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!

Higher employment rates mean higher wages for low-wage workers

Low-wage jobs pay better when the local labor market is tight. This is because an employer who can’t easily replace one worker with another will tend to pay current workers more to incentivize them to stay and also will tend to invest more in equipment and training that make workers more productive and grows the economy.

One way to see this relationship is to look at what percent of people living in an area have a job and the wages near the bottom of the area’s wage distribution. For example, for workers between the ages of 25 and 54 in the ~100 largest metro areas, a one percentage point increase in the share of the age group with a job results in $0.13 an hour in additional wages for the first decile full-time wage earner in the metro area, equivalent to more than $200 extra per worker per year (in November 2018 dollars).

Employment rates (the share of the age group with a job) have been rapidly increasing since 2012 and show no sign of slowing (and perhaps even show signs of accelerating, as higher wages pull more people off the sidelines). If the trend is allowed to continue until the employment rate returns to its late 1990s rate, the employment-rate-related real wage boost for low-wage full-time workers would be between $500 and $600 a year.

cbsa_p10wage_epop

Data source

This relationship has been pointed out countless times, for example by Dean Baker, but it worth reiterating, using the latest data. I’ve calculated these figures from the Current Population Survey public use microdata, using the latest two years of monthly data (December 2016-November 2018). The results are stored here in csv file, for those curious about which dot is which metro area. I’ve also added the union membership rate and the unemployment rate for the area to the file. Wages are in November 2018 dollars, adjusted for inflation within the 2-year period by the regional CPI-U. The largest metro areas are the 97 center-based statistical areas (CBSAs) with at least 300 valid wage observations during the 2-year period. The python code that generates the results is available here. I use a set of programs called the bd CPS to standardize the CPS data from 1994 to present, which are available as a GitHub repo.

 

Possible pieces of the long-term unemployment puzzle

Top US economists recently shared insights and data on the increase in the share of unemployed who are long-term unemployed. While unemployment is currently low, about one-fifth of those who are without a job and looking for one have been doing so for more than six months. As Martha Gimbel pointed out, in “1969 (the last time the unemployment rate was at 3.7%), [long-term unemployed] were 4.7% of the unemployed.” In other words, long-term unemployed used to be much more rare during periods of low unemployment.

Abigail Wozniak added that “more [long-term unemployment] is a clear trend,” citing research done for the CEA that gives insight into the trend. It’s very hard to argue, in light of this data, that something structural is not happening here.

I’m hoping to add a few points to the discussion, the first of which I have not seen made. To do so, I focus on the period from 1998 to present, which has a more stable female labor force participation rate than the period from 1969 to 1997. In 1998, the long-term share of unemployed was 14.1 percent, compared to 21.6 percent in the 12 months ending November 2018. The source for the following calculations and charts is basic CPS microdata.

Educational specialization

In 1998, the highest level of education for 46 percent of the US labor force was a high school degree or less, while about 25 percent had a bachelor’s degree or more (the rest had either some college but no degree, or an associate degree). In contrast, the most recent twelve months of data show 35 percent of the workforce with a high school degree or less and 37 percent with a bachelor’s degree or more. One result of this long-term structural change, almost by definition, is increased specialization in the educational training of the workforce.

Educational specialization may mean less fungibility in employment. To illustrate this point in a highly exaggerated way, it may take an unemployed person with a BA in Mesopotamian history longer to find a job in the field than it does for someone with a GED who quit at Wendy’s. This explanation may be particularly relevant during a period with a tight labor market and a growing economy, when an unemployed person with advanced education is less desperate to take the first available job and when job opportunities are much more plentiful for those with less education.

We can see this point by looking at the long-term share of unemployed by their highest level of educational attainment (figure 1). Among unemployed with a bachelor’s degree, 23.8 percent are long-term unemployed (27 weeks or more), compared to 18.7 percent for those without a high school degree.

Figure 1.

educ_ltu

So there are two things going on here. First, the advanced-education share of the overall labor force has increased. Second, more-educated unemployed people are more likely to be long-term unemployed. The effect of combining these two is a higher long-term share of all unemployment.

This first point is further substantiated because people with advanced education have become relatively more likely to be unemployed. During 1998, the unemployment rate for college grads was 2.0 percent, compared to 2.5 percent over the past year, and the unemployment rate for those without a high school degree was 10.5 percent, compared to 8.1 percent over the past year.

Age and unemployment duration

But education is only a small part of the story. This is obvious because the long-term unemployed share of every educational group has increased over the same 20-year period. A second explanation, which was covered by Gray Kimbrough in the original twitter discussion, is the age composition of the workforce. The same “specialization” story may apply to age.

If we look at the long-term share of unemployed by age, we see that older unemployed people are more likely to be long-term unemployed than younger unemployed people (figure 2). Among unemployed people age 21 to 25, for example, 16.7 percent have been unemployed for 27 weeks or longer, compared to 30.6 percent for unemployed people age 56 to 60.

Figure 2.

age_ltu

Again, like the education story, the composition of the labor force is shifting towards those more likely to be long-term unemployed. In 1998, people age 56 to 60 made up 5.5 percent of the labor force, compared to 9.4 percent today. The age 21-25 share of the labor force has been relatively stable over the period, declining slightly from 10.1 percent in 1998 to 10.0 percent over the last year. Likewise, unemployment rates by age further support the argument. In 1998, the unemployment rate for those age 56 to 60 was 2.6 percent, compared to 2.9 percent today. The 1998 unemployment rate for those age 21 to 25 was 7.0 percent, compared to 6.2 percent today.

Beyond the story of older workers being relatively more specialized, part of the relationship between age and long-term unemployment may be coming from older workers being pushed out of jobs. For more on this topic, please read the fascinating deep dive by ProPublica.

Estimating the compositional effect from age and education

While evidence suggests that more education and older workers explain some of the increase in the long-term share of unemployment, I’m not convinced it can fully explain the 7.5 percentage point increase in the measure over the last 20 years. Re-weighting the last 12 months of CPS labor force observations to match the education and age composition of the 1998 labor force shows that 2.2 percentage points of the 7.5 percentage point increase in long-term unemployment can be explained by age and education. The majority of the increase is therefore not explained by age and education.

One business cycle with two troughs

As a last thought on the issue, measures such as the prime-age employment-to-population ratio show that the US labor market is still recovering from the recession of 2001. A long, jobless recovery creates a situation where people who might otherwise be in the labor force and short-term unemployed are instead in other categories, such as caring for elder relatives, disabled, or in school. If this is the case, it would increase the long-term share of unemployment over the period since 2001.

An alternative way to look at long-term unemployment is to consider the share of unemployed people (age 25 to 54 shown) who are employed one year later. The trend for this indicator (figure 3) suggests that if the US economy is allowed to run hot for a few more years, it may reverse the portion of long-term unemployment effects that are cyclical and not structural.

Figure 3.

jobfinding

I hope the above helps to clarify or guide some thinking on the important topic, even if it can only explain one-third of the puzzle.