Market Analysis

Lessons From Lots and Lots of Wage Metrics

A new report on wage growth sheds light on the challenges of calculating economic data.

A Council of Economic Advisers (CEA) report released last week[i] argues US wages are rising faster than Labor Department measures indicate. Are they riiiiiiiiiiight?[ii] We aren’t here to settle the debate—rather, we believe it highlights the fact there is more than one way to measure any segment of economic activity, and pretty much all metrics have their merits (and shortcomings). Hence, we believe investors trying to get the lay of the land should look at all available data, not just one measurement of a given category.

The comparison point for the CEA report is the Bureau of Labor Statistics’ (BLS)[iii] Current Employment Statistics (CES) survey. This survey includes one of the most commonly cited wage figures in financial media: average hourly and weekly earnings figures based on a survey of about 149,000 businesses and government agencies. The calculation is simple and easy to understand —divide total hours worked by total reported payrolls (excluding bonuses and other forms of nonwage compensation). Per July’s CES survey, average hourly earnings grew 2.7% y/y (compared to 3.0% y/y for average weekly earnings).[iv] This number drove fears American workers’ wages are falling behind their cost of living after the BLS’ July Consumer Price Index (CPI) was up 2.9% y/y. (August’s CES, released late last week, showed wage growth accelerating to 2.9% y/y.) However, besides some high-level reasons why these concerns are off, excluding bonuses and the like from earnings figures is kind of a big omission! These accounted for 30% of employer payroll costs as of June 2018 and have been slowly rising as a percentage of the total for decades.[v] Seems like important information to be aware of.

The CEA report takes a different tack. First, it adjusts for workforce demographic changes stemming from the trend of experienced, high-earning baby boomers retiring while younger, less experienced, lower-paid workers take their place. Conventional wage growth metrics don’t reflect this ongoing shift. Second, the CEA’s method uses the Bureau of Economic Analysis’s (BEA) Personal Consumption Expenditures (PCE) inflation gauge rather than the BLS’s CPI. PCE, the authors argue, better accounts for consumers’ tendency to buy cheaper substitutes when certain goods get more expensive. Third, it incorporates non-wage benefits, such as bonuses, medical insurance, vacation time and parental leave. With these adjustments, it shows inflation-adjusted worker compensation rose 1.0% y/y in Q2—well above the BLS’s 0.1% y/y measurement.[vi] Including the effects of last year’s tax reform bill, the CEA estimated real take-home pay rose a whopping 1.4% y/y in Q2.[vii]

Both approaches are useful, depending on what you are looking for. If you are interested in pure wages, the Labor Department mostly has you covered, though its broad measure doesn’t necessarily reflect individual earners. For a more expansive view, something like the CEA’s figures may be more helpful. Plus, tracking the differences between them can highlight trends in compensation and labor markets. Having two different options/perspectives is good!

But having more than two is even better. As it happens, these aren’t the only wage measurements in town. The BLS also calculates the Employer Cost Index (ECI) each quarter. The ECI tracks business spending on total employee compensation, “including roughly 20 different categories of nonwage benefits, such as paid leave, health insurance, and retirement plans.”[viii] In our view, this is a more nuanced and complete picture than mere wages—it comes closer to reflecting what workers actually receive in return for their time and talents. In Q2, total compensation growth measured 0.6% q/q (2.8% y/y).[ix] In a similar vein, the BEA monitors perhaps the broadest possible measure of employee compensation, going beyond wages, salary and pensions to cover forms of compensation employees never see—private insurance, social insurance (think: unemployment insurance) and the like. Basically, this is the sum cost of employing someone—a useful snapshot of firms’ labor costs, which could influence decisions about whether (or how much) to hire versus automate.

Yet none of these measures necessarily captures the experience of individual earners. Enter the Atlanta Fed’s wage growth tracker. Instead of tracking aggregate, economy-wide average wages, it observes individual workers over time to see how their earnings fluctuate. This controls for workforce demographics shifts, similar to the CEA. In July, the 12-month moving average registered 3.2% y/y growth, and it has fairly consistently outperformed BLS wage growth over the past several years.[x]

Wages aren’t the only data category with multiple measurements. Labor markets have BLS payrolls, ADP payrolls, the Job Openings and Labor Turnover survey (JOLT), jobless claims, the Challenger, Gray and Christmas job cut report and the Fed Labor Market Conditions Index (which combines 19 different measures). For inflation, you may pick from CPI, chained CPI, the Implicit Price Deflator for Personal Consumption Expenditures (PCE), the Billion Prices Index and the GDP Deflator. National economic output has Gross Domestic Product (GDP), Gross National Product (GNP), Gross Value Added (GVA) and Final Domestic Demand. For any broad category, there are a handful of ways to slice it

For investors, there are several takeaways here. First, calculating economic data is hard! Reality is complicated, and trying to capture it perfectly via calculations and figures is more of an ideal than an achievable goal. Consequently, all data points have limitations, and it is worth being aware of them. This is why we recommend drawing on several if possible. We aren’t saying you have to look up nine different labor market gauges any time you wonder whether the job market is getting tighter. But viewing a couple more (judiciously selected) metrics likely offers a more well-rounded understanding and lowers the likelihood one narrow stat skews your perception. But most importantly, don’t presume any economic data are predictive for stocks. Most data are backward-looking. Some, like employment data, are especially so. Stocks, meanwhile, swiftly price them in and look forward. Thus, if it seems there is a disconnect, consider the possibility the data aren’t as meaningful as they might appear.



[i] You can read the full report here, though we won’t judge if you just scan the executive summary.

[ii] Yes, in celebration of the back-to-school season, we recently re-watched Back to School.

[iii] This is a division of the Labor Department.

[iv] Source: Bureau of Labor Statistics, as of 9/11/2018. https://www.bls.gov/news.release/pdf/realer.pdf

[v] Source: Council of Economic Advisers citing Bureau of Labor Statistics data, as of 9/6/2018. https://www.bls.gov/news.release/pdf/eci.pdf

[vi] Source: Council of Economic Advisers, as of 9/6/2018.

[vii] Ibid.

[viii] Ibid.

[ix] Source: Bureau of Labor Statistics, as of 9/6/2018. https://www.bls.gov/news.release/pdf/eci.pdf

[x] Source: Federal Reserve Bank of Atlanta, as of 9/6/2018.


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