The Ambrosini Critique

Delong:

One of the embarrassing dirty little secrets of economics is that there is no such thing as economic theory properly so-called. There is simply no set of foundational bedrock principles on which one can base calculations that illuminate situations in the real world. Biologists know that every cell runs off instructions for protein synthesis encoded in its DNA. Chemists start with what the Heisenberg and Pauli principles plus the three-dimensionality of space tell us about stable electron configurations. Physicists start with the four fundamental forces of nature. Economists have none of that. The “economic principles” underpinning their theories are a fraud–not bedrock truths but mere knobs twiddled and tunes so that th right conclusions come out of the analysis.

What are the “right” conclusions? It depends on what type of economist you are, for [there] are two types. One type chooses, for non-economic and non-scientific reasons, a political stance and a political set of allies, and twiddles and tunes their assumptions until they come out with conclusions that please their allies and their stance. The other type takes the carcass of history, throws it into the pot, turns up the heat, and boils it down, hoping that the bones and the skeleton that emerge will teach lessons and suggest principles that will be useful to voters, bureaucrats, and politicians as they try to guide our civilization as it slouches toward utopia.

There’s a third type of economist that cooks a pot of history to discover interesting patterns for the sake of discovering interesting patterns. Ironically, given the existence of the first type of economist, you should only listen to the third type for policy advice.

Here’s the cross-state plot of the effect of immigrants on native wages:

This time you’ll notice that I plotted the changes in the proportion of immigrants against the change in wages (these are changes from the 1990 to 2000 censuses). In effect, I’m controlling for fixed features of states. This makes the effect size (a slope of about 0.25 and statistically insignificant) a little closer to the average effect size reported by Longhi, Nijkamp and Poot.

I’m jumping ahead of my own narrative, but why does it seem ingrained in people (including economists) that immigrants must be competing with native workers? Why is complementarity so much more unlikely in people’s priors?

We don’t have this bias when it comes to new technologies. In fact, we have the opposite bias. Despite the popularity of the phrase “creative destruction”, quality ladder models are much less common than expanding variety models of growth. Anyone who suggests there may be a negative externality of R&D in competitive industries gets jumped all over.

Can’t immigration be seen as a type of innovation? Immigrants bring more with them than their L.

Angus Deaton:

Structural estimation is useful, not only for the estimates (whose credibility is often undercut by the panoply of supporting assumptions that are required to obtain them), but for understanding the empirical predictions of the theory.

Do we do this with DSGEs? We try to replicate known patterns in the data, but I don’t think we use these models to find potential new patterns.

In one of my myriad of yet-unfinished papers I report things I call “simulated comparative statics”. I can’t solve the model, so I have the computer generate numerical derivatives. People don’t like ‘em. Can I market them better? “Simulated theorems”? “Generated predictions”? “Computed hypotheses”?

When you first take a grad level economics class, it occurs to you the discipline appears to be “social physics”. Where you thought the discipline was “a tool for understanding and social criticism and an instrument for intellectual enlightenment” you begin to suspect its “a tool for social engineering and an instrument for progressive politics”. The suspicion morphs into a full blown conspiracy theory when you learn the math behind the welfare theorems.

But look at how normative analysis is actually done in economics. Consumers of models compare their intuitions about how policy should work against the prescriptions of the model and use this difference to evaluate the model. “Wow, this model says money is too loose right now. I know that’s wrong, why does the model get it wrong?”

One of the reasons to reject real business cycle models is because they provide no room for macroeconomic management. The dissatisfaction with exogenous growth models is that they’re silent about institutions. There’s a heavy bias against models of wage inequality that rely on transient features of workers; the problems *must* be structural. Unemployment must be responsive to aggregate demand manipulations and so they can’t be a result of real frictions. And so on.

Despite these examples, after a couple years of doing this, I’ve come to believe that this is an acceptable way to evaluate models. There is no THE MODEL of the economy and our intuitions are basically trustworthy when it comes to social arrangements. Our intuitions are data. If a model gives counterintuitive policy implications, it bears the burden of showing us why our intuitions are incorrect.

Why is it always assumed that “philosophy” or “pictures and talk” are substitutes for mathematical analysis? I mean, they’re compliments right?

Robustness is a fledgling literature in Macro. The primary concern of robust analysis is that we don’t know the exact model of the economy and this model uncertainty has policy implications. Also, the math is neat.

Uncertainty about what the correct model is causes optimal policy to give heavy weight to worse-case scenarios.

Ellison and Sargent found a pretty neat application of robustness. The Fed staff are a bunch of academics who believe they know the true model of the economy. They use the “true” model to make forecasts and those forecasts are usually really good. The FOMC is the policy making branch of the Fed. They take the staff’s forecasts, produce their own forecasts and then set policy. It turns out the FOMC’s forecasts are worse than the staff’s. Those dumb policy makers, right?

Wrong. It turns out that because the FOMC is uncertain about the true model of the economy, they won’t take the staff’s model as the true model. The optimal response to this uncertainty would lead them to worry more about worse-cases. As Ellison and Sargent say, the FOMC “can be bad forecasters and good policymakers”.

… every time I run into a statement like this: “But all interesting models involve unrealistic simplifications, which is why they must be tested against data.”

We can know things a priori, but we can’t know how important those things are a priori. A model can tell us how a particular mechanism operates, but it can’t tell us how important that mechanism is. Effect size can only be determined by looking at the data.

I’m sure there’s something wrong with my brain that can’t internalize this fact. I just read Landsburg’s Big Questions and this was his major theme. (BTW, I liked the book and here’s a review at /.)

“Let’s look at several of the problems that happened over the past few years in the financial sector, and see how legislative efforts have attempted to address them. (Spoiler alert: not very well.)”

Mike tells this story:

Our regulator’s goal isn’t to make a system in which there are never failures but a system in which failures are cleaned up in an orderly and nondisruptive fashion. Like an elaborate game of Jenga, even removing the smallest piece can collapse the entire structure, and regulators need to be able to remove any piece without having the entire real economy collapse.

This is a great story. Can it be rationalized? What objective function of regulators would lead them to aim to prevent “disruption”? A disruption now and again might be good by standard measures of welfare. Does Mike have a public choice model in mind? Do bailouts improve the chances of re-election?

Is the system as precarious as Mike suggests? Bailouts are sold to the public using a counterfactual that is rarely, if ever, observed. Namely, if the bailed out institutions were allowed to fail, it would have produced an undesirable level of systematic risk. When have failed banks caused systematic risk? The Great Depression had bank runs which caused a bad situation to get worse. But bank runs weren’t, by far, leading the causal chain. You need deflationary expectations, no deposit insurance and no branch banking to get those sorts of bank runs.

Besides, is the recent banking crisis evidence of the system’s precariousness or evidence against it? A long time transpired between banking crises in the US.

UPDATE: What is systemic risk?

Angus Deaton on the “project evaluation” craze:

Randomized controlled trials cannot automatically trump other evidence, they do not occupy any special place in some hierarchy of evidence, nor does it make sense to refer to them as “hard” while other methods are “soft”. These rhetorical devices are just that; a metaphor is not an argument… thirty years of project evaluation in sociology, education and criminology was largely unsuccessful because it focused on whether projects work instead of on why they work.

and

The wholesale abandonment in American graduate schools of price theory in favor of infinite horizon intertemporal optimization and game theory has not been a favorable development for young empiricists. Empiricists and theorists seem further apart now than at any period in the last quarter century. Yet reintegration is hardly an option because without it there is no chance of long term scientific progress.

and after listing a number of papers that he thinks have a good mix of theory and data, he says:

In all of this work, the project, when it exists at all, is the embodiment of the theory that is being tested and refined, not the object of evaluation in its own right, and the field experiments are a bridge between the laboratory and the analysis of “natural” data.

Science is about finding underlying mechanisms. Its not about testing hypotheses and

[H]eterogeneity is not a technical problem, but a symptom of something deeper, which is the failure to specify causal models of the processes we are examining. This is the methodological message of this lecture, that technique is never a substitute for the business of doing economics.

I use the “project evaluation” and “experiment” rhetoric in one of my papers. I might have to rethink the organization of that paper…