Introduction
Inflation is perhaps the least well understood phenomenon in economics. Once said to be exclusively a monetary phenomenon, our current predicament is significantly more complicated and there is little consensus as to the root cause. Until recently the concern was that inflation would run permanently too low. The topic garnered little interest from the public but has seen a sharp reversal in recent months and now ranks as the top concern amongst voters. Indeed, how to appropriately measure inflation is often cause for debate.
February saw the Personal Consumption and Expenditures (PCE) Index (the Fed’s preferred measure) print an astonishing 6.35% year-over-year increase while the less volatile “core” PCE index (which excludes food and energy) rose 5.4%; both 40-year highs. This has led many to worry about the possibility of structurally higher prices going forward and the risk of inflation expectations becoming “unanchored” (though there has been some doubt as to the importance of inflation expectations for controlling the price level).
As we consider the outlook for inflation it is critical to understand which parts of the economy are currently causing the problem and what that means for the risks going forward. To untangle this riddle, I went deep (and I mean deep) into the data and examined the ~235 categories of goods and services considered in the PCE index. The goal is to uncover if inflation is broadly distributed or confined to select categories that are having an outsized impact. The methodology is loosely based on research from the Federal Reserve Bank of San Francisco.
Methodology
To begin the analysis, I classified each underlying category into one of three groups:
- Above Trend: For categories in this group the current rate of inflation is higher than the pre-Pandemic average.
- At Trend: Inflation for products and services in this group are broadly in line with the pre-Pandemic trend.
- Below Trend: Prices in this category are registering inflation below the pre-Pandemic average.
To classify the categories, I ran the following regression for the period January 2010 through February 2022:
Where:
Πi,t = the YoY log-change in the price index for category ‘i’ in month ’t’
αi = regression intercept
Di,t = a dummy variable that takes a value of 1 beginning at the start of the Pandemic in February 2020 and 0 otherwise
βi = regression coefficient for dummy variable
∈i,t = regression error term
In this setting the regression intercept, αi, represents the average rate of inflation during the pre-Pandemic period January 2010 through January 2020. The coefficient βi is the differential intercept term and gives us the change in inflation during the Pandemic period. If βi is positive and statistically significant then we can conclude that inflation for category ‘i’ is higher today than during pre-Pan; these categories are classified as “Above Trend”. Conversely, if βi is negative and statistically significant then we conclude inflation for category ‘i’ is lower today than it was pre-Pan; these are the “Below Trend” categories. Finally, if βi is not statistically significant then there is no detectable change between the pre-Pandemic and post-Pandemic periods for product ‘i’; such categories are placed in the “At Trend” group.
Inflation Deep Dive
The below table summarizes the number of categories in each group and the corresponding weight of each group in the Core PCE calculation:
The Above Trend group consists of 101 separate products & services and comprises ~56% of the weight in the Core PCE index. This suggests that over half of all spending is currently running above trend and putting substantial pressure on consumer’s wallets. In contrast, only 17% of spending is currently running below the pre-Pandemic trend which suggests that there isn’t much of an offset to the rising prices in other parts of the economy. Finally, 72 categories are currently classified as At Trend which implies that current inflation is consistent with observed price level changes pre-Pan. However, the At Trend categories only comprise ~28% of overall spending which is not sufficient to keep prices anchored.
Core PCE can be broadly decomposed into Goods and Services. In the core PCE data there are 68 separate Goods and 147 Service categories. To get a sense of whether Goods or Services are contributing more significantly to inflation we can break down the Trend groups by classification.
The plot below depicts the percentage of all Goods and Service categories contained by each Trend bucket. Approximately 60% of all Goods and 40% of all Services are currently running at Above Trend inflation. The At Trend group is dominated by Services while the Below Trend group is evenly split. Taken together, these figures imply that Goods are primarily responsible for the acceleration in inflation that we are witnessing, but there are potential upside risks if some of the At Trend Services categories inflect higher. A key determinant for keeping Services prices anchored will be a sustained recovery in the labor force in service-related sectors (housing, transportation, food service, childcare, etc.).
To understand where the trends in inflation may be headed, I reconstructed a separate price index for the Above, At and Below Trend groupings. Even though 101 categories are currently logging Above Trend inflation it could be the case that the pace of acceleration is cooling or even rolling over which would suggest some near-term abatement in headline numbers. Conversely, Below Trend figures could be inflecting higher and moving from a net negative contribution to net positive contribution which would mean headline figures are likely to worsen.
The below chart depicts the percentage YoY change in PCE for each of the Above, At and Below price indices. The results are a bit jarring. It appears that each of the classifications are accelerating higher. The Above Trend group started to climb higher basically at the onset of the Pandemic and is currently clocking a 6% YoY change. Interestingly, the Above Trend categories showed the most subdued inflation in the pre-Pan period running at a little more than 1% YoY for almost 10 years. This makes the rapid rise all the more disturbing as it suggests potentially significant damage to the supply chains of the underlying categories.
The At Trend group saw an initial steep decline and stayed low for most of 2020 but has been resurgent in 2021 and ’22. The 4.5% change in February is now notably higher than the 1%-3% range that the index saw pre-Pan. Indeed, the limited sample size may be the only thing keeping these categories in the At Trend group. This observation lends some credence to our earlier conjecture that some Service categories in this classification may be at risk of inflecting higher.
The trajectory of the Below Trend group offers, perhaps, the most interesting results. Historically, this group recorded the highest inflation figures of the three with a pre-Pan range of ~2%-4% and considerably more volatility. At the onset of the Pandemic inflation for this group declined precipitously and spent most of 2020 and part of 2021 in negative territory. Outright deflation for this group was responsible for initially keeping a lid on inflation, but now the situation is distinctly different. Of the three classes, the Below Trend group has experienced the most dramatic snapback from approx. -1.4% in February 2021 to 3.3% one year later. Inflation in this group remains below the top end of the pre-Pan range which suggests near term upside risk as these categories continue to recover.
Having decomposed PCE into the Above, At and Below trend classes we can finally put the pieces together to determine the net impact to headline Core PCE. The following plot charts the cumulative contribution of each bucket to Core PCE. The dark blue region is the cumulative impact of categories that post-Pandemic are considered Above Trend, the dark red region shows the impact of the At Trend categories and dark green the Below Trend. Overlaid on the chart is headline Core PCE in gold.
It’s important to recall that the bucket classifications (and consequently the color scheme) are based on post-Pandemic results. Just because a category is running Above Trend today does not mean that pre-Pandemic its contribution to Core PCE was necessarily positive. Indeed, what we observe is that many categories that are today running Above Trend and contributing significantly to inflation were actually net detractors for most of the 2010’s as evidenced by the sub-zero dark blue region from ~2011-2020. Even today some categories that are considered At Trend are contributing negatively to inflation, though these categories are disappearing fast!
As of February (again, latest data) the Above Trend categories are contributing ~3.30% to Core PCE, At Trend is contributing (net) 1.20% and the Below Trend categories are contributing ~.50%. As we expected from the prior results, there are very few categories now that are acting to offset inflation (i.e., net negative).
Concluding Remarks
In this post I went deep into the inflation data to get a more granular picture of where inflation is running hot and how the underlying trends are developing. We broke down the data into Goods v. Services inflation and classified the ~235 categories into separate buckets based on deviations from pre-Pandemic averages. The results indicate that across almost all categories inflation is positive and accelerating. The key near term risk appears to be At Trend categories flipping to Above Trend in the coming months as the sample size broadens and the underlying trend reveals itself. On balance the results suggest that Core PCE is likely to be higher over the next few months which will have significant implications for the direction of monetary policy.
This post gave me ample opportunity to work with the underlying PCE datasets available through NIPA. Managing this much data was certainly a bit of a beast, but also instructive as it really gave me a sense of the challenge involved in measuring each of these items monthly. If you’re wondering about the various considerations that economists make when constructing a price index (as well as the high possibility for error!) check out this EconTalk podcast with Susan Houseman on manufacturing. It’s exceptionally good and really makes you think about the implicit assumptions you often make when working with price data.
Until next time, thanks for reading!
-Aric Lux.
