Consumer Price Index (CPI)
Issue
The Consumer Price Index (CPI) is one measure of consumer prices. The Bureau of Labor Statistics (BLS) CPI program produces monthly data on changes in the prices paid by urban consumers for a representative basket of goods and services. The BLS data sets allows us to review price increases (or declines) on more than 200 categorical items. (Link)
While my home shopping econometric expert and I were comparing prices from a recent trip to the grocery store, we realized there had been a significant price increase in our Cream of Wheat®. In fact, the cereal had increased by more than 6 percent. However, the CPI November report from the BLS stated:
The Consumer Price Index for All Urban Consumers (CPI-U) was unchanged in November (2011) on a seasonally adjusted basis, the U.S. Bureau of Labor Statistics reported today. Over the last 12 months,the all items index increased 3.4 percent before seasonal adjustment.
So how do the items we purchase prices relate to the CPI?
CPI: Index Weighting
It turns out there is a relationship between our breakfast cereal and the CPI. Although our individual cereal price is in the “basket of goods”, it is a small contributor to the overall index. The index is heavily weighted toward food, but even more so for housing and transportation. 
This is one criticism of the index – that it does not clearly reflect the items that we purchase every day. Who of us purchases a home once a week?! In other words, our cereal is mixed in with a variety of goods and services, some of which we seldom use.
CPI: Applied
The CPI however, is actually a pretty handy consumer tool, especially if one is going to make a major purchase beyond daily consumables. As an example, in 2006 a price drop in televisions at Best Buy® caught my eye. I decided to save the January/February sale catalog and watch prices over the years. After our recent morning cereal discussion, I recovered the January 2006 catalog and starting comparing prices. What I found is that a 32-inch television in the 2006 catalog had decreased in price by 86.3 percent in 2012. When I looked at the “basket” of television items in the CPI, the index noted a 61.5 percent decrease from 2006 to 2010, and 82.7 percent decrease from 2001 to 2010. (PDF) Another way to look at this is that your 32-inch television, if you purchased one in 2006, is worth 61.5 percent less, not including depreciation!
Conclusion
The CPI is a good tool for not only reviewing price trends, but forecasting what we might expect in price increases or decreases from a large assortment of items we purchase. This broad base of items range from daily purchases on consumables to housing purchases, which may only happen once or twice in a lifetime.
Maybe I will wait another year before I get that 52 inch big screen.
For a monthly analysis of these data see the Center for Economic and Policy Research (cepr).
Coastal Tourism: The Economics of Sand
Issue
I am researching coastal economies. One of the first questions I am asking is this: What are the key factors affecting coastal economic regions? I theorized that transportation would be one of the key drivers.
However, my research so far indicates that the quality of the coastal resource is much larger determinate of coastal economic success. (PDF)
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.
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.
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)
State Gross Domestic Product (GDP)
In This Together – Not Really!
Recently the Post and Courier published an article on South Carolina 2009 GDP. (See GDP Discussion.) Wells Fargo’s Mark Vitner provided the color commentary:
“This recession was very much centered on housing, manufacturing and financial services, and those three industries are much more important to the South than the nation as a whole.”
The Devil is in the Details
Unfortunately, this article is about South Carolina and not “the South.” What particularly grabbed my attention was the reference to financial services. I did not believe that South Carolina financial services were much more important within the state than to financial services in the United States as a whole. In fact a little research, apparently not provided to Mr. Vitner, indicates GDP is significantly LESS as a percentage of the South Carolina total than of the United States – 6.6 percent versus 9.7 percent. Another surprise is that manufacturing is a significantly LARGER portion of the state’s GDP than the national figures – 18.4 percent versus 12.7 percent (PDF).
Summary
The bottom line is this: We are not like the national economy. It is a poor comparison because South Carolina is too small. More appropriately, the reporter compares South Carolina with Georgia and North Carolina but misses Florida, the big dog in the region. At least at the macro level, South Carolina’s numbers are better than our neighbors’.
And that’s something to build on.
GDP Explained (Third Quarter 2010)
The Quarterly Data Mind Melt
Gross Domestic Product (GDP) is a huge data set managed by the Bureau of Economic Analysis (BEA). On a quarterly basis, I receive a number of emails announcing the latest data from the BEA. Most economists, including Dean Baker, give concise analyses of these data. But even with one page summaries, I wonder where these data come from and what exactly they are talking about, since the analysis is usually out of context. Furthermore, the data are national in scope and tell very little about what is going on in my state or the relationship between the national data and the state or regional economy.
Third Quarter 2010 Perspective
The third quarter briefing is an excellent example of how these data are developed over a period of time. In fact, the “advance” third quarter numbers are actually estimates, not final numbers. (Most skim over this fact.)
“Real gross domestic product – the output of goods and services produced by labor and property located in the United States – increased at an annual rate of 2.0 percent in the third quarter of 2010, (that is, from the second quarter to the third quarter), according to the ‘advance’ estimate released by the Bureau of Economic Analysis. In the second quarter, real GDP increased 1.7 percent.”
A technical note describes assumptions, data and how “advance” estimates are calculated. The method is described in detail, which is one of the truly great features of these data. This release goes on the state:
“The change in real private inventories added 1.44 percentage points to the third-quarter change in real GDP after adding 0.82 percentage point to the second-quarter change. Private businesses increased inventories $115.5 billion in the third quarter, following increases of $68.8 billion in the second quarter and $44.1 billion in the first.”
These statements allow the reader to delve deeper into the data set. But where did these data come from? The BEA has a number of interactive tables so you can explore the data in more detail. The $115.5 billion is found in Table 5.6.6B., “Change in Real Private Inventories by Industry, Chained Dollars” (PDF). This happens to be an important number because most economist, including me, believe inventory building is not sustainable. Therefore subtracting inventories 1.44 percent from the total growth, final GDP is a measly .6 percent, close to zero. Likely not what most are looking for.
You may have noted recent news stories of the private sector trying to move-up or expand Black Friday? That’s because retailers hope to decrease the temporary inventory bubble they have created.
National, State and Local Comparisons
If you are like me, national data is fine, but I like to know how they sync with the regional economy. State and local data lag behind national data by about two years (PDF). That is quite a long time. However, there are a number of ways an analyst can create an index comparing national and state level data, with reasonable assumptions, to produce a current trend for the regional economy. That would be particularly helpful here in South Carolina when discussing automobile inventories and the effect an increase in inventory has on both short- and long-term investment and employment.
Economic Impact – Forecasting Definition
As a result of the BP Gulf Oil spill, persons are using the term “Economic Impact” or “Impact Analysis” more frequently. The definition of impact analysis is: “When the exogenous changes occur because of the actions of only one “impacting agent” (or a small number of such agents) and when the changes are expected to occur in the short run (e.g., next year), this is usually called impact analysis”. (Miller and Blair, 2009)
The definition for forecasting is: “If we project the levels of final demand for outputs of all sectors in an economy fives years hence, and estimate, using the Leontief inverse, the outputs from all sectors that will be needed to satisfy this demand, this is an exercise in forecasting”. (Miller and Blair, 2009)
The issues in the Gulf are going rely on a combination of these to analysis techniques.
