Democrats Make the Best Republicans, Economically Speaking

For many national election cycles, I have listened to the Republicans express their platform positions of: Smaller Government, Lower Taxes, Smaller Federal Debt, and Higher Economic Growth.   As I am now living off of my savings, these topics are ever more important to me; especially the last one!  So, as I listen to all of the current Presidential Primary debates, rhetoric and talking heads, it is difficult to distinguish how any of the candidates will help my current standard of living be maintained.  So back to the data and facts I go, to help me understand the actual execution against the 4 Republican Platform positions mentioned above.  As it turns out, my previous posts were a great place to start.

In this post, the Presidents since JFK have been evaluated on many Federal Budget Spending categories as was done in the earlier post.   They have been evaluated individually and collectively as Republicans and Democrats.  So, in addition to updating the data with one more year of data from the 2017 Federal Budget Package, I also added a new category of the Dow Jones Industrial Average at beginning of each year over this same timeframe.  This, with the Gross Domestic Product, gives a good overview of the macro economic picture.  In summary, the Democrats did better in delivering 3 of the 4 Republican Platform components listed above!  Surprised like me??  Read on.

As way of review for those of you who are new my blog here is a bit of background. Looking at the actual Federal Spending by year would clearly bias conclusions for the most recent Presidential terms since our economy and budgets are steadily growing. Therefore, all of my analysis is based on the growth rate of budget spending expressed as percent growth compounded annually for each Presidential term.  There are an equal number of terms for both Republicans and Democrats in my analysis with each party also having one 4 year term.  In addition, Kennedy/Johnson were combined into one 8 year term as was Nixon/Ford.  Since Obama has submitted the 2017 Budget, the 2016 budget is half over, the 2016 estimate should be fairly close and will round out his 8 year term.

Although there are several ways to establish compound growth rate for these Presidential terms, I am using a statistical tool called Control Charts.  They are graphs of the actual budget spending, by year. An average is calculated for each Presidential term, which in this case is the exponential growth average.  Around this average, are placed "Upper and Lower Control Limits" which are the plus/minus 3 sigma boundaries of annual spending. This allows for the determination of any statistical anomalies during a Presidential term, which in turn would unduly influence the compounded growth rate calculation.  For example, here is the growth of the Dow Jones Industrial Average (DJIA) during Obama's term:

The time frame on this is from 1961, JFK, through 2016, end of Obama's term.  At the end of this graph you will notice the colors are stronger, which means that all the statistical calculations were done over the 8 year term of Obama, 2009 - 2016.  In this 8 year section of the graph, you will notice 3 colors.  The green "zone" is the +- 1.5 sigma zone where most of any consistent results should fall.  Where it turns from white to red is the +- 3 sigma boundary or Upper Control Limit (UCL) and the Lower Control Limit (LCL) where 99.7% of any consistent results should fall.  For Obama, this would mean that there are not any "outlier" years in his term, and ,therefore, the calculated Annual Compound Growth Rate (CAGR) of 10.8% is accurate.  The CAGR can be found in right hand box under the graph just above the light blue highlighted number.  To note, double clicking on any graph or picture will allow you to see a larger version.

Staying with the DJIA, here is a graph for the term of Bush 2:

Over his 8 year term, the years all fall within the UCL and LCL and all but one in the "Green Zone" signifying his years are consistent (no outliers).  His CAGR for the Dow Jones is 4.0%.  This same analysis was done for 14 Federal Budget and economic categories for each Presidential term from JFK forward.  The good news is that the graphs for all Presidential terms did not show any statistical outlier years and, therefore, their spending growth rates are true.

Below you will see the summary table of the Presidential terms with all the budget categories evaluated for each term.  There are two key numbers for each term and budget category: the average annual spending which is presented for context (Federal Debt is at the end of Term) and the Compound Annual Growth Rate (CAGR) which is the key focus of this analysis.

As I have done in the past, to evaluate each Term relative to the others, I have defined "Best" as Receipts with the highest growth rates and Outlays, Deficits, Supplementals and Debt with the lowest growth rates.  In short anything that makes the Debt fall.  In the table above, magenta color reflects the "Best" performance in each budget category.  Likewise, the orange color represents the "Worst" performance.  Any underlined number was found to be statistically different from all other results.  At the bottom of the table is the average growth for all Democratic terms and the average growth for all the Republican terms.

Below are my highlights from the table related to Republican platform components mentioned at the beginning of this post:

1. SMALLER GOVERNMENT:  Total Federal Outlay growth was 1% smaller for Democrats.  Obama had the lowest growth at 1.5%.  I also evaluated Government employee growth.  Unfortunately, this data only existed from 1981 to 2016.  In this case the Democrats grew Federal employees 1.6% slower than Republicans.  So, Democrats actually create SMALLER government than Republicans!
2. LOWER TAXES:  Total Receipt or Tax growth was lower for Republicans by 3.6%.  The lowest Receipt growth was 3.2% for Bush 2.  And Republicans argue LOWER taxes yield HIGER economic growth (trickle down)???  Unfortunately this does not seem to be the case, since the Real GDP grew 1% slower for Republicans AND the Dow Jones grew 1.3% slower as well.  Another way to look at Receipts/Taxes was to ratio the average annual receipts divided by the average GDP for each Term.  In this case the Republican "Tax" rate is only 0.1% lower than Democrats.  So, Republicans do deliver on this platform element.  But...
3. SMALLER FEDERAL DEBT:  Every President has grown the Federal Debt!  However, the Democrats grow the debt 5.1% slower than Republicans.  Debt growth was the smallest for Kennedy/Johnson at 2.9%.  So, the combination of higher taxes and smaller government by Democrats has caused lower growth in the Debt.  
4. HIGHER ECONOMIC GROWTH:  Real GDP, which represents economic growth, was 1% higher for Democrats.  Kennedy/Johnson had the highest growth at 5.4%.  Using the Dow Jones Industrial Average as another Economic indicator shows growth 1.3% higher for Democrats.  Clinton had the highest growth at 20.4%.  So, again, the Democrats grow the economy faster than Republicans!

So, in the upcoming election, if you are Socially Conservative and Fiscally Conservative, put any money you have under your mattress.  However, if you are Socially Liberal and Fiscally Conservative, do I have a Political Party for you.

Social Security and Medicare Cost per Person - Update

My last post on Social Security and Medicare costs per person was published in 5/23/2011 and a great deal has happened since then, especially with the Affordable Care Act (ACA).  I am sure that this will become a hot topic during the Presidential campaign along with the Social Security Trust Fund.  So I thought it might be worth a look at the current status of both of these trust funds and also do some forecasting using the latest Federal Budget (2016) forecasts through 2020.

As before, I used The Federal Budget Package for 2016 published by The Office of Management and Budget as well as the Historical Tables.  All of this is easily found on The Government Publishing Office site.  When the 2016 budget was published, the 2015 fiscal years was nearly complete, so even though 2015 is shown as an estimate, I am treating it as a very good estimate which may not necessarily be the case from 2016 onward.  In addition to doing my own forecasting of the inflows, outflows and resulting Trust fund balances out to 2060, I also have converted the 2016 budget forecasted and historical Social Security and Medicare outflows to Outflow/Person using census forecasts for the population over 65.  This ratio is then forecasted out to 2060 but converted back to Total Outflow to determine the Trust Fund Balances out to 2060.  [Note:  The Social Security Trust fund I am forecasting includes the Disability Fund which is used by people under 65 but this fund is less than 10 of the Social Security Trust Fund.  Also, there are some people who do draw on Social Security before 65, which I will evaluate later in this post.  Likewise the Medicare Trust Fund is the combined total of the Hospital and Supplemental Funds.  Lastly, the ratios of money you paid in to what you will take out  for either SS or Medicare remains the same from the 5/23/2011 post].

Looking at Social Security first, remember that the payroll tax rates, which you see on your W-2, have not increased since 1992.  Prior to this date, the payroll tax rates had been increased about every two years.  Prior to 1992, the SS Trust Fund balance forecast was not in jeopardy but each year since 1992, the forecasts have become increasingly dismal.  Also remember that there is a cap on wages to which the SS tax rate is applied, which limits in the Trust Fund inflow.  Below is a graph of the OASDI Trust Fund Balance (Old Age and Survivors and Disability Insurance) out to 2060 under 4 different scenarios.

1. The blue line is the result of taking the 2016 budget forecast growth rates on Inflow (taxes mostly) and Outflow (benefits paid), then calculating the resulting balance of the fund.  You see that it goes negative in 2032.
2. The purple line represents what would happen if the cap on wages taxed was immediately removed in 2015, with all other assumptions the same as in #1.  This extends the life of the fund to 2052 assuming that benefits paid do NOT change.
3. The red line is the result of taking the CAPPED wages from #2 but applying a payroll tax rate that would exist in 2015 if payroll taxes had continued to be raised every two years at their historical rate.  To note, this tax rate would save OASDI Trust fund even with CAPPED wages.
4. The green line has the same assumption as #3, except that wages had never been capped and the tax rate had gone up every two years from 1992,  in line with the history prior to 1992.

As you can see, the solution to the OASDI Fund is to increase the payroll tax or significantly reduce benefits.  It seems removing the cap is really not a TAX INCREASE but would cause the wealthy to pay more of the load.  This would buy some time to figure more acceptable solutions…tax rate hike or benefit reductions.

I also tried to look at the OASDI Fund using the most favorable growth rates in Inflow and Outflow, to determine if "rose colored glasses" might tell us anything different.  The highest growth in Inflow (taxes) was from 1995 to 2014 at 4.1% compounded annually. The lowest growth in Outflow (benefits) was from 2009 to 2014 at 4.1% compounded annually.  Using these two growth rates the Fund goes negative in 2043!  The bad news is that the 2016 Budget Forecast for these growth rates is 3.5% for Inflow and 4.8% for Outflow which results in the Fund going negative 2032 (the blue line in the graph above). Finally, converting the Outflow to Outflow/Person and finding the lowest growth rate for this number, the lowest is from 2009 to 2014 at 1.5% compounded annually.  If this forecast were used along with over 65 population growth the OASDI Trust Fund would never go negative!  By the way, the average per person benefit in this scenario is $1,563 per month in 2015.

Thinking about all of this Inflow and Outflow made me think about my own decision: When do I start to take my SS benefits???  So, out with the excel spreadsheet. Using the benefit estimates from by Social Security Annual Statement for age 62, 66 and 70, I created a simple model.  I assumed my  benefits would increase at 2% per year starting whatever year I began taking them.  Here is what I found to be my total benefits paid to me:

                                     Start at 62                    Start at 66                   Start at 70

Live to 80                     $495,356                      $495,353                    $459,997

Live to 90                     $837,912                      $964,469                 $1,031,902

If taking these benefits early allowed me to not to draw this amount from any IRA's, there is an additional benefit of growth in the IRA which amounts to (at 5% growth) about another $40,000 in the 4 years from 62 to 66, which buys a few more years!  Therefore, I started taking by benefits at 62 since I don't have long life genes in my family and I wanted to get in while the Fund is still solvent.

Now on to Medicare.  Again, I did some forecasting using Inflow (taxes mostly) and Outflow (benefits) using different growth rates on these numbers.  In addition, I also converted Outflow to Outflow/Person and studied this growth rate in different periods of time.  Using these different forecasts, I determined the resulting Medicare Trust Fund balance which is graphed below.

1. The dark blue line uses the growth rates calculated over the 20 years from 1995 to 2014 for both Inflow and Outflow/Person.  This goes slightly negative for 14 years and then recovers.
2. The green line uses the growth rates calculated over the 6 years from 2009 to 2014 for both Inflow and Outflow/Person.  This scenario never yields a negative Fund balance.
3. The light blue line uses the growth rates calculated from 2015 to 2020 in the Budget Forecast.  Here the Fund Balance never goes negative.
4. The red line uses the 20 year growth rates from 1995 to 2014 on Inflow and Total Outflow.  This goes negative in 2032.
5. The purple line uses the 6 year growth rates from 2009 to 2014 on Inflow and Total Outflow.  This goes negative in 2028.
6. The orange line represents the 6 year Budget Forecast growth from 2015 to 2020 for Inflow and Outflow.  This forecast does not go negative.  

What is the big difference in the growth rates from 2015 to 2020 being forecasted by the government.  Might this be the impact on Medicare of the ACA that has been often discussed?  Lets look at growth in for Total Medicare Outflow in an exponential control chart.

As you can see, the growth rate in Medicare Outflow (and Outflow per Person) since 2012 is down to 1.7% (the number in the table just above the blue shaded number).  This includes 3 years of actuals and is a statistically significant change.  For reference, the growth from 1995 to 2011 was 7.3%.  I am sure the Government is glad to see the low numbers for 2012-2015 and is happy to forecast this into the future as a way of claiming success on ACA which was signed into law in March of 2010.  The first insurance sign ups began in October of 2013.  If these numbers hold up, it could be that the Trust Fund might live on after all.

Republicans Versus Democrats on Debt and Budget Elements

Since the "Great Recession" is over, Presidential campaigns are looming and Obama has submitted his final Budget, I have decided to update some past postings in which I have rated the Presidential terms (and Parties) since Kennedy.  As before, I have downloaded all the historical spending and forecasts from the 2016 Budget package.  These can be found on the Office of Management and Budget website.

Looking at the actual budget incremental numbers by year would clearly bias conclusions for the most recent Presidential terms since our economy is steadily growing.  Therefore, all of my analysis is based on the growth rate of budget numbers expressed as percent growth compounded annually for each Presidential term.  There are an equal number of terms for both Republicans and Democrats in my analysis with each party also having one 4 year term.  In addition, Kennedy/Johnson were combined into one 8 year term as was Nixon/Ford.  Since Obama has submitted the 2016 Budget, the 2015 budget is half over, so that estimate should be fairly close and I am using the 2016 estimate to round out his 8 year term.

Below is the graph for Obama's 8 year term and this same technique was used for all Presidential terms on each budget category.

The total Budget Outlays are graphed above, beginning in 1961 through 2020, with the last 6 years being budget estimates.  The statistics are calculated, in this example, from 2009 thru 2016 which will be Obama's 8 years.  You will notice some red boundaries on the graph, for this period, which reflect the bounds of expected variation in the annual Outlays.  This would let you know if there was "unusual" year among his 8.  Centered under the graph, is a table of statistics, including the Compound Annual Growth Rate (CAGR) of 1.5% which is just above the blue highlighted entry.  Therefore, during Obama's 8 years (1 1/2 are forecasted), Government spending has grown 1.5% each year, compounded.  You should be able to d-click on this graph to see a larger version.

This type of analysis was done for every Presidential Term on the following Budget categories:

• Real GDP
• Nominal GDP
• Individual Income Tax Receipts
• Payroll Tax Receipts (Social Security and Medicare)
• Corporate Tax Receipts
• Total Receipts
• Total Outlays
• Annual Deficit
• Supplemental Spending (off budget - calculated by Total Debt - sum of deficits)
• Total Debt at the end of the year.

I also calculated, for evaluation, three additional categories of Budget line growth minus GDP growth.  This would show if a budget item is growing faster (positive number) or slower (negative number) than the economy:

• Total Receipt growth minus GDP growth
• Total Outlay growth minus GDP growth
• Total Debt growth minus GDP growth 

The table below summarizes these findings.

As I have done in the past, to evaluate each Term relative to the others, I have defined "Best" as Receipts with the highest growth rates and Outlays, Deficits, Supplementals and Debt with the lowest growth rates.  In short anything that makes the Debt fall.  In the table above, magenta color reflects the "Best" performance in each budget category.  Likewise, the orange color represents the "Worst" performance.  Any underlined number was found to be statistically different from all other results.  At the bottom of the table is the average growth for all Democratic terms and the average growth for all the Republican terms.

Below are my highlights from the table:

1. Real GDP, which represents economic growth, grew 1% better for Dems.  Kennedy/Johnson had the highest growth at 5.4%
2. Individual and Corporate Tax receipt growth was higher for Dems (6.1% and 2.1% respectively).  Could it be that HIGHER taxes yield HIGER economic growth???  Notice for Bush 1, who had the lowest growth rate in both Individual and Corporate Taxes, he also had the lowest Real GDP!
3. Total Receipt growth was 3.6% higher for Dems which is likely due to both higher tax rates and higher economic growth.
4. Total Outlay growth was 1%  for Republicans.  I thought they valued SMALLER government.
5. Supplemental Spending was 20 times higher for Republicans!!  Reagan had the highest rate at 46.6%.
6. Total Federal Debt grew 5% faster under Republicans!  Reagan had the highest Debt growth at 15.1% likely caused by the high Supplemental.  The only other term with double digit growth was Bush 1 at 11.8% when his Outlay growth was twice the Receipt growth.

Finally, to determine the Best and Worst Presidential Terms, I evaluated each of Budget Categories I listed earlier, eliminating GDP but keeping Real GDP.  I also included the 3 additional calculated categories bulleted above.

I assigned 2 points for the best growth rate in each category and 1 point for second best.  (There was a tie in one category).  Likewise I assigned -2 points for the worst growth rate and -1 point for second worst.  Finally I added up the scores for each Presidential Term.  Here are the results:

I was very surprised by these results as they are different than in my earlier post!  So I tried a different approach using only Real GDP and my 3 calculated categories which should have negated any inflation issues.  To my surprise again, the results were very much the same.  Carter dropped to 4th (inflation issues) and Reagan switched with Bush 1 for last place!

I know there are many ways to evaluate a Presidency, with historians having a strong say.  However, as a data monger, I am glad that I have some data analysis approaches to use as I begin to think about the upcoming election cycle and all the rhetoric.  Good luck to us all!

Climate Change Modeling Has Some Problems. History Might Help!

On September 19, Steven E. Koonin wrote an article for the Wall Street Journal titled "Climate Science Is Not Settled" .  Although there are many interesting observations in the article, what caught my attention was the topic of Climate Models. http://online.wsj.com/articles/climate-science-is-not-settled-1411143565?mod=WSJ_hp_RightTopStories   The basic message is that all of the modeling that has been done cannot accurately predict our current climate results.  Here are a couple of quotes from the article:

"As a result, the models give widely varying descriptions of the climate's inner workings. Since they disagree so markedly, no more than one of them can be right."

"Although the Earth's average surface temperature rose sharply by 0.9 degree Fahrenheit during the last quarter of the 20th century, it has increased much more slowly for the past 16 years, even as the human contribution to atmospheric carbon dioxide has risen by some 25%. This surprising fact demonstrates directly that natural influences and variability are powerful enough to counteract the present warming influence exerted by human activity."


Models are ways of predicting a future result which in this case is the Average Annual Temperature of Earth.  For most of us and in our experience, the range of highest to lowest temperatures seems to occur within a year, which captures all of the seasons.  So no surprise, that modelers are focused on the Average Annual Temperature and all the "influencers" that occur within a year.  By studying many years, say 200 or so, you could get a good idea if this Annual Average Temperature predicted by the models, is accurate.  (Useful worldwide temperatures date back only to 1880, hence the 200 year history.)

If you wanted to predict (or even measure) the Average Annual Temperature on Earth, clearly you would not gather climate data only for March and September!  You would be missing some important information.  Likewise, as we take the Average Annual Temperature and find that it is increasing recently and setting records, as compared to 1880, MIGHT WE BE MISSING SOMETHING BY NOT LOOKING AT OTHER CENTURIES OR MILLENNIUMS??  Might there be other temperature cycles we need to understand before we can declare that "these are the highest temperatures we have ever seen, and, therefore, caused by human influences?"  The operative word here is "we".  Could there be some other temperature cycles beyond those that occur within a year which could be affecting the climate models?  Should we instead be looking at the Average Century Temperature for instance?

As many of my posts suggest, there is a great deal to be learned from studying the history of any problem, so I will try that approach again.  The history of worldwide climate data has all been within the last 200 years.  However, there is some data that goes back 800,000 years and this amount of history might give us some insight to the "cycles" of both temperature and the corresponding CO2 levels, which are often in the news.  This data comes from ice core samples, 2 sites in Antarctica are titled EPICA and Vostok (temperature data is listed a degrees centigrade variation from present); and one Arctic site called GISP2 in Greenland (temperature is degrees centigrade).  The process and science of these measurements can be read at  http://www.climatedata.info/Proxy/Proxy/icecores.html but I will focus on the data itself which can also be downloaded from this site.  But to be clear, this is very localized data and does not reflect the Global Temperature.  However, it is the relative changes over time at this location which can help us understand if there are any longer term "temperature cycles" that are affecting the globe such as the Milankovitch Cycles.

Here are the key points that are supported by the graphs and analysis which follow:

1. Earth is getting warmer!  It is supposed to be getting warmer since we have been in an interglacial warming period that began about 17,000 years ago.  This warming began at the end of a long glacial period and when combined with the interglacial warming period, lasts about 100,000 years.  These cycles have been repeated for at least 800,000 years.  There is a longer term temperature affect in play beyond the 4 seasons in a year.
2. If you look at the previous interglacial warming period 127,000 years ago, we have not yet reached the previous high of 4.84 degrees.  100 years ago we were 1.8 degrees hotter than we are today, because we are at "0", the base line for all these temperature measurements.  We are not yet as warm as we have been in previous temperature cycles.
3. The most recent interglacial temperature rise has taken longer than the previous two, but not yet reached their highs.  The pattern and length of our recent interglacial rise, however, looks very similar to that of 422,000 years ago.  This also holds true for CO2 levels.  Using this as the benchmark, we could have 11,000 more years of warming before hitting the historical highs.  This might yet be another even longer temperature cycle we need to understand.
4. Looking at the last 50,000 years of temperatures, which begins in the middle of the last glacial period, you see a very rapid rise in temperature about 10,000 years ago.  Since this time, the temperature has been fairly stable, until the last 600 years, when the temperatures dropped to a new, lower stable level.  This is the period of highest human activity.
5. Using only the last 50 or 200 years is not nearly enough data to understand the amount and sources of the earth's changing temperature.  Without this understanding, we cannot model or assess the impact of the human contribution to Global Warming.  We need utilize and model at least 422,000 years of history.  If we have not yet hit the historical highs from all these years ago, how do we conclude that humans are to blame?  Are we the cause of global warming, or rather are WE to adapt to the predictable warming yet to come as our ancient forefathers have done???

ANALYSIS AND GRAPHS

The EPICA ice is the deepest, so it yields the greatest amount of history going back 800,000 years.  Below you will find a control chart (X, MR) of the Temperature data.  The upper chart is the actual temperatures (tracked as plus/minus from the current temperature) and the lower chart is a measure of the variation in these numbers.

The first 400,000 years show a different pattern with a range of temperatures from 3.15 to -9.63 than the last 400,000 years, with a temperature range of 4.84 to -10.58.  You will also see a change in the patterns in the second half of more distinct and rapid rise in temperatures (interglacial periods) and longer lower temperatures (glacial periods).  This cycle of glacial and interglacial periods has also been increasing from 74,000 to 113,000 over past 3 cycles or about 20% increase each cycle.  The most recent cycle is not yet over and is already 130,000 years.  Also notice that the variation in the temperatures is also increasing, which for the modeler, creates issues in creating accurate forecasts.  Below you will see the similarities to the corresponding CO2 levels.  Notice however, that there is not a large increase in the CO2 variation.

Now, comparing the first 400,000 years to the more recent 400,000 years can help us determine if the change in cycle patterns and ranges results in a statistical difference for temperature or CO2.

In the last 400,000 years, temperature dropped .49 degrees or 9.7% while CO2 actually rose 0.9%.  Here is another anomaly for modeling CO2 and temperature.  The pattern of the most recent temperature rise is quite different from the previous 2 interglacial rises which are quite steep and short.  However, the interglacial rise 422,000 years ago looks very similar to the most recent rise, and needs a closer look.

The first two interglacial periods below have the same average temperature and CO2's but the second one is less than a third the length of the first, 28,600 years vs 8,000.

For the third and fourth interglacial periods, the average temperature has risen, the CO2 levels are the same, but the length of time is about the same, 8,200 years and 8,000.

Finally, comparing the most recent interglacial to the similar one 422,000 years earlier, the average temperatures and CO2 levels are the same, but has not yet reached the highest temperature or CO2's of the past.  Also, the length of the most recent period is 17,000 years.  Could we be repeating the interglacial warming pattern of 422,000 years ago?  Could it take 11,000 more years to reach our interglacial high and only then know if "we" caused it?

The next several graphs of EPICA data will focus on the last 422,000 years, which actually cover the same time frame as the Vostok data.  To show how similar both the data sources are, the next 4 control charts (X,MR) will show the EPICA and Vostok temperatures followed by the CO2 levels.

EPICA and Vostok Temperature

Patterns very similar

EPICA and Vostok CO2

Patterns very similar

The next analysis will show the changes in average temperatures over  each of these 4 glacial/interglacial cycles using EPICA data.  I have done the same analysis on the EPICA CO2 data as well as the Vostok Temperature and CO2 data.  To prevent graphic overload, I will summarize all of this in a table at the end of this section.

The cycles here are defined as the maximum interglacial temperature to the next maximum interglacial temperature.  Each figure contains the X Chart, a histogram, the F and t statistic and a summary of the two cycle's data ("before" and "after").  The blue highlight in the lower left corner signifies a statistical difference either in the averages or the variations between the two cycles.  For the first two cycles, the average temperature dropped .46 degrees or 9%.

From the second to third cycles, the average temperature dropped .48 degrees or 8.8%.

From the third cycle to the fourth cycle (most recent), the average temperature rose .6 degrees or 10%.

However, comparing the first cycle to the fourth, the average temperatures are NOT statistically different!  This coupled with the 17,000 year interglacial temperature rise pattern studied earlier, leads me to conclude that any modeling needs to consider at least 422,000 years of history to establish all of the independent variables that could impact temperature changes.  Since good climate databases go back only a couple of hundred years, this could partially explain the problem with the current climate models.

Note below that the Vostok temperature for the first and last cycles, shows a difference when the EPICA data shows the temperature to be the same.  This is further evidence that the current interglacial warming may not be over.

Looking at the most recent interglacial rise of the last 17,000 years using another ice core database might help reinforce the point of needing more history to get accurate models.  The GISP2 data includes 50,000 years of temperature data at a much more granular level, about every year.  This control chart (X, MR) shows that there has been a dramatic shift up in temperature about 10,000 years ago and has been pretty steady since.  So, we need to focus on the significant event of 10,000 years ago!  It sure looks like this is just a part of the interglacial warming that began 17,000 years ago.  But, lets look closer at these last 10,000 years to determine if the human affect of global warming can be seen.

By zooming in on these last 10,000 years, which appeared to be fairly stable, shows even more information.  In the last 900 years the temperature dropped 1.6 degrees from earliest average.  But, over the last 300 years, the temperature is rising again, but has not yet returned to the earlier highs.  So again, our issue is not what happened in the last 300 years, but what happened 10,000 years ago.  Our current models will just not help with this question!  To confirm that recent human industrialization is the cause of "global warming", we would need to see global temperatures statistically higher than we have seen in the last 422,000 years!

In conclusion, to show the human affect of global warming, we need to see CO2 and temperature levels that have not been seen in human history.  That has not yet happened!  Modeling needs to include more of our earth's history in order to forecast when, and how high, our current interglacial warming period will go.  Only then can we know if, or how much, humans have impacted this interglacial cycle.

Homicide Statistics Bias by Gun Politics

A recent email I was forwarded, contained a list of Homicides/100,000 citizens for many countries around the world.  The email subject was "Eye Opener" and began with the title "World Murder Statistics".  What followed was a list of 109 Countries, with Honduras at the top of the list with 91.6 Homicides / 100,000.  Last on this list was the USA with 4.2 Homicides / 100,000 citizens.  The email ended with this statement:

 "ALL the countries (109) above America have 100% gun bans. It might be of interest to note that SWITZERLAND (not shown on this list)has NO MURDER OCCURRENCE!However, SWITZERLAND'S law requires that EVERYONE....

1. Own a Gun
2. Maintain Marksman qualifications....regularly
3. "Carry"........a Weapon."

As has been my habit when I see a list of numbers, I first went to the source of the data to confirm what I saw in the email.  Indeed, there is data supplied by the United Nations Office on Drugs and Crime (UNODC), which is different than the email's claimed source of the World Health Organization.  The following links will take you to these data summarized in an active table, but on these sites there are links to the complete data set from the UNODC which I downloaded and found to be the same as these links.

List of countries byIntentional Homicides / 100,000 Inhabitants

List of countries by firearm-related death rate per 100,000 inhabitants 

List of countries by gun ownership rate per 100 inhabitants

I first began to understand the "109 countries above America" and what this meant.  In order to find the Honduras rate of 91.6,  the email was referencing Intentional Homicide data.  All the data in the email were correct for all countries EXCEPT America!  The email stated that the United States rate was 4.2 / 100,000 but from the UNODC data set, the United States rate was 4.8.  In addition, there were 102 countries with rates higher than the US (not 109) and, not stated in the email, 104 countries with Homicide Rates LESS than the US.

In order to understand if all 102 (109) countries with rates worse that the US indeed had "gun bans", I utilized Gun Politics  for more information.  In summary, I could not find any country with a "100% ban" on guns.  However, many of these countries do indeed have stronger gun restrictions, but in these cases there are ways to obtain and possess a gun.  But to be clear, there are an equal number of countries with Homicide Rates LESS than the US that have more restrictive gun laws than the US.  So, it appears that restrictive gun laws do not seem to predict Homicide Rates.  But to test this I did download Gun Ownership data to correlate to Homicides which I will cover later.

Now, I wanted to investigate the comments about Switzerland! The statement that they have "no murder occurrance" is not accurate.  In fact, on this same list their Homicide Rate is 0.7 with 15 countries lower than Switzerland.  And finally, gun control in Switzerland is based on a militia concept as seen from this quote from Gun Politics.

Switzerland practices universal conscription, which requires that all able-bodied male citizens keep fully automatic firearms at home in case of a call-up. Every male between the ages of 20 and 34 is considered a candidate for conscription into the military, and following a brief period of active duty will commonly be enrolled in the militia until age or an inability to serve ends his service obligation.^[76] During their enrollment in the armed forces, these men are required to keep their government-issued selective fire combat rifles and semi-automatic handguns in their homes.^[77] They are not allowed to keep ammunition for these firearms in their homes, however, and ammunition is stored at government arsenals. Up until September 2007, soldiers received 50 rounds of government-issued ammunition in a sealed box for storage at home.^[78] Swiss gun laws are considered to be restrictive.^[79] 

So this law does not apply to everyone, but only to males.  They are required to keep a gun in the home for immediate call up to the militia (after serving in the armed forces) and does not mention anything about "carrying" a gun.  The marksmanship requirement I could not find either.  However, the most interesting fact was left out of the email.  Although required to keep the firearm at home, THEY HAVE NO AMMUNITION AT HOME!  All of it is stored in government run arsenals!  No wonder the death rate is so low!  Guns at home without ammo.

Now, moving on the actual data.  First I thought it interesting that this email used Homicide Rates by all methods.  There is a database of Homicide by Firearms by the UNODC which I found and began to look at relative to guns/firearms.  This database has fewer countries participating but there are still 70.

Applying statistics to all these lists, I was first interested in statistical differences between lower and higher rates.  I evaluated this using control charts with limits based on population sigma since the data were not time ordered, just alphabetical.  First we will look at Gun Ownership per 100 Residents.

The X chart at the top, clearly shows only one outlier country which is the US!  All other countries are within the normal range of per capita ownership.  This is probably not new news to most of you.

Next I looked at Homicide by Firearm Rates for Total, Homicide and Suicide.

There are two countries outside the upper limit signifying outside the "norm" for Total Firearm Homicides.  These two countries are El Salvador and Honduras.  If we take the 95% confidence (2 sigma), Columbia, Guatemala, and Sweden could also be considered outside the norm.  Switzerland is considered to have less restrictive gun laws, but Honduras more restrictive.

For Firearm Homicide Rates, the two outliers are Guatemala and Hungary,  At 95% confidence, add El Salvador, Honduras, Japan and Sweden.  Japan is considered to have more restrictive gun laws than the US.

Finally, the Firearm Suicide Rate shows Netherlands and Zimbabwe to be uniquely high.  Netherlands is considered to be more restrictive in their gun laws.

I could not find any correlations between gun ownership per 100 inhabitants and any of the firearm rates as seen below:

As you can see, between the two graphs, there is a small white box with a -2.8 which means the correlation is non existent and all others even weaker!  The number of guns don't correlate to any of the firearm death rates so I would conclude that other factors, including culture are more important.

My takeaway from this closer look at the email and the corresponding data is the culture of guns and gun politics has very little to do with firearm homicides and suicides.  It is time for the different groups battling over gun laws to take a new direction to make their respective cases!

Apple - Stock Price Drop Justified??

Following on my post of October 26, 2012, Corporate Quarterly Earnings Report's Negative Effect on Wall Street, I began to wonder if the recent $200 drop in Apple's stock price would correlate to its actual financial performance.  To note, the price began dropping in mid-September 2012, and might be now stabilizing as of this writing.

To begin my investigation, I collected, from SEC filings, Apple quarterly Revenue and Earnings figures back to March 1993.  As you have probably gathered in my other posts or from reading the information at my website (www.sustainthegain.com), I am not a fan of using Indices of these quarterly figures relative to a previous period!  Remember, a trend of one (recent quarter compared to one previous one) is not significant!  However, to reinforce this idea, I will produce a few examples of this analysis technique and make a few comments on them.  After these examples, I will return to the more meaningful analysis technique using the actual Revenue and Earnings data.

I have displayed these indices in a control chart, in order to gain some statistical reference!  I will start with Revenue, and in particular, Index versus Previous Quarter.

The first thing to notice is that at first glance, this chart appears to be stable at an average index of 1.065.  Compounded quarterly, this is a Compound Annual Growth Rate (CAGR) of 26.6%.  More importantly, the last two quarters ending Sept and Dec of 2012 are NOT uniquely different and, therefore, don't suggest any reason for a decrease in stock price.

There are some other things to take away from this chart.  The Upper and Lower Control Limits (UCL, LCL) are 1.78 and 0.35.  This means that any single quarter's index would need to be greater than 1.78 or less than 0.35 to be "out of the ordinary"!  As I have said, most companies are spending precious time explaining indices of 1.05 or .96 when a single, unique explanation is fruitless since only the common causes are acting on the results.  Whatever explanation is offered will now falsely become part of their institutional memory.   However, there is a distinct change in the pattern of these indices beginning at the middle of the chart, which is actually, March 2004, so lets take a closer look at this change.

 Although not obvious in the first graph, there has been a statistically relevant change 3/04 when the average index rose from 1.012 to 1.124.  This is the equivalent of increasing the CAGR from 4.9% to 59.6%.  Sounds great, but still nothing showing up for the 9/2012 stock drop!  My conclusion is that there was one sustainable positive change in 3/2004, and a positive "bump" in 12/1999 which could not be sustained.  My research on Apple SEC filings turned up a major accounting change in 2004 whereby the Revenue was reported differently!  It is pretty clear that the OND quarter is the highest index each and every year, since 2004!  This is one of the rare examples of indices, in control chart form, will indeed highlight a sustainable change.  The good news is that the actual quarterly results show this change as well, so still no need to use Index versus a Previous Quarter.

Below is the same Quarterly Revenue, but displayed as Index versus Year Ago (IYA).

It is more obvious that there is a change around 3/2004, but look how messy the individual indices are!  And the OND quarter pattern change is not indicated.  Also, the width of the UCL and LCL is quite large and the average 4 quarter index is 1.44 since 3/2004.  But, the last 3 quarters are all closer to the LCL than to the average, which is a signal of a possible change.  Could this explain the stock price drop??  I doubt that anyone on Wall Street is using control charts on indices!

Would Earnings as either Index Quarter Ago or Index Year Ago, show anything more??

 IQA does not show a average index shift in 3/2004 but the high OND quarter, each year, can be seen after this date.  The average index in Earnings is .962 which means that the earnings are shrinking!  This is likely an issue with the very low index at the beginning of the chart.  After 3/2004, the average index is 1.22.  Now look at IYA to see if there is anything more insights.

In the IYA case, the rise in the average index again shows near 3/2004.  But the most striking thing on this chart is the reduced variation in the earnings index after the accounting change of 3/2004.

In summary, the use of indices turned up only one sustainable change in performance since 1993, which was the accounting change in 2004.  We also did NOT see any significant changes in 9/2012 which would explain the stock price drop.  Sooooooo, we will move on to control charts using the actual quarterly results, starting again with Revenue.

Using the actual quarterly data for Revenue, you can find 4 timeframes of stable performance:  first from 3/1993 to 12/1995 when the CAGR was 18.3%;  then 1/1996 to 9/2004 when the Revenue dropped and CAGR was -0.6%;  next from 12/2004 to 6/2010 when the Revenue rose and the CAGR was 33.3%;  finally from 9/2010 to 12/2012 when Revenue jumped and the CAGR rose slightly to 35.5%.  The increasing variation (width of the blue UCL and the yellow LCL) in 9/2004 and 12/2010 is consistent with increasing quarterly values.  

In 1/1996, Windows 95 was introduced and most likely explains the drop in Revenue 1/1996!  In the following years, Jobs became CEO, Mac OS 9 ships, G4 Cube introduced, Apple Stores Open, i-Pod ships, Mac OS X ships and i-Tunes starts late 2003.  In 2004 we have the accounting change, 17" mac display and i-pod mini.  So what was the 2004 breakthrough......you pick, but my guess is accounting!  Had the pattern of Revenue (high OND quarters), I might have said this breakthrough was  i-Tunes.   The i-phone launches in 2007, 3G in 2008, but it is the i-Pad and i-Phone 4 that both launch in 2010 which creates the jump in 9/2010 and maybe only the i-Pad.  

However, trying to explain the stock price drop is more difficult!  Wall Street does not analyze using these techniques so they were not aware of the rise in CAGR!  The last two OND quarters were at or just above the UCL, but this should have been good news!  You can see that the Spring and Summer quarters had lower revenue but not outside the LCL!  It does appear that if you averaged all 4 quarters of 2012, you would get a number that falls right on the green trend line.  I decided to check this by obtaining the annual revenue numbers since 1993 which gave me the following graph.

Since 12/2004, the annual Revenue has had stable, predictable CAGR of 41% with 2012 landing right on the trend line!  So, it seems that Revenue should not have the caused of the stock to drop.  Could it have been Earnings??  Below is a graph of actual quarterly data.

There are only two sustainable breakthroughs in Earnings when there were 3 in Revenue (4 stable timeframes), but remember that one of the Revenue breakthroughs was the Accounting change that applied only to Revenue.  The Windows 95 intro in 1/96 did yeild a couple negative earnings quarters but not a shift in the Earnings.  In 12/2000 there was large loss in Earnings after 3 years of positive growth.  This lines up quite well with when Jobs became CEO.  He likely took a big write down after which the Earnings CAGR took off at 57.9%.  Then, simultaneous with Revenue, the Earnings jumped in 9/2010 but CAGR dropped to 40.1%.  Even if Wall Street had been tracking Earnings growth with control charts, the stock price should have dropped before 2012 since the 4 quarters of 2011 would have been sufficient to get a signal of this change.  But I'm sure they were not doing such an analysis!  An argument could be made that the OND 2011 and OND 2012 for Earnings were approximately the same value, when Wall Street would have expected at least a 20% year on year increase.  This was likely the stock downfall, but it is clear from the control chart that OND 2012 is just a random, non-significant result that fell between the control limits and should have been given no special consideration.  Had the stock problem been due to the i-Phone 5 intro and the "Apple Maps" problem, this should have shown up in Revenue.

An important note about Apple Quarterly Earnings News Releases:  In my 10/26/12 article referenced at the beginning of this post, I gave many examples of companies that spent significant time trying to explain every non-significant up or down in their results using Index Year Ago as the basis.  However, Apple does NOT report this way.  Every News Release follows the exact same format: the first paragraph reports actual results of this quarter and the same quarter year ago, but they do NOT use indices; the second paragraph gives sales figures but again avoids IYA; the third paragraph reports the dividends declared; the fourth and fifth paragraphs use the phrases "We are thrilled" and "We are excited" to describe the records they have set in Sales, Revenue and Earnings.  But they never attempt to tie a particular product event directly to a change from the quarter year ago!  Way to go Apple for not poisioning their institutional memory.   Might this be a contributor to their success??