Author: JW Mason

Making Sense of Changes in State-Local Debt

In a previous post, I pointed out that state and local governments in the US have large asset positions — 33 percent of GDP in total, down from nearly 40 percent before the recession. This is close to double state and local debt, which totals 17 percent of GDP. Among other things, this means that a discussion of public balance sheets that looks only at debt is missing at least half the picture.

On the other hand, a bit over half of those assets are in pension funds. Some people would argue that it’s misleading to attribute those holdings to the sponsoring governments, or that if you do you should also include the present value of future pension benefits as a liability. I’m not sure; I think there are interesting questions here.

But there are also interesting questions that don’t depend on how you treat the existing stocks of pension assets and liabilities. Here are a couple. First, how how do changes in state credit-market debt break down between the current fiscal balance and other factors, including pension fund contributions? And second, how much of state and local fiscal imbalances are financed by borrowing, and how much by changes in the asset position?

Most economists faced with questions like these would answer them by running a regression. [1] But as I mentioned in the previous post, I don’t think a regression is the right tool for this job. (If you don’t care about the methods and just want to hear the results, you can skip the next several paragraphs, all the way down to “So what do we find?”)

Think about it: what is a regression doing? Basically, we have a variable a that we think is influenced by some others: b, c, d … Our observations of whatever social process we’re interested in consist of sets of values for a, b, c, d… , all of them different each time. A regression, fundamentally, is an imaginary experiment where we adjusted the value of just one of b, c, d… and observed how a changed as a result. That’s the meaning of the coefficients that are the main outputs of a regression, along with some measure of our confidence in them.

But in the case of state budgets we already know the coefficients! If you increase state spending by one dollar, holding all other variables constant, well then, you increase state debt by one dollar. If you increase revenue by one dollar, again holding everything else constant, you reduce debt by one dollar. Budgets are governed by accounting identities, which means we know all the coefficients — they are one or negative one as the case may be. What we are interested in is not the coefficients in a hypothetical “data generating process” that produces changes in state debt (or whatever). What we’re interested in is how much of the observed historical variation in the variable of interest is explained by the variation in each of the other variables. I’m always puzzled when I see people regressing the change in debt on expenditure and reporting a coefficient — what did they think they were going to find?

For the question we’re interested in, I think the right tool is a covariance matrix. (Covariance is the basic measure of the variation that is shared between two variables.) Here we are taking advantage of the fact that covariance is linear: cov(x, y + z) = cov(x, y) + cov(x, z). Variance, meanwhile, is just a variable’s covariance with itself. So if we know that a = b + c + d, then we know that the variance of a is equal to the sum of its covariances with each of the others. In other words, if y = Σ xn then:

(1) var(y) = Σ cov(y, xn)

So for example: If the budget balance is defined as revenue – spending, then the variance of some observed budget balances must be equal to the covariance of the balance with revenue, minus the covariance of the balance with spending.

This makes a covariance matrix an obvious tool to use when we want to allocate the observed variation in a variable among various known causes. But for whatever reason, economists turn to variance decompositions only in few specific contexts. It’s common, for instance, to see a variance decomposition of this kind used to distinguish between-group from within-group inequality in a discussion of income distribution. But the same approach can be used any time we have a set of variables linked by accounting identities (or other known relationships) and we want to assess their relative importance in explaining some concrete variation.

In the case of state and local budgets, we can start with the identity that sources of funds = uses of funds. (Of course this is true of any economic unit.) Breaking things up a bit more, we can write:

revenues + borrowing = expenditure + net acquisition of financial assets (NAFA).

Since we are interested in borrowing, we rearrange this to:

(2) net borrowing = expenditure – revenue + NAFA = fiscal balance – NAFA

But we are not simply interested in borrowing,w e are interested in the change in the debt-GDP ratio (or debt-GSP ratio, in the case of individual states.) And this has a denominator as well as a numerator. So we write:

(3) change in debt ratio = net borrowing – nominal growth rate

This is also an accounting identity, but not an exact one; it’s a linear approximation of the true relationship, which is nonlinear. But with annual debt and income growth rates in the single digits, the approximation is very close.

So we have:

(4) change in debt ratio = expenditure – revenue + NAFA – nominal growth rate * current debt ratio

It follows from equation (1) that  the variance of change in the debt ratio is equal to the sum of the covariances of the change with each of the right-side variables. In other words, if we are interested in understanding why debt-GDP ratios have risen in some years and fallen in others, it’s straightforward to decompose this variation into the contributions of variation in each of the other variables. There’s no reason to do a regression here. [2]

So what do we find?

Here’s the covariance matrix for combined state and local debt for 1955 to 2013.  “Growth contrib.” refers to the last term in Equation (4). To make reading the table easier, I’ve reversed the sign of the growth contribution, fiscal balance and revenue; that means that positive values in the table all refer to factors that increase the variance of debt-ratio growth and negative values are factors that reduce it. [3]

Debt Ratio Growth Growth Contrib. Fiscal Balance Revenue Expenditure NAFA & Trusts
Debt Ratio Growth 0.18
Growth Contrib. (-) 0.10 0.11
Fiscal Balance (-) 0.03 0.04 0.13
Revenue (-) 0.08 0.24 0.12 5.98
Expenditure 0.11 0.28 -0.01 5.86 5.87
NAFA & Trusts 0.06 -0.05 0.13 -0.01 -0.14 0.23

How do we read this? First of all, note the bolded terms along the main diagonal — those are the covariance of each variable with itself, that is, its variance.  It is a measure of how much individual observations of this variable differ from each other. The off-diagonal terms, then, show how much of this variation is shared between two variables. Again, we know that if one variable is the sum of several others, then its variance will be the sum of its covariances with each of the others.

So for example, total variance of debt ratio growth is 0.18. (That means that the debt ratio growth  in a given year is, on average, about 0.4 percentage points above or below the average growth rate for the full period.) The covariance of debt-ratio growth and (negative) growth contribution is 0.10. So a bit over half the debt-ratio variance is attributable to nominal GDP growth. In other words, if we are looking at why the debt-GDP ratio rises more in some years than in others, more of the variation is going to be explained by the denominator of the ratio than the numerator. Next, we see that the covariance of debt growth with the (negative) fiscal balance is 0.03. In other words, about one-sixth of the variation in annual debt ratio growth is explained by fiscal deficits or surpluses.

This is important, because most discussions of state and local debt implicitly assume that all change in the debt ratio is explained this way. But in fact, while the fiscal balance does play some role in changes in the debt ratio — the covariance is greater than zero — it’s a distinctly secondary role.  Finally, the last variable, “NAFA & Trusts,” explains about a third of variation in debt ratio growth. In other words, years when state and local government debt is rising more rapidly relative to GDP, are also years in which those governments are adding more rapidly to their holdings of financial assets. And this source of variation explains about twice as much of the historical pattern of debt ratio changes, as the fiscal balance does.

Since this is probably still a bit confusing, the next table presents the same information in a hopefully clearer way. Here see only the covariances with debt ratio growth — the first column of the previous table — and they are normalized by the variance of debt ratio growth. Again, I’ve flipped the sign of variables that reduce debt-ratio growth. So each value of the table shows the share of variation in the growth of state-local growth ratios that is explained by that component. There is also a second column, showing the same thing for state governments only.  

Component State + Local State Only
Nominal Growth (-) 0.52 0.30
Fiscal Balance (-) 0.17 0.31
Revenue (-) -0.41 0.07
Expenditure 0.58 0.24
… of which: Interest 0.06 0.03
Trust Contrib. and NAFA 0.33 0.37
… of which: Pensions 0.01 0.02

I’ve added a couple variables here — interest payments under expenditure and pension contributions under NAFA and Trusts. Note in particular the small value of the latter. Pension contributions are quite stable from year to year. (The standard deviation of state/local pension contributions as a percent of GDP is just 0.07, versus around 0.5 for nontrust NAFA.)  This says that even though most state and local assets are in pension funds, pension contributions contribute only a little to the variation in asset acquisition. Most of the year to year variability is in governments’ acquisition of assets on their own behalf. This is helpful: It means that if we are interested in understanding variation in the growth of debt over time, or the role of assets vs. liabilities in accommodating fiscal imbalances, we don’t need to worry too much about how to think about pension funds. (If we want to focus on the total increase in state debt, as opposed to the variation over time, then pensions are still very important.)

If we compare the overall state-local sector with state governments only, the picture is broadly similar, but there are some interesting differences. First of all, nominal growth rates are somewhat less important, and the fiscal balance more important, for state government debt ratio. This isn’t surprising. State governments have more flexibility than local ones to independently adjust their spending and revenue; and state debt ratios are lower, so the effect on the ratio from a given change in growth rates is proportionately smaller. For the same reason, the effect of interest rate changes on the debt ratio, while small in both cases, is even smaller for the lower-debt state governments. [4]

So now we have shown more rigorously what we suggested in the previous post: While the fiscal balance plays some role in explaining why state and local debt ratios rise at some times and fall at others, it is not the main factor. Nominal growth rates and asset acquisition both play larger roles.

Let’s turn to the next question: How do state and local government balance sheets adjust to fiscal imbalances? Again, this is just a re-presentation of the data in the first table, this time focusing on the third column/row. Again, we’re also doing the decomposition for states in isolation, and adding a couple more items — in this case, the taxes and intergovernmental assistance components of revenue, and the pension contribution component of NAFA. The values are normalized here by the variance of the fiscal balance. The first four lines sum to 1, as do the last three. In effect, the first four rows of the table tells us where fiscal imbalances come from; the final three tell us where they go.


Component State + Local State Only
Revenue, of which: 0.94 1.01
… Taxes 0.50 0.93
… Intergovernmental 0.18 -0.04
Expenditure (-) 0.06 -0.01
Trust Contrib. and NAFA, of which: 1.04 0.92
… Pensions 0.10 -0.49
Borrowing (-) -0.04 0.08

So what do we see? Looking at the first set of lines, we see that state-local fiscal imbalances are entirely expenditure-driven. Surprisingly, revenues are no lower in deficit years than in surplus ones. Note that this is true of total revenues, but not of taxes. Deficit years are indeed associated with lower tax revenue and surplus years with higher taxes, as we would expect. (That’s what the positive values in the “taxes” row mean.) But this is fully offset by the opposite variation in payments from the federal government, which are lower in surplus years and higher in deficit years. During the most recent recession, for example, aggregate state and local taxes declined by about 0.4 percent of GDP. But federal assistance to state and local governments increased by 0.9 percent of GDP. This was unexpected to me: I had expected most of the variation in state budget balances to come from the revenue side. But evidently it doesn’t. The covariance matrix is confirming, and quantifying, what you can see in the figure below: Deficit years for the state-local sector are associated with peaks in spending, not troughs in revenue.

muni-budgets
Aggregate State-Local Revenue and Expenditure, 1953-2013

Turning to the question of how imbalances are accommodated, we find a similarly one-sided story. None of the changes in state-local budget balances result in changes in borrowing; all of them go to changes in fund contributions and direct asset purchases. [5] For the sector as a whole, in fact, asset purchases absorb more than all the variation in fiscal imbalances; borrowing is lower in deficit years than in surplus years. (For state governments, borrowing does absorb about ten percent of variation in the fiscal balance.) Note that very little of this is accounted for by pensions — less than none in the case of state governments, which see lower overall asset accumulation but higher pension fund contributions in deficit years. Again, even though pension funds account for most state-local assets, they account for very little of the year to year variation in asset purchases.

So the data tells a very clear story: Variation in state-local budget balances is driven entirely by the expenditure side; cyclical changes in their own revenue are entirely offset by changes in federal aid. And state budget imbalances are accommodated entirely by changes in the rate at which governments buy or sell assets. Over the postwar period, the state-local government sector has not used borrowing to smooth over imbalances between revenue and spending.

 

[1] The interesting historical meta-question, to which I have no idea of the answer, when and why regression analysis came to so completely dominate empirical work in economics. I suspect there are some deep reasons why economists are more attracted to methodologies that treat observed data as a sample or “draw” from a universal set of rules, rather than methodologies that focus on the observed data as the object of inquiry in itself.

[2] I confess I only realized recently that variance decompositions can be used this way. In retrospect, we should have done this in our papers in household debt.

[3] Revenue and expenditure here include everything except trust fund income and payments. In other words, unlike in the previous post, I am following the standard practice of treating state and local budgets separate from pension funds and other trust funds. The last line, “NAFA and Trusts”, includes both contributions to trust funds and acquisition of financial assets by the local government itself. But income generated by trust fund assets, and employee contributions to pension funds, are not included in revenue, and benefits paid are not included in expenditure. So the “fiscal balance” term here is basically the same as that reported by the NIPAs and other standard sources.

[4] This is different from households and the federal government, where higher debt and, in the case of households, more variable interest rates, mean that interest rates are of first-order importance in explaining the evolution of debt ratios over time.

[5] It might seem contradictory to say that a third of the variation in changes in the debt ratio is due to the fiscal balance, even though none of the variation in the fiscal balance is passed through to changes in borrowing. The reason this is possible is that those periods when there are both deficits and higher borrowing, also are periods of slower nominal income growth. This implies additional variance in debt growth, which is attributed to both growth and the fiscal balance. There’s some helpful discussion here.

 

(This post is based on a paper in process. I probably will not post any more material from this project for the next month or so, since I need to return to the question of potential output.)

 

Lost in Fiscal Space

Arjun and Jayadev and I have a working paper up at the Washington Center for Equitable Growth on the conflict between conventional macroeconomic policy and Lerner-style functional finance. Here’s the accompanying blogpost, cross-posted from the WCEG blog.

 

One pole of current debates about U.S. fiscal policy is occupied by the “functional finance” position—the view usually traced back to the late economist Abba Lerner—that a government’s budget balance can be set at whatever level is needed to stabilize aggregate demand, without worrying about the level of government debt. At the other pole is the conventional view that a government’s budget balance must be set to keep debt on a sustainable trajectory while leaving the management of aggregate demand to the central bank. Both sides tend to assume that these different policy views come from fundamentally different ideas about how the economy works.

A new working paper, “Lost in Fiscal Space,” coauthored by myself and Arjun Jayadev, suggests that, on the contrary, the functional finance and the conventional approaches can be understood in terms of the same analytic framework. The claim that fiscal policy can be used to stabilize the economy without ever worrying about debt sustainability sounds radical. But we argue that it follows directly from the standard macroeconomic models that are taught to undergraduates and used by policymakers.

Here’s the idea. There are two instruments: first, the interest rate set by the central bank; and second, the fiscal balance—the budget surplus or deficit. And there are two targets: the level of aggregate demand consistent with acceptable levels of inflation and unemployment; and a stable debt-to-GDP ratio. Each instrument affects both targets—output depends on both the interest rate set by monetary authorities and on the fiscal balance (as well as a host of other factors) while the change in the debt depends on both new borrowing and the interest paid on existing debt. Conventional policy and functional finance represent two different choices about which instrument to assign to which target. The former says the interest rate instrument should focus on demand and the fiscal-balance instrument should focus on the debt-ratio target, the latter has them the other way around.

Does it matter? Not necessarily. There is always one unique combination of interest rate and budget balance that delivers both stable debt and price stability. If policy is carried out perfectly then that’s where you will end up, regardless of which instrument is assigned to which target. In this sense, the functional finance position is less radical than either its supporters or its opponents believe.

In reality, of course, policies are not followed perfectly. One common source of problems is when decisions about each instrument are made looking only at the effects on its assigned target, ignoring the effects on the other one. A government, for example, may adopt fiscal austerity to bring down the debt ratio, ignoring the effects this will have on aggregate demand. Or a central bank may raise the interest rate to curb inflation, ignoring the effects this will have on the sustainability of the public debt. (The rise in the U.S. debt-to-GDP ratio in the 1980s owes more to Federal Reserve chairman Paul Volcker’s interest rate hikes than to President Reagan’s budget deficits.) One natural approach, then, is to assign each target to the instrument that affects it more powerfully, so that these cross-effects are minimized.

So far this is just common sense; but when you apply it more systematically, as we do in our working paper, it has some surprising implications. In particular, it means that the metaphor of “fiscal space” is backward. When government debt is large, it makes more sense, not less, to use active fiscal policy to stabilize demand—and leave the management of the public debt ratio to the central bank. The reason is simple: The larger the debt-to-GDP ratio, the more that changes in the ratio depend on the difference in between the interest rate and the growth rate of GDP, and the less those changes depend on current spending and revenue (a point that has been forcefully made by Council of Economic Advisers Chair Jason Furman). This is what we see historically: When the public debt is very large, as in the United States during and immediately after the Second World War, the central bank focused on stabilizing the public debt rather than on stabilizing demand, which means responsibility for aggregate demand fell to the budget authorities.

We hope this paper will help clarify what’s at stake in current debates about U.S. fiscal policy. The question is not whether it’s economically feasible to use fiscal policy as our primary instrument to manage aggregate demand. Any central bank that is able to achieve its price stability and full employment mandates is equally able to keep the debt-to-GDP ratio constant while the budget authorities manage demand. The latter task may even be easier, especially when debt is already high. The real question is who we, as a democratic society, trust to make decisions about the direction of the economy as a whole.

UPDATE: Nick Rowe has an interesting response here. (And an older one here, with a great comments thread following it.)

The Action Is on the Asset Side

Let’s talk about state and local government balance sheets.

Like most sectors of the US economy, state and local governments have seen a long-term increase in credit-market debt, from about 8 percent of GDP in 1950 to 19 percent of GDP in 2010, before falling back a bit to 17 percent in 2013. [1] While this is modest compared with federal-government and household debt, it is not trivial. Municipal bonds are important assets in financial markets. On the liability side, state and local debt operates as a political constraint at the state level and often plays a prominent role in public discussions of state budgets. Cuts to state services and public employee wages and pensions are often justified by the problem of public debt, municipal bond offerings are a focal point for local politics, and you don’t have to look far to find scare stories about an approaching state  or local debt crisis.

muni-debt
State and Local Government Debt, 1953-2013

 

My interest in state and local debt is an extension of my work (with Arjun Jayadev) on household debt and on sovereign debt. The question is: To what extent to historical changes in debt ratios reflect the balance between revenue and expenditure, and to what extent do they represent monetary-financial factors like inflation and interest rates? The exact balance of course depends on the sector and period; what we want to steer people away from is the habit of assuming that balance sheet changes are a straightforward record of real income and spending flows. [2]

The first thing to note about state and local debt is that, as the first figure shows, only about 40 percent of it is owed by state governments, with the majority is owed by the thousands of local governments of various types. Of the 10 percent of GDP or so owed by local governments, about half is owed by general-purpose governments (cities, counties and towns, in that order), and half by special purpose districts, with school districts accounting for about half of this (or a bit over 2 percent of GDP). This is interesting because, as the  figure below shows, the majority of state and local spending is at the state level.

muni-spending
State and Local Government Spending, 1953-2013

 

This imbalance goes back to at least the 1950s and 1960s, when local governments accounted for just over half of combined state and local spending, but more than three-quarters of combined state and local government debt. The explanation for the different distributions of spending and debt over different levels of government is simple: While state governments account for a larger share of total state and local spending, local governments account for about two-thirds of state and local capital spending. In the US, most infrastructure spending is the responsibility of local governments; direct service provision, which requires buildings and other fixed assets, is also disproportionately local. State government budgets, on the other hand, include a large proportion of transfer spending, which is negligible at the local level. Since debt is mainly used to finance capital spending, it’s no surprise that the distribution of debt looks more like the distribution of capital spending than like the distribution of spending in general.

This is an interesting fact in itself, but it also is a good illustration of an important larger point that should be obvious but is often ignored: The main use of debt is to finance assets. This simple point is for some reason almost always ignored by economists — both mainstream and heterodox economists regard the paradigmatic loan as a consumption loan. [3] Among other things, this leads to the mistaken idea that credit-market debt reflects — or at least is somehow related to — dissaving. When in fact there’s no connection.

For households and businesses, just as for state and local governments, the majority of debt finances investment. [4] This means that additions to the liability side of the balance sheet are normally simultaneous with additions to the asset side, with no effect on saving. If anything, since most assets are not financed entirely with debt, most transactions that increase debt require saving to increase also. (Homebuyers normally get a mortgage and make a downpayment.) Sovereign governments are the only economic units whose borrowing mainly finances gaps between current revenue and current expenditure. Again, this point is missed as much by heterodoxy as by the mainstream. Just flipping over to the next tab in my browser, I find a Marxist writing that “Debt has become so high that the personal savings rate in the United States actually became negative.” Which is a non sequitur.

The fact that most state-local debt is at the local level, while most spending is at the state level, is a reflection of the fact that debt is used to finance capital spending and not spending in general. But in and of itself this fact doesn’t tell us anything about how much changes in the state-local debt ratio reflect fiscal deficits or dissaving. It still could be true that state and local debt mainly reflects accumulated fiscal deficits.

As it turns out, though, it isn’t true at all. As the next figure shows, historically there is no relationship between changes in the state-local debt ratio and the state-local fiscal balance.

muni-debtyears

Here, the vertical axis shows the change in the ratio of aggregate state and local debt to GDP over the year. The horizontal axis shows the aggregate fiscal balance, with surpluses positive and deficits negative. So for instance, in 2009 the debt ratio increased by about one point, while state and local governments ran an aggregate budget deficit of close to 6 percent of GDP. [5] If changes in the debt ratio mainly reflected fiscal deficits, we would expect most of the points to fall along a line sloping down from upper left to lower right. They really don’t. Yes, 2009 has both very large deficits and a large rise in the debt ratio; but 2007 has the largest aggregate surpluses, and the debt ratio rose by almost as much. Eyeballing the figure you might see a weak negative relationship; but in this case your eyeballs are fooling you. In fact, the correlation is positive. A regression of the change in on debt on the fiscal balance yields a coefficient of positive 0.11, significant at the 5 percent level. As I’ll discuss later, I’m not sure a regression is a good tool for this job. But it is good enough to answer the question, “Is state and local debt mainly the result of past deficits?” with a definite No.

How can state and local fiscal balances vary without changing the sector’s debt? The key thing to recognize about state and local government balance sheets is that they also have large financial asset positions. In the aggregate, the sector’s net financial wealth is positive; unlike the federal government, state and local governments are net creditors, not net borrowers, in financial markets. As of 2013, the sector as a whole had total debt of 18 percent of GDP, and financial assets of 34 percent of GDP. As the following figures show, the long-term rise in state and local assets is much bigger than the rise in debt. Now it is true that most of these assets are held in pension funds, rather than directly. But a lot of them are not. In fact, for state governments — though not for the state-local sector as a whole — even nontrust assets exceed total debt. And whether or not you want to attribute pension assets to the sponsoring government, contributions to pension funds are important margin on which state budgets adjust.

State and Local Financial Assets, 1953-2013
State and Local Financial Assets, 1953-2013

 

Combined State-Local Financial Net Wealth

 

As the final figure shows, since the mid 1990s the aggregate financial assets of state-local government have exceeded aggregate debt in every single state. (Alaska, with government net financial wealth in excess of 100 percent of gross state product, is off the top of the chart, as is Wyoming.) This is a change from the 1950s and 1960s, when positive and negative net positions were about equally common. Nationally, the net credit position of state and local governments was equal to 16 percent of GDP in 2013, down from over 20 percent in 2007.

These large asset positions have a number of important implications:

1. To the extent that state and local governments run deficits in recessions, they are can be financed by reducing net acquisition of assets rather than by issuing more debt. And historically it seems that this is how they mostly are financed, especially in recent cycles. So if we are interested in whether state and local budgets behave procyclically or anticyclically, the degree of flexibility these governments have on the asset side is going to be a key factor.

2. Some large part of the long-term increase in state and local debt can be attributed to increased net acquisition of assets. This is especially notable in the 1980s, when there were simultaneous rises in both state debt and state financial assets. And changes in assets are strongly correlated across states. I.e. the states that increase their debt the most in a given year, tend to also be the ones that increased their assets the most — in some periods, higher debt is actually associated with a shift toward a net creditor position.

3. Low interest rates are not so clear an argument for increased infrastructure spending as people often assume, given that little of this spending currently happens at the federal level. Yes, an individual project may still look more cost-effective, but set against that is the pressure to increase trust fund contributions.

4. If state and local governments face financial constraints on current spending, these are at least as likely to reflect the terms on which they must prefund future expenses as the terms on which they can borrow.

The second point is the key one for my larger argument. Debt is part of a financial system that evolves independently of the system comprising “real” income and expenditure. They connect with each other, but they don’t correspond to each other. The case of state and local governments is somewhat different from households and the federal government — for the latter two, changes in interest rates play a major role in the evolution of debt ratios (along with changing default rates for households), while net acquisition of financial assets is not important for the federal government. But in all cases, purely financial factors play a major role in the evolution of debt ratios, along with changes in nominal income growth rates, which explain about a third of the variation in state-local debt ratios over time. And in all cases the divergence between the real and financial variables is especially visible in the 1980s.

With respect to state and local governments specifically, point 4 may be the most interesting one. Why do state and local governments hold so much bigger asset positions than they used to? What is the argument for prefunding pension benefits and similar future expenses, rather than meeting them on a pay-as-you-go basis? And how do those arguments change if we think the current regime of low interest rates is likely to persist indefinitely? It’s not obvious to me that either public employees or public employers are better off with funded pensions. Unlike in the private sector, public employees don’t need insurance against outliving their employer. It’s not obvious why governments should hold reserves against future pension payments but not against other equally large, equally predictable future payments. Nor is it obvious how much protection funded pensions offer against benefit cuts. And if interest rates remain lower than growth rates, prefunding pensions is actually more expensive than treating them as a current expense. I see lots of discussion about how state and local government funds should be managed, but does anyone ask whether they should hold these big funds at all?

In any case, given the very large asset positions of state and local governments, and the large cyclical and secular variation in net acquisition of assets, it’s clear that we shouldn’t imagine there’s any connection between sate and local debt and state and local fiscal positions. And we shouldn’t assume that the main financial problem faced by state and local governments is the terms they can borrow on. Most of the action is on the asset side.

 

[1] My critique of Piketty comes from the same place.

[2] All data in this post comes from the Census of Governments.

[3] This is true of economic theory obviously, but it’s also true of a lot of empirical work. When Gabriel Chodorow-Reich was hired at Harvard a few years ago, for instance, his job market paper was an empirical study of credit constraints on business borrowers that ignored investment and treated credit as an input into current production.

[4] For households, nearly 70 percent of debt is accounted for by mortgages, with auto loans and student debt accounting for another 10 percent each. (Admittedly, spending in the latter two categories is counted as consumption the national accounts; but functionally, cars and diplomas are assets.) Less than 10 percent of household debt looks like consumption loans.

[5] This is different from the number you will find in the national accounts. The main reasons for the difference are, first, that the Census works on a strict cashflow basis, and, second, that it consolidates pension and other trust funds with the sponsoring government. (See here.) This means that if a pension fund’s benefit payments exceed its income in a given year, that contributes to the deficit of the sponsoring government in the Census data, but not in the national accounts. This is what’s responsible for the very large deficits reported for 2009. If we are interested in credit-market debt the Census approach seems preferable, but there are some tricky questions for sure. All this will be discussed in more detail in the paper I’m writing on state and local balance sheets.

 

EDIT: Followup here.

Thinking about Monetary Policy

There’s been even more ink spilled lately than usual over the reasons monetary policy seems to have lost its mojo, and what it would take to get it back. Admittedly a lot of it is the same dueling pronouncements over whether helicopter money must always or can never work, but with the volume turned up a notch.

From my point of view, the conceptual issues here are simpler than you’d guess from the shouting. It comes down to two questions. First, how much control does the central bank have over the terms on which various economic units can adjust their balance sheets by selling assets or issuing new liabilities? And second, how many units would increase their spending on goods and services if they could more easily make the required balance sheet adjustments? Obviously, these questions are not straightforward. And they have to be answered jointly — to be effective, monetary policy has to reach not just the elasticity of the financial system in general, but its elasticity at the points where it meets financially-constrained units. But in principle, it’s simple enough.

The whole question, it seems to me, is made more confusing than it needs to be by two bad habits of economists. First is the tendency to think of the economy as a tightly articulated system, with just a few degrees of freedom. (This is one way of describing the focus on equilibrium.) To an economist, the economy is like a pool of water, where a disturbance to any part of it leads to a rapid adjustment of the whole system to a final state that can be described on the basis of a few parameters, without any information about specific components. What’s the alternative? The economy is like a pile of rocks: Disturbances may remain local rather than being transmitted to the whole system; less information about the structure can be derived from a few global parameters and more depends on the contingent states of the individual components; and stability is the result not of rapid adjustment, but rather of buffers that make adjustment unnecessary. Economists’ fixation on tightly-articulated systems tempts us to think about a single parameter (the interest rate, the money supply) changing uniformly through the economy (and often over all of time), and economic units fully adjusting their behavior in response.  It leads to a focus on the ultimate endpoint of an adjustment process rather than its next step. This yields stronger, and often paradoxical, conclusions than you would reach if you imagined beliefs and behavior changing locally and incrementally.

The second vice is economists’ incorrigible tendency to mistake the map for the territory. Like the first, this leads us to overvalue formal logical analysis at the expense of the concrete and historical. It also leads us to take an abstract representation that was adopted to clarify a particular question in a particular context, and treat it as an object in itself, as if it descried a self-contained world. Anyone who’s spent time around economists will have noticed their habit of regarding any label on a variable in an equation, as a physical object out there in the world. There’s nothing wrong — it should go without saying — with formal, logical analysis; as Marx said, abstraction is the social scientist’s equivalent of the microscope or telescope. The difference is that economists treat models as toy train sets rather than as tools. In the case of monetary policy, it works like this. The central bank adopts a policy tool which, in the institutional context at the time, gives them adequate control over the overall pace of credit expansion. Economists abstract from the — genuinely, but only for the moment  — irrelevant details of exactly how this instrument works, and postulate a direct connection with the economic outcome it is meant to control. To make communication with other economists easier, they often also construct a model where just exactly the intervention carried out by the central bank is what’s needed to restore the Walrasian optimum. This may be harmless enough as long as the policy framework persists. But the dogmatic insistence that “the central bank sets the money supply” or “the central bank sets the interest rate” is a source of endless confusion when the instrument is changing to something else.

So coming back to the concrete situation, how much can the Fed influence the expansion of bank balance sheets, and how much is expenditure on current production held down by the inelasticity of bank balance sheets? In the idealized financial world of circa 1950, the answer was simple. Commercial bank liabilities were deposits; deposits expanded through investment loans to business and households; and the total volume of deposits was strictly limited by the reserves made available by the Fed. The situation today is more complicated. But we have a better chance of making sense of it if we don’t get distracted by brain teasers about “M”.

 

I wrote this a month or two ago and didn’t post it for some reason. As a critical post, it really ought to have links to examples of the positions being criticized; but at this point it doesn’t seem worth the trouble.

Links for October 14

Now we are making progress. This piece by CEA chair Jason Furman on “the new view” of fiscal policy seems like a big step forward for mainstream policy debate. He goes further than anyone comparably prominent in rejecting the conventional macro-policy wisdom of the past 30 years. From where I’m sitting, the piece advances beyond the left edge of the current mainstream discussion in at least three ways.

First, it abandons the idea of zero interest rates as a special state of exception and accepts the idea of fiscal policy as a routine tool of macroeconomic stabilization. Reading stuff like this, or like SF Fed President John Williams saying that fiscal policy should be “a first responder to recessions,” one suspects that the post-1980s consensus that stabilization should be left to the central banks may be gone for good. Second, it directly takes on the idea that elected governments are inherently biased toward stimulus and have to be institutionally restrained from overexpansionary policy. This idea — back up with some arguments about  the“time-inconsistency” of policy that don’t really make sense — has remained a commonplace no matter how much real-world policy seems to lean the other way. It’s striking, for instance, to see someone like Simon Wren-Lewis rail against “the austerity con” in his public writing, and yet in his academic work take it as an unquestioned premise that elected governments suffer from “deficit bias.” So it’s good to see Furman challenge this assumption head-on.

The third step forward is the recognition that the long-run evolution of the debt ratio depends on GDP growth and interest rates as well as on the fiscal balance. Some on the left will criticize his assumption that the debt ratio is something policy should be worried about at all — here the new view has not yet broken decisively with the old view; I might have some criticisms of him on this point myself. But it’s very important to point out, as he does, that “changes in the debt ratio depend on two factors: the difference between the interest rate and the growth rate… and the primary balance… The larger the debt is, the more changes in r – g dwarf the primary balance in the determination of debt dynamics.” (Emphasis added.) The implication here is that the “fiscal space” metaphor is backward — if the debt ratio is a target for policy, then a higher current ratio means you should focus more on growth, and that responsibility for the “sustainability” of the debt rests more with the monetary authority than the fiscal authority. Admittedly Furman doesn’t follow this logic as far as Arjun and I do in our paper, but it’s significant progress to foreground the fact the debt ratio has both a numerator and a denominator.

If you’re doubting whether there’s anything really new here, just compare this piece with what his CEA chair predecessor Christina Romer was saying a decade ago — you couldn’t ask for a clearer statement of what Furman now rejects as “the old view.” It’s also, incidentally, a sign of how far policy discussions — both new view and old view — are from academic macro. DSGE models and their associated analytic apparatus don’t have even a walk-on part here. I think left critics of economics are too quick to assume that there is a tight link — a link at all, really — between orthodox theory and orthodox policy.

 

Why do stock exchanges exist? I really enjoyed this John Cochrane post on volume and information in financial markets. The puzzle, as he says, is why there is so much trading — indeed, why there is any trading at all. Life cycle and risk preference motivations could support, at best, a minute fraction of the trading we see; but information trading — the overwhelming bulk of actual trading — has winners and losers. As Cochrane puts it:

all trading — any deviation of portfolios from the value-weighted market index — is zero sum. Informed traders do not make money from us passive investors, they make money from other traders. It is not a puzzle that informed traders trade and make money. The deep puzzle is why the uninformed trade, when they could do better by indexing. …

Stock exchanges exist to support information trading. The theory of finance predicts that stock exchanges, the central institution it studies, the central source of our data, should not exist. The tiny amounts of trading you can generate for life cycle or other reasons could all easily be handled at a bank. All of the smart students I sent to Wall Street for 20 years went to participate in something that my theory said should not exist.

At first glance this might seem like one of those “puzzles” beloved of economists, where you describe some real-world phenomena in terms of a toy model of someone maximizing something, and then treat the fact that it doesn’t work very well as a surprising fact about the world rather than an unsurprising fact about your description. But in this case, the puzzle seems real; the relevant assumptions apply in financial markets in a way they don’t elsewhere.

I like that Cochrane makes no claim to have a solution to the puzzle — the choice to accept ignorance rather than grab onto the first plausible answer is, arguably, the starting point for scientific thought and certainly something economists could use more of. (One doesn’t have to accept the suggestion that if we have no idea what social needs, if any, are met by financial markets, or if there is too much trading or too little, that that’s an argument against regulation.) And I like the attention to what actual traders do (and say they do), which is quite different from what’s in the models.

 

Yes, we know it’s not a “real” Nobel. So the Nobel went to Hart and Holmstrom. Useful introductions to their work are here and here. Their work is on contract theory: Why do people make complex ongoing agreements with each other, instead of just buying the things they want? This might seem like one of those pseudo-puzzles — as Sanjay Reddy notes on Twitter, the question only makes sense if you take economists’ ideal world as your starting point. There’s a whole genre of this stuff: Take some phenomenon we are familiar with from everyday life, or that has been described by other social scientists, and show that it can also exist in a world of exchange between rational monads. Even at its best, this can come across like a guy who learns to, I don’t know, play Stairway to Heaven with a set of spoons. Yes, getting the notes out takes real skill, and it doesn’t sound bad, but it’s not clear why you would play it that way if you weren’t for some reason already committed to the gimmick. Or in this case, it’s not clear what we learn from translating a description of actual employment contracts into the language of intertemporal optimization; the process requires as an input all the relevant facts about the phenomenon it claims to explain. What’s the point, unless you are for some already committed to ignoring any facts about the world not expressed in the formalism of economics? This work — I admit I don’t know it well — also makes me uncomfortable with the way it seems to veer opportunistically between descriptive and prescriptive. Is this about how actual contracts really are optimal given information constraints and so on, or is it about how optimal contracts should be written? Anyway, here’s a more positive assessment from Mark Thoma.

 

Still far from full employment. Heres’ a helpful report from the Center for Economics and Policy Research on the state of the labor market. They look at a bunch of alternatives to the conventional unemployment rate and find that all of them show a weaker labor market than in 2006-2007. Hopefully the Clinton administration and/or some Democrats in the Senate will  put some sharp questions to FOMC appointees over the next few years about whether they think the Fed as fulfilled its employmnet mandate, and on what basis. They’ll find some useful ammunition here.

 

Saving, investment and the natural rate. Here’s a new paper from Lance Taylor taking another swipe at the pinata of the “natural rate”. Taylor points out that if the “natural” interest rate simply means the interest rate at which aggregate demand equals potential output (even setting aside questions about how we measure potential), the concept doesn’t make much sense. If we look at the various flows of spending on goods and services by sector and purpose, we can certainly identify flows that are more or less responsive to interest rates; but there is no reason to think that interest rate changes are the main driver of changes in spending, or that “the” interest rate that balances spending and potential at a given moment is particularly stable or represents any kind of fundamental parameters of the economy. Even less can we think of the “natural” rate as balancing saving and investment, because, among other reasons, “saving” is dwarfed by the financial flows between and within sectors. Taylor also takes Keynes to task (rightly, in my view) for setting us on the wrong track with assumption that households save and “entrepreneurs” invest, when in fact most of the saving in the national accounts takes place within the corporate sector.

 

On other blogs, other wonders:

At Vox, another reminder that the rise in wealth relative to income that Piketty documents is mainly about the rising value of existing assets, not the savings-and-accumulation process he talks about in his formal models.

Also at Vox: How much did Germany benefit from debt forgiveness after World War II? (A lot.) EDIT: Also here.

Is there really a “global pivot” toward more expansionary fiscal policy? The IMF and Morgan Stanley both say no.

Another one for the short-termism file: Here’s an empirical paper suggesting that when banks become publicly traded, their management starts responding to short-run movements in their stock, taking on more risk as a result.

Matias Vernengo has a new paper on Raul Prebisch’s thought on business cycles and growth. Prebisch would be near the top of my list of twentieth century economists who deserve more attention than they get.

I was just at Verso for the release party for Peter Frase’s new book Four Futures, based on his widely-read Jacobin piece. I don’t really agree with Peter’s views on this — I don’t see the full replacement of human labor by machines as the logical endpoint of either the historical development of capitalism or a socialist political project — but he makes a strong case. If the robot future is something you’re thinking about, you should definitely buy the book.

 

EDIT: Two I meant to include, and forgot:

David Glasner has a follow-up post on the inconsistency of rational expectations with the “shocks” and comparative statics they usually share models with. It’s probably not worth beating this particular dead horse too much more, but one more inconsistency. As I can testify first-hand, at most macroeconomic journals, “lacks microfoundations” is sufficient reason to reject a paper. But this requirement is suspended as soon as you call something a “shock,” even though technology, the markup, etc. are forms of behavior just as much as economic quantities or prices are. (This is also one of Paul Romer’s points.)

And speaking of people named Romer, David and and Christina Romer have a new working paper on US monetary policy in the 1950s. It’s a helpful paper — it’s always worthwhile to reframe abstract, universal questions as concrete historical ones — but also very orthodox in its conclusions. The Fed did a good job in the 1950s, in their view, because it focused single-mindedly on price stability, and was willing to raise rates in response to low unemployment even before inflation started rising. This is a good example of the disconnect between the academic mainstream and the policy mainstream that I mentioned above. It’s perfectly possible to defend orthodoxy macroeconomic policy without any commitment to, or use of, orthodox macroeconomic theory.

 

EDIT: Edited to remove embarrassing confusion of Romers.

Links for October 6

More methodenstreit. I finally read the Romer piece on the trouble with macro. Some good stuff in there. I’m glad to see someone of his stature making the  point that the Solow residual is simply the part of output growth that is not explained by a production function. It has no business being dressed up as “total factor productivity” and treated as a real thing in the world. Probably the most interesting part of the piece was the discussion of identification, though I’m not sure how much it supports his larger argument about macro.  The impossibility of extracting causal relationships from statistical data would seem to strengthen the argument for sticking with strong theoretical priors. And I found it a bit odd that his modus ponens for reality-based macro was accepting that the Fed brought down output and (eventually) inflation in the early 1980s by reducing the money supply — the mechanisms and efficacy of conventional monetary policy are not exactly settled questions. (Funnily enough, Krugman’s companion piece makes just the opposite accusation of orthodoxy — that they assumed an increase in the money supply would raise inflation.) Unlike Brian Romanchuk, I think Romer has some real insights into the methodology of economics. There’s also of course some broadsides against the policy  views of various rightwing economists. I’m sympathetic to both parts but not sure they don’t add up to less than their sum.

David Glasner’s interesting comment on Romer makes in passing a point that’s bugged me for years — that you can’t talk about transitions from one intertemporal equilibrium to another, there’s only the one. Or equivalently, you can’t have a model with rational expectations and then talk about what happens if there’s a “shock.” To say there is a shock in one period, is just to say that expectations in the previous period were wrong. Glasner:

the Lucas Critique applies even to micro-founded models, those models being strictly valid only in equilibrium settings and being unable to predict the adjustment of economies in the transition between equilibrium states. All models are subject to the Lucas Critique.

Here’s another take on the state of macro, from the estimable Marc Lavoie. I have to admit, I don’t care for way it’s framed around “the crisis”. It’s not like DSGE models were any more useful before 2008.

Steve Keen has his own view of where macro should go. I almost gave up on reading this piece, given Forbes’ decision to ban on adblockers (Ghostery reports 48 different trackers in their “ad-light” site) and to split the article up over six pages. But I persevered and … I’m afraid I don’t see any value in what Keen proposes. Perhaps I’ll leave it at that. Roger Farmer doesn’t see the value either.

In my opinion, the way forward, certainly for people like me — or, dear reader, like you — who have zero influence on the direction of the economics profession, is to forget about finding the right model for “the economy” in the abstract, and focus more on quantitative description of concrete historical developments. I expressed this opinion in a bunch of tweets, storified here.

 

The Gosplan of capitalism. Schumpeter described banks as capitalism’s equivalent of the Soviet planning agency — a bank loan can be thought of as an order allocating part of society’s collective resources to a particular project.  This applies even more to the central banks that set the overall terms of bank lending, but this conscious direction of the economy has been hidden behind layers of ideological obfuscation about the natural rate, policy rules and so on. As DeLong says, central banks are central planners that dare not speak their name. This silence is getting harder to maintain, though. Every day there seems to be a new news story about central banks intervening in some new credit market or administering some new price. Via Ben Bernanke, here is the Bank of Japan announcing it will start targeting the yield of 10-year Japanese government bonds, instead of limiting itself to the very short end where central banks have traditionally operated. (Although as he notes, they “muddle the message somewhat” by also announcing quantities of bonds to be purchased.)  Bernanke adds:

there is a U.S. precedent for the BOJ’s new strategy: The Federal Reserve targeted long-term yields during and immediately after World War II, in an effort to hold down the costs of war finance.

And in the FT, here is the Bank of England announcing it will begin buying corporate bonds, an unambiguous step toward direct allocation of credit:

The bank will conduct three “reverse auctions” this week, each aimed at buying the bonds from particular sectors. Tuesday’s auction focuses on utilities and industries. Individual companies include automaker Rolls-Royce, oil major Royal Dutch Shell and utilities such as Thames Water.

 

Inflation or socialism. That interventions taken in the heat of a crisis to stabilize financial markets can end up being steps toward “a more or less comprehensive socialization of investment,” may be more visible to libertarians, who are inclined to see central banks as a kind of socialism already. At any rate, Scott Sumner has been making some provocative posts lately about a choice between “inflation or socialism”. Personally I don’t have much use for NGDP targeting — Sumner’s idée fixe — or the analysis that underlies it, but I do think he is onto something important here. To translate the argument into Keynes’ terms, the problem is that the minimum return acceptable to wealth owners may be, under current conditions, too high to justify the level of investment consistent with the minimum level of growth and employment acceptable to the rest of society. Bridging this gap requires the state to increasingly take responsibility for investment, either directly or via credit policy. That’s the socialism horn of the dilemma. Or you can get inflation, which, in effect, forces wealthholders to accept a lower return; or put it more positively, as Sumner does, makes it more attractive to hold wealth in forms that finance productive investment.  The only hitch is that the wealthy — or at least their political representatives — seem to hate inflation even more than they hate socialism.

 

The corporate superorganism.  One more for the “finance-as-socialism” files. Here’s an interesting working paper from Jose Azar on the rise of cross-ownership of US corporations, thanks in part to index funds and other passive investment vehicles.

The probability that two randomly selected firms in the same industry from the S&P 1500 have a common shareholder with at least 5% stakes in both firms increased from less than 20% in 1999Q4 to around 90% in 2014Q4 (Figure 1).1 Thus, while there has been some degree of overlap for many decades, and overlap started increasing around 2000, the ubiquity of common ownership of large blocks of stock is a relatively recent phenomenon. The increase in common ownership coincided with the period of fastest growth in corporate profits and the fastest decline in the labor share since the end of World War II…

A common element of theories of the firm boundaries is that … either firms are separately owned, or they combine. In stock market economies, however, the forces of portfolio diversification lead to … blurring firm boundaries… In the limit, when all shareholders hold market portfolios, the ownership of the firms becomes exactly identical. From the point of view of the shareholders, these firms should act “in unison” to maximize the same objective function… In this situation the firms have in some sense become branches of a larger corporate superorganism.

The same assumptions that generate the “efficiency” of market outcomes imply that public ownership could be just as efficient — or more so in the case of monopolies.

The present paper provides a precise efficiency rationale for … consumer and employee representation at firms… Consumer and employee representation can reduce the markdown of wages relative to the marginal product of labor and therefore bring the economy closer to a competitive outcome. Moreover, this provides an efficiency rationale for wealth inequality reduction –reducing inequality makes control, ownership, consumption, and labor supply more aligned… In the limit, when agents are homogeneous and all firms are commonly owned, … stakeholder representation leads to a Pareto efficient outcome … even though there is no competition in the economy.

As Azar notes, cross-ownership of firms was a major concern for progressives in the early 20th century, expressed through things like the Pujo committee. But cross-ownership also has been a central theme of Marxists like Hilferding and Lenin. Azar’s “corporate superorganism” is basically Hilferding’s finance capital, with index funds playing the role of big banks. The logic runs the same way today as 100 years ago. If production is already organized as a collective enterprise run by professional managers in the interest of the capitalist class as a whole, why can’t it just as easily be managed in a broader social interest?

 

Global pivot? Gavyn Davies suggests that there has been a global turn toward more expansionary fiscal policy, with the average rich country fiscal balances shifting about 1.5 points toward deficit between 2013 and 2016. As he says,

This seems an obvious path at a time when governments can finance public investment programmes at less than zero real rates of interest. Even those who believe that government programmes tend to be inefficient and wasteful would have a hard time arguing that the real returns on public transport, housing, health and education are actually negative.

I don’t know about that last bit, though — they don’t seem to find it that hard.

 

Taylor rule toy. The Atlanta Fed has a cool new gadget that lets you calculate the interest rate under various versions of the Taylor Rule. It will definitely be useful in the classroom. Besides the obvious pedagogical value, it also dramatizes a larger point — that macroeconomic variables like “inflation” aren’t objects simply existing in the world, but depend on all kinds of non-obvious choices about measurement and definition.

 

The new royalists. DeLong summarizes the current debates about monetary policy:

1. Do we accept economic performance that all of our predecessors would have characterized as grossly subpar—having assigned the Federal Reserve and other independent central banks a mission and then kept from them the policy tools they need to successfully accomplish it?

2. Do we return the task of managing the business cycle to the political branches of government—so that they don’t just occasionally joggle the elbows of the technocratic professionals but actually take on a co-leading or a leading role?

3. Or do we extend the Federal Reserve’s toolkit in a structured way to give it the tools it needs?

This is a useful framework, as is the discussion that precedes it. But what jumped out to me is how he reflexively rejects option two. When it comes to the core questions of economic policy — growth, employment, the competing claims of labor and capital — the democratically accountable, branches of government must play no role. This is all the more striking given his frank assessment of the performance of the technocrats who have been running the show for the past 30 years: “they—or, rather, we, for I am certainly one of the mainstream economists in the roughly consensus—were very, tragically, dismally and grossly wrong.”

I think the idea that monetary policy is a matter of neutral, technical expertise was always a dodge, a cover for class interests. The cover has gotten threadbare in the past decade, as the range and visibility of central bank interventions has grown. But it’s striking how many people still seem to believe in a kind of constitutional monarchy when it comes to central banks. They can see people who call for epistocracy — rule by knowers — rather than democracy as slightly sinister clowns (which they are). And they can simultaneously see central bank independence as essential to good government, without feeling any cognitive dissonance.

 

Did extending unemployment insurance reduce employment? Arin Dube, Ethan Kaplan, Chris Boone and Lucas Goodman have a new paper on “Unemployment Insurance Generosity and Aggregate Employment.” From the abstract:

We estimate the impact of unemployment insurance (UI) extensions on aggregate employment during the Great Recession. Using a border discontinuity design, we compare employment dynamics in border counties of states with longer maximum UI benefit duration to contiguous counties in states with shorter durations between 2007 and 2014. … We find no statistically significant impact of increasing unemployment insurance generosity on aggregate employment. … Our point estimates vary in sign, but are uniformly small in magnitude and most are estimated with sufficient precision to rule out substantial impacts of the policy…. We can reject negative impacts on the employment-to-population ratio … in excess of 0.5 percentage points from the policy expansion.

Media advisory with synopsis is here.

 

On other blogs, other wonders

Larry Summers: Low laborforce participation is mainly about weak demand, not demographics or other supply-side factors.

Nancy Folbre on Greg Mankiw’s claims that the one percent deserves whatever it gets.

At Crooked Timber, John Quiggin makes some familiar — but correct and important! — points about privatization of public services.

In the Baffler, Sam Kriss has some fun with the new atheists. I hadn’t encountered Kierkegaard’s parable of the madman who tells everyone who will listen “the world is round!” but it fits perfectly.

A valuable article in the Washington Post on cobalt mining in Africa. Tracing out commodity chains is something we really need more of.

Buzzfeed on Blue Apron. The reality of the robot future is often, as here, just that production has been reorganized to make workers less visible.

At Vox, Rachelle Sampson has a piece on corporate short-termism. Supports my sense that this is an area where there may be space to move left in a Clinton administration.

Sven Beckert has edited a new collection of essays on the relationship between slavery and the development of American capitalism. Should be worth looking at — his Empire of Cotton is magnificent.

At Dissent, here’s an interesting review of Jefferson Cowie’s and Robert Gordon’s very different but complementary books on the decline of American growth.

Links for September 23

I am going to strive to make these posts weekly. People need things to read.

 

The trouble with macro. I haven’t yet read any of the latest big-name additions to the “what’s wrong with macroeconomics?” pile: Romer (with update), Kocherlakota, Krugman, Blanchard. I should read them, maybe I will, maybe you should too. Here’s my own contribution, from a few years ago.

 

Tankus notes. You may know Nathan Tankus from around the internet. I’ve been telling him for a while that he should have a blog. He’s finally started one, and it’s very much worth reading. I’m having some trouble with one of his early posts. Well, that’s how it works: You comment on what you disagree with, not the things you think are smart and true and interesting — which in this case is a lot.

 

The shape of the elephant. Branko Milanovic’s “elephant graph” shows the changes in the global distribution of income across persons since 1980, as distinct from the more-familiar distribution of income within countries or between countries. The big story here is that while there has been substantial convergence, it isn’t across the board: The biggest gains were between the 10th and 75th percentiles of the global distribution, and at the very top; gains were much smaller in the bottom 10 percent and between the 70th and 99th percentiles. One question about this has been how much of this is due to China; as David Rosnick and now Adam Corlett of the Resolution Fondation note, if you exclude China the central peak goes away; it’s no longer true that growth was unusually fast in the middle of the global distribution. Corlett also claims that the very slow growth in the upper-middle part of the distribution — close to zero between the 75th and 85th percentiles — is due to big falls in income in the former Soviet block and Japan. Initially I liked the symmetry of this. But now I think Corlett is just wrong on this point; certainly he gives no real evidence for it.  In reality, the slow growth of that part of the distribution seems to be almost entirely an artifact due to the slow growth of population in the upper part of the distribution; correct for that, as Rosnick does here, and the non-China distribution is basically flat between the 10th and 99th percentiles:

Source: David Rosnick
Source: David Rosnick

Yes, there does seem to be slightly slower growth just below the top. But given the imprecision of the data we shouldn’t put much weight on it. And in any case whatever the effect of falling incomes in Japan and Eastern Europe (and blue-collar incomes in the US and western Europe), it’s trivial compared to the increase in China. Outside of China, the global story seems to be the familiar one of the very rich pulling ahead, the very poor falling behind, and the middle keeping pace. Of course, it is true, as the original elephant graph suggested, that the share of income going to the upper-middle has fallen; but again, that’s because of slower population growth in the countries where that part of the distribution is concentrated, not because of slower income gains.

It’s important to stress that no one is claiming that Branko’s figures are wrong, and also that Branko is on the side of the angels here. He’s been fighting the good fight for years against the whiggish presumption of universal convergence.

 

Equality of opportunity and revolution. Speaking of Branko, here he is on the problem with equality of opportunity:

Upward mobility for some implies downward mobility for the others. But if those currently at the top have a stronghold on the top places in society, there will no upward mobility however much we clamor for it. … In societies that develop quickly even if a lot of mobility is about positional advantages, … it can be compensated by creating enough new social layers, new jobs and by making people richer. …

In more stagnant societies, mobility becomes a zero-sum game. To effect real social mobility in such societies, you need revolutions that, while equalizing chances or rather improving dramatically the chances of those on the bottom, do so at the cost of those on the top. … The French Revolution, until Napoleon to some extent reimposed the old state of affairs, was precisely such an upheaval: it oppressed the upper classes (clergy and nobility) and promoted the poorer classes. The Russian revolution did the same thing; it introduced an explicit reverse discrimination against the sons and daughters of former capitalists, and even of the intellectuals, in the access to education.

I think this is right. The principle of equality of opportunity is incompatible, not just practically but logically, with the principle of inheritance. The only way to realize it is to deprive those at the top of their power and privileges, which by definition is possible only in a revolutionary situation. This is one reason why I have no interest in a political program defined, even in its incremental first steps, in terms of equality of income or wealth. The goal isn’t equality but the abolition of the system which makes quantitative comparisons of people’s life-situations possible.

The post continues:

There is also an age element to such revolutions which fundamentally alter societies and lift those from below to the top. The young people benefit. In a beautiful short novel entitled “The élan of our youth” Alexander Zinoviev, a Russian logician and later dissident, describes the Stalinist purges from a young man’s perspective. The purges of all 40- or 50-year old “Trotskyites” and “wreckers” opened suddenly incredible vistas of upward mobility for those who were 20- or 25-year old.  They could hope, at best, to come to the positions of authority in ten or fifteen years; now, that were suddenly thrown in charge of hundreds of workers, became chief designers of airplanes, top engineers of the metro. What was purge and Gulag for some, was upward mobility for others.

As this suggests, the overturning ofhierarchies didn’t stop with the revolutions themselves — that was the essential content of the various purges, to prevent a new elite from consolidating itself. I’ve always wondered how much vitality revolutionary France and Russia gained from these great overturnings. There are an enormous number of working-class people in our society, I have no doubt, who would be much more capable of running governments and factories, designing airplanes and subways, or teaching economics for that matter, than the people who get to do it.

 

We simply do not know — but we can fake it. Aswath Damodaran has a delicious post on the valuations that Elon Musk’s bankers came up with to justify Tesla’s acquisition of Solar City. The basic problem in these kinds of exercises is that the same price has to look high to the shareholders of the acquired company and low to the shareholders of the acquiring company. In this case, the Solar City shareholders have to believe that the 0.11 Tesla shares they are getting are worth more than the Solar City share they are giving up, while the Tesla shareholders have to believe just the opposite — that one Solar City share is worth more than the 0.11 Tesla shares they are giving for it. You can square this circle by postulating some gains from the combination — synergies! efficiencies! or, sotto voce, market power — that allows the acquirer to pay a premium over the market price while still supposedly getting a bargain. Those gains may be bullshit but at least there’s a story that makes sense. But as Damodaran explains, that isn’t even attempted here. Instead the two sets of advisors (both ultimately hired by Musk) simply use different assumptions for the growth rates and cost of capital for the two companies, generating two different valuations. For instance, Tesla’s advisors assume that Solar City’s existing business will grow at 3-5% in perpetuity, while Solar City’s advisors assume the same business will grow at 1.5-3%. So one set of shareholders can be told that a Solar City share is definitely worth less than 0.11 Tesla shares, while the other set of shareholders can be told that it is definitely worth more.

So what’s the interest here? Obviously, it’s always fun to se someone throwing shoes at the masters of the universe. But with my macroeconomist hat on, the important thing is it’s a snapshot of the concrete sociology behind the discounting of future cashflows. Whenever we talk about “the market” valuing some project or business, we are ultimately talking about someone at Lazard or Evercore plugging values into a spreadsheet. This is something people who imagine that production decisions are or can be based on market signals — including my Proudhonist friends — would do well to keep in mind. Solar City lost money last year. It lost money this year. It will lose money next year. It keeps going anyway not because “the market” wants it to, but because Musk and his bankers want it to. And their knowledge of the future isn’t any better founded than the rest of ours. Now, you could argue that this case is noteworthy because the projections are unusually bogus. Damodaran suggests they aren’t really, or only by degree. And in any case this sort of special pleading wouldn’t work if there were an objective basis for computing the true value of future cashflows. I suspect it was precisely Keynes’ experience with real-world financial transactions like this that made him stress the fundamental unknowability of the future.

 

Uber: The bar mitzvah moment. While we’re reading Damodaran, here’s another well-aimed shoe, this one at Uber. As he says, pushing down costs is not enough to make profits. You also need some way of charging more than costs. You need some kind of monopoly power, some source of rents: network externalities; increasing returns, and the financing to take advantage of them; proprietary technology; brand loyalty; explicit or implicit collusion with your competitors. Which of these does Uber have? maybe not any? Uber’s foray into self-driving cars is perhaps a way to generate rents, though they’re more likely to accrue to the companies that actually own the technology; I think it’s better seen as a ploy to convince investors for another quarter or two that there are rents there to be sought.

Izabella Kaminska covers some of the same territory in what may be the definitive Uber takedown at FT Alphaville. Though perhaps she focuses overmuch on how awful it would be if Uber’s model worked, and not enough on how unlikely it is to.

 

On other blogs, other wonders. 

San Francisco Fed president John Williams writes, “during a downturn, countercyclical fiscal policy should be our equivalent of a first responder to recessions.” Does this mean that MMT has won?

Mike Konczal: Trump is full of policy.

My friend Sarah Jaffe interviews my friend Vamsi, on the massive strikes going on in India.

The Harry Potter books are bad books and and have a bad, childish, reactionary view of the world. So does J. K. Rowling.

The Mason-Tanebaum household has its first byline in the New York Times this week, with Laura’s review of the novel Black Wave in the Sunday books section.

 

 

Potential Output: Why Should We Care?

Brian Romanchuk has a characteristically thoughtful post making “the case against growth and stimulus.” He’s responding to pieces by Larry Summers and John Cochrane arguing that macroeconomic policy should focus more on output growth.

Brian has two objections to this. First, environmental resource constraints are real. Not in an absolute sense — in principle a given throughput of physical inputs can be associated with an arbitrarily high GDP. But in our economies as currently organized there is a tight connection between rising GDP and increased use of fossil fuels. Even leaving aside climate change concerns, that means that faster growth may well be cut off by a spike in oil prices. [1] The second objection is that the link between higher growth and better labor-market outcomes may not be as tight as Summers suggests. In Brian’s view, things like public investment may not do much for incomes at the bottom because the

U.S. labour market is obviously segmented. The “high skill” segments are doing relatively well… Non-targeted “demand management” (such as infrastructure spending) is probably going to require creating jobs for college-educated workers. (You need an engineering degree to sign off on plans, for example.) It is a safe bet that the job market for college graduates would become extremely tight before the U-6 unemployment rate even begins to close on its historical lows. This would cause inflationary pressures…

This suggests that the focus should be on direct job-creation programs for people left out of the private market, rather than policies to raise aggregate demand.

Since I am (very slowly) making an argument that there is space for more expansionary policy, evidently I disagree.

Before saying why, I should add one other argument on Brian’s side. One reason to be against “growth” as a political project is that higher GDP does not increase people’s wellbeing. In my view this is clearly true for countries with per-capita GDP above $15,000-20,000 or so. This is a moderately respectable view these days, though obviously a minority one. For most economists the case for growth is still so obvious it doesn’t even need stating — having more stuff makes people happier.

I don’t believe that. But I still think it’s worth arguing that there is more space for expansionary policy to raise GDP. For three reasons:

First, I think Summers and Cochrane are right (!) about the importance of tight labor markets to raise wages, flatten the income distribution and increase the social power of working people more broadly. I don’t think you would have had the mass social movements of the 1960s and 70s (even on such apparently non-economic ones as feminism and gay rights) if there hadn’t been a long period of very tight labor markets. [2] The threat of unemployment maintains the power of the boss in the workplace, and that reinforces all kinds of other hierarchies as well.

Corollary to this, I’m not convinced that the labor market is as segmented as Brian suggests. I think that in many cases, people with more credentials get to the front of the queue for the same jobs, as opposed to competing for a distinct pool of jobs. It seems to me the historical evidence is unambiguous that when overall unemployment falls there are disproportionate gains for those at the bottom.

Second, I think the idea of a hard ceiling to potential output is an important part of the logic of scarcity that hems in our political imagination in all kinds of harmful ways. Yes, infrastructure spending, and sometimes also increased social spending, even a basic income, can be presented as measures to boost demand and output. But you can also look at it the other way — these are good things on the merits, and the claim that they will boost output is just a way of defusing arguments that we “can’t afford” them. To me, the policy importance of saying we are far from any real supply constraint is not that higher output is desirable in itself (apart from its labor-market effects); it’s that it strengthens the argument for public spending that’s desirable for its own sake.

Third, on a more academic level, I think the idea of a fixed exogenous potential output is one of the most important patches (along with the “natural rate of interest”) covering up the disconnect between the “real exchange” world of economic theory and the actual monetary production economy we live in. Assuming that the long-run path of output is fixed by real supply-side factors is a way of quarantining monetary and demand factors to the short run. So the more space we open up for demand-side effects, the more space we have to analyze the economy as a system of money claims and payments and coordination problems rather than the efficient allocation of scare resources

 

[1] As it happens, this was the the topic of the first real post on this blog.

[2] The best discussion of this link I know of is in Armstrong, Glyn and Harrison’s Capitalism Since 1945. Jefferson Cowie’s more recent book on the ’70s makes a similar case for the US specifically.

 

(I wrote this post a month ago and for some reason never posted it.)

 

UPDATE: There’s another argument I meant to mention. When I look around I see a world full of energetic, talented, creative people forced to spend their days doing tedious shitwork and performing servility. I find it morally offensive to claim that a job at McDonald’s or in a nail salon or Amazon warehouse is the fullest use of anyone’s potential. When Keynes says that we will build “our New Jerusalem out of the labour which in our former vain folly we were keeping unused and unhappy in enforced idleness,” he doesn’t have to mean literal idleness. In a society in which aggregate expenditure was constantly pushing against supply constraints, millions of people today who spend the working hours of the day having the humanity slowly ground out of them would instead be developing their capacities as engineers, artists, electricians, doctors, scientists. To say that most of the jobs we expect people to do today make full use of their potential is a vile slander, even if we are only measuring potential by the narrow standards of GDP.

Can We Blame Low Labor Participation on Past High Unemployment?

Fifth post in a series. Posts one, two, three and four.

We know that US GDP fell sharply in 2008-2009. We know that none of that decline has been made up by faster growth since the recession: GDP today is about 14 percent below the pre-2008 trend, a gap that shows no sign of closing. We also know that one-third of that shortfall is accounted for by slower productivity growth, and the remaining two-thirds by slower employment growth.

To put numbers on it: Over the past decade, US employment rose by a total of 6 percent, or about 0.5 percent per year. This is about half the rate of employment growth over the last ten years before the recession, and less a quarter the average rate for the postwar period as a whole. 2000-2010 was the first decade since the Depression in which US employment actually fell. Since the unemployment rate today is very close to that of ten years ago, this whole slowdown is accounted for by a decline in laborforce participation.

Employment growth, unlike productivity growth, was already slowing prior to the recession, and  pre-recession forecasts predicted a further slowdown comparable to what actually occurred. This is consistent with a widely-held view that the slowdown in employment is the result of demographic and other structural factors, not of the recession or demand weakness in general. In the next couple posts, I want to take a critical look at this claim. How confident should we be that employment would be the same today in a counterfactual world where the 2008-2009 didn’t happen? How responsive might employment be to stronger demand going forward? And more broadly, how much do changes in laborforce participation seem to be explained by more or less exogenous factors like demographics, and how much by demand and labor-market conditions?

The rest of this post is about an approach to this question that did not produce the results I was hoping for. So I probably won’t include this material in whatever paper comes out of these posts. But as we feel our way into reality it’s important to note down the dead ends as well as the routes that seem promising. And even though this exercise didn’t help much in answering the big questions posed in the previous paragraph, it’s still interesting in its own right.

*

Can the fall in laborforce participation be explained as a direct, predictable effect of the rise in unemployment during the recession? It seems like maybe it can. The starting point is the observation that unemployed workers are much more likely to drop out of the laborforce than people with jobs are. You can see this clearly in the BLS tables on employment transitions. As the figure below shows, about 3 percent of employed people exit the laborforce each month, a fraction that has been remarkably stable since the data begins in 1990. Meanwhile, about 20 percent of unemployed people drop out of the laborforce each month.

transitions1

On the face of it, this 17-point difference suggests an important role for the unemployment rate in changes in labor force participation. All else equal, each year-point of additional unemployment should reduce the labor-force participation rate by two points. (0.17 x 12 = 2.) So you would think that much of the recent fall in laborforce participation could be explained simply by the rise in unemployment during the recession.

When I thought of this it seemed very logical. It would be easy to do a counterfactual exercise, I thought, showing how laborforce participation would have evolved simply based on the historical transition rates between employment, unemployment and out of the laborforce, and the actual evolution of employment and unemployment. If you could show that something like the actual fall in laborforce participation was a predictable result of the rise in unemployment during the recession, that would support the idea that demand rather than “structural” factors are at work. And even if it wasn’t that strong positive evidence, it would suggest skepticism about similar counterfactual exercises using historical participation rates by age and so on.

I mean, it makes sense, right? Unemployed people are much more likely to leave the workforce than employed people, so a rise in unemployment should naturally lead to a decline in laborforce participation. But as the figure below shows, the numbers don’t work.

What I did was start with the populations of employe, unemployed and not-in-the-laborforce people at the end of the recession in December 2009. Then I created a counterfactual scenario for the remaining period using the actual transition rates between employment and unemployment but the pre-recession average rates for transitions between not in the workforce and unemployment and employment. In other words, just knowing the average rates that people move between employment, unemployment and out of the workforce, and the actual shifts between employment and unemployment, what path would you have predicted for laborforce participation over 2010-2016?

transitions2The heavy gray line shows the historical fraction of the population aged 16 and over who are not in the laborforce. The black line shows the results of the counterfactual exercise. Not very close.

There turn out to be two reasons why the counterfactual exercise gives such a poor fit. Both are interesting and neither was obvious before doing the exercise. The first reason is that there are  surprisingly large flows from out of the labor force back into it. Per the BLS, about 7 percent of people who report being out of the labor force in a given month are either employed or unemployed (i.e. actively seeking work) the following month. This implies that the typical duration of being out of the workforce is less than a year — though of course this is a mix of people who leave the workforce for just a month or two and people who leave for good. For present purposes, the important thing is that exogenous changes to the employment-population ratio decline quickly, with a half-life of only about a year. So while the historical data suggests that a rise in unemployment like we saw in 2008-2009 should have been associated with a large rise in the share of the population not in the laborforce, it also suggests that this effect should have been transitory — a couple years after unemployment rates returned to normal, participation rates should have as well. This is not what we’ve seen.

The large gross movements in and out of the laborforce mean that sustained lower participation rates can’t be straightforwardly understood as the “echo” of high unemployment in the past. But they do also tend to undermine the structural story — if the typical stint outside the laborforce lasts less than a year it can hardly be due to something immutable.

The second reason why the counterfactual doesn’t fit the data was even more surprising, at least to me. I constructed my series using the historical average transition rates into and out of the workforce. But transition rates during the recession and early recovery departed from the historical average in an important way: unemployed workers were significantly less likely to exit the workforce. This turns out to be the normal pattern, at least over the previous two business cycles — if you look back to that first figure, you can see dips in the transition rate from unemployed to out of the workforce in the early 1990s and early 2000s downturns as well. The relationship is clearer in the next figure, a scatter of the unemployment rate and the share of unemployed workers leaving the workforce each month.

transitions3

 

As you can see, there is a strong negative relationship — when unemployment was around 4 percent in 1999-2000 and again in 2006-2007, about a quarter of the unemployed exited the laborforce each month. But at the peak of the past recession when unemployment reached 10 percent, only 18 percent of the unemployed left the laborforce each month. That might not seem like a huge difference, but it’s enough to produce quite different dynamics. It’s also a bit surprising, since you would think that people would be more likely to give up searching for work when unemployment is high than when when it is low. The obvious explanation would be that the people who are out of work when the unemployment rate is low are not simply a smaller set of the same people who are out of work when the rate is high, but are different in some way. The same factors that keep them at the back of the hiring queue may make also be likely to push them out of the laborforce altogether. Extended unemployment insurance might also play a role.

It would be possible to explore this further using CPS data, which is the source for the BLS tables I’m working with. No doubt there are papers out there describing the different characteristics of the unemployed in periods of high versus low unemployment. (Not being a labor economist, I don’t know this literature.) But I am going to leave it here.

Summary: The fact that unemployed people are much more likely to leave the laborforce than employed people are, suggests that some part of the fall in laborforce particiaption since 2008 might be explained by the lingering effects of high unemployment in the recession and early recovery. But this story turns out not tow work, for two reasons. First, the rapid turnover of the not in the laborforce population means that this direct effect of high unemployment on participation is fairly shortlived. Second, the rate at which unemployed people exit the laborforce turns out to be lower when unemployment is high. Together, these two factors produce the results shown in the second figure — the fall in participation you would predict based simply on high unemployment is steeper but shorter-lived than what actually occurred. The first factor — the large flows in and out of the laborforce — while it vitiates the simple story I proposed here, is consistent with a broader focus on demand rather than demographics as an explanation for slow employment growth. If people are frequently moving in and out of the laborforce, it’s likely that their decisions are influenced by their employment prospects, and it means they’re not determined by fixed characteristics like age. The second factor — that unemployed people were less likely to give up looking for jobs during 2009-2011, as in previous periods of high unemployment — is, to me, more surprising, and harder to fit into a demand-side story.

Employment, Productivity and the Business Cycle

Fourth post in a series. Posts one, two and three.

Empirically-oriented macroeconomists have recognized since the early 20th century that output, employment and productivity move together over the business cycle. The fact that productivity falls during recessions means that employment varies less over the cycle than output does. This behavior is quite stable over time, giving rise to Okun’s law. In the US, Okun’s law says that the unemployment rate will rise by one point in each 2.5 point shortfall of GDP growth over trend — a ratio that doesn’t seem to have declined much since Arthur Okun first described it in the mid-1960s. [1]

It’s not obvious that potential should show this procyclical behavior. As I noted in the previous post, a naive prediction from a production function framework would be that a negative demand shock should reduce employment more than output, since business can lay off workers immediately but can’t reduce their capital stock right away. In other words, productivity should rise in recessions, since the labor of each still-employed worker is being combined with more capital.

There are various explanations for why labor productivity behaves procyclically instead. The most common focus on the transition costs of changing employment. Since hiring and firing is costly for businesses, they don’t adjust their laborforce to every change in demand. So when sales fall in recessions, they will keep extra workers on payroll — paying them now is cheaper than hiring them back later. Similarly, when sales rise businesses will initially try to get more work out of their existing employees. This shows up as rising labor productivity, and as the repeated phenomenon of “jobless recoveries.”

Understood in these terms, the positive relationship between output, employment and productivity should be a strictly short-term phenomenon. If a change in demand (or in other constraints on output) is sustained, we’d expect labor to fully adjust to the new level of production sooner or later. So over horizons of more than a year or two, we’d expect output and employment to change in proportion. If there are other limits on production (such as non-produced inputs like land) we’d expect output and labor productivity to move inversely, with faster productivity growth associated with slower employment growth or vice versa. (This is the logic of “robots taking the jobs.”) A short-term positive, medium term negative, long-term flat or negative relationship between employment growth and productivity growth is one of the main predictions that comes out of a production function. But it doesn’t require one. You can get there lots of other ways too.

And in fact, it is what we see.

prod-emp correl

The figure shows the simple correlation of employment growth and productivity growth over various periods, from one quarter out to 50 quarters. (This is based on postwar US data.) As you can see, over periods of a year or less, the correlation is (weakly) positive. Six-month periods in which employment growth was unusually weak are somewhat more likely to have seen weak productivity growth as well. This is the cyclical effect presumably due to transition costs — employers don’t always hire or fire in response to short-run changes in demand, allowing productivity to vary instead. But if sales remain high or low for an extended period, employers will eventually bring their laborforce into line, eliminating this relationship. And over longer periods, autonomous variation in productivity and labor supply are more important. Both of these tend to produce a negative relationship between employment and productivity. And that’s exactly what we see — a ten-year period in which productivity grew unusually quickly is likely to be one in which employment grew slowly. (Admittedly postwar US data doesn’t give you that many ten-year periods to look at.)

Another way of doing this is to plot an “Okun coefficient” for each horizon. Here we are looking at the relationship between changes in employment and output. Okun’s law is usually expressed in terms of the relatiojship between unemployment and output, but here we will look at it in terms of employment instead. We write

(1)    %ΔE = a (g – c)

where %ΔE is the percentage change in employment, g is the percentage growth in GDP, c is a constant (the long-run average rate of productivity growth) and a is the Okun coefficient. The value of a says how much additional growth in employment we’d expect from a one percentage-point increase in GDP growth over the given period. When the equation is estimated in terms of unemployment and the period is one, year, a is generally on the order of 0.4 or so, meaning that to reduce unemployment by one point over a year normally requires GDP growth around 2.5 points above trend. We’d expect the coefficient for employment to be greater, but over short periods at least it should still be less than one.

Here is what we see if the estimate the equation for changes in output and employment for various periods, again ranging from one quarter up to ten years. (Again, postwar US data. The circles are the point estimates of the coefficients; the dotted lines are two standard errors above and below, corresponding to a standard 95% confidence interval.)

emp on output

What’s this show? If we estimate Equation (1) looking at changes over one quarter, we find that one percentage point of additional GDP growth is associated with just half a point of additional employment growth. But if we estimate the same equation looking at changes over two years, we find that one point of additional GDP growth is associated with 0.75 points of additional employment growth.

The fact that the coefficient is smallest for the shorter periods is, again, consistent witht he conventional understanding of Okun’s law. Because hiring and firing is costly, employers don’t fully adjust staffing unless a change in sales is sustained for a while. If you were thinking in terms of a production function, the peak around 2 years represents a “medium-term” position where labor has adjusted to a change in demand but the capital stock has not.

While it’s not really relevant for current purposes, it’s interesting that at every horizon the coefficient is significantly below zero. What this tells us is that there is no actual time interval corresponding to the “long run” of the model– a period long enough for labor and the capital stock to be fully adjusted but short enough that technology is fixed. Over this hypothetical long run, the coefficient would be one. One way to think about the fact that the estimated coefficients are always smaller, is that any period long enough for labor to adjust, is already long enough to see noticeable autonomous changes in productivity. [2]

But what we’re interested in right now is not this normal pattern. We’re interested in how dramatically the post-2008 period has departed from it. The past eight years have seen close to the slowest employment growth of the postwar period and close to the slowest productivity growth. It is normal for employment and productivity to move together for a couple quarters or a year, but very unusual for this joint movement to be sustained over nearly a decade. In the postwar US, at least, periods of slow employment growth are much more often periods of rapid productivity growth, and conversely. Here’s a regression similar to the Okun one, but this time relating productivity growth to employment growth, and using only data through 2008.

prod on empWhile the significance lines can’t be taken literally given that these are overlapping periods, the figure makes clear that between 1947 and 2008, there were very few sustained periods in which both employment and productivity growth made large departures from trend in the same direction.

Put it another way: The past decade has seen exceptionally slow growth in employment — about 5 percent over the full period. If you looked at the US postwar data, you would predict with a fair degree of confidence that a period of such slow employment growth would see above-average productivity growth. But in fact, the past decade has also seen very low productivity growth. The relation between the two variables has been much closer to what we would predict by extrapolating their relationships over periods of a year. In that sense, the current slowdown resembles an extended recession more than it does previous periods of slower growth.

As I suggested in an earlier post, I think this is a bigger analytic problem than it might seem at first glance.

In the conventional story, productivity is supposed to be driven by technology, so a slowdown in productivity growth reflects a decline in innovation and so on. Employment is driven by demographics, so slower employment growth reflects aging and small families. Both of these developments are negative shifts in aggregate supply. So they should be inflationary — if the economy’s productive potential declines then the same growth in demand will instead lead to higher prices. To maintain stable prices in the face of these two negative supply shocks, a central bank would have to raise interest rates in order to reduce aggregate spending to the new, lower level of potential output. Is this what we have seen? No, of course not. We have seen declining inflation even as interest rates are at historically low levels. So even if you explain slower productivity growth by technology and explain slower employment growth by demographics, you still need to postulate some large additional negative shift in demand. This is DeLong and Summers’ “elementary signal identification point.”

Given that we are postulating a large, sustained fall in demand in any case, it would be more parsimonious if the demand shortfall also explained the slowdown in employment and productivity growth. I think there are good reasons to believe this is the case. Those will be the subject of the remaining posts in this series.

In the meantime, let’s pull together the historical evidence on output, employment and productivity growth in one last figure. Here, the horizontal axis is the ten-year percentage change in employment, while the vertical axis is the ten-year percentage change in productivity. The years are final year of the comparison. (In order to include the most recent data, we are comparing first quarters to first quarters.) The color of the text shows average inflation over the ten year period, with yellow highest and blue lowest. The diagonal line corresponds to the average real growth rate of GDP over the full period.

e-p scatter

What we’re looking at here is the percentage change in productivity, employment and prices over every ten-year period from 1947-1957 through 2006-2016. So for instance, growth between 1990 and 2000 is represented by the point labeled “2000.” During this decade, total employment rose by about 20 percent while productivity rose by a total of 15 percent, implying an annual real growth of 3.3 percent, very close to the long-run average.

One natural way to think about this is that yellow points below and to the right of the line suggest negative supply shocks: If the productive capacity of the economy declines for some reason, output growth will slow, and prices will rise as private actors — abetted by a slow-to-react central bank — attempt to increase spending at the usual rate. Similarly, blue points above the line suggest positive supply shocks. Yellow points above the line suggest positive demand shocks — an increase in spending can increase output growth above trend, at least for a while, but will pull up prices as well. And blue points below the line suggest negative demand shocks. This, again, is Delong and Summers’ “elementary signal identification point.”

We immediately see what an outlier the recent period is. Both employment and productivity growth over the past ten years have been drastically slower than over the preceding decade — about 5 percent each, down from about 20 percent. 2000-2010 and 2001-2011 were the only ten-year periods in postwar US history when total employment actually declined. The abruptness of the deceleration on both dimensions is a challenge for views that slower growth is the result of deep structural forces. And the combination of the slowdown in output growth with falling prices — especially given ultra-low interest rats — strongly suggests that we’ve seen a negative shift in desired spending (demand) rather than in the economy’s productive capacities (supply).

Another way of looking at this is as three different regimes. In the middle is what we might call “the main sequence” — here there is steady growth in demand, met by varying mixes of employment and productivity growth. On the upper right is what gets called a “high-pressure economy,” in which low unemployment and strong demand draw more people into employment and facilitates the reallocation of labor and other resources toward more productive activity, but put upward pressure on prices. On the lower left is stagnation, where weak demand discourages participation in the labor force and reduces productivity growth by holding back investment, new business formation and by leaving a larger number of those with jobs underemployed, and persistent slack leads to downward pressure on prices (though so far not outright deflation). In other words, macroeconomically speaking the past decade has been a sort of anti-1960s.

 

[1] There are actually two versions of Okun’s law, one relating the change in the unemployment rate to GDP growth and one relating the level of unemployment to the deviation of GDP from potential. The two forms will be equivalent if potential grows at a constant rate.

[2] The assumption that variables can be partitioned into “fast” and “slow” ones, so that we can calculate equilibrium values of the former with the latter treated as exogenous, is a very widespread feature of economic modeling, heterodox as much as mainstream. I think it needs to be looked at more critically. One alternative is dynamic models where we focus on the system’s evolution over time rather than equilibrium conditions. This is, I suppose, the kind of “theory” implied by VAR-type forecasting models, but it’s rare to see it developed explicitly. There are people who talk about a system dynamics approach, which seems promising, but I don’t know much about them.