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By now, you may be getting very tired of the Christmas carol equivalent of "The Wheels on the Bus Go Round and Round".
We're talking of course about "The Twelve Days of Christmas" song. You know, the one with "five goooooolden rings" and "a partridge in a pear tree".
What most people don't fully appreciate about the carol is just how much stuff is being doled out in the twelve days it spans. Not even PNC Wealth Management, the people who calculate the value of the gifts being given out on each day in the song each year, really get it.You see, they treat the math as if only one gift is being given out on each of the 12 days, whether it be a single set of 10 lords-a-leaping on Day 10 or 3 French hens on Day 3. But here's the thing - if you pay close attention to the lyrics, many of the gifts are repeated on each subsequent day after being given out.
So instead of just one partridge in a pear tree, the recipient of all this true love would actually have 12 - one partridge in a pear tree for each day the song goes on. The "goooooolden rings" would be pretty cool to get, because you'd be getting 40 of them, which you could then equally divide among the 40 milking maids would would start arriving in batches of eight on Day 8.
Needless to say, this is a lot of stuff to keep track of, which is why we've created today's tool, which you can use to keep track of how many total items have been transferred as gifts through each day of Christmas. Just enter the day, and we'll tell you just how many items we're talking about....
And there you have it. Going back to the title of our post "Why Christmas Only Has 12 Days", we find that after 12 days, the gift recipient in the song has accumulated 364 items - enough for every other day of the year (except for leap years, but don't tell the 10 lords.)
You can get a visual sense of all the gifts given during these twelve days in the following image, which was the inspiration for today's post!
This is our final post for 2012 - we'll see you again in the new year. In the meantime, we'll leave you with the only tolerable version of "The Twelve Days of Christmas" that we could find:
Have a Merry Christmas!
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"The evolutionary advantage to compete and succeed in the world of business as in the world at large - that is the thanks we owe to the ice age winters." |
In the wake of the Newtown school massacre, we've noted a strong uptick in our site traffic by people wanting to find out how different the U.S. might be if the nation adopted Canada's much more restrictive firearms laws. This post gathers all our analysis on that topic from 2011 in one place.
We examine the FBI's data on the race of victims and their killers. We find that the vast majority of offenders prefer to kill their own kind (that evidence is borne out elsewhere, where criminals also seem to prefer killing other criminals!)
We want to compare the U.S. and Canada's murder statistics, but find we can't do a direct comparison because Canada is significantly lacking in two things the U.S. has in much greater quantities: blacks and Hispanics!
We work out how to get around Canada's demographic deficiencies in reporting its homicides to be able to directly compare the populations of both nations.
We determine the real difference in the number of homicides per 100,000 people between Canada and the most demographically-similar-to-Canada portion of the U.S. population.
This is the one post that has drawn the most attention since the shootings in Connecticut. We break down the number of homicides per 100,000 by method for Canada and the most demographically-similar-to-Canada portion of the U.S. population, finding that Canada's much more strict laws regulating firearms "saves" about one life for every 100,000 people, although Canadian homicide offenders have adapted to the lack of firearms available to them by making murder more brutal.
We find that there's an additional price to be paid for saving that one life for every 100,000 people with strict gun control laws. It turns out that after adjusting for the major demographic differences between the two nations, Canada is a much more violent place than is the U.S. (Ed. At least Canadians are polite, eh? Just don't cross them....)
Do Canada's stricter gun-control laws reduce the number of suicides per 100,000 people compared to the U.S.? We find the answer is not at all....
Update: Doc Palmer picks up on a report that indicates the U.S. is also much less violent than the U.K., Sweden, Belgium and Holland - all places that also feature much more restrictive gun laws than does the U.S....
We now have dividend futures data through the fourth quarter of 2013. Our chart below shows how the expected future for dividends looks:
Given all the dividend-related activity following the 6 November 2012 election in the United States, where companies have acted to pull dividends from 2013 into 2012 instead to beat a now guaranteed dividend tax increase, we anticipate that there may be quite a bit of error in the actual dividends that will be paid in 2012-Q4 and for 2013-Q1. We believe the value that will actually be recorded for 2012-Q4 will be about $0.42 per share higher than what we've shown on the chart above based on how much money appears to have been transferred from 2013-Q1.
Looking at the history of the expected future for that quarter, 2013-Q1's expected cash dividend of $7.88 per share is down considerably from the high value of $8.30 per share that was expected to be paid in that quarter back on 17 October 2012. Almost all of the decline in the level of expected dividends for 2013-Q1 has taken place since 15 November 2012.
Looking forward now in time, the expected level of cash dividends for the S&P 500 looks as if the first three quarters for the U.S. economy in 2013 will be lackluster. The fourth quarter looks as if it will be better by comparison, but even here we've already seen some erosion in investor expectations for that future quarter.
Here, the expected dividends for that quarter first debuted on 13 December 2012 at $8.90 per share. That has fallen to $8.84 through the futures for 19 December 2012.
We hope you've enjoyed 2012. As we've long forecast, 2013 will be a very different story....
Today's data visualization exercise features the Bureau of Labor Statistics' data reporting the number of U.S. teens working either full or part-time, which goes back to January 1968. Our first chart improves on the BLS' data, by showing how both full and part-time working 16 to 19 year olds make up the complete teen employment scene:
Looking at the chart, we see that full-time jobs for teens peaked in 1979, while we see that part-time jobs for teens peaked in 1999. Overall, the number of part-time jobs for teens has been more stable than the number of full-time jobs, which have been declining since 1980.
The decline in full-time jobs for teens is especially visible in our second chart, which shows how the relative share of full-time jobs for teens has declined in stages over time:
As a short primer to the reasons why the decline in the relative share of full-time jobs for U.S. teens looks the way it does, we'll point you to one document, which reveals the history of both the U.S. federal and California's minimum wages. Note the timing of when major shifts occur in our two charts with the dates listed....
Following on the heels of our finding that the increase in the share of single person households over time is the primary factor in the observed increase in U.S. income inequality for households over the last six decades, we thought it might be interesting to share what we found in the U.S. Census' data from 1940 onward regarding the growth trend of Americans living alone.
Our chart below reveals the general trend for how single person households grew from 7.7% of all U.S. households in 1940 to an estimated 27.5% in 2011.
Here, we find that the percentage share of single person households in the U.S. doubled in the 28 years from 1940 to 1968. It then took another 20 years for the percentage share of single person households to more than triple its 1940 level, reaching that mark in 1988. Since that time, the growth rate of householders living alone has sharply decelerated. The percentage share of single person households has only increased by 3.5% in the last 23 years.
In essence, the number of single-person households in the U.S. grew exponentially from 1940 into the mid-1960s, then steadily from then until about the early 1980s and at a decelerating pace in the years since.
U.S. Census Bureau. Households by Size: 1960 to Present. [Excel spreadsheet]. Accessed 16 December 2012.
U.S. Census Bureau. Historical Census of Housing Tables: Living Alone. Accessed 16 December 2012.
There we were, surfing the web for ideas of what to get a certain 9-year old boy for Christmas, when we stumbled into something that made us suddenly sit up and say "That is so cool!"
That something is the Air Swimmer Remote Control Inflatable Flying Shark. Here's a Youtube video of it in action:
We like it because it combines a boy's love of flying R/C vehicles with nature's perfect predator! And as an added bonus, it echoes some of the more fun scenes from the 2010 Doctor Who Christmas special, many of which were excerpted and remixed with appropriate music in the following video preview:
The real preview for the 2010 Doctor Who Christmas special is available here. The episode is simply brilliant, with one of the best twists ever in retelling Charles Dickens' classic Christmas Carol story. Very highly recommended!
And at the very least, we've also answered Mark Cuban's problem of what to get his fellow multi-millionaire venture investors on Shark Tank for Christmas this year!
How do we know that the Fed pays attention to the things we write? Previously, commenting on the apparent lack of effect that the Fed's latest round of quantitative easing has had on stock prices, we observed:
- The Fed is doing it wrong. In the two previous rounds of QE, the Fed purchased large quantities of U.S. Treasuries. So far in this round, the Fed is only purchasing Mortgage Backed Securities. The stock market just doesn't get the same bang for the buck as when the Fed buys up Treasuries, which acts to reduce long-term interest rates across a wider swath of the economy, which is really what helped boost stock prices in earlier rounds.
- QE, as an effective policy, is running out of gas. The interest rates that the Fed might hope to lower in its QE programs started off at a much lower level, and a lot closer to their minimum zero level, than in its previous incarnations. With less room to maneuver, the Fed's actions just don't have the same oomph they once did.
And now, the Fed has announced that they've gotten the message and are going to start "doing it right" and also buy Treasury securities, which will give this latest generation of QE more "oomph". Interestingly, they've also announced the economic targets that must be satisfied before they will discontinue the plan. All together, that suggests to us that they're thinking the future for the economy in much of 2013 will be somewhat worse than other official sources are letting on....
It looks like that as far as the pace of layoffs in the U.S. is concerned, the impact of Hurricane Sandy lasted for just three weeks.
Assuming that the volatility we've previously noted dies down, we should have enough data to begin projecting the new trend in initial unemployment benefit claim filings within a few weeks.
Suppose we converted a house to run entirely off the grid on green, renewable energy sources like solar or wind, so that we would never again have to pay an electric bill or generate any carbon emissions for the power it consumes, as President Obama would seem to desire all Americans do. What possible environmental harm would we cause by lighting it with the soon-to-be-banned 100-watt incandescent bulbs, which we might note are far more friendly for the environment and are much less costly than their CFL replacements? And if the answer is "none", why must we have the government progressively ban all incandescent light bulbs from production?
On a development note, we can't help but notice that if we combined the site traffic our tools for determining individual, family and household income distribution percentile rankings, they would collectively represent the most popular tool ever on Political Calculations. So guess what will be coming soon!...
And speaking of coming soon, here's our Christmas countdown clock!
We're going to look at the change in the U.S. employment situation since the total level of employment in the U.S. peaked five years ago in November 2007, but first, let's look at the change since October 2012.
Through November 2012, the U.S. employment situation for young adults Age 20-24 was good, for all older adults it was bad, and for teens, it was "meh".
Overall, some 6,000 more teens and 62,000 young adults than in October 2012 gained jobs, while some 190,000 fewer individuals Age 25 and older were counted as being employed. Doing the math, the net change in the number of jobs in the month from October 2012 to November 2012 came in for a loss of 122,000.
The total number of employed Americans fell by that number to 143,262,000 in November 2012, which is 3,333,000 less than the so-far all-time peak number of of 146,595,000 Americans who were counted as having jobs in November 2007.
The number of employed teens in the U.S. has declined from 5,927,000 in that month to 4,479,000 some five years later. Over this period of time, the number of young adults Age 20-24 with jobs has fallen by 405,000 from 14,001,000 to 13,596,000 and the number of older adults has fallen by 1,480,000 from 126,667,000 to 125,187,000.
Looking at the total decline in the number of employed Americans through November 2012, jobs lost by U.S. teens account for 43.4%, young adults for 12.2% and adults Age 25 and older account for 44.4% of all jobs that have disappeared from the U.S. economy over the last five years.
In November 2007, teens represented 4.0% of the entire U.S. workforce. In November 2012, teens account for just 3.1% of the reduced U.S. workforce. At this point, jobs that were most likely to have been held by teens are 14 times more likely to have been negatively affected by the employment situation over the past five years than their numbers among the entire U.S. workforce would suggest.
In retrospect, it seems that the U.S. Congress' action to boost the minimum wage by nearly 41% in three stages from 2007 through 2009 without doing anything to boost the revenues of teen employers by an appropriate percentage to compensate them for their higher costs of doing business during this period of time wasn't such a hot idea.
According to S&P's latest Monthly Dividend Action Report [Excel spreadsheet], the month of November 2012 was a record month that saw some 3,327 U.S. companies make some kind of declaration involving their dividends (that's not the record!) Here are the astounding numbers:
197 companies acted to increase their cash dividend in the 8th best month on record (since January 2004), and the most in any November on record. The all-time record for regular dividend increases announced in a single month is 246, which was set in February 2007.
228 companies acted to make a special cash dividend payment to their investors, the most ever. To put that number in context, the most announcements that companies would pay an extra dividend in an entire year was 233 in 2007. For the period of time for which we have data, the average number of extra dividends announced per month from January 1994 through October 2012 is 33. The previous record of 97 in one month was set in December 2010.
But this drive to pay out dividends in 2012 before the tax rates on them goes up in 2013 masks the deteriorating situation for many companies in the U.S. The number of companies announcing they would cut their cash dividend payments in the month of November 2012 rose to 27, up one from the previous month.
To put that increase in perspective, the average number of companies that act to decrease their cash dividends in a non-recession month is 4. The 54 dividend cuts that have been announced in just the last two months alone is more than would be expected in an entire average non-recession year.
In the chart above, it takes at least 10 companies announcing dividend cuts in a given month for the U.S. economy to be considered to be in recessionary territory. Through November, U.S. companies have announced 151 dividend cuts in 2012.
In Part 1, we pointed out that companies waited a little over a week after the re-election of Barack Obama on 6 November 2012 to begin responding to the guarantee of higher taxes on dividends that would take effect on 1 January 2013. In today's post, we're simply going to point out how much money they've have pulling into 2012 to avoid those higher taxes waiting in 2013 and from where:
As of the dividend futures data available for 10 December 2012, companies such as Walmart and numerous others have collectively pulled about 4.3% of the total amount of dividends that had been projected to be paid out in the first quarter of 2013 into the fourth quarter of 2012 instead.
While some companies like Oracle have simply pulled ahead the dividends that they had originally intended to pay out in the first, second and third quarters of 2013, at least 123 other companies at this writing have announced they will pay a special dividend before the end of 2012. One of those companies, Costco, has actually taken out a loan to pay a special dividend to its investors before the higher dividend tax rates of 2013 take effect.
These companies are rushing to take these actions because many of their largest shareholders fall into the income range that will be most negatively impacted by the higher taxes on dividends. We estimate that over two-thirds of all dividends go to these individuals, who are often the primary owners or founders of the companies that pay out dividends.
The planet Neptune has never been seen by anyone looking at the night sky through just their own eyes. So distant is it from the sun that the light it reflects toward the Earth is so faint that the planet is effectively invisible in the darkness of night. And yet, the outermost large planet of our solar system was discovered by astronomers who knew exactly where to look....
Following William Herschel's discovery of Uranus in 1781, the world's astronomers went to work to observe and describe the seventh planet of the solar system, taking detailed measurements of its trajectory in space.
Forty years later, French astronomer Alexis Bouvard published detailed tables describing Uranus' orbit about the sun. More than that however, his tables incorporated the lessons learned about planetary orbits from Johannes Kepler and Sir Isaac Newton to chart the path Uranus would follow into the future.
But then, something strange happened. Significant discrepancies between Bouvard's projected path for Uranus and its actual orbit began to be observed - irregularities that were not observed in the tables he had created to describe the orbital paths of the planets Jupiter and Saturn using the same methods. Soon, observations and detailed measurements confirmed that Uranus was moving along a path that was not described by Bouvard's careful calculations.
These irregularities led Bouvard to hypothesize that an as yet unseen eighth planet in the solar system might be responsible for what he and other astronomers were observing.
Over twenty years later, astronomer Urbain Le Verrier was working on the problem, taking a unique approach to resolving it.
What made Le Verrier's work unique is that he applied the math developed by Sir Isaac Newton to describe the gravitational attraction between two bodies to solve the problem. Here, he used Newton's theory to anticipate where an as yet unknown, but more distant planet also orbiting the sun would have to be to create the effects observed upon the position of the planet Uranus in its orbit.
Le Verrier completed his calculations regarding the position of the hypothetical eighth planet on 1 June 1846. A little over three months later, on 23 September 1846, the planet Neptune was observed for the first time at almost exactly the position in space where Le Verrier predicted it would be, confirming Newton's gravitational theory in the process.
We're going to do something similar today to explain why household income inequality in the United States has increased over time, even though there has been no change in individual income inequality.
Our first chart below is based on data taken from the U.S. Census' data [Excel spreadsheet] on the inflation-adjusted median and mean income for all Americans from 1947 through 2010, which we've presented in terms of constant 2010 U.S. dollars. For reference, we've also indicated the NBER's official periods of recession in the U.S. during this period with the shaded red vertical bands on the chart:
Next, we took the U.S. Census' breakdown of inflation-adjusted median income for both men and women for each of these years [Excel spreadsheet] and used the math that applies to log-normal distributions to construct the combined median income that applies to individuals. Our results are shown in the chart below, along with the actual median incomes reported by the U.S. Census so we can compare our calculated results with them:
As you can see, our calculated results in creating a weighted median from the subsets of median income data for men and women are very close to the actual real median income numbers for all individuals. Here, because per capita income has been demonstrated to follow a log-normal distribution, we are able to use this math to either combine or extract subsets of data that have never been officially presented.
As an aside, we achieved the results above by treating the reported median income data the way we might calculate a weighted average. The beauty of the log-normal distribution math is that we can do this with medians, which we ordinarily could not do otherwise.
In the chart above, you can see the effect of the changing composition of the U.S. workforce, as the relative share of women earning incomes in the United States has increased since 1947. In 1947, the median income for individuals is much closer to the median income for men than it is for women. By 2010 however, we see that the median income for individuals is about halfway in between the median incomes for men and for women, reflecting that nearly equal share that both sexes now have among all individual income earners in the U.S.
The U.S. Census Bureau provides the median income data for individuals (or persons), men and women. It also reports median income data for both male and female wage or salary earners [Excel spreadsheet], whom we'll simply describe as Working Men and Working Women.
Using the math we demonstrated above with this data, we can extract the median incomes for two categories of people for whom the U.S. Census has never reported median incomes: men and women with incomes who do not earn wages or salaries, or as we'll describe them from now on, Non-Working Men and Non-Working Women! Today, we're putting what we found for all U.S. individual income earners together for the first time:
Now, let's combine our median income earners into two-person households, pairing working men and women, working men and non-working women, non-working men and working women and finally non-working men and non-working women. We've shown our results below, along with the U.S. Census' official median income for U.S. households:
Well, look at that! The households formed by our single-wage and salary income earning couples from 1947 through 2010 closely parallels the actual real median income for U.S. households with a working man and non-working woman over that time (except for the years 1974 through 1977, where there seems to be an anomaly in the Census' data for working men - and here, the actual median splits the difference!) Also keeping in mind that the actual median household income might include the income contributions of additional people (say individuals between the ages of 16 and 24 who might be working part time at minimum wage jobs while also attending school and living at home with their parents), which likely accounts for the difference between the two, we've pretty much just demonstrated that we can successfully model basic U.S. households using just the data that applies for U.S. individuals.
But wait! What about single person households? Our next chart throws them into the mix as well!
Using the figures for 2010, we approximated the income percentiles for each of our single and two-person median income earning households. The table below reveals our results (our model should put each approximated percentile within 0.2 of the actual percentile!):
Household Type | 2010 Median Income | Approximate Income Percentile |
---|---|---|
Working Men and Working Women | $64,075 | 61.4 |
Working Men and Non-Working Women | $50,026 | 50.7 |
Working Women and Non-Working Men | $49,344 | 50.1 |
Non-Working Men and Women | $35,295 | 36.7 |
Working Men Only | $37,102 | 38.6 |
Working Women Only | $26,973 | 27.7 |
Non-Working Men Only | $22,371 | 22.4 |
Non-Working Women Only | $12,924 | 11.5 |
It occurs to us that all we would need to increase the income inequality among households in the United States is to increase the nation's percentage of single person households among all households. That would work by increasing the number of households at the lower end of the income spectrum, even though it would have absolutely no effect upon the measured income inequality for individuals. The U.S. Census Bureau shows the change in the number of single person households since 1960:
Here's the U.S. Census Bureau's Gini index measure of the amount of income equality among U.S. households for the years from 1947 through 2010:
And here is the Gini index measure of the amount of income equality among U.S. individuals for the years from 1947 through 2005 (the data since 2005 is presented here - it's similar to all that recorded since 1960 in the chart below):
The relevant data in the chart above is the Gini measure indicated with the hollow circles, which is based on the "fine", or more detailed, income bins reported by the U.S. Census in its annual Current Population Survey. The other data in the chart, indicated by solid diamonds, represents income distribution data reported by the U.S. Census in larger, or more "coarse" income bins, which are less detailed and are therefore a much less accurate measure of the nation's level of income inequality in any given year.
Looking at where all the data in these three charts intersect and overlap, What we find is that since 1960, the level of income inequality for U.S. individuals as measured by the "fine" Gini index is nearly constant, but has increased significantly for U.S. households. What has changed over that time is the composition of U.S. households, with a steady increase in the percentage of single person households.
Without a corresponding increase in the measured income inequality for U.S. individuals, the increase in the measured income inequality for U.S. households has been almost entirely driven by the increase in the number of single person households over time.
So income inequality among U.S. households isn't increasing because the rich are getting richer. That means that policies intended to right this situation by going after the rich in the name of "fairness" are guaranteed to fail, because the real cause of the increase in income inequality among U.S. households over time is something that cannot be fixed by such actions.
If only the people pushing such policies could see that....
And that concludes our eighth anniversary post. Thank you for joining us today - we greatly appreciate your choice to spend so much time with us (we really do try to draft shorter posts!)
Our anniversary posts typically represent the biggest ideas and celebration of the original work we develop here each year. Here are our landmark posts from previous years:
Kitov, Ivan. "Modeling the evolution of Gini coefficient for personal incomes in the USA between 1947 and 2005," MPRA Paper 2798, University Library of Munich, Germany. 2007.
Lopez, J Humberto and Servén, Luis. "A Normal Relationship? Poverty, Growth and Inequality". World Bank Policy Research Working Paper 3814, 2006.
Pinkovskiy, Maxim and Sala-i-Martin, Xavier. "Parametric Estimations of the World Distribution of Income". NBER Working Paper No. 15433. October 2009.
Political Calculations. The Distribution of Income for 2010: Households. 14 September 2011.
U.S. Census Bureau. Changing American Households. [PDF document]. C-SPAN. 4 November 2011. p. 6.
U.S. Census Bureau. Table P-2. Race and Hispanic Origin of People by Median Income and Sex: 1947 to 2010. [Excel spreadsheet]. September 2011.
U.S. Census Bureau. Table P-4. Race and Hispanic Origin of People (Both Sexes Combined) by Median and Mean Income: 1947 to 2010. [Excel spreadsheet]. September 2011.
U.S. Census Bureau. Table P-53. Wage or Salary Workers (All) by Median Wage and Salary Income and Sex: 1947 to 2010. [Excel spreadsheet]. September 2011.
Wendt, Phil. Income Disparity by the Numbers. Phil Wendt's Studio. 26 December 2011.