A Bargaining Framework for Local Development Policy Choices

A Bargaining Framework for Local Development Policy Choices

Richard C. Feiock Askew School of Public Administration and Policy Florida State University [email protected] & Annette Steinacker Dept of Politics and Policy Claremont Graduate University [email protected]

Paper presented at the Annual Meeting of the American Political Science Association Annual Meeting, August 27-30, 2003. We wish to thank John Scholz and Max Neiman for their comments on an earlier version. This paper is based upon work supported by the National Science Foundation under Grant No. 0214174. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

A Bargaining Framework for Local Development Policy Choices Local governments regularly negotiate agreements with private organizations. Issuing bonds, managing pension plans, contracting out service delivery, and attracting economic development require local officials to negotiate deals with self-interested private actors. In these contexts the public interest depends upon the ability of governments to bargain effectively, yet we know very little about the conditions under which governments may be effective bargainers or how government institutions affect bargaining outcomes. If the incentive structures of public organizations lead to ineffective bargaining strategies, local officials may “give away the store.” Nowhere is this concern more evident than in economic development policy. In response to fiscal stress and employment demands some cities actively court businesses, including offering substantial location incentives to attract new firms. For example, Indianapolis offered American Airlines $294 million in subsidies to attract a new maintenance hub, which worked out to over $47,000 per job created (Sullivan, 2002). In other communities, local government policy regarding growth and development is radically different. Some cities attempt to keep out new development and use restrictive zoning, growth management regulations and other policies to limit the influx of high-service/low-tax land uses and preserve an advantaged economic position (Feiock Lubell and Jeong, 2003). In the last decade as the economy grew at an unprecedented rate, even the poorest cities began to reap economic benefits from the expansion and the intense economic competition among cities seemed to abate. Nonetheless, some of the poorest areas, often with substantial minority populations, still actively recruited any economic development opportunities that might come their way including landfills, casinos, and other potentially undesirable environmental or social land uses. At the other end 1

of the scale, more localities become prosperous and faced political pressures to limit growth, protect the quality of life that contributed to their prosperity, and ignore the impact of accelerating housing prices on the poor and the middle class. Growth policy remained critical to cities but the balance in type of policies pursued had shifted. Now that the economic expansion has ended, state budgets are being slashed, and negative impacts of welfare reform are likely to be felt most in cities, the balance in development policy may shift again. Economic competition across cities is likely to accelerate with more efforts to court firms in fiscally stressed areas to add needed jobs and more efforts to impose tight growth restrictions in fiscally sound cities to prevent higher service demands. While both location incentives and growth management policies have been studied extensively, they have not been investigated within an integrated theoretical framework. This paper begins to fill this lacuna by offering a conceptualization of growth policy as a continuum based on the distribution of benefits from development between the pubic and private sectors. The literature identifies three general types of development policies: traditional location incentives that subsidize development; progressive policies that provide incentives but with strings attached (also referred to as Type II policy in the literature); and growth management policies. Extant work has tended to examine one or at most two of these policy types in isolation. In particular, the growth management literature has developed almost independent of the economic development literature, particularly with regard to policy adoption (Baldassare and Potash, 1982; Baldassare and Wilson, 1996; Bollens, 1990; Dubin, et. al., 1992; Logan and Zhou, 1989, 1990; Neiman and Donovan, 1992; Warner and Molotch, 1995). The economic development literature tends to focus on either the existence of progressive city regimes that use Type II policies or the dominance of the local 2

growth machine which favors traditional pro-business subsidies (Clarke and Gaile, 1989; DeLeon, 1992; Eisenger, 1988; Elkin, 1987; Elkins, 1995; Goetz, 1990, 1994; Logan and Molotch, 1987; Miranda and Rosdil, 1995; Reese, 1998; Stone, 1989). We link the three policy types by placing them on a scale that measures the proportion of development benefits captured by the private rather than the public sector. At one extreme unrestricted location incentives allow private interests to capture most of the surplus created by new development. In the middle of the spectrum, with progressive development policies that impose extractions or other conditions on development, government and businesses divide the benefits. At the other extreme, growth management policies provide no incentives and impose costs on new development, reserving the benefits to the public sector. The 'price' that firms pay for development opportunities ranges from negative (generous subsidies received) to close to zero (incentives mixed with constraints) to positive (extractions imposed) depending on the policies cities adopt and the specific deals they negotiate with firms. The approach to economic development that we use is based on a series of bargaining models. The first section of this paper outlines the limitations of current efforts to model local economic development -- the Prisoners Dilemma game and perfect competition -- and illustrates how a bilateral bargaining model avoids many of these problems. Recasting economic development issues in a bargaining framework provides three advantages: (1) even with the most stringent set of assumptions in the basic model, bilateral bargaining is a more accurate portrayal of critical features in the real world; (2) this perspective generates a set of testable empirical predictions from a comprehensive theoretical perspective, integrating several of the separate frameworks used previously; (3) the approach also generates policy suggestions for cities that could lead to development outcomes closer to the city 3

preferences, whether pro-growth or anti-growth. The second section describes the Nash bargaining model and applies it to the specific case of bargaining over growth policies. This model allows us to incorporate insights from previous development research within a more rigorous theoretical framework that leads to a very specific empirical specification. City competition for development is captured by the firm's outside option. The primary limitation of the Prisoner's Dilemma model -- that all cities are equally desirable to businesses except for the concessions offered -- is avoided by incorporating these features into the city's outside option. Municipal need for development to counter economic and fiscal problems is captured in the discount rate reflecting the city's time preference. Finally, risk aversion allows political factors to enter the model by explicitly considering that political leaders face different utility functions than the city as a whole. The third section describes our current research efforts and the instruments we have developed to operationalize the bargaining model. We then describe our analysis strategy and ongoing work that will test the model and identify the impact of strategic considerations on both a city's choice of general development policy and the specific concessions made to firms. The fourth section extends the bargaining model to repeated play of the game with different firms over time. We describe how future work will incorporate dynamic features of growth policies neglected in extant research by extending our basic model to include the impact of a city development decisions on its future options. This extension focuses on the possibility that the city's general development policy can act as a signal to firms that the city is either a 'tough' or 'soft' agent, equivalent in development policy to slow- or pro-growth areas. Responses to specific requests for concessions also 4

send signals to future firms and contribute to reputation building. Models of Local Economic Development Policy While typically applied in an informal way, a single-play Prisoner's Dilemma game or a perfectly competitive market model underlies much of previous research on local economic development. Political scientists interested in explaining city decisions to adopt location incentives have generally applied some variation of the Prisoner's Dilemma model (Bowman, 1988; Grady, 1986; Green and Fleischmann, 1989, 1991; Peretz, 1986; Rubin and Rubin, 1987; Schneider, 1989; Wolman, 1988). In this model, the two players are two cities each deciding whether to offer incentives (defect) or not (cooperate). Because of the payoff structure, each player has a dominant strategy of offering concessions. If only one player defects, that city would attract all new development at the expense of the other. The Prisoner’s Dilemma model is limited by inappropriate assumptions for this case -principally the homogeneity of cities and firms. This payoff structure, which drives the results, can be challenged on several grounds. First, it assumes all cities have the same objective function, that they all value development and receive the same marginal benefit from a project. The existence of slow-growth cities provides evidence that neither is true. Not all cities maximize growth nor do they equally value benefits from development projects. Second, the model assumes that the value of concessions is the determining factor in a firm's location decision. If cities were homogeneous on all other characteristics, then development incentives would set the price of exactly equivalent goods and would be the critical factor to a firm. Any city refusing to offer concessions would lose. However, as we will argue below, cities differ on many characteristics that are critical to businesses -- access to market, an educated labor force, wage rates, available space. This difference in quality may lead firms to pay different prices for 5

different locations. The Prisoner's Dilemma model has not incorporated either the heterogeneous nature of cities or the differences in value each city may place on attracting growth. The second effort to model city competition has focused on relationships between firms and cities, rather than relationships between cities. This approach uses a perfectly competitive marketplace model with the product either the location sites, which cities supply and firms buy, or the economic development (i.e. jobs), which firms supply and cities buy. In the most common application of this approach, economists treat firm's location decisions as cost minimization problems with location incentives and other public policies as one set of contributing factors (reviews of this literature are given in Bartik, 1991 and Wasylenko, 1997). In this model the focus is not on explaining city use of location incentives, but on assessing their influence on firms' behavior. This framework is an improvement over the Prisoner's Dilemma layout -- it can incorporate heterogeneity in cities, although the typical empirical specification has not adequately accounted for the price/quality trade-off. However, it is still limited by the assumption that there are sufficient numbers of buyers and sellers so that no single participant can influence the price of the good. In reality, the diversity of both cities and firms is so great that the competitive market would be very thin, with few participants for any possible transaction. In fact, most of the assumptions underlying a perfectly competitive market are violated in the market for development, limiting the usefulness of this perspective. Of the five assumptions for a perfectly competitive market -homogenous products, multiple buyers and sellers, no barriers to entry or exit, perfect information, and rational actors maximizing utility -- only the last one holds. It is violation of the first two assumptions that are critical in undermining the applicability of the competitive marketplace in this case. Whether the product is considered the location sites cities offer or 6

the development benefits firms offer, the product is not homogenous. Cities offer extremely diverse factor endowments -- such as wage rates, skilled workforce, transportation access, land cost, agglomeration benefits. It is this total bundle of goods that the firm buys. Trade-offs among the factors that compose the bundle, as well as a trade-off in price versus overall quality of the location, will influence the development market. Price alone, as represented by city subsidies or imposed costs, is not the sole determining factor in a location decision. Incorporating this heterogeneity into the competitive market framework implies there are multiple markets for each product (i.e. city type) each with different clearing prices. Similarly, if the product is business development, firms do not all have the same impact on cities -- the number of jobs, wage level, impact on traffic, impact on land price are just a few of the differential effects. All suggest that the firms are no more homogeneous than the cities, and that no single market exists for them either. One corollary of the multiple buyers and sellers assumption is that no individual actor can affect the equilibrium market price of the product. All act as price takers, so if the assumption held, there again would be no variation in the price that a city could charge. Any increase due to costly regulations or failure to provide incentives would price the city out of the development market. However, the preceding discussion suggests that the assumption of multiple buyers and sellers breaks down in this market. Even though there are thousands of cities, from the firm's perspective only a few may be acceptable once the differences in endowment bundles are considered. And even though there are thousands of firms, from the city's perspective there may be few they would want, once differences in their economic benefits and costs are recognized, or few firms that would be interested in that city. In the development marketplace, we cannot assume either a large number of cities where a firm might 7

locate or a large number of firms that might invest in a particular city (Feiock, 2002). Given that central assumptions underlying the competitive market model and the Prisoner's Dilemma Model are not met, consideration of a model that reflects these differences might lead to better explanations of economic development decisions. A bilateral bargaining model is one way to incorporate both heterogeneity and corresponding limited numbers of market participants. Also as reported in much anecdotal evidence, the outcome of development deals are frequently determined by negotiation between a single city and firm, so the model also reflects the empirical world (Guskind, 1990).

Rubenstein/Nash Bargaining Model Since the bargaining model has not been applied to this area of urban policy previously, we will start with the most basic model to establish a baseline perspective (Morrow, 1994). In addition, one of the goals of this project is to develop policy tools for local government officials and a simpler model is more likely to be used. Overviews of bargaining models have been written by several authors; this presentation draws on Binmore, 1992; Binmore and Dasgupta, 1987; Binmore, et. al, 1986; Kreps; 1990; and Osborne and Rubinstein, 1990, 1994. The simplest bargaining game is one of complete information. Complete information games assume that both participants know all of the relevant characteristics of their opponent from the beginning. There is no uncertainty. The Nash model which is the basis for this game is similar to one of "splitting a pie". There is a valued item (the pie) that will be divided between two players. Each side wants more of the item and the more that any one player gets the less that the other will receive. Not all of the item needs to be given to the players -- some could be 8

left on the table -- but that outcome is undesirable from the perspective of both participants. Either player could choose to walk away from the negotiations at some point because he could get a better deal with someone else. In the case of location incentives, the pie is equal to the total net economic value of the new development -- including the local public benefits of an enhanced tax base and economy. This value sets the maximum that the city could provide to the firm and still break even. The rationality and complete information assumptions preclude a city from accepting a deal that would make them worse off than before. The two players in the negotiation are the city and the firm trying to reach an agreement over what percentage of this value the city will get to keep. If the firm locates in the city without any development policy concessions, the city receives all of the economic costs and benefits of the project. A high outside option city could collect all the benefits form the development, typically through imposing additional extractions on the firm to cover any city development costs. Places with few outside options lose all the benefits by charging no fees and often providing valuable location incentives to businesses. The outcome variable, xi, falls in the interval [0,1] representing the proportion of development benefits kept by the city. The payoff for the firm is x1 and for the city x2. The set of possible agreements is: X = {(X1, X2) | X1, X2 ∃ 0; (X1+ X2) = 1}. This states that if agreement is reached, the outcome values for each players must lie between 0 and 1, or 0 and 100 percent of the available benefits, and that the total benefits are divided between the two. There is also a breakdown point (β 1, β 2) which is the outcome reached if the parties fail to reach an agreement. Breakdown occurs when one player leaves the negotiation in order to take up his outside option -- the best he could do outside the game with this particular player. This can also be characterized as his reservation price -- the minimum value the player 9

would need to consider this particular deal. If negotiations break down because one player takes his outside option (β 1), the other player also receives the value of his outside option (β 2). This game is solved by use of the Nash bargaining solution. The negotiated outcome (X1, X2) will be the point, or argument, in the set X defined earlier at which the Nash product is maximized. Each player gets the most that he can given the characteristics of the other player. These relevant characteristics are each player's level of risk aversion (in this case, the player's expectation that the game will continue for another round), their levels of time preference (discount rate), and the outside options available to each. This is shown in the model presented below. While the Nash model is a cooperative game, Rubinstein has shown that the Nash solution will also be the unique subgame perfect equilibrium for the non-cooperative model providing that the time between offers approaches zero (Rubinstein, 1982). In a complete information game, since all players can calculate the final outcome with certainty, technically only one offer (the subgame perfect result) is made. The offer/counteroffer structure collapses, implying time between 'offers' is zero. The Nash framework is easier to illustrate than the Rubinstein version, so it will be used to develop the basic complete information game. The Nash solution assigns a payoff (Xi) to each player of the value of his breakdown point or outside option (β i) plus some proportion (α or 1-α) of what remains of the total pie after these values have been distributed (1 - β 1 - β 2). The size of this proportion is determined by the player's time preference and risk aversion -- both related to the value lost if the offer/counteroffer cycle was played out. If the sum of the breakdown points is greater than one, both players could be made better off exercising their outside options than in the negotiated game, and we should expect a breakdown of the game. If they sum to zero, the players are indifferent between the negotiated result or their outside 10

options. Following the traditional approach in these models, we assume that both will then take the negotiated outcome. Arg (X1 - β 1)α (X2 - β 2)1 - α Max Where: β i = the breakdown point of player i α i = log δ i log δ 1+ log δ 2 1-α i = log δ 1 log δ 1+ log δ 2 δ i = 1 - ρi = the discount factor of player i 1 + θi ρi = expectation of player i that the game will continue for another round (risk aversion) = [0,1] θi = discount rate of player i (time preference) = [0,1] The solution for this game is: Player one: x1 = β ??+ α (1 - β 1 - β 2) Player two: x2 = β 2?+ (1- α) (1 - β 1 - β 2)

Hypotheses from the Nash Model Comparative statics from the Nash solution equations lead to six hypotheses concerning the effects of each player's outside option (endowment), time preference (immediate economic concerns), and risk aversion (characteristics of the bargaining agent). This set of hypotheses demonstrate one of the advantages of using the Nash model to study city-firm negotiations. Working from a well specified theoretical framework leads to a better understanding of the influences of the explanatory variables. Earlier research typically assumed simple linear relationships and ignored the interdependency among the variables that the Nash model specifies. Including these interactive effects of the independent variables could result in different policy recommendations since a one unit change in a variable will no longer generate the same impact on the negotiated outcome for all cities. The size of that effect will 11

depend on the levels of the other explanatory variables, and for some cities the substantive effect may not be great enough to advocate making that change. While this specification change is intuitively plausible, the bargaining model provides theoretical justification for it as well. H1: The higher the player's outside option value, the higher the outcome value for him. (First derivative of outside option with respect to outcome is positive). Cities with desirable location features will retain a higher level of development benefits. H2: The higher the player's risk aversion, the lower the outcome value for him. (First derivative of risk aversion with respect to outcome is negative). Cities that face fiscal and economic pressures demand less from any development deal. H3 : The higher the player's time preference, the lower the outcome value for him. (First derivative of time preference with respect to outcome is negative). City political actors who fear political retaliation for not winning new developments will demand less from any deal. H4 : The magnitude of the positive relationship between the outside option and the outcome value will be stronger as the player's risk aversion increases. (Cross-partial derivative of risk aversion with respect to outcome and option is positive). Regarding the last hypothesis, if a bargaining agent for the city such as the mayor is highly risk averse, she places a low probability on seeing another round of negotiations. Therefore, she wants to increase the odds that the city will get the development in this round. Her strategy will be to expand the size of the potential solution set by lowering the city request to the minimum that it could get with certainty. This guaranteed minimum is the value of the city's outside option. A very risk averse mayor asks only for the city's outside option, so differences in the level of the option variable strongly influence the size of the negotiated outcome. If the mayor is not risk averse, she believes the firm is not likely to withdraw from negotiations, so she uses a strategy that demands more than the city's outside option. The value of the outside option has less impact in determining the outcome with the more risk acceptant mayor.

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H5 : The magnitude of the positive relation between the outside option and the outcome will be stronger at higher levels of time preference. (Cross-partial derivative of time preference with respect to outcome and outside option is positive). The argument for this effect is analogous to the one for risk aversion. A strong time preference is equivalent to facing a high interest rate, so the value of development is perceived as declining quickly if it does not occur soon. At high values of time preference, cities want to get development underway immediately. Again, they will not ask for more than their outside option because that would increase the risk that the offer would fall outside the solution set for the firm. On the other hand, when interest rates are low, the value of growth declines slowly, and the city is more willing to make demands above its outside option. Just as with risk aversion, the outside option has its strongest impact on the result when it is the only offer the city will make.

H6 : The magnitude of the negative relation between risk aversion and the outcome will be lower at higher levels of time preference. (The cross-partial derivative of time preference with respect to risk and the outcome is positive).

Again at low levels of time preference, the perceived value of development declines slowly and does not constrain the city's negotiation offer. When there is no immediate need for new growth, other factors such as the level of the mayor's risk aversion can have a strong impact on the negotiated results. However, as the rate of time preference increases, the city, regardless of the mayor's risk aversion level, wants a deal today. All mayors would be compelled to make low requests to increase the odds of winning the current development deal. The influence of risk aversion in determining the outcome declines at these higher levels of time preference. 13

EMPIRICAL RESEARCH STRATEGY Our ongoing research effort tests the basic Nash model in two ways. Because the solution to a game is the set of player strategies that are best responses to the other player and that lead to an equilibrium outcome, we first consider the city to be playing against a known population of firms and the firm's three parameter values can be set to the population mean values. While the city does not expect to encounter a firm with these identical values, the expected value of the parameters over repeated interactions with different firms would equal these population means. Choosing a general development strategy that is optimal in anticipation of this overall population provides the city with the highest payoffs over the long-run if it never engages in specific negotiations with individual firms. Therefore, our first test of city choice of development policy is based on expected play against the average firm. With the firm's values held constant, the outcome is a function of the city characteristics only. The problem is now decision theoretical rather than strategic. The following specification is based on the comparative statics from the equation for the city solution. The units of analysis are all cities in the data set described below; the dependent variable is an index of the development policies -- incentives and constraints -- adopted by the city. A vector of control variables are added to account for state level policies and constraints. Y = α + β 1 Xc1 + β 2 Xc2 + β 3 Xc3 + β 4 Xc1 * Xc2 + β 5 Xc1 * Xc3 + β 6 Xc2 * Xc3 + β 7 Xc7 where X1 = outside option X2 = time preference X3 = risk aversion X7 = control variables Y = policy index 14

Because the dependent variable incorporates policies selected over time, the independent variables also will include more than the current values on these dimensions, using average values of the variables for the preceding five years. The second test applies our basic Nash model to specific negotiated development offers. One advantage of the bargaining model over other approaches to economic development is the recognition that outcomes depend on both player's strategies. Therefore, a test that specifically incorporates information about the firm and the relationship of its critical parameters to those of the city is desirable. The following specification is derived from the comparative statics predictions from both the firm and city solution equations. The combination of the two equations illustrates that the outcome depends on the relative magnitude of the parameters of the two players. For example, while the city will capture more of the benefits from development if its outside option (alternative development options) is high, it receives less if the firm's outside option is also high. The difference between the two player's characteristics determines the proportions in the division of the pie. In the following specification, the dependent variable is the value to the firm of the specific city offers -- either additional incentives, waiver of some growth management requirements, or new costs imposed to win city approval of the project. The units of analysis are the outcomes from specific location deals, information that will be a part of this project's data collection efforts. All independent variables are normalized to constrain the scale for both firm and city factors to comparable ranges. The same control variables reflecting state policies will also be included.

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Hypotheses regarding the effects of each variable, where the variables are now differences between the two players' values on each factor, are the same as those for the general Nash model. Since all variables are defined to be the difference between the firm and city values on factors, each measures the level of the firm's advantage and the coefficients are expected to be positive. The greater the firm's advantage in its outside option (Xf1 - Xc1), the higher the value of the concessions it receives (β 1 > 0); the greater its advantage in time preference (so that Xf2 - Xc2 0); and so on. Y = α + β 1 (Xf1 - Xc1) + β 2 (Xf2 - Xc2) + β 3 (Xf3 - Xc3) + β 4 ( Xf1*Xf2 - Xc1*Xc2 ) + β 5 (Xf1*Xf3 - Xc1*Xc3) + β 6 (Xf2*Xf3 - Xc2*Xc3) + β 7 X7 where: X1 = outside option, f=firm value; c=city value X2 = time preference X3 = risk aversion X7 = control variables Y = value to the firms of negotiated concessions This specification recognizes that it is the difference between the two players' values on the parameters that determines the final outcome. While cities with high option values and low time preference and risk aversion in general will do better in negotiations, they will do less well when they play against a strong firm, one that also has a high option value, low time preference, and low risk aversion.

Research Design Most work on business location decisions and city development policies focus on a national set of large cities competing for development across metropolitan areas. The handful of studies that 16

considered intrametropolitan competition among a city and its suburbs examined only a single metropolitan area (Charney, 1983; Luce, 1994; McGuire, 1985; McHone, 1986; Wasylenko, 1980). The importance of government policies, tax and service levels in location choices increases dramatically once competition is narrowed to a single metropolitan area, yet intrametropolitan relationship among cities is understudied, primarily because of the scarcity of data on smaller cities within metropolitan areas. Even fewer studies have incorporated both inter-and intra-metropolitan competition. Nevertheless, cities do compete at both levels. Consideration of both levels of competition would affect the model in two ways. First, the range of possible values of the independent variables is likely to differ with inclusion of smaller suburbs. Population size is associated with different government structures and homogeneity of the population, which affect political calculations of elected officials. Large cities also tend to have larger staffs with higher levels of expertise. Second, the values for the both the city's and firm's outside options depend respectively on their competitive position with or attractiveness to cities in other MSAs and suburbs within the same MSA. Therefore, we have undertaken an extensive data collection effort targeted to all incorporated places with populations greater than 10,000 in 12 purposively selected metropolitan areas. This will permit a more inclusive picture of city competitive pressures and development outcomes. There are approximately 500 localities with a population over 10,000 in the 12 areas. The 12 metropolitan statistical areas (MSAs) were selected from the 56 MSAs with an estimated 1999 population of at least 1 million. Two selection criteria were used: the cities' growth trends relative to their region's trend and the level of local competition among cities in the MSA. Two MSAs were chosen in each of the four Census Bureau regions -- one that had unemployment rates 17

below its regional average in both 1995 and 1999 (a 'growth' city) and one that had unemployment rates higher than its regional average (a 'declining' city) in both years. The cities are shown in the table below. Selection of a declining city in the West was slightly problematic, given that most below average growth areas were smaller municipalities. Salt Lake City was selected because it experienced an increase in unemployment during this five year period, while the rest of the metropolitan areas saw fairly significant decreases. Within each region, metropolitan areas were selected to be as similar as possible on the basis of general population size and percentage of the population that lives in the dominant city, while as divergent as possible on their relationship to the regional unemployment rate. All 12 MSAs are between 1.5 and 3 million population, making them large enough to be nationally competitive business sites, but not subject to unusual growth forces that could affect mega-metropolitan places such as Los Angeles or New York City. On the second selection criteria, four MSAs were chosen that had multiple central cities, so local competition between several large cities in the same area could be evaluated as well as the competition between a dominant central city and its suburbs. One multi-city growth and one multi-city declining MSA were chosen in both a declining and a growth region. Table One: Selection of Metro Areas City Boston Buffalo Milwaukee Cleveland Denver Salt Lake City San Francisco-San Jose-Oakland Riverside-San Bernardino San Antonio Houston Tampa-St. Petersburg Miami-Ft. Lauderdale

Region NE NE MW MW W W W W S S S S

City growth growth declining growth declining growth declining growth declining growth declining growth declining

Regional growth slightly declining slightly declining growth growth declining declining declining declining slightly growing slightly growing slightly growing slightly growing

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Local competition level Single central city Single central city Single central city Single central city Single central city Single central city Multiple central cities Multiple central cities Single central city Single central city Multiple central cities Multiple central cities

Model Operationalization and Data Collection The Nash model identifies general factors that will affect negotiation outcomes in any situation -the outside option, time preference, and risk aversion of each player. In order to apply this general model to different bargaining situations, these concepts must be measured in a manner specific to the particular situation and players. Since multiple factors determine most of these concepts, each of the final empirical tests of the bargaining process will be composed of a set of structural equations; a set of measurement equations that specify the operationalized relationships for the general concepts and one that specifies the relationship between these measures of the concepts and the final development outcome. The same basic factors will be used in measures for both tests of the model -- general policies and specific deals. However, the general policy index will include policies adopted at different points in time, which requires that the independent variables for those city measures capture more stable features underlying a longer time period. When the dependent variable is the specific concession, the time frame is more immediate, requiring the independent variables also to reflect conditions at the time the deal was made. Therefore, both intermediate-term and short-term versions of the variables will be constructed. A mail questionnaire (attached as Appendix 1), currently in the field collects information on the authorization and use of the three types of city growth policies, the value of specific negotiated deals, local political institutions and election results, the role of neighborhood groups, land use and zoning information, and supplemental city financial data. The policy instruments specified in the questionnaire include location incentives, progressive development, and growth management policies identified from previous studies (Feiock, 1990; Clingermayer and Feiock, 1992; Neiman and Lewis, 2001; Sullivan and Green, 2001). The instrument also identifies if the city offered a specifically negotiated location 19

package in the last 24 months. For those who answer yes, a series of additional questions about the two most recent offers will be asked including: the date of the final outcome, the firm involved, specific incentives and extractions, estimates of jobs to be created or retained, and whether there was a formal contract stating the obligations of the firm receiving the deal. Where a formal agreement was used, information on specified employment and investment levels and any claw-back provisions will be gathered. While this type of data can be difficult to elicit in a mail questionnaire, the format and data collection procedures will closely follow the methods used by Sullivan and Green (2001) who gathered similar information for urban rural communities. We anticipate using telephone follow-ups to obtain this information for some cases. In a subset of the cities, we will also conduct personal interviews with local development officials, mayors, and others involved in city negotiations with firms. A large number of the smaller cities may never have negotiated a deal, resulting in a distribution with a substantial number of zeros and missing data on any firm level variables. This is addressed by estimate the models with two different measures of the dependent variable: as a dichotomous measure (negotiated deal, did not) and then as a continuous variable treating the non-deals as zeros. The estimation procedures for the two models will be adjusted using probit and tobit procedures respectively rather than OLS (King, 1989; Long, 1997; Maddala, 1981; Sigelmann and Zang, 1999).

Measures of City and Firm Characteristics We next describe our strategy for measuring city outside options, time preference, and risk aversion. Outside option values indicate how well each player could do if this particular negotiation fell apart and each took their next best alternative. All places experience some level of economic 20

development, regardless of the outcome of any individual negotiation. The benefit from this natural level of development is the city's alternative if it loses a specific project -- its real outside option. Cities with higher levels of expected natural growth have stronger bargaining positions. We create an index measuring the city's natural attractiveness based on characteristics found to be important in business location decisions, such as wage rates, transportation accessibility, and others. Both the factors found to be important for intermetropolitan and those for intrametropolitan location choices will be included. All economic sectors will be classified into 20 groups. For each group, traditional business location factors are used as independent variables, and the dependent variable was the change in proportion of national establishments in the area. Based on the estimated regression coefficients, predicted growth in establishments in each sectors are calculated. Higher levels of expected natural growth would indicate a higher reservation price or outside option for the city. A final regression equation including intermetropolitan location factors, intrametropolitan location factors, controls for regional economic conditions, and controls for government policies will be estimated for each economic group. The dependent variable will be the change in establishments as described above over a five year period, 1992-1997. Predicted values from the separate economic sector equations will be combined to create an estimate of expected natural growth in the city with higher values indicating a higher city reservation price or outside option. Time preference reflects how important it is to attract economic development today rather than in the future. Since cities often pursue development to increase jobs or their tax base, communities with higher unemployment rates or fiscal stress are likely to place a greater value on attracting development and soon. The more severe the problem, the more important it is to get relief as soon as possible, and 21

the more willing the city is to accept an outcome with a lower value due to generous location concessions than to wait for a better deal in the future. The greater the city's perceived need for development, the higher the discount rate it would apply. This time pressure will be measured in two slightly different ways. For the general policy equation where a more stable measure is desirable, demographic expenditure needs and local revenue capacity will be incorporated, using the Ladd and Yinger (1989) fiscal stress index. This index is based on the underlying city and intergovernmental structural conditions that affect need for city services, cost of an average unit, and local revenue raising capacity. For the second empirical test using negotiated deals, variables that reflect current economic pressures will be added to the fiscal stress measure. Unemployment and poverty put pressure on the city to attract jobs quickly. The severity of the problem will depend on the local area's economic prosperity compared to overall prosperity. The city unemployment rate relative to the regional rate and poverty rate relative to regional rate will capture these immediate problems. Risk aversion in the bargaining framework has a very precise meaning -- it is the actor's belief that the specific development deal will be lost if the negotiations are delayed for another round. For local government officials their risk aversion would result from both the level of their constituents' desire for economic development (losing a deal is costly) and the set of local government institutions that could protect the political actors from electoral retribution if he fails to satisfy those preferences. High values of risk aversion in this case occur only when losing a deal is costly to political officials. Measures of risk aversion will be different for the two empirical models. For the first test on city development policies, it will depend on general government structures rather than the features of any 22

specific individuals. These institutions create the same incentives and accountability framework for all political officials, regardless of who holds the office. For the second test on negotiated deals, risk aversion will also be dependent on the specific individuals in these offices and local conditions at the time of the deal, such as time until the next election and previous electoral margin of victory. Both the city council and the chief executive play significant roles in policy adoption, while the chief executive tends to have a greater role in negotiation of specific deals. Therefore, information on both will be used to create the institutional measure, while the short-term measure will concentrate on the role of the chief executive. The political incentives created by local government institutions has been linked to the adoptions of both economic development incentives (Sharp and Elkins, 1991; Fleischman and Green, 1992; Feiock and Kim 2001) and growth management restrictions (Feiock, 2003b; Feiock Lubell and Jeong, 2003; Gerber and Phillips, 2001). In particular, the mayor council form of government creates powerful incentives for political opportunism and credit claiming (Clingermayer and Feiock, 2001; Frant, 1996). Even in cities with slow-growth constituencies, the mayor is likely to have some level of risk aversion. Residents typically still value some growth; they are just more particular about what type and less willing to support large concessions to win it. The greater the consensus among voters that growth has limited value or the stronger and more visible the slow-growth advocacy groups, the more likely that the mayor will take a less risk averse, tougher bargaining stance. Heterogeneity in resident preferences and ability to express them will affect the city executive's willingness to pursue development at any cost.

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Mayor-council government is also more responsive to popular demands than council-manager systems that insulate local decisions from community pressures (Lineberry and Fowler, 1967; Lyons, 1978; Feiock Lubell and Jeong 2003). Schneider, Teske, and Minstrom (1995) found mayor-council systems encouraged entrepreneurial behavior in suburban governments. We anticipate that this enhanced political sensitivity, need to demonstrate leadership and to offer new ideas will lead mayors more than city managers to pursue pro-growth policies and provide larger concessions in order to claim credit for accomplishments under their administrations. The shorter the term of office, the greater the need to establish a reelection record and secure development deals as soon as possible. This behavior, however, will be moderated by the composition of the mayor's constituency. He should also be more responsive to diversity in citizen preferences and various interest groups’ political activity than a city manager. Diversity in constituency preferences also moderates the impact of city council structure. In general, large city councils and district election promote incentives to use development as a means of political distribution (Feiock 2003; Clingermayer and Feiock 2001), indicating higher risk aversion for these council systems. Given that the costs of development projects tend to be concentrated in the immediate neighborhood (traffic congestion, environmental degradation) while the benefits are city-wide (jobs and additional tax revenue), support for development projects is more likely to be divided in district councils. The more heterogeneous the city in a district council system, the more likely that a variety of preferences on development policy will be openly debated and more moderate options adopted.

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Constituency preferences on growth have been related to socioeconomic status and racial or ethnic background. Sonenshein (1993) found support for slow-growth initiatives in Los Angeles was highest among white liberals, with a significant drop for both African-Americans and Hispanics, but with the Hispanic drop often only about half that for African-Americans. Creation of a slow-growth coalition in San Francisco faced similar problem keeping all the likely supporters involved -- environmentalists, neighborhood populists, and traditionally liberal ethic and racial groups (DeLeon, 1992; DeLeon and Powell, 1989). We will estimate constituency preferences in the city by the level and heterogeneity of socioeconomic status and racial/ethnic background. Effective ability to express these preferences will depend on the existence of various interests groups (business, neighborhood, environmental, and others), their level of activity as assessed by city officials, and any formal requirements that gives these groups on official roll in development decisions. Risk aversion will therefore be measured by a vector of factors that reflect local government institutions -- type of chief executive, city council size, election structure for the council, length of term for both council and mayor, interest group activity, and formalized requirements for group involvement in development approval. Outcomes of the negotiation game also depend on the firm's value of the three general variables. These factors will only be used in the second test of the model based on specific negotiated deals where strategic interactions are possible. Information on several of the firm's parameters will be gathered by the survey, and others will be obtained based on their general economic sector (retail, finance, etc.) and matches to secondary data sources (number of employees, sales, payroll) to assess market power and economic position of the firm. 25

The firm's outside option is based on both the attractiveness of alternative city locations and the probability that the firm would receive concessions from any of those locales. Attractiveness of other locations will be measured for both inter- and intra-metropolitan competition and will depend on both the city characteristics relative to their competitors and the number of alternatives in an area. We measure firm's potential alternative locations based on the negotiating city's national/regional competitive position as measured by its percentile rank in a location endowment index, the number of municipalities in its metropolitan area, the percentage of land available for development among those cities, and a survey measure of the extent of competition with other cities. The probability that a firm would be welcomed in another city primarily depends on the economic benefits that they generate. The greater the firm's economic effect through additional payroll or jobs, the more likely a comparable city would be willing to offer substantial location incentives. The firm's outside option is then the product of the probability of finding a comparable city or better and the firm's estimated payroll impact. The second parameter, time preference, is low for most firms because delays during the negotiation stage of a deal are relatively insignificant to the present value of the project. For a few sectors delay will be more costly -- projects that need to make an early financial commitment and face substantial interest payments before revenue from the project begins. These conditions most commonly hold in retail and office building development. Also for some sectors, making a commitment to market area before competitors enter is critical. Time preference will be estimated based on the firm's economic sector, its bond rating, and level of competition in the market area. For many firms, time preference will be treated as zero, giving them the expected advantage over cities.

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For the risk aversion parameter, the firm's negotiating agent is also likely to be more risk neutral than any city representative. His risk aversion is an assessment of the chance that the city would totally walk away from this deal -- no other offer would be considered and the development would never occur anywhere. Since the firm can always choose to pay the city full taxes as its last 'offer' or to locate at an alternative site, the probability that the company would never be able to build its establishment is zero. Given this advantage, we assume in this study that all firms risk aversion will be a value near zero. `The data collection described above will generate an extensive set of variables for empirical analysis and at a level of analysis never used before (city/firm dyads). This large scale data analysis will be supplemented by field work in the 12 MSAs, using interviews with local officials involved in development policy and related negotiations to elaborate on the implications from the bargaining model and nuances that cannot be captured with standard variables. The dynamic nature of the negotiations can be explored for particular cases, utilizing the analytical narrative framework (Bates, et. al., 1998). Applying the Nash bargaining model to the specific case of negotiations over growth policies allows us to incorporate insights from previous development research within a more rigorous theoretical framework that leads to a very specific predictions and empirical specifications. This research strategy also provides the basis for extensions to related development issues. The next section describes one of those extensions, indicating the fertile nature of the research programme.

Extensions for the Repeated Play/Reputation Game The Nash bargaining model provides a starting point for analysis of economic development choices in cities. This framework can be extended to incorporate additional features of the real world 27

or focus on different aspects of the process. Our future research will extend the work described in the previous section by relaxing the complete information assumption. This allow us to focus on the effect of the city repeatedly bargaining with different firms over time and the possibility of developing a 'strong' reputation of not giving away excessive benefits. The model integrates the two levels of policy decisions that cities make in this area -- the selection of general policies that apply to any future development projects and the selection of city responses to specific requests for concessions. Since the general policy will not be optimal if applied to all requests, the city could increase its payoffs if it adjusted the general policy to specific firm requests. In addition, selecting a stringent set of policies sets a high entry price, requiring more firms to ask for concessions and providing more opportunities for the city to extract benefits. However, these policies also send a signal that the city is an expensive place to do business and may discourage firms from even approaching the city, lowering the city's payoff through missed opportunities. If the city consistently adjusts its policies for individual firms, then future entrants are more likely to ignore the general policies as non-binding. Finally, given that city politicians' objective functions may differ from those of the city voters, establishment of general policies as binding constraints on their actions may improve the payoffs for some cities. The role of general policies as binding constraints on politicians and as credible signals to future players is an important question in determining a city's development policy. The general structure of the game is that the city selects general development policies in the first stage, and then decides to enforce or waive the policy in each succeeding stage when approached by a firm requesting concessions. Firms update their beliefs about the city type after each stage.

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This scenario will be treated as a repeated Chain Store game where the city is the monopolist attempting to establish a reputation by its early actions in order to maximize its payoffs over time (Selten, 1978). The city that corresponds to the 'strong monopolist' in this game is the high outside option, growth management city that restricts development and never waives its constraints on growth. The 'normal monopolist' is the typical city that would like to keep as much of the development benefits as possible. Milgrom and Roberts (1982) showed that when the potential entrants to a market (equivalent to firms considering entry to a city) are uncertain about the strategy the monopolist will play (offer concessions or not), it is possible for some monopolists to increase his final payoff by bluffing. Uncertainty may exist because the monopolist is not a unitary actor and the different participants may prefer different strategies, in the same way that the city and its negotiation agent may have different utility functions or time horizons leading to divergent strategies. Kreps and Wilson (1982) showed that the payoffs of the monopolist player are determined by the relative value of his discount rate and his payoff, and the number of iterations already played. For some set of these values, the monopolist would obtain a higher payoff from responding aggressively to market entry than from cooperating. The potential entrant will not know the value of these parameters, so he cannot be certain if the monopolist really is one who will never concede or one who would concede but is trying to improve his payoff by bluffing. These sources of uncertainty are related to the parameters of the bargaining model. Uncertainty about the city's payoff can be due to uncertainty about either its time preference, which indicates how quickly the payoff decreases if negotiations continue, or uncertainty about the value of development to the city, which is related to its outside option. And uncertainty about the risk aversion level of the city's negotiating agent can lead to uncertainty about the strategy the city really will play. The same data 29

gathered to measure these parameters can also be used in assessment of the reputation model. There are two significant policy questions that can be answered from analysis of this game. First, we can derive the conditions under which a city could establish a credible reputation to deter concession requests. Considering the general solution to this game, it will depend on the relative parameter values for the city, particularly the discount rate in relation to the expected payoffs generated by cities with different outside options. We can characterize these as different 'regimes' -- groups of cities defined by their parameter values that could be 'strong' monopolists and those that could not be. Using data gathered in this study, we can then determine how many cities would actually fall into these regimes. We can also directly test the reputation model by comparison of the predictions of outcomes in each regime to actual outcomes (Morton, 1999). Second, we can determine for which cities deviations from the general development policy will be more fruitful in reaching a city's development goals over the long run.

Conclusion In applying the general concepts of the Nash model to the specific case of bargaining over location incentives, most of the ideas from previous development research have been incorporated but in a more rigorous theoretical framework that leads to a very specific empirical specification. City competition for development is captured by the firm's outside option. The limitation of the Prisoner's Dilemma model -- that all cities are equally desirable to businesses except for the concessions offered -is avoided by incorporating these features into the city's outside option. Municipal need for development to counter economic and fiscal problems is captured in the discount rate reflecting the city's time 30

preference. Finally, risk aversion allows political factors to enter the model by explicitly considering that the elected officials face a different utility function than the city as a whole. Information from archival sources, surveys, and field work and theoretical modeling will be utilized in tests of the bargaining models outlined above. Theoretically, the use of a bargaining model approach improves upon earlier models of city competition and city decisions to offer location incentives. The heterogeneity of cities which permits some to charge additional development fees while others offer extensive subsidies is systematically included in the decision process, providing a way to integrate the currently separate lines of research on growth management and economic development policies. The model also includes the impact of the strategic interactions between cities and firms, which are significant factors when the market has a small number of participants. The empirical analysis testing hypotheses from the three bargaining models will differ from prior work in three ways: the use of a large national data set from a variety of metropolitan areas, the integration of information on both the city's adoption of general development polices and its specific responses to firm requests, and finally, use of firm-level data to directly assess the relative power and interactions between the firm and city. The extent of city control over their own growth and fiscal well-being has been a source of debate in urban politics since Paul Peterson's argument in City Limits (1981) that external economic factors constrain local governments so much that they are nearly powerless to guide their own development. In the 1980s and early 1990s the research emphasis was on the effectiveness of city efforts to attract business, assuming this was the growth objective for all cities. With changes in the national economy, differences in city growth objectives and negotiation power has been recognized, and the research emphasis has shifted from determining when cities should offer growth subsidies to when 31

they should impose growth constraints. Yet these are parts of a single process where cities and firms divide the benefits of development. Developing and testing the bargaining framework for the entire range of growth policies promises to enhance our understanding of the relationship between local economic and political factors, city development choices, and the final level of city growth. It also will provide important insights into the contexts and institutional arrangements that allow governments to bargain effectively.

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Stone, C. 1989. Regime Politics: Governing Atlanta 1946-1988. Lawrence, KS: University of Kansas Press. Sullivan, D. and G. Green. 1999. “Business Subsidies and Municipal Controls.” Journal of Urban Affairs, 21(3): 267-279. Sutton, John. "Non-cooperative Bargaining Theory: An Introduction," Review of Economic Studies, October, 1986. pp. 709-724. Swanstrom, T. 1985. The Crises of Growth Politics: Cleveland, Kucinich, and the Challenge of Urban Populism. Philadelphia, PA: Temple University Press. Warner, K. and H. Molotch. 1995. "Power to build: How development persists despite local controls." Urban Affairs Review. 30: 378-406. Wassmer, R. W. 1992. "Property Tax Abatement and the Simultaneous Determination of Local Fiscal Variables in a Metropolitan Area." Land Economics 68: 263-82. Wasylenko, M. 1980. "Evidence of fiscal differentials and intrametropolitan firm relocation." Land Economics. 56: 339-49. Wasylenko, M. 1997. "Taxation and economic development: The state of the economic literature." New England Economic Review. 37-52. Welch, S. and T. Bledsoe. Urban Reform and its Consequences: A Study in Representation. Chicago: University of Chicago Press, 1988. Wolkoff, M. 1983. "The Nature of Property Tax Abatement Awards," Journal of the American Planning Association 49: 77-84. Wolman, H. 1988. "Local economic development policy: What explains the divergence between policy analysis and political behavior?" Journal of Urban Affairs. 10: 19-28.

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STRATEGIC ECONOMIC DEVELOPMENT

This survey is part of a study of local government economic development policy sponsored by the National Science Foundation and endorsed by the National League of Cities. The survey is being conducted by Florida State University and Claremont Graduate University. It is designed to collect information on local governments’ economic development activities. Please return this questionnaire to: Richard C. Feiock, Ph.D. Askew School of Public Administration and Policy Florida State University Tallahassee FL 32303-2250

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We first ask some general questions about your community and local government economic development goals.

1. Each city pursues a number of visions. However, it is possible that in a city some visions are more or less important. In thinking about the overall direction of land use and development policy in your city, please indicate how important each of the following is as a feature of your city’s policies. (Check a box between 1-5 for each of the following, with “1” considered to be “not at all important” and “5” to be “very important”.)

Not at all Important 1

A place to raise families and children A source of jobs for workers An environment friendly to all businesses A community of single family home owners A source of high quality professional services A destination for tourists A recreation and entertainment center A place of high income residents A community that improve the lives of the poor A retail shopping center

Very Important 2

? ? ? ? ? ? ? ? ? ?

? ? ? ? ? ? ? ? ? ?

3

? ? ? ? ? ? ? ? ? ?

4

? ? ? ? ? ? ? ? ? ?

5

? ? ? ? ? ? ? ? ? ?

2. How important would you say promotion of economic development is in your city? (Please check the category that best reflects your judgment.)

? ? ? ?

Not at all important Somewhat important Important Very Important

3. Does your local government provide financial support for any county-wide or regional economic development organizations? ? Yes ? No

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4. Which of the following activities are the focus of most of the economic development efforts in your city? (Check the box with the one response that best describes your city.) ? Retaining existing businesses ? Recruiting new business ? Retaining existing AND recruiting new business about equally ? Other (Please specify), _____________________ ? Not Relevant (city does not engage in any development activities)

5. Which of the following size businesses would you say are the primary target of your city's economic development policies? (Please check only one.) ? Those under 20 employees ? Those between 20 and 49 employees ? Those between 50 and 99 employees ? Those between 100 and 200 employees ? Those with more than 200 employees ? Not Relevant 6. Please rate the priority of the following types of development as targets of your city’s development policy (Check a box between 1-5 for each of the following, with “1” considered to be “not at all a priority” and “5” to be “highest priority”.)

Not at all a Priority 1

Residential Commercial/retail Office/business services Wholesale Manufacturing/industrial Mixed use, commercial and office Mixed use, with residential component Other _________________(specify)

Highest Priority 2

? ? ? ? ? ? ? ?

? ? ? ? ? ? ? ?

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3

? ? ? ? ? ? ? ?

4

? ? ? ? ? ? ? ?

5

? ? ? ? ? ? ? ?

7. Where are the majority of economic development activities carried out by your local government? (Check the box with the one response that best describes your city.)

? Office of the mayor ? Office of the manager or administrator ? A separate economic development department ? One or more other city government departments ? A non-governmental organization such as a chamber of commerce ? Another government such as the county ? Not Relevant (no development activities) 8. What was your local government budget for business recruitment and development incentives last year? (Please fill in the amount of that budget in the following space.) $____________________ 9. How many professional and support staff personnel (including yourself) does your local government currently have working on economic development related activities? (Please include any part-time staff. For example, 20 hours per week equals 0.5)

Number of professional staff:

______

Number of support staff: ______

Next we would like to get information about your city’s economic base and land use. 10. What is your best estimate of the percentage of land within the city used in the following ways? (Please provide an estimated percentage.)

Vacant Residential Manufacturing/Industrial Retail Service/Offices Government Other (please specify) ________________

_______ % _______ % _______ % _______ % _______ % _______ % _______ %

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11. How would you characterize the rate of growth for the following categories of development in the last three years? (Check a box between 1-5 for each of the following, with “1” to be low or no growth, “3”to be moderate growth, and “5” to be high growth.)

Low Growth - - - - - - - - - ? Residential Commercial/retail Office/business services Wholesale Manufacturing/industrial Mixed use, commercial and office Mixed use, with residential component Other _________________ (specify)

1

2

? ? ? ? ? ? ? ?

? ? ? ? ? ? ? ?

High Growth

3

? ? ? ? ? ? ? ?

4

? ? ? ? ? ? ? ?

5

? ? ? ? ? ? ? ?

12 . How severely does your city experience the following problems with economic development? (Check a box between 1-5 for each of the following, with “1”indicating not at all a problem and “5”indicating a very severe problem

for your city.) Not at all a Very Severe Problem - - - - - - - - - - - - - ? Problem Brownfield or other contamination issues Lack of developable land Cost of land Quality of labor is low Overall shortage of labor Lack of investment capital Traffic congestion Declining market due to loss of population Too many similar products or services Competition from nearby communities Citizen/neighborhood opposition Media opposition Aging and obsolete infrastructure High taxes Excessive or time-consuming regulation Other (please specify) _______________________

1

2

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

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3

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

4

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

5

? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Next we ask several questions regarding development policies and activities in your city.

13. The following are a set of economic development activities that some cities use. We have included some programs that involve the private sector or non-profit agencies as well (Please check all policies that are used in your city.)

? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

Community Redevelopment Agency to promote projects in specific areas Local government owned industrial or business park One or more State enterprise zones City-operated job training program Lobbying government units to locate government facilities in your community Annexing property to provide serviced land for new business Engaging in land assembly for eventual resale to private sector Using Business Improvement Districts or a Main Street program Aggressive code enforcement or use of eminent domain on blighted parcels Expedited rezoning or variance procedures for new projects Zoning or building code relief Density bonuses for specific site amenities Engaging in public/private ventures Engaging in joint ventures with other cities to encourage development Other (Please specify)___________________________________________

14. Growth management and residential development programs are related to economic development efforts in some areas. Th e following are a list of some of these programs, including some state programs (Please check all that are used in your city). ? Impact fees for new development ? Open space or farmland preservation programs ? Urban growth boundary ? Assistance in brownfield remediation ? Tax base sharing ? Housing linkage fees ? Housing trust fund ? Mandatory inclusionary zoning ? Incentives for affordable housing units in new developments ? State low-income housing tax credit program ? Other______________________________(specify )

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15. Some cities engage in promotional activities or efforts to centralize information about development opportunities in their city. Does your local government do any of the following? (Check all that apply.) ? Use a promotional brochure or media advertising ? Direct mailing to prospective businesses ? Call on prospective businesses ? Provide an ombudsman or technical services to small business ? Maintain a site map of locations for potential business development ? Provide information on all development incentives and programs in the area ? Has streamlined review of licenses and permits, 'fast track permitting' ? Has a centralized permitting agency, 'one stop shopping' ? Has a written economic development plan

16. We would now like to get information about location incentive policies offered by the City. Please indicate all of the location incentive programs that are generally available to business that locate in your city (Check all that apply .) ? Property tax abatements ? Sales tax abatement or rebates ? Tax credits to businesses ? Tax increment financing ? Grants to business ? Infrastructure improvements ? Free land/land write downs ? Subsidized buildings ? Job training subsidies ? Utility rate reduction ? Low-cost loans to businesses ? City issued bonds ? Relief from development fees ? Other (specify)____________

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17. For each of the location incentives in the previous question, we would like to get information about the size of firms that have received incentives. Please estimate the number of firms within each size category receiving incentives in the last 12 months. # of Small # of Moderate # of Large firms (less than firms (20 to firms (more than 20 employees) 100 employees) 100 mployees) Property tax abatements ________ _________ ________ Sales tax abatement or rebates ________ _________ ________ Tax credits to businesses ________ _________ ________ Tax increment financing ________ _________ ________ Grants to business ________ _________ ________ Infrastructure improvements ________ _________ ________ Free land/land write downs ________ _________ ________ Subsidized buildings ________ _________ ________ Job training subsidies ________ _________ ________ Utility rate reduction ________ _________ ________ Low-cost loans to businesses ________ _________ ________ City issued bonds ________ _________ ________ Relief from development fees ________ _________ ________ Other (specify)____________ ________ _________ ________

Next we ask several questions regarding negotiated location incentive packages offered to businesses by your

city. 18. Some cities negotiate specific development incentives with individual firms that are outside their normal set of incentives programs generally available. Approximately how often does the city negotiate specific incentive packages with new businesses? ? Never (IF NEVER, please skip to question 22.) ? Rarely ? Less than half of the time ? About half of the time ? More than half of the time ? Almost every time

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19. How often does your local government perform a cost/benefit or fiscal impact analysis before offering business incentives? ? Never ? Sometimes ? Usually ? Always 20. How often does your local government require a performance agreement (e.g. number of jobs created) as a condition for providing business incentives? ? Never (IF NEVER, please skip to question 21.) ? Sometimes ? Usually ? Always ? Not Relevant

(city never provides business incentives)

20A. IF YOUR CITY REQUIRES A PERFORMANCE AGREEMENT, Has the requirement for a performance agreement ever impeded a potential incentive deal? ? No ? Yes 20B. How often does your local government include a “clawback” clause in performance agreements, requiring businesses to return government incentives if they do not fulfill the performance agreement? ? Never ? Sometimes ? Usually ? Always 20C. Has the "clawback" provision ever been successfully used that you can recall? ? No need, all firms met their performance goals ? No, a firm did not met its goal, but the city did not recover benefits ? Yes, a firm did not met its goal and the city recovered some of its costs through the clawback provision

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21. For the last two incentive agreements your city negotiated could you identify the types of incentives included and your best estimate of the value of the incentives?

A. INCENTIVE PACKAGE # 1 _______________________________ Estimated total value of incentive package # 1 $__________________ ? ? ? ? ? ? ? ? ? ? ? ? ?

Property tax abatements Estimated amount $____________ Sales tax abatement or rebates Estimated amount $____________ Tax credits to businesses Estimated amount $____________ Grants to business Estimated amount $____________ Infrastructure improvements Estimated amount $____________ Free land/land write downs Estimated amount $____________ Subsidized buildings Estimated amount $____________ Job training subsidies Estimated amount $____________ Utility rate reduction Estimated amount $____________ Low-cost loans to businesses Estimated amount $____________ City issued bonds Estimated amount $____________ Relief from development fees or exactions Estimated amount $____________ Other Estimated amount $____________

Did the city provide any other non-financial assistance to the firm, such as expedited rezoning or assistance in land acquisition? ? No ? Yes, (Please describe) _________________________________________ Approximately, how many jobs did the firm bring to the city? _________ At about what average pay? ___________ What type of business does the firm do (e.g. retail, printing, banking)? __________________________ If it is a matter of public record or does not violate confidentiality requirements, please provide the name of the firm that was offered this incentive package. _________________________________________________ (name of firm)

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B. INCENTIVE PACKAGE # 2 _______________________________ Estimated total value of incentive package # 2 $__________________ ? ? ? ? ? ? ? ? ? ? ? ? ?

Property tax abatements Estimated amount $____________ Sales tax abatement or rebates Estimated amount $____________ Tax credits to businesses Estimated amount $____________ Grants to business Estimated amount $____________ Infrastructure improvements Estimated amount $____________ Free land/land write downs Estimated amount $____________ Subsidized buildings Estimated amount $____________ Job training subsidies Estimated amount $____________ Utility rate reduction Estimated amount $____________ Low-cost loans to businesses Estimated amount $____________ City issued bonds Estimated amount $____________ Relief from development fees or exactions Estimated amount $____________ Other Estimated amount $____________

Did the city provide any other non-financial assistance to the firm, such as expedited rezoning or assistance in land acquisition? ? No ? Yes, (Please describe) _________________________________________ Approximately, how many jobs did the firm bring to the city? _________ At about what average pay? ___________ What type of business does the firm do (e.g. retail, printing, banking)? __________________________ If it is a matter of public record or does not violate confidentiality requirements, please provide the name of the firm that was offered this incentive package. _________________________________________________(name of firm)

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22. For those businesses that are relocating to your city, where are they generally moving from (check one):

? ? ? ? ?

Another location in your county or metro area Another location in your state Out of state Don’t know Not applicable

23. For businesses that move out of your city, are they generally moving to (check one): ? Another location in your county or metro area ? Another location in your state ? Out of state ? Don’t know ? Not applicable

24. In thinking about your local efforts to encourage economic development, how would you compare your city’s policies with your neighboring local governments? (Please check one of the following.) ? My city does LESS THAN its neighboring localities ? My city does ABOUT THE SAME as its neighboring localities ? My city does SOMEWHAT MORE than its neighboring localities ? My city does MUCH MORE than its neighboring localities ? Don’t Know

25. Would you say that competition with other communities for economic development causes your community to offer more incentives to businesses than would be the case with out such a sense of rivalry? ? Yes ? No ? Don’t Know ? Not applicable

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Finally, we would now like to get some information about the participants in the development process in your city. 26. In general, how often are economic development issues controversial in your community? ? Never controversial ? Sometimes controversial ? Usually controversial ? Always controversial 27. Which of the following groups would you say are most likely to actively oppose development projects in your city? (Check all that apply.) ? Media ? Neighborhood groups or homeowner associations ? Environmental groups ? Other (Please Specify) ___________________________________ 28. How would you describe the role neighborhood groups play in the development process in your city? ? Very active, frequently win concessions from developers ? Very active, occasionally win concessions from developers ? Involved, but conflict with developers is rare ? Involved, but typically have no impact on the proposed project ? Not very active ? Not applicable (please skip to question 30) 29. Can you remember a case in the last year in which a neighborhood group: (check all that apply) ? Stopped a project; developers withdrew completely ? Forced changes in the project, such as reduced size or change in development type ? Won financial concessions from a developer ? Negotiated other neighborhood relief for possible negative effects of the project ? No instance of neighborhood group influencing a development that I can recall

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30. Please check the frequency of interaction your local government has with each of the following organizations regarding economic development (Check “Does Not Exist” if your community, county, or region does not have a given organization.) Does Exists, But Not exist No Contact

FREQUENCY OF INTERACTION Yearly Quarterly Monthly Weekly

1 Regional planning commission/MPO

?

?

?

?

?

?

2. Chamber of Commerce

?

?

?

?

?

?

3. Council of Government

?

?

?

?

?

?

4. Local public-private development organization ? (e.g. industrial/economic/community development corporation operating only in your community)

?

?

?

?

?

5. County/Regional public-private development org. ? (e.g. industrial/economic/community development corporation operating in more than 1 community)

?

?

?

?

?

6. Private consultants

?

?

?

?

?

?

7. Neighborhood associations

?

?

?

?

?

?

8. Citizen advisory group

?

?

?

?

?

?

9. University/College

?

?

?

?

?

?

10. Community College/Technical Institute

?

?

?

?

?

?

11. Churches/Religious organizations

?

?

?

?

?

12. Private lending institutions

?

?

?

?

?

13. Real estate or property developers

?

?

?

?

?

14. Utility companies

?

?

?

?

?

15. Officials/agencies in other cities

?

?

?

?

?

16. County government officials/agencies

?

?

?

?

?

17. State government officials/agencies

?

?

?

?

?

18. Federal government officials/agencies

?

?

?

?

?

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31. Please rate the importance of each person in the city’s economic development policy activity. Only one individual should be checked in the first column, but you may check as many as needed for columns two and three. City Government

Most Important (check one)

Not Important Important (check as many as needed) ? ?

City mayor

?

City chief administrative officer

?

?

?

City assistant adminis trative officer

?

?

?

City development director

?

?

?

City intergovernmental specialist

?

?

?

City finance officer

?

?

?

City budget analyst

?

?

?

Development organization director

?

?

?

Chamber of Commerce director/president

?

?

?

Other _____________________

?

?

?

32. How many council members are elected by district and at-large in your city? District _____________(number of members) At-large_____________(number of members)

33. How long are the terms of office for the following local offices? Mayor ____________(years) Council members elected at-large ____________(years) Council members elected by district ____________(years)

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34. Which of the following apply to the office of mayor in your community? (check all that apply) ? Mayor is directly elected by voters ? Mayor is a full-time position ? Mayor serves as a voting member of council ? Mayor can veto council-passed measures ? Mayor can initiate hiring or firing the manager or chief administrator ? Mayor appoints department heads ? Mayor presents the budget to council

Please provide you job title or position, your agency and email address below. Your Department:

___________________________________

Your position or title:

___________________________________

Your email address:

___________________________________

Would you like to receive the final report summarizing the survey results? ? YES, I would like to receive the final report ? NO, I would not like to receive the final report

Thank you again for your participation in this study. suggestions you may have.

We would welcome any additional comments and

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