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Many economic development organizations (EDOs) have embraced the mission to support entrepreneurial firms in their communities. EDOs engage in their entrepreneurial ecosystems, in part, by providing resources, sometimes in the form of business incentives.

The purpose of this report is to provide practitioners and policymakers with insights regarding the use of these incentives and guidance for offering incentives to entrepreneurial firms. Researchers and policymakers use a wide range of definitions for “entrepreneurial firm” and “incentive,” making it difficult to categorize and describe the current state of entrepreneurial firm incentives. Multiple additional research challenges, including a lack of data on program outcomes, hinder the ability to draw definitive policy guidance from both program evaluations and academic research. This report strives to sort this tangle of material into a framework that is helpful for policymakers and economic development practitioners.

Typology

The most common types of state and local incentives for entrepreneurial firms are financial, fiscal, and services. Incentives for entrepreneurial firms are, for the most part, divided into two target categories: small business entrepreneurs and innovation- or technology-oriented entrepreneurs. New or young firms are rarely the defined target for state and local incentives.

State and local financial incentives are primarily intended to fill small business funding gaps and address the regional disparity in private equity investment. They may take the form of debt, equity investment, or grants. The most prominent type of fiscal incentive is a tax credit for angel investors, which is intended to address the funding gap by encouraging more private investment. Services incentives include, for example, business advice and training, technical assistance, professional services, access to innovation spaces and networks, and referrals.

State and local governments have increasingly recognized that incentives designed either for all small businesses or for only technology-oriented businesses with high growth potential leave out many types of entrepreneurial firms that contribute to community and economic development. In response, these governments are devising new approaches to support growth-oriented and second-stage small businesses, inclusive entrepreneurship and social enterprises, and microenterprises.

Entrepreneurial firm incentives in practice

Incentive program names, types, targets, and mechanisms tell only part of the story. Each location’s entrepreneurial ecosystem context and program implementation practices shape the impact of its entrepreneurial firm incentives. The following six implementation issues can influence incentive effectiveness.

• Incentives are only a minor component of the entrepreneurial ecosystem.
• Incentive program rules may inadvertently constrain access and limit participation.
• Awareness of and access to incentive programs remains a challenge without a consistent pathway for entrepreneurial firms to navigate offerings.
• Most individual incentive programs are very small, providing relatively small amounts of money and assisting a limited number of companies per year.
• BIPOC and women entrepreneurs, as well as entrepreneurs in rural communities and distressed urban locations all remain underserved. Existing programs, then, are primarily engaging a narrow segment of entrepreneurial firms. A new approach that serves all entrepreneurial firms is needed.
• Careful program design and active project management can improve effectiveness.

Outcomes

Research challenges limit the specific policy guidance that can be gleaned from academic studies, formal program evaluations, and annual reports. Many of the most robust studies examine federal programs rather than smaller, heterogeneous state and local incentive programs. Despite these limitations, a review of research resources has yielded some insights regarding best practices in the field of entrepreneurial incentives.

• Small business lending programs can be effective, but most stand-alone state and local small business loan programs are too small to have a substantial community- or firm level impact. The programs themselves may fill a gap in credit access, but they are still a minuscule segment of the small business credit universe. Good management practices, technical expertise, sustained outreach, and effective compliance procedures are necessary to ensure a chance for success – all of which are a challenge for programs that manage a small number of transactions per year.
• Research tends to highlight the risks associated with public funds for private equity investment, but this strategy remains popular. Even successful private equity investors generate few breakout successes and tolerate many company failures. State and local governments face an even greater challenge in achieving success because their goals are for firms receiving investments to create a substantial number of new jobs and remain in the state over the long term. Experienced managers and good management practices play an especially important role in equity programs.
• Grants appear to have positive firm-level effects, including employment and sales growth, that should yield community-level benefits, as well. The scale and scope of most grant programs, however, suggest that community-level outcomes would not be widely felt.
• Angel investor tax credits appear to have a positive but limited impact on the firm and community. Research in this area, however, is not definitive. Program design may mean that the tax credits disproportionately or unintentionally go to company insiders who may have made the investment even without the tax credit. Community-level benefits would not be widely felt except in the unusual case of a breakout company success.
• Tax incentives are not the best method of helping entrepreneurial firms. At best, they have indirect positive effects; at worst, they have a negative impact. Transaction costs can diminish the value of refundable or transferrable tax credits, diverting intended resources away from the entrepreneurial firm.
• Services to entrepreneurial firms appear to generate positive firm-level outcomes, but it is not clear which types of services are most valuable. Service offerings must be sufficiently staffed and funded to be effective.

Guidance and Conclusion

1. Design incentives to leverage other resources and boost the ecosystem.
2. Strengthen incentive management and implementation procedures to improve program effectiveness.
3. Establish data and research standards to help researchers and evaluators determine best practices.

Extracting Meaning from Data

Travis Howe, Ewing Marion Kauffman Foundation

Our world is becoming increasing Funesian in that we are perceiving and storing more and more information in the form of data. But, as with Funes, access to information is not the same as understanding. Are we also better at extracting meaning from all of this data? What does understanding rely on – is it only possible through sophisticated data-processing techniques or is something else required? This paper will briefly discuss three common pitfalls related to the challenge of extracting meaning from data.

Visual Analytics for Entrepreneurship Research

Rahul C. Basole, Accenture AI

Visual analytics, which fuses data visualization with analytical models, is a promising emerging methodological approach in the enterprise sciences that aims to alleviate these issues. Broadly considered, visual analytics enables scholars and practitioners to more rapidly digest data, see patterns, spot trends, and identify outliers, thereby improving comprehension, memory, and decision making. It also facilitates the proposition and hypothesis generating process and ultimately accelerates time-to-insight. In addition to augmenting human intelligence, visualizations also aid in communication and explanation of complex phenomena, the critical last mile in data-driven research. The impact of visual analytics can thus be quite substantial for both entrepreneurship research and practice.

Computational Modeling Approach to Understanding Entrepreneurial Ecosystems

Yushim Kim, Arizona State University

The notion of an “ecosystem for entrepreneurship” has emerged as a promising conceptual framework because the decision to engage in entrepreneurship is made as entrepreneurs identify, interpret, and act upon opportunities that are embedded in a system. The entrepreneurial ecosystem (hereafter, EE) involves entrepreneurs as well as other critical actors, such as financial firms, universities, and public organizations that support new and growing firms. The concept also includes the entrepreneurial processes and institutional constraints that are interlaced together. The EE has been described as a system of “dynamic local, social, institutional, and cultural processes and actors that encourage and enhance new firm formation and growth”.

The literature does not give clear answers to critical questions, such as how institutional and individual actors are interlaced together and which conditions are necessary to ensure vibrant and sustainable innovation systems. The understanding of the delicate and complex relations among various ingredients that enable successful EEs may not easily develop without the aid of a suitable framework and tools. This thought piece introduces an approach that can be fruitful in exploring this challenge and such questions.

Measuring Digital Entrepreneurship at the Grassroots: What Role Will It Play in Community Resilience?

Karen Mossberger, Arizona State University; Caroline Tolbert, University of Iowa; Scott LaCombe, Smith College

The landscape of entrepreneurship has been changing because of digital technologies, and we have lacked the tools to adequately capture these trends and their impacts on communities. This is especially true for measuring the development and effects of microenterprises and start-ups that are too small or too new to be counted in traditional sources such as government data on small businesses.

Yet the internet has also led to experimentation with large, new datasets – often referred to as “big data” – to generate new policy-relevant insights. The analysis of such data has proliferated with increased computational power and the use of machine learning and algorithms.

We present new data on the density of active domain name websites in communities as a measure of local economic activity. GoDaddy, which is the world’s largest registrar of domain names, has collaborated with researchers from University of Iowa and Arizona State University, sharing de-identified data on the 20 million active U.S. domain name websites that have traffic and services attached to them. At least three-quarters of these websites are commercial.