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CLOSUP Working Paper Series Number 33
February 2014
Transformation of America’s Metropolitan Area Economies: Lessons from Four Decades
George A. Fulton, Donald R. Grimes, Yuanlei Zhu
Institute for Research on Labor, Employment, and the Economy and
Research Seminar in Quantitative Economics
This paper is available online at http://closup.umich.edu
Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the Center for Local, State, and Urban Policy or any sponsoring agency
Center for Local, State, and Urban Policy Gerald R. Ford School of Public Policy
University of Michigan
TRANSFORMATION OF AMERICA’S METROPOLITAN AREA ECONOMIES:
LESSONS FROM FOUR DECADES
DRAFT
George A. Fulton Donald R. Grimes
Yuanlei Zhu
Institute for Research on Labor, Employment, and the Economy and
Research Seminar in Quantitative Economics
Prepared for: Center for Local, State, and Urban Policy (CLOSUP)
Gerald R. Ford School of Public Policy
February 2014
Financial support for this study was provided by the Center for Local, State, and Urban Policy (CLOSUP) at the Gerald R. Ford School of Public Policy, and by the Office of the Provost, at the University of Michigan. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the Center for Local, State, and Urban Policy or any sponsoring agency.
Abstract
With a unique approach and expanded data measures, this study attempts to contribute to the research on what leads metro economies in the United States to function the way they do, what makes some of the economies more successful than others, and what policy handles, if any, can improve their profiles. The primary tool for analysis is regression, and two measures, income and employment, are used to represent economic success. Two dimensions of analysis are considered: time and space (geography). For time, we investigate the hypothesis that behavioral relationships can vary in a meaningful way depending on the time period selected for analysis, while other relationships remain robust over time. For space, we compare results for metro areas in the “rust belt” region of the country with those for metro areas collectively in the nation. To address the constraints, or “tyranny,” of best practices, we carry out an analysis of residuals to gain insight into which metro areas least conformed to the fit of the general model, and why. The results suggest that findings can be quite sensitive to the time period selected, but also that there are structural and policy-related drivers that are more robust to different time periods and geographies. Among the strongest indicators of the well-being of a metro area are the initial conditions in the metro area, the industry structure, the innovative environment, crime, and educational attainment. Metro areas fit the income model reasonably well. Some areas did not conform as well to the fit of the employment model; those areas tended to be rapidly growing economies located in the South and West regions of the country.
Acknowledgments
[To be written.]
Transformation of America’s Metropolitan Area Economies: Lessons from Four Decades
George A. Fulton, Donald R. Grimes, and Yuanlei Zhu Institute for Research on Labor, Employment, and the Economy
and Research Seminar in Quantitative Economics
University of Michigan
Introduction During the past three or four decades, the U.S. economy at times has been on an
extended ride so invigorating as to inspire some experts to declare the business cycle
dead. At other times, the ride has been so rocky that people despaired of ever returning
to better times. And throughout these times, both good and bad, there has been a wide
variance in performance among the regions and localities that make up the national
economy. A fair amount of research has been carried out on the performance of
metropolitan areas in the United States, attempting to gain insights on the critically
important but difficult questions of what the key drivers are to their economic evolution
and what the policy handles are that can improve their profiles. With a unique approach
and expanded data measures, this paper attempts to extend the analysis to date of what
leads metro economies to function the way they do and what makes some of these
economies more successful than others.
The genesis of the study was a single question posed by colleagues: “Why have
some localities in the country that have suffered from structural decline been relatively
more successful in remaking their economies, such as Pittsburgh, than have others, such
as Detroit?” The accompanying question was, “What lessons for Detroit can be learned
from Pittsburgh…and beyond?” The study mushroomed into an econometric modeling
analysis including all of the metro areas in the United States collectively, and benefited
from initial guidance provided by a panel of experts on how success is measured, what
the predictors of success are, and what role, if any, policymakers can have in promoting
success. The following sections of the paper outline our approach and measures, discuss
how they compare with other studies, and then provide a summary analysis of the
regression results. We follow this by considering a residual analysis to determine which
metro areas are outliers, either positive or negative, to the fit equations, and whether we
can determine any consistency in their profiles. A concluding section closes the paper.
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Innovations in the Study Several embellishments to previous studies are tendered in this study, including:
1. Extending the data base for metropolitan areas to forty years (1969 to 2009),
much longer than is typical for small economic regions.
2. Taking advantage of the longer time period of available data to segment the
estimation period into sequential sub-intervals.
3. Investigating spatial differences among select regions of the country.
4. Making a considerable investment in assembling new series for variables that
were judged to be promising economic drivers.
5. Conducting an analysis of residuals to identify those metro areas that showed
the least conformation to the general model.
We elaborate on each of these items in turn.
1. Regression analysis in general is carried out in two dimensions: time and space
(geography). Because data limitations are so severe when analyzing economic behavior
in geographies as small as metropolitan areas, statistical investigations have often focused
on relatively narrow time intervals. As a consequence, inferences on the effectiveness of
economic drivers and policy handles are, by necessity, drawn from time intervals that
might not, indeed will not, reflect all of the behavioral relationships outside of the period.
To address this concern, we first expended great effort to assemble data that spanned a
forty-year interval, from 1969 to 2009, a longer contemporary period than for any
regression-based study of metro areas of which we are aware. The data were also
adjusted where necessary to maintain consistent metro-area definitions over time for 366
areas, and to account for idiosyncrasies such as metro areas overlapping state boundaries.
All data expressed in real terms were deflated by the price deflator representing the
closest proximity to the metro area.
2. Because of the comparative volatility of local economies, we hypothesized that
we would learn more (and results would be less misleading) by looking in combination at
shorter, sequential time periods within the longer time interval. Few other statistical,
analytical studies on metro areas to date have broken a time range into intervals. We
generated regression results for ten- and twenty-year intervals, but in the paper we focus
on the twenty-year groupings, with results for the ten-year intervals contained in the
appendix.
3
The concept of sequential periods for analysis was motivated by our initial
descriptive work for the study. The twenty metropolitan areas with the largest and
smallest increase between 1969 and 2009 in real personal income minus transfer
payments per capita (one of the dependent variables chosen for our analysis) are shown in
tables 1 and 2. The two tables provide the same information for different time intervals.
Table 1 shows the change broken out into four ten-year intervals and table 2 shows two
twenty-year intervals. Also included are our original focus metro area, Detroit, and its
“peer” areas as identified by characteristics suggested by our panel of experts.1
For many metro areas there is a wide variation in the area’s performance by time
interval, particularly by decade. The metro area with the greatest increase between 1969
and 2009 in real personal income minus transfers per capita was Bridgeport-Stamford-
Norwalk, Connecticut. The area’s relative performance by decade, however, has a
surprisingly large variance. Between 1969 and 1979 the area ranked 73rd among the 366
metro areas, while it ranked first between 1979 and 1989 and second between 1989 and
1999. In the most recent decade, 1999 to 2009, it ranked 347th in income growth, near
the bottom of the income performance rankings.
The fluctuations for Midland, Texas, are even more dramatic. While this metro
area ranked 14th overall, in the first (1969 to 1979) and last (1999 to 2009) decades it
ranked second and fourth, respectively, among the metro areas, while in the middle two
decades it ranked 356th and 248th, respectively. Midland’s roller-coaster ride is in large
part the consequence of the vagaries of the market for petroleum.
In the most recent decade, 1999 to 2009, the Detroit area had one of the weakest
performances, ranking 359th out of 366 metro areas. But in the preceding ten years
(1989 to 1999), Detroit was in the top quartile in terms of income growth, ranking 85th
among the metro areas. These results show that any one decade is not necessarily
prologue to the next decade. Not surprisingly, an area’s performance over twenty-year
intervals, shown in table 2, tends to be more stable, although even here there is
substantial variation over time in the economic performance of some regions.
1These characteristics include geography (Midwest-Northeast region), similar size (population) in 1969, and concentration of earnings in manufacturing in 1969 (location quotients exceeding one, based on the private nonfarm sector).
Table 1. Change in Personal Income Minus Transfers Per Capita (2009$), 1969 to 2009: 10-Year Intervals Metropolitan Statistical Area 1969–2009 Rank 1969–1979 Rank 1979–1989 Rank 1989–1999 Rank 1999–2009 Rank Metro Areas with Largest Increases, 1969–2009 Bridgeport-Stamford-Norwalk, CT $29,010 1 $ 4,745 73 $15,783 1 $12,811 2 –$ 4,329 347 Washington-Arlington-Alexandria, DC-VA-MD-WV 23,450 2
6,106 26 8,865 10 4,605 82 3,875 27
Naples-Marco Island, FL 22,913 3 1,100 340 13,440 2 2,983 186 5,389 16 Boston-Cambridge-Quincy, MA-NH 21,520 4 2,518 268 10,545 3 7,085 14 1,371 111 Sebastian-Vero Beach, FL 21,319 5 5,372 41 10,323 4 4,658 77 965 129 San Francisco-Oakland-Fremont, CA 21,210 6 6,447 21 5,140 77 10,657 4 –1,035 259 Jacksonville, NC 21,118 7 1,774 319 4,475 107 3,491 144 11,378 2 Boulder, CO 20,870 8 4,057 123 6,294 37 8,508 6 2,011 78 San Jose-Sunnyvale-Santa Clara, CA 20,386 9 7,467 9 4,876 86 13,825 1 –5,781 363 Lafayette, LA 20,127 10 9,413 3 –1,175 343 4,655 79 7,234 7 Charlottesville, VA 18,935 11 2,749 248 8,514 13 4,274 92 3,396 31 Santa Fe, NM 18,341 12 4,354 96 5,879 48 5,220 52 2,888 44 Sioux Falls, SD 18,324 13 6,671 19 1,355 271 7,207 10 3,091 40 Midland, TX 18,114 14 10,324 2 –2,440 356 2,196 248 8,034 4 Houma-Bayou Cane-Thibodaux, LA 17,932 15 7,533 8 –4,150 361 4,028 105 10,521 3 Houston-Sugar Land-Baytown, TX 17,906 16 6,600 20 2,249 227 8,000 7 1,057 123 Barnstable Town, MA 17,840 17 2,082 305 7,798 20 7,148 13 812 140 Trenton-Ewing, NJ 17,832 18 5,484 38 9,152 9 2,991 185 205 189 Baltimore-Towson, MD 17,759 19 4,575 83 5,549 61 3,751 126 3,884 26 Santa Cruz-Watsonville, CA 17,751 20 5,863 31 3,198 164 10,641 5 –1,950 298 Metro Areas with Smallest Increases, 1969–2009 Riverside-San Bernardino-Ontario, CA 2,702 347 4,291 101 1,492 260 –623 352 –2,458 312 Michigan City-La Porte, IN 2,559 348 4,134 118 –1,061 341 1,747 277 –2,261 309 Youngstown-Warren-Boardman, OH-PA 2,549 349 2,948 223 422 307 1,520 290 –2,342 310 Longview, WA 2,443 350 4,110 119 –778 334 589 330 –1,478 278 Visalia-Porterville, CA 2,283 351 4,929 62 –2,965 357 780 325 –461 227 Hanford-Corcoran, CA 2,149 352 6,096 27 –3,519 359 –2,671 365 2,242 69 Bakersfield-Delano, CA 2,143 353 5,307 44 –2,118 352 –1,654 361 607 162 Madera-Chowchilla, CA 1,921 354 8,929 5 –5,748 363 –1,520 360 260 182 Anderson, IN 1,723 355 2,910 228 1,368 270 1,670 281 –4,224 346 Saginaw-Saginaw Township North, MI 1,596 356 4,187 115 –990 337 2,354 241 –3,956 340 Mansfield, OH 1,384 357 1,861 314 1,899 240 486 334 –2,863 322 Stockton, CA 1,277 358 2,834 238 –693 332 1,383 301 –2,247 308 Elkhart-Goshen, IN 1,157 359 487 354 3,829 135 2,351 242 –5,510 360 Yuma, AZ 1,116 360 3,145 202 –632 331 –2,490 364 1,093 121
Table 1 continued. Change in Personal Income Minus Transfers Per Capita (2009$), 1969 to 2009: 10-Year Intervals
Area 1969–2009 Rank 1969–1979 Rank 1979–1989 Rank 1989–1999 Rank
1999–2009 Rank Metro Areas with Smallest Increases, 1969–2009 (continued) Muskegon-Norton Shores, MI 1,046 361 1,403 331 133 316 3,304 163 –3,793 336 Jackson, MI 948 362 1,745 320 157 314 2,895 194 –3,850 337 El Centro, CA 238 363 6,897 16 –3,871 360 –3,449 366 661 154 Merced, CA 21 364 3,065 209 –921 336 –1,427 358 –695 241 Flint, MI –1,780 365 3,488 167 –1,282 345 2,547 222 –6,533 366 Lake Havasu City-Kingman, AZ –3,222 366 –1,010 365 370 308 –1,216 356 –1,365 273 Metro Areas with Characteristics Comparable to Detroit Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 15,055 47 2,742 250 6,374 35 4,206 97 1,732 93 Hartford-West Hartford-East Hartford, CT 14,804 49 3,247 193 9,269 8 942 318 1,347 112 St. Louis, MO-IL 13,495 75 3,017 216 4,737 92 5,112 59 629 159 Pittsburgh, PA 12,118 102 4,270 106 2,526 207 4,719 74 603 163 Chicago-Joliet-Naperville, IL-IN-WI 11,941 108 3,582 159 3,568 144 5,196 53 –405 224 Cincinnati-Middletown, OH-KY-IN 11,741 114 2,557 265 4,113 120 6,183 28 –1,113 263 Milwaukee-Waukesha-West Allis, WI 11,625 118 3,777 146 2,464 214 5,531 43 –147 209 Columbus, OH 11,403 124 2,862 235 5,174 75 5,168 54 –1,802 294 Indianapolis-Carmel, IN 10,665 153 3,004 218 4,593 101 5,275 51 –2,207 307 Providence-New Bedford-Fall River, RI-MA 10,641 155 1,902 313 6,033 41 1,600 283 1,106 120 Cleveland-Elyria-Mentor, OH 7,213 260 3,454 170 1,370 268 3,815 119 –1,426 277 Buffalo-Niagara Falls, NY 6,487 281 2,451 273 2,319 222 1,066 316 652 156 Detroit-Warren-Livonia, MI 5,558 303 2,926 226 3,407 152 4,488 85 –5,263 359
Table 2. Change in Personal Income Minus Transfers Per Capita (2009$), 1969 to 2009: 20-Year Intervals Metropolitan Statistical Area 1969–2009 Rank 1969–1989 Rank 1989–2009 Rank Metro Areas with Largest Increases, 1969–2009 Bridgeport-Stamford-Norwalk, CT $29,010 1 $20,528 1 $8,482 23 Washington-Arlington-Alexandria, DC-VA-MD-WV 23,450 2 14,971 4 8,479 24 Naples-Marco Island, FL 22,913 3 14,540 6 8,372 26 Boston-Cambridge-Quincy, MA-NH 21,520 4 13,063 9 8,456 25 Sebastian-Vero Beach, FL 21,319 5 15,696 2 5,623 87 San Francisco-Oakland-Fremont, CA 21,210 6 11,587 15 9,622 15 Jacksonville, NC 21,118 7 6,249 188 14,869 1 Boulder, CO 20,870 8 10,351 27 10,519 8 San Jose-Sunnyvale-Santa Clara, CA 20,386 9 12,343 12 8,043 30 Lafayette, LA 20,127 10 8,238 83 11,889 4 Charlottesville, VA 18,935 11 11,264 18 7,671 35 Santa Fe, NM 18,341 12 10,233 30 8,108 28 Sioux Falls, SD 18,324 13 8,026 91 10,298 11 Midland, TX 18,114 14 7,884 97 10,230 13 Houma-Bayou Cane-Thibodaux, LA 17,932 15 3,383 319 14,549 2 Houston-Sugar Land-Baytown, TX 17,906 16 8,849 61 9,057 16 Barnstable Town, MA 17,840 17 9,880 34 7,960 31 Trenton-Ewing, NJ 17,832 18 14,637 5 3,196 179 Baltimore-Towson, MD 17,759 19 10,124 31 7,635 37 Santa Cruz-Watsonville, CA 17,751 20 9,061 54 8,690 20 Metro Areas with Smallest Increases, 1969–2009 Riverside-San Bernardino-Ontario, CA 2,702 347 5,782 214 –3,081 361 Michigan City-La Porte, IN 2,559 348 3,073 333 –514 326 Youngstown-Warren-Boardman, OH-PA 2,549 349 3,370 320 –821 331 Longview, WA 2,443 350 3,332 321 –889 333 Visalia-Porterville, CA 2,283 351 1,964 357 319 303 Hanford-Corcoran, CA 2,149 352 2,578 344 –429 321 Bakersfield-Delano, CA 2,143 353 3,190 330 –1,047 336 Madera-Chowchilla, CA 1,921 354 3,181 331 –1,260 340 Anderson, IN 1,723 355 4,278 289 –2,554 356 Saginaw-Saginaw Township North, MI 1,596 356 3,197 329 –1,602 348 Mansfield, OH 1,384 357 3,761 307 –2,377 354 Stockton, CA 1,277 358 2,142 353 –865 332 Elkhart-Goshen, IN 1,157 359 4,317 287 –3,160 362 Yuma, AZ 1,116 360 2,513 346 –1,398 342 Muskegon-Norton Shores, MI 1,046 361 1,536 363 –490 325
Table 2 continued. Change in Personal Income Minus Transfers Per Capita (2009$), 1969 to 2009: 20-Year Intervals Area 1969–2009 Rank 1969–1989 Rank 1989–2009 Rank Metro Areas with Smallest Increases, 1969–2009 (continued) Jackson, MI 948 362 1,902 359 –954 334 El Centro, CA 238 363 3,026 334 –2,788 358 Merced, CA 21 364 2,143 352 –2,122 352 Flint, MI –1,780 365 2,206 350 –3,986 365 Lake Havasu City-Kingman, AZ –3,222 366 –641 366 –2,581 357 Metro Areas with Characteristics Comparable to Detroit Philadelphia-Camden-Wilmington, PA-NJ-DE-MD 15,055 47 9,116 50 5,939 73 Hartford-West Hartford-East Hartford, CT 14,804 49 12,515 10 2,289 228 St. Louis, MO-IL 13,495 75 7,754 103 5,741 83 Pittsburgh, PA 12,118 102 6,796 149 5,322 97 Chicago-Joliet-Naperville, IL-IN-WI 11,941 108 7,150 129 4,791 122 Cincinnati-Middletown, OH-KY-IN 11,741 114 6,671 157 5,070 106 Milwaukee-Waukesha-West Allis, WI 11,625 118 6,241 189 5,384 96 Columbus, OH 11,403 124 8,036 90 3,367 170 Indianapolis-Carmel, IN 10,665 153 7,597 107 3,068 186 Providence-New Bedford-Fall River, RI-MA 10,641 155 7,935 95 2,706 203 Cleveland-Elyria-Mentor, OH 7,213 260 4,824 264 2,388 223 Buffalo-Niagara Falls, NY 6,487 281 4,769 265 1,717 257 Detroit-Warren-Livonia, MI 5,558 303 6,334 181 –775 330
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3. Regional results can differ from those estimated over the nation as a whole in
ways that are worth investigating. In our case, the original interest was in the “rust belt”
region, which our expert panel suggested comprised the Midwest and Northeast census
regions. Our analysis thus includes metro areas in three regions of the country: the nation
in total; the combined Midwest-Northeast region; and the balance of the United States.
4. The most time-consuming task in the project was constructing new or
improved data series that were not available for previous studies, yet seemed promising
contributors to our equation estimates. Much of the information came from raw records
and also involved hand-entering the data from the earlier periods. Guidance on “picking
our spots” for this investment of time was provided by suggestions from previous studies
or guidance from our experts. The following areas were targeted: (i) the environment for
innovation, particularly as measured by patents, where we individually processed two-
million-plus raw records provided by the U.S. Patent Office, subdividing the annual data
by four major industry categories for every metro area in the country. We also
constructed various series for university research activity, including research
expenditures and college enrollment; (ii) metro area crime count and rate, also subdivided
by year into violent and property crimes, from raw records provided by the FBI; (iii) state
and local tax revenue by metro area; and (iv) an economic diversity index. We used a
field-tested algorithm we designed to fill in missing data values due to disclosure issues
for employment and income, thus enabling us to have a full set of data for these items and
those derived from them. We also discovered a scale published by the U.S. Department
of Agriculture on physical natural amenities by county, which does not appear to have
been considered in previous studies.
5. This whole exercise in seeking out drivers and strategies for success comes
with a cautionary note, and that is, beware the “tyranny” (constraints) of best practices.
Some structures and approaches may be well-suited to some places and not to others. To
gain insight into which areas might be outliers to the fit of the general model, we carried
out a simple analysis of the (studentized) residuals to identify the metropolitan areas that
might qualify. To push the questioning of the approach one step further, we note that a
few members of our expert panel felt that specific public policies undertaken have had
little effect at all. They opined instead that success rests with decisions made by
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individual firms based on their products and process, and even on location decisions
motivated by personal preferences of company leadership. All of our experts agree,
however, that the data can be instructive.
Data Definitions and Sources The extension of the data base to encompass four decades has been discussed in
point (1) above, and the construction of several previously unavailable series is touched
on in point (4) above. Issues of measurement will be raised for the individual series,
when appropriate, in stepping through the regression model and its results. The
definitions and sources of the variables used in the study are summarized in table 3, and
descriptive statistics for the variables over successive twenty-year periods are
documented in table 4.
Table 3. Variable Definitions and Sources Variables Definition Source Dependent variables Change in real per capita personal income minus transfer payments
Change in real per capita personal income minus transfer payments
Bureau of Economic Analysis
% Change in employment Percentage change in employment Bureau of Economic Analysis Independent variables used in regressions Per capita personal income minus transfer payments Per capita personal income minus transfer payments Bureau of Economic Analysis Natural log of MSA population Natural log of MSA population Bureau of Economic Analysis Share of agriculture in total employment Ratio of agricultural employment to total employment Bureau of Economic Analysis Share of mining in total employment Ratio of mining employment to total employment Bureau of Economic Analysis Share of construction in total employment Ratio of construction employment to total employment Bureau of Economic Analysis Share of manufacturing in total employment Ratio of manufacturing employment to total
employment Bureau of Economic Analysis
Share of finance, ins. in total employment Ratio of finance, ins. employment to total employment Bureau of Economic Analysis Share of government in total employment, excluding military
Ratio of government excluding military employment to total employment
Bureau of Economic Analysis
Share of military in total employment Ratio of military employment to total employment Bureau of Economic Analysis Share of mining in total earnings Ratio of mining earnings to total earnings Bureau of Economic Analysis Share of construction in total earnings Ratio of construction earnings to total earnings Bureau of Economic Analysis Share of durables in total earnings Ratio of durables earnings to total earnings Bureau of Economic Analysis Share of nondurables in total earnings Ratio of nondurables earnings to total earnings Bureau of Economic Analysis Share of finance, ins. in total earnings Ratio of finance, ins. earnings to total earnings Bureau of Economic Analysis Share of health services in total earnings Ratio of health services earnings to total earnings Bureau of Economic Analysis Share of military in total earnings Ratio of military earnings to total earnings Bureau of Economic Analysis Share of government in total earnings, excluding military Ratio of government excluding military earnings to
total earnings Bureau of Economic Analysis
Share of population with bachelor's degree or higher Percentage of population with bachelor's degree or higher
U.S. Census Bureau
Share of foreign-born Percentage of foreign-born population U.S. Census Bureau Share of poverty Percentage of population in poverty U.S. Census Bureau Chemical patents per 1,000 Number of chemical-related patents per 1,000
population U.S. Patent & Trademark Office
IT patents per 1,000 Number of IT (information technology) related patents per 1,000 population
U.S. Patent & Trademark Office
Industrial excluding motor vehicle patents per 1,000 Number of industrial excluding motor vehicle related patents per 1,000 population
U.S. Patent & Trademark Office
Motor vehicle patents per 1,000 Number of motor vehicle related patents per 1,000 population
U.S. Patent & Trademark Office
Total crimes per 1,000 Total crime (violent and property) counts per 1,000 population
FBI Crime Report
Table 3 continued. Variable Definitions and Sources Variables Definition Source Independent variables used in regressions (continued) State & local government tax percentage Ratio of state and local government tax revenue to
personal income U.S. Census Bureau
Share of population age 65 or more Percentage of the population age 65 and over U.S. Census Bureau College enrollments per 1,000 Number of postsecondary school enrollments
per 1,000 population National Center for Education Statistics
Research expenditures per 1,000,000 University research expenditures per 1,000,000 population
National Center for Education Statistics
Airport passengers per capita Number of enplaned passengers per capita Bureau of Transportation Statistics July temperature minus January temperature July temperature minus January temperature Weather Underground Right-to-work dummy Right-to-work state dummy variable National Right-to-Work Legal
Defense Foundation Natural Amenities Scale Physical natural amenity index U.S. Department of Agriculture Southwest(SW=1, WT= –1,Oth=0) Regional dummy variables U.S. Census Bureau Southeast(SE=1, WT= –1,Oth=0) Regional dummy variables U.S. Census Bureau Midwest(MW=1, WT= –1,Oth=0) Regional dummy variables U.S. Census Bureau Northeast(NE=1, WT= –1,Oth=0) Regional dummy variables U.S. Census Bureau Independent variables tried in regressions Diversity Index Index of the MSA’s diversity among industries Calculated using Bureau of
Economic Analysis employment data Number of postsecondary schools Number of postsecondary schools in MSA National Center for Education Statistics Share of population age 24 or less Percentage of the population age 24 or younger U.S. Census Bureau January temperature Average January temperature Weather Underground July temperature Average July temperature Weather Underground Violent crimes per 1,000 Violent crime counts per 1,000 population FBI Crime Report Property crimes per 1,000 Property crime counts per 1,000 population FBI Crime Report Air freight per capita Air freight (tons) per capita Bureau of Transportation Statistics Public research expenditures per 1,000,000 Public university research expenditures per 1,000,000
population National Center for Education Statistics
Private research expenditures per 1,000,000 Private university research expenditures per 1,000,000 population
National Center for Education Statistics
Table 4. Descriptive Statistics for the Variables: 20-Year Intervals Variables Mean Std Dev Mean Std Dev
Dependent variables 1969–1989 1989–2009 Change, real per capita personal income minus transfer payments 3490.51 1798.95 3356.87 2747.95 % Change in employment 69.28 60.25 34.43 29.72 Independent variables used in regressions 1969/1970 1989/1990 Per capita personal income minus transfer payments 18573.020 3546.040 25022.120 5132.510 Natural log of MSA Population 12.145 1.110 12.434 1.052 Share of agriculture in total employment 0.060 0.055 0.040 0.039 Share of mining in total employment 0.009 0.024 0.008 0.021 Share of construction in total employment 0.052 0.016 0.054 0.015 Share of manufacturing in total employment 0.185 0.099 0.126 0.070 Share of finance, ins. in total employment 0.057 0.020 0.065 0.020 Share of government excl. military in total employment 0.153 0.069 0.145 0.055 Share of military in total employment 0.052 0.092 0.030 0.059 Share of mining in total earnings 1.197 3.134 1.241 3.368 Share of construction in total earnings 7.021 2.534 6.310 2.091 Share of durables in total earnings 15.495 12.472 12.872 9.790 Share of nondurables in total earnings 10.272 8.117 8.398 6.443 Share of finance, ins. in total earnings 4.112 1.980 4.453 2.396 Share of health services in total earnings 4.216 1.689 8.037 2.686 Share of military in total earnings 4.385 9.588 3.426 8.133 Share of government excl. military in total earnings 16.485 8.450 17.998 7.591 Share of population with bachelor's degree or higher 10.722 4.164 18.926 6.263 Share of foreign-born 2.945 2.806 4.444 5.080 Share of poverty 14.434 6.489 13.708 4.985 Chemical patents per 1,000 0.035 0.096 0.041 0.100 IT patents per 1,000 0.022 0.036 0.036 0.074 Industrial excluding motor vehicle patents per 1,000 0.052 0.039 0.053 0.037 Motor vehicle patents per 1,000 0.016 0.019 0.016 0.023 Total crimes per 1,000 31.015 16.184 53.022 18.482 State & local government tax revenue percentage 10.364 1.495 9.903 1.229 Share of population age 65 or more 9.459 3.219 12.477 3.542 College enrollments per 1,000 51.568 58.082 71.665 58.531 Research expenditures per 1,000,000 13.72 41.08 76.89 21.38 Airport passengers per capita 0.486 0.714 0.846 1.646 July temperature minus January temperature 41.652 10.372 41.652 10.372 Right-to-work dummy 0.470 0.500 0.470 0.500 Natural Amenities Scale 3.810 1.243 3.810 1.243
Table 4 continued. Descriptive Statistics for the Variables: 20-Year Intervals Variables Mean Std Dev Mean Std Dev Independent variables used in regressions (continued) 1969/1970 1989/1990 Southwest(SW=1, WT= –1,Oth=0) –0.087 0.537 –0.087 0.537 Southeast(SE=1, WT= –1,Oth=0) 0.120 0.700 0.120 0.700 Midwest(MW=1, WT= –1,Oth=0) 0.063 0.665 0.063 0.665 Northeast(NE=1, WT= –1,Oth=0) –0.052 0.573 –0.052 0.573 Independent variables tried in regressions 1969/1970 1989/1990 Air freight (tons) per capita 7.540 18.521 8.263 40.621 Diversity Index 0.212 0.041 0.213 0.029 Number of postsecondary schools 5.776 13.082 23.161 50.891 Share of population age 24 or less 47.782 4.712 37.694 4.455 January temperature 34.395 12.803 34.395 12.803 July temperature 76.046 5.616 76.046 5.616 Public university research expenditures per 1,000,000 12.28 40.68 67.40 196.48 Private university research expenditures per 1,000,000 1.45 6.45 9.49 60.92 Violent crimes per 1,000 2.03 1.41 5.35 3.25 Property crimes per 1,000 29.42 14.95 48.05 15.91
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Previous Studies The seminal research study on metropolitan areas that follows the general
approach we have chosen, that being a regression analysis of the evolution of local
economies in the United States, is Blumenthal, Wolman, and Hill (2009). As in our
study, the authors examine the drivers of metro economic performance, in their case
modeling the change in Gross Metropolitan Product and employment for the single
decade of the 1990s and over 244 metro areas that have a large central urban core. Our
study tests many of the same drivers as are found in their analysis, but other measures are
unique to one model or the other. They find that initial-year economic structure,
agglomeration economies (proxied by size of population), human capital (measured by
share of the population with a bachelor’s degree or more), and presence of state right-to-
work laws are positively and significantly related to Gross Metropolitan Product and
employment growth, while the economic age of the area, percentage of black residents,
and average wage at the beginning of the period are negatively and significantly related
to both.
Blumenthal et al. make a particular point of the vulnerability of these models to
the problem of omitted variables because of the challenging measurement issues
confronting those who take on data-intensive research on small economies. They
demonstrate this point, and their contribution here, by adding three variables of their own
to the model and observing that regional dummy variables, included to control for spatial
autocorrelation and other possible omitted variables that may vary by region, are reduced
in significance. The effects of a few other variables in their specification were contrary
to their expectations, as is common in this research, and which they attribute in part to the
time interval of the 1990s over which the model is estimated.
In our study, we attempt to build on the foundation provided by Blumenthal et al.
in the manner outlined in the introductory section. Our primary focus is on
understanding the evolution of U.S. metro area economies over a period longer than a
decade, first by extending the time range of the measures over several decades and then
by seeking out behavioral differences over intervals of time among a full set of 366 U.S.
metropolitan areas. One point of interest to us is comparisons between the earlier and
later periods, to seek out tendencies on what might be—or might not be—prologue to
future outcomes. We also rise to the challenge of Blumenthal et al. to fill in some of the
measurement gaps so as to contribute to a more complete structural specification of metro
15
area econometric models, without simultaneously sacrificing the fit period. Finally, we
supplement previous research by dissecting the model geographically, both by exploring
differences in fit for a few selected regions, and by investigating which metro areas are
outliers to the aggregate results estimated over 366 areas.
Beyond the Blumenthal et al. article there has been voluminous academic research
on American urban areas, with much of the contemporary research exploring differences
in growth between cities and suburbs. Of greater relevance to our current work is the
evolution of metro area economies over time, and the drivers, both structural and policy-
related, that appear to underlie their relative success patterns. Pack (2002), for instance,
argues that urban growth is not simply a matter of choice (policy or market forces), but
also of idiosyncrasy, fate, and history. This stems from her findings that regional growth
varies widely and is vulnerable to shocks, and thus policies based on the experience of
earlier periods are often inappropriate. Glaeser and Shapiro (2001), on the other hand,
find that urban growth in the 1990s looked extremely similar to urban growth during the
prior post-World War II decades, and was determined by three large trends: (1) faster
growth in cities with strong human capital bases; (2) movement to warmer, drier places;
and (3) faster growth in cities built around the automobile. To add a wrinkle, Erickcek
and McKinney (2009) raise the possibility that smaller metro areas might behave
differently than larger urban areas—a possibility that we plan to explore with our data set
in future research.
A number of articles in the literature posit specific drivers as key contributors to
urban area economic growth. A scan of those articles, together with suggestions from our
expert panel, led us to consider the following as potential explanatory factors of national
urban growth: urban structure (initial conditions), industry (economic) structure,
demographics, innovative environment, amenities, regional effects, and a series of
measures susceptible to shorter-term policy initiatives. The last of these include such
persistent state and local budget issues as education, crime, taxes and business climate,
and connectivity to the global economy. Several of the other factors, such as industry
structure or demographics, have sufficiently long time horizons over which significant
change can occur, making them suitable as control variables in the shorter term. For all
of the concepts, the challenge is to come up with proxy measures, and to understand the
limitations of the measures and what’s in play and what’s not in the policy landscape.
16
Each of these concepts and their proxy measures will be addressed more fully in
the discussion of our model and its estimation. Here we first consider previous findings
on the efficacy of certain drivers related to the economic performance of metro areas.
Initial Conditions
Population size at the beginning of the period has been used as a measure of
initial conditions in local economies. Glaeser and Shapiro (2001) find no statistically
significant relationship between initial metro area population and economic growth. In
contrast, Blumenthal et al. find a significantly positive effect of population size in 1990
on metro area growth over the following decade, which they interpret as reflecting the
agglomeration economy advantages of large areas, including productivity advantages.
Glaeser, Kolko, and Saiz (2001) argue that although urban economies have traditionally
been viewed as having advantages in production, as firms have become less bound by
location, the success of cities may hinge more and more on their role as centers of
consumption.
Industry Structure
Several decades of forecasting economic activity for regional and local economies
has convinced us that differing industry structure among these areas is at the crux of their
differing economic outcomes over varying periods of time. This is undoubtedly the
source of much of the dramatic movement across decades in economic outcome rankings
documented in tables 1 and 2 in a previous section. In the literature on urban economies,
the most frequently tested variable measuring the effects of industry structure on
economic performance has been manufacturing’s share of employment or earnings,
which typically is found to be negatively related and is associated with characteristics of
the “old” economy—high-paid, low-skilled activity vulnerable to the spreading global
economy. Glaeser, Scheinkman, and Shleifer (1995), for instance, find that
manufacturing’s share of employment in 1960, the beginning of their observation period,
is negatively related to growth in income and population between 1960 and 1990.
Blumenthal et al. find contrary to these expectations, including their own, a positive
relationship between a metro area’s manufacturing share of employment in 1990 and
growth over the subsequent decade in employment and Gross Metropolitan Product.
They attribute this unexpected result, correctly in our view, to the fact that their
estimation period coincides with manufacturing’s relatively more favorable prospects
over the 1990s. They also include in their consideration of industry structure the share of
17
employment in the finance-insurance-real-estate sector, and find the measure to be
positively related to the change in Gross Metropolitan Product, which they see as an
outcome of a higher-value service sector orientation.
Demographics
On demographics, a frequent focus in the literature is on the racial composition of
the population and its influence on the relative success of local economic outcomes. The
measure most commonly analyzed is the percentage of the target population that is
African-American (excluding Hispanics), typically strongly related to the area’s poverty
status, and hypothesized to contribute to weaker economic outcomes. Blumenthal et al.
find that initial racial demographics did affect economic performance negatively over the
1990s. Glaeser, Scheinkman, and Shleifer (1995) find that racial composition and
segregation were uncorrelated with urban growth across all cities between 1960 and
1990, but in cities with large nonwhite communities, segregation is positively related to
population growth. In terms of the more general measure of poverty across all racial
lines, Partridge and Rickman (2008) find that metropolitan-wide job growth is associated
with a stronger safety net in medium and smaller metro areas.
Blumenthal et al. include measures for the proportion of the population that is not
of traditional working age (both those age 24 years or younger and those 65 years or
older) in the initial period (1990), observing that their labor force participation rates
remain significantly lower than those of the prime working-age cohort. They find that
these measures of demographic structure do not affect economic performance over their
period of estimation.
Innovative Environment
An innovative environment is increasingly viewed to be an important driver of
economic growth as the New Economy evolves—that is, among other advantages,
scientific development promotes economic development. The classic example is the
growth of Research Triangle Park in North Carolina, the largest research park in the
United States in terms of both employees and acreage. Link and Scott (2000) provide an
economic history of the Park, from vision through its eminent status at the turn of this
century. Aided by an analytic model of the Park’s growth, they argue that, over time,
new companies adopted the area’s innovative environment, and their success can be
explained by the continuity of entrepreneurial leadership enjoyed there for over thirty
years. Glaeser, Kerr, and Ponzetto (2009) suggest that entrepreneurship is higher when
18
fixed costs are lower and when there are more entrepreneurial people, which in turn have
some relationship to smaller establishment size.
Such studies highlight the complexity of the innovative and entrepreneurial
environment, and particularly of measuring it adequately for small, open economies.
Most common in past research is to narrow the focus to the presence of research
institutions, particularly those associated with universities. Results have been mixed.
Pack (2002), for instance, finds a positive relationship between the presence of
universities and per capita income growth, whereas Blumenthal et al. report that the
presence of very active research universities is not statistically significant in either of
their economic outcome models. Goldstein and Renault (2004) posit that the research
and technology creation functions of universities generate significant knowledge
spillovers that result in enhanced regional economic development that otherwise would
not occur—but that the contribution is small compared with other factors.
Fundamentally, though, the problem is to capture in well-defined measures the path
between university research activity and measures of economic outcome.
Variables Related to Policy Decisions
For didactic purposes, we sort several economic drivers discussed in the literature
and group them under the general heading of policy-related variables. These
hypothesized drivers include those that have been central to contemporary budget
deliberations across state and local governments. Among this group of drivers is
educational attainment, one of the most scrutinized concepts in the recent literature in
terms of its relationship with regional economic performance. As a policy matter, former
Chicago Fed President and CEO Michael Moskow notes (in Mattoon, 2006) that the
relationships among education, productivity, and economic growth have never been
clearer, but financial support for higher education has waned while costs continue to rise.
He states that the perception of higher education as an important public good has eroded,
as it is increasingly seen as a private good with the benefits accruing to the student in the
form of higher wages and quality of life.
Glaeser and Saiz (2004) make perhaps the strongest summary statement in the
academic literature on the value of education to the community, observing that for more
than a century, educated cities have grown more quickly than comparable cities with less
human capital. Adding rigor to this statement is their evaluation that the claim survives a
battery of other control variables, metropolitan area fixed effects, and tests for reverse
19
causality. They argue that skilled cities are growing because they are becoming more
economically productive, not because they are becoming more attractive places to live.
They suggest that in large part, the success of skilled cities results from their being better
at adapting to economic shocks.
Glazer and Grimes (2010) lend support to the notion that educational attainment is
a predictor of regional economic success. They find that almost all states in the highest
per capita income category are over-concentrated compared with the nation in the
proportion of wages coming from knowledge-based industries; they have a high
proportion of adults with a four-year degree or more; they have a big metropolitan area
with even higher per capita income than the state; and, in that big metropolitan area, a
high proportion of the residents have a four-year degree or more. Blumenthal et al. also
find that the share of the population with a bachelor’s degree or higher is positively and
significantly related to Gross Metropolitan Product and employment growth.
An area’s business climate is often identified as an important factor in its
economic success. One element of the business environment clearly under the umbrella
of the policy rubric is state and local taxation. Monchuk, Miranowski, Hayes, and
Babcock (2007) are among those who find that state and local tax burdens have important
impacts on economic growth, but the literature is not definitive on this issue. Part of the
lack of clarity here is that the tax structure can be complex and finding a way to represent
it is fraught with measurement issues.
One of the most controversial areas related to the environment for business is
right-to-work legislation, which gives employees the option of working in establishments
without having to join a union, even if co-workers are union members. There is much
disagreement about such legislation, with some saying it’s essential for business success
and others saying it’s not necessary and may even be detrimental. Bartik (1985) finds a
positive effect on the location decisions of manufacturing plants associated with the
presence of right-to-work laws, and Tannenwald (1997) also finds a positive relationship
between such laws and economic activity, as do Blumenthal et al.2 But the authors of the
last article pose as one possible interpretation of their result, “the presence or enactment
of right-to-work legislation is a proxy for a more general positive business-friendly
2The results of right-to-work legislation could depend on whether the dependent variable is employment (positive sign) or income per capita (negative sign), if the legislation is viewed as a union-avoidance measure. But it might take some time after the enactment of the legislation for the effect to be reflected in the results.
20
political climate in the state that transcends the issue of union organization” (p. 615). In
the same vein, Grimes and Ray (1988) point out that differences among states in the
presence or absence of these laws may reflect more general social, economic, and
political differences.
Another salient point is that, with the economy becoming increasingly more
global, attention has turned to the connectivity of localities to the outside world, primarily
through air traffic. Both Brueckner (2003) and Green (2007) find a positive relationship
between some measure of airport traffic and economic activity, and Blumenthal et al.
observe a positive and significant relationship with employment growth. Blonigen and
Cristea (2012) find that air service has a positive and significant effect on regional
growth, with the magnitude of the effects differing by the size of the metropolitan area
and its industrial specialization.
Amenities
Several studies in the literature have touted amenity-rich environments as a
catalyst for local economic growth, viewing them as magnets for attracting businesses
and workers and thus creating jobs. Amenities can be either natural or human-created.
The latter would seem to be important, but measurement has proven difficult, and thus
quantitative analysis is sparse. More common is the assessment of natural amenities,
typically using some measure of local climate as a gauge. For example, Glaeser and
Shapiro (2001) and Blumenthal et al. find some association between warmer climates and
urban growth. Dorfman, Partridge, and Galloway (2011) find that natural amenities
matter most in the employment patterns for high-skill workers in the subset of U.S.
counties that are micropolitan, where their presence can be a deciding factor in location
decisions. Deller, Lledo, and Marcouiller (2008), armed with a more sophisticated model
for natural amenities, conclude that higher-amenity areas do experience faster growth, but
that some level of value-added development may be required to realize that growth.
(Bribes, such as tax incentives, are not classified as amenities in the literature.)
Corruption
In our meetings on this project, the topic of corruption came up periodically.
Glaeser and Saks (2004) find that more educated states, and to a lesser degree, richer
states, have less corruption. They observe a weak negative relationship between
corruption and employment and income growth, and conclude that the correlation
21
between development and good political outcomes occurs because more education
improves political institutions.
General Model and Estimating Equations
With the number of timeseries variables we constructed and assembled, we
initially had the intention of using a panel study approach. Due to the potential
importance of variables constructed from the Bureau of the Census which were not
available annually, however, we settled on cross-section analysis. The independent
variable measures are included in the equations at the beginning of each time interval,
and the dependent variables measure the change over the time interval. We estimated the
equations over four sequential ten-year intervals from 1969 to 2009, and two sequential
twenty-year intervals over the same period. The specification of the model follows.
Dependent Variables
Both our panel of experts and a review of the literature led us to choose two
measures, the change in inflation-adjusted (real) income per capita and the change in
employment, as our primary indicators of an area’s economic performance, although
many other gauges were put forward.3 The change in income is meant to reflect the
changing wealth of an area, while employment change is a measure of the variation in its
size. Although the first measure may appear to be more compelling, employment growth
is often viewed as desirable in its own right.4 And there seems to be some consensus
among researchers that there should be multiple measures of success.
We tested three measures of change in real income in our estimated equations:
(1) personal income per capita; (2) personal income minus transfer payments per capita;
and (3) earnings per capita. For each measure, we examined both the dollar change and
the percentage change. The findings for all three income measures and two functional
forms were broadly similar, and we settled on one measure for reporting in the paper: the
real dollar change in personal income minus transfers per capita.
The values for real income are expressed in 2009 dollars, using as price deflators
the area- or region-specific consumer price index for all urban consumers. If a
3Examples include aggregate value of land, population change, and amount of money being invested in the area. 4See, for example, Blumenthal et al. (p. 606). If the per capita earnings of an area increase while the population decreases due to poorer people being pushed out, there is some question as to whether that should be viewed as a success. Some have also argued that success represents a positive deviation from expectations, but such a concept is difficult to measure.
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metropolitan area was part of a consolidated statistical area that had its own price index,
then that area-specific index was used. If that was not the case, then the appropriate
regional (Northeast, South, Midwest, or West) price index was used.
For employment as the dependent variable, we experimented with both total
employment and private-sector employment, each in the form of absolute and percentage
changes. We settled on reporting the results for the percentage change in total metro
area employment.
Independent Variables
The independent variables that form the general model to be tested and the
rationale for their inclusion were largely itemized in our review of previous studies. In
sum, the model can be expressed as follows:
∆Y = α + β (initial conditions) + γ (industry structure) + δ (demographics) + π
(innovative environment) + μ (short-term policy variables) + ζ (amenities) + η (regional
effects) + ε,
where: ∆Y = either the real dollar change in personal income minus transfers per capita,
or the percentage change in total metro area employment; α, β, γ, δ, π, μ, ζ, and η =
coefficients (α = the intercept of the equation); ε = the error term of the equation; and the
independent variable concepts are shown in parentheses. Among the independent
variable concepts, not itemized in the section on previous studies are the regional effects,
which are the typical dummy variables included in such studies to account for spatial
autocorrelation and to control for omitted variables that may vary by region.5
The proxy measures that represent each of the right-hand side variables in the
general equation are as follows (with the signs expected a priori on the associated
coefficients in parentheses):
Initial Conditions
• Personal income per capita excluding transfers at the beginning of the period
for the income equation (signs indeterminate)
• Population (log) at the beginning of the period (signs indeterminate depending
on the effect on performance of agglomeration economies)
5See, for example, Blumenthal et al., pp. 612–13.
23
Industry Structure
• A set of variables representing the importance of an industry in a metro area,
measured by employment or earnings share (signs determined by industry
conditions in the estimation period)
Demographics
• Share of the population that is foreign-born (positive sign for employment,
negative sign for income)
• Share of the population in poverty (negative signs)
• Share of the population 65 years of age or older (negative signs)
Innovative Environment
• Patents (utility) awarded per 1,000 population, subdivided into four industry
groupings: chemical, information technology, industrial except motor vehicles,
and motor vehicles (positive signs)
• University research expenditures per million population (positive signs)
Short-Term Policy-Related Variables
• Educational attainment: share of the population with a bachelor’s degree or
more (positive signs)
• College enrollment per 1,000 population (indeterminate signs depending on the
dominance of its direct or spinoff effect)
• Total crimes per 1,000 population (negative signs)
• State and local government tax revenue as a percentage of personal income
(negative signs)
• Dummy variable for location in a right-to-work state, value of 1 for presence
(positive signs as indicator of business climate)
• Airport passengers per capita (positive signs)
Amenities
• Temperature extremes: July temperature minus January temperature (negative
signs)
• Natural Amenities Scale (positive signs)
Regional Effects
• Dummy variables for four regions of the country: Southwest, Southeast,
Midwest, and Northeast (indeterminate signs)
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Many of these proxy measures have been introduced in the review of previous
studies. More detail on the rationale for their inclusion in the model and their expected
effects is provided in the discussion of the equation estimation results.
Estimation Results for the National Model The results of estimating our income and employment models over the two most
recent twenty-year periods in our data set, 1969 to 1989 and 1989 to 2009, are
summarized here for 366 metropolitan areas in the nation. (Estimation results for ten-
year intervals are contained in the appendix.) Tests were carried out for
heteroskedasticity, and it was determined that this was not a problem. Recall that the
independent variable measures are included in the equations at the beginning of each time
interval, and the dependent variables measure the change over the time interval.
To gain an initial overall impression of the results, we assembled table 5, a
summary table that shows the signs and significance of the estimated parameters for the
four equations representing the earlier and later periods for both income and employment.
The signs are identified in the cells of the table, with parameter values significant at the
5 percent level or better (based on p-values) indicated by the shaded cells.
About half a dozen observations stand out in table 5. Initial population size has a
positive effect on income growth and a negative effect on employment growth, with most
of the results significant. The three industry structure variables that are generally
significant are mining, much of which is based on the energy sector, and with a switch in
sign between periods for income; finance, which has a significantly positive effect across
the board; and some component of manufacturing. For the innovative environment, both
IT patents and industrial patents (excluding motor vehicles) have a consistently positive
and mostly significant influence over the four models. Among the policy variables, the
two that stand out are crime, which is consistently negative and usually significant; and
the educational attainment variable (share of the population with a bachelor’s degree or
more), which has a consistently positive effect that is significant for income.
It is heartening that a number of the variables highlighted here were among those
that we assembled for this study because they were not previously available but showed
promise of contributing to the analysis. Also encouraging is the last line of the table,
which indicates that the models fit the data quite well, with cross-section R-square
statistics ranging from 0.52 to 0.66.
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The details of the estimation results are shown in table 6 for income and in table 7
for employment. For each time period, the parameter values are beside the variable
names with the p-values shown below in parentheses. The results are reviewed here, for
both income and employment, by independent variable in turn.
26
Table 5. Summary of Parameter Signs and Significance for (1) Change in Personal Income Minus Transfers Per Capita (2009$) and (2) Change in Employment 20-Year Intervals, 1969–2009, United States (1) Income (2) Employment (Shaded entries significant at 5% level) ’69–’89 ’89–’09 ’69–’89 ’89–’09 Intercept – – + + Initial conditions Personal income per capita excluding transfers + + * * Population (log) + + – – Industry structure Share agricultural * * + + Share mining – + – – Share construction – + + + Share manufacturing * * – – Share durables – – * * Share nondurables + – * * Share finance, insurance + + + + Share health services + + * * Share government ex. military – + + + Share military – + – – Demographics Share foreign-born – – + + Share poverty + + + + Share population 65 or more + – + – Innovative environment Chemical patents per 1,000 pop. + – – – IT patents per 1,000 pop. + + + + Industrial ex. motor vehicle patents per 1,000 pop. + + + + Motor vehicle patents per 1,000 pop. – – – – Research expenditures per 1,000,000 pop. – – – + Variables related to policy decisions Share bachelors + + + + + College enrollment per 1,000 pop. – – – – Total crimes per 1,000 pop. – – – – State & local govt. tax % personal income – – – + Dummy right to work – + + + Airport passengers per capita * * + + Amenities July temp. minus Jan. temp. – – – + Natural Amenities Scale * * + + Regional effects Southwest(SW=1, WT= –1,Oth=0) – – + + Southeast(SE=1, WT= –1,Oth=0) + – + – Midwest(MW=1, WT= –1,Oth=0) – + + – Northeast(NE=1, WT= –1,Oth=0) + – + – N 366 366 366 366 R-squared 0.57 0.62 0.66 0.52 *Not included in final equation.
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Table 6. Estimation Results for Change in Personal Income Minus Transfers Per Capita (2009$): 20-Year Intervals, 1969–2009 United States 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Intercept –2662.506 –5859.194 (0.367) (0.117) Initial conditions Personal income per capita excluding transfers 0.262 0.038 (0.000) (0.492) Population (log) 250.455 69.457 (0.042) (0.654) Industry structure Share mining –89.503 246.243 (0.029) (0.000) Share construction –62.803 49.710 (0.218) (0.541) Share durables –35.306 –97.673 (0.044) (0.000) Share nondurables 2.062 –5.373 (0.925) (0.847) Share finance, insurance 197.429 211.060 (0.015) (0.002) Share health services 88.477 134.238 (0.260) (0.024) Share government ex. military –7.407 9.939 (0.719) (0.700) Share military –41.334 144.800 (0.025) (0.000) Demographics Share foreign-born –88.857 –140.852 (0.100) (0.000) Share poverty 28.364 243.674 (0.397) (0.000) Share population 65 or more 80.540 –30.169 (0.110) (0.562) Innovative environment Chemical patents per 1,000 pop. 58.394 –1294.958 (0.958) (0.324) IT patents per 1,000 pop. 8458.215 7058.021 (0.029) (0.001) Industrial ex. motor vehicle patents per 1,000 pop. 2105.581 13391.629 (0.618) (0.007) Motor vehicle patents per 1,000 pop. –6538.296 –7254.799 (0.416) (0.288) Research expenditures per 1,000,000 pop. –7.758 –2.300 (0.044) (0.010) Variables related to policy decisions Share bachelors + 283.681 279.006 (0.000) (0.000) College enrollment per 1,000 pop. –1.845 –13.546 (0.603) (0.000)
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Table 6. Estimation Results for Change in Personal Income Minus Transfers Per Capita (2009$): cont’d. 20-Year Intervals, 1969–2009 United States 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Variables related to policy decisions (continued) Total crimes per 1,000 pop. –15.488 –38.440 (0.089) (0.000) State & local govt. tax % personal income –72.378 –48.135 (0.412) (0.679) Dummy right to work –66.371 1360.275 (0.842) (0.000) Amenities July temp. minus Jan. temp. –34.534 –1.678 (0.037) (0.934) Regional effects Southwest (SW=1, WT= –1,Oth=0) –752.613 –914.908 (0.033) (0.024) Southeast (SE=1, WT= –1,Oth=0) 1289.005 –872.392 (0.000) (0.008) Midwest (MW=1, WT= –1,Oth=0) –108.351 1587.350 (0.722) (0.000) Northeast (NE=1, WT= –1,Oth=0) 1332.890 –17.026 (0.000) (0.965) N 366 366 R-squared 0.57 0.62
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Table 7. Estimation Results for Change in Employment: 20-Year Intervals, 1969–2009 United States 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Intercept 130.497 10.227 (0.042) (0.803) Initial conditions Population (log) –18.652 –6.807 (0.000) (0.000) Industry structure Share agricultural 1.392 0.073 (0.024) (0.864) Share mining –2.475 –0.932 (0.027) (0.200) Share construction 6.556 6.245 (0.000) (0.000) Share manufacturing –0.432 –0.602 (0.395) (0.050) Share finance, insurance 6.648 3.349 (0.000) (0.001) Share government ex. military 1.231 0.347 (0.036) (0.328) Share military –0.094 –0.048 (0.831) (0.884) Demographics Share foreign-born 0.353 0.319 (0.737) (0.428) Share poverty 0.086 1.383 (0.879) (0.001) Share population 65 or more 1.800 –0.622 (0.061) (0.235) Innovative environment Chemical patents per 1,000 pop. –30.678 –23.517 (0.163) (0.080) IT patents per 1,000 pop. 244.666 22.185 (0.001) (0.296) Industrial ex. motor vehicle patents per 1,000 pop. 242.588 158.181 (0.003) (0.002) Motor vehicle patents per 1,000 pop. –116.975 –28.989 (0.446) (0.666) Research expenditures per 1,000,000 pop. –0.063 0.000 (0.408) (0.966) Variables related to policy decisions Share bachelors + 1.255 0.181 (0.282) (0.675) College enrollment per 1,000 pop. –0.011 –0.041 (0.868) (0.279) Total crimes per 1,000 pop. –0.438 –0.275 (0.030) (0.004) State & local govt. tax % personal income –1.829 0.267
30
Table 7. Estimation Results for Change in Employment: 20-Year Intervals, 1969–2009 cont’d. United States 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Variables related to policy decisions (continued) Dummy right to work 3.936 17.109 (0.540) (0.000) Airport passengers per capita 14.323 2.492 (0.000) (0.008) Amenities July temp. minus Jan. temp. –0.611 0.668 (0.096) (0.003) Natural Amenities Scale 20.840 2.725 (0.000) (0.175) Regional effects Southwest (SW=1, WT= –1,Oth=0) 2.465 5.658 (0.725) (0.204) Southeast (SE=1, WT= –1,Oth=0) 3.578 –2.604 (0.582) (0.462) Midwest (MW=1, WT= –1,Oth=0) 11.333 –5.414 (0.078) (0.159) Northeast (NE=1, WT= –1,Oth=0) 3.659 –9.704 (0.606) (0.017) N 366 366 R-squared 0.66 0.52
31
Initial Conditions
We have included in the income equations personal income per capita
excluding transfers at the beginning of each period as a measure of initial conditions in
the metro area economies. As shown in table 6, in both twenty-year intervals (1969 to
1989 and 1989 to 2009), initial income levels have a positive effect on the change in
income over the period. This effect appears to have dwindled between the two intervals,
however, with the coefficient shrinking by an order of magnitude and becoming
insignificant in the more recent period.
Population size at the beginning of the period also has been selected as a measure
of initial conditions in the local economies. As shown in tables 6 and 7, initial population
size has a positive effect on real per capita income increases and a negative effect on
employment growth over both periods, with most of the results significant but shrinking
in magnitude over time. To the extent that agglomeration economies in larger areas
account for the positive effect on income growth, consistent with the interpretation of
Blumenthal et al. discussed earlier, the effects seem to be dwindling over time. This
would be consistent with the observation of Glaeser, Kolko, and Saiz (2001) on the
increasing mobility of firms and the lessening need to congregate for production
efficiencies. Our results also suggest that larger metro areas are more prone to face
declining employment over time, but that this phenomenon has slowed more recently.
Industry Structure
Both casual observation and more rigorous research suggest that the industry
makeup of local economies is integral to their economic success patterns. Thus, it is
crucial to account for industry structure while striving to isolate other phenomena
contributing to economic behavior. Here we control for the concentration in an area of
multiple industries, measured in each case at the beginning of the period by earnings
share in the income equations and jobs share in the employment equations.
In the income equations, four industries show significant coefficients for both
periods, and one other industry effect is significant for the later period. A higher share of
mining activity at the beginning of the period had a negative and significant effect on
income growth (as well as on employment growth) in the earlier interval, and a
significantly positive effect in the later period. These results are consistent with changes
in the price of oil over these periods. The same pattern for military activity reflects a
32
significant escalation in the defense budget in the first decade of the 2000s to prosecute
the wars in Iraq and Afghanistan.
The share of activity in durable goods manufacturing was negatively and
significantly related to income increases in both periods, consistent with most other
studies that consider manufacturing’s share, but inconsistent with the findings of
Blumenthal et al. This lends greater support to the reasoning that their estimation period
just happened to coincide with manufacturing’s relatively more favorable prospects over
the 1990s. The much smaller and statistically insignificant effect of the nondurable
goods share of activity suggests that the negative relationship between the growth in
income and manufacturing’s share is mostly related to durable goods behavior.
Manufacturing’s share had a significantly negative effect in the later period in our
employment equation.
The share of activity in finance has a positive and significant effect on both
income increases and employment growth in both periods, with a slightly larger effect in
the later period on income and a slightly smaller effect on employment. This is
consistent with the findings of Blumenthal et al., which they see as an outcome of a
higher-value service sector orientation. Unique to our study, we also included health
services in our income equation (the data were not available to test the effect of health
services on employment change), and found a positive and significant effect in the later
period, consistent with this industry’s growing influence in the economy.
Demographics
We include three measures in our income and employment equations in the
category of demographics: the share of the population that is foreign-born, the share of
the population that is classified as being in poverty, and the share of the population age
65 years or older.
The share of the foreign-born population had a more negative and a significant
effect on income in the later period, but an insignificant effect on employment,
suggesting disproportionate numbers of lower-paid workers in this group.
One of the more puzzling results in our study is the finding that the share of the
population in poverty had a positive and significant effect on both income and
employment change in the most recent period. This is counter-intuitive; one hypothesis
might be that higher poverty levels in 1989 prompted more activity in programs to assist
the poor, but that seems to be a stretch. This is one issue we leave unresolved.
33
The measure for the share of the population age 65 years or older was included
in the equation specifications to account for, in part, the dependent population of the area,
or at least the much lower labor force participation rates of the cohort.6 In both the
income and employment equations, its effect was mixed and not significant. Blumenthal
et al. also found that measures of demographic structure did not affect economic
performance over their period of estimation.
Innovative Environment
As an innovative environment is increasingly being perceived as a ticket to
economic success, it has become imperative to put forward some proxy measures of this
complex concept to test this claim. This was our motivation in assembling and
organizing a comprehensive data set on patents over time by metro area and major
industry category—only one facet of innovation, but an important one, and one that has
not been adequately captured in prior studies for lack of a complete set of measures.
Our results indicate that the granting of IT-related patents per 1,000 population
are positively and significantly related to income growth in both periods, and to
employment growth in the earlier period. The income and employment effects are less
strong in the later period. The effect on income of industrial patents (excluding motor
vehicles) per 1,000 population, on the other hand, is much stronger in the later period,
and overall, industrial patents make a significant contribution to income and employment
growth. In contrast, the granting of both chemical patents per 1,000 population and
patents related to motor vehicle manufacturing per 1,000 population were generally
unrelated to income and employment growth for the nation as a whole. Any variations in
these results regionally are considered below.
We also assembled a series on real university research expenditures per capita
in an attempt to capture this contribution of universities to the local economy. As well as
providing educated workers, research universities bring in funding, produce goods and
services, attract private industry (see Blumenthal et al., p. 612), and perforce create an
amenity-rich environment around them. Unexpectedly, research spending had a negative
effect on income growth and no effect on employment growth in either period. This
counter-intuitive result may reflect the fact that major research universities are located in
metro areas with a high level of educational attainment, a measure that is also included in
6Note that the measure of the population age 65 years or older excludes those entering that status during the decade.
34
our model, as are the variables tracking the granting of patents, and those drivers could be
picking up most of the explanatory power. In previous studies, assessing the effects of
research spending has produced mixed results, but it is difficult to believe that the
research and technology creation functions of universities—if isolated and measured
properly—do not result in enhanced regional economic development that otherwise
would not occur.
Variables Related to Policy Decisions
Among the half-dozen economic drivers we categorized as policy-related
variables, educational attainment and crime are the most robust in the estimating
equations. The level of educational attainment, as measured by the share of the
population with a bachelor’s degree or more, has consistently been found in prior studies
to be a major determinant of the economic success of regions—regardless of the set of
control variables and tests of reverse causality. Our results support these findings for
income, with educational attainment showing a stable and highly significant positive
effect over the earlier and later periods. Our results are less convincing for the impact of
educational attainment on employment, however, which is positive but not significant
and records a smaller effect in the later period. These results are not entirely unlike
Blumenthal et al. in that they find a stronger relationship between education and Gross
Metropolitan Product than between education and employment. That the income
relationship is stronger than the one for employment is not inconsistent with the general
rationale that more educated regions are becoming more economically successful because
they are becoming more productive.
Of course, educational achievement can be valued in the labor market by
accomplishments other than receiving a bachelor’s or an advanced university degree.
There are studies, for instance, that find a positive outcome for the economy of an
increasing share of the population attaining some college education short of a bachelor’s
degree. Neither Blumenthal et al. nor our prior work found a significant effect on
regional economic outcomes of this education cohort, however.
As a measure of the presence of universities in the local economy, we included
college enrollment per 1,000 population in the model. This variable had a negative
effect on both income and employment in both periods, and was usually not significant.
In the case of income, that result was undoubtedly due to the typically low-income status
of students, thus bringing down the per capita average in a region. The spinoff effect of
35
having a larger share of the work force with relatively high incomes due to the presence
of a college is likely captured by the more targeted variable in the estimating equations
representing the share of the population with a bachelor’s degree or more.
One of the strongest variables among the estimating equations is the crime rate
per 1,000 population. This concept has been inadequately represented in previous
studies, largely because of measurement issues, which is what prompted us to assemble
and organize metropolitan area series from raw records provided by the FBI. In the
income equation, the effect of the crime rate was negative in both the earlier and later
periods, as expected, and significant in the later period. In the employment equation, the
coefficient on the crime rate was negative (as expected) and significant in both periods.
The negative impact on employment is smaller over time, but on income it is larger over
time.
Observing that all crimes committed are not equal, we hypothesized that more
serious crimes may be more influential on economic outcomes. We assembled data
series on the violent crime subcomponent of total crimes, and substituted that concept for
the total in each of the four estimating equations. In both periods for the income
equation, the coefficient on the violent crime rate was significant and had a larger
negative value than the one associated with the total crime rate. In the employment
equation, the effect of violent crimes was greater than the total in the later period, but
positive and not significantly different from zero in the earlier period. Because we were
more confident in the total crime rate measures, and those data yielded more consistent
results, we settled on the total concept for our final estimating equations.
The first of two measures in our estimating equations directly targeting an area’s
business climate is state and local taxation. Specifically, we include in our estimating
equations a variable representing state and local government tax revenue as a
percentage of personal income, assembled from data provided by the U.S. Census
Bureau. Results have been mixed among previous studies that investigate the impact of
taxes on regional economic growth. The tax structure is complex, but it is clear in
assessing the tax burden of a metropolitan area that both state and local tax policy have to
be included.
In our estimating equations, state and local tax rates usually had a negative effect
on economic outcomes, as expected, but the coefficients were consistently not significant.
As in a number of other studies, the largely inconclusive results could reflect the
36
difficulties of fine-tuning the tax burden measure. Also a consideration, though, is that
state and local governments have to provide services for the people who live there and for
the people and the industries they want to attract into the region—and it takes revenue to
do that. Studies have consistently indicated that those services are valued by
constituents.
Our second driver directly related to business climate is right-to-work legislation,
which gives employees the option of working in establishments without having to join a
union, even if co-workers are union members. We include in our estimating equations a
dummy variable for location in a right-to-work state, with a value of 1 representing the
presence of the legislation. When a metropolitan area crossed state boundaries, it was
assigned a state based on the location of its major city.7 The right-to-work dummy
variable had a positive and significant effect on income growth in the later period, as it
did on employment growth in that period, as hypothesized. It was not statistically
significant for either outcome measure in the earlier period. For employment growth, the
effect is also larger in the later period.
As pointed out in other studies, the precise interpretation of the results is not
obvious. One interpretation, of course, is to view the dummy variable more narrowly as
representing unionization, and positive effects of the presence of right-to-work legislation
on economic outcomes as signaling less proclivity for businesses to locate or invest in
regions with closed shops. The argument is that they instead are more attracted to
environments with greater workforce flexibility, and where they can avoid the possibility
of union pay differentials.
An alternative, more inclusive interpretation, and one to which we ascribe, is that
the dummy variable is a proxy for a more general business-friendly environment. The
difficulty in being definitive here is that a dummy variable measure is not sufficiently
articulated to extract a more focused finding.
With the economy becoming increasingly more global, the connectivity of both
people and goods to the outside world has become more important, primarily through air
traffic. In an attempt to capture the effect of connectivity on the growth of metropolitan
area economies, we tested two measures in turn in our estimating equations: air freight
(in tons) per capita and the number of enplaned passengers per capita. We settled on the
7Note that MSAs located in Michigan are not included in our measure despite recently enacted right-to-work legislation in the state because its effective date falls outside of our estimation range.
37
number of enplaned passengers per capita as having the better explanatory power.
Airport passenger traffic had a positive and significant effect on employment growth in
both the earlier and later periods, although the coefficient was smaller in the later period.
No relationship was found between airport passenger traffic and income growth, and the
variable was not included in the income equations.
Amenities
Amenity-rich environments are often viewed as a catalyst for local economic
growth. We include two measures of natural amenities in our estimating equations:
temperature as a proxy for local climate and a Natural Amenities Scale to represent a
more comprehensive measure of the environmental attractiveness of a region.
We tested two concepts of temperature: seasonal temperature extremes in the
locality, and the range of those temperatures. In the first equation specification, we
included both the average temperature in July and the average temperature in January,
hypothesizing that warmer temperatures are associated with a more attractive economic
environment for workers. In the second specification, we included instead a measure of
the difference between the average temperatures in July and January, hypothesizing that
more moderate temperature ranges are preferred. The second concept had more
explanatory power, and was included in the final specification.
Our hypothesis of more moderate temperature ranges being associated with
positive economic outcomes, reflected by negative signs on the coefficient, is supported
by the results for income growth. These results indicate that metropolitan areas with a
smaller temperature change between the seasons had the highest income growth, although
with a weaker effect in the later period. The results are inconclusive for employment
growth, with the relationship being reversed (i.e., a positive sign on the coefficient) in the
later period.
The second measure of physical amenities is a scale published by the U.S.
Department of Agriculture that to our knowledge has not been used in previous studies in
our topic area. The Natural Amenities Scale is a measure of the physical characteristics
of a county area that enhance the location as a place to live. The scale was constructed
by combining six measures that reflect environmental qualities most people prefer.
These measures are warm winter, winter sun, temperate summer, low summer humidity,
topographic variation, and water area.
38
The natural amenities forming the scale were a positive contributor to
employment growth in both periods, but were significant only in the earlier period—
contrary to expectations about the growing importance of the natural environment to
decisions by individuals and businesses on where to locate. No relationship was found
between the scale and income growth, and the variable was not included in the income
equations.
Regional Effects
We include in the estimating equations dummy variables for four regions of the
country, using aggregations of the nine U.S. Census Bureau divisions: Southwest,
Southeast, Midwest, and Northeast.8 These variables are included to account for spatial
autocorrelation and to provide a control for possible omitted variables that may vary by
region. They were largely insignificant in the employment equations, but were often
significant in the income equations.9
Independent Variables Tested But Not Included in the Final Estimating Equations
It may be helpful to future researchers to identify those variables that were tested
in the research process but were not included in the final model specifications. Most
often, these measures had less explanatory power than other variations of the concept, but
in other cases, they exhibited little explanatory power in their own right. The list follows.
1. The number of post-secondary schools in the metropolitan area
2. The share of the population age 24 or younger
3. Average January temperature
4. Average July temperature
5. Violent crime count and rate per 1,000 population
6. Property crime count and rate per 1,000 population
7. Air freight (tons) per capita
8. Public university research expenditures
9. Private university research expenditures
10. Diversity index: an index of a metropolitan area’s diversity among its industries (employment-based Herfindahl Index)
8The New England and Middle Atlantic divisions make up the Northeast region; the East North Central and West North Central divisions make up the Midwest region; the South Atlantic, East South Central, and West South Central divisions make up the Southeast region; and the Mountain and Pacific divisions make up the Southwest region. 9The coefficients on the regional dummy variables are the differential from the average effects for all regions.
39
Many of these variables are mentioned throughout the analysis of the estimation results.
Estimation Results for the Regional Model We also estimated both income and employment change equations on two subsets
of the U.S. metropolitan areas: the combined Northeast and Midwest regions as defined
by the U.S. Census Bureau, and the regions collectively making up the balance of the
country. Our panel of experts identified a combined Northeast-Midwest region as
constituting the “rust belt,” and this is our primary focus in this section. The results for
the region making up the balance of the country, containing over 60 percent of the metro
areas in the United States, are more similar to the results for all of the metro areas
collectively in the nation. For the rust belt, the results in some instances differ in ways
worth noting from the results for the nation as a whole; in other instances, the results are
fairly consistent across the geographies—and both of these occurrences are of interest to
us.
The high-level similarities and differences between the estimation results for the
nation and for the rust belt can be best gleaned from table 8 for income and table 9 for
employment, summary tables that show the signs and significance of the estimated
parameters for the equations representing the earlier period (1969 to 1989) and the later
one (1989 to 2009). The signs are identified in the cells of the table, with parameter
values significant at the 5 percent level or better (based on p-values) indicated by the
shaded cells. The details of the estimation results for the Northeast-Midwest region are
shown in table 10 for income and table 11 for employment. 10
For the change in income, the most similar patterns in signs and significance
between U.S. and rust belt results can be found in table 8 for the following drivers: initial
levels per period of per capita personal income excluding transfers, several of the
industry structure variables (the share of durable goods, finance, and military),
educational attainment, and crime. 11
10Similar tables for the balance of the country can be found in the appendix. 11Note that the results for the right-to-work dummy variable in the rust belt region should be discounted because there were very few metro areas in the region located in right-to-work states.
40 Table 8. Summary of Parameter Signs and Significance for Change in Personal Income Minus Transfers Per Capita (2009$): 20-Year Intervals, 1969–2009 United States, Northeast-Midwest Region, and Rest of United States National Northeast-
Midwest Rest of U.S.
(Shaded entries significant at 5% level) ’69–’89 ’89–’09 ’69–’89 ’89–’09 ’69–’89 ’89–’09 Intercept – – + – – – Initial conditions Personal Income per capita ex. transfers + + + + + + Population (log) + + – – + – Industry structure Share mining – + – – – + Share construction – + + + – + Share durables – – – – – – Share nondurables + – – – + – Share finance, insurance + + + + + + Share health services + + + – – + Share government ex. military – + – – + + Share military – + – + – + Demographics Share foreign-born – – + + – – Share poverty + + – + + + Share population 65 or more + – + + + – Innovative environment Chemical patents per 1,000 pop. + – – + + + IT patents per 1,000 pop. + + + – + + Indust. ex. mot. veh. patents per 1,000 pop. + + – + – + Motor vehicle patents per 1,000 pop. – – – – + – Research expenditures per 1,000,000 pop. – – – – – – Variables related to policy decisions Share bachelors + + + + + + + College enrollments per 1,000 pop. – – + – – – Total crimes per 1,000 pop – – – – – – State & local govt. tax % personal income – – – – – – Dummy right to work – + – + – + Amenities July temp. minus Jan. temp. – – + + – – Regional effects Southwest(SW=1, WT= –1,Oth=0) – – * * * * Southeast(SE=1, WT= –1,Oth=0) + – * * * * Midwest(MW=1, WT= –1,Oth=0) – + * * * * Northeast(NE=1, WT= –1,Oth=0) + – * * * * MW dummy(MW=1,Oth=0) * * – + * * N 366 366 143 143 223 223 R-squared 0.57 0.62 0.63 0.77 0.58 0.61 *Not included in final equation.
41
The results for initial-period income indicate the same relationship among metro
areas in the rust belt as in the nation overall—initial income levels have a positive effect
on the change in income over both periods. In both geographies, the effect seems to have
dwindled between the earlier and later periods. Once industry structure is controlled for,
the most consistent drivers of income change, both in the nation and in the rust belt, are
the positive effect of educational attainment and the negative effect of the crime rate. For
both the nation and the region, educational attainment is positive and highly significant in
the later period, and the crime rate is negative and also highly significant in the later
period.
For the change in income, the greatest differences in signs and significance
between the U.S. and rust belt results occur among the following variables: the initial
population size in each period, the share of the foreign-born population, and IT patents
per 1,000 population.
The initial population size has a positive effect on income change in both periods
for the national results, which we interpreted as reflecting agglomeration economies in
larger areas, although the magnitude of this effect is diminished during the later period.
For the rust belt, we observe a negative effect in both periods, although neither is
significant, suggesting that there are no additional agglomeration gains among the rust
belt metro areas. The share of the foreign-born population had a negative sign in both
periods for the national results and positive signs for the rust belt results, although most
of the coefficients were not significant. To the extent that any inferences can be drawn
from these findings, the foreign-born cohort could be higher-paid overall relative to
workers in general in the rust belt. In terms of the innovative environment, the effect on
income growth of the granting of IT-related patents per 1,000 population was positive
and consistently significant for the nation, but not significant for the region in either
period. The region does not appear to be a focal point for this activity. On the other
hand, our expectation was that the granting of motor vehicle patents would be related to
income growth in the rust belt region, but as with the nation, this relationship was not
observed in the results.
Among the other variables, results between the nation and the rust belt were
generally mixed. The overall fit of the equations, reflected by the R-square statistic, was
superior for the rust belt region in both periods.
42
For the change in employment, the most similar patterns in signs and significance
between the U.S. and rust belt results are shown in table 9 for the following variables: the
initial population size in each period, a few of the industry structure variables (the share
of agriculture and manufacturing), the innovative environment for industry, educational
attainment, airport passengers per capita, and natural amenities.
The initial population size has a negative effect on employment change for both
periods and geographies, and it is usually significant, but its impact is diminished in the
later period. Success rates in granting industrial patents yield consistently positive
results, usually significant, across periods and geographies; for the region, the effects are
stronger over time. The effect of educational attainment on the change in employment is
consistently positive across the board, as with income change, but in the case of
employment it is significant only in the earlier period and for the rust belt. That the
relationship between education and income growth is stronger than the one for
employment growth is consistent with the reasoning that better-educated workers are
more productive and thus earn a higher wage, but that the presence of a better-educated
workforce is less important to the creation of additional jobs in an area. Our measure of
geographic connectivity—airport passengers per capita—had a consistently positive
effect on employment change across periods and geographies, but it was significant only
for the national results. The effect on employment change was also positive across the
board for the Natural Amenities Scale, but for both the nation and the rust belt it was
significant only in the earlier period—again contrary to the notion of the growing
importance of the natural environment to decisions by individuals and businesses on
where to locate.
For change in employment, the greatest differences in sign and significance
between the U.S. and rust belt results are among the demographic variables: the share of
the foreign-born population, the share of the population that is classified as being in
poverty, and the share of the population age 65 years or older.
In contrast to the U.S. results, the share of the foreign-born population had a
consistently negative effect on employment change, although all of the coefficients were
insignificant. The foreign-born appear not to be a meaningful component of job growth
in the rust belt region to date.
43
Table 9. Summary of Parameter Signs and Significance for Change in Employment: 20-Year Intervals, 1969–2009 United States, Northeast-Midwest Region, and Rest of United States National Northeast-Midwest Rest of U.S. (Shaded entries significant at 5% level) ’69–’89 ’89–’09 ’69–’89 ’89–’09 ’69–’89 ’89–’09 Intercept + + + + + – Initial conditions Population (log) – – – – – – Industry structure Share agricultural + + + + + + Share mining – – – + – – Share construction + + + + + + Share manufacturing – – – – – – Share finance, insurance + + + + + + Share government ex. military + + – + + + Share military – – – – + – Demographics Share foreign-born + + – – – + Share poverty + + – – + + Share population 65 or more + – – – + – Innovative environment Chemical patents per 1,000 pop. – – – – + – IT patents per 1,000 pop. + + + – + + Indust. ex. mot. veh. patents per 1,000 pop. + + + + + + Motor vehicle patents per 1,000 pop. – – – – – – Research expenditures per 1,000,000 pop. – + – + + – Variables related to policy decisions Share bachelors + + + + + – + College enrollments per 1,000 pop. – – – + – – Total crimes per 1,000 pop – – + – – – State & local govt. tax % personal income – + + – – + Dummy right to work + + – + + + Airport passengers per capita + + + + + + Amenities July temp. minus Jan. temp. – + + + – + Natural Amenities Scale + + + + + + Regional effects Southwest(SW=1, WT= –1,Oth=0) + + * * * * Southeast(SE=1, WT= –1,Oth=0) + – * * * * Midwest(MW=1, WT= –1,Oth=0) + – * * * * Northeast(NE=1, WT= –1,Oth=0) + – * * * * MW dummy(MW=1,Oth=0) * * – – * * N 366 366 143 143 223 223 R-squared 0.66 0.52 0.66 0.65 0.68 0.41 *Not included in final equation.
44
As it turns out, the share of the population in poverty, which had an inexplicably
positive effect on employment change in the U.S. results, takes on the expected negative
sign in both periods for the rust belt region, although it is not significant in either case.
The results for the share of the population age 65 or older were more conclusive for the
rust belt region, with the expected negative sign that was significant in both periods,
reflecting the much lower labor force participation rates of this cohort.
Among the other explanatory variables for employment change, results between
the nation and the rust belt were generally mixed. The overall fit of the equations,
measured by the R-square statistic, was similar for both the nation and the rust belt in the
earlier period and higher for the rust belt in the later period.
In summary, there appear to be sufficient differences in the national and regional
results that there is yield in estimating regional equations rather than drawing inferences
from national estimates when the region is of primary interest. It is also informative,
however, to find that the effect of some policy-related variables appears to be consistent
across geographies. The results in this section suggest that included in the list of those
variables for income growth would be supporting education and deterring crime; and for
employment growth, providing an innovative environment for industry, and perhaps
enhancing airport connectivity and being good stewards of the natural environment.
45
Table 10. Estimation Results for Change in Personal Income Minus Transfers Per Capita (2009$): 20-Year Intervals, 1969–2009 Northeast-Midwest Region 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Intercept 2471.972 –18142.190 (0.714) (0.005) Initial conditions Personal income per capita ex. transfers 0.473 0.001 (0.001) (0.990) Population (log) –174.522 –2.950 (0.435) (0.989) Industry structure Share mining –0.138 –82.765 (0.999) (0.595) Share construction 64.829 297.533 (0.663) (0.068) Share durables –104.843 –95.158 (0.006) (0.004) Share nondurables –59.580 –1.257 (0.209) (0.979) Share finance, insurance 26.674 172.982 (0.835) (0.026) Share health services 13.139 –53.298 (0.917) (0.520) Share government ex. military –43.154 –19.052 (0.437) (0.623) Share military –97.857 189.568 (0.063) (0.000) Demographics Share foreign born 109.067 19.868 (0.373) (0.852) Share poverty –43.734 279.567 (0.702) (0.018) Share population 65 or more 14.630 309.912 (0.914) (0.004) Innovative environment Chemical patents per 1,000 pop. –709.240 612.009 (0.581) (0.641) IT patents per 1,000 pop. 12455.359 –720.431 (0.093) (0.866) Industrial ex. motor vehicle patents per 1,000 pop –6.975 6846.413 (0.999) (0.296) Motor vehicle patents per 1,000 pop. –7043.230 –2481.762 (0.468) (0.698) Research expenditures per 1,000,000 pop. –7.000 –0.080 (0.205) (0.940) Variables related to policy decisions Share bachelors + 125.219 355.257 (0.300) (0.000) College enrollment per 1,000 pop. 1.864 –26.035 (0.778) (0.000)
46
Table 10. Estimation Results for Change in Personal Income Minus Transfers Per Capita (2009$): cont’d. 20-Year Intervals, 1969–2009 Northeast-Midwest Region 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Variables related to policy decisions (continued) Total crimes per 1,000 pop. –5.296 –55.183 (0.773) (0.000) State & local govt. tax % personal income –91.370 –285.927 (0.587) (0.073) Dummy right to work –576.275 1138.620 (0.419) (0.076) Amenities July temp. minus Jan. temp. 11.326 252.279 (0.873) (0.000) Regional effects MW Dummy(MW=1, Oth=0) –962.648 1396.348 (0.211) (0.035) N 143 143 R-squared 0.63 0.77
47 Table 11. Estimation Results for Change in Employment: 20-Year Intervals, 1969–2009 Northeast-Midwest Region 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Intercept 65.117 1.618 (0.320) (0.971) Initial conditions (Population (log)) –5.064 –0.404 (0.041) (0.827) Industry structure Share agricultural 2.539 3.423 (0.008) (0.000) Share mining –2.593 1.534 (0.185) (0.413) Share construction 6.520 3.725 (0.003) (0.010) Share manufacturing –1.088 –0.661 (0.020) (0.017) Share finance, insurance 0.279 1.316 (0.832) (0.052) Share government ex. military –0.234 0.010 (0.678) (0.973) Share military –0.746 –0.219 (0.168) (0.630) Demographics Share foreign born –0.061 –0.566 (0.957) (0.355) Share poverty –0.725 –0.706 (0.464) (0.239) Share population 65 or more –2.610 –1.603 (0.043) (0.014) Innovative environment Chemical patents per 1,000 pop. –25.621 –15.239 (0.032) (0.061) IT patents per 1,000 pop. 60.235 –21.352 (0.343) (0.361) Indust. ex. motor veh. patents per 1,000 pop. 84.628 105.071 (0.138) (0.007) Motor vehicle patents per 1,000 pop. –163.603 –19.212 (0.064) (0.610) Research expenditures per 1,000,000 pop. –0.068 0.003 (0.186) (0.663) Variables related to policy decisions Share bachelors + 2.070 0.112 (0.040) (0.786) College enrollment per 1,000 pop. –0.009 0.024 (0.864) (0.515) Total crimes per 1,000 pop. 0.373 –0.067 (0.028) (0.400) State & local govt. tax % personal income 0.435 –0.899 (0.775) (0.320)
48
Table 11. Estimation Results for Change in Employment: 20-Year Intervals, 1969–2009 cont’d. Northeast-Midwest Region 1969 to 1989 1989 to 2009 (p-values shown in parentheses) Variables related to policy decisions (continued) Dummy right to work –13.667 4.842 (0.031) (0.191) Airport passengers per capita 0.340 0.389 (0.958) (0.852) Amenities July temp. minus Jan. temp. 0.113 0.476 (0.864) (0.189) Natural Amenities Scale 8.888 2.198 (0.003) (0.232) Regional effects MW Dummy(MW=1,Oth=0) –9.064 –1.799 (0.171) (0.660) N 143 143 R-squared 0.66 0.65
49
Analysis of Residuals: Metropolitan-Area Outliers In this section we investigate the pattern of the residuals generated by the
estimated equations for our four national models, that is, the earlier and later periods for
the change both in real per capita income minus transfer payments and in employment.
We do this for two reasons. First, and more generally, a graphical analysis of the
residuals is a valuable tool in model validation. Model validation is frequently an
overlooked step in econometric modeling, other than reporting the R2 statistics from the
equation fits (the fraction of the total variability in the outcome variables that is
accounted for by the model). Such numerical methods for model validation are useful,
but graphical methods are a less narrowly focused test result and provide a broader
impression of the relationship between the models and the data. We are not familiar with
any other study in the literature on identifying the success patterns of metro areas that
explored a graphical analysis of the model residuals (other than the infrequent comment
that not all areas would necessarily fit a general model well), in part perhaps because of
the standard assumption that the models are well-behaved with random errors. In the
case of estimating the economic behavior of hundreds of metro areas across the country
with the severe data limitations that are inherent in such an exercise, it is unlikely that the
models will be so well-behaved.
The second, and more specific, reason for the graphical residual analysis is to
identify those metro areas that did not conform well to the fit of the general model. In
this type of analysis, there are always going to be outliers; the question is whether there is
something systematic about them. Specifically, it is informative to identify those metro
areas that are outliers to the fit of the model, and ascertain whether there are any
organized patterns related to these outliers. For instance, are there issues of spatial
autocorrelation, where the error in one location is correlated with errors in other affected
geographic areas?12 For outlier metro areas, the model could be misspecified in that the
model is not “complete,” that is, variables might have been omitted that are important in
explaining an outcome variable. Alternatively, some events, or “exogenous shocks,” that
could not be modeled may have affected economic outcomes in these regions in a
significant way.
12In our regression analysis, we introduced regional dummy variables to capture some of the potential issues of spatial autocorrelation. Unlike autocorrelation between periods, there could be many dimensions of spatial autocorrelation.
50
It is not terribly surprising to find that the general model does not fit certain areas
as well as it fits other metro areas, and it is also not difficult to identify those outlying
areas. It is much more challenging to uncover all of the reasons for the weaker fit in
those cases, but a few general patterns do emerge. We now turn to a discussion of our
observations.
In the figures that follow, the residuals generated by estimating the general model
across 366 metro areas are plotted against the estimated change for each of the outcome
variables. The results for the estimates of the change in personal income (minus
transfers) are shown in figures 1 and 2 for the earlier and later periods, respectively. The
results for the change in employment are shown in figures 3 and 4 in the same time
sequence. Each figure is accompanied by a table that provides a key lining up the
residual outliers with the corresponding metro area names. For the purpose of this
analysis, we transformed the residuals from the regression estimates into studentized
residuals, which are the quotients resulting from the division of a residual by an estimate
of its standard deviation. Typically, the standard deviations of residuals in a sample vary
greatly from one data point to another, particularly in regression analysis; thus, it does not
make sense to compare residuals at different data points without first studentizing,13 an
important technique in the detection of outliers.
The studentized residuals are plotted against the estimated change in income for
the period 1969 to 1989 in figure 1. The overriding observation is that the outlier
residuals are evenly distributed in sign. The sixteen largest studentized residuals in
absolute value are identified by number in the figure and in the accompanying key,
representing all of the residuals more distant than two standard deviations from the mean.
Eight of the studentized residuals are positive (four in the Northeast-Midwest region,
which contains about 40 percent of the metro areas in the country), and eight are negative
(three in the Northeast-Midwest region). The locations of the outlier metro areas
identified in the key accompanying the figure do not suggest any clear geographic
pattern. Some states, such as Florida and California, have metro areas with relatively
large residuals, both positive and negative. Texas, which has a large number of metro
areas, does not have any large outliers, similar to the Midwest (all of the rust belt outliers
are in the Northeast). 13Dividing by an estimate of scale is called studentizing, analogous to standardizing and normalizing. Studentized residuals are summarized in Wikipedia: http://en.wikipedia.org/wiki/Studentized_residual
51
Key to Figure 1: Studentized Residuals for Income Change Regression, 1969–89 (Northeast-Midwest metro areas shown in bold) Rank Area Studentized Residual
1 Atlantic City, NJ 4.33 2 Bridgeport-Stamford-Norwalk, CT 4.01 3 Sebastian-Vero Beach, FL 3.85 4 Palm Bay-Melbourne-Titusville, FL –3.84 5 San Diego-Carlsbad-San Marcos, CA –2.74 6 Fairbanks, AK –2.63 7 Elmira, NY –2.40 8 Oxnard-Thousand Oaks-Ventura, CA 2.26 9 Cumberland, MD-WV –2.19
10 Punta Gorda, FL –2.18 11 Lawton, OK 2.17 12 Ithaca, NY –2.15 13 Lake Havasu City-Kingman, AZ –2.14 14 Manchester-Nashua, NH 2.09 15 Trenton-Ewing, NJ 2.09 16 Vallejo-Fairfield, CA 2.02
Figure 1Studentized Residuals for Income Change Regression, 1969 – 89
– 5
– 4
– 3
– 2
– 1
0
1
2
3
4
5
0 2000 4000 6000 8000 10000 12000 14000 16000
NEMW metro areasRest of country metro areas
StudentizedResiduals
Estimated Change in Income
13
106 5
8
4
9 7
2
11
1213
14 1516
Figure 1Studentized Residuals for Income Change Regression, 1969 – 89
– 5
– 4
– 3
– 2
– 1
0
1
2
3
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5
– 5
– 4
– 3
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0
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0 2000 4000 6000 8000 10000 12000 14000 160000 2000 4000 6000 8000 10000 12000 14000 16000
NEMW metro areasNEMW metro areasRest of country metro areasRest of country metro areas
StudentizedResiduals
Estimated Change in Income
1133
101066 55
88
44
99 77
22
1111
12121313
1414 15151616
52
The story is broadly the same for the change in income for the period 1989 to 2009,
shown in figure 2. Here, too, the outlier residuals are evenly distributed in sign. Again,
eight of the outliers are positive (three in the Northeast-Midwest region), and eight are
negative (two in the Northeast-Midwest region). When we consider the areas that make
up the outliers, it is difficult to identify what Ann Arbor, Michigan, and Hinesville,
Georgia, have in common that could explain why the model is over-predicting their
income growth over this period—or why Pascagoula, Mississippi, and Sheboygan,
Wisconsin, would both be substantially exceeding the expectations of the model. Also,
unlike the results for the earlier period, the Midwest region and Texas have metro areas
with relatively large outliers, and the Northeast region has only one, Atlantic City, New
Jersey. In sum, the search for consistencies and commonalities to improve the general
specification of the model is more complicated than can be gleaned from this overview
analysis, and would also involve more specific insights into some of the individual areas.
But the fact that the outliers are balanced between positive and negative results is
encouraging news for the predictive capabilities of the income model.
This is not so much the case for the employment model, which makes that model
more interesting but also introduces more concerns than for the income model. Indeed,
one of our main findings from the analysis of residuals is that the income equations are a
better fit than the employment equations for the economic behavior we are modeling.
The plot of the studentized residuals for the change in employment from 1969 to 1989 is
shown in figure 3. Of the fourteen outliers identified in the figure, twelve are positive,
and eleven of those are in the South and West regions (the other is in the Northeast-
Midwest region). The two negative outliers are also in the South and West regions.
The studentized residuals from the employment equation over the 1989 to 2009
period, shown in figure 4, are even more striking. All ten of the outliers identified in the
figure are positive, and all ten are located in the South and West regions of the country.
The residuals for both periods suggest that there are some large, unexplained employment
gains in several metro areas in these regions over the forty-year period from 1969 to
2009. This would seem to be the most obvious place to start exploring whether there are
some consistent factors missing from the employment model that would enhance our
knowledge of what is contributing to the stronger-than-expected employment outcomes
in these metro areas. Later, we make an initial pass at looking into this.
53
Key to Figure 2: Studentized Residuals for Income Change Regression, 1989–2009 (Northeast-Midwest metro areas shown in bold) Rank Area Studentized Residual
1 Houma-Bayou Cane-Thibodaux, LA 3.38 2 Ann Arbor, MI –3.31 3 Hinesville-Fort Stewart, GA –3.31 4 Pascagoula, MS 3.02 5 Winston-Salem, NC –2.96 6 Sheboygan, WI 2.63 7 Peoria, IL 2.62 8 Atlantic City, NJ –2.62 9 Punta Gorda, FL –2.53
10 Columbus, IN 2.50 11 Jacksonville, NC 2.39 12 Napa, CA 2.31 13 Midland, TX –2.29 14 Bakersfield, CA –2.18 15 Myrtle Beach-Conway-North Myrtle Beach, SC –2.17 16 New Orleans-Metairie-Kenner, LA 2.07
Figure 2Studentized Residuals for Income Change Regression, 1989–2009
– 4
– 3
– 2
– 1
0
1
2
3
4
– 4000 – 2000 0 2000 4000 6000 8000 10000 12000 14000 16000
StudentizedResiduals
Estimated Change in Income
NEMW metro areasRest of country metro areas
1
3
116
5
9
47
2
12
131415
16
8
10
Figure 2Studentized Residuals for Income Change Regression, 1989–2009
– 4
– 3
– 2
– 1
0
1
2
3
4
– 4
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0
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– 4000 – 2000 0 2000 4000 6000 8000 10000 12000 14000 16000– 4000 – 2000 0 2000 4000 6000 8000 10000 12000 14000 16000
StudentizedResiduals
Estimated Change in Income
NEMW metro areasNEMW metro areasRest of country metro areasRest of country metro areas
11
33
111166
55
99
4477
22
1212
131314141515
1616
88
1010
54
Key to Figure 3: Studentized Residuals for Employment Change Regression, 1969–89 (Northeast-Midwest metro areas shown in bold) Rank Area Studentized Residual
1 Palm Coast, FL 7.91 2 St. George, UT 4.60 3 Hinesville-Fort Stewart, GA 4.45 4 Honolulu, HI –3.65 5 Las Vegas-Paradise, NV 2.37 6 Prescott, AZ 2.31 7 St. Cloud, MN 2.28 8 Orlando-Kissimmee, FL 2.26 9 Riverside-San Bernardino-Ontario, CA 2.26
10 Punta Gorda, FL 2.23 11 Naples-Marco Island, FL 2.15 12 Miami-Fort Lauderdale-Pompano Beach, FL –2.14 13 Lafayette, LA 2.10 14 Anchorage, AK 2.09
Figure 3Studentized Residuals for Employment Change Regression, 1969 – 89
– 4
– 2
0
2
4
6
8
10
– 50 0 50 100 150 200 250 300 350
NEMW metro areasRest of country metro areas
StudentizedResiduals
Estimated Change in Employment (Thousands)
1
3
10658
4
97
2
11
12
13 14
Figure 3Studentized Residuals for Employment Change Regression, 1969 – 89
– 4
– 2
0
2
4
6
8
10
– 4
– 2
0
2
4
6
8
10
– 50 0 50 100 150 200 250 300 350– 50 0 50 100 150 200 250 300 350
NEMW metro areasNEMW metro areasRest of country metro areasRest of country metro areas
StudentizedResiduals
Estimated Change in Employment (Thousands)
11
33
1010665588
44
9977
22
1111
1212
1313 1414
55
Key to Figure 4: Studentized Residuals for Employment Change Regression, 1989–2009 Rank Area Studentized Residual
1 St. George, UT 8.58 2 Palm Coast, FL 4.52 3 McAllen-Edinburg-Mission, TX 3.17 4 Laredo, TX 3.07 5 Bend, OR 3.05 6 Austin-Round Rock, TX 3.00 7 Fayetteville-Springdale-Rogers, AR-MO 2.97 8 Provo-Orem, UT 2.94 9 Las Vegas-Paradise, NV 2.87
10 Coeur d'Alene, ID 2.74
Figure 4Studentized Residuals for Employment Change Regression, 1989–2009
– 4
– 2
0
2
4
6
8
10
– 40 – 20 0 20 40 60 80 100 120
StudentizedResiduals
Estimated Change in Employment (Thousands)
NEMW metro areas
Rest of country metro areas1
3
106
5
8
4 97
2
Figure 4Studentized Residuals for Employment Change Regression, 1989–2009
– 4
– 2
0
2
4
6
8
10
– 4
– 2
0
2
4
6
8
10
– 40 – 20 0 20 40 60 80 100 120– 40 – 20 0 20 40 60 80 100 120
StudentizedResiduals
Estimated Change in Employment (Thousands)
NEMW metro areasNEMW metro areas
Rest of country metro areasRest of country metro areas11
33
101066
55
88
44 9977
22
56
Table 12. Studentized Residuals for the Income and Employment Models: Metropolitan Area Outliers Studentized Residuals (Outliers shown in bold) Income Employment Full MSA Name 1969–89 1989–2009 1969–89 1989–2009 Atlantic City, NJ 4.33 –2.62 0.62 0.83 Bridgeport-Stamford-Norwalk, CT 4.01 1.03 –0.48 –0.72 Sebastian-Vero Beach, FL 3.85 1.00 0.89 –0.42 Palm Bay-Melbourne-Titusville, FL –3.84 0.23 –0.33 0.49 San Diego-Carlsbad-San Marcos, CA –2.74 0.19 0.07 –0.45 Fairbanks, AK –2.63 0.18 1.15 –1.94 Elmira, NY –2.40 –0.37 –1.99 –0.54 Oxnard-Thousand Oaks-Ventura, CA 2.26 0.99 0.40 –0.65 Cumberland, MD-WV –2.19 –0.03 –1.63 –0.64 Punta Gorda, FL –2.18 –2.53 2.23 –0.94 Lawton, OK 2.17 –1.36 –0.78 –1.78 Ithaca, NY –2.15 –0.71 –0.12 –0.76 Lake Havasu City-Kingman, AZ –2.14 –1.17 1.80 0.07 Manchester-Nashua, NH 2.09 –0.09 0.31 –0.58 Trenton-Ewing, NJ 2.09 –0.03 –0.30 1.01 Vallejo-Fairfield, CA 2.02 0.34 –0.03 –0.54 Houma-Bayou Cane-Thibodaux, LA –0.28 3.38 0.42 1.34 Ann Arbor, MI 0.42 –3.31 1.82 –0.02 Hinesville-Fort Stewart, GA –0.93 –3.31 4.45 1.37 Pascagoula, MS –0.45 3.02 –0.11 –0.30 Winston-Salem, NC 1.53 –2.96 0.76 –0.32 Sheboygan, WI 1.45 2.63 –0.08 0.46 Peoria, IL 0.10 2.62 –0.79 0.12 Columbus, IN 1.18 2.50 0.43 1.35 Jacksonville, NC 1.15 2.39 0.32 1.12 Napa, CA 1.41 2.31 –1.39 0.47 Midland, TX –0.23 –2.29 –0.07 0.13 Bakersfield, CA –0.50 –2.18 –0.11 –0.04 Myrtle Beach-Conway-North Myrtle Beach, SC 1.38 –2.17 1.58 0.77 New Orleans-Metairie-Kenner, LA –1.34 2.07 –0.42 –1.16 Palm Coast, FL –0.99 –0.40 7.91 4.52 St. George, UT –0.51 –0.43 4.60 8.58 Honolulu, HI 0.59 –0.60 –3.65 –1.95 Las Vegas-Paradise, NV –1.13 –0.71 2.37 2.87 Prescott, AZ –1.46 –1.79 2.31 0.92 St. Cloud, MN 1.44 1.27 2.28 0.91 Orlando-Kissimmee, FL –0.48 –1.11 2.26 1.33 Riverside-San Bernardino-Ontario, CA 0.40 –1.65 2.26 1.46 Naples-Marco Island, FL 0.08 1.47 2.15 0.11 Miami-Fort Lauderdale-Pompano Beach, FL –1.00 –0.08 –2.14 –0.23 Lafayette, LA 1.41 0.45 2.10 1.87 Anchorage, AK 1.75 –1.43 2.09 –0.33 McAllen-Edinburg-Mission, TX –0.21 –1.23 1.79 3.17 Laredo, TX –0.26 0.32 0.44 3.07 Bend, OR 1.09 0.19 1.77 3.05 Austin-Round Rock, TX 0.74 0.40 1.83 3.00 Fayetteville-Springdale-Rogers, AR-MO 1.79 1.38 –0.04 2.97 Provo-Orem, UT –0.29 –0.67 1.88 2.94 Coeur d'Alene, ID 0.93 –0.10 1.71 2.74
57
The outliers from the plots of the residuals are shown in table 12, which
consolidates the values for the identified outlying studentized residuals for all four
estimating equations. The first two columns of data show the studentized residuals for
the income change equations, and the third and fourth columns show the results for the
employment change equations. Values of the studentized residuals that have an absolute
value greater than two are highlighted in bold.
There were thirty metropolitan areas where the studentized residual from either of
the income equations had an absolute value greater than two. Of those thirty metro areas,
the studentized residuals for exactly half of them flipped signs between the two periods.
On the other hand, there were twenty-one metro areas where the studentized residual
from either of the employment equations had an absolute value greater than two. Of
those twenty-one areas, the residuals for only three saw sign reversals between the two
periods. Thus, areas that had large unexplained employment gains in one period also
tended to have unexplained employment gains in the other period. Either the
employment model is missing some drivers that could help explain some of this behavior,
or in other instances there may be shocks outside of the model that can account for the
stronger-than-expected growth in employment in selected metro areas in the South and
West regions of the country.
In an attempt to move the analysis forward, we made an initial pass at trying to
account for some of the strong employment growth, beyond what the variables in our
estimating equations were able to pick up, for those outliers in the South and West
regions of the country. Often the strong employment growth is associated with rapid
growth in population, and we hypothesized that this could be due in part to lesser
geographic or legal restrictions on growth. We were able to obtain recent single-point-in-
time data on both geographic and zoning indices for half of the outlier metro areas
identified by the estimating equations for employment.14 Specifically, the geographic
indices are a measure of the percentage of land that is difficult to develop, either because
it is covered by wetlands or because it is steep. The index values are between zero and
one (for example, Abilene, Texas, scores 0.019, while San Francisco, California, scores
0.73). The zoning indices come from the Wharton Residential Land Use Regulatory
Index, which measures the stringency of land use regulations across cities. It is
14http://real.wharton.upenn.edu/~saiz/
58
constructed as a z-score, with high numbers representing strict zoning and low numbers
representing loose zoning (for example, Boulder, Colorado, scores a 3.1, while Pine
Bluff, Arkansas, scores a –1.76).
Of the twenty-one metro areas identified as having outlier residuals for the
employment model, only eleven of them have these data available. Among those eleven
areas, there is some support for the hypothesis that land use or availability matters. The
results are summarized in table 13.
Table 13. Geographic and Zoning Restrictions on Growth Selected U.S. Metropolitan Areas
Positive (P) or Stringency of Negative (N) Land Use Land Accessible Metro Area Residual Regulations to Develop
Austin, TX P Loose –0.283 Much 0.038 McAllen, TX P Loose –0.449 Much 0.009 Lafayette, LA P Loose –0.103 Much 0.020
Fayetteville, AR P Loose –0.404 Moderate 0.289 Las Vegas, NV P Loose –0.692 Moderate 0.321 St. Cloud, MN P Loose –0.115 Moderate 0.206
Miami, FL N Strong 0.945 Little 0.766
Orlando, FL P Moderate 0.316 Moderate 0.361
Riverside, CA P Moderate/ Strong 0.526 Moderate 0.379
Provo, UT P Moderate 0.208 Little 0.596 Naples, FL P Moderate 0.289 Little 0.756
Notes: The land use regulation indices are a z-score, with higher numbers representing stricter zoning. The land accessibility indices are a measure of the percentage of land that is difficult to develop.
In summary, out of the eleven areas with complete data, the first seven listed in
the table fit the land use and regulation hypotheses well, the next two fit only the land
availability hypothesis, and the remaining two areas don’t fit either hypothesis. So, we
have learned something from this exercise, but clearly, further research is called for to
more fully understand the economic behavior of these regions.
Of course, exogenous shocks that are difficult to internalize in a model can also
be the cause for unusual strength or weakness in the evolution of an area’s economy.
Examples can be found among some of the metro areas with larger outliers. The border
59
town of Laredo, Texas, received a significant shot in the arm for employment in the later
period from the introduction of NAFTA, as did Atlantic City, New Jersey, for income in
the earlier period with the opening of casino gaming there. On the other hand, the
collapse of the high-paying auto industry in Ann Arbor, Michigan (a drop in industry
employment from 19,100 in 1990 to 4,200 in 2009) contributed to its under-performance
in income growth in the later period. More examples can be found. This suggests that
not all of the large misses in modeling the economy are due to internal modeling
shortcomings. To move forward in understanding the success patterns of metropolitan
areas, future research needs to dig more deeply into the reasons why certain regions did
not conform as well to the general model specification.
Conclusion
Our study strives to extend the insights of prior research on what leads
metropolitan area economies in the United States to function the way they do, what
makes some of the local economies more successful than others, and what policy-related
handles, if any, can improve their profiles. In some respects, we covered ground similar
to studies that preceded ours. In several important ways, however, our approach and
measures were unique to this literature. We looked at a forty-year time interval, much
longer than is typical for this subject, and moreover, we segmented our estimation period
into sequential sub-intervals. We built a data base to support these fit periods, and
assembled new series for variables that were judged to be promising economic drivers
but that were not previously available. And we conducted an analysis of the regression
residuals to determine what metro areas did not conform as well to the fit of the general
model.
We found, consistent with a number of previous studies, that among the strongest
indicators of the well-being of a metro area are: its initial conditions (particularly related
to the size of the population); industry structure (especially related to mining, finance,
manufacturing, and health services); educational attainment; right-to-work legislation (or
more generally, a business-friendly environment); and for employment, its airport
connectivity. With data that we assembled or discovered, we added to that list of
favorable results the crime rate, the innovative environment as measured by industrial
and IT patents awarded, and for employment, amenities associated with the natural
environment. We found less support than a number of other studies have found for the
60
notion that the share of the population in poverty is important to aggregate economic
outcomes.
More generally, our approach was structured in a manner to add more depth to
those studies based on econometric modeling methods. We demonstrated the point that
the behavior of these small, open economies can be quite volatile over shorter intervals of
time, so that it is important to have longer fit periods for the equations in order to
generate reliable coefficient estimates. An example is a study by Blumenthal et al.,
which found that the share of manufacturing activity was positively related to economic
outcome variables, contrary to the dominant trend over the past forty years, because their
estimation period happened to coincide with manufacturing’s relatively more favorable
prospects over the decade of the 1990s. In addition, by estimating our model over
sequential twenty-year time intervals, we demonstrated that the impact of the economic
drivers can change over time, so that currency of the fit period is important. For
example, our estimates suggest that the effect of agglomeration economies in metro areas
is shrinking over time, the influence of health services is growing, the importance of
industrial innovation is increasing, and the negative impact of crime on regional income
has expanded over time. Thus, results for a prior period might not be prologue to future
outcomes.
In addition, we constructed a complete set of new or improved measures for select
economic drivers. In doing so, we were able to contribute to a more complete structural
specification of a metro area econometric model without simultaneously sacrificing the
fit period. Several of the new measures, including the crime rate and awarded patents,
proved to be significant additions to the equation estimates.
Also unique to our study is an analysis of the residuals generated by the
estimating equations. This served the purpose of more complete model validation, as
well as identifying those metro areas that did not conform as well to the fit of the
equations. We found that in general the income equations were a better fit than the
employment equations. In the employment equations, the most systematic errors were
associated with several metro areas in the South and West regions of the country that
were growing at more rapid rates than were understood by our general model. We found
some evidence that, in part, this could be due to lesser geographic or legal restrictions on
growth in those areas, and that some exogenous shocks played a role, but more research
61
is required to uncover a more complete answer. The main point is that it is important to
move the research forward by gaining a greater understanding of why these models don’t
work as well in certain geographic areas.
There is an even more fundamental question in this area of research: Do public
policies have much effect on economic outcomes for these local economies? There are
those who take the view that public-policy-related actions that have been undertaken had
little effect at all. Instead, success rests with decisions made by individual firms based on
their products and process, and even on location decisions motivated by personal
preferences of company leadership. Others argue that urban growth is not simply a
matter of choice, but also of idiosyncrasy, fate, and history—regional growth is
particularly vulnerable to shocks. Our view is that although many of the drivers of
metropolitan area economies do not have short time horizons to affect change, including
public-policy-related drivers, there is an opportunity to move economies onto a more
favorable longer-term growth path with sensible policy-induced change.
62
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