Unemployment Definition (BLS)
Unemployment Data
Unemployment numbers are one of the few data sets that are reported and analyzed in the media. Unfortunately, most of the current media analysis is flawed because writers don’t understand the definition of unemployment as reported by the Bureau of Labor Statistics (BLS). Here is a link to that definition(pdf). This post is to help you understand the basic definition of unemployment.
Key Points for Everyday Analysis:
Civilian Labor Force (labor force): These are the people who are counted, age 16 and older. It does not include folks in institutions such as prisons, nursing homes, military, etc.
Employed: This term applies to anyone did any work on the 12th of each month as paid employee at a farm or business, 15 hours or more in a family business, or had job but was on vacation, sick, absent due to bad weather, etc. Even those holding more than one job are counted only once.
Unemployed: People who weren’t employed on the 12th, but were available to work and were looking for work over the past four weeks.
Unemployment Rate Calculation: The ratio of unemployed to civilian labor force, expressed as percent.
Analysis Discussion:
What happens in the labor force makes a difference in the unemployment rate – specifically when people enter and exit. As an example, if more people enter the labor force than can find a job, the unemployment rate goes up.
Always consider the three classifications in the calculation; labor force, employment and unemployment. Focus on trends, not individual points. Compare trends, not points, from one year to the next. Think about what happens in the labor force during the year, such as a big layoff, teachers being hired in the fall, hurricanes. Review the Current Employment Statistics (CES) establishment survey data for clues to employment changes by industry.
Don’t confuse the neighborhood unemployment rate with the official BLS unemployment rate. True, if your neighbor is unemployed her unemployment rate is 100 percent, but this number has no correlation to the official unemployment rate.
Think of the unemployment rate as a tide. Thus, a drop of water tells you very little. Only by standing back and looking at the coastline can you discern the effects of water level change. You may not like the BLS definition, but the trend it produces is powerful.
How is unemployment calculated? See Unemployment Calculation Method and Documentation
Unemployment South Carolina- April 2010
The Post and Courier missed the main point in the April unemployment numbers. The story here is the labor force, or in this case the decrease in labor force (moving in the wrong direction). Mary Graham from the Charleston Metro Chamber got it right by thinking about this from a seasonal perspective. The surprise is that there should have been an up tick, but instead the labor force dropped a whopping 18K from April 2009, a bad year in itself. In addition to these data, employment actually DROPPED 17K from 2009 (BLS). There is nothing in the data to be pleased with.
I would encourage the Post to speak with experts on these data or become more educated before writing an article. These are important data that need to be reported accurately since they affect so many.
The pdf link tells the real story of unemployment for the state of SC. Note the disturbing drop in labor force and the deep employment recession. (pdf)
LAUS Unemployment Calculation Method and Documentation
Unemployment Method Description
Each month the Bureau of Labor Statistics (BLS) publishes national, state and local unemployment statistics. The results are reported in the local media, usually with a brief analysis along with a human interest story. Unfortunately, the story often does not match the data. One reason is the users are not familiar with the strict definition of unemployment as defined by the BLS. I would encourage anyone who has doubts about that definition to review it first before jumping into this detailed post of unemployment calculation. See definition of unemployment.
Statistics: Root of the Published Results
Calculating unemployment is a statistical process. You could stop here, but I encourage you to keep reading since this post gives the sources of those calculations and breaks it all down into bit size (non-math) pieces. We will give a brief explanation of each part of the process with source documents, where available, and links, if necessary, to key terms.
Why is the unemployment calculation process so complex? There are two primary reasons: 1) timing and 2) cost. The series is published every month for a number of labor market regions. Wouldn’t it be great if we could go out and actually count the number of people who are employed or unemployed, and just for fun, determine how many people are in the labor force every month? This process would be labeled a census. In this country, that is done once every 10 years.
Even if we could compile and report the results each month, imagine the expense involved. The next best process then, is to survey (estimate) the population and estimate the number who fall into each category, along with some general demographic information. The process starts with a monthly employment survey, administered by the Census Bureau, named the Current Population Survey or CPS. The data from this survey are used by BLS in statistical models to calculate unemployment rates.
Background
Let’s keep in mind the unemployment rate published by general media is the U-3 rate. There are actually 6 rates that provide different estimates of unemployment. The U-3 is the middle estimate. In South Carolina, the U-1 rate was 5.6 percent and the U-6 rate was 18.4 percent, averaged between the third quarter of 2008 and the third quarter of 2009. The U-3 rate during this same time period was 10.6 percent. This tells us that the unemployment rate is exact, given a certain level of statistical accuracy based on specific criteria. The following statistical process looks at how the rate is developed, regardless of level.
Statistical Process: Four primary Steps
Step One: CPS
Step one is the CPS. This is a national survey, completed in each state, done on a monthly basis.
Like any statistical survey sample, we know there is truth and error in the data. The question is, what is the true value and what is error, or noise? In a survey we need to model (statistically) the difference, allowing us to calculate the accuracy of our results in a consistent fashion. In this case, it’s for states and special regions. The BLS LAUS program uses the monthly Census data in a signal-plus-noise (SNP) model – actually two models – which when combined, estimate the true labor force for divisions and states. (Page 37 pdf)
The SNP model estimates also incorporate historical CPS auxiliary data. The end result is seasonal-, trend- and irregularity-adjusted employment/unemployment characteristics at the national level. (Page 37 pdf)
Step Two: Monthly Benchmark
In the past, large adjustments in employment/unemployment data were required at year’s end to match the national CPS sample because state monthly totals were not summing to the national CPS totals. That process has now been modified. The monthly data is bench-marked, real time, in two ways. First, census division models are constructed and controlled to the national CPS level, and second, state models are controlled to their appropriate census division estimates. We now have a statistical model of labor force, employment and unemployment for the nation, census regions, states and other special geographies. (Page 38 pdf)
Summary: Steps One and Two
Clearly there is a fair amount of math within this process. However, in its simplest form, a survey is taken throughout the country by the Census Bureau for a number of different geographies each month. Larger regions are more accurate than smaller ones. Census regions total to the national CPS. The BLS then works with the CPS data to create state data that is controlled to the appropriate census region, providing consistency month to month with the national results. We now have an estimate of labor force, employment and unemployment at the state level that is consistent with the national CPS survey.
Keep in mind each step involves error. So it is important to remember that as good as this process is, variability is not completely eliminated. That is one reason that trend analysis is important when analyzing these data.
Step Three: Estimates for sub-State Labor Market Areas (LMA)
The third step estimates unemployment and employment for areas within a state, such as a metropolitan statistical areas (MSA), county or city (sub-state). These typically are data that the media reports. Up until now our estimates have been for states, census regions and the nation as a whole.
With state level controls, local unemployment estimates are derived from local unemployment insurance (UI) statistics, based on two covered employee building blocks: 1) those with benefits and 2) those with exhausted benefits. These data allow for estimates of those unemployed and expected to be unemployed. New entrants and re-entrants cannot be estimated using this process. Instead, those data are estimated from national data based on demographics.
Local employment is estimated using the Current Employment Statistics (CES) and Quarterly Census of Employment and Wages (QCEW), or covered workers. These place-of-work estimates need to be adjusted to place-of-residence. This is accomplished with decennial census data. Data for each labor market area is adjusted to sum to the state total, calculated above. Finally, estimates for parts of Local Market Areas (LMAs) are primarily computed using the number of claims versus local population. (Page 39 pdf)
Keep in mind that not all those in the labor force are estimated in this process. Primarily, two groups not covered are those in agriculture and “all other,” which includes self-employed workers.
Step Four: Year-End Benchmark Correction or Smoothing
Smoothing is a year-end process that collects and distributes any irregularities that are noted throughout the year that were not a part of the original series. Therefore, mid-year data, unlike final smoothed data from prior years, still needs to go through a smoothing process. Trend analysis, when comparing prior year data with current data, is recommended. This will reduce the risk of misinterpreting the variance between the two data sets as a result of computations alone. (Page 39 pdf)
Summary: Steps Three and Four
Generally step three uses local data to determine who is and who is not employed, but is still an estimate. Smoothing in step four is generally a clean-up process to make the data as robust as possible for future use.
Conclusion
The methodological sources I have provided are being updated from April 1997. The basic process (1997) is the same with the exception of the monthly benchmarking and year end smoothing, incorporated in 2010. One important note to this process is the results are only as good as the inputs. States that take their UI data collection seriously are more accurate and thus provide a better picture.
I want to thank the Southeast BLS Regional Analysis Team for the assistance in helping me understand and interpret the LAUS detailed statistical documentation.
Workforce Professionals and O*NET
International Association of Workforce Professionals (IAWP)
O*NET Data Set (xlsx)
Presentation (ppt-pdf)
Presentation Live Live Scribe
The Occupational Information Network (O*NET) is sponsored by the US Department of Labor/Employment and Training Administration (USDOL/ETA). I primarily use O*NET as an engine in other databases I develop. I also use O*NET data as the basic building blocks for statistical analysis on a variety of subjects. The data is found in the developers corner on the O*NET site.
This post compares O*NET data bases over the past seven years. In particular, we wanted to know if there was a significant difference in occupational educational requirements from 2003, (version 5) compared to 2009 (version 14). We did not complete any specific statistical significance calculations, mainly as a result of sample size; however, the results are interesting.
Example
We used Dental Assistant as our test sample. We found that there was a decrease in the number of persons which worked as Dental Assistants with only a high school degree in 2009 versus 2003. To off-set that decline there was an increase in Post High School Certificates, AA, and Masters Degrees. See Data Sets, Dental Assistant Calculation.
This indicates there is a different level of education required in 2009 than 2003 for this occupation. O*NET does not tell us why the variance which could be related to increased competition, new technology, certification requirements or other regulatory (insurance) requirements.
For person that work with other trying to locate new work, this is a good place to determine if the applicant has the necessary educational requirements to compete in the job market. For you convenience, I have completed all the calculations on occupations re-surveyed by O*NET between these two time periods. As a result of limited funding, O*NET has only updated approximately 50 occupations. This limited data, still provide UI and human resource persons general guidelines to the change education requirements in both service and goods producing occupations.
O*NET Work Activities Report
For some increased education is not an option. For UI staff, O*NET provides a variety of useful tools to access an applicant’s compatibility with new and likely different work. One such too is the CUSTOM report. See presentation slides. The customer report allows for an analysis of O*NET Descriptors, and in particular WORK ACTIVITIES. When we check work activities and related occupations, and select GO, at the bottom is a list of occupations, with similar work activities and the occupations outlook. This is just one example of the many ways O*NET can assist in breaking through the skills gap barrier.
Developers
If you are a developer, especially in the area of human resources, take another look at O*NET data. I believe you will find it intuitive, relational, and compatible with most of your current database designs enabling you to assist your HR staff and company being more affective in resource development and allocation.
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.
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.
United States – South Carolina Unemployment Comparison
I had a recent question as to why South Carolina (SC) unemployment rates appear to be lower than the United States (U.S.) rates before 2001 and higher after 2001. With this question we need to back up a few steps. There are three factors which, when taken into account, explain unemployment rate expected variation.
First, the U.S. unemployment rate is calculated using the Current Population Survey or CPS. State and local unemployment data is calculated using Local Area Unemployment Statistics or LAUS. These are different programs with different methodologies, although related. Many times, as reported in the press, these data do not corroborate.
Second, compatibility of reference data sets. Many forget that SC is a state with a population of only about 5 million persons. The U.S. has 300 plus million persons. In other words it is more likely than not that SC is not a representative sample of the U.S. So there is no reason to believe that what happens either in the U.S. or in SC will affect the other by the same proportion at any time, historical or otherwise! This is an issue not only in this case, but with many data comparisons- the old apple and oranges problem. Because they appear to be related, we make a big deal out of it when in fact the two are independent events.
Alternatively, it is more likely the economies of Georgia or North Carolina, our trading partners, will have a greater impact on SC, in both the short and long run.
A third factor, and a complicated one at that, is the change in survey method over time. There have been significant survey updates in 1996, 2000 (Census), and 2003 to mention a few. Often data is labeled with codes indicating a methodological change in the series. The Bureau of Labor Statistics often describes these changes in great detail with formula- yikes! It is safe to say, the changes do impact the data, sometimes with sudden jumps up or down, considered by some to be either favorable or unfavorable. The fact is, it was a change in method and all data still sums to 1. Unfortunately, the next day you will still be either employed, unemployed or looking for work in this data set. Here is a link to the summary of differences.
NOTE: The BLS does make mistakes, most recently with the Current Employment Statistics. However, they are a group with integrity who fix mistakes, correct data, and describe the impact. What I like most about the BLS is their drive continually to improve data through refining their techniques. Businesses and individuals who use these data have benefited significantly.
How to Think About Unemployment Data-
Example: As a former labor market analyst, I emphasize the big picture, the trend. Here is a link to a spreadsheet that compares, for the fun of it, SC, MI, US and SD back to 1976. Note how differently these states labor forces have reacted to this and other booms and recessions. Recently, the MI labor force has crashed, likely driven by the employment outlook, thus creating a high unemployment rate. SC, on the the other hand, has strong labor force growth, maybe too strong (over new employment), creating higher unemployment. This is better than a labor force crash, however! Have you ever been to SD? I am poking fun at a former neighboring state of one where I previously lived. The point is that each state has its own economy, and it is best to understand that economy and focus on what it can and can’t do. In SC, we have been hit hard in the textile industry (another plant announced a closure today), missed the finance crash, but took it on the chin in the auto parts industry (about 2000 jobs since 2007). We are a rural state where persons have a difficult time migrating to new jobs. We are also a state where people go to spend money- tourism. Persons have been tight with that money recently. Most employment and unemployment data makes sense when we take a look at a regions industry make-up (mix), and how those industries manage over time in up and down economies.
February 09 Employment
The Post and Courier had a nice article on the February 09 unemployment rate. The data, as one can imagine, is complex ,and therefore it can be helpful to step back and take a look at the big picture as to the root causes of unemployment and where it might be going.
Currently SC unemployment is 11.5 percent. Unemployment in the Low-Country (Charleston MSA) is 9.1 percent. The unemployment rate is calculated by looking at labor force, employed and unemployed workers. I have captured the graphs from the Bureau of Labor Statistics that display how these different data affect each other.
Note that labor force numbers continue to rise. In other words, there continues to be a net gain of new persons entering the workforce. However, at the same time, there is a decrease in the number of available jobs. This increasing gap is one reason for the spike in the unemployment rate. The other item that one notices is the irregularity of the graphs. The reason is that some industries are seasonal, like hospitality and construction. Therefore, in SC it is not uncommon to see large jumps in employment and unemployment depending on the time of year. I originally thought that the recent one percent jump month to month was unusual. However, after a little research, I found an example of a similar jump in 2007. Only this time it was a drop of .6 percent from March to April.
Unemployment also varies across our state by region. One reason is the variation in industry distribution. The linked table calculates industry employment percentages of SC versus the MSA. Note that 53 percent of SC employment is in industries hard hit by the downturn versus a subset of 47 percent locally. The Charleston region also has a slightly higher percentage of government employment (including education) that adds slightly more stability to the region. This industry distribution is, in fact, born out in the numbers.
The other factor which affects unemployment is type of job and skills required for employment. Many of the jobs that have been brought into SC are low-skill jobs, including warehousing, retail, and some manufacturing jobs. Unfortunately, in a poor economy those are typically the first to see layoffs, regardless of the industry – easy come easy go. As a result of the lower skill level, job characteristics, and industries affected, SC as a whole has taken a harder hit then what would be predicted.
It actually could be significantly worse if we had an uncontrolled construction industry or a financial sector with a larger presence. Fortunately both of those industries were only a part of our overall economy. I believe it is possible for SC’s unemployment to hit 14 percent for reasons mentioned above and then stabilize. It will, of course, take government, educational institutions, and private industry all working closely together to decrease that rate. Time to roll up the sleeves again.
November 2008 Unemployment
Here are some numbers to digest. These are from Dean Bakers BLOG Center for Economic and Policy Research.
Dean goes on to say-
The December employment report showed the economy losing 524,000 jobs in December. It also showed sharp upward revisions to job losses in the prior two months, bringing job loss over the last three months to 1,531,000. This is the highest 3-month total since the months immediately following the end of World War II, although the job losses in the 1958 and 1974-75 recessions were larger relative to the size of the workforce.
With the length of the average workweek getting shorter, the decline in hours worked has been even more rapid than the drop in employment. From September to December, the index of hours worked for production workers fell at a 9.4 percent annual rate. This rate of decline in hours would be equivalent to losing 12.8 million jobs over the course of a year, if the length of the workweek remained constant.
We saw that today, 01/14/09, with the announcement of the Charleston Metro Chamber cutting hours.
Recenty The Post and Courier highlighted “experts” who couldn’t figure out why the unemployment rate in SC is higher than the rate in the United States. The reason is very clear; SC is NOT like the United States in many ways and should not be compared directly without looking at the details. First here is the unemployment-nov-08 from the BLS. I like these tables since they show the relationship between employment, labor force, unemployment and the unemployment rate. Note the STEEP drop-off in employment. Wow! This is a big drop. It could be a lot worse though, depending on what the labor force does. We will have to wait and see. So one can imaginewhat the unemployment rate would look like, if instead of companies cutting back hours like the Chamber, those were actual layoffs. Regardless, as Dean Baker stated these hourly cutbacks are real and will continue to affect the economy.
Back to our comparison. I reviewed a number of data sets but chose two that highlight the differences between SC and the United States. I chose Education (Census) and what is called Location Quotient (LQ) from the BLS. Education compares SC educational attainment rates with the United States. LQ compares industry concentration between the United Sates and SC. See ed_lq-data-nov-ui
The top table is education. Note the difference in higher educational attainment. We know that persons with more education on AVERAGE are less affected than persons with a lower education. The lower table is LQ or the concentration of industries. Note how SC has a higher concentration of Manufacturing, Construction (HOMES) and Retail than the United states as a whole. All three of these industries have been hit hard. But also note the lower concentration in Health Care than in the United Sates as a whole. Of course, Health Care is one of the bright spots for new jobs, thus our not rec eivingthe full benefit of that industry. So there are significant differences between SC and the United States. Therefore, I would expect to see the SC unemployment rate rise much faster than the US in the months ahead. The GOOD news! We do not have much of a finance sector!
