Data Dependent

The market is now placing nearly a 100% probability on a September rate cut based on Powell’s recent comments on inflation progress. We will learn more from Powell after the Fed’s July 30-31 meeting comments. As of today, we’re asking the question: might this expectation be too optimistic? Powell has made it abundantly clear that any rate policy changes will be “data dependent.” To dig deeper into this, we need to understand the specific data on which the policy depends. So, what exact data does the Fed depend upon? This is straightforward. Here are the data points: 1. Inflation Data (CPI and PPI), 2. Employment Data (Job Creation and Employment Levels), and 3. Economic Growth Data (GDP and forecasted GDP). Without further ado, let us get into the data.

Inflation Data

Below is a five-year chart of the CPI. One can see that inflation progress stalled in mid-2023.

Below we zero in on the past year. One can see that we’ve just pierced the 3% level over a one-year period for the first time in the recent inflation episode. This is certainly good news, but It’s hard to tell from the data if this is now a trend moving toward the Fed’s 2% target.

The Fed has said they will cut rates prior to reaching 2% due to the lag effects of policy changes. So, the jury is still out, and the visibility is a bit murky. Our take is that we probably need to see CPI inflation trending down AND see a 2.5% level. Given this outlook, the primary questions become, 1) will we see a continuation of the deflationary trend? 2) will we see 2.5%? and 3) when will we see it? To help answer these questions, we want to zero in on the PPI and M2 money growth.

Below is one year change in PPI over the past five years. One can see plain as day we are moving back up to slightly inflationary commodity prices. Given that these so-called producer prices are cost inputs into the final consumer product outputs, we need to pay attention to this. It could be that this uptick in producer prices could soon find its way into consumer prices. It is now a pivotal time for PPI, if we stay flat, all is good, if we move higher, it might be a more challenging inflation fight than many expect.

To gain some perspective on what is driving this CPI and PPI dynamic, we think it is instructive to look at M2 money growth relative to inflation data. Here it is below.

The lagged relationship between money growth and PPI is strong and obvious. The lagged relationship between those two things and consumer prices is less strong and less obvious. Changes in money stock is clearly driving producer prices but only influencing consumer prices. The consumer price dynamic has more to consider (things like rates, income, confidence, employment, etc.), This is what makes forecasting inflation hard. So let’s turn to Employment data which we know the Fed relies on for rate decisions.

Employment Data

Employment is an inverse indicator. The Fed needs to see weak or weakening employment levels to cut rates. To help assess this, we are going to look at two data series. The first is the Employment Level. In this data series, we are seeing weakening employment. This might be the data which is leading to Powell’s concilliatory rate cut language.

Let’s look at another series below, Job Openings. The availability of new jobs is weakening.

A continuation of the employment and jobs trends could very well lead to a rate cut in September. We think it will come down to economic growth and the raw inflation numbers combined.

Economic Growth Data

As can be seen below, GDP growth is healthy. But there is a pretty big lag in GDP numbers (we get number well after the fact and revisons to those numbers over time can me material. It makes assessing current economic conditions tricky.

To help address this inherent unreliability of GDP, the Atlanta Fed developed GDP Now for a contemporary forecast (rather than relying on trailing data) of economic growth. This is what it looks like below:

The GDP picture looks strong and stable. Unless there is a dramatic and swift economic turn, the Fed is not getting a read on economic weakness that they need to cut rates imminently.

Putting it All Together

As of today, we don’t think the Fed has in the data what it needs in the data to cut rates. We think to cut in September, we need to see: 1) Inflation below 3% and well into the 2% range, 2) steady and low M2 money growth, 3) Continuing job market weakness, and some softening in GDP data and forecasts. Only the employment data is giving the Fed what they need right now. By September, we would need to see additional data to cause the Fed to cut. If we are being data dependent in our assessment, we woud conclude a 100% probability of a rate cut is probably too optimistic, particularly given current M2 money growth levels.

DISCLOSURES

The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a price index of a basket of goods and services paid by urban consumers. Percent changes in the price index measure the inflation rate between any two time periods. The most common inflation metric is the percent change from one year ago. It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas. To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date. In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (CPILFESL) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs. Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.

The Producer Price Index (PPI) measures the average change over time in the prices domestic producers receive for their output. It is a measure of inflation at the wholesale level that is compiled from thousands of indexes measuring producer prices by industry and product category. The index is published monthly by the U.S. Bureau of Labor Statistics (BLS).

Before May 2020, M2 consists of M1 plus (1) savings deposits (including money market deposit accounts); (2) small-denomination time deposits (time deposits in amounts of less than $100,000) less individual retirement account (IRA) and Keogh balances at depository institutions; and (3) balances in retail money market funds (MMFs) less IRA and Keogh balances at MMFs.

Beginning May 2020, M2 consists of M1 plus (1) small-denomination time deposits (time deposits in amounts of less than $100,000) less IRA and Keogh balances at depository institutions; and (2) balances in retail MMFs less IRA and Keogh balances at MMFs. Seasonally adjusted M2 is constructed by summing savings deposits (before May 2020), small-denomination time deposits, and retail MMFs, each seasonally adjusted separately, and adding this result to seasonally adjusted M1.

The civilian noninstitutional population is defined as: persons 16 years of age and older residing in the 50 states and the District of Columbia, who are not inmates of institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

Total Nonfarm Job Openings are a measure of all jobs that are not filled on the last business day of the month. A job is considered open if a specific position exists and there is work available for it, the job can be started within 30 days, and there is active recruiting for the position.

Total Nonfarm Job Openings are measured by the Job Openings and Labor Turnover Survey (JOLTS) and published by the Bureau of Labor Statistics (BLS). These data are a unique economic indicator of unmet demand for labor and labor shortages. Economists, government officials, and researchers use Job Openings as a measure of tightness within job markets.

Gross domestic product (GDP), the featured measure of U.S. output, is the market value of the goods and services produced by labor and property located in the United States.For more information, see the Guide to the National Income and Product Accounts of the United States (NIPA) and the Bureau of Economic Analysis.

GDPNow is a nowcasting model for gross domestic product (GDP) growth that synthesizes the bridge equation approach relating GDP subcomponents to monthly source data with factor model and Bayesian vector autoregression approaches. The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the US Bureau of Economic Analysis.

The Federal Reserve Bank of Atlanta's GDPNow release complements the quarterly GDP release from the Bureau of Economic Analysis (BEA). The Atlanta Fed recalculates and updates their GDPNow forecasts (called "nowcasts") throughout the quarter as new data are released, up until the BEA releases its "advance estimate" of GDP for that quarter. The St. Louis Fed constructs a quarterly time series for this dataset, in which both historical and current observations values are combined. In general, the most-current observation is revised multiple times throughout the quarter. The final forecasted value (before the BEA's release of the advance estimate of GDP) is the static, historical value for that quarter.

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