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Posts tagged with facts

Advertising rises and falls with the economy, though how much is a matter of debate. Randall Rothenberg … points to the remarkable stability of advertising at about 2% of GDP since 1919, when the data began to be collected.

(Source: economist.com)





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

image

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


hi-res




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

hi-res




There are very few facts I think “everyone should know”. The huge income differences across countries are an exception.

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Everyone should know that income per person in Burundi is about 1% of in the U.S. (yes, even though there’s a recession on).

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And everybody should know a rough quantitative history of the world.

13 minutes by Tyler Cowen & Alex Tabarrok




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: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’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.

hi-res




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


hi-res




[In] Against Method … [Paul] Feyerabend divides his argument into an abstract critique followed by a number of historical case studies.

The abstract critique is a reductio ad absurdum of … the belief that a single methodology can produce scientific progress. Feyerabend … identifies four features of methodological monism: the principle of falsification, a demand for increased empirical content, the forbidding of ad hoc hypotheses and the consistency condition.

He then demonstrates that these features [together would] imply that science could not progress….

Wikipedia

(Source: Wikipedia)




by @Macro_Tourist

Interest rates since 3000 B.C.

  • credit crisis of 33 A.D.
  • code of Justinian
  • Uruk, “city of sheepfolds”, had a writing system, counting system, and calendar system
  • 16% rates in Athens 600 B.C.
  • Solon’s reforms 594 B.C.
  • early interest rates just used 1 unit of money per unit of stuff per unit of time; no decimalised share prices in the Code of Hammurabi (1772 B.C.)
  • Temples as proto-banks

With regards to big data, just think how much work @Macro_Tourist (and Sidney Homer) had to do to put these graphs together versus recording some twitter history or server logs. Talk about wealth of information versus interesting information.

Closer zoom:

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Sidney Homer: “Each generation is inevitably surprised by interest rates”

(Source: twitter.com)