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Blogging on Employment and Job Markets in US Metro Areas

Labor Share of Income, Labor Force Participation, and Wage Growth

Labor share of income and prime age labor force participation rate 1990 to 2016
The labor share of income (the blue line in the above chart) and prime age labor force participation rate (the red line) have both recently increased after long-term declines (labor share of income since the 1970s, and LFPR since the 1990s). Though it is too early to say if those long-term trends have been reversed, these are both positive developments for the labor market.

As shown in the above chart, the labor share of income increased sharply after 2014, and prime age LFPR did so a year later. The unemployment rate, which has fallen steadily from 10% in late 2009, was 5.6% in December 2014, 5.0% in December 2015, and as of the latest reading (May 2017) is 4.3%.

The rate of wage growth, shown in the below chart, has recently increased, with a CAGR (compound annual growth rate) of 2.38% for the 2014 to 2016 period compared to a CAGR of 2.05% for the 2010 to 2014 period (these are nominal growth rates and do not account for inflation).

private sector wage growth 2008 to 2016
Given these changes – increase in wage growth, LFPR, labor share of income, amidst a continued decline of the unemployment rate – 2014 represents a turning point for the labor market.

The increase in wage growth from 2.05% to 2.35%, however, obscures what is going on in the different segments of the labor market. At the industry level, we can see a marked difference in wage growth over these two periods:

wage growth by industry, 2010 to 2014 vs 2014 to 2016
For the 2010 to 2014 period higher wage industries saw higher rates of wage growth. By comparison, in the 2014 to 2016 period wage growth was much more even across industries as shown by a relatively flat slope of the trend line.

The difference is even more dramatic at the occupational level:

wage growth by industry, 2010 to 2014 vs 2014 to 2016

While higher-wage occupations saw higher rates of wage growth over the 2010 to 2014, lower-wage occupations saw higher rates of growth over 2014 to 2016. Note the difference in the trend lines, and the different scales of the Y-axis (wage growth) for the two periods. This shows that the lower-wage end of the job market is finally reaping benefits from the continuing economic recovery.

May 2017 Nonfarm Payroll Update

As of May nonfarm payroll employment was 146.7 million (this is the preliminary figure from the BLS, and most likely will be adjusted in the coming months), an increase of 2.2 million from a year earlier. In percentage terms, this is an increase of 1.5% from a year earlier. May marks the 81st consecutive month of year-on-year employment growth. As can be seen in the chart below, the rate of employment growth has been declining since a peak in early 2015:

Nonfarm payroll year-on-year growth as of May 2017
Though the rate of employment growth has been slowing for over two years now, the prime age (25 to 54) labor force participation rate (LFPR) has been increasing since September 2015 (when it hit a low of 80.6%) and is now at 81.5%. This is still significantly below the prime age LFPR of the 1990s and 2000s expansions, suggesting there is still room for employment to grow (see the below FRED chart).

Prime Age (25 to 54) labor for participation rate 1990 thru May 2017

Metro Area April 2017 Nonfarm Payroll Update

Of the nation’s 51 largest metro areas (those with populations of a million or more as of the 2010 census), this table shows the large metro areas with the top 10 year-on-year growth rates in nonfarm payroll employment as of April:

Metro areas - top 10 nonfarm payroll employment growth rates - April 2017
For comparison, the national year-on-year growth rate of nonfarm payroll employment was 1.45% as of April 2017, and was 1.85% as of April 2016. All of these metro areas have seen the rate of employment growth drop from a year earlier, though their growth rates are still well above the national average. Note that Orlando, Florida has the highest rate of employment growth, and did as of April 2016, too. Also note that all of these metro areas are in the South or the West.

Of the largest metro areas, only five saw no growth or negative growth compared to a year earlier:

Metro areas with zero or negative nonfarm payroll employment growth April 2017
New Orleans is the only large metro area to have experienced negative year-on-year employment growth in both April 2017 and April 2016. The other four metro areas to post negative year-on-year employment growth as of April 2017 saw decent employment growth a year earlier. At 1.92%, Rochester, New York was above the national employment growth rate of 1.85% as of April 2016.

Here’s a table showing for all 51 large metro areas year-on-year growth for nonfarm payroll employment for April 2017 and April 2016, as well as rankings, sorted by the April 2017 growth rate:
Metro area year-on-year nonfarm payroll employment growth April 2017

Household Debt Update

The New York Fed released its Quarterly Report on Household Debt and Credit updated through the first quarter of 2017 accompanied by this press release. The NY Fed’s report includes this graph:

Total debt balance and its composition
The press release noted that “household debt reached $12.73 trillion in the first quarter of 2017 and finally surpassed its $12.68 trillion peak reached during the recession in 2008.” And that set off lots of speculation that a financial crisis could be just around the corner.

While the 2017 Q1 dollar figure is indeed higher than the peak reached in 2008, household debt as a percentage of GDP at 66.9% is considerably below the 87.1% peak of 2008.

house hold debt as a percentage of GDP, 2003Q1 thru 2017Q1

In other words, households are not nearly as leveraged as they were in the run-up to the financial crisis.

The NY Fed’s report contains a wealth of data on household debt and credit. One thing I found interesting is how the composition of household debt has changed over the past several years. Altering the Fed’s above graph to show percentage of household debt by category gives us this:

composition of household debt, percentages
While mortgage debt remains the largest component, as a percentage of household debt it is now at 67.8%, down from over 73% in 2008, and below where it was in 2003. Student loans now account for over 10% of household debt, over three times its percentage in 2003.

table of components of household debt

Update: at the request of reader “MetroGuy”, I’ve added this chart showing household debt as a percentage of personal income, rather than as a percentage of GDP:

household debt as a percentage of personal income, 2003Q1 thru 2017Q1

The pattern is much the same, with the latest figure household debt as a percentage of personal income (77.5% as of 2017 Q1) down considerably from the peak (103.9% in 2009 Q1) and now back down to 2003 levels.

Metro Area Unemployment and Labor Force Changes

As a follow up to my recent post on March unemployment rates for large metro areas, this post will focus the change in labor force and unemployment rates. The labor force and unemployment figures shown are taken form this BLS news release. As in the previous post, this post will focus on the 51 largest metro areas, those with populations of 1 million or more as of the 2010 census.

Before looking at the metro area level data, a quick look at the national level: the national unemployment rate was 4.6% in March, down from 5.1% in March 2016. Over that twelve month period the civilian labor force increased by 1.06 million. Among the 51 largest metro areas as a group, the labor force increased by 1.13 million. This means that outside of those large metro areas, the labor force shrank by 70,000.

When looking at changes in labor force and unemployment, keep in mind that a decrease in the unemployment rate means that the percentage change in labor force is greater than the percentage change in the number of unemployment persons. If the labor force decreased and the unemployment rate fell (as experienced by several metro areas over the past year – see below), the number of unemployed person decreased by more than the labor force.

The below table shows the labor force change and unemployment rates for the twenty metro (of the 51 largest metro areas) that saw the largest decreases in unemployment rates from a year earlier:

labor force changes for large metro areas with decrease in unemployment rates March 2017 vs March 2016
The Chicago metro area, which had the largest unemployment rate decrease, saw its labor force decrease by 1.7%. St Louis and Pittsburgh also saw relatively large decreases in their labor forces (1.9% and 0.9% respectively). Only one metro area saw a labor force decrease larger than that of Chicago or St Louis – Rochester, NY (not shown in the above table, but included in the table at the end of this post) with a 3% decrease. In terms of number of persons, Chicago’s labor force decrease was the largest. The metro areas with notable labor force increases are Phoenix (3.7%), Seattle-Tacoma (2.2%), Denver (2.1%), Portland, Oregon (1.7%), and Hartford (1.7%).

Here’s a table showing the metro areas where the March unemployment rate was unchanged from or higher than a year earlier:

labor force and unemployment changes March 2017 vs March 2016
With the exception of Cleveland, these metro areas all saw significant labor force increases. Notably, although the four Texas metro areas (Austin, Dallas-Fort Worth, Houston, San Antonio) all saw their unemployment rates increase from a year earlier, as a group they account for a labor force increase of over 238,000, which is over 20% of the national increase. It is further worth nothing that the Dallas-Fort Worth metro area’s labor force increase is the largest of any metro area (though not in percentage terms) and accounts for over 12% of the national increase.

Below is a table showing labor force and employment changes over the year ending in March for all 51 large metro areas in order of the percent change in labor force. Though there are a few exceptions, we see the labor force growing in warm weather and western metro areas, and shrinking in cold weather metro areas.

Changes in labor force and unemployment rates for large metro areas March 2017 vs March 2016

Where New Grads Can Find a Roof and a Paycheck

Jed Kolko (Chief Economist at Indeed) and Ralph McLaughlin (Chief Economist at Trulia) look at job opportunities for new graduates and housing affordability:

The bad news is that the local markets with the most opportunities for young grads are among the least affordable. The good news is that some lower-cost markets also offer numerous opportunities for recent grads, though not as many as the priciest markets. While there’s no place that offers the magic combination of extensive job opportunities and easily affordable housing (and if that place existed, it probably wouldn’t stay affordable for long), we found six metros where you can spend a bit less on housing without giving up too much on the job options.

Click the graphic below to read the whole post:

Where New Grads Can Find a Roof and a Paycheck

March Metro Area Unemployment Update

The BLS recently released March unemployment figures for metro areas, so let’s take a look, focusing on the nation’s 51 largest metro areas (those that had population of 1 million or more as of the 2000 census):

Denver had the lowest unemployment rate at 2.4% (down from 3.6% as of March 2016), and is the only large metro area with an employment rate less than 3%. A year earlier, March 2016, Austin, Texas had the lowest rate at 3.1%. In March the national unemployment rate was 4.6%, down from 5.1% a year earlier.

Compared to year earlier, six large metro areas saw their unemployment rates drop by more 1%, Chicago with the largest drop of 1.8%:

Of the 51 largest metros, only eight did not see a decrease in their unemployment rate from a year earlier. Of those eight, two were unchanged from a year earlier: Baltimore and Memphis. Here are the six that saw an increase in unemployment from March 2016:

Interestingly, four of those six are Texas metro areas. As noted above, as of March 2016, Austin, Texas had the lowest unemployment rate of any large metro area at 3.1%.

Here is a table showing the March 2016 unemployment rate, March 2017 unemployment rate, and the change in unemployment rate for all 51 large metro areas. The table can be sorted by any of its columns.

Metro AreaMarch 2016
March 2017
Chicago, IL6.3%4.5%-1.8%
Las Vegas, NV6.1%4.8%-1.3%
Denver, CO3.6%2.4%-1.2%
Indianapolis, IN4.7%3.5%-1.2%
Seattle-Tacoma, WA4.8%3.7%-1.1%
Providence, RI5.9%4.8%-1.1%
Milwaukee, WI4.7%3.7%-1.0%
Portland, OR4.9%3.9%-1.0%
St. Louis, MO5.0%4.0%-1.0%
New York, NY5.0%4.1%-0.9%
Los Angeles, CA5.0%4.2%-0.8%
Hartford, CT5.9%5.1%-0.8%
San Diego, CA4.9%4.2%-0.7%
Philadelphia, PA5.3%4.6%-0.7%
Inland Empire, CA6.0%5.3%-0.7%
Pittsburgh, PA6.1%5.4%-0.7%
Phoenix, AZ4.7%4.1%-0.6%
New Orleans, LA5.5%4.9%-0.6%
Sacramento, CA5.6%5.0%-0.6%
Birmingham, AL5.6%5.0%-0.6%
Atlanta, GA5.1%4.6%-0.5%
San Francisco, CA4.0%3.5%-0.5%
San Jose, CA4.1%3.6%-0.5%
Orlando, FL4.4%3.9%-0.5%
Jacksonville, FL4.6%4.1%-0.5%
Boston, MA3.8%3.4%-0.4%
Columbus, OH4.4%4.0%-0.4%
Cincinnati, OH4.6%4.2%-0.4%
Hampton Roads, VA4.6%4.2%-0.4%
Miami, FL5.0%4.6%-0.4%
Tampa, FL4.5%4.1%-0.4%
Salt Lake City, UT3.6%3.2%-0.4%
Detroit, MI5.5%5.1%-0.4%
Oklahoma City, OK4.1%3.8%-0.3%
Charlotte, NC4.7%4.4%-0.3%
Louisville, KY4.7%4.4%-0.3%
Washington, DC3.9%3.7%-0.2%
Minneapolis-St. Paul, MN4.0%3.8%-0.2%
Richmond, VA4.1%3.9%-0.2%
Raleigh, NC4.3%4.1%-0.2%
Kansas City, MO4.4%4.2%-0.2%
Buffalo, NY5.4%5.2%-0.2%
Rochester, NY4.9%4.8%-0.1%
Baltimore, MD4.5%4.5%0.0%
Memphis, TN5.0%5.0%0.0%
Nashville, TN3.5%3.6%0.1%
Austin, TX3.1%3.6%0.5%
San Antonio, TX3.6%4.1%0.5%
Dallas-Fort Worth, TX3.8%4.3%0.5%
Houston, TX4.9%5.7%0.8%
Cleveland, OH5.5%6.5%1.0%
data source: Bureau of Labor Statistics

The Death of Retail Has Been Greatly Exaggerated

The news is full of doom and gloom for retailers, driven by some high profile bankruptcies and store closings by national chains such as Sports Authority, Sears, Payless Shoes, J.C. Penny, Macy’s and others. Explanations for the difficulties faced by brick and mortar retail focus on the rise of online shopping (most notably Amazon) and the decline of suburban shopping malls.

But is brick and mortar retail dying? As gauged by employment, hardly. As shown in the chart below, retail employment is following the same trend as total nonfarm payroll employment, and both have been growing steadily after bottoming out in 2009.

total nonfarm payroll employment and nonfarm payroll employment
When looking at retail employment as a percentage of nonfarm payroll employment, we can see that it has declined from 12% in 1990 to 10.9% as of 2016. More importantly, the rate of decline has been fairly constant over that entire sixteen year period, and has not accelerated with the rise of ecommerce or millennials’ preference for the urban core over suburbs.

retail employment as a percentage of total nonfarm payroll employment
We are seeing a decline in employment in two specific retail subsectors: general merchandise stores (which include department stores) and clothing stores. The below chart shows the annual change for total nonfarm employment, retail excluding general merchandise and clothing, general merchandise stores, and clothing stores. The datapoints for March 2017 show the percentage change from March 2016.

Annual change in retail employment
As of March 2017 (the most recent figures available from the BLS), total nonfarm employment is up 1.5% from a year earlier, with retail excluding general merchandise and clothing up 1.0%, general merchandise stores down 1.6%, and clothing stores down 0.5%. Total retail employment including general merchandise and clothing stores (not shown in the above chart) is up 0.4% from a year earlier.

In short, while general merchandise stores and clothing stores are having a rough year so far, overall retail employment continues to grow with the rest of the economy.

Metro Area Population Growth – Current and Previous Expansions

Using the NBER’s method of measuring expansions from trough to peak (see this NBER page on expansions and contractions) our current expansion is still chugging along 82 months after the June 2009 trough. The previous expansion, from November 2001 through December 2007 (referred to as “the 2001-07 expansion” for the rest of this post), was 73 months in duration.

With the Census Bureau’s recent release of 2016 population estimates for metro areas, I’ve compared the growth rates for large metro areas (the 51 metro areas with populations of 1 million or more as of the 2010 census) for the current and previous expansions. The Census Bureau’s population estimates are for July 1st of each year. For this comparison of population growth, for the current expansion I’ll use the CAGR (compound annual growth rate) over the 2009 to 2016 period, and for the previous expansion the CAGR over the 2001 to 2007 period. At the end of this post is a sortable table showing the population growth rates for both expansions, as well as the difference between the two, for all 51 large metro areas.

Before reviewing the metro area population growth rates, for the US national population the CAGR was 0.93% for the 2007-10 expansion and 0.74% for the current expansion.

Here are metro areas with the top ten rates of population growth for the 2001-07 expansion (left) and the current expansion (right):

large metro areas top 10 population growth rates, 2001-07 expansion and 2009-16
Just as the national rate of population growth has slowed during the current expansion compared to the 2001-07 expansion, so have the growth rates of the fastest growing in metro areas. For the 2001-07 expansion, the top ten growth rates ranged from 2.36% to 4.19%, while for the current expansion the rates range from 1.66% to 2.91%. For each metro area in both top ten lists, the rate of population growth has slowed during the current expansion, with the exception of Denver, Colorado. The metro areas in both top ten lists area also all in warm weather states, again with the exception of Denver.

Texas metro areas feature prominently with Austin, San Antonio, and Houston in both lists, joined by Dallas-Fort Worth in the list for the current expansion. On top of that, Austin went from the sixth fastest population growth for the 2001-07 expansion to #1 for the current expansion. The other Texas metros rose in rank, too (Houston from #10 to #3, San Antonio from #9 to #5, and Dallas-Fort Worth from #12 to #6).

North Carolina was the only other state to have more than one metro areas show up in either list, with Raleigh at #2 in both lists, Charlotte at #7 for the 2001-07 expansion, and #9 for the current expansion.

The below table shows the metro areas with the ten largest increases in population growth rates for the current expansion compared to the previous expansion:

large metro areas, top 10 increases in population growth rates, 2001-07 expansion and 2009-16
At the top of this list is New Orleans with its negative growth rate for the 2001-07 expansion showing the impact of Hurricane Katrina (the New Orleans metro area’s 2006 population was 25% below that of 2005). Next on the list are tech hubs San Francisco and San Jose, both with growth rates of 1.21% (well above the national rate) for the current expansion compared to anemic growth rates well below the national rate of population growth for the 2001-07 expansion.

Next, the below table shows metro areas with decreases in population during the current expansion:

large metro areas with negative population growth, 2001-07 expansion and 2009-16
These five metro areas that have seen negative population growth during the current expansion also saw negative population growth during the 2001-07 expansion (with the exception of Hartford, Connecticut), though at steeper rates of decline. Note that, again with the exception of Hartford, these are Rust Belt cities. The only other large metro area to experience negative population growth during the 2001-07 expansion was New Orleans, which as noted above, was due to Hurricane Katrina.

Lastly, here is the sortable table of all 51 large metro areas:

Population Growth
Population Growth
Austin, TX3.00%2.91%-0.09%
Raleigh, NC3.67%2.32%-1.36%
Houston, TX2.36%2.17%-0.18%
Orlando, FL3.13%2.09%-1.04%
San Antonio, TX2.37%2.07%-0.30%
Dallas-Fort Worth, TX2.08%1.90%-0.18%
Denver, CO1.45%1.85%0.40%
Nashville, TN2.06%1.73%-0.33%
Charlotte, NC2.78%1.72%-1.06%
Phoenix, AZ3.01%1.66%-1.35%
Seattle-Tacoma, WA1.11%1.53%0.43%
Las Vegas, NV4.19%1.52%-2.66%
Oklahoma City, OK1.32%1.49%0.17%
Jacksonville, FL2.18%1.47%-0.71%
Washington, DC1.31%1.44%0.13%
Salt Lake City, UT1.41%1.43%0.02%
Atlanta, GA2.37%1.43%-0.94%
Miami, FL0.99%1.40%0.40%
Portland, OR1.36%1.36%-0.01%
Tampa, FL1.84%1.33%-0.51%
Inland Empire, CA3.15%1.22%-1.93%
San Francisco, CA0.04%1.21%1.17%
San Jose, CA0.22%1.21%0.98%
New Orleans, LA-3.34%1.19%4.54%
San Diego, CA0.61%1.16%0.55%
Columbus, OH1.27%1.13%-0.15%
Sacramento, CA1.84%1.06%-0.78%
Indianapolis, IN1.33%0.97%-0.36%
Richmond, VA1.55%0.94%-0.61%
Minneapolis-St. Paul, MN0.96%0.92%-0.04%
Boston, MA0.06%0.82%0.76%
Kansas City, MO1.04%0.77%-0.27%
Louisville, KY1.01%0.63%-0.38%
Los Angeles, CA0.16%0.59%0.43%
Baltimore, MD0.59%0.54%-0.05%
New York, NY0.11%0.49%0.38%
Hampton Roads, VA0.72%0.49%-0.23%
Cincinnati, OH0.59%0.39%-0.20%
Philadelphia, PA0.48%0.31%-0.18%
Birmingham, AL0.74%0.28%-0.47%
Memphis, TN1.03%0.27%-0.76%
Milwaukee, WI0.26%0.21%-0.05%
St. Louis, MO0.40%0.14%-0.26%
Chicago, IL0.30%0.13%-0.18%
Providence, RI0.07%0.12%0.05%
Rochester, NY0.06%0.01%-0.05%
Buffalo, NY-0.40%-0.03%0.36%
Hartford, CT0.57%-0.03%-0.60%
Detroit, MI-0.32%-0.05%0.27%
Pittsburgh, PA-0.41%-0.08%0.33%
Cleveland, OH-0.39%-0.18%0.22%
data source: Census Bureau

West Coast Metro Areas – Employment Growth and Housing

Up and down the West Coast, recent conversations about housing usually include the word “crisis”, as a few Google searches will attest. During the current expansion, the supply of new housing has not kept pace with employment growth, which drives demand for housing.

In a recent post, I compared nonfarm payroll (NFP) employment growth to housing permits issued over the 2011 to 2016 period for 51 large US metro areas (those that had populations of 1 million or more as of the 2010 census). This post will focus on just West Coast metro areas. The table below shows housing and employment figures for eight West Coast metro areas, and can be sorted by any column. The right-most column is the ratio of housing units per increase in employment, referred to as the “H/E ratio”, introduced in a previous post.

West Coast Metro Areas – 2011 to 2016 Housing Permits and Employment Growth

Metro Area2011-2016 NFP
2011-2016 NFP
% of units
housing units
per NFP
Portland, OR163.12.6%65,77449.4%0.40
Seattle-Tacoma, WA299.32.8%120,89059.0%0.40
San Diego, CA184.02.3%46,72767.8%0.25
Sacramento, CA127.32.4%27,64815.8%0.22
Inland Empire, CA262.13.4%50,14425.6%0.19
San Jose, CA219.03.8%38,34573.4%0.18
San Francisco, CA405.83.2%64,24465.6%0.16
Los Angeles, CA667.02.0%92,99272.0%0.14
data source: Bureau of Labor Statistics, Census Bureau

For these eight West Coast metro areas employment growth has been relatively strong over the 2011 to 2016 period with a CAGR (compound annual growth rate) that ranges from 2.0% for LA to 3.8% for San Jose. By comparison the national rate was 1.8%.

The Seattle-Tacoma and Portland metro areas both have H/E ratios of 0.40 (above the national large metro average of 0.34), while the California metro areas’ H/E ratios are significantly lower, ranging from 0.25 for San Diego, to 0.14 for LA. In short, relative to employment growth, Seattle and Portland have issued considerably more housing permits than the large California metro areas.

West Coast metro areas H/E ratio - housing units per employment increase

Repeating the caveat from my earlier posts on this subject: the H/E ratio tells us only about the incremental supply of housing units to be added, and incremental demand as gauged by increases in employment, and does not tell us anything about the existing housing stock or the existing employment base. That is, it does not give us the entire picture of supply and demand for housing.