Wage growth is in the pipeline

If you haven’t looked at the US wage distribution recently, you might be surprised to see nominal growth of 7.0 percent in 2019 Q3 and 6.7 percent in Q2 in first decile usual weekly earnings. My own calculations show an even stronger 8.7 percent year over year increase in October 2019. The increase in the first decile wage seems to be coming from state, local, and company increases to the minimum wage. It is also an indication that higher wages may soon be coming to the workers in the middle of the income distribution.

First, the data:

First decile wages and growth

The dark blue lines are the BLS quarterly data on first decile usual weekly earnings for full-time wage and salary earners. The light green lines (bd CPS) calculate the same series on a monthly basis.

The BLS and bd CPS series both show first decile wage growing at its strongest rate since the late 1990s. One conventional story here is that a tight labor market, as measured by a low unemployment rate, makes it harder for employers to find qualified replacements for employees who quit, which makes existing employees more likely to get raises. As a result, wage growth is usually strongest when the labor market is tight.

But there’s a catch. BLS uses the same sample to report nominal median wage growth of 3.6 percent in Q3 and 3.7 percent in Q2. BLS data on real average hourly earnings, which adjusts nominal earnings data for inflation, shows 1.2 percent real wage growth in October (nominal wage growth of 3.0 percent and inflation of 1.8 percent). This isn’t terrible, but it’s no seven percent. With the unemployment rate historically low, why isn’t median and average wage growth stronger?

Leaning on unemployment to fully explain wage growth is clearly not working. An alternative measure of whether a labor market is tight is the employment rate for people age 25-54. This measure is currently at it’s highest level since 2007 but is still the equivalent of more than a million jobs away from its late 1990s and early 2000s level. In other words, there’s evidence that the labor market is still over a million jobs away from full employment.

Maybe it’s better to ask instead why wages are growing so rapidly at the first decile and whether it means anything for wage growth more broadly. The data show that the number of full-time workers earning $400 per week or less has fallen to 7.5 million in the latest three months from 14.7 million in the same three months of 2014. The story here seems to be higher local and state minimum wages and higher company minimum wages at large employers. Several areas and employers moved the minimum wage to above $10 per hour ($400 for a 40-hour week) during the past five years.

Higher minimum wages could eventually translate into higher median wages. For example, the first decile and median wage tend to move together:

First decile and median wage growth

In the period of faster wage growth and tighter labor markets during the late 1990s, first decile wage growth accelerated and median wage growth accelerated shortly after. If employment trends continue, it seems reasonable to expect an increase in the median wage of five percent or more in 2020.

But the current period is not the late 1990s. Business fixed investment and labor productivity growth are both particularly weak now compared to then. Negative net capital investment and increasing payrolls suggest an aggregate situation akin to fewer tools per worker, which casts doubt on forecasts of sustained real wage growth.

Technical Note

Unlike the first two charts, I use some additional steps to smooth out the third chart comparing median and first decile wage growth. I calculate the wage in each month as the wage in the previous three months combined, so October 2019 is based on combined microdata from August, September, and October 2019. Additionally, I use the Census X13as program to seasonally adjust the results. The growth rate is based on this resultant seasonally-adjusted nominal wage.

In both sets of charts, I replicate the BLS process of taking a “binned median” to reduce how much the data reflect breaks around certain round numbers (like $400 per month).

Do regional inflation differences affect nationwide real wage estimates?

Some basic facts about the US economy point to the possibility that inflation is overstated in measures of real wage growth for workers at the very bottom of the wage distribution. If this is true, it would mean that low-wage earners have actually seen more wage growth than published estimates suggest.

First, the places that have a higher state or local minimum wage than the federal minimum wage tend to be in the west region of the US, and sometimes in the northeast or midwest regions, but rarely in the south. The regional differences in minimum wage create large differences in the regional distribution of low wage earners. In November 2018, the population (including children) is divided by region as follows: midwest: 20.8%, northeast: 17.6%, south: 37.8%, and west: 23.8%. In contrast, people earning $8.00 per hour or less are distributed as follows: midwest: 19.2%, northeast: 16.3%, south: 51.3%, and west: 13.2%. In other words, low wage earners are disproportionately in the south and disproportionately not in the west.

Second, in recent years inflation has been higher in the west region, compared to the south or midwest regions. This is largely because inflation has recently been driven by housing shortages, which are particularly severe in the west region. Price growth from December 2017 to December 2018 was 1.9% nationwide, 1.3% in the midwest, 1.7% in the northeast, 1.5% in the south, and 3.1% in the west.

The above creates a potential issue for estimates of real wage growth for low wage earners. This is because nearly all such estimates apply the nationwide rate of inflation (for all urban consumers) to workers who disproportionately live in the south, which has less inflation than the nation as a whole. Likewise, low-wage workers are not nearly as likely to live in the west, which has higher inflation than the nation as a whole.

To test this, I used CPS microdata to calculate the real wage for the fifth percentile wage earner (nationwide) using both the usual CPI-U (the CPI-U-RS is preferred, but that would have complicated analysis) and using the regional CPI for each of the four regions. That is, in the regional CPI estimate, each wage observation is adjusted using the price index for the region where the person lives.

rw_by_cpi

The results show that the story above does apply, but that the effects are very minimal and not particularly cumulative. The total cumulative difference since 1989 is less than three percent. In other words, it’s true that low wage earners did better than published estimates suggest, but the effect is not very big. It’s important to point out that adjusting for age, or including only full-time workers, eliminates most of the cumulative impact. Usually findings this trivial are not written up as a blog post, but they might be of interest to people who have the same question. Here’s the jupyter notebook used to calculate the results.

Comments and feedback are always welcome. Please let me know if I did something wrong, so I can learn!