Haver Analytics
Haver Analytics


Paul L. Kasriel

Mr. Kasriel is founder of Econtrarian, LLC, an economic-analysis consulting firm. Paul’s economic commentaries can be read on his blog, The Econtrarian.   After 25 years of employment at The Northern Trust Company of Chicago, Paul retired from the chief economist position at the end of April 2012. Prior to joining The Northern Trust Company in August 1986, Paul was on the official staff of the Federal Reserve Bank of Chicago in the economic research department.   Paul is a recipient of the annual Lawrence R. Klein award for the most accurate economic forecast over a four-year period among the approximately 50 participants in the Blue Chip Economic Indicators forecast survey. In January 2009, both The Wall Street Journal and Forbes cited Paul as one of the few economists who identified early on the formation of the housing bubble and the economic and financial market havoc that would ensue after the bubble inevitably burst. Under Paul’s leadership, The Northern Trust’s economic website was ranked in the top ten “most interesting” by The Wall Street Journal. Paul is the co-author of a book entitled Seven Indicators That Move Markets (McGraw-Hill, 2002).   Paul resides on the beautiful peninsula of Door County, Wisconsin where he sails his salty 1967 Pearson Commander 26, sings in a community choir and struggles to learn how to play the bass guitar (actually the bass ukulele).   Paul can be contacted by email at econtrarian@gmail.com or by telephone at 1-920-559-0375.

Publications by Paul L. Kasriel

  • In an April 16, 2024 Bloomberg News article entitled “What If Fed Rate Hikes Are Actually Sparking US Economic Boom?”, it is argued that the Fed’s increase in the federal funds rate from 0.08% in March 2022 to 5.33% in July 2023, which, in turn, pushed up other interest rates, stimulated US domestic aggregate demand for goods and services by increasing interest income to holders of fixed-income assets. Wow! This novel hypothesis, if valid, turns monetary policy theory on its head.

    Let’s look at some data. As can be seen in Chart 1, there does indeed seem to be some positive correlation between the level of the federal funds rate and the level of personal interest income. For example, when the Federal Open Market Committee (FOMC) hiked the federal funds rate from the beginning of 2016 through the first quarter of 2019, personal interest income moved up sympathetically. Although personal interest income had started increasing in 2014, before the FOMC had begun raising the federal funds rate. Similarly, as the FOMC began hiking the federal funds rate in 2022, personal interest income starting rising, too. So far, so good for this hypothesis that FOMC federal funds hikes raise personal interest income.

  • USA
    | Apr 01 2024

    April Odds and Ends

    I enjoy examining data. It used to be a hobby that I got paid to do. Now, it is just a hobby. Below are some random sets of data of that I found interesting. Perhaps some others will, too.

    As shown in Chart 1, starting in 2022, household interest payments as a percent of their after-tax income (Disposable Personal Income) started rising after declining in 2020 and 2021. By Q4:2023, household interest payments as a proportion of after-tax income had moved up to 5.4%. This compares with 5.0% in Q4:2019, just before the Covid pandemic hit the US. Notice that the main driver in of this proportional increase in household interest payments has been non-mortgage debt. With many 30-year home-mortgage rates locked in at around 3% in 2020 and 2021, households have been able to increase their spending relative to their after-tax income by increasing consumer loans, such as credit card and auto debt. Although household debt-service ratios are rising, they are a far cry from those that obtained just before the onset of the Global Financial Crisis, but …

  • In its January 31, 2024 FOMC statement, the Fed said: “In assessing the appropriate stance of monetary policy, the Committee will continue to monitor the implications of incoming information for the economic outlook.” The translation of this Fedspeak is that the Fed’s target level of the federal funds going forward would depend on the forthcoming data as they relate to the Fed’s dual mandates of promoting price stability and full employment. But what if the data upon which the Fed were depending to determine the level of the federal funds rate were undependable? In what follows, I will provide examples of “undependable” data and recommend a solution for how the Fed might conduct monetary policy in the face of undependable data.

    In the Bureau of Labor Statistics (BLS) February 2024 Employment Situation, it was reported that the level of January 2024 nonfarm business establishment payrolls was 157,533 thousand, revised down from its preliminary estimate of 157,700 thousand. Mind you, this is just the first revision of January 2024 nonfarm payrolls. When the BLS releases its March 2024 Employment Situation report, there will be a second revision to January 2024 nonfarm payrolls. And then in 2025, there will be annual “benchmark” revisions to 2024 nonfarm payrolls, including those of January 2024. The level of February 2024 nonfarm payrolls reported on March 8, 2024, 157,808 thousand, was said up 275 thousand compared to the first-revised January 2024 level of nonfarm payrolls. However, compared to the first-reported level of January 2024 nonfarm payrolls, the level of February 2024 nonfarm payrolls was up only 108 thousand. And, of course, in the next two 2024 BLS Employment Situation reports, the February 2024 level of nonfarm payrolls will be revised twice. Because of monthly and annual revisions, the monthly reports of nonfarm payrolls would seem to be undependable data upon which the Federal Reserve might use to determine monetary policy.

    On March 14, 2024, the Census Bureau reported that the level of February 2024 retail sales increased 0.6% compared to the revised level of January 2024 retail sales. However, the level of January 2024 had been revised down by $3,581 million or 0.5% from the originally-reported level. So, the level of February 2024 retail sales was up only 0.06%, not 0.6% from the originally-reported level of January 2024 retail sales. Based on revised data in the February 2024 retail sales report, in the three months ended January 2024, retail sales contracted at an annualized rate of 3.8%. Based on the data reported in the January 2024 retail sales report, in the three months ended January 2024, retail sales contracted at an annualized rate of only 1.8%, less than half the rate of contraction exhibited by the data revised in the February 2024 retail sales report. Again, monthly revisions to retail sales data would suggest that these data are undependable for the purposes of guiding monetary policy.

    The next problematic economic report I will discuss is the Consumer Price Index (CPI), more specifically, the Owners’ Equivalent Rent (OER) component of the CPI. At 26.7% of the CPI, OER has the “heaviest” weight in the CPI. That OER has such a high weight in the CPI is understandable given that the US homeownership rate is about 66%. My quarrel is not with the weight of OER but how it is estimated. From what I have read about this estimation process is that a sample of homeowners are asked by the BLS what the respondents think their detached dwelling/condo/townhouse would rent for. How many homeowners, especially owners of detached houses, have a reasonably accurate estimate of what their abode would rent for?

    OER was reported to have increased month-to-month annualized 6.94% in January 2024 compared to a 5.22% annualized increase in December 2023. The CPI excluding OER monthly increase was 2.66% annualized in January 2024 compared to 2.03% in December 2023. The month-to-month annualized change in the CPI-All Items was 3.73% in January 2024 compared to 2.83% in December 2023. The BLS received queries as to why there was such a relatively large percent increase in the January 2024 OER compared to December 2023. On February 29, 2024, the BLS issued a statement saying that there are now annual updates effective in January of a year in the weighting of the OER in terms of owner-occupied detached dwellings versus condos/townhouses. The BLS said that “[i]n January 2024, the proportion of OER weighted toward single-family-detached homes increased by approximately 5 percentage points.” My point, again, is not that OER is unimportant, but that its measurement is, for lack of a better term, “flaky”. Given the difficulty in accurately measuring OER, the European Union excludes OER from its calculation of EU consumer price inflation. Plotted in Chart 1 are the year-over-year percent changes in the All-Items CPI (the blue bars) and the CPI excluding OER (the red line). The year-over-year change in the CPI excluding OER in February 2024 was 2.27%, close enough to 2% for Federal Reserve work.

  • Before he took over hosting The Tonight Show” in September 1962, Johnny Carson hosted an afternoon quiz television show called “Who Do You Trust” (originally titled “Do You Trust Your Wife”). I used to watch it when I came home from school before digging into my homework. Looking at the revisions to the monthly report of nonfarm payroll employment, the show’s title gained more relevance to me now. Billions of dollars, maybe trillions, of transactions in financial instruments take place in reaction to the first estimate of the change in nonfarm payrolls on the day each month that the Bureau of Labor Statistics (BLS) releases its report. That first estimate of the monthly change in nonfarm payrolls gets revised in each of the following two months. But those revisions, although sometimes mentioned by the mainstream financial media, tend to be put aside in the frenzy of trading that takes place in the nanosecond after the BLS releases its monthly report of the Employment Situation.

    I decided to take a look at how much the monthly changes in total nonfarm payrolls get revised from their preliminary estimate to their “final” estimate two months later. (I put quotation marks around final because there are of course, subsequent benchmark revisions.) Plotted in Chart 1 are the BLS first estimates for monthly changes in total nonfarm payrolls (the red bars) and its third estimates (the blue bars). (Ignore the November and December 2023 data points inasmuch as the BLS has not yet reported its final estimates for the changes in nonfarm payrolls for these months.) Plotted in Chart 2 are the monthly differences in changes in total nonfarm payrolls between the third estimate by the BLS and its first estimate. Notice that in the 10 months ended October 2023, there is only one month, July 2023, in which the third estimate is greater than the first estimate. Plotted in Chart 3 are the 10-month cumulative totals in the monthly differences between the third estimates of the changes in total nonfarm payrolls and the first estimates. Attention should be focused on the last data point, October 2023, which reads minus 417 thousand. So, in the first 10 months of 2023, the cumulative total of monthly changes in nonfarm payrolls was 417 thousand less when the third estimate of the monthly change in nonfarm payrolls is compared with the first estimate. But these 417 thousand jobs that got revised away probably had no effect on the analyses of the state of the US labor market made by the talking heads on CNBC and Bloomberg when the November and December 2023 first estimates of changes in nonfarm payrolls were reported by the BLS.

  • The second guesstimate by the Bureau of Economic Analysis (BEA) of Q3:2023 real Gross National PRODUCT’s annualized growth came in at 5.2%, up from the first guesstimate of 4.9%. Along with these data, the BEA reported its first guesstimate of annualized growth in real Gross Domestic INCOME (GDI) , 1.5%. In theory, both GDP and GDI should be the same. Both represent the value of goods and services produced in the economy. GDP calculates this value by adding up the value of expenditures in the economy – personal consumption, business expenditures, including the change in inventories, government expenditures and the change in net exports. Income is earned by some entities for the production of goods and services. So, GDI is the sum of wages, profits, interest income, rental income and taxes minus production/import subsidies.

    As I mentioned above, in theory, real GDP and real GDI should be the same. But, in practice, they are not. Plotted in Chart 1 are the quarterly observations of the year-over-year percent changes in real GDI (blue line) and real GDP (red line) from 2010 through Q3:2023. Also plotted in Chart 1 are the quarterly observations of the percentage point differences between the year-over-year percent changes in real GDI and real GDP (the green bars). Notice that in the three quarters ended Q3:2023, these differences have widened out considerably, widened out to the negative side. The median difference from Q1:2010 through Q4:2022 has been 0.09 percentage points. That’s close enough for government work for saying real GDI and real GDP, as separately calculated, are the same. But in the four quarters ended Q3:2023, the median difference has been negative 1.97 percentage points. In Q3:2023 by itself, the difference between the year-over-year percent change in real GDI and real GDP was minus 3.16 percentage points, the widest absolute difference between changes in real GDI and real GDP in the period staring in Q1:2010 through Q3:2023. Granted, the real GDI data point for Q3:2023 is the BEA’s first guestimate of it.

  • I don’t have access to the Blue Chip survey of economists’ forecasts of various economic data anymore, so I can’t answer my question. But I do have access to consumers’ inflation forecasts and these forecasts are terrible. Plotted in the chart below are monthly observations of consumers’ forecasts of year-ahead inflation as reported in the University of Michigan Consumer Sentiment Survey (the blue bars). Also plotted in the chart are monthly observations of the actual (until revised) year-over-year percent changes in the All-Items Consumer Price Index (the red line). The CPI percent changes are lagged such that they line up with month in which the consumers’ forecasts were surveyed. For example, in May 2020, consumers were forecasting that the year-over-year inflation rate in May 2021 would be 3.2% (the height of the blue bar in May 2020). As luck would have it, the actual CPI inflation rate turned out to be 4.9% (the height of the red line in May 2020). In October 2022, consumers were forecasting that the year-over-year inflation rate in October 2023 would be 5.0%. In fact, it turned out to be 3.2%. In the latest November survey, consumers are forecasting that inflation will be 4.5% in the 12 months ahead. Given that the sum of the monetary base plus commercial bank credit grew by only 0.7% in the 12 months ended October 2023, my bet is that the year-over-year percent change in the CPI in November 2024 will be much lower than 5.0%.

  • Part un was written by me way back on March 14, 2020. I should have paid more attention to my 2020 commentary so that I would not have thought that household spending would be less resilient as it has been so far in 2023. Moreover, I would not have called for a recession to commence in Q2:2023.

    In Chart 1 below are plotted monthly observations of the M2 money supply as a percent of nominal Disposable Personal Income (DPI). From January 2015 through December 2019, the median value of this ratio was 91.8%. Then, after the federal government started writing Covid-aid checks to households and businesses, checks financed by the Fed and banking system, the ratio of M2 to DPI reached a high of 118.9% in January 2022. As of September 2023, the ratio had declined to 102.1%, much below its January 2022 high, but also materially above its 2015-2019 median value.

  • In this commentary I will explain the relationship between the shape of the yield curve, the behavior of nominal thin-air credit and the behavior of real aggregate demand for goods and services. Specifically, I will argue that there is a positive relationship between the “slope” of the yield curve and the percent change in thin-air credit. Further, I will argue that there is positive relationship between percent changes in real thin-air credit and percent change in real domestic aggregate demand for goods and services. For example, the steeper the slope in the yield curve, the faster will nominal thin-air credit grow. In turn, the faster will real domestic aggregate demand grow. The yield curve concept, I will be referring to is the spread between the yield on the Treasury 10-year security vs. the overnight federal funds rate (hereafter referred to as the yield spread). The importance of the federal funds rate in this yield spread will be explained later in this commentary. Thin-air credit for the purposes of this commentary will be defined as the sum of the asset categories of securities and loans on the books of depository institutions (currently, primarily commercial banks). “Thin-air” refers to the fact that the system of depository institutions has the unique ability among various other private lending entities to create credit figuratively out of thin air. (The central bank also has the ability to create credit out of thin-air. In fact, the central bank creates the “seed money” that enables the depository institution system to create thin-air credit.) The important implication of the ability to create credit out of thin-air is that the borrower can spend borrowed funds without necessitating any other entity to reduce its current spending. Thus, when new thin-air credit is created, we can assume that nominal current spending will increase, all else the same. (We cannot determine, a priori, what will be purchased by the borrowers of this new thin-air credit. It could be newly-produced goods and services or it could be existing assets, physical or financial.) We cannot make this same assumption when new credit is extended by non-depository entities. For example, if an individual extends credit, in order to fund that loan, she either has to cut back on her current spending or run down her current deposit holdings. If she reduces her current spending, i.e., increases her saving, no new net spending will occur from this loan because she will merely be transferring her spending ability to the borrower. If she funds the loan by running down her deposits, then there will be some net new spending in the economy. In economist jargon, this would be an example of an increase in the velocity of money. An increase in the velocity of money is another way of stating that there has been a decrease in the public’s demand for money balances. I am not aware of any statistic that measures total transactions in the US economy, transactions that include not only expenditures for newly-produced goods and services but also purchases of existing assets. So, I must rely on expenditures recorded in the National Income and Product Accounts (where the GDP data are reported). Because the bulk of thin-air credit created by US depository institutions goes to domestic borrowers, I have chosen Gross Domestic Purchases as my measure of aggregate domestic demand.

    If you intend to read further, I would advise you have some Red Bulls on ice. It’s going to be a long one. Plotted in Chart 1 are quarterly averages of the level of federal funds rate (the blue line) and the yield spread between the Treasury 10-year security and the federal funds rate (the red bars). The gray shaded areas demarcate periods of recession. Notice as the federal funds rate declines, the spread tends to widen. Similarly, as the federal funds rate rises, the spread tends to narrow, sometimes going into negative territory. Why might this be, especially, why might the spread narrow when the federal funds rate rises? Remember, the Federal Reserve sets the level of federal funds rate. It does so by regulating the supply of cash reserves it creates out of thin-air in relation to the amount of these reserves demanded collectively by depository institutions (hereafter, banks). Prior to March 26, 2020, the Fed imposed reserve requirements on banks. That is, banks were required to hold as cash reserves (either on deposit at the Fed or as coin/currency in their vaults) in an amount equal to a specified percentage of their deposits. The Fed varied this percentage from time to time for reasons known only to the Fed. Prior to October 9, 2008, when the Fed began paying interest on reserves held by banks, these required reserves largely defined banks’ demand for reserves. If the Fed wanted to increase the level of the federal funds rate, it would reduce the amount of reserves it supplied relative to banks’ demand for reserves. Even if the Fed had not imposed reserve requirements on banks, they still would have had a demand for reserves. Banks would want to maintain a certain level of reserves to cover their clearings with other banks. As mentioned above, on October 9, 2008, the Fed began paying interest to banks on their reserves holdings. The reason it did so was to increase banks’ demand for reserves. In November 2008, the Fed began its first round of quantitative easing (QE), which added reserves to the banking system, but had not yet lowered its federal funds target to zero. In order to prevent the federal funds rate from falling to zero, the Fed induced the banking system to hold these extra reserves by paying them interest on their reserves holdings. Eventually, the Fed lowered its target for the federal funds rate to zero, yet the Fed continued to pay interest on reserves held by banks’ as it still does to this day. Why?

  • There is an economist in Chicago who has been known to see a silver lining behind every cloud. For example, after some natural disaster that resulted in hundreds of millions of dollars of damage to structures, this economist had been known to say that on the bright side, think of the rebuilding activity that will take place. By this logic, if the federal government wanted to increase the pace of economic activity, it could call on the US Air Force to carpet bomb some selected suburb, giving the residents plenty of notice to vacate the their premises with their irreplaceable possessions. (You might want to Google Bastiat’s “broken window fallacy” for the nonsense of this). I bring this up because after the debt-ceiling increase/extension legislation was signed into law on June 3, 2023, analysts, who unlike the aforementioned Chicago economist, see a cloud behind every silver lining. Even before the debt-ceiling bill hit the desk of President Biden, these nattering nabobs of negativity (you youngsters can Google this phrase) were saying that the rebuilding of Treasury balances at the Fed would suck liquidity out of the financial system, which, in turn, would cause all sorts of unspecified problems in the financial markets.

    All else the same, it is true that an increase in Treasury deposits at the Fed would drain reserves from the banking system. But all else has not been the same in this case. Let’s go to Chart 1. Plotted in Chart 1 are the four-week billions of dollars changes in reserve balances held at the Fed by depository institutions (the blue bars), Treasury deposits at the Fed (the red line) and reverse repurchase agreements (RRPs) with the Fed (the green line). The last data points plotted are for the week ended June 28, 2023. In the four weeks ended June 28, Treasury deposits at the Fed increased by $360 billion, which, all else the same, would have drained that amount of reserves from the banking system. Yet, in the four weeks ended June 28, reserves at the Fed contracted by only $29 billion (the last blue bar plotted). Evidently, all else was not the same. One major factor that was not the same was the amount of RRPs executed with the Fed. An increase in RRPs drains reserves from the banking system; a decrease in RRPs adds reserves. In the four weeks ended June 28, RRPs executed with the Fed declined by $344 billion, which offset all but $16 billion of the reserves drained via the increase in Treasury balances at the Fed.

  • The year-over-year change in the All-Items CPI for May 2023 was 4.13%. My forecast is that the year-over-year change in the All-Items CPI for June 2023 will be less than 4.13%. Barring revisions, the seasonally-adjusted month-to-month percent change in the June CPI would have to be 1.19% non annualized for the year-over-year change in the June All-Items CPI to be equal to May’s 4.13%. Coincidentally (I think), the last time the month-to-month non-annualized change in the CPI was as high as 1.19% was June 2022, when it was exactly 1.19%. If the June 2023 All-Items CPI increases by 0.55% (non-annualized), the average non-annualized percent change in the CPI in the three months ended May 2023, the June 2023 year-over-year change in the CPI would slow to 3.47% vs. May’s 4.13%.

    This is not economics. Rather, it is arithmetic. And it is all about that June 2022 base. (It took me a while, but I got there.) Plotted in Chart 1 are the month-to-month annualized percent changes in the All-Items CPI (the blue bars) along with the monthly observations of the year-over-year percent changes in the All-Items CPI (the red line). The June 2022 CPI increased a whopping annualized 15.22%. The June 2022 level of the CPI is the base for the June 2023 year-over-year percent change observation. With such a high June 2022 base, the bias is for a slowing in the year-over-year percent change in June 2023. The year-over-year percent changes in the All-Items CPI beyond June 2023 are not likely to slow as much because the high June 2022 base will drop out of the calculation. However, in the 11 months ended May 2023, the All-Items CPI has increased an annualized 3.17%. So, barring some negative supply shock in the remainder of 2023, the year-over-year change in the December 2023 All-Items CPI is likely to be much lower than the 6.44% for December 2022. For example if the CPI increases a non-annualized 0.3% from June through December 2023, the December 2023 year-over-year change in the All-Items CPI would be 3.59%. I believe that the monthly non-annualized changes in the All-Items CPI will, on average, be less than 0.3%. Thus, I believe that the year-over-year change in CPI as of December 2023 will be less than 3.59%.

  • Yes, but energy prices fell by 3.6% month-to-month and food prices were up only 0.2%. Used motor vehicle prices account for only 2.75% of the CPI while food and energy prices account for 20.30% of the CPI. Typically each month some consumer prices rise and some prices fall. That is why when we try to measure the overall change in consumer prices we use a weighted price index, the weights being determined by the estimated relative importance of the different items purchased by a representative household.

    Let’s look at the annualized percent changes in the All-Items CPI over one month, 3 months, six months and 12 months, which are plotted in Chart 1. As of May 2023, the annualized percent change in the CPI was 4.13%, 3.17%, 2.20% and 1.50% over 12 months, six months, three months and one month, respectively. In May 2022, these changes were 8.50%, 9.21%, 9.69% and 11.62%.

  • The Bureau of Economic Analysis’s first guess at real GDP annualized growth in Q1:2023 was a paltry 1.1%. Bear in mind that the BEA does not yet have March 2023 data for business inventories, net exports or construction expenditures. With only full January and February inventories data, the estimated change in real business inventories in Q1 “contributed” minus 2.3% to the annualized percent change in Q1 real GDP. Who knows, perhaps the March inventories data will reduce the magnitude of the drag on real GDP growth from this component.

    In contrast to the drag on Q1 real GDP growth from real business inventories, real Personal Consumption Expenditures (PCE) contributed 2.5% to Q1 annualized real GDP growth. At an annualized rate, Q1 real PCE increased 3.7% versus 1.0% in Q4:2022 and the fastest growth in real PCE since the 12.1% recorded in Q2:2021. Given that real PCE accounted for around 71% of real GDP in Q1, the economy seemingly was on fire in Q1, right?

    Wrong! Let’s take a look at the behavior of monthly real PCE as compared to its quarterly average. This is shown in Chart 1 below. The blue bars in Chart 1 represent the month-to-month annualized percent changes in real PCE. The red bars represent the quarter-to-quarter annualized percent changes in real PCE. So, the annualized growth of 3.7% in real PCE in Q1:2023 was the result of the outsized 17.6% annualized growth in January 2023 PCE growth. Monthly real PCE contracted in February and March 2023. In fact, real PCE contracted in four of the past five months. It seemed as though a myriad of measures of economic activity were very strong in January, very strong, as a former president might say. Could it have been that January 2023 was uncharacteristically warm?