Estimated Energy Use
Linked is graphic showing the different sources and uses of energy in the United States. I first ran across this graphic in Input – Output Analysis (Miller and Blair) in their discussion of energy and economic impacts. I encourage you to “do the the math” addition or subtraction in this case. These data are often referenced as sources and uses of energy. As an example, residential structures consume over 35 percent of the usable energy. This graphic details how that can be possible.
These data also bring into question the green energy movement including; jobs, energy savings, and industry impact. It is clear to this analyst that conservation would have a greater impact on our energy use short term, than new forms of green energy like wind or solar. This is not to say these new forms of energy generation are not important, only that there is the reality of our current energy system efficiency (see graphic) as a result of the system the United States currently deploys.
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)
Economic-Ecologic Commodity Flow Impact
Detailed Example of Economic-Ecologic Commodity Flows
The ‘TEAM’ model has done the heavy lifting calculating coefficients for a ecologic commodity flows. The classification was first created by Johnson and Bennett (1981). They used the ecologic terms commodities for inputs and sinks for discharges or outputs. Miller and Blair (2009)
The ecologic matrix is defined using matrix algebra. In example (econ_ecol) (pdf), matrices A, R and Q are created from the table of commodity flows. Matrices R* and Q* are calculated using the following formula:
R* =R(I – A)ˉ¹ Reflect the amount of ecologic inputs required directly and indirectly to deliver a dollar’s worth of industry output. In the example, 0.455 represents 0.455 units of water required to deliver a dollar’s worth of agricultural products!
Q* =Q(I – A)ˉ¹ Reflect the amount of ecologic output associated directly and indirectly with a dollar of industry output. In the example, 0.358 means that associated with one dollar’s worth of manufacturing goods to final demand is production of 0.358 units of hydrocarbon pollutants!
Industry may shy from these formula. The opportunity for industry however is to review manufacturing processes using these data to uncover opportunities to reduce waste or identify material/process substitutions (cost savings). For those interested in economic-ecologic impact, this opens, for the first time through the TEAM model, the capability for applied analysis in both industry and transportation development projects.
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.
