Quantcast

Posts tagged with unemployment

It’s much easier to destroy than to build. I can destroy not just one £30,000 car, but all the £30,000 cars in a car park, with a little planning and maybe a few hundred expenditure. And I could do a decent job of destroying a car with only £20.

image
Nothing in his pockets but knives and lint.

Same with houses—fire for example is a very effective tool per-effort for ruining lives. Four skinny pirates with a modicum of guns & ammo can hijack a vessel that cost $10 million or $100 million to build.

image

File:Somali Pirates.jpg

image

Despite it being so easy to destroy, where I live things are quite peaceful. Nobody slashes all the tyres in a car park, for example. Why? Economic theory says that when the cost of something goes down we’ll see more of it. Shouldn’t this be true as well for the destruction of other peoples’ lives & property?

Read More




The Brookings Institution says it cares about unemployment among 18–24-year olds in the U.S. I know there are some unemployed EEUUse 18–24-year-olds who subscribe to isomorphismes in your tumblr feeds.

So if you are whom this seminar is supposed to help—how do you feel about people getting paid possibly $170k and $65k salaries to write this?:

  • forging strong vertical relationships
  • industry needs should drive workforce development policy
  • Outreach to SME’s
  • A sector approach
  • organizing industry sector panels that bring together leaders from the worlds of industry, education, and labor
  • By establishing clear goals and empowering regions, state leaders can set a platform for … action
  • Goal-setting is a major platform-setting function
  • bringing in a third-party to facilitate discussion can make all the difference
  • Encouraging cross-sector collaboration at the regional level
  • States can help strengthen metro-level workforce development by improving access to useful data
  • encourage greater cooperation across state agencies involved in economic and workforce development
  • By introducing innovation in the very structure and organization of its state agencies, Kansas is better positioned to ensure cooperative, cross-agency action on critical economic and workforce development priorities.

Those are some of the bolded parts of their “distilled” 6-page “main takeaways”.

Are you being served?

(Source: brookings.edu)




I don’t think so, for the following reasons:

  • Companies are still run imperfectly.
  • Costs are still not as efficiently optimised as possible.
  • Workers don’t produce output at the highest conceivable rate in their current occupation.
  • Plenty of potential engaged in low-productivity work, unemployed, or otherwise not producing (e.g., in school).
  • People do wrong, imperfect things all the time. So there’s room for improvement.

I realise this argument is incomplete. Just because there’s room to grow doesn’t mean we’ll get there. However I think this line of reasoning may prove productive even if my version of it doesn’t get quite there. So let’s press on.

Half a century after Solow, many economists and rest-of-us still think of economic growth as an exogenous “magical” process driven by abstract words such as “technology” or “skill” or “trade” or “innovation”—rather than as the macro sum of correct micro decisions taken by individuals at the company two towns over.

Solow growth model

Some of that surely is to blame on things like Y=C+I+G+NX. Everything “the government” spends is G—regardless of whether it’s spent on a really good idea, implemented well, or on a pie-in-the-sky promise of a half-price incinerator with huge cost overrun. This is like "economists’ K working to constrain our thinking". Or like the Mpemba effect where one first assumes temperature is one-dimensional (false) and then infers that “you have to go through here to get to there”.

 

I prefer to think about a time-varying graph

image.

  • Most of the people (nodes) in the graph I do not trade with directly.
  • The stores I shop at are “popular” nodes. Everyone in my neighbourhood hops 1 to our grocery store. And the grocery store hops 1 or 2 to many many suppliers all over the country (and outside our country).
  • Salaried people have one very-very thick edge connecting to their sole employer.
  • And so on.

With this model it’s less tempting to use abstract words like “technology” without getting more specific. Clearly this new highway will reduce transit times for many of the transaction-edges. And therefore reduce costs broadly, ergo growth. But its effects are (a) localised to those who touch that edge (or an edge that touches that edge) (b) helping some more than others (even hurting a few on net) and [c] rather than being inevitable, come from the good work of individuals, who could have screwed it up. Now it’s tempting to, rather than wish for some magical entrepreneurs or inventors, look at ordinary decisions; not divide the world into “government vs private enterprise”; consider the individuals in particular places who are helped or hurt rather than an average … basically I find this much more grounded and less prone to theoretical histrionics.

Instead of focussing on growth as a 1-D number (Mpemba), trying to correlate it with size of government or regress it against “trust index” or “ethnofract index” or other abstract highfalutins, the GDP number can only be gotten by integrating all the micro elements—which is, I think, as it should be. Then instead of counting on the magical 2% number to stay around 2% year after year, it’s nicely surprising if the total value of transactions this year was again at its record historical high. And when people can squeeze even a higher total of value throughput through the year (out of what? out of natural resources? out of ingenuity? out of Saudi muppets?) you have to wonder where that growth came from—if it was from a sequence of very old “industrial revolutions” or from many companies doing things a little better this year than they did last year. (¬∀, but on net.)

 

Will smart machines make low-wage jobs redundant?

P Krugman asserts in his blog post about Robert Gordon’s paper

machines may soon be ready to perform many tasks that currently require large amounts of human labor. …[I]t’s all too easy to make the case that most Americans will be left behind, because smart machines will end up devaluing the contribution of workers, including highly skilled workers whose skills suddenly become redundant.

So: yes, armies of back-office pencil-pushers have been replaced in the IT revolution by computers. Thank goodness: what a boring, repetitive worklife. But besides record-keeping and verification and copying and automated checking of things, what is it that humans do that’s been replaced by machines? Travel agents? Brick-and-mortar stores with poor selection and high prices? A lot of things computers are good at, like spidering the web, is not something that we previously paid humans to do.

For example Indeed.com, a billion-dollar company created within the last decade, uses electronic computation, networks, and O(100) software engineers to usefully index job search results from disparate sources across the Web. Who lost that job? This is basically a billion-dollar free lunch vis-à-vis unemployment.

Another example that comes to mind is the auto-scan machines at the grocery store. These machines actually make a single clerk more productive. Not that her wages went up necessarily, but the efficiency of the economy did.

So why do peole think automation will replace low-wage jobs? If it’s based on evidence rather than me sitting on my couch and spitballing I’m willing to listen. But from my armchair I see people computing electronic tasks that no human used to do, Siri computing what the iPhone users said, Toyota and NewBalance augmenting humans with machines, machine learning/AI/statistical forecasting making digital things better but again not disemploying anyone.

Despite the appeal of using widespread unemployment as a basis for dystopian fiction, I don’t see anyone scrambling at the profit opportunity to make window-washing droids, janitorial droids, pizza-delivery droids, anything that requires operating in a broad ambiente. The machines seem to be great for repeating the same task in a well-defined scenario, same as factory robots at the Toyota since decades ago.




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

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.

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




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.