Tag: macroeconomic history

Does the Fed Still Believe in the NAIRU?

(I write occasional opinion pieces for Barron’s. This one was published there in October 2024. My previous pieces are here.)

Not long ago, there was widespread agreement on how to think about monetary policy. When the Federal Reserve hikes, this story went, it makes credit more expensive, reducing spending on new housing and other forms of capital expenditure. Less spending means less demand for labor, which means higher unemployment. With unemployment higher, workers accept smaller wage gains, and slower wage growth is in turn passed on as slower growth in prices — that is, lower inflation. 

This story, which you still find in textbooks, has some strong implications. One is that there was a unique level of unemployment consistent with stable 2% inflation — what is often called the “nonaccelerating inflation rate of unemployment,” or NAIRU. 

The textbook story also assumes that  wage- and price-setting depend on expectations of future prices. So it’s critical for central banks to stabilize not only current inflation but beliefs about future inflation; this implies a commitment to head off any inflationary pressures even before prices accelerate. On the other hand, if there is a unique unemployment rate consistent with stable inflation, then the Fed’s mandate is dual only in name. In practice, full employment and price stability come to the same thing.

In the early 21st century, all this seemed sufficiently settled that fundamental debates over monetary policy could be treated as a question for history, not present-day economics.

The worldwide financial crisis of 2007-2009 unsettled the conversation. The crisis, and, even more, the glacial recovery that followed it, opened the door to alternative perspectives on monetary policy and inflation. Jerome Powell, who took office as Federal Open Market Committee chair in 2018, was more open than his predecessors to a broader vision of both the Fed’s goals and the means of achieving them. In the decade after the crisis, the idea of a unique, fundamentals-determined NAIRU came to seem less plausible.

These concerns were crystallized in the strategic review process the Fed launched in 2019. That review resulted, among other things, in a commitment to allow future overshooting of the 2% inflation target to make up for falling short of it. The danger of undershooting seemed greater than in the past, the Fed acknowledged.

One might wonder how much this represents a fundamental shift in the Fed’s thinking, and how much it was simply a response to the new circumstances of the 2010s. Had Fed decision-makers really changed how they thought about the economy?

Many of us try to answer these questions by parsing the publications and public statements of Fed officials. 

A fascinating recent paper by three European political scientists takes this approach and carries it to a new level. The authors—Tobias Arbogast, Hielke Van Doorslaer and Mattias Vermeiern—take 120 speeches by FOMC members from 2012 through 2022, and systematically quantify the use of language associated with defense of the NAIRU perspective, and with various degrees of skepticism toward it. Their work allows us to put numbers on the shift in Fed thinking over the decade. 

The paper substantiates the impression of a move away from the NAIRU framework in the decade after the financial crisis. By 2019-2020, references to the natural rate or to the need to preempt inflation had almost disappeared from the public statements of FOMC members, while expressions of uncertainty about the natural rate, of a wait-and-see attitude toward inflation, and concern about hysteresis (long-term effects of demand shortfalls) had become more common. The mantra of “data dependence,” so often invoked by Powell and others, is also part of the shift away from the NAIRU framework, since it implies less reliance on unobservable parameters of economic models. 

Just as interesting as the paper’s confirmation of a shift in Fed language, is what it says about how the shift took place. It was only in small part the result of changes in the language used by individual FOMC members. A much larger part of the shift is explained by the changing composition of the FOMC, with members more committed to the NAIRU gradually replaced by members more open to alternative perspectives. 

The contrast between 2014-2018 Chair Janet Yellen and Powell is particularly noteworthy in this respect. Yellen, by the paper’s metric, was among the most conservative members of the FOMC, most committed to the idea of a fixed NAIRU and the need to preemptively raise rates in response to a strong labor market. Powell is at the opposite extreme — along with former Vice Chair Lael Brainard, he is the member who has most directly rejected the NAIRU framework, and who is most open to the idea that tight labor markets have long-term benefits for income distribution and productivity growth. The paper’s authors suggest, plausibly, that Powell’s professional training as a lawyer rather than an economist means that he is less influenced by economic models; in any case, the contrast shows how insulated the politics of the Fed are from the larger partisan divide.

Does the difference in conceptual frameworks really matter? The article’s authors argue that it does, and I agree. FOMC members may sincerely believe that they are nonideological technicians, pragmatically responding to the latest data in the interests of society as a whole. But data and interests are always assessed through the lens of some particular worldview. 

To take one important example: In the NAIRU framework, the economy’s productive potential is independent of monetary policy, while inflation expectations are unstable. This implies that missing the full employment target has at worst short-term effects, while missing the inflation target grows more costly over time. NAIRU, in other words, makes a preemptive strike on any sign of inflation seem reasonable. 

On the other hand, if you think that hysteresis is real and important, and that inflation is at least sometimes a question of supply disruptions rather than unanchored expectations, then it may be the other way round. Falling short of the employment target may be the error with more lasting consequences. This is a perspective that some FOMC members, particularly Powell and Brainard, were becoming open to prior to the pandemic.

Perhaps even more consequential: if there is a well-defined NAIRU and we have at least a rough idea of what it is, then it makes sense to raise rates in response to a tight labor market, even if there is no sign, yet, of rising inflation. But if we don’t believe in the NAIRU, or at least don’t feel any confidence about its level, then it makes more sense to focus more on actual inflation, and less on the state of the labor market.

By the close of the 2010s, the Fed seemed to be well along the road away from the NAIRU framework. What about today? Was heterodox language on inflation merely a response to the decade of weak demand following the financial crisis, or did it represent a more lasting shift in how the Fed thinks about its mission?

On this question, the evidence is mixed. After inflation picked up in 2022, we did see some shift back to the older language at the Fed. You will not find, in Powell’s recent press conferences, any mention of the longer-term benefits of a tight labor market that he pointed to a few years ago. Hysteresis seems to have vanished from the lexicon. 

On the other hand, the past few years have also not been kind to those who see a tight link between the unemployment rate and inflation. When inflation began rising at the start of 2021, unemployment was still over 6%; two years later, when high inflation was essentially over, unemployment was below 4%. If the Fed had focused on the unemployment rate, it would have gotten inflation wrong both coming and going.

This is reflected in the language of Powell and other FOMC members. One change in central-bank thinking that seems likely to last, is a move away from the headline unemployment rate as a measure of slack. The core of the NAIRU framework is a tight link between labor-market conditions and inflation. But even if one accepts that link conceptually, there’s no reason to think that the official unemployment rate is the best measure of those conditions. In the future, we are likely to see discussion of a broader set of labor-market indicators.

The bigger question is whether the Fed will return to its old worldview where tight labor markets are seen as in themselves an inflationary threat. Or will it stick with its newer, agnostic and data-driven approach, and remain open to the possibility that labor markets can stay much stronger than we are used to, without triggering rising inflation? Will it return to a single-minded focus on inflation, or has there been a permanent shift to giving more independent weight on the full employment target? As we watch the Fed’s actions in coming months, it will be important to pay attention not just to what they do, but to why they say they are doing it.

 

FURTHER THOUGHTS: I really liked the Arbogast et al. paper, for reasons I couldn’t fully do justice to in the space of a column like this.

First of all, in addition to the new empirical stuff, it does an outstanding job laying out the intellectual framework within which the Fed operates. For better or worse, monetary policy is probably more reliant than most things that government does on a consciously  held set of theories.

Second, it highlights — in a way I have also tried to — the ways that hysteresis is not just a secondary detail, but fundamentally undermines the conceptual foundation on which conventional macroeconomic policy operates. The idea that potential output and long-run growth (two sides of the same coin) are determined prior to, and independent of, current (demand-determined) output, is what allows a basically Keynesian short-run framework to coexist with the the long-run growth models that are the core of modern macro. If demand has lasting effects on the laborforce, productivity growth and potential output, then that separation becomes untenable, and the whole Solow apparatus floats off into the ether. In a world of hysteresis, we no longer have a nice hierarchy of “fast” and “slow” variables; arguably there’s no economically meaningful long run at all.1

Arbogast and co don’t put it exactly like this, but they do emphasize that the existence of hysteresis (and even more reverse hysteresis, where an “overheating” economy permanently raises potential) fundamentally undermine the conventional distinction between the short run and the long run.

This leads to one of the central points of the paper, which I wish I’d been able to highlight more: the difference between what they call “epistemological problematization” of the NAIRU, that is doubts about how precisely we can know it and related “natural” parameters; and “ontological problematization,” or doubts that it is a relevant concept for policy at all. At a day to day operational level, the difference may not always be that great; but I think — as do the authors — that it matters a lot for the evolution of policy over longer horizons or in new conditions.

The difference is also important for those of us thinking and writing about the economy. The idea of some kind of “natural” or “structural” parameters, of a deeper model that abstracts from demand and money, deviations from which are both normatively bad and important only in the short term — this is an incubus that we need to dislodge if we want to move toward any realistic theorizing about capitalist economies. It substitutes an imaginary world with none of the properties of the world that matter for most of the questions we are interested — a toy train set to play with instead of trying to solve the very real engineering problems we face.

I appreciate the paper’s concluding agnosticism about how far the Fed has actually moved away form this framework. As I mentioned in the piece, I was struck by their finding that among the past decade’s FOMC members, Powell has moved the furthest away from NAIRU and the rest of it. If nothing else, it vindicates some of my own kind words about him in the runup to his reappointment.2

This is also, finally, an example of what empirical work in economics ought to look like.3 First, it’s frankly descriptive. Second, it asks a question which has a quantitative answer, with substantively interesting variation (across both time and FOMC members, in this case.) As Deirdre McCloskey stressed in her wonderful pamphlet The Secret Sins of Economics, the difference between answers with quantitative and qualitative answers is the difference between progressive social science and … whatever economics is.

What kind of theory would actually contribute to an … inquiry into the world? Obviously, it would be the kind of theory for which actual numbers can conceivably be assigned. If Force equals Mass times Acceleration then you have a potentially quantitative insight into the flight of cannon balls, say. But the qualitative theorems (explicitly advocated in Samuelson’s great work of 1947, and thenceforth proliferating endlessly in the professional journals of academic economics) don’t have any place for actual numbers.

A qualitative question, in empirical work, is a question of the form “are these statistical results consistent or inconsistent with this theoretical claim?” The answer is yes, or no. The specific numbers — coefficients, p-values, and of course the tables of descriptive statistics people rush through on their way to the good stuff — are not important or even meaningful. All that matters is whether the null has been rejected.

McCloskey, insists, correctly in my view, that this kind of work adds nothing to the stock of human knowledge. And I am sorry to say that it is just as common in heterodox work as in the mainstream.

To add to our knowledge of the world, empirical work must, to begin with, tell you something you didn’t know before you did it. “Successfully” confirming your hypothesis obviously fails this test. You already believed it! It also must yield particular factual claims that other people can make use of. In general, this means some number — it means answer a “how much” question and not jsut a “yes or no” question. And it needs to reveal variation in those quantities along some interesting dimension. Since there are no universal constants to uncover in social science, interesting results will always be about how something is bigger, or more important in one time, one country, one industry, etc. than in another. Which means, of course, that the object of any kind of empirical work should be a concrete historical development, something that happened at a specific time and place.

One sign of good empirical work is that there are lots of incidental facts that are revealed along the way, besides the central claim. As Andrew Gelman observed somewhere, in a good visualization, the observations that depart from the relationship you’re illustrating should be as informative as the ones that fit it.

This paper delivers that. Along with the big question of a long term shift, or not, in the Fed’s thinking, you can see other variation that may or may be relevant to the larger question but are interesting facts about the world in their own right. If, for example, you look at the specific examples of language they coded in each category, then a figure like shows lots of interesting fine-grained variation over time.

Also, in passing, I appreciate the fact that they coded the terms themselves and didn’t outsource the job to ChatGPT. I’ve seen a couple papers doing quantitative analysis of text, that use chatbots to classify it. I really hope that does not become the norm!

Anyway, it’s a great paper, which I highly recommend, both for its content and as a model for what useful empirical work in economics should look like.

 

At Barron’s: Americans Owe Less Than They Used To. Will the Fed Change That?

(I write a monthly opinion piece for Barron’s. This one was published there in September.)

Almost everyone, it seems, now agrees that higher interest rates mean economic pain. This pain is usually thought of in terms of lost jobs and shuttered businesses. Those costs are very real. But there’s another cost of rate increases that is less discussed: their effect on balance sheets.

Economists tend to frame the effects of interest rates in terms of incentives for new borrowing. As with (almost) anything else, if loans cost more, people will take less of them. But interest rates don’t matter only for new borrowers, they also affect people who borrowed in the past. As debt rolls over, higher or lower current rates get passed on to the servicing costs of existing debt. The effect of interest rate changes on the burden of existing debt can dwarf their effect on new borrowing—especially when debt is already high.

Let’s step back for a moment from current debates. One of the central macroeconomic stories of recent decades is the rise in household debt. In 1984, it was a bit over 60% of disposable income, a ratio that had hardly changed since 1960. But over the next quarter-century, debt-income ratios would double, reaching 130%. This rise in household debt was the background of the worldwide financial crisis of 2007-2008, and made household debt a live political question for the first time in modern American history.

Household debt peaked in 2008; it has since fallen almost as quickly as it rose. On the eve of the pandemic, the aggregate household debt-income ratio stood at 92%—still high, by historical standards, but far lower than a decade before.

These dramatic swings are often explained in terms of household behavior. For some on the political right, rising debt in the 1984-2008 period was the result of misguided government programs that encouraged excessive borrowing, and perhaps also a symptom of cultural shifts that undermined responsible financial management. On the political left, it was more likely to be seen as the result of financial deregulation that encouraged irresponsible lending, along with income inequality that pushed those lower down the income ladder to spend beyond their means.

Perhaps the one thing these two sides would agree on is that a higher debt burden is the result of more borrowing.

But as economist Arjun Jayadev and I have shown in a series of papers, this isn’t necessarily so. During much of the period of rising debt, households borrowed less on average than during the 1960s and 1970s. Not more. So what changed? In the earlier period, low interest rates and faster nominal income growth meant that a higher level of debt-financed expenditure was consistent with stable debt-income ratios.

The rise in debt ratios between 1984 and 2008, we found, was not mainly a story of people borrowing more. Rather, it was a shift in macroeconomic conditions that meant that the same level of borrowing that had been sustainable in a high-growth, low-interest era was unsustainable in the higher-interest environment that followed the steep rate hikes under Federal Reserve Chair Paul Volcker. With higher rates, a level of spending on houses, cars, education and other debt-financed assets that would previously have been consistent with a constant debt-income ratio, now led to a rising one.

(Yes, there would later be a big rise in borrowing during the housing boom of the 2000s. But this is not the whole story, or even the biggest part of it.)

Similarly, the fall in debt after 2008 in part reflects sharply reduced borrowing in the wake of the crisis—but only in part. Defaults, which resulted in the writing-off of about 10% of household debt over 2008-2012, also played a role. More important were the low interest rates of these years. Thanks to low rates, the overall debt burden continued to fall even as households began to borrow again.

In effect, low rates mean that the same fraction of income devoted to debt service leads to a larger fall in principal—a dynamic any homeowner can understand.

The figure nearby illustrates the relative contributions of low rates and reduced borrowing to the fall in debt ratios after 2008. The heavy black line is the actual path of the aggregate household debt-income ratio. The red line shows the path it would have followed if households had not reduced their borrowing after 2008, but instead had continued to take on the same amount of new debt (as a share of their income) as they did on average during the previous 25 years of rising debt. The blue line shows what would have happened to the debt ratio if households had borrowed as much as they actually did, but had faced the average effective interest rate of that earlier period.

As you can see, both reduced borrowing and lower rates were necessary for household debt to fall. Hold either one constant at its earlier level, and household debt would today be approaching 150% of disposable income. Note also that households were paying down debt mainly during the crisis itself and its immediate aftermath—that’s where the red and black lines diverge sharply. Since 2014, as household spending has picked up again, it’s only thanks to low rates that debt burdens have continued to fall.

(Yes, most household debt is in the form of fixed-rate mortgages. But over time, as families move homes or refinance, the effective interest rate on their debt tends to follow the rate set by the Fed.)

The rebuilding of household finances is an important but seldom-acknowledged benefit of the decade of ultra-low rates after 2007. It’s a big reason why the U.S. economy weathered the pandemic with relatively little damage, and why it’s growing so resiliently today.

And that brings us back to the present. If low rates relieved the burden of debt on American families, will rate hikes put them back on an unsustainable path?

The danger is certainly real. While almost all the discussion of rate hikes focuses on their effects on new borrowing, their effects on the burden of existing debt are arguably more important. The 1980s—often seen as an inflation-control success story—are a cautionary tale in this respect. Even though household borrowing fell in the 1980s, debt burdens still rose. The developing world—where foreign borrowing had soared in response to the oil shock—fared much worse.

Yes, with higher rates people will borrow less. But it’s unlikely they will borrow enough less to offset the increased burden of the debt they already have. The main assets financed by credit—houses, cars, and college degrees—are deeply woven into American life, and can’t be easily foregone. It’s a safe bet that a prolonged period of high rates will result in families carrying more debt, not less.

That said, there are reasons for optimism. Interest rates are still low by historical standards. The improvement in household finances during the post-2008 decade was reinforced by the substantial income-support programs in the relief packages Congress passed in response to the pandemic; this will not be reversed quickly. Continued strong growth in employment means rising household incomes, which, mechanically, pushes down the debt-income ratio.

Student debt cancellation is also well-timed in this respect. Despite the fears of some, debt forgiveness will not boost  current demand—no interest has been paid on this debt since March 2020, so the immediate effect on spending will be minimal. But forgiveness will improve household balance sheets, offsetting some of the effect of interest rate hikes and encouraging spending in the future, when the economy may be struggling with too little demand rather than (arguably) too much.

Reducing the burden of debt is also one of the few silver linings of inflation. It’s often assumed that if people’s incomes are rising at the same pace as the prices of the things they buy, they are no better off. But strictly speaking, this isn’t true—income is used for servicing debt as well as for buying things. Even if real incomes are stagnant or falling, rising nominal incomes reduce the burden of existing debt. This is not an argument that high inflation is a good thing. But even bad things can have benefits as well as costs.

Will we look back on this moment as the beginning of a new era of financial instability, as families, businesses, and governments find themselves unable to keep up with the rising costs of servicing their debt? Or will the Fed be able to declare victory before it has done too much damage? At this point, it’s hard to say.

Either way we should focus less on how monetary policy affects incentives, and more time on how it affects the existing structure of assets and liabilities. The Fed’s ability to steer real variables like GDP and employment in real time has, I think, been greatly exaggerated. Its long-run influence over the financial system is a different story entirely.

Fisher Dynamics Revisited

Back in the 2010s, Arjun Jayadev and I wrote a pair of papers (one, two) on the evolution of debt-income ratios for US households. This post updates a couple key findings from those papers. (The new stuff begins at the table below.)

Rather than econometric exercises, the papers were based on a historical accounting decomposition —  an approach that I think could be used much more widely. We separated changes in the debt-income ratio into six components — the primary deficit (borrowing net of debt service payments); interest payments; real income growth; inflation; and write downs of debt through default — and calculated the contribution of each to the change in debt ratios over various periods. This is something that is sometimes done for sovereign debt but, as far as I know, we were the first to do it for private debt-income ratios.

We referred to the contributions of the non-borrowing components as “Fisher dynamics,” in honor of Irving Fisher’s seminal paper on depressions as “debt deflations.” A key aspect of the debt-deflation story was that when nominal incomes fell, the burden of debt could rise even as debtors sharply reduced new borrowing and devoted a greater share of their income to paying down existing debt. In Fisher’s view, this was one of the central dynamics of the Great Depression. Our argument was that something like a slow-motion version of this took place in the US (and perhaps elsewhere) in recent decades.

The logic here is that the change in debt-income ratios is a function not only of new borrowing but also of the effects of interest, inflation and (real) income growth on the existing debt ratio, as well as of charge offs due to defaults.

Imagine you have a mortgage equal to double your annual income. That ratio can go down if your current spending is less than your income, so that you can devote part of your income to paying off the principal. Or it can go down if your income rises, i.e. by raising the denominator rather than lowering the numerator. It can also go down if you refinance at a lower interest rate; then the same fraction of your income devoted to debt service will pay down the principal faster. Our of course it can go down if some or all of it is written off in bankruptcy.

It is possible to decompose actual historical changes in debt-income ratios for any economic unit or sector into these various factors. The details are in either of the papers linked above. One critical point to note: The contributions of debt and income growth are proportional to the existing debt ratio, so the higher it already is, the more important these factors are relative to the current surplus or deficit.

Breaking out changes in debt ratios into these components was what we did in the two papers. (The second paper also explored alternative decompositions to look at the relationship been debt ratio changes and new demand from the household sector.) The thing we wanted to explain was why some periods saw rising debt-income ratios while others saw stable or falling ones.

While debt–income ratios were roughly stable for the household sector in the 1960s and 1970s, they rose sharply starting in the early 1980s. The rise in household leverage after 1980 is normally explained in terms of higher household borrowing. But increased household borrowing cannot explain the rise in household debt after 1980, as the net flow of funds to households through credit markets was substantially lower in this period than in earlier postwar decades. During the housing boom period of 2000–2007, there was indeed a large increase in household borrowing. But this is not the case for the earlier rise in household leverage in 1983–1990, when the debt– income ratios rose by 20 points despite a sharp fall in new borrowing by households.

As we explained:

For both the 1980s episode of rising leverage and for the post-1980 period as a whole, the entire rise in debt–income ratios is explained by the rise in nominal interest rates relative to nominal income growth. Unlike the debt deflation of the 1930s, this ‘debt disinflation’ has received little attention from economists or in policy discussions.

Over the full 1984–2011 period, the household sector debt–income ratio almost exactly doubled… Over the preceding 20 years, debt–income ratios were essentially constant. Yet households ran cumulative primary deficits equal to just 3 percent of income over 1984–2012 (compared to 20 percent in the preceding period). The entire growth of household debt after 1983 is explained by the combination of higher interest payments, which contributed an additional 3.3 points per year to leverage after 1983 compared with the prior period, and lower inflation, which reduced leverage by 1.3 points per year less.

We concluded:

From a policy standpoint, the most important implication of this analysis is that in an environment where leverage is already high and interest rates significantly exceed growth rates, a sustained reduction in household debt–income ratios probably cannot be brought about solely or mainly via reduced expenditure relative to income. …There is an additional challenge, not discussed in this paper, but central to both Fisher’s original account and more recent discussions of ‘balance sheet recessions’: reduced expenditure by one sector must be balanced by increased expenditure by another, or it will simply result in lower incomes and/or prices, potentially increasing leverage rather than decreasing it. To the extent that households have been able to run primary surpluses since 2008, it has been due mainly to large federal deficits and improvement in US net exports.

We conclude that if reducing private leverage is a policy objective, it will require some combination of higher growth, higher inflation, lower interest rates, and higher rates of debt chargeoffs. In the absence of income growth well above historical averages, lower nominal interest rates and/or higher inflation will be essential. … Deleveraging via low interest rates …  implies a fundamental shift in monetary policy. If interest-rate policy is guided by the desired trajectory of debt ratios, it no longer can be the primary instrument assigned to managing aggregate demand. This probably also implies a broader array of interventions to hold down market rates beyond traditional open market operations, policies sometimes referred to as ‘financial repression.’ Historically, policies of financial repression have been central to almost all episodes where private (or public) leverage was reduced without either high inflation or large-scale repudiation.

These papers only went through 2011. I’ve thought for a while it would be interesting to revisit this analysis for the more recent period of falling household debt ratios. 

With the help of Arjun’s student Advait Moharir, we’ve now brought the same analysis forward to the end of 2019. Stopping there was partly a matter of data availability — the BEA series on interest payments we use is published with a considerable lag. But it’s also a logical period to look at, since it brings us up to the start of the pandemic, which one would want to split off anyway.

The table below is a reworked version of tables in the two papers, updated through 2019. (I’ve also adjusted the periodization slightly.) 

Due to …
Period Annual PP Change in Debt Ratio Primary Deficit Interest Growth Inflation Defaults
1929 – 1931 3.7 -5.5 2.9 2.8 2.9 *
1932 – 1939 -1.2 -1.5 2.4 -1.6 -0.7 *
1940 – 1944 -3.8 -1.6 1.3 -2.5 -1.9 *
1945 – 1963 2.6 2.5 2.6 -1.5 -0.8 *
1964 – 1983 0.0 0.8 5.1 -2.4 -3.5 *
1984 – 1999 1.7 -0.3 7.5 -2.9 -2.1 -0.4
2000 – 2008 4.5 2.4 7.2 -1.7 -2.5 -0.8
2009 – 2013 -5.4 -3.7 5.8 -3.1 -2.3 -2.4
2014 – 2019 -2.0 -1.4 4.6 -3.4 -1.3 -0.6

Again, our central finding in the earlier papers was that if we compare the 1984-2008 period of rising debt ratios to the previous two decades of stable debt ratios, there was no rise in the primary deficit. For 1984-2008 as a whole, annual new borrowing exceeded debt service payments by 0.7 percent of income on average, almost exactly the same as during the 1964-1983 period. (That’s the weighted average of the two sub-periods shown in the table.) Even during the housing boom period, when new borrowing did significantly exceed debt service, this explained barely a third of the difference in annual debt-ratio growth (1.6 out of 4.5 points).

The question now is, what has happened since 2008? What has driven the fall in debt ratios from 130 percent of household income in 2008 to 92 percent on the eve of the pandemic?

In the immediate aftermath of the crisis, sharply reduced borrowing was indeed the main story. Of the 10-point swing in annual debt-ratio growth (from positive 4.5 points per year to negative 5.4), 6 points is accounted for by the fall in net borrowing (plus another 1.5 points from higher defaults). But for the 2014-2019 period, the picture is more mixed. Comparing those six years to the whole 1984-2008 period of rising debt, we have a 4.7 point shift in debt ratio growth, from positive 2.7 to negative 2. Of that, 2.1 points is explained by lower net borrowing, while almost 3 points is explained by lower interest. (The contribution of nominal income growth was similar in the two periods.) So if we ask why household debt ratios continued to fall over the past decade, rather than resuming their rise after the immediate crisis period, sustained low interest rates are at least as important as household spending decisions. 

Another way to see this is in the following graph, which compares three trajectories: The actual one in black, and two counterfactuals in red and blue. The red counterfactual is constructed by combining the average 1984-2008 level of net borrowing as a fraction of income to the actual historical rates of interest, nominal income growth and defaults. The blue counterfactual is similarly constructed by combining the average 1984-2008 effective interest rate with historical levels of net borrowing, nominal income growth and defaults. In other words, the red line shows what would have happened in a world where households had continued to borrow as much after 2008 as in the earlier period, while the blue line shows what would have happened if households had faced the same interest rates after 2008 as before. 

As the figure shows, over the 2008-2019 period as a whole, the influence of the two factors is similar — both lines end up in the same place. But the timing of their impact is different. In the immediate wake of the crisis, the fall in new borrowing was decisive — that’s why the red and black lines diverge so sharply. But in the later part of the decade, as household borrowing moved back toward positive territory and interest rates continued to fall, the more favorable interest environment became more important. That’s why the blue line starts rising after 2012 — if interest rates had been at their earlier level, the borrowing we actually saw in the late 2010s would have implied rising debt ratios. 

As with the similar figures in the papers, this figure was constructed by using the law of motion for debt ratios:

where b is the debt-income ratio, d is the primary deficit, i is the effective interest rate (i.e. total interest payments divided by the stock of debt), g is income growth adjusted for inflation, π is the inflation rate, and sfa is a stock-flow adjustment term, in this case the reduction of debt due to defaults. The exact sources and definitions for the various variables can be found in the papers. (One note: We do not have a direct measurement of the fraction of household debt written off by default for the more recent period, only the fraction of such debt written down by commercial banks. So we assumed that the ratio of commercial bank writeoffs of household debt to total writeoffs was the same for the most recent period as for the period in which we have data for both.)

Starting from the actual debt-ratio in the baseline year (in this case, 2007), each year’s ending debt-income ratio is calculated using the primary deficit (i.e. borrowing net of debt service payments), the share of debt written off in default, nominal income growth and the interest rate. All but one of these variables are the actual historical values; for one, I instead use the average value for 1984-2007. This shows what the path of the debt ratio would have been if that variable had been fixed at its earlier level while the others evolved as they did historically.  In effect, the difference between these counterfactual lines and the historical one shows the contribution of that variable to the difference between the two periods.

Note that the interest rate here is not the current market rate, but the effective or average rate, that is, total interest payments divided by the stock of debt. For US households, this fell from around 6 percent in 2007 to 4.4 percent by 2019 — less than the policy rate did, but still enough to create a very different trajectory, especially given the compounding effect of interest on debt over time. So while expansionary monetary policy is not the whole story of falling debt ratios since 2008, it was an important part of it. As I recently argued in Barrons, the deleveraging of US households is unimportant and under appreciated benefit of the decade of low interest rates after the crisis.

 

The Return of the Renter

Every month, the Census releases new numbers on new housing construction. As an indicator of current economic conditions, June’s numbers didn’t give any dramatic news one way or another. But they did highlight a trend that I think should get more attention: the decline of single-family housing in the US.

To market watchers, housing is an important sign of business cycle turning points. A well-known article argues that Housing Is the Business Cycle.  From this point of view, June’s numbers were not very informative. They told the same story the last several months’ did: After steadily rising from the end of the recession, housing construction has stabilized — housing starts and permits issued have been basically unchanged since early 2017. Last month’s housing starts were almost exactly the same as last summer’s. The fact that housing construction is no longer rising might perhaps be seen as a sign of economic weakness; but it’s hard to take it as a sign of a crisis or imminent downturn.But pulling back from the month by month variation, the most recent numbers reflect two related trends that may be more important than the ups and downs of the business cycle.

The first trend is the secular decline in housing construction. Housing starts, while higher than  a few years ago, are still very low by historical standards — not just compared with the boom period of the 2000s, but with most earlier periods as well. On a per capita basis, new housing construction is at a level seen only at the bottom of the worst recessions before 2007.  Compared with an annual average of 6.5 new units per thousand people in the 1980s and 1990s, the current rate is less than 4 per thousand, and shows no sign of returning to the old rate.

It’s hard to say how much this decline in new housing construction is a specifically post-bubble-and-crisis phenomenon, and how much it reflects longer-term trends. People sometimes suggest that low rates of housing construction are the flipside of the housing boom of the 2000s. There was a strong case for this in the years immediately after the recession, when the fraction of vacant houses was well above historical levels. But since then, the inventory of vacant houses has come down toward more normal levels.

Meanwhile, if we look at new housing construction per capita over a longer period, there is a fairly steady long-term decline – it’s not clear that the most recent period is exceptional. If you draw an exponential trend from 1959 through 1999 (the start of the housing bubble), as shown in the figure below, the current level of housing starts falls right on that trend. And relative to the shortfall in new construction during 2008-2015 is not too much greater than the excess of new construction during 1999-2007. To put it another way, the percentage decline in housing starts per capita over the past 20 years, is not much bigger than the average decline over any 20 year period since the 1950s. 

Of course, this is just one way of looking at the numbers. There are many ways to draw a trend! And one might argue that, historically, the top of a boom should see new housing starts well above trend, suggesting that the recent decline is something new after all. You might also reasonably wonder whether the long term trend has any substantive meaning at all. The political economy of housing the 1950s and 1960s was different from today on all sorts of levels. It wouldn’t be hard to look at the same data in terms of a structural break, rather than — or in addition to — a downward trend.

For macroeconomic purposes, though, it doesn’t necessarily matter. Whether it reflects the ongoing effects of the subprime crisis  or whether it reflects longer-term factors — slowing population growth, an aging population, the end of suburbanization – or whether it’s some mix of both, the decline in new housing construction remains an important economic fact.

Among other things, it is important for macroeconomic policy. Mortgage lending is central to the financial system: Housing accounts for over 70 percent of household debt, and housing finance plays a central role in financial instability. Conversely, residential construction is the economic sector most sensitive to financial conditions, and to monetary policy in particular. So the shrinking weight of housing in the economy may be a factor in the Federal Reserve’s inability to restore growth and full employment after the crisis. Looking forward, if conventional monetary policy works primarily through residential construction, and residential construction is a permanently smaller part of the economy, that is another argument for broadening the Fed’s toolkit.

Housing construction may be down for the count, at least compared with historical levels. But — and this is the second trend – it is not down across the board. The recent decline is limited to single family housing. Multifamily construction has been quite strong, at least by the standards of the post-1990 period. Compared with the two decades before 2007, single-unit housing starts in the past year are down by a third. Multifamily starts are up by a third. Per capita multifamily housing starts are actually higher than they were at the height of the housing boom. These divergent trends imply a major shift in the composition of new housing. Through much of the 1990s, less than 10 percent of new housing was in multifamily projects. Today, the share is more like 30 percent. This is a dramatic change in the mix of housing being added, a shift change visible across much of the country in the form of suddenly-ubiquitous six-story woodframe apartment buildings. The most recent housing data released suggests that, if anything, this trend is still gathering steam: A full third of new housing in June was in multifamily buildings, an even higher proportion than we’ve seen in recent years. In the areas that the Census designates as metropolitan cores, the shift is even more dramatic, with the majority of new housing units now found in multifamily buildings. 

The shift in new construction away from single-family houses is consistent with the decline in homeownership. At 64 percent of households, the share of homeowners is 5 points lower than it was in the mid-2000s. In fact it’s back almost exactly where it was 30 years ago, before the big expansion in homeownership of the 1990s and 2000s. 

To be sure, multifamily housing and rental housing are not the same thing. But there is a very substantial overlap. Over 80 percent of detached single-family homes are owned by their occupants. Less than 20 percent of units in larger buildings are, and the share drops as the number of units in the building rises. While homeownership rates have fallen across the board over the past decade, these relative patterns have not changed. (See the figure below.) So it’s fair to say that the decline of homeownership and the shift toward multifamily developments are, if not the same trend, at least closely linked.The aggregate figures understate the decline in homeownership, because over this period the population has also been aging, and older families are much more likely to own their homes. (For a good discussion of these trends, see here.) For younger families, homeownership rates are lower than they have been in many decades. Compared with 40 years ago, homeownership rates are substantially lower for every age group except those 65 or older. Even compared with a decade ago, there has been a substantial fall in homeownership rates in younger age groups. As a result, the typical homeowner today is much older than in the past. Only a quarter of US homeowners today are younger than 45, compared with nearly half in the 1980s.

The same pattern is visible over the post-housing crash period, as shown in the figure below. Among those aged 30-44 – the ages when most Americans are starting families – the rate of homeownership is nearly 10 points lower than it was just a decade ago. The shift in housing construction toward multifamily buildings reflects the fact that Americans in their prime working years are much more likely to be renters than they used to be. This shift is important for politics as well as the economy. Tenant organizations were once an important vehicle for mass politics in American cities. In the progressive imagination of a century ago, workers were squeezed from one side by landlords and high rents just as they were squeezed from the other by bosses and low wages.   

After World War II, the focus of housing politics shifted away from tenants’ rights, and toward broadening access to home ownership. This shift reflected a genuine expansion of homeownership to middle class and working class families, thanks to a range of public supports — supports, it should be noted, from from which African-Americans were largely excluded. But it also reflected a larger vision of democratic politics in terms of a world of small property owners. Homeowners were expected — not without reason — to be more conservative, more ready to imagine themselves on the side of property owners in general. As William Levitt, developer of the iconic Long Island suburb, is supposed to have said: “No man who owns his own house and lot can be a communist.”

The idea of a property-owning democracy has deep roots in the American political imagination, and can be part of a progressive vision as well as a conservative one. Baby bonds – an endowment or grant given to everyone at the start of their life — are supposed to be a way to broaden property ownership in a way that opens up rather than shuts down possibilities for radical change. Here for example is Darrick Hamilton in his 2018 TED Talk. “Wealth,” he says, 

is the paramount indicator of economic security and well-being. It provides financial agency, economic security… We use words like choice, freedom to describe the benefits of the market, but it is literally wealth that gives us choice, freedom and optionality. Wealthier families are better positioned to finance an elite, independent school and college education, access capital to start a business, finance expensive medical procedures, reside in neighborhoods with higher amenities… Basically, when it comes to economic security, wealth is both the beginning and the end.

Descriptively, there’s certainly some truth to this. And with homes by far the most important form of middle-class wealth, policies to promote homeownership have been supported on exactly these grounds. Homeowners enjoy more security, stability, a cushion against financial setbacks, and the ability to pass their social position on to their children. The policy problem, from this point of view, is simply to ensure that everyone gets to enjoy these benefits. 

One way to keep people secure in their homes is to allow more people to own them. This has been the focus of US housing policy for most of the past century. But another way is to give tenants more of the protections that only homeowners currently enjoy. Outside a few major cities, renting has been assumed to be a transitory stage in the lifecycle, so there was little reason to worry about security of tenure for renters. A few years ago I was a guest on a radio show on rent control, and I suggested that apart from affordability,  an important goal of rent regulation was to protect people’s right to remain in their homes. The host was genuinely startled: “I’ve never heard someone say that a person has the right to remain in their home whether they own it or not.”

There are still plenty of people who see the decline in homeownership as a problem to be solved. But the shift in the housing stock toward multifamily units suggests that the trend toward increased  renting is unlikely to be reversed any time soon. (And even many single-family homes are now owned by investors.) The experience of the past 15 years suggests that, in any case, home ownership offers less security than we used to think.

If more and more Americans remain renters through their adult lives, the relationship with the landlords may again approach the relationship with the employer in political salience. Strengthening protections for tenants may again be the basis of political mobilization. And people may become more open to the idea that living in a place, whether or not you own it, gives you a moral claim on it — as beautifully dramatized, for example, in the 2019 movie The Last Black Man in San Francisco. 

We may already be seeing this shift in the political sphere. In recent years, there has been a resurgence of support for rent regulation. A ballot measure for statewide rent control failed in California, but various bills to extend or strengthen local rent regulation have gotten significant support. Oregon recently passed the nation’s first statewide rent control measure. And in New York, Governor Cuomo signed into law a sweeping bill strengthening rent regulation where it already exists — mainly New York City – and opening the way for municipalities around the state to pass their own rent regulations.

The revival of rent regulation reflects, in the first instance, political conditions – in New York, years of dogged organizing work by grassroots coalitions, as well as the primary defeats of most of the so-called Independent Democratic Conference, nominal Democrats who caucused with Republicans and gave them control of the State Senate. But it is not diminishing the hard work by rent-regulation supporters to suggest that the housing-market shift toward rentals made the terrain more favorable for them. When nearly half the population are renters, as in New York State, there is likely to be more support for rent regulation. The same dynamic no doubt played a role in the opposition to Amazon’s new headquarters in Queens: For most residents, higher property values meant higher rents, not windfall gains. 

To be sure, the United States is not (yet) New York. The majority of American families still live in homes they own. But as the new housing numbers remind us, it’s a smaller majority than it used to be, and likely to get even smaller in the future. Which suggests that, along with measures to democratize property-ownership, there is a future for measures like rent control, to ensure that non-property owners also have a secure claim on their part of our common wealth.


(Figures 1, 3 and 4 are my analysis of series from FRED: HOUST, HOUST1F, COMPUTSA, and POPTHM. Figure 2 is from the Census Housing and Vacancy Survey. Figures 5 and 6 are my analysis of ACS data.) 

Acquisitions as Corporate Money Hose

Among the small group of heterodox economics people interested in corporate finance, it is common knowledge that the stock market is a tool for moving money out of the corporate sector, not into it.  Textbooks may talk about stock markets as a tool for raising funds for investment, but this kind of financing is dwarfed by the payments each year from the corporations to shareholders.

The classic statement, as is often the case, is in Doug Henwood’s Wall Street:

Instead of promoting investment, the U.S. financial system seems to do quite the opposite… Take, for example, the stock market, which is probably the centerpiece of the whole enterprise. What does it do? Both civilians and professional apologists would probably answer by saying that it raises capital for investment. In fact it doesn’t. Between 1981 and 1997, U.S. nonfinancial corporations retired $813 billion more in stock than they issued, thanks to takeovers and buybacks. Of course, some individual firms did issue stock to raise money, but surprisingly little of that went to investment either. A Wall Street Journal article on 1996’s dizzying pace of stock issuance (McGeehan 1996) named overseas privatizations (some of which, like Deutsche Telekom, spilled into U.S. markets) “and the continuing restructuring of U.S. corporations” as the driving forces behind the torrent of new paper. In other words, even the new-issues market has more to do with the arrangement and rearrangement of ownership patterns than it does with raising fresh capital.

The pattern of negative net share issues has if anything only gotten stronger in the 20 years since then, with net equity issued by US corporations averaging around negative 2 percent of GDP. That’s the lower line in the figure below:

Source

 

Note that in the passage I quote, Doug correctly writes “takeovers and buybacks.” But a lot of other people writing in this area — definitely including me — have focused on just the buyback part. We’ve focused on a story in which corporate managers choose — are compelled or pressured or incentivized — to deliver more of the firm’s surplus funds to shareholders, rather than retaining them for real investment. And these payouts have increasingly taken the form of share repurchases rather than dividends.

In telling this story, we’ve often used the negative net issue of equity as a measure of buybacks. At the level of the individual corporation, this is perfectly reasonable: A firm’s net issue of stocks is simply its new issues less repurchases. So the net issue is a measure of the total funds raised from shareholders — or if it is negative, as it generally is, of the payments made to them.

It’s natural to extend this to the aggregate level, and assume that the net change in equities outstanding similarly reflects the balance between new issues and repurchases. William Lazonick, for instance, states as a simple matter of fact that “buybacks are largely responsible for negative net equity issues.” 4 But are they really?

If we are looking at a given corporation over time, the only way the shares outstanding can decline is via repurchases.5 But at the aggregate level, lots of other things can be responsible — bankruptcies, other changes in legal organization, acquisitions. Quantitatively the last of these is especially important.   Of course when acquisitions are paid in stock, the total volume of shares doesn’t change. But when they are paid in cash, it does. 6 In the aggregate, when publicly trade company A pays $1 billion to acquire publicly traded company B, that is just a payment from the corproate sector to the household sector of $1 billion, just as if the corporation were buying back its own stock. But if we want to situate the payment in any kind of behavioral or institutional or historical story, the two cases may be quite different.

Until recently, there was no way to tell how much of the aggregate share retirements were due to repurchases and how much were due to acquisitions or other causes.7 The financial accounts reported only a single number, net equity issues. (So even the figure above couldn’t be produced with aggregate data, only the lower line in it.) Under these circumstances the assumption that that buybacks were the main factor was reasonable, or at least as reasonable as any other.

Recently, though, the Fed has begun reporting more detailed equity-finance flows, which break out the net issue figure into gross issues, repurchases, and retirements by acquisition. And it turns out that while buybacks are substantial, acquisitions are actually a bigger factor in negative net stock issues. Over the past 20 years, gross equity issues have averaged 1.9 percent of GDP, repurchases have averaged 1.7 percent of GDP, and retirements via acquisitions just over 2 percent of GDP. So if we look only at corporations’ transactions in their own stock, it seems that that the stock market still is — barely — a net source of funds. For the corproate sector as a whole, of course, it is still the case that the stock market is, in Jeff Spross’ memorable phrase, a giant money hose to nowhere.

The figure below shows dividends, gross equity issues, repurchases and M&A retirements, all as a percent of GDP.

Source

What do we see here? First, the volume of shares retired through acquisitions is consistently, and often substantially, greater than the volume retired through repurchases. If you look just at the aggregate net equity issue you would think that share repurchases were now comparable to dividends as a means of distributing profits to shareholders; but it’s clear here that that’s not the case. Share repurchases plus acquisitions are about equal to dividends, but repurchases by themselves are half the size of dividends — that is, they account for only around a third of shareholder payouts.

One particular period the new data changes the picture is the tech boom period around 2000. Net equity issues were significantly negative in that period, on the order of 1 percent of GDP. But as we can now see, that was entirely due to an increased volume of acquisitions. Repurchases were flat and, by the standard of more recent periods, relatively low. So the apparent paradox that even during an investment boom businesses were paying out far more to shareholders than they were taking in, is not quite such a puzzle. If you were writing a macroeconomic history of the 1990s-2000s, this would be something to know.

It’s important data. I think it clarifies a lot and I hope people will make more use of it in the future.

We do have to be careful here. Some fraction of the M&A retirements are stock transactions, where the acquiring company issues new stock as a kind of currency to pay for the stock of the company it is acquiring.8 In these cases, it’s misleading to treat the stock issuance and the stock retirement as two separate transactions — as independent sources and uses of funds. It would be better to net those transactions out earlier before reporting the gross figures here. Unfortunately, the Fed doesn’t give a historical series of cash vs. stock acquisition spending. But in recent years, at least, it seems that no more than a quarter or so of acquisitions are paid in stock, so the figure above is at least qualitatively correct. Removing the stock acquisitions — where there is arguably no meaningful issue or retirement of stock, jsut a swap of one company’s for another’s — would move the M&A Retirements and Gross Equity Issues lines down somewhat. But the basic picture would remain the same.

It’s also the case that a large fraction of equity issues are the result of exercise of employee stock options. I suspect — tho again I haven’t seen definite data — that stock options accout for a large fraction, maybe a majority, of stock issues in recent decades. But this doesn’t change the picture as far as sectoral flows goes — it just means that what is being financed is labor costs rather than investment.

The bottom line here is, I don’t think we heterodox corporate finance people have thought enough about acquisitions. A major part of payments from corporations to shareholders are not distribution of profits in the usual sense, but payments by managers for control rights over a production process that some other shareholders have claims on. I don’t think our current models handle this well — we either think implicitly of a single unitary corporate sector, or we follow the mainstream in imagining production as a bouillabaisse in where you just throw in a certain amount of labor and a certain amount of capital, so it doesn’t matter who is in charge.

Of course we know that the exit, the liquidity moment, for many tech startups today is not an IPO — let alone reaching profitability under the management of early investors — but acquisition by an established company. But this familiar fact hasn’t really made it into macro analysis.

I think we need to take more seriously the role of Wall Street in rearranging ownership claims. Both because who is in charge of particular production processes is important. And because we can’t understand the money flows between corporations and households without it.

 

Reading Notes: Demand and Productivity

Here are two interesting articles on demand and productivity that people have recently brought to my attention.

The economic historian Gavin Wright — author of the classic account of the economic logic of the plantation — just sent me a piece he wrote a few years ago on the productivity boom of the 1990s. As he said in his email, his account of the ‘90s is very consistent with the suggestions I make in my Roosevelt paper about how strong demand might stimulate productivity growth.

In this article, Wright traces the idea that high wage regions will experience faster productivity growth back to H. J. Habbakuk’s 1962 American and British Technology in the Nineteenth Century. Then he assembles a number of lines of evidence that rapid wage growth drove the late-1990s productivity acceleration, rather than vice versa.

He points out that the widely-noted “productivity explosion” of the 1920s — from 1.5 percent a year to over 5 percent — was immediately preceded by a period of exceptionally strong wage growth: “The real price of labor in the 1920s … was between 50 and 70 percent higher than a decade earlier.” [1] The pressure of high wages, he suggests, encouraged the use of electricity and other general-purpose technologies, which had been available for decades but only widely adopted in manufacturing in the 1920s. Conversely, we can see the productivity slowdown of the 1970s as, at least in part, a result of the deceleration of wage growth, which — Wright argues — was the result of institutional changes including the decline of unions, the erosion of the minimum wage and other labor regulations, and more broadly the shift back toward “‘flexible labor markets,’ reversing fifty years of labor market policy.”

Turning to the 1990s, the starting point is the sharp acceleration of productivity in the second half of the decade. This acceleration was very widely shared, including sectors like retail where historically productivity growth had been limited. The timing of this acceleration has been viewed as a puzzle, with no “smoking gun” for simultaneous productivity boosting innovations across this range of industries over a short period. But “if you look at the labor market, you can find a smoking gun in the mid-1990s. … real hourly wages finally began to rise at precisely that time, after more than two decades of decline. … Unemployment rates fell below 4 percent — levels reached only briefly in the 1960s… Should it be surprising that employers turned to labor-saving technologies at this time?” This acceleration in real wages, Wright argues, was not the result of higher productivity or other supply-side factors; rather “it is most plausibly attributed to macroeconomic conditions, when an accommodating Federal Reserve allowed employment to press against labor supply for the first time in a generation.”

The productivity gains of the 1990s did, of course, involve new use of information technology. But the technology itself was not necessarily new. “James Cortada [2004] lists eleven key IT applications in the retail industry circa 1995-2000, including electronic shelf levels, scanning, electronic fund transfer, sales-based ordering and internet sales … with the exception of e-business, the list could have come from the 1970s and 1980s.”

Wright, who is after all a historian, is careful not to argue that there is a general law linking higher wages to higher productivity in all historical settings. As he notes, “such a claim is refuted by the experience of the 1970s, when upward pressures on wages led mainly to higher inflation…” In his story, both sides are needed — the technological possibilities must exist, and there must be sufficient wage pressure to channel them into productivity-boosting applications. I don’t think anyone would say he’s made a decisive case , but if you’re inclined to a view like this the article certainly gives you more material to support it.

*

A rather different approach to these questions is this 2012 paper by Servaas Storm and C. W. M. Naastepad. Wright is focusing on a few concrete episodes in the history of a particular country, which he explores using a variety of material — survey and narrative as well as conventional economic data. Storm and Naastepad are proposing a set of general rules that they support with a few stylized facts and then explore via of the properties of a formal model. There are things to be learned from both approaches.

In this case the model is simple: output is demand-determined. Demand is either positive or negative function of the wage share (i.e. the economy is either wage-led or profit-led). And labor productivity is a function of both output and the wage, reflecting two kinds of channels by which demand can influence productivity. And an accounting identity says that employment growth is qual to output growth less labor productivity growth. The productivity equation is the distinctive feature here. Storm and Naastepad adopt as “stylized facts” — derived from econometric studies but not discussed in any detail — that both parameters are on the order of 0.4: An additional one percent growth in output, or in wages, will lead to an 0.4 percent growth in labor productivity.

This is a very simple structure but it allows them to draw some interesting conclusions:

– Low wages may boost employment not through increased growth or competitiveness, but through lower labor productivity. (They suggest that this is the right way to think about the Dutch “employment miracle of the 1990s.)

– Conversely, even where demand is wage-led (i.e. a shift to labor tends to raise total spending) faster wage growth is not an effective strategy for boosting employment, because productivity will rise as well. (Shorter hours or other forms of job-sharing, they suggest, may be more successful.)

– Where demand is strongly wage-led (as in the Scandinavian countries, they suggest), profits will not be affected much by wage growth. The direct effect of higher wages in this case could be mostly or entirely offset by the combination of higher demand and higher productivity. If true, this has obvious implications for the feasibility of the social democratic bargain there.

– Where demand is more weakly wage-led or profit-led (as with most structuralists, they see the US as the main example of the latter), distributional conflicts will be more intense. On the other hand, in this case the demand and productivity effects work together to make wage restraint a more effective strategy for boosting employment.

It’s worth spelling out the implications a bit more. A profit-led economy is one in which investment decisions are very sensitive to profitability. But investment is itself a major influence on profit, as a source of demand and — emphasized here — as a source of productivity gains that are captured by capital. So wage gains are more threatening to profits in a setting in which investment decisions are based largely on profitability. In an environment in which investment decisions are motivated by demand or exogenous animal spirits (“only a little more than an expedition to the South Pole, based on a calculation of benefits to come”), capitalists have less to fear from rising wages. More bluntly: one of the main dangers to capitalists of a rise in wages, is their effects on the investment decisions of other capitalists.

Saving and Borrowing: A Response to Klein

Matthew Klein has a characteristically thoughtful post disagreeing with my new paper on income distribution and debt. I think his post has some valid arguments, but also, from my point of view, some misunderstandings. In any case, this is the conversation we should be having.

I want to respond on the specific points Klein raises. But first, in this post, I want to clarify some background conceptual issues. In particular, I want to explain why I think it’s unhelpful to think about the issues of debt and demand in terms of saving.

Klein talks a great deal about saving in his post. Like most people writing on these issues, he treats the concepts of rising debt-income ratios, higher borrowing and lower saving as if they were interchangeable. In common parlance, the question “why have households borrowed more?” is equivalent to “why have households saved less?” And either way, the spending that raises debt and reduces saving, is also understood to contribute to aggregate demand.

This conception is laid out in Figure 1 below. These are accounting rather than causal relationships. A minus sign in the link means the relationship is negative.

 

We start with households’ decision to consume more or less out of their income. Implicitly, all household outlays are for consumption, or at least, this is the only flow of household spending that varies significantly. An additional dollar of household consumption spending means an additional dollar of demand for goods and services; it also means a dollar less of savings. A dollar less of savings equals a dollar more of borrowing. More borrowing obviously means higher debt, or — equivalently in this view — a higher debt-GDP ratio.

There’s nothing particularly orthodox or heterodox about this way of looking at things. You can hear the claim that a rise in the household debt-income ratio contributes more or less one for one to aggregate demand as easily from Paul Krugman as from Steve Keen. Similarly, the idea that a decline in savings rates is equivalent to an increase in borrowing is used by Marxists as well as by mainstream economists, not to mention eclectic business journalists like Klein. Of course no one actually says “we assume that household assets are fixed or nonexistent.” But implicitly that’s what you’re doing when you treat the question of what has happened to household borrowing as if it were the equivalent of what has happened to household saving.

There is nothing wrong, in principle, with thinking in terms of the logic of Figure 1, or constructing models on that basis. Social science is impossible without abstraction. It’s often useful, even necessary, to think through the implications of a small subset of the relationships between economic variables, while ignoring the rest. But when we turn to  the concrete historical changes in macroeconomic quantities like household debt and aggregate demand in the US, the ceteris paribus condition is no longer available. We can’t reason in terms of the hypothetical case where all else was equal. We have to take into account all the factors that actually did contribute to those changes.

This is one of the main points of the debt-inequality paper, and of my work with Arjun Jayadev on household debt. In reality, much of the historical variation in debt-income ratios and related variables cannot be explained in terms of the factors in Figure 1. You need something more like Figure 2.

Figure 2 shows a broader set of factors that we need to include in a historical account of household sector balances. I should emphasize, again, that this is not about cause and effect. The links shown in the diagram are accounting relationships. You cannot explain the outcomes at the bottom without the factors shown here. [1] I realize it looks like a lot of detail. But this is not complexity for complexity’s sake. All the links shown in Figure 2 are quantitatively important.

The dark black links are the same as in the previous diagram. It is still true that higher household consumption spending reduces saving and raises aggregate demand, and contributes to lower saving and higher borrowing, which in turn contributes to lower net wealth and an increase in the debt ratio. Note, though, that I’ve separated saving from balance sheet improvement. The economic saving used in the national accounts is quite different from the financial saving that results in changes in the household balance sheet.

In addition to the factors the debt-demand story of Figure 1 focuses on, we also have to consider: various actual and imputed payment flows that the national accounts attribute to the household sector, but which do not involve any money payments to or fro households (blue); the asset side of household balance sheets (gray); factors other than current spending that contribute to changes in debt-income ratios (red); and change in value of existing assets (cyan).

The blue factors are discussed in Section 5 of the debt-distribution paper. There is a much fuller discussion in a superb paper by Barry Cynamon and Steve Fazzari, which should be read by anyone who uses macroeconomic data on household income and consumption. Saving, remember, is defined as the difference between income and consumption. But as Cynamon and Fazzari point out, on the order of a quarter of both household income and consumption spending in the national accounts is accounted for by items that involve no actual money income or payments for households, and thus cannot affect household balance sheets.

These transactions include, first, payments by third parties for services used by households, mainly employer-paid premiums for health insurance and payments to healthcare providers by Medicaid and Medicare. These payments are counted as both income and consumption spending for households, exactly as if Medicare were a cash transfer program that recipients then chose to use to purchase healthcare. If we are interested in changes in household balance sheets, we must exclude these payments, since they do not involve any actual outlays by households; but they still do contribute to aggregate demand. Second, there are imputed purchases where no money really changes hands at all.  The most important of these are owners’ equivalent rent that homeowners are imputed to pay to themselves, and the imputed financial services that households are supposed to purchase (paid for with imputed interest income) when they hold bank deposits and similar assets paying less than the market interest rate. Like the third party payments, these imputed interest payments are counted as both income and expenditure for households. Owners’ equivalent rent is also added to household income, but net of mortgage interest, property taxes and maintenance costs. Finally, the national accounts treat the assets of pension and similar trust funds as if they were directly owned by households. This means that employer contributions and asset income for these funds are counted as household income (and therefore add to measured saving) while benefit payments are not.

These items make up a substantial part of household payments as recorded in the national accounts – Medicare, Medicaid and employer-paid health premiums together account for 14 percent of official household consumption; owners’ equivalent rent accounts for another 10 percent; and imputed financial services for 4 percent; while consolidating pension funds with households adds about 2 percent to household income (down from 5 percent in the 1980s). More importantly, the relative size of these components has changed substantially in the past generation, enough to substantially change the picture of household consumption and income.

Incidentally, Klein says I exclude all healthcare spending in my adjusted consumption series. This is a misunderstanding on his part. I exclude only third-party health care spending — healthcare spending by employers and the federal government. I’m not surprised he missed this point, given how counterintuitive it is that Medicare is counted as household consumption spending in the first place.

This is all shown in Figure 3 below (an improved version of the paper’s Figure 1):

The two dotted lines remove public and employer payments for healthcare, respectively, from household consumption. As you can see, the bulk of the reported increase in household consumption as a share of GDP is accounted for by healthcare spending by units other than households. The gray line then removes owners’ equivalent rent. The final, heavy black line removes imputed financial services, pension income net of benefits payments, and a few other, much smaller imputed items. What we are left with is monetary expenditure for consumption by households. The trend here is essentially flat since 1980; it is simply not the case that household consumption spending has increased as a share of GDP.

So Figure 3 is showing the contributions of the blue factors in Figure 2. Note that while these do not involve any monetary outlay by households and thus cannot affect household balance sheets or debt, they do all contribute to measured household saving.

The gray factors involve household assets. No one denies, in principle, that balance sheets have both an asset side and a liability side; but it’s striking how much this is ignored in practice, with net and gross measures used interchangeably. In the first place, we have to take into account residential investment. Purchase of new housing is considered investment, and does not reduce measured saving; but it does of course involve monetary outlay and affects household balance sheets just as consumption spending does. [2] We also have take into account net acquisition of financial assets. An increase in spending relative to income moves household balance sheets toward deficit; this may be accommodated by increased borrowing, but it can just as well be accommodated by lower net purchases of financial assets. In some cases, higher desired accumulation of financial asset can also be an autonomous factor requiring balance sheet adjustment. (This is probably more important for other sectors, especially state and local governments, than for households.) The fact that adjustment can take place on the asset as well as the liability side is another reason there is no necessary connection between saving and debt growth.

Net accumulation of financial assets affects household borrowing, but not saving or aggregate demand. Residential investment also does not reduce measured saving, but it does increase aggregate demand as well as borrowing. The red line in Figure 3 adds residential investment by households to adjusted consumption spending. Now we can see that household spending on goods and services did indeed increase during the housing bubble period – conventional wisdom is right on that point. But this was a  spike of limited duration, not the secular increase that the standard consumption figures suggest.

Again, this is not just an issue in principle; historical variation in net acquisition of assets by the household sector is comparable to variation in borrowing. The decline in observed savings rates in the 1980s, in particular, was much more reflected in slower acquisition of assets than faster growth of debt. And the sharp fall in saving immediately prior to the great recession in part reflects the decline in residential investment, which peaked in 2005 and fell rapidly thereafter.

The cyan item is capital gains, the other factor, along with net accumulation, in growth of assets and net wealth. For the debt-demand story this is not important. But in other contexts it is. As I pointed out in my Crooked Timber post on Piketty, the growth in capital relative to GDP in the US is entirely explained by capital gains on existing assets, not by the accumulation dynamics described by his formula “r > g”.

Finally, the red items in Figure 2 are factors other than current spending and income that affect the debt-income ratio. Arjun Jayadev and I call this set of factors “Fisher dynamics,” after Irving Fisher’s discussion of them in his famous paper on the Great Depression. Interest payments reduce measured saving and shift balance sheets toward deficit, just like consumption; but they don’t contribute to aggregate demand. Defaults or charge-offs reduce the outstanding stock of debt, without affecting demand or measured savings. Like capital gains, they are a change in a stock without any corresponding flow. [3] Finally, the debt-income ratio has a denominator as well as a numerator; it can be raised just as well by slower nominal income growth as by higher borrowing.

These factors are the subject of two papers you can find here and here. The bottom line is that a large part of historical changes in debt ratios — including the entire long-term increase since 1980 — are the result of the items shown in red here.

So what’s the point of all this?

First, borrowing is not the opposite of saving. Not even roughly. Matthew Klein, like most people, immediately translates rising debt into declining saving. The first half of his post is all about that. But saving and debt are very different things. True, increased consumption spending does reduce saving and increase debt, all else equal. But saving also depends on third party spending and imputed spending and income that has no effect on household balance sheets. While debt growth depends, in addition to saving, on residential investment, net acquisition of financial assets, and the rate of chargeoffs; if we are talking about the debt-income ratio, as we usually are, then it also depends on nominal income growth. And these differences matter, historically. If you are interested in debt and household expenditure, you have to look at debt and expenditure. Not saving.

Second, when we do look at expenditure by households, there is no long-term increase in consumption. Consumption spending is flat since 1980. Housing investment – which does involve outlays by households and may require debt financing – does increase in the late 1990s and early 2000s, before falling back. Yes, this investment was associated with a big rise in borrowing, and yes, this borrowing did come significantly lower in the income distribution that borrowing in most periods. (Though still almost all in the upper half.) There was a debt-financed housing bubble. But we need to be careful to distinguish this episode from the longer-term rise in household debt, which has different roots.

 

[1] Think of it this way: If I ask why the return on an investment was 20 percent, there is no end to causal factors you can bring in, from favorable macroeconomic conditions to a sound business plan to your investing savvy or inside knowledge. But in accounting terms, the return is always explained by the income and the capital gains over the period. If you know both those components, you know the return; if you don’t, you don’t. The relationships in the figure are the second kind of explanation.

[2] Improvement of existing housing is also counted as investment, as are brokers’ commissions and other ownership transfer costs. This kind of spending will absorb some part of the flow of mortgage financing to the household sector — including the cash-out refinancing of the bubble period — but I haven’t seen an estimate of how much.

[3] There’s a strand of heterodox macro called “stock-flow consistent modeling.” Insofar as this simply means macroeconomics that takes aggregate accounting relationships seriously, I’m very much in favor of it. Social accounting matrices (SAMs) are an important and underused tool. But it’s important not to take the name too literally — economic reality is not stock-flow consistent!

 

How State Budgets Adjust

Here is a figure from the paper I’m presenting at the Eastern Economics Association meetings next weekend, on state and local government balance sheets:

State Government Finances 1999-2013. Source: Census of Governments, author’s analysis

This figure is just for aggregate state governments. It shows total borrowing (red), net acquisition of financial assets (blue), and the overall fiscal balance (black, with surplus as positive). It also shows the year over year change in the ratio of state debt to GDP (the gray dotted line). A number of interesting points come out here:

  • Despite statutory balanced-budget requirements, state budgets do show significant cyclical movement, from aggregate deficits of around 0.5 percent of GDP in recent recessions to surpluses as high as 0.5 percent of GDP in the expansions of the 1980s and 1990s (not shown here). Individual state governments show larger movements.
  • Shifts in state government fiscal balances are accommodated almost entirely on the asset side of the balance sheet. When state government revenue exceeds current expenditure, they buy financial assets; when revenue falls or expenditure rises, they sell financial assets (or buy less). State governments borrow in order to finance specific capital projects; unlike the federal government, they do not use credit-market borrowing to close gaps between current expenditure and revenue. (As I show in the paper, this is still true when we look at state governments cross-sectionally rather than aggregate data.) Between 2005 and 2009, state budgets moved from an aggregate surplus of around 0.3 percent of GDP to an aggregate deficit of around 0.5 percent. But borrowing over this period was completely flat – the entire shortfall was made up by reduced acquisition of financial assets.
  • The ratio of state government debt to GDP rose over the Great Recession period, by a total of about 2 points. While this is small compared with the increase in federal debt over the same period, it is certainly not trivial. Among other things, rising state debt ratios have been used as arguments for austerity and attacks on pubic-sector unions in a number of states. But as we see here, the entire rise in state debt-GDP ratios over this period is explained by slower growth. The ratio rose because of a smaller denominator, not a bigger numerator.
  • State debt ratios rose around the same time that state budgets moved into deficit. But there is no direct relationship between these two developments. Deficits were financed entirely through a reduction in assets. Simultaneously, the drastic slowdown in growth mean that even though state governments significantly reduced their borrowing, in dollar terms, during the recession, the ratio of debt to income rose. It is true, of course, that both the deficits and the growth slowdown were the result of the recession. But the increase in state debt ratios would have been exactly the same if state budgets had not moved to deficit at all.
  • Since 2010 there has been a simultaneous fall in state government borrowing and acquisition of assets. When these two variables vary together (as they also do across governments in some periods) it suggests that there is some autonomous balance sheet adjustment going on that can’t be reduced to the net financial position changing to accommodate real flows. (The fact that offsetting financial positions cannot in general be netted out is one of the main planks of Bezemer’s accounting view of economics.)

The pattern is similar in the previous recession. Although there was some increase in borrowing as state governments moved into deficit in 2002-2003, the large majority of the financing was on the asset side.

The larger significance of all this, and the data underlying it, is discussed more in the paper.  I will post that here next week. In the meantime, the two big takeaways are, first, that a lot of historical variation in debt ratios are driven by the effect of different nominal growth rates on the existing debt stock rather than by new borrowing; and that state governments don’t finance budget imbalances on the liability side of their balance sheets, but on the asset side.

(Earlier posts based on the same work here and here.)

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.)

 

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.