Yesterday’s Gains Versus Today’s Realties

Abstract

The past decade has been one of the more turbulent in terms of the economic pressures felt by localities. Through three national-level surveys taken at five-year intervals over the previous decade, the authors examine changes in the use of economic development strategies employed by localities with populations more than 10 000. Despite reporting having moved away from a reliance on business incentives to a broader set of strategies, we find that after the recent recession, localities are relying on business incentives at their highest levels in 10 years. Moreover, the most recent survey results suggest that there are observable patterns among localities and their use of first-, second-, and third-wave strategies.

Keywords

local economic development economic development theory economic development strategy economic development practice

Introduction

Since 1999, the United States has experienced two significant recessions. The first recessionary period occurred from March to November 2001 and the other from December 2007 to June 2009 (National Bureau of Economic Research, 2011). The duration of the most recent economic decline is the longest of the post–World War II recessions, having lasted nearly 18 months. Given these events, the period between 1999 (a time of relative stability and prosperity) and 2009 (a period of retraction) provides us with unique insight into the changing realities of local economic development practices. According to Koven and Lyons (2010), local economic development during times of recession generally “. . . causes government to tend to use its remaining resources to ensure the continuation of basic services rather than expending them on development efforts” (p. 66). Examining more recent data will enable us to draw some conclusions about the changing landscape of local economic development tools and strategies in light of the turbulent economic condition in the United States.

Local governments across the United States have always struggled with crafting their own economic destinies while balancing a variety of competing interests (Blakely & Leigh, 2010; Eisinger, 1988; Koven & Lyons, 2010). Following the ebb and flow of these efforts, researchers have examined a number of issues related to the pursuit of economic development at the local level. For example, urban affairs scholars have explored issues of mobile capital (Elkins, 1995; Williamson, Imbroscio, & Alperovitz, 2003), job creation tools (Eisinger, 1988), and economic revitalization (Orr & West, 2002). Similarly, the economic development literature has looked at issues of federalism (Blakely & Leigh, 2010; Hall, 2010), globalization (Blakely & Green Leigh, 2010; Porter, 2000), education (Vogel & Keen, 2010), and tools and incentives (Blair & Reese, 1999; Koven & Lyons, 2010; Zheng & Warner, 2010) as they relate to local economic development.

To date, scholarship has identified an evolution of economic development practices that include at least three distinct phases or “waves” of development strategies (Bradshaw & Blakely, 2002; Olberding, 2002; Koven & Lyons, 2010; Zheng & Warner, 2010). First-wave strategies look outward for economic growth through the attraction of firms with the promise of financial incentives and infrastructure investment. Second-wave strategies shift the focus inward by leveraging economic development efforts to retain and expand existing firms. Third-wave development strategies focus “more broadly on community-level economic development strategies” through “small business, microenterprise, and community economic development strategies” (Zheng & Warner, 2010, p. 326).

According to Koven and Lyons (2010), these three waves are a useful metaphor for the practice of local economic development, in part because “. . . it places attraction and retention within an evolutionary progression” (p. 118). In fact, in 2010, Zheng and Warner were able to use this three-wave framework and data from 1994 to 2004 to conclude that many local governments learn important lessons from their economic development practices and do progress in an evolutionary manner—even while still relying on strategies from all three waves. Although Zheng and Warner are able to illustrate policy learning over time as it relates to economic development practice, the past decade of economic development has been turbulent and examining updated data is timely.

This article seeks to explore the trends in local economic development through national-level survey data to answer the following questions:

What changes have occurred in local economic development over the past decade?

Are there patterns and observable relationships in what types of tools local governments use for economic development?

In a post-recessionary world, are localities favoring one set of strategies over another?

Because many scholars have described the practice and usage of economic development strategies as evolutionary, and in keeping with Koven and Lyons (2010) assertion that local government resources are redirected toward basic services, we expect that there have been significant shifts over time in the use of certain strategies. Certainly the recession has shifted the attention of local governments to business attraction and retention (first- and second-wave strategies), and based on that, we expect those strategies to be key approaches in the local economic development toolbox. In light of the significant differences among localities in terms of economic health, size and type of government, and access to technical expertise, it is likely that there are patterns in the types of tools local governments use for economic development.

In exploring these postulations, the article briefly outlines the academic literature on local economic development efforts, then moves on to a discussion of the data, methodology, and findings, and concludes with lessons learned.

Local Economic Development: Literature Review

Although localities engaged in economic development practice much earlier, it was not until the 1960s that academics began to earnestly examine some of the major issues surrounding these economic development efforts (Fitzgerald & Leigh, 2002). Academic interest in the process and practice of local economic development rapidly increased as suburbanization ensued and questions of equity, central city decline, and racial disparities emerged as urban problems that were, at least partly, related to the economic development practice and needs of a community (Banfield, 1970; Fitzgerald & Leigh, 2002; Molotch, 1976). Academic interest surged again as de-industrialization wreaked havoc on many local economies (Eisinger, 1988). In the most recent years, the literature has started to move toward environmental concerns as they relate to economic development practices—the so-called sustainable development literature (Blakely & Leigh, 2010). Numerous scholars have identified three waves of economic development strategies used at the local level (see, e.g., Koven & Lyons, 2010). In this way, most related literature point to a progression through sets of chronological and overlapping tools and strategies that start at business recruitment and culminate at socially related goals and environmental sustainability issues.

From the earliest research, economic development was generally defined as some variant of wealth creation—whether that meant jobs, taxes, or some combination of the two (Bartik, 1990; Blakely & Bradshaw, 2002; Koven & Lyons, 2010; Wolman & Spitzley, 1996). Following in this purpose, many local governments offered financial incentives, infrastructure provision, and job training services, among others, to encourage growth in jobs and tax revenues (Blair & Reese, 1999). Many of these activities can be characterized as the so-called first-wave strategies that seek to attract businesses to the community to create jobs, wealth, and tax revenues. Although many scholars have illustrated that these financial incentives create competition among communities and do not always result in economic benefits to the community, they remain a popular tool among local governments (Goss & Phillips, 1999; Koven & Lyons, 2010; Zheng & Warner, 2010). Industrial recruitment efforts can be traced back to at least the 1930s when “[e]conomic development practice was concentrated on creating a good business climate through tax abatements, loan packages, infrastructure, and land development” (Blakely & Leigh, 2010, p. 57).

Many scholars have pointed out that the use of business attraction incentives (Wave 1 strategies) often evolve into business retention strategies as an emphasis in their community’s economic development practice (Blakely & Leigh, 2010; Bradshaw & Blakely, 1999; Koven & Lyons, 2010). During the 1960s, both the academic and political literature began to suggest that local economic development efforts providing incentives to private businesses did not actually result in additional jobs; rather “. . . jobs were transferred between locations” (Blakely & Leigh, 2010, p. 59). Thus, in part as an effort to stem the loss of jobs to competing entities, economic development practices began to include and emphasize business retention strategies and tools.

More recently, the academic literature has identified and refined its understanding of a third wave of strategies being used in local economic development practices. Koven and Lyons (2010) characterize the third wave as having a focus on “. . . quality-of-life and environmental concerns, social costs of growth, redistribution issues, and the role of government . . .” (pp. 124-125). With this focus, Wave 3 tools have been used to support “community-level economic development strategies and public investment(s) to improve quality of life and social justice and empower local communities” (Zheng & Warner, 2010, p. 326). Because of Zheng and Warner’s (2010) broad definition of Wave 3 strategies that incorporates the multiple classifications of third-wave approaches present in the literature, we similarly characterize Wave 3 strategies as both community development (including sustainability initiatives) and small business development efforts.

Although the three waves of local economic development are often viewed as evolutionary, many scholars point out that all three of the waves are often used simultaneously (Bradshaw & Blakely, 1999; Zheng & Warner, 2010). In fact, Blakely and Leigh (2010) explicitly states that, “. . . we can continue to observe the dominant characteristic of each phase in economic development practice today . . .” (p. 62). In an analysis of economic development practices from 1994 to 2004, Zheng and Warner (2010) find localities moving from a primary reliance on incentives to a more inclusive set of strategies. However, given the extraordinary economic decline of recent years, it is of interest to examine whether this progression continues. This article will now turn to the data and methods used to examine the changes in local economic development practice from 1999 to 2009 in the United States.

Data and Methods

Our analysis is based on national-level surveys conducted by the International City/County Management Association (ICMA) for the years 1999, 2004, and 2009 (the survey was conducted in conjunction with the National League of Cities). For each of these three years, the survey was sent to all municipalities with populations more than 10,000 and to counties with populations more than 50,000. For the purposes of our analysis, we believe there are distinctions to be made between the economic development activities, resources, and abilities of municipalities and counties, and for this reason, we have chosen to include only municipalities. By examining only municipalities, 912 respondents remain in 1999, 637 in 2004, and 729 in 2009.

Despite the efforts of the ICMA and the National League of Cities to include several key demographic variables, including population, form of government, and geographic location, there was a need to supplement this information with additional variables that would allow for the testing of differences in the variations in the use of certain economic development tools. To that end, we included selected socioeconomic data from the 2005-2009 five-year American Community Survey (ACS) for each responding municipality.¹

The survey response rate ranged from 19.6% in 2004 to as high as 31.5% in 1999, with 126 municipalities returning surveys across all three years. In light of the limited number, for the comparison of economic development strategies over time, we elected to examine responses from all respondents across each survey year to assure the robustness of the analysis. Surprisingly, through an examination of several key demographic and institutional variables (jurisdiction type, population, region, metropolitan status, and form of government), we identified a high degree of stability for each characteristic across all three years (see Table 1). As a result, we are confident in making comparisons of economic development strategies over time. To explore the trends and changes in local economic development practice a number of statistical techniques were employed. These techniques included descriptive statistics, comparison of means, and principal component analysis.

Table 1.

Comparison of Respondents Across Survey Years (Percentage of Governments Reporting)

	1999	2004	2009
Jurisdiction type
City	80.2	78.3	78.1
Town	9.9	8.9	8.6
Village	4.9	6.9	5.9
Township	3.7	4.9	6.0
Borough	1.3	0.9	1.4
Population
>1 million	0.4	0.0	0.3
500,000-1,000,000	0.1	0.9	0.7
250,000-499,999	1.4	0.9	1.5
100,000-249,999	6.9	7.5	6.9
50,000-99,999	15.6	15.5	16.3
25,000-49,999	25.3	25.1	25.1
10,000-24,999	50.2	49.9	49.2
Region
Northeast	16.6	14.3	14.0
North Central	31.0	32.3	33.3
South	27.7	31.4	29.1
West	24.7	22.0	23.6
Metropolitan status
Central city	18.8	19.6	19.1
Suburban city	60.7	62.6	65.4
Independent city	20.5	17.7	15.5
Form of government
Mayor–council	20.1	21.5	21.7
Council–manager	76.4	76.5	74.2
Commission	1.2	0.3	1.5
Town meeting	1.3	1.3	2.2
Representative meeting	1.0	0.5	0.4

Broad Trends in Economic Development: 1999-2009

Several important foundational aspects of local economic development practice can be examined with these data sets to begin to understand the changing landscape of economic development. These aspects include responsibility and competition issues with local economic development and funding and barriers to economic development practice.

Responsibility for Economic Development

With 67.6% in 2009 and 68.4% in 2004, municipalities have, and continue to be, the primary entity responsible for local economic development. Across both years, approximately 20% of localities report that a nonprofit development corporation functions as the principal coordinator of local economic development efforts. In comparing communities that use economic development corporations and those that exercise responsibility for economic development themselves, we find that areas with lower rates of poverty, higher home values, and higher household incomes are statistically more likely to eschew economic development corporations for their own internal processes. A possible reason for this finding is that affluent communities are more likely to have the resources necessary to have a robust internal economic development operation than areas in distress.

In the most recent survey year, 60.8% of localities report having a chamber of commerce as a participant in the local economic development process, which is somewhat increased over 57.1% in 2004. Despite this comparatively robust level of involvement, chambers of commerce have experienced a considerable decrease in their recognition as economic development partners since the 1999 survey, when 74.7% of localities reported chambers as an economic development participant. One possible explanation for the shift away from the chambers may be related to an increasing reliance on economic development corporations, which is at least partly supported by the data in Table 2. However, the possibility does exist that this may be more of a semantic distinction on the part of respondents given that a growing number of economic development corporations are housed within or are connected to chambers of commerce. Without further examination, there exist a number of possibilities for the changing role of the chamber. Counties (46.6%) and economic development corporations (40.6%) are the next two most frequently reported economic development partners. With the exception of the chambers of commerce, economic development participants have varied very little over the past decade.

Table 2.

Trends in Local Economic Development (Percentage of Governments Reporting)

	2009	2004	1999
Responsibility for economic development
Local government	67.6	68.4	n/a
Nonprofit	20.0	20.2	n/a
Other	5.5	11.5	n/a
Written economic development plan
Yes	48.7	41.8	52.6
Local budget allocation linked to ED priorities
Yes	38.8	n/a	n/a
How long has locality had ED plan?
<3 years	21.0	n/a	n/a
3-5 years	20.4	n/a	n/a
>5 years	58.6	n/a	n/a
Participants in local economic development
Chamber of commerce	60.8	57.1	74.7
County	46.6	39.2	40.5
Economic Development Corporation	40.6	37.4	n/a
Regional organizations	34.0	31.2	n/a
State	31.1	27.9	27.6
Federal government	7.0	8.0	7.8
Competitors for local economic development
Nearby local governments	74.6	59.0	80.6
Other local governments within state	62.7	49.1	63.8
Other states	46.2	38.3	41.8
Local governments in surrounding states	44.4	38.8	40.4
Foreign countries	19.9	19.3	10.4

Number of competitors by characteristic	Mean (SD)

Median household income
<$50,000	2.72 (1.52)*	n/a	n/a
≥$50,000	2.31 (1.42)*	n/a	n/a
Median home value
<$250,000	2.66 (1.52)*	n/a	n/a
≥$250,000	2.18 (1.36)*	n/a	n/a
Percentage in poverty
<15%	2.34 (1.44)*	n/a	n/a
≥15%	2.77 (1.50)*	n/a	n/a

Note. ED = economic development; n/a = not applicable. Values in parentheses are standard deviations.

p < .05.

In some ways, whether or not a municipality has a written economic development plan is an indicator of the extent to which municipalities are willing to institutionalize their economic development efforts as part of their normal course of operations and more strategically engage the economic development planning activities within their jurisdictions. In 2009, slightly less than half (48.7%) of the responding municipalities report having a written economic development plan; this is slightly down from 52.6% in 1999. Of those localities with a plan, nearly 59% have been in place for more than 5 years, 20.4% report between 3 and 5 years, and 21% have been in existence for less than 3 years. With the recessionary pressures on local budgets, many municipalities may be postponing the creation of written plans until their economic conditions improve. Despite a lack of resources now, localities that take the opportunity to use the recessionary period as a period of planning are less likely to lose time planning during the immediate period of economic expansion. Similarly, understanding the degree to which localities are basing resource allocation decisions on those written plans is a measure of the extent to which municipalities are ensuring alignment between the budgeting process and their economic development priorities. Only 38.8% of localities report linking the local government budget allocation process to economic development priorities. However, 49.5% of respondents who indicated they have a written economic development plan report that their overall local government budgeting allocation process is linked to economic development priorities, compared to only 27.6% of respondents who do not have a written economic development plan (p < .01). In this way, localities that take the time to engage in the process of developing a written economic development plan are significantly more likely to link their budgeting processes to economic development priorities. In fact, a simple logistic regression model analysis reveals that the odds for connecting the budget allocation process to economic development priorities are 2.6 times greater for those localities with a written economic development plan.

Competition for Economic Development

Municipalities have experienced an increase in the numbers and types of competitors (other localities) they face in pursuit of economic development. In 2009, nearly 75% of localities reported that they most frequently compete with nearby and other in-state local governments. This is a significant increase over the 59% in 2004 but similar to the 81% in 1999. Beginning with the 2004 survey, the number of localities reporting competition for economic development from foreign countries nearly doubled. Two significant international trade events occurred during the period that likely led to the increased foreign competition felt by localities. The first is the North American Free Trade Agreement that created a free trade zone opening up Canada, Mexico, and the United States and the other was the admission of China to the World Trade Organization. To be sure, municipalities are progressively feeling the competition for local economic development from abroad as local and national economies are becoming more interconnected with that of the world economy. In this way, local economic development is becoming as much about competing internationally as it is nationally and locally.

It is interesting to note that with regard to competition for economic development, communities with lower median household incomes, lower median home values, and higher rates of poverty report having greater numbers of competitors than do their wealthier counterparts (see Table 2). Although the relative impact of each is not examined here, we do know that the three indicators are interrelated and are representative of key markers of municipal distress. And, it is not entirely surprising that distressed areas face the greatest amount of competition as they generally compete for the lowest skilled jobs and therefore are under the greatest amount of pressure from multiple competitors (Wilson, 1996).

Funding and Barriers

Many forces both internal and external to the locality influence the economic development activities of municipalities. The diversity in capacity, funding, and policy tools can certainly shape the localities’ ability to pursue economic development priorities. As previous literature has illustrated, local governments have a number of tools at their disposal related to funding economic development efforts. Table 3 highlights the trends of some of the major funding tools used. Funding from local revenues remains the most frequently reported source of funding across all three years. Likewise, tax increment financing has remained at the top of the list in terms of reported economic development funding sources. Localities with populations more than 100,000 are more likely to employ tax increment financing as a funding strategy, with 51.5% of them reporting using tax increment financing to fund economic development programs, compared with only 30.4% of localities with populations less than 100,000 (p < .01). This is not surprising given the technical and legislative authority necessary to enact this type of funding mechanism. State and federal grants-in-aid are also commonly reported economic development funding sources—although each shows a decrease in usage from 1999 to 2009. Between 2004 and 2009, municipalities increased their reliance on sales and hotel/motel taxes to fund economic development activities. Although there is no direct irrefutable evidence to suggest a reason for the change, it is likely that the increase is at least partly due to a decrease in the absolute dollar value in federal and state grants-in-aid.

Table 3.

Trends in Local Economic Development: Funding and Barriers (Percentage of Governments Reporting)

	2009	2004	1999
Economic development funding sources
Local revenues	77.2	66.1	81.6
Tax increment financing	32.4	23.9	35.4
State grants-in-aid	24.8	19.5	32.3
Federal grants-in-aid	21.7	17.7	27.4
Hotel/motel taxes	20.7	16.0	n/a
Sales tax	19.1	15.4	n/a
Special assessment districts	10.0	7.8	12.6
General obligation bonds	8.8	7.1	11.4
Revenue bonds	7.3	6.1	10.7
Barriers to economic development
Availability of land	48.3	46.2	57.0
Cost of land	47.7	43.2	41.0
Lack of capital	45.7	27.5	37.2
Lack of building	33.9	34.1	n/a
Limited major employers	31.1	25.4	25.3
Inadequate infrastructure	23.7	19.3	n/a
Citizen opposition	21.3	17.0	30.7
Environmental regulations	20.0	16.2	n/a
Taxes	18.0	14.9	n/a
Lack of skilled labor	16.2	12.4	42.2
Distance from major markets	15.1	11.9	n/a
High cost of housing	13.7	16.0	n/a
Traffic congestion	13.6	19.0	25.5
Lengthy permit process	11.2	8.2	n/a
Lack of political support	10.0	7.5	n/a
Declining market due to population loss	8.1	3.9	4.9
High cost of labor	7.7	25.4	n/a
Poor quality of life	5.2	3.9	n/a

Note. n/a = not applicable.

Localities face any number of barriers when pursuing economic development (see Table 3). For the 2009 and 2004 surveys, availability and cost of land were among the top barriers to economic development. And in the most recent survey, localities report a lack of capital as one of their top two barriers to economic development. According to Koven and Lyons (2010), in times of economic downturn, governments are more likely to focus on basic service delivery rather than expanding economic development initiatives; after all, businesses are likely struggling and “. . . government intervention is less likely to influence their investment decisions . . .” (p. 66). In light of continuing issues with credit markets and the economy, it is not unexpected that municipalities are finding it more difficult to secure the necessary capital for economic development activities in place of direct funding from general revenues already marked for basic services.

Municipalities experiencing excessive labor costs as a barrier to economic development have decreased from 25.4% in 2004 to 7.7% in 2009. Moreover, the number of localities facing a shortage of skilled labor has also decreased significantly: from a high of 42.2% in 1999 to 16.2% in 2009. These significant changes are most likely related, in part, to the increased incidence of underemployment and overall labor market supply and demand changes as a result of the recent recession.² Adding to the uncertainty of economic development planning is an inability to know the extent to which these barriers to economic development may return to prerecessionary levels. Compounding the duress currently endured by localities are the previously discussed issues of lack of capital and the prohibitive cost of land. It is through these indicators that it becomes very apparent that municipalities are struggling to make sense of an environment increasingly inhospitable to economic development.

Economic Development Strategies in a Post-recessionary World

As discussed earlier, three waves of strategies have been documented within local economic development practice: first-wave strategies sought to attract firms through the use of incentives, second-wave strategies focused economic development efforts inward by emphasizing strategies designed to retain and expand existing firms, and third-wave strategies shifted focus to community and small business development activities in support of local economic development activities. Building on the work of Zheng and Warner (2010), we compare the mean scores for municipalities regarding the three waves of economic development and find a continuation of the trend to employ multiple economic development strategies simultaneously. In the analysis that follows, we examine, in succession, the three waves of economic development strategies using two lenses. Through the first lens, we attempt to identify changes in the use of the three waves and strategies within waves over the past decade. Then, through the second lens, we turn our attention to the most recent year (the post-recessionary period) to examine which strategies within waves are likely to be used in conjunction with each other, and also to determine what factors may explain the differences between municipal use of those within wave strategies.

First-Wave Strategies

Broadly examining the use of first-wave strategies across the three survey years reveals that local governments have almost uniformly increased their utilization of these tools in the most recent survey year. Perhaps most telling about the path localities are taking in the context of the recent economic recession is a substantially increased reliance on business incentives over previous years (see Table 4). Where Zheng and Warner (2010) found a decreasing reliance on incentives (first-wave activities) over time (from 88.4% in 1994 to 54.6% in 2004), the most recent ICMA data reveal that local government reliance on incentives has increased dramatically to 89.2% in 2009. Presumably, as localities experienced dramatic losses of employment and businesses, they had few other choices but to strengthen their business attraction efforts to offset significant losses in the community. In addition, the mean score (the average number of selected strategies on the survey) for first-wave activities increased from 3.31 in 2004 (Zheng & Warner, 2010, p. 328) to 4.83 in 2009, higher than each of the means reported in the previous study (see Table 5). What may be most concerning about this upward trend, however, is the degree to which localities are measuring the effectiveness of the incentives they are offering. In the 2004 survey, more than half of the localities reported measuring the effectiveness of their incentives, which was up significantly from the 1999 survey. Alarmingly, the number of localities taking a look at the degree to which their first-wave strategies are effective is now at an all time low of approximately 31% (see Table 4).

Table 4.

Trends in First, Second, and Third Waves of Economic Development Strategies (Percentage of Governments Reporting)

	2009	2004	1999
First wave: Incentives offered
Local government has written business attraction plan	22.7	19.8	15.1
Local government offers business incentives	89.2	55.6	67.7
Locality measures effectiveness of incentives	30.9	56.5	34.5
Zoning/permit assistance	62.1	37.7	47.5
Tax increment financing	51.7	33.4	35.6
Infrastructure improvements	50.3	36.4	49.7
Tax abatements	43.6	31.4	36.6
One-stop permit issuance	37.3	22.1	26.1
Grants	31.3	20.1	30.2
Free land	26.6	17.0	26.6
Low-cost loans	24.6	18.2	26.2
Special assessment districts	23.7	13.7	12.9
Federal/state designated enterprise zones	22.9	16.5	17.9
Locally designated enterprise zones	22.6	12.9	18.5
Tax credits	19.1	12.4	16.3
Training support	19.1	13.7	20.4
Regulatory flexibility	13.7	6.9	15.8
Relocation assistance	12.5	8.6	12.0
Subsidized buildings	8.1	5.0	7.1
Employee screening	7.3	6.1	8.8
Utility rate reduction	6.9	7.1	12.4
Second wave: Business retention activities
Local government has written business retention plan	22.4	16.5	29.6
Partnering with chamber, others	68.2	n/a	n/a
Surveys of local businesses	48.0	42.4	48.5
Partnering with local governments	44.0	34.9	31.6
Business roundtable	32.5	33.1	33.0
Business clusters/industrial districts	25.7	18.5	n/a
Local business publicity program (community-wide)	23.7	23.9	25.9
Ombudsman program	21.8	15.7	18.0
Revolving loan fund	18.8	22.0	28.9
Export development assistance	7.5	6.3	7.6
Replacing imports with locally supplied goods	4.1	1.6	1.9
Third Wave: Community and small business development
Community development activities
High quality of life	63.0	n/a	n/a
High-quality physical infrastructure	50.6	n/a	n/a
Affordable housing	40.6	n/a	n/a
Efficient transportation systems	37.3	n/a	n/a
Environmental sustainability	32.6	n/a	n/a
Community development corporation	32.0	34.1	50.9
Job training	29.6	29.2	58.2
Community development loan fund	21.1	26.4	53.8
Affordable, quality child care	8.1	18.4	n/a
Small business development activities
Local government has written small business development plan	13.3	16.5	24.4
Small business development center	23.2	23.9	26.8
Revolving loan fund	22.8	22.8	34.5
Matching improvement grants—physical upgrades	20.9	14.4	19.8
Marketing assistance	20.2	14.8	16.0
Business incubator	14.3	12.2	13.5
Management training	8.8	8.6	9.2
Microenterprise program	7.0	7.7	n/a
Executive on loan	6.0	4.2	7.2
Vendor/supplier matching	5.8	3.1	4.8

Note. n/a = not applicable.

Table 5.

Economic Development Wave Type Overall Mean Scores

	Mean (SD)
Economic development wave type	2009	2004	1999
First wave	4.83 (3.28)	3.19 (3.64)	4.21 (3.94)
Second wave	2.94 (2.16)	3.44 (2.97)	3.56 (2.61)
Third wave	4.36 (3.13)	2.33 (2.72)	3.30 (3.24)
N	729	637	912

Since 1999, an increasing number of municipalities are reporting having a written business attraction plan, the central idea behind first-wave strategies, which typically include zoning/permit assistance, tax increment financing, infrastructure improvements, and tax abatements. When asked to report on their top two most frequently used incentives, localities indicate that tax abatements and tax increment financing are at the top of their lists.

A comparison of socioeconomic variables for the totality of the first-wave tools reveals that localities with greater poverty levels, higher percentages of minority residents, lower percentages of residents with at least a bachelor’s degree, lower home values and household income levels, and larger populations rely more heavily on the first-wave strategies than do their more educated, higher income levels, fewer minority residents counterparts (see Table 6).

Table 6.

First-Wave Strategies Comparison of Means

Characteristic	Mean (SD)
Population (small)
<25,000	4.29 (3.05)*
≥25,000	5.36 (3.42)*
Population (large)
<100,000	4.62 (3.11)*
≥100,000	6.93 (4.12)*
Jurisdiction type
City	5.33 (3.32)*
Noncity	3.09 (2.48)*
Form of government
Non–council–manager	4.44 (3.03)
Council–manager	4.97 (3.36)
Median household income
<$50,000	5.93 (3.52)*
≥$50,000	4.01 (2.83)*
Median home value
<$250,000	5.54 (3.38)*
≥$250,000	3.59 (2.69)*
Percentage unemployment
≤8%	4.93 (3.11)*
>8%	5.61 (3.39)*
Percentage in poverty
<15%	4.25 (3.01)*
≥15%	6.02 (3.51)*
Percentage White
<75%	5.52 (3.31)*
≥75%	4.51 (3.22)*
Percentage Black
<15%	4.63 (3.23)*
≥15%	5.71 (3.36)*
Percentage Hispanic
<15%	4.70 (3.28)
≥15%	5.21 (3.27)
Percentage older than 25 years with BA
<35%	5.38 (3.40)*
≥35%	3.84 (2.80)*
Metropolitan Statistical Area type
Central	6.45 (3.69)²
Suburban	4.19 (2.92)^1,3
Independent	5.58 (3.40)²
Region
Northeast	2.78 (2.63)^2,3,4
North Central	5.56 (3.21)^1,4
South	5.49 (3.41)^1,4
West	4.23 (2.95)^1,2,3

Note. Values in parentheses are standard deviations. Superscript numbers indicate group pairings with significant differences (p < .05).

p < .05.

An examination of metropolitan statistical area (MSA) type indicates statistically significant differences regarding first-wave mean scores when comparing groups by MSA type. Central (urban) localities have the highest first-wave mean score (6.45), whereas suburban localities have the lowest (4.19). Independent localities (rural), not located in an MSA, have a mean score of 5.58. Between group differences are significant at the p < .05 level for each comparison, except for central to independent, which is significant at the p < .10 level. Finally, type of jurisdiction, which is certainly related to population size but is not synonymous, has implications for the types and number of economic development activities that might be pursued by a locality. The mean score for city localities for first-wave strategies was 5.33 compared with 3.09 for noncity localities.³

When examining the 20 first-wave tools, it is possible to identify five subgroups that localities are more likely to use in conjunction with each other. Specifically, a principal component analysis of the first-wave strategies reveals that the items cluster on five components, each having an Eigenvalue greater than 1, and when considered in combination, explain 47.61% of the variance (see Table 7). Financial assistance tools make up the first component and include four strategies providing monetary or financial assistance or relief in the form of grants, low-cost loans, relocation assistance, and subsidized buildings. Tax incentives are the common theme of the second component and include the commonly used tax credits and abatements, but also includes the more complex enterprise zones.⁴ Built environment incentives comprise the third component and include incentives such as free land, infrastructure improvements, and tax increment financing.⁵ Regulatory assistance is the fourth component and includes a set of incentives that attempt to provide regulatory support for local businesses. Finally the fifth component can be characterized as human resource–based incentives for business attraction.

Table 7.

First-Wave Principal Component Analysis (Rotated Factor Loadings)

	1. Financial assistance tools	2. Tax incentive tools	3. Built environment incentives	4. Regulatory assistance	5. Human resource–based incentives
First-wave strategies
Subsidized buildings	.684
Relocation assistance	.675
Low-cost loans	.589
Grants	.579
Locally designated enterprise zones		.729
Tax credits		.626
Federal-/state-designated enterprise zones		.607
Tax abatements		.527
Free land or land write-downs			.617
Infrastructure Improvements			.586
Tax increment financing			.540
Regulatory flexibility				.707
Zoning/permit assistance				.685
One-stop permit issuance				.628
Employee screening					.771
Training support					.652
Eigenvalue	2.06	1.97	1.60	1.55	1.39
Percentage of variance	11.46	10.93	8.90	8.58	7.74

Further examination of these components reveals some interesting patterns. Population size and identification as a city (rather than other municipal classifications) have a significant effect on the mean scores for the majority of the components (see Table 8), with localities that are cities and with larger populations employing more first-wave components. Localities with professional city managers have higher mean scores related to regulatory assistance and human resource–based incentives. This is not surprising given that these sets of tools tend to be more technical and require a greater degree of expertise, such as that commonly associated with professional city managers. And finally, localities with lower median household income, lower median home values, higher unemployment rates, higher percentages of minority residents, and those with more of their residents in poverty employ more strategies within first-wave components, producing higher mean scores for those components. This suggests that these localities are still very much focused on business attraction activities, which is likely related to their ongoing attempts to address the economic challenges they face.

Table 8.

First-Wave Strategies Comparison of Means by Component

Characteristic	Mean (SD)
Population (small)	<25,000	≥25,000
1: Financial assistance tools	0.67 (0.95)	0.86 (1.08)
2: Tax inventive tools	0.99 (1.10)	1.18 (1.19)
3: Built environment incentives	1.13 (1.01)	1.44 (1.05)
4: Regulatory assistance	1.03 (0.92)	1.23 (0.93)
Population (large)	<100,000	≥100,000
1: Financial assistance tools	0.72 (0.97)	1.21 (1.34)
2: Tax inventive tools	1.02 (1.12)	1.66 (1.28)
4: Regulatory assistance	1.10 (0.92)	1.40 (0.95)
5: Human resource–based incentives	0.23 (0.52)	0.62 (0.77)
Jurisdiction type	City	Noncity
1: Financial assistance tools	0.88 (1.07)	0.34 (0.67)
2: Tax inventive tools	1.19 (1.19)	0.71 (0.91)
3: Built environment incentives	1.42 (1.03)	0.82 (0.92)
4: Regulatory assistance	1.18 (0.92)	0.96 (0.95)
5: Human resource–based incentives	0.32 (0.60)	0.08 (0.33)
Form of government	Non–council–manager	Council–manager
4: Regulatory assistance	1.01 (0.95)	1.17 (0.92)
5: Human resource–based incentives	0.20 (0.48)	0.29 (0.58)
Median household income	<$50,000	≥$50,000
1: Financial assistance tools	1.02 (1.15)	0.57 (0.86)
2: Tax inventive tools	1.48 (1.24)	0.78 (0.97)
3: Built environment incentives	1.49 (1.05)	1.13 (1.01)
5: Human resource–based incentives	0.40 (0.64)	0.16 (0.46)
Median home value	<$250,000	≥$250,000
1: Financial assistance tools	0.89 (1.10)	0.54 (0.82)
2: Tax inventive tools	1.39 (1.21)	0.54 (0.79)
3: Built environment incentives	1.45 (1.01)	0.99 (1.02)
5: Human resource–based incentives	0.33 (6.11)	0.14 (0.43)
Percentage unemployment	≤8%	>8%
2: Tax inventive tools	1.09 (1.13)	1.33 (1.23)
5: Human resource–based incentives	0.23 (0.53)	0.36 (0.63)
Percentage in poverty	<15%	≥15%
1: Financial assistance tools	0.61 (0.90)	1.07 (1.17)
2: Tax inventive tools	0.85 (1.04)	1.54 (1.23)
3: Built environment incentives	1.18 (1.02)	1.50 (1.05)
5: Human resource–based incentives	0.18 (0.48)	0.42 (0.67)
Percentage White	<75%	≥75%
1: Financial assistance tools	0.92 (1.06)	0.69 (0.99)
2: Tax inventive tools	1.24 (1.23)	1.01 (1.11)
4: Regulatory assistance	1.25 (0.93)	1.07 (.92)
5: Human resource–based incentives	0.38 (0.64)	0.21 (00.51)
Percentage Black	<15%	≥15%
5: Human resource–based incentives	0.25 (0.55)	0.34 (0.61)
Percentage Hispanic	<15%	≥15%
2: Tax inventive tools	1.08 (1.14)	1.08 (1.17)
Percentage older than 25 years with BA	<35%	≥35%
1: Financial assistance tools	0.90 (1.08)	0.52 (0.83)
2: Tax inventive tools	1.27 (1.22)	0.75 (0.93)
3: Built environment incentives	1.40 (1.05)	1.10 (0.99)
5: Human resource–based incentives	0.33 (0.60)	0.14 (0.45)
Metropolitan Statistical Area type	Central	Suburban	Independent
1: Financial assistance tools	1.08 (1.19)²	0.61 (0.88)^1,3	1.04 (1.17)²
2: Tax inventive tools	1.51 (1.25)²	0.89 (1.06)^1,3	1.35 (1.21)²
3: Built environment incentives	1.60 (0.99)²	1.16 (1.01)^1,3	1.44 (1.12)²
5: Human resource–based incentives	0.48 (0.73)²	0.17 (0.46)^1,3	0.38 (0.62)²
Region	Northeast	North Central	South	West
1: Financial assistance tools	0.46 (0.85)^2,3	0.87 (1.06)¹	0.88 (1.10)¹	0.66 (0.89)
2: Tax inventive tools	0.80 (1.07)^2,3	1.33 (1.21)^1,4	1.26 (1.15)^1,4	0.67 (0.97)^2,3
3: Built environment incentives	0.50 (0.71)^2,3,4	1.69 (0.98)^1,3,3	1.33 (0.97)^1,2	1.12 (1.07)^1,2
4: Regulatory assistance	0.83 (0.89)^3,4	1.04 (0.86)³	1.28 (0.94)^1,2	1.26 (0.99)¹
5: Human resource–based incentives	0.08 (0.34)³	0.24 (0.53)³	0.41 (0.65)^1,2,4	0.23 (0.54)³

Note. Cut points reflect natural breaks in the data. Only significant differences are displayed (p < .05) for dichotomous categories. Superscript numbers indicate group pairings with significant differences (p < .05).

Second-Wave Strategies

Examining the tools of wave two strategies reveals a more mixed picture than that for first-wave strategies. After a decline in 2004, there has been an increase in the number of localities reporting having a written business retention plan (see Table 4). Partnerships with the local chamber of commerce are the most frequently reported business retention strategy among localities. Municipalities are also surveying local businesses as a means of soliciting input for ways in which local businesses could be assisted. Localities are also reporting the development of partnerships with local businesses without going through an intermediary organization (i.e., chamber of commerce) as one of their strategies for business retention. In these ways, localities are seeking input from local businesses either through specific strategies or direct or indirect partnerships in an attempt to understand their needs and ways in which these needs can be met. These findings are consistent with many of the suggestions that Koven and Lyons (2010) make for localities to help deal with economic downturns in their economic development activities. Specifically, these authors indicate that strategic planning and network building can be valuable tools to combat declining economic conditions. Certainly, the Wave 2 strategies show an increasing, albeit small in some cases, use of tools that are consistent with this advice.

An examination of the socioeconomic characteristics of the communities and Wave 2 strategies reveals a more mixed picture than with Wave 1 strategies. The only statistically significant relationship is with race (p <.05). Communities with a higher percentage of Black residents have a higher tendency to engage in Wave 2 strategies, and conversely, communities with a higher percentage of white Residents have lower mean scores for Wave 2 strategies (see Table 9).

Table 9.

Second-Wave Strategies Comparison of Means

Characteristic	Mean (SD)
Population (small)
<25,000	2.89 (2.16)
≥25,000	3.00 (2.17)
Population (large)
<100,000	2.88 (2.16)
≥100,000	3.53 (2.11)
Jurisdiction type
City	3.10 (2.16)
Noncity	2.41 (2.09)
Form of government
Non–council–manager	2.74 (2.20)
Council–manager	3.01 (2.14)
Median household income
<$50,000	3.06 (2.03)
≥$50,000	2.85 (2.25)
Median home value
<$250,000	2.87 (2.08)
≥$250,000	3.08 (2.29)
Percentage unemployment
≤8%	3.08 (2.22)
>8%	2.96 (2.10)
Percentage in poverty
<15%	2.87 (2.23)
≥15%	3.09 (2.01)
Percentage White
<75%	3.27 (2.11)*
≥75%	2.79 (2.17)*
Percentage Black
<15%	2.85 (2.18)*
≥15%	3.36 (2.05)*
Percentage Hispanic
<15%	2.94 (2.16)
≥15%	2.97 (2.16)
Percent older than 25 years with BA
<35%	2.92 (2.10)
≥35%	2.98 (2.26)
Metropolitan Statistical Area type
Central	3.12 (2.12)
Suburban	2.83 (2.15)
Independent	3.11 (2.26)
Region
Northeast	2.17 (2.03)^2,3,4
North Central	2.98 (2.12)¹
South	3.13 (2.14)¹
West	3.13 (2.24)¹

Note. Values in parentheses are standard deviations. Superscript numbers indicate group pairings with significant differences (p < .05).

p < .05.

A principal component analysis of second-wave strategies reveals that the various tools cluster on three components, each having an Eigenvalue greater than 1, and when considered in combination, explain 48.95% of the variance (see Table 10). Community and business partnerships, either through direct partnerships or the offering of a revolving loan fund, comprises the first component. Community-provided direct business assistance through such programs as an ombudsman, engaging in local business publicity, and hosting a business roundtable are included in the second component. In this way, these activities help to foster effective partnerships with and help retain local businesses. The third and final component within second-wave strategies includes efforts to minimize imports and maximize exports to aid in business retention and expansion.

Table 10.

Second-Wave Principal Component Analysis (Rotated Factor Loadings)

	1. Community and business partnerships	2. Community-provided direct business assistance	3. Export/import-focused retention activities
Second-wave strategies
Partnering with other local governments	.739
Revolving loan fund program	.610
Partnering with chamber, others	.600
Ombudsman program		.684
Local business publicity program		.626
Business roundtable		.575
Replacing imports with locally supplied goods			.840
Export development assistance			.555
Eigenvalue	1.85	1.83	1.22
Percentage of variance	18.45	18.26	12.24

Despite the lack of significant differences in means for component scores along the demographic categories examined, an interesting pattern does emerge. For localities with lower household incomes, more of their residents in poverty, and more of their residents being minorities, the mean scores for the component titled community and business partnerships are higher (see Table 11). In fact, for those categories, it is the only Wave 2 component with a statistically significant different mean. Moreover, for those communities with larger populations and higher home values, the component community that provided direct business assistance is the only one with statistically different means.

Table 11.

Second-Wave Strategies Comparison of Means by Component

Characteristic	Mean (SD)
Population	<100,000	≥100,000
2: Community-provided direct business assistance	0.76 (0.90)	1.00 (0.99)
Jurisdiction type	City	Noncity
1: Community and business partnerships	1.37 (0.96)	1.08 (0.99)
Form of government	Non–council–manager	Council–manager
1: Community and business partnerships	1.16 (0.95)	1.36 (0.97)
Household income	<$50,000	≥$50,000
1: Community and business partnerships	1.40 (0.92)	1.24 (1.00)
Home value	<$250,000	≥$250,000
2: Community-provided direct business assistance	0.73 (0.87)	0.87 (0.97)
Percentage in poverty	<15%	≥15%
1: Community and business partnerships	1.26 (1.01)	1.41 (0.88)
Percentage White	<75%	≥75%
1: Community and business partnerships	1.45 (0.91)	1.24 (0.99)
Percentage Black	<15%	≥15%
1: Community and business partnerships	1.28 (0.99)	1.45 (0.86)
Region	Northeast	North Central	South	West
1: Community and business partnerships	1.03 (1.04)^2,3,4	1.36 (1.00)¹	1.34 (0.89)¹	1.36 (0.97)¹
2: Export/import-focused retention	0.04 (0.24)³	0.12 (0.34)	0.15 (0.40)¹	0.12 (0.38)

Note. Cut points reflect natural breaks in the data. Only significant differences are displayed (p < .05). Superscript numbers indicate group pairings with significant differences (p < .05).

Third-Wave Strategies

Third-wave strategies often include both community development and small business development strategies (Koven & Lyons, 2010; Zheng & Warner, 2010). Community development activities focus on enriching the quality of communities through economic, socially, and environmentally focused programs. Municipalities most frequently report focusing on improving their quality of life as a way of supporting economic development. In the same vein, high-quality infrastructure and affordable housing are also commonly used community development activities. Following closely behind are efficient transportation systems and environmental sustainability. Because a broader definition and options of community development activities were not included in earlier surveys, we are unable to identify if trends exist in the use of these types of strategies. There are a number of third-wave strategies, however, that are increasingly finding a place within local economic development toolkits. For the purpose of this analysis, we have included both small business development activities⁶ and community development activities as third-wave strategies.

Over the past decade, the number of jurisdictions reported having written small business development plans has steadily declined. Localities do report relying on small business development centers most frequently, however, and, in light of the fact that many small business development centers are housed within universities, it may be that many municipalities have handed over the responsibility for supporting small businesses to these centers, although this is something that these data cannot prove. Revolving loan funds and matching improvement grants for physical upgrades are also frequently reported as small business development strategies. Although most of the third-wave tools in these data show little variation across the years, several tools have shown a substantial decline since 1999—primarily the community development activity related tools (see Table 4).

A comparison of the socioeconomic data with the Wave 3 strategies indicates that communities with larger populations and those localities identified as cities have higher mean scores for Wave 3 strategies. In addition, those localities with higher rates of poverty and racial minority populations have higher Wave 3 mean scores (see Table 12).

Table 12.

Third-Wave Strategies Comparison of Means

Characteristic	Mean (SD)
Population (small)
<25,000	1.16 (1.67)**
≥25,000	1.41 (1.78)**
Population (large)
<100,000	1.25 (1.72)**
≥100,000	1.65 (1.75)**
Jurisdiction type
City	1.38 (1.78)*
Noncity	0.97 (1.49)*
Form of government
Non–council–manager	4.20 (3.30)
Council–manager	4.41 (3.32)
Median household income
<$50,000	4.72 (3.25)*
≥$50,000	4.09 (3.34)*
Median home value
<$250,000	4.34 (3.21)
≥$250,000	4.39 (3.49)
Percentage unemployment
≤8%	4.18 (3.34)
>8%	4.53 (3.19)
Percentage in poverty
<15%	4.14 (3.28)*
≥15%	4.80 (3.34)*
Percentage White
<75%	5.04 (3.43)*
≥75%	4.03 (3.21)*
Percentage Black
<15%	4.13 (3.27)*
≥15%	5.34 (3.33)*
Percentage Hispanic
<15%	4.37 (3.39)
≥15%	4.33 (3.10)
Percentage older than 25 years with BA
<35%	4.51 (3.20)
≥35%	4.08 (3.51)
Metropolitan Statistical Area type
Central	1.55 (1.84)
Suburban	1.21 (1.63)
Independent	1.28 (1.96)
Region
Northeast	0.81 (1.38)^3,4
North Central	1.13 (1.69)³
South	1.57 (1.78)^1,2
West	1.45 (1.80)¹

Note. Values in parentheses are standard deviations. Superscript numbers indicate group pairings with significant differences (p < .05).

p < .05.

A principal component analysis of third-wave strategies reveals that the items cluster on four components, each having an Eigenvalue greater than 1, and when considered in combination, explain 47.05% of the variance (see Table 13). Entrepreneurial expansion strategies are included in the first component. Through these tools, localities attempt to provide structural support for the expansion of entrepreneurial activities. Environmental sustainability and quality of life is the common theme of the second component. Localities engaging in these strategies focus on areas such as high-quality physical infrastructure, efficient transportation systems, and environmental sustainability. The third component includes strategies that provide financial and institutional support for community or small business development activities. This includes community and revolving loan funds and community development corporations. The final component includes human investment strategies that aim to address issues of capacity through training and stability in the home through affordable housing and childcare.

Table 13.

Third-Wave Principal Component Analysis (Rotated Factor Loadings)

	1. Entrepreneurial expansion	2. Environmental sustainability and quality of life	3. Community and small business development support	4. Human investment strategies
Third-wave strategies
Management training	.667
Small business development center	.639
Executive on loan/mentor	.601
Business incubator	.585
Vendor/supplier matching	.548
Marketing assistance	.526
Microenterprise program	.509
High quality of life		.698
Efficient transportation systems		.677
High-quality physical infrastructure		.630
Environmental sustainability		.605
Community development loan fund			.818
Revolving loan fund			.774
Community development corporation			.574
Affordable, quality child care				.685
Job training				.567
Affordable housing				.564
Eigenvalue	2.65	2.32	1.96	1.54
Percentage of variance	14.70	12.89	10.89	8.57

Differences in mean scores for components are related to differences in mean scores for the demographic categories (see Table 14). Interestingly, while form of government does not reveal statistically significant mean score differences for the overall wave score, when considering the component analysis there is a significant difference in mean score for the component human investment strategies, with those localities having a council manager revealing a higher mean score for this component. This is in keeping with a similar finding in Wave 1 components, where localities with council-managers were engaged in more technically complex strategies.

Table 14.

Third-Wave Strategies Comparison of Means by Component

Characteristic	Mean (SD)
Jurisdiction type	City	Noncity
1: Entrepreneurial expansion	0.92 (1.41)	0.63 (1.11)
2: Environmental sustainability and quality of life	1.93 (1.38)	1.52 (1.33)
4: Human investment strategies	0.84 (.91)	0.58 (0.81)
Form of government	Non–council–manager	Council–manager
4: Human investment strategies	0.66 (0.78)	0.83 (0.93)
Household income	<$50,000	≥$50,000
1: Entrepreneurial expansion	0.97 (1.48)	0.76 (1.24)
3: Community and small business development	0.84 (.99)	0.70 (0.97)
Percentage in poverty	<15%	≥15%
2: Environmental sustainability and quality of life	1.75 (1.38)	2.00 (1.36)
Percentage White	<75%	≥75%
1: Entrepreneurial expansion	1.11 (1.43)	0.73 (1.30)
3: Community and small business development	0.90 (1.05)	0.69 (0.94)
4: Human investment strategies	0.90 (0.96)	0.73 (0.86)
Percentage Black	<15%	≥15%
1: Entrepreneurial expansion	0.77 (1.31)	1.23 (1.47)
3: Community and small business development	0.70 (0.95)	1.02 (1.09)
4: Human investment strategies	0.75 (0.89)	0.95 (0.92)
Percentage Hispanic	<15%	≥15%
3: Community and small business development	0.80 (1.01)	0.63 (0.88)
Region	Northeast	North Central	South	West
1: Entrepreneurial expansion	0.49 (0.97)^3,4	0.69 (1.26)³	1.09 (1.46)^1,2	1.01 (1.46)¹
2: Environmental sustainability and quality of life	1.45 (1.26)^3,4	1.74 (1.37)⁴	1.91 (1.36)¹	2.11 (1.43)^1,2
3: Community and small business development	0.59 (0.90)²	0.90 (1.03)¹	0.75 (0.93)	0.67 (0.99)
4: Human investment strategies	0.49 (0.77)^3,4	0.73 (0.83)	0.86 (0.89)¹	0.94 (1.02)¹

Note. Cut points reflect natural breaks in the data. Only significant differences are displayed (p < .05). Superscript numbers indicate group pairings with significant differences (p < .05).

Lessons Learned

Returning to the research questions posed in this article it is possible to draw some lessons from this research.

What Changes Have Occurred in Local Economic Development Over the Past Decade?

The most compelling change in local economic development efforts over the past decade seems to be an almost uniform increase in the use of first-wave strategies. These first-wave strategies, directed at business attraction, include some of the most contentious economic development tools: financial incentives, free land, tax credits, and other monetary and service assistance. It would seem that, to some degree, municipalities have returned to a time where any increase in jobs and development is worth the risk associated with the use of financial incentives to attract businesses. In addition to this overwhelming increase in the use of first-wave strategies, localities report a substantial drop in the measurement of the effectiveness of these incentives. This combination of changes may indicate a return, to some degree, to the old adage of “Shoot anything that flies: Claim anything that falls” (Rubin, 1988). More simply stated, local governments are not only returning to the first-wave business attraction strategies but are also widely measuring whether those incentives are effective in their economic development efforts. Although no one can fault local governments for engaging in a broad array of strategies to attract economic development, they should still be vigilant about the extent to which those practices are indeed producing outcomes. In an era of declining revenues and an increasing number of budgetary demands, localities are ill advised to pursue any economic development approach available to them without regard for their effectiveness.

Are There Patterns and Observable Relationships in What Types of Tools Local Governments Use for Economic Development?

The data illustrate some interesting socioeconomic status-related lessons. When it comes to first-wave strategies—some of the most contentious tools—the communities that have higher unemployment rates, lower home values, larger numbers of individuals in poverty, lower education levels, and higher numbers of minority residents actually more often engage in these Wave 1 strategies. In addition to a higher propensity to engage in Wave 1 strategies, these poorer communities with higher percentages of minority residents also tend to engage more frequently in the Wave 2 and Wave 3 strategies. Furthermore, the communities with higher rates of poverty and lower income levels face statistically significant higher numbers of reported competitors (minority population is not statistically significant). Overall, this may indicate that poorer communities are in, or feel that they are in, greater need of economic development and are therefore more willing to engage in all types of economic development strategies. While the jury is still out on the efficacy of business attraction incentives, research has questioned whether the widespread use of them contributes to unnecessary competition and unsuccessful results (see, e.g., Goss & Phillips, 1999; Wassmer & Anderson, 2001). Although it is likely the case that distressed areas are inclined to pursue all types of economic development strategies, the important lesson must surely be that those communities should engage in a strategic self-examination of how each economic development strategy is either supporting or working against one another in pursuit of increased economic activity. In this way, these communities have the greatest need for technical expertise in the area of economic development.

Additionally, patterns can be identified in these data with regard to certain tools being routinely used in conjunction with each other within each set of strategies. Lessons to be gleaned from these pairings include the finding that council–manager forms of governments seem to have a higher propensity to engage in the more technical and complicated sets of tools for economic development. For example, within Wave 1 strategies, a municipality that has a council–manager form of government is more likely to engage in regulatory assistance and human resource–based incentives for business attraction. Arguably these two sets of tools are the more complex and possibly advanced of the Wave 1 strategies. This same pattern is seen in the other two waves as well. This may point to some lessons for level of complexity and professional ability of a council–manager versus other forms of government.

In a Post-recessionary World, are Localities Favoring One Set of Strategies Over Another?

In light of the new ICMA data several lessons can be gleaned from the economic development efforts of municipalities during a difficult economic climate. Related to the overall increase in business attraction efforts are the municipalities’ major competitors for economic development. In 2009, a greater percentage of municipalities reported that every competitor (nearby local governments, other local governments in the state, other states, local governments in surrounding states, and foreign countries) increased from 2004. In addition, those communities with higher rates of poverty report having a greater number of competitors than do other communities. These data also illustrate some interesting lessons for the major barriers to local economic development efforts. Specifically, although land, cost, and availability issues have remained one of the largest barriers to local economic development, the lack of capital has increased substantially as a barrier since 2004. This is certainly a byproduct of the economic recession in the most recent years; however, a silver lining of sorts, is that both the high cost of labor and the lack of skilled labor as a barrier to local economic development have decreased from 1999.

Further Research

Although this article has provided some interesting and significant findings related to local economic development practice during turbulent economic times, much research is left to be done on this subject. This article identifies a number of interesting relationships between communities and the economic development tools they employ, which all warrant further study. Several key questions emerge from this study: What long-term impacts will be seen by local governments’ increasing return to first-wave strategies? What is the true relationship between the various forms of government (city manager–council vs. mayor–council) and the skill sets needed for the various individual tools? Is there something unique about city managers that enables them (or encourages them) to pursue certain strategies over others? This study is, however, limited by the lack of longitudinal data on individual localities. Following the same cities over time could help identify changes in perspectives from the individual level. This article, however, has taken the first step to identify patterns in local economic development policy with the use of a national level dataset. Future research could also explore some of these questions from a qualitative lens to gain a better understanding of the individual nuances of the communities involved in economic development activities.

Footnotes

Acknowledgements

The authors would like to thank the reviewers for their time and effort and thoughtful recommendations.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Bios

Jeffery L. Osgood Jr., PhD, is the Associate Director of the Center for Social and Economic Policy Research and an assistant professor of political science at West Chester University. His research interests focus on economic development and public policy and administration.

Susan M. Opp, PhD, is an assistant professor of political science at Colorado State University. Her research interests include environmental policy, sustainable economic development, and public administration.

R. Lorraine Bernotsky, DPhil, is the Director of the Center for Social and Economic Policy Research and a professor of political science at West Chester University. She is a methodologist, whose research interests include policy evaluation, program evaluation, and survey research methodology.

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