorities attempted to exploit it. Permanently raising inflation in hopes that this would permanently lower unemployment would eventually cause firms' inflation forecasts to rise, altering their employment decisions.

Economists responded by developing "micro-founded" macroeconomic models, ones that built up from the sum of microeconomic models. The most commonly used of these models is called, awkwardly, dynamic stochastic general equilibirum (DGSE). Much of my study time this semester involves learning the math behind this. What's the next step forward from DGSE? Are these models better than the old Keynesian models? How do we even define "better"? These are all hot topics in macro at the moment. There's been a recent spat in the economics blogosphere that illustrates this -- what follows are a few highlights.

Back in 2009 Paul Krugman (NYT columnist, Nobel winner, and Woodrow Wilson School professor) wrote an article titled "How Did Economists Get It So Wrong?" that included this paragraph:

As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth. Until the Great Depression, most economists clung to a vision of capitalism as a perfect or nearly perfect system. That vision wasn’t sustainable in the face of mass unemployment, but as memories of the Depression faded, economists fell back in love with the old, idealized vision of an economy in which rational individuals interact in perfect markets, this time gussied up with fancy equations. The renewed romance with the idealized market was, to be sure, partly a response to shifting political winds, partly a response to financial incentives. But while sabbaticals at the Hoover Institution and job opportunities on Wall Street are nothing to sneeze at, the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess.

Last month Stephen Williamson wrote this:

[Because of the financial crisis] There was now a convenient excuse to wage war, but in this case a war on mainstream macroeconomics. But how can this make any sense? The George W era produced a political epiphany for Krugman, but how did that ever translate into a war on macroeconomists? You're right, it does not make any sense. The tools of modern macroeconomics are no more the tools of right-wingers than of left-wingers. These are not Republican tools, Libertarian tools, Democratic tools, or whatever.

A bit of a sidetrack, but this prompted Noah Smith to write a long post (that is generally more technical than I want to get in to here) defending the idea that modern macro models (like DSGE) are in fact ideologically biased, even if that's not their intent. Near the end:

So what this illustrates is that it's really hard to make a DSGE model with even a few sort-of semi-realistic features. As a result, it's really hard to make a DSGE model in which government policy plays a useful role in stabilizing the business cycle. By contrast, it's pretty easy to make a DSGE model in which government plays no useful role, and can only mess things up. So what ends up happening? You guessed it: a macro literature where most papers have only a very limited role for government.

In other words, a macro literature whose policy advice is heavily tilted toward the political preferences of conservatives.

Back on the main track, Simon Wren-Lewis, writing at Mainly Macro, comes to Krugman's defense, sort of, by saying that its conceivable that an aggregate model might actually be more defensible than a micro-founded one in certain circumstances.

This view [Krugman's view that aggregate models may still be useful] appears controversial. If the accepted way of doing macroeconomics in academic journals is to almost always use a ‘fancier optimisation’ model, how can something more ad hoc be more useful? Coupled with remarks like ‘the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth’ (from the 2009 piece) this has got a lot of others, like Stephen Williamson, upset. [skipping several paragraphs]

But suppose there is in fact more than one valid microfoundation for a particular aggregate model. In other words, there is not just one, but perhaps a variety of particular worlds which would lead to this set of aggregate macro relationships....Furthermore, suppose that more than one of these particular worlds was a reasonable representation of reality... It would seem to me that in this case the aggregate model derived from these different worlds has some utility beyond just one of these microfounded models. It is robust to alternative microfoundations.

Back on the main track, Krugman followed up with an argument for why its OK to use both aggregate and microfounded models.

And here's Noah Smith writing again, "Why bother with microfoundations?"

Using wrong descriptions of how people behave may or may not yield aggregate relationships that really do describe the economy. But the presence of the incorrect microfoundations will not give the aggregate results a leg up over models that simply started with the aggregates....

When I look at the macro models that have been constructed since Lucas first published his critique in the 1970s, I see a whole bunch of microfoundations that would be rejected by any sort of empirical or experimental evidence (on the RBC side as well as the Neo-Keynesian side). In other words, I see a bunch of crappy models of individual human behavior being tossed into macro models. This has basically convinced me that the "microfounded" DSGE models we now use are only occasionally superior to aggregate-only models. Macroeconomists seem to have basically nodded in the direction of the Lucas critique and in the direction of microeconomics as a whole, and then done one of two things: either A) gone right on using aggregate models, while writing down some "microfoundations" to please journal editors, or B) drawn policy recommendations directly from incorrect models of individual behavior.

The most recent is from Krugman, wherein he says (basically) that models that make both small and big predictions should be judged more on the big than the small.

This is just a sampling, and likely a biased one as there are many who dismiss the criticism of microfoundations out of hand and thus aren't writing detailed responses. Either way, the microfoundations models are dominant in the macro literature now, and the macro-for-policy-folks class I'm taking at the moment focuses on micro-founded models (because they're "how modern macro is done").

So what to conclude? My general impression is that microeconomics is more heavily 'evolved' than macroeconomics. (You could say that in macro the generation times are much longer, and the DNA replication bits are dodgier, so evolving from something clearly wrong towards something clearly better is taking longer.)

Around the same time that micro was getting problematized by Kahneman and others who questioned the rational utility-maximizing nature of humans, thus launching behavioral economics revolution -- which tries to complicate micro theory with a bit of reality -- the macroeconomists were just  getting around to incorporating the original microeconomic emphasis on rationality. Just how much micro will change in the next decades in response to the behavioral revolution is unclear, so expecting troglodytesque macro to have already figured this out is unrealistic.

A number of things are unclear to me: just how deep the dissatisfaction with the current models is, how broadly these critiques (vs. others from different directions) are endorsed, and what actually drives change in fields of inquiry. Looking back in another 30-40 years we might see this moment in time as a pivotal shift in the history of the development of macroeconomics -- or it may be a little hiccup that no one remembers at all. It's too soon to tell.

Updates: since writing this I've noticed several more additions to the discussion:

Overhead at WWS

Last week a classmate of mine at the Woodrow Wilson School shared this story, which I in turn share with permission.

Today G and I were doing our impossible econ problem set in Schultz Café. It was about consumer surplus so there were some nice geometric properties, and it was fun finding the areas of the triangles and trapezoids. I said out loud, "I don't how to do it the econ way, G. I only know how to do it the 9th-grade-math way."

Guess who was sitting right behind us?

Christopher Sims.

For background, search this article for the paragraph on Sims and the SAT. Maybe this is why economics folks might think we public policy students aren't so great at math? Related: how to fight impostor syndrome.

Understatement of the day

It is ironic that modern capitalist societies engage in public campaigns to urge individuals to be more attentive to their health, while fostering an economic ecosystem that seduces many consumers into an extremely unhealthy diet. According to the United States Centers for Disease Control, 34% of Americans are obese. Clearly, conventionally measured economic growth – which implies higher consumption – cannot be an end in itself.

That's economist Ken Rogoff, asking "Is Modern Capitalism Sustainable?". And of course it goes beyond ironic; it's tragic. Changes in policy that address that "economic ecosystem" itself are usually considered outside the realm of public health, which is exactly why public health folks have to (and do) engage on broader policy issues.

Pick your model

I enjoyed this piece by Dani Rodrik at Project Syndicate:

Indeed, though you may be excused for skepticism if you have not immersed yourself in years of advanced study in economics, coursework in a typical economics doctoral program produces a bewildering variety of policy prescriptions depending on the specific context. Some of the frameworks economists use to analyze the world favor free markets, while others don’t. In fact, much economic research is devoted to understanding how government intervention can improve economic performance. And non-economic motives and socially cooperative behavior are increasingly part of what economists study.

As the late great international economist Carlos Diaz-Alejandro once put it, “by now any bright graduate student, by choosing his assumptions….carefully, can produce a consistent model yielding just about any policy recommendation he favored at the start.” And that was in the 1970’s! An apprentice economist no longer needs to be particularly bright to produce unorthodox policy conclusions.

Nevertheless, economists get stuck with the charge of being narrowly ideological, because they are their own worst enemy when it comes to applying their theories to the real world. Instead of communicating the full panoply of perspectives that their discipline offers, they display excessive confidence in particular remedies – often those that best accord with their own personal ideologies.

Is it that bad? Well, statistician Kaiser Fung of the blog Numbers Rule Your World) says that it's actually much worse and that Rodrik doesn't go far enough as he compares Rodrik's point with a critique of economic modeling in Emanuel Derman's new book Models Behaving Badly (which I haven't read yet):

My own view, informed by years of building statistical models for businesses, is more sympathetic with Derman than Rodrik. There is no way that economic (by extension, social science) models can ever be similar to physics models. Derman draws the comparison in order to disparage economics models. I prefer to avoid the comparison entirely.

The insurmountable challenge of social science models, which constrains their effectiveness, is that the real drivers of human behavior are not measurable. What causes people to purchase goods, or vote for a particular candidate, or become obese, or trade stocks is some combination of desire, impulse, guilt, greed, gullibility, inattention, curiosity, etc. We can't measure any of those quantities accurately.

Testing treatments in policy

The students at the Woodrow Wilson School have a group blog on public policy called 14 Points. I've been helping promote the blog for a while but just got around to writing my first submission this week. It's titled "Testing Treatments: Building a culture of evidence in public policy". Here's an excerpt:

Similar lessons can be gleaned from the history of surgical response to breast cancer. In The Emperor of All Maladies (2010), a new history of cancer, oncologist Siddhartha Mukherjee chronicles the history of such failed interventions as the radical mastectomy. Over a period of decades this brutal procedure – removing the breasts, lymph nodes, and much of the chest muscles – became the tool of choice for surgeons treating breast cancer. In the 1970s rigorous trials comparing radical mastectomy to more limited procedures showed that this terribly disfiguring procedure did not in fact help patients live longer at all. Some surgeons refused to believe the evidence – to believe it would have required them to acknowledge the harm they had done. But eventually the radical mastectomy fell from favor; today it is quite rare. Many similar stories are included in a free e-book titled Testing Treatments (2011).

As a society we’ve come to accept that medical devices should be tested by the most rigorous and neutral means possible, because the stakes are life and death for all of us. Thousands of people faced with deadly illnesses volunteer for clinical trials every year. Some of them survive while others do not, but as a society we are better off when we know what actually works. For every downside, like the delay of a promising treatment until evidence is gathered properly, there is an upside – something we otherwise would have thought is a good idea is revealed not to be helpful at all.

Under normal circumstances most new drugs are weeded out as they face a gauntlet of tests for safety and efficacy required before FDA licensure. The stories of the humanitarian-exemption stent and the radical mastectomy are different because these procedures became more widely used before there was rigorous evidence that they helped, though in both cases there were plenty of anecdotes, case studies, and small or non-controlled studies that made it look like they did. This haphazard, post-hoc testing is analogous to how policy in many other fields, from welfare to education, is developed. Many public policy decisions have considerable impacts on our livelihoods, education, and health. Why are we not similarly outraged by poor standards of evidence that leads to poor outcomes in other fields?

Read the rest at 14 Points, and check out the posts by my classmates.

Why study economics?

As a follow-up to my last post on values and humility in economics, I thought the following video (which Mankiw shared on his blog) of Steve Marglin talking about heterodox economics is great:

Marglin gives two reasons to study mainstream economics in his talk. One of them resonates strongly with me because it is part of why I'm studying economics right now; it is the language of power. While I'm more interested in morbidity and mortality than I am in interest rates, much of health policy, aid policy, and development policy is done by -- or strongly influenced by -- those who speak the language of economics. For the other reason Marglin gives (and of course, there are many others) you'll have to watch the talk (it's good).

Values and humility in economics

Greg Mankiw is a Harvard economist, former chairman of President Bush's Council of Economic Advisers, and currently an advisor to the Romney presidential campaign. He teaches a large introductory economics course at Harvard and writes both the widely used Principles of Economics and a blog that displays the same crisp, eminently-readable prose as his textbook. In a show of solidarity with the Occupy Boston movement, some of his students walked out of that class earlier this year. Much has been written about the walkout  (Update: here's the students' open letter and a response that outlines why walking out of this particular class isn't the most informed move.) Still I wanted to highlight Mankiw's column in yesterday's New York Times, titled  "Know what you're protesting." I share some of his reaction:

But my second reaction was sadness at how poorly informed the Harvard protesters seemed to be. As with much of the Occupy movement across the country, their complaints seemed to me to be a grab bag of anti-establishment platitudes without much hard-headed analysis or clear policy prescriptions. Ironically, the topic of the lecture that the protesters chose to boycott was economic inequality, including a discussion of recent trends and their causes.

Fair. But later in the piece Mankiw says something that really rankles (emphasis added):

I don’t claim to be an economist of Paul Samuelson’s stature. (Probably no one alive can.) But like him, I have written a textbook that has introduced millions of students to the mainstream economics of today. If my profession is slanted toward any particular world view, I am as guilty as anyone for perpetuating the problem.

Yet, like most economists, I don’t view the study of economics as laden with ideology. Most of us agree with Keynes, who said: “The theory of economics does not furnish a body of settled conclusions immediately applicable to policy. It is a method rather than a doctrine, an apparatus of the mind, a technique for thinking, which helps the possessor to draw correct conclusions.”

That is not to say that economists understand everything. The recent financial crisis, economic downturn and meager recovery are vivid reminders that we still have much to learn. Widening economic inequality is a real and troubling phenomenon, albeit one without an obvious explanation or easy solution. A prerequisite for being a good economist is an ample dose of humility.

I'll preface my reaction to this with my own dose of humility: my studies at the Woodrow Wilson School this semester are my first exposure to serious economics, and I'm realizing every day that I have ever more to learn. I think it can be helpful to approach a field with fresh eyes so I hope my thoughts here won't be entirely discredited by my fresh arrival to the dismal science.

That said, No! This seems like denial, pure and simple. My impression is that one of the areas where economists have most often failed to display humility is when thinking of and talking about the interaction of their values and methodologies.  Yes, economics has epistemological limitations, but these are equaled or surpassed by its axiological limitations, and may be more consequential because -- unlike with the more readily acknowledged methodological shortcomings -- economists themselves don't always make clear the values implicit in their worldviews. I think economists and those who are impacted by their views (ie, everyone else) would benefit from clearer statements of how values impact economics.

Mankiw's textbook does very briefly address the political philosophies underlying views on income redistribution, from utilitarianism to liberalism to libertarianism (pages 442-3 in the 5th edition). But this is halfway through the text and only in the context of the chapter on "income inequality and poverty." In reality, your views on maximizing utility for all versus (at another extreme) only caring about how policies affect the poorest have an impact on pretty much every piece of welfare economics. Here from what I can tell Mankiw is quite mainstream -- when considering the effects of a particular policy using the tools of welfare economics, the underlying philosophical preferences are almost always assumed. The conclusions of those studies are then touted as positive statements ("Policy X is bad for the economy") when that may or may not be true, depending on whether you share the same fundamental normative roots.

Prof. Mankiw spoke at Princeton on October 20 (you can view the lecture here) and it was a well-presented talk. His remarks were broad and intelligent, though maybe a bit constrained by the fact that he is associated with a presidential campaign and thus can't rock the boat too much (even with a disclaimer that his remarks were his own). In that talk Mankiw similarly began by emphasizing the need for economists to show greater humility in light of recent failures; he then proceeded to discuss a good number of specific policy recommendations with quite a bit of confidence. My biggest question coming out of the lecture was how to square the Mankiw who calls for greater humility from economists with the Mankiw who makes policy prescriptions. If we don't know with certainty what the impacts of particular policies will be or how to do more than tweak the performance or recovery of an economy, why not start with the policies least likely to do harm to the most vulnerable members of society? That would generally be my preference, growing out of my own nascent political philosophy.

In his textbook (5th edition page 35) Mankiw evenly explicitly notes that "Economists give conflicting advice sometimes because they have different values." This is true, and if anything it is under-emphasized. Elsewhere Mankiw has been more direct, contrasting the philosophies of Nozick and Rawls and noting how those might result in very different policy prescriptions on taxation. Mankiw ends up closer to Nozick and so it's no surprise that his policy prescriptions are for lower corporate taxes (re-emphasized in the Princeton talk as one of the things on which he very strongly agrees with Romney).

How is this anything but a heavy dose of ideology being injected into economics? How can we square this with the Mankiw who says he doesn't see the study of economics as "laden with ideology"? Part of the problem is that there are figures such as Mankiw who are concurrently serious researchers on scientific questions within economics and proponents of normative preferences in the political sphere. Can the outside observer tell when an economist is being one and not the other? Can economists realize this in themselves? When you couch these preferences in the language of economics without making the underlying values explicit, it's hard to believe that the field is not laden with ideology. To the extent that he doesn't even recognize how these value statements pervade the field, Mankiw is -- in his own words -- as guilty as anyone for perpetuating the problem.

Beyond economic growth

Jean Dreze and Amartya Sen, writing in Outlook India ("Putting Growth In Its Place") argue that India should see economic growth as a means to an end and not the end in and of itself. Whether you see GDP growth or human development as an end will shape whether India's recent history is an extraordinary history or something much more grim:

So which of the two stories—unprecedented success or extraordinary failure—is correct? The answer is both, for they are both valid, and they are entirely compatible with each other... Indeed, economic growth is not constitutively the same thing as development, in the sense of a general improvement in living standards and enhancement of people’s well-being and freedom. Growth, of course, can be very helpful in achieving development, but this requires active public policies to ensure that the fruits of economic growth are widely shared, and also requires—and this is very important—making good use of the public revenue generated by fast economic growth for social services, especially for public healthcare and public education.

On a more specific social policy, they comment on how conditional cash transfers -- the hot social policy of the moment (of the decade?) -- worked in Latin America precisely because some level of public social services were already in place, and the condition of receiving the transfer was often utilizing those services. They argue that India can't shortcut around investing in social services, skipping straight to the transfers and waiting for things to get better.

In Latin America, conditional cash transfers usually act as a complement, not a substitute, for public provision of health, education and other basic services. The incentives work for their supplementing purpose because the basic public services are there in the first place. In Brazil, for instance, basic health services such as immunisation, antenatal care and skilled attendance at birth are virtually universal. The state has done its homework—almost half of all health expenditure in Brazil is public expenditure, compared with barely one quarter (of a much lower total of health expenditure) in India. In this situation, providing incentives to complete the universalisation of healthcare may be quite sensible. In India, however, these basic services are still largely missing, and conditional cash transfers cannot fill the gap.

Cash transfers are increasingly seen as a potential cornerstone of social policy in India, often based on a distorted reading of the Latin American experience in this respect. There are, of course, strong arguments for cash transfers (conditional or unconditional) in some circumstances, just as there are good arguments for transfers in kind (such as midday meals for school children). What is remarkably dangerous, however, is the illusion that cash transfers (more precisely, “conditional cash transfers”) can replace public services by inducing recipients to buy health and education services from private providers. This is not only hard to substantiate on the basis of realistic empirical reading; it is, in fact, entirely contrary to the historical experience of Europe, America, Japan and East Asia in their respective transformation of living standards. Also, it is not how conditional cash transfers work in Brazil or Mexico or other successful cases today.

Here's the rest of the article.

"Small Changes, Big Results"

The Boston Review has a whole new set of articles on the movement of development economics towards randomized trials. The main article is Small Changes, Big Results: Behavioral Economics at Work in Poor Countries and the companion and criticism articles are here. They're all worth reading, of course. I found them through Chris Blattman's new post "Behavioral Economics and Randomized Trials: Trumpeted, Attacked, and Parried." I want to re-state a point I made in the comments there, because I think it's worth re-wording to get it right. It's this: I often see the new randomized trials in economics compared to clinical trials in the medical literature. There are many parallels to be sure, but the medical literature is huge, and there's really one subset of it that offers better parallels.

Within global health research there are a slew of large (and not so large), randomized (and other rigorous designs), controlled (placebo or not) trials that are done in "field" or "community" settings. The distinction is that clinical trials usually draw their study populations from a hospital or other clinical setting and their results are thus only generalizable to the broader population (external validity) to the extent that the clinical population is representative of the whole population; while community trials are designed to draw from everyone in a given community.

Because these trials draw their subjects from whole communities -- and they're often cluster-randomized so that whole villages or clinic catchment areas are the unit that's randomized, rather than individuals -- they are typically larger, more expensive, more complicated and pose distinctive analytical and ethical problems. There's also often room for nesting smaller studies within the big trials, because the big trials are already recruiting large numbers of people meeting certain criteria and there are always other questions that can be answered using a subset of that same population. [All this is fresh on my mind since I just finished a class called "Design and Conduct of Community Trials," which is taught by several Hopkins faculty who run very large field trials in Nepal, India, and Bangladesh.]

Blattman is right to argue for registration of experimental trials in economics research, as is done with medical studies. (For nerdy kicks, you can browse registered trials at ISRCTN.) But many of the problems he quotes Eran Bendavid describing in economics trials--"Our interventions and populations vary with every trial, often in obscure and undocumented ways"--can also be true of community trials in health.

Likewise, these trials -- which often take years and hundreds of thousands of dollars to run -- often yield a lot of knowledge about the process of how things are done. Essential elements include doing good preliminary studies (such as validating your instruments), having continuous qualitative feedback on how the study is going, and gathering extra data on "process" questions so you'll know why something worked or not, and not just whether it did (a lot of this is addressed in Blattman's "Impact Evaluation 2.0" talk). I think the best parallels for what that research should look like in practice will be found in the big community trials of health interventions in the developing world, rather than in clinical trials in US and European hospitals.

Microfinance Miscellany

I had a conversation yesterday with a PhD student friend (also in international health) about the evaluation of microcredit programs. I was trying to summarize -- off the top of my head, never a good idea! -- recent findings, and wasn't able to communicate much. But I did note that like many aid and development programs, you get a pretty rosy picture when you're using case studies or cherry-picked before-and-after evaluations without comparison groups. So I was trying to describe what it looks like to do rigorous impact evaluations that account for the selection biases you get if you're just comparing people who self-select for taking out loans versus controls. After that discussion, I was quite happy to come across this new resource on David Roodman's blog: yesterday DFID released a literature review of microfinance impacts in Africa.

On a related note, Innovations for Poverty Action hosted a conference on microfinance evaluation last October, and many of the presentations and papers presented are available here. The "What Are We Learning About Impacts?" sections includes presentations given by Abhijit Banerjee (PDF) and Dean Karlan (PDF) of Yale. Worth reading.