Other labour market stats also deteriorating

 Most analysts of the US share and bond markets watch the payrolls employment data to decide what's happening to the labour market, and by inference, the economy.

But there are other statistical series, from different sources, which give an interesting insight into what's happening right now.  

First, two surveys which produce similar results (and have done for more than 30 years), the results of "jobs hard to fill" question by the NFIB (National Federation of Independent Businesses), and, the results of the "jobs plentiful" question asked by the Conference Board for its survey of consumer confidence.  Both are declining, an indication that the labour market is loosening, i.e., that employment is decreasing.

The second pair are from official sources:  the "quit rate" and the level of vacancies.  The "quit rate" asks employers how many of their employees resign of their own accord.  Obviously, the lower it is, the worse the job market is.   

All of these indicators point to, at worst, a levelling off of the economic decline from the middle of last year, and possibly, the beginnings of an economic recovery, but also suggest a slowdown since early this year.  

A similar conclusion can be drawn from other indicators, which I haven't listed. "Jobs hard to find" (from the Conference board), which fell until January this year, and has risen sharply since; small business hirings, which rose until January and have fallen  since; and job openings (from the BLS) which peaked in November, and have been trending down since.

One indicator which has risen over the last couple of months is overtime hours in manufacturing.  This would suggest that employers are reluctant to hire more workers because of the uncertainty, and are working existing workers for longer hours.  Interesting.

I'll combine all these indicators into a composite index, and will share it with you shortly.

 In the meanwhile, my conclusion holds.  The US started a recovery in the middle of last year, and that ended early this year.  The next lower turning point is prolly several months away.

Is business confidence pointing towards a downturn?

 Yesterday, I posted a piece about global business confidence.  I am in the process of expanding and altering my databases, and adding more business and consumer confidence time series as well as revising  my time series processing programs.  

The time series I plotted was a composite of two different business confidence indicators, one for the USA and one for OECD Europe as a whole.  

I've since added some more business confidence series, which has allowed me to calculate business confidence indicators covering more of the world.  

The chart below shows G7 and G11 industrial production and business confidence.  

20 years ago, the G7 (the USA, the UK, Germany, France, Italy, Japan and Canada) were key to the world economy and served as a good proxy for it.  But other countries grew fast, and we needed to take them into account.  So we got the G11:  the original G7 plus China, India, Russia and Brazil.  Together they make up roughly 2/3rds of world GDP.  Also on the chart is G11 business confidence.  (All the indicators are my calculations, using weights derived from purchasing power parity GDPs.)

As can be seen, G7 and G11 industrial production move in similar cyclical patterns (which is what the deviation from moving trend is designed to portray) but in fact the G11 growth rate has been much higher than the G7.  Over the last 20 years, the G11 growth rate has been a full percentage point per annum higher than the G7, and over the last couple of years, the gap has been even higher.  

What's happening now is that G11 business confidence is falling, while G11 industrial production is picking up steam, because China is recovering, even while G7 IP is slowing.  Clearly, business confidence tends to lead the cycle, turning down before the economy does, and also up before it does too, though the lead at the top of the cycle is longer than at the bottom.  So this divergence now between G11 IP and business confidence is very interesting.  The business cycle gave a false signal in 2004, and so it's not infallible.  But remember, it leads the cycle, by between 6 months and a year, so its decline is yet to be fully reflected in economic activity.

We are, I think, at a cusp, and conventional indicators might not work too well, because the uncertainty and disruption caused by Trump's tariff follies are unique in my long professional experience.  Logic leads me to believe that the combination of extreme uncertainty and big blockages to world trade will lead to rising inflation in the US, and lower growth everywhere.  But we won't know for a few more months, by when it will be too late. In the meantime, the big question is how much US tariffs will damage world growth outside the US, especially in China.  I am following the latest releases of economic and financial data in a way I haven't for many years. 

In response to the question I pose in my title for this post, the answer is most likely yes.

Click on the chart to enlarge it.

My Australian coinciding index

 I haven't updated my Australian coinciding index, which is designed to coincide with the business cycle, for a while now, for a variety of personal reasons, some small, some much larger.  At any rate, I have just updated most of the Australian time series I monitor, and have calculated my coinciding and leading indices.  

I thought I would just confirm that my coinciding index is still correlated with the Ozzie business cycle.  To measure the business cycle, I use an average of real GDP and real GDE (gross domestic expenditure).  For a commodity-based economy, GDE is a better guide to the business cycle than GDP, and is better capable of being influenced by monetary and fiscal policy.

I have expressed both time series (my index and the GDP/GDE average) relative to their moving trend. 

As can be seen, my coinciding index follows the GDP/GDE average closely, though my coinciding index is much further below the GDP/GDE benchmark over the last year than (I suspect) it should be, which may be an artefact of the deviation from moving trend calculation.  [See the second chart below.] Note that neither GDP nor GDE form part of my coinciding index because they are quarterly, and therefore published with a lag.

The MT at the end of the time series is estimated by fitting a OLSQ trend to the data from 01/2018 to 2025, i.e, the last 6.5 years, which have been greatly distorted by the covid crash and the wild rebound from it.  I will investigate whether a slightly longer period for the last few years should be used. [Update: I've tweaked the algorithm, which reduces the deviation below the long-term trend over the last 2 years, but I haven't updated the charts]

Anyway, it seems clear enough that my coinciding index is an accurate estimate of the business cycle.  Expressed as a YoY percentage change, my coinciding index has levelled off, but obviously at a big negative value (hence its continued fall relative to its longer-term moving trend)

Will the Ozzie economy recover from now on?  I suspect it will, but Trump's tariff shilly-shallying isn't just making it harder for business to plan ahead, it's also making it harder for economists to forecast!  All I can do is monitor the data closely, which I will do.  In the meantime, I believe that Australia has passed the lower turning point in the cycle, and although it won't be immune to Trump's idiocies, the recovery will be sustained.

China has further to fall

The chart below shows the year-on-year percentage change in my leading and coinciding indices for China.  A coinciding index is designed to follow the economic cycle, while a leading index is designed to lead the economic cycle.  In the chart, the leading index has been plotted with a nine months lag, so that its peaks and troughs approximately coincide with the cycle.  From 2014-2018, the leads were longer, and during China's repeated covid lockdowns over the last 3 years, they were shorter. 

The leading index appears to have troughed, consistent with recent shifts in monetary policy by the People's Bank of China.  But as always, these won't affect the economic cycle for several months.  It is likely that the real economy will only bottom in the middle of 2024. 

What does this mean for the world economy?  China's economic importance is overstated by its official GDP data, but I estimate that it is something between 10 and 15% of the world economy.  In my calculations, I use a weight of 11%, which is perhaps at the lower end.   This compares with the US at ~21%, the Euro Area/Zone of 17%, Japan at 6%, India at ~5% and the UK at 3%.  Because currencies move around a lot, these weights are based on PPP (Purchasing power parity) exchange rates.  (You might see news reports which give different weights, but these often use current exchange rates, which are volatile and can easily change direction.)

So, the US is embarking on a new economic upturn, Europe and the UK are still mired in recession, and Japan is beginning a slow recovery.  China's weak economy will retard world growth.  From an Australian perspective, it will also mean bad news for the prices of Australia's exports, including iron ore, which is already reflecting economic reality, and is falling fast.

China also faces major structural, long-term negatives for growth, but cyclical indices like my leading and coinciding indices aren't designed to pick these up.   Among the negatives China faces are its declining population, its massive housing crisis, and its continued skew away from private consumption.   Given these long-term structural issues, the traditional Chinese stimulus involving massive debt-financed residential housing and infrastructure development may not be feasible or even work.  

This chart shows the gap between the official GDP data and my GDP estimate.   My estimates, and those of other people, suggest that China's actual GDP is 1/3rd or more below its official level.  Note that my alternate GDP estimate is falling even though the government's estimate for GDP is still rising.

Why bother with seasonal adjustment?

Why bother with seasonal adjustment, extreme adjustment, and smoothing?  Because often the unprocessed data are not very clear.

The chart below shows the original (unadjusted), seasonally and extreme-adjusted, and the smoothed data for Turkey's total vehicle sales.

Notice the extreme seasonality.   

If you just looked at the original data, you might conclude from December 2023's (seasonal) spike that vehicle sales were doing very well.   But in fact, on a seasonally adjusted basis, vehicle sales are down from earlier this year, which is consistent with the sharp monetary tightening by the Bank of Türkiye over the last six months.   In fact, the BOT started raising interest rates in July, which is also when the seasonally adjusted sales peaked.

The smoothed version of the seasonally adjusted data has turned down, suggesting that this is a downturn which will last more than a couple of months.  The smoothed data peaked in July at 105,000 units and have fallen steadily since to 103,000 units in December.

 

Click to see clearer image

Here's what the data look like over a shorter time period:

Click to see clearer image

I should point out that the seasonal and extreme adjustment are done by programs I've written, based on algorithms created by the US Bureau of the Census.  I make no manual adjustments; it's all automated.  However, I can choose the length and type of the smoothing.  In this case, I used a 3-month centred linear moving average of a 9-month centred linear moving average with weights calculated by the Bureau of the Census for the X-11 seasonal adjustment program.  This is called a 3 x 9 moving average.  "Linear" refers to the pattern of weights, which is the same for each month in the central span of the time series.  You can also use quadratic or quartic moving averages.  The moving averages are "centred", because a conventional moving average lags the turning points in the underlying data. If you use different moving averages for different time series, because they have different "spikiness", you would get spurious variations in cyclical turning points between the series, not derived from the underlying data, but a function of the length of the moving average you've chosen. 

Quite technical stuff, but I thought you might be interested. 

Extending PMIs back in time

For many countries, the PMI indicator is very useful.  They are released on the first few days of the months for the previous month, and they give a very good idea of the business cycle in each country.  For some countries, I have PMI data going back 25 years, for others just 10, or less, either because the people who compile the PMI indicators (S&P Global) haven't been doing it for longer than that, or because I just don't have the data (you have to be a subscriber to get back data).  But if you want to analyse economic cycles, it helps a lot to have a long run of data.   

What you can do is use other business surveys to estimate back data for the PMIs.  

The chart below shows business confidence for Austria compared with the PMI data.  You can see the close correlation.

Here's a similar comparison for Turkey, where the business confidence time series goes back to 1987, but the PMI (my data) only goes back to 2012.

I'm working on similar analyses for other countries, which will allow me to include a few other countries in my small-country world PMI  ("Small 11", which will soon expand to the "Small 14")

Incidentally, the charts show that Austria's economy is falling more slowly (remember, the PMI/Business confidence surveys lead), pointing towards an upturn in Europe later this year, while Turkey's economy is slipping into recession after their Central Bank raised rates dramatically after the president was re-elected. (He wouldn't permit it before.)  Interest rates in Turkey are far from peaking.

Fed surveys spike in June

There are 5 regional Fed surveys: the Empire State; The Philly Fed; the Richmond Fed's, the Dallas Fed's and the Kansas Fed's.  Normally, even though these surveys don't cover the whole country, the correlation with the ISM (which does cover the whole country) is close.   In June, the average of the 5 (PERDK) and the ISM went in different directions.  It's happened before, but not that often.  

Was it the bottom of the cycle?  Prolly not, but what do I know?

Just thought I'd give you a heads-up.  😉

US Labour mkt in May

Traded markets (shares, bonds, commodities and currencies) tend to be moved by the latest data.  This is, in a way, logical, because new data can make you re-assess your positions.  Was the latest data point stronger than you expected.  Yes?  No?  The market adjusts its expectations, and so it increases or decreases its weightings allocated towards (long) or against (short) certain assets.

So, a stronger than expected rise in US payrolls employment in May suggested that the economy was strong, and so company profits would be better.  But, wait a bit, wouldn't a stronger economy make it more likely that the Fed would raise rates?   Except, the unemployment rate went up, so maybe that would make the Fed more cautious.  There was something for everyone in the jobs report.  The share market duly spiked up, the US dollar rose, as did bond yields and commodity prices.

I prefer to look at trends, rather than the latest monthly movements.  What are they?

• Employment growth continues to slow.  It's still positive, but the trend is clearly down
• The change in the unemployment rate over 6 months, which tracks the economy, except it is inverted (unemployment goes up when the economy goes down), is rising.  In fact, the little blip in unemployment over the last few months matches the mini bounce we've seen in the ISM and PMI indices.
• The 'jobs plentiful' subcomponent of the consumer confidence index has slid to new lows, after (surprise!) a mini-spike.
• Overtime hours didn't rise in the month, which would have been a sign of strength.  Indeed, the downward trend since February last year is still intact, though the trend levelled off during the "blip" we saw in the first few months of this year.

Conclusions:

• The latest monthly spike in payrolls is not a sign of a strengthening economy;
• The "blip" in the economy is starting to wane, but ... ;
• ... it is not yet in recession.
• Which means, given inflation still being higher than the Fed's target, that the Fed has prolly not stopped raising the Fed Funds rate.

A most interesting point in the current US business cycle.  

I show only two of the possible charts, the change in payrolls employment, which is positively correlated with the cycle, and the change in the unemployment rate, which is negatively correlated, and so has been plotted inverted.  If you are interested in the others, drop me a note in the comments.  As usual, because of some mysterious Blogger algorithm, the charts are fuzzy, and will be clearer if you click on them.

Austria's PMI slumps to a new low

As I've mentioned before, the state of Austria's economy gives a good guide to how the whole of Europe's going.  Exports equal 56% of Austria's GDP.  If manufacturing is slumping in Austria, it's mostly because Europe is sliding.  Compare the difference here with large continental economies like the United States. 

After extreme adjustment, Austria's PMI is now lower than during the Covid crash.  That's because my extreme-adjustment algorithm regards a downward spike lasting just 2 months as an aberration (outside the 2 sigma limit for the error term, if you want to get technical).    Business confidence, which, as you'd expect, correlates reasonably well with the PMI, recovered in December, January and February, paralleling the bump in the PMI, but fell again since then.   

I remain convinced that there will be a European recession.  We've had a mini bounce in the European economy, and that's now over.

[See also Austria: PMI vs Business Confidence]

Click on chart to see a clearer image.

Overtime hours rally, but is it a cyclical turn

 Overtime hours in manufacturing correlate well with the cycle.   They ticked up in January, before retreating a little in February.  We'll see the next data point on 7th April.  In the meantime, my forecast is that they might have temporarily stopped falling but that this pause will last only a couple of months before it resumes.  

Big 8 PMI ticks up in January

 After falling month on month for a while, the GDP-weighted extreme-adjusted PMI for the big 8 (USA, Euro zone, China, Japan, UK, Brazil, India, Russia) ticked up a little in January.  

Is this the low point?  Well, obviously it could be.  But I think the mild rebound is prolly as much to do with European gas prices falling sharply, because of very mild weather and increased gas supplies.  A physical shortage of gas would cause an immediate economic crunch, whereas monetary tightening only takes effect with a lag.  So the removal of a gas shortage, or the fears of a gas shortage, will cause a rapid rebound.   In fact, the end of the gas crisis has emboldened the ECB to raise rates to fight inflation.  It is expected to raise its discount rate by 0.5% at its next meeting later this week.  And the impact of that will play out over the next year.

In my judgement, this is not the bottom of the business cycle.  But there may be a slowdown in the rate of decline, or a levelling off over the next couple of months before the slide resumes.

We shall see.

US existing home sales point to GFC recession

 Existing home sales in the US, like most time series associated with the housing sector, lead the cycle.

The chart below shows home sales vs my monthly indicator, which tracks GDP quite well.  Note how home sales peaked in 2005, but only really started to fall fast in 2007.   This plunge preceded the GFC (global financial crisis) of 2008/2009.  History seems to be repeating itself.  However, as bond yields and therefore mortgage rates may have peaked, home sales may be close to a trough.  If there is a deep recession, the trough may be postponed.  Banks are in a much sounder financial state than they were then, on the face of it, but there's plenty of debt owed by emerging markets and zombie companies.  What drove the GFC to its depths was actual and potential banking collapses.  What will (most probably) drive the current cycle into its depths will be the surge in commodity prices, which only peaked in June last year, and central banks overtightening. 

Big 8 & Small 6 all trending south

 I talked about the Big 8 and the Small 6 when I first introduced the concept, here.

Both indices continue to decline.  The global business cycle continues to slow.

US PMI slides again

 As usual, the thick green line is the one to focus on, because it minimises the random fluctuations.  Growth is still positive, but is clearly trending lower.

Deep recession in 2023?

The chart below shows the deviations from trend of the CRB commodity price index, and of my index of world industrial production.  The CRB deviation from trend has been smoothed, inverted and lagged 18 months (i.e., plotted with an 18 months lag)

Why should rising commodity prices lead to a recession later on?

First, there is the income transfer.  When commodity prices double or quadruple, then the exporting country  experiences a jump in income, and consumers in importing countries experience a fall in (real) income.  The two should offset each other, but they do not.  Consumers have no alternative to reducing their spending when the prices of food and energy rise.  But commodity exporting countries do not need to spend the extra income they're getting.  Of course, they do eventually spend it, but the delays involved reduce world demand and output.

Second, a surge in commodity prices leads to a jump in general inflation, as rising commodity prices spill over into the rest of the economy.  Policymakers respond to rising inflation; Central Banks by tightening policy (raising interest rates, and reversing quantitative easing); governments by (perversely) tightening spending and raising taxes.  This especially applies to developing countries, which are more dependent on foreign borrowing to fund expenditure.  These countries often have no choice but to tighten fiscal policy.

Third, these tightening measures lead to a fall in asset prices, including commercial property and housing.  Often the decline in asset prices leads to banking crises and collapses.   A credit crunch is added to the original downward impetus, making the downturn even more severe.

These are general patterns ― the specifics in every cycle are different.  But it will do to go on with.

So a rise in commodity prices leads to a subsequent downturn in economic activity.  The relationship is inverted, and also lagged.  The effect of surging commodity prices isn't immediate, it lags ― by an average of 18 months, as the chart below shows.

Relative to its trend, the rise in commodity prices (plotted as a negative in the chart, because the effect of commodity prices increases is inverse to the effect on economic activity) is even larger than in 1972/73.  And that implies an even deeper recession than in 1974/75.

However, this is only one of possible indicators pointing towards recession.  I will examine others over the next couple of weeks.

Covid distorts the stats

 After rebuilding my programs and updating my data banks, I have started to once again be able to calculate world industrial production and world GDP.  The way I do that is to calculate IP and GDP for each continent.  

I was the first person to calculate monthly world industrial production back in 1992.  I did it to explain movements in commodity prices, because OECD industrial production didn't cover China, Brazil, Russia, etc.  By factoring in these fast-growing economies, the surge in commodity prices made sense.  Now, of course, including large developing economies in our calculations is standard.

To create my early version of world industrial production, I did a lot of trawling through old physical copies of the UN Monthly Statistical Bulletin and other paper data sources to obtain industrial production data to feed my indices, and I have kept those time series more or less up to date, though recently, various factors led to my letting them get a couple of years out of date.  

I have now updated these records, and can once again calculate world industrial production and GDP, as well as regional indices.  

The chart below shows GDP-weighted IP and GDP for South America,  extreme-adjusted, which helps reduce the Covid-related  downwards spike in 2020.  I have also expressed the indices as a percentage of their long-term moving trend.  As economies develop, the trend growth rate gradually slows.  Expressing the underlying series as a percentage of its own moving trend allows us to understand the business cycle better.

One interesting result is that industrial production has rebounded much more strongly relative to trend and on a year-on-year basis than GDP, despite both falling year-on-year by similar percentages.   Otherwise, as you can see, industrial production usually tends to have very similar cycles to GDP.

I should be able to calculate world GDP and industrial production tomorrow, and I'll post the results then.

World growth decelerates again

Despite China's and Russia's PMIs rebounding a little, the average PMI for the big 8 economies/regions fell a little in May.  The index covers eight countries/regions: the USA, Euro zone, UK, Japan, China, Brazil, India & Russia.  Each component is extreme-adjusted and then weighted by PPP GDP to derive the total.

The chart below shows the Big 8  PMI compared with the extreme-adjusted year-on-year change in industrial production for the Big 8, also weighted by PPP  GDP.   The advantage of the PMI indicator is that we have data for May, but industrial production is only available to end March, and even then not all component country IP data are available.  

US average May PMIs down

 As usual, focus on the average of the two national purchasing manager indices, the green line.  This reduces the statistical 'noise' or the random measurement fluctuations around the underlying trend. As often happens, over the month the two surveys moved in opposite directions, this time, the ISM up and the PMI down.  But the average continues to slide.

Note that I have extreme-adjusted both series, and that has attenuated the sharp 2-month downward spike due to Covid in early 2020.

My current projects

 

I'm still working my way through a list of improvements to my time series programs and databases.  They always take longer than I thought they would.  Programming, especially in Visual Basic, is time-consuming, because one almost always makes errors in coding, from simple typos to errors in the logic, but VBA's clumsy way of interrogating values during execution makes tracking down coding errors tedious.   

I'm also resurrecting my APL  programs, but since they work off my Excel spreadsheets, reading from and writing to spreadsheets, they need the VBA programs to work.

My current project is to create a composite monthly series which gives a reasonable estimate of GDP for the USA, and to extend this series back to WW1.  Ultimately, I might write a paper on this for publication.  In the meantime, I'm busy creating a continuous series for the volume of US retail sales from several disparate series going back to 1919.  I'll show y'all the results when I'm done.

UK business confidence

 After my analysis of Russian business confidence, I also looked at UK business confidence.  This has an even better correlation with the overall PMI  than it does in Russia.  You'd have to conclude from these data that growth is slipping, but remains at the peaks of previous business cycles.  However, *if* it keeps on slipping, you'd be looking at recession in H2 2023, entirely consistent with the rise in the Bank of England's discount rate, as extreme measures introduced to support the economy during covid are withdrawn.