Skip to main content
SpringerLink
Log in

The impact of retail establishments in hinterlands on the export role of retail establishments in rural places

  • Original Paper
  • Published:
The Annals of Regional Science Aims and scope Submit manuscript

Abstract

Rural retail trade industries can serve an export function for and contribute to the economic growth of a rural place. The importance of a retail trade industry to the economy of a rural place depends on its market area. Since those market areas often extend into a place’s hinterlands, retail trade can effectively have an export component for the place itself. Yet empirical determination of the export role of a rural retail trade industry has been historically confounded by data limitations which limit testing for and identification of an industry’s market area. Using geographic information system tools to define variables for differing driving distances (as contrasted with circles defined by Euclidean distance) from a place, this paper tests for the market areas of seven retail trade industries in rural places and for the impact of retail trade in hinterlands on those market areas. This paper contributes to the relevant literature in three ways. First, it tests formally for a retail industry’s “market area driving time.” Prior articles which have incorporated distance into their analyses have not generally tested for the actual travel times more appropriate for such analyses. Second, it finds that not undertaking such testing might produce misleading results regarding the relationship between a place and the hinterlands in its market areas. Third, the regression results suggest subtlety in the export nature of rural retail trade, with some industries losing their export nature when competing businesses appear in hinterlands (Weak Export Retail industries) and other industries retaining their export nature under those circumstances (Strong Export Retail industries).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Articles which have addressed simultaneity between two variables in which one dependent variable is a count variable have generally assumed that the second variable is either binary or that it has a linear reduced form (e.g., Windmeijer and Santos Silva 1997; Wooldridge 2010: 742–747). Those models are not relevant because we have a count-data regressor which is possibly endogenous, whose reduced form will not be linear.

  2. Wooldridge (2010: 748) notes that the Mullahy’s estimator does not impose restrictions on the distribution of \({y}_\mathrm{N}\) conditional on z, other than standard rank conditions for identification. Our presentation of Mullahy’s model is similar to that in Wooldridge (2010: 747–748).

  3. The vector z will include the elements of x \(_\mathbf{1}\) which are exogenous and the instruments for \({y}_{n}\) (the endogenous variable).

  4. We estimated Eq. (3) using a GAUSS software procedure made available through the Centre for Microdata Methods and Practice at the Institute For Fiscal Studies (Windmeijer 2002).

  5. All of the \(42\, \hbox {R}^{2}\)’s exceeded 0.49, with thirty-one of the \(\hbox {R}^{2}\)’s exceeding 0.60.

  6. Data on the exact sizes of establishments and their distribution, which would preferable, are not readily available. There are data available on size classes of establishments, the number of establishments that exist in a particular range of employment levels. However, using those categories would lead to problematic discontinuities in empirical analyses.

  7. The states were Colorado, Idaho, Montana, Utah, Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota and South Dakota.

  8. Microsoft MapPoint calculations are based on posted speeds and average travel time. The default settings were used to construct our radii and were cross-checked with sample data in ArcGIS as well as Google Maps.

  9. The dependent variable and the regressors in the \(P\) test regression vary according to the hypothesis chosen to be null.

  10. Both the 20- and 30-min driving time are rejected when they are paired, and the 30- and 40-min intervals are accepted when they are paired. The 50-min interval is superior when tested against the 40-min interval and is no different than the 60-min interval. When the 20-min distance is tested against the 50-min interval, both intervals are rejected. The same is true when the 20-min interval is tested against the 60-min interval.

  11. It would be tempting to search all of the regression results for explanations of the differences between the results here and those in Mushinski and Weiler (2002). Because their definition of neighboring areas as the remainder of the county in which a place was located allowed for market areas of differing shape and size, such a search was not meaningfully possible.

References

  • Beavon KSO, Hay AM (1978) Long run average cost, price and Christaller’s concepts of range: an explanatory note. Geography 63(2):98–100

    Google Scholar 

  • Berry BJL (1967) Geography of market centers and retail distribution. Prentice-Hall Inc, Englewood Cliffs

    Google Scholar 

  • Berry BJL, Garrison WL (1958) A note on central place theory and the range of a good. Econ Geogr 34(4):304–311

    Article  Google Scholar 

  • Cho S-H et al (2011) Relationship between value of open space and distance from housing locations within a community. J Geogr Syst 12:393–414

    Article  Google Scholar 

  • Christaller W (1933) Die zentralen Orte in Süddeutschland: Eine ökonomisch geographische Untersuchung über die Gesetzmäßigkeit der Verbreitung and Entwicklung der Siedlungen mit städtischen Funktionen. Doctoral dissertation (Gustav Fischer Verlag, Jena). Translated by Baskin C (1966) Central places in Southern Germany. Prentice Hall, Englewood Cliffs

  • Davidson R, MacKinnon JG (2004) Econometric theory and methods. Oxford University Press, Cambridge

    Google Scholar 

  • Davidson R, MacKinnon JG (1981) Several tests for model specification in the presence of alternative hypotheses. Econometrica 49(3):781–793

    Article  Google Scholar 

  • Deller SC, Harris TR (1993) Estimation of minimum market thresholds for rural commercial sectors using stochastic frontier estimators. Reg Sci Perspect 23(1):3–17

    Google Scholar 

  • Eaton BC, Lipsey RG (1982) An economic theory of central places. Econ J 92:56–72

    Article  Google Scholar 

  • Golledge RG, Rushton G, Clark WAV (1966) Some spatial characteristics of Iowa’s dispersed farm population and their implications for the grouping of central place functions. Econ Geogr 42(3):261–272

    Article  Google Scholar 

  • Hanjoul P, Beguin H, Thill J-C (1989) Advances in the theory of market areas. Geogr Anal 21(3):185–196

    Article  Google Scholar 

  • Henderson JW, Kelly TM, Taylor BA (2000) The impact of agglomeration economies on estimated demand thresholds: an extension of Wensley and Stabler. J Reg Sci 40(4):719–733

    Article  Google Scholar 

  • Isard W (1956) Location and space-economy. Wiley, New York

    Google Scholar 

  • Kaiser MS, Cressie N (1997) Modeling Poisson variables with positive spatial dependence. Stat Probab Lett 35:423–432

    Article  Google Scholar 

  • Launhardt W (1885) Mathematische Begrundung der Volkswirtschaftslehre. W. Englemann, Leipzig

    Google Scholar 

  • Lösch A (1954) The economics of location. Yale University Press, New Haven

    Google Scholar 

  • Mullahy J (1997) Instrumental-variable estimation of count data models: applications to models of cigarette smoking behavior. Rev Econ Stat 79(4):586–593

    Article  Google Scholar 

  • Mulligan GF (1984) Agglomeration and central place theory: a review of the literature. Int Reg Sci Rev 9(1):1–42

    Article  Google Scholar 

  • Mulligan GF, Fik TJ (1994) Price and location conjectures in medium- and long-run spatial competition models. J Reg Sci 34(2):179–198

    Article  Google Scholar 

  • Mulligan GF, Fik TJ (1995) Consistent price and location conjectures in spatial competition models. Ann Reg Sci 29:91–109

    Article  Google Scholar 

  • Mulligan GF, Partridge MD, Carruthers JI (2012) Central place theory and its reemergence in regional science. Ann Reg Sci 48:405–431

    Article  Google Scholar 

  • Mushinski D, Weiler S (2002) A note on the geographic interdependencies of retail market areas. J Reg Sci 42(1):75–86

    Article  Google Scholar 

  • O’Kelly ME, Miller H (1989) A synthesis of some market area delimitation models. Growth Change 20(3):14–33

    Article  Google Scholar 

  • Rietveld P et al (1999) On the relationship between travel time and travel distance of commuters. Ann Reg Sci 33:269–287

    Article  Google Scholar 

  • Rushton G, Golledge RG, Clark WAV (1967) Formulation and test of a normative model for the spatial allocation of grocery expenditures by a dispersed population. Ann Assoc Am Geogr 57(2):389–400

    Article  Google Scholar 

  • Shields M, Kures M (2006) Black out of the blue light: an analysis of Kmart store closing decisions. J Retail Consum Serv 14:259–268

    Article  Google Scholar 

  • Shonkwiler JS, Harris TR (1993) A non-Gaussian time series analysis of rural retail business counts. J Reg Sci 33:37–48

    Article  Google Scholar 

  • Shonkwiler JS, Harris TR (1996) Rural retail business thresholds and interdependencies. J Reg Sci 36: 617–630

    Google Scholar 

  • Shonkwiler JS, Harris TR (1997) Interdependence of retail business. Growth Change 27:520–533

    Google Scholar 

  • Thill J-C (1992) Spatial competition and market interdependence. Pap Reg Sci 71(3):259–275

    Article  Google Scholar 

  • Thill J-C (1986) A note on multipurpose and multistop shopping, sales, and market areas of firms. J Reg Sci 26:775–778

    Article  Google Scholar 

  • US Department of Commerce (2002) Census of retail trade: geographic area series. Bureau of the Census, Washington, DC

  • Windmeijer F (2002) ExpEnd, a GAUSS programme for non-linear GMM estimation of exponential models with endogenous regressors for cross-section and panel data. Centre for Microdata Methods and Practice. Institute for Fiscal Studies, London

  • Windmeijer FAG, Santos Silva JMC (1997) Endogeneity in count data models: an application to demand for health care. J Appl Econom 12:281–294

    Article  Google Scholar 

  • Wooldridge JM (2010) Econometric analysis of cross section and panel data, 2nd edn. The MIT Press, Cambridge

    Google Scholar 

  • Yu T-H et al (2012) Assessing the residential property tax revenue impact of a shopping center. J Real Estate Financ Econ 45:604–621

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank Wade Hudson for his research assistance, Eric Thompson for an insightful comment, participants in a seminar at the University of Colorado-Denver, and the Centre For Microdata Methods and Practice at the Institute For Fiscal Studies and the Department of Economics at the University College London for providing the GAUSS programming code used in our estimation.

Author information

Authors and Affiliations

  1. Department of Economics, Colorado State University, Fort Collins, CO, 80523-1771, USA

    David Mushinski & Stephan Weiler

  2. Department of Economics, Applied Statistics, and International Business, New Mexico State University, P.O. Box 30001/MSC3CQ, Las Cruces, NM, 88003-8001, USA

    Benjamin Widner

Authors
  1. David Mushinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Weiler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benjamin Widner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Mushinski.

Appendix: Description of the hypothesis testing analysis

Appendix: Description of the hypothesis testing analysis

The details of the hypothesis testing analysis are provided using Table 3, which presents \(P\) test results for three industries: Gasoline Stations (4471), Health and Personal Care Stores (4461) and Building Material and Supplies Dealers (4441). The analysis for the Gasoline Stations industry is presented first, since a clear testing result is obtained. Table 3 indicates that when the 20-min driving distance is tested against the 30-min distance, the 20-min driving distance is accepted when it is the null hypothesis and the 30-min distance is rejected when it is the null hypothesis. These results imply that the 20-min distance is preferred when compared with the 30-min distance. Table 3 also indicates that when the 30-min driving distance is tested against the 40-min driving distance, the 30-min driving distance is accepted when it is the null hypothesis and the 40-min driving distance is accepted when it is the null hypothesis. The testing results are inconclusive; both driving distances are equally acceptable as a possible driving distance. When this result is combined with the testing result for the 20- versus 30-min driving times, it is concluded that the 20-min driving time is superior over both the 30- and 40-min driving times. Test results for the 40- and 50-min driving times indicate that the former is more likely when compared with the latter; the 40-min driving time is accepted when it is the null hypothesis and the 50-min driving time is rejected. Combining this testing result with the earlier test result, one may conclude that the 20-min driving time is the most likely. Since the test of the 50- and 60-min driving times are inconclusive, we ultimately concluded that the market area is households within a 20-min driving time.

Table 3 Hypothesis test results for all driving times for three industries
Full size table

Health and Personal Care establishments are examined next, as testing results for adjacent driving times yield two preferred driving times with one of those times rejected when the two driving times are tested against each other. Testing results for adjacent driving times support 30 min as being more likely when tested against the 20- and 40-min driving times, and they support the 50-min driving time when tested against the 20- and 60-min driving times. Since testing for adjacent driving times is inconclusive with regard to the 30- and 50-min driving times, the 30-min driving time was tested against the 50-min driving time. A test of the 30-min driving time against the 50-min driving time indicates that the 30-min driving time is preferred. Thus, the industry’s market area includes households within a 30-min driving time.

Finally, the test results for the Building Materials Establishments industry are considered since they provide an example of an inconclusive analysis. The pairwise tests indicated only that the 20-min driving distance is not superior when tested against the 30-min driving distance. Otherwise, no conclusions are possible for driving distances of 30 min or more. In cases such as this, we concluded that the more likely market area is the shortest driving time among the times for which one can reach no conclusions. Because inclusion of the incremental area does not affect the relationship, it does not matter to the relationship. In a situation where all other things are equal, we chose shorter driving times over longer ones. Thus, we concluded that the most likely driving time is 30 min. Table 4 identifies test results for the remaining industries.

Table 4 Hypothesis test results for all driving times for four industries
Full size table
Table 5 Regression results at market area driving times
Full size table
Table 6 Regression results at market area driving times
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mushinski, D., Weiler, S. & Widner, B. The impact of retail establishments in hinterlands on the export role of retail establishments in rural places. Ann Reg Sci 52, 469–487 (2014). https://doi.org/10.1007/s00168-014-0595-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00168-014-0595-3

JEL Classification

Search

Navigation