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Agglomeration Economies and Business Startups on Native American Tribal Areas

Agglomeration Economies and Business Startups on Native American Tribal Areas. Christopher S. Decker, Ph.D. Department of Economics University of Nebraska – Omaha And David T. Flynn Director, Bureau of Business and Economic Research & Department of Economics University of North Dakota

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Agglomeration Economies and Business Startups on Native American Tribal Areas

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  1. Agglomeration Economies and Business Startups on Native American Tribal Areas Christopher S. Decker, Ph.D. Department of Economics University of Nebraska – Omaha And David T. Flynn Director, Bureau of Business and Economic Research & Department of Economics University of North Dakota Association for University and Business Research Annual Conference Indianapolis, IN October, 2011

  2. Motivation • Long-term phenomenon: Poverty rates are higher in non-metropolitan than metropolitan regions (Fisher, 2007) • On Native American Indian reservations poverty rates can be triple the national average (Benson, Lies, Okunde, and Wunnava, 2011) • Recent (anecdotal) evidence identifying several instances of successful enterprises on Native American Indian reservations of the Great Plains (Clement, 2006)

  3. Questions • What are the determinants of business startups on Native American Indian reservation areas? • How does this compare with non-Native American rural areas? • Focus: the role “Information Technology” agglomeration (IT agglomeration) plays • Focus: State of South Dakota

  4. Why South Dakota? • Home to many Native American tribes • Cheyenne River, Pine Ridge, Rosebud, Yankton, Lower Brule, Crow Creek, and parts of the Standing Rock and Sesseton • Boundaries (roughly) follow county lines • According to Leichenko (2003) • Much of the available data is county-level • Native American counties in South Dakota historically among the poorest in the nation • Yet, they have experienced substantial improvement in recent years • Example: Shannon County (Pine Ridge Sioux)

  5. Native American Rural Counties: Leichenko (2003)

  6. Annual Growth in Business Starts – County Aggregates (NETS)

  7. Share of Rural Startups Located in Native American Counties (NETS)

  8. Business Startups in Native American Counties: 2000 - 2007 (NETS)

  9. Business Startups and Agglomeration Economies • A common reason for lackluster growth in rural economies has been that they tend to lack agglomeration economies (Gabe, 2003, 2004; Carlino, 1980) • Lack ready access to productive capital • Limited access to educated, skill-relevant and experienced labor force • Limited transportation and communication infrastructure

  10. Recent Research and Agglomeration • Yet, evidence suggests some substantial growth in rural business starts • Recent research (e.g. Decker, Thompson, and Wohar, 2009; Domazlicky and Weber, 2006; Latzko, 2002) suggests that traditional measure of agglomeration (such as population density, etc.) may be playing a less critical role in regional economic development

  11. Perhaps a Refined Measure of Agglomeration Would be Helpful • Clement (2006): examples of new Native American businesses that exploit Computing and Information Technology (IT) to promote consumer outreach and sales growth • Suggests that local economies taking advantage of IT development • Inexpensive computing equipment • IT labor skills more prevalent, easier to acquire • Software written to be more generally accessible and relevant to a broad number of industries • Can lead to greater geographic dispersion of IT-related capital and labor skills

  12. “IT” Agglomeration • Le Bas and Miribel (2005) constructed an IT Agglomeration measure • Identified industries which appear to rely heavily upon, or have increased their usage of IT and IT-related inputs in recent years • Found that IT Agglomeration significantly enhanced labor productivity in existing firms

  13. Le Bas and Miribel’s IT agglomeration • Based on employment data by industry • Comprised of a variety of different sectors • Computer & electronics, wholesale trade, information services, financial services, professional services, educational services • Common concentration measure (used in our paper) : IT “Location Quotient”

  14. Model Variables • Model variables and construction follow Gabe (2003, 2004) • Dependent variable • STARTi,t – new business starts in county i, year t • Independent Variables (one year lag) • IT_LQi,t-1 – IT Location quotient (+) • ESTABi,t-1 – number of establishment in operation (+) • TAX_INCi,t-1 - ratio of tax revenue to personal income (-) • SPEND_POP-,t-1 – government spending per capita (+) • WAGE_WAGESDi,t-1– relative per capita wages in county i to SD (-) • NL_NLSDi,t-1– relative non labor costs to SD (-)

  15. The General Model • Note: independent variables enter estimation in natural log form to facilitate interpretation of coefficients as elasticities

  16. The DATA…. • Covers the period 1990 to 2007 annually • STARTS, ESTAB, all employment data – National Establishment Time-Series database (NETS) – Walls and Associates • Population and income data – Regional Economic Information Service (REIS) – BEA • Tax revenue and government spending data – Census of Governments (various years)

  17. The DATA…. • Two panel data sets • Native American Counties • South Dakota Rural, non-Native American Counties

  18. The DATA…. • Native American Counties • 1990 - 2007 • Average number of Starts: 185 • Average number of Establishments: 2,971 • Non-Native American Counties • 1990 - 2007 • Average number of Starts: 3,421 • Average number of Establishments: 49,280

  19. Estimation Procedure • Following Gabe (2003) – model STARTS using models applicable to count data • Poisson vs. Negative Binomial • Fixed Effects vs. Random Effects • OLS – dependent variable: ln(STARTS/ESTAB) • Not uncommon • Intuitive appeal • Restricts ESTAB’s effect to be unit elastic

  20. Estimation & Specification • Wu-Hausman test favors the Fixed Effects model over the Random Effects model • Count models: • Poisson – conditional mean = conditional variance • Restriction caused by the model • Negative Binomial (NB) – conditional mean > conditional variance • Applicable when data is over-dispersed • Failure to account for over-dispersion can lead to inflated standard errors • Likelihood Ratio tests favor NB

  21. NB model estimation results

  22. OLS Results: ln(STARTS/ESTAB)

  23. Preliminary Research Extensions • Startups don’t necessarily translate into regional success • Survival characteristics of rural businesses versus metropolitan area businesses • Agglomeration economies (as traditionally defined) would favor metropolitan concerns • Survival characteristics of Native American rural businesses versus non-Native American rural businesses • Reasons for difference? Perhaps minority access to financial capital?

  24. Rural vs. Metropolitan Area (NETS) • Rural survival rates higher than metro (reg1 = rural)

  25. Native American versus non-Native American (NETS) • Nat. Am. survival rates higher than non-Nat. Am.

  26. Conclusion • IT Agglomeration seems to stimulate business startups • Marginal impact higher in Native American Indian Counties • Survival characteristics of rural vs. metro businesses in SD • Survival characteristics of Native American vs. non-Native American rural businesses • Full-parametric analysis would be helpful.

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