Local economic development priorities say a lot about the wealth of a community. Municipalities with strong economies are less interested in jobs than those with a weak economy and are more likely to pursue environmental sustainability and social equity goals. Municipalities with weak economies focus primarily on the basics; jobs and tax revenues. The results, however, are the same; while there are statistically significant correlations between economic development programs and self-reported measures of success, when actual results are substituted, the statistical significance disappears.
In a previous post I reviewed the accuracy of reporting in local economic development programs. In this article, using the same data set from the International City Manager’s Association (ICMA), I focus on the alignment of economic conditions (barriers), local economic development priorities, programs and self-reported results. This is the first exploratory study to test connections across the entire continuum from planning to results. Previous studies have focused only on a portion of this continuum.
As with my previous study on reporting accuracy, municipalities are the focus of this inquiry because of the consistency of their organization, roles, and responsibilities. Counties, special districts, and non-profits can be organized in a number of ways, making it difficult to compare survey results. The complete ICMA survey instrument included 25 questions. Twenty-two of these questions were close-ended with a predetermined set of multiple choice answers. The number of multiple choice options in these close-ended questions ranged from 2 to 32. The questions involved planning (motivation, barriers, priorities), programs (tools and incentives), and claims of success. The menu of priorities included traditional (creating jobs, increasing the tax base) and Type II programming (quality of life, environmental sustainability, and social equity) . A summary of the responses from municipalities on priorities and claims of success is provided in Table 1. The priorities of municipalities in the survey are fairly consistent, with over 85% indicating that increasing jobs and the local tax base, along with improving quality of life are priorities. The responses are more varied for environmental sustainability (42%), and social equity (25%). As for success in meeting these priorities, just over 89% of municipalities claim some level of success with job growth, tax growth, improvement in the quality of life, and progress towards environmental sustainability. Claims of success on social equity are substantially less, dropping to approximately 76%.
Organizing the Data for Analysis
Prior to analysis, the data was organized into four general categories consistent with the structure of the survey; 1) planning (motivation/ barriers), 2) priorities, 3) programs (tools/incentives), and 4) reporting. Measures for planning (motivation/barriers) and programs (tools/incentives) were developed by combining multiple responses through factor analysis. Priorities and reporting were based on individual responses from the survey. The factor analysis started with 79 items relating to motivation (10), barriers (21), and tools and incentives (48). The items associated with motivation, barriers and tools and incentives, all measured on four-point Likert scales, were reduced through factor analysis to 13 measures and tested for internal consistency (α).
The measures developed for the motivation category are “progressive agenda” (α: .725), constructed with four items, and “organizational change” (α: .698), constructed with three items (Table 2). Four measures were created for barriers to success using twelve items (3 for each measure). These measures are “development constraints” (α: .656), “weak economy” (α:.660), “strong economy” (α:.661), and “labor constraints” (α:.575) (Table 3). Finally, seven measures were created for tools and incentives using 24 items. These measures are “direct business support” (α:.792), “sustainability programs” (α:.732), “marketing” (α:.731), “finance” (α:.719), “investment” (α: .681), “contributions” (α: .664), and “assistance” (α:.703) (Table 4). A consolidated description of all the measures is provided in Table 5.
Testing Program Alignment
The next step to explore the survey data was to test the relationships between 1) programs (motivation/barriers) and priorities, 2) priorities and programs (tools/incentives), 3) programs (tools/incentives) and 4) reporting. The test between measures are organized along a planning-programming-reporting continuum (Figure 1).
The first set of measures, representing motivation and barriers, was tested for correlation with priorities established by municipalities using binomial logistic regression. All five measures were grouped and tested against the five priorities. Table 6 provides the details of the analysis for all five models. In summary, all five models were found to be, overall, statistically significant. In terms of relationships between specific measures and priorities, a significant positive relationship was found between an economy with labor constraints (antecedent) and prioritizing job growth (OR 3.117), and between a progressive agenda (antecedent)` and prioritizing quality of life (OR 2.061), environmental sustainability (OR 4.143) and social equity (OR 3.698). A significant negative relationship was identified between a strong economy (OR .563) and development constraints (OR .510) in establishing job growth as a priority, and between a weak economy and establishing environmental sustainability (OR.641) or quality of life as a priority (OR .627). The organizational change measure had no significant connection to any of the five priorities.
The next step was to test the five priorities with the seven programmatic constructs (tools and incentives). An Independent-sample T-Test was conducted for all possible pairings of priorities and tools and incentives. The results of this analysis are displayed in Table 7. Twenty-two of a possible thirty-five relationships were found to be statistically significant (p <.05). Sixteen of these, using Cohen’s d, had a small effect (>.20), and five had a medium effect (>.50). The strongest relationships were job growth (priority/antecedent) with direct support (d:.84), sustainability (d:.50) and finance (d:.60); environmental sustainability (priority/antecedent) with sustainability (d:.69); and social equity (priority/antecedent) with sustainability (d:.77).
The final step in exploring the connections between measures was to analyze the relationship between program constructs (tools and incentives) and claims of success. For this analysis, a binary dummy variable was created to measure success with a response of “none” being a “0” and responses of “somewhat successful” and “very successful” being a “1”. Binomial logistic regression was then used to test the relationship between the tools and incentives measures (collectively) and the binary success variables for each of the five priorities. The results of the five models are presented in Table 8. All five models were found to be statistically significant. In terms of relationships between specific program measures and success, a statistically significant positive relationship was found between seven measures and claim of success. The investment measure was strongly associated with success with job growth (OR 1.824), tax growth (OR 2.761), quality of life (OR 3.231) and environmental sustainability (OR 2.616). Assistance was strongly associated with quality of life success (OR 2.046), sustainability was associated with success in environmental sustainability (OR 3.893) and social equity (OR 1.899).
Figure 2 provides a diagram illustrating the statistically significant relationships for individual components of the binomial logistic models, and moderate effects (Cohen’s d) from the t-tests.
The above analysis of the ICMA data set reveals some internal connectivity between motivation, priorities, and organization (programs) consistent with past research. Municipalities with strong economies are less interested in jobs than those with a weak economy. And municipalities with weak economies are not likely to establish environmental sustainability as a priority. The pattern of data appears logical in this phase of the study – and consistent with past studies (Reese & Fasenfest, 2004, p. 12; Warner & Zheng, 2013). Weak economies focus on the basics, and progressive municipalities pursue environmental sustainability and social equity. The association of public investment (Investment) with results in several categories likely reflects the ability of the municipality to tie direct expenditures with tangible results through operating or capital budget processes.
This exploratory study reveals that local economic development priorities are as unique as the municipality where they are set. Unlike other disciplines in the public sector, finding common ground is difficult. Police departments have crime rates, fire departments have response times, and public works departments have common unit costs that are measurable and repeatable. Local economic development priorities and programs are a reflection of the wealth of a community and the struggle to build or maintain that wealth. Municipalities with strong economies are less interested in jobs than those with a weak economy and are more likely to pursue environmental sustainability and social equity goals. Municipalities with weak economies focus primarily on the basics; jobs and tax revenues. The results, however, are the same; while there are statistically significant correlations between economic development programs and self-reported measures of success, when actual results are substituted, the statistical significance disappears.
About the author: Bill Farley has 30 years of experience in local economic and community development as a public official, entrepreneur and corporate executive. He is a former instructor of public policy and public finance at the University of Southern California Price School of Public Policy. He is currently advising organizations on local economic policy while completing a PhD in Public Policy and Administration at Virginia Commonwealth University.