Causes of Retail Pull in Nebraska Counties and Towns A Thesis By Rex Nelson

Introduction  Motivation for study – to understand rural declines  Improve effectiveness of rural development efforts

Introduction Observations in Rural America:  Rural population declines  Rural business district declines  Declining services available to rural dwellers  Declining quality of life for rural residents  Declining job opportunities  Shrinking tax base  Decaying rural infrastructure

Introduction Goal: Improved Understanding  Learn to modify, if not change trends  Manage impacts  Focus rural development efforts  Improved public policy  Better use of scarce resources

Retail Trade  This study uses retail trade as a benchmark to measure relative economic performance  It is a net by which wealth is captured and transferred to new ownership  In recipient community: creates jobs and economic multiplier effects  In doner communities: opposite effects

(1) Leistritz, Ayres and Stone Retail Trade  Rural counties in Iowa, Kansas Missouri and Nebraska  15% retail leakage in 1970’s  20% in 1990’s 1  Lost Jobs  Population declines

(2) Reilly 1931, (3) Craig et al (4) Rathge Causes in the Literature  Central Place Theory  Distance and geographic area 2  Distance and demand thresholds 3  Population and demographics  8.4% of U.S. counties accounted for 93% of growth 1990 to  Age and shopping patterns  Income

(5) Darling (6) Yanagida et al Causes in the Literature  Retail environment  Size and quality 5  Presence of big box retailers  Transportation  Highway access  Agriculture dependence 6

Theory  Retail Trade =  People  Money  A place to trade

Theory  Retail Strength is represented by:  County Trade Pull Factor (CTPF)  County taxable retail sales / average of state aggregate taxable retail sales  Measures retail trade coming into or “leaking” out of a community

(7) Darling Theory  CTPF = f(CB, BP, RE)  Where:  CB is Customer Base  BP is Buying Power  RE is Retail Environment

Theory Dependent Variable  CTPF represents Retail Strength

Dependent Variables CB  POPROOT  MJRHWY  DIST BP  INCOME  CIIV RE  VALUE

Theoretical Model CTPF=f(POPROOT, MJRHWY, DIST, INCOME, CIIV, VALUE)

Dependent Variables  POPROOT: square root of population of dominant city within each county  MJRHWY: location on interstate highway  DIST: for communities over 2500, distance from major trade center of 10,000 population

Dependent Variables  INCOME: per capita household income for residents in county  CIIV: size and direction of flow of commuter income  VALUE: per capita value of property value

Dependent Variables CB  POPROOT  MJRHWY  DIST BP  INCOME  CIIV RE  VALUE Seitz and Darling 2003 CB  URBMASS  MJRHWY  CIIVBP  INCOMERE  VALUE  MARKETCAP

Dependent Variables CB  POPROOT  MJRHWY  DIST BP  INCOME  CIIV RE  VALUE Upendram and Darling 2004 CB  URBMASS (sq root)  MJRHWY  CIIVBP  INCOMERE  VALUE

Data  All 93 Nebraska Counties  Includes the three major metro counties  CTPF from University of Nebraska Department of Ag Economics: “Nebraska Retail Pull Factors for Counties – 2002”  Sales tax data  Values greater than one – stronger than average retail sales  Implies incoming trade

Data  POPROOT: population of dominant city in the county – the trade center  From 2000 census  Square root used to reduce variance  CIIV: Derived from U.S. Dept. of Commerce Bureau of Economic Research  Index developed by Darling at KSU  BEA Adjustment for Residence divided by total income  Signed for positive index for job centers

Data  MJRHWY: county given a value of one for location on an interstate highway  DIST: Distance county trade center lies from nearest trade center of >10000  Towns under 2500 assigned value of zero  Towns over 2500 assigned number of miles

Data  INCOME: Per capita income in county from 2000 census (1999)  VALUE: Per capita commercial property value, real and personal  Includes all retail and industrial property

Results and Conclusions  CTPF  Mean 0.56  Median 0.47  Skewness 0.89

Results and Conclusions Profile of retail sales underlying CTPF:  Three metro counties capture 61% of retail sales  Top six counties (6.5%) capture 71%

Results and Conclusions  Adjusted R-Squared73.4  The model is effective at explaining retail trade  All variables significant at the 10% level  POPROOT was 66% correlated with VALUE, all others <55% in correlation matrix

Results and Conclusions  DIST: coefficient positive and significant at 1% level  Relatively large  A trade center city 50 miles from another trade center could surmise that 0.16 of CTPF was due to favorable location

Results and Conclusions  MJRHWY: Coefficient positive and significant at the 1% level  Location on interstate may add 0.17 to CTPF all other things being equal  Highway investments have and will continue to impact trade patterns

Results and Conclusions  POPROOT: Coefficient positive and significant at the 10% level  Larger cities do have a significant advantage  Managing for population growth may improve retail pull over time

Results and Conclusions  VALUE: Coefficient positive and significant at the 1% level  The value of commercial property gives some measure of retail performance  Investment in the retail environment may improve retail trade

Results and Conclusions  INCOME: Coefficient positive and significant at the 1% level  $10,000 increase in INCOME may cause increase of 0.16 in CTPF  High income cities may draw less outside trade than CTPF indicates

Results and Conclusions  CIIV: Coefficient positive and significant at 1% level

Results and Conclusions Retail Trade:  Is a function of Customer Base, Buying Power, and the Retail Environment  Customer base can be represented by population, interstate highway access and distance to trade center  Buying power can be measured as income and commuter income  Retail environment can be represented by value of commercial property

Further Research  Longitudinal Study  Alternatives to CTPF  Testing model on other state data sets  “Big Box Retailer” variable  Demographic variables  Agricultural influence  Finer highway grid  Define study boundaries by trade areas rather than state borders