Political Calculations
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January 24, 2017

For the last three years, the U.S. government and the governments of the 50 states and the District of Columbia have been running an experiment on live human beings.

That experiment seeks to answer the question of whether providing "free" health insurance coverage through the Medicaid welfare program to low-income earning Americans whose household incomes fall between 100% and 138% of the poverty threshold would improve their health through increased access to health care resources enough to save lives.

Medicaid Expansion and Non-Expansion States with estimate of eligible population in Non-Expansion States - Source: White House, Archived: https://web.archive.org/web/20140702152608/http://www.whitehouse.gov//expanding-medicaid

The 50 states and the District of Columbia were divided through a political process into two groups in 2014, when the Patient Protection and Affordable Care Act (aka "Obamacare") went into effect. In that year, the governments of 26 states and the District of Columbia had acted to expand the eligibility of these low-income earning American households for their respective Medicaid programs, while the other 24 states did not. In the 24 states that did not act to expand their state's Medicaid welfare program, low income earning households were instead eligible for highly-subsidized health insurance coverage through their state's Affordable Care Act marketplace (or ACA exchange), which they would be able to opt out of if they chose instead to pay the ACA's "shared responsibility" tax.

Divided into these two groups, between Medicaid-expansion states and non-expansion states, we should be able to tell whether the expansion of eligibility for the Medicaid welfare program for low-income earning but non-impoverished American households saved lives through the National Center for Vital Statistics' data on each state's age-adjusted mortality rates given that this portion of income earners represents a significant share of each state's population.

In theory, because the expansion of eligibility of the Medicaid welfare program should improve the access of these low-income earning households to costly health care services by eliminating the need of these households to pay for their medical treatment, we should expect to see a noticeable reduction in the mortality rates for all causes in each Medicaid-expansion state that would not be evident in the non-Medicaid expansion states.

But to do the job properly, we need to take into account any trends in age-adjusted mortality rates that existed in the period prior to the implementation of Obamacare in 2014, which establishes a counterfactual for what we should expect mortality rates from all causes to be in each state in the absence of any expansion in Medicaid eligibility.

We did that for each state and the District of Columbia for the five full years from 2009 through 2013 using linear regression, which we used to project what each state's age-adjusted mortality rate would be in 2014. We can then compare those projected results with the actual age-adjusted mortality rates that was recorded for each state in 2014, which is the most recent year for which the NCHS has published final data for deaths at this time. All that data is presented in the following table (if you're accessing this article on a site that republishes our RSS news feed. and the table hasn't been rendered properly, you can see the original here).

Age-Adjusted Mortality Rates in 50 States and District of Columbia, 2009-2014
State 2009
Alabama 921.3 939.7 933.6 926.7 925.2 927.7 909.1 No
Alaska 755.0 771.5 747.8 731.4 724.4 715.6 736.8 No
Arizona 652.2 693.1 688.9 682.9 674.2 688.4 661.7 Yes
Arkansas 874.6 892.7 895.3 897.5 893.8 903.7 883.7 Yes
California 652.0 646.7 641.3 630.4 630.1 622.1 605.7 Yes
Colorado 688.1 682.7 677.8 665.6 655.4 649.2 664.4 Yes
Connecticut 684.1 652.9 660.6 648.2 646.3 634.3 646.5 Yes
Delaware 753.5 769.9 764.2 745.4 726.8 728.6 734.0 Yes
District of Columbia 812.7 792.4 755.9 757.2 752.0 727.1 743.8 Yes
Florida 673.7 701.1 677.1 669.9 663.4 661.5 662.0 No
Georgia 818.4 845.4 815.7 808.6 806.2 800.5 801.9 No
Hawaii 619.7 589.6 584.9 586.5 590.8 576.0 588.7 Yes
Idaho 721.3 731.6 745.0 726.6 730.6 735.1 723.8 No
Illinois 743.5 736.9 737.4 728.7 724.0 719.9 726.0 Yes
Indiana 815.8 820.6 825.1 827.5 832.2 836.2 822.3 No
Iowa 724.7 721.7 722.7 718.3 723.7 720.6 722.9 Yes
Kansas 760.2 762.2 767.2 761.0 757.7 759.8 759.3 No
Kentucky 898.7 915.0 910.3 916.3 899.9 909.2 906.3 Yes
Louisiana 888.3 903.8 886.6 898.6 897.7 899.1 894.2 No
Maine 757.7 749.6 752.8 730.4 754.2 741.1 739.0 No
Maryland 762.6 728.6 715.8 709.1 710.4 688.1 699.5 Yes
Massachusetts 680.3 675.0 676.3 657.9 663.5 655.4 663.0 Yes
Michigan 785.9 786.2 784.2 774.2 782.3 776.8 783.7 Yes
Minnesota 651.8 661.5 659.2 649.5 651.0 650.5 647.0 Yes
Mississippi 926.1 962.0 956.1 942.9 959.6 963.7 937.6 No
Missouri 804.6 819.5 812.0 803.0 807.7 806.3 807.0 No
Montana 758.0 754.7 760.6 732.4 761.3 748.7 732.1 No
Nebraska 716.1 717.8 719.8 719.0 714.7 717.0 718.2 No
Nevada 784.8 795.4 789.7 774.6 769.8 767.6 749.2 Yes
New Hampshire 677.3 690.4 710.4 687.5 679.1 689.2 706.2 Yes
New Jersey 694.8 691.1 690.6 677.6 676.4 671.0 665.7 Yes
New Mexico 739.4 749.0 748.9 744.6 731.8 736.9 749.6 Yes
New York 667.1 665.5 665.4 652.1 649.3 645.2 636.5 Yes
North Carolina 800.7 804.9 790.9 786.4 777.6 772.7 775.9 No
North Dakota 719.4 704.3 697.4 701.2 709.7 699.7 692.7 Yes
Ohio 813.4 815.7 821.8 817.9 811.2 815.3 810.0 Yes
Oklahoma 890.5 915.5 910.9 891.5 910.7 908.7 897.5 No
Oregon 733.1 723.1 724.1 706.6 717.5 706.6 706.7 Yes
Pennsylvania 770.8 765.9 776.0 759.2 761.3 758.9 750.2 No
Rhode Island 717.6 721.7 707.3 686.5 709.6 693.2 700.9 Yes
South Carolina 818.2 854.8 839.5 835.2 837.8 843.0 829.1 No
South Dakota 689.3 715.1 720.6 712.3 679.3 696.5 710.4 No
Tennessee 867.2 890.8 879.0 880.6 881.1 885.0 880.0 No
Texas 754.3 772.3 751.6 753.3 751.6 749.3 745.3 No
Utah 658.7 703.2 699.1 700.0 710.4 724.3 709.6 No
Vermont 681.6 718.7 711.0 700.1 710.6 716.2 694.8 Yes
Virginia 749.3 741.6 741.6 730.2 724.8 719.4 717.5 No
Washington 709.8 692.3 690.4 681.5 679.3 669.1 672.9 Yes
West Virginia 949.7 933.6 953.2 939.3 923.8 926.1 929.1 Yes
Wisconsin 708.9 719.0 721.1 707.8 720.1 718.7 712.1 No
Wyoming 776.4 778.8 754.6 748.3 731.7 722.0 742.4 No

Collectively, from 2009 through 2013, the 26 states and the District of Columbia that acted to expand their Medicaid welfare program eligibility in 2014 averaged annual declines in their age-adjusted mortality rates of 4.0 deaths per 100,000 population. Meanwhile, the 24 states that did not expand their Medicaid welfare program eligibility in 2014 saw an average annual decline in their mortality rates of 1.3 deaths per 100,000 population in the years from 2009 through 2013.

The chart below visualizes the age-adjusted mortality rate projected for each state in 2014 along with the actual mortality rate that was reported in 2014.

Mortality Rates by State After Implementation of Obamacare in 2014, Projected and Actual Age-Adjusted Deaths for All Causes per 100,000 Population

Collectively, there was very little difference in the average projected change in the age-adjusted mortality rate and the average actual mortality rate for the 26 states and the District of Columbia that chose to expand their Medicaid programs in 2014. The average actual rate was 0.2 deaths per 100,000 population higher than the projected decline based on the existing trend from 2009-2013, which is not significantly significant. If expanding the eligibility for Medicaid in these states produced life-saving benefits, you cannot tell the difference with this data.

The age-adjusted mortality rates in the 24 states that didn't expand their Medicaid welfare programs declined by 4.1 deaths per 100,000 population compared to what would have been expected in 2014 based on the preceding trends in these states from 2009 through 2013. We suspect that also is noise in the data.

These are interesting results, which if repeated again in both 2015 and 2016, would confirm that the expansion of Medicaid did very little to produce noticeable life-saving benefits for the Americans who were enrolled in it (one potential explanation for that result is presented here). With the single year of data we do have, it would appear that the Affordable Care Act's expansion of Medicaid provided very little benefit to the portion of the U.S. population that earns the lowest incomes outside of those falling below the poverty line in the jurisdictions where the expansion was implemented.


U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 60. Number 3. Deaths: Final Data for 2009. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2009. [PDF Document]. 8 May 2013.

U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 61. Number 4. Deaths: Final Data for 2010. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2010. [PDF Document]. 8 May 2013.

U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 63. Number 3. Deaths: Final Data for 2011. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2011. [PDF Document]. 27 July 2015.

U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 63. Number 9. Deaths: Final Data for 2012. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2012. [PDF Document]. 31 August 2015.

U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 64. Number 2. Deaths: Final Data for 2013. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2013. [PDF Document]. 16 February 2016.

U.S. Centers for Disease Control. National Vital Statistics Reports. Volume 65. Number 4. Deaths: Final Data for 2014. Table 19. Number of deaths, death rates, and age-adjusted death rates for major causes of death: United States, each state, Puerto Rico, Guam, American Samoa, and Northern Marianas, 2014. [PDF Document]. 30 June 2016.

HealthPocket. Expansion of Medicaid in 2014. [Online Document]. 10 July 2014. Accessed 18 January 2017.

U.S. Census Bureau. Annual Estimates of the Resident Population for the United States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2016 (NST-EST2016-01). [Excel Spreadsheet]. 7 December 2016. Accessed 18 January 2017.

Blase, Brian. New Gruber Study Raises Major Questions About Obamacare's Medicaid Expansion. Forbes. [Online Article]. 27 November 2016. Accessed 18 January 2017.

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January 23, 2017

As far as the markets were concerned, Trump Inauguration Week, a.k.a. Week 3 of January 2017, was a really lackluster experience where the S&P 500 mainly drifted sideways.

Alternative Futures - S&P 500 - 2017Q1 - Standard Model with Connected Dots Between 29 December 2016 and 14 February 2017 - Snapshot on 2017-01-20

Believe it or not, the sideways market is remarkable because U.S. stocks have been going nowhere for longer than at any time since 1957. CNBC describes what that means for the Dow:

If it seems like the stock market's crawl to nowhere over the past month has been particularly strange, that's because it has been. It turns out the gap between the Dow's high and low prices over the past month is a tiny 1.4 percent — the narrowest gap in data going back to 1957....

Before this 1.4 percent range (1.42 to be precise), the previous records for lowest one-month ranges were 1.85 percent in August 2005, 2.17 percent in December 2004 and 2.25 percent all the way back in November 1964. Yes, 1964.

We don't know what this means for future performance. Some traders have suggested a narrow range indicates increased volatility to come. But there hasn't been anything else like this: the narrowest range we've ever seen, while the market is at an all-time high.

We don't know what it means for future performance either, but we can offer some insight. Given how stock prices work, they either need to absorb as yet unannounced changes in the fundamental expectations for dividends that will be paid at specific points of time in the future, or investors need to shift their time horizon from where they've currently fixed their forward-looking attention (we think to the end of 2017-Q2, coinciding with when the Fed is next expected to hike short term interest rates), to another point of time in the future that has different expectations for dividends associated with it.

As for the upcoming week, there's nothing in the news as yet to suggest a significant change in course at this writing, but perhaps we'll get some noise out of Washington D.C. now that the U.S. government has a new boss.

Looking back at Week 3 of January 2017, here are the headlines that seemed notable, although not much new information was revealed from a market-moving perspective.

Tuesday, 17 January 2017
Wednesday, 18 January 2017
Thursday, 19 January 2017
Friday, 20 January 2017

Barry Ritholtz summarized the week's markets and economic news in terms of its positives and negatives .

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January 20, 2017

You know it's the middle of soup season when you see that Walmart has established an island for Campbell's Tomato and Chicken Noodle Soups in its main grocery aisle.

Walmart Campbell's Soup Island - December 2016

Walmart's motto is "everyday low prices", which for an iconic can of Campbell's Tomato Soup in December 2016 means a cost to consumers of $1.00 per can.

To put that everyday low price into historical context, we've charted the advertised discounted sale price of a single Number 1 can of Campbell's Condensed Tomato Soup for each month from January 1898 through December 2016.

Campbell's Tomato Soup - Discounted Sale Unit Price per Can - January 1898 to December 2016
That's 118 years worth of consumer prices for a single product!

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January 19, 2017

We're following up a prediction we made back in September 2016 today:

Looking toward the current quarter, there is early evidence that points to an increase in the acceleration of private debt in the U.S. economy in the early months of 2016-Q3, suggesting a more positive outcome for GDP than was recorded in 2016-Q2 lies ahead.

The following chart shows what happened after all the data for 2016-Q3 came in, which took until December 2016 to get:

Acceleration (Change in Year Over Year Compounded Growth Rate) of Private Debt in the United States, January 2006 - September 2016

Two things really stand out:

  1. The acceleration of private debt in the U.S. economy sharply escalated in 2016-Q3.
  2. The trailing year average of the annualized percent change of quarterly real GDP stopped falling after having done so continuously for 14 months and began rising in August 2016.

We think that the main boost to the private sector economy came in the energy sector, following the bottoming of oil prices in early February 2016, which then went on to rebound to levels where domestic oil producers could justify taking on debt to expand their operations.

That improvement brought an extended period of economic contraction to an end for the U.S. energy sector. According to Reuters, that change in fortune is likely to continue in 2017.

U.S. petroleum producers are looking forward to better times in 2017 as the industry has passed the low-point in the cycle and embarked on the road to renewed expansion.

Domestic oil and gas production hit a trough in the first half of 2016 and showed signs of rising in the second half as drilling picked up in response to higher prices....

The downward trend in oil and gas output has been arrested by a significant upturn in the number of rigs drilling onshore, especially in West Texas.

In the private sector, the decision to take on debt can be considered to be a bet on the future, which is why this particular measure would appear to be valuable for anticipating changes in GDP.

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January 18, 2017
Run!  Stock Market Chaos!

How can you tell if a given company's stock is overvalued, undervalued or priced just right?

If you're the kind of trader who likes to try their hand at picking winners and avoiding losers, that's the million dollar question, isn't it?

But for stocks of firms that pay dividends, there may be a tangible way to determine the answer to that question, provided that you know how those firms will act to change their dividends in the future. And as it happens, we have a speculative forecast for how five S&P 500 firms are expected to do just that in 2017, but who, as yet, have not declared what they will do. The following list summarizes the forecast for dividends for each of the companies:

  • Comcast (NASDAQ: CMCSA) - 15% increase
  • Home Depot (NYSE: HD) - 14% increase
  • Cisco Systems (NASDAQ: CSCO) - 15% increase
  • TJX Companies (NYSE: TJX) - 16% increase
  • Vulcan Materials (NYSE: VMC) - 25% increase

Being able to do something useful with this knowledge requires some familiarity with the concept of the dividend discount model, where stock prices are recognized as representing the approximate net present value of all dividends that will be paid to shareholders in the future, and also the part of the efficient market hypothesis that says that stock prices will almost instantaneously incorporate information about the future as it becomes known.

[In reality, the relationship between the current prices of stocks and their expected future dividends per share is more complex than these two simple assumptions suggest, but we can still put them to work to do something interesting. (Remember the motto: All models are wrong, but some are useful!)]

The way we'll do that is to chart the relationship between the stock prices at the beginning of the last several years and the actual annual dividends per share that each company would go on to pay out during each of those years.

Projected Trends for Stocks Forecast to Boost Dividends in 2017, Forward Annual Dividends Per Share vs January Price Per Share

For the historical data shown in this chart, what we're effectively doing is pretending that investors had perfect knowledge of what dividends each firm would pay out during the year to come, which they incorporated into the closing stock price for each company on the first trading day of each year. We then calculated linear trend lines to approximate the relationship between the first trading day of the year's stock price for each company and the forward annual dividends per share that each would pay, extending the trend lines out past the point of the forecast dividends per share for 2017. We then added one last point, indicating the forecast annual dividends per share for 2017 for each company along with its stock price as of the close of 6 January 2017.

As you can see, we obtained some pretty interesting results. For three of the firms, Comcast, TJX and Cisco Systems, we find that the forecast data points for 2017 are all within a fairly small margin of error of the trendlines projected from the historic data. This result suggests that the stock prices of these firms are priced about right.

But we have some significant deviations when we look at the forecast values for both Home Depot and Vulcan Materials, where Home Depot's stock would appear to be undervalued and Vulcan Materials would appear to be overvalued.

Are they though? Could VMC be experiencing something of a speculative bubble that could soon pop? Or could investors be expecting the board of Vulcan materials to boost the company's dividend by more than what has been forecast for 2017?

Similar questions arise about Home Depot. Is the stock price being unnaturally depressed by investor pessimism about the company's future prospects? Or does HD's board have plans for a much smaller dividend increase than the forecast anticipates?

And how might potential changes in either Home Depot's or VMC's stock buyback programs change the dividend per share math (and stock prices) for the firms?

All you need to do to pick winning trades is to answer questions like these! We should know most of the answers to the questions we've asked in this post by the end of 2017-Q1, if not early in 2017-Q2.

On a final note, the kind of analysis we just showed isn't new by any stretch, but more often than not, when it has been presented, has typically been purely backwards-looking. What we're doing that's new is applying it to look forward in time with information that is still potentially actionable. We're looking forward to finding out how it all plays out.

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