We’ve updated our visualized Medicaid enrollment data. These data run through May of 2016.


From The Secret War: Spies, Cyphers, and Guerrillas, 1939-1945, by Max Hastings:


“The first requirement for successful use of secret data is that commanders should be willing to analyze it honestly. Herbert Meyers, a veteran of Washington’s National Intelligence Council, defined his business as the presentation of ‘organized information’; He argued that ideally intelligence departments should provide a service for commanders resembling that of ship and aircraft navigation systems. Donald McLachlan, a British naval practitioner, observed: ‘Intelligence has much in common with scholarship, and the standards which one demanded in scholarship are those which should be applied to intelligence.’ “ 

Hastings goes on to reference German commanders after the war blaming their intelligence failures on Hitler’s “refusal to countenance objective assessment of evidence” – especially if the reports were unfavorable [to his views].’


Useful lessons here even outside the military and national security domains, but seems especially timely these days. Bad enough to try to fool others. Fooling yourself is especially dangerous.


Visualized Medicaid enrollment trends, by health plan (and fee-for-service) along with mix by demographic characteristics


This is as we – or at least most of us – would expect.

Compared to people in states that did not expand Medicaid under the ACA, newly available Medicaid in States that expanded the program enabled newly covered people to reduce their unpaid bills and debts. 

Luojia Hu, Robert Kaestner, Bhashkar Mazumder, Sarah Miller, and Ashley Wong wrote: 

The Effect of the Patient Protection and Affordable Care Act Medicaid Expansions on Financial Well-Being (PDF)

NBER Working Paper No. 22170, April 2016

Their abstract:

We examine the effect of the Medicaid expansions under the 2010 Patient Protection and Affordable Care Act (ACA) on financial outcomes using credit report data for a large sample of individuals. We employ the synthetic control method (Abadie et al., 2010) to compare individuals living in states that expanded Medicaid to those that did not. We find that the Medicaid expansions significantly reduced the number of unpaid bills and the amount of debt sent to third- party collection agencies among those residing in zip codes with the highest share of low income, uninsured individuals. Our estimates imply a reduction in collection balances of around $600 to $1,000 among those who gain Medicaid coverage due to the ACA. Our findings suggest that the ACA Medicaid expansions had important financial impacts beyond health care use 

Good to have evidence

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Monmouth County, NJ has sold two nursing homes for a total price of $32.2 million. Sold by auction were the 174 bed John L. Montgomery Care Center in Freehold to the Allaire Healthcare Group and the 135 bed Geraldine L. Thompson Care Center in Wall Township to Preferred Care Holdings

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With a relative minimum of fuss, Genesee County, NY is finalizing the sale of its nursing home. Final State approval of the transfer is on the agenda.

The sale includes the 160 bed skilled nursing facility (SNF), 13 slots for adult day health care, and an 80 bed assisted living facility. 

Sale price is $15.2 million

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Gambling is intrinsically a zero-sum game. Though there may be locally, in the aggregate, there’s no economic multiplier. So it’s never made sense to me as a viable strategy for economic development. At best, it’s a break-even. And, the odds are that, it simply transfers money from one region to another, mostly from poor to wealthy.

Thanks Lucy Dadayan, for documenting actual results!

State and local government gambling revenues have softened significantly in recent years. States and localities derive the bulk of gambling-related revenues from three major sources — lotteries, accounting for about two-thirds of gambling revenue; commercial casinos; and racinos. Lottery revenue declined by 0.7 percent in real (inflation-adjusted) terms in fiscal year 2015, with twenty- seven states reporting declines. This was the second consecutive decline. Casinos experienced dramatic growth during the 1990s, but that growth slowed over the past decade. In recent years, much of the growth has shifted to racinos — hybrids of casinos and racetracks — as more states have approved such facilities. Revenues from casinos and racinos combined increased by 1.1 percent in real terms in 2015, but that growth is mostly attribut- able to two states, Maryland and Ohio, which legalized casino/ racino operations after the Great Recession and opened more facilities in fiscal year 2015.

The recent geographic expansion of gambling created stiff competition as facilities vie for the same pool of consumers, par- ticularly in the northeastern region of the nation, where weaken- ing growth has been partly attributable to market saturation and industry cannibalization. Between 2008 and 2015, inflation- adjusted tax and fee revenues from commercial casinos grew by more than $1.3 billion in states with newly authorized casinos, but declined by $1.4 billion in states with established casinos, for a net decline of 1.5 percent nationally. 

At least let’s not fool ourselves that this is an easy funding source for government.

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Just a reminder. I first published this in 2006 under a my old SignalHealth label and again here in 2011. It seems to get more current every day. Certainly, there are a lot of tax “plans” being floated that confirm one of the self-reinforcing loops depicted (influencing changes in public policy to the advantage of the already advantaged).


Inequality is both effect and cause.

Inequality of income and resources is indeed a significant, even profound social, political and economic problem. But this did not just happen suddenly and, by itself, it’s not enough to explain the current unrest. That’s more because a large proportion of the public has come to the conclusion (rightly, I think) that the game is rigged.

Here’s an alternative, systems picture of what’s happening and has been happening for several decades. It’s the self-reinforcing nature of the current system and its inequalities. This shows three examples of “the rich getting richer,” one in the public sector, one in the private sector, and one private/public combination. (Note that I did this a few years back on an earlier version of a sister site.)

Connecting the Social Dots Rich get Richer


Other self-reinforcing loops might well be added, such as the ownership of the media and its messaging. Implicitly, it’s part of “buying influence.”

The key points is that this unhealthy system is self-reinforcing and that fixing it must occur in multiple interrelated domains. It ripples through politics, government, education, finance, and business. Perhaps it would be better to assume that, until proven otherwise, any major component of our economy works this way.

Increasing taxes on very high income individuals would contribute, but it’s far from enough. These self-reinforcing loops need to be reversed, or at least be counter-balanced.

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Ben Wellington, who runs the excellent I Quant New York  did a nice job here unpacking NYC taxi charge data and found that two different systems installed in taxis calculate driver tips differently. 

And he estimates the more generous (?) system produces $5.2 million in tips above what the other system provides. Of course, riders have been unaware and Wellington’s analysis suggests that the Taxi Commission and drivers were probably unaware as well. This is why we should “use the damn data.” And it’s why public data should be open to the public. Even the best intentioned and most capable public officials do not have the time or resources to explore it all. Making data public enables people like Wellington to do their own explorations. 

So, even when it’s in a good visualization, it’s not just a matter of looking at the data. It’s also a matter of thinking about it. It’s also a matter of following its logic and asking questions about what each type of data really represents and how different fields relate to one another (and often to what’s missing).

Wellington makes some cogent suggestions. Here are the summary points, but you ought to take a look at his post:

  • The TLC (Taxi and Limousine Commission) should fix the in-cab payment systems to make them consistent with one another.
  • The TLC should release taxi data directly to the public, not through FOIL.
  • When doing data science, look at the raw data.
  • Link to or publish your data sources.

All excellent suggestions. Kudos to Ben Wellington. 

And, by the way, check this post of Wellington’s as well. If you’ve ever had a NYC MTA Metrocard, the value of which you did not fully exhaust, this is an excellent example of how Open Data can lead to very concrete suggestions that save the public a lot of money. The value of this one is even greater than from tips.

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Andrew Cuomo, Governor of New York in his combined State of the State and Budget Address, January 21, 2015.

“When people complain about high taxes in New York, they’re talking about the property tax.”

OK, how much, for what and where? Mostly schools and secondarily counties. These data are for over 3,200 local governments outside of New York City. They include:

  • School districts
  • Counties
  • Cities
  • Towns
  • Villages
  • Fire Districts
The data are from annual financial reports submitted by each local government to the Office of the State Comptroller. In some cases, local governments have not submitted their reports or have not submitted them timely. Not all local governments use the same fiscal year. For purposes of simplicity, in all cases, the data are displayed based on the calendar year that fiscal years end. 

Property Tax by Region Outside NYC by Type of Municipality 2013 Public Signals LLC









Here’s a table with the summary numbers:

Table of Property Taxes NYS Outside NYC FY 2013 by Region by Type Public Signals LLC









And the trends? Again, schools. Though it’s hard to see in the detail, in the aggregate, village property taxes now exceed those levied by cities.

Local Government Property Taxes NYS Outside NYC by Type of Government 1998 2013











As is so often the case, there’s lots of variability. In this graphic, each dot represents a local governmental unit, color coded by type, and organized by region.

Scatter of Property Taxes per $1 000 Value by Region Color Coded by Municipal Type 2013 Public Signals LLC









Hope you find this useful.

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