The Department of Labor reported that for the week ending February 4, seasonally adjusted initial claims were 234,000, down from the previous week’s unrevised level of 246,000. The 4-week moving average was 244,250, a decrease of 3,750 from the previous week’s unrevised average of 248,000. Compared to a year earlier, the 4-week moving average is down 12.1%. Here’s a graph from FRED:
Now that metro area unemployment rates for December have been released, it’s time for an update. For metro areas with populations of 1 million or more as of the 2010 census, here are the 10 lowest unemployment rates:
Topping the list, is Boston with an unemployment rate of 2.5%. Only two other large metros had an unemployment rate below 3%: Denver (2.6%), and Salt Lake City (2.7%). For comparison, the national unemployment rate for December was 4.5%.
Among large metro areas, Boston also tops the list for largest decrease in the unemployment rate over the course of 2016, dropping 1.5% from an already low rate of 4% in December, 2015:
Other metro areas to have seen a decrease in unemployment of 1% or more were Las Vegas (1.2%), Detroit (1.1%), Hartford, CT (1.1%), Seattle-Tacoma (1.0%), and Providence, RI (1.0%). The national unemployment rate fell 0.3% over the course of 2016. The outlier in this list is Detroit with an unemployment rate of 10.4% as of December, more than twice the national rate.
Of the 51 metro areas with population of at least 1 million, only twelve saw unemployment increase over the course of 2016:
Most of these metros had low unemployment rates as of December, 2015, and for most of them the increase has only been a fraction of a percent. However, Cleveland not only saw the largest increase in unemployment over 2016, it also has the highest unemployment rate of 10.7% as of December, 2016. Only Detroit comes close with its 10.4% unemployment rate (though Detroit’s unemployment rate decreased in 2016). All other larger metro areas posted unemployment rates less than 5.5% in December.
With the release of metro area nonfarm payroll employment figures for December, we can compare the rate of job growth over the course of 2016. The table below shows the top 10 rates of 2016 job growth for metro areas with populations greater than 1 million (as of the 2010 census), and the change in employment since December, 2007 (the table also includes corresponding figures for national employment growth, for comparison). The sparkline chart on the right side of the table includes a bar for each year (2008 through 2016) showing the percent change in employment since 2007.
At the top of the list, with employment growth of 4.2% (three times the national growth rate) is Orlando, Florida. The next three top growth rates are for the Salt Lake City, San Jose, and Seattle-Tacoma metro areas, tied at 3.4%. Next in this ranking are Jacksonville, Florida and Dallas-Fort Worth at 3.3%, followed by Denver and Sacramento at 3.2%. Atlanta, tenth in this top 10 list at 2.7%, saw employment growth almost double the national rate.
When looking at the change in employment growth since 2007, seven of these top 10 are at 10% or more, with San Jose, California posting the highest rate of 18.1%. Atlanta and Jacksonville, though they’ve seen less than 10% employment growth since 2007, are both significantly above the national rate of 4.9%. Only Sacramento, California at 3.7% has seen a lower rate of growth since 2007 than the national rate.
Moving on to metro areas with populations between 500,000 and 1 million:
At the top of this list are Sarasota, Florida and Provo, Utah both with 3.8% employment growth in 2016. Next are Spokane, Washington (3.6%), Boise, Idaho and Deltona, Florida (both at 3.5%).
When looking at the change in employment growth since 2007, half of this group has seen 10% or more, with Provo, UT posting a very healthy 22.9% (over four times the national rate). Not so far behind is McAllen, Texas with 18.4%. Only two of these top ten, Spokane, Washington and Deltona, Florida are showing lower growth since 2007 than the national rate of 4.9%.
Below is the top 10 list for metro areas with populations between 200,000 and 500,000:
Prescott, Arizona tops this list with 50%, followed closely by Ann Arbor, Michigan at 4.8%.
As we saw above with, half of this group has seen employment growth of 10% or more since 2007, with College Station, Texas posting 22.2%. However, Reno, Nevada, despite healthy employment growth of 3.4% in 2016 is still 0.5% below its 2007 level of employment. Prescott, Arizona, with 5% employment growth in 2016, has seen only 1.2% employment growth since 2007.
When looking these three tables, showing where metro area employment growth was highest in 2016, we can see that roughly half (and more than half for the top ten of metros with populations of 1 million or more) have seen relatively strong growth since 2007. The other half have seen employment growth recover more slowly and/or more recently over the course of the great recession. The red and blue bars in the sparkline charts convey this visually.
Also apparent from these three top then lists is that metro employment growth in 2016 was strongest in the West, particularly California, Washington, and Utah, and warm weather states, mostly Texas and Florida. Notably absent are metro areas in the Northeast and most of Midwest (two smaller midwestern cities, Ann Arbor, Michigan and Sioux Falls, South Dakota are in the top ten for metro areas with population between 200,000 and 500,000).
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:
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:
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:
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:
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.
Check out this awesome visualization of the Shifting Incomes for American Jobs from Nathan Yau at Flowing Data. Nathan’s interactive chart shows the distributions of incomes for major occupational groups over several decades:
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%.
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:
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):
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.
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.
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.
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.