Competing for Global Capital or Local Voters?The Politics of Business Location Incentives

Online Appendix

Appendix Name PageI Balance between City-Level Political Institutions B & C

- Table A1 Descriptive Statistics and Differences between Mayors and Council Managers

C

II Balancing between States with and without Default Mayor Clauses

D-H

- Figure A1: Map of States with Default Mayor Regulations E

- Table A2 Balance Test (States with and without Default Provision)

F

- Table A3 Share of ICMA Cities with Mayors (in States with and without Default Provision)

G

- Table A4 Entropy Balancing (Treatment-State has Default Mayor Policy)

H

III Robustness Tests I-O- Table A5 Electoral Institutions and the Average Size of Tax

IncentivesN

- Table A6 Association between Electoral Institutions and Performance Criteria

O

A

Online Appendix I: Balance between City-Level Political Institutions

Although treatment and control have very similar levels of per capita

income and economic structures, Table A1 shows that mayor-council

systems are significantly: 1) more common in populous, suburban cities; 2)

less likely to compete for foreign projects; 3) more concentrated in the

North Central region of the United States.

More non-balance is observed on economic policy choices. These

powerful mayors are more likely to have development plans and

development corporations. By contrast, executives in council-manager

systems are more likely to invest in environmental sustainability and

cultural programs. Because these are post-treatment variables, measuring

decisions made by officials after the establishment of electoral institutions,

they cannot (and should not) be addressed in our regression analysis. We

present them because they illustrate an unmistakable pattern that aligns

with our theory. Mayor-council systems are associated with policies

favoring short-term incentives for commercial development, whereas

executives in council-manager systems are associated with policies that

have more diffuse economic benefits. This table presents non-balance on

observable, pre-treatment characteristics, however, it indicates that omitted

variable bias is a severe threat to statistical inference.

B

Mean SD Mean SD Mean SD T-Statistic P-ValueShare of Observations in SurveySurvey Year = 1999 (n=1,042) 0.58 0.49 0.48 0.50 0.62 0.49 4.50 0.000Survey Year = 2004 (n=726) 0.41 0.49 0.36 0.48 0.43 0.50 2.31 0.021Survey Year = 2009 (n=844) 0.47 0.50 0.43 0.50 0.48 0.50 1.80 0.072Dependent VariablesAverage Value of Incentives (Millions USD, ln) 0.37 0.85 0.44 0.96 0.34 0.81 -1.86 0.063Performance criteria for incentives (=1) 0.59 0.49 0.51 0.50 0.61 0.49 3.23 0.001Cost-Benefit analysis for incentives (=1) 0.73 0.45 0.65 0.48 0.75 0.43 3.46 0.001Number of performance criteria 1.01 0.04 0.84 0.07 1.07 0.05 2.55 0.011Structural VariablesState requires default mayor (=1) 0.36 0.48 0.53 0.50 0.31 0.46 -7.90 0.000Population (8-point scale) 5.14 1.09 5.28 1.03 5.09 1.11 2.83 0.005Per capita income (ln USD) 10.15 0.53 10.11 0.45 10.16 0.55 0.96 0.339Core city in Metropolitan Statistical Area (MSA) 0.19 0.39 0.17 0.38 0.20 0.40 0.99 0.321Suburb in MSA 0.63 0.48 0.61 0.49 0.64 0.48 0.82 0.413Independent city not in MSA 0.19 0.39 0.22 0.41 0.18 0.39 -1.43 0.154Main competition is foreign (=1) 0.20 0.40 0.17 0.38 0.22 0.41 1.81 0.070Northeast region (=1) 0.14 0.35 0.17 0.38 0.13 0.34 -1.99 0.047North Central region (=1) 0.32 0.47 0.49 0.50 0.27 0.44 -8.12 0.000South (=1) 0.29 0.45 0.22 0.42 0.31 0.46 3.32 0.001West (=1) 0.24 0.43 0.11 0.31 0.28 0.45 6.92 0.000National League of City SurveyAgriculture is economic base (=1) 0.07 0.25 0.07 0.25 0.07 0.25 -0.01 0.994Agriculture is economic focus (=1) 0.02 0.15 0.03 0.17 0.02 0.15 -0.65 0.516Manufacturing is economic base (=1) 0.26 0.44 0.29 0.45 0.25 0.43 -0.95 0.345Manufacturing is development focus (=1) 0.28 0.45 0.29 0.45 0.28 0.45 -0.19 0.850Service is economic base (=1) 0.46 0.50 0.42 0.49 0.47 0.50 1.20 0.232Service is development focus (=1) 0.43 0.50 0.43 0.50 0.43 0.50 -0.04 0.964Land zoned for commercial development (%) 22.05 11.87 22.55 11.49 21.90 11.99 -0.54 0.591Local government has development plan (=1) 1.45 0.50 1.55 0.50 1.42 0.49 -2.89 0.004Years with development plan (3-point scale) 2.39 0.80 2.29 0.84 2.41 0.79 1.15 0.250Has community dev't corporation (=1) 0.33 0.47 0.40 0.49 0.31 0.46 -2.09 0.037Has community dev't loan fund (=1) 0.23 0.42 0.24 0.43 0.23 0.42 -0.42 0.678Environmental sustainability program (=1) 0.33 0.47 0.27 0.44 0.35 0.48 2.01 0.045Efficient transportation system (=1) 0.38 0.49 0.33 0.47 0.39 0.49 1.45 0.148High quality infrastructure (=1) 0.53 0.50 0.51 0.50 0.53 0.50 0.55 0.581Job training program (=1) 0.29 0.45 0.24 0.43 0.31 0.46 1.56 0.120Affordable child care program (=1) 0.08 0.28 0.08 0.27 0.08 0.28 0.16 0.873Affordable housing (=1) 0.42 0.49 0.37 0.49 0.44 0.50 1.44 0.150Culture and recreation programs (=1) 0.64 0.48 0.57 0.50 0.66 0.48 1.95 0.051

Table A1: Descriptive Statistics and Difference between Mayors and Council-Managers

Note: Author’s calculation based on1) ICMA/NLC Survey for information on cities; 2) ICAincentives.com for information on the number and value of incentives. Data is pooled for three survey years (1999, 2004, and 2009).

Full Sample Treatment=Mayor Control=Manager BalanceVariables

C

Online Appendix II: between States with and without Default Mayor

Clauses

Our identification strategy for mayor-council institutions employs

state laws making mayor-council systems the default form of government.

An important clarification is in order about our use of the Nelson data. Our

measure is merely one indicator from a collection of indicators that Nelson

(2010, 2011) uses to construct an index for 49 US states based on the

number of variations of form of government by state law. The ultimate

purpose is to examine how state laws shape the ability of municipalities to

form hybrid governments (Nelson 2011). Although Nelson concludes that

hybrid governments are quite rare, she finds that state laws shape the

ability of municipalities to shape their local institutions.

This complete index, while appropriate for Nelson’s purpose, is

problematic in our study. First, we are not interested in the customization

of institutions. Our concern is the endogenous choice of selecting mayor-

council institutions over council-manager institutions or vice versa. Second,

some of the indicators that she includes, such has “home rule,” not only

affect institutions; they also affect the fiscal discretion of political leaders.

This clearly violates the exclusion restriction for an instrumental variables

analysis. Consequently, we prefer to focus on a single dimension of state

laws governing municipal form of government that we can justify for our

instrumental variable regression analysis.

D

In Figure A2, we map the states that had default clauses in 2004

(midway through our sample), while in Table A2, we provide balance tests

using census data. In Table A3 we provide descriptive statistics by state.

E

Figure B1: States with Default Mayor Regulations(Green Share= States with Default Mayors, Source (Nelson 2011))

F

Mean SD Mean SD Mean SD T-Statistic P-ValuePopulationPopulation 3,572,777 3,947,324 3,544,633 3,042,485 4,132,930 4,644,487 0.483 0.631Households (#) 2,068,237 2,197,946 2,072,055 1,714,764 2,378,191 2,569,028 0.453 0.653Households with kids (%) 35.57 3.16 35.45 1.50 35.87 3.17 0.536 0.594High school grads (%) 81.87 4.37 82.11 4.06 81.66 4.55 -0.341 0.735College grads (%) 24.08 4.75 23.09 3.87 24.55 4.43 1.152 0.255Foreign population (%) 7.26 5.68 6.12 5.25 7.95 5.80 1.093 0.280Speak foreign language (%) 12.66 8.93 11.45 9.21 13.37 8.91 0.709 0.482Live in same house (%) 54.11 5.11 54.66 3.87 53.68 5.71 -0.653 0.517Drive alone to work (%) 76.12 7.24 77.50 6.00 77.17 2.93 -0.249 0.805Males/Female 97.30 2.88 97.28 2.07 97.31 3.42 0.027 0.979IncomePer capita income 33,670.98 5,521.45 32,992.63 4,296.76 33,650.15 5402.08 0.441 0.661Total income (Millions $) 156542.20 189860.20 152736.90 150551.50 181826.20 223682.80 0.493 0.625Income over $75,000 (%) 20.73 6.06 19.36 5.68 21.53 5.78 1.264 0.213Below poverty line (%) 12.35 2.93 12.31 2.35 12.21 3.17 -0.120 0.905Surface area (sq. miles) 74,393.78 96,677.08 67,618.72 26,561.28 64,211.65 59,518.85 -0.233 0.817Pop per mile 366.53 1311.93 213.59 300.49 521.00 1804.79 0.733 0.467Construction share (%) 6.80 1.40 6.45 1.14 7.01 1.58 1.315 0.196Retail share (%) 6.82 1.23 6.68 0.80 7.10 1.01 1.496 0.142Professional share (%) 8.08 3.31 7.54 2.54 8.36 2.60 1.060 0.295Health share (%) 9.77 1.83 9.62 1.24 9.89 1.88 0.558 0.580Government share (%) 18.76 5.56 17.67 3.90 17.57 3.66 -0.088 0.930ManufacturingEstablishments 6879.73 8131.11 7114.16 5824.22 7840.82 9772.98 0.289 0.774Change in establishments (%) -1.89 6.38 -1.99 5.83 -1.49 5.15 0.308 0.759Employment 291,390 303,148 324,558 236,915 316,712 350,750 -0.085 0.933Employment share (%) 8.26 3.35 9.49 3.70 8.16 2.49 -1.464 0.150Change in employment (%) -10.50 7.47 -9.35 6.39 -11.39 6.41 -1.064 0.293Earnings 19,900,000 23,200,000 21,500,000 17,500,000 22,200,000 27,400,000 0.086 0.932Change in earnings (%) 12.55 5.36 14.34 6.00 12.52 3.90 -1.248 0.219Value added 37,100,000 39,500,000 41,800,000 32,900,000 40,000,000 44,700,000 -0.142 0.888Change in value added (%) 5.49 16.31 7.49 20.94 4.29 12.27 -0.651 0.518Capital Expenditures 2,461,705 2,882,258 2,678,063 1,937,541 2,726,621 3,504,671 0.055 0.957Change in CapEx (%) -12.84 22.67 -5.86 23.39 -17.40 20.51 -1.773 0.083Government RoleFederal expenditures 41,582.49 43,228.97 38,440.03 32,576.45 48,529.59 51495.37 0.753 0.455Total government earnings 25,865.61 28,824.91 23,896.68 21,199.16 29,979.38 34800.23 0.677 0.502Total government employment 449,882.40 461,545.60 436,736.60 342,548.40 515,280.30 552763.80 0.549 0.586Local government employment 260,346 308,464 268,604 242,973 295,334 362,494 0.280 0.781Local government payroll ($1000s) 734,969 1,021,291 762,990 902,130 830,454 1,165,079 0.211 0.833Local government revenue ($1000s) 19,500,000 27,500,000 19,800,000 23,600,000 22,300,000 31,800,000 0.298 0.767Local gov. revenue ($ per capita) 3232.24 1475.22 3235.74 792.81 3007.26 547.46 -1.158 0.253Local government taxes ($1000s) 7249612.00 9626494.00 7674894.00 10500000.00 8034280.00 9744603.00 0.120 0.905Property tax share (%) 74.95 17.61 74.20 17.42 76.81 16.83 0.511 0.612Local government debt ($1000s) 20500000.00 28300000.00 19000000.00 24400000.00 24700000.00 32400000.00 0.639 0.526Local government debt ($ per capita) 3121.51 1557.13 2867.11 1052.15 3116.78 1368.52 0.668 0.508ElectionVotes in 2004 election 2,397,948 2,415,704 2,405,452 1,889,661 2,758,461 2,803,329 0 1 Change in Votes (2004 to 2008) 16.01 6.16 14.77 4.66 16.96 7.22 1.163 0.251Republican share (2004) 52.30 10.37 55.29 8.30 51.29 8.53 -1.585 0.120Democrat share (2004) 46.52 10.38 43.65 8.47 47.51 8.40 1.530 0.133Owner occupied housing (%) 67.47 5.97 68.75 4.40 67.84 4.30 -0.695 0.491Multunit housing (%) 24.07 9.77 22.12 9.32 24.08 7.37 0.797 0.430Share of income of top 10% 0.42 0.03 0.42 0.03 0.43 0.04 0.961 0.342

Table A2: Balance Test (States with and without Default Provision)Variables All States Default Mayor No Default Balance

G

StateCities w/Mayors Default Provision Cities w/Mayors Default Provision Cities w/Mayors Default Provision

Alabama 42.9% 1 50.0% 1 75.0% 1

Alaska . 1 100.0% 1 0.0% 1

Arizona 0.0% 0 0.0% 0 0.0% 0

Arkansas 75.0% 1 50.0% 1 50.0% 1

California 2.6% 0 2.0% 0 0.0% 0

Colorado 0.0% 1 0.0% 1 4.3% 1

Connecticut 25.9% 0 50.0% 0 7.1% 0

Delaware . 0 . 0 50.0% 0

Florida 7.3% 0 8.6% 0 22.0% 0

Georgia 0.0% 0 26.7% 0 16.0% 0

Idaho 0.0% 1 100.0% 1 85.7% 1

Illinois 35.7% 1 28.1% 1 30.8% 1

Indiana 100.0% 1 96.8% 1 95.0% 1

Iowa 11.8% 1 37.5% 1 0.0% 1

Kansas 37.5% 1 22.2% 1 0.0% 1

Kentucky 100.0% 0 23.5% 0 94.2% 0

Louisiana 83.3% 0 50.0% 0 50.0% 0

Maine 0.0% 0 33.3% 0 0.0% 0

Maryland 0.0% 0 0.0% 0 40.0% 0

Massachusetts 10.8% 0 18.2% 0 0.0% 0

Michigan 18.5% 1 15.2% 1 19.1% 1

Minnesota 23.1% 0 31.3% 0 19.4% 0

Mississippi 0.0% 0 100.0% 0 100.0% 0

Missouri 7.0% 1 33.3% 1 12.9% 1

Montana 33.3% 0 . 0 0.0% 0

Nebraska 66.7% 1 . 1 80.0% 1

Nevada . 0 0.0% 0 0.0% 0

New Hampshire 0.0% 0 40.0% 0 0.0% 0

New Jersey 43.8% 1 54.5% 1 29.4% 1

New Mexico 0.0% 1 0.0% 1 33.3% 1

New York 60.4% 1 33.3% 1 26.7% 1

North Carolina 0.0% 1 0.0% 1 0.0% 1

North Dakota 0.0% 1 . 1 50.0% 1

Ohio 74.5% 0 33.3% 0 25.9% 0

Oklahoma 0.0% 0 0.0% 0 52.4% 0

Oregon 12.5% 0 0.0% 0 12.5% 0

Pennsylvania 10.0% 0 33.3% 0 20.0% 0

Rhode Island 20.0% 0 20.0% 0 50.0% 0

South Carolina 30.0% 0 9.1% 0 13.3% 0

South Dakota 25.0% 0 0.0% 0 0.0% 0

Tennessee 91.7% 1 26.3% 1 23.1% 1

Texas 5.0% 0 4.0% 0 2.6% 0

Utah 100.0% 0 50.0% 0 50.0% 0

Vermont 0.0% 0 0.0% 0 66.7% 0

Virginia 0.0% 0 0.0% 0 4.3% 0

Washington 25.0% 0 33.3% 0 29.4% 0

West Virginia 0.0% 0 50.0% 0 . 0

Wisconsin 83.3% 1 28.0% 1 51.5% 1

Wyoming 50.0% 1 . 1 100.0% 1

Only includes cities covered in ICMA database. Data on default clause from Nelson.

1999 2004 2009Table A3: Share of ICMA Cities with Mayors (in States with and without Default Provision)

H

Table A4: Entropy Balancing(Treatment =State has Default Mayor Policy)Before:

Mean Variance Skewness Mean Variance Skewness Mean Variance Skewness Mean Variance SkewnessJobs created (ln) 3.88 2.54 -0.64 3.90 2.19 -0.49 3.88 2.54 -0.64 3.88 1.88 -0.45Capital value (ln, US) 11.58 51.43 -0.89 9.84 57.05 -0.46 11.58 51.43 -0.89 11.58 44.38 -1.03Population (1 to 7) 2.99 2.70 0.40 3.30 2.95 0.13 2.99 2.70 0.40 2.99 3.00 0.32Unemployment Rate (%) 6.90 55.47 10.10 6.47 8.06 1.43 6.90 55.47 10.10 6.90 10.42 1.35Other Taxes 0.77 0.18 -1.28 0.71 0.21 -0.93 1.85 3.12 1.72 1.85 2.64 1.13Foreign Competition=1 1.85 3.12 1.72 2.75 4.23 1.87 0.27 0.20 1.05 0.27 0.20 1.05Brand New Investment=1 0.27 0.20 1.05 0.22 0.17 1.38 0.40 0.24 0.41 0.40 0.24 0.41Economic Development Plan=1 0.40 0.24 0.41 0.39 0.24 0.45 0.77 0.18 -1.28 0.77 0.18 -1.28LocationNortheast=1 0.09 0.08 2.82 0.11 0.10 2.48 0.09 0.08 2.82 0.09 0.08 2.82Northcentral=1 0.61 0.24 -0.43 0.21 0.16 1.46 0.61 0.24 -0.43 0.61 0.24 -0.43South=1 0.22 0.17 1.32 0.62 0.24 -0.47 0.22 0.17 1.32 0.23 0.17 1.32Metro Area=1 0.55 0.25 -0.21 0.64 0.23 -0.57 0.55 0.25 -0.21 0.55 0.25 -0.21Surburb=1 0.26 0.19 1.08 0.27 0.20 1.04 0.26 0.19 1.08 0.26 0.19 1.08SectorAutomotive =1 0.11 0.10 2.52 0.06 0.05 3.89 0.11 0.10 2.52 0.11 0.10 2.52Basic Materials=1 0.09 0.08 2.90 0.09 0.08 2.90 0.09 0.08 2.90 0.09 0.08 2.90Consumer Goods=1 0.11 0.10 2.46 0.10 0.09 2.66 0.11 0.10 2.46 0.11 0.10 2.46Creative Industries=1 0.01 0.01 11.52 0.02 0.02 7.26 0.01 0.01 11.52 0.01 0.01 11.52Electronics=1 0.04 0.04 4.92 0.03 0.03 5.52 0.04 0.04 4.92 0.04 0.04 4.92Food and Drinks=1 0.10 0.09 2.71 0.09 0.08 2.87 0.10 0.09 2.71 0.10 0.09 2.71Industrial Goods=1 0.14 0.12 2.10 0.15 0.13 1.93 0.14 0.12 2.10 0.14 0.12 2.10Information Tech.=1 0.07 0.07 3.32 0.08 0.08 3.00 0.07 0.07 3.32 0.07 0.07 3.32Tourism=1 0.03 0.03 5.76 0.02 0.02 6.96 0.03 0.03 5.76 0.03 0.03 5.77Life Sciences=1 0.09 0.08 2.90 0.11 0.10 2.43 0.09 0.08 2.90 0.09 0.08 2.90Non-Renewable Energy=1 0.01 0.01 11.52 0.03 0.03 5.52 0.01 0.01 11.52 0.01 0.01 11.49Energy=1 0.02 0.02 6.23 0.01 0.01 8.44 0.02 0.02 6.23 0.02 0.02 6.23Services=1 0.15 0.13 1.95 0.15 0.13 1.96 0.15 0.13 1.95 0.15 0.13 1.95Survey Year2004 =1 0.26 0.19 1.11 0.20 0.16 1.47 0.26 0.19 1.11 0.26 0.19 1.112009 =1 0.41 0.24 0.36 0.57 0.25 -0.29 0.41 0.24 0.36 0.41 0.24 0.36

Treatment=Elected Mayor Control=Council Treatment=Elected Mayor Control=Council

I

Appendix III: Robustness Tests

Our main results in the paper using Entropy Balancing are robust to

alternative methods. We present additional tests in this appendix.

Incentive Offers

In Models 1 and 2 of Table A5, we first estimate the probability that a

municipality offered an incentive during the survey year using a probit

specification. It is immediately clear that the size of the coefficient for the

dummy variable for mayor-council institutions (Elected Mayors)—the key

independent variable—is effectively zero. The bivariate relationship is

insignificant and the size of the coefficient declines significantly with

additional controls (Model 2). Clearly, the propensity to offer an incentive

is driven by structural factors such as population size, regional location, and

characteristics of the municipality (e.g., a suburb or metro area).1 This

makes sense, as we noted above, because almost all cities, regardless of

electoral institutions, now offer some form of incentives. What really

matters is how much they offer and the oversight of their programs. City-

years offering zero incentives were dropped from the analysis in Models 3–

7.

This means that 1,284 of our projects were allocated to the 837

municipalities that did offer incentives. Some of these municipalities show

up multiple times in the project data, as they offered incentives to multiple

projects in a given year, which is why our n is larger than the number of 1 Unfortunately, project-specific controls (e.g., sector, size, etc.) are not available for projects that were not offered incentives, so we are forced to rely only on municipal-level covariates. The marginal effect should therefore be treated as tentative.

J

cities. Of the cities that offered incentives, the median number of

incentivized projects was 1 per year and the mean was 1.18 per year, with

404 offering incentives to more than one project. The number of incentives

per municipality at the 95th percentile was 3, with one location offering 95

targeted incentives during the calendar year.2 Structuring the data at the

project level allows us to focus on the behavior of the municipalities that did

offer an incentive, which allows us to ascertain whether the amount of

money allocated for individual projects was higher for mayor-council

systems.

<Insert Table A5 About Here>

To this end, in Models 3 to 7, we regress the natural log of the value

of the incentive package that was offered by a municipality on Elected

Mayors. 3 Model 3 provides the bivariate relationship, controlling only for

survey-year fixed effects to make sure that we wipe out any trending in the

use of incentives over time. We use ordinary least squares (OLS) with

errors clustered at the municipal-level to account for non-independence

among projects in the selection of electoral institutions and the use of

incentives. We find that mayor-council systems offer an additional 29% in

incentives per project than other institutions.

In Model 4, we include a battery of control variables at the level of the

municipality and project. At the municipal level, we use controls at the city

2 We drop this outlier from our analysis, reducing our n to 1,176.3 We considered treating no incentives as $0 and estimated the full-model of the total value, but we discarded this option, as we believed it would bias in favor of our hypothesis, because mayoral systems are marginally more likely to offer incentives in the first place.

K

level discussed above (e.g., population, metro area, suburb, foreign

competition, region of the country (e.g., Northeast), etc.). In addition, we

include a number of control variables at the project level. First, we include

the natural log of the total number of new jobs created (Jobs) and the log of

capital expenditures (Capex). We also include a dummy variable if the

investment was in the energy or electricity industry (an industry that is

granted more generous incentives than other industries) and a dummy

variable if this is a new investment (New) as opposed to an expansion

project.

Our control variables behave as expected. Larger investments, in

terms of jobs and capital expenditures receive larger incentive allocations,

along with new investments and those in the energy sector. Our key

independent variable, Elected Mayors, remains statistically significant and

substantially large. Mayors in mayor-council systems offer an additional

32% in incentives per investment, accounting for the size, sector, and type

of investment. The results are robust to the inclusion of sector fixed effects

in Model 5 and state fixed effects in Model 6. In Model 7, we include

additional controls for the level of unemployment, existing tax policies, and

municipal development plan. We run these last because of concerns that

these variables may post-date the allocation of incentives.

Given the fact that an incentive was offered, it is clear that these

mayors pay far greater amounts for similarly situated projects. In dollar

L

terms, the difference amounts to $172,242 more in incentives on an average

project than council-managers.

One concern is that although we are comparing cities of the same

population, region, and even level of economic development program

professionalism, the underlying size of municipal budgets can largely shape

the ability of local leaders to offer incentives. Using survey data on the size

of the annual economic development budget, we run models 8 & 9 that

scale the size of the incentives as a percentage of the local economic

development budget. Although this variable is the most theoretically

appropriate measure, it has a large number of missing values and thus we

present this primarily as a robustness test of our original estimates. Again,

mayor-council institutions are associated with a larger percentage of their

economic development budget being offered as incentives.

Incentive Oversight

With the first two dependent variables (Models 1–10 in Table C2), we

use a probit specification, coding the dependent variable as 1 for the

existence of the oversight program and 0 otherwise. Models 11–15 assess

the number of criteria using OLS. All independent variables are measured

in the same year of the survey unless otherwise noted.

As expected, there is considerable variation across municipalities in

just this minimum requirement. Although 72% of managers in our sample

M

indicate that they always perform a cost-benefit analysis of incentives, only

59% of municipalities have specific performance requirements.4 Looking

directly at the number of performance criteria, 21 cities employ all six

items, 939 employ none, and the average city employs about one item.

We present our regressions in Table A6. All models are estimated with

robust standard errors clustered at the municipal level and we report

marginal probabilities for easier interpretation of the coefficients. Models 1

and 6 test how mayor-council institutions affect performance without any

control variables. Even without any control variables, we find that mayor-

council systems are associated with an over 10.5% decline in the probability

of having performance requirements on incentives (Model 1) or conducting

a formal cost-benefit analysis (Model 5). As our H2 predicts, electoral

institutions lead to a decrease in the probability of oversight mechanisms.

Our results are similar when we include survey-year fixed effects in Models

2 and 6.

<Table A6 about Here>

In Models 3 and 8 we include our control variables. Although few of

our control variables are statistically significant, two variables have a large

and statistically significant effect on oversight. First, municipalities with

larger populations are more likely to have greater oversight of incentives.

More interestingly, foreign competition, or at least the perception of

competition with foreign localities, leads to an increased use of oversight

4 The most common performance requirement is based on the number of new jobs created.

N

mechanisms. The substantive effects of performance requirements and

cost-benefit analyses decline slightly with the inclusion of these control

variables. Municipalities with mayor-council systems are 10.5% less likely

to require performance criteria for incentives and 7.2% less likely to require

a cost-benefit analysis of incentive programs. Looking at the regressions on

the number of criteria, we find that mayor-council systems employ about

0.33 fewer criteria than council-manager systems. These results are robust

across the different specifications, including regional fixed effects (Models

4, 9, and 14) and the additional control variables for the unemployment rate

and existing tax policies (Models 5, 10, and 15).

These results support H2, although with an important caveat—they

are based on a survey of municipalities, which could be prone to a number

of potential measurement problems. Self-reported oversight and

perceptions of “foreign competition” can suffer from issues of lying or

perception biases. These errors, however, are more likely to bias against a

significant coefficient on mayor-council systems, either because of

attenuation bias or because mayoral systems are likely to exaggerate the

level of oversight.

O

Table A5: Electoral Institutions and the Average Size of Tax Incentives (Project-Level)

Probit Probit OLS OLS OLS OLS OLS OLS OLS(1) (2) (3) (4) (5) (6) (7) (8) (9)

Elected Mayors 0.096* 0.014 0.297* 0.342** 0.329** 0.305** 0.279** 1.135* 0.824(0.053) (0.039) (0.168) (0.167) (0.155) (0.125) (0.130) (0.591) (0.691)

Population 0.120*** 0.142** 0.132** 0.080** 0.076* -0.550*** -0.711***(0.019) (0.059) (0.058) (0.041) (0.040) (0.203) (0.267)

Foreign Competition 0.086** 0.001 0.032 0.125 0.199 -0.172 -0.017(0.038) (0.139) (0.135) (0.116) (0.123) (0.507) (0.377)

Metro Area 0.138** -0.071 -0.054 0.027 0.080 0.447 0.556(0.060) (0.203) (0.197) (0.176) (0.181) (0.851) (0.824)

Suburb -0.215*** 0.364** 0.367** 0.251 0.293* 0.287 0.327(0.045) (0.174) (0.170) (0.166) (0.172) (0.506) (0.527)

Jobs created (ln) 0.438*** 0.445*** 0.424*** 0.420*** 0.500*** 0.557***(0.048) (0.048) (0.047) (0.048) (0.088) (0.094)

Capital value (ln, US) 0.041*** 0.039*** 0.051*** 0.049*** 0.057*** 0.068***(0.008) (0.008) (0.008) (0.008) (0.019) (0.020)

Energy Project 2.285** 1.546 2.165 2.227 2.128** -1.862**(1.039) (1.001) (1.481) (1.493) (0.931) (0.836)

Brand New Investment 0.175* 0.112 0.160* 0.136 0.078 -0.214(0.105) (0.102) (0.095) (0.098) (0.209) (0.234)

Unemployment Rate (%) -0.000 0.091(0.004) (0.073)

Other Taxes (1 to 5) -0.001 -0.018(0.023) (0.230)

Development Plan=1 0.098*** 0.126 0.112 0.084 -0.674 -0.209(0.036) (0.139) (0.138) (0.126) (0.749) (0.509)

Pbar/Constant 0.533 0.543 13.051*** 9.930*** 10.283*** 11.038*** 10.956*** -0.419 -2.462***(0.157) (0.318) (0.416) (0.685) (0.680) (0.807) (0.713)

Survey Year FE Yes Yes Yes Yes Yes Yes Yes Yes YesRegional FE No Yes No Yes Yes No No Yes NoState FE No No No No No Yes Yes No YesSector FE No No No No Yes Yes Yes No YesObservations 2,205 2,115 1,176 1,136 1,136 1,163 1,116 430 430City 1475 1399 446 425 425 438 424 167 167States 48 48 45 45 45 45 45 34 34(Pseudo) R-squared 0.00556 0.282 0.009 0.240 0.264 0.367 0.369 0.290 0.419Log Likelihood -1523 -1458 -2294 -2217 -2217 -2265 -2181 -1019 -1019Chi-Squared 3.448 299.0RMSE 1.696 1.497 1.481 1.391 1.403 2.217 2.125

Offered incentive=1Independent/Dependent Variables

Value of Incentive in Millions USD (ln) Value of Incentive as % of Budget

(*** p<0.01, ** p<0.05, * p<0.1). For Models 1 & 2 the unit of analysis is the city and the dependent variable is an incentive anytime in the calendar year and uses a probit analysis on whether an incentive was offered (marginal effects displayed) for a specific project. Robust standard errors are clustered at the state level. For Models 3 -7, the unit of analysis is the individual project. And the depednent variable is the natural log (ln) of size of the incentive in millions of USD. Models 8 and 9 standardize the incentive by the municipality's budget. Data on incentives is from 2011 and 2012, but basic city information was captured in different ICMA/NLC surveys. Fixed effects address confounding based on survey year. Models 3 to 9 employ OLS on the sample of projects which were offered incentives. Models 4 to 7 introduce city and project-level controls, including sector (Model 5) and state fixed effects (Model 6 & Model 9). Robust standard errors are clusterd at the city level.

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Table A6: Association between Electoral Institutions on Performance Criteria

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)Elected Mayors -0.113** -0.115** -0.100** -0.108** -0.095** -0.111*** -0.112*** -0.095** -0.077* -0.058 -0.288** -0.392*** -0.327*** -0.358*** -0.312***

(0.048) (0.049) (0.048) (0.047) (0.045) (0.042) (0.042) (0.041) (0.042) (0.039) (0.125) (0.112) (0.101) (0.107) (0.095)Population 0.050*** 0.052*** 0.045*** 0.041*** 0.034*** 0.027* 0.181*** 0.180*** 0.162***

(0.013) (0.016) (0.015) (0.011) (0.012) (0.015) (0.037) (0.045) (0.050)Foreign Competition 0.149*** 0.141*** 0.130*** 0.097*** 0.092*** 0.068** 0.294*** 0.283*** 0.236**

(0.038) (0.038) (0.037) (0.028) (0.029) (0.029) (0.090) (0.091) (0.089)Metro Area -0.044 -0.040 -0.043 0.064 0.074 0.059 0.047 0.068 0.020

(0.051) (0.054) (0.055) (0.060) (0.061) (0.068) (0.139) (0.143) (0.141)Suburb -0.039 -0.024 -0.021 -0.015 0.004 0.004 -0.122 -0.117 -0.103

(0.035) (0.036) (0.038) (0.040) (0.043) (0.040) (0.096) (0.099) (0.100)Unemployment Rate (%) -0.001 0.007 0.006

(0.004) (0.004) (0.019)Other Taxes 0.016* 0.020* -0.026

(0.008) (0.010) (0.021)Development Plan=1 0.100*** 0.172*** 0.594***

(0.037) (0.031) (0.074)Pbar/Constant 0.592 0.592 0.592 0.592 0.595 0.723 0.723 0.723 0.723 0.727 1.376*** 2.731*** 2.362*** 2.105*** 1.804***

(0.105) (0.145) (0.155) (0.162) (0.188)Survey Year FE No Yes Yes Yes Yes No Yes Yes Yes Yes No Yes Yes Yes YesRegion FE No No No Yes Yes No No No Yes Yes No No No Yes YesObservations 1,106 1,106 1,106 1,106 1,083 1,106 1,106 1,106 1,106 1,083 1,106 1,106 1,106 1,106 1,083State Clusters 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48(Pseudo) R-Squared 0.00712 0.00930 0.0324 0.0513 0.0612 0.00933 0.00948 0.0373 0.0589 0.0934 0.006 0.213 0.243 0.249 0.274Log Likelihood -742.4 -740.7 -723.5 -709.3 -686.4 -646.2 -646.1 -628.0 -613.9 -575.9 -2108 -1979 -1957 -1953 -1897Chi-Squared 5.294 8.700 40.42 66.46 140.9 6.733 7.601 49.62 96.85 148.9RMSE 1.630 1.452 1.426 1.422 1.404Marginal effects using probit analysis. Robust standard errors, clustered at state level, in parentheses (*** p<0.01, ** p<0.05, * p<0.1). The unit of analysis is the city within each state. Fixed effects address confounding based on survey year (1999, 2004, 2009). Models 1 to 4 study performance criteria. Models 4 to 8 analyze cost-benefit analysis programs. And Models 9 to 12 test whether mayors are subject to more or less performance criteria.

Performance Criteria (=1) Cost Benefit Analysis (=1)Independent/ Dependent Variables

Performance Criteria (0 to 6)

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