Posts tagged with data

Research focuses on real wages—wages that are adjusted for inflation. Getting data on wages is tricky. But accounting for inflation is even harder. (For example, workers often paid rent informally, meaning that there are few records around).

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And so it is unsurprising that researchers differ in their estimations of real wages. Some, such as Peter Lindert and Jeffrey Williamson, suggest that full-time earnings for British common labourers, adjusted for inflation, more than doubled in the seventy years after 1780. But Charles Feinstein argued that over the same period, British real wages only increased by around 30%. It’s a bit of a … mess.

Most people agree that after about 1840, real wages did better. Nicholas Crafts and Terence Mills shows that from 1840 to 1910, real wages more than doubled. Their findings are mirrored by other researchers ….

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in almost all British cities, mortality conditions in the 1860s were no better—and were often worse—than in the 1850s. In Liverpool in the 1860s, the life expectancy fell to an astonishing 25 years. It was not until the two subsequent decades that rises in life expectancy were found

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Since [2008], the [US] labor force participation rate (LFPR) has dropped from 66 percent to 63 percent. [Out of 314M people.] Many people have left the labor force because they are discouraged … (U.S. Bureau of Labor Statistics data indicate that a little under 1 million people fall into this category)….
…Knowing the reasons why people have left (or delayed entering) the labor force can help us [guess] how much of the ↓ might … ↑ if the economy ↑ and how much is permanent. (For more on this topic, see here, here, and here.)

The chart … shows the distribution of reasons in the fourth quarter of 2013…. Young people [usually say they] are not in the labor force … because they are in school. Individuals 25 to 50 years old who are not in the labor force mostly [say they] are taking care of their family or house. After age 50, disability or illness becomes the primary reason [given]—until around age 60, when retirement begins to dominate.
…
Of the 12.6 million increase in individuals not in the labor force, about 2.3 million come from people ages 16 to 24, and of that subset, about 1.9 million can be attributed to an increase in school attendance (see the chart below).

—Ellyn Terry

HT @conorsen
off-topic sidenote: the natural cohort —vs— year adjustments, like “the baby boom has shifted 7 years since 7 years ago” are an economic example of the covariant/contravariant distinction

Since [2008], the [US] labor force participation rate (LFPR) has dropped from 66 percent to 63 percent. [Out of 314M people.] Many people have left the labor force because they are discouraged … (U.S. Bureau of Labor Statistics data indicate that a little under 1 million people fall into this category)….

…Knowing the reasons why people have left (or delayed entering) the labor force can help us [guess] how much of the ↓ might … ↑ if the economy ↑ and how much is permanent. (For more on this topic, see herehere, and here.)

The chart … shows the distribution of reasons in the fourth quarter of 2013…. Young people [usually say they] are not in the labor force … because they are in school. Individuals 25 to 50 years old who are not in the labor force mostly [say they] are taking care of their family or house. After age 50, disability or illness becomes the primary reason [given]—until around age 60, when retirement begins to dominate.

Of the 12.6 million increase in individuals not in the labor force, about 2.3 million come from people ages 16 to 24, and of that subset, about 1.9 million can be attributed to an increase in school attendance (see the chart below).

Ellyn Terry

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HT @conorsen

off-topic sidenote: the natural cohort —vs— year adjustments, like “the baby boom has shifted 7 years since 7 years ago” are an economic example of the covariant/contravariant distinction


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The Audacity of Despair

by David Simon (creator of The Wire)

  • arch cynicism about the public purpose of television
  • The Wire is not hyperbolic about our inability to solve our own problems.
  • The news media buries and forgets relevant information.
  • New Orleans was not destroyed by Hurricane Katrina. An untethered barge breached the retaining wall, destroying the Ninth Ward.
  • Three years later during Hurricane Gustav, another barge was unsecured in the same canal.
  • The Wire is not about sinister people doing sinister things. There’s no fun in that. There’s no drag in writing a show about bad guys and good guys. First of all, it’s not credible. And second of all, it’s not where the real evil lurks.
  • As a reporter: “Every time someone dragged out a statistic, I immediately distrusted it as [probably fabricated or] dubious [method]”
  • Management: No sooner does someone invent a useful measure of institutional progress, than someone else begins to game it to the point that the measure becomes useless.
  • "In my city [Baltimore], every single effort to quantify progress was an effort by somebody to advance themselves.”
  • People are promoted or leave to another job before anyone figures out what they got was dross.
  • Cops retire with a pension despite making zero progress in 40 years in the war on drugs.
    https://imageshack.us/a/img607/7854/13v152.gif
    http://www.washingtonpost.com/blogs/wonkblog/files/2013/05/embarrassing-drug-graph.jpg
    http://dev.ijreview.com/wp-content/uploads/2013/01/US-drugs-prison-population-graph.gif
  • Why? is the only of the 5W’s+1H that matters. That could have made journalism “a game for grown-ups”.
  • Bulls∗∗t US government claims about progress in Vietnam.
  • More profitable for Chicago Tribune Company’s shareholders to stop asking Why?—and lay off reporters.
    http://i.imgur.com/1MpTn2n.png
  • This was due to their monopoly: they didn’t need top-quality journalism to compete. But the drop in quality, if efficient at the time, made the papers soft targets when the Web became big half a decade later.
  • He thinks Internet reporting is less magazine-like and more frothy. I contend ∃ both.
  • Crime wasn’t going down anymore. So robberies became larcenies. Aggravated assaults became common assaults. Felonies were leached down to misdemeanors.” Robberies in southwest Baltimore went down 70%. The commander was promoted to head of CID. Next boss went in, crime went up 70%, he took the flack.
  • "40% decline in crime, but the murder rate stayed constant. [red flag] The only thing that that says rationally is that they’ve opened up a gun range in West Baltimore and they’re better shots.”
  • Any reporter who had any sense of his beat would know this was a huge red flag, would dig deeper into the data and call the complainants.
  • "How is it that we’re able to talk about this in an entertainment medium—television—but not in journalism?”
  • Curfew for Blacks in Baltimore (fallacious arrests). ACLU tries to sue, but by the time it wends its way through the courts the practice has stopped; the Mayor has become Governor.
  • "If you walk into The Other America and ask people how they feel about certain things, you’re likely to hear how they feel.”
  • "We stole facts from real life, but thematically the people we stole the most from were Euripides, Aeschylus, and Sophocles."

(Source: youtube.com)




U.S. homelessness dropped nearly 17% over the past eight yearsvia The State of Homelessness in the USA

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I’m trying to do retrospectives on financial predictions as I stumble across them on the Web. Here’s one that turned out correct: @EpicureanDeal said not to buy Blackstone Group when it went public.

"A little knowledge is a dangerous thing." Like, say, a little knowledge of cool and funky rich people or private equity deals from the paper. I’d rather be financially illiterate than taken hold of this bag.

From 35, to 25, to 15, to 5. Since the market bottom $BX has quintupled which is basically in line with the S&P.

Correlation since 2008 of the S&P to $BX has been 95%, so you can rule out a “complementary growth” argument for the buy.

Reproducible analysis:

require(quantmod)
getSymbols("BX")
chartSeries(BX)
reChart(up.col='yellow', dn.col='light blue', color.vol=FALSE)
getSymbols("SPY")
chartSeries(BX/SPY)         #quantmod automatically matches subsets for you!
reChart(up.col='yellow', dn.col='light blue', color.vol=FALSE)
bx <- BX['2008:']
sp <- SPY['2008:']
cor(bx,sp)










A billion chronically hungry people in the world via The Economist
As you can see from the right-hand scale, during the 1990&#8217;s and 2000&#8217;s the &#8220;bottom billion&#8221; poorest people have been starving or close to it.
Even though the right-hand scale is more important, the lines get graphical emphasis.
Therefore the two pictures, though nearly equivalent in absolute terms, tell very different stories:about a spiking crisis and increasing failure to deal with poverty during rich-world recession
about marginal improvements that continue despite a rich-world financial debacle.

Both stories were told by the Food and Agriculture Organisation, of the United Nations.
Of course statistical bodies revise estimates all the time.
But still this juxtaposition warns us to question the facticity of numbers appearing in charts.
All data come from somewhere. Just because the numbers appear on a chart doesn&#8217;t make them correct.

A billion chronically hungry people in the world via The Economist

  • As you can see from the right-hand scale, during the 1990’s and 2000’s the “bottom billion” poorest people have been starving or close to it.
  • Even though the right-hand scale is more important, the lines get graphical emphasis.
  • Therefore the two pictures, though nearly equivalent in absolute terms, tell very different stories:
    1. about a spiking crisis and increasing failure to deal with poverty during rich-world recession
    2. about marginal improvements that continue despite a rich-world financial debacle.
  • Both stories were told by the Food and Agriculture Organisationof the United Nations.
  • Of course statistical bodies revise estimates all the time.
  • But still this juxtaposition warns us to question the facticity of numbers appearing in charts.
  • All data come from somewhere. Just because the numbers appear on a chart doesn’t make them correct.

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The Speenhamland allowance scale enacted in 1795 effectively set a floor on the income of labourers according to the price of bread.

When the gallon loaf cost 1s, the laborer was to have a weekly income of 3s for himself. … Weekly wages of 3s are equal to …3.72 pounds of bread per day for a single labourer. This is an important figure to remember as the Speenhamland allowance.

As a pound of bread provides about 1100 calories, the allowance gave the labourer a total of 4100 calories per day. An agricultural labourer doing 8-10 hours of vigorous work can easily require 3000 calories/day. It is evident that the Speenhamland allowance provided just above the bare means of subsistence.




What jobs do the 1% have? by Bajika, Cole, and Heim

BCH and the US government did all the work here. My only contribution was to highlight

  1. professions I didn’t expect to see like pilot, farmer, government, teacher
  2. some “standard narratives”:
    • the one about “lawyers and doctors”
    • (I don’t know why these two get grouped together, since one works in abstractions and the other works in gore…but whatever, that is a narrative)
    • the one about “study hard and you’ll get ahead” (scientists, professors, computer, maths)
    • and “real estate developers”
 

Obviously the top 1.5M earners aren’t important to the exclusion of the other 311M Estadounidenses, the 145M employed Estadounidenses, or everyone else.

Equally obvious is that

fraction of lawyers in the one percent is not the same as fraction of one percent who are lawyers

(some lawyerly deeds are more lucrative than others … same for doctors.)

Still, if you’re 

  • choosing a career
  • thinking about social justice
  • trying to understand how the world works

then you might want to find out about rich people. It might be better to do so with, you know, actual facts, rather than for example listening to a bunch of programmers b*tch about how much money lawyers and doctors make.

 

Back to Bajika Cole & Heim. Why is it that this basic information wasn’t known? BCH, Pikkety Saez, and a few others who have bothered to parse data to answer simple questions seem to get fairly good citations. Are economics researchers so bent on complicated research that they won’t “arb” citations by doing something a non-PhD could do?

It is well known that the share of US income going to the top percentiles has increased dramatically over 1986–2006.  Piketty and Saez found that the top ¹⁄1000’s share of pre-tax income (ex cap gains) in the United States that was received by the top ¹⁄1000 rose from 2.2% to 8.0%.

But we don’t know what these people typically do for a living. Kaplan and Rauh (2010) looked through publicly-available information on top executives of publicly-traded firms, financial professionals, law partners, and professional athletes and celebrities. Despite making various extrapolations beyond what is directly available in publicly-available data sources, they were only able to identify the occupations of 17% of the top ¹⁄1000 of income earners.

We tabulated individual income tax return data from the U.S.Treasury Department on what share of top income earners work in each type of occupation. Through this method we are able to account for the occupations of almost all top earners – for example, for over 99% of primary taxpayers in the top ¹⁄1000.

(I liberally edited without [] or ….)

They also looked at spouses of the well-paid, computed income shares, computed growth rates, and broke down the incomes into

  • 1% ex ½% (rank 1,500,000–750,000)
  • ½% ex 0.1% (rank 750,000–150,000)
  • 0.1% (rank 150,000–1)

. All of this is at the end of the PDF, after the bibliography.

Anyway let’s give BCH a hand for providing us with useful information.

"Theory is easy. Data are hard."

(Source: web.williams.edu)










3D map of the large-scale distribution of dark matter, reconstructed from measurements of weak gravitational lensing with the Hubble Space Telescope.
via davidaedwards

3D map of the large-scale distribution of dark matter, reconstructed from measurements of weak gravitational lensing with the Hubble Space Telescope.

via davidaedwards


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