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.

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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

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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

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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

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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.

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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.

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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

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Estimated Change in Income

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106 5

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9 7

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1213

14 1516

Figure 1Studentized Residuals for Income Change Regression, 1969 – 89

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StudentizedResiduals

Estimated Change in Income

1133

101066 55

88

44

99 77

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1111

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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

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Estimated Change in Income

NEMW metro areasRest of country metro areas

1

3

116

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47

2

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131415

16

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Figure 2Studentized Residuals for Income Change Regression, 1989–2009

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33

111166

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4477

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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

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StudentizedResiduals

Estimated Change in Employment (Thousands)

1

3

10658

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97

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11

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13 14

Figure 3Studentized Residuals for Employment Change Regression, 1969 – 89

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Estimated Change in Employment (Thousands)

11

33

1010665588

44

9977

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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

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– 40 – 20 0 20 40 60 80 100 120

StudentizedResiduals

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NEMW metro areas

Rest of country metro areas1

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106

5

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4 97

2

Figure 4Studentized Residuals for Employment Change Regression, 1989–2009

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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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