Last month I discovered, much to my surprise, that even though I pay my SCE&G utility bill in full and on time, I actually owe SCE&G money – at one point more than $90. We had spent a lot of money when we replaced our HVAC unit, turned down the hot water heater, purchased a more efficient refrigerator, and installed LED light bulbs. Behind the scene and not identified on my bill is an adjustment that increases and decreases the actual amount owed based on a dubious calculation called Weather Normalization Adjustment (WNA).

The most distressing part is that this is done without warning or allowing us to opt out of this practice. I noticed this not as a result of a high electric bill but as a kWh price that jumped from $0.1045 to $0.1433. When I called to inquire about this rate hike, I was told of this invisible calculation. And this has been going on for more than a year. The information provided by SCE&G through the energy analyzer is also not correct since it includes the WNA. In other words, we are not given the actual monthly electric cost because it is not displayed. Perplexed, dismayed, and downright angry, I decided to look into this further.

Power companies use what is called Optical Climate Normals Method as a way to hedge against the variations experienced by the utility to balance their costs, including weather, fuel costs, and swings in the economy. WNA expands this hedging method to the unsuspecting customer’s bill! WNA is an adjustment to a monthly electric bill based on how that month’s temperature varies from an average, likely 15 years.

This process is different than a balanced payment which “zeros” at the end of the year (estimated annual bill divided by 12). The impetus for making these adjustments is the company’s real desire to lessen the burden on customers when there are significant temperature variances from normal that result in burdensome electric bills – especially for those on fixed incomes. Unfortunately, the process does not work. This is not because SCE&G folks are bad people or not trying. They are doing their best, but  the statistics will not produce the results they desire within the pilot program of one year.

Statistics- Timeline Importance

Typically, the devil is the details and this case is no different. I contacted the U.S. Department of Commerce National Climatic Data Center and inquired about the appropriate process for using historical data to predict future weather patterns to balance to zero within one year. It turns out that it is inappropriate to use average historical data with this method to predict future temperature. Unfortunately, it just doesn’t work. The results are the proof. The pilot program was to last one year, but as of last month I still owed $60 … oops … while most others owe much, much more!

Assumptions vs. Pay the Bill!

Assumption: Stationary Time Series. This model apparently assumes the past average will continue into the future. Unfortunately, the impetus for the process change was an outlier (colder than normal winter). This should have been the first clue not to take historical data to fix an anomaly. There is no indication that the opposite will occur any time soon to offset the observed outlier. It is possible that this could have been tested by not taking only the average temperature of the last 15 years, but by sampling 15 years of data from the last 60 years and evaluating the sample means. If nothing else, this would have made  clear to decision makers the significant uncertainty in predicting future weather patterns with historical data. This knowledge may have resulted in a different strategic decision.

Assumption: Trending Data. The alternative to stationary is trending data. That is to say that this formula requires the random variable to sum to zero. However, based on National Oceanic and Atmospheric Administration data, there is a trend, understood or not. Therefore, the formula will not sum to zero. Hence my $60 debit. Further complicating the process is the fact the model needs to be updated regularly, likely a timely and expensive proposition. Taking all the changes into account, it will still be 10 or more years before one can expect a balanced bill.

Assumption: No Modeling Necessary. Complex statistical problems need to be modeled. Modeling assists the analyst in understanding how well the formula describes situation or event. The hope is to avert a knee-jerk reaction based on erroneous assumptions or data. The best way to understand a new method is through a statistical model, which would provide a probability of the formula zeroing out a person’s electric bill after one year. This is highly unlikely, given the data, and the model would provide us with that information. So instead of having a one-year pilot program we might need a 20 year pilot program or, better yet scrap the formula and create another solution, which is what I suggest. The statistical model by itself is outdated when it comes to forecasting weather.

Assumptions: Balancing Costs Independent of Consumer Action. The WNA calculation is done on the kWh used. As an example, an abnormally cold temperature will kick in the WNA calculation, resulting in a consumer debit off the books! The result is consumers believe they have a lower electric bill and thus turn the thermostat up, believing all is well. The unintended consequence is to use more energy, not less.

The WNA circumvents economic “substitution,” whereby consumers make a different choice to maximize their needs. In this case substitution could include decisions to turning the heat down, switching to a different energy source, turning the heat off in a vacant room, or other actions designed to reduce energy use. In our household we thought all was OK. The result was we turned i[ the heat not knowing that the actually energy billed was more than what we were being charged. Energy use management does not work without full disclosure by the provider of the effects the consumer’s actions, something SCE&G preaches on a daily basis over the air waves!

Conclusion

My recommendation to SCE&G is to stop the program and have customers pay their debit bill over the next eight months before this program gets even more out of control than it already is. If the company wants to hedge, they can do it with their time and money – not mine. As for people who cannot pay their bill, during extreme weather events it is important to continue to work with those individuals to create a predictable balanced payment plan zeroing the balance over a predetermined number of months. Statistics can really assist in this process. The rest of us need to be allowed to opt out or join SCE&G’s balanced payment plan.

I chose to opt out, paid the balance of the WNA to SCE&G (likely the only SCE&G customer in the state that has a zero electric bill balance) and informed them I would happily pay the actual kWh usage in full in the future. I was informed that if I did that, they would send out a bill collector to collect my “funny money” WNA payment, which by the way, is supposed to balance after twelve months. I wonder what the statistics say about either of those independent events happening in the future!

Model Example Results

We ran a model below for demonstration purposes. A person might assume that a WNA should be close to zero for most of the time, but this is not true. In fact, it can get quite far from zero. The following plot shows percentiles for the maximum positive deviation from zero in 12 periods. (This is based on 100,000 random sequences of length 12.) Notice that 15 percent of the time, you never have a positive sum (balance). Fifty-five percent of the time the maximum deviation is 2 or less, while 95 percent of the time, the maximum positive deviation is less than 7. Results for a different standard deviation (σ) are the same, but with the maximum sum values multiplied by σ.

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).

The Bowl Championship Series (BCS) ranking process is a failure by any measure. The good news is that it finally appears the powers-that-be are going to work out a playoff system. But what is the root cause of the problematic BCS rankings? Why don’t they work? And what type of numerical system might meet the needs of a college football ranking system?

Statistics: Or Lack of!

A cursory review of BCS statistics quickly identifies the main problem, which is that is the people who created these “methods” do not appear to use any form of statistics. Further limiting the public’s understanding of these data is that the methods used to calculate rankings are not available. In other words, they have not been peer-reviewed in any meaningful way – and subscribe to the “trust me” method!

We know the accuracy is questionable at best or scandalous at worst, since we never read or hear about odds, confidence intervals, error, probability or other common statistical references when referring to these data. We also know intuitively that around each number there is error. If the error is not displayed, we know we cannot trust neither the numbers nor the authors – hence the ruckus around these rankings.

The Champ: Play-off

The great thing about a playoff for college football, like every other major sports league, is that you know the answer at the end. The best team on that day is the final one standing. End of debate. Rodney Harrison was recently asked who he liked in the NFL playoff and his answer was that it is hard to estimate since anything can happen in a playoff game. Well said. The challenge with a college playoff system is not that it wouldn’t work, because it would. Rather, it cuts the number of bowl games in half. Ouch, that is a lot of lost revenue!

The Champ: Numerical Calculation

I will disclose my bias for a playoff system since, as Rodney stated, anything can happen. But I believe there is likely a method that would, in fact, provide a numerical answer that most would agree with. First, the method needs to be made public, and it should be a method that has a history of success. “Odds” are, of course, one system, but in reviewing the odds estimates for the BCS championship game, there were many conflicting estimates with some odds makers suggesting a difference of only a point or two. In other words, it was too close to call.

Odds is an interesting process (better than the “look what I made up” numerical process), but probability estimates are the only real tool we have that could pick a winner. Odds and probability sound similar but in fact are quite different. The difference:

• Probability is used to express sensitivity, specificity and predictive value. It is the proportion of people in whom a particular characteristic, such as a positive test, is present.
• Odds is the ratio of two complementary probabilities. (PDF)

Along the probability line is a process called Evidence Based Management (EBM) which uses Bayesian analysis.

Bayes Theorem: a statistical principle for combining prior knowledge of the classes with new evidence gathered from data. See Introduction to Data Mining Chapter 5 pp: 228-229) (PDF)

EBM with Bayesian analysis states: What was thought before the test was done, combined with the test result is greater than what is thought after the test result. In other words, what you thought you knew before the football contest, the game, and what you think afterward – LSU is still No. 1 syndrome! It is this process that could provide an answer to who is No. 1 regardless of the date, time or opponent,* effectively removing the Rodney affect, but not likely the debate!

Conclusion

I am not sure that the BCS question is all that important or worth a lot of time in the context of solving the world’s problems, but if we are going to do the math, let’s at least try to make the process transparent, thoughtful and based on some sort of peer-reviewed science. Frankly, that is the only way my team will EVER have a chance at a BCS championship!

*Note: I do not address “style” points: a non-sportsmanship concept.

The Gini Coefficient, developed by the Italian statistician Corrado Gini, is the most commonly used measure of inequality. The coefficient varies between 0, which reflects complete equality and 1, which indicates complete inequality (one person has all the income or consumption, all others have none). (The World Bank) We wanted to use this method to look at income distribution throughout South Carolina, but first we had to understand the formula.

At first glance, there is a fair amount of math needed to calculate the coefficient. Make no mistake, this is and can be a very complex formula, utilizing probability sampling, bootstrapping, confidence intervals and other statistical methodology. We however, tried to keep it applied, and therefore used the most basic variation:

After sorting out the symbolism, we created a sample problem (PDF).  The sample problem allowed us to work through the math in a structured process. The value of  ”doing the math” is that one gains an understanding as to how different variables affect the formula. The PDF contains two versions of the sample problem, one showing the formula and the other with plugged numbers. Note how unlike most of the available examples, we show a calculation needed prior to using the formula.  In this case (dollars strata) TIMES (number of persons). That’s because the analyst may need to do a number of calculations prior to applying the formula.

The Formula: Results

We applied the formula to the classic income distribution (wealth share) problem, using Census, Household and Family Income Report B19001, for each county in South Carolina. These data have 16 income strata. We found the formula is particularly sensitive to changes in the top two strata, not necessarily the number of persons, but average dollar value. In other words, ”the tail wags the dog” in this formula. The other critical piece of information needed is what value to assign the highest strata. The census uses approximately $400,000 as an approximation for the average top strata dollar figure.  They calculate this number using volumes of data, so it’s good enough for me.

After making our calculations, the formula really did reveal a number of interesting trends. One, the impact of the economy on higher wage earners – in the case of these data – is very delayed. In other words, higher income households continued to make money well into the latest recession. The other revealing attribute is the affect of a rising tide. A rising tide does in fact lift boats, but some higher than others and in the process it also sinks a few!  In this case,  households with higher incomes grew at a proportionally higher rate than those with lower incomes, and in some counties, household income (high and low) was hit particularly hard.

Conclusions

Now that you understand the formula, if you use these data, the Census Bureau has already done the Gini Coefficient income calculations for you! Yes, to my surprise the the Bureau has been doing this calculation since the 1990s.  The file is B19083. It may sound like I have given you a shortcut but now you have to figure out the new GUI American Community Survey interface. Good Luck!

Acknowledgement: Thank you to the staff at the US Census Bureau for assisting me in understanding key drivers of the Gini Coefficient.

It models passenger and freight travel across all modes, and it assesses costs, benefits, and impacts across a range of economic responses and societal perspectives. To  integrate this range of features, TREDIS operates as four separate but interconnected modules:

• Travel Cost
• Market Access
• Economic Adjustment, and
• Benefit Cost

For more information see:

• TREDIS User Manual
• TREDIS Variable List

For a more detailed explanation of Input-Output Analysis see: Input-Output Analysis: Foundations and Extensions, by Miller and Blair. (Amazon)

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)

The truth concerning some economic impact models is seldom revealed in the media, much less a publication such as The Washington Post. The Post recently printed an article (PDF) on organizations overstating their employment economic impacts.  In the case cited, the numbers are made up to persuade gullible lawmakers to reduce regulation to create more jobs.

Employment Impacts

In all fairness to the economists who manufacture numbers, if lawmakers do not take the time to understand the data they use to make policy decisions, who is at fault?  In the article some analysts published their model methods, and I applaud that.  You may not agree with what they have done, but at least you can evaluate the model accuracy.

Employment impacts include:

• Direct – The actual number the company will supposedly hire.
• Indirect – The number of employees other businesses will hire as a result of direct hiring.
• Induced – The number of employees hired as a result of a rising economic tide in the study area.

Often the numbers are summed and presented as direct impact. In the article, one economist argues that induced jobs are accurate, and they are if you count food service and lawn care workers. But let’s not confuse those jobs with high-paying drilling jobs by providing a total without accompanying employment detail.

Assumption Pitfalls

It is the assumptions, then, that are at the heart of the debate. Two common mythological tweaks that significantly change employment impacts are geography selected (not discussed), and initial direct impact calculations. The article provides an example of a flawed direct assumption:

“The Wood Mackenzie study also makes assumptions about current policy. For example, it assumes that current regulations limit the number of Gulf of Mexico exploratory wells to 20 a year. But the number of exploration wells being drilled now is already well above that. As a result, gulf exploration would have little effect on job creation.”

This results in an estimate that is the equivalent to taking a 10 year old employment number, taking the difference from that time period to present and stating it as the new impact!

Conclusion

Currently firms that sell impact software have been unwilling to set guidelines for appropriate use of their software. A simple one is to require publishing model assumptions. It is true that some information is confidential to organizations. But on the other hand, if one is asking for public support to do something that benefits your organization, is it not reasonable to provided the data behind your model? Kudos to The Washington Post for publishing this piece. It is clear we need more transparency in the impact business.

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.

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