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).
Labor Force Forecast
Issue
Three data sets are needed to forecast the unemployment rate: labor force growth, employment and unemployment. One of these, labor force growth, is used to gauge the effects new employment has on unemployment. Forecasting labor force growth, therefore, is an important part of the process. It benchmarks the number of jobs the economy needs to create to maintain or reduce the current unemployment rate.
Process
Recently the Center for Economic Policy Research (CEPR) demonstrated this calculation. It seems straight forward enough. But these guys are good, so let me take the words out of the article and focus on data explained. The goal is to estimate the number of jobs the economy needs to add to keep pace with the labor force growth. The bogey is 90,000. What data sets do we need to arrive at that number?
Congressional Budget Office (CBO) Key Assumptions in CBO’s Projection of Potential Output
Table 2.2 Potential Labor Force Growth 2010-2014 = 0.7 percent/year
BLS Current Employment Statistics (CES) Payroll Employment January of 2008 total non-farm employment 137,996,000 (138)
142 million *0.7 percent= 994,000/12 equals approximately 83,000 jobs a month.
Conclusion
CEPR also uses data exclusively from the BLS – its growth estimate and employment to population ratio (EPOP) – to derive another estimate. One key calculation in the second estimation is the the number of self-employed workers. CEPR put this at 6 percent. The number is estimated from the Household Survey Table A8. Let’s look at September 2011: Self-employed 8.878 million divided by total employed, 128.565 million, equals 6.9 percent – close to the CEPR estimate. Both calculations suggest the labor force is growing something south of 90,000, but no higher. This is an important calculation because it gives a strong indication of how strong, or weak in this case, our economy employment growth is at the present time. Currently our economy is barely producing jobs at a rate of 0.7 percent (.007) a month, pretty sad performance.
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.
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!
2010 October Unemployment
Forecasting Unemployment
The Bureau of Labor Statistics recently posted October unemployment statistics for South Carolina. The state’s unemployment actually dropped, which is a positive sign and was not expected. It tends to be difficult to forecast any trend from one set of data, especially when we hope for a better economy but have no way to know whether that hope is grounded.
History of September to October
I thought I would do a back of the envelope review of the employment change trend from September to October. As to be expected, it is all over the place, but there are some interesting relationships that become apparent and are typically glossed over in monthly reports. (PDF) What is revealed is the link between labor force, employment and unemployment.
From 2005 to 2007, employment and the labor force moved together. The result of the way these two variables interact is the third category, which is unemployment. In 2008 the bottom fell out of employment, with unemployment shooting to the moon and the labor force making a steady march south. In 2010, it seems that employment has overshot the capability of the labor market, so it is reasonable to expect that employment will moderate going forward.
Final Thoughts
What I do not like is the potential for the labor force to make a strong comeback and for employment to flatten. The result would be more unemployment. The best case is continued strong employment, which decreases unemployment while allowing the labor force to expand at a moderate pace.
Update 12.03.2010
Jobs Byte – Some times I get it right!
Productivity, Wages and Demand
Productivity and Wages: Successful Business Partners (PDF)
“Productively measures how efficiently economic inputs are converted into output, which are the goods and services that business sells. So when more is produced with the same or less we can increase income (that is value added) and potentially increase profit.”
Productively has one underling assumption, demand. If there is no demand then productively is not a factor. If demand declines, similar to our current situation, productively is everything. We see companies trying to deal with lack of demand by laying off large numbers of employees. Those left, do work harder and longer hours, but likely are producing significantly less as a result of decreased demand. This is one of the reasons wages are flat, there is simply no way to increase prices even-though everyone is working harder.
Productivity and Wages: Successful Business Partners
This paper provides a sample calculation which demonstrates how to think about wages and productively when plugged into your demand formula.
Sources:
Bureau of Labor Statistics – Productivity
CEPR – Price Byte
Unemployment and Productivity
Productivity Enhances Job Growth
The Post and Courier recently published an article on unemployment and productivity. The article suggests that productivity is a cause of unemployment. In fact, it is the other way around! Being more productive (efficient), means gaining market share, which creates an opportunity for more employment. The alternative, low productivity and inflated cost structure, decreases employment.
Demand: The Lost link
The link between productivity and employment rests with demand. Decline in demand shrinks employment. The best example is the housing market. A productive builder may be able to capture a greater market share, but that share is in a declining market. The overall effect is that his employees work longer hours with the same technology (they do more with less). But the industry sheds workers due to limited demand, not because of increased productivity. Wages during this time stay flat or decline due to thin margins and little growth potential.
Show Me The Data
Two data tools that assist us in this discussion are Current Employment Statistics (CES) and Labor Productivity databases from the Bureau of Labor Statistics (BLS). CES tracks hours worked, while the labor productivity database tracks hours as well as output. A quick glance at these data indicate that – sure enough – hours have increased. Output per person is up while earnings have declined and employment has shrunk. The astute observer will, however, note that hours have been dropping since 2007 and likely rebounded recently only in response to the need to rebuild critical inventory levels. I would expect hours to continue their slide in the short term.
Easy to Cut, Hard to Invent
Looking forward, cutting cost is easy: Don’t sign the check, reduce the crew, punish vendors for late shipments – the list is endless. But what is needed now in part, is new products that create demand. In the long run, this is the only way employment will increase.
All of which means businesses have plenty of hard work cut out for them.
South Carolina Lazy? I don’t think so!
Lazy: When Noise Interferes with the Signal
Recently the Post and Courier ran an article highlighting a Business Week analysis that said South Carolina was the eighth laziest state in the union! Typically, subjective words used to describe data pop a red flag that warns me of impending data misuse doom.
The Data Set
The American Time Use Survey (ATUS), measures the time people spend doing various activities such as work, childcare, housework, watching television, volunteering and socializing. Hence this is an activity survey, not a lazy survey. The data are collected by the Census Bureau and sponsored by the Bureau of Labor Statistics (BLS). I ran a query to understand the nature of the survey, data availability and error rates. I called in the big guns from Global Pragmatica LLC to assist in converting the data from a ASCIDAT file to my JMP statistical software package format. These folks are experts in scripting and were a huge help. Thank you!
These data are collected regionally but analyzed nationally. There is about a 90-percent chance, or level of confidence, that an estimate based on a sample will differ by no more than 1.6 standard errors from the “true” population value because of sampling error. No estimates are made for state level data, and one University of Minnesota analyst stated she was not aware of state level error estimates.
It is inappropriate to analyze these data at the state level without calculating the error inherent in the data. If you did that, the analysis would be interesting but useless when comparing one state to another. Why?
Sports Activity Variable Analysis
For a test sample, I choose state level geography,with sports as a variable activity. This category captures the respondent’s participation in sports, exercise and recreational activities. To extract the data from the system, I used a tool created by the University of Minnesota called the American Time Use Survey -X. The data needs to be processed by a statistical package, in this case my JMP program. An analysis of people participating in sports activities indicates that South Carolina would rank 22nd out of 50 states in terms of average minutes spent participating in sports in a 24 hour period – not bad. However, upon further inspection of South Carolina’s 2009 detailed weighted data, the state could rank anywhere from 12th to 23rd,based on national error rates! (PDF) Unfortunately, since these are state data, the results are meaningless. That’s because the sample is simply too small, which is one of many buried statistical problems. This 2009 sample included a total of 200 people, where 166 recorded zero sports activity minutes. (PDF) In fact, the median is zero, which is another red flag for this data set. A review of other states’ data revealed the same issue. This is a fascinating national data set. But unfortunately, analysis of non-national geographies yields unreliable results.
June 2010 South Carolina Unemployment
The Post and Courier wrote and excellent article on the June employment situation here in SC. By focusing on the labor force, the P and C is highlighting a major issue not only for our state but for the country. From the labor analyst perspective, one of the disturbing trends is the “discouraged worker”. The Bureau of Labor Statisics defines Discouraged workers (Current Population Survey) as:
“Persons not in the labor force who want and are available for a job and who have looked for work sometime in the past 12 months (or since the end of their last job if they held one within the past 12 months), but who are not currently looking because they believe there are no jobs available or there are none for which they would qualify.” The data suggests a worsening situation on the labor front.
Here is a table (PDF) with the BLS discouraged worker annual data. I would like to think these data are about to turn, especially with the number of temporary workers being hired. However, one issue which may prevent that, is the skills gap.
