I put together some links for economists. These are standard tools and services, and you may have seen most of them already.
But I’m sure this list is missing something you know of. So I’m asking you to have a look and contribute to posterity.
Adam Ozimek asks, “Can Economics Change Your Mind?”
In this skeptical view, economists and those who read economics are locked into ideologically motivated beliefs—liberals versus conservatives, for example—and just pick whatever empirical evidence supports those pre-conceived positions. I say this is wrong and solid empirical evidence, even of the complicated econometric sort, changes plenty of minds.
Just to make myself clear, only a human himself can change his mind, and economics can’t. And since the question is basically about learning, not economics, I reformulate the question accordingly: Can Learning Change Your Mind?
The rest turns out to be simple. If I want to change my mind big time, I take an issue I know nothing about and read some research. There will be surprises.
But if I happen to discover big surprises in the area of my competence, I become suspicious. Evidences don’t drop down like Newtonian apples. They flow like a river. Then learning is a flow, too. It’s a continuous process that brings no surprises if you learn constantly.
Where does continuity come from? First, from discounting new studies. New studies have standard limitations, even being factually and methodologically correct. Most frequent limitations concern long-term relationships, external validity, general equilibrium effects. Second, from the nature of the economy itself. Research in economics often speaks in yes-no terms, while economic processes are continuous. For marketing purposes, researchers formulate questions and answers like “Does X cause Y?”, which is a yes-no question tested with regressions. But causation is not about p-values in handpicked models. Causation is also the degree of impact. But this degree jumps wildly even within different specifications of a single model. That means I need a lot of similar studies to change my mind about X and Y.
Removing one letter from Bertrand Russell, “One of the symptoms of approaching nervous breakdown is the belief that one work is terribly important.”
Going back to Adam’s initial (yes-no) question, I’d say yes, some economists “are locked into ideologically motivated beliefs,” and yes, some economists produce knowledge that other people can learn from. These two groups overlap, but it’s no obstacle to good learning.
PS: In his post, Adam Ozimek also asked to submit studies that changed one’s mind. Since I see mind-changing potential as a function of novelty, I’d recommend a simple source of mind-changing studies: visit RePEc’s top cited studies list and read carefully the papers you haven’t read yet. There will be surprises.
Complex systems happen to have probabilistic, rather than deterministic, properties, and this fact made social sciences look deficient next to the real hard sciences (as if hard sciences predicted weather or earthquakes better than economics predicts financial crises).
What’s the difference? When today’s results differ from yesterday’s results, it’s not because authors get science wrong. In most cases, these authors just study slightly different contexts and may obtain seemingly contradictory results. Still, to benefit from generalization, it’s easier to take “slightly different” as “the same” and treat the result as a random variable.
In this case, “contradictions” get resolved surprisingly simply: by replicating the experiment and collecting more data. In the end, you have a distribution of the impact over studies, not simply of the impact within a single experiment.
Schoenfeld and Ioannidis show the dispersion of results in cancer research (“Is everything we eat associated with cancer?”, 2012):
Each point indicates a single study that estimates how much a given ingredient may contribute to getting cancer. The bad news: onion is more useful than bacon. The good news: we can say that a single estimate is never enough. A single study is not systematic, even after a peer review.
The recent attempt to reproduce 100 major studies in psychology confirms the divergence: “A large portion of replications produced weaker evidence for the original findings.” In this case, they also found a bias in reporting.
Economics also has reported effects varying across papers. By Eva Vivalt (2014):
This chart reports how conditional cash transfers affect different outcomes, measured in standard deviations. Cash transfers exemplify the rule: The impact is often absent, otherwise it varies (sometimes for the worse). For more, check this:
The dispersion of the impact is not a unique feature of randomized trials. Different estimates from similar papers appear elsewhere in economics. It’s most evident in literature surveys, especially those with nice summary tables: Xu, “The Role Of Law In Economic Growth”; Olken and Pande, “Corruption in Developing Countries”; DellaVigna and Gentzkow, “Persuasion.”
The problem, of course, is that the evidences are as good as their reproducibility. And reproducibility requires data on demand. But how many authors can claim that their results can be replicated? A useful classification by Levitt and List (2009):
Naturally-occurring data occurs naturally, so we cannot replicate it at will. A lot of highly cited papers rely on the naturally occurring data from the right-hand side methods. That’s, in fact, the secret. When an author finds a nice natural experiment that escapes accusations of endogeneity, his paper becomes an authority on the subject. (Either because natural experiments happen rarely and competing papers aren’t appearing, or because the identification looks so elegant that the readers fall in love with the paper.) But this experiment is only one point on the scale. It doesn’t become reliable just because we don’t know where the other points would be.
The work based on controlled data gets less attention, but this work gives a systematic account of causal relationships. Moreover, these papers cover the treatments of a practical sort: well-defined actions that NGOs and governments can implement. This seamless connections is a big burden, since taping “naturally-occurring” evidences to policies adds another layer of distrust between policy makers and researchers. For example, try to connect this list of references in labor economics to government policies.
Though to many researchers “practical” is an obscene word (and I don’t emphasize this quality), reproducible results are a scientific issue. What do reproducible results need? More cooperation, simpler inquiries, and less reliance on chance. More on this is coming.
In A Fistful of Dollars, Clint Eastwood challenges Gian Maria Volonte with the words, “When a man with .45 meets a man with a rifle, you said, the man with a pistol’s a dead man. Let’s see if that’s true. Go ahead, load up and shoot.”
That’s the right words to challenge big data, which recently reappeared in economics debates (Noah Smith, Chris House via Mark Thoma). Big data is a rifle, but not necessary winning. Economists must have special reasons to abandon small datasets and start messing with more numbers.
Unlike business, which only recently discovered the sexiest job of the future, economists do analytics for the last 150 years. They deal with “big data” for half of that period (I count from 1940, when the CPS started). So, how can the new big data be useful to them?
Let’s find out what big data offers. First of all, more information, of course. Notable cases include predicting the present with Google and Joshua Blumenstock’s use of mobile phones in development economics. Less notable cases encounter the same problem: a decline in the quality of data. Compare long surveys that development economists collect when they do experiments versus what Facebook dares to ask its most loyal users. Despite Facebook having 1.5 bn. observations, economists end up with much better evidences. That’s not about depth alone. Social scientists ask clearer questions, find representative respondents, and take nonresponses seriously. If you do a responsible job, you have to construct smaller but better samples like this.
Second, big data comes with its own tools, which, like econometrics, are deeply rooted in statistics but ignorant about causation:
The slogan is: to predict and to classify. But economics does care about cause and effect relations. Data scientists dispense with these relations because the professional penalty for misidentification is lower than in economics. And, honestly, at this stage, they have more important problems to solve. For example, much time still goes into capacity building and data wrangling.
The task requires predicting whether a person survived the crash or not. The chart says that children had more chances to survive than old passengers, while for the rest age didn’t matter. A regression tree captures this nonlinearity in the age, while logit regression does not. Hence, the big data tool does better than the economics tool.
But an economist who remembers to “always plot the data” is ready for this. Like with other big data tools, it’s useful to know the trees, but something similar is already available on the econometrics workbench.
There’s nothing ideological in these comments on big data. More data potentially available for research is better than less data. And data scientists do things economists can’t. The objection is the following. Economists mostly deal with the problems of two types. Type One, figuring out how n big variables, like inflation and unemployment, interact with each other. Type Two, making practical policy recommendations for the people who typically read nothing more than executive summaries. While big data can inform top-notch economics research, these two problems are easier to solve with simple models and small data. So, a pistol turns out to be better than a rifle.
Chris Blattman noted that economists lack evidences on important policies. That’s true for foreign aid programs, which Chris mentioned. But defined broadly, policy making in poor countries can source evidences from elsewhere. NBER alone supplies 20 policy-relevant papers each week. And so does the World Bank, which recently studied its own economy:
About 49 percent of the World Bank’s policy reports … have the stated objective of informing the public debate or influencing the development community. … About 13 percent of policy reports were downloaded at least 250 times while more than 31 percent of policy reports are never downloaded. Almost 87 percent of policy reports were never cited.
In an ideal world, policy makers would read more and adjust their economies to the models we already know thanks to the decades of thorough research. This is not happening because policy makers are managers, not researchers with well-defined problems. And, as Russell Ackoff said, managers do not solve problems they manage messes.
Governments have their own limits of the messes they can deal with. Economists in research, on the contrary, simplify messes to tractable models. Let’s take one of the most powerful ideas in development: structural changes. Illustrated by Dani Rodrik:
The negative slope of the fitted values says that people moved from more productive to less productive industries over time. Which, of course, is a bad structural change. We can blame politics for this or whatever, but it’s hard to separate politics and, say, incompetence.
Emerging (and not so emerging) economies love the idea of employment growing in productive sectors. Even reports on sub-Saharan Africa regularly refer to knowledge economies and high-value-added industries. But in the end, many nations have something like that picture. (Oh, those messes.)
Did economists learn to manage messes better than public officials? Well, that’s what development economics is trying to accomplish. While it doesn’t include “general equilibrium effects” (the key takeaway from Daron Acemoglu), the baseline for judging the effectiveness of assessment programs is way below this and other criticisms. The baseline is eventually the intuition of a local public official—and policies that he would otherwise enact, if there were no evidence based programs.
Instead, these assessment programs provide simple tools for clear objectives. NGOs and local governments can expect something specific.
What about big evidence based policies? They require capacity building. At the extreme, look at the healthcare reform in the United States. Before anything happened, the Affordable Care Act already contained 1,000 pages. Implementation was difficult. Could a government in Central Africa implement a comparable reform, even having abundant evidences on healthcare in the US or at home?
Economists start to ignore the problem of implementation as the potential impact of their insights increases. The connection is not direct, but if you simplify a complex problem, you get a solution for the simplified problem. Someone else must complete the solution, and that becomes the problem.
Not today’s news, but The New York Times and other major newspapers have a great influence on public policy. Key government documents, like budgets and congressional hearings, mention “the new york times” about 38,000 times (see Government Printing Office website with Google Search), while an economist from the top 10—who studies his topic for decades but doesn’t write for the public regularly—gets mentioned in the same documents just 10 times. So, even if economists know something (like a big secret about inflation), it’s up to the media to deliver this knowledge.
Where do the media source their information from, in turn? The Times explains:
If someone doesn’t see the black line for the references to researchers, it’s because the line had been drawn over the zero axis.
(That was a post of envy, of course.)