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Lost and Found: Ancient BEA County Personal Income Data (1929, 1940, 1950, 1959, 1962, 1965, 1967-1984)

A key data set used in my dissertation on highways and local economic growth about 25 years ago was a personal income and earnings by industry file of U.S. counties that covered the early beginnings of BEA data collection efforts.  Since that time, the data has vanished from the public domain without a trace. It’s been replaced with the current 1969-2000 and 2001-2017 local personal income series.  The latter series use different regional accounting methodologies than the earlier series.  So, the two series 1929-1984 and 1969-2000/2001-2017 can’t simply be spliced together.

I am posting the data here it the hopes that somebody finds it useful (e.g., historians, regional scientists, members of the general public).   I discovered an old SAS version of the file on a 3-1/2 inch floppy disk, which was fortunately still convertible into a more accessible CSV (comma-separated values) format.  Unfortunately, the original suppression codes were not retained, either because my data conversion did not preserve the missing values, the missing value indicators were never available in the first place, or missing values were never properly written out into the SAS file.  Therefore, data users will need to realize that a field zero represents either a real zero or a field that was suppressed for confidentiality reasons.  If it is a real zero, the values for nested industry earnings categories will add up to the earning totals.  If not, there will be residuals.  In many instances, you will be able to reasonably estimate the missing values by a combination of summing to totals and interpolating over time.

A few notes about the file layout.  The SAS file contained sector numbers but no actual sector descriptions.  I was able to identify the descriptions using several different sources and inserted them into CSV file.

The column headings y29-y84 represent years.  That is to say, y29 is income, population and earnings data for 1929, y40 is income, population and earnings, . . .

Fipstate is the fips state number

Fipcount is the fips county number

Fipcode is a unique county code.  It is mathematically defined as 1,000Xfipstate+fipcount

Locality is the locality (i.e., county, city, parish) name.

Secnum is the sector number.  It is somewhat different from the current BEA line code.  Moreover, the industrial classification system is SIC (Standard Industrial Classification System), the predecessor to the NAICS (North American Industry Classification System) used now.  However, the BEA line code is unique to BEA.

Earnings and income figures are expressed in thousands of dollars.  The exceptions are per capita income (actual value) and population (hundreds of dollars).

Good luck. 

Posted in Economics. Tagged with , , , , , , , , , , , .

Resurgence of the Country? How Performance on One Prosperity Measure Trumps Urban Triumphalism

In an article entitled Why Some Places Prosper and Others Do Not published in 2009, Andy Isserman and colleagues argued that the conventional measures of economic competitiveness provide a skewed picture of the wellbeing of communities across the United States and motivate policy initiatives that may very well hinder place-based prosperity. Per capita income is a suspect measure because it fails to incorporate regional cost-of-living differences and ignores distributional issues. Economic growth is problematic because it doesn’t gauge improvement in the average welfare of existing community residents. Economic growth can raise or lower average income, and it often benefits in-commuters and migrants rather than existing residents. Therefore, it has an uncertain effect on community wellbeing. From a community vantage point, both the quality and the beneficiaries of the growth matter.

In lieu of those commonly adopted but somewhat flawed measures, Isserman introduced the idea of a “prosperity index” that captures essential characteristics of economically and socially healthy communities. This index was built on four component measures, all of them derived from the U.S. Census Bureau, Summary File 3 (i.e., the decennial census “long form” questions): (a) percentage of individuals living below the poverty level, (b) unemployment rate, (c) percentage of the 16-19 population that dropped out of high school and (d) percentage of the occupied housing units that has one or more of four undesirable physical and financial conditions (i.e., incomplete plumbing or kitchen facilities, over-occupancy, or unaffordability). A county notched one point for each component that exceeded the corresponding U.S. average value for the measure. Therefore, the prosperity index ranges from zero (below the U.S. on all four measures) to four.

When Isserman et al. crunched the numbers and mapped the prosperity results for all 3,140 U.S. counties back in 2000, a surprising and geographically uneven picture emerged. Contrary to what might be expected, the most “prosperous” counties are located in many of the slower growing areas of the Midwest and Northeast. Fast-growing Sunbelt counties generally rated poorly. A notable exception is Virginia, a Sunbelt state where the Shenandoah Valley and northern/central areas of the state fare very well. Isserman focused most of their attention on rural differences. They found that prosperous rural counties had “diverse, vigorous” economies with more entrepreneurship, greater social capital, and higher per capita incomes.

Prosperity by County, Number of Criteria Achieved, 2000

This portrait of a successful community described by Isserman stands in marked contrast to that described by Harvard economist Edward Glaeser. In his book Triumph of the City, Professor Glaeser touts the benefits of urban agglomeration, its accompanying creative chaos and ability to integrate poor newcomers. Such places stimulate productivity, learning, and innovation, and maximize migrant economic potential. Though he does not offer an urban-friendly prosperity index, one could imagine constructing one with weighted variables representing population density, patent activity, educational achievement levels, migration rates, and demographic diversity.

To investigate this issue further, we updated the prosperity index to the most current period available. With the U.S. Census Bureau American Community Survey available, it is now possible to revise the prosperity index each year using the 5-year estimates. The geographical pattern that emerges for 2006-2010 is not all that different than 2000 with the exception of a more fragmented Midwestern picture and greater geographical diffusion of prosperity.

Prosperity by County, Number of Criteria Achieved, 2010

The values were then charted along the USDA rural-urban continuum (which runs from low values for counties in highly urbanized metro areas to high values for non-metropolitan counties with less urbanization and greater isolation from metropolitan areas). The figure below shows the prosperity values weighted by population for each continuum category.

Prosperity by Rural-Urban Continuum

Non-metropolitan areas generally did better in 2006-2010 on average than metro areas (2.22 prosperity index for nonmetro counties compared to 2.05 for metro counties) and very rural non-metro counties generally do better than less rural non-metro counties. Clearly, lower land rents in the rural periphery and thus better performance on the housing criterion account for this gap. But, this gap also grew from 2000 to 2010 and decreasing housing affordability in metropolitan areas provides only part of the explanation. For the very largest metro counties (1 million or more), about two-thirds of the loss in average prosperity is explained by lower housing criterion performance. For the second metropolitan category (metro areas of 250,000 to 1 million), four-fifths of the regression occurred along the dimensions of poverty and unemployment.

Metropolitan areas are a heterogenous bunch. So, it is worthwhile to take a closer look at the success stories. Generally speaking, the top performers are smaller, slower growth cities located in the interior of the country. Yet several larger metropolitan areas score in the top 50 (Rochester, NY; Pittsburgh, PA; Minneapolis, MN; Raleigh, NC; Cincinatti, OH, St. Louis, MO; Richmond, VA; Salt Lake City, UT; Baltimore, MD; San Diego, CA). And, what about Glaeser’s hometown and inspiration for Triumph of the City? Well, it ranks a distant 245th.

Rank Metropolitan Area Population Prosperity
1 Lewiston, ID-WA 60,249 4.0000
1 Dover, DE 156,918 4.0000
1 Sioux Falls, SD 221,095 4.0000
1 Sheboygan, WI 115,328 4.0000
1 Bloomington-Normal, IL 166,706 4.0000
1 Appleton, WI 222,359 4.0000
1 Lincoln, NE 296,056 4.0000
1 Scranton–Wilkes-Barre, PA 561,113 4.0000
1 Oshkosh-Neenah, WI 165,032 4.0000
1 Fond du Lac, WI 100,919 4.0000
1 Ogden-Clearfield, UT 526,394 4.0000
1 Colorado Springs, CO 622,809 4.0000
1 Des Moines-West Des Moines, IA 552,889 4.0000
1 Wausau, WI 132,644 4.0000
1 Norwich-New London, CT 272,360 4.0000
1 Green Bay, WI 302,755 4.0000
1 Holland-Grand Haven, MI 261,376 4.0000
1 Glens Falls, NY 128,795 4.0000
1 York-Hanover, PA 428,175 4.0000
1 Bismarck, ND 105,488 4.0000
1 Johnstown, PA 144,741 4.0000
1 Fargo, ND-MN 201,499 4.0000
1 Reading, PA 407,310 4.0000
1 Olympia, WA 243,563 4.0000
1 La Crosse, WI-MN 132,045 4.0000
1 Altoona, PA 127,071 4.0000
1 Columbus, IN 75,855 4.0000
1 Fort Walton Beach-Crestview-Destin, FL 182,076 4.0000
1 Pittsfield, MA 131,528 4.0000
1 Worcester, MA 791,855 4.0000
1 Dubuque, IA 92,547 4.0000
1 Fairbanks, AK 94,439 4.0000
1 Rochester, MN 182,816 4.0000
34 Provo-Orem, UT 495,922 3.9801
35 Albany-Schenectady-Troy, NY 865,982 3.9621
36 Springfield, IL 207,990 3.9389
37 Cedar Rapids, IA 254,571 3.9187
38 Omaha-Council Bluffs, NE-IA 845,820 3.8913
39 Elizabethtown, KY 114,956 3.8775
40 Madison, WI 557,744 3.8566
41 Rochester, NY 1,049,836 3.8286
42 St. Cloud, MN 186,075 3.7950
43 Pittsburgh, PA 2,358,313 3.7940
44 Allentown-Bethlehem-Easton, PA-NJ 812,027 3.7068
45 Wenatchee, WA 108,155 3.6564
46 Minneapolis-St. Paul-Bloomington, MN-WI 3,229,181 3.6487
47 Syracuse, NY 658,811 3.6290
48 Davenport-Moline-Rock Island, IA-IL 376,736 3.6084
49 Jefferson City, MO 147,772 3.5991
50 Raleigh-Cary, NC 1,069,694 3.5356
51 Charlottesville, VA 197,279 3.4432
52 Fayetteville-Springdale-Rogers, AR-MO 445,626 3.4362
53 Harrisburg-Carlisle, PA 541,758 3.4270
54 Eau Claire, WI 158,847 3.3870
55 Portland-South Portland-Biddeford, ME 513,139 3.3848
56 Lynchburg, VA 248,742 3.3824
57 Knoxville, TN 685,335 3.3725
58 Grand Forks, ND-MN 98,107 3.3194
59 Burlington-South Burlington, VT 209,381 3.2610
60 Cincinnati-Middletown, OH-KY-IN 2,110,398 3.2515
61 St. Joseph, MO-KS 126,030 3.2371
62 St. Louis, MO-IL 2,792,309 3.2217
63 Waterloo-Cedar Falls, IA 165,838 3.2206
64 Richmond, VA 1,235,365 3.2072
65 Binghamton, NY 252,181 3.2037
66 Idaho Falls, ID 124,736 3.1966
67 Winchester, VA-WV 125,382 3.1908
68 Buffalo-Niagara Falls, NY 1,137,266 3.1900
69 Topeka, KS 231,386 3.1704
70 Wichita Falls, TX 150,953 3.1323
71 Evansville, IN-KY 355,854 3.1250
72 Logan, UT-ID 119,575 3.1045
73 Kennewick-Richland-Pasco, WA 238,406 3.1024
74 Salt Lake City, UT 1,090,848 3.0831
75 Baltimore-Towson, MD 2,683,160 3.0749
76 Champaign-Urbana, IL 228,688 3.0731
77 Canton-Massillon, OH 405,334 3.0715
78 Amarillo, TX 245,177 3.0201
79 Peoria, IL 376,046 3.0155
80 San Angelo, TX 109,673 3.0153
81 Grand Junction, CO 142,284 3.0000
81 Deltona-Daytona Beach-Ormond Beach, FL 496,053 3.0000
81 San Diego-Carlsbad-San Marcos, CA 3,022,468 3.0000
81 Akron, OH 703,093 3.0000
81 Kingston, NY 182,782 3.0000
81 Lebanon, PA 131,341 3.0000
81 Warner Robins, GA 134,880 3.0000
81 San Luis Obispo-Paso Robles, CA 265,577 3.0000
81 Sherman-Denison, TX 119,111 3.0000
81 Boulder, CO 290,177 3.0000
81 Sandusky, OH 77,454 3.0000
81 Cheyenne, WY 89,221 3.0000
81 Barnstable Town, MA 217,483 3.0000
81 Ames, IA 87,594 3.0000
81 Jacksonville, NC 169,207 3.0000
81 Williamsport, PA 116,376 3.0000
81 Bremerton-Silverdale, WA 247,336 3.0000
81 Manchester-Nashua, NH 399,555 3.0000
81 Casper, WY 73,520 3.0000
81 Bridgeport-Stamford-Norwalk, CT 905,342 3.0000
81 Columbia, MO 168,187 3.0000
81 Lancaster, PA 511,250 3.0000
81 Great Falls, MT 80,562 3.0000
81 Oxnard-Thousand Oaks-Ventura, CA 809,080 3.0000
81 Santa Rosa-Petaluma, CA 474,047 3.0000
81 Owensboro, KY 113,588 3.0000
81 Bay City, MI 108,156 3.0000
81 Lubbock, TX 276,139 3.0000
81 Fort Collins-Loveland, CO 291,162 3.0000
81 Ithaca, NY 100,612 3.0000
81 Poughkeepsie-Newburgh-Middletown, NY 666,353 3.0000
81 State College, PA 151,411 3.0000
81 Janesville, WI 159,964 3.0000
81 Bangor, ME 152,934 3.0000
81 Midland, TX 132,103 3.0000
81 Coeur d’Alene, ID 134,851 3.0000
81 Utica-Rome, NY 298,865 3.0000
81 Iowa City, IA 148,620 3.0000
81 Waco, TX 229,587 3.0000
81 Billings, MT 154,044 3.0000
81 Erie, PA 279,234 3.0000
81 Honolulu, HI 936,984 3.0000
81 Elmira, NY 88,708 3.0000
81 Panama City-Lynn Haven, FL 166,798 3.0000
125 Sioux City, IA-NE-SD 141,794 2.9946
126 Roanoke, VA 304,995 2.9500
127 San Jose-Sunnyvale-Santa Clara, CA 1,793,888 2.9392
128 Abilene, TX 163,092 2.9175
129 Washington-Arlington-Alexandria, DC-VA-MD-WV 5,416,691 2.9049
130 Springfield, MO 427,566 2.8676
131 Anchorage, AK 368,414 2.7716
132 Charleston, WV 304,033 2.7428
133 Morgantown, WV 125,691 2.7376
134 Boston-Cambridge-Quincy, MA-NH 4,489,250 2.7243
135 Columbus, OH 1,798,377 2.7116
136 Johnson City, TN 195,735 2.7052
137 Oklahoma City, OK 1,218,920 2.6999
138 Texarkana, TX-Texarkana, AR 134,447 2.6798
139 Kansas City, MO-KS 1,999,718 2.6498
140 Boise City-Nampa, ID 598,730 2.6143
141 Harrisonburg, VA 122,328 2.6125
142 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 5,911,638 2.6076
143 Little Rock-North Little Rock-Conway, AR 681,812 2.5972
144 Columbia, SC 744,145 2.5775
145 Duluth, MN-WI 278,337 2.5667
146 Wheeling, WV-OH 148,354 2.5501
147 Hartford-West Hartford-East Hartford, CT 1,203,823 2.5247
148 Tulsa, OK 917,367 2.5013
149 Hagerstown-Martinsburg, MD-WV 264,648 2.4871
150 Nashville-Davidson–Murfreesboro–Franklin, TN 1,541,541 2.4844
151 Dallas-Fort Worth-Arlington, TX 6,154,265 2.4513
152 Asheville, NC 416,276 2.4397
153 Terre Haute, IN 172,066 2.4387
154 Longview, TX 210,226 2.4314
155 San Francisco-Oakland-Fremont, CA 4,244,889 2.4104
156 Rapid City, SD 123,078 2.4088
157 Wichita, KS 609,383 2.4045
158 Virginia Beach-Norfolk-Newport News, VA-NC 1,663,070 2.3926
159 Florence-Muscle Shoals, AL 146,208 2.3724
160 Lexington-Fayette, KY 459,761 2.3708
161 Blacksburg-Christiansburg-Radford, VA 161,013 2.3630
162 Seattle-Tacoma-Bellevue, WA 3,356,089 2.3267
163 Baton Rouge, LA 787,961 2.3224
164 Victoria, TX 114,094 2.3106
165 Jackson, MS 533,673 2.2958
166 Cumberland, MD-WV 102,434 2.2714
167 Kingsport-Bristol-Bristol, TN-VA 307,637 2.2521
168 Huntington-Ashland, WV-KY-OH 287,112 2.2380
169 Indianapolis-Carmel, IN 1,717,259 2.2101
170 Fort Smith, AR-OK 294,478 2.2064
171 San Antonio, TX 2,057,782 2.1980
172 Huntsville, AL 401,694 2.1957
173 Killeen-Temple-Fort Hood, TX 388,448 2.1907
174 Albuquerque, NM 862,165 2.1827
175 Milwaukee-Waukesha-West Allis, WI 1,539,897 2.1734
176 Pocatello, ID 88,334 2.1728
177 Winston-Salem, NC 468,922 2.1679
178 Athens-Clarke County, GA 188,932 2.1668
179 College Station-Bryan, TX 219,058 2.1557
180 Bloomington, IN 189,263 2.1149
181 Dayton, OH 843,218 2.0843
182 Montgomery, AL 370,554 2.0801
183 Fort Wayne, IN 412,067 2.0801
184 Charlotte-Gastonia-Concord, NC-SC 1,687,440 2.0776
185 Lake Charles, LA 196,414 2.0371
186 Grand Rapids-Wyoming, MI 772,621 2.0137
187 Prescott, AZ 209,260 2.0000
187 Auburn-Opelika, AL 135,010 2.0000
187 Racine, WI 194,736 2.0000
187 Napa, CA 134,051 2.0000
187 Naples-Marco Island, FL 316,931 2.0000
187 Gainesville, GA 175,001 2.0000
187 Lawton, OK 119,902 2.0000
187 Tyler, TX 203,393 2.0000
187 Kalamazoo-Portage, MI 323,831 2.0000
187 Palm Bay-Melbourne-Titusville, FL 540,583 2.0000
187 Palm Coast, FL 91,806 2.0000
187 Houma-Bayou Cane-Thibodaux, LA 206,559 2.0000
187 Monroe, MI 152,784 2.0000
187 Monroe, LA 174,957 2.0000
187 Sumter, SC 106,601 2.0000
187 Missoula, MT 107,288 2.0000
187 Mansfield, OH 125,980 2.0000
187 Springfield, OH 139,374 2.0000
187 Alexandria, LA 152,475 2.0000
187 Jackson, TN 114,171 2.0000
187 Lafayette, LA 267,302 2.0000
187 El Paso, TX 772,280 2.0000
187 Lima, OH 106,586 2.0000
187 Atlantic City, NJ 273,162 2.0000
187 Ann Arbor, MI 343,947 2.0000
187 Michigan City-La Porte, IN 110,937 2.0000
187 St. George, UT 134,033 2.0000
187 Elkhart-Goshen, IN 196,855 2.0000
187 Muncie, IN 117,344 2.0000
187 Mount Vernon-Anacortes, WA 115,231 2.0000
187 Ocean City, NJ 97,684 2.0000
187 Trenton-Ewing, NJ 364,445 2.0000
187 Goldsboro, NC 120,102 2.0000
187 Lawrence, KS 109,052 2.0000
187 Lewiston-Auburn, ME 107,882 2.0000
187 Farmington, NM 127,517 2.0000
187 Spokane, WA 461,262 2.0000
187 New Haven-Milford, CT 856,688 2.0000
187 Dothan, AL 142,718 2.0000
187 Corvallis, OR 84,158 2.0000
187 Santa Cruz-Watsonville, CA 256,901 2.0000
187 Odessa, TX 133,015 2.0000
187 Punta Gorda, FL 159,385 2.0000
187 Parkersburg-Marietta-Vienna, WV-OH 162,214 2.0000
187 Santa Barbara-Santa Maria-Goleta, CA 416,051 2.0000
232 Weirton-Steubenville, WV-OH 125,101 1.9467
233 Lansing-East Lansing, MI 463,602 1.9463
234 Augusta-Richmond County, GA-SC 544,180 1.9376
235 Gainesville, FL 260,930 1.9361
236 Tuscaloosa, AL 213,754 1.9244
237 Denver-Aurora, CO 2,464,415 1.9241
238 Bowling Green, KY 121,861 1.9008
239 Brunswick, GA 109,099 1.8734
240 South Bend-Mishawaka, IN-MI 318,951 1.8356
241 Youngstown-Warren-Boardman, OH-PA 571,975 1.8331
242 Clarksville, TN-KY 263,531 1.8314
243 Louisville/Jefferson County, KY-IN 1,261,825 1.8132
244 Austin-Round Rock, TX 1,627,571 1.8109
245 New York-Northern New Jersey-Long Island, NY-NJ-PA 18,700,715 1.7916
246 Jonesboro, AR 118,032 1.7907
247 Durham, NC 488,508 1.7819
248 Birmingham-Hoover, AL 1,115,485 1.7718
249 Houston-Sugar Land-Baytown, TX 5,709,313 1.7387
250 Wilmington, NC 349,522 1.7274
251 New Orleans-Metairie-Kenner, LA 1,105,020 1.7211
252 Savannah, GA 335,980 1.7103
253 Detroit-Warren-Livonia, MI 4,345,978 1.6882
254 Charleston-North Charleston, SC 641,930 1.6685
255 Florence, SC 203,499 1.6629
256 Toledo, OH 653,650 1.6395
257 Danville, VA 106,934 1.5905
258 Shreveport-Bossier City, LA 393,350 1.5788
259 Hattiesburg, MS 139,092 1.5539
260 Sarasota-Bradenton-Venice, FL 694,819 1.5414
261 Morristown, TN 134,876 1.5414
262 Orlando-Kissimmee, FL 2,083,626 1.5406
263 Beaumont-Port Arthur, TX 384,583 1.4900
264 Atlanta-Sandy Springs-Marietta, GA 5,125,113 1.4835
265 Decatur, AL 151,867 1.4509
266 Hickory-Lenoir-Morganton, NC 362,665 1.4223
267 Sacramento–Arden-Arcade–Roseville, CA 2,107,092 1.4044
268 Columbus, GA-AL 290,204 1.3588
269 Lafayette, IN 197,590 1.3377
270 Joplin, MO 172,590 1.3335
271 Cleveland-Elyria-Mentor, OH 2,086,589 1.3313
272 Chattanooga, TN-GA 518,288 1.3292
273 Kokomo, IN 99,458 1.3244
274 Chicago-Naperville-Joliet, IL-IN-WI 9,384,661 1.3080
275 Tampa-St. Petersburg-Clearwater, FL 2,745,350 1.2938
276 Tallahassee, FL 360,391 1.2849
277 Providence-New Bedford-Fall River, RI-MA 1,602,822 1.2731
278 Springfield, MA 691,119 1.2210
279 Greenville-Mauldin-Easley, SC 621,286 1.1896
280 Gulfport-Biloxi, MS 241,122 1.1759
281 Portland-Vancouver-Beaverton, OR-WA 2,170,801 1.1755
282 Rockford, IL 347,539 1.1541
283 Cleveland, TN 113,882 1.1466
284 Valdosta, GA 134,631 1.0994
285 Jacksonville, FL 1,319,195 1.0897
286 Phoenix-Mesa-Scottsdale, AZ 4,080,707 1.0807
287 Reno-Sparks, NV 416,860 1.0096
288 Battle Creek, MI 137,112 1.0000
288 Spartanburg, SC 278,167 1.0000
288 Port St. Lucie, FL 413,981 1.0000
288 Rome, GA 95,810 1.0000
288 Myrtle Beach-Conway-North Myrtle Beach, SC 258,267 1.0000
288 El Centro, CA 168,052 1.0000
288 Longview, WA 101,244 1.0000
288 Redding, CA 176,906 1.0000
288 Santa Fe, NM 141,702 1.0000
288 Gadsden, AL 104,066 1.0000
288 Hot Springs, AR 95,290 1.0000
288 Hinesville-Fort Stewart, GA 76,996 1.0000
288 Pine Bluff, AR 101,419 1.0000
288 Brownsville-Harlingen, TX 393,566 1.0000
288 Niles-Benton Harbor, MI 157,232 1.0000
288 Flagstaff, AZ 131,824 1.0000
288 Ocala, FL 326,833 1.0000
288 Bellingham, WA 195,993 1.0000
288 Vallejo-Fairfield, CA 410,042 1.0000
288 Greenville, NC 182,542 1.0000
288 Greeley, CO 242,860 1.0000
288 Jackson, MI 161,569 1.0000
288 Decatur, IL 110,719 1.0000
288 Sebastian-Vero Beach, FL 135,518 1.0000
288 Chico, CA 218,635 1.0000
288 Saginaw-Saginaw Township North, MI 202,336 1.0000
288 Bend, OR 154,568 1.0000
288 Flint, MI 433,054 1.0000
288 Lake Havasu City-Kingman, AZ 199,177 1.0000
288 Laredo, TX 240,346 1.0000
288 Las Vegas-Paradise, NV 1,895,521 1.0000
288 Kankakee-Bradley, IL 112,100 1.0000
288 Las Cruces, NM 201,670 1.0000
288 Muskegon-Norton Shores, MI 173,223 1.0000
288 Mobile, AL 408,620 1.0000
288 Greensboro-High Point, NC 709,142 1.0000
288 Dalton, GA 139,835 1.0000
288 Modesto, CA 509,682 1.0000
288 Pascagoula, MS 159,143 1.0000
288 Danville, IL 82,033 1.0000
288 Merced, CA 250,699 1.0000
288 Anderson, IN 131,444 1.0000
288 Fayetteville, NC 357,122 1.0000
288 Burlington, NC 147,072 1.0000
288 Anderson, SC 183,691 1.0000
288 Cape Coral-Fort Myers, FL 606,165 1.0000
288 Carson City, NV 55,375 1.0000
288 Anniston-Oxford, AL 117,149 1.0000
288 Eugene-Springfield, OR 347,156 1.0000
289 Pensacola-Ferry Pass-Brent, FL 445,778 0.9949
290 Macon, GA 231,172 0.9767
291 Miami-Fort Lauderdale-Pompano Beach, FL 5,478,869 0.8702
292 Albany, GA 157,528 0.7407
293 Los Angeles-Long Beach-Santa Ana, CA 12,723,781 0.6992
294 Memphis, TN-MS-AR 1,301,248 0.6796
295 Salisbury, MD 123,362 0.6423
296 Rocky Mount, NC 150,649 0.6266
297 Salem, OR 383,639 0.5767
298 Riverside-San Bernardino-Ontario, CA 4,114,751 0.5127
299 Corpus Christi, TX 423,717 0.4217
300 Medford, OR 200,587 0.0000
300 Yakima, WA 236,542 0.0000
300 Vineland-Millville-Bridgeton, NJ 155,456 0.0000
300 Yuba City, CA 164,580 0.0000
300 Tucson, AZ 964,462 0.0000
300 Bakersfield, CA 815,693 0.0000
300 Lakeland, FL 590,116 0.0000
300 Madera, CA 147,738 0.0000
300 McAllen-Edinburg-Mission, TX 736,973 0.0000
300 Fresno, CA 908,830 0.0000
300 Salinas, CA 407,435 0.0000
300 Visalia-Porterville, CA 429,404 0.0000
300 Stockton, CA 673,613 0.0000
300 Hanford-Corcoran, CA 151,122 0.0000
300 Pueblo, CO 156,244 0.0000
300 Yuma, AZ 190,526 0.0000

Posted in Economics, Uncategorized, Virginia.

Will Health Care Reform Kill or Mend the Economy?

The Patient Protection and Affordable Care Act/Health Care and Education Reconciliation Act (henceforth referred to as “Health Care Reform” or the “Affordable Care Act”) was adopted ostensibly to improve health insurance coverage, decrease health care costs, and improve health care quality. On the first point, most pundits agree. Health Care Reform should substantially boost coverage. The Congressional Budget Office estimates that it will increase coverage from an estimated 83 percent of U.S. legal nonelderly residents in 2010 to 94 percent by 2019. But, there is a wide range of opinion of the impact of the legislation on both cost and quality. For instance, the liberal-leaning Center for American Progress places the expected cost savings on the order of 1.5% per year which they attribute to the cost-reducing effects of prospective changes in payment systems, competitive effects of newly instituted health insurance exchanges, and technology improvements. Others on the political right disagree, arguing that it will further escalate costs because it entrenches a flawed third-party Fee For Service payment system, will result in short-run shortages because of inelastic health workforce labor supply, and is replete with tax and regulatory provisions that will increase costs.

Some of the most heated controversy but most meager investigation centers on the macroeconomic aspects of Health Care Reform. Simply put, what is the impact of reform on jobs and output? Implicit in the name of the Republican house sponsored Repealing the Job Killing Health Care Act Bill is that Affordable Care Act will result in enormous job losses, an especially unsettling prospect in the kind of jobless economic recovery we have been experiencing these last two years. On the other side of the aisle, House Minority Leader Nancy Pelosi has announced that Health Care Reform will “create 4 million jobs – 400,000 jobs almost immediately.” This figure appears to be loosely (very loosely) based on a Center for American Progress Study that shows 400,000 jobs created in 2014 under a best-case cost reduction scenario.

Somewhere in the middle is the Congressional Budget Office. The CBO Director, Douglas Elmendorf, has issued the terse statement that the “effects of the legislation on overall employment would be small.” However, net job figures are not provided to quell this swirling controversy. So, speculation and hyperbole continue.

One reasonable scenario is offered in a new paper by Juergen Jung and Chung Tran. They find that an Affordable Care Act type policy produces a “7 percent increase in aggregate spending on healthcare,” “steady state output decreases by up to 2 percent,” and the public experiences an “increase in stock of health capital.” They conclude that the “reform is socially beneficial as welfare gains are observed for most generations.” In sum, there are slightly negative macroeconomic outcomes but net social benefits.

Another way of looking at the Affordable Care Act is to focus on its fiscal effects. The Act was scored by the CBO to be deficit reducing at the time it was written. In the spring of 2010, it was estimated to provide $143 billion in savings over the period FY2010-2019 with much of the savings to occur earlier in the period and most of the spending impetus beginning in 2014 when Medicaid is expanded and the subsidized insurance exchanges open. There have been several modifications since that time (such as the repeal of new 1099 reporting requirements) that affect some of the particulars but you get the general gist. In the traditional Keynesian economic modeling world, deficit reduction is contractionary fiscal policy and has a dampening effect on output and employment.

Source: Congressional Budget Office

If these annual budgetary expenditure and revenue estimates are mapped onto the policy variables of a respected commercial economic model called REMI, the employment trajectory closely follows the budget trajectory (see this paper for further methodological details) with one exception. During the budgetary surplus years, employment losses associated with the act peak in 2013 with over 800,000 jobs lost. However, beginning in 2015 when the spending kicks in, jobs are added. This pattern continues even as the federal government continues to accumulate small budget surpluses and ends on 2019 with almost 236,000 more jobs than would have occurred otherwise. This figure is net employment and includes both industry winners and losers. The winners? Health care services industries see a gain of over 470,000 jobs and insurance carriers see over 39,000 additional jobs. There are also employment gains in the pharmaceutical and medical equipment manufacturing industries. But, offsetting losses occur in many other industries, including retail trade, construction, and services.

Why might this happen? Well, Health Care Reform significantly channels spending from general consumer goods and services to health care goods and services. Moreover, health care spending is almost entirely spent in the U.S. and has a strong U.S. supply chain. But, it displaces general consumption spending that has significant import leakages. Think of it as having features of a successful “Buy American” program.

Unfortunately, this analysis is limited in many ways. For one thing, it doesn’t fully account for some perverse labor market effects of the legislation. For instance, the effect of the employer penalty (“Play or Pay”) feature of Health Care Reform is similar to instituting a higher minimum wage. Since employers can’t shift the costs of health insurance to low wage employees (as they tend to do with the costs of benefits), they will employ fewer of them or shift from employing full-time workers to part-time workers. The Lewin Group places the likely magnitude of this reduction in quantity of labor demanded at 157,300 to 366,200. The Act will also have profound effect on labor supply. The Medicaid and insurance exchange subsidies will effectively increase some household incomes and increase effective marginal tax rates. Therefore, some individuals will elect to drop out of the labor market. The CBO estimates that the net labor supply effect will be to reduce employment by approximately 800,000 by 2021.

Health Care Reform has ramifications for many other variables that could affect the macroeconomy, but they are difficult to quantify given the current state of knowledge. Mentioned earlier was the interminable debate about the impact of Health Care Reform on health care costs, an issue unlikely to be resolved anytime soon. There are also questions about the business and state government administrative costs of implementing the tax and regulatory provisions. Lastly, expanded health coverage should make people healthier. Healthier people should be more likely to work, be more productive, and live longer. In the process, they should impose lower disability and workers comp expenses (but raise retirement costs). Unfortunately, little is known about the magnitude of these other effects.

Given the current state of the knowledge, however, a few things seem reasonably clear. Health Care Reform is likely to have a relatively small effect on the macroeconomy. But, it is more likely to be a modest negative than modest positive impact. Which brings us back to the Jung and Tran study. If the effect of Health Care Reform is to effectively “kill” some jobs but make many more people better off, is that necessarily a bad thing?

Posted in Economics, Health, politics, Uncategorized. Tagged with , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , .

Roanoke Comes of Age

The Roanoke Metropolitan Statistical Area (MSA) (which currently consists of the counties of Botetourt, Craig, Franklin, and Roanoke, and the cities of Roanoke and Salem) has made slow but steady relative progress over the last century on one key measure of economic development. Per capita personal income as measured against the U.S. benchmark has advanced from 68.3% in 1929 to 96.7% in 2009, an all-time high, with a few fits and starts in between.  

Roanoke MSA Per Capita Personal Income, 1929-2009

Source: Bureau of Economic Analysis, Regional Economic Information System

Better yet, when adjustments are made for differences in the cost-of-living using a new regional price parity index, it bests the U.S. by almost 10% and even manages to inch past Virginia with $43,449 compared to $43,406.

Per Capita Personal Income Adjusted for Regional Price Levels, 2009

Source: Bureau of Economic Analysis, Regional Economic Information System and Aten, Figueroa, and Martin (2011)

At the beginning of the period, this convergence reflected Roanoke’s growth as a manufacturing center and regional transportation hub as well as the same rising tides that have lifted boats throughout the southeast. But, more recently, the causes have been varied.  As the region began to lose some of its industrial luster, the services sector gradually picked up much of the slack and the economy became more diversified.  Another factor is the much slower population growth of the Roanoke metro area compared to the rest of the U.S. and Virginia in the last three decades. 

Roanoke MSA Annual Population Change

Source: Bureau of Economic Analysis, Regional Economic Information System

The population growth has been uneven among age groups, with younger adult cohorts more likely to migrate for education, employment, and lifestyle reasons at the same time that the region attracts retirees because of its natural amenities, low cost-of-living and excellent medical services. This continued migration pattern combined with aging-in-place baby boomers has produced a relatively much larger senior and near-retirement population and much smaller young-adult population than Virginia as a whole.

Percentage of Population by Age Group

Source: U.S. Census Bureau, 2010 Census

These demographics are reflected in personal income statistics.  Transfer payments as a percentage of total personal income have grown at a faster clip than elsewhere and now constitute almost 20 percent, compared to 18 percent for the U.S. and just 13 percent for Virginia.

Transfer Payments as Percentage of Personal Income

Source: Bureau of Economic Analysis, Regional Economic Information System

The key factor in the growth of transfer payments, particularly over the last 10 years, are government medical payments for programs like Medicare and Medicare.  The recent recession has also created a recent spurt in retirement, disability, and public assistance transfers.

Transfer Payments by Source (Billions of 2009 dollars)

Source: Bureau of Economic Analysis, Regional Economic Information System

The enormous growth in medical transfer payments helps to explain some industrial restructuring that is occurring in the Roanoke region. As people retire and age, their disposable income generally decreases with a decline in earnings but their medical spending increases as a result of greater need for medical care and eligibility for public insurance such as Medicare and Medicaid. Therefore, health services has displaced retail trade (which has remained largely flat in terms of employment) as the largest industrial sector in the region.

Roanoke MSA's Largest Industries by Employment, 2000-2010

Source: Virginia Employment Commission, Quarterly Census of Employment and Wages

Whether the continued upward trajectory in per capita income is sustainable is questionable.  Federal entitlements cuts are increasingly eyed as key to federal deficit reduction efforts, and the Affordable Care Act tasks government agencies with finding ways to reduce spiraling health care costs.  Moreover, the continued attrition of young adults from the workforce could erode the economic competitiveness of the region. On the other hand, economic and population growth are not the same as economic development. Indeed, one recent study found that slow-growing counties in non-metropolitan areas tend to prosper more than fast-growing counties.  But, more about that later. 


Posted in Economics, Uncategorized, Virginia. Tagged with , , , , , , , , , , , , , , , , , , , , , , , , .

Dentists in Space

The average dentist to population ratio nationwide was 64.7 per 100,000 residents in 2007. However, 255 counties across the U.S. did not have a professionally active dentist. The first figure below shows the distribution of both professionally active dentists (i.e., public and non-profit sector dentists plus dentists in private practices) as well as dentists in private practice by rural urban continuum category which runs from low values (counties in highly urbanized metro areas) to high values (non-metropolitan counties with less urbanization).

Dentists per 100,000 residents, 2007

Source: Health Resources and Services Administration, Area Resource File

A couple of things stand out.  Not surprisingly, dentists like other health professionals are relatively more abundant in more urbanized areas.  The most rural of the continuum categories have fewer than half of the relative number of dentists as the larger metro area categories.  On the other hand, the disparities for dentists in private practice are lower.  These dentists are more responsive to local demand characteristics.

Part of the reason for the lower relative number of private practitioners in more rural areas is the existence of speciality care.  Relatively few dental specialists (e.g., orthodontists, oral surgeons, periodontists, endodontists) operate in rural markets because they must draw from a much wider market diameter to maintain a profitable practice. It makes good business sense for them to locate in more centralized urban locations.  Even after accounting for these specialists, there is still a sizable disparity.  A large reason for the remaining difference is the tendency of dentists like other professionals to locate in commercial areas.  More rural counties have fewer and smaller commercial activity centers.

Dentists per 100,000 residents, 2007

Source: Health Resources and Services Administration, Area Resource File

Regional dentist disparities have grown over the previous decade for both professionally active and private practice dentists with large metro counties gaining the most dentists on a per capita basis.  Only one rural continuum category (8=”Completely rural or less than 2,500 urban population, adjacent to a metro area”) actually saw a slight decrease in dentists.

Change in Dentists per 100,000 residents, 1998-2007 



Source: Health Resources and Services Administration, Area Resource File

These disparities are likely to grow worse before they become better.  Non-metro counties, particularly those down the rural-urban continuum, have a much higher percentage of dentists at retirement age (65+ years) and near retirement age (55-64 years) and proportionally few young dentists.

Percentage of Private Practice Dentists by Age Group, 2007

Source: Health Resources and Services Administration, Area Resource File

This pattern may be partly related to changing graduate locational preferences. Recent cohorts of dental school graduates have indicated a strong preference for more urban or suburban practice locations. For instance, only 5.2 percent of recent graduating seniors stated that they intend to practice in areas with fewer than 10,000 residents (Okwuje, Ifie, Eugene Anderson, and Richard W. Valachovic. 2009.  Annual ADEA survey of dental school seniors: 2008 graduating class Journal of Dental Education 73, 8: 1009-1032).  If this trend continues, the price of dental care will need to increase in order to attract more dentists to rural areas absent any policy intervention.

Posted in Economics, Health, Uncategorized. Tagged with , , .

G.I. (Globaloney International) Joe

The Wall Street Journal (“Plot Change: Foreign Forces Transform Hollywood Films“) reports that Hollywood movie studios are moving away from producing movies that appeal to primarily goers in favor of blockbusters that favor a more international clientele. Because of shrinking U.S. audiences, growing global incomes, and the increasing costs of major studio productions, standard theater fare like romantic comedies and films geared to particular places and people like say southerners, metropolitan twenty-somethings, and African Americans are being phased out in favor of a new hegemonic planetary monoculture.

Apparently nothing is safe from the global makeover. For instance, take the Real American Hero, G.I. Joe. Strip him of his American character, add brawny sidekicks drawn from the world’s exotic hotspots and voilá, you’ve now got yourself a violent international special force that will appeal to the lowest common denominator worldwide.

And, let’s face it, the message is not that different from the motto one Armed Forces branch (in this case, the Navy) fashioned for itself: “A Global Force for Good” and the perorations of a Commander in Chief from nowhere in particular who views himself as a Messianic postmodernistic antidote to national identity.

So, is what’s good for Hollywood, good for the United States? It all depends on what you mean by “is” and of course what you mean by “the United States.” If it’s the bigger, better, and badder borderless one envisioned by the new international media oligopolies, it’s plenty good and profitable to boot. If it’s the distinctive North American republic that George Washington warned in his farewell address to avoid “foreign entanglements,” it’s pretty much the end of the line. And, if you don’t particularly like that situation, the new G.I. Joe will see to it that you do.

Join the Army. Visit exotic places. Meet strange people. And, bill them.

Posted in Entertainment, politics, Uncategorized. Tagged with , , , , , , , , , , , , , .

The Magic Loogie

Back when I served on City Council in Cumberland, Maryland, there was an incident that is now worth recollecting. After a meeting where there might have been a contentious issue on the agenda, I returned to my vehicle in the City parking lot to find that some disgruntled citizen had apparently “hocked a loogie” on the front of my truck windshield. I calmly entered the vehicle, pumped the window washer a few times, ran the windshield wipers, and watched the large wad of snot and spittle at first resist and then swish back and forth until it broke into smaller pieces and slithered downward to the grill. After less than a minute, everything was spic and span again.

Over a political career of nearly eight years, I probably encountered a dozen or so similar incidents: trash dumped in the yard after a fluoridation vote (probably the closest we ever came to mandating universal health care coverage), anonymous letters and e-mails with hateful messages, a few taunts, and one threatened punch-up. After my resignation, I received a lovely little thank you card that read on the outside “Thank You” to reveal upon opening “for leaving” signed by the pseudonyms of a dozen or so malcontents.

All in all, you learn to take the occasional insult, veiled threat, and gesture of ill will in stride and recognize that it comes with the terrain of political representation. You’re never ever going to satisfy everyone. Moreover, people often have strong reactions to public policy issues. Some citizens can even be a little cantankerous. But it never escalated to the level that it required publicity or law enforcement action. End of story.

Beginning of story for some Congressional politicians who would have us believe that they’ve never been on the receiving end of an insult or taunt. In the past week, a dozen or so Congressmen have come forward with stories of harassment surrounding their positions on recently passed health care legislation. Some serious. Most not. Whether revelations of the latter are just peculiar demonstrations of political thin skin, lame attempts to gain sympathy, or coordinated efforts to demonize public demonstrators is still open to question. What is quite clear, however, is that many of the incidents either didn’t happen as told or involve ordinary, run-of-the-mill insults of a kind that merit no public controversy whatsoever.

As evidence of the latter consider the following. Democratic Congressman Bart Stupak charged that one constituent lectured that “You will rue the day you did this, Mr. Stupak.” Not to be outdone in the victimization department, a Republican Congresswoman retorts that she received an ugly call wishing that she had broken her back in a recent accident. And, of course, there were the “really gross expressions to young, Congressional staffers” telephoned to Congressman Anthony Weiner’s office. For goodness sakes, folks, call in the Marines.

And, then there is the spitting incident. Exhibit Congressman Emmanual Cleaver. In headlines that evoked images of the Selma to Montgomery marches, news headlines blared that a black congressman, Congressman Emmanuel Cleaver, was subject to racial taunts and spat in the face by an angry and vindictive teaparty activist in a tumultuous crowd. Fortunately, the entire incident was captured on video. What the tape clearly shows is a rather orderly crowd of demonstraters and a gentleman cupping his hands, shouting, and inadvertedly releasing a spray.

Fortunately too, Americans long ago became inured to conspiracy theories involving saliva projectiles. We can thank Jerry Seinfeld’s debunking of Cosmo Kramer for that.

Posted in Entertainment, politics. Tagged with , , , , , , , , .

Paved with Good Intentions: The Next Corridor “H”

The Cumberland (MD) Times-News is reporting this morning that the Allegany County Commissioners have placed their seal of approval on a proposal to build what amounts to “Son of Corridor H,” a multilane highway to connect Cumberland to the Robert C. Byrd east-west boondoggle to the south. Prodded by a group called the “North South Highway Corridor Committee” and the Greater Cumberland Committee to support a joint resolution, the commissioners stated: “We feel this is a very, very important issue for Allegany County and economic development.”

Since the proposed highway would require no local match, one can certainly understand the attraction of a gigantic public works project like this one, especially during these recessionary times. If it ever comes to fruition decades from now (assuming that there aren’t revolutionary technology developments in the transportation industry like travel pods), it would temporarily pump hundreds of millions in construction monies into the area and create hundreds of jobs, though many of the jobs would be filled by non-resident workers for outside contractors. Once the highway opened, it would have a marginal impact on local employment. You’d see some rearranging of the economic geography with retail establishments clustering closer to highway exits. You’d see more residential sprawl. For an investment of $1 billion or so, the affected counties might experience a net impact of a few hundred permanent jobs, mostly in low paying service and retail trade sectors.

A terrible thing to waste

Another economic development dead end

Why? New highways and expanded highways have their largest economic impacts where existing capacity bottlenecks and agglomeration economies exist. But, let’s face it: Route 220, which runs in the direction of the proposed corridor is not experiencing any bottlenecks. It’s a lightly traveled thoroughfare. When you get past Rawlings, MD, you can often drive for miles without encountering an oncoming car. Thus, a new highway would have very small total user benefits. That’s why Wilbur Smith Associates found that similar highways built in lightly populated areas as part of the Appalachian Highway System have a negative ROI. We’ve known these things for decades. Former ARC Executive Director, Ralph Widner, who oversaw the planning and construction of much of the ARC highway system acknowledged the mistake years later in a pensive 1990 article in Economic Development Quarterly in which he faulted the ARC for placing too little emphasis on developing human resources.

When the numbers don’t add up, expect the proponents to reach elsewhere for support. They’ll tout the improved highway safety and potential for reduced accidents (without acknowledging the increased pollutants and deleterious effects on, for example, asthmatics). They’ll assert that it will improve national defense, citizen evacuation, and police mobilization (without acknowledging that it improves criminal and terrorist movement and is associated with increased crime as well). They’ll hold aloft a few advocacy studies with poor research designs purporting to show how the areas will thrive economically as a result of the new asphalt. They’ll argue that the Marcellus shale discovery changes the entire economic rationale.

Who are the biggest losers in this economic development equation? First, the local public who fall for yet another economic development whopper, and lose valuable time in developing worthwhile economic development projects created through publicly engaged planning which focuses on the area’s assets, including human and natural resources. Second, everything else. The proposed corridor would cut through another fairly intact forest area, inducing a pattern of fragmentation that will render an entire swath of wilderness reaching several miles in each direction useless as an environmental asset, devastating ecological services, and destroying biodiversity.

A terrible thing to waste

A terrible thing to waste

Ezekial 38:20 warned us of what a wrathful god could do:

So that the fishes of the sea, and the fowls of the heaven, and the beasts of the field, and all creeping things that creep upon the earth, and all the men that are upon the face of the earth, shall shake at my presence, and the mountains shall be thrown down, and the steep places shall fall, and every wall shall fall to the ground.

Turns out that we are quite capable of doing ourselves in without any heavenly ire.

Posted in Economics, Environment. Tagged with , , , , , .

All that twitters is not gold: Why you don’t need to buy that new Twitter for Dummies book

Here it goes:

You don’t need to buy that new Twitter for Dummies book. Way too much filler. It is redacted to fewer than 1,600 characters on

Done. It took only 117 or so characters, which means that I was far more frugal than required to post on the Twitter social networking and microblogging system, which allows up to 140 characters. That’s the first thing you need to know in order to use Twitter. The next is to register your account on But, not too many people are tripping up on this last part because there are purportedly over 24 million registered users at the moment.

Here’s the rest:

  • There is shorthand code for accomplishing Twitter operations. Just post them with your message. Here are a few: (1) send direct message (D username message), (2) follow twitterer (F username), (3) send public reply (@username message)
  • If you want to notify your audience of some keywords associated with a message, add a hashtag #. Go to to see if the hashtag is already being used.
  • Twitter has a search engine available at
  • You can download applications that allow you to route blog post titles and social bookmarks to Twitter (e.g.,
  • You can download applications that allow you to route tweets (messages on Twitter) to blogs and other social media.
  • Killer apps

    Killer apps

  • You might want to use an aggregator like FriendFeed ( if you have too many social media feeds to track.
  • You can find celebrities and politicians at
  • Twit and nittwit on Twitter @joebiden

    Twit and nittwit on Twitter @joebiden

  • There are several clients that run from your desktop and make it easier to follow your feeds. TweetDeck ( is the best.
  • A directory of twitterers can be found at
  • That's twellow, not gold.

    That's twellow, not gold.

  • You can grade your twitter presence at and TwitterGrader
  • Tweet dreams.

    Posted in Internet, Uncategorized. Tagged with , , , , , , , , , , , , , , , , , , , , .

    Simply George Jones

    Country music long ago lost its defiant frontier spirit, its improvisational character, its penchant for misbehaving (drinking, cheating, and fighting). Its songs were rooted in regions and honored memory. The singers themselves were imperfect specimens: they had odd bodyshapes, barbershop haircuts, and weren’t great singers in the technical sense. Many led broken lives. In other words, they were real people.

    That’s gone now. In its stead we have a hyperprocessed Madison Avenue product. It’s still vaguely rural or rather ex-urban but there’s no discernable place. It’s been deracinated. Also, no more drinking and carousing. The forgetable themes seem to be chosen by focus groups and increasingly celebrate different aspects of politically correct consumerism. The melodies are often recycled from older songs and given a pop beat. A procession of perfect blonde twenty-something singer-models strut around in music videos to sexually titillate. In sum, it’s been so thoroughly mainstreamed that it’s practically been absorbed into the global monoculture.

    Therefore, it was pleasure to see George Jones in concert last night at the Charlottesville Pavillion. Simple alliterative name. Two syllables. The young waitress at The Nook couldn’t fathom who was performing that evening: “Just some old guy from the 70s.” Seventy-eight years old in September to be precise. He sported a paunch. He missed the high notes.

    But, he lived the lyrics that he wrote and the songs are a spontaneous reflection on the challenges and passions of life. Here in the real world.

    Or, as he tells it in “The Grand Tour”

    Step right up, come on in
    If you’d like to take the grand tour
    . . .
    I have nothing here to sell you.
    Just some things that I will tell you.

    Ladies and Gentleman, Mr. George Jones.

    Posted in Economics, Entertainment, politics, Uncategorized. Tagged with , , , , , .