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Pull Factors: A Measure of Retail Sales Success Estimates for 77 Oklahoma Cities (2018)

Introduction

Whether people live in a small town or a major metropolitan area, they have the power to spend their money where they choose. This notion is very important to most cities, since many local government services (police, fire, parks and recreation) are heavily dependent on tax revenue from local retail sales (Semuels, 2017). It is helpful for cities to know the relative health of their retail sector – and in particular, if they are losing retail dollars when local residents shop elsewhere. To assess this, a calculation known as a “Pull Factor” is typically used. A pull factor is a measure of how well local retail stores are able to capture the sales of local and non-local people (see box). Because it compares actual retail spending in a city to that city’s population, it can be used to assess whether people are coming into the community to shop – or if people are leaving the community to shop elsewhere. Shopping online can also have repercussions for sales tax collections. Businesses currently only collect sales tax for online transactions in states where they have a presence (Whitacre, Ferrell and Hobbs, 2009); however, a recent 2018 Supreme Court decision has cleared the way for more taxation of online purchases (Liptak et al., 2018). This can impact the amount of revenue that local governments receive.

 

What is a Pull Factor?

Pull factors measure the relative strength of a city’s ability to attract retail shoppers. They are a quantitative measure of how the retail trade sector of a community is performing, put into an easily interpretable number.

 

Interpreting a Pull Factor

  • PF < 1: The city is losing local retail shoppers to other areas
  • PF = 1: The city is capturing retail shopping activity exactly equal to its population
  • PF > 1: The city is attracting non-resident retail shoppers (in addition to its own population)

A pull factor of 1.15 would indicate that the retail sector is attracting non-resident consumers equal to 15 percent of the city’s population.

 

Pull factor analysis is important because it puts the health of the retail sector into a number that is easy to interpret. For example, if a city has a pull factor of less than 1, it is not capturing the retail sale expenditures of the local residents. In this case, retail spending is leaking out of the city and being spent in other locations. In contrast, a city with a pull factor of greater than 1 is capturing the entire expected retail sale spending of local residents – plus some extra. Pull factors can be used as indicators of the relative health of a community’s retail sector.

 

Large cities, such as Tulsa, typically have pull factors greater than 1 because they have an abundant number of retail stores with a variety of goods to offer. Because of this, these cities typically capture the “leakage” from nearby smaller cities, which have fewer stores and often see residents leave to shop in the bigger city markets. These smaller cities, such as Sperry (population 1,206), usually have pull factors of less than 1 because the city’s retail sector is smaller and generally struggles to keep all the spending within the city limits. Not only do these cities have a smaller retail sector, but they generally do not have the diversity and abundance of products that people want in their town. The retail sector is driven by population and disposable income, and a smaller population may not be able to support the volume of sales necessary for some types of goods and services. However, it is possible for some smaller cities to have strong pull factors – if they serve as hubs for surrounding rural areas and are relatively distant from larger towns with more developed retail sectors. This report discusses how pull factors are calculated (including the websites where data is available) and constructs them for the largest city in each of Oklahoma’s 77 counties, using data from 2016.

 

While it is possible to calculate pull factors for counties (as opposed to cities), this publication concentrates on cities because the decision to “go shopping” is typically focused on a particular location with specific stores or amenities in mind. The city-level measures detailed here help provide a basic overview of how the largest town in each county is performing in terms of retail activity. Furthermore, the largest county in the state, Oklahoma County, does not collect a sales tax.

 

Data and Methodology

The data that goes into the city pull factor calculation includes city and state-level per capita income (PCI), population, tax rate and total retail sales collected (see box below). There are two main websites that can be used to gather this data. The population and PCI data (for both the city and the state) can be found on the United States Census website (www.census.gov). The link in the box can be used for all cities with populations greater than 5,000. For smaller cities, the information can be found with the Census’ American Factfinder tool. The PCI data is taken from the American Community Survey table B19301. The PCI is on a moving average over the past five years (for example, 2012-2016). Since this is the case, it is not as accurate as an annual estimate, but typically is the best source available. The population measures for this report also are taken from the same American Community Survey (table B01003). Yearly updates are available for cities using the Census’ annual population estimates. Meanwhile, the tax rate and sales tax collections can be found on the Oklahoma Tax Commission website (again, for both the individual city and the state total). Using the OK Tax Commission link in the box, users should select “View Public Reports” and then “Tax by NAICS Report” before selecting the information (tax type, city, date) of interest. Note that the Tax Commission’s reports are broken out by North American Industrial Classification System (NAICS) codes, and that codes 44-45 represent the retail sector. Sales tax is collected on other sectors within a city as well, such as entertainment, recreation and food services. These are an important part of the health of a city. However, this fact sheet only focuses on the predefined retail sector (NAICS codes 44-45) and the sales that storefront businesses collect. For these specific NAICS codes, the numbers available from this system represent the retail sales taxes collected by a city. To get the total amount of retail sales in a city, the total amount of retail sales sector tax collections should be divided by the city sales tax rate (which is also available from the Tax Commission’s site). The June 2016 numbers were used for this analysis, since they contain a full year of data on retail sales tax collections. A step-by-step guide for constructing a city-level Pull Factor is available in Shideler and Malone (2017).

 

As the formula in the box shows, all of this information is combined to calculate a “Trade Area Capture (TAC)” which is an estimate of the number of shoppers the retail area attracts for a given year. A PCI ratio is used in the denominator to adjust for income levels in the city versus the state. If the city PCI is above average, it requires the numerator to be larger to keep a positive pull factor. This feeds into the idea that retail sales are a factor of population and the disposable income of the residents. Finally, the Pull Factor is calculated by dividing the TAC by the overall population of the city. The Pull Factor indicates whether the retail market attracts non-local customers (i.e. has a value > 1.0) or loses local customers (i.e. has a value < 1.0).

 

The Pull Factor Formula (and online data sources)

Pull factors are based on a measure of “Trade Area Capture” (TAC) which estimates the total number of shoppers an area attracts. The TAC is then divided by the city’s population to get the Pull Factor.

 

Caculated TAC = RS / ( [RS_state / P_State] x [PCI / PCI_state ] ) ; Pull factor = ( Trade Area Capture )  / (Population )

 

Variable included:

RS: Retail Sales Tax Collections (city level)

RSState: Retail Sales Tax Collections (state level)

Available from: OK Tax Commission Public Reports:  https://oktap.tax.ok.gov/OkTAP/Web/_/#1

 

Variable included:

P: Population (city level)

PState: Population (state level)

PCI: Per Capita Income (city level)

PCIState: Per Capita Income (state level)

Available from: Census Quickfacts Website: https://www.census.gov/quickfacts/fact/table/US/PST045217

 

Pull Factors for 77 Oklahoma Cities

 

(2016 data)

This report calculates city-level pull factors for the largest city in each Oklahoma county, using the most recent data available (2016) (Figure 1). The city population is also listed. The county containing each city displays a color corresponding to four levels of city Pull Factors, ranging from the highest (over 2.0) to the lowest (less than 1.0). Table 1 displays the relevant information for each of the 77 cities by population category.

 

City-level Pull Factors for the Largest Town in each Oklahoma County (2016).

 

Figure 1. City-level Pull Factors for the Largest Town in each Oklahoma County (2016).

 

Discussion

Since each city displayed in Figure 1 was selected because it was the largest in its county, it probably has a stronger retail sector than many surrounding, smaller towns – and likely captures shoppers from those areas. Thus, only a small portion of the cities listed have a pull factor of less than 1. Most of the cities with pull factors less than 1 are found in the western half of the state, with quite a few in the southwestern quadrant. Many of these towns have less than 3,000 people and are within driving distance of larger cities [Cheyenne (Elk City), Mangum (Altus), Walters (Lawton) and Cordell (Weatherford)]. In the southeast quadrant, the largest cities in most counties have relatively strong pull factors (> 2). This may be because they are further away from larger cities (or with less direct routes to alternative shopping locations), and have developed retail sectors that cater to the needs of local residents and those living in the nearby towns. These southeastern towns also are generally larger in population (none are smaller than 1,000) compared to the southwestern cities noted above.

 

The three largest cities in the state have pull factors only slightly larger than 1 (Oklahoma City, 1.15; Tulsa, 1.37; Norman, 1.10). This still reflects they are able to attract non-locals to shop there – and in some ways masks how popular their retail sectors actually are. In Oklahoma City, for instance, the pull factor of 1.15 indicates that the local retail sector is not only capturing the expected shopping of the 638,000 residents, but also 95,000 non-residents (638,000 x 0.15). That is a sizeable portion of the surrounding counties! Similarly, Tulsa’s pull factor of 1.37 suggests that it is capturing an additional 149,000 shoppers on top of its 403,000 population (403,000 x 0.37 = 149,000). Thus, they are likely capturing many shoppers from neighboring cities like Bixby and Owasso, as well as shoppers from Creek, Rogers and Wagoner counties.

 

Table 1 demonstrates that pull factors can vary widely across cities with similar populations. For instance, Seiling and Cheyenne both have around 850 people, but Seiling’s pull factor is over twice that of Cheyenne. This may be due to Seiling capturing sales to small nearby communities like Taloga (population 303) and several unincorporated areas (Chester, Orion, Bado). Alternatively, Cheyenne does not have as many surrounding rural towns that might support their retail sector. In the same manner, Perry and Sulphur are both around 5,000 in population, but the pull factor for Perry (which is within driving distance of Stillwater) is less than half that of Sulphur’s. This is true in larger towns as well: Claremore (population 19,069) has a pull factor of 2.00, while El Reno (population 18,786) has a pull factor of only 0.92 – likely due to El Reno’s proximity to the OKC metropolitan area. These differences are largely dependent upon the types of amenities available in or near the communities. For example, Sulphur is located just outside of the Chickasaw National Forest, is 3 miles from the Chickasaw Cultural Center, and is home to the Chickasaw Nation’s Artesian Hotel, Casino and ARTesian Gallery and Studios. Similarly, Claremore is home to Rogers State College and the Claremore Expo Center, both of which bring numerous visitors to town for special events.

 

Conclusion

While the pull factor is an easy way for communities to measure the retail trade in their communities, it does have some limitations. First, it can leave communities wanting in terms of policy prescriptions; that is to say, how does someone increase the pull factor in their community? While the answer is to increase retail sales, it is difficult to determine how to go about doing that without an influx of population, income or new attraction in town. Shopping patterns and trends also are determined by other factors, such as commuting patterns to employment centers and life stages, which many communities also feel to be beyond their control. Second, retail leakage does not automatically equate to a business opportunity; there may be insufficient demand in a community (either due to lack of population or preferences), such that it makes sense for residents to purchase goods and services elsewhere. It is recommended, then, that the community using pull factors also conduct additional analysis, such as population thresholds or gap analysis (which uses pull factor analysis for each individual sector rather than all retail (Shideler and Malone, 2017)). Such analysis provides a better sense of which sectors might actually present opportunities for a viable business.

 

Table 1. City-level Pull Factors, by Population

 

Table 1A. Population <1,999

  FIPS Code County City PCI (2016) Population (July, 2016)
Population <1,999          
  40129 Roger Mills Cheyenne 22,010 833
  40043 Dewey Seiling 22,677 863
  40053 Grant Medford 26,562 1,015
  40045 Ellis Shattuck 27,667 1,246
  40025 Cimarron Boise City 26,458 1,266
  40059 Harper Laverne 24,605 1,344
  40007 Beaver Beaver City 19,897 1,454
  40003 Alfalfa Cherokee 25,505 1,516
  40057 Harmon Hollis 19,625 1,962
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
Population <1,999          
  Cheyenne 0.03 4,552,322 759 0.91
  Seiling 0.04 12,840,747 2,077 2.41
  Medford 0.04 5,739,888 793 0.78
  Shattuck 0.03 9,870,995 1,309 1.05
  Boise City 0.03 7,661,028 1,062 0.84
  Laverne 0.0225 6,863,564 1,023 0.76
  Beaver City 0.03 9,471,060 1,746 1.2
  Cherokee 0.0325 12,425,634 1,787 1.18
  Hollis 0.02 8,415,047 1,573 0.8

 

Table 1B. Population 2,000 - 2,999

  FIPS Code County City PCI (2016) Population (July, 2016)
2,000 - 2,999          
  40067 Jefferson Waurika 20,470 2,097
  40029 Coal Coalgate 18,055 2,120
  40127 Pushmataha Antlers 16,999 2,548
  40093 Major Fairview 24,790 2636
  40085 Love Marietta 16,857 2710
  40077 Latimer Wilburton 18,463 2717
  40061 Haskell Stigler 17,553 2740
  40033 Cotton Walters 19,101 2854
  40149 Washita Cordell 26,800 2900
  40055 Greer Mangum 20,709 2922
  40091 McIntosh Eufaula 18,549 2929
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
2,000 - 2,999          
  Waurika 0.03 9,454,614 1,695 0.81
  Coalgate 0.03 12,262,806 2,492 1.18
  Antlers 0.035 23,378,195 5,046 1.98
  Fairview 0.04 22,152,435 3,279 1.24
  Marietta 0.02 21,903,233 4,767 1.76
  Wilburton 0.035 20,131,265 4,000 1.47
  Stigler 0.03 45,043,136 9,415 3.44
  Walters 0.03 9,467,434 1,818 0.64
  Cordell 0.03 147,209,912 2,015 0.69
  Mangum 0.03 11,298,260 20,012 0.69
  Eufaula 0.035 32,210,129 6,371 2.18

 

Table 1C. Population 3,000 - 4,999

  FIPS Code County City PCI (2016) Population (July, 2016)
3,000 - 4,999          
  40005 Atoka Atoka 15,365 3076
  40069 Johnston Tishomingo 15,287 3077
  40081 Lincoln Chandler 20,676 3133
  40117 Pawnee Cleveland 22,541 3221
  40107 Okfuskee Okemah 14,180 3262
  40113 Osage Pawhuska 17,276 3521
  40075 Kiowa Hobart 23,043 3666
  40105 Nowata Nowata 17,106 3717
  40141 Tillman Frederick 17,120 3744
  40095 Marshall Madill 19,047 3864
  40011 Blaine Watonga 16,004 3921
  40001 Adair Stilwell 12,584 4019
  40073 Kingfisher Kingfisher 25,983 4784
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
3,000 - 4,999          
  Atoka 0.03 51,682,810 12,341 4.01
  Tishomingo 0.03 22,943,876 5,507 1.79
  Chandler 0.04 49,390,420 8,764 2.8
  Cleveland 0.035 39,329,852 6,402 1.99
  Okemah 0.035 20,337,678 5,262 1.61
  Pawhuska 0.03 16,661,246 3,538 1
  Hobart 0.04 20,441,223 3,255 0.89
  Nowata 0.03 13,530,187 2,902 0.78
  Frederick 0.035 11,292,266 2,420 0.65
  Madill 0.03 58,849,952 11,336 2.93
  Watonga 0.05 21,160,565 4,851 1.24
  Stilwell 0.0325 44,126,238 12,865 3.2
  Kingfisher 0.035 59,196,058 8,359 1.75

 

Table 1D. Population 5,000-6,999

  FIPS Code County City PCI (2016) Population (July, 2016)
5,000 - 6,999          
  40099 Murray Sulphur 22,531 5042
  40103 Noble Perry 25,214 5056
  40151 Woods Alva 27,376 5120
  40023 Choctaw Hugo 15,699 5257
  40035 Craig Vinita 18,155 5563
  40063 Hughes Holdenvile 12,643 5680
  40049 Garvin Pauls Valley 20,120 6206
  40087 McClain Purcell 22,185 6442
  40015 Caddo Anadarko 19,179 6768
  40041 Delaware Grove 28,073 6835
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
5,000 - 6,999          
  Sulphur 0.03 55,131,185 8,977 1.78
  Perry 0.0325 28,144,719 4,095 0.81
  Alva 0.0425 59,528,416 7,978 1.56
  Hugo 0.035 61,661,408 14,410 2.74
  Vinita 0.03 67,346,490 13,610 2.45
  Holdenvile 0.05 30,577,984 8,873 1.56
  Pauls Valley 0.045 82,049,301 14,962 2.41
  Purcell 0.04 74,556,839 12,330 1.91
  Anadarko 0.035 47,867,468 9,157 1.35
  Grove 0.034 133,720,790 17,476 2.56

 

Table 1E. Population 7,000 - 9,999

  FIPS Code County City PCI (2016) Population (July, 2016)
7,000 - 9,999          
  40089 McCurtain Idabel 17,293 7,007
  40133 Seminole Seminole 17,771 7,424
  40135 Sequoyah Sallisaw 17,731 8,602
  40079 Leflore Poteau 20,126 8,687
  40097 Mayes Pryor Creek 20,975 9,520
  40145 Wagoner Coweta 20,966 9,673
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
7,000 - 9,999          
  Idabel 0.03 74294802 15,762 2.25
  Seminole 0.04 77646101 16,030 2.16
  Sallisaw 0.04 91,128,695 18,856 2.19
  Poteau 0.03 117,943,743 21,501 2.48
  Pryor Creek 0.0375 125,180,744 21,896 2.3
  Coweta 0.03 77,748,063 13,605 1.41

 

Table 1F. Population 10,000 - 16,999

  FIPS Code County City PCI (2016) Population (July, 2016)
10,000 - 16,999          
  40083 Logan Guthrie 19,250 11,492
  40139 Texas Guymon 21,832 11,703
  40039 Custer Weatherford 22,041 11,978
  40009 Beckham Elk City 25,292 11,997
  40111 Okmulgee Okmulgee 16,816 12,239
  40153 Woodward Woodward 25,827 12,543
  40115 Ottawa Miami 17,877 13,484
  40051 Grady Chickasha 22,881 16,423
  40021 Cherokee Tahlequah 18,336 16,741
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
10,000 - 16,999          
  Guthrie 0.03 90,787,037 17,303 1.51
  Guymon 0.04 103,172,218 17,338 1.48
  Weatherford 0.04 135,140,871 22,495 1.88
  Elk City 0.045 165,428,851 23,997 2
  Okmulgee 0.04 96,322,767 21,016 1.72
  Woodward 0.04 175,968,867 24,997 1.99
  Miami 0.0365 105,807,456 21,715 1.61
  Chickasha 0.03969 158,578,059 25,427 1.55
  Tahlequah 0.0325 196,212,569 39,261 2.35

 

Table 1G. Population 17,000 - 29,999

  FIPS Code County City PCI (2016) Population (July, 2016)
17,000 - 29,999          
  40123 Pontotoc Ada 21,263 17,371
  40013 Bryan Durant 18,130 17,583
  40121 Pittsburg McAlester 21,166 18,206
  40017 Canadian El Reno 21,145 18,786
  40131 Rogers Claremore 22,406 19,069
  40065 Jackson Altus 21,845 19,422
  40037 Creek Sapulpa 22,018 20,928
  40137 Stephens Duncan 23,051 22,985
  40071 Kay Ponca City 22,909 24,527
  40019 Carter Ardmore 25,217 25,107
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
17,000 - 29,999          
  Ada 0.04 231,781,537 39,993 2.3
  Durant 0.04375 198,193,428 40,107 2.28
  McAlester 0.035 240,036,158 41,608 2.29
  El Reno 0.04 99,833,854 17,322 0.92
  Claremore 0.03 232,404,493 38,055 2
  Altus 0.0375 159,132,792 26,726 1.38
  Sapulpa 0.04 171,711,166 28,612 1.37
  Duncan 0.035 2,083,472,323 33,161 1.44
  Ponca City 0.035 229,714,509 36,789 1.5
  Ardmore 0.0375 324,695,154 47,241 1.88

 

Table 1H. Population 30,000 - 99,999

  FIPS Code County City PCI (2016) Population (July, 2016)
30,000 - 99,999          
  40125 Pottawatomie Shawnee 20,823 31,465
  40147 Washington Bartlesville 29,204 36,647
  40101 Muskogee Muskogee 19,695 38,352
  40119 Payne Stillwater 20,719 49,504
  40047 Garfield Enid 24,095 51,004
  40131 Comanche Lawton 21,892 94,653
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
30,000 - 99,999          
  Shawnee 0.03 369,730,880 65,144 2.07
  Bartlesville 0.03 352,914,623 44,336 1.21
  Muskogee 0.04 369,195,603 68,776 1.79
  Stillwater 0.035 480,511,471 85,088 1.72
  Enid 0.035 494,903,434 75,358 1.48
  Lawton 0.04125 694,637,079 116,414 1.23

 

Table 1I. Population 100,000+

  FIPS Code County City PCI (2016) Population
(July, 2016)
100,000+          
  40027 Cleveland Norman 28,466 122,180
  40143 Tulsa Tulsa 28,104 403,090
  40109 Oklahoma Oklahoma City 27,370 638,367
 GRAND TOTAL     OK STATE TOTAL 25,628 3,923,561
  City Tax Rate Retail Sales ($) (2016) Trade Area Capture Pull Factor
100,000+          
  Norman 0.04 10,467,614,712 134,913 1.1
  Tulsa 0.031 4,240,207,309 553,545 1.37
  Oklahoma City 0.03875 5,453,492,574 731,028 1.15

 

 

References

Liptak, B., Casselman, B., and Creswell, J. (2018). “Supreme Court Widens Reach of Sales Tax for Online Retailers.” New York Times. Available online: https://www.nytimes.com/2018/06/21/us/politics/supreme-court-sales-taxes-internet-merchants.html

 

Semuels, A. (2017). “All the Ways Retail’s Decline Could Hurt American Towns.” The Atlantic. Available online: https://www.theatlantic.com/business/archive/2017/05/retail-sales-tax-revenue/527697/

 

Shideler, D. and Malone, T. (2017). Measuring Community Retail Activity. Oklahoma Cooperative Extension Service Fact Sheet AGEC-1049.

 

Whitacre, B. Ferrell, S. and Hobbs, J. (2009). E-commerce and Sales Taxes: What You Collect Depends on Where You Ship. Oklahoma Cooperative Extension Service Fact Sheet AGEC-1022.

 

Ryan Loy

Undergraduate Research Assistant

 

Brian Whitacre

Professor and Extension Economist

 

Dave Shideler

Associate Professor and Extension Economist

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