see things differently

Posts tagged with basic facts

What is the world made of?…There are twelve basic building blocks.

Six of these are quarks—- they go by the interesting names of up, down, charm, strange, bottom and top. (A proton, for instance, is made of two up quarks and one down quark.) The other six are leptons—- these include the electron and its two heavier siblings, the muon and the tauon, as well as three neutrinos.

There are four fundamental forces in the universe: gravity, electromagnetism, and the weak and strong nuclear forces. Each of these is produced by fundamental particles that act as carriers of the force…: …photon…graviton…eight…gluons…three…W+, … W- , … Z.

The behavior of all of these particles and forces is described with impeccable precision by the Standard Model, with one notable exception: gravity.

Alberto Güijosa

Gauging the frothiness of the webby/techy/san-fran VC market.

Source: Mark Suster. Propagated via one of tumblr’s owners, who added:

Based on the NVCA statistics on the venture capital industry, there are [approximately] 1,000 early stage financings every year….

And somewhere around 50 - 100 of them exit for more than $100mm every year. So 5-10% of the companies financed by VCs end up exiting for more than $100mm.

Mathematical PS: These are value-at-risk numbers, just upside-down.

hi-res

David Hale laying out the basic facts, in one minute each, on:

US housing demand
developing-country demand for automobiles
volatility in agricultural prices

Did you know?

Americans burn down 400,000 houses every year.
$7 trillion wealth loss for Americans from house price declines.

(Source: openmarkets.cmegroup.com)

Africans drink 7 litres of commercial beer per year.
Chinese drink 35 litres of commercial beer per year.
Americans drink 70+ litres of commercial beer per year.

(minute 7)

From my own little corner of the Earth, it looks like home-brewed beer is growing in appeal—as are micro-brews and wines & ciders made from fruits with a little more natural variation.

So it’s interesting that—just when my crowd is being led by Pied Piper Pollan away from Corporate Consistency-topia into the Land of Natural Individual Variation—those climbing up the Ladder of Disposable Income might drift the opposite direction.

I was going to try to make an alluring mathematical comment on this story, but I’m out of steam. Here are the mathematical concepts involved in this story:

“direction” — implies ∃ beer space, ∋ beer vectors
this is a perceptual space — what are the dimensions? Is it linear?
how would you mathematically model variable-versus-consistent beer tastes?

Maybe as a contour plot / heatmap of confidence intervals? Or a Schwartz distribution?

I wouldn’t assume that the variation is Gaussian. Whatever the taste / smell space looks like, a lot of the variation in homebrewing is due to creativity (discontinuous leaps to elsewhere in the space) — not just to production “errors” (which might in fact be normal).

PS Tusker Beer rules.

(Source: economist.com)

length of time spent jobless in various American recessions

via the Economic Policy Institute

Relatedly, here’s Alan Krueger & Andreas Mueller on reservation wages:

This paper presents findings from a survey of 6,025 unemployed workers who were interviewed every week for up to 24 weeks in the fall of 2009 and spring of 2010. Our main findings are: (1) the amount of time devoted to job search declines sharply over the spell of unemployment; (2) the self-reported reservation wage predicts whether a job offer is accepted or rejected; (3) the reservation wage is remarkably stable over the course of unemployment for most workers, with the notable exception of workers who are over age 50 and those who had nontrivial savings at the start of the study; (4) many workers who seek full-time work will accept a part-time job that offers a wage below their reservation wage; and (5) the amount of time devoted to job search and the reservation wage help predict early exits from Unemployment Insurance (UI).

via @tylercowen

hi-res

The ratio of US jobseekers to US jobs stands at 4:1.
via John Irons (of argmax.com fame)

The jobs-to-seekers ratio rises immediately during a recession, but does not decrease as quickly after the recession ends. (Is this true in general?)

The 2011 jobs-to-seekers ratio, broken down by sector.

hi-res

The last decade’s debt record for several rich countries.

3-month Bond Yields owed by some of them: (SOURCE: Bloomberg)

Japan   .10%
UK      .41%
Germany .28%
US      .04%

And here’s one of the yield curves (US’):

(Remember, higher yield means the debt costs more to service for the country that’s borrowing.)

hi-res

Earthquakes

> data(quakes)> head(quakes)

     lat   long depth mag stations

1 -20.42 181.62   562 4.8       41

2 -20.62 181.03   650 4.2       15

3 -26.00 184.10    42 5.4       43

4 -17.97 181.66   626 4.1       19

5 -20.42 181.96   649 4.0       11

6 -19.68 184.31   195 4.0       12

> summary(quakes)

      lat              long           depth            mag      

 Min.   :-38.59   Min.   :165.7   Min.   : 40.0   Min.   :4.00  

 1st Qu.:-23.47   1st Qu.:179.6   1st Qu.: 99.0   1st Qu.:4.30  

 Median :-20.30   Median :181.4   Median :247.0   Median :4.60  

 Mean   :-20.64   Mean   :179.5   Mean   :311.4   Mean   :4.62  

 3rd Qu.:-17.64   3rd Qu.:183.2   3rd Qu.:543.0   3rd Qu.:4.90  

 Max.   :-10.72   Max.   :188.1   Max.   :680.0   Max.   :6.40  

    stations     

 Min.   : 10.00  

 1st Qu.: 18.00  

 Median : 27.00  

 Mean   : 33.42  

 3rd Qu.: 42.00  

 Max.   :132.00

> plot(quakes, pch=20, col=rgb(0,0,0,.1) , lwd=.6)

> require(ggplot2) > qplot(data = quakes, x = lat, y = long, size = exp(mag), color = mag, alpha = I(.8))

UPDATE: In the comments, Sean Mulcahy shared his much better post on earthquakes: http://seanmulcahy.blogspot.com/2011/11/global-earthquakes-desktop.html. He shows how to grab up-to-date earthquake data from the U.S. Geological Survey and display it with R’s maps package. Hooray!

Fair Trade cocoa price, 1996-2006

You can see from the above graph that fair trade certifiers aim not just to raise, but to raise and stabilise the price a farmer or cooperative receives for produce.

Fact: There are many fair trade certifying bodies, this data comes from Flo-CERT GmbH, a non-profit based in Bonn. Flo-CERT pays X employees to verify that cocoa, coffee, and other popular consumption products are farmed and sold according to Fair Trade standards.

How much extra are you willing to pay these people (their efforts are part of the extra cost of fair trade goods) so that the farmers are guaranteed predictable revenues?

Fact 2: The loathèd corporation Starbucks has been paying stable above-market rates for their coffee for years.

Fact 3: The charts above depict a one-dimensional price. Of course each coffee/cocoa bean is unique; so is every farm and every farmer. For a commodity to be traded from hand to hand to hand, it needs to be standardised. But a big buyer like Starbucks which deals through its own channels with farmers might pay a higher price simply because it’s also requiring a higher grade of beans — thus leaving the middle quality ones to be sold at the regular market rate.

Conclusion: Nothing is as simple or clear-cut as it at first seems.

hi-res

Average Wages by US County, 1Q2011

When people pontificate about national politics, I find the dialogue too generalistic.

“Technology X will create green jobs”.
“Wall Street is making more than Main Street.”
“The United States needs to invest more money in high tech. Routine production is the way of the past.”
“Semiconductors will move our nation forward. High-value-added, that’s what we need.”
Supply side vs. Keynesians.
“Welfare recipients really need job training.”
“Operation Twist is a miserable failure”
“53% of people pay their taxes”
“The top 5% of income earners pay half of all tax revenue”
“Teachers are undervalued”
“Rich people are overprivileged”
Any statements about movements in the total unemployment rate of an entire continent.

These discussions ignore most of the interesting variation and lose touch with real places. And, certain facts that are obvious if you’re familiar with the more specific numbers seem “miraculous” when you just hear one nation-level statistic. (Tax statistics are one such.)

Consider the US unemployment rate, for example. Not only does that figure make it sound like the same 9.5% are unemployed — not true, it’s just an aggregate of all hirings & firings and business openings & business closings — but the unemployment rate in Dane, WI, doesn’t really affect me, because I live in Monroe, IN. If I see some really, really, really compelling place — like Travis, TX — I might uproot my entire life and thenceforth be affected by the data in Travis, TX. And a nearby, culturally good place like Louisville is relevant. I moved to Louisville for a while for a job. But mostly, I need to focus on improving the economy in Monroe, IN.

I remember very well, when I was running my first business, reading grim economic news about the rest of the country. Mall-dwelling retard businesses, national franchises leveraged on the assumption that all of their new franchisees will face good economic conditions … they were affected by the national statistics, but not me. The newspapers kept shouting about how bad things were and I didn’t see it at all.

I think if people were primed by reading a table like this before engaging in debates, a lot fewer overly-generalistic ideas would be floated. Looking at regional variation puts me in a frame of mind that’s more specific, more sub_i sub_j, in touch with data and out of touch with theory.

N America is too big for any one’s imagination. Europe is too big for any one’s imagination. Africa is too big for any one’s imagination. China is too big for any one’s imagination. India is too big for any one’s imagination. Theory makes the world seem small, which is necessary to be able to comprehend huge topics. But Theory can make you overconfident. Data humble you.

The question

“How will policy X create green jobs in Monroe County? in Travis County? in Lancaster County?”

gets my gears running very differently than the question

“How will policy X create green jobs?”

. Importantly, the first question is more bullsh~t-proof. Even though logically a “Create green jobs” type of claim should be evaluated as the sum total of all green jobs created in every county.

Third number from the right is weekly income.

Table 1. Covered(1) establishments, employment, and wages in the 323 largest counties,
first quarter 2011(2)
                                                                                                       
                                                                                                       
County	                        Average weekly wage
United States(6).........	935
	
San Juan, PR.............	598
Peoria, IL...............	944
Santa Clara, CA..........	1863
Macomb, MI...............	941
Clayton, GA..............	844
Wayne, MI................	1021
Brazoria, TX.............	922
Saginaw, MI..............	760
Stark, OH................	703
Butler, PA...............	799
New York, NY.............	2634
Hartford, CT.............	1260
Fulton, GA...............	1370
Washington, PA...........	867
Snohomish, WA............	968
Genesee, MI..............	742
Fort Bend, TX............	979
Jefferson, TX............	920
Forsyth, NC..............	891
Montgomery, TX...........	886
Hennepin, MN.............	1197
Harris, TX...............	1258
Weld, CO.................	776
Winnebago, IL............	769
Oakland, MI..............	1019
Catawba, NC..............	692
Cuyahoga, OH.............	953
Middlesex, MA............	1370
Mecklenburg, NC..........	1231
Marin, CA................	1103
San Diego, CA............	1003
Worcester, MA............	908
Anoka, MN................	829
Milwaukee, WI............	929
Douglas, CO..............	1069
San Francisco, CA........	1723
Lorain, OH...............	750
Sedgwick, KS.............	816
Caddo, LA................	736
Washington, OR...........	1120
Erie, PA.................	695
Cass, ND.................	765
Whatcom, WA..............	745
Los Angeles, CA..........	1046
Hamilton, IN.............	924
Benton, AR...............	1110
Howard, MD...............	1141
Somerset, NJ.............	1867
Bexar, TX................	838
Contra Costa, CA.........	1210
Nueces, TX...............	748
New Castle, DE...........	1194
Bristol, MA..............	791
Essex, MA................	955
Henrico, VA..............	1027
Ramsey, MN...............	1093
Dane, WI.................	878
Scott, IA................	725
Ottawa, MI...............	714
Westmoreland, PA.........	716
De Kalb, GA..............	992
Fayette, KY..............	811
Ingham, MI...............	879
Travis, TX...............	1002
Tuscaloosa, AL...........	778
Muscogee, GA.............	749
Frederick, MD............	904
Hillsborough, NH.........	975
Lucas, OH................	793
Charleston, SC...........	774
Cook, IL.................	1145
Collin, TX...............	1075
Virginia Beach City, VA..	717
Fairfield, CT............	1888
Vanderburgh, IN..........	729
Rockingham, NH...........	857
Camden, NJ...............	903
Lake, IN.................	791
St. Louis, MN............	722
King, WA.................	1185
Pulaski, AR..............	819
Oklahoma, OK.............	837
Elkhart, IN..............	698
Larimer, CO..............	795
Mercer, NJ...............	1283
Multnomah, OR............	918
Allegheny, PA............	997
Greenville, SC...........	770
Dallas, TX...............	1156
Maricopa, AZ.............	889
Sacramento, CA...........	1025
Santa Barbara, CA........	869
Tulsa, OK................	825
Kanawha, WV..............	797
Denver, CO...............	1212
Will, IL.................	793
Plymouth, MA.............	815
Suffolk, MA..............	1625
Kalamazoo, MI............	816
Jefferson, AL............	919
Ada, ID..................	773
Polk, IA.................	940
Minnehaha, SD............	748
Shelby, TN...............	915
Richmond City, VA........	1071
Calcasieu, LA............	768
Cumberland, ME...........	835
Buncombe, NC.............	676
Guilford, NC.............	802
Webb, TX.................	590
Benton, WA...............	959
Mobile, AL...............	741
New Haven, CT............	956
New London, CT...........	960
Lafayette, LA............	847
Lancaster, PA............	734
Washington, AR...........	726
Greene, MO...............	661
Yellowstone, MT..........	721
Middlesex, NJ............	1191
Erie, NY.................	794
Mahoning, OH.............	632
Dauphin, PA..............	889
Northampton, PA..........	791
Spokane, WA..............	751
Placer, CA...............	876
Hillsborough, FL.........	880
McHenry, IL..............	727
Harford, MD..............	844
Barnstable, MA...........	759
Norfolk, MA..............	1066
Essex, NJ................	1229
Broome, NY...............	703
Philadelphia, PA.........	1079
Madison, AL..............	978
Ventura, CA..............	964
Orange, FL...............	805
Palm Beach, FL...........	886
Wyandotte, KS............	826
Franklin, OH.............	920
Williamson, TN...........	1054
Galveston, TX............	827
Fairfax, VA..............	1479
Lee, FL..................	711
Shawnee, KS..............	751
Onondaga, NY.............	831
Newport News City, VA....	826
Clark, WA................	800
Pima, AZ.................	768
Kern, CA.................	790
Escambia, FL.............	690
Queens, NY...............	844
Suffolk, NY..............	972
Cumberland, NC...........	695
New Hanover, NC..........	741
Chesapeake City, VA......	724
Brown, WI................	803
Montgomery, AL...........	764
Adams, CO................	806
Collier, FL..............	767
Oneida, NY...............	708
Hamilton, OH.............	992
Luzerne, PA..............	684
Bell, TX.................	736
Chesterfield, VA.........	830
Alameda, CA..............	1183
Cobb, GA.................	962
Allen, IN................	747
Berks, PA................	780
Lexington, SC............	650
Boulder, CO..............	1050
Polk, FL.................	668
Chatham, GA..............	752
Richmond, GA.............	743
Linn, IA.................	847
Montgomery, MD...........	1311
Hinds, MS................	778
Denton, TX...............	780
Outagamie, WI............	747
Waukesha, WI.............	902
Lehigh, PA...............	879
Smith, TX................	739
Salt Lake, UT............	856
Jefferson, CO............	929
Baltimore City, MD.......	1081
Cumberland, PA...........	815
Delaware, PA.............	1003
Utah, UT.................	681
Manatee, FL..............	668
Marion, IN...............	987
Jefferson, LA............	831
Dakota, MN...............	895
St. Louis, MO............	973
Lancaster, NE............	711
Richmond, NY.............	758
Lake, OH.................	774
Norfolk City, VA.........	861
Alachua, FL..............	730
Burlington, NJ...........	957
York, PA.................	789
Fresno, CA...............	709
Sonoma, CA...............	846
Miami-Dade, FL...........	874
Gwinnett, GA.............	879
Du Page, IL..............	1076
Sangamon, IL.............	907
Jefferson, KY............	873
Kent, MI.................	792
Olmsted, MN..............	968
Washoe, NV...............	789
Monroe, NY...............	847
Clackamas, OR............	798
Lane, OR.................	672
Orange, CA...............	1035
San Bernardino, CA.......	754
Nassau, NY...............	1015
Montgomery, OH...........	782
El Paso, TX..............	626
Tarrant, TX..............	900
Riverside, CA............	748
San Joaquin, CA..........	752
Broward, FL..............	834
Ocean, NJ................	746
Bronx, NY................	818
Davidson, TN.............	927
Hidalgo, TX..............	556
Duval, FL................	891
Seminole, FL.............	735
Honolulu, HI.............	821
St. Joseph, IN...........	723
Boone, MO................	692
Douglas, NE..............	853
Passaic, NJ..............	921
Bucks, PA................	855
Richland, SC.............	794
Chittenden, VT...........	878
Orleans, LA..............	983
Knox, TN.................	750
Brazos, TX...............	659
Cameron, TX..............	546
McLennan, TX.............	727
Pierce, WA...............	821
El Paso, CO..............	812
Champaign, IL............	750
Albany, NY...............	937
Chester, PA..............	1164
Lackawanna, PA...........	665
Horry, SC................	534
Tulare, CA...............	622
Lake, FL.................	586
Marion, FL...............	614
Pasco, FL................	596
Pinellas, FL.............	765
Volusia, FL..............	629
Kane, IL.................	777
East Baton Rouge, LA.....	831
St. Louis City, MO.......	1037
Atlantic, NJ.............	772
Bergen, NJ...............	1152
Lubbock, TX..............	653
Solano, CA...............	921
Arapahoe, CO.............	1130
Monmouth, NJ.............	945
Jackson, OR..............	644
Anchorage Borough, AK....	958
Bernalillo, NM...........	781
Rockland, NY.............	991
Spartanburg, SC..........	761
Stanislaus, CA...........	748
Bibb, GA.................	699
Johnson, KS..............	955
Morris, NJ...............	1462
Washington, DC...........	1540
Sarasota, FL.............	722
Clay, MO.................	850
Weber, UT................	642
Baltimore, MD............	920
Providence, RI...........	895
Davis, UT................	704
Brevard, FL..............	801
Stearns, MN..............	700
Orange, NY...............	755
Summit, OH...............	841
Yakima, WA...............	606
Winnebago, WI............	831
San Luis Obispo, CA......	742
Santa Cruz, CA...........	814
McLean, IL...............	904
Madison, IL..............	738
Prince Georges, MD.......	933
Montgomery, PA...........	1198
Rutherford, TN...........	771
Loudoun, VA..............	1093
St. Clair, IL............	709
Union, NJ................	1199
Wake, NC.................	917
Marion, OR...............	699
Clark, NV................	790
Dutchess, NY.............	917
Kitsap, WA...............	798
Harrison, MS.............	668
Monterey, CA.............	808
San Mateo, CA............	1485
Jackson, MO..............	894
St. Charles, MO..........	744
Westchester, NY..........	1332
Prince William, VA.......	808
Washtenaw, MI............	925
Gloucester, NJ...........	766
Kings, NY................	725
Leon, FL.................	722
Hampden, MA..............	812
Thurston, WA.............	800
Arlington, VA............	1549
Butler, OH...............	781
Hamilton, TN.............	785
Durham, NC...............	1276
Hudson, NJ...............	1509
Williamson, TX...........	953
Yolo, CA.................	892
Lake, IL.................	1230
Anne Arundel, MD.........	958
Alexandria City, VA......	1226

Data notes:

There’s a lot of variation in number of counties per American state. For example, Indiana (36k sq mi) has 92 counties whilst Massachusetts (10 k sq mi) has 14.
Also, this is only private employers which skews some of the Maryland and Virginia numbers.
Also, this is a look at employed people, and it doesn’t count benefits.

Some raw-data observations:

average income in New York County is $2,600/week but only $800/week in the Bronx.
San Francisco and Arlington, VA are about $1000/week less than New York County.
Incomes in Indianapolis (Marion County) are a joke on a national scale. Even if you include people in Carmel (Hamilton County) it’s still less than $1000/week. I thought all of those Lilly people made a tidy bundle; I guess they’re too few to bring up the average.
I should ddply this data.
There seem to be a lot of $600’s $700’s $800’s. That basically checks out with median household income of $51k. Although households can comprise two individual incomes.