Political Calculations
Unexpectedly Intriguing!
30 June 2017
Source: https://www.bja.gov/JusticeToday/images/SmartPolicingLogo.jpg

Nearly two years ago, we featured a story about a software system called PredPol, a data analytics tool that promised to deliver lower crime rates through algorithms designed to recognize patterns behind a series of crimes to better anticipate how to position police officers to intervene in the activities of criminals.

The results from the pilot studies for the predictive software were promising, enough so that it received a wider test in real-life policing in being applied to more regions within the cities where it was being tested. In October 2016, Kristian Lum and William Isaac of the Human Rights Data Analysis Group released the results of their study into how well PredPol was performing in reducing crime, but what they found gives cause for concern about the data that is being used to direct police activities in the U.S. cities that have adopted the data analytics approach to fighting crime.

Lum and Isaac discovered that the software had a real shortfall because of biases in the police records that were being fed into it.

While police data often are described as representing "crime," that's not quite accurate. Crime itself is a largely hidden social phenomenon that happens anywhere a person violates a law. What are called "crime data" usually tabulate specific events that aren't necessarily lawbreaking – like a 911 call – or that are influenced by existing police priorities, like arrests of people suspected of particular types of crime, or reports of incidents seen when >patrolling a particular neighborhood.

Neighborhoods with lots of police calls aren't necessarily the same places the most crime is happening. They are, rather, where the most police attention is – though where that attention focuses can often be >biased by gender and racial factors.

Focusing on Oakland, California, Lum and Isaac tested the bias of PredPol using race and income level as observable characteristics with crimes involving illegal drug use, the incidence of which studies have indicated are relatively uniform across the racial and income demographics of Oakland's population.

But you wouldn't know that from the results of the PredPol's predictive software using Oakland's police data.

Our recent study... found that predictive policing vendor PredPol's purportedly race-neutral algorithm targeted black neighborhoods at roughly twice the rate of white neighborhoods when trained on historical drug crime data from Oakland, California. We found similar results when analyzing the data by income group, with low-income communities targeted at disproportionately higher rates compared to high-income neighborhoods.

The reason for that turned out to have been directly embedded in the data the race-neutral PredPol software used to direct police activities. Because the data reflected the increased level of law enforcement activities that already existed in the city's primarily black and low income neighborhoods, the software directed police to increase their intervention efforts in the areas where they were already disproportionately focusing their attention.

That increased attention would then be reinforced in an adverse feedback loop, as the police records being generated from their new increased activity would tend to amplify the already disproportionate law enforcement activities in these areas, which would have consequences for a police department already accused of practicing racial profiling in law enforcement.

The software-directed adverse feedback loop would also open the city up to the "squeezing balloon" problem we noted in our previous coverage, where increasing police pressure in one area would result in increases in the incidence of crime in other areas, which would now be more likely to escape both detection and intervention.

What the results indicate is that using data analytics to effectively reduce crime is more complicated than simply factoring racial or income factors out of the software packages used to maximize the return on police investment. They can be useful, but the GIGO principle definitely applies.

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29 June 2017

Every now and again, a famous economist will say something that not only stands apart from everything else they've said and done, it turns out to be the defining quote of their entire life, regardless of what other achievements they may have had or contributions they may have made in their field. We thought it might be fun to revisit some of the more famous, or really, infamous, things that several famous economists have said that might very well be placed on their tombstones as their final epitaph.

Here's a very famous one about the U.S. stock market....

Stock prices have reached "what looks like a permanently high plateau," Irving Fisher, Yale economist, told members of the Purchasing Agents Association at its monthly dinner meeting....

- Irving Fisher, 16 October 1929, less than two weeks before the U.S. stock market began to crash, marking the unofficial beginning of the Great Depression.

It's been said that if you laid every economist in the world on the ground end-to-end, you would never reach a conclusion. As proof, here's an opposing viewpoint on the U.S. stock market.

It really does now look like President Donald J. Trump, and markets are plunging. When might we expect them to recover?

Frankly, I find it hard to care much, even though this is my specialty. The disaster for America and the world has so many aspects that the economic ramifications are way down my list of things to fear.

Still, I guess people want an answer: If the question is when markets will recover, a first-pass answer is never.

- Paul Krugman, 9 November 2016, after which, the U.S. stock market quickly rose to reach a series of all-time-record highs over the next several months.

Some statements have to do with the health of the U.S. economy.

The Federal Reserve is not currently forecasting a recession.

- Ben Bernanke, 10 January 2008. Some time later, the National Bureau of Economic Research would mark December 2007 as the peak of the U.S. economy's period of expansion before it entered into the "Great Recession", where January 2008 was its first month of economic contraction.

But rest assured, all is well for the U.S. economy now.

"Would I say there will never, ever be another financial crisis?" Yellen said at a question-and-answer event in London.

"You know probably that would be going too far but I do think we're much safer and I hope that it will not be in our lifetimes and I don't believe it will be," she said.

- Janet Yellen, 27 June 2017

It's almost like the Fed Chair is asking for that to be her economic epitaph!

Ben Bernanke's Epitaph

Image Credit: Tombstone Builder


28 June 2017

Earlier in 2017, the state of Georgia ran a social welfare experiment, which resulted in the state decreasing the number of welfare recipients enrolled in the state's Supplemental Nutrition Assistance Program (SNAP) by over 200,000 individuals in a single month.

Georgia's secret? Back in January 2016, the state launched a pilot program in three of the state's counties requiring all able-bodied food stamp recipients without children to find work or lose their SNAP benefits. In August 2016, the state expanded the program to 24 counties, and also set a hard deadline of 1 April 2017 where all childless, able-bodied SNAP beneficiaries in these counties would become ineligible for the state's food stamp program unless they were employed.

The following chart uses available data from the U.S. Census (Excel spreadsheet) and the USDA's Food and Nutrition Service shows the number of individuals enrolled in Georgia's SNAP (or Food Stamp) program from January 1981 through March 2017 to show the impact of Georgia's welfare-to-work initiative:

Georgia Number of People Enrolled in SNAP Benefits (Food Stamps), January 1981 through March 2017

Perhaps the most remarkable aspect of the data is the sheer drop in enrollment levels in the month before the hard deadline came and went, where if jobs were truly hard to obtain for the affected SNAP benefit recipients, we should have seen the enrollment levels stay elevated through March 2017 before seeing such a sharp decline resulting from their loss of eligibility. It also far outstrips the state's previous experience in enforcing work requirements in its food stamp welfare program to reduce its welfare rolls, which came as part of the 1996 Welfare Reform Act.

That's the positive power of incentives. Faced with the hard deadline to get a job before their food stamps ran out, over 200,000 people preemptively entered into the job market in the month before they would lose their welfare benefit.

Better still, there are greater benefits for the state and nation as a whole from Georgia's success (the excerpt from the 24 May 2017 article below references Georgia's SNAP enrollment data through February 2017):

Georgia is seeing a significant decrease in the number of people receiving food stamps as the improving economy lifts many recipients into new jobs and frees them from the fear of going hungry.

The number of Georgians getting food stamps has dropped by 300,000 from 1.9 million in April 2013 to 1.6 million. That decrease of 16 percent in the federally funded program saves taxpayers tens of millions of dollars monthly.

Georgia will expand the state's SNAP work requirements initiative to an additional 60 counties in 2018. Georgia has 159 counties in total, but appears to be prioritizing its more highly populated ones in rolling out its SNAP benefit work requirements, so we would anticipate that future benefit cutoff deadlines won't prompt the same scale of preemptive enrollment declines.

Data Bleg

If anyone can point us to where the FNS archives its monthly data for the number individuals covered by SNAP benefits, so we can fill in the missing months of data for 2016 in our chart, we would greatly appreciate it! (Otherwise, we'll have to wait for the U.S. Census Bureau to get around to updating its records.) We do plan to revisit the topic again several months from now so we can see how things evolved in the months following the deadline.


27 June 2017
Seattle Mayor Ed Murry Signs Seattle Minimum Wage Ordinance, 3 June 2014 - Source: http://murray.seattle.gov/seattle-mayor-ed-murray-signs-minimum-wage-bill/

On April Fool's day in 2015, the minimum wage in the City of Seattle was increased by city ordinance from $9.47 per hour (130% of the federal minimum wage of $7.25 per hour) to $11.00 per hour (152% of the federal minimum wage), which would be followed by two additional minimum wage hikes within the next two years.

The second minimum wage hike mandated by Seattle's 2014 city ordinance took place on New Year's Day in 2016, when the same city ordinance mandated that the minimum wage at businesses with more than 500 employees in the city rise to $13.00 per hour (179% of the federal minimum wage), while small employers were required to increase the minimum wages that they pay to $12.00 per hour (166% of the federal minimum wage). On New Year's Day 2017, Seattle's minimum wage was hiked once more for the city's largest employers to $15 per hour (207% of the federal minimum wage), while small businesses were required to pay at least $13 per hour (159% of federal minimum wage).

As anybody with common sense might reasonably predict, mandating such a series of increases in the cost of labor for a business in such a short period of time without also mandating increased revenues to support it would likely lead to reductions in the amount of labor consumed by the businesses affected by the ordinance. And in fact, a new NBER paper authored by a team of University of Washington economists who had unique access to the payroll data of affected business found exactly that.

But that's the expected result from the analysis. What was surprising was that the authors used the highly comprehensive data set to which they had access in a successful attempt to replicate the results of one of the most controversial minimum wage studies on record: the 1994 Card-Krueger case study of the relative effect of a minimum wage increase upon employment in the fast food industry in adjacent communities in New Jersey and Pennsylvania.

This paper examines the impact of a minimum wage increase for employment across all categories of low-wage employees, spanning all industries and worker demographics. We do so by utilizing data collected for purposes of administering unemployment insurance by Washington’s Employment Security Department (ESD). Washington is one of four states that collect quarterly hours data in addition to earnings, enabling the computation of realized hourly wages for the entire workforce. As we have the capacity to replicate earlier studies’ focus on the restaurant industry, we can examine the extent to which use of a proxy variable for low-wage status, rather than actual low-wage jobs, biases effect estimates.

We further examine the impact of other methodological choices on our estimates. Prior studies have typically drawn “control” cases from geographic regions immediately adjoining the “treatment” region. This could yield biased effect estimates to the extent that control regions alter wages in response to the policy change in the treatment region. Indeed, in our analysis simple geographic difference-in-differences estimators fail a simple falsification test. We report results from synthetic control and interactive fixed effects methods that fare better on this test. We can also compare estimated employment effects to estimated wage effects, more accurately pinpointing the elasticity of employment with regard to wage increases occasioned by a rising price floor.

Our analysis focusing on restaurant employment at all wage levels, analogous to many prior studies, yields minimum wage employment impact estimates near zero. Estimated employment effects are higher when examining only low-wage jobs in the restaurant industry, and when examining total hours worked rather than employee headcount.

What makes their success in replicating the results of the Card-Krueger study by filtering the Seattle data to reproduce its limitations is significant in that it effectively invalidates Card and Krueger's 1994 finding that minimum wage increases have no effect upon employment. Simply put, the limited nature of the data that Card and Krueger used to support their earlier study of the effect of New Jersey's 1992 minimum wage hike almost certainly led them to miss its true effect on employment after it went into effect.

Source: https://www.bls.gov/opub/reports/minimum-wage/2015/home.htm / https://www.bls.gov/opub/reports/minimum-wage/2015/image/minimum_wage_image_2015.jpg

This same issue of data detail has come up before with economists who rely upon income tax data to measure income inequality, which similarly fails to capture the true nature of the distribution of income by not providing the additional individual-level detail that other data sets provide. We've described the knowing use of such limited data without acknowledging its limitations as "analytical malpractice", which in the worst cases, crosses the ethical line into outright pseudoscience.

To be fair, we believe that the Card and Krueger's case study was a good faith effort that applied a novel approach to attempt to measure the impact of a minimum wage hike on employment. The limitations of the data they had available however meant they were weren't capable of detecting the reduction in labor hours that occurred across all employees in the industry, which the Seattle minimum wage study indicates would have negatively affected many whose wages are above the levels that would be directly impacted by minimum wage increases.

Since we've touched on the topic of pseudoscience in this post, particularly where the limitations of data are concerned, we should note that there's more going on with respect to the analysis of the impact of Seattle's minimum wage hikes that more strongly fits into that category. Specifically, as Jonathan Meer has observed:

This paper not only makes numerous valuable contributions to the economics literature, but should give serious pause to minimum wage advocates. Of course, that’s not what’s happening, to the extent that the mayor of Seattle commissioned *another* study, by an advocacy group at Berkeley whose previous work on the minimum wage is so consistently one-sided that you can set your watch by it, that unsurprisingly finds no effect. They deliberately timed its release for several days before this paper came out, and I find that whole affair abhorrent. Seattle politicians are so unwilling to accept reality that they’ll undermine their own researchers and waste taxpayer dollars on what is barely a cut above propaganda.

That sounds startingly similar to the "battle of the experts" dynamic described by former antitrust litigator David Gelfand in our Examples of Junk Science series, which we should note also fails the Goals, Progress, Challenges, Inconsistencies, Models and Falsifiability categories in our checklist for detecting junk science.

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26 June 2017

For a market where the S&P 500 isn't changing very much, there's a remarkable about of tension building up in it. Or more accurately, there is tension building in the halls of the big investment banks where big bets are being made, such as JPMorgan, whose top quantitative analyst is seeing great cause for concern that hasn't translated into market volatility (via ZeroHedge):

After getting virtually every market inflection point in 2015, and early 2016, so far 2017 has not been Marko Kolanovic's year, whose increasingly more bearish forecasts have so far been foiled repeatedly by the market, and the same systematic traders that he periodically warns about. As a reminder, his most recent warning came last week, when he cautioned that even a modest rebound in VIX could lead to dramatic losses for vol sellers. As a reminder, here is the punchline from his latest note:

Days like May 17th and similar events "bring substantial risk for short volatility strategies. Given the low starting point of the VIX, these strategies are at risk of catastrophic losses. For some strategies, this would happen if the VIX increases from ~10 to only ~20 (not far from the historical average level for VIX). While historically such an increase never happened, we think that this time may be different and sudden increases of that magnitude are possible. One scenario would be of e.g. VIX increasing from ~10 to ~15, followed by a collapse in liquidity given the market’s knowledge that certain structures need to cover short positions.

So in light of a market that refuses to post even the smallest of drawdowns (we are not sure if the words "selling", "correction" or "crash" have been made illegal yet), has Kolanovic thrown in the towel and declared smooth seas ahead? To the contrary: in a note released late last night, he echoes warnings made recently by both Citi and BofA, and predicts that receding monetary accommodation from ECB and BOJ will likely lead to "market turmoil, and a rise in volatility and tail risks" and just in case there is some confusion, he reiterates what he said last week, namely that the "key risk of option selling programs is market crash risk."

What Kolanovic is describing is a realistic mechanism by which stock prices might suddenly change dramatically for the worse. But is there anything to it?

That's where we have an angle on the story. Going by our dividend futures-based model, we would see that kind of potential plunge in stock prices as a sudden shift in investor focus from 2017-Q4, where our model suggests that investors are largely holding their attention at this time, to instead focus on 2017-Q3, where if such an event happened, it would likely coincide with a 300-350 point decline in the value of the S&P 500.

Alternative Futures - S&P 500 - 2017Q2 - Standard Model - Snapshot on 23 June 2017

Now, here's the catch. For that to happen, something would have to fundamentally change in the expectations that investors have about the future to compel them to focus on 2017-Q3 instead of 2017-Q4.

One entity with the power to do just that is the Federal Reserve, which can in effect "command" investors to focus on 2017-Q3 through the statements its officials make about their plans for the timing of the Fed's next change in the Federal Funds Rate, which would affect all short term interest rates in the U.S. At present, our model suggests that investors only see a 16% probability of them taking that kind of action in or by its September 2017 Federal Open Market Committee meeting, but some Fed officials have been pushing in that direction, which explains why investors are not 100% focused on 2017-Q4 as the most likely timing for the Fed's next interest rate adjustment.

Another factor that can shift the attention of investors is the changing expectations for future earnings in the companies that make up the S&P 500. For example, should oil prices fall even further than they have in the last several weeks, that could reignite the concern that the oil and gas sector of the U.S. economy is in for a new round of economic distress, which could lead investors to focus on these companies in the near term, pulling the index down along the way.

Or, oil prices could rise sharply, which would both boost the oil and gas sector of the U.S. economy while straining other sectors, which would also have the same effect. Or, they could rise just enough, contributing to the kind of inflation that would potentially prompt the Fed to pull the trigger on its next rate hike sooner than investors are expecting today.

In all this, the random onset of new information is the potential trigger for unleashing a significant change in the S&P 500, where the interactive dynamics are both very complex and periodically chaotic.

And that's just considering how changes in how far forward in time investors are focusing their attention might affect stock prices. If the expectations for future dividends themselves change, that would very directly affect stock prices, which adds a whole other level of complexity in how stock prices behave.

Speaking of which, if you want to know which scenario might apply before Kolanovic's mechanism becomes engaged, you might want to keep up on the information coming into the market....

Monday, 19 June 2017
Tuesday, 20 June 2017
Wednesday, 21 June 2017
Thursday, 22 June 2017
Friday, 23 June 2017

Elsewhere, Barry Ritholtz summarized the positives and negatives for the economy for Week 3 of June 2017.

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23 June 2017

Summer 2017 is turning out to be something of a disappointment at the cineplex. So, with that thought in mind, we're going to do what millions of teens this summer are doing and turning to YouTube for the kind of fun that used to come with Hollywood's biggest blockbusters, but which seems to be in too short of supply this summer. Enjoy!...

But wait, we have a double feature!

In other, important entertainment news, Hollywood director Joel Schumacher has finally apologized for Batman and Robin, which almost completely counteracted all that was good at the movies in the summer of 1997.


22 June 2017

We're almost to the end of 2017-Q2, so we'll take one last snapshot of how the pace of dividend cuts being reported in our ongoing real-time sampling are stacking up for the calendar quarter. First, here's how the second quarter of 2017 compares with the preceding quarter of 2017-Q1:

Cumulative Announced Dividend Cuts in U.S. by Day of Quarter in 2017, 2017-Q1 and 2017-Q2, Snapshot on 2017-06-21

As of 21 June 2017, the number of dividend cuts announced during 2017-Q2 is slightly higher than what was reported in 2017-Q1, with 44 in the current quarter's sample as compared to the previous quarter's 41 as of the same relative point of time in the quarter.

But 2017-Q2 is well behind the year ago quarter of 2016-Q2's total of 59 dividend cut announcements through the similar point of time in the quarter....

Cumulative Announced Dividend Cuts in U.S. by Day of Quarter, 2016-Q2 vs 2017-Q2, Snapshot on 2017-06-21

All in all, the number of dividend cuts in the quarter are consistent with recessionary conditions being present in the U.S. economy.

In our sampling, about 41% of the firms announcing decreases in their dividend payments to their shareholding owners are in the oil and gas sector of the U.S. economy, which follows from the reduced revenues they're earning with reduced oil prices in the global market.

There is also a high percentage of financial firms and real estate investment trusts in the mix, which combine to account for 25% the total. The remaining firms come from seven different industries, most notably chemical producers that produce agricultural fertilizers, where that industry accounts for 11% of the sampled 44 dividend cutting firms during in the quarter.

Data Sources

Seeking Alpha Market Currents. Filtered for Dividends. [Online Database]. Accessed 21 June 2017.

Wall Street Journal. Dividend Declarations. [Online Database]. Accessed 21 June 2017.


21 June 2017

The U.S. government passed a unique milestone in May 2017, where it has now cumulatively borrowed more than $1 trillion from the public since President Obama was sworn into office in January 2009, just so it can loan the money back out to Americans who need to borrow money to go to college in the form of Federal Direct Student Loans.

Money Borrowed by the U.S. Government to Finance the Federal Direct Student Loan Program, FY 1998 (October 1997) through FY 2017 (May 2017)

President Obama is directly responsible for this state of affairs. After being sworn into office on 20 January 2009, his first major domestic policy act was to sign the American Recovery and Reinvestment Act of 2009 into law on 17 February 2009 in an attempt to jump start a U.S. economy that had fallen into deep recession. Better known as the "Stimulus Bill", the act boosted the subsidy amount and quantity of Pell Grants paid to low and middle income-earning Americans attending college, but not by enough to cover more than one-third of the average annual cost of a university education, where American students who received these grants would then have to make up the difference through taking out student loans that are subsidized by the U.S. government.

Then, on 30 March 2010, President Obama signed the Health Care and Education Reconciliation Act of 2010, which resulted in the U.S. government taking over the student loan industry from the private sector.

President Barack Obama signed a law Tuesday that he said will end subsidies for banks that guarantee federal student loans, saving $68 billion over 11 years by making loans directly through the U.S. Department of Education. 

The overhaul of the student loan industry is part of the Health Care and Education Reconciliation Act of 2010, which was passed by Congress to reform the nation's health care system. 

According to the White House, starting July 1 all federal student loans will be direct loans administered through private companies that have performance-based contracts with the DOE. 

At present, the law appears set to fail on delivering these promised savings to U.S. taxpayers. For that portion of the story, please scroll down and click through!...

Previously on Political Calculations

U.S. Student Loan Implosion - We looked at the Federal Direct Student Loan program from the perspective of the student borrowers, where $137 billion worth of loans that have come due are either delinquent (more than 90 days without any payment being made) or are in default (more than 270 days without any payment being made).

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20 June 2017

How likely is it that the U.S. economy will go into recession sometime in the next year?

The combination of rising short term interest rates and falling yields for long term bonds is often considered to be a harbinger of recession in the U.S. economy. And with the Fed having left its Zero Interest Rate Policy behind while the yields on long-term U.S. Treasuries have been falling in recent weeks, we thought it was time to dust off the version of the Recession Probability Track visualization tool that we introduced in September 2007 to indicate the odds that the U.S. economy would be in recession up to a year later.

Back then, the historical data we showed in our Recession Probability Track indicated that the odds of a recession beginning in the next year had already peaked back at 50% back on 4 April 2007, which is to say that it was signaling that there was at least even odds of the U.S. having a recession begin by 4 April 2008. As it turned out, the National Bureau of Economic Research determined that the U.S. economy expanded all the way through December 2007, before beginning to contract into recession in January 2008, where what we now call the "Great Recession" is considered to have begun in December 2007, coinciding with the peak of the preceding period of economic expansion, marking Month 0 of the recession that would last until the NBER determined that it bottomed in June 2009.

The last time we showed the Recession Probability Track was 6 November 2008. Just over a month later, the Fed would implement its Zero Interest Rate Policy, which would render the Recession Probability Track useless as a recession prediction tool for the next 7 years, until the Fed stopped holding its thumb on the zero end of the scale in December 2015.

Today, some four small interest rate hikes later, the Fed can still be considered to be applying pressure at that end of the scale, but not so much as to prevent the Recession Probability Track from registering a non-zero probability....

Recession Probability Track, 2 January 2014 through 19 June 2017

At 0.17%, we find that there's very little probability of what the NBER might someday declare to be a full-blown recession breaking out in the U.S. economy sometime between now and 19 June 2018. At least, not one propositioned on Jonathan Wright's yield curve-based recession forecasting model, which factors in the one-quarter average spread between the 10-year and 3-month constant maturity U.S. Treasuries and the corresponding one-quarter average level of the Federal Funds Rate. If you'd like to do that math for yourself, we have also built a very popular tool to help with that!

That doesn't mean however that the U.S. economy might escape without experiencing some level of distress. As we saw from mid-2014 through 2016 in oil and gas production states in particular, along with some other states whose major industries are linked to the health of that sector of the economy, it is possible for some degree of significant economic contraction to occur without leading to a national recession.

For other takes on the same data trends and what it might mean for the prospects of a U.S. recession, be sure to check out Kevin Erdmann's thoughts on the flattening yield curve and Joshua Brown's perception that flattening is not threatening, as well as Mike Shedlock's contrarian speculation from a month ago.

Finally, we should also note that China has seen its yield curve invert in recent months, where our recession forecasting tool has become increasingly popular among readers there, even though it wasn't specifically designed to consider China's economic situation and history, so we don't know how well it might work for assessing that nation's recession odds.


19 June 2017

The Federal Reserve surprised no one with the announcement that its Federal Open Market Committee (FOMC) had voted to hike U.S. short term interest rates by a quarter point.

For our dividend futures-based model for forecasting the S&P 500, investors appeared fully focused on 2017-Q2 in setting stock prices on the day of the Fed's announcement, which given the influence that the Fed has over the future expectations of investors, was to be expected.

Alternative Futures - S&P 500 - 2017Q2 - Standard Model - Snapshot on 16 June 2017

But what happened after that is somewhat telling. Although the top line number for the value of the S&P 500 didn't change much, the action of stock prices in the days following the announcement combined with the week's major economic news (and to be honest, in the weeks preceding the announcement) suggest a division within the component companies that make up the index has intensified over what we observed in Week 1 of June 2017.

Where there that gets interesting is that what the S&P 500 is communicating about the expected timing of the Fed's next rate hike action is very different from what other futures-based models are communicating.

Our model suggests that investors are currently betting on 2017-Q4 as the likely timing for the Fed's next rate hike, currently giving about a 72% probability of that being the case, with the remainder banking on the Fed taking action in 2017-Q3. CME Group's Fedwatch model however is currently giving almost the opposite odds between the two quarters, with an 84% probability that the FOMC will act to hike its Federal Funds Rate at the conclusion of its 20 September 2017 meeting.

Update 2:30 PM EDT: We stand corrected, where we appear to have gotten our wires crossed in reading the FedWatch FFR futures! According to CNBC, "Market expectations for a September rate hike are just 13 percent, according to the CME Group's FedWatch tool." That's a lot closer to what the dividend futures are telling us.

It will be fun to watch how both indicators evolve over the next several weeks. From the perspective of our dividend futures-based model, it will take some substantial good economic news to boost the odds that the Fed will next hike interest rates in September 2017, which would be accompanied by a decline in stock prices. Conversely, if that news is bad and the Fed is compelled to delay its next action until later in 2017, then the S&P 500 would find itself drifing sideways to slightly higher in the weeks ahead.

Right now, we think that the context of the current market environment from the major market news stories of the second full week of June 2017 are supporting what dividend futures appear to be anticipating for the future for the S&P 500, but here they are so you can judge for yourself.

Monday, 12 June 2017
Tuesday, 13 June 2017
Wednesday, 14 June 2017
Thursday, 15 June 2017
Friday, 16 June 2017

For a succinct summary of the week's positives and negatives for the economy, be sure to check out Barry Ritholtz' latest!

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16 June 2017

It is a crime to even be in possession of some numbers in United States. In the following video, Sam from Wendover Productions explains this seemingly strange state of affairs:

If you'd like to try your hand at identifying other prime numbers that might someday also be made illegal to possess in America, join the Great Internet Mersenne Prime Search, which just confirmed the 45th Mersenne prime number to be discovered back on 2 September 2016 after 8 years of computer-aided labor!

As for the biggest prime number known today, which is the 49th Mersenne prime, it was confirmed as such on 7 January 2016:

Of course, for non-mathematicians, no prime number really exists until it's printed out on paper.

Just be sure that it's not one of the ones that you don't want the cops to catch you with!

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15 June 2017

How well are typical American households faring so far in 2017?

To answer that question, we're going to turn to a unique measure of the well-being of a nation's people called the national dividend. The national dividend is an alternative way to measure of the economic well being of a nation's people that is primarily based upon the value of the things that they choose to consume in their households, which makes it very different and by some accounts, a more effective measure than the more common measures that focus upon income or expenditures throughout the entire economy, like GDP, which have proven to not be well suited for the task of assessing the economic welfare of the people themselves.

In our case, we've developed the national dividend concept that had been originally conceived by Irving Fisher back in 1906, but which fell by the wayside in the years that followed because of the challenge of collecting the kind of consumption data needed to make it a reality. That kind of data exists today, which is why we've been able to bring it back to life.

With that introduction now out of the way, let's update the U.S.' national dividend through the end of April 2017 following our previous snapshot through the end of 2016.

Monthly National Dividend, January 2000 through April 2017

In the first four months of 2017, we see that in nominal terms, the national dividend has risen strongly following a lackluster 2016. That observation holds after adjusting for inflation, which suggests that the typical American household is benefiting from real growth.

You can see that perhaps better with our calculation of the year over year growth rates for the nominal and inflation-adjusted national dividend, which we show in the following chart from January 2001 through April 2017.

Year Over Year Growth Rates for the Monthly National Dividend, January 2001 through April 2017

Year to date, the upward trend for 2017 appears to be much better than the previous downward trend through 2016 was.

Previously on Political Calculations

The following posts will take you through our work in developing Irving Fisher's national dividend concept into an alternative method for assessing the relative economic well being of American households.


Chand, Smriti. National Income: Definition, Concepts and Methods of Measuring National Income. [Online Article]. Accessed 14 March 2015.

Kennedy, M. Maria John. Macroeconomic Theory. [Online Text]. 2011. Accessed 15 March 2015.

Political Calculations. Modeling U.S. Households Since 1900. 8 February 2013.

Sentier Research. Household Income Trends: April 2017. [PDF Document]. 23 May 2017. [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 approximating the national dividend.]

U.S. Bureau of Labor Statistics. Consumer Expenditure Survey. Total Average Annual Expenditures. 1984-2015. [Online Database]. Accessed 7 February 2017.

U.S. Bureau of Labor Statistics. Consumer Price Index - All Urban Consumers (CPI-U), All Items, All Cities, Non-Seasonally Adjusted. CPI Detailed Report Tables. Table 24. [Online Database]. Accessed 13 June 2017.

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14 June 2017

Where have all the jobs for teens in America gone? That's a question that was just asked in Redondo Beach, California on 6 June 2017:

While it may have been a rite of passage for previous generations, but more and more teenagers are spurring summer jobs for more schooling, according to the Bureau of Labor Statistics.

Traditionally, July is the month with the highest teen employment participation rate because school is out of session, are many sports and extracurricular activities, but the number has been decreasing dramatically in recent years. In July 2016, for instance, the teen labor force participation rate was 43.2 percent, down almost 30 percentage points from the high point of 71.8 percent in July 1978 and 10 percentage points from a decade ago, according to the BLS.

While the report shows that close to half of teens are working during the summer, that number is significantly lower than previous generations. Several reasons have popped up to explain the decline in teen workforce participation, including more older workers working past retirement age to more low-skill immigrants taking jobs that would otherwise go to teens, to teens foregoing jobs because of the low pay.

We'll stop there because neither the California journalist reporting the story, nor the BLS analyst who wrote the report that the journalist is citing, ever mention the phrase "minimum wage", which is a very remarkable omission, particularly for the BLS.

Unfortunately, Bloomberg's reporting on the topic isn't much better:

Why aren't teens working? Lots of theories have been offered: They're being crowded out of the workforce by older Americans, now working past 65 at the highest rates in more than 50 years. Immigrants are competing with teens for jobs; a 2012 study found that less educated immigrants affected employment for U.S. native-born teenagers far more than for native-born adults. Parents are pushing kids to volunteer and sign up for extracurricular activities instead of working, to impress college admission counselors. College-bound teens aren't looking for work because the money doesn't go as far as it used to. "Teen earnings are low and pay little toward the costs of college," the BLS noted this year. The federal minimum wage is $7.25 an hour. Elite private universities charge tuition of more than $50,000.

Just as a quick aside, isn't that just a completely bizarre juxtaposition in those last two sentences? As if attending an "elite private university" was a common occurrence when in reality, over 70% of Americans in college are enrolled in much less costly public universities? What an elitist snob!

But at least they mentioned the minimum wage when discussing teen summer jobs, which puts them well ahead of both the Redondo Beach Patch and the Bureau of Labor Statistics in the February 2017 edition of its Monthly Labor Review.

Let's do a quick comparison between the job market that adults (Age 20 and older) see and the one that teenagers (Age 16-19) see. And better still, let's make it more local for the Redondo Beach journalists, where we'll just look at California's labor force and employment statistics for these demographic groups, which presents the data as rolling twelve month averages to control for annual seasonality in the data. Let's first look at the Age 20 and up group's employment situation in the state for the period from January 2004 through April 2017:

California Non-Teen (Age 20+) Labor Force and Total Employed Trailing Twelve Month Average, January 2004  - April 2017

In this chart, we can see that California's adult labor force has been affected by things like recessions, but is pretty unaffected by minimum wage hikes. Next, let's focus in on just California's noninstitutionalized population of working age (16 to 19 years old) residents, whose total population in the state has largely ranged between 2.0 and 2.2 million from 2004 through the present:

California Teen (Age 16-19) Labor Force and Total Employed Trailing Twelve Month Average, January 2004  - April 2017

In this chart, we see that whenever minimum wage increase are approved or are implemented, adverse trends in teen employment levels follow. But then, that's what we should expect to happen whenever the cost of hiring the least educated, least skilled and least experienced portion of the U.S. workforce is arbitrarily increased by legislative whims.

It's the sort of thing that carries a social cost for the communities where teens live. Bloomberg's snobbish reporting on the topic was partially redeemed with the following observations:

All this studying has obvious benefits, but a single-minded focus on education has disadvantages, too. A summer job can help teenagers grow up as it expands their experience beyond school and home. Working teens learn how to manage money, deal with bosses, and get along with co-workers of all ages.

A summer job can even save lives. In a study released last month by the National Bureau of Economic Research, researchers analyzed the effects of two Chicago programs providing students with part-time jobs along with mentors for the summer. The programs had little apparent effect on the teens' later employment or education—a big concern in itself—but arrests for violent crime plunged, by 42 percent for one program and 33 percent for the other, an effect felt for at least a year after the programs ended. If teens got nothing else out of the jobs programs, the researchers suggested, they were at least "learning to better avoid or manage conflict."

Those kinds of real life lessons are all too lacking in far too many of today's schools and college campuses.

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13 June 2017

Who is really paying Philadelphia's controversial soda tax? And how much, if at all, has it affected the economy of the City of Philadelphia?

MRU: Tax Revenue and Deadweight Loss
The answer to the first question involves the concept of tax incidence, which considers the all too common reality that the people and businesses that are intended target of a tax are not necessarily the source of the money that pays the tax. For Philadelphia, what we want to find out is what percentage of the tax is being paid by the distributors who were specifically targeted to be taxed by the city versus the percentage of the tax that is being passed through to the city's consumers of sweetened beverage products subject to the tax.

Meanwhile, the answer to the second question involves the economic concept of deadweight losses to an economy, which can arise whenever the optimum equilibrium between supply and demand for goods or services that maximizes the benefits to a population of consumers and producers is prevented from being achieved. If a deadweight loss exists, it represents the amount of economic activity that has been directly lost because of the imposition of the tax, which tells us the degree to which the city's economy may have shrunk as a result.

We ran the numbers to calculate both sets of figures back in March 2017 for Philadelphia's newly imposed tax on a wide array of naturally and artificially-sweetened beverages distributed for sale within Philadelphia's city limits, but now, we have more finalized numbers for the amount of tax revenue actually being generated by the tax through the first four months of 2017 and also a more refined estimate of the quantity of sweetened beverages that would have been distributed in the city in the absence of the soda tax, which will allow us to better estimate both the tax incidence and deadweight loss that has resulted from Philadelphia's beverage tax to date.

We've plugged those updated numbers into the tool we built to do the math below, where our "time period of interest" is the first four months of 2017, which is the most current data we have at this writing. If you're accessing this article on a site that republishes our RSS news feed, please click here to access a working version of the tool.

Supply and Demand Chart Data (Considering the Effect of a Tax)
Input Data Values
Tax Rate [cents per ounce]
Unit Price of Item Without the Tax [cents per ounce]
Quantity Demanded Before the Tax [millions of ounces/time period of interest]
Unit Price of Item With the Tax [cents per ounce]
Quantity Demanded After the Tax [millions of ounces/time period of interest]

Tax Incidence of Philadelphia's Soda Tax
Calculated Results Values
Taxes Collected [millions/time period of interest]
Portion of Tax Paid by Consumers
Portion of Tax Paid by Distributors
Deadweight Loss of Tax
Deadweight Loss from Tax [millions/time period of interest]

Acme Markets: Discounted Sale Price, 9 June 2017 through 15 June 2017
In the tool above, we also updated the "Unit Price of Item With the Tax" to reflect the discounted sale price for a 12-pack of 12 fluid ounce cans of Coca Coca Coke Classic at the Acme Markets at 19th and Oregon, which applies for the week of 9 June 2017 through 15 June 2017. Dividing the sale price of $5.66 by 144 ounces works out to be a discounted unit price of 3.93 cents per ounce. Meanwhile, the same ShopRite location that we used in our previous example is still selling a 2-liter (67.6 fluid ounces) of Coca Cola Coke Classic for $3.00 per bottle, which gives an upper end for the unit price of 4.43 cents per ounce, so the tax incidence will vary for Philadelphia consumers by the locations where they might shop.

Using the updated numbers we've entered as the default values in the tool above, we find that for our time period of interest, January through April 2017, the total amount of taxes collected by Philadelphia through its new beverage tax is $25.6 million. Of this tax, the majority (66%) of the actual burden of paying the tax is being passed through to the city's population of sweetened beverage consumers, with only a lesser fraction (34%) coming out of the pockets of the city's soft drink distributors (at ShopRite this week, it would be 99.3% consumer, 0.7% distributor). We also estimate the deadweight loss of the tax through the first four months of 2017 totals $6.777 million.

Multiplying the dollar figures by a factor of three allows us to project the tax collections and deadweight loss will be for all of 2017, assuming the level of post-tax consumption established during the first four months of the year continues. With that assumption, Philadelphia would be on track to collect $76.8 million, which will fall over 17% short of the $92.4 million that city officials were counting upon to fund park improvements and the expansion of pre-K schooling in the city to up to 6,000 children, as well as to boost the city's general fund. Given the projected numbers, we would anticipate that one or more of these initiatives is going to be scaled back, unless the city reduces spending elsewhere in its budget to make up the difference.

Meanwhile, the projected deadweight loss of economic activity to the city from its controversial tax would be $20.3 million for the year. This figure is the total amount by which the controversial soda tax imposed by the city's civic leadership would be responsible for shrinking the economy of the city of Philadelphia.

You're more welcome to use our tool to update these estimates as new information comes out, or to consider what the impact of a similar tax might be in other jurisdictions. If you're so inclined, you can even do the math for the image of the supply and demand curves that we included at the top of the article!

Image Credit: Marginal Revolution University, as extracted from their informative YouTube video on Tax Revenue and Deadweight Loss!

Update 3 August 2017: Menzie Chinn, who has really had it in for us ever since we prominently featured his work in our Examples of Junk Science series and exposed his bizarre, stalking-like behavior, disputes our analysis, claiming that we should have accounted for a reduction in negative externalities, or rather, a reduction in bad things that would be associated with greater sweetened beverage consumption, such as obesity-related health conditions, as is often claimed by soda tax supporters.

Thing is, Philadelphia's soda tax was passed purely because of its promise to put revenue in the city's coffers. Any potential health benefits from imposing the tax were deliberately not taken into account by the city's leaders, where the tax they did put in place will minimize the potential to realize any such positive benefit because of the adverse behaviors it encourages.

They may exist however, where actual post-tax sweetened beverage consumption and health cost data within Philadelphia will be needed to quantify what those benefits might be. That will be difficult for this situation however, considering that purchases of sweetened drinks by Philadelphia residents has shifted toward the suburbs to avoid the tax, which is one of the major reasons why Philadelphia is failing to hit its tax revenue projections.

So they're not even getting the full cost benefits of reducing sugary beverage consumption that the quantity of taxed beverages to date would suggest are taking place, so whether that factor is accounted for will be one thing that we would look toward in future research findings of any realized health benefits from the implementation of Philadelphia's soda tax, which to the best of our knowledge, is work that has yet to be done.

Not that such a thing would stop a confirmed repeat-offending junk scientist from offering unfounded claims, especially one who has an established history of either cooking data or knowingly omitting relevant data to achieve his analytical results while also engaging in what in our view is very peculiar and creepy conduct.

Update 1 September 2017: Speaking of unfounded claims, it turns out that Chinn's highly limited and very peculiar understanding of economics is even junkier than we first thought. Talk about cooking in a predetermined outcome!

Previously on Political Calculations

Presented in reverse chronological order....

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About Political Calculations

Welcome to the blogosphere's toolchest! Here, unlike other blogs dedicated to analyzing current events, we create easy-to-use, simple tools to do the math related to them so you can get in on the action too! If you would like to learn more about these tools, or if you would like to contribute ideas to develop for this blog, please e-mail us at:

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