Labor Market Information: An Overview
Issue
My friend and business associate Gary Crossley provides labor market information (LMI) to a variety of organizations nationwide. Recently he sent me one of his overview presentations to post on Moore Data.
Most analysts believe they have a grip on labor market data, but what Gary and I find is that this is not so. The reality is analysts tend not to stray very far from unemployment statistics, rarely giving any weight to other key data sets that fill in the labor market knowledge gap. Below is Gary’s presentation. I have provided the appropriate links to corresponding Bureau of Labor Statistics (BLS) web sites.
Presentation Analysis (PDF)
Slides 9-11: Employment: Provides a general definition for employed and unemployed along with basic calculations.
Slides 14,15: Quarterly Census of Employment and Wages (QCEW) discusses data sources and uses of data.
Slides 17,18: Current Employment and Statistics (CES) discusses data sources and uses of data.
Slides 20 – 23: Occupational Employment Statistics (OES) discusses data sources, uses and programs.
Slides 25, 26: Local Area Unemployment Statistics (LAUS) discusses data sources and uses of data. For detailed calculation of unemployment see Unemployment Calculation Post.
Slide 28: Mass Layoff Statistics (MLS) discusses general program.
Slides 29, 30: These slides provide a link to the Bureau of Labor Statistics Handbook of Methods. This is a particularly good resource for analysts. The handbook not only describes method, but also what programs use these data as an engine to drive their information programs. The handbook also provides a detailed account of data limitations, which is a plus when determining appropriateness of data for various uses. Slide 30 provides a nice “cheat sheet” to the different data sets.
Slides 33 – 45: These slides list the different entities that provide data portals. Of course you can analyze these data yourself, but the challenge is understanding the nuances of the data so that one does not come to the wrong conclusion.
Slides 45 – 47: These slides touch on supply and demand, training, and military data. However, one of the more interesting and eye-opening data sources is the Census data set of military service-related disabilities. These data can be found at Veterans.
Conclusion
LMI data is readily available on the web. Most competent analysts will use two or three data sets attempting to triangulate to find the “answer.” Gary has provided a basic reference that will assist the user in thinking about the different data sources and how they may help you answer your labor market question. Thanks Gary.
State and Local Government Employment
Issue
The Richmond Federal Reserve recently published an informative article on the recession and government tax shortfalls. The analysis included the affect on government employment. “State and local governments employed nearly 20 million workers in the U.S. That is about 15 percent of total payroll employment in the nation, more than the manufacturing and construction industries combined. As a result of the fiscal duress, state and local governments have been cutting jobs and more are likely to follow.” (PDF)
Economic Impact: Cutting Government Workers
I am not sure anyone would argue that an efficient and productive government is not a good thing for almost everyone. However, arbitrary employment cuts have a significant negative affect on an economy.
As an example, a Targeting Economic Development study using Analytic Hierarchy Process (AHP) showed the impact of government workers. Cox et al. (2000). The study showed that within a three-county region in Virginia, that the State and Local Government, non-education, sector created 32 jobs per million dollars of output. It was the No. 1 industry for this region out of the top 20 studied. The industry also had the 15 lowest average wages of the top 20 but the highest value-added effect (total Virginia/dollars of output) of 1.30. The next-closest industry, oil and gas, was 1.17.
Value-added includes employee compensation, proprietor income (i.e. self employment), other property-type income (i.e. rents and profits), and indirect business tax (i.e. sales tax paid to business). So if government cuts employment, indirect and induced dollars flowing to private sector industries are significantly reduced.
Conclusion
Having an efficient and productive workforce is important for both the government and private sectors. Random cutting, however, will lead to direct negative economic impacts in the private sector at a time when we are all looking for a sign of an improved economy.
Michael Porter vs States Governor’s
This is a great article from Martin Schram on states budget deficits and productivity. Dr. Michael Porter told the governors productivity, “sets standard for whether your particular state is going to succeed.” ”If you are productive you can be prosperous. If not, you can’t.” To be productive a state needs to invest in education and infrastructure. If not they will be regulated to the back of the pack both nationally and globally!
Occupational Wage Data: Let’s Get it Right
Issue
Recently there has been a rash of articles in which someone is trying to make arguments for reducing salaries of some other class of workers’ salaries – but not their own! The Bureau of Labor Statistic (BLS) is the natural choice for those who wish to quote wage and salary information. Unfortunately, without exception, BLS salary databases have been mixed-up, misused or are simply the wrong data set to make the point.
NCS vs OES
The two most common databases for wage and salary information are the National Compensation Survey (NCS) and Occupational Employment Statistics (OES) programs. Both of these surveys (Technical Note on Survey Error) provide information on wages and salaries by occupation, but each has different strengths. Primarily, the OES is the larger survey and can provide a greater range of occupations and areas, while the NCS is conducted by personal visit and can provide greater depth by obtaining occupational work level.
The BLS states:
“the NCS occupational work level is based on the duties and responsibilities of the job. An architect, for example, who directs a major project would typically be more highly compensated than an architect preparing a small part of a project under direct supervision. To determine these ‘levels of work,’ each occupation is evaluated using four factors. This system also allows for pay comparisons to be made across occupations (for example, comparing architects to accountants with similar levels of responsibility).”
Two other primary differences stated by the BLS include:
“1) the OES provides information for more different occupations. The NCS, on the other hand, provides information on the wages for the occupations it covers at specific levels of work, rather than just an average for all workers in the occupation.
2) the OES provides information for the nation, for states, and for all metropolitan areas. The NCS provides information for the nation, for selected metropolitan and nonmetropolitan areas and for the 9 Census divisions.”
National vs. Local Data
As an analyst, I prefer to start with national data, which frames the question and provides a reasonable and defensible position when it comes to wages. Local wage data then provides specific detail within that framework. It is important to understand the difference in wages levels, median and mean wages, so as not to confuse the reader or end up comparing apples to oranges.
Conclusion
Unfortunately most of the “cut to greatness” articles are missing the real problem, which is productivity. See Productivity. It is easy to pontificate about wages, but for sustained growth, lower costs and increased value delivered, productivity, the education and tools that help people perform, needs to increase.
Economic Development: Analytic Hierarchy Process
Issue
Economic development continues to be a focus for states – especially here in South Carolina as the state strives to attract businesses. The most recent challenge for economic development agencies is the diverse visions of what economic development should provide for the community. These visions seem to have become more divisive as tensions increase due to a lack of success. Where once new jobs were the only factor to consider. These days, new jobs compete with environmental concerns, quality of life, wage rates, tax incentives and a host of business, community and regulatory concerns.
AHP
The Analytic Hierarchy Process (AHP) economic development targeting tool is one solution to meeting the needs of diverse perspectives. AHP is able to compare or rate varied perspectives, assisting policy makers in narrowing economic development strategies. The process was developed by Saaty. (Saaty, T. L., and Alexander, J.M. (1989) Conflict Resolution: The Analytical Hierarchy Approach. New York: Praeger.) Saaty’s approach is outlined in Targeting Regional Economic Development. Goetz et al (2009).
Process
The process is a method that weights or prioritizes outcomes when several considerations are relevant. For example, when attempting to reconcile survey results taken from different constituents, the process uses pairwise comparisons of several outcomes. The goal of AHP, when comparing different criteria, is to determine relative importance of each criterion in achieving the goal. The math includes solving a “weighting” problem using an eigenvector P (Matrix) corresponding to an eigenvalue equal to K, or the matrix rank (Saaty 1980). Typically the Lanczon Algorithm is used to calculate the matrix. In this case, the hierarchy of importance (range from equal importance to extreme importance) is limited to 9 levels to reduce error.
Results
Traditional industry targeting methods answer a variety of questions independently of one other. However, without some type of preference elicitation process, those results simply do NOT answer the question of which firm or industry is most attractive to the region. One of the earliest applications of AHP to solve this problem, was Cox et al. (2000). The example below demonstrates how Cox used the process to rank regional expectations or needs that were most important to the community. These criteria were then compared to industry characteristics or used to develop a strategy of industry selection, rejection or negotiation parameters.
