Month: July 2023

By Paul Gomme and Peter Rupert

The BEA release of the advance estimate of Q2 Real GDP showed an increase of 2.4% at a seasonally adjusted annual rate. The BEA noted:

The increase in real GDP reflected increases in consumer spending, nonresidential fixed investment, state and local government spending, private inventory investment, and federal government spending that were partly offset by decreases in exports and residential fixed investment. Imports, which are a subtraction in the calculation of GDP, decreased.

Bureau of Economic Analysis, July 27, 2023

The 1.6% increase in Personal Consumption Expenditures (PCE) represented nearly half of the contribution to overall growth, due to the fact that consumption is about 70% of total output. Real non-residential fixed investment increased 7.7% while real residential fixed investment declined 4.2%.

On a year-over-year basis, inflation as measured by the Personal Consumption Expenditures Price Index is the lowest since mid-2022, rising 3.0%, a full percentage point over the Fed’s 2% target. However, as emphasized in earlier posts to this blog, year-over-year measures are quite sluggish. Our preferred measure, the 3-month annualized inflation rate, is 2.5% in June, slightly higher than the 2.3% recorded in May. For what it’s worth, the 1-month annualized PCE inflation rate in June was 2.0%, up from May’s 1.5%.

As an aside, it seems curious to us that commentators are quite comfortable annualizing quarterly growth rates (as emphasized by the headline numbers for GDP growth), but are reticent to do the same with price data (for which headlines compute 12-month growth rates). Perhaps consistency is too much to ask.

What’s significant is that output growth accelerated from 2.0% in the first quarter of 2023 to 2.4% in the second quarter. In this context, the BEA’s discussion of the second quarter, quoted above, is strange. The BEA focused on the fact that GDP increased, listing off the major components that contributed to this increase (consumption, investment, government spending and imports) while noting those that detracted from the increase (exports and residential investment); see the following table.

	Quarter 1	Quarter 2
Output	2.0%	2.4%
Consumption	4.2%	1.6%
Investment	-11.9%	5.7%
– Non-residential	0.6%	7.7%
– Structures	15.8%	9.7%
– Equipment	-8.9%	10.8%
– Intellectual Property	3.1%	3.9%
– Residential	-4.0%	-4.2%
Government	5.0%	2.6%
Exports	7.8%	-10.8%
Imports	2.0%	-7.8%

But why did output growth increase? The answer lies principally in the swings in investment and imports growth. Investment growth rose from -11.9% to 5.7%, transforming it from a drag on output growth to a contributor. Drilling deeper into investment, the growth rate of residential investment was largely unchanged (-4.0% to -4.2%). The big increase in non-residential investment growth was driven principally by investment in equipment, rising from -8.9% to 10.8%. To be sure, the growth in non-residential structures was very strong (9.7%), but it grew even faster in the first quarter (15.8%).

The growth rate of imports fell from 2.0% to -7.8%. However, since imports enter with a negative sign in the output identify, Y=C+I+G+X-M, the lower growth rate of imports contributed positively to output growth.

As noted by the BEA, growth of exports was negative in the second quarter (-10.8%) while it was positive in the first quarter (7.8%). While growth of consumption and government spending were both positive, their growth rates fell which has the effect of lowering quarter two output growth relative to the first quarter.

Overall, the strong growth in GDP coupled with the subdued (though still above the 2% target) price change shows a still-resilient real economy that is disregarding the increases in the Fed Funds rate. Based on available data, it is hard to make the case for a nascent recession in the U.S.

Personal Income

On Friday, the Bureau of Economic Analysis released personal income data. Real personal income growth fell from 8.5% in the first quarter to 2.5% in the second (both at annualized rates).

Disposable income data is also available on a monthly basis. The chart below shows that the first quarter was driven by very strong growth in January (21.9%) while the second quarter was hampered by negative growth in April (-0.4%).

Employment Cost Index

On Friday, the Bureau of Economic Analysis also released updated employment cost index data. Growth in the wages and salaries component fell from 4.9% in the first quarter to 4.1% in the second.

Overall, the current data suggest that the real economy continues to chug along at a respectable clip and the price numbers indicate that Fed policy is helping push down inflation. The Fed has indicated that the round of tightening is not yet over and the strength of the real economy gives no reason to alter that view.

By Paul Gomme and Peter Rupert

The BLS announced the CPI and PPI for June. The CPI rose 0.18% from May to June on a seasonally adjusted basis, 2.18% when annualized, and was up 3.1% year over year. Rather than strip out a lot of goods that comprise the CPI, we look at the CPI on a three month average basis. As shown in the graph, the year over year continues to decline while both the annualized monthly and the 3 month ticked up slightly. The CPI-X (ex food and energy) differs, however. The annualized monthly rate grew 1.91%, below the Fed’s 2% target.

Of course, the question the Fed faces comes down to: What has been happening to the trend in inflation? Given the 2% target, the Fed hopes to see progress toward that goal. But, as we have commented on in several previous posts, what measure of inflation is the best indicator? Headline CPI (PCE)? Core CPI (PCE)? The Fed’s new Supercore CPI (PCE) (the price of services excluding energy and housing services)? Paul Krugman (NYT, July 11) has his own measure of supercore: “My preferred measure these days is “supercore,” which excludes food, energy, used cars and shelter (because official measures of housing costs still reflect a rent surge that ended a year ago.)” And goes on to say he is not a fan of the Fed’s measure, “The Fed has a different measure of supercore — non-housing services — but when you look at the details of that indicator, it’s a dog’s breakfast of poorly measured components that I find hard to take seriously.” Here is a graph of the Fed’s supercore measure:

So what are we to make of all of this? The main problem is that all of these measures are purely arbitrary; there is no underlying theory as to how, or what to measure. The BLS states, “Inflation can be defined as the overall general upward price movement of goods and services in an economy. The U.S. Department of Labor’s BLS has various indexes that measure different aspects of inflation.” These new so-called supercore measures omit various categories of goods that are purchased in the market. The overall CPI is comprised of 243 commodities and services measured in 32 different geographic areas. To show just how big these omissions are, the following table recreates a subset of the BLS table where the relative importance is the share of total expenditure used to weight the respective change and the May 2023-June 2023 column gives the monthly change:

Expenditure category	Relative importance	May 2023-June 2023
All items	100%	0.2
Food	13.4%	0.1
Energy	6.9%	0.6
All items less food and energy	79.7%	0.2
Services less energy services	58.3%	0.3
Shelter	34.7%	0.4

The table above shows that the core CPI includes about 79.7% of all expenditures. Removing shelter from the core means the price measure includes only 45% of the things we buy. If only services prices are included that would vastly reduce the set of prices used to calculate inflation.

The BLS announced that the Producer Price Index (PPI) for final demand increased 0.1% from May to June or 1.65% annualized. Year over year it increased 0.24%. Our 3 month measure fell -0.69%, the third consecutive month with a decline.

Trend Inflation

What we (the writers of this blog) mean by “trend inflation” is a series that tracks the movements of actual monthly inflation but somewhat smoothed (taking out the more extreme fluctuations). As we’ve discussed before, this is a signal-extraction problem. Every month, we get a new CPI number from which we want to make an inference concerning the current trend inflation rate. Trend inflation is the “signal”. However, the news (the new CPI number) is a noisy indicator of this signal, both because the CPI is reported in levels and because of transitory factors affecting monthly inflation. Further, the nature of the data is such that we cannot easily extract the signal (trend inflation) from the data. With all of this in mind, the reason for looking at core or supercore CPI inflation is that it has a higher signal-to-noise ratio than CPI inflation. Both seem promising in that they omit the more volatile price components of CPI: in the case of core CPI, food and energy; in the case of super core CPI, lots of other stuff. However, the charts above show that over the past couple of years, core CPI inflation has been running well above actual CPI inflation while the Fed’s supercore inflation has been well below actual.

An alternative to slicing and dicing CPI is to compute inflation over a horizon longer than a month. The idea in this case is that the monthly noise is an independent draw (in the jargon of economics, it’s independently and identically distributed) while trend inflation has memory and changes relatively slowly over time. Consider headline inflation. Typically, it’s calculated as the 12 month percentage change in the CPI. But a different way to think about this calculation is as the average of the 12 one month inflation rates. If there is no change in trend inflation over this period of time, the 12 noise terms will have a mean of roughly zero, and the year-over-year inflation rate will provide a good estimate of trend inflation. The rub is that there have been changes in trend inflation over the past couple of years. Consequently, the year-over-year inflation rate also takes an average of trend inflation over the past year. This is why, in the chart above, year-over-year inflation has consistently exceeded the monthly inflation rate for the last year and a half.

But there’s nothing magic about year-over-year inflation rates, and we’ve previously advocated for annualizing the three month inflation rate as a good compromise between capturing changes in trend in a timely fashion, and seeing through the noise in monthly inflation rates. By construction, the three month inflation rate tracks movements in monthly inflation while removing much of the noise – as can be seen in the chart above. One thing that the three month inflation rate lacks is a fancy name like supercore; oh, and the NYT soap box.

By Paul Gomme and Peter Rupert

The BLS released the Job Openings and Labor Turnover Survey (JOLTS) for May on July 6. The data reveal a pretty mixed view. Job openings fell 496,000 and now stands at 9.8 million vacancies. The number of unemployed workers in May was 6.1 million so that there were 1.6 jobs available for each unemployed worker.

The JOLTS data also contains information on the rate at which workers are hired, laid off, quit and job openings. The rates are determined by dividing by the level of employment. The JOLTS covers about 95% of all nonfarm payroll jobs in the US. While the openings rate declined the hiring rate and the quit rate rose. The layoff rate remains at one of its lowest rates since the inception of the JOLTS in December of 2000. Note that all of the other rates are well above their historic average.

The JOLTS data provides some evidence for those looking for nascent signs of a recession. Quit and hiring rates fell through the March-November 2001 and December 2007-June 2009 recessions, and both series started falling prior to the latter recession. The declining quit and hiring rates since early 2022 fit this pattern. Unfortunately, JOLTS only covers three recessions, so it’s tough to make much of the historic precedents, particularly since the February-April 2020 recession was due to COVID-19. Another potentially confounding factor is that both quits and hires are at historically high rates, and the recent declines may be due to reversion to the mean.

The data on initial and continued claims for unemployment insurance also came out on July 6. Initial claims rose slightly but remain relatively subdued. Continued claims have been trending down over the past few months.

On July 7, the employment situation report for June was released by the BLS and showed a payroll employment increase of 209,000 but downward revisions for April and May totaling 110,000 for the two months. One way to think about these changes is that roughly half of the employment gains reported for June were offset by the downward revisions in the previous two months. The employment change in the private sector was the smallest increase over the past couple of years.

The phrase “little changed” was peppered throughout the press release for the Employment Situation Report. The report highlighted gains in employment in government (60,000), health care (41,000), social assistance (24,000) and construction (23,000). Yet the gains in professional and business services, and leisure and hospitality (both 21,000) were described as “little changed”. It would seem that the difference between a gain warranting notice, and “little changed” is 22,000.

Average hours worked in the non-farm sector inched up 0.1 hours to 34.4. That increase, combined with the increase in private employment led to about a 5% in total hours of work in the private sector. There was little change in average hourly earnings.

One way to combine data from JOLTS and the employment report is through the Beveridge curve which plots the vacancy rate (from JOLTS) against the unemployment rate (the household survey). As seen in the chart below, the data up to and including the Great Recession (2007-09) appears to lie on a stable Beveridge curve. After the Great Recession, the Beveridge curve shifted out. And after the pandemic, the Beveridge curve shifted out yet again, with the most recent data in the north west quadrant. One interpretation of an outward shift in the Beveridge curve is that it reflects lower efficiency in the matching process between jobs and workers. Under this interpretation, at a given level of unemployment, firms need to post more vacancies in order to fill jobs. An alternative interpretation is that the outward shifts in the Beveridge curve are due to lower costs of recruiting workers: firms post more vacancies because doing so is simply much cheaper that in the past. One way to distinguish between these two alternative interpretations is to look at how quickly vacancies are filled: less efficient matching says it should take longer while lower job posting costs implies a shorter period of time. Unfortunately, the speed at which vacancies are filled is no longer available.

According to the BLS household survey, employment increased 273,000, the number of people unemployed fell by 140,000 and the labor force increased by 133,000. The participation rate and employment to population ratio were unchanged. The unemployment rate fell from 3.65% to 3.57%.