Tag: Data

Black Lives Matter

 

It is true that young black men are being killed disproportionately — killed brutally, ruthlessly, and unjustly. And we need to talk about it if we hope to put an end to it.

We have data, and that data has been studied carefully. We know, based on that, that police are not the ones doing the killing. We know, based on that data, that police do not disproportionately kill young black men.

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Note: These pieces can be enjoyed a la carte or as one of a series you can access here: Part One, Part Two, Part Three, Part Four A young woman sits at a small table opposite a little girl about preschool age. She lays several objects in a neat row in front of the preschooler […]

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The early afternoons of September 2019 found me racing off from my online work to arrive breathless at a small outbuilding where I was being trained in my new job working with kids who had autism. The bell affixed to the front door jangled as I entered, glanced at the large digital clock high up […]

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Confession: I’m a data nerd. So, when a colleague shared the Media Bias Chart® with me a couple years ago, I dove right in to see where my preferred news sources landed on the chart (and was somewhat surprised at some of what I saw.) Eventually the “ooooh-shiny” of it wore off and I moved […]

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During the COVID-19 shutdown, how many political leaders have claimed legitimacy because they are using the “Science and the Data”? For many people who crave certainty, the experts reassure them that they are receiving information they can rely on, in making the important daily decisions of their lives. As the lockdown continues in some states, […]

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In my last post, I wrote about the large number of corona-virus posts we were seeing on Ricochet. I do realize that is my own problem and no one else’s, but a part of me wonders what this obsession about this deadly virus might say about our culture. The usual explanations for this fascination make […]

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Coronavirus Update

 

President Trump’s team has come out with an initial briefing on the coronavirus outbreak, offering facts, cautions, and pushing back against panic. Johns Hopkins University has an excellent data visualization tool, constantly updating data on maps: “2019-nC0V Global Cases (by Johns Hopkins CSSE). CNET has a fact-based story, with lots of links, that is being regularly updated; it is now titled: “Coronavirus cases pass 11,000, US declares emergency: Everything we know.”

This was a display of competent communication to the American public, treated as adults. Dr. Redfield gave the numbers. Dr. Fauci then explained the question posed by people on Ricochet, including me: why is this different from the well-known annual deaths from the seasonal flu?* With the numbers and the differentiation in place, the briefers laid out a series of screening and quarantine steps that will go into full effect Sunday. Anyone who has been in the province where the outbreak started will be quarantined for 14 days, while those coming from other areas with known infections would be screened and then go into “self-quarantine.” They were also careful to speak of sympathy and compassion for the Chinese people who have been affected, directly or with family losses.

Here is the video and the whole transcript, of the press briefing, followed by the text of the presidential proclamation. Both the transcript and the proclamation are posted on the White House website.

Data Is Just Like Oil, Other Than These Few Minor Differences

 

You may have heard something about “data being the new oil” or some such. Just as petroleum drove economies in the 20th century, so will digital information in the 21st. I really started hearing about this framing after a May 2017 cover story by The Economist. The piece had a pretty snappy lede:

An oil refinery is an industrial cathedral, a place of power, drama and dark recesses: ornate cracking towers its gothic pinnacles, flaring gas its stained glass, the stench of hydrocarbons its heady incense. Data centres, in contrast, offer a less obvious spectacle: windowless grey buildings that boast no height or ornament, they seem to stretch to infinity. Yet the two have much in common. For one thing, both are stuffed with pipes. In refineries these collect petrol, propane and other components of crude oil, which have been separated by heat. In big data centres they transport air to cool tens of thousands of computers which extract value—patterns, predictions and other insights—from raw digital information. Both also fulfill the same role: producing crucial feedstocks for the world economy. Whether cars, plastics or many drugs—without the components of crude, much of modern life would not exist. The distillations of data centres, for their part, power all kinds of online services and, increasingly, the real world as devices become more and more connected. Data are to this century what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and—crucially—new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways. It changes the rules for markets and it demands new approaches from regulators. Many a battle will be fought over who should own, and benefit from, data.

“Deplorables” Pwning the Info Wars? Blame Canada!

 

Fans of Brexit’s Vote Leave campaign might remember Dominic Cummings’s reflections on the uses (and abuses) of data in politics. Cummings, often hailed as the mastermind behind Vote Leave, is an eloquent advocate for how getting the data science right contributed to Vote Leave‘s success, and he has a prickly – even “psychopathic” – reputation as a man who won’t suffer data-science fools (or at least those whom he deems foolish) gladly.

No doubt Cummings is right that charlatanism infests the ranks of political “data scientists”, but a more charitable term than “charlatanism” for much iffy “data science” might be “ad-hockery”: Adventurous wunderkinds promote ad-hoc heuristics which seem to work well enough, or which work until they don’t, or which may work, but which haven’t yet been vetted by systematic scientific reasoning. Ad-hoc heuristics aren’t inherently deceptive, or incapable of delivering what they claim to deliver. They deserve to be met with plenty of skepticism, of course, but skepticism needn’t always include suspicion of fraud.

Governors & Spending: The Numbers

 

Uniquely for presidential candidates, governors have a fairly directly comparable fiscal record. Going through the data, I was surprised by a few things and wondered if the Ricochetti could explain some matters. I include the top line table here, but you can find the year by year data at usgovernmentspending.com. The numbers are in nominal dollars and cap out at 2015, the last completed year. If anyone wants to explore the (sadly, very ugly) sheet I made this from, I’d really appreciate any error corrections; PM me and I’ll email it to you.

 Budgets Party First Ran (from 2004) Governor State Spending Growth
1  1990-1993 D->R 2012 Roemer Lousiana 9.33%
2  2004-2007  R 2008 Romney Mass. 8.22%
3  2006-2010  R 2012 Huntsman Utah 8.17%
4 2000-2007  R 2016 Bush Florida 7.97%
5  1995-2003  R 2012 Johnson New Mexico 7.29%
6  1998-2002  R 2008 Gilmore Virginia 7.26%
7  2003-2011  D 2008 Richardson New Mexico 7.26%
8  1987-2001  R 2008 Thompson Wisconsin 7.21%
9  1992-2003  D 2004 Dean Vermont 6.54%
10  1997-2007  R 2008 Huckabee Arkansas 6.41%
11  1999-2007  D 2008 Vilsack Iowa 5.66%
12  2001-2016  R 2012 Perry Texas 5.65%
13  1995-2006  R 2016 Pataki New York 5.11%
14  1995-2001  R 2004 Bush Texas 4.79%
15  2003-2011  R 2012 Pawlenty Minnesota 4.49%
16  2007-2015  D 2016 O’Malley Maryland 2.08%
17  2011-?  R 2016 Kasich Ohio 0.18%
18  2010-2018  R 2016 Christie New Jersey 0.01%
19  2011-?  R 2016 Walker Wisconsin -0.49%
20  2008-2016  R 2016 Jindal Louisiana -0.64%
21  2011-2015  I->D 2016 Chafee Rhode Island -1.38%

Firstly, I was wrong about Rick Perry. When he ran last year, I criticized him for running on a two-note platform of being a wall-builder and a budget-cutter. Don’t get me wrong: I like both walls and budget cuts, but Perry made no apologies for being the most important wall opponent (Gary Johnson made a big deal about the border governors knowing about this sort of thing, awkwardly, since no other border state governor, including his own state, shared his views) and his spending before the 2012 primary was terrible.

Not All News Is Bad News

 

Focusing on the hourly media cycle gets mighty depressing for us news junkies. A stagnant economy, wars all around, desperate migrants flooding Europe and the US. But Hans Rosling, a public health professor in Sweden, shows the incredibly great news happening over the longer term. For the few of you who missed Thursday’s episode of Nyheder on the Danish Broadcasting Corporation’s DR network, I’ve uploaded a clip that deserves to go viral.

The Pernicious Lie: Liberals, Civil Rights, and Southern Voting Patterns

 

On Facebook today, a liberal friend claimed that “[racist] Democrats fled to the Republican party when the [Democrats] started talking about civil rights legislation.” I pointed out that that was completely untrue. The only prominent Democrat who became a Republican was Strom Thurmond who — as a Democrat — famously ran for president on a pro-segregation platform and filibustered civil rights legislation in the Senate; as a Republican, though, he had black staff, and voted to make Martin Luther King, Jr.’s birth a national holiday and Clarence Thomas an Associate Justice of the Supreme Court. In contrast, George Wallace, Robert C. Byrd, Bull Connor, Orval Faubus, etc. all stayed Democrats.

I asked him why — if his narrative were true — Southerners continued to support Democrats for more than 30 years after the Civil Rights Movement. To which he replied, “My point was the [Democrats’] hold on the South began to die with the Civil Rights Act. That was when the GOP started to gain traction.” I again replied that that was completely untrue; Democrats maintained their grip on the South well into the 1990s.

The Rule of 13 in House and Senate Elections

 

If you want to predict House or Senate elections, a useful notion is what I call the Rule of 13. It says that if a district is misaligned with your partisanship by more than 13 points, then, to a close approximation, you have zero chance of winning that district. The rule predicts the following: (i) Mark Pryor is sure to lose his Senate reelection bid in Arkansas, (ii) Mitch McConnell is sure to win his reelection bid in Kentucky, (iii) if voters become convinced that challenger Greg Orman is, for all intents and purposes, a Democrat, then Pat Roberts is sure to win his reelection; (iv) although Alaska, Georgia, Louisiana, and North Carolina are conservative states, they are not conservative enough to invoke the Rule of 13; accordingly the Democratic candidates in those states at least have a chance of winning; (v) although the West Virginia 2nd and 3rd House races are called “tossups” by some prognosticators, the Rule of 13 says that the Republican candidates (Alex Mooney and Evan Jenkins) will win for certain.

The Rule of 13 is formally defined as follows. First, define the partisan index of a district according the most recent presidential vote in that district. For example, consider the situation of Rep. Mike Ross (D-Ark.) near the end of his sixth term in office, 2010-12. At that time, the most recent presidential election was the 2008 race between John McCain and Barack Obama. In Ross’s district (based on lines redrawn after the 2010 census) McCain received 166,247 votes and Obama received 103,478 votes. McCain’s share of the two-party vote in the district was thus 61.6%. Meanwhile, McCain’s two-party vote share in the nation was 46.0%. Define the partisan index of Ross’s district as the difference of those two numbers. Thus, the district’s partisan index was “Republican 15.6.” (The Cook Political Report constructs a similar “Partisan Voting Index,” except it bases its number on an average of the prior two presidential elections. Some research I’ve conducted suggests that the partisanship of a district follows a random walk, which implies that only the most recent presidential election is relevant in predicting the political views of a district; prior elections do not provide any more information.)

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Many people don’t trust the government to report inflation correctly. I don’t trust the government in general, but I am less skeptical about the data aggregators. Even if they are doing it less than ideally, they are most likely doing it consistently, which means at least the changes in the data are meaningful, even if […]

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