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December 2016 Nonfarm Payroll Update

With the BLS release of December nonfarm payroll (NFP) numbers we can compare 2016 job growth to that of 2015. First, though let’s take a look at year-on-year (YoY) changes since 1990:

Nonfarm Payroll December 2016 update

While the economy continues to add jobs, the rate is slowing: 2.03 million in 2016 compared to 2.79 million in 2015. In percentage terms, NFP employment grew by 1.4% in 2016 compared to 2.0% in 2015. Here’s table showing employment growth (in millions) and the YoY percent change for each year of the current expansion:

Table: NFP Employment Growth 2010 to 2016

2016 has posted the smallest job growth since 2010, and slightly less than 2011. Job growth for the current expansion may have peaked in 2014.

Here’s a table showing NFP employment growth in 2016 and 2015 at the industry level:

NFP employment contribution by industry 2015 and 2016

A few observations:

  • In 2016, as in 2015, growth was largely being driven by these four industries:
    • Education and health services
    • Professional and business services
    • Trade, transportation, and utilities
    • Leisure and hospitality
  • Construction’s contribution for 2016 was less than half that of 2015 in percentage terms.
  • As noted above, manufacturing and state governments switched from positive contributions (adding jobs) in 2015 to a negative (losing jobs) in 2016.
  • Compared to 2015, local governments were a significant source of job growth.

This chart shows the contribution to NFP employment by industry for both 2015 and 2016:

graph of contribution by industry to NFP employment growth 2015 and 2016

Mean vs Median

Mean or median, which should we pay more attention to? First, for some background, the difference between the mean and median:

The mean is the average, the result of dividing the sum of two or more values by the number of values. So for three values, X, Y, and Z, the mean is (X+Y+Z)/3.

The median is the middle value in a set of values sorted in ascending or descending order. If the sample contains an even number of values, the median is defined as the mean of the middle two. To use X, Y, and Z again, if X > Y and Y > Z, then the median will be Y. No matter how many values we have, the median will be middle point of the dataset, with half of the remaining values above the median and the other half of the remaining values below the median.

As an example, lets look at a hypothetical sample of seven salaries for statisticians in a given city. From lowest to highest the salaries are:

$72,000  $75,000  $78,000  $82,000  $85,000  $88,000  $96,000

The mean, or average, of these salaries is $82,286, while the median is the middle value, $82,000. In this case the mean and median differ by only a small amount, so one may be tempted to conclude that we can use them interchangeably. However, the mean and median will not always be so close.

In many real-world situations, such as with salaries or house prices, the mean and the median often differ substantially. This is due to outliers, abnormally low or high values, which have a greater effect on the mean than the median. To illustrate this, let’s add a substantially higher salary to list above, an eighth statistician who has a salary of $145,000. Maybe this statistician is a manager, or for some other reason receives a much higher salary than the other statisticians in our sample. In any case, the average of these eight salaries is $90,125. The median of these eight salaries is $83,500. So with the addition of one outlier, the average has increased dramatically (by $7,839, or 9.5%) while the median’s increase is much smaller (by $1,500, or 1.8%). And this why the median is more often cited than the mean or average when comparing salaries or house prices among cities or over time: outliers or abnormal values have much less impact on the median.

Hourly and Weekly Earnings in the July Jobs Report

The July jobs report, released by the BLS last Friday, shows that wages (specifically average hourly earnings) are now growing at their fastest rate since the end of the great recession. The number most often cited was 2.6% year-on-year growth rate for both June and July. That figure is based on the seasonally adjusted series supplied by the BLS. I prefer to use the unadjusted series, and to smooth out its monthly volatility with a three-month moving average, and then calculate year-on-year growth (labeled as “3moMA %ch YoY” in the graph below). The results are very similar, with June and July showing post-recession high growth rates of 2.8%.

private sector hourly earnings 2016 July
When we look at average weekly earnings, however, while the growth rates are the same for June and July at 2.8% (as above, that’s the year-on-year growth rate of the three-month moving average), this is not a post-recession high. Weekly earnings growth is stuck in the same range it’s been in since 2011:

private sector weekly earnings 2016 July
Here’s another graph showing the both hourly and weekly earnings growth (again, the year-on-year growth rates of the three-month moving average):

private sector hourly and weekly earnings 2016 July
The difference, of course, is explained by average weekly hours worked, also included in the BLS’s jobs report. While average weekly hours has been between 34 and 35 since mid-2011, the year-on-year growth rate has been negative for most of 2016 as the below graph shows.

private sector average weekly hours 2016 July

June 2016 Nonfarm Payroll Update

The release of June nonfarm payroll (NFP) numbers was greeted with headlines such as “Job Growth Surged in June.” Of course, that surge was in comparison to May. The month-on-month changes can be quite volatile, and for that I reason I prefer to look at year-on-year (YoY) changes.

Nonfarm Payroll June 2016 update
June’s NFP employment was up 2.5 million from a year earlier. While that shows that job growth is continuing, it also means the rate of job growth has been slowing since early 2015, as can be seen in the above chart. In the current expansion, the highest year-on-year job growth was 3.1 million in February 2015. In percentage terms, June’s YoY growth was 1.8% compared to 2.3% in February 2015 (see the red series in the bottom section of the above chart). For 2016 YTD (January thru June), average YoY growth is 1.9%, only slightly higher than the average since January 2012 of 1.8%. The slow, but steady job growth of this expansion continues.

NFP employment growth for the first half of 2016 was 1.12 million, compared to 1.39 million for the first half of 2015 (a drop of 19%).

Using the seasonally adjusted industry-level NFP data I’ve prepared a comparison of the first half of 2016 to that of 2015 (2016H1 and 2015H1 in the below graph), showing us how job growth is shifting.

Employment contribution by industry 2015H1 and 2016H1
A few industry-level observations:

  • Education and health services continues to be largest source of job growth, contributing 29.8% in 2016H1, up from 25.7% in 2015H1.
  • Professional and business services remains the second largest source of job growth, but its contribution dropped to 17.2% for 2016H1 from 22.8% for 2015H1.
  • Construction’s contribution for 2016H1 was 4.5%, less than half that of 2015H1 (9.5%).
  • Manufacturing was drag on employment in 2016H1 (-2.3%), compared to a 2.3% contribution in 2015H1.
  • Local government contribution more than tripled to 6.6% in 2016H1 from 2.0% for 2015H1.