Transformation of America’s Metropolitan Area Economies: Lessons from Four Decades

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.

2

Innovations in the Study Several embellishments to previous studies are tendered in this study, including: 1. Extending the data base for metropolitan areas to forty years (1969 to 2009), much longer than is typical for small economic regions.

2. Taking advantage of the longer time period of available data to segment the estimation period into sequential sub-intervals.

3. Investigating spatial differences among select regions of the country.

4. Making a considerable investment in assembling new series for variables that were judged to be promising economic drivers.

5. Conducting an analysis of residuals to identify those metro areas that showed the least conformation to the general model.

We elaborate on each of these items in turn.

1. Regression analysis in general is carried out in two dimensions: time and space (geography). Because data limitations are so severe when analyzing economic behavior in geographies as small as metropolitan areas, statistical investigations have often focused on relatively narrow time intervals. As a consequence, inferences on the effectiveness of economic drivers and policy handles are, by necessity, drawn from time intervals that might not, indeed will not, reflect all of the behavioral relationships outside of the period.

To address this concern, we first expended great effort to assemble data that spanned a forty-year interval, from 1969 to 2009, a longer contemporary period than for any regression-based study of metro areas of which we are aware. The data were also adjusted where necessary to maintain consistent metro-area definitions over time for 366 areas, and to account for idiosyncrasies such as metro areas overlapping state boundaries.

All data expressed in real terms were deflated by the price deflator representing the closest proximity to the metro area.

2. Because of the comparative volatility of local economies, we hypothesized that we would learn more (and results would be less misleading) by looking in combination at shorter, sequential time periods within the longer time interval. Few other statistical, analytical studies on metro areas to date have broken a time range into intervals. We generated regression results for ten- and twenty-year intervals, but in the paper we focus on the twenty-year groupings, with results for the ten-year intervals contained in the appendix.

3 The concept of sequential periods for analysis was motivated by our initial descriptive work for the study. The twenty metropolitan areas with the largest and smallest increase between 1969 and 2009 in real personal income minus transfer payments per capita (one of the dependent variables chosen for our analysis) are shown in tables 1 and 2. The two tables provide the same information for different time intervals.

Table 1 shows the change broken out into four ten-year intervals and table 2 shows two twenty-year intervals. Also included are our original focus metro area, Detroit, and its “peer” areas as identified by characteristics suggested by our panel of experts. 1 For many metro areas there is a wide variation in the area’s performance by time interval, particularly by decade. The metro area with the greatest increase between 1969 and 2009 in real personal income minus transfers per capita was Bridgeport-StamfordNorwalk, 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.

1

These 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 Metro Areas with Largest Increases, 1969–2009 Bridgeport-Stamford-Norwalk, CT $29,010 1 $ 4,745 73 $15,783 1 $12,811 2 Washington-Arlington-Alexandria, DCVA-MD-WV 23,450 2 6,106 26 8,865 10 4,605 82 Naples-Marco Island, FL 22,913 3 1,100 340 13,440 2 2,983 186 Boston-Cambridge-Quincy, MA-NH 21,520 4 2,518 268 10,545 3 7,085 14 Sebastian-Vero Beach, FL 21,319 5 5,372 41 10,323 4 4,658 77 San Francisco-Oakland-Fremont, CA 21,210 6 6,447 21 5,140 77 10,657 4 Jacksonville, NC 21,118 7 1,774 319 4,475 107 3,491 144 Boulder, CO 20,870 8 4,057 123 6,294 37 8,508 6 San Jose-Sunnyvale-Santa Clara, CA 20,386 9 7,467 9 4,876 86 13,825 1 Lafayette, LA 20,127 10 9,413 3 –1,175 343 4,655 79 Charlottesville, VA 18,935 11 2,749 248 8,514 13 4,274 92 Santa Fe, NM 18,341 12 4,354 96 5,879 48 5,220 52 Sioux Falls, SD 18,324 13 6,671 19 1,355 271 7,207 10 Midland, TX 18,114 14 10,324 2 –2,440 356 2,196 248 Houma-Bayou Cane-Thibodaux, LA 17,932 15 7,533 8 –4,150 361 4,028 105 Houston-Sugar Land-Baytown, TX 17,906 16 6,600 20 2,249 227 8,000 7 Barnstable Town, MA 17,840 17 2,082 305 7,798 20 7,148 13 Trenton-Ewing, NJ 17,832 18 5,484 38 9,152 9 2,991 185 Baltimore-Towson, MD 17,759 19 4,575 83 5,549 61 3,751 126 Santa Cruz-Watsonville, CA 17,751 20 5,863 31 3,198 164 10,641 5 Metro Areas with Smallest Increases, 1969–2009 Riverside-San Bernardino-Ontario, CA 2,702 347 4,291 101 1,492 260 –623 352 Michigan City-La Porte, IN 2,559 348 4,134 118 –1,061 341 1,747 277 Youngstown-Warren-Boardman, OH-PA 2,549 349 2,948 223 422 307 1,520 290 Longview, WA 2,443 350 4,110 119 –778 334 589 330 Visalia-Porterville, CA 2,283 351 4,929 62 –2,965 357 780 325 Hanford-Corcoran, CA 2,149 352 6,096 27 –3,519 359 –2,671 365 Bakersfield-Delano, CA 2,143 353 5,307 44 –2,118 352 –1,654 361 Madera-Chowchilla, CA 1,921 354 8,929 5 –5,748 363 –1,520 360 Anderson, IN 1,723 355 2,910 228 1,368 270 1,670 281 Saginaw-Saginaw Township North, MI 1,596 356 4,187 115 –990 337 2,354 241 Mansfield, OH 1,384 357 1,861 314 1,899 240 486 334 Stockton, CA 1,277 358 2,834 238 –693 332 1,383 301 Elkhart-Goshen, IN 1,157 359 487 354 3,829 135 2,351 242 Yuma, AZ 1,116 360 3,145 202 –632 331 –2,490 364

1999–2009

Rank

–$ 4,329

347

3,875 5,389 1,371 965 –1,035 11,378 2,011 –5,781 7,234 3,396 2,888 3,091 8,034 10,521 1,057 812 205 3,884 –1,950

27 16 111 129 259 2 78 363 7 31 44 40 4 3 123 140 189 26 298

–2,458 –2,261 –2,342 –1,478 –461 2,242 607 260 –4,224 –3,956 –2,863 –2,247 –5,510 1,093

312 309 310 278 227 69 162 182 346 340 322 308 360 121

Table 1 continued. Change in Personal Income Minus Transfers Per Capita (2009$), 1969 to 2009: 10-Year Intervals Area 1969–2009 Rank Metro Areas with Smallest Increases, 1969–2009 (continued) Muskegon-Norton Shores, MI 1,046 361 Jackson, MI 948 362 El Centro, CA 238 363 Merced, CA 21 364 Flint, MI –1,780 365 Lake Havasu City-Kingman, AZ –3,222 366 Metro Areas with Characteristics Comparable to Detroit Philadelphia-Camden-Wilmington, PA-NJDE-MD 15,055 47 Hartford-West Hartford-East Hartford, CT 14,804 49 St. Louis, MO-IL 13,495 75 Pittsburgh, PA 12,118 102 Chicago-Joliet-Naperville, IL-IN-WI 11,941 108 Cincinnati-Middletown, OH-KY-IN 11,741 114 Milwaukee-Waukesha-West Allis, WI 11,625 118 Columbus, OH 11,403 124 Indianapolis-Carmel, IN 10,665 153 10,641 155 Providence-New Bedford-Fall River, RI-MA Cleveland-Elyria-Mentor, OH 7,213 260 Buffalo-Niagara Falls, NY 6,487 281 Detroit-Warren-Livonia, MI 5,558 303

1969–1979

Rank 1979–1989

Rank 1989–1999 Rank

1999–2009

Rank

1,403 1,745 6,897 3,065 3,488 –1,010

331 320 16 209 167 365

133 157 –3,871 –921 –1,282 370

316 314 360 336 345 308

3,304 2,895 –3,449 –1,427 2,547 –1,216

163 194 366 358 222 356

–3,793 –3,850 661 –695 –6,533 –1,365

336 337 154 241 366 273

2,742 3,247 3,017 4,270 3,582 2,557 3,777 2,862 3,004 1,902 3,454 2,451 2,926

250 193 216 106 159 265 146 235 218 313 170 273 226

6,374 9,269 4,737 2,526 3,568 4,113 2,464 5,174 4,593 6,033 1,370 2,319 3,407

35 8 92 207 144 120 214 75 101 41 268 222 152

4,206 942 5,112 4,719 5,196 6,183 5,531 5,168 5,275 1,600 3,815 1,066 4,488

97 318 59 74 53 28 43 54 51 283 119 316 85

1,732 1,347 629 603 –405 –1,113 –147 –1,802 –2,207 1,106 –1,426 652 –5,263

93 112 159 163 224 263 209 294 307 120 277 156 359

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 Metro Areas with Largest Increases, 1969–2009 Bridgeport-Stamford-Norwalk, CT $29,010 1 $20,528 1 $8,482 Washington-Arlington-Alexandria, DC-VA-MD-WV 23,450 2 14,971 4 8,479 Naples-Marco Island, FL 22,913 3 14,540 6 8,372 Boston-Cambridge-Quincy, MA-NH 21,520 4 13,063 9 8,456 Sebastian-Vero Beach, FL 21,319 5 15,696 2 5,623 San Francisco-Oakland-Fremont, CA 21,210 6 11,587 15 9,622 Jacksonville, NC 21,118 7 6,249 188 14,869 Boulder, CO 20,870 8 10,351 27 10,519 San Jose-Sunnyvale-Santa Clara, CA 20,386 9 12,343 12 8,043 Lafayette, LA 20,127 10 8,238 83 11,889 Charlottesville, VA 18,935 11 11,264 18 7,671 Santa Fe, NM 18,341 12 10,233 30 8,108 Sioux Falls, SD 18,324 13 8,026 91 10,298 Midland, TX 18,114 14 7,884 97 10,230 Houma-Bayou Cane-Thibodaux, LA 17,932 15 3,383 319 14,549 Houston-Sugar Land-Baytown, TX 17,906 16 8,849 61 9,057 Barnstable Town, MA 17,840 17 9,880 34 7,960 Trenton-Ewing, NJ 17,832 18 14,637 5 3,196 Baltimore-Towson, MD 17,759 19 10,124 31 7,635 Santa Cruz-Watsonville, CA 17,751 20 9,061 54 8,690 Metro Areas with Smallest Increases, 1969–2009 Riverside-San Bernardino-Ontario, CA 2,702 347 5,782 214 –3,081 Michigan City-La Porte, IN 2,559 348 3,073 333 –514 Youngstown-Warren-Boardman, OH-PA 2,549 349 3,370 320 –821 Longview, WA 2,443 350 3,332 321 –889 Visalia-Porterville, CA 2,283 351 1,964 357 319 Hanford-Corcoran, CA 2,149 352 2,578 344 –429 Bakersfield-Delano, CA 2,143 353 3,190 330 –1,047 Madera-Chowchilla, CA 1,921 354 3,181 331 –1,260 Anderson, IN 1,723 355 4,278 289 –2,554 Saginaw-Saginaw Township North, MI 1,596 356 3,197 329 –1,602 Mansfield, OH 1,384 357 3,761 307 –2,377 Stockton, CA 1,277 358 2,142 353 –865 Elkhart-Goshen, IN 1,157 359 4,317 287 –3,160 Yuma, AZ 1,116 360 2,513 346 –1,398 Muskegon-Norton Shores, MI 1,046 361 1,536 363 –490

Rank 23 24 26 25 87 15 1 8 30 4 35 28 11 13 2 16 31 179 37 20 361 326 331 333 303 321 336 340 356 348 354 332 362 342 325

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

8 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 twomillion-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

9 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 Dependent variables Change in real per capita personal income minus transfer payments % Change in employment Independent variables used in regressions Per capita personal income minus transfer payments Natural log of MSA population Share of agriculture in total employment Share of mining in total employment Share of construction in total employment Share of manufacturing in total employment Share of finance, ins. in total employment Share of government in total employment, excluding military Share of military in total employment Share of mining in total earnings Share of construction in total earnings Share of durables in total earnings Share of nondurables in total earnings Share of finance, ins. in total earnings Share of health services in total earnings Share of military in total earnings Share of government in total earnings, excluding military Share of population with bachelor's degree or higher Share of foreign-born Share of poverty Chemical patents per 1,000 IT patents per 1,000 Industrial excluding motor vehicle patents per 1,000 Motor vehicle patents per 1,000 Total crimes per 1,000

Definition

Source

Change in real per capita personal income minus transfer payments Percentage change in employment

Bureau of Economic Analysis

Per capita personal income minus transfer payments Natural log of MSA population Ratio of agricultural employment to total employment Ratio of mining employment to total employment Ratio of construction employment to total employment Ratio of manufacturing employment to total employment Ratio of finance, ins. employment to total employment Ratio of government excluding military employment to total employment Ratio of military employment to total employment Ratio of mining earnings to total earnings Ratio of construction earnings to total earnings Ratio of durables earnings to total earnings Ratio of nondurables earnings to total earnings Ratio of finance, ins. earnings to total earnings Ratio of health services earnings to total earnings Ratio of military earnings to total earnings Ratio of government excluding military earnings to total earnings Percentage of population with bachelor's degree or higher Percentage of foreign-born population Percentage of population in poverty Number of chemical-related patents per 1,000 population Number of IT (information technology) related patents per 1,000 population Number of industrial excluding motor vehicle related patents per 1,000 population Number of motor vehicle related patents per 1,000 population Total crime (violent and property) counts per 1,000 population

Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis

Bureau of Economic Analysis

Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis Bureau of Economic Analysis U.S. Census Bureau U.S. Census Bureau U.S. Census Bureau U.S. Patent & Trademark Office U.S. Patent & Trademark Office U.S. Patent & Trademark Office U.S. Patent & Trademark Office FBI Crime Report

Table 3 continued. Variable Definitions and Sources Variables Independent variables used in regressions (continued) State & local government tax percentage Share of population age 65 or more College enrollments per 1,000 Research expenditures per 1,000,000 Airport passengers per capita July temperature minus January temperature Right-to-work dummy

Definition

Source

Ratio of state and local government tax revenue to personal income Percentage of the population age 65 and over Number of postsecondary school enrollments per 1,000 population University research expenditures per 1,000,000 population Number of enplaned passengers per capita July temperature minus January temperature Right-to-work state dummy variable

U.S. Census Bureau

Natural Amenities Scale 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) Independent variables tried in regressions Diversity Index

Physical natural amenity index Regional dummy variables Regional dummy variables Regional dummy variables Regional dummy variables

Number of postsecondary schools Share of population age 24 or less January temperature July temperature Violent crimes per 1,000 Property crimes per 1,000 Air freight per capita Public research expenditures per 1,000,000

Number of postsecondary schools in MSA Percentage of the population age 24 or younger Average January temperature Average July temperature Violent crime counts per 1,000 population Property crime counts per 1,000 population Air freight (tons) per capita Public university research expenditures per 1,000,000 population

Private research expenditures per 1,000,000

Private university research expenditures per 1,000,000 population

Index of the MSA’s diversity among industries

U.S. Census Bureau National Center for Education Statistics National Center for Education Statistics Bureau of Transportation Statistics Weather Underground National Right-to-Work Legal Defense Foundation U.S. Department of Agriculture U.S. Census Bureau U.S. Census Bureau U.S. Census Bureau U.S. Census Bureau Calculated using Bureau of Economic Analysis employment data National Center for Education Statistics U.S. Census Bureau Weather Underground Weather Underground FBI Crime Report FBI Crime Report Bureau of Transportation Statistics National Center for Education Statistics National Center for Education Statistics

Table 4. Descriptive Statistics for the Variables: 20-Year Intervals Variables Dependent variables Change, real per capita personal income minus transfer payments % Change in employment Independent variables used in regressions Per capita personal income minus transfer payments Natural log of MSA Population Share of agriculture in total employment Share of mining in total employment Share of construction in total employment Share of manufacturing in total employment Share of finance, ins. in total employment Share of government excl. military in total employment Share of military in total employment Share of mining in total earnings Share of construction in total earnings Share of durables in total earnings Share of nondurables in total earnings Share of finance, ins. in total earnings Share of health services in total earnings Share of military in total earnings Share of government excl. military in total earnings Share of population with bachelor's degree or higher Share of foreign-born Share of poverty Chemical patents per 1,000 IT patents per 1,000 Industrial excluding motor vehicle patents per 1,000 Motor vehicle patents per 1,000 Total crimes per 1,000 State & local government tax revenue percentage Share of population age 65 or more College enrollments per 1,000 Research expenditures per 1,000,000 Airport passengers per capita July temperature minus January temperature Right-to-work dummy Natural Amenities Scale

Mean

Std Dev

1969–1989 3490.51 1798.95 69.28 60.25 1969/1970 18573.020 3546.040 12.145 1.110 0.060 0.055 0.009 0.024 0.052 0.016 0.185 0.099 0.057 0.020 0.153 0.069 0.052 0.092 1.197 3.134 7.021 2.534 15.495 12.472 10.272 8.117 4.112 1.980 4.216 1.689 4.385 9.588 16.485 8.450 10.722 4.164 2.945 2.806 14.434 6.489 0.035 0.096 0.022 0.036 0.052 0.039 0.016 0.019 31.015 16.184 10.364 1.495 9.459 3.219 51.568 58.082 13.72 41.08 0.486 0.714 41.652 10.372 0.470 0.500 3.810 1.243

Mean

Std Dev

1989–2009 3356.87 2747.95 34.43 29.72 1989/1990 25022.120 5132.510 12.434 1.052 0.040 0.039 0.008 0.021 0.054 0.015 0.126 0.070 0.065 0.020 0.145 0.055 0.030 0.059 1.241 3.368 6.310 2.091 12.872 9.790 8.398 6.443 4.453 2.396 8.037 2.686 3.426 8.133 17.998 7.591 18.926 6.263 4.444 5.080 13.708 4.985 0.041 0.100 0.036 0.074 0.053 0.037 0.016 0.023 53.022 18.482 9.903 1.229 12.477 3.542 71.665 58.531 76.89 21.38 0.846 1.646 41.652 10.372 0.470 0.500 3.810 1.243

Table 4 continued. Descriptive Statistics for the Variables: 20-Year Intervals Variables Independent variables used in regressions (continued) 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) Independent variables tried in regressions Air freight (tons) per capita Diversity Index Number of postsecondary schools Share of population age 24 or less January temperature July temperature Public university research expenditures per 1,000,000 Private university research expenditures per 1,000,000 Violent crimes per 1,000 Property crimes per 1,000

Mean

Std Dev 1969/1970 –0.087 0.537 0.120 0.700 0.063 0.665 –0.052 0.573 1969/1970 7.540 18.521 0.212 0.041 5.776 13.082 47.782 4.712 34.395 12.803 76.046 5.616 12.28 40.68 1.45 6.45 2.03 1.41 29.42 14.95

Mean Std Dev 1989/1990 –0.087 0.537 0.120 0.700 0.063 0.665 –0.052 0.573 1989/1990 8.263 40.621 0.213 0.029 23.161 50.891 37.694 4.455 34.395 12.803 76.046 5.616 67.40 196.48 9.49 60.92 5.35 3.25 48.05 15.91

14

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-towork 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 policyrelated, that appear to underlie their relative success patterns. Pack (2002), for instance, argues that urban growth is not simply a matter of choice (policy or market forces), but also of idiosyncrasy, fate, and history. This stems from her findings that regional growth varies widely and is vulnerable to shocks, and thus policies based on the experience of earlier periods are often inappropriate. Glaeser and Shapiro (2001), on the other hand, find that urban growth in the 1990s looked extremely similar to urban growth during the prior post-World War II decades, and was determined by three large trends: (1) faster growth in cities with strong human capital bases; (2) movement to warmer, drier places; and (3) faster growth in cities built around the automobile. To add a wrinkle, Erickcek and McKinney (2009) raise the possibility that smaller metro areas might behave differently than larger urban areas—a possibility that we plan to explore with our data set in future research.

A number of articles in the literature posit specific drivers as key contributors to urban area economic growth. A scan of those articles, together with suggestions from our expert panel, led us to consider the following as potential explanatory factors of national urban growth: urban structure (initial conditions), industry (economic) structure, demographics, innovative environment, amenities, regional effects, and a series of measures susceptible to shorter-term policy initiatives. The last of these include such persistent state and local budget issues as education, crime, taxes and business climate, and connectivity to the global economy.

Several of the other factors, such as industry

structure or demographics, have sufficiently long time horizons over which significant change can occur, making them suitable as control variables in the shorter term. For all of the concepts, the challenge is to come up with proxy measures, and to understand the limitations of the measures and what’s in play and what’s not in the policy landscape.

16 Each of these concepts and their proxy measures will be addressed more fully in the discussion of our model and its estimation. Here we first consider previous findings on the efficacy of certain drivers related to the economic performance of metro areas.

Initial Conditions Population size at the beginning of the period has been used as a measure of initial conditions in local economies. Glaeser and Shapiro (2001) find no statistically significant relationship between initial metro area population and economic growth. In contrast, Blumenthal et al. find a significantly positive effect of population size in 1990 on metro area growth over the following decade, which they interpret as reflecting the agglomeration economy advantages of large areas, including productivity advantages.

Glaeser, Kolko, and Saiz (2001) argue that although urban economies have traditionally been viewed as having advantages in production, as firms have become less bound by location, the success of cities may hinge more and more on their role as centers of consumption.

Industry Structure Several decades of forecasting economic activity for regional and local economies has convinced us that differing industry structure among these areas is at the crux of their differing economic outcomes over varying periods of time. This is undoubtedly the source of much of the dramatic movement across decades in economic outcome rankings documented in tables 1 and 2 in a previous section. In the literature on urban economies, the most frequently tested variable measuring the effects of industry structure on economic performance has been manufacturing’s share of employment or earnings, which typically is found to be negatively related and is associated with characteristics of the “old” economy—high-paid, low-skilled activity vulnerable to the spreading global economy.

Glaeser, Scheinkman, and Shleifer (1995), for instance, find that

manufacturing’s share of employment in 1960, the beginning of their observation period, is negatively related to growth in income and population between 1960 and 1990.

Blumenthal et al. find contrary to these expectations, including their own, a positive relationship between a metro area’s manufacturing share of employment in 1990 and growth over the subsequent decade in employment and Gross Metropolitan Product.

They attribute this unexpected result, correctly in our view, to the fact that their estimation period coincides with manufacturing’s relatively more favorable prospects over the 1990s. They also include in their consideration of industry structure the share of

17 employment in the finance-insurance-real-estate sector, and find the measure to be positively related to the change in Gross Metropolitan Product, which they see as an outcome of a higher-value service sector orientation.

Demographics On demographics, a frequent focus in the literature is on the racial composition of the population and its influence on the relative success of local economic outcomes. The measure most commonly analyzed is the percentage of the target population that is African-American (excluding Hispanics), typically strongly related to the area’s poverty status, and hypothesized to contribute to weaker economic outcomes. Blumenthal et al. find that initial racial demographics did affect economic performance negatively over the 1990s.

Glaeser, Scheinkman, and Shleifer (1995) find that racial composition and

segregation were uncorrelated with urban growth across all cities between 1960 and 1990, but in cities with large nonwhite communities, segregation is positively related to population growth. In terms of the more general measure of poverty across all racial lines, Partridge and Rickman (2008) find that metropolitan-wide job growth is associated with a stronger safety net in medium and smaller metro areas.

Blumenthal et al. include measures for the proportion of the population that is not of traditional working age (both those age 24 years or younger and those 65 years or older) in the initial period (1990), observing that their labor force participation rates remain significantly lower than those of the prime working-age cohort. They find that these measures of demographic structure do not affect economic performance over their period of estimation.

Innovative Environment An innovative environment is increasingly viewed to be an important driver of economic growth as the New Economy evolves—that is, among other advantages, scientific development promotes economic development. The classic example is the growth of Research Triangle Park in North Carolina, the largest research park in the United States in terms of both employees and acreage. Link and Scott (2000) provide an economic history of the Park, from vision through its eminent status at the turn of this century. Aided by an analytic model of the Park’s growth, they argue that, over time, new companies adopted the area’s innovative environment, and their success can be explained by the continuity of entrepreneurial leadership enjoyed there for over thirty years. Glaeser, Kerr, and Ponzetto (2009) suggest that entrepreneurship is higher when

18 fixed costs are lower and when there are more entrepreneurial people, which in turn have some relationship to smaller establishment size.

Such studies highlight the complexity of the innovative and entrepreneurial environment, and particularly of measuring it adequately for small, open economies.

Most common in past research is to narrow the focus to the presence of research institutions, particularly those associated with universities. Results have been mixed.

Pack (2002), for instance, finds a positive relationship between the presence of universities and per capita income growth, whereas Blumenthal et al. report that the presence of very active research universities is not statistically significant in either of their economic outcome models. Goldstein and Renault (2004) posit that the research and technology creation functions of universities generate significant knowledge spillovers that result in enhanced regional economic development that otherwise would not occur—but that the contribution is small compared with other factors.

Fundamentally, though, the problem is to capture in well-defined measures the path between university research activity and measures of economic outcome.

Variables Related to Policy Decisions For didactic purposes, we sort several economic drivers discussed in the literature and group them under the general heading of policy-related variables.

These

hypothesized drivers include those that have been central to contemporary budget deliberations across state and local governments.

Among this group of drivers is

educational attainment, one of the most scrutinized concepts in the recent literature in terms of its relationship with regional economic performance. As a policy matter, former Chicago Fed President and CEO Michael Moskow notes (in Mattoon, 2006) that the relationships among education, productivity, and economic growth have never been clearer, but financial support for higher education has waned while costs continue to rise.

He states that the perception of higher education as an important public good has eroded, as it is increasingly seen as a private good with the benefits accruing to the student in the form of higher wages and quality of life.

Glaeser and Saiz (2004) make perhaps the strongest summary statement in the academic literature on the value of education to the community, observing that for more than a century, educated cities have grown more quickly than comparable cities with less human capital. Adding rigor to this statement is their evaluation that the claim survives a battery of other control variables, metropolitan area fixed effects, and tests for reverse

19 causality. They argue that skilled cities are growing because they are becoming more economically productive, not because they are becoming more attractive places to live.

They suggest that in large part, the success of skilled cities results from their being better at adapting to economic shocks.

Glazer and Grimes (2010) lend support to the notion that educational attainment is a predictor of regional economic success. They find that almost all states in the highest per capita income category are over-concentrated compared with the nation in the proportion of wages coming from knowledge-based industries; they have a high proportion of adults with a four-year degree or more; they have a big metropolitan area with even higher per capita income than the state; and, in that big metropolitan area, a high proportion of the residents have a four-year degree or more. Blumenthal et al. also find that the share of the population with a bachelor’s degree or higher is positively and significantly related to Gross Metropolitan Product and employment growth.

An area’s business climate is often identified as an important factor in its economic success. One element of the business environment clearly under the umbrella of the policy rubric is state and local taxation. Monchuk, Miranowski, Hayes, and Babcock (2007) are among those who find that state and local tax burdens have important impacts on economic growth, but the literature is not definitive on this issue. Part of the lack of clarity here is that the tax structure can be complex and finding a way to represent it is fraught with measurement issues.

One of the most controversial areas related to the environment for business is right-to-work legislation, which gives employees the option of working in establishments without having to join a union, even if co-workers are union members. There is much disagreement about such legislation, with some saying it’s essential for business success and others saying it’s not necessary and may even be detrimental. Bartik (1985) finds a positive effect on the location decisions of manufacturing plants associated with the presence of right-to-work laws, and Tannenwald (1997) also finds a positive relationship between such laws and economic activity, as do Blumenthal et al. 2 But the authors of the last article pose as one possible interpretation of their result, “the presence or enactment of right-to-work legislation is a proxy for a more general positive business-friendly 2

The results of right-to-work legislation could depend on whether the dependent variable is employment (positive sign) or income per capita (negative sign), if the legislation is viewed as a union-avoidance measure. But it might take some time after the enactment of the legislation for the effect to be reflected in the results.

20 political climate in the state that transcends the issue of union organization� (p. 615). In the same vein, Grimes and Ray (1988) point out that differences among states in the presence or absence of these laws may reflect more general social, economic, and political differences.

Another salient point is that, with the economy becoming increasingly more global, attention has turned to the connectivity of localities to the outside world, primarily through air traffic. Both Brueckner (2003) and Green (2007) find a positive relationship between some measure of airport traffic and economic activity, and Blumenthal et al. observe a positive and significant relationship with employment growth. Blonigen and Cristea (2012) find that air service has a positive and significant effect on regional growth, with the magnitude of the effects differing by the size of the metropolitan area and its industrial specialization.

Amenities Several studies in the literature have touted amenity-rich environments as a catalyst for local economic growth, viewing them as magnets for attracting businesses and workers and thus creating jobs. Amenities can be either natural or human-created.

The latter would seem to be important, but measurement has proven difficult, and thus quantitative analysis is sparse. More common is the assessment of natural amenities, typically using some measure of local climate as a gauge. For example, Glaeser and Shapiro (2001) and Blumenthal et al. find some association between warmer climates and urban growth. Dorfman, Partridge, and Galloway (2011) find that natural amenities matter most in the employment patterns for high-skill workers in the subset of U.S. counties that are micropolitan, where their presence can be a deciding factor in location decisions. Deller, Lledo, and Marcouiller (2008), armed with a more sophisticated model for natural amenities, conclude that higher-amenity areas do experience faster growth, but that some level of value-added development may be required to realize that growth.

(Bribes, such as tax incentives, are not classified as amenities in the literature.)

Corruption In our meetings on this project, the topic of corruption came up periodically.

Glaeser and Saks (2004) find that more educated states, and to a lesser degree, richer states, have less corruption.

They observe a weak negative relationship between

corruption and employment and income growth, and conclude that the correlation

21 between development and good political outcomes occurs because more education improves political institutions.

General Model and Estimating Equations With the number of timeseries variables we constructed and assembled, we initially had the intention of using a panel study approach.

Due to the potential

importance of variables constructed from the Bureau of the Census which were not available annually, however, we settled on cross-section analysis.

The independent

variable measures are included in the equations at the beginning of each time interval, and the dependent variables measure the change over the time interval. We estimated the equations over four sequential ten-year intervals from 1969 to 2009, and two sequential twenty-year intervals over the same period. The specification of the model follows.

Dependent Variables Both our panel of experts and a review of the literature led us to choose two measures, the change in inflation-adjusted (real) income per capita and the change in employment, as our primary indicators of an area’s economic performance, although many other gauges were put forward. 3 The change in income is meant to reflect the changing wealth of an area, while employment change is a measure of the variation in its size. Although the first measure may appear to be more compelling, employment growth is often viewed as desirable in its own right. 4 And there seems to be some consensus among researchers that there should be multiple measures of success.

We tested three measures of change in real income in our estimated equations: (1) personal income per capita; (2) personal income minus transfer payments per capita; and (3) earnings per capita. For each measure, we examined both the dollar change and the percentage change. The findings for all three income measures and two functional forms were broadly similar, and we settled on one measure for reporting in the paper: the real dollar change in personal income minus transfers per capita.

The values for real income are expressed in 2009 dollars, using as price deflators the area- or region-specific consumer price index for all urban consumers.

3

If a

Examples include aggregate value of land, population change, and amount of money being invested in the area.

4 See, 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.

22 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)

5

See, for example, Blumenthal et al., pp. 612–13.

23 Industry Structure • A set of variables representing the importance of an industry in a metro area, measured by employment or earnings share (signs determined by industry conditions in the estimation period) Demographics • Share of the population that is foreign-born (positive sign for employment, negative sign for income) • Share of the population in poverty (negative signs) • Share of the population 65 years of age or older (negative signs) Innovative Environment • Patents (utility) awarded per 1,000 population, subdivided into four industry groupings: chemical, information technology, industrial except motor vehicles, and motor vehicles (positive signs) • University research expenditures per million population (positive signs) Short-Term Policy-Related Variables • Educational attainment: share of the population with a bachelor’s degree or more (positive signs) • College enrollment per 1,000 population (indeterminate signs depending on the dominance of its direct or spinoff effect) • Total crimes per 1,000 population (negative signs) • State and local government tax revenue as a percentage of personal income (negative signs) • Dummy variable for location in a right-to-work state, value of 1 for presence (positive signs as indicator of business climate) • Airport passengers per capita (positive signs) Amenities • Temperature extremes: July temperature minus January temperature (negative signs) • Natural Amenities Scale (positive signs) Regional Effects • Dummy variables for four regions of the country: Southwest, Southeast, Midwest, and Northeast (indeterminate signs)

24 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 tenyear 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.

25 The details of the estimation results are shown in table 6 for income and in table 7 for employment. For each time period, the parameter values are beside the variable names with the p-values shown below in parentheses. The results are reviewed here, for both income and employment, by independent variable in turn.

26 Table 5. Summary of Parameter Signs and Significance for (1) Change in Personal Income Minus Transfers Per Capita (2009$) and (2) Change in Employment 20-Year Intervals, 1969–2009, United States

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 R-squared *Not included in final equation.

(1) Income (2) Employment (Shaded entries significant at 5% level) ’69–’89 ’89–’09 ’69–’89 ’89–’09 – – + + + +

+ +

*

*

–

–

* – – * – + + + – –

* + + * – – + + + +

+ – + –

+ – + –

* *

* *

+

+

*

*

+ –

+ –

– + +

– + –

+ + +

+ + –

+ + + – –

– + + – –

– + + – –

– + + – +

+ – – – – *

+ – – – + *

+ – – – + +

+ – – + + +

– *

– *

– +

+ +

– + – +

– – + –

+ + + +

+ – – –

366 0.57

366 0.62

366 0.66

366 0.52

27 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)

28 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 R-squared

366 0.57

366 0.62

29 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 R-squared

366 0.66

366 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 6

Note 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 7

Note that MSAs located in Michigan are not included in our measure despite recently enacted right-towork 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) 8

The 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.

9 The 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

10

Similar tables for the balance of the country can be found in the appendix.

Note 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.

11

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

NortheastMidwest

Rest of U.S.

(Shaded entries significant at 5% level) 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 R-squared *Not included in final equation.

’69–’89 –

’89–’09 –

’69–’89 +

’89–’09 –

’69–’89 –

’89–’09 –

+ +

+ +

+ –

+ –

+ +

+ –

– – – + + + – –

+ + – – + + + +

– + – – + + – –

– + – – + – – +

– – – + + – + –

+ + – – + + + +

– + +

– + –

+ – +

+ + +

– + +

– + –

+ +

– +

– +

+ –

+ +

+ +

+ –

+ –

– –

+ –

– +

+ –

–

–

–

–

–

–

+ – –

+ – –

+ + –

+ – –

+ – –

+ – –

– –

– +

– –

– +

– –

– +

–

–

+

+

–

–

– + – + *

– – + – *

* * * * –

* * * * +

* * * * *

* * * * *

366 0.57

366 0.62

143 0.63

143 0.77

223 0.58

223 0.61

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) 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 R-squared *Not included in final equation.

’69–’89 +

’89–’09 +

’69–’89 +

’89–’09 +

’69–’89 +

’89–’09 –

–

–

–

–

–

–

+ – + – + + –

+ – + – + + –

+ – + – + – –

+ + + – + + –

+ – + – + + +

+ – + – + + –

+ + +

+ + –

– – –

– – –

– + +

+ + –

– +

– +

– +

– –

+ +

– +

+ –

+ –

+ –

+ –

+ –

+ –

–

+

–

+

+

–

+ – –

+ – –

+ – +

+ + –

– – –

+ – –

– + +

+ + +

+ – +

– + +

– + +

+ + +

– +

+ +

+ +

+ +

– +

+ +

+ + + + *

+ – – – *

* * * * –

* * * * –

* * * * *

* * * * *

366 0.66

366 0.52

143 0.66

143 0.65

223 0.68

223 0.41

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 R-squared

143 0.63

143 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 R-squared

143 0.66

143 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.

12

In 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).

13

Dividing 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 Figure 1

Studentized Residuals for Income Change Regression, 1969 – 89 Studentized Residuals 5 4

NEMW metro areas Rest of country metro areas

1 2

3

3 11

2

8

16

14

15

1 0 –1 –2

13 6

9

7

–3

12

10

5

4

–4 –5 0

2000

4000

6000

8000

10000

12000

14000

16000

Estimated Change in Income

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

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 NortheastMidwest 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 Figure 2

Studentized Residuals for Income Change Regression, 1989–2009 Studentized Residuals 4 3

NEMW metro areas Rest of country metro areas

1

4 10

6

7 12

2

11

16

1 0 –1 –2 15

14

13 9

8

–3

5 2

3

–4 – 4000

– 2000

0

2000

4000

6000

8000

10000

12000

14000

Estimated Change in Income

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

16000

54 Figure 3

Studentized Residuals for Employment Change Regression, 1969 – 89 Studentized Residuals 10 NEMW metro areas Rest of country metro areas 8

1

6 2

3

4 7

13 9

8

14

5 10

6

2

11

0

–2

12

4

–4 – 50

0

50

100

150

200

250

300

Estimated Change in Employment (Thousands)

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

350

55 Figure 4

Studentized Residuals for Employment Change Regression, 1989–2009 Studentized Residuals 10

NEMW metro areas

1

Rest of country metro areas

8

6 2

4 7

6

4

5 8

2

3

9

10

0

–2

–4 – 40

– 20

0

20

40

60

80

100

120

Estimated Change in Employment (Thousands)

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

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 0.62 0.83 4.33 –2.62 Bridgeport-Stamford-Norwalk, CT 1.03 –0.48 –0.72 4.01 Sebastian-Vero Beach, FL 1.00 0.89 –0.42 3.85 Palm Bay-Melbourne-Titusville, FL 0.23 –0.33 0.49 –3.84 San Diego-Carlsbad-San Marcos, CA 0.19 0.07 –0.45 –2.74 Fairbanks, AK 0.18 1.15 –1.94 –2.63 Elmira, NY –0.37 –1.99 –0.54 –2.40 Oxnard-Thousand Oaks-Ventura, CA 0.99 0.40 –0.65 2.26 Cumberland, MD-WV –0.03 –1.63 –0.64 –2.19 Punta Gorda, FL –0.94 –2.18 –2.53 2.23 Lawton, OK –1.36 –0.78 –1.78 2.17 Ithaca, NY –0.71 –0.12 –0.76 –2.15 Lake Havasu City-Kingman, AZ –1.17 1.80 0.07 –2.14 Manchester-Nashua, NH –0.09 0.31 –0.58 2.09 Trenton-Ewing, NJ –0.03 –0.30 1.01 2.09 Vallejo-Fairfield, CA 0.34 –0.03 –0.54 2.02 Houma-Bayou Cane-Thibodaux, LA –0.28 0.42 1.34 3.38 Ann Arbor, MI 0.42 1.82 –0.02 –3.31 Hinesville-Fort Stewart, GA –0.93 1.37 –3.31 4.45 Pascagoula, MS –0.45 –0.11 –0.30 3.02 Winston-Salem, NC 1.53 0.76 –0.32 –2.96 Sheboygan, WI 1.45 –0.08 0.46 2.63 Peoria, IL 0.10 –0.79 0.12 2.62 Columbus, IN 1.18 0.43 1.35 2.50 Jacksonville, NC 1.15 0.32 1.12 2.39 Napa, CA 1.41 –1.39 0.47 2.31 Midland, TX –0.23 –0.07 0.13 –2.29 Bakersfield, CA –0.50 –0.11 –0.04 –2.18 Myrtle Beach-Conway-North Myrtle Beach, SC 1.38 1.58 0.77 –2.17 New Orleans-Metairie-Kenner, LA –1.34 –0.42 –1.16 2.07 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 –1.95 –3.65 Las Vegas-Paradise, NV –1.13 –0.71 2.37 2.87 Prescott, AZ –1.46 –1.79 0.92 2.31 St. Cloud, MN 1.44 1.27 0.91 2.28 Orlando-Kissimmee, FL –0.48 –1.11 1.33 2.26 Riverside-San Bernardino-Ontario, CA 0.40 –1.65 1.46 2.26 Naples-Marco Island, FL 0.08 1.47 0.11 2.15 Miami-Fort Lauderdale-Pompano Beach, FL –1.00 –0.08 –0.23 –2.14 Lafayette, LA 1.41 0.45 1.87 2.10 Anchorage, AK 1.75 –1.43 –0.33 2.09 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-intime 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.

14

http://real.wharton.upenn.edu/~saiz/

It is

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

Metro Area

Positive (P) or Negative (N) Residual

Stringency of Land Use Regulations

Land Accessible to Develop

Austin, TX McAllen, TX Lafayette, LA

P P P

Loose Loose Loose

–0.283 –0.449 –0.103

Much Much Much

0.038 0.009 0.020

Fayetteville, AR Las Vegas, NV St. Cloud, MN

P P P

Loose Loose Loose

–0.404 –0.692 –0.115

Moderate Moderate Moderate

0.289 0.321 0.206

Miami, FL

N

Strong

0.945

Little

0.766

Orlando, FL

P

0.316

Moderate

0.361

Riverside, CA

P

Moderate Moderate/ Strong

0.526

Moderate

0.379

Provo, UT Naples, FL

P P

Moderate Moderate

0.208 0.289

Little Little

0.596 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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