Political Calculations
Unexpectedly Intriguing!
September 30, 2014
Signed LeBron James Miami Heat Jersey - Source: U.S. Treasury Department - http://www.treasury.gov/auctions/treasury/gp/riverside_genmerch.html

Earlier this year, the hapless Cleveland Cavaliers signed a two-year deal for its former player, NBA superstar LeBron James, for $42.2 million.

Was that a smart business decision for the Cavs?

To find out, we'll do some of the same math that economist LeRoy Brooks did to estimate that Cleveland's signing of LeBron James could add nearly $500 million to the economy of northeast Ohio. Only instead of considering James' potential impact on the region, we'll just look at the only real revenue that the team itself can fully count upon: its regular season ticket sales.

Why just regular season ticket sales? First, we need to exclude the possibility that the Cavaliers will benefit from additional ticket sales during postseason games, because that's not guaranteed revenue. After all, even with having signed the world's top ranked professional basketball player to its roster, there is no absolute guarantee that the Cavaliers will make it to the post season even though they have significantly improved their prospects. And that would be particularly true in the event that James is injured during the term of the contract.

In fact, to account for the potential of injury during the course of the season, we'll add an adjustment to account for the potential that James will play less than 100% of the team's home games during the season.

We'll also need to rule out money from other sources, such as television contracts, since those are largely locked up at this point until the league negotiates new contracts to replace the current ones, which will expire two years from now. The team does stand to benefit from licensing Cavalier's jerseys with James' name and number on it during this period of time, but we want to keep the math simple.

Here are the basic numbers we'll be working with:

Cleveland’s home-ticket prices last year averaged $68.17, according to TiqIQ. In 2009–10, the last season James played in Cleveland prior to leaving for Miami, Cavs ticket prices averaged $195. Last season, Cavs fans paid $202.74, on average, to watch Miami beat Cleveland.

Miami had the NBA’s highest average ticket price last season, at $245. To account for the lower cost of living in Cleveland, let’s make what still might be a conservative estimate: Cavs tickets go for $210, on average (remember — this doesn’t mean there won’t be plenty of seats for far less). Cavs attendance last year averaged 17,329 per game. With James, the Cavs are likely to fill up their arena’s capacity of 20,562. Spread over 41 home games, James could bring in $129 million in additional ticket revenue for Cleveland.

Let's now find out if it really made business sense for Cleveland to bring LeBron James back to northeast Ohio. If you're reading this article on a site that republishes our RSS news feed, click here to access a working version of this tool!

Ticket and Attendance Data
Input Data Without Superstar When Superstar Plays for Visiting Team
Average Sale Price of Home Game Ticket
Average Home Game Attendance During Season
Superstar Contract Data
Number of Home Games per Season
Number of Seasons Superstar Will Play for Team Under Contract
Maximum Compensation That Superstar Will Earn for Playing for Home Team
What Percentage of Home Games Is the Superstar Likely to Play? [Enter value between 0 and 100]

Should Your Team Sign That Superstar?
Calculated Results Values
Potential Additional Revenue per Home Game from Ticket Sales
Likely Additional Revenue per Regular Season from Home Game Ticket Sales
Likely Additional ReguRevenue Over Term of Superstar Contract
Potential Profit (or Loss if Negative) Over Term of Superstar Contract
The Bottom Line

Playing with the numbers, we find that the "LeBron effect" on the teams home game ticket prices can justify the expense of signing a "max" deal with him for the Cavaliers, even if the superstar ultimately misses playing as many as 79.4% of the team's home games!

Believe it or not, this kind of math applies outside of the world of sports. For example, it can help media organizations like People and Hello! determine if it makes sense to pay A-list celebrities like Brad Pitt and Angelina Jolie some $5 million for exclusive copies of their wedding photographs to put on their magazine covers. Or for that matter, how much they should pay D-list celebrities, like the Kardashians, for whatever photos they're hawking these days.

We'll close by sharing the classic video of Frank Caliendo impersonating Morgan Freeman reading the letter that LeBron James wrote to explain why he wanted to return to Cleveland, which is simply outstanding.



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September 29, 2014

How many of you recall what we wrote at the beginning of September 2014 about the dynamics of a stock market where investors are shifting their focus from one point in time in the future to another?

What we observe is that at present, investors would appear to be focused on 2014-Q4 in setting today's stock prices, which is a relatively recent development - one that has come about largely as a result of better than expected earnings reported by many companies in the S&P 500 in recent months. Prior to that development, investors had been largely focused on 2015-Q2, which is the period during which many believe the Fed will begin hiking short term interest rates.

But the danger in focusing on the fourth quarter of 2014 is such that any shift in focus by investors to another future quarter would be associated with either flat or falling stock prices. And then there is the question of what expectations of the future will replace those of the soon to expire 2014-Q3. Or the next to expire 2014-Q4. Investors can hold their attention on 2014-Q4 through the end of the third quarter of 2014, but that won't be an option for long in the fourth quarter.

It's that kind of scenario with expectations that explains why the month of October is historically feared by investors, because it can come with the greatest downside potential when compared with every other calendar month. If those new expectations are more negative than the ones they replace, then the stock market's reaction will likely be as well.

As best as we can tell, investors were switching their forward-looking focus back and forth between 2014-Q4 and 2015-Q2 as they were making their investment decisions and setting stock prices last week, which accounts for the volatility. Our rebaselined model of the potential alternative trajectories that stock prices are likely to take perhaps best illustrates what we think was going on:

Alternative Futures for S&P 500, 30 June 2014 - 30 September 2014 (Baseline for Projections Set Back from 1 Year to 2 Years Earlier), Snapshot on 26 September 2014

Now for the bad news. We got the data for what the S&P 500's dividends are expected to be in 2015-Q3, and the scenario isn't good. Here's what we see after we calculate the change in the year-over-year growth rate from quarter to quarter for each of the future quarters for which we have data.

Change in Growth Rates of Expected Future Trailing Year Dividends per Share with Daily and 20-Day Moving Average of S&P 500 Stock Prices, through 26 September 2014

In terms of basic growth potential, none of the future quarters is positive. The best case scenario, where investors would be steadily focused on 2014-Q4 or 2015-Q2 in setting stock prices, would be consistent with stock prices largely moving sideways within a relatively narrow band (say plus or minus 5%).

If investors shift their focus among these and the other alternative futures, then stock prices are likely to be very volatile, with a great likelihood of decline. The worst case scenario here would be for investors to suddenly make their investment decisions according to the expectations associated with 2015-Q1, which would coincide with a crash for stock prices in the long dreaded 10% (or more) correction.

The only thing that we will guarantee is that sometime during this next quarter, in the absence of a major fiscal crisis, investors will stop paying attention to 2014-Q4.

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September 26, 2014

When you were a kid, did you ever wrap your bike around a tree?

You know what we mean. You were out riding around and having fun with a bunch of your friends, and then did something meant to impress them that, in retrospect, turned out to not be very bright thing to have attempted, with the result of that unfortunate life lesson involving extensive road rash treatment for yourself and the need to untangle the frame of your hopelessly damaged bike from the tree trunk where it impacted.

Now, what if the frame of your next bike was designed to be wrapped around a tree, post or other object? And what if that was a key selling point for the bike? Would you be interested?

It could well be if you were concerned about the risk of having your new bike stolen. In fact, the fear of that risk motivated three Chilean design students to develop a bicycle frame with an integrated locking device - one where the bike would be rendered completely unrideable if it were ever cut. Via Core77, meet the "Yerka":

One thing we appreciate about the bike's design is that it eliminates the need to carry a bike lock while riding, which boosts its convenience factor. The trade off for that capability though is performance - the bike's frame will be heavier and less capable of absorbing impacts than a traditional frame design.

It also differentiates it from this competing concept, which basically makes the seat into the bicycle version of the "Club" steering wheel lock.

But that also means that the "Yerka" shouldn't be ridden in a way where the frame might accidentally get wrapped around a tree on accident!

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September 25, 2014

Earlier this week, we indicated that we were about to get our first glimpse of the expected future for the S&P 500's quarterly dividends per share for the third quarter of 2015.

We now have it! Here's the snapshot of the amount of cash dividends expected to be paid in each quarter between the present and the 2015-Q3, according to the Chicago Board of Exchange's implied future dividends per share indicators:

S&P 500 Past and Expected Future Dividends per Share, 2013-Q1 through 2015-Q3

This chart mixes the historic data for the S&P 500's quarterly dividends per share recorded by Standard & Poor [Excel spreadsheet] from 2013-Q1 through 2014-Q2 with the expected future dividends recorded by the CBOE's dividend futures contracts.

But because of how the CBOE's dividend futures work, there's a mismatch between the two sources of data. Here, the dividend futures contracts run through the third Friday of the month ending the indicated calendar quarter, while the S&P data runs to the very end of month ending the indicated calendar quarter. That mismatch in terms results in discrepancies between the quarterly dividends reported by each source of data.

For most quarters, the discrepancies that result from that term mismatch is relatively small, but our readers should expect that the data indicated for the future quarters will be revised.

That's especially true for the almost completed quarter of 2014-Q3, where the value indicated in the chart by the CBOE's dividend futures represents the amount of cash dividends paid out by S&P 500 companies between the time the 2014-Q2 dividend futures contract expired and the just expired 2014-Q3 dividend futures contract. Because we're still in the period before official end of the calendar quarter used by S&P, that figure will very likely be revised.

Leaving aside changes in the actual expectations for the amount of dividends that will be paid in future quarters, we should also note that the biggest discrepancy between what the futures data indicates and what S&P will finally record the data to have been will occur in the data for the fourth quarter of the current year and the first quarter of the next one, with the typical adjustment being that the data for the fourth quarter of the current year will tend to be adjusted upward and the data for the first quarter of the next year will tend to be adjusted downward when S&P reports its figures.

Update 25 September 2014 11:41 AM EDT: Oh, by the way, in case anyone is wondering why stock prices are down so much and so seemingly unexpectedly this morning, these future expectations are why. We'll explain more next Monday, and then we'll go on vacation for a week....

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September 24, 2014
Source: ehp.niehs.nih.gov/121-a26/

Last week, shortly after the U.S. Census Bureau released its data on the distribution of income in the United States for 2013, we generated our tool that lets you estimate what your income percentile might be. Today, in response to a request from one of our readers, we're going to go in the opposite direction!

Our tool below reverses the math we originally developed to model the cumulative distribution of income in the U.S. to instead estimate the income that approximately corresponds to a given income percentile. All you need to do is to enter the income percentile for which you're interested in estimating the corresponding income, and we'll put you within a few percentage points of it!

If you're reading this article on a site that republishes our RSS news feed, click here to access a working version of this tool!

Income Percentile
Input Data Values
Income Percentile [Enter a value between 1 and 99]

Approximate 2013 Income for Demographic Group
Calculated Results Values
Individuals
- Men
- Women
Families
Households

The tool above should be most accurate in the range of incomes that fall between the tenth and ninety-fifth percentiles for each group, which is an artifact of the original data, which doesn't provide much detail for the distribution of income at these extreme ends of the income spectrum.



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September 23, 2014

In previously analyzing what broke the job market in the United States, we pointed to the passage of the Patient Protection and Affordable Care Act (more popularly known as "Obamacare") and offered a hypothesis for why the number of part time jobs in the U.S. has not increased with the economic recovery from recession as many had predicted.

But it is evident that the feared outcome of more part time jobs being created at the expense of full time jobs has, at least since the Fed began stimulating the U.S. economy through its various quantitative easing programs, has not come to pass. What we observe instead is that it is part time employment that has not increased in any meaningful way since Obamacare became law.

So we'll offer a hypothesis for why that might be: the Affordable Care Act's lowered definition of the number of hours that need to be worked in a week to qualify as full time employment is leading employers to create considerably fewer jobs where the number of hours worked falls in the range between 30 and 35 hours per week.

That range is significant because the Bureau of Labor Statistics and the U.S. Census Bureau, who collect the data on the number of Americans working in full time and part time jobs, did not change their definition of the number of hours of work each week that qualifies as full time employment: a minimum of 35 hours per week.

It is then that part of the job market that Obamacare has broken, and there is some evidence that the U.S. job market is not adding a meaningful number of jobs within that margin. And we suspect that the absence of jobs being created within that margin plays a big role in the assessment of Americans that there is and has been no meaningful economic recovery since the official end of the last recession.

Today, we're going to make the transition from noting that there is "some evidence" to instead note that we now have "definitive evidence". Here's the proof of our hypothesis, in which we've visualized the data for the number of Americans counted as being employed in part time jobs by the number of hours they work each week for the years from December 2008 through the present.

Trailing Twelve Month Average of Number of Americans Working Part Time by Hours of Work Each Week, December 2008 through Present

In this chart, we've taken the non-seasonally adjusted data reported by the U.S. Bureau of Labor Statistics in their Employment and Earnings reports for each month from January 2008 through August 2014 and calculated the twelve month trailing average of the number of Americans counted as being employed part time by the number of hours they work each week, which allows us to account for the effect of seasonality in the data. In doing that, we omitted the data for September 2009 from our calculations because it was such a clear outlier for the surveyed data results.

Having done that exercise, we can use the graphed results to verify that the passage of the Affordable Care Act (ACA) is principally responsible for the deterioration of the part-time job market since it was signed into law on 23 March 2010.

What we observe is that the number of Americans employed part time peaks in March 2010, just as the law was passed. After that time, employers began reducing the number of people on their part time payrolls pretty much across the board, but most significantly for those working "near full time" - between 30 and 34 hours per week.

That level is particularly significant because the ACA redefined the number of hours that an employee could work before being classified as a full time employee for the purpose of enforcing the law's employer mandate, which requires employers with more than 30 full time equivalent employees to either pay a large tax of $2,000 for each uninsured employee above that number or to provide costly health insurance coverage for all their employees at a cost of 3 to 7 times the amount of the tax per uninsured employee.

Consequently, in seeking to avoid having such dramatically higher expenses or taxes imposed upon them, U.S. employers began significantly reducing the number of these near full time workers from their payrolls almost immediately after the passage of the law.

But the way that the Affordable Care Act's employer mandate would be implemented carried another catch for employers, which ensured that additional job losses would occur for Americans working in near full time jobs in the months ahead of key dates specified in the law. Business owner and blogger Warren Meyer explains:

In March 2010, the PPACA was passed. Looking at the jobs data, one can date the stall in the economic recovery almost precisely from the date the PPACA was passed (e.g. here).

The more important date, though, is January 1, 2013. This is a date that every business owner was paying attention to at the time but which seems entirely lost on the media. All the media was focused on the start-date of the employer mandate on January 1, 2014. Why was the earlier date important? Let's go back in time.

At that time, the employer mandate had yet to be delayed. The PPACA and IRS rules in place at the time called for a look-back period in 2013 where actual hours worked for each employee would be tracked to determine whether the employee would classified as full or part-time on the Jan 1, 2014 start date. So, if a company wanted to classify an employee as part-time at the start of the employer mandate (and thus avoid penalties for that employee), that employee needed to be converted to part-time as early as possible, preferably before 2013 even started and at worst by mid-year 2013 [sorry, I typo-ed these dates originally].

Unlike the government, which apparently waits until after the start-up date to begin building large pieces of major computer systems, businesses often tackle problems head on and well in advance. Faced with the need to have employees be working 29 hours or less a week in the 2013 look-back period, many likely started making changes back in 2012. Our company, for example, shifted everyone we could to part time in the fourth quarter of 2012. I know from talking to the owners of several restaurant chains that they were making their changes even earlier in 2012. One employee of mine went to Hawaii in October of 2012 and said that all the talk among the resort employees was how they were getting cut to part-time over Obamacare.

Yes, the employer mandate was eventually delayed, but by the time the delay was announced, every reasonably forward-looking company that was going to make changes had already done so. Having made the changes, there is no way they were going to switch back, and then back yet again when the Administration finally stumbles onto an actual implementation date.

If this chart gets any traction over the next few days, expect to see a lot of ignorance as PPACA defenders claim that the fall in low-wage work hours can't possibly have anything to do with the PPACA because the employer mandate has not even started. Now you know why this argument is wrong. The PPACA, and associated IRS implementation rules, drove companies to convert full-time to part-time jobs as early as 2012.

And that's why we see the number of Americans employed on a near full time basis drop in steps in the months ahead of the key Obamacare employer mandate implementation dates of January 1, 2013 and January 1, 2014.

Altogether, we observe that as many as 1,151,000 near full time jobs have been lost from the U.S. economy as a direct result of the passage of the Patient Protection and Affordable Care Act since the time of its passage through August 2014. Moreover, because this reduction in employment is the result of legislation that applies across the entire nation, we must consider this change to be a contributor to structural unemployment for the U.S. economy.

No wonder Obamacare's employer mandate is so increasingly unpopular!

Previously on Political Calculations

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September 22, 2014

Although the Federal Reserve met, issued a statement, and Fed Chair Janet Yellen held a press conference last week, none of it meaningfully affected the trajectory of stock prices, which continued to behave consistently with investors focused on the future quarter of 2014-Q4 in setting their expectations for making investment decisions. As expected, our rebaselined model pretty much nailed where stock prices ended the week.

Alternative Trajectories for S&P 500 Stock Prices, Third Quarter of 2014, Rebaselined Model (Baseline Set 2 Years Earlier), Snapshot on 19 September 2014

Next week should prove interesting because we'll start getting our first glimpse into the expectations for the future quarter of 2015-Q3!

Analyst Notes

What was the earliest point in time at which our rebaselined model could have accurately predicted where stock prices ended up on 19 September 2014?

As we'll demonstrate with the following animation, the earliest we could have anticipated that the S&P 500 would close at that particular level was ten days earlier, after the expectations for 2014-Q4 shifted on 10 September 2014. We'll pick up the action as stock prices began transitioning from investors being focused on the more distant future quarter of 2015-Q2 to the nearer future quarter of 2014-Q4 after 25 August 2014.

Alternative Trajectories for S&P 500 Stock Prices, Third Quarter of 2014, Rebaselined Model (Baseline Set 2 Years Earlier), Animation from 25 August 2014 through 19 September 2014

As we noted last week, we anticipate that stock prices will continue to more closely pace the alternative trajectories indicated by our rebaselined model until mid-October 2014, when the final echoes from the major noise events of a year ago finally dissipate, allowing us to resume using our standard model.

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September 19, 2014

Are you an environmentally-minded person who enjoys cold beer, but feels guilty about using all that electricity to keep beer cool in your refrigerator? Wouldn't you be better off if you could keep your beer cold without drawing any power from the grid at all?

The obvious answer to the question is yes, and believe it or not, there's an invention that doesn't use any electric power but which can keep your beer at cool temperatures all year round, even outside.

The secret is to use a geothermal storage system, which takes advantage of the stable and cool temperatures below ground, which in many areas of the world, just happen to be at temperatures that are nearly ideal for storing beer. That geologic characteristic attracted the attention of a team of four inventors from the small island of Mors in northern Denmark, who launched eCool, a hand-driven conveyor system for storing cans of beer at the depths where it can be kept cool and drinkable all year round.

eCool in-ground beer storage system

Core77 describes what you need to to install the device:

To install the roughly four-foot-long device, you bore a hole into the ground using a garden drill, though they advise that "[the eCool] can be installed with a shovel as well, if you're a real man." Once you've got the hole dug, you insert the cylindrical device into the ground, then load it with up to 24 cans of quaff.

Depending upon where you live, you may need to go deeper to get to your desired temperature, but in principle, using the earth itself to cool your beer is very do-able! Here's an animiation of the system in action:

And to think, for just $369, it could be yours! If you really care about the environment and keeping your beer cool, isn't that a small price to pay?

Other Stuff We Can't Believe Really Exists

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September 18, 2014

After looking at the overall history of the net change in the number of full time and part time jobs in the U.S. since 1968, we had a question that relates to the job market today: What broke the job market in the United States?

To see what we mean, let's focus in on the period since the official beginning of the so-called "Great Recession" in December 2007, when the previous period of economic expansion in the U.S. last peaked according to the National Bureau of Economic Research before falling into recession. The chart below measures the net change in the number of full time, part time and the total number of jobs from that point through the present.

Net Change in Full Time and Part Time Employment Since December 2007 (Beginning of 'Great Recession'), Through August 2014

Here, we see a large increase in the number of part time jobs during the official period of recession as the number of full time jobs plummeted, which coincides with the displacement of Americans working full time during the recession. That increase in part time jobs however basically ends after the U.S. officially exited from the recession, where we also observe the rate of decline in the number of full time jobs begin to decelerate.

By December 2009 however, the number of full time jobs begins to make a robust recovery, as the number of part time jobs begins to decrease slightly, suggesting that a number of Americans working part time transitioned to working full time.

And then, things begin to go haywire in and after March 2010. What had been a robust recovery in full time employment suddenly becomes choked off as working Americans were shunted back into part time employment. The number of Americans working in full time jobs then began to fall until November 2010, which coincides with the Federal Reserve's announcement that it would intervene in the U.S. economy by beginning a new round of quantitative easing to stimulate economic growth.

Shortly afterward, the number of full time jobs began to increase again, but the number of part time jobs remained stagnant. The pattern where Americans working in part time jobs transitioned to full time employment is no longer present, and unlike previous periods of economic expansion, there is no general increase in the number of Americans working part time as the economy recovers.

We won't keep you in suspense. The most likely culprit behind the growth of part time jobs in the U.S. economy was the passage of Obamacare, officially known as the Patient Protection and Affordable Care Act. The chart below shows the stagnation of part time employment in the United States since the Affordable Care Act (ACA) was signed into law in March 2010.

Net Change in Full Time and Part Time Employment Since March 2010 (Passage of Obamacare), Through August 2014

That outcome is contrary to what many expected after the passage of the ACA, where the law's changes to the number of hours that an employee could work before being qualified as a full time employee for whom the employer would be required to provide costly health insurance coverage would lead employers to create fewer full time jobs and more part time jobs.

Here, the law changed the definition of what is considered to be full time employment from an average of 35 hours per week to just 30 hours per week.

But it is evident that the feared outcome of more part time jobs being created at the expense of full time jobs has, at least since the Fed began stimulating the U.S. economy through its various quantitative easing programs, has not come to pass. What we observe instead is that it is part time employment that has not increased in any meaningful way since Obamacare became law.

So we'll offer a hypothesis for why that might be: the Affordable Care Act's lowered definition of the number of hours that need to be worked in a week to qualify as full time employment is leading employers to create considerably fewer jobs where the number of hours worked falls in the range between 30 and 35 hours per week.

That range is significant because the Bureau of Labor Statistics and the U.S. Census Bureau, who collect the data on the number of Americans working in full time and part time jobs, did not change their definition of the number of hours of work each week that qualifies as full time employment: a minimum of 35 hours per week.

It is then that part of the job market that Obamacare has broken, and there is some evidence that the U.S. job market is not adding a meaningful number of jobs within that margin. And we suspect that the absence of jobs being created within that margin plays a big role in the assessment of Americans that there is and has been no meaningful economic recovery since the official end of the last recession.

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September 17, 2014

We have a project underway where we're looking at the net change in the number of Americans who work as full time or part time employees over different periods of time. The chart below shows all the data the Bureau of Labor Statistics and the U.S. Census Bureau have reported since they began regularly tracking each type of employment since January 1968.

Net Change in Full Time and Part Time Employment Since January 1968, Through August 2014

One of the things that stands out in looking at the chart is that part time employment actually grows during periods of recession, while full time employment would appear to take the hit, where a good part of what we're seeing is the result of previously full-time workers being pushed into more marginal, part-time employment. Interestingly though, one other thing that we observe is that while part time employment tends to rise sharply during periods of recession, during periods of economic expansion, it tends to rise slowly with the growing economy.

But with two very noticeable exceptions. The first exception is the period from 1994 through 2001, which coincides with a change in the U.S. Census Bureau's methodology for collecting and reporting employment status data beginning in January 1994. As such, we really can't break out the effect of what might be the result of that change in methodology from other changes affecting the U.S. job market at that time.

The second exception is the period since the official end of the December 2007-June 2009 recession through the present day, where it would seem that the U.S. economy has simply lost the ability to generate new part time jobs outside of recession, suggesting that aspect of the U.S. job market has broken down.

One last major point of interest is the surge in the number of Americans working in full time employment that we observe during the peak of the Dot-Com Bubble in 2000. Perhaps if not for that unsustainable bump, Americans would not have noticed the recession of 2001 as the number of Americans working full time returned to grow at the level of the pre-bubble trend.

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September 16, 2014
Image Source: http://tax.illinois.gov/businesses/taxinformation/income/corporate.htm

Where do you stack up in the distribution of income within the United States?

We can help you answer this question using the data that the U.S. Census Bureau has collected on the total money income earned by individual Americans, as well as for the families and households into which Americans gather themselves!

If you're a visual person, we'll first present the information graphically in animated chart form and then we'll present a tool where you can get a more precise estimate of what your percentile ranking is within each of these groups. In the charts below, first find the income that applies for you on the horizontal axis, then move directly upward to the curve that defines the cumulative distribution of income. Once you've found your place on S-shaped curve in each chart, look directly to the vertical scale on the left hand side of the chart to determine your approximate U.S. income percentile ranking.

The animated chart below will take you through the following progression, showing the cumulative total money income distribution for U.S. individuals, households and families in 2016. Each of the charts will be displayed for five seconds and will cycle back to the beginning after running through each of the charts. The charts will also indicate the median and average income earned by each category.

Animation: Cumulative Distribution of Total Money Income for U.S. Individuals, Families, and Households in 2016Update 13 September 2017: We've updated this animation to feature charts showing the U.S. Census Bureau's income income data for the 2016 calendar year (data for 2017 won't be available until September 2018). Please click here to see the original animation using 2013's income distribution data!

That's it for the pictures - let's find out more precisely where you really fit in! To find out where you, your family or your household ranks among each of these categories, just enter your personal income, your family's income, which includes the incomes of your spouse and other family members who live with you, and also the combined income of just the people who live within the walls of the same household that you do. We'll do some quick math and provide a more better estimate of the percentage of all American individuals, families and households that you outrank given the incomes you enter than you can get from simply reading the chart above!

And as a bonus, we'll also break down the numbers for your Individual income to tell you how you compare to your fellow male and female Americans.

It all starts below! (Unless you're accessing this article through a site that simply republishes our RSS news feed, in which case, you should click through to our site to access a working version of our tool....)

Update 13 September 2016: Now updated with the U.S. Census Bureau's just released income distribution data for individuals, families and household data for the 2015 calendar year!

Income Data
Input Data Values
Select Year of Interest
Your Personal Total Money Income
Your Family's Combined Total Money Income
Your Household's Combined Total Money Income

Your Estimated U.S. Income Percentile Ranking
Calculated Results Values
Among All U.S. Individuals with Incomes
 - Among All U.S. Men with Incomes
 - Among All U.S. Women with Incomes
Among All U.S. Families
Among All U.S. Households

For our readers who live outside of the United States, you can still get in on the action if you convert your income from your local currency into U.S. dollars first!

Update 20 April 2016: If you want a more precise estimate of your income percentile ranking within the U.S., be sure to check out Don't Quit Your Day Job's Income Percentile Calculator. PK uses a more refined version of the U.S. Census Bureau's income data to estimate the distribution of total money income within the United States, which means that compared to our tool, which will put you in the right seating section of the ballpark, his tool can put you in the right row of the seating section. [Be sure to read the comments at that site for a discussion of the tradeoffs we have chosen to make to produce our tool above!]

Notes

The default data we've presented in the tool above represents the average total money income of U.S. individuals, families and households for 2013. Oh, and as a bonus, you can also see where you would have fit in the U.S. income distributions we've modeled going back to 2010 by selecting your year of interest.

In the tool above, your percentile ranking indicates the percentage of Americans who either share your income or earn less than you do. As such, it tells you what percentage of the population you're above in the income-earning food chain.

For example, a percentile ranking of zero would indicate that you are at the very bottom end of the American income spectrum, while a percentile ranking of 100 indicates that you are effectively at the very top end. A percentile rank of 50.0 would indicate that you're within spitting range of being the middle of all Americans, as our tool should be able to place most people within 0.2% of their actual percentile ranking.

Finally, if you're looking for the income data for this year, please note that the U.S. Census Bureau will report the data it collects for this year sometime in September of next year. The delay isn't all bureaucratic - they send out the surveys for income in March of each year, just as or after most Americans fill out their income taxes for the previous year so their income figures are still fresh in their memories, and then it can take the Census Bureau's statisticians up to six months to sort it all out and make some kind of coherent sense of it all!

Income Inequality

If you're seeking to understand what really drives income inequality in the United States, including what has caused it to appear to increase since 1947, you've come to the right place! Here's a short list of our previous posts on the topic:

  • The Discovery of the Unseen (2012) - we go where so-called experts on income inequality fear to tread and reveal that U.S. household income inequality has increased over time mostly because more Americans live alone!
  • The Real Story of "Rising" U.S. Income Inequality - we find that social factors, rather than economic ones, account for virtually all of the claimed increase in income inequality over time.
  • The Major Trends in U.S. Income Inequality Since 1947 (2013, Part 1) - we revisit the U.S. Census Bureau's income inequality data for American individuals, families and households to see what it really tells us.
  • The Widows Peak (2013, Part 2) - we identify when the dramatic increase in the number of Americans living alone really occurred and identify which Americans found themselves in that situation.
  • The Men Who Weren't There (2013, Part 3) - our final anniversary post installment explores the lasting impact of the men who died in the service of their country in World War 2 and the hole in society that they left behind, which was felt decades later as the dramatic increase in income inequality for U.S. families and households.
  • Debunking Income Inequality Theory - we revisit the inflation and deflation of the Dot-Com stock market bubble to demonstrate how the topmost and bottommost portions of the spectrum of U.S. income earners realized directly proportional benefits to each other, even though the measure of income inequality made it seem like income inequality increased during that period.

Data Sources for 2013 Incomes

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-01. Selected Characteristics of People 15 Years Old and Over by Total Money Income in 2013. Total Work Experience, Both Sexes, All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-01. Selected Characteristics of People 15 Years Old and Over by Total Money Income in 2013. Total Work Experience. Male. All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-01. Selected Characteristics of People 15 Years Old and Over by Total Money Income in 2013. Total Work Experience. Female. All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-11. Income Distribution to $250,000 or More for Males and Females: 2013. All Races. Male. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Person Income Tables. PINC-11. Income Distribution to $250,000 or More for Males and Females: 2013. All Races. Female. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Family Income Tables. FINC-01. Selected Characteristics of Families by Total Money Income in 2013. All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Family Income Tables. FINC-06. Percent Distribution of Families, by Selected Characteristics Within Income Quintile and Top 5 Percent in 2013. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Family Income Tables. FINC-07. Income Distribution to $250,000 or More for Families: 2013. All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Household Income Tables. HINC-01. Selected Characteristics of Households by Total Money Income in 2013. All Races. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Household Income Tables. HINC-05. Percent Distribution of Households, by Selected Characteristics Within Income Quintile and Top 5 Percent in 2013. [Excel Spreadsheet]. 16 September 2014.

U.S. Census Bureau. Current Population Survey 2014 Annual Social and Economic (ASEC) Supplement. 2013 Household Income Tables. HINC-06. Income Distribution to $250,000 or More for Households: 2013. All Races. [Excel Spreadsheet]. 16 September 2014.

Data Sources for 2014 Incomes

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2014. Male. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2014. Female. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-01. Selected Characteristics of People 15 Years and Over, by Total Money Income in 2014, Work Experience in 2014, Race, Hispanic Origin, and Sex. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-07. Income Distribution to $250,000 or More for Families: 2014. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-01. Selected Characteristics of Families by Total Money Income in: 2014. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-06. Income Distribution to $250,000 or More for Households: 2014. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-01. Selected Characteristics of Households by Total Money Income in: 2014. [Excel Spreadsheet]. 16 September 2015. Accessed 16 September 2015.

Data Sources for 2015 Incomes

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-01. Selected Characteristics of People 15 Years and Over, by Total Money Income in 2015, Work Experience in 2015, Race, Hispanic Origin, and Sex. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2015. Male. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2015. Female. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-01. Selected Characteristics of Families by Total Money Income in: 2015. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-07. Income Distribution to $250,000 or More for Families: 2015. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-01. Selected Characteristics of Households by Total Money Income in: 2015. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-06. Income Distribution to $250,000 or More for Households: 2015. [Excel Spreadsheet]. 13 September 2016. Accessed 13 September 2016.

Data Sources for 2016 Incomes

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2016. Male. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-11. Income Distribution to $250,000 or More for Males and Females: 2016. Female. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-01. Selected Characteristics of People 15 Years and Over, by Total Money Income in 2016, Work Experience in 2016, Race, Hispanic Origin, and Sex. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-07. Income Distribution to $250,000 or More for Families: 2016. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table FINC-01. Selected Characteristics of Families by Total Money Income in: 2016. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-06. Income Distribution to $250,000 or More for Households: 2016. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table HINC-01. Selected Characteristics of Households by Total Money Income in: 2016. [Excel Spreadsheet]. 12 September 2017. Accessed 12 September 2017.


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