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Will tighter monetary policy in the US induce a gentle easing in labour market pressures or something unpleasant?

WARNING: THIS POST I BELIEVE IS ONE OF MY MORE IMPORTANT AND INTERESTING BUT IT IS QUITE TECHNICAL. SO, IF YOU WANT TO SCROLL STRAIGHT DOWN TO WHAT IS A DIGESTIBLE CONCLUSION, I WON’T MIND…

The FT’s Unhedged newsletter last week set out clearly the arguments with respect to the soft versus hard landing debate in the US and, by implication, other advanced economies. Key protagonists on the soft landing side of the debate include Fed governor Christopher Waller and Fed chairman Jerome Powell, while on the hard side are the likes of former IMF chief economist Oliver Blanchard, and former US treasury secretary Larry Summers. The FT’s chief economics commentator Martin Wolf also sides with the hard guys. (To be clear, the difference between a hard and soft landing is whether in a recession there is a meaningful rise in unemployment.)

The reason this is so important for investors is that the hardness/softness of the landing will determine how risky assets perform over the next year or so. It is of course important for workers too – unemployment can be a traumatic experience.

What is fascinating is that both sides in the debate use the same mathematical construct known as the Beveridge Curve to prove their case. They cannot both be right so one of them must have used a sleight of hand – an assumption that does not stand up to scrutiny or a factual error. This post tries to identify the culprit.

(BTW, the Beveridge Curve plots vacancy rates against unemployment rates and can be derived both theoretically and empirically. The vacancy rate is calculated by dividing the number of jobs being advertised by labour demand, the latter being filled plus vacant positions. In other words, it represents the percentage of labour demand that is unsatisfied. Thus, the higher the vacancy rate, the tighter the labour market. Currently, the labour market in the US – and indeed in other key economies – is currently very tight. Not only is the unemployment rate at a very low 3.5pct (July) but the vacancy rate is also very high at around 7pct.)

The case for a soft landing was set out in a speech on 30 May this year by member of the Federal Reserve Board of Governors, Christopher Waller. The argument has received support from other Fed members, including chairman Jerome Powell.

Waller’s paper was rebutted in July by Oliver Blanchard, Lawrence Summers, and Alex Domash. Two further rebuttals, one from each side, followed. I shall henceforth call these four papers, S1, H1, S2, and H2.

Chart 1 below is from S1 and is of actual unemployment and vacancy rates from December 2000 to December 2019 – blue dots. The regular line of black dots is the “fitted relationship between the log of vacancies and the log of unemployment over this period” and represents the empirically derived Beveridge Curve. However, Waller calls this line “predicted” not “fitted” – in a later slide he uses the same black line and calls it “fitted”. It is not clear why he uses different terminology in the two slides but in my opinion describing the line as predicted implies that it is a theoretical construct which it isn’t.

Chart 1 (from S1): Actual unemployment and vacancy rates from December 2000 to December 2019

Chart 2 below depicts four theoretical “steady state” curves – coloured – and the empirically derived Beveridge Curve – black – from Chart 1 – note in the legend the use of the term “fitted” rather than “predicted”. The curves, whether the theoretical steady state curves or the empirically derived curve, are convex. This intuitively makes sense: when the labour market is tight, rises in vacancies generate fewer and fewer hires, resulting in smaller reductions in unemployment. This issue of convexity is important later so bear it in mind.

A steady state curve represents the relationship between unemployment and vacancy rates assuming that unemployment is not changing i.e. it is steady. Each of the four coloured curves represents a different separations rate, the separations rate being a measure of the rate at which people are losing or leaving their jobs i.e. becoming unemployed. So, for a separations rate of 1.5pct – green line – unemployment would be steady if unemployment/vacancy rates were 6pct/4pct, or 10pct/1pct, the former representing a stronger growth environment, the latter a weaker one.

Now, steady states do not exist in the real world since separations and thus unemployment rates are always rising or falling. In other words, as economies expand and contract, unemployment/vacancy rate pairs jump from one coloured curve to another. Hence, the need to look at the empirically derived Beveridge Curve which indeed dissects the four coloured theoretical curves. For example, moving from where the black line crosses the blue line to where it crosses the orange line represents a rise in the separations rate from 1pct to 1.5pct. This rise in separations then causes the unemployment rate to rise and the vacancy rate to fall.

Finally, there is one other parameter that determines the relationship between unemployment and vacancy rates: matching efficiency. This is a measure of the extent to which those looking for jobs are suited to the jobs being advertised. The curves in Chart 2 assume that matching efficiency is fixed. However, in the real world, matching efficiency rises and falls over the cycle, and causes the curves to shift down and left – better matching efficiency – or up and right – worse.

Chart 2 (from S1): Beveridge Curves and Recoveries

Chart 3 is the same as Chart 1 except the post-covid recovery is included and the empirically derived Beveridge Curve is omitted. If the empirically derived Beveridge Curve was included it would not come close to the Mar 2022 data point. Again, keep this in mind for later.

Chart 3 (from S1): Vacancies and unemployment

The three different groupings of blue dots – curves – represent distinct periods. Not only do they represent distinct periods, but they also contain a direction of travel, a chronology within the groupings. Thus, in Chart 4 I have annotated Chart 3 with arrows.

Arrow 1 depicts the eight years leading up to the 2008 crisis during which unemployment gradually rose, then shot up during the crisis itself. Arrow 2 represents the recovery period that followed the 2008 crisis, up to just prior to the pandemic outbreak. Arrow 3 relates to the very rapid jump in unemployment from 4.4pct to 14.7pct in April 2020 – the dots are monthly readings, so this one-month jump does not show up in Charts 3 or 4 as a series of dots. Arrow 4 is the post-covid period of recovery since April 2020.

Chart 4: Four curves representing four distinct periods

Waller’s argument is that the vacancy rate – labour market pressure – can fall, thus easing inflation pressure, without causing joblessness to rise, a soft landing. His argument is presented visually in Chart 5 as the red arrow. (Forget for the time being the suggestion that when the vacancy rate falls to where it was prior to the pandemic and the unemployment rises slightly to around 4pct, there will be no wage/inflation pressure. A challenge to this assumption is for another blog post.)

Chart 5: What the soft landers expect (red arrow)

Waller then supports his argument for the Arrow 5 scenario by overlaying the top left part of Chart 4, specifically the points representing Jan 2019 and March 2022, with a curve that assumes a fixed separations rate – see Chart 6 below. In order for the curve to pass through the Mar 2022 point, a separations rate of 1.23pct is required, which Waller notes is “a little above current levels”. In other words, it reflects reality.

The final part of Waller’s argument is that to get back to the vacancy rate of 4.5pct that prevailed in Jan 2020 prior to the pandemic, we can travel down the curve to point B (Chart 6), which would imply a gentle rise in the unemployment rate to around 4.2-4.3pct, not significantly different to the unemployment rate of 4.0pct in Jan 2020.

The convexity of the curves is key to Waller’s argument. Convexity means that curves become steeper as the labour market tightens i.e. in the top left. Moreover, Waller uses a curve with a fixed separations rate of 1.23pct which is much steeper in the top left than the empirically derived curve, allowing him to argue that a decline in the vacancy rate will not be accompanied by a meaningful increase in unemployment.

Chart 6 (from S1): Beveridge Curve and future unemployment

I believe Waller has used a sleight of hand in relation to the separations rate.

First, he has used a curve that assumes the separations rate is fixed – at 1.23pct – arguing that this – rather than a real world, empirically derived curve that sees separations rates changing over the course of the business cycle – should be used when vacancy rates are high as they are now.

Second, I cannot find support for his note that the separations rate of 1.23pct that he uses to produce his curve is “a little above current levels”. In his paper he rightly points out that separations from employment include layoffs and quits, but then he says that, “Separations consist largely of layoffs”. This is blatantly false! In July, total separations numbered 5.931 million, of which layoffs were just 1.327 million. Quits on the other hand totalled 4.237 million. In other words, separations consist largely of quits not layoffs!

It is possible that he said that separations consist largely of layoffs so that to calculate a separations rate he could use layoffs only – 1.327 million layoffs divided by total number of jobs of 152.536 million is 0.9pct which would justify the “a little above current levels” remark. Using total separations gives a separations rate of 3.9pct. The curve based on this rate would pass nowhere near the Mar 2022 point in Chart 6, thus nullifying Waller’s argument.

In his closing remarks, Waller says, “I’m not arguing that the unemployment rate will end up exactly as the Beveridge curve I’ve drawn [Chart 6] suggests. But I do think it quite plausible that the unemployment rate will end up in the vicinity of what the Beveridge curve currently predicts.” Again, if the curve that he has drawn is wrong, whether because separations are not fixed, or because the separation rate is in fact far higher than his 1.23pct, his argument crumbles.

Now, onto H1, the first rebuttal, which makes two points:

  1. The shift in the curve from pre-covid Arrows 1/2 to post-covid Arrow 4 (Chart 5 above) represents a deterioration in matching efficiency and/or higher reallocation and thus an increase in the natural rate of unemployment. In other words, the labour market is much tighter than may be suggested by the current unemployment rate. Intuitively, it makes sense that covid induced a deterioration in matching efficiency. For example, companies keen to make supply lines more secure by repatriating manufacturing may have found that there were insufficient workers willing to fill such jobs. Lockdowns and quarantine would also have disrupted matching.

  2. Arrow 5 (Chart 5) is not in fact a move down a very steep curve (Chart 6) as Waller suggests, but, according to the authors of H1, a jump from one curve to another. Such a jump would necessarily require a strong improvement in matching efficiency which, according to the authors of H1, has never happened before.

The H1 line of reasoning is visualised in Chart 7 below. The authors point out that moving from B to A (depicted in Charts 5 and 6) as Waller and other Fed officials would like would, requires an improvement in matching – improved matching efficiency/lower reallocation – that has simply never happened before, nor is supported by theory.

Chart 7 (from H1): Stronger activity, lower matching efficiency, and higher reallocation

H1 states:

Going back to figure 3 [my Chart 7], a decrease in the equilibrium from B to A—which is what some Fed officials would like to achieve—would require a large downward shift of the matching relation.

This could happen if either matching efficiency increased or reallocation decreased sufficiently. Figures 4b and 4c show that this is not happening so far, and there is little reason to expect it to happen: It is clear that the COVID-19 crisis will have substantial reallocation implications, especially as the implications of telework become more apparent. And it is not surprising that higher reallocation, with workers moving across sectors and across space, may lead to a sustained decline in matching efficiency. Tighter monetary policy, which will rotate the aggregate activity relation to the right, is unlikely to shift the matching relation at all. Thus, one must expect movements along the matching relation, with the decrease in vacancies associated with an increase in unemployment.

Turning to the empirical evidence, looking at the historical relation between job vacancies and unemployment going back to the 1950s and analyzing the trajectory of unemployment after vacancies come down from a peak, there has never been a historical example where the job vacancy rate came down in a substantial way without a significant increase in unemployment. To look at the evidence over a long time period, one can extend the JOLTS vacancy series back to the 1950s using data constructed by Regis Barnichon (2010), who makes use of the Help-Wanted Index published by the Conference Board to create a vacancy rate series from 1951 to 2000. This is done in figure 5 [my Chart 8 below].

Figure 5 [my Chart 8] plots separately each vacancy rate peak plus and minus eight quarters to visualize the movement in the unemployment rate after each vacancy peak between 1951 and 2019. The eight quarters before a peak are shown in blue and the eight quarters after a peak are shown in orange (this is related to Diamond and Şahin (2015), who instead look at movements in unemployment and vacancies after each business cycle trough). The figure shows that in every historical example, the unemployment rate rose substantially in the eight quarters after the vacancy rate reached its maximum.

Chart 8 (from H1): After vacancies reached a peak, the unemployment rate always rose as the vacancy rate fell

Note: Vacancy rate peaks were determined using the quarter when vacancies reached a local maximum and were nonincreasing for two consecutive quarters. Quarterly data are calculated using the average monthly vacancy and unemployment rate within each quarter. The vacancy rate is defined as the total number of nonfarm job openings divided by the size of the labor force. Vacancy data from 2001 onwards use estimates from the Job Openings and Labor Turnover Survey, while vacancy data before 2001 use vacancy estimates constructed from Barnichon (2010) using the Help-Wanted Index published by the Conference Board. All values are seasonally adjusted. Figure is adapted from Diamond and Şahin (2015). Sources: Bureau of Labor Statistics, JOLTS, Barnichon (2010); authors’ calculations.

Sure enough, the Beveridge Curve has never moved vertically downwards as Waller suggests it can. As the authors of H1 state in relation to past recessions, “There was no free lunch, and there is no reason to expect one today.”

Interestingly, H1 does not pick up on Waller’s sleight of hand in relation to incorrectly stating that “Separations consist largely of layoffs”. I don’t know whether I have got something wrong, whether they did not spot this, or whether they felt that mentioning it would unnecessarily complicate their argument which was not so much about where the curve was but whether one could travel vertically down it – no meaningful increase in unemployment. In relation to the first possibility, it is a statement of fact that separations do not consist largely of layoffs, so if I have got something wrong it is in relation to something else.

Onto the second rebuttal, S2, written by Andrew Figura, Associate Director Program Direction Section Research and Statistics at the Fed, and Christopher Waller, published on 29 July.

Cutting straight to the chase, S2 states that the difference between its and H1’s framework is that H1’s does not take account of separations while its does. Moreover, it states that, “Separations increase importantly in business cycle downturns, and most research attributes to separations a key role in driving cyclical movements in unemployment. As a result, a model that excludes separations will have a hard time explaining movements in unemployment.”

This statement appears to include a blatant error, namely that if S2 states that “Separations increase importantly”, why does S1 use a curve based on a fixed separations rate of 1.23pct in Chart 6? Also, to repeat, the actual separations rate, based on July data, is 3.9pct, the curve for which goes nowhere the March 2020 data point, thus not supporting Waller’s argument.

Finally, onto the third rebuttal, H2, written by Blanchard, Domash, and Summers, and published last Monday, 1 August. The authors write, “We looked at their note [S2] with interest, in the hope of being educated on a more optimistic view of the American economy’s soft landing prospect. Unfortunately, our judgment is that it contains misleading conclusions, errors, and factual mistakes.” Strong stuff.

H2 points out, as I have above, that S1 and S2 used a theoretical curve that is very steep in the top left to argue their case, rather than one of the many empirically derived curves for various post war recessions, or an average thereof, which are all much flatter. H2 also notes that the historical case that most closely resembles today was prior to the 1969/70 recession, the slope for which was quite flat.

The authors do note that, “Given that the current vacancy rate is outside of historical experience, anything is obviously possible. But based on the evidence, we see no reason to change our conclusions.”

H2 also rebuts S2’s claim that it did not take account of separations in its framework. H2’s point is that in steady state, separations are equal to hirings, and therefore that they can be used interchangeably – H2 uses hirings rather than separations. Where there is a difference, according to H2, is in relation to the definition of the hiring rate, what S1 calls the job finding rate. H1 calculates the hiring rate by dividing hires by the labour force – number of employed plus number of unemployed. S1 calculates it by dividing hires by number of employed only. Since the unemployment rate is low, this difference is immaterial.

Finally, H2 notes that S1/S2 argue that,

during the global financial crisis, from 2007 to 2009, separations increased by 50 percent; it is easy to check that, in fact, separations decreased during that period by 22 percent. (This may seem surprising, given the large decrease in employment. It reflects the fact that quits decreased more than layoffs increased during that period.)

H2’s conclusion states that,

We understand the desire of senior officials to hold out the soft landing prospect of a return to target inflation without spiking unemployment. Yet this desire cannot justify flawed analysis or incorrect criticism of serious work on the issue.

Ouch.

I have not seen a further rebuttal from the soft landers but if one is published it will have to address the hard landers’ very serious accusations.

Conclusion

The hard landers argue that for labour market and inflation pressures to ease, unemployment must rise sharply. The soft landers such as Fed chairman Jerome Powell and other senior Fed members, argue that there is a path to a soft landing i.e. that inflation pressures can be eased without a significant rise in unemployment.

I could not see any factual errors made by the hard landers in their two papers, and they certainly appeared able to bat away easily all accusations thrown at them by the other side. However, the hards appear to have found factual errors in the softs’ two papers, ones that have not been rebutted. Furthermore, it appears that I picked up on a factual error made by the softs that the hards did not spot, namely the one about separations consisting largely of layoffs. They don’t!

I conclude that a) it is likely that unemployment will rise sharply as the vacancy rate falls – a hard landing and, b) Fed officials are using dodgy economics.

Hard landings are bad, but when the most important economists at the most important central bank in the most important economy in the world start using dodgy and factually incorrect arguments, we should all be concerned.

The views expressed in this communication are those of Peter Elston at the time of writing and are subject to change without notice. They do not constitute investment advice and whilst all reasonable efforts have been used to ensure the accuracy of the information contained in this communication, the reliability, completeness or accuracy of the content cannot be guaranteed. This communication provides information for professional use only and should not be relied upon by retail investors as the sole basis for investment.

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