Quantcast

Posts tagged with sub i sub j

The racial category Asian lumps together widely diverse groups with no common language, phenotype, or culture who come to the U.S. under vastly different circumstances…

How do you mash together Laotian war refugees and Japanese business investors and come up with an average or mean experience?…

So let’s get it straight. The term “Asian” in the U.S. was chosen by Asian American activists as an alternative to the pejorative “Oriental.” The Oriental is the creation of Europeans for whom the Orient was an object of curiosity and a source of riches to be studied and exploited. In modern times, the study of the Orient, especially in contrast with the civilized world of the Occident (aka Europe), solidified an idea of Orientals as exotic, potentially dangerous Others.

Activists back in the 1960s decided they wanted to reject the label Oriental and call themselves Asian American instead. Subsequent generations of Asian Americans have gathered as a coalition under the Asian American banner in order to resist being treated like Orientals. But don’t get it twisted, the idea of an Asian or Oriental race is a creation of white people, not of Asians.

Scot Nakagawa (via untilasinglesolitonsurvives)

 

Just like Thomas Sowell said:

an amalgamation of widely varying subgroups

I recently saw some solid predictions in a real estate projection, but they were solid because the subgroups were actually fairly similar. It was like a rational middle ground between everyone being a special individual snowflake with no similarities to anyone else, and stereotypes that ignore the variation within subgroups. If you can find a subgroup that actually is fairly homogeneous, and you can predict that subgroup, then you can multiply your predictions out and get somewhere helpful with the arithmetic.

Of course, defining realistic subgroups occupies a lot of minds in marketing, because if you can find a mass of similar people you can market to all of them at once, and that’s a business opportunity. In the case I’m thinking of, though, it was like tying changes in a metro area to demographic shifts. But rather than imply “All old people are the same" by just saying "the population is aging", it talked about a specific kind of older person with less variable wants and means.

Here’s a pretty good example:

Messrs Kharas and Rogerson calculate that the number of poor in “non-fragile” states has fallen from almost 2 billion in 1990 to around 500m now; they think it will go on declining to around 200m by 2025. But the number of poor in fragile states is not falling—a testament both to the growing number of poor, unstable places and to their fast population growth. This total has stayed flat at about 500m since 1990 and, the authors think, will barely shift until 2025.

Lesson being, if you consider a couple different sorts of people living under $2/day—refugees, slum-dwellers, rural people in large medium-income countries, poor in poor countries (eg Papua)—then you’re at least looking at more homogeneous components, whereas lumping together refugees with an American businessman who made negative income this year is just way too amalgamated.

(Source: baysian)




Thank you, steel manufacturing companies, and thank you, chemical processing companies, for giving us the time to read. —Hans Rosling

Totally good point about how the mechanisation of the rich world has allowed us to have so many professors, doctors, photographers, lawyers, and social media managers.

 

But I wonder: why is laundry so important?

There has to be a good reason; no one working with their hands for 70+ hours a week would choose to do an extra 10 hours of labour a week if they could avoid it. But I know from experience that, in my world, if you don’t do laundry for months at a time, nothing bad happens to you.

What did I do instead of laundry? I’ve taken a few options, some of which would have been available to poor humans now or in the past:

  1. wash clothing with the excess soapy water that falls off me in the shower (not available to them)
  2. turn clothing inside out and leave it outside (requires a lot of socks but before the 19th century no one was wearing socks anyway)

The second you would think poor people could do pretty easily. I used my porch, which got sun and wind and blew away, over time, most of the smells

So what’s the reason they couldn’t do that? I have a few theories.

  • They laboured with their bodies, getting much sweatier than I do at my computer.
  • Bugs and germs were more prevalent in their environment and got in their clothing if it weren’t soaped — or at least exposed to ammonia rising off the castle pissing grounds.
  • They got dirtier, muddier, muckier. But why would you need to deal with that?
  • Having clean clothes raised your appeal to the opposite sex, and social status went along with that as it goes along with attractiveness today. Clean isn’t necessary; it’s just sexy (on average).

Anyway, I wonder if it isn’t the other changes to the modern OECD environment (reduction in bugs and reduction in manual labour) that made for the progress. Nowadays I just use the washer when I’ve exercised or played in the mud.

If the wash was always just a way of keeping up with the Joneses, however, then we can’t congratulate the washing machine for saving us necessary labour — it just helps us live out our autocompetitive rank obsessions in other ways now the elbow’s been surpassed on that dimension.




"There is more difference within the sexes than between them."
‒Ivy Compton-Bennett, Mother and Son

"In all of human biology, there is no greater difference than of that between men and women."
—Some biology notes I found online

These two statements sound like rhetorical opposites, but in fact both are true.

(Says me. I can’t prove this, but I bet that taking everything into consideration, divisions between men & women are greater than those between liberals & conservatives, blacks & non-blacks, tall & short, sick & well, D&D players and people who get laid, etc.)

Let me show how both statements can logically live together harmoniously.

Just like how most men are slower than female Olympians, but at the same time the average man is faster than the average woman.

NB: Not real data.

Measurement

Even when differences are statistically significant enough to draw conclusions (such as: “boys sprint faster than girls”), the magnitude may be really small so that the difference, while indisputable, is also unimportant. (“Statistical significance” is a confusing term in this respect.)

Consider that there are many ways you could measure differences among people. Here are some that come up frequently in the gender wars, grouped suggestively:

  • height, weight, curvature
  •   IQ, SAT scores, reading tests
  • speed, throwing distance, fine motor skills
  • communication skills, emotional intelligence
  • went to college, profession is engineer
  • finding things in the refrigerator, ability to focus, ability to multitask

There are many ways to measure each of these “dimensions”. For example, does "speed" mean in the 100m dash, 200m dash, marathon, trail running, bike race, or triathlon? While the answers wouldn’t be independent, they wouldn’t be one-to-one either.

A billion points in a million-dimensional space

Now you are faced with 6.7 billion points in an N-dimensional space, where N is the number of things you could measure. Let’s say like a billion points in a million-dimensional space. (Some dimensions may be collinear.)

On the one hand, there are always lots of pink and blue dots mixing in with each other (e.g. men who sew better than most women)‒and directly from Ivy’s point, the distance among pinks (variation among men) is greater than the distance from the pink centroid to the blue centroid (variation between men and women).

At the same time, though, if you had to choose just one factor by which to color these dots and get maximal classification power, it would have to be gender.

In other words, gender differences may generate a maximally separating hyperplane, but Euclidean distances between differently-gendered points are often small, and Euclidean distances between same-gendered points are often large.




data from the US Drug Enforcement Agency’s System To Retrieve Information on Drug Evidence

A few points about these pictures which I’ll be elaborating on in future posts:

  • sub i, sub j: There is significant variation from city to city and presumably dealer to dealer or customer to customer, since they plot interquartile range.
  • 3-D data: Since both purity and quantity affect the price, we’re really talking about a “price surface” — just like a volatility surface or the yield curve on Treasurys. And in fact there are even more dimensions to the data since it could be cut differently, and … well, I won’t say what makes for good coke.
  • data collection: Do you really believe these numbers? Some undercover cop probably solicited drugs (I didn’t read the methodology section but just guessing). Does that seem like an error-free data collection process? But the same goes for macroeconomic data, financial data from companies, and so on. It comes from somewhere, it’s not “the truth” necessarily.










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

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.