In this note we review the April reading of the Average Hourly Earnings (AHE) and we layout the prospect for wages and total compensation in Q2. Overall, we finally see some moderation ahead. But we continue to expect wage growth to remain well above the level consistent with the Fed 2% target. Translated: no FF cuts in 2023. (For the record, we did see the preliminary reading of the long-term expectations from the Michigan survey. We will update the relevant models early next week)
Our recent calls
Our AHE and ECI calls turned out to be correct. Last month (our notes here and here), we argued that the published MoM of the Average Hourly Earnings in March was biased down by composition effects and seasonal adjustment issues. Based on that analysis, the models made two predictions: (i) the MoM of AHE in April was expected to round to 0.5%, and (ii) the Employment Cost Index (ECI) was expected to go roughly sideways in Q1. Both predictions turned out to be correct as the AHE grew 0.5% MoM in April (way above consensus), and the ECI accelerated slightly (also above consensus).
AHE in April
In our estimates, AHE in April was biased down (slightly) by seasonal adjustment issues. Figure 1 shows the MoM of AHE (total private, the black bar) and 10 sub-sectors. Last month, we pointed out that one sector (“other services”) biased down the March reading due to seasonal adjustment issues (our note here). In April, a similar thing happened with another sector (“Information”) which posted a 0.9% MoM contraction in seasonally adjusted terms. Figure 2 shows the published level of AHE “Information” non-seasonally adjusted (the blue line), seasonally adjusted by the BLS (the red line), and seasonally adjusted by UnderlyingInflation (the thick black line). Because the NSA level ticked up in April and the series seasonally adjusted in-house went sideways, we conclude that the published BLS MoM (sa) of the AHE was biased down by around 2-3bps in April by seasonal adjustment issues of the “Information” sector.
(Once again, we recommend to be extremely careful when interpreting SA (or even NSA) figures on your Bloomberg terminal screen. Please, be aware that several series are now distorted because traditional seasonal adjustment filters are introducing noise/biases due to Covid distortions. See our notes here and here).
Figure 1. MoM (sa) of AHE and subsectors.
Figure 2. Level of Average Hourly Earnings in “Information”
Note: “UI” stands for UnderlyingInflation. “SA” and “NSA” stand for “seasonally adjusted” and “non-seasonally adjusted” respectively.
In our estimates, AHE in April was boosted by “residual calendarity”, and it was unaffected by composition effects. Figure 3 shows the fit of our AHE model that controls for the number of weekend days in a month (a.k.a. “residual calendarity” effect) and shifts across industries. Specifically, Figures 3 shows the MoM of AHE (the orange line) together with the fitted values of the model (in the figure, the extreme values above 1 and below -0.5 recorded during Covid have been omitted to help visualizing the fit). The R^2 of the model is 0.5. In April, the fitted values of the model is essentially identical to the published figure. This implies that, according to the model, the reading in April was boosted by a higher number of weekend days (10 in April), while sectoral shifts played a limited role. Put it simply: given the observables, in the end we got precisely what one should have expected, which explains why we got our prediction right.
Figure 3. Published AHE MoM (orange) and model fit (blue)
Wages going forward: AHE in May
We expect a lower reading of AHE in May (0.3% MoM). The reason behind our forecast is simple. In March, we got 48bps (sa). Our model estimates that “residual calendarity” boosted the April number by about 12-14bps, with no distortions from shifts across sectors. Therefore, a reasonable call for May (which has only 8 weekend days) is for a solid 0.3% MoM sa.
Wages going forward: Wages and total compensation in Q2
Back at the beginning of February (our note here) we wrote that we expected the average of all measures of wages and total compensation to tick up in Q1 in QoQ saar terms (we argued along the same lines last December here). Back then, our notes went clearly against consensus. In the end, wages and total compensation not only did accelerate -on average- last quarter, but they went even beyond our expectations. Figure 4 shows the updated chart that plots the average across all measures of wages and total compensation. The last point in Figure 4 is our forecast for Q2. Considering the on-going slowdown of the labor market, our models suggest that wages and total compensation should decelerate a bit in Q2. Nevertheless, we expect the average across all measures at 4.5% (QoQ saar), about 1¼pp above its pre-Covid mean.
Figure 4. Measures of wages and total compensation (QoQ, ar)
Note: the measures of wages and total compensation (gray lines) in the figure are: Average Hourly Earnings (AHE), Average Weekly Earnings (AWE), the Atlanta Fed Wage Tracker (AFWT), the Employer Cost Index (ECI), the ECI wages and salaries, Compensation Per Hour (CPH), and aggregate compensation (the CPH numerator). All measures are at quarterly frequency, QoQ saar. The blue line shows the average across measures. The dotted horizontal lines are the pre-Covid and post-Covid means.
Implications for the Fed staff and the FOMC
No cuts. Some tentative good news here and there for the Fed when looking at the current quarter. But it takes time to get to a level of wage growth consistent with the 2% target. Nobody can be sure whether wages are putting upper pressure on consumers prices right now, but certainly we cannot exclude it. As such, while we think that some moderation in wage growth might show up in the coming months, we continue to exclude the Fed can cut rates this year.