Federal Spending Back on the Increase?

Federal spending is back on the upswing. Or, at least it is according to the President’s budget which shows an increase of 7% percent in Fiscal Year (FY) 2015 (October 2014 through September 30, 2015) compared with the prior year.[1] 

The President’s Budget has federal spending growing on average 5.4 percent a year from FY2015 through FY2020 with national defense increasing essentially 0 percent.[2] Although slower than the 6.6 percent annual average growth from FY2000 through the peak in FY2011, the return to some growth in spending is good news for states and metropolitan statistical areas (MSAs) that are dependent on federal contract spending.

We’re not out of the woods yet. A January 2015 Congressional Budget Office report indicates that defense and nondefense funding are equal to or below the FY 2015 budget caps,[3] but the President’s Budget for FY 2016  ignores the caps put in place by the Budget Control Act of 2011 and modified by the Bipartisan Budget Act of 2013.  If the caps are exceeded, a sequestration will reduce federal discretionary spending by approximately $139 billion in FY2016 below the President’s Budget request.[4] About 64 percent of the reduction will occur in defense spending based on current laws.

So which metro area economies are most dependent on revenues derived from contract work for the federal government? Which ones are most at-risk if we see another round of sequestration? Chmura Economics & Analytics took a look at federal contract spending data and assigned it a metro geography based on where the awarded firm performed the work and adjusted it for time of performance since some contracts are awarded for work that is performed over a number of years.

Out of 381 MSAs in the country, the Washington-Arlington-Alexandria, DC-VA-MD-WV MSA topped the list of federal spending with $71 billion in FY 2014. Dallas-Fort Worth-Arlington, TX was a far second with $19.3 billion, and Los Angeles-Long Beach-Anaheim, CA was third at $14.8 billion in FY 2014.

A better way to assess the risk of a region to potential cuts in federal spending is to consider the concentration relative to employment. From that perspective, Idaho Falls, ID ranked the highest ($47,691 per employee); followed by California-Lexington Park, Maryland ($39,940); Amarillo, Texas ($31,407); and Huntsville, Alabama ($30,799).

The interactive map and table below show the dependence of all MSAs in the nation on federal spending.

Research support was provided by Patrick Clapp.

 

[1] President’s Budget FY 2016, Table S-1

[2] President’s Budget FY 2016, Table 28-1 Net Outlays by Function, Category, and Program

[3] https://www.cbo.gov/sites/default/files/cbofiles/attachments/49889-sequestration.pdf

[4] President’s Budget FY 2016, Table 5.6—Budget Authority for Discretionary Programs: 1976–2020

Hollowing Out of the Middle Class?

We’ve heard a lot about the hollowing out of the middle class.  That is, a trend of solid job growth for middle-class Americans which turned into contraction during the Great Recession. This contraction persisted after the recession ended as more of the jobs held by the middle class moved offshore, were consolidated into fewer jobs, or were lost to productivity gains resulting from technology and innovation.

Was this hollowing out driven by the recession or is it indicative of a new norm? Chmura economists looked at this issue in 2013 and verified a clear hollowing out of the middle class, as shown in the chart below.[1] During the period from 2001 through 2011, wages were mostly stagnant, with gains exceeding inflation for only those occupations on the high end of the wage scale. Also, job growth was mainly isolated to those jobs paying the least and those paying the most (see our related post Where the Jobs Are for more).

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Now, with data available through 2014 and three additional years’ distance from the end of the recession, are the jobs in the middle faring any better?

Using the same methodology as in 2013, the results demonstrate a continuation of the trends identified in the 2011 data with modest improvement. Wages grew slower than the pace of inflation for all but the 7th, 9th and 10th deciles, with deciles 2, 3, and 6 showing the slowest growth. Specifically, inflation-adjusted wages declined 3.5%, 5.0%, and 3.3% in deciles 2, 3, and 6, respectively. Similar to our findings in 2013, job growth has been the fastest in the 10th, 9th, and 1st deciles. Also, the middle class—represented by deciles 4, 6, and 7—showed negative job growth between 2004 and 2014, albeit relatively smaller declines than reported in the previous blog. 

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On a more positive note, employment grew in the 5th decile, which includes occupations such as dental assistants, medical secretaries, and customer service representatives. In addition, employment grew in a number of middle class occupations between 2004 and 2014, and many are expected to continue to grow, as demonstrated in the sample selection of occupations in the table below.   

  Avg Ann
Empl
Growth
2004-2014
Forecast
Avg Ann
Empl Growth
2014-2024
Decile
Interpreters and Translators 7% 4% 7
Health Technologists and Technicians, All Other 7% 2% 7
Medical Equipment Repairers 6% 3% 7
Insulation Workers, Mechanical 5% 4% 7
Occupational Therapy Aides 5% 3% 4
Medical Assistants 4% 3% 4
Industrial Machinery Mechanics 4% 2% 7
Computer User Support Specialists 4% 1% 7
Pharmacy Technicians 4% 2% 4
Physical Therapist Assistants 3% 3% 7
Medical Appliance Technicians 3% 1% 6
Machinists 1% 1% 6
Source: Chmura Economics & Analytics and JobsEQ®

Growth in high-demand occupations such as these may lead to more competitive wages for the middle-deciles that could reverse current trends. For now, the data help explain the frustration that many have felt towards slow job recovery and wage growth post-recession. Stagnant wages have not been limited to the middle; spending power declined for seven of the ten deciles. Meanwhile, the middle-wage deciles in this analysis accounted for more than 40% of all jobs where job growth has been slow, at best, for those workers over the past decade.

Research assistance for this post was provided by Johnny Constable, Claire Brunner, and Leah Deskins.

[1] This analysis was created by using over 800 occupations identified by the Bureau of Labor Statistics. We broke the occupations into ten groups based on employment and wages earned and analyzed each deciles based on job and wage growth.

Economic Impact: Jobs are being created but workers are underemployed

The unemployment rate is dropping and jobs are becoming more plentiful, but that doesn’t mean workers are in their ideal jobs.

Some people who are working are “underemployed.”

The Bureau of Labor Statistics officially defines underemployment as someone who wants to work full-time and can only find part-time work.

Arizona and California had the highest number of underemployment among states for the four quarters that ended in March, BLS data shows.

Underemployment can be measured at the state level by looking at the difference between two different jobless rates that the BLS compiles.

Arizona and California had the largest gap between the two rates, having a difference of 6.2 percentage points each. Nevada followed with 6.1 percentage point difference.

The state with the least amount of underemployment, because workers are employed part time for economic reasons, was North Dakota with a 1.8 percentage point gap.

For the U.S., the difference between the two rates that the BLS compiles was 4.4 percentage points for the four quarters that ended in March.

Virginia ranked with the 32nd gap in the nation at 3.8 percentage points.

However, BLS’s definition does not capture those who are working in an occupation below their level of qualifications. For example, it doesn't count someone with a master’s degree who is working as a retail salesperson.

There is no official measure of such underemployment by occupation.

But it can be estimated by comparing the educational skills  of residents in a region to the education achievements required by occupations employed by industries in the same region.

Looking at all 381 metropolitan statistical areas, the three MSAs with the largest surpluses of high-skilled workers are Barnstable, Mass., with a 13.2 percentage point surplus; Washington, D.C. with a 12.5 percentage point surplus; and San Francisco, with an 11.5 percentage point surplus.

Some of those are desirable areas to live that attract many high-skilled residents. Some of those are college towns where a lot of graduates choose to stay.

The three MSAs with the largest deficits of high-skilled workers are Hanford-Corcoran, Calif., with a 16.3 percentage point deficit; Hinesville, Ga., with a 14.7 percentage point deficit; and Cumberland, Md., with a 14.6 percentage point deficit.

In Virginia, the Richmond MSA is ranked 78th nationally with a 1.3 percentage point surplus of high-skilled workers, indicating that underemployment here is not as much of an issue as it is in other regions.

The Hampton Roads MSA has a 3.4 percentage point deficit of high-skilled workers (ranked 177th), indicating a less severe issue of underemployment by occupation.

Viewing underemployment across the nation shows that even as the unemployment rate continues to drop, underemployment will vary by metropolitan area.

Underemployment in the United States

Many students who graduated during the Great Recession and over the last few years were unable to find jobs for which they were trained. The so-called underemployed workers are employed in an occupation below their level of qualification. For example, a graduate with a Bachelor’s Degree in economics who is waiting tables or working at a retail store is considered underemployed. Chmura calculates a proxy for underemployment by comparing educational attainment supply and demand in a given labor market at various skill levels.

Some metropolitan statistical areas (MSAs) around the country have a higher percentage of underemployed than others.  MSAs in Massachusetts, the District of Columbia, and California top the list of regions that possess a surplus of high-skilled workers in the latest update to Chmura Economics & Analytics’ underemployment dataset.

Interactive FeatureUnderemployment is a useful supplement to other indicators of labor market health. The traditional measure of unemployment from the Bureau of Labor Statistics does not distinguish between workers who are employed in a position aligned with their skills and education. Workers who are underemployed and not necessarily contributing as much as they could to the labor market, represent potential lost productivity, wages, and tax revenue for the region.

High underemployment in a region may also be a positive measure, reflecting the desire of workers to live in a particular area (like the scenic Cape Cod waterfront of Barnstable Town, Massachusetts) and/or higher standards for occupations in certain regions (such as for computer occupations in San Francisco).

Chmura’s underemployment proxies for MSAs, along with more detailed methodology and definitions, are available on our website and at the county, MSA, and state levels within JobsEQ®.

Research assistance for this post was provided by Patrick Clapp.

Using Job Postings to Measure Employment Demand

An article in the Harvard Business Review recently touched on Why Job Postings Don’t Equal Jobs, explaining that these data should be considered unreliable when trying to estimate job demand under various circumstances. Specifically:

  • Professional-type jobs are more likely to be posted online
  • Companies often advertise the same job multiple times, and
  • For job boards that require payment to post openings, firms may post more openings when there is a discount offered, whether or not they currently need those workers.

A few additional concerns were not mentioned in that article:

  • Some jobs are posted for legal reasons, such as firms sponsoring foreign workers for permanent residence (green card). Firms have to “test” the labor market by advertising those jobs even though they have hired a foreign worker already and have no intention of hiring someone else. Most of these cases are professional jobs as well.
  • The methods used to collect and clean online postings and estimate trends over time can be problematic.

The methods used to obtain and clean job postings data are varied and are typically closely guarded. For an objective review of several providers of these data, see the Vendor Product Review:  A Consumer’s Guide to Real-time Labor Market Information. Vendors scrape and spider job boards automatically and manually, code results into anywhere from 5 to 70 data elements, and deduplicate 60 to 90 percent of job ads. Based on around 4 million job postings daily, and assuming ads are only duplicates (not triplicates, etc.), that could mean anywhere from 1.2 to 1.8 million job postings are thrown out as duplicates every day.

Methods for analyzing the postings range from keyword searches to natural language processing and text analytics, but small details in methodology can have outsized effects on what gets counted. Take, for example, the difference between searching job postings on Indeed.com for registered nurses using different keywords such as “rn,” “registered nurse,” or “registered nurses.”

Job Trends from Indeed.com

Source: Indeed.com

This simple search raises a few questions:

  • Which keyword or collection of search terms best represents job postings for registered nurses?
  • Do the keyword results change by region? 
  • In another field, how might different data providers distinguish between R, the statistical programming language, and H.R. (Human Resources) or R&D (Research & Development)?

The answers to these types of questions will likely vary by data provider and should be considered before relying on the data for analysis.

Many providers make a concerted effort to improve collection, parsing, and deduplication methods; however, significant changes in methodologies can cause additional confusion and inconsistency in job advertisement data if used in analysis over time. Changes to the deduplication methodology used by The Conference Board, for example, resulted in revisions lowering estimates by about 460,000 jobs for every month in the series. The overall curves were fairly consistent, showing similar shape and trends, but anyone relying on the actual levels for measuring or forecasting employment demand could find old estimates too high by hundreds of thousands of jobs.

Impact of revisions, HWOL data series

In summary, the use of online job postings data to glean labor market information is promising, but there are a number of concerns that suggest these data are not sufficient replacements for traditional labor market data.

Research assistance for this post was provided by Patrick Clapp.