2012Q3 GDP: An Upward Revision, As Expected

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We were really amused to read Ed Morrissey's lead in his take on the BEA's release of its second estimate for how much the U.S. economy grew in the third quarter of 2012:

When I wrote about the advance estimate from the Commerce Department on Q3 GDP hitting 2.0%, some commenters warned that the report — which came out just before the election — would be sharply revised after it. They were right, although the revision went in the opposite direction they suspected. The revised Q3 GDP number jumped upward to 2.7%, the best quarter of the year, although still substantially a stagnation number, especially when one sees the source of the growth

While Ed then goes on to note the role of government spending and particularly defense spending in boosting the GDP growth rate for the quarter, we really can't help wonder who the commenters who picked the wrong direction for the revision are.

Because clearly, they aren't us! Here's what we wrote nearly one month ago:

As we expected, economic growth in the United States picked up in the third quarter of 2012, increasing from the annualized growth rate of 1.3% recorded in 2012-Q2 to 2.0% in the BEA's initial GDP estimate for 2012-Q3.

We had forecast that real GDP would be recorded at $13,602.8 billion in terms of constant 2005 U.S. dollars, and the BEA's initial estimate came in at $13,616.2 billion - a difference of $13.4 billion, or within 0.01% of the BEA's recorded figure.

The BEA will revise this figure twice more before the end of the year. We anticipate that it will be adjusted upward, as the third quarter of 2012 likely recorded the U.S.' strongest economic performance for the year.

And here's what we wrote nearly a month before that:

Although we're coming off a quarter where the U.S. economy could reasonably be described as being in a "microrecession", as we had long expected, we anticipate that the GDP growth rate will be stronger in the third quarter of 2012 by comparison. Going by the dividend futures data we've had available since November 2011, the third quarter of 2012 has always looked as if it would be the strongest in 2012. Much like how the second quarter of 2008 was the strongest quarter in that year, even though the U.S. economy had already peaked and had begun falling into recession earlier in December 2007.

We'll give the next-to-final word to the Emperor:

But alas, when it came to serious analysis of data in real time, the Emperor was no Moradmin Bast. Geek out on that if you will....

Your Paycheck Over the Cliff

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Assuming that President Obama continues his policy of not negotiating in good faith with U.S. congressional leaders on federal budget matters, what might your paycheck look like in 2013 after President Bush's 2003 income tax rate cuts expire?

That matters to you because if you earn either wages or a salary from a job, your income taxes will increase. And with that increase, so will the amount of taxes that you have withheld from each of your paychecks also go up.

We wondered what the potential impact of that might be on a working American's paycheck, so we adapted our paycheck withholding tool for 2012 by incorporating the withholding tax rates that would apply under 2003's original rules, updating the income thresholds to account for inflation.

That tool is below - just enter the indicated data for all the typical items that might affect the amount of federal income taxes that will be withheld from your paycheck and we'll work out how much federal income taxes will be withheld from your paycheck beginning in January 2013, as well as the percentage change from the amount that would be withheld from a paycheck you would receive in 2012.

Your Paycheck and Tax Withholding Data
Category	Input Data	Values
Basic Pay Data	Annual Pay
	Pay Period	Daily Weekly Biweekly Semimonthly Monthly Quarterly Semiannually Annually
Federal Withholding Data	Filing Status	Single Married
	Number of Withholding Allowances	0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
401(k) or 403(b) Contributions	Pre-Tax Contributions (%)
	After Tax Contributions (%)
Flexible Spending Accounts	Health Care FSA Annual Contribution
	Dependent Care FSA Annual Contribution

Your Paycheck Data
Calculated Results		Values
Amount of Typical Paycheck
Federal Income Tax Withholding
Estimated Results	Under 2012 Law	After Fiscal Cliff
Federal Income Taxes Withheld from Your Paycheck
Percentage Change in Taxes Withheld Compared to 2012 Law

In doing this exercise, we've taken the IRS' withholding tax income thresholds for both 2001 and 2012 and updated them to account for inflation, which we estimated based on the increase in Social Security's maximum taxable earnings from those years to 2013. We've also taken into account the reduced maximum amount of any health care flexible spending account contributions you might make in 2013, which will occur independently of other changes the nation's tax law thanks to ObamaCare.

As a result, the amount of federal income tax withholding from your paycheck that is estimated in this tool should be very close to the right ballpark for what you might actually see in your paychecks beginning in January 2013.

The Flat Tax the U.S. Effectively Already Has

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Harvard economist Greg Mankiw offered a unique observation following the release of the findings of a CBO study on the effective marginal tax rates that many Americans really pay on their incomes, after taking into account any government assistance they might receive and the income levels at which their welfare benefits phase out:

The Congressional Budget Office has a new study of effective federal marginal tax rates for low and moderate income workers (those below 450 percent of the poverty line). The study looks at the effects of income taxes, payroll taxes, and SNAP (the program formerly known as Food Stamps). The bottom line is that the average household now faces an effective marginal tax rate of 30 percent. In 2014, after various temporary tax provisions have expired and the newly passed health insurance subsidies go into effect, the average effective marginal tax rate will rise to 35 percent.

Here's a chart from the CBO's report illustrating Mankiw's observation for the effective marginal tax rates that applied in 2012:

In 2012, the federal government's poverty guidelines would put the federal poverty level (FPL) at $11,170 for a single person household, at $15,130 for a two-person household, $19,090 for a three person household and at $23,050 for a four-person household. These values would correspond to 100% of the federal poverty level indicated in the CBO's chart above.

But what can we do with Mankiw's observation? Mankiw offered the following idea:

What struck me is how close these marginal tax rates are to the marginal tax rates at the top of the income distribution. This means that we could repeal all these taxes and transfer programs, replace them with a flat tax along with a universal lump-sum grant, and achieve approximately the same overall degree of progressivity.

So what if we did just that? How might you fare under that kind of tax code? And how much money would the U.S. government collect if we adopted a flat income tax like the one that would seem to be in effect in practice, if not in law?

Let's find out! In our tool below, enter the flat tax income tax rate that you might like to see as well as the amount of a universal lump sum grant that might apply per person. Then enter the unique data that might apply for your household and we'll do the rest, calculating what the data you input would mean for you and for the U.S. Treasury's coffers, outputting the data in the tables below!...

Flat Tax Data
Input Data	Values
Flat Income Tax Rate [%]
Value of One Individual Tax Credit [$]
Your Household's Tax Data
Your Household's Total Money Income
Number of Individuals Covered on Your Tax Return	1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

How Well Would You Do With Your Flat Tax?
Calculated Results	Values
Your Basic Income Tax, Before Tax Credits
Your Tax Credits
Do You Owe, Or Will You Get a Refund?
Amount of Taxes You Owe, Or...
Amount of Your Refund
Your Effective Income Tax Rate and After Tax Income
Your Effective Income Tax Rate (After Tax Credits)
Your After Tax Income

How Well Would the Federal Government Do With Your Flat Tax?
Estimated Results		Values
Aggregate Total Money Income
Aggregate Income Taxes, Before Tax Credits
Aggregate Total Tax Credits
How Much Money Would the Government Collect?
Estimated Results	With Your Flat Tax in 2010	Actual Income Tax in 2010
Aggregate Income Taxes, After All Tax Credits
... as a Percent of 2010 GDP

For estimating how much money the U.S. federal government would collect in income taxes, we based our calculations upon the distribution of income for U.S. households in 2010. As such, the amount of tax collections estimated in our tool represents how much income taxes that the federal government might collect following a deep recession in the United States.

Our default data of a 30% flat income tax rate and a universal lump sum grant (or tax credit) of $4,300 per person works out to nearly match the U.S. federal government's actual total tax collections in 2010, which represented about 6.2% of the nation's Gross Domestic Product (GDP) for that year.

To achieve the same results as 2010 with a 35% flat income tax rate, the amount of the individual tax credit must be increased to $5,500.

Putting those results into statistical context, since the end of World War 2, the federal government's tax collections from personal income taxes has steadily averaged 8.0% of GDP, with a standard deviation of 0.8% of GDP. Personal income tax collections of just 6.2% of GDP as were collected in 2010 fall more than two standard deviations below the federal government's average level of income tax collections, which is something we would only expect to have happened in just under 2 of the 65+ years since 1945.

What that means is that the the U.S. federal government's current income tax rates are more than capable of collecting higher amounts of taxes in a healthier economy. That many in the federal government are so actively pursuing higher effective marginal income tax rates today is really an indication that they don't believe the economy is going to be getting healthier any time soon!

References

Congressional Budget Office. Effective Marginal Tax Rates for Low- and Moderate-Income Workers. [PDF document]. November 2012.

This Chart, Explained!

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Earlier this year, we posed the following challenge for our readers:

Why does the following chart, which spans 50 years of data for the United States in the post World War 2 era, look the way it does?

In this chart, we observe that the ratio of the U.S. National Average Wage Index starts off at a level 127.3% of the U.S.' GDP per Capita in 1951, slowly rises to peak at 137.8% of GDP per Capita ten years later in 1961, then falls steadily for the next three decades until 1994 when it flattened out at around 88.3% of the U.S.' GDP per Capita.

Since then, it has been as high as 91.3% of GDP per Capita in 2001, and as low as 86.2% of GDP per Capita in 2006. In 2010, the ratio of the U.S. National Average Wage Index to GDP per Capita is 88.6%.

What we can't explain is why these patterns exist. How can the average wage earned by individuals in the U.S. go from being as much as 37.8% higher than the U.S.' GDP per Capita over forty years ago to being steadily 11.4% below that quantity three decades later. What factors caused this ratio to first rise, then fall, then stabilize?

Several of our readers responded, offering the following possibilities:

• Why the ratio was rising from 1950 to 1961:
• Casualties in World War 2 reducing the pool of able-bodied men available to work, which boosted wages.
• Why the ratio fell so dramatically from 1961 to 1994:
• Technology replacing human capital in the workforce.
• An increasing share of women entering the workforce at lower wages.
• Baby boomers entering the U.S. workforce at lower wages.
• Declining union membership in the private sector.
• A declining portion of GDP going to wages.
• Why the ratio leveled off after 1994:
• Baby boomers no longer entering the U.S. workforce.

It took time to run down all these possibilities, but we now know the basic answer, at least for the period since 1961: the declining portion of GDP going to wages!

We were able to eliminate the other possibilities largely because the timing of factors affecting each of them failed to correlate with the trends we observed in the chart. For example, the number of women entering the U.S. workforce has been rising steadily for all years since 1951, and so have their wages - there have been no surges in female employment that might have sharply reduced wages with respect to GDP after 1961 as we might expect to see if women were responsible for what we've observed.

The entry of Baby Boomers into the U.S. workforce at first seemed like a better fit in explaining the decline, with the oldest Baby Boomers turning the working age of 16 in 1962, but here, only a portion of this population would have done so, with a majority waiting to graduate from high school two years later before entering the workforce, and another very large percentage postponing that event for up to another four years following their graduation from college.

If Baby Boomers were responsible for what we observe, we would see the ratio continue rising but top out in the early-to-mid 1960s. Instead, that clearly happened in the late 1950s and very early 1960s, years before the leading edge of the Baby Boomers reached Age 16.

Similar logic applies for the stabilization of the ratio following 1994. Here, the youngest Baby Boomers, born in 1964, would have reached the working age of 16 in 1980. Adding in two more years of high school and four years of college would have nearly all of the last Baby Boomers entering the U.S. workforce no later than 1986. The stabilization in the ratio didn't begin for another eight years.

Likewise, the role of technology, such as the wide scale introduction of the transistor in the 1950s and 1960s, following by the rapid development of computing technology in the late 1980s and 1990s doesn't well correlate with what we observe. Here, if technology were the culprit, we would expect to see a second decline in the ratio following the widespread adoption of computing technology in the late 1990s and early 2000s. That we don't see such a second decline indicates that technology doesn't explain what we observe.

Like the timing of Baby Boomers entering the U.S. workforce, the timing of the decline of union membership fails to explain what we observe in the chart. Here, union membership in the U.S. peaked in the early 1950s, then entered a period of slow and steady decline until 1974, after which union membership entered into more of a free fall thanks to the loss of automotive industry jobs as a result of the Arab oil embargo, when U.S. consumer demand shifted strongly in favor of more fuel efficient vehicles made by foreign car makers (a situation repeated again in 2008). The slow decline of union membership in the 1950s and 1960s is not consistent with the timing of the decline we observe in the ratio after 1961 and the lack of a similar decline following 2008's event rules out this possibility.

And that leaves just a declining portion of GDP going to wages. Interestingly though, when we went into the Bureau of Economic Analysis' data on the composition of GDP by sources of income, we found that total labor compensation as a percentage of GDP has been rock steady since 1951, averaging 88.6% of GDP and ranging between 86.8% and 90.7% of GDP in the 60 years from 1951 through 2011.

But the composition of that labor compensation has changed! Here, the percentage of labor compensation represented by wages and salaries has fallen from 95% in 1951 to just over 80% in 2011. Meanwhile, the portion of labor compensation represented by employer contributions to Social Security and Medicare (or employer FICA contributions), as well as for regular pensions and health insurance has risen considerably, taking a larger and larger share of total labor compensation for U.S. workers. Our new chart below shows how and when these other forms of compensation cut into U.S. wages and salaries:

Moreover, the timing of when those changes in the composition of labor compensation in the U.S. took place correlate very well with what we observe in the chart. Here, we observe increases in the employer portion of FICA taxes in 1954, 1957, 1959, 1960, 1962, 1963, 1966 (when the Medicare tax was introduced), 1967, 1969, 1971, 1973, 1978, 1979, 1981, 1982, 1984, 1985, 1986, 1988 and 1990. The FICA tax rate has then increased from 1.5% of income in 1951 to 7.65% in 1990, where it has held steady through 2011.

We likewise observe double-digit increases over the previous year for labor compensation in the form of pensions and employer-provided health insurance taking place from 1955 through 1957, 1959, and in each year from 1964 through 1981.

Together, these forms of employee compensation have displaced wage and salary income in the measure of GDP, which largely explains why the ratio of the National Average Wage Index first topped out with respect to GDP per capita in the U.S. in the late 1950s, then dropped after 1961 exactly as we've observed.

That also includes the stabilization in the ratio after 1994, as the year-over-year growth rate for employer-provided pension and health insurance slowed significantly after that year as U.S. employers adopted more effective cost control strategies for their pension and health insurance-related employee compensation expenses. These measures have included shifting away from unsustainable defined benefit pension plans in favor of defined contribution plans, such as 401(k) and 403(b) type retirement saving plans, as well as increasing the amount of the co-payments and annual deductibles paid by employees for their employer-provided health insurance.

And that's it! Our thanks to our readers for their suggestions in helping us finally explain that chart!

References

The data for the changing composition of labor compensation over time is taken from the BEA's interactive data showing GDP and Personal Income data, specifically Table 1.12, which shows the U.S.' national income by type of income.

The Discovery of the Unseen

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The planet Neptune has never been seen by anyone looking at the night sky through just their own eyes. So distant is it from the sun that the light it reflects toward the Earth is so faint that the planet is effectively invisible in the darkness of night. And yet, the outermost large planet of our solar system was discovered by astronomers who knew exactly where to look....

Following William Herschel's discovery of Uranus in 1781, the world's astronomers went to work to observe and describe the seventh planet of the solar system, taking detailed measurements of its trajectory in space.

Forty years later, French astronomer Alexis Bouvard published detailed tables describing Uranus' orbit about the sun. More than that however, his tables incorporated the lessons learned about planetary orbits from Johannes Kepler and Sir Isaac Newton to chart the path Uranus would follow into the future.

But then, something strange happened. Significant discrepancies between Bouvard's projected path for Uranus and its actual orbit began to be observed - irregularities that were not observed in the tables he had created to describe the orbital paths of the planets Jupiter and Saturn using the same methods. Soon, observations and detailed measurements confirmed that Uranus was moving along a path that was not described by Bouvard's careful calculations.

These irregularities led Bouvard to hypothesize that an as yet unseen eighth planet in the solar system might be responsible for what he and other astronomers were observing.

Over twenty years later, astronomer Urbain Le Verrier was working on the problem, taking a unique approach to resolving it.

What made Le Verrier's work unique is that he applied the math developed by Sir Isaac Newton to describe the gravitational attraction between two bodies to solve the problem. Here, he used Newton's theory to anticipate where an as yet unknown, but more distant planet also orbiting the sun would have to be to create the effects observed upon the position of the planet Uranus in its orbit.

Le Verrier completed his calculations regarding the position of the hypothetical eighth planet on 1 June 1846. A little over three months later, on 23 September 1846, the planet Neptune was observed for the first time at almost exactly the position in space where Le Verrier predicted it would be, confirming Newton's gravitational theory in the process.

We're going to do something similar today to explain why household income inequality in the United States has increased over time, even though there has been no change in individual income inequality.

From Darkness to Discovery

Our first chart below is based on data taken from the U.S. Census' data [Excel spreadsheet] on the inflation-adjusted median and mean income for all Americans from 1947 through 2010, which we've presented in terms of constant 2010 U.S. dollars. For reference, we've also indicated the NBER's official periods of recession in the U.S. during this period with the shaded red vertical bands on the chart:

Next, we took the U.S. Census' breakdown of inflation-adjusted median income for both men and women for each of these years [Excel spreadsheet] and used the math that applies to log-normal distributions to construct the combined median income that applies to individuals. Our results are shown in the chart below, along with the actual median incomes reported by the U.S. Census so we can compare our calculated results with them:

As you can see, our calculated results in creating a weighted median from the subsets of median income data for men and women are very close to the actual real median income numbers for all individuals. Here, because per capita income has been demonstrated to follow a log-normal distribution, we are able to use this math to either combine or extract subsets of data that have never been officially presented.

As an aside, we achieved the results above by treating the reported median income data the way we might calculate a weighted average. The beauty of the log-normal distribution math is that we can do this with medians, which we ordinarily could not do otherwise.

In the chart above, you can see the effect of the changing composition of the U.S. workforce, as the relative share of women earning incomes in the United States has increased since 1947. In 1947, the median income for individuals is much closer to the median income for men than it is for women. By 2010 however, we see that the median income for individuals is about halfway in between the median incomes for men and for women, reflecting that nearly equal share that both sexes now have among all individual income earners in the U.S.

Extracting The Unseen

The U.S. Census Bureau provides the median income data for individuals (or persons), men and women. It also reports median income data for both male and female wage or salary earners [Excel spreadsheet], whom we'll simply describe as Working Men and Working Women.

Using the math we demonstrated above with this data, we can extract the median incomes for two categories of people for whom the U.S. Census has never reported median incomes: men and women with incomes who do not earn wages or salaries, or as we'll describe them from now on, Non-Working Men and Non-Working Women! Today, we're putting what we found for all U.S. individual income earners together for the first time:

Constructing Households

Now, let's combine our median income earners into two-person households, pairing working men and women, working men and non-working women, non-working men and working women and finally non-working men and non-working women. We've shown our results below, along with the U.S. Census' official median income for U.S. households:

Well, look at that! The households formed by our single-wage and salary income earning couples from 1947 through 2010 closely parallels the actual real median income for U.S. households with a working man and non-working woman over that time (except for the years 1974 through 1977, where there seems to be an anomaly in the Census' data for working men - and here, the actual median splits the difference!) Also keeping in mind that the actual median household income might include the income contributions of additional people (say individuals between the ages of 16 and 24 who might be working part time at minimum wage jobs while also attending school and living at home with their parents), which likely accounts for the difference between the two, we've pretty much just demonstrated that we can successfully model basic U.S. households using just the data that applies for U.S. individuals.

But wait! What about single person households? Our next chart throws them into the mix as well!

Using the figures for 2010, we approximated the income percentiles for each of our single and two-person median income earning households. The table below reveals our results (our model should put each approximated percentile within 0.2 of the actual percentile!):

Household Type	2010 Median Income	Approximate Income Percentile
Working Men and Working Women	$64,075	61.4
Working Men and Non-Working Women	$50,026	50.7
Working Women and Non-Working Men	$49,344	50.1
Non-Working Men and Women	$35,295	36.7
Working Men Only	$37,102	38.6
Working Women Only	$26,973	27.7
Non-Working Men Only	$22,371	22.4
Non-Working Women Only	$12,924	11.5

It occurs to us that all we would need to increase the income inequality among households in the United States is to increase the nation's percentage of single person households among all households. That would work by increasing the number of households at the lower end of the income spectrum, even though it would have absolutely no effect upon the measured income inequality for individuals. The U.S. Census Bureau shows the change in the number of single person households since 1960:

Here's the U.S. Census Bureau's Gini index measure of the amount of income equality among U.S. households for the years from 1947 through 2010:

And here is the Gini index measure of the amount of income equality among U.S. individuals for the years from 1947 through 2005 (the data since 2005 is presented here - it's similar to all that recorded since 1960 in the chart below):

The relevant data in the chart above is the Gini measure indicated with the hollow circles, which is based on the "fine", or more detailed, income bins reported by the U.S. Census in its annual Current Population Survey. The other data in the chart, indicated by solid diamonds, represents income distribution data reported by the U.S. Census in larger, or more "coarse" income bins, which are less detailed and are therefore a much less accurate measure of the nation's level of income inequality in any given year.

Intersections and Connections

Looking at where all the data in these three charts intersect and overlap, What we find is that since 1960, the level of income inequality for U.S. individuals as measured by the "fine" Gini index is nearly constant, but has increased significantly for U.S. households. What has changed over that time is the composition of U.S. households, with a steady increase in the percentage of single person households.

Without a corresponding increase in the measured income inequality for U.S. individuals, the increase in the measured income inequality for U.S. households has been almost entirely driven by the increase in the number of single person households over time.

So income inequality among U.S. households isn't increasing because the rich are getting richer. That means that policies intended to right this situation by going after the rich in the name of "fairness" are guaranteed to fail, because the real cause of the increase in income inequality among U.S. households over time is something that cannot be fixed by such actions.

If only the people pushing such policies could see that....

And that concludes our eighth anniversary post. Thank you for joining us today - we greatly appreciate your choice to spend so much time with us (we really do try to draft shorter posts!)

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!

References

Kitov, Ivan. "Modeling the evolution of Gini coefficient for personal incomes in the USA between 1947 and 2005," MPRA Paper 2798, University Library of Munich, Germany. 2007.

Lopez, J Humberto and Servén, Luis. "A Normal Relationship? Poverty, Growth and Inequality". World Bank Policy Research Working Paper 3814, 2006.

Pinkovskiy, Maxim and Sala-i-Martin, Xavier. "Parametric Estimations of the World Distribution of Income". NBER Working Paper No. 15433. October 2009.

Political Calculations. The Distribution of Income for 2010: Households. 14 September 2011.

U.S. Census Bureau. Changing American Households. [PDF document]. C-SPAN. 4 November 2011. p. 6.

U.S. Census Bureau. Table P-2. Race and Hispanic Origin of People by Median Income and Sex: 1947 to 2010. [Excel spreadsheet]. September 2011.

U.S. Census Bureau. Table P-4. Race and Hispanic Origin of People (Both Sexes Combined) by Median and Mean Income: 1947 to 2010. [Excel spreadsheet]. September 2011.

U.S. Census Bureau. Table P-53. Wage or Salary Workers (All) by Median Wage and Salary Income and Sex: 1947 to 2010. [Excel spreadsheet]. September 2011.

Wendt, Phil. Income Disparity by the Numbers. Phil Wendt's Studio. 26 December 2011.

Street Cars & Light Rail Concepts : Catistrophic Boondoggles

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I can't imagine what the debate is all about here, a street car can only, and for ever, go to the same place it was originally designed to go. When the tracks are laid down, it's over. No matter how hard you try, the street car will always wind up taking the same route every day until it falls apart.  But a bus can go anywhere it's needed day in and day out including other cities. And when it becomes outdated, it can be sold for other uses. Can you sell a street car so the new owner can go camping in it?

 What a concept. Who knew?

The Streetcar Fantasy
Source: Randal O'Toole, "The Streetcar Fantasy," Heartland Institute, November 2012.

December 6, 2012
There are many myths surrounding the benefits a city can expect from streetcar lines. In reality, streetcars are an outdated mode of transportation that is unlikely to bring in any economic development, says Randal O'Toole, a senior fellow with the Cato Institute.

•Streetcars only travel about 8 miles per hour.
•Furthermore, they have fewer seats than buses, which leaves many riders standing.
•As far as safety concerns, streetcars weigh over 60,000 pounds and gaps in the street create dangerous conditions for cyclists.
•Additionally, streetcars are limited in transportation destinations, which require massive investment to expand streetcar lines.
•However, it costs around $30 million per mile of rail line.

Advocates of the streetcar argue that operating costs are cheaper than buses. This is because a streetcar can move just as many people as a single bus driver can. However, according to the National Transit Database, the cost of operating one streetcar mile is three to four times higher than operating one bus mile. More importantly, the streetcar does not carry enough people each trip to make up for the costs. Indeed, it costs a lot to maintain streetcars -- $1 of maintenance for every $2 spent on operations.

Advocates also argue that the overall economic benefits derived from streetcars through development outweigh the other costs associated with operations and maintenance. However, in nearly every city that claimed economic development as a result of streetcars, many developers and businesses were offered lavish subsidies to promote the local economy. Take, for example, the experience of Portland, Oregon:

•Officials claimed that the streetcar generated billions of dollars of economic development.
•The streetcar served two neighborhoods of roughly equal size. One neighborhood received millions of dollars in subsidies for economic development while the other did not.
•Predictably, the first neighborhood attracted as much as 75 times the investment the second neighborhood did, proving that it was the subsidies, not the streetcars, which were essential to economic development.

Lastly, proponents argue that streetcars will reduce dependence on automobiles and improve overall environmental conditions. But Portland, known as the most successful streetcar line in the nation, has seen a decline in Portland-area transit commuting because of the high costs of streetcars and cutbacks in bus services. Additionally, streetcars consume more energy than automobiles do. The average car, for example, consumes about 3,450 British thermal units (BTUs) per passenger mile whereas streetcars consume an average of 7,000 BTUs.

FHA Housing Authority Collapsing : Another Progressive Victory

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Let's see, what did chicken little say about the nut that fell on it's head, 'the sky is falling' right? Well, the Federal Housing Administration, and others in the Obama administration, believes that the sky is not falling even though the entire universe is collapsing on their collective heads.

And why are they denying there is a problem, easy, it's the progressive socialist agenda in full flower, ignore the problem and it will go away. Really, it's not about a successful program or a losing one, it's all about the ideology. The progressive agenda in this case dictates, pay attention to acquiring new voters by sustaining, insuring mortgages that everyone knows will fail. Who cares anyway, not the voter that went to the poles this November?

It's a win win situation for the progressive, no matter what actually happens, press conferences  that said 'nothing to see here' were used for the last decade about Fannie and Freddie are being used today for the same reasons, the progressive knows the taxpayer will step up to the plate and pay the bill all the while the voter base of dependents grows.

It doesn't matter that there isn't any money any more, remember it's always  about the agenda, the politics, the control and the getting and keeping power over others.

Federal Housing Administration: the Next Housing Bailout
Source: Joe Gyourko, "FHA, the Next Housing Bailout: Update and Evaluation," American Enterprise

December 6, 2012
The Federal Housing Administration (FHA) recently released its 2012 fiscal year actuarial review of the single family mortgage insurance fund and concluded it was insolvent. It is likely that the FHA will be the next government bailout for the housing industry, says Joe Gyourko, the Martin Bucksbaum professor of Real Estate, Finance and Business & Public Policy at the Wharton School of Business.

•The value of the fund is measured by looking at the difference between the expected revenues of the insurance program and expected costs associated with the defaults of underlying mortgages.
•This year, the value was reported to be negative $13.48 billion.
•As a result, it would need $50 billion to $100 billion for the program to be solvent.

Even more troubling is that a recent Federal Reserve policy that keeps interest rates lower for a longer period by FHA's actuarial reviewer will result in greater losses for the FHA mortgage insurance portfolio.

Critics of the program long argued that important risks were being undervalued. Even with improvements to the model used to predict default and claims, it does not capture all the risks.

Currently, potential liabilities are 41 times the total capital reserves. No major entity operates, or should operate, with its core capital reserves so highly leveraged (a measure of the concentration of debt usage for private companies).

For the past several years, the FHA has expected future insurance proceeds to cover past losses, only to be disappointed. The FHA argues that it will grow out of insolvency based on estimates of net worth of the next seven years of mortgage insurance that the FHA is expected to write. However, it is based on mortgages that have not been taken out by borrowers yet.