Unemployment and Migration
One issue we note in unemployment levels is the relationship of employment and unemployment to migration and population change. I took the liberty to compare population change by county over the last eight years using the 2000 Decennial Census and the 2008 American Community Survey (ACS). Unfortunately, I have a data conflict since I am using two different sources. Early ACS data (2000 to 2003) provides data for a select group of counties in the state, while naturally the Decennial Census is done only once over a 10 year period. However, reviwing these data together revealed some startling results.
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I analyzed population c
hange from 2000 through 2008 and compared that percentage change with December 2009 unemployment data. As one might imagine the numbers are all over the map (map to be included at a later date), literally, but there are general themes which float to the surface.
If you live in an expanding county, one that has added population from 2000, it is more likely that you have a job. Counties with a population change of over 10 percent, had the lowest median unemployment rate, 10.5 percent, while counties which expereinced a decrease in population had a median rate of 16.9 percent. Counties with insignificant change, had a median rate of 13.9 percent, while small counties experienced a 16.2 percent rate.
The December 2009 unemployment ranges between 8.8 to 21.4 percent. Population change ranges between minus 6.8 to plus 27.3 percent. This represents significant variation among counties and suggests a mismatch of population to available work. Can South Carolina match work to where workers live. This may be extremely difficult as a result competitiveness in transportation, technology and training. This is not to say that a rural workforce is less skilled, but instead has less access to opportunity.
Boeing is an excellent example of this phenomenon. Boeing is locating in a growing Metropolitan Statistical Area (MSA), supported by state of the art technology, a world class transportation infrastructure, and a primary education system which can adapt to the companies needs. In order to capture one of these opportunities, a rural workforce, in all probability, will need to move or commute.
The number of persons who make this tough decision, in some cases leaving family, property, and heritage, may hold in their hands the future of South Carolina’s unemployment rate.
Unemployment- Let’s add it up one more time!
The post and courier reported the December monthly unemployment data for South Carolina. Unemployment is the most often quoted number and also the most misunderstood of the economic data floating around. Even for a seasoned professional, it is easy to get crossed up. This particular article referenced two unrelated data sets while discussing South Carolina unemployment.
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When citing unemployment detail, employment numbers need to be culled from the same data set. The recession started in December of 2007. Employment since then decreased by 113,820, not the 109,900 in the article. However, what is most interesting, and left out, is persons on unemployment increased by 150,693 over this same time period! (Employment pdf) So not only are there less employed, but also more unemployed. It is likely this number will creep higher over the next couple of months to 14 percent (Feb 09), partly as a result of re-benchmarking and partly due to the South Carolina economy. But where did the 109,900 come from? That number is Unadjusted Current Employment Statistics (CES) employment data from the “establishment survey”, a different program. I really like these data and often use them in conjunction with unemployment data thus providing a better picture of where jobs may have been gained or lost. CES data in this situation, should not be compared to unemployment data. Unemployment data is derived from the Current Population Survey (CPS), “the household survey”. This survey is the labor force measure for the nation.
CES Data- the poser
CES is reported two ways. Over longer timelines, Seasonally Adjusted CES data is a better estimate of employment, while Unadjusted Seasonal data is more acccurate in real time. I have to admit I tend to default to Unadjusted Seasonal data, but there is a time and place for Adjusted and this may be that time. My default to unadjusted is a result of statistical variations with Seasonally Adjusted data, most could give a hoot about- but they are there.
“CES data are a coincident economic indicator and are often cited in national and local newspapers, magazines, and reports. This press generates enthusiasm, curiosity and a wealth of outside material for supplementary reading. The College of Business Administration at the University of South Carolina uses seasonally adjusted employment as an indicator of current employment trends in South Carolina. The regional Federal Reserve Banks use CES data in easy-to-understand economic applications….”. Source: Bureau of labor Statistics
The CES table shows the difference in data sets for the state of South Carolina. (CES pdf) The unadjusted is 109,900 with adjusted coming in at a loss of 102,200. 102 seems a little soft in this economy and, consequently, it is another reason for not quoting the sum of change in the face of unemployment data from the household survey. The bigger picture is which direction is employment going in the future. I will try to address that by looking at migration.
It’s Not Easy Being Green
It is important to examine some of the recent work being done in the quantification of Green Jobs. A group called the Workforce Information Council, comprised of leading statisticians/economists from the federal Bureau of Labor Statistics and state labor market information directors, have produced a key report titled “Measurement and Analysis of Employment in the Green Economy (October 2009).” (pdf)
The Study Group defined a green job is one in which the work is essential to products or services that improve energy efficiency, expand the use of renewable energy, or support environmental sustainability. The job involves work in any of these green economic activity categories: Renewable Energy and Alternative Fuels; Energy Efficiency and Conservation; Pollution, Waste, and Greenhouse Gas (GHG) Management, Prevention, and Reduction; Environmental Cleanup and Remediation and Waste Clean-up and Mitigation; Sustainable Agriculture and Natural Resource Conservation; and/or, Education, Regulation, Compliance, Public Awareness, and Training and Energy Trading.
You Cannot Prove the Null
This sounds like statistics better go to the next site–stop before you do that. I think we can help. In 2009, I joined the SAS JMP® software user group and receive periodic updates and explanations on different aspects of statistics. Since using JMP®, my whole world has become significantly (no pun intended) less complicated.
The latest issue of JMPer Cable (pdf) (Issue 26 Winter 2010 pp 6-9), has a short and informative article by Ramirez and Bailey on significant testing. Questions we analysts need to ask ourselves, from a data standpoint include: is there a difference, does the data tell me anything, or is the simple comment “that’s interesting” good enough or do we need to have that discussion with the marketing guys again? Regardless, this article explains the null hypothesis (no change) and the alternative hypothesis in a few easy to understand pages. The authors do this with short informative examples and more importantly the intuitive computer display from the JMP® statistical package. This article is a conversation, not a lecture, allowing one to absorb concepts that frankly can be confusing.
10 Years of Less Employment
Employment
The economy lost another 85,000 jobs in December 2009. It is not uncommon for most of us to focus on the economy month to month, however there are bigger numbers looming on the horizon which are just as important. Recently, Dean Baker calculated employment losses for the decade. He estimates private employment declined by a little over 1.5 million for the decade. In addition to that he states based on the annual benchmark revision, total employment loss is closer to 2.4 million.
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Employment and unemployment calculations are confusing. One reason is the different surveys and assumptions which are used to calculate employment and unemployment. The employment which is referenced above is calculated using what is referred to as the establishment survey. The establishment survey is the Bureau of Labor Statistics Current Employment Statistics (CES) program.
Using historical data from CES, one can calculate the employment losses over the decade. My spreadsheet (pdf) (in 1000’s) demonstrates two ways to calculate this number, one based on last month of 1999 versus the last month of 2009 and an alternative which takes an average over each year, 1999 and 2009, and calculates the difference. Regardless these are large numbers. It is important to note that these are establishment (CES) data rather than the Current Population Survey data. As demonstrated, one is able to make this 1,549,000 jobs loss calculation.
The last portion of this article highlights something called a benchmark revision. Since the CES is a survey the BLS “checks” and makes revisions to the data, by comparing it to a census of employment. This census is the Quarterly Census of Employment and Wages (QCEW). There is actually a good deal of work that goes into this method and the BLS needs to get credit for going though the process.
What is interesting about this year, which Mr. Baker notes, is that we will experience an unusually high adjustment (pdf). Past adjustments have been plus or minus two-tenths. This year the adjustment could be a downward (worse) adjustment of six-tenths or more. Putting these two numbers together one derives the 2.4 million plus/minus job loss over the last decade.
