Political Calculations
Unexpectedly Intriguing!
June 28, 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.

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June 27, 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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June 26, 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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June 23, 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.

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June 22, 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.

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