Macro Roundup

Evidence from Sweden finds a strong relationship between cognitive ability and earnings up to annual earnings of ~$64k. There are no significant differences among the top three income percentiles.

Description automatically generated “…The figure above shows cognitive ability levels expressed with annual wage on the horizontal axis. Cognitive ability plateaus at high levels of occupational success. Precisely in the part of the wage distribution where cognitive ability can make the biggest difference, its right tail, cognitive ability ceases to play any role. Cognitive ability plateaus around €60,000 [$64,200] at under a standard deviation above the mean. There are no significant differences in ability between the three top income percentiles, despite there being 594 cases in each percentile bin and despite those in the 100th percentile earning more than double the wage of those in the 98th percentile. Past a certain wage threshold, having a higher wage is no longer telling of cognitive ability. The average score individuals in the top percentile achieved as adolescents on the cognitive-ability test is 7.15 ± 0.11 (95% confidence interval). On a stanine scale this amounts to less than a standard deviation (+0.86) above average. …”

The Evidence

Cognitive ability

“…We draw on standardized test results of cognitive ability among male conscripts. The Swedish military enlistment procedure consisted of a series of physical, psychological, and intellectual tests all men had to take at age 18–19. Motivation for participation in military service was not a factor for evaluation as avoiding enlistment by obtaining low-ability scores was not possible (Lindqvist and Vestman, 2009). Early-life measurement of cognitive ability circumvents endogeneity problems which may arise when using ability scores measured after job market entry (e.g. Aldén, Hammarstedt and Neuman, 2017): One can plausibly assume that learning on the job is steeper in highly paid jobs and that these learning effects may reduce the role of innate ability in later-life cognitive skills. The enlistment procedure included an assessment of cognitive ability similar to the AFQT used in the United States (Carlsson et al., 2015), which was found a better predictor of wages than IQ test scores (Borghans et al., 2016). There were separate paper and pencil tests for verbal understanding, technical comprehension, spatial ability, and logic. Each test consisted of 40 items presented in order of increasing difficulty and speed (Carlstedt and Mårdberg, 1993) and grades from each dimension were combined into a normally distributed stanine scale ranging from 1 to 9 (Lindqvist and Vestman, 2009). This measure has been frequently used in medical studies (e.g. Åberg et al., 2009) and in the social sciences, for example for assessing managers’ intelligence (Adams, Keloharju and Knüpfer, 2018), entrepreneurs’ balance in skill sets (Aldén, Hammarstedt and Neuman, 2017), the importance of social origin in achieving promotions to managerial positions (Bihagen, Nermo and Stern, 2013), and the effect of smart teachers on student performance (Grönqvist and Vlachos, 2016). With only minor revisions implemented over the years, this procedure evaluated the same four underlying dimensions of ability throughout the full observation period….”

Labour-market success

“…We consider two success measures, individuals’ average annual wage and their average occupational prestige during a 11 year career window (age 35–45) centred on the age of 40 (Haider and Solon, 2006). We measure annual gross wage—directly reported by employers to the Swedish tax authorities—in hundreds of thousands of Swedish krona (roughly equivalent to units of 10,000 present-day Euros). We include years of part-time employment and of zero-income, and our measure includes bonuses paid out as additional, directly taxable wage income. We adjust annual wages for inflation (using an OECD consumer price index for Sweden, with base year 2012) to make wages comparable between individuals who entered the labour market in different calendar years where the same nominal incomes represent different levels of purchasing power. See Figure 2B and Table 1 for descriptives of the wage variable. To demonstrate the robustness of our findings, we change to snapshot annual wages in individuals’ 2nd, 10th, and 20th year of labour-market participation in Appendix A. Because these snapshots do not require a balanced panel of individuals with 11 years of labour-market participation, we can include up to 238,000 earners in these analyses. Our second measure of labour-market success is occupational prestige, measured on the International Socio-Economic Index (ISEI) scale, where higher values indicate occupations with greater social status (Ganzeboom, De Graaf and Treiman, 1992). We obtain ISEI scores from employees’ registered occupation which are available for 49,022 employees. Following the above operationalization, we measure individuals’ multi-year average occupational prestige (see Appendix A for prestige in the 2nd, 10th, and 20th career year). Ranging from 16 (farm workers) to 90 (judges), ISEI averages 48.9 and density peaks at low (e.g. industrial workers, construction workers), intermediate (e.g. office clerks, policemen), and sub-top levels (e.g. accountants, engineers; see Figure 2C)…”

Results

“…Figure 3A shows the conditional mean, geometric mean, and median wage for each cognitive-ability score. The figure reproduces the positive effect of ability on wage identified in earlier work (Coward and Sackett, 1990; Herrnstein and Murray, 1994; Ng et al., 2005; Strenze, 2007; Jokela et al., 2017). The rank correlation between ability and wage equals ρ = 0.400 (P < 0.001; N = 59,387). The monotonicity of the relationship in Figure 3A is consistent with previous studies (Lubinski, 2016; Gensowski, 2018) rejecting the claim that past a certain threshold having even higher cognitive abilitydoes not matter (Gladwell, 2008). The most able Swedish men can expect to make more money than less able others. The substantive magnitude of the relationship is, however, modest, as the worst scoring men on average still earn more than a third of the salary of the best scoring men. The most cognitively able individuals clearly cannot expect outlandish labour-market returns (Rosen, 1981; Frank and Cook, 1995; Borghans and Groot, 1998; Neal and Rosen, 2000; Brynjolfsson and McAfee, 2011; Mankiw, 2013). Figure 3B flips the axes, conditioning ability on wage to allow an evaluation of the comparative intelligence of top earners. Each dot represents the average ability level for a wage percentile. The figure reveals a marked contrast between the body and the tail of the wage distribution, with a strong correspondence at intermediate wage levels, while above a certain wage level average ability plateaus at an average of around 7.25. This plateauing of the wage–ability relation occurs at approximately SEK600,000 annual wage (about €60,000). In the three top wage percentiles, that earn between SEK800,000 and SEK8,400,000 annually, the relationship even slightly reverses. As in our model simulations (Figure 1C), there are no significant differences in ability between the three top income percentiles, despite there being 594 cases in each percentile bin and despite those in the 100th percentile earning more than double the wage of those in the 98th percentile. This result supports our hypothesis, and it suggests that the inverse of Gladwell’s (2008) claim does hold: Past a certain wage threshold, having a higher wage is no longer telling of cognitive ability. The average score individuals in the top percentile achieved a adolescents on the cognitive-ability test is 7.15 ± 0.11 (95 per cent confidence interval). On a stanine scale this amounts to less than a standard deviation (+0.86) above average. Figure 3C shows the same 100 cognitive ability levels but this time expressed with wage percentiles on the horizontal axis. The upper part of the distribution exhibits the predicted concave pattern, just as it does in Figure 3B. This lends further support to our hypothesis.

“…Figure 4 shows results when using occupational prestige instead of wage as measure of career success. Results are similar as for wage: Figure 4A shows that expected prestige monotonically increases in cognitive ability, with extremely intelligent people having the best job-market prospects. Yet when ability is conditioned on occupational prestige in Figure 4B, we find no systematic pattern in how average cognitive ability varies between those with ISEI scores 70 and above. Individuals in these professions (judges, lawyers, professors, and doctors) achieved an average cognitive- ability test score of 7.13 ± 0.04, which is less than a standard deviation (+0.80) above the mean….”

Marc Keuschnigg, Arnout van de Rijt and Thijs Bol, “The Plateauing of Cognitive Ability Among Top Earners,” European Sociological Review, 28 January 2023, https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcac076/7008955

Chris Marsh notes that during the pandemic for every additional $1T in currency and deposits, American households saw $6T in non-monetary wealth driven by equities and real estate. “…Household net worth expanded by USD34 trillion from end-2019 through end-2021, an increase of >150% of end-2019 GDP in only 2 years. This wealth increase was mainly due to the increase in financial and non-financial assets (housing). The chart below shows the change in the value of outstanding assets held by households for select items from end-2019. Currency and deposits held by households began the sharp increase in the first quarter of 2020, during the initial lockdowns, and increased by about USD4½ trillion in total. The increase in equity and real estate assets, mainly due to valuation adjustments, followed—and in value terms was a much larger influence on household net worth. At its peak, equity and investment fund shares, which fell in value by USD6 trillion by end-March 2020, increased nearly USD18 trillion by end-2021 on end-2019, or USD24 trillion peak-to-trough. The declines experienced last year during the interest rate normalization have weighed on such wealth since, but the levels remained above pre-pandemic norms. Real estate assets continued to increase in value still in 2022Q3, though at a diminishing pace as housing feels the strain of higher rates. Still, nearly USD13 trillion was added to household wealth through real estate since end-2019. Put another way, at its peak, every additional USD1 trillion in monetary wealth during the pandemic came to be reflected in roughly USD6 trillion in non-monetary wealth, mainly equities and housing….”

Chris Marsh, “Lessons from the Great Reflation,” Money: Inside and Out, February 5, 2023, https://moneyinsideout.exantedata.com/p/lessons-from-the-great-reflation

Lessons from the Great Reflation

INFLATION SHOCKS ARE LIKE LONDON BUSES—you wait over a decade for one to come along and three arrive at once.

And so, as of early-2023, Britain is navigating the aftermath of Brexit, a global pandemic, and the near-unprecedented energy shock from Russian irredentism. These shocks have combined to push up headline consumer prices by double digits since the summer and core inflation by above 6 percent more recently—the highest since the aftermath of the ERM crisis in 1992.

Though the shocks are weighed differently, the challenge is similar elsewhere.

The pandemic-related fiscal-monetary shock was greatest in the United States, the energy shock largest for mainland Europe, while the UK sits somewhere between the two—plus Brexit.

The narrative repeats in other countries—or is felt simply through spillovers to global traded goods prices and supply chains. Even Japan is showing signs of inflationary life.

In Australia, where CPI has followed the global trend upwards to reach 7.8%YoY in Q4, the Treasury announced last July a “wide‑ranging review of the Reserve Bank of Australia” to be completed next month. The report provides “an important opportunity to ensure that our monetary policy framework is the best it can be, to make the right calls in the interests of the Australian people and their economy.” A website accompanies this review.

Others will rightly question the origins of our Great Reflation—and the size and speed of the associated policy response.

Abstracting from the energy and Brexit shocks, how might we reflect upon the pandemic-related monetary shock, reflation, and associated monetary policy response? Where did it all go wrong?

In short, as during and after the Great Financial Crisis (GFC), macroeconomics has been found wanting. This time, dropping money from monetary macroeconomics left central banks asleep at the wheel when inflation hit.

We pointed out this problem more than two years ago, in one of the first posts on this blog, with the title “The big myth about money and inflation”

The key point we made at the time (end-2020), was: “If money printing is associated with actual demand generation (via government spending, or transfers), then the link into inflation is likely to be much stronger. This is a simple difference, between different types of monetary expansion. But somehow it has been missed by many, leading to ‘forecasting disasters’ by those who are ‘religious’ about the role of money in inflation dynamics.””

We wish we had made the argument more forcefully, and repeated it more often, as it turned out to be rather crucial for global macro trends in 2021-2022.

What happened to money?

The pandemic-related expansion in broad—and base—money has surely played a crucial, though not the exclusive, role in the recent acceleration in inflation. Only last week the Bank for International Settlements concluded such a link exists. “There is indeed evidence that money growth and inflation have been closely linked recently. For example, across countries, there is a statistically and economically significant positive correlation between excess money growth in 2020 and average inflation in 2021 and 2022.”

It is natural therefore to question how money features in the calibration of monetary policy today—during and since the pandemic.

Unfortunately, there is little calibration to such money aggregates in today’s state-of-the-art in macro models. Why?

Well, exactly 20 years ago the publication of Michael Woodford’s Interest and Prices: Foundations of a Theory of Monetary Policy sought “to provide theoretical foundations for a ruled-based approach to monetary policy.” This book, and associated research, had an enormous impact on academic macroeconomics and central banking.

Partly this agenda was only research aping central banking practice. But Interest and Prices and associated neo-Keynesian research legitimised the view that money can be largely ignored in macroeconomic modelling and policymaking.

Consider the related paper presented at the ECB in 2006, on the role of money in the conduct of monetary policy, later published in the JMCB, where Woodford quotes former-Fed governor Laurence Meyer insisting that “money plays no explicit role in today’s consensus macro model, and it plays virtually no role in the conduct of monetary policy.”

This quote was lifted from a 2002 speech by then-Bank of England Deputy Governor Mervyn King on the role of money in the economy—more on which later.

Crucially, in this 2006 paper—“How Important is Money in the Conduct of Monetary Policy?”—Woodford questioned the ECB’s two-pillar approach to monetary policy, in particular the separation of economic analysis from monetary developments in forecasting and policy.

This is because the research agenda in Interest and Prices did not require money to be explicitly modelled—and money was ignored in a cashless limit.

The paradigm combines sticky prices and dynamic stochastic general equilibrium (DSGE) modelling—allowing for the “endogeneity of private-sector expectations”—with simple monetary policy rules to analyse inflation control and stabilisation policy.

But why no money? Well, the simplest answer—and the one given in Chapter 1 of Woodford’s book—is that the practice of monetary policy was, and is still, concerned with the choice of interest rate to achieve policy objectives (e.g., an inflation target.) The policy rate is chosen exogenously and (base) money adjusts endogenously, through open-market operations, to achieve this target rate.

Thus was born “Keynesian macroeconomics without the LM curve.”

The theoretical corollary of dropping money was the observation that a money demand function can always be grafted onto the model—with no impact on the resulting solution for the price level.

The textbook treatment by Jordi Galí of UPF and Barcelona School of Economics, explains that when added, money demand plays “no independent role in determining the equilibrium;” instead money adjusts endogenously given real output, the price level, and interest rate (p. 28).

Crucially, the framework assumes that money demand always equals supply.

Yet occasions have emerged historically when an imbalance might emerge between the two, something contemplated seriously in the 1970s and worth exploring again.

Disequilibrium money

What if money supply does not at all times equal money demand?

This seems like an awkward question today, as we usually think markets continuously clear. But such disequilibrium was considered a serious possibility during the Great Inflation in the 1970s; money might be a buffer for the adjustment of the community to structural changes in the financial system.

In “Disequilibrium money — a note” Charles Goodhart reviewed this possibility while at the Bank of England in the early 1980s—later made public as part of a collection of essays.

As Goodhart explains, disequilibrium money was popularised by David Laidler and others during the breakdown of money demand functions in the 1970s: “The essence of this approach is to regard money holdings as in some large part being used as a buffer stock… economic agents may rationally and optimal decide to respond to the continuing stream of developments… not by a thoroughgoing, continuous reconsideration of their full economic dispositions, but allowing such shocks to impinge initially upon certain assets/liabilities whose characteristics make them suitable to act as buffers.”

And money holdings are, “pre-eminently suitable to be used by agents as buffer stocks… shocks in all other markets will tend to have a counterpart effect on cash flows. For example, if goods are rationed and subject to price control, as occurred during wartime and in socialist economies, the initial effect of that is likely to find a reflection in holdings of money.”

Goodhart did not imagine the impact of a pandemic and related lockdowns. But a period of rationing provides a decent enough metaphor, when government fiscal-monetary support was the counterpart to a build up of private saving—taken largely in monetary form thanks to central bank balance sheet expansion.

Importantly, if money indeed serves a a buffer in times of upheaval, supply need not equal money demand—creating disequilibrium at the heart of household balance sheets.

This issue was raised again in 2007 when Goodhart—in a lecture in honour of Maurice Peston, published by the National Institute of Economic and Social Research—took on directly Woodford’s argument at the ECB.

In this lecture—“Whatever became of the Monetary Aggregates?”; free here—Goodhart warns that the “downgrading of the role of the monetary aggregates in current models, and in forecasting future inflation, has gone too far.”

Why so? First recall the problem with the New Keynesian DSGE paradigm: “There appears to be no need to look at what is happening to money in [Woodford’s] system to achieve the main macro-economic variables of importance to social welfare. In practice, … a demand-for-money function is fully consistent with the above three equations… [but with such a demand shock] … you learn nothing more from looking at money than you already knew from looking at inflation, output and interest rates.”

Goodhart goes on: “But it is not true that all shocks to money are demand-side. The bulk of money is in the form of commercial bank liabilities, and banks can behave very differently over time… If there is a supply shock to money, with certain groups now getting more, or less, access to funding, for example when banks provide mortgages to a wider group of households on easier terms, will this not feed back into the IS curve? Of course it will.”

Goodhart is not thinking of goods rationing here, or pandemic lockdowns, but money from the changing disposition of banks in credit provision—an echo of the structural changes to the United Kingdom monetary system in the 1970s. These changes (“regulatory and structural”) brought about “consequential and autonomous changes in bank behaviour” coincident with the breakdown of money demand relations at that time, according to his disequilibrium essay.

We lament parenthetically that in 2007, in his response to Woodford, Goodhart does not reference this disequilibrium paper.

In any case, Goodhart went on to worry about overlooking the impact of money supply changes on wealth, through asset prices such as housing, and the long-run link between such asset prices and expenditure—more on which shortly.

Goodhart ends the paper with a decision tree for thinking about money shocks.

If money growth is not consistent with the paths for output, inflation and interest rates, then the policymaker should ask if it is a money demand or supply shock. If the former, it can be ignored. If the latter, it matters—and ought to elicit a policy response.

In short, as Goodhart warned, state-of-the-art monetary models of two decades ago—those that infiltrated the temples of central banking—could leave policymakers blindsided in the event of a shock to the money supply—especially if money serves as a buffer for saving, exactly as occurred during the pandemic.

But if we allow for such a buffer, how should it be managed?

Unwinding monetary overhangs

There is a large literature on managing “monetary overhangs” such as those that followed the great wars of the last century or the post-communist transition. These overhangs found expression in shortages of goods and incipient inflation as households attempted to cash in cumulated savings.

The analogy to the post-pandemic challenge is striking. Yet the absence of policy discussion of our monetary disequilibrium, drawing on prior experience, is equally striking. Perhaps the shock and overhang wasn’t quite as large. But the disequilibrium was considerable still.

Consider the discussion of quantitative tightening in recent years. This was largely conducted in terms of fine tuning the policy stance or, with the Bank of England, to making space for future upward expansion. It has not been framed in terms of calibrating policy to the unwind of the household monetary disequilibrium.

It’s worth briefly recalling how such overhangs were thought to be resolved.

Indeed, for the late Rudiger Dornbusch, such overhangs could be managed in two ways. First, a “big, possibly one-shot inflation” could be used to return real money balances to their equilibrium levels. Second, the liquid assets created previously could be “immobilised” by being “written off, consolidated as debt or retired by asset exchanges or budget surpluses.”

Curiously, according to Dornbusch, once this stock problem is resolved, inflation will only continue if there is an underlying flow problem that remains. And it has been found that historically, “the release of a monetary overhang does not result in permanent inflation, much less a hyperinflation.”

Seen in this light, the monetary policy debate fell short.

There was a monetary disequilibrium. This could be dealt with through a combination of temporary, though perhaps multi-year, inflation alongside quantitative tightening to withdraw the build up of money and reshape private portfolios. But there is no need to panic about persistent inflation unless a residual flow imbalance remained once the pandemic was resolved.

But this discussion could not happen, of course, because central banks dropped money aggregates two decades before. Instead of being proactive, Inflation forecast targeting central banks became reactive in setting policy to incoming inflation—despite the fact this inflation was baked in the cake due to the pandemic intervention.

Once we acknowledge a monetary disequilibrium exists, it needs to be unwound. But by what mechanism does it transmit? This is where the monetarists come into their own.

Monetarist transmission

For all that Milton Friedman wrote about money, it is difficult to find a clear distinction between the source of the money supply shock and the resulting adjustment. For example, should we should distinguish for analytical purposes between a shock to high powered—or outside—as opposed to broad money? Recent experience suggests we should.

In his Theoretical Framework for Monetary Analysis in 1970, Friedman devotes only about half a page of the forty-five in total to the drivers of the money supply. For Friedman, at that stage at least, the quantity theory of money was sacrosanct—further details could be ignored.

Still, taking as given the money supply shock, the monetarists can be said to provide the richest description of the resulting transmission throughout the macroeconomy.

Consider first the traditional Keynesian description of monetary transmission. Again, here’s Goodhart (from a different paper, page 161): “The effect of a change in the money supply [due to open market operations in the Keynesian framework] is seen to be like a ripple passing along the range of financial assets, diminishing in amplitude and in predictability as it proceeds farther along from the initial disturbance. This ‘ripple’ eventually reaches to the long end of the financial market, causing a change in yields, which will bring about a divergence between the cost of capital and the return on capital.”

Hence money transmits through the yield curve into investment decisions, then investment and activity—and, perhaps, inflation.

For the monetarists there was a much richer process in play.

Drawing on detailed studies with Anna Schwartz, Milton Friedman’s description in his Wincott Lecture in 1970 is as clear as anything we can hope to imagine: “One of the most difficult things to explain in simple fashion is the way in which a change in the quantity of money affects income. Generally, the initial effect is not on income at all, but on the prices of existing assets, bonds, equities, houses, and other physical capital. This effect, the liquidity effect stressed by Keynes, is an effect on the balance-sheet, not on the income account. An increased rate of monetary growth, whether produced through open-market operations or in other ways, raises the amount of cash that people and businesses have relative to other assets. The holders of the now excess cash will try to adjust their portfolios by buying other assets. But one man’s spending is another man’s receipts. All the people together cannot change the amount of cash all hold—only the monetary authorities can do that.”

He continues, original emphasis: “However, as people attempt to change their cash balances, the effect spreads from one asset to the another. This tends to raise the prices of assets and to reduce interest rates, which encourages spending to produce new assets and also encourages spending on current services rather than on purchasing existing assets. That is how the initial effect on balance-sheets gets translated into an effect on income and spending.”

In summary, Friedman noted: “…monetarists insist that a far wider range of assets and of interest rates must be taken into account. They give importance to such assets as durable and even semi-durable consumer goods, structures and other real property.”

Or, returning to Goodhart’s survey, Keynesians: “expect people to buy financial assets when they feel that they have larger money balances than they strictly require (given the pattern, present or prospective, of interest rates), whereas monetarists expect the adjustment to take place through ‘direct’ purchases of a wider range of assets, including physical assets such as consumer durables. (Page 164.)”

For monetarists, then, a money shock feeds slowly into income and prices, first into physical and financial assets, durable and non-durable goods, and through balance sheets to income and spending. This happens echo Goodhart’s conclusions in response to Woodford’s cashless approach, though he was careful to insist at the time: “I am far from being a card-carrying monetarist.”

Pandemic wealth

If this description best fits the transmission of a monetary shock to income, can we visualise it?

The exceptional increase in household wealth during the pandemic was noted here previously (here and here.) Household net worth expanded by USD34 trillion from end-2019 through end-2021, an increase of >150% of end-2019 GDP in only 2 years.

This wealth increase was mainly due to the increase in financial and non-financial assets (housing).

The chart below shows the change in the value of outstanding assets held by households for select items from end-2019. Currency and deposits held by households began the sharp increase in the first quarter of 2020, during the initial lockdowns, and increased by about USD4½ trillion in total. The increase in equity and real estate assets, mainly due to valuation adjustments, followed—and in value terms was a much larger influence on household net worth.

At its peak, equity and investment fund shares, which fell in value by USD6 trillion by end-March 2020, increased nearly USD18 trillion by end-2021 on end-2019, or USD24 trillion peak-to-trough. The declines experienced last year during the interest rate normalisation have weighed on such wealth since, but the levels remained above pre-pandemic norms.

Real estate assets continued to increase in value still in 2022Q3, though at a diminishing pace as housing feels the strain of higher rates. Still, nearly USD13 trillion was added to household wealth through real estate since end-2019.

Put another way, at its peak, every additional USD1 trillion in monetary wealth during the pandemic came to be reflected in roughly USD6 trillion in non-monetary wealth, mainly equities and housing.

Alternatively, we can visualise how the increase in household nominal money balances fed various prices—as below, note the different scales. Within the Personal Consumption Expenditure (PCE) deflator, durable goods accelerated first, though lagging equities (seen above), followed by non-durable goods and, lastly, services. Average hourly earnings jumped initially on compositional effects, but has since kept up with the PCE deflator.

Conclusions

Our Great Reflation has taken many by surprise. Here we have reflected on the monetary aspects—downplaying the energy shock and Brexit challenge.

Central banks in particular have been found reactive, responding to incoming inflation rather than inflation targeting proper. That’s a sign a panic.

One reason for this panic is the new Keynesian work program that reached some kind of peak twenty years ago with Woodford’s Interest and Prices. This approach encouraged the idea that money could be ignored—but as Goodhart warned at the time, there was a risk that the implications of a money supply shock would be missed. And the pandemic created for the community a huge money supply shock—and this disequilibrium is being unconsciously unwound through inflation.

It will be interesting to read the official post mortems for our recent experience.

The first chance to reflect comes in Australia next month, with the review of the RBA. Skimming through the main macro model used by the RBA, money doesn’t appear as an input for forecasting—though perhaps it is hidden somewhere in the annex. They too have been a victim of the new Keynesian group think.

We might also note some reflection already in the UK last summer, when the Bank’s Chief Economist Huw Pill asked: “What did the monetarists ever do for us?”

Pill takes a step towards revising the role of money in these models: “The analysis I have presented explores whether monetary quantities play an additional role in the transmission of monetary policy to aggregate spending (and thus pricing) decisions, beyond that already captured by the impact of changes in the official policy interest rate. Expressed in the context of the canonical new Keynesian model of monetary policy transmission, this essentially revolves around whether it is sufficient for the policy rate alone to appear in the ‘dynamic IS curve’ equation in that model, or whether other measures of monetary and financial conditions are required.”

In short, Pill suggests perhaps the dynamic IS curve might need updating. It’s a pity he does not go further. Recall that Goodhart wondered how a money supply shock might “feed back into the IS curve” through asset prices and spending.

In other words, the household balance sheet matters—not just money, but also due to the knock on of money supply shocks—and this should be reflected in our state-of-the-art thinking.

Curiously, Pill’s speech draws heavily on the 2002 speech by Mervyn King mentioned above and referenced by Woodford.

In reflecting upon our recent monetary malaise, therefore, we might recall once more the fact that monetary macroeconomics continually fails to integrate balance sheets and financial structures. This time, central banks have been blindsided in the pandemic as household balance sheets strengthened—a decade ago, fiscal policy was calibrated without due regard for household balance sheet weakness.

Finally, we end on an even more unhappy note: none of the above can be used to endorse the policy tightening response over the past year—which has been based on panic in the absence of a structured analytical framework. And so the prospect of further policy errors abound.

The Economist reports that between mid-2020-2022 America’s lower earners saw cumulative inflation of 13.5% whereas middle earners saw 15.5%. The difference was largely driven by car prices and fuel expenditures.

“…Middle-income earners, not the poorest, appear to have borne the brunt of America’s current bout of inflation. Research by Xavier Jaravel of the London School of Economics has yielded a distinctive shape—an inverted-U curve—that illustrates the distributional effects of inflation in America from mid-2020 to mid-2022. For the lowest earners, inflation was about 13.5%. For those in the middle of the spectrum, inflation was closer to 15.5%. The wealthiest faced inflation of about 14%. Since 2020 nominal wages have increased at an average annual rate of about 4.2% for Americans as a whole. The lowest-earning quartile, however, has seen the biggest gains, with their wages up by 5.3% on average during the same period….”

Economist Staff, “Inflation usually hits America’s poor hardest. Not this time,” The Economist, February 2, 2023, https://www.economist.com/graphic-detail/2023/02/02/inflation-usually-hits-americas-poor-hardest-not-this-time

Inflation usually hits America’s poor hardest. Not this time

It is often seen as an axiomatic truth about the pernicious effect of rising prices: the lowest earners suffer the most. The calculation is straightforward. Those on low incomes must spend a greater share of their earnings on food and rent, so when the prices for those items soar, they have less left over for everything else. The rich and the middle class, by contrast, are better insulated. This pattern has been observed over many decades, in many countries (see left-hand chart). But America’s current bout of inflation may be an exception to the rule: middle-income earners, not the poorest, appear to have borne the brunt of it (see right-hand chart).

Research by Xavier Jaravel of the London School of Economics has yielded a distinctive shape—an inverted-U curve—that illustrates the distributional effects of inflation in America from mid-2020 to mid-2022. During that time consumer prices rose by an average of 15%. Yet that average masked considerable variation. For the lowest earners, inflation was about 13.5%. For those in the middle of the spectrum, inflation was closer to 15.5%. The wealthiest faced inflation of about 14%. That the rich suffered less is unsurprising. But why have the poor also been somewhat sheltered?

The answer is in large part down to Americans’ reliance on cars. Mr Jaravel used annual expenditure shares from consumer surveys to work out who spends how much on what. Middle-income earners, he found, spend more than others on both petrol and vehicles relative to their wages. That may reflect school runs, longer commutes by car to work, more frequent trips to shops and the like. In the two years that he examined, inflation was extreme for petrol (the price of which more than doubled) and cars (used-vehicle prices rose by about 50%). By extension that hurt the middle class most. A further breakdown of the data reveals even more nuance: at all levels of income, rural Americans faced higher rates of inflation than their urban compatriots because they spent more hours behind the wheel.

A difference of two percentage points in inflation over the course of two years may not seem like much. But it comes on top of a divergence in nominal wage growth that has also, unusually, benefited Americans at the low end of the income scale. Since 2020 nominal wages have increased at an average annual rate of about 4.2% for Americans as a whole. The lowest-earning quartile, however, has seen the biggest gains, with their wages up by 5.3% on average during the same period. Slightly lower inflation makes those gains even bigger in real terms.

This suggests that the combination of a tight labour market and unevenly distributed inflation has made America a little more equal over the past few years, a striking difference with how things normally play out. Unfortunately, it may also be a blip. Wage growth has started to soften as the economy slows. Food inflation has become more severe. On both counts low-income earners are the most vulnerable. The sorry old truth that inflation hits the poor hardest still applies—just not always.

Michael Spence and Belinda Azenui note that labor productivity between the tradable and non-tradable sectors started to diverge in 1998, and by 2021 per-employee value-added in tradable sectors was nearly double the non-tradable sectors.

Description automatically generated “…Value-added per employee is a measure of labor productivity. In America’s tradable sector, it has risen steadily over the last two decades in both manufacturing and services, reaching roughly $185,000 (in chained 2012 dollars) in 2021. Over the same period, productivity growth in this sector averaged nearly 3%. The non-tradable economy [net government] is just 0.57% per annum over the last 20 years. This reflects below-average productivity levels and, in most cases, low-to-moderate productivity growth in the large-employment sectors. There was not always a large gap between the tradable and non-tradable sectors. On the contrary, as the chart shows, labor productivity was about $100,000 across the economy in 1998. But by 2021, after more than two decades of steady divergence, per-employee value-added in the tradable sector was nearly double the level in the non-tradable sector….”

Michael Spence and Belinda Azenui, “American’s Widening Productivity Gap,” Project Syndicate, February 6, 2023, https://www.project-syndicate.org/commentary/us-productivity-growth-non-tradable-sectors-labor-shortages-by-michael-spence-and-belinda-azenui-2023-02?barrier=accesspaylog

America’s Widening Productivity Gap

MILAN/GRANVILLE, OHIO – The United States has a productivity problem, though one would never know it from looking only at the industries producing goods and services that are traded internationally. Because these goods and services account for only one-third of GDP and slightly over 20% of employment, as is typical for a developed economy, it is important also to consider the non-tradable sector that comprises the remaining two thirds of the economy.

The economy’s tradable sector comprises agriculture, forestry, fishing, and manufacturing – the production of goods, as either final or intermediate products – which in 2021 accounted for one-third of tradable value-added. The tradable sector also includes services such as research and development, consulting, information, and much of finance. Taken together, services account for about two-thirds of tradable value-added – a share that has increased over the last two decades.

Value-added for a firm or industry is calculated by subtracting purchased inputs like energy and intermediate products – excluding labor and capital – from total sales in dollars. It can be understood as the value created by the combination of labor and capital. That value is then captured as income for the labor (forming the upper limit on the average compensation of employees in the sector) and returns for the owners of the capital.

Value-added per employee is thus a measure of labor productivity. And, in America’s tradable sector, it has risen steadily over the last two decades in both manufacturing and services, reaching roughly $185,000 (in chained 2012 dollars) in 2021. Over the same period, productivity growth in this sector averaged nearly 3%. If this had been true of the non-tradable part of the economy – including large-employment sectors such as government, health care, traditional retail, accommodation and food service, education, and construction – nobody would have to be worried about productivity.

There is no good way to measure government productivity, because markets do not price the value created by services that are generally not sold. For accounting purposes, value-added for the government is measured by the costs of labor and capital, the assumption being that democratic collective-choice mechanisms will eliminate services whose costs exceed the perceived benefits. But that approach does not provide much insight into productivity: the fact that value-added per government employee has grown very slowly – by 0.25%, on average, over two decades – just means that, on average, labor and capital costs grew at roughly the same pace as employment.

For the rest of the non-tradable economy, however, we can measure productivity growth, and the results – based on industry-specific data from the Bureau of Economic Analysis for employment and real value-added (in billions of chained 2012 dollars) – are very different than the tradable picture and far from rosy: just 0.57% per annum over the last 20 years. This reflects below-average productivity levels and, in most cases, low-to-moderate productivity growth in the large-employment sectors.

For example, in 2021, hospitality employs 12 million people, has value-added per employee of $41,355 – less than one-third of the $130,000 national average – and productivity growth of 0.26%. The health-care and social-assistance sector employs 20 million people, with value-added per employee of $73,624 and productivity growth of 0.71%. For construction, the figures are 7.6 million, $87,425, and -1.21%, respectively.

There was not always a large gap between the tradable and non-tradable sectors. On the contrary, as the chart shows, labor productivity was about $100,000 across the economy in 1998. But by 2021, after more than two decades of steady divergence, per-employee value-added in the tradable sector was nearly double the level in the non-tradable sector. Chart, line chart

Description automatically generated By definition, there is no external supply or demand in non-tradable sectors, and thus no external competition or specialization. These sectors must therefore be supplied by domestic and even local entities. Put simply, the supply and demand side must match.

Today, however, many non-tradable parts of the economy – including all the large-employment ones – are experiencing labor shortages. This is partly because factors like stress, safety issues, low compensation, and lack of flexibility are driving workers away from these jobs. But skills gaps are also playing a role. Given this, removing barriers to the acquisition of higher-level skills and incomes is a critical component of any structural-transition agenda.

Demand for health care, hospitality, and construction services is not going to drop. So, labor-supply constraints may lead to higher wages and higher prices, raising incomes and measured labor productivity to some extent. But more must be done to boost productivity growth in low-productivity sectors, thereby mitigating supply constraints, which population aging is set to exacerbate.

Digital technologies have been an important driver of productivity growth in rapidly expanding industries. Given recent breakthroughs in robotics and artificial intelligence, there is every reason to believe this will continue. But progress in high-end services and manufacturing alone is not enough. These powerful technologies must also be applied in low-value-added, low-wage, and low-productivity-growth parts of the economy.

Given labor-supply constraints and economic shocks – linked, for example, to climate change and geopolitics – the case for productivity-boosting interventions is clear. Unless policymakers use a combination of investment and incentives to reverse negative productivity trends, the US will achieve modest growth, at best. Worse, the growth that it does attain will be highly uneven, excluding many from its benefits.