Political Calculations
Unexpectedly Intriguing!
June 6, 2014

How much does each individual crime cost victims and society on average in the U.S.?

That was the subject of a 2010 paper by Kathryn McCollister, Michael French and Hai Fang, in which they tabulated the direct, indirect and total cost of various types of crime in the United States. We've taken the data they originally presented in terms of 2008 U.S. dollars and updated in to be in terms of 2014 U.S. dollars to create both the chart below and the more detailed table presented below it:

The table below provides these total figures and also breaks down both the tangible (direct) costs and the intangible (indirect) costs of each type of crime.

Tangible Plus Intangible Per-Offense Cost for Different Crimes in the U.S., 2014 U.S. Dollars
Type of Offense Tangible Cost Intangible Cost Total Cost
Murder \$1,415,085 \$9,295,559 \$9,891,157
Rape/Sexual Assault \$45,423 \$219,828 \$265,121
Aggravated Assault \$21,441 \$104,631 \$117,841
Robbery \$23,534 \$24,858 \$46,588
Arson \$18,090 \$5,652 \$23,237
Motor Vehicle Theft \$11,599 \$288 \$11,861
Stolen Property \$8,780 N/A \$8,780
Household Burglary \$6,793 \$353 \$7,115
Embezzlement \$6,034 N/A \$6,034
Forgery and Counterfeiting \$5,797 N/A \$5,797
Fraud \$5,541 N/A \$5,541
Vandalism \$5,351 N/A \$5,351
Larceny/Theft \$3,879 \$11 \$3,889

We should note that the total cost of each type of crime does not necessarily equal the sum of the tangible and intangible costs, as there is some overlap in the accounting of various costs that go into the individual tangible and intangible categories.

### Reference

McCollister, Kathryn E., French, Michael T. and Fang, Hai. The Cost of Crime to Society: New Crime-Specific Estimates for Policy and Program Evaluation. Drug Alcohol Depend. Apr 1, 2010; 108(1-2): 98–109. Published online Jan 13, 2010. doi: 10.1016/j.drugalcdep.2009.12.002. Table 5.

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June 3, 2014

Now that we have Sentier Research's report on median household income for the U.S. in April 2014, we're now able to update our charts showing the evolution of the second U.S. housing bubble as it has progressed through its inflation phase. Our first chart looks at the relationship between the trailing twelve month average of median new home sale prices and median household income since December 2000, which allows us to directly compare the second U.S. housing bubble with the first:

With the trailing twelve month average of median new home sale prices falling for the second time in three months, the data suggests that we are seeing the second U.S. housing bubble continue to go through a peaking process.

We should note that we don't necessarily expect the same kind of deflation process that followed the peaking of the first U.S. housing bubble. The April 2014 data indicated that while median new home sale prices fell, the quantity of new homes sold rose. That combination is somewhat healthier than the outright collapse in both sales and prices that defined the deflation phase of the first U.S. housing bubble.

To put the state of the bubble market into the historical context of what a non-bubble driven housing market looks like, our next chart expands the time frame of the first chart back to 1967, which corresponds to the oldest data directly published by the U.S. Census Bureau's on the median income earned by all U.S. households.

Our final chart goes back four years further in time, to December 1963, to capture the oldest data we have showing the trailing twelve month average of median new home sale prices since that time:

### References

Sentier Research. Household Income Trends: April 2014. [PDF Document]. Accessed 30 May 2014. [Note: We have converted all the older inflation-adjusted values presented in this source to be in terms of their original, nominal values (a.k.a. "current U.S. dollars") for use in our charts, which means that we have a true apples-to-apples basis for pairing this data with the median new home sale price data reported by the U.S. Census Bureau.]

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 30 May 2014.

### Previously on Political Calculations

We were among the first to declare that a second housing bubble was forming in the U.S. economy, and we were the first to back it up with an objective framework of analysis and data. Our ongoing analysis is chronicled below....

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May 30, 2014

Ben Schmidt has done some interesting work in visualizing data, with one of his more recent projects involving creating a Sankey diagram of the connections between various college majors and the professions where the people who majored in those field have reported they found work.

Mostly, there's a strong connection between one's major and one's career, but we couldn't help but notice that those who studied Journalism in college tended to end up just about anywhere else:

The Top 10 careers where journalism majors find work, ranked from most popular to least, include:

1. Marketing and Sales Managers
2. Elementary and Middle School Teachers
3. Miscellaneous Managers, Including Funeral Service
4. Lawyers, Judges, Magistrates and Other Judicial
5. Retail Salespersons
7. Wholesale and Manufacturing Sales Representatives
8. First Line Supervisors of Retail Sales Workers
9. Postsecondary Teachers
10. Market Research Analysts and Marketing Specialists

We find it pretty interesting that legal careers rank fourth, especially since those careers require considerably more schooling than the other listed professions, most of which don't require much in the way of schooling, much less schooling in journalism. We wonder what we should make from that?

Perhaps what that tells us is that the people who study journalism are really looking for an easy degree. After all, if you're going to have to bust your back end to earn a law degree after you get some other degree first, why not spend the first four years of your post-secondary education studying something that sounds so much more pretentious than a humble business degree, but that allows you to party more while still keeping the door open to a meaningful career in so many marketing and sales professions in case the law school thing doesn't work out?

Because apparently, it's not like you really have to learn anything like how to actually report news as a journalism major in college these days.

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May 13, 2014

What percentage of an average U.S. business' revenue goes to pay wages and salaries? Or employer-provided health insurance benefits? How much goes to pay debt? How much gross business income is retained on average to be invested in new capital?

You can spend a lot of time searching the web to try to answer basic questions like these and still fall short of filling in much of the whole picture, or you can take advantage of our already having done that for you! Our chart below presents our results graphically:

Here's how we came up with that visual breakdown. Starting with 100% of all revenue, or income, taken in by a business, we first find that through the end of 2013, the share of that amount that goes to compensate labor is 61%, with the remaining 39% being accounted for by capital.

Breaking down the labor side of the accounting, we find that 69% of that portion of total business revenue goes to actually pay wages and salaries, and 31% goes to provide employee benefits.

Continuing to drill down, we found that 30.4% of all benefits is represented by paid leave and supplemental pay, which covers things like vacations, holidays, overtime, bonuses and shift differentials. Employer provided health insurance accounts for 27.5% of all employee benefits, with employer provided life insurance adding an extra 1.5%. Pension and retirement benefits represent 15.6% of all money spent on employee benefits. Finally, mandated benefits for compensating labor, which includes the portions of Social Security and Medicare paid by employers as well as unemployment insurance and workers compensation, makes up the remaining 25% of money spent on employee benefits in the U.S.

Looking at the capital side of the ledger, we find that money here is split into three main categories: payments to debtholders (40% of capital), payments to business owners or shareholders (31.6% of capital) and money that stays with the business to support new capital investments (28.4% of capital).

That's how each of these major categories break down as either a percentage of labor or of capital. Our table below goes one step further and reveals the percentage share of each of these major categories consume of the total income generated in the U.S. going into 2014:

Percentage Share of Component of Labor or Capital of Total Business Income, 2014
Category Component of Labor or Capital Share of Total Revenue (or Income)
Labor Wages & Salaries 42.1%
Paid Leave & Supplemental Pay 5.7%
Employer Provided Health Insurance 5.2%
Pension & Retirement Benefits 2.9%
Mandated Benefits 4.7%
Employer Provided Life Insurance 0.3%
Capital Payments to Debtholders 15.6%
Payments to Shareholders 12.3%
Retained Earnings 11.1%

If you want to drill down even deeper on your own, our data sources are presented below....

### References

U.S. Bureau of Economic Analysis. National Income and Product Accounts Tables. Table 1.12. National Income by Type of Income. [Online Database]. Accessed 10 May 2014.

U.S. Bureau of Labor Statistics. Employer Costs for Employee Compensation - December 2013. [PDF Document]. 12 March 2014.

Bolton, Patrick, Mehran, Hamid and Shapiro, Joel. Executive Compensation and Risk Taking. Federal Reserve Bank of New York Staff Reports. Staff Report No. 456. [PDF Document]. June 2010. Revised November 2011.

Ameta, Michael. Factset Dividend Quarterly. [PDF Document]. 24 March 2014.

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March 27, 2014

Following our most recent post looking at the current trends in U.S. median new home sale prices, we were forwarded the following question:

"Can you quickly and easily 'real dollar' this chart?"

By "this chart", our inquisitor is likely referring to the following chart, showing the overall trend for median new home sale prices with respect to median household income since 1967:

And the answer to their question is: "Why, yes we can!"

It's not as pretty as our nominal value chart, which better describes the world in which people actually live and buy things, but it does clearly show housing prices defying the post-2000 recession as real median incomes fell during the first U.S. housing bubble, and again at present in the second U.S. housing bubble, as median new house prices began rising in 2011, but with no meaningful increase in median incomes to support them.

As for what a non-bubble driven housing market looks like, since we've already demonstrated that household income is *the* primary driver of home prices, we should see a close coupling between median incomes and median sale prices, with both either rising or falling at rates consistent with those observed over extended period of time.

That we're instead seeing nearly vertical movements for prices with respect to household income indicates that other factors have created a situation where housing is becoming increasingly unaffordable.

After all, if the families who earn the median household income are increasingly unable to afford the median price of new homes for sale, something other than the fundamental driver of house prices is seriously skewing the real estate market.

And history tells us that the other factors that can affect home prices do not have any significant "staying power".

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February 25, 2014

It's time once again to take a snapshot of the major trends in S&P 500 stock prices against their trailing year dividends per share over time! Our first chart shows each of the major trends that have existed in the U.S. stock market since December 1991, all the way through to 21 February 2014:

Our next chart zooms in on the current trend, which has been in place since 4 August 2011, and has extended through 21 February 2014 (the chart picks up the action from the end of 2011-Q1 on 30 June 2011):

The difference between the regression equations representing the main trajectory of stock prices with respect to deviations in both charts is attributable to the differences in the data presented in each. The first chart shows the average of the S&P 500's daily closing prices during each calendar month, while the second chart shows the S&P 500's daily closing prices.

Since 4 August 2011, the S&P has behaved in what we would describe as an orderly manner, where the residual variation in stock prices about their central trajectory would appear to be adhering to a normal distribution, where stock prices would be likely to fall between the indicated +/- one standard deviation curves some 68.4% of the time, between the +/- two standard deviation curves some 95% of the time, and between the +/- three standard deviation curves some 99.7% of the time.

While this is a characteristic of periods of relative order in the market, our readers should note that over the long term, stock prices are not normal. Since the statistical hypothesis that stock prices are behaving normally during relative periods of order in the market cannot be ruled however, we can use the tools of standard statistical analysis to gain more insight into their behavior during these periods.

One interesting and new observation we can offer is that the size of the standard deviation for the residual variation of stock prices about the main trend with respect to their dividends per share would appear to coincide with differences between the change of the year-over-year growth rates of dividends per share expected in different future quarters.

Which is something that should perhaps be expected if investors do indeed periodically shift their forward-looking focus from one future quarter to another in setting today's stock prices, since it would account for a good portion of the variation we observe in stock prices during relative periods of order in the market.

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January 22, 2014

Last Friday, 17 January 2014, President Obama secretly contacted us for information about the number of Americans who might be impacted by a significant increase in the U.S. federal minimum wage.

Really. Our friends at the NSA tipped us off to the visit and here's the screen shot of our site traffic log of the President's inquiry:

Specifically, President Obama was interested in our work where we estimated the income distribution of U.S. hourly workers using data originally published by the Congressional Budget Office in 2006.

So he got numbers, but numbers that applied before the last series of increases in the U.S. federal minimum wage. Numbers we later used to show that lots of jobs within the range of incomes affected by the minimum wage increase of 2007 disappeared from the economy. Before the last recession.

Alas, they are also old numbers. And as a source of vital information more trusted by the Executive Office of the President than say the New York Times or government bureaucrats, we thought we'd take this opportunity to update them somewhat. Our chart below shows what we found when we tapped the U.S. Census Bureau's most current data available on the distribution of wage and salary income earned by Americans in 2012 (as the data for 2013 won't be collected until this upcoming March, nor will it be available to the public until September.)

There are two key thresholds shown in this chart. The first is at \$15,080, which corresponds to the annual income that an individual who earned the U.S. federal minimum wage might earn if they worked full-time, 40 hours per week, all year long. Americans who fall below this level today are predominantly those who work fewer than 40 hours per week or who work fewer than 52 weeks per year. That's approximately 39.3 million Americans.

The next threshold is at \$21,008, which corresponds to the annual income that an individual who earns an hourly wage equal to \$10.10 per hour might earn if they worked 40 hours per week, 52 weeks per year. In 2012, there were approximately 14.7 million Americans who earned wages or salaries with an equivalent full-time hourly wage of greater that \$7.25 per hour, but less than or equal to \$10.10 per hour.

As we indicated yesterday, approximately half of these individuals are between the ages of 16 and 24. Which is a shame, because it sure would be nice if they could earn just a little bit of money through a job so they don't have to rack up quite so much student loan debt.

But then, that's a very profitable racket for Uncle Sam, which is one reason why President Obama may have chosen to sign off on this particular nudge. They'll just need that college education to be considered for the new higher minimum wage jobs that don't even really require a college degree.

### References

U.S. Census Bureau. Current Population Survey. Annual Social and Economic (ASEC) Supplement.Table PINC-10. Wage and Salary Workers--People 15 Years Old and Over, by Total Wage and Salary Income in 2012, Work Experience in 2012, Race, Hispanic Origin, and Sex. Both Sexes. [Excel Spreadsheet]. 17 September 2013. Accessed 21 January 2014.

January 21, 2014

We thought it might be fun to update some of our previous analysis of the U.S. minimum wage, now that we have both minimum wage and inflation data through 2013. First, we should note that there is more than one minimum wage in the United States, with the federal minimum wage often setting the effective floor for those states that don't set their minimum wage at a higher level. Here's what the last 20 years looks like for the federal minimum wage and those states that do set their minimum wage at significantly higher levels:

While kind of cool to look at, with so many different minimum wages, we think that most people would rather refer to a single number that represents what we'll call the national average minimum wage. So we calculated what that single number is for each year from 1994 through 2013, weighting each state's minimum wage by its percentage of the U.S. population. Our next chart shows the results of our calculating the nominal (non-inflation-adjusted) minimum wage for each year of the last two decades:

But wait! What about inflation? Well, we redid the math we did above to calculate the national average minimum wage to adjust it for inflation as well, showing the last 20 years in terms of constant 2013 U.S. dollars!

Now for a real challenge! How would changing the national average minimum wage affect the people most likely to earn the minimum wage?

For that, we'll need to create a demand curve, pairing the inflation-adjusted national average minimum wage with the number of Americans between the ages of 16 and 24 who earn incomes. Or rather, the U.S. teens and young adults who collectively have represented approximately half of all Americans who earn incomes that are consistent with the minimum wage over the past decade (and probably much longer than that, since the BLS only provides data on the characteristics of minimum wage earners back to 2002).

Our U.S. minimum wage demand curve chart is below. The data for the number of teens and young adults with income is taken from the U.S. Census' Annual Social and Economic Supplements going back to 1994 (the first year the Census began publishing this data in digital-friendly format). The data for state minimum wages is from the Bureau of Labor Statistics, where we adjusted the data to account for inflation, according to data also published by the BLS. All we did was to connect the dots, or at least those that weren't skewed by the impact of the 1997-2003 Dot-Com Bubble, and run a simple linear regression....

Going by the regression, for every \$1 increase in the inflation-adjusted national average minimum wage (expressed in terms of constant 2013 U.S. dollars), some 1,265,000 fewer teens and young adults can expect to have incomes.

So what would increasing the U.S. minimum wage to \$10.10 per hour, as desired by a number of politicians in Washington D.C., affect the teens and young adults who make up half of all minimum wage earners? Especially if the politicians have no plan or even a clue for how to increase the revenues earned by the businesses that pay U.S teens and young adults to cover the higher cost of keeping these least experienced, least educated and least skilled Americans on their payrolls?

To answer that question, we built a tool you can use to answer that question for yourself, or perhaps for some other hypothetical minimum wage that you would like to consider. Enjoy!

Proposed Minimum Wage Data
Input Data Values
Proposed Minimum Wage [in 2013/14 U.S. Dollars]

Impact Upon American Teens and Young Adults
Calculated Results Values
Number with Incomes After Proposed Minimum Wage is Implemented

To put that result in context, we estimate that about 26.6 million teens and young adults earned incomes in 2013 (we won't have a firm number until September 2014, as the data won't be collected until this upcoming March.) That means that about 1 in 8 Americans between the ages of 16 and 24 could reasonably expect to no longer be able to earn incomes after this particular minimum wage increase would go into effect. Unless there's a "lot" of inflation!...

As for those pushing the minimum wage increase, we would recommend that if they really think its a good idea, they should commit to doing everything possible to have the increase take effect in April 2014.

December 6, 2013

What is the lasting economic impact of the men who died while serving their country in war?

In our last installment, we showed that the single greatest factor behind the exponential increase in the number of single person households after the 1930s was the loss of over 416,000 men who died while in military service during the Second World War. The vast majority of these men had been drafted into military service, all between the ages of 18 and 37, where they were inducted after being selected through a lottery process operated by their local draft boards.

That lottery process ensured that approximately equal numbers of healthy American men by year of birth would enter into military service, which explains why the average age of the men who served in the U.S. military during World War 2 was 26. And since over 87% of U.S. military casualties in the war occurred in 1944 and 1945, enough time had elapsed from when President Franklin D. Roosevelt imposed the draft by executive order for the population of American casualties to reflect the age distribution of those who had been drafted.

If these more than 416,000 men had lived, they would have turned Age 65 in the years from 1970 through 1992. Instead, if we assume that the number of American casualties are approximately equally distributed by age, reflecting the age distribution of those who served, beginning in 1970 at least 15,735 fewer men reached Age 65 in each year for the next 22 years than would have been the case otherwise. (87% of the over 416,000 deaths of U.S. servicemen during the war is 361,920, which divided by the 23 years between 1970 and 1992, works out to be 15,735 men per year.)

### The Hole They Left Behind in American Society

How can you measure the economic impact of men who died?

The only way that can really be done is to measure the size of the hole they left behind in society. Here, that hole can be measured by the population of women who lost their husbands and boyfriends, who because the number of women outnumbered the number of men after the end of the war, meant that many would have to go on to either live with their families or alone as they aged. Many of the men who had been their contemporaries in age were no longer there.

Since most American women in this era were homemakers, a large majority of these women would be dependent upon their families or upon survivors benefits for widows for their income. Others would find work and earn wages or salaries, but with little education or previous work experience, most would go on earn very low incomes throughout their lives.

But not just low incomes. Their incomes were the lowest of all income earners in the United States:

In this chart, we can see the median incomes earned by women who worked (and earned wage or salary income) and those who did not (whose income came from other sources, such as survivors' benefits provided by the U.S. government.) We can also see how they compared to American men and to typical American households consisting of at least one man and woman for the years from 1947 through 2012, all adjusted for the effect of inflation to be in terms of constant 2012 U.S. dollars.

By the time many of these women reached Age 65, they would find themselves living alone in increasing disproportionate numbers, as their parents passed away for those who continued to live with their families or because that's how they had lived for years. The number of households in the United States consisting of just one Age 65 woman living alone would rise dramatically from 1960 through 1992 as a direct consequence of the deaths of so many men during World War 2:

But it is the absence of men surviving to be at least Age 65 from 1970 through 1992 that would have the greatest economic impact on the nation, because the men who were drafted into service represented the majority of casualties for the war. Because they didn't survive and because they didn't live and work to support their families after they came home, that resulted in a very large increase in the number of Age 65+ women living alone beginning in 1970. And since we've already established that such women represent the lowest income earners in the United States, the large increase in this segment of the population caused the amount of income inequality measured among U.S. families and households to begin increasing in these years:

In the chart above, we see that a steady increase in the Gini coefficient, the most common measure of income inequality in the U.S., begins to take place for both households and families in 1970 after bottoming in the late 1960s. Meanwhile, we see that the amount of income inequality among individual American income earners remains essentially flat during that time.

The only way that can happen is if the composition of U.S. households changed so that they consisted of greater numbers of lower income earning households and families. In this case, the change was driven by an increasing number of single person households, and specifically by an increasing number of single person households consisting of women over the Age of 65.

### The Women Who Survived the War

How do we know that it was an increase in the number women over Age 65 living alone who drove the rising trend in the amount of income inequality observed among U.S. families and households? After all, that change would also be occurring at the time in which the very large Baby Boom generation, who had been born beginning in 1946, would also be moving out from their parents' homes and setting up their own households in increasing numbers after leaving high school or college.

The difference between these different groups is their income. Almost all baby boomers leaving home or school and establishing their own households would be working and earning wages or salaries, which would put them much higher in the income spectrum for all American income earners, as we showed in our earlier chart showing the median incomes earned by typical one or two-person households.

But the vast majority of women Age 65 or older living alone wouldn't be working - not at that point in their lives. They would instead be drawing upon Social Security benefits for their income. And as it happens, we can demonstrate that the annual amount of income collected by the typical recipients of Social Security benefits is fully consistent with the median income earned by women without wage or salary incomes:

And since Social Security benefits are typically paid to individuals over Age 65, that means that we're looking specifically at the population of women who were the direct contemporaries by age for the men who died in the Second World War.

From there, it is the combination of a large increase in the share of U.S. households consisting of elderly women living alone and their very low incomes that allows us to single out this group as being the primary driver of the rising trend in income inequality among all U.S. households and families. Here, without changing the distribution of income earned by individual Americans, we can account for much of the observed increase in the years after the 1960s. Simply recognizing that the number of Americans at the very lowest end of the income spectrum increased is enough to account for much of that change, given how the math behind the Gini coefficient works.

The men who weren't there also explains why the increase in the income inequality among U.S. families and households shifted to grow at a much slower pace after 1992 (or really, 1993, if you want to include the smaller impact of the 17-year olds who were allowed to enlist to serve in World War 2 in 1945). Men who were 17 or younger in that year were much more likely to survive to reach Age 65, which explains why the percentage share of women Age 65 or older living alone begins to fall and the percentage of men Age 65 living alone increases after 1992, as shown in our third graph above.

### Conclusion

Obviously, there's more to the story of why the amount of observed income inequality among U.S. families and households has increased in the years since 1970, as the men who didn't survive World War 2 only accounts for one of the factors behind the changing composition of U.S. households that have driven that change since that time. Higher divorce rates, the increase of out-of-wedlock births and the expansion of the welfare state enabled much of the increase in single income-earner households that combine to explain virtually all of the increase we observe in income inequality among America's families and households since 1970.

But it was the lasting impact of the men who died in World War 2, and the women who survived them to go on to populate the very lowest end of the American income spectrum that explains why it started and also a good portion of why the distribution of income in the United States has evolved as it has.

To us, it's more remarkable that so many economists and politicians insist on focusing on the opposite end of the income spectrum in attempting to blame the highest income-earning Americans for that increase. It's much like the con artist's or magician's trick of misdirection, where they're trying to conceal what really happened in using deceptive means to distract attention away from it, so the uneducated and misinformed buy into such a flawed perception of reality.

That their latest "solution" for that poorly conceived problem has already been shown to be utterly useless as a result is likely lost upon them.

And that observation concludes our ninth anniversary post.

### Celebrating Political Calculations' Anniversary

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:

• A Year's Worth of Tools (2005) - we celebrated our first anniversary by listing all the tools we created in our first year. There were just 48 back then. Today, there are nearly 300....
• The S&P 500 At Your Fingertips (2006) - the most popular tool we've ever created, allowing users to calculate the rate of return for investments in the S&P 500, both with and without the effects of inflation, and with and without the reinvestment of dividends, between any two months since January 1871.
• The Sun, In the Center (2007) - we identify the primary driver of stock prices and describe a whole new way to visualize where they're going (especially in periods of order!)
• Acceleration, Amplification and Shifting Time (2008) - we apply elements of chaos theory to describe and predict how stock prices will change, even in periods of disorder.
• The Trigger Point for Taxes (2009) - we work out both when, and by how much, U.S. politicians are likely to change the top U.S. income tax rate. Sadly, events in recent years have proven us right.
• The Zero Deficit Line (2010) - a whole new way to find out how much federal government spending Americans can really afford and how much Americans cannot really afford!
• Can Increasing the Minimum Wage Boost GDP? (2011) - using data for teens and young adults spanning 1994 and 2010, not only do we demonstrate that increasing the minimum wage fails to increase GDP, we demonstrate that it reduces employment and increases income inequality as well!
• 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!

We celebrated our 2013 anniversary in three parts, since we were telling a story too big to be told in a single blog post! Here they are:

• 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.

Image Credits: Michael Wu.

December 5, 2013

Discovery is the seeing of what has never before been seen. There's no greater rush than the realization that we've turned up something that answers bigger questions about how the world really works that no one else has seen or appreciated. It drives us not just to dig deeper, but intrigues us to go on to build new foundations of understanding.

And it's all the more intriguing to us when it's unexpected.

Earlier this year, we constructed a mathematical model of the number of households in the United States since 1900. Using data from the U.S. Census Bureau, we found that there was a one-time upward shift in the overall trajectory in the number of U.S. households that occurred after 1947, which we attributed to the introduction of mass-production techniques to U.S. home construction at that time, which made houses more affordable to more people. The chart below updates the information we originally presented with data through 2013:

We later went on to consider the case of single person households. Our next chart likewise updates what we had originally presented to include data through 2013:

In this second chart, we can see that the relative share of single-person households in the United States has increased significantly over time. Since our mathematical models for both would appear to closely follow the actual data, we can use them to closely approximate how that relative share has evolved over time:

Here, we observe that the number of single person households has grown from being 5.5% of all U.S. households in 1900 to be approximately 26.8% in 2013. What's more, we see that the change over time has followed an S-shaped curve, where the relative share of single person households among all U.S. households was growing at an increasing pace up through 1967, but whose growth has slowed dramatically since.

The timing of that change is troubling. In terms of the demographics of the U.S. population, we would expect the number of single person households within the nation to begin rising dramatically after 1964, which is when the oldest members of the leading edge of the baby boom generation would turn 18 and begin establishing their own households.

We would also expect to see a dramatic increase coincide with the increase in divorce rates that occurred in the U.S. after 1967:

But after 1967 is when we see the growth rate of the share of single person households begin to decelerate, so we must discount the increase in the divorce rate as a driving factor behind the increase in the number and share of single person U.S. households.

Instead, the data indicates that the great increase in the relative share of single person households took place much earlier, with the greatest growth in the 1940s and 1950s, which also means that it couldn't possibly have been driven by the Baby Boomers.

Clearly, it had to be driven by people older than the Baby Boomers. Unfortunately, the U.S. Census Bureau's historical data for the age demographics of persons living alone is severely lacking before 1967, which is when they began paying closer attention to that particular aspect of the composition of American households.

It occurred to us though that most people, once they are well established in their living arrangements, don't change them very often. If we want to find out what compelled Americans to begin living alone in single person households in increasing numbers in the 1940s, we should consider the population of adult Americans who were living alone long afterward.

So we dug deeper into the U.S. Census Bureau's data, and extracted the number of Americans living alone Age 65 or older for the years for which that data is available. Our next chart shows that data along with that for the number of all single person households in the U.S.

Our next chart presents the share of Age 65+ single person households among all single person households in the U.S. from 1960 through 2013, for the years for which the U.S. Census Bureau provides data.

Even though we have only two points of data to go by in establishing what the pre-1967 trend before looked like, for 1960 and for 1965, it would appear that is enough to explain why 1967 is significant as the break point in the growth rate of the number of single person households in the United States. It would appear that 1967 marks the point in time when the number of Americans Age 65 or older living alone peaked as a share of the entire population of Americans living in single person households.

We next took things to the next level deeper, looking at the number of both Age 65+ Men and Women living alone for the years for which the U.S. Census Bureau provides that information.

What's unexpected in this chart is that we see that there is a clear break in the trend that takes place after 1992. After 1992, we find that the number of Age 65+ men living alone begins to increase at a faster rate at the same time that the increase in the number of Age 65+ women living alone begins to decelerate.

What makes this significant is because that simple change in trajectory provides the key to unlocking why the population of Americans living alone in single person households really took off in the 1940s.

Working backwards in time, a person who turned Age 65 in 1992 would have been born in 1927. Going forward in time again, that same person would have turned 18 years old in 1945, the last year of the Second World War.

As it happens, 18 is the minimum age that an American man would have been subjected to the draft lottery during World War 2, which in 1945, would mean a high likelihood of seeing combat in operations that saw some of the highest casualty rates during World War 2.

But a man born one year later, in 1928, would most likely miss seeing service in the war altogether unless they specifically enlisted if they were 17 years old in 1945. American men born in that year or later would therefore be much more likely to live to be Age 65 or older. Our chart below shows both the birth years for which a American man alive during World War 2 that correspond to their eligibility to enlist or be drafted into military service for the war, as well as the year for which they would turn Age 65 if they survived.

The vast majority of men who served in the U.S. armed forces during World War 2 were conscripted into service through the draft lottery, which placed American men between the ages of 18 and 37 in approximately equal numbers by age. The oldest men who were drafted, who would have been Age 37 in 1942, would have turned Age 65 in 1970. Meanwhile, the oldest men who voluntarily enlisted into service during the war, who would have been Age 45 in 1941, would have turned Age 65 in 1961.

During the war, over over 416,000 American men died in military service between 1941 and 1945, with most of these deaths concentrated in 1944 and 1945. If they had lived, the period from 1961 through 1992 would represent when they would have turned Age 65.

For those that died in the war however, the period from 1961 through 1992 would represent when their surviving spouses, or rather, their widows, who were presumably at or near the same age, reached Age 65.

So the data for the Age 65+ population who live alone does indeed tell us why so many Americans began living alone in such large numbers in the 1940s, just as it tells us why the population of American Age 65 or older who live alone has evolved as it has in much more recent years.

Since 1992, we see that both the number of American women living alone over Age 65 is growing at a decreasing rate, while the population of men living alone over Age 65 is growing at an increasing rate, as compared to the years before. Both changes may be attributed to the larger number of men, born in the years 1928 and afterward, who were too young to see combat during the Second World War.

And that's why the number of widows peaks in the years from 1990 through 1992, which we can see in the percentage share of all U.S. households represented by the number of Age 65+ women living alone:

Note that as a percentage of all U.S. households, the number of men Age 65 or older living alone was essentially flat in the years from 1970 through 1992, which corresponds to the ages when men selected for the draft in World War 2 would have turned that age.

The widows peak in the early 1990s is the unexpected discovery to which we alluded earlier in this post. In our next installment, we'll reveal how its existence answers a much bigger question when we consider the lasting impact of the men who died while serving their country.

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December 4, 2013

Because it has suddenly become relevant, thanks to Pope Francis' first Evangelii Gaudium, we thought we might revisit the trends in income inequality in the United States.

In the chart below, we've presented the Gini ratios (or coefficients) for U.S. households, families and individuals that have been calculated using the U.S. Census Bureau's collected data on the incomes earned by Americans for each year from 1947 through the most recent data available.

The Gini coefficient is the most commonly used measure of the amount of income inequality in a society.

In the chart above, we see that the amount of income inequality among individual Americans increased in the years from 1947 to 1960. This corresponds to the period of time following the end of World War 2's wage and price controls on 9 November 1946, which had prevented Americans from being able to earn incomes that matched the value of their true level of productivity. The U.S. federal government had imposed the controls early in the war in attempting to minimize its cost, however the major consequence of imposing such equality-enforcing measures was to create major shortages that deprived the majority of Americans of access to adequate supplies of many basic goods and necessities throughout the war years at the same time that it prevented the most productive Americans from reaching their full income-earning potential.

After 1960 however, the post-war income adjustments reached their natural limits and the level of income inequality among U.S. individuals stabilized. The Gini coefficient for U.S. individuals has been essentially flat ever since, falling within a very narrow range.

That observation is significant because if income inequality in the United States was really rising as a result of economic factors that concentrate an increasing amount of income into progressively fewer hands, we would not observe this outcome because income payments are made to individuals, not to households and not to families. That basic reality means that a rising level of economically-driven income inequality would be most prominently evident among the distribution of income for individuals as measured by the Gini coefficient if it were actually taking place - just like it did in the post-World War 2 recovery years from 1947 through 1960.

We do however observe such a rising trend in measured income inequality in the Gini coefficients calculated for U.S. households and for U.S. families. Interestingly, we don't see much of that change occurring when economic factors were actually driving up the level of income inequality among individual American income earners in the years from 1947 through 1960. Instead, we see that the overall trend for household and family inequality was basically flat during this time, which then continued through the 1960s. It's not until 1970 that we find that a rising trend in the amount of income inequality for U.S. households and families begin to take hold.

From 1970 through 1992, we see that the amount of income inequality among U.S. households and families increases steadily - and since we don't observe a similar trend among individuals, we must conclude that social factors, such as the changing composition of the nation's families and households over time, are the primary cause of that trend.

After 1992, we see what appears to be a sharp increase in the Gini coefficient. This turns out to be the result of a major change in the U.S. Census Bureau's data collection methodology, as they increased the maximum amount of income that a household or family could report up to \$1 million from \$300,000. This change resulted a one-time upward shift in the calculated values for the U.S. Gini coefficient for households and families for the years following 1992.

What's more significant though is that the rising trend for income inequality among U.S. households and families shifted to grow more slowly over time after 1992. Once again, without any meaningful change in the Gini coefficient calculated for U.S. individuals to indicate the potential influence of economic factors, something else had to change in U.S. society to produce that shift.

And what that something else was is something we'll explore in the very near future....

Given the very strong correlation between year-over-year percentage changes observed in household and family Gini coefficients from 1968 through 1976, we applied the annual percentage changes given by the Gini data for U.S. families to estimate the household values for the period pre-dating 1967, when the Census began to officially collect this data.

The U.S. Census Bureau does not include income from capital gains in its data for total money income, which accounts for the difference that exists between the estimates of Gini coefficients between this data and that reported by the Congressional Budget Office (CBO), which incorporates capital gains data collected by the Internal Revenue Service (IRS) on income tax returns. Since this data only covers "tax units", which doesn't distinguish between households, families or unique individuals living on their own or within households or families, much less their changing composition over time, economists who blindly rely upon CBO or IRS data are incapable of providing an accurate assessment of the actual structure of income inequality in the U.S., which invalidates much of their analysis and policy prescriptions.

Income from capital gains is really deferred income, which accumulates over time and is realized only when assets, such as real estate or the shares of a business owned by an individual, are sold. For many such asset owners, the realization of capital gains represents a rare or irregular event, which is why this source of income is not included in the Census Bureau's total money income statistics.

In reality, the income accumulated through capital gains is often directly proportionate to the income that the asset owners realize in the form of wages, salaries, dividends or rents throughout the term of their asset ownership, which is captured by the Census' income survey, and which is why we may rely solely upon that data to determine what trends exist in income inequality over time. While the Census' income data may not capture the full magnitude of such trends, which might be periodically exaggerated due to things like stock market or real estate bubbles, its data will most certainly capture whether a trend exists and give a very good indication of its comparative strength.

The bottom line is that the Census Bureau's total money income data is the only valid source of data that allows us to determine the impact that individual income earners have within their families and households. It is the only data that can possibly allow anyone to determine whether economic or social changes are behind the trends occurring in the development of income inequality over time. Using other sources of data that do not allow the impact of individuals to be measured or isolated without acknowledging its limited utility in making any legitimate determination on the state of income inequality within a population is the equivalent of analytical malpractice.

The Census Bureau's total money income data also excludes non-cash transfers of income, which includes benefits that large numbers of low-income earning individuals, families or households might enjoy such as housing assistance, which means that the Census Bureau's reported Gini coefficients understate the effective incomes of Americans who qualify for such government welfare programs. Since it also excludes capital gains as we've already discussed, which are predominantly earned by those at the upper end of the U.S. income spectrum, using the Census' total money income statistics to determine the level of income inequality in the United States works out to be somewhat of a wash in terms of the effect on the Census' calculated Gini coefficients. These values should be therefore considered to be estimates that are in the right ballpark.

### References

Kitov, Ivan O. and Kitov, Oleg I. The Dynamics of Personal Income Distribution and Inequality in the United States. [PDF Document]. Fifth Meeting of the Society for the Study of Economic Inequality (ECINEQ). July 2013.

U.S. Census Bureau. Historical Income Tables: Income Inequality. Table F-4. Gini Ratios for Families, by Race and Hispanic Origin of Householder. [Excel Spreadsheet]. Accessed 2 December 2013.

U.S. Census Bureau. Historical Income Tables: Income Inequality. Table H-4. Gini Ratios for Households, by Race and Hispanic Origin of Householder. [Excel Spreadsheet]. Accessed 2 December 2013.

Weinberg, Daniel H. U.S. Census Bureau, Current Population Reports: A Brief Look at Postwar U.S. Income Inequality. [PDF Document]. P60-191. June 1996.

Update 4 December 2013, 4:45 PM EST: Well, guess who decided to make this post even more relevant today?

The bad news for President Obama's claims, as well as those of his supporters, is that our post today is really Part 1 of a three part series that pretty much fully undermines their arguments. Unless they've found a way to get Obamacare to work well enough to resuscitate the dead, their proposed solutions will only impose greater hardships on the living....

Speaking of which, here are the links to Part 2 and Part 3, which will connect through once the posts are live.

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