ANALYSIS OF RETAIL TRENDS AND TAXABLE SALES FOR THE COMMUNITIES IN KIOWA COUNTY, OKLAHOMA

AE-02184 ANALYSIS OF RETAIL TRENDS AND TAXABLE SALES FOR THE COMMUNITIES IN KIOWA COUNTY, OKLAHOMA Suzette Barta, Extension Assistant, OSU, Stillwater (405) 744-6186 Susan Trzebiatowski, Student Assistant, OSU, Stillwater (405) 744-6186 Kent Orrell, Agric./4-H & CED Kiowa County, OSU, Hobart (580)-726-5643 Stan Ralstin, Area Community Development Specialist, OSU, Enid (580) 233-5295 Mike D. Woods, Extension Economist, OSU, Stillwater (405) 744-9837 OKLAHOMA COOPERATIVE EXTENSION SERVICE OKLAHOMA STATE UNIVERSITY November 2002

Analysis Of Retail Trends And Taxable Sales For The Communities in Kiowa County, Oklahoma Suzette Barta Susan Trzebiatowski Mike Woods Extension Assistant Student Assistant Extension Economist Room 527, Ag. Hall Room 527, Ag. Hall Room 514, Ag. Hall Oklahoma State University Oklahoma State University Oklahoma State University Stillwater, OK 74078-6026 Stillwater, OK 74078-6026 Stillwater, OK 74078-6026 sdb1113@okstate.edu susanft@okstate.edu mdwoods@okstate.edu Kent Orrell Stan Ralstin Ext. Ed., Ag/4-H & CED Area Ext. Comm. Dev. Specialist 300 S. Main, Courthouse Annex 205 W. Maple, Suite 610 Hobart, OK 73651-4016 Enid, OK 73701-4011 orrellbk@okstate.edu ralstin@okstate.edu ABSTRACT The goal of this paper is to provide an analysis of taxable sales for the communities in Kiowa County. Basic data is used to provide estimates of trade area capture and pull factors. Reported sales tax data is also used to analyze trends in the county and area. "Oklahoma State University, in compliance with Title VI and VII of the Civil Rights Act of 1964, Executive Order 11246 as amended, Title IX of the Education Amendments of 1972, Americans with Disabilities Act of 1990, and other federal laws and regulations, does not discriminate on the basis of race, color, national origin, sex, age, religion, disability, or status as a veteran in any of its policies, practices or procedures. This includes but is not limited to admissions, employment financial aid, and educational services." "Readers may make verbatim copies of this document for non-commercial purposes by any means."

ANALYSIS OF RETAIL TRENDS AND TAXABLE SALES FOR THE COMMUNITIES IN KIOWA COUNTY, OKLAHOMA INTRODUCTION Oklahoma communities have been concerned with all aspects of economic development for the past several years. Creating new jobs and additional income is of concern to rural communities and urban areas alike. Often, retailing is viewed as a "service" sector dependent on the "basic" sectors such as oil, manufacturing, and agriculture. Export sectors produce goods and services sold outside the local or regional economy. Service sectors tend to circulate existing local dollars rather than attracting "new" outside dollars. The retail sector is important, though, as retail activity reflects the general health of a local economy. Retail sales also produce sales tax dollars, which support municipal service provision. Many local communities are promoting a "shop at home" campaign to keep local retail dollars in the community. It will not be possible to stop all out-of-town spending or sales leakage s for a local economy. Opportunities for improvement do frequently exist, however. Kiowa County is involved in the Initiative for Rural Oklahoma leadership program, and Mountain Park, Mountain View, Snyder, Roosevelt, Hobart, Gotebo, and Lone Wolf are the seven communities in the county that collect a city sales tax. The information in this study may help these communities identify key areas for improvement. Specifically, the objectives of this study are: 1. Utilize reported sales tax data to analyze trends in the county and area, and 2. Provide estimates of trade area capture and market attraction. 3. Provide estimates of market attraction, broken out by SIC code. 1

METHODOLOGY AND DATA SOURCES A trade area analysis model frequently used is "trade area capture." Trade area capture is calculated by dividing the city's retail sales by state per capita retail sales. The figure is adjusted by income differences between the state and relevant local area. The specific equation utilized is: TAC = RS PS Where: TACc=Trade Area Capture by city, RSc=Retail Sales by city, RSs=Retail Sales for the state, Ps=State Population, PCIc=Per Capita Income by county, and PCIs=Per Capita Income for the state. C RS C X PCI PCI Trade area capture figures incorporate both income and expenditure factors, which may be influencing retail trade trends. An underlying assumption of the trade area capture estimate is that local tastes and preferences are similar to that of the state as a whole. If a trade area capture estimate is larger than city population then two explanations are possible: 1) the city is attracting customers outside its boundaries or 2) residents of the city are spending more than the state average. Trade area capture figures can be utilized to estimate the amount of sales going to outside consumers. To do this a pull factor, which is a measure of an economy's retail sales gap, is derived using trade area capture figures and city population: S C S Where: PFc=City Pull Factor, and Pc=City Population. PF C TAC = PC C 2

A pull factor of 1.0 means the city is drawing all its customers from within its boundaries but none from the outside. A pull factor of 1.50 means the city is drawing non-local customers equal to 50 percent of the city population. A pull factor of less than one means the city is not capturing the shoppers within its boundaries or they are spending relatively less than the state average. This is considered leakage of retail sales or a retail sales gap. Additional discussion of trade area capture and pull factors can be found in the references cited in this report (Barta and Woods; Harris; Stone and McConnon; Hustedde, Shatter, and Pulver). The Oklahoma Cooperative Extension Service has been conducting pull factor/gap analysis and sales tax analysis since 1991 (Woods, 1991). City pull factors and trade area capture figures are calculated for fiscal years 1980 through 2001. Data used were sales tax returns as reported by the Oklahoma Tax Commission. These figures do not include all retail sales (only taxable sales) in an area but provide a proxy. Population data were obtained from the Oklahoma State Data Center and were consistent with figures from the1980, 1990, and 2000 Census. Income figures were taken from Bureau of Economic Analysis estimates for counties. Similar income data for cities were not available so county income was used as a proxy. 3

TAXABLE SALES ANALYSIS Sales tax returns as reported by the Oklahoma Tax Commission for cities and towns in Kiowa County are listed in Table 1 for the fiscal years 1980 to 2001. Sales tax returns are important to a city because they reflect the general health of a local economy and also represent significant revenue for the city budget. In FY 2001, Hobart collected over $1 million in sales tax at a 4.0% tax rate. Also collecting at a 4.0% rate in 2001 were Snyder (collecting over $214,000) and Mountain View (collecting over $131,000). The remaining towns collected at a 3.0% rate in 2001. Roosevelt collected nearly $24,000, Gotebo collected nearly $28,000, Lone Wolf collected over $54,500, and Mountain Park collected over $13,000. Figure 1A plots estimated taxable sales for the same time period in both actual dollars and inflation-adjusted dollars for Hobart. Sales are estimated from the sales tax returns and the sales tax rate that is reported. The Consumer Price Index is used to adjust for inflation. When taxable sales have been adjusted for inflation, Figure 1A shows that real sales for Hobart have generally declined since 1983. Figure 1B shows only the inflation-adjusted or real sales for Snyder, Mountain Park, Lone Wolf, and Mountain View. Snyder and Mountain View both show the same general decline in real sales since the early 1980 s. Lone Wolf s real sales have been pretty constant around the $700,000-$900,000 level (1980 inflation-adjusted dollars), and Mountain Park has been relatively constant at about $200,000 (1980 inflation-adjusted dollars). Figure 1C shows the same information for the two smallest towns in Kiowa County, Roosevelt and Gotebo. Roosevelt shows the same pattern of decline that many of the other towns in the county have shown. Gotebo does too, but also shows a moderate trend of growth from 1997 to 2001. Table 2 lists trade area capture figures for cities and towns in Kiowa County from 1980 to 2001. The trade area capture for Hobart was at a maximum of 6,014 occurring in 1980. This 4

means that in 1980 Hobart captured the retail sales of 6,014 persons. Other towns that hit their high trade area levels in 1980 are Lone Wolf (287) and Mountain View (878). Snyder hit their high in 1983, attracting 1,112 shoppers. Roosevelt hit a high of 243 in both 1982 and 1987. Gotebo hit a high of 253 in 1985. Finally, Mountain Park attracted their high of 157 shoppers in 1986. Hobart and Snyder are the only two towns to reach a trade area of over 1,000 people at any point. Figure 2A presents a graphic of these same trade area capture figures for Hobart and Snyder. Hobart s trade area capture has fallen from about 6,000 in 1980 to below 4,000 in recent years. Snyder s trade area capture has tended to stay at or slightly below 1,000. Figure 2B shows trade area capture for the other towns in Kiowa County. Mountain View s trade area has declined from around 850 to below 400 in 2000. The remaining towns have been consistently below 300. Table 3 lists pull factors for the cities and towns in Kiowa County for the years 1980 to 2001. The pull factor for Hobart ranges from 0.868 to 1.270. Recently, these pull factors have tended to be about 0.94. The interpretation is that Hobart is capturing about 94% of the local shoppers, but did not attract any non-local shoppers. (This may not be exactly true. It is likely that some rural residents travel to Hobart to shop for some items; however, this activity is more than offset by the out-shopping by local residents. It is likely that residents of Hobart do some of their shopping in Lawton, Clinton, and Weatherford.) Several of the towns have 2001 pull factors in the range of 0.48 to 0.53, including Lone Wolf, Gotebo, Snyder, and Mountain View. Roosevelt s is slightly lower at 0.405, and Mountain Park s is the lowest at 0.16. Hobart is the only community to ever have a pull factor greater than 1.0. This indicates that in those years, Hobart attracted a number of shoppers that was greater than its own population. Recall that Hobart s population is about 4,000. Figure 3 shows pull factors for cities and towns in Kiowa County with a reported sales tax. Hobart clearly stands apart on this chart, and is probably the historical center of trade for the 5

county. Mountain Park is at the bottom of the chart, but the remaining towns are really clumped together in the middle, with no one community clearly emerging in second place. Figure 4 shows pull factors for 461 cities that have sales tax return information available. The pull factors are presented as a group average by city size. The highest pull factors fall in the size categories 5,001 to 10,000 and 10,001 to 25,000 and 25,001 to 50,000 in population. The smallest pull factors fall in the range for cities less than 1,000 in population. Figure 5A plots Hobart and Snyder s pull factors compared to other cities with population 1,000-5,000. The average level is represented by the series of empty boxes. Hobart has tended to run right above the average, while Snyder has been below the average. Figure 5B plots pull factors for Roosevelt, Mountain View, Mountain Park, Lone Wolf and Gotebo versus the average pull factor for other towns with a population less than 1,000. Again, the average level is represented by the series of empty boxes. Mountain View and Roosevelt were both above the average in the 1980s, but most recently, all of the communities are below the average for other towns of similar size. 6

Table 1 Sales Tax Collections for Cities and Towns in Kiowa County 1980-2001 Hobart Lone Wolf Roosevelt Gotebo Months Rate Collected Months Rate Collected Months Rate Collected Months Rate Collected 1980 12 2.00% $399,367.09 12 2.00% $19,044.78 12 2.00% $15,461.59 5/7 2.0%/3.0% $14,980.10 1981 12 2.00% $439,112.71 12 2.00% $21,429.30 12 2.00% $17,606.17 12 3.00% $19,637.00 1982 12 2.00% $503,788.50 11/1 2.0%/3.0% $22,733.94 12 2.00% $20,400.84 12 3.00% $21,350.52 1983 12 2.00% $520,712.80 12 3.00% $33,284.85 12 2.00% $21,417.93 12 3.00% $23,342.48 1984 6/6 2.0%/3.0% $589,622.48 12 3.00% $33,624.07 3/9 2.0%/3.0% $23,262.70 12 3.00% $23,240.89 1985 12 3.00% $765,521.97 12 3.00% $35,568.22 12 3.00% $27,745.18 12 3.00% $36,617.60 1986 12 3.00% $757,083.11 12 3.00% $31,861.16 12 3.00% $27,717.87 12 3.00% $31,627.79 1987 12 3.00% $715,752.48 12 3.00% $31,619.54 12 3.00% $33,979.31 12 3.00% $29,700.11 1988 12 3.00% $692,649.47 12 3.00% $33,317.95 12 3.00% $32,905.45 12 3.00% $25,983.84 1989 12 3.00% $692,949.19 12 3.00% $36,069.42 12 3.00% $34,465.20 12 3.00% $29,347.80 1990 12 3.00% $701,930.97 12 3.00% $32,322.39 12 3.00% $32,508.04 12 3.00% $24,751.68 1991 12 3.00% $701,410.22 12 3.00% $31,898.06 12 3.00% $32,040.05 12 3.00% $23,445.14 1992 12 3.00% $711,830.04 12 3.00% $34,111.57 12 3.00% $32,818.36 12 3.00% $25,228.94 1993 12 3.00% $718,325.32 12 3.00% $39,975.23 12 3.00% $32,018.14 12 3.00% $23,493.05 1994 12 3.00% $724,559.99 12 3.00% $36,351.85 12 3.00% $29,237.42 12 3.00% $25,248.78 1995 6/6 3.0%/4.0% $850,499.98 12 3.00% $36,659.99 12 3.00% $28,219.84 12 3.00% $23,305.87 1996 12 4.00% $977,901.23 12 3.00% $42,598.60 12 3.00% $25,805.39 12 3.00% $25,556.96 1997 12 4.00% $960,769.47 12 3.00% $41,797.24 12 3.00% $23,614.94 12 3.00% $23,345.55 1998 12 4.00% $976,404.68 12 3.00% $46,151.19 12 3.00% $28,992.58 12 3.00% $25,095.45 1999 12 4.00% $997,014.58 12 3.00% $56,781.18 12 3.00% $25,622.81 12 3.00% $25,819.91 2000 12 4.00% $1,004,470.90 12 3.00% $44,117.84 12 3.00% $23,579.50 12 3.00% $26,134.91 2001 12 4.00% $1,047,491.91 12 3.00% $54,516.06 12 3.00% $23,786.60 12 3.00% $27,840.86 7

Table 1 (Continued) Sales Tax Collections for Cities and Towns in Kiowa County 1980-2001 Mountain Park Snyder Mountain View Months Rate Collected Months Rate Collected Months Rate Collected 1980 12 2.00% $7,702.11 12 2.00% $72,605.99 12 2.00% $58,314.58 1981 12 2.00% $7,182.79 12 2.00% $79,594.57 12 2.00% $59,376.00 1982 12 2.00% $7,232.12 12 2.00% $90,990.70 12 2.00% $71,598.13 1983 12 2.00% $7,858.36 4/8 2.0%/3.0$ $130,741.99 12 2.00% $72,917.20 1984 7/5 2.0%/3.0% $10,136.59 3/9 3.0%/4.0$ $179,109.11 12 2.00% $60,317.49 1985 12 3.00% $21,074.13 12 4.00% $198,826.02 12 2.00% $79,977.79 1986 12 3.00% $23,030.26 12 4.00% $179,364.29 12 2.00% $72,270.09 1987 12 3.00% $18,787.86 12 4.00% $180,941.88 12 2.00% $55,984.80 1988 12 3.00% $15,977.63 12 4.00% $183,587.39 12 4.00% $105,390.87 1989 12 3.00% $28,453.12 12 4.00% $187,012.14 12 4.00% $114,057.63 1990 12 3.00% $10,698.12 12 4.00% $202,036.78 12 4.00% $118,696.34 1991 12 3.00% $13,546.23 12 4.00% $200,140.25 12 4.00% $111,710.42 1992 12 3.00% $12,617.60 12 4.00% $213,890.34 12 4.00% $115,060.58 1993 12 3.00% $12,809.58 12 4.00% $199,720.55 12 4.00% $111,019.04 1994 12 3.00% $14,140.61 12 4.00% $208,231.59 12 4.00% $109,878.51 1995 12 3.00% $12,938.10 12 4.00% $199,685.52 12 4.00% $117,209.19 1996 12 3.00% $13,680.33 12 4.00% $200,192.36 12 4.00% $117,645.05 1997 12 3.00% $11,463.97 12 4.00% $198,529.39 12 4.00% $128,347.47 1998 12 3.00% $13,702.54 12 4.00% $201,972.64 12 4.00% $136,747.33 1999 12 3.00% $13,162.11 12 4.00% $207,004.72 12 4.00% $117,327.27 2000 12 3.00% $9,322.53 12 4.00% $210,641.49 12 4.00% $110,336.40 2001 12 3.00% $13,385.61 12 4.00% $214,738.63 12 4.00% $131,146.33 8

Figure 1A. Estim ated Retail Sales for Hobart, Actual and Inflation-Adjusted, 1980-2001 $28,000,000.00 $26,000,000.00 $24,000,000.00 $22,000,000.00 $20,000,000.00 $18,000,000.00 $16,000,000.00 $14,000,000.00 $12,000,000.00 $10,000,000.00 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Hobart Hobart-Adjusted 9

Figure 1B. Estimated Retail Sales for Lone Wolf, Mt. Park, Snyder, and Mt. View - Inflation Adjusted Only $4,500,000.00 $4,000,000.00 $3,500,000.00 $3,000,000.00 $2,500,000.00 $2,000,000.00 $1,500,000.00 $1,000,000.00 $500,000.00 $0.00 1980 1981 1982 1983 1984 1985 1986 1987 1988 10 1989 1990 1991 1992 1993 1994 1995 1996 Lone Wolf-Adjusted Mt Park Adjusted Snyder Adjusted Mt View Adjusted 1997 1998 1999 2000 2001

$1,000,000.00 $900,000.00 $800,000.00 $700,000.00 $600,000.00 $500,000.00 $400,000.00 Figure1C. Estimated Retail Sales for Roosevelt and Gotebo - Inflation Adjusted Only $300,000.00 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Roosevelt Adjusted Gotebo Adjusted 11

Table 2 Trade Area Capture for Cities and Towns in Kiowa County 1980-2001 Hobart Lone Wolf Roosevelt Gotebo Year Trade Area Population Trade Area Population Trade Area Population Trade Area Population Capture Capture Capture Capture 1980 6,014 4,735 287 613 233 396 171 457 1981 5,054 4,950 247 650 203 400 151 500 1982 5,997 5,050 258 700 243 400 169 550 1983 5,895 5,050 251 750 242 400 176 550 1984 4,918 4,900 232 650 177 350 160 500 1985 5,288 4,800 246 650 192 350 253 500 1986 5,167 4,850 217 650 189 300 216 500 1987 5,118 4,650 226 650 243 300 212 500 1988 4,728 4,600 227 700 225 300 177 500 1989 4,659 4,600 243 700 232 300 197 500 1990 4,482 4,265 206 576 208 325 158 370 1991 4,787 4,179 218 562 219 325 160 369 1992 4,592 4,115 220 555 212 325 163 368 1993 4,448 4,129 248 555 198 331 145 375 1994 4,291 4,064 215 545 173 332 150 374 1995 4,390 4,027 220 541 170 333 140 378 1996 4,263 3,931 248 526 150 331 149 375 1997 3,962 3,905 230 521 130 335 128 380 1998 3,770 3,817 238 511 149 333 129 378 1999 3,627 3,734 275 499 124 330 125 375 2000 3,469 3,997 203 500 109 280 120 272 2001 3,744 3,997 260 500 113 280 133 272 12

Table 2 (Continued) Trade Area Capture for Cities and Towns in Kiowa County 1980-2001 Mountain Park Snyder Mountain View Year Trade Area Population Trade Area Population Trade Area Population Capture Capture Capture 1980 116 557 1,093 1,848 878 1,189 1981 83 550 916 1,850 683 1,200 1982 86 500 1,083 1,850 852 1,200 1983 89 550 1,112 1,850 825 1,200 1984 86 500 988 1,850 624 1,150 1985 146 500 1,030 1,850 829 1,000 1986 157 450 918 1,850 740 1,050 1987 134 450 970 1,850 600 1,000 1988 109 450 940 1,900 540 1,000 1989 191 450 943 1,850 575 950 1990 68 467 968 1,592 568 1,090 1991 92 467 1,025 1,563 572 1,068 1992 81 467 1,035 1,542 557 1,051 1993 79 475 928 1,549 516 1,054 1994 84 474 925 1,526 488 1,037 1995 78 478 900 1,514 528 1,027 1996 80 474 873 1,478 513 1,003 1997 63 480 819 1,472 529 997 1998 71 477 780 1,439 528 975 1999 64 473 753 1,408 427 953 2000 43 390 727 1,509 381 880 2001 64 390 767 1,509 469 880 13

7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Figure 2A. Trade Area Capture for Hobart and Snyder, 1980-2001 14 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Hobart Snyder

1,000 900 800 700 600 500 400 300 200 100 0 Figure 2B. Trade Area Caputure for the Remaining Towns in Kiowa County, 1980-2001 2001 15 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Lone Wolf Roosevelt Gotebo Mountain Park Mountain View

Table 3 Pull Factors for Cities and Towns in Kiowa County 1980-2001 Hobart Lone Wolf Roosevelt Gotebo Mountain Park Snyder Mountain View 1980 1.270 0.468 0.588 0.375 0.208 0.592 0.739 1981 1.021 0.379 0.507 0.301 0.150 0.495 0.569 1982 1.188 0.369 0.607 0.308 0.172 0.585 0.710 1983 1.167 0.335 0.606 0.320 0.162 0.601 0.688 1984 1.004 0.357 0.507 0.321 0.173 0.534 0.543 1985 1.102 0.378 0.548 0.506 0.291 0.557 0.829 1986 1.065 0.335 0.631 0.432 0.349 0.496 0.705 1987 1.101 0.348 0.810 0.425 0.299 0.525 0.600 1988 1.028 0.325 0.749 0.355 0.242 0.495 0.540 1989 1.013 0.346 0.772 0.395 0.425 0.510 0.605 1990 1.051 0.358 0.639 0.427 0.146 0.608 0.522 1991 1.146 0.387 0.673 0.434 0.198 0.655 0.535 1992 1.116 0.397 0.651 0.442 0.174 0.671 0.530 1993 1.077 0.446 0.599 0.388 0.167 0.599 0.489 1994 1.056 0.395 0.522 0.400 0.177 0.606 0.471 1995 1.090 0.407 0.509 0.371 0.163 0.594 0.514 1996 1.084 0.471 0.453 0.396 0.168 0.590 0.511 1997 1.014 0.441 0.388 0.338 0.131 0.556 0.531 1998 0.988 0.465 0.448 0.342 0.148 0.542 0.542 1999 0.971 0.552 0.377 0.334 0.135 0.535 0.448 2000 0.868 0.406 0.388 0.442 0.110 0.482 0.433 2001 0.937 0.520 0.405 0.488 0.164 0.509 0.533 16

1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 Figure 3. Pull Factors for Cities and Towns in Kiowa County, 1980-2001 17 Hobart Lone Wolf Roosevelt Gotebo Mountain Park Snyder Mountain View 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 1982 1981 1980

1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 Figure 4. Average Pull Factor by City Size, 1980-2001 18 Less 1000 1-5 5-10 10-25 25-50 Grtr 50 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

2001 1.400 1.200 1.000 0.800 0.600 0.400 0.200 0.000 Figure 5A. Pull Factors for Hobart and Snyder v. Other Cities with Population 1,000-5,000 2000 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Hobart Snyder 1,000-5,000 19 1981 1980

Figure 5B. Pull Factors for the Remaining Towns in Kiowa County v. Other Towns with Population Less than 1,000 0.900 0.800 0.700 0.600 0.500 0.400 0.300 Lone Wolf Roosevelt Gotebo Mountain Park Mountain View Less 1,000 0.200 0.100 0.000 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 20

Detailed Sales Gap Analysis for Cities and Towns in Kiowa County For purposes of this study, a sales gap analysis refers to a pull factor study that has been analyzed by SIC code for the 8 retail sectors. Sales gap coefficients may be interpreted in exactly the same manner as are pull factors. See Table 4 for a sales gap analysis for the communities in Kiowa County. Table 5 provides detailed descriptions of the 8 retail SIC categories. For Hobart s Building and Gardening Materials, the number of shoppers has declined from a high of 1,835 in FY 2000 to 1,513 in FY 2001. Hobart's population is about 4,000; thus, in 2001, this sector of the Hobart economy was capturing a number of shoppers that was equal to about 38% of the town s population. In 2001, Mountain View captured 353 shoppers in this category for a gap coefficient of 0.40. The category of General Merchandise tends to be dominated by Wal-Mart. Wal-Mart reports all its sales under this category (even though it sells clothing, grocery items, etc. as well). In general, towns that have a Wal-Mart will post sales gap coefficients that are greater than 1.0 for this category. Towns without a Wal-Mart will post gap coefficients less than 1.0. Hobart is the only town in the county with a Wal-Mart; consequently, it is the only town with a gap coefficient greater than 1.0 in this category. Consumers tend to appreciate the convenience of shopping for groceries close to home. In addition, most residents outside of the city limits will travel into the small town grocery store to shop; consequently, it is common to find that even very small towns post high gap coefficients for this sector. For several of the communities in Kiowa County, this sector has the highest gap coefficient, although the value is not necessarily greater than 1.0 for all of them. These include: Hobart (1.47), Lone Wolf (0.60), Roosevelt (0.48), Snyder (0.98), and Mountain View (0.85). SIC category 55 is difficult to interpret because motor vehicle and gasoline sales are exempt from municipal sales tax in Oklahoma. Most of the sales tax collection reported under this category appears 21

to stem from auto parts stores and other retail sales from gas stations. For instance, most gas stations sell snack items, tires, some auto parts, oil, anti-freeze, etc. A good location on a state highway or interstate can bolster sales tax collections in this category. Gotebo did particularly well in this category, attracting 777 shoppers in 2001, which is almost 3 times its own population. Apparel sales are reported under SIC 56. It is very difficult for small to medium sized towns to post high sales coefficients in the category of apparel. Many small towns have nearly zero sales in this category, and it is common to see sales gap coefficients that are less than 0.10 in these towns. Cities with large malls tend to be the most successful at capturing the market. This turns out to be quite true for the small towns in Kiowa County. The only town that was able to break the 10% level was Hobart with a gap coefficient of 0.38. SIC 57 reports Furniture and Home Furnishings. Also included are appliance and electronics stores, drapery and floor covering stores, and music stores. This category is generally viewed from the perspective that most furniture purchases are made in either Tulsa or Oklahoma City. Oklahoma City, for example, has a large cluster of retail furniture stores centralized in one geographic area. Kiowa County residents probably do purchase some furniture in Oklahoma City, but probably also purchase some in Lawton as well. Lone Wolf posted the highest gap coefficient in this category, attracting 165 shoppers for a coefficient of 0.33. Eating and Drinking Places, SIC 58, is one of the most straightforward retail sectors. It contains restaurants and bars. Restaurants and bars in Hobart captured 2,242 customers in FY 2001. Recently, restaurants in Hobart have attracted a number of shoppers is equal to about 56% of its population. Snyder attracted 495 shoppers in 2001 and Lone Wolf attracted 238. 22

SIC 59, or Miscellaneous Retail, contains a host of retail activity, including pharmacies, florists, liquor stores, and antique stores. Mountain View has the strongest coefficient in this category, posting a 0.398. Snyder s pull factor is next at 0.389, followed by Hobart with 0.305.. 23

Table 4 Retail Sales Gap Analysis by Standard Industrial Classification (SIC) Code: Fiscal 1998-2001 Hobart Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 1,351 1,651 1,835 1,513 0.354 0.442 0.459 0.379 General Merchandise (53) 4,889 4,783 4,956 5,082 1.281 1.281 1.240 1.271 Food Stores (54) 6,539 6,027 5,877 5,890 1.713 1.614 1.470 1.474 Automobile Dealers & Gas Stations (55) 1,224 2,100 1,857 2,311 0.321 0.562 0.465 0.578 Apparel & Accessory Stores (56) 3,078 2,683 2,172 1,525 0.806 0.719 0.543 0.382 Furniture & Home Furnishings (57) 668 939 1,051 838 0.175 0.251 0.263 0.210 Eating & Drinking Places (58) 2,441 2,540 2,253 2,242 0.640 0.680 0.564 0.561 Miscellaneous Retail (59) 1,568 1,365 1,217 1,218 0.411 0.366 0.304 0.305 Lone Wolf Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 48 18 72 15 0.095 0.036 0.144 0.029 General Merchandise (53) 9 6 10 8 0.017 0.012 0.021 0.015 Food Stores (54) 340 348 353 302 0.665 0.697 0.705 0.604 Automobile Dealers & Gas Stations (55) 3 4 8 2 0.005 0.007 0.016 0.003 Apparel & Accessory Stores (56) 0 1 0 8 0.000 0.002 0.001 0.017 Furniture & Home Furnishings (57) 77 1 --- 165 0.152 0.002 --- 0.329 Eating & Drinking Places (58) 122 236 88 238 0.239 0.473 0.176 0.477 Miscellaneous Retail (59) 86 231 --- 96 0.169 0.462 --- 0.193 24

Roosevelt Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 3 7 4 15 0.010 0.022 0.015 0.055 General Merchandise (53) 6 8 3 8 0.017 0.025 0.012 0.027 Food Stores (54) 87 92 125 135 0.262 0.278 0.445 0.483 Automobile Dealers & Gas Stations (55) 5 11 7 1 0.016 0.033 0.025 0.005 Apparel & Accessory Stores (56) 2 1 0 1 0.006 0.002 0.000 0.002 Furniture & Home Furnishings (57) 28 18 11 11 0.083 0.055 0.039 0.038 Eating & Drinking Places (58) 26 35 27 24 0.077 0.108 0.096 0.087 Miscellaneous Retail (59) 146 --- 34 25 0.438 --- 0.123 0.089 Gotebo Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 87 160 140 74 0.231 0.427 0.513 0.274 General Merchandise (53) 3 1 3 1 0.009 0.003 0.011 0.003 Food Stores (54) 166 178 183 173 0.439 0.474 0.674 0.637 Automobile Dealers & Gas Stations (55) 892 751 650 777 2.359 2.002 2.389 2.856 Apparel & Accessory Stores (56) 3 2 3 0 0.007 0.005 0.010 0.000 Furniture & Home Furnishings (57) 2 5 8 9 0.006 0.013 0.029 0.034 Eating & Drinking Places (58) 0 0 --- 4 0.000 0.000 --- 0.015 Miscellaneous Retail (59) 23 21 18 18 0.061 0.055 0.065 0.065 25

Mountain Park Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 25 2 1 0 0.053 0.004 0.004 0.000 General Merchandise (53) 2 1 1 1 0.004 0.003 0.002 0.002 Food Stores (54) 94 0 0 0 0.198 0.000 0.000 0.000 Automobile Dealers & Gas Stations (55) 0 0 1 1 0.001 0.000 0.002 0.004 Apparel & Accessory Stores (56) 0 0 0 0 0.001 0.001 0.000 0.000 Furniture & Home Furnishings (57) 12 5 0 2 0.025 0.010 0.001 0.006 Eating & Drinking Places (58) 30 37 15 129 0.063 0.079 0.039 0.331 Miscellaneous Retail (59) 10 9 11 14 0.021 0.020 0.029 0.035 Snyder Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 343 366 315 44 0.238 0.260 0.209 0.029 General Merchandise (53) 336 373 386 327 0.234 0.265 0.255 0.217 Food Stores (54) 1,638 1,653 1,605 1,487 1.138 1.174 1.063 0.985 Automobile Dealers & Gas Stations (55) 346 286 317 290 0.241 0.203 0.210 0.192 Apparel & Accessory Stores (56) 9 10 1 4 0.006 0.007 0.001 0.003 Furniture & Home Furnishings (57) 147 164 145 146 0.102 0.117 0.096 0.097 Eating & Drinking Places (58) 503 388 542 495 0.349 0.276 0.359 0.328 Miscellaneous Retail (59) 226 267 414 587 0.157 0.190 0.275 0.389 26

Mountain View Trade Area Capture Sales Gap Coefficient* FY 1998 FY 1999 FY 2000 FY 2001 FY 1998 FY 1999 FY 2000 FY 2001 Building, Gardening & Merchandise (52) 541 398 438 353 0.555 0.418 0.498 0.401 General Merchandise (53) 44 13 14 10 0.045 0.014 0.016 0.011 Food Stores (54) 766 719 462 747 0.785 0.754 0.525 0.848 Automobile Dealers & Gas Stations (55) 290 235 199 205 0.298 0.247 0.226 0.233 Apparel & Accessory Stores (56) 343 373 43 3 0.351 0.392 0.049 0.003 Furniture & Home Furnishings (57) 330 125 94 55 0.338 0.131 0.106 0.063 Eating & Drinking Places (58) 79 105 104 93 0.081 0.110 0.118 0.105 Miscellaneous Retail (59) 386 355 370 350 0.396 0.373 0.421 0.398 27

TABLE 5 TYPES OF BUSINESSES DESCRIBED BY THE RETAIL SIC CODES 52 Building Materials 58 Eating and Drinking Places Lumber yards including home centers Paint and wallpaper stores Glass stores 59 Miscellaneous Retail Hardware stores Drug and proprietary stores Retail Nurseries Liquor Stores Lawn and garden supply stores Mobile Home dealers Used merchandise stores including antique stores and pawn shops Sporting goods stores 53 General Merchandise Stores Book stores Variety stores Stationary stores Department stores Jewelry stores Warehouse clubs Hobby, toy, and game shops General combination merchandise stores Camera and photographic supplies stores Gifts, novelties and souvenirs 54 Food Stores Luggage and leather goods stores Grocery stores (Supermarkets) Sewing, needlework, and piece goods stores Convenience stores both with and without gasoline Catalog and mail order sales (includes e- Meat and fish markets commerce stores) Fruit and vegetable markets Vending machine operators and direct selling Candy, nut and confectionery stores establishments Dairy stores Fuel oil dealers Retail Bakeries Bottled gas dealers Florists 55 Automotive Dealers and Gasoline Service Stations Tobacco Stores Motor vehicle dealers (new and used) Tire stores Auto supply stores Gasoline stations Boat dealers RV dealers Motorcycle dealers 56 Apparel and Accessory Stores Men and boys apparel Women s apparel and accessories Children and infant s wear Family apparel Shoe stores Custom tailor and seamstresses 57 Furniture and Home Furnishings Stores Furniture stores Floor covering stores Drapery, curtains and upholstery stores Pottery and crafts made and sold on site Household appliance stores Radio and TV and consumer electronics stores Computer and computer software stores Record and prerecorded tapes stores Musical instruments stores Newsstands Optical goods stores Cosmetic stores Pet and pet supply stores Hearing aid and artificial limb stores Art dealers Telephone and typewriter stores 28

BUSINESS DEVELOPMENT STRATEGIES Retail trade trends reflect the overall health of a local economy. All out shopping or sales leakage cannot be stopped. Often, larger economic trends (State-National-Global) overwhelm retail opportunities. There are programs and actions which can assist retail trade activities, however. Concerned leaders and business persons can focus on business development by forming a business assistance committee to begin implementing some of the assistance activities or working with the existing chamber of commerce. The following activities were in part of a retail trade improvement program. These activities can improve the climate for business and show the community's commitment to support local business. 1. Analyze the local business sector to identify needs and opportunities to be pursued by the program. Businesses often do not have the resources to study the economy (local, regional, and national) and how they fit in. They need practical data and analysis that will help in their individual business decision making. In particular, economic analysis can identify voids in the local or regional market that can possibly be filled by expanding or new business. Examples of analysis include the pull factor analysis reported here and consumer surveys to identify needs and opportunities. In addition to economic analysis, information is needed on the needs or problems of individual businesses and of the business district as a whole. As needs are identified, action can be taken to improve the situation. For example, a business may need help in preparing a business plan to qualify for financing. Perhaps the appearance of buildings and vacant lots is detrimental to attracting people to be business district, or perhaps poorly coordinated store hours are a hindrance. Once these needs are identified, a business development program can initiate action. A periodic survey of local business needs can form the basis of a business development program's work plan. 29

2. Provide management assistance and counseling to improve the efficiency and profitability of local businesses. Many local businesses are owner-operated, earn low profits, and have difficulty obtaining financing. Businessmen often need additional education and training in improving business management skills like accounting, finance, planning, marketing, customer, relations, merchandising, personnel management, or tax procedures. This assistance and counseling can be provided through seminars and one-to-one aid. Sources of assistance include the Service Corps of Retired Executives (SCORE), Small Business Development Center program sponsored by the Small Business Administration, Universities, Technology Centers, Oklahoma Department of Commerce, and the Cooperative Extension Service. The intent is to aid small businesses in becoming more competitive. 3. Assist new business start-up and entrepreneurial activity by analyzing potential markets and local skills and matching entrepreneurs with technical and financial resources. Establishing a business incubator is another way to assist new businesses. An incubator is a building with shed space or service requirements that reduce start-up costs for new businesses. Incubators have been successful in many locations but are not the right answer for every town. A successful incubator must have long-range planning, specific goals, and good management in order to identify markets and entrepreneurs. 4. Promote the development of home-based enterprises. Home-based work by individuals is increasing because of the flexibility offered and because in some areas, it may be the most realistic alternative. Home-based enterprises can include a great variety of full or part-time occupations such as food processing, quilting, weaving, crafts, clothing assembly, mail order processing, or assembling various goods. 30

5. Provide assistance in identifying and obtaining financing. Small businesses often have difficulty obtaining long-term bank financing for expansion because they lack assets to mortgage, cannot obtain affordable terms or rates, or cannot present a strong business plan. A business development program can identify public loan programs and package them with private loans to make projects feasible. 6. Provide assistance in undertaking joint projects such as: improved appearance improved management of the commercial area building renovation preparation of design standards joint promotions and marketing organizing independent merchants special activities and events fund raising improved customer relations uniform hours of operation Undertaking these projects requires cooperation, good organization, and efficient management. These projects can improve a business district's competitive position and attract new customers. The Oklahoma Main Street Program provides many good examples of towns working together for economic revitalization. The Main Street Program developed 31

by the National Trust for Historic Preservation, is built around the four points of organization, design, promotion, and economic restructuring. 7. Develop a one-stop permit center. There is great deal of red tape involved in starting a business including registering a name, choosing a legal form, and determining what licenses, permits, or bonds are needed. Other concerns include internal revenue service requirements, unemployment insurance, sales tax permits, and state withholding taxes. Having this type of information available in one location will make life easier for potential businesses. 8. Involve active organizations and the media. Groups such as the chamber of commerce, civic clubs, etc. can encourage a healthy business climate. The local media can also support small business and aid in developing awareness of the importance of local business. 32

SUMMARY This report has analyzed taxable sales trends for the cities and towns in Kiowa County: Hobart, Mountain Park, Lone Wolf, Snyder, Mountain View, Gotebo, and Roosevelt. The level of taxable sales in Hobart, for example, has mostly grown in nominal terms since 1988. After correcting for inflation, however, taxable sales have declined since about 1982. As the centrally located county seat for Kiowa County, Hobart probably is the trade center for residents in the county. Even so, trade area and pull factors have been dropping for Hobart and for the second largest town of Snyder. Most of the other towns in the county are quite small and tend to capture about half of their own populations. It is likely that Hobart or other regional trade centers are destinations for much of their residents shopping. Whether we like it or not, the existence of a Wal-Mart store can turn a town into a trade center for the county or the region. Kiowa County, for instance, has only one Wal-Mart in Hobart. According to the store locator at www.walmart.com, the closest Wal-Mart Supercenter for residents of Hobart is located 28 miles away in Altus. It is likely that Altus as well as Lawton, Weatherford, and Clinton are all retail destinations for residents of Kiowa County, including Hobart. 33

REFERENCES Barta, S.D. and M.D. Woods. Gap Analysis as a Tool for Community Economic Development. WF 917, Oklahoma Cooperative Extension Service, Oklahoma State University, <http://agweb.okstate.edu/pearl/agecon/resource/wf-917.pdf>, 2000. Harris, Lone Wolf R. "Commercial Sector Development in Rural Communities: Trade Area Analysis." Hard Times: Communities in Transition. Western Rural Development Center, WREP 90, September 1985. Hustedde, R., R. Shatter, and G. Pulver, Community Economic Analysis: A How To Manual. Ames, Iowa. North Central Regional Center for Rural Development, 1984. Oklahoma Department of Commerce, Research and Planning Division. Population Estimates for State, Counties, and Cities, Oklahoma: April 1, 1980-July 1, 1989. December 1990. Oklahoma Tax Commission City Sales Tax Collections Returned to Cities and Towns in Fiscal, 1980 to 2001. (Fiscal Year End-June 30). Stone, K. and J.C. McConnon, Jr. "Trade Area Analysis Extension Program: A Catalyst for Community Development," Proceedings of Realizing Your Potential as an Agricultural Economist in Extension. Ithaca, New York, August 1984. Tennessee Valley Authority. "Focus on the Future," Workbook provided at RedArk Development Authority Symposium on Economic Development Leadership, Shawnee, Oklahoma, June 1986. U.S. Department of Commerce Bureau of The Census. Resident Population by County, 1990 to 2001. http://www.census.gov/populations/extimates/county/ (June 2002). U.S. Department of Commerce, Bureau of Economic Analysis. "Personal Income by Major Source and Earnings by Major Industry," Regional Economic Information System, 1980 to 2000. Woods, Mike D. Retail Sales Analysis in Oklahoma By County, 1977, 1982, 1987. Bulletin B-801, Agricultural Experiment Station, Oklahoma State University, October 1991. 34