Mitt Romney: Corporation a Person?
Issue
Is a corporation a person?
Analysis
Mitt actually got this one right. According to Ross, Westerfield and Jaffe, a corporation is defined as a:
“Form of business organization that is created as a distinct “legal person”composed of one or more actual individuals or legal entities. Primary advantages of a corporation include limited liability, ease of ownership transfer, and perpetual succession.”
This is a classic example of where our legal and financial systems define the rights of an organization similar to a “person”, but where in no way does the entity look, feel, or act like anyone we know, hence the confusion.
Trade Environmental Assessment Model (TEAM)
Environmental Economic Impacts
TEAM is a suite of software tools developed by Abt Associates for the Environmental Protection Agency (EPA) National Center for Environmental Economics to assess the environmental impacts of international trade agreements.
The ECONOMIC resolution is 4 digit NAICS sector level.
The ENVIRONMENTAL resolution TEAM currently analyzes are four separate environmental release and resource use categories: air, water, and carbon dioxide pollutants along with energy consumption by fuel type.
The GEOGRAPHIC resolution is the state level.
This tool is an exciting, and likely the first, applied environmental economic impact calculation tool. Imagine for a moment a community considering the development of an industrial park with a certain mix of industries. Wouldn’t it be useful to estimate the environmental economic impact this development could have on a regional community. TEAM can assist in this evaluation.
The EPA has done an excellent job in development, analysis, and peer review. The model is based on standard economic methodology, grounded with sources such as Duchin and Steenge, Miller and Blair, Leontief, and the National Research Council among others.
Further, as a result of the model design, outputs dovetail with; transportation, economic, land use, and environmental impact models as part of the final economic/environmental impact deliverables.
Utility Industry Economic Environmental Impact Example
This is an example of output created by the TEAM model. The example uses the sector 2211- Electric Power Generation, Transmission and Distribution to demonstrate a portion of the model functionality. This example is only scratching the surface and for introductory purposes only. In my view the possibilities for TEAM are limitless. (pdf)
I have added an example from Miller and Blair (2009), which demonstrates the matrix algebra used to create these coefficients. The process is intuitive and flexible, demonstrating the value of applied industry – environmental analysis. (pdf)
Rural Technology and Transportation
Issue
Recently, I traveled to a remote region of western North Carolina. The only communication technology provided by the local motel was rabbit-ear TV! Cell, telephone (old school), Wi-Fi, cable and Internet (smart phone, computer, iPad) service were not available. But communication was not a problem since there were plenty of guns and ammunition around.
But seriously, I wonder if there is still a technology – communication or otherwise – gap as first reported by the United States Department of Agriculture (USDA) in 1997. That report, titled “Is There A Rural-Urban Technology Gap“, analyzes the technology gap between rural and urban areas.
Analysis
Without the benefit of a follow-up report, I speculate there remains a technology variance between rural and urban areas. However, the main culprit may not be the lack of desire to invest in technology itself but, instead, transportation costs associated with both the cost of manufacturing (cost of goods sold – import parts) and the cost to ship the finished product from the manufacturing facility. It is these transportation costs that determine the type of product that can be competitively produced in rural areas? Important to this conversation is that capital investment in the production process does not reduce these costs. Ultimately only plant location and/or plant capability (rail, water, truck) can lower them.
Early in my manufacturing career, we built a plant that was designed to ship 80 percent by truck and 20 percent by rail. After being in operation for a few years, the plant was shipping 80 percent by rail and 20 percent by truck. A significant plant upgrade along with a rail extension was necessary to make this possible, but it made an immediate profitability impact through the reduction of transportation costs of both raw material and finished goods. Ultimately this cost reduction lowered the cost per unit and made us more competitive in the market place. The cost advantage remains in place to this day.
Conclusion
Today advanced technologies can be found in most communities. What is not always available is a cost-competitive transportation model used to import manufacturing components and ship finished goods. Small rural communities may give a boost to their economic development efforts by eliminating this barrier for current and future businesses.
Note: As we left western North Carolina, on the side of a remote road was parked a classic Airstreamer with a sign that stated it had full Wi-Fi access – but there was no tow vehicle hooked up to pull it down the road!
Grocery Stores: The Profitability Index
Issue
The Post and Courier recently posted an article titled Super Market Central that raises more questions than it answers. The article compares the number of grocery stores in affluent Mount Pleasant, S.C., compared with North Charleston, S.C. One Mount Pleasant store owner was quoted as saying “We want to be in markets where there are households with families.” Actually, North Charleston and Mount Pleasant both have an average of 2.5 persons per household! (Source: City-data.com) After review a number of data-intensive sites such as city data and the U.S. Census, I was able to confirm many points in the article and, like the author, identify differences in these communities. Many, such as race and income, are obvious. However, this still does not explain the disparity between the numbers of grocery stores in one community versus another.
Empirical Research
It turns out this is a significant problem, not only here in the low-country, but across the United States. One source “Closing the grocery gap in low-income areas,” identifies key issues. Other research from CA Food Policy Advocates suggests:
“One promising model, among others, that has emerged involves the conversion of existing corner stores, typically depending upon sales of alcohol, tobacco and sodas, into neighborhood groceries selling healthy foods. Because so many of the necessary costs — rent, utilities, space, and management possessing some degree of both business skills and familiarity with neighborhood preferences — already are present, the conversion can be relatively inexpensive and, in fact, provide the store with additional opportunities to be profitable. A viable neighborhood grocery store represents multiple policy gains, including food access, nutrition and fitness, transportation, community development and crime reduction.”
Unfortunately, the quote describes symptoms, but not the cause of the problem. The root cause is how capital is rationed to achieve the highest return. The Post and Courier article states margins for grocery stores (a basic commodity) are around 1.5 percent. This is pretty poor even by commodity standards. In fact, one would have to wonder why anyone would go into this business, especially a small business, as suggested above. There simply are not enough retained earnings to make a living! However, to understand why Mt. Pleasant has more grocery stores than North Charleston, we must look for the answer among the financial tools used to make capital allocation strategic decisions – in other words, to build new grocery stores.
The Profitability Index
Most firms’ capital budgeting process uses some sort of discounted cash flows, the most common being net present value (NPV). Although there are other methods, such as payback or average accounting return, we assume our grocery stores use a variation of NPV. In the capital budgeting process, a project is accepted if NPV is greater than 1 and rejected if it is less than 1. That basically means if the project is accepted it will make money (hopefully). We will assume that both North Charleston and Mount Pleasant grocery projects have positive NPVs. So far so good. Unfortunately, investment capital is limited, especially when risk is factored in. We therefore can choose only one project. To do that, we run both projects back through the profitability index.
Profitability Index (PI) = Present Value (PV) of cash flows subsequent to initial investment/ Initial Investment
Again if PI is greater than 1 we accept the project, if it’s less than 1 we reject it. When using NPV, we make a go, no-go decision. However, when applying PI, projects are ranked according to the ratio of present value to initial investment. The project with the best potential return (greater than 1) is funded. It is Mount Pleasant in this case. The project is funded, as the article states, not because of corn flake sales, but because of special item sales, which less affluent customers avoid. Special items sales create a better return (profit) on capital invested in the Mount Pleasant location.
Conclusion
Both projects are in fact profitable. But one provides a slightly better return. At this point corporate culture also comes into play. For example, “what we did last week, which worked, will likely work in our next venture” … and so on. One can see this pattern in Mount Pleasant – the me too effect. This happens in part because firms generate positive NPVs because of prior investments, leveraging their current market position. An organization does need to make a profit, whether it is the small corner store or a large grocery chain. Without that profit, the store will cease to exist.
In the end a different model is needed (not currently in the domain of the typical grocery store) that incorporates social networking, transportation, specific product offerings, efficient security and product distribution. This comprehensive model leverages capital not only for the current project, but for indirect cash flows of future business ventures yet to be determined in the same locale. Extending the scope of the investment decision breaks the current boom-bust grocery store location cycle. The question is how to get business owners to adopt this perspective.
Choosing a Career: ASAP
Issue
I discuss the results of our economy from both an employment and unemployment perspective on a regular basis on this blog. It is fine to record and analyze market results, but how as individuals can we have a proactive positive impact on our careers; whether looking for new employment, enhancing our current situation, or looking toward future career objectives?
Process
Colleague and career consultant Gary Crossley has written a short article (PDF) about choosing a career. This four step process is about taking a measured approach to selecting the career right for a person. Using Assessment, Skills, Analysis, and Preparation (ASAP) can only lead to a more logical approach to discovering your best path to an interesting career field. (Workforce Links PDF)
Hotel Impact on “Tourism”: Methodology for Estimating Economic Impact
Issue
This impact analysis was created by Julie Flowers, a retired statistician for the South Carolina Parks and Recreation Department, using Travel Industry of America (U.S. Travel Association) data. Julie is a pretty sharp statistician and does a nice job of outlining what is important when thinking about tourism impacts.
Analysis
Ms. Flowers uses IMPLAN to create a final economic impact based on TIA data. To create an economic impact, we need either spending or employment patterns by industry. As Ms. Flowers points out, there is no “tourism” industry. Our closest proxy is the Hospitality and Leisure super-sector. When it comes to hospitality, the big dog is lodging (hotel and motels). This is not apparent at first glance, but without this industry there is no tourism other than tenting! Within the hospitality industry it is clear that other businesses leverage lodging’s strength.
Hotel Impacts
TIA developed a strong method for collecting and analyzing hospitality data that is generally available to the public. What comes to light when exploring economic relationships within the travel industry is that for every dollar spent on lodging, $3.60 is spent on travel-related items – food, retail, recreation, etc. In employment, that number leaps to 5.3 jobs* for every job in the hotel industry. In other words, the hotel industry provides the anchor for other businesses to flourish. TIA data states that every $1 million in travel spending creates 13.5 jobs outside the hotel industry. Therefore, every million dollars spent in hotels likely generates over 70 jobs in hospitality-related industries!
Conclusion
Hospitality analysis requires a fair amount of data to create economic impacts, in this case supplied by TIA. Furthermore, TIA provides a solid methodology for justifying these spending patterns. Ms. Flowers’ process starts with a analysis of spending, then plugs the data into an economic analysis program, IMPLAN. With this transparent method further analysis is possible, uncovering the deeper relationships within this cluster. In this case, the end result provides a more clear picture of how supporting the hotel industry leads to significant gain in both employment and industry output within hospitality related industries.
*Spreadsheet addition error in report.
Manufacturing: Decline or Revitalization?
Issue
The Post and Courier recently printed an article from the Associated Press on the national economy. It is an interesting article in that unlike many articles of this type, there is a limited amount of talk, and actually some interesting data. Unfortunately, most of the data points were taken out of context and in one instance actually mislead the reader. Of particular interest are the manufacturing data.
Manufacturing Expansion – NOPE!
The data which were quoted appear to be from the U.S. Census, but are actually from the Federal Reserve Board.
“U.S. manufacturing output expanded in May at the slowest pace in 20 months”
Actually manufacturing declined* by -0.4 percent. The Federal Reserve goes on to explain these data in more detail:
“In April (2011), manufacturing output fell 0.4 percent after increasing 0.6 percent in March. The rates of change for manufacturing were also revised down for both January and February; lower estimates for the production of cigarettes, petroleum products, pharmaceuticals, microprocessors, and military aircraft contributed to the downward revisions. The index for manufacturing in April was 4.6 percent above its year-earlier level. Capacity utilization for manufacturing moved down 0.4 percentage point to 74.4 percent, a rate 10.0 percentage points above its trough in June 2009 but still 4.6 percentage points below its average from 1972 to 2010.”
Analysis: Wish the Late 80s Were Back
When evaluating manufacturing, two important measurements are production and capacity utilization (CU). Production (Federal Reserve, St. Louis) had been increasing since the end of the recession. Because this trend was broken well before reaching production output established late in the past decade, April’s release was disturbing.
More troublesome however, is the continued long term slide in CU. Fortunately, we rebounded from the recession in this statistic too, but again the numbers seem to be leveling off. Most manufacturers operate best when they run between 80 to 83 percent of full capacity. Any number higher than this typically means that the manufacturer has to bring old, less efficient equipment on line. So although there is an increase in production, efficiencies actually drop. In addition, high CUs tend to dominate the business model, leaving other areas of the business to suffer, such as quality (think Toyota).
Unfortunately, this is not our current problem. The current state of production is low capacities resulting in machines sitting idle, workers being laid off and budgets being reduced – all of which are a real drag on the recovery. So how do we get back on track?
Solutions
Solutions to America’s long term decline were the subject of a paper by Timothy J. Bartik, “Thoughts on American Manufacturing Decline and Revitalization” back in 2003. He outlines six ways to support manufacturers. We have noted these suggestions over the years but maybe now, as a result of hitting a manufacturing ceiling, it is the time to take a hard look at policies such as retraining, capital formation and access to information to improve this industry’s competitiveness.
For the best information on the economic indicators, see The Federal Reserve Bank of Richmond (National Economic Indicators)
*See Major Industry Groups Manufacturing (April)
SC State Unemployment March 2011
Issue
I do not review these data every month because unemployment data is more useful when the analysis focuses on the labor trends. Unfortunately, the Post and Courier is more interested in reporting hype and misinformation than telling us what the data actually says. This is a shame since they are wasting people’s time, including economist Frank Hefner’s, who I am sure pointed out what I am about to say, based on his comments:
“College of Charleston economist Frank Hefner said the unemployment rate does not tell the whole story. The recovery in the past year has been slow, he said, and fewer people are in the workforce, such as those individuals who are discouraged and no longer looking for work.”
The bottom line, which Dr. Hefner eluded to, is there is no reason to be “elated” about in this jobs picture!
Incorrect Analysis: Again
Jezzz. I constantly feel that I need to correct the Post and Courier on this point! Mixing data sets to fit the story misleads the reader. Adjusted and unadjusted unemployment numbers are two completely different data processes – apples and oranges. State adjusted employment for the month of March increased by 3,746. A little different than the 15,700 noted in the article! (PDF)
Unemployment Analysis: Current Employment Statistics Benchmark
This analysis uses data from the Bureau of Labor Statistics. Stated above, South Carolina gained 3,746 jobs in March. The major sticking point, however, is the labor force dropped by 3,199 persons from February to March, and by almost 18,000 from March 2010. Three numbers come together to create the unemployment rate: labor force (LF), employment and unemployment. It is not possible to adjust one with out adjusting one of the others. If we assume a LF scenario that is neither growing or declining – very conservative considering South Carolina’s population is growing – we see the unemployment rate remained flat at 10 percent from February 2011 to March 2011. See PDF.
Regardless of the meager employment growth, some is better than none! However, employment changes by the minute in the state. So what does the final employment picture look like for March? The Current Employment Statistics program (adjusted) provides clues to the result of all those changes from month to month and year to year. These data explain why an accounting professional the Post and Courier interviewed may be challenged in finding employment. The business services industry, accountants included, actually declined in employment from the previous month. Even so, over the past year there has been an improvement of almost 20,000 jobs in this major sector. Unfortunately, 94 percent are not in the accounting field! Where was the growth? It turns out it is right where it has been and should be this time of year, in leisure and hospitality.
Conclusion
It appears that the current recovery, which is already lagging significantly behind other recoveries, is going to be slow at best. With an increase in commodities prices (essentially a excise tax on disposable income – i.e. fuel) and the loss of 10,500 jobs in government employment this past year, “elated” would not describe the way many people feel about the current state of economy.
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.
