As far as who should get it, who I’d like to see getting it, I would go with Sargent and/or Hall. Some recognition for the incomplete market model/precautionary behavior might be nice too, but it’s probably not “happening” enough. Maybe a Barro, Romer & Acemoglu combo for growth? Ill-timed? Too ironic?

Interview with Thomas Sargent

New York University economist on the state of modern macro, polar models of bank regulation and the problem of persistently high unemployment.

Here‘s my previous post on his “where to draw the line” paper.

Policy Distortions and Aggregate Productivity with Heterogeneous Plants

We formulate a version of the growth model in which production is carried out by heterogeneous plants and calibrate it to US data. In the context of this model we argue that differences in the allocation of resources across heterogeneous plants may be an important factor in accounting for cross-country differences in output per capita. In particular, we show that policies which create heterogeneity in the prices faced by individual producers can lead to sizeable decreases in output and measured TFP in the range of 30 to 50 percent. We show that these effects can result from policies that do not rely on aggregate capital accumulation or aggregate relative price differences. More generally, the model can be used to generate differences in capital accumulation, relative prices, and measured TFP.

Beyond the main point, this paper is interesting because it puts U-shaped average cost firms to good use in a macro model.

It’s also comforting that whether people make any sense is highly autocorrelated across contributions, so once you skim the site the first time you should have a pretty good idea of who you’re better off simply ignoring. I only hope that the guest list will expand over time and, in particular, that it’s going to include a SED point of view, which is currently largely missing.

What Determines Productivity? [NBER WP]

Economists have shown that large and persistent differences in productivity levels across businesses are ubiquitous. This finding has shaped research agendas in a number of fields, including (but not limited to) macroeconomics, industrial organization, labor, and trade. This paper surveys and evaluates recent empirical work addressing the question of why businesses differ in their measured productivity levels. The causes are manifold, and differ depending on the particular setting. They include elements sourced in production practices—and therefore over which producers have some direct control, at least in theory—as well as from producers’ external operating environments. After evaluating the current state of knowledge, I lay out what I see are the major questions that research in the area should address going forward.

The discussion is authoritative but the fundamental measurement-theory disconnect that’s at work here is making everything look like speculation. (Due to the fact that TFP is measured as a residual, there’s always the possibility that you’re picking up unmeasured or mis-measured inputs or external effects.)

In any case, one take-away here, especially for macro- oriented people, is that while macro models usually take aggregate TFP as being exogenous and potentially highly volatile, in reality it’s probably the result of extensive selection (entry/exit), market structure and endogenous management choices all of which are, needless to say, responsive to the state of the economy and policy in particular.

Aydemir and Borjas point out that if the data is subject to substantial measurement error, that in itself is enough to bias the estimate downwards in absolute value (i.e. towards zero) and they explore this possibility in this paper:

Attenuation Bias in Measuring the Wage Impact of Immigration [PDF]

Although economic theory predicts an inverse relation between relative wages and immigration-induced supply shifts, it has been difficult to document such effects.  The weak evidence may be partly due to sampling error in a commonly used measure of the supply shift, the immigrant share of the workforce.  After controlling for permanent factors that determine wages in specific labor markets, little variation remains in the immigrant share.  We find significant sampling error in this measure of supply shifts in Canadian and U.S. Census data.  Correcting for the resulting attenuation bias can substantially increase existing estimates of the wage impact of immigration.

Even so, as Lucas once famously pointed out, there aren’t any second-generation busboys so maybe this is one issue where taking the dynastic or long-run point of view is maybe more appropriate.

Trends in U.S. hours and the labor wedge

From 1980 until 2007, U.S. average hours worked increased by thirteen percent, due to a large increase in female hours. At the same time, the U.S. labor wedge, measured as the discrepancy between a representative household’s marginal rate of substitution between consumption and leisure and the marginal product of labor, declined substantially. We examine these trends in a model with heterogeneous households: married couples, single males and single females. Our quantitative analysis shows that the shrinking gender wage gaps and increasing labor income taxes observed in U.S. data are key determinants of hours and the labor wedge. Changes in our model’s labor wedge are driven by distortionary taxes and non-distortionary factors, such as cross-sectional differences in households’ labor supply and productivity. We conclude that the labor wedge measured from a representative household model partly reflects imperfect household aggregation.

Convergence in Macroeconomics: The Labor Wedge [PDF] is one of the best introductions to the “labor wedge”.

The results in the Cociuba and Ueberfeldt paper are somewhat “mechanical” since they don’t attempt to model the gender wage gap, yet conditional on a given (declining) sequence of this gap and historical tax data, their model gives largely the right answers: women work more, men work slightly less, while total labor supply actually increases.

In a baseline model, higher effective marginal tax rates (the joint effect of consumption and labor income taxes) tend to reduce consumption and labor supply while increasing leisure, by making marginal hours not worth working. In the data, taxes are increasing over the 1980-2000s period which means that either (a) the baseline model of labor supply is all wrong; or (b) there were countervailing changes in the after-tax wage due to reduced discrimination, changes in productivity or otherwise changes in the composition of the population. This paper makes (b) work quantitatively, imperfectly, under a common set of assumptions about functional forms and parameter values.

This paper deals with the long-run stylized facts of aggregate labor supply but the same method could be applied at quarterly frequencies. We might be then tempted to conclude that recessions are periods of increased discrimination against women! Joking aside, I’m trying to make the obvious point that there’s probably a lot more to the labor wedge than just taxes and secular changes in gender discrimination.

Business Cycles With A Common Trend in Neutral and Investment-Specific Productivity [PDF]

This paper identifies a new source of business-cycle fluctuations. Namely, a common stochastic trend in neutral and investment-specific productivity. We document that in U.S. postwar quarterly data total factor productivity (TFP) and the relative price of investment are cointegrated. We show theoretically that TFP and the relative price of investment are cointegrated if and only if neutral and investment-specific productivity share a common stochastic trend. We econometrically estimate an RBC model augmented with a number of real rigidities and driven by a multitude of shocks. We find that in the context of our estimated model, innovations in the common stochastic trend explain a sizable fraction of the unconditional variances of output, consumption, investment, and hours.

They can replicate this stylized fact by augmenting a standard RBC model with a production function for the investment good of the form  where is the resulting quantity of the investment good and is the quantity of consumption good used for investment (the input). The relative price of the investment good is then given, under competitive markets, by

where is the stationary and is the nonstationary component of TFP.

Production of the consumption good is done with the usual Cobb-Douglas technology

where is capacity utilization, is the stationary component of the productivity shock and is the permanent component.

By imposing in their estimation that TFP and the relative price of investment are cointegrated, they can replicate the stylized fact. Following the ML estimation of the log-linear version of the model they argue that this common shock is (potentially?) a major driving force for aggregate fluctuations. This might be the case but I am more curious about what this all means.

As I understand their paper, their claim is that following a positive technological shock, not only is TFP higher but that the “effective productivity” for investment goods is even higher. This goes well with what we know about the long-run trend in durable good prices, e.g. Gordon 1990, but does little in terms of explaining business cycles.

TFP is a black box which fluctuates considerably when measured by the Solow residual and, given the results in this paper, durables productivity turns out to be an even greater mystery. Even worse, the conventional Solow residual—adjusted for capacity utilization or not—is most likely a very poor measure of TFP. Gali [PDF] has a summary and references for this view.

Did Schmitt-Grohe and Uribe really “find a new source of business-cycle fluctuations” if they can’t tell us what it is?

We assemble a dataset on technology adoption in 1000 bc, 0 ad, and 1500 AD for the predecessors to today’s nation states. Technological differences are surprisingly persistent over long periods of time. Our most interesting, strong, and robust results are for the association of 1500 AD technology with per capita income and technology adoption today. We also find robust and significant technological persistence from 1000 BC to 0 AD, and from 0 AD to 1500 AD. The evidence is consistent with a model where the cost of adopting new technologies declines sufficiently with the current level of adoption. (JEL N10, O33, O47)

and…

Amplification Mechanisms in Liquidity Crises
Arvind Krishnamurthy

I describe two amplifications mechanisms that operate during crises and discuss the benefits of policy given each mechanism. The first mechanism involves asset prices and balance sheets. A negative shock to agents’ balance sheets causes them to liquidate assets, lowering prices, further deteriorating balance sheets and amplifying the shock. The second mechanism involves investors’ Knightian uncertainty. Unusual shocks to untested financial innovations increase agents’ uncertainty about their investments, causing them to disengage from markets and amplifying the crisis. Liquidity provision by the central bank alleviates the crisis in both mechanisms. Ex ante policies such as liquidity/capital requirements may also be beneficial. (JEL E32, E44, G01, G21, G32)

An IDEAS search will reveal working paper versions of these articles, which are freely downloadable, although there’s no guarantee regarding their freshness.

Where to Draw the Line: Stability versus Efficiency

What kinds of assets should financial intermediaries be permitted to hold and what kinds of liabilities should they be allowed to issue? This paper reviews how tensions involving stability versus efficiency and regulation versus laissez-faire have for centuries run through macroeconomic analysis of these questions.

The paper mixes benchmark models (e.g. Diamond and Dybvig), discussion of recent, 2010 papers and history.

Some of the questions discussed:

• Which institutions should be covered by deposit insurance? To what sort of regulatory regime should they be subjected?
• What are the benefits and risks in extending deposit insurance to institutions previously not covered by it, in the event of a run?
• What assets should the central bank hold?
• How is money different from bonds? How is it different from credit? How should it be different? What is money?
• How can bundling assets improve economic performance and how can it go wrong?

The way Sargent puts everything together makes it clear that these issues were there for a long time. On the other hand, the focus within the profession and the field where elsewhere.

SED is almost surely the best conference/event of the year. If you like RED—and let’s face it, what kind of monster would not worship RED—then you’re going to love SED.

Also, you might be asking But why aren’t you, Agent Continuum, there at the SED, getting your  mind blown? To which I can only reply, good question, Sir, very good question! Oh, well, there’s always next year (Belgium?!).

Global Climate Change and the Resurgence of Tropical Disease: An Economic Approach

We study the impact of global climate change on the prevalence of tropical diseases using a heterogeous agent dynamic general equilibrium model. In our framework, households can take actions (e.g., purchasing bednets or other goods) that provide partial protection from disease. However, these actions are costly and households face borrowing constraints. Parameterizing the model, we explore the impact of a worldwide temperature increase of 3C. We find that the impact on disease prevalence and especially output should be modest and can be mitigated by improvements in protection efficacy.

All is well, the technical exercise goes through fine, but I’m not convinced at all that market incompleteness is relevant here. Is there any evidence that an inability to borrow is what keeps the marginal household from purchasing mosquito bed nets?

Unfortunately, the evidence we do have suggests that financial market imperfections are not all that important, at least not compared with what looks like aggressive discounting and self-centered preferences:

Moonshine or the Kids?

[...] The Obamzas have no mosquito net, even though they have already lost two of their eight children to malaria. They say they just can’t afford the $6 cost of a net. Nor can they afford the $2.50-a-month tuition for each of their three school-age kids. [...] But Mr. Obamza and his wife, Valerie, do have cellphones and say they spend a combined $10 a month on call time. In addition, Mr. Obamza goes drinking several times a week at a village bar, spending about $1 an evening on moonshine. By his calculation, that adds up to about $12 a month — almost as much as the family rent and school fees combined. [...] Other villagers said that Mr. Obamza drinks less than the average man in the village (women drink far less). Many other men drink every evening, they said, and also spend money on cigarettes. [...]

Economists are uncomfortable with either tweaking preferences or with “behavioral” models, but I’m not a card-carrying member of the profession (yet!) so I might as well suggest their potential relevance here.

Yet another macro model with endogenous mortality is nice, but I think we would get a lot more out of a model of intra-household allocation, given evidence like this. I don’t mean to pick on Gollin and Zimmermann, it just that I was underwhelmed by the motivation for their modeling choices.

Side note, posting should revert to the previous regime, which was “whenever I get around to it”. The Sunday evening (or Monday morning) arrival of the NEP-DGE mailing list is the usual shock.

Posting has been light. I’ve been taking some time off. I’m sure that my 4 or so return visitors will understand.

Update: I see Athreya removed the text from his personal web page. Too bad. He was making many sensible points and he was telling the truth about the pompous political hacks plaguing the Economics blogging community.

Update #2: The drama continues. First of all, someone made a Scribd copy of the text. Second, I’m not surprised by the pathetic reactions of the bloggers targeted. Their response only makes Athreya’s points more obvious, if that’s even possible.

Finite State Markov-chain Approximations to Highly Persistent Processes

This is potentially important since most of the shocks used in DSGE macroeconomic models have to have a highly persistent AR(1) structure and, traditionally, this was approximated via the Tauchen method for numerical applications.  (This paper is from the Vol XIII, issue 3 of RED, which was published online a while ago.)

Industry Evidence on the Effects of Government Spending

This paper investigates industry-level effects of government purchases in order to shed light on the transmission mechanism for government spending on the aggregate economy. We begin by highlighting the different theoretical predictions concerning the effects of government spending on industry labor market equilibrium. We then create a panel data set that matches output and labor variables to shifts in industry-specific government demand. The empirical results indicate that increases in government demand raise output and hours, but lower real product wages and productivity. Markups do not change as a result of government demand increases. The results are consistent with the neoclassical model of government spending, but they are not consistent with the New Keynesian model of the effects of government spending.

I want to better understand what they do about the potential endogeneity of government actions and why a different instrument gave different results in an earlier version. To be honest, I tend to zone-out when I look over regression tables and their discussion but this seems worth the effort.

On a side-note, I subscribe only to NEP-DGE and not NEP-MAC since MAC has a horribly low signal-to-noise ratio and most of the time DGE is a subset of MAC, the good papers I would like to read anyway. This paper is actually one of those rare exceptions that make it worth scanning the MAC archives once in a while.

The Diamond-Dybvig model has three basic elements:

• Long-term investments that are more productive than short-term investments;
• A random need for liquidity on the part of an individual; and
• Private information about an individual’s need for liquidity.

With these elements, Diamond and Dybvig (DD hereafter) show that it is desirable for people to pool their funds and jointly invest in productive long-term investments, while allowing individuals to withdraw their funds on demand, even before the end of the life of the long-term investments. Furthermore, they show that it is also desirable to set payouts for early withdrawals high enough so that if every person in the pool withdrew his funds early, there would not be enough funds available to meet every withdrawal.

I didn’t know (of this) model before I saw this so it’s all news to me. If you’re familiar with this line of research, please let us know what you make of it in the comment section!

Financial Factors in Economic Fluctuations [IDEAS]

We augment a standard monetary DSGE model to include a banking sector and financial markets. We fit the model to Euro Area and US data. We find that agency problems in financial contracts, liquidity constraints facing banks and shocks that alter the perception of market risk and hit financial intermediation — ‘financial factors’ in short — are prime determinants of economic fluctuations. They have been critical triggers and propagators in the recent financial crisis. Financial intermediation turns an otherwise diversifiable source of idiosyncratic economic uncertainty, the ‘risk shock’, into a systemic force.

This is a huge model: there are many types of agents, bank deposits and money generating  liquidity services, shares of the wage bill and capital rent have to be borrowed and entrepreneurs rent out the capital stock subject to a “risk” shock, under a wealth constraint. There is a “financial accelerator” where good shocks mean high wealth and high lending, and therefore more investment and even better wealth. There’s also a “debt deflation” feature where nominal liabilities and assets are sensitive to the price level, and therefore, through the wealth constraint, so is activity. These are features from many papers integrated into one model.

There are 16 shocks and the model is nonstationary. There is a straightforward transformation that is stationary, though, and they estimate a log-linearized version of the stationary transformation, with state-of-the-art methods I don’t really understand (honestly).

I’ll skip the gory details and get to the point by discussing the main shock that drives the results: in the model entrepreneurs purchase installed physical capital and then observe an idiosyncratic shock to the value of this capital. They can observe it costlessly but banks (who finance part of the initial capital purchase) can observe it only costly (costly state verification). The variance of this shock is what the authors call “risk shock”. This time-variant variance has a complex structure, AR(1) with an anticipated component (p lags) to its shock (the shock of the shock, not the shock, of course!).

is the risk shock, the variance of the distribution out of which, in every period, each entrepreneur draws her idiosyncratic shock.

The authors claim that this shock, , can account for most of the aggregate fluctuations in the US and the EU in recent times, including the “Great Recession”, in other words, recessions happen when entrepreneurial activity becomes riskier. Does this make any sense? Is there any corroborating Finance evidence regarding this risk shock? Because of its high explanatory power it’s correlated with everything, but is it structural? I don’t know.

There are a lot of free parameters and many lags in this exercise, so the cynical bottom line is that it better fit well, but beyond that, what sort of evidence or criteria we might invoke to evaluate this result?

Can this result be replicated with a much leaner model, in term of features, or by traditional calibration standards? I’ll follow-up with a post if I find someone trying.

His talk stresses fundamentals: expectations of future taxes depress activity now, these expectations are being realized (mandates are implicit taxes, by the way).  Japan’s depression was driven by low productivity due to changes in the composition of firms (the Hayashi & Prescott hypothesis).

More striking, he argues for (a) banning financial intermediation and (b) 100% reserve commercial banking with government bonds are reserves. The reason is that financial intermediation is socially unproductive and mainly an attempt to give the illusion of insurance against aggregate shocks (in excess of what differences in tastes for risk would imply). — I would really like to see some follow-up on these policy proposals. Besides, there’s no obvious reason commercial banks (with their short-term liabilities) should lend to businesses (risky, long-term assets).

The “Effective Measures Against the Recession” presentation I linked is from July 6 and it has 50 slides. Later in the month he gave a lengthier presentation, of 69 slides, with an extended focus on this argument for financial reform, “The Financial Crisis and Its Consequences”, but I cannot find it online. At the time, the slides circulated via emails, if I remember correctly.

There are several off-the-shelf critiques of his analysis: the Solow residual is not a measure of productivity, what about sticky prices and money, there’s more to Japan than the survival of bad firms, and so on. That being said, I still think he makes good points and I would really like to see him publish at least a paper-length treatment of these views, although I do realize that sweeping policy reform papers are not stylish for theoreticians.

Search models of the labor market are interesting for macroeconomics not because they’re substantial in their treatment of unemployment but also because they feature externalities (posting a vacancy has benefits and costs not directly accruing to the firm) and multiple equilibria (if you’re unwilling to close the model with some type of bargaining).

I’m reading some of the later chapters in the book and I’ll probably return with a later post. If you read it already, please let us know what you thought and share a comment below!

Modern Macroeconomic Models as Tools for Economic Policy (2009 Annual Report/Essay) [PDF]

A while ago Kocherlakota circulated his views on the “state of macro”, the (in)famous “stuff.pdf” that’s now off his Minnesota website. Those were his views on the academics of Macroeconomics. This essay is about the state of central banking vis-a-vis the use of coherent models and economic theory for policy.

I think these are the main points he’s making, correct me if you think I’m wrong or incomplete:

• The use of “New Keynesian” models for regular policy was a positive development, overall. There are some remarks mirroring his Model fit and model selection paper and an approving reference to CKM’s Not Yet Useful critique;
• He supports work on sun-spots, “self-fulfilling prophesies” and the like, so that at least part of the volatility in the model is generated by the structure of the interaction rather than by changes in preferences, technology or endowments (i.e. “fundamentals”);
• Search-type frictions are important, or at least that’s how I read his “modeling the exchange process” comments;
• Heterogeneity (and the market incompleteness it requires) is important, especially for doing a better job at capturing financial market frictions and failures;
• It’s hard to solve models with several frictions (or deviations from the baseline model) but there is progress;
• Academic macroeconomists have done a bad job in terms of communicating their progress to the wider profession and to policy-makers; and, therefore
• The views of the profession in general, of policy-makers and of the public in general significantly lag behind the research frontier.

Overall, his comments are far too forgiving towards the central banking establishment, but that’s a highly subjective assessment, to say the least.

This school of thought has been influential in labor economics and development, mostly due to the interests of its founding fathers. As any radical movement that seeks to redefine what empirical economics is, the bold claims of “proper science” made by these authors lead to considerable backlash from established economists who suddenly found themselves in a position to defend their “structuralist” work as fundamentally meaningful.

Last week I linked and briefly commented on Acemoglu’s working paper, Theory, General Equilibrium and Political Economy in Development . His opposition to the most extreme views out there is important because his work on the settlers’ mortality instrument has introduced a larger growth and development readership to some of the basic experimentalist tools and mindset.

Getting back to the symposium, I find most of the replies, by Keane, Sims and Stock to be rather skeptical and negative, while Leamer is less than comfortable allowing the experimentalists to claim his “con in econometrics” critique as their own. My own view is that the methods and concerns pushed by the experimentalists are valuable but they don’t constitute a panacea or a scientific revolution in empirical economics. I also find the snide criticism of Macroeconomics in the tradition of Prescott & Kydland as misguided and counterproductive (this is, in part, what gets them in trouble with Sims).

That being said, let me just highlight some of the more insightful or fun passages from the symposium:

From Keane’s reply:

What has always bothered me about the “experimentalist” school is the false sense of certainty it conveys. The basic idea is that if we have a “really good instrument” we can come up with “convincing” estimates of “causal effects” that are not “too sensitive to assumptions.” Elsewhere I have written an extensive critique of this experimentalist perspective, arguing it presents a false panacea, and that all statistical inference relies on some untestable assumptions (Keane, 2010b)

Keane on consensus in labor economics:

A main theme of the Angrist and Pischke paper is the “proof is in the pudding”argument. They claim that labor economics, utilizing experimental methods, has left other fields in the dust. Specifically, labor has developed wide consensus on a broad range of questions, while fields like macro and industrial organization remain in disarray. Hearing this claim makes me feel like Count Almaviva when he’s told that Figaro is of noble blood: “Where am I? Who am I?” As far as I can see, labor economists don’t agree on much of anything.

Sims responding to the swipes taken by Angrist and Pischke at Macroeconomics:

The Angrist and Pischke essay does not mention what seems to me the main advance in macroeconometrics: the interaction of vector autoregressions, structural vector autoregressions, and econometrically estimated dynamic stochastic general equilibrium models, which has led to broad consensus on, for example, the consequences of shifts in central bank interest rate policy.

Sims’ conclusion:

Natural experiments, difference-in-difference, and regression discontinuity design are good ideas. They have not taken the con out of econometrics—in fact, as with any popular econometric technique, they in some cases have become the vector by which “con” is introduced into applied studies. Furthermore, overenthusiasm about these methods, when it leads to claims that single-equation linear models with robust standard errors are all we ever really need, can lead to our training applied economists who do not understand fully how to model a dataset. This is especially regrettable because increased computing power—and the new methods of inference that are arising to take advantage of this power—make such narrow, overly simplified approaches to data analysis increasingly obsolete.

All the papers are relatively easy to read and non-technical and I found the discussion of various landmark papers, on capital punishment and class size for example, to be good introductions and provide good references, so you might see some of those papers featured in future posts.

There are a lot of people going weak in the knees over Duflo and her randomized control trials gospel, to which I say *yawn*. Yes, conditional on you deciding that increasing school attendance is desirable, experimenting to see which kind of subsidy is more cost-effective is a good idea, but that’s a terribly uninteresting and narrow question. It might be important but we don’t gain much insight into anything, as scientists, by obsessing over it.

This kind of perspective ends up suggesting, perhaps inadvertently, that poverty is something that’s fixable with bureaucratic tinkering with subsidies, taxes and aid. It’s pretty obvious it’s not.

Randomized controlled trials or “behavioral” economics  have very little if anything to say about the really important open questions out there, e.g. on international income and growth rate differences, insurance and self-insurance and the causes of fluctuations.