Agglomeration Economies and Business Startups on Native American Tribal Areas

Christopher S. Decker, Ph.D.Department of EconomicsUniversity of Nebraska – Omaha And David T. FlynnDirector, Bureau of Business and Economic Research &Department of EconomicsUniversity of North Dakota

Association for University and Business Research Annual ConferenceIndianapolis, INOctober, 2011

MotivationLong-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)

QuestionsWhat 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

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)

Native American Rural Counties: Leichenko (2003)

Annual Growth in Business Starts – County Aggregates (NETS)

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

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

Total 1,782Key Sectors Health Care 248 Waste Management and Remediation Services 227 Retail Trade 136 Professional, Scientific, and Technical Services 134 Construction 119 Accommodation and Food Services 59 Wholesale Trade 56 Real Estate and Rental and Leasing 55 Arts, Entertainment, and Recreation 52 Transportation and Warehousing 45 Educational Services 45

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

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

Perhaps a Refined Measure of Agglomeration Would be HelpfulClement (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

“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

Le Bas and Miribel’s IT agglomerationBased on employment data by industryComprised 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”

, ,

, ,

_ i IT SD IT

i TOT SD TOT

EMP EMPIT LQ

EMP EMP

Model VariablesModel 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 (-)

The General Model

Note: independent variables enter estimation in natural log form to facilitate interpretation of coefficients as elasticities

, , 1 , 1

, 1 , 1

, 1 , 1 ,

( , _ ,

_ , _ ,

_ , _ , ).

i t i t i t

i t i t

i t i t i t

START f ESTAB TAX INC

SPEND POP WAGE WAGESD

NL NLSD IT LQ

The DATA….Covers the period 1990 to 2007 annuallySTARTS, 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)

The DATA….Two panel data sets

◦ Native American Counties◦ South Dakota Rural, non-Native American Counties

Bennett Jackson Codington Aurora Deuel Jerauld SullyBuffalo Roberts Day Beadle Douglas Jones YanktonCharles Mix Shannon Gregory Bon Homme Edmunds KingsburyCorson Todd Hughes Brookings Fall River LakeDewey Ziebach Hyde Brown Faulk Lawrence

Lyman Brule Grant McPhersonMarshall Butte Haakon MinerMellette Campbell Hamlin MinnehahaMoody Clark Hand PerkinsStanley Clay Hanson PotterTripp Custer Harding SanbornWalworth Davison Hutchinson Spink

Native American Counties Non-Native Rural American Counties

The DATA….Native American Counties

◦1990 - 2007◦Average number of Starts:

185◦Average number of Establishments:

2,971Non-Native American Counties

◦1990 - 2007◦Average number of Starts:

3,421◦Average number of Establishments:

49,280

Estimation Procedure

1. Following Gabe (2003) – model STARTS using models applicable to count data

◦Poisson vs. Negative Binomial◦Fixed Effects vs. Random Effects

2. OLS – dependent variable: ln(STARTS/ESTAB)

◦Not uncommon◦Intuitive appeal◦Restricts ESTAB’s effect to be unit elastic

Estimation & SpecificationWu-Hausman test favors the Fixed Effects

model over the Random Effects modelCount 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

NB model estimation resultsConstant -4.85 *** -2.92 ***

(1.68) (0.80)ln(ESTAB) *** 0.73 *** 0.45 ***

(0.24) (0.10)ln(IT_LQ) *** 1.15 ** 0.38 ***

(0.51) (0.13)ln(TAX_INC) * -0.31 ** -0.48 ***

(0.12) (0.09)ln(SPEND_POP) *** 0.13 0.38 ***

(0.13) (0.08)ln(WAGE_WAGESD) *** -0.21 -0.42

(1.21) (0.34)ln(NL_NLSD) -0.22 ** -0.08 *

(0.11) (0.05)

No. Obs 160 833Log likelihood -483.87 -2,875.54Wald χ2(6) *** 24.91 *** 99.15 **LR statistic - Poisson restriction test 147.24 *** 1821.00 ***

Standard errors reported in parentheses* - 10 percent significance** - 5 percent significance*** - 1 percent significance

Native American Counties Non-Native American Counties

OLS Results: ln(STARTS/ESTAB)

Constant -5.41 *** -5.17 ***(1.23) (0.52)

ln(IT_LQ) 1.53 ** 0.41 ***(0.67) (0.17)

ln(TAX_INC) -0.28 ** -0.44 ***(0.14) (0.11)

ln(SPEND_POP) 0.10 0.35 ***(0.14) (0.09)

ln(WAGE_WAGESD) -0.04 -0.50(1.40) (0.44)

ln(NL_NLSD) -0.20 -0.10 *(0.13) (0.05)

No. Obs 160 833F-stat 2.15 * 8.21 ***

R2 0.11 0.14

Standard errors reported in parentheses

* - 10 percent significance

** - 5 percent significance

*** - 1 percent significance

Native American Counties Non-Native American Counties

Preliminary Research ExtensionsStartups don’t necessarily translate into

regional successSurvival characteristics of rural businesses

versus metropolitan area businesses◦Agglomeration economies (as traditionally defined)

would favor metropolitan concernsSurvival characteristics of Native American

rural businesses versus non-Native American rural businesses◦Reasons for difference? Perhaps minority access to

financial capital?

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

Native American versus non-Native American (NETS)

Nat. Am. survival rates higher than non-Nat. Am.

ConclusionIT Agglomeration seems to stimulate

business startupsMarginal impact higher in Native American

Indian CountiesSurvival characteristics of rural vs. metro

businesses in SDSurvival characteristics of Native American

vs. non-Native American rural businessesFull-parametric analysis would be helpful.