“Entrepreneurship, Innovation, and Public Policy: Evidence from Major U.S. Cities”

Scott W. Hegerty, Ph.D.

World Economy Research Institute

October 19, 2023

Major issues

• How do “cities” grow and attract entrepreneurs?
• What are the economic effects of increased entrepreneurship?
• How can this be measured?
• How do relationships vary between cities?

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Trends in U.S. cities

• U.S. Industrial Transformation:

Manufacturing 🡪 Services 🡪 Producer Services

• Demographics:

Suburbanization (decreasing share of core-city population)� Migration from “Rust Belt” to “Sunbelt”

• Remote work: revival based on costs?

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Monolithic vs. Diverse cities

• Detroit (in the automobile heyday)
• Chicago (today)
• San Francisco (over time; vs. L.A.)

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• Tolerance for “failure”?
• Mechanism: Entrepreneurship🡪 Growth
• Diversification/knowledge spillovers/input markets

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Literature

• Krugman (1991): Increasing RTS
• Barro and Sala-i-Martin (1991): Regional convergence
• Armington and Acs (2002): firm birth rates: related to industrial density, population and income growth
• Glaeser et al. (2010): “Entrepreneurial people” (not cost-related)

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Public policy

• Within the city proper�Rebuilding population in the “new” �economy�“Creative class” (R. Florida)
• Within metro areas�Fragmentation and cooperation: Chicago as an example�Impact on inequality
• Between metro areas (and states!)�Convergence vs. divergence?�Explaining variation (while controlling for everything else)

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An Empirical Study

• Measure entrepreneurship for large U.S. Metropolitan Statistical Areas
• Preliminary analysis: Connections to poverty (including < age 18), inequality, income
• Extensions: Different size thresholds, multivariate model

Two measures:

107 MSAs with population > 500K

• 1) Small-firm job creation 2016-2020 (growth, %): njc1620
• 2) Average # of employees in small firms (as a share of the total):�2016, 2018, 2020: afjc3

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Evidence of Divergence?

Correlations with demographics

Entrepreneurship and Inequality

Entrepreneurship and Income

Leaders and laggards: +1.5σ > median

Leaders and laggards: +1.0σ < median

What if MSA pop >1,000,000? (N = 53)

A story of two metros

• Rochester, NY: 332K (1950)🡪211K (2020)

Kodak, Xerox (but also Rochester Institute of Technology)

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• Jacksonville, FL 204K (1950) 🡪950K (2020)

Fidelity, other financial services

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National trends + state policy?

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Future covariates

• Pop. growth, income growth, education, industry type/density
• Can capture between-metro variation
• But need to control for various external factors

Conclusions/New Directions

• Able to calculate two “entrepreneurship” variables

🡪Can compare against others in the literature

• Classify high/low values🡪 Case studies
• Growth increases with levels: Divergence?
• Bivariate associations (little connection) 🡪 Formal model
• Related to policies?

Thank you!

• S-Hegerty@neiu.edu