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
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)

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

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
Key Sectors
Health Care
Waste Management and Remediation Services
Retail Trade
Professional, Scientific, and Technical Services
Construction
Accommodation and Food Services
Wholesale Trade
Real Estate and Rental and Leasing
Arts, Entertainment, and Recreation
Transportation and Warehousing
Educational Services
1,782
248
227
136
134
119
59
56
55
52
45
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 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
“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 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”
 E M Pi , IT
IT _ L Q  
 EM P
i ,T O T




 E M PSD , IT

 E M PSD ,T O T



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 (-)
The General Model
ST A R Ti , t  f ( E ST A B i , t 1 , T A X _ IN C i , t 1 ,
SP E N D _ P O Pi , t  1 , W A G E _ W A G E SD i , t 1 ,
N L _ N L SD i , t 1 , IT _ L Q i , t 1 ,  i , t ).

Note: independent variables enter estimation in
natural log form to facilitate interpretation of
coefficients as elasticities
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)

The DATA….

Two panel data sets
◦ Native American Counties
◦ South Dakota Rural, non-Native American Counties
Native American Counties
Bennett
Buffalo
Charles Mix
Corson
Dewey
Jackson
Roberts
Shannon
Todd
Ziebach
Non-Native Rural American Counties
Codington
Day
Gregory
Hughes
Hyde
Lyman
Marshall
Mellette
Moody
Stanley
Tripp
Walworth
Aurora
Beadle
Bon Homme
Brookings
Brown
Brule
Butte
Campbell
Clark
Clay
Custer
Davison
Deuel
Douglas
Edmunds
Fall River
Faulk
Grant
Haakon
Hamlin
Hand
Hanson
Harding
Hutchinson
Jerauld
Sully
Jones
Yankton
Kingsbury
Lake
Lawrence
McPherson
Miner
Minnehaha
Perkins
Potter
Sanborn
Spink
The DATA….

Native American Counties
◦ 1990 - 2007
◦ Average number of Starts:
◦ Average number of Establishments:

185
2,971
Non-Native American Counties
◦ 1990 - 2007
◦ Average number of Starts:
◦ Average number of Establishments:
3,421
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 & 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
NB model estimation results
Native American Counties
Constant
ln(ESTAB)
***
ln(IT_LQ)
***
ln(TAX_INC)
*
ln(SPEND_POP)
***
ln(WAGE_WAGESD)
***
ln(NL_NLSD)
No. Obs
Log likelihood
Wald χ2(6)
LR statistic - Poisson restriction test
Standard errors reported in parentheses
* - 10 percent significance
** - 5 percent significance
*** - 1 percent significance
***
-4.85
(1.68)
0.73
(0.24)
1.15
(0.51)
-0.31
(0.12)
0.13
(0.13)
-0.21
(1.21)
-0.22
(0.11)
***
***
**
**
**
160
-483.87
24.91 ***
147.24 ***
Non-Native American Counties
-2.92
(0.80)
0.45
(0.10)
0.38
(0.13)
-0.48
(0.09)
0.38
(0.08)
-0.42
(0.34)
-0.08
(0.05)
***
***
***
***
***
*
833
-2,875.54
99.15 **
1821.00 ***
OLS Results: ln(STARTS/ESTAB)
Constant
ln(IT_LQ)
ln(TAX_INC)
ln(SPEND_POP)
ln(WAGE_WAGESD)
ln(NL_NLSD)
Native American Counties
-5.41 ***
(1.23)
1.53 **
(0.67)
-0.28 **
(0.14)
0.10
(0.14)
-0.04
(1.40)
-0.20
(0.13)
No. Obs
F-stat
2
R
Standard errors reported in parentheses
*
- 10 percent significance
** - 5 percent significance
*** - 1 percent significance
Non-Native American Counties
-5.17 ***
(0.52)
0.41 ***
(0.17)
-0.44 ***
(0.11)
0.35 ***
(0.09)
-0.50
(0.44)
-0.10 *
(0.05)
160
2.15 *
833
8.21 ***
0.11
0.14
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?
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.
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.
