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Economic impacts of adoption and fundraising strategies in animal shelters
Preventive Veterinary Medicine 113 (2014) 423–429
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Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
Economic impacts of adoption and fundraising strategies in animal shelters Emily Lord a , Nicole Olynk Widmar a , Annette Litster b,∗ a
Department of Agricultural Economics, Purdue University, 403 West State Street, West Lafayette, IN 47907, United States Department of Veterinary Clinical Sciences, Purdue University, School of Veterinary Medicine, 625 Harrison Street, West Lafayette, IN 47907, United States b
a r t i c l e
i n f o
Article history: Received 1 May 2013 Received in revised form 22 November 2013 Accepted 9 December 2013
Keywords: Adoption fees Animal-shelter adoptions Animal-shelter economics Adoption strategies Computer-based simulation modeling tool Reduction of adoption fees
a b s t r a c t The adoption strategies used in animal shelters can have a large impact on the total number of adoptions and donations that take place. Reducing adoption fees during peak kitten or puppy season is one way to reduce inventories and increase the number of open spaces to save more lives, but does not necessarily increase the financial well-being of the shelter if the per-animal costs exceed the revenues generated. We developed a stochastic model to simulate the expected costs, revenues, and net income of a hypothetical animal shelter for various alternative management strategies, based on US conditions. A total of 8 scenarios were developed and compared to the base-case scenario (BC). In the model, scenarios which decreased or waived adoption fees caused total costs to increase due to the escalating costs associated with increasing the total number and density of animals housed. This effect was especially pronounced when adoptions were free. When the return on money invested in additional fundraising was predetermined to be ‘good’ (rather than ‘fair’ or ‘poor’), net shelter income did exceed costs – but even ‘fair’ return increased net shelter income compared to the BC. Of the eight scenarios compared to BC, the mean monthly net income was significantly different from that in the BC in all eight scenarios (p < 0.01). In contrast, variances were different (p < 0.01) in five of the eight scenarios (and the uncertainty that comes with high variance would make planning difficult for shelter managers); however, the variance in net income did not differ from the BC for any of the scenarios investigating returns to additional spending on promotion and fundraising. In these scenarios, because the extra cost involved is relatively low compared to the other scenarios, the potential risk of a reduction in net shelter revenue is reduced. When shelters are aware of the positive and negative impacts of various adoption strategies on mean net income and variation in net income, shelter managers can better strategize saving animal lives and meeting shelter goals, while maintaining the financial health and functionality of the operation. © 2013 Elsevier B.V. All rights reserved.
1. Introduction The overall goal of shelters is to adopt animals into homes that are willing to provide the animal with love and long-term commitment (Weiss and Gramann, 2009).
∗ Corresponding author. Tel.: +1 765 418 3186; fax: +1 765 496 1108. E-mail address: [email protected] (A. Litster). 0167-5877/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.prevetmed.2013.12.002
Importantly, as stated by Winograd (2009), ‘adopting an animal means a shelter does not kill that animal’. Additionally, adoptions free space for additional animals to enter the shelter system, thereby enabling shelters to save more lives (because fixed assets such as buildings and cages can be reused). Many animal shelters share similarities in their mission statement; all of them have animals as their main focus, and many focus on rescuing, rehabilitating, and rehoming
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animals. A common mission (whether stated or implicit) of many animal shelters is to affect the largest “possible” number of animal lives. Mission statements are usually considered the cornerstone of an organization’s strategic plan and are designed to inspire and motivate the organization’s members; mission statements help determine the allocation of resources, but organizations must generate considerable managerial support to be successful (Bart, 1997). Adoptions are the core of how most animal shelters seek to achieve their goals and objectives, but sound strategies must underpin adoption protocols to maximize lives saved and achieve the shelter mission. To create a positive environment for adoptions, shelters must identify their optimal strategies – taking into account that each community is unique. Just as a business would need to tailor its strategies to operating in a suburban location where the vast majority of customers visit by car compared to a downtown location with considerable foot traffic, shelters must assess the communities in which they operate to determine the shelters’ optimal approaches for success. Furthermore, shelters must complete internal assessments of personnel to determine the unique skills or competencies of the people who work or volunteer for the organization to enhance success. Our aim was to analyze hypothetical adoption and fundraising strategies to determine their impacts on adoption numbers and finances, by developing a computerbased simulation modeling tool. The general shelter strategies investigated were: altering adoptions fees and associated adoption numbers; creating a ‘continued giving environment’; promoting adoption events; and re-evaluating adoption criteria. Specific scenarios investigated were: altering adoption fees and total numbers of animals handled; analyzing low, fair, and high returns to additional promotion spending; and investigating zerofee adoptions. Sensitivity analysis was performed on key shelter-performance and financial-performance variables. 2. Materials and methods 2.1. Model development We created an animal-sheltering model and simulation in Excel (Microsoft, Seattle, WA) and @RISK Version 5.7 (Palisade Corp., Newfield, NY). The model allows us to estimate the individual-animal costs to shelters for puppies, kittens, dog, and cats, and creates a hypothetical shelter. The development of a hypothetical shelter enabled investigation into the hypothetical shelter’s financial situation when various adoption strategies were used. Because animal shelters differ greatly (financially, geographically, and in their management strategies), a base-case scenario (BC) was generated to allow for discussion surrounding the positive and negative consequences of various adoption strategies. The model output can assist shelter managers in evaluating their current adoption strategies, as well as considering new adoption strategies to increase their shelter’s performance. Several key economic variables were incorporated into our model. Monthly animal-related costs, monthly fixed costs, and monthly revenues were included; each was
Table 1 Assumed variable costs for dogs and puppies. Dog
Cost ($)
Puppy
Cost ($)
Cost of spay (female)
15.00
15.00
Cost of neuter (male)
12.00
Food cost (for 36-day stay)
26.75
DA2 PP vaccine (two doses)
14.00
Bordetella vaccine Rabies vaccine Blood testing (≥6 years) Heartworm testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Dog supplies
6.00 1.25 4.00 3.00 11.00 12.00 2.00 7.00 6.00 3.15 10.00
Cost of spay (female) Cost of neuter (male) Food cost (for 21-day stay) DA2 PP vaccine (three doses) Bordetella vaccine Rabies vaccine Blood testing Heartworm testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Puppy supplies
12.00 7.23 20.00 6.00 1.25 0.00 3.00 10.00 12.00 2.00 5.00 4.00 1.25 10.00
adjusted according to the adoption strategy being simulated. Costs and revenues vary on a monthly basis due to numerous factors which are largely shelter-specific (and often related to seasonal patterns); however, for simplicity (and for lack of input data), we assumed that monthly revenues and expenses were consistent from month to month. 2.2. Revenue estimation Total revenues for the shelter included revenues from adoption fees for dogs, cats, puppies, and kittens, as well as any retail revenue, donations or sponsorships received, grants received, interest revenue, and income from special events. The hypothetical shelter represented in this analysis is representative of no particular shelter, but parameters were compared to various shelters reporting information on Charity Navigator (www.charitynavigator.org) to ensure that our hypothetical scenarios are feasible. The adoption fees used in the BC scenario were $150 for dogs, $175 for puppies, $50 for cats, and $95 for kittens. Further details of revenue estimation are provided in Appendix A. 2.3. Cost estimation Shelters have both animal-specific and fixed costs. The categories for individual-animal expenses we used included core vaccines recommended for shelters against: feline viral rhinotracheitis, calicivirus, and panleukopenia (FVRCP) for cats (Richards et al., 2006) and caninedistemper virus, canine parvovirus, canine adenovirus-2, and parainfluenza virus (DA2PP), and Bordetella bronchiseptica (intranasal) for dogs (AAHA et al., 2011). In addition to vaccine costs, key costs included in the model were for: food, spaying/neutering and other veterinary costs, and other general care for the dogs, cats, puppies, and kittens in shelter care. Vaccine costs were obtained from various supplier websites in January, 2013. Further details of cost estimation are provided in Tables 1 and 2 and in Appendix B. All costs were for US conditions.
E. Lord et al. / Preventive Veterinary Medicine 113 (2014) 423–429 Table 2 Assumed variable costs for cats and kittens. Cat
Cost ($)
Kitten
Cost ($)
Cost of spay (female)
12.00
12.00
Cost of neuter (male)
7.00
Food cost (for 36-day stay)
6.78
FVRCP vaccine (two doses)
8.00
Cost of spay (female) Cost of neuter (male) Food cost (for 21-day stay) FVRCP vaccine (three doses) Rabies vaccine Blood testing FeLV/FIV testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Kitten supplies
Rabies vaccine Blood testing (≥6 years) FeLV/FIV testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Cat supplies
1.25 3.00 3.00 10.00 12.00 2.00 5.00 4.00 2.15 10.00
7.00 3.93 12.00 1.25 0.00 3.00 10.00 12.00 2.00 5.00 4.00 1.00 10.00
2.4. Stochastic budgeting Key parameters were stochastic (i.e. determined from a distribution using computer simulations) in our model. We used @RISK Version 5.7 (Palisade Corp., Newfield, NY) to evaluate the model and key outcomes: monthly revenue (MR), monthly costs (MC), and monthly net income (MNI). We ran 10,000 iterations for each adoption strategy investigated. We used triangular distributions to parameterize the yearly: adoption numbers for dogs, cats, puppies, and kittens; the probability of needing treatment in each species; the cost of treatment per stay in each species; and average number of days spent in the shelter for each species. Triangular distributions are used throughout this analysis; triangular distributions are used in cases which sample data are limited because only minimum, maximum, and most-likely values are needed to parameterize the model (Olynk and Wolf, 2009). Throughout this analysis, the BC was created as an example for comparison purposes only, and reflects no specific animal shelter. Assumptions for the BC are outlined in Appendices A and B through the construction of the base model. Means and variances were compared between the BC and every other scenario investigated by using a two-sample t-test with unequal variances (for comparing means) and a variance-ratio test (for variance) in Stata 12 (Stata Statistical Software: Release 12. College Station, TX: StataCorp LP.) All tests were evaluated 2-sided, at alpha = 0.01 (a “low” alpha, to adjust partially for multiplicity). Further details of stochastic budgeting are provided in Tables 3 and 4 and in Appendix C. 2.5. Simulations of adoption strategies employed Various scenarios were investigated to represent potential shelter situations under varying conditions. The price elasticity of demand is the percent change in quantity divided by the percent change in price (Varian, 2003). The sign of the elasticity of demand is generally negative, as demand curves slope downward (Varian, 2003). Therefore, it was assumed that as the adoption fee increases that fewer total adoptions would take place as fewer animals
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Table 3 Minimum, most likely, and maximum values for triangular distributions of stochastic variables in shelter model. Dog Annual numbers adopted 900 Minimum Most likely 1500 Maximum 2100 Probability of treatment (%) 2 Minimum 20 Most likely 40 Maximum Treatment cost per stay ($) 5 Minimum 50 Most likely 500 Maximum Days spent in shelter 2 Minimum 15 Most likely 90 Maximum
Cat
Puppy
Kitten
950 1200 1650
350 650 1050
950 1400 1800
2 20 40
5 20 55
5 20 55
5 40 350
5 50 500
5 40 350
2 15 90
2 15 45
2 15 45
are demanded at the higher price. Throughout the scenarios investigated, the adoption fees and adoption numbers considered represent an assumed elasticity of demand for shelter animals between −0.2 and −0.8.Scenarios investigated included: Scenario IfeeD10 (increased adoption fees with decrease in total adoption numbers by 10%) and Scenario IfeeD20 (increased adoption fees with decrease in total adoption numbers by 20%): adoption fees were increased from the BC to $200 per dog, $225 per puppy, $75 per cat, and $120 per kitten, and total adoption numbers were decreased by 10% and 20%, respectively, from the BC; Scenario DfeeI10 (decreased adoption fees with an increase in total adoption numbers by 10%) and Scenario DfeeI20 (decreased adoption fees with an increase in total adoption numbers by 10%): adoption fees were lowered from the BC scenario rates to $100 per dog, $125 per puppy, $25 per cat, and $70 per kitten, and total adoption numbers were increased by 10% and 20%, respectively, from the BC; Scenarios LprRet (low donation returns from promotions), FprRet (fair donation returns from promotions), and HprRet (high donation returns from promotions), in which returns on additional spending on promotions generated relatively, low, fair, or high, returns respectively in Table 4 Correlation matrix among stochastic parameters of the shelter model. Probability of treatment Probability of treatment Cost of treatment per stay Average days spent in shelter Annual number of animal adoptions
Cost of treatment per stay
Average days spent in shelter
1 0.3
1
0.3
0
1
0
0
−0.6
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Fig. 1. Mean monthly net income, adoption revenue, and animal costs across scenarios (thousands of $).
terms of donation revenue. Additional returns to promotion spending in this model was defined as a 1.5%, 13.6%, and 24.2% increase from the baseline monthly donations of $66,000. To assess returns to additional spending on promotions, the initial $10,000 of spending from the BC scenario continued to generate $6.60 per $1.00 spent while the additional $2000 was assessed at three levels generating $8.00, $4.50, and $0.50 in donations per $1.00 spent. Scenarios LFeeIncAn30 ($0 fee adoptions with 30% simultaneous increase in adoptions) in which adoption fees were reduced to $0.00 and investigated alongside the simultaneous increase in total animals adopted by 30%. 3. Results 3.1. Base-case (BC) scenario MAC was 51% of total costs. Summary statistics, including mean, range, and standard deviation of outcomes for the MNI, MC, and MR from 10,000 iterations of the BC scenario and all other scenarios investigated are in Table 5. Fig. 1 displays the mean monthly net income, adoption revenue, and animal costs for each of the scenarios investigated in this analysis. 3.2. Changing adoption fees and adoption numbers (scenarios IfeeD10, IfeeD20, DfeeI10 and DfeeI20) Minimum, most likely, and maximum values on the distributions for dogs, cats, puppies, and kittens are shown in Table 3 for the BC; changes in the minimum, most likely, and maximum adoption numbers used in simulations for increased and decreased adoption numbers were adjusted by 10% and 20%, as indicated by the scenario1 . Impacts of
1 Maximum, most likely, and minimum values on the distributions for dogs were 2310, 1650, and 990; for cats were 1815, 1320, and 1045; for puppies were 1155, 715, and 385; and for kittens were 1980, 1540, and 1045 when adoption numbers were increased by 10%. Correspondingly, maximum, most likely, and minimum values on the distributions for dogs
Fig. 2. Per animal revenue, cost, and average days in shelter by animal type in the base case and increased adoption fee scenarios (BC and IfeeD20 scenarios, respectively).
changes in adoption fees and associated changes in adoption numbers were assessed on MR, MC, and MNI. Under IfeeD10 mean MNI increased to $7.0K when adoption fees were increased (and an associated reduction in adoption by 10% was incorporated); the variance of MNI increased significantly over the BC. Mean MNI decreased to $-28.4K when adoption fees were decreased and an associated increase in adoptions by 10% was considered. Fig. 2 shows the per animal adoption revenue (under the BC and IfeeD20 scenarios), per animal total costs, and average days in the shelter by animal type. Dogs, cats, and kittens all have higher per-animal costs than per-animal adoption revenue; puppies generate only $2.43 more, on average, in adoption revenues in the BC scenario than they cost.
were 1890, 1350, and 810; for cats were 1485, 1080, and 855; for puppies were 945, 585, and 315; and for kittens were 1620, 1260, and 855 when adoption numbers were decreased by 10%.
E. Lord et al. / Preventive Veterinary Medicine 113 (2014) 423–429
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Table 5 Summary statistics of key outputs from the base-case scenario and alternative adoption strategies (10,000 iterations each scenario). Scenario
Monthly net income ($)
Total monthly revenue ($)
Total monthly costs ($)
Base-case scenario (BC)
Min Mean Max SD
−54,536 −9273 23,875 11,857
126,284 139,945 153,030 3983
119,400 149,218 198,987 11,621
Increased adoption fees with decrease in all animal adoption number parameters by 10% (Scenario IfeeD10)
Min Mean Max SD
−40,951 6994 40,615 11,029
133,582 148,608 164,865 4716
113,890 141,613 194,228 10,420
Increased adoption fees with decrease in all animal adoption number parameters by 20% (Scenario IfeeD20)
Min Mean Max SD
−32,928 8623 35,913 9629
128,080 142,650 156,015 4195
108,368 134,027 180,501 9167
Decreased adoption fees with increase in all animal adoption number parameters by 10% (Scenario DfeeI10)
Min Mean Max SD
−82,323 −28,442 8019 12,626
118,235 128,359 138,821 2997
118,319 156,802 213,191 12,587
Decreased adoption fees with increase in all animal adoption number parameters by 20% (Scenario DfeeI20)
Min Mean Max SD
−94,340 −33,017 3276 13,758
120,467 131,392 141,656 3313
124,398 164,409 228,833 13,762
Poor return from additional fundraising spending (Scenario LprRet)
Min Mean Max SD
−65,351 −10,293 23,156 11,848
126,782 140,945 154,610 3999
118,539 151,238 211,461 11,576
Fair return from additional fundraising spending (Scenario FprRet)
Min Mean Max SD
−51,068 −2257 29,122 11,751
136,042 148,944 163,185 4011
119,825 151,201 203,208 11,448
High return from additional fundraising spending (Scenario HprRet)
Min Mean Max SD
−38,393 4743 39,563 11,670
143,423 155,944 168,864 3990
119,784 151,202 199,377 11,400
Free adoptions with increase in all animal adoption number parameters by 30% (Scenario LfeeIncAn30)
Min Mean Max SD
−138,588 −76,986 −37,753 14,878
95,000 95,000 95,000 –
132,753 171,986 233,588 14,878
p-Value comparing meansa
p-Value comparing variancesb
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.941 <0.001 0.368 <0.001 0.111 <0.001 <0.001
a
The mean monthly net income in the base case scenario was compared to every other scenario via the use of a t-test. In all cases the p < 0.01, therefore we reject the null hypothesis that the means are equal. b The variance of the net income in the base case scenario was compared to every other scenario via the use of a variance ratio test; p < 0.01 indicates we reject the null hypothesis that the variances are equal.
3.3. Shelter promotions and events (scenarios LprRet, FprRet and HprRet)
to the increasing variable costs associated with handling more animals (Table 5, Figs. 1 and 2).
The additional $2000 spent on fundraising did not improve MNI when poor return on additional promotional spending was simulated; only the high return got the shelter into a positive MNI (although the variance in MNI did not differ from the BC scenario; all P ≥ 0.11).
4. Discussion
3.4. Free adoptions with simultaneous increased adoption numbers (scenarios LfeeIncAn30) When adoption fees were reduced to $0.00 and total adoption numbers were increased, the financial performance of the shelter was dramatically reduced – partly due to the elimination of adoption revenue and partly due
Simulation of animal-shelter economic outcomes allows shelter managers to ‘test run’ alternative adoption (or management) strategies and to determine the financial consequences. Empowering managers to model expected outcomes (including building in stochastic variables to take into account uncertainty and variation in key parameters) could help shelters to assess the outcomes of various management strategies efficiently and improve the shelters’ decision-making capabilities. In the hypothetical shelter we simulated, changing adoption fees and associated adoption numbers had a
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pronounced impact on the MNI of the shelter. Further, in many of the scenarios investigated, the variance in MNI increased significantly over the BC scenario. Beyond variation due to changing strategies, shelters might experience month-by-month variation in financial performance, which could lead to increased difficulty in financial management. It is often suggested, and expected, that the number of adoptions decrease when adoption fees are raised and increase when they are lowered. However, the exact relationship between fees and the number of adoptions is expected to vary depending on shelter-specific and location-specific factors. Reducing adoption fees to increase the number of adoptions (e.g. during an adoption fair or holiday season) could increase adoption rates of the shelter, ultimately having a positive impact on the number of lives saved. Recall that throughout this analysis when prices increased the total adoption numbers declined , with scenarios representing price elasticities between −0.2 and −0.8; variation from these assumptions may have significant impacts on adoption numbers resulting from changing prices. However, as we illustrated in this analysis, increasing the numbers of animals moved through the shelter could increase costs disproportionately to the associated increase in adoption-fee revenue, due to the escalating costs associated with the increased number and density of animals housed. Therefore, we suggest that reductions in adoption fees (with an increase in adoption numbers) must be considered in conjunction with strategies to raise funds or increase revenues through other avenues, and/or strategies to decrease costs. A financial dilemma is faced with the reduction of adoption fees. The question facing shelter managers is whether to allow the animal to be adopted for less than the total cost spent on the animal or to continue putting money into the animal to wait for another potential adopter, while also foregoing the opportunity to save an additional life. Considerations that must be taken into account are the sunk costs of each animal that is currently being housed at the shelter. Weiss and Gramann (2009) address the adoptionfee reduction stating, ‘implementing a free adult cat adoption program in shelters around the country could dramatically affect the lives of thousands of shelter cats who otherwise either would reside in the shelter for months awaiting adoption or be euthanized’. An example of reducing adoption fees can be found at Stray Rescue of St. Louis. Stray Rescue of St. Louis permanently reduced their adoption fees from $150 to $75 to encourage more adoptions (www.strayrescue.org, 2012). In addition to assessing adoption fees, adoption promotions are crucial to the success of a shelter. Adoption promotions also make shelter pets accessible to a wider audience of potential adopters and publicize the value of the human–animal bond and of saving the lives of homeless pets (Weiss and Gramann, 2009). The efficiency of promotion and event expenses in generating dollars of donations (or other forms of revenue for the shelter) or adoptions must be considered. The examples in this analysis held additional spending constant at $2000 and varied only the level of donations generated for every dollar of spending, resulting in substantial differences in MR and MNI (Fig. 1, Table 5).
Managing money well and demonstrating a positive impact on the community’s pet population, as well as demonstrating progress toward meeting stated shelter goals and objectives, can increase the likelihood of donations to flow into the shelter. Promotion includes adoption events that not only promote adoption, but also involve the community in the shelter’s purpose. According to Hibbert and Horne (1997), ‘the attitudes respondents talked about which guide donation decisions, related to three aspects of a charity’s operations: the work carried out by the charity, its efficient and effective use of funds, the nature of the request for a contribution and the associated feelings of obligation to give’. Once donors have gained confidence in the organization, barriers to giving generally dissipate; donations then become habitual and donors usually require little (if any) additional information (Hibbert and Horne, 1997). However, potential donors could consider withholding funding support for charities they felt were spending too large of a percentage of funds raised on activities that had no direct benefit to its beneficiaries; this was especially the case for groups that spent “too much” money on administrative costs, more specifically office space and staff (Hibbert and Horne, 1997). Shelter administrative and policy decisions can also impact adoption rates. These include providing weekend and evening opening hours for adoptions; encouraging interactions between potential adopters and pets; facilitating an effective and motivated volunteer network; asking why a pet is being relinquished; and providing an environment that is attractive and convenient for potential adopters (Fournier and Geller, 2004). The question of the quality of adoptive homes often arises when adoption fees are being set, but Weiss and Gramann (2009) point out that although there are plenty of potential homes available, unnecessarily stringent adoption criteria can prevent these animals from finding permanent homes and create negative feelings in the local community. Additionally, shelter workers sometimes create certain assumptions based on applications and can risk drawing conclusions that are incorrect, lessening the chance for potential adopters to save a life (Weiss and Gramann, 2009). Required review of adoption decisions by a group of shelter workers and/or volunteers ensures that one individual is not the sole determinant of the adoption and creates opportunities to realign adoption protocols with the mission of the shelter to maximize adoptions. The types of animals available for adoption are also an important determinant of adoption choices (Weiss et al., 2012) and this is in part determined by shelter-intake protocols. In order to maximize the ability to save lives and optimize the allocation of shelter resources, shelters must identify adoptable animals that are not likely to be delayed in the shelter due to medical reasons (Litster et al., 2011). Taking many high-risk animals into the shelter might change the cost of veterinary care, for example, beyond that which was modeled using the distributions presented in this analysis. It is important for shelters to assess their own costs and parameterize cost distributions which represent their own situation. Shelter managers are cautioned to take into account the expected MNI and variation in MNI resulting
E. Lord et al. / Preventive Veterinary Medicine 113 (2014) 423–429
from various shelter management strategies. Both revenue and cost streams, as well as the variability in those streams, are expected to impact shelter performance and viability. In particular, shelters must pay careful attention to the management of fixed versus variable (predominantly animal-related) costs. In shelters that have higher per-animal costs than their adoption fees, increasing of adoption numbers without corresponding increases in revenues – via fees or donations – will result in a worsening financial outcome for the shelter. 5. Conclusions Increasing the total number of animals adopted might seem like a logical goal for shelters. However, our stochastic simulations showed that increasing animal numbers (without increasing adoption fees or donations) caused costs to increase faster than total revenues. This example illustrates the necessity for shelter managers to clearly understand variable or animal-related costs versus fixed costs and identify where their revenue is generated. Conflict of interest statement The authors declare that they have no financial or profession conflicts of interest that might inappropriately influence this paper. Acknowledgements This study was supported, in part, by a grant from the Maddie’s Fund® . The Purdue Maddie’s Shelter Medicine Program is underwritten by a grant from Maddie’s Fund® , The Pet Rescue Foundation (www.maddiesfund.org), helping to fund the creation of a no-kill nation.
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Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.prevetmed.2013.12.002. References American Animal Hospital Association (AAHA) Canine Vaccination Task Force, Welborn, L.V., DeVries, J.G., Ford, R., Franklin, R.T., Hurley, K.F., McClure, K.D., Paul, M.A., Schultz, R.D., 2011. AAHA canine vaccination guidelines. J. Am. Anim. Hosp. Assoc. 47, 1–42. Bart, C.K., 1997. Sex, lies, and mission statements. Bus. Horiz. 40, 9–10. Fournier, A.K., Geller, E.S., 2004. Behavior analysis of companion-animal overpopulation: a conceptualization of the problem and suggestions for intervention. Behav. Soc. Issues 12, 51–68. Hibbert, S.A., Horne, S., 1997. Donation dilemmas: a consumer behavior perspective. Int. J. Nonprofit Volunt. Sect. Mark. 2, 261–274. Litster, A., Allen, J., Mohamed, A., He, S., 2011. Risk factors for delays between intake and veterinary approvals for adoption on medical grounds in shelter puppies and kittens. Prev. Vet. Med. 101, 107–112. Olynk, N.J., Wolf, C.A., 2009. Stochastic economic analysis of dairy cattle artificial insemination reproductive management programs. J. Dairy Sci. 92, 1290–1299. Stray Rescue of St. Louis, 2010. We’ve Lowered Adoption Fees to $75. . .Permanently!, Retrieved from: http://strayrescue.org/ node/3285 (accessed 25.06.13). Richards, J.R., Elston, T.H., Ford, R.B., Gaskell, R.M., Hartmann, K., Hurley, K.F., Lappin, M.R., Levy, J.K., Rodan, I., Scherk, M., Schultz, R.D., Sparkes, A.H., 2006. The 2006 American Association of feline practitioners feline vaccine advisory panel report. J. Am. Vet. Med. Assoc. 229, 1405–1441. Varian, H., 2003. Intermediate Microeconomics: A Modern Approach. W.W. Norton & Co., New York, NY. Weiss, E., Gramann, S., 2009. A comparison of attachment levels of adopters of cats: fee-based adoptions versus free adoptions. J. Appl. Anim. Welf. Sci. 12, 360–370. Weiss, E., Miller, K., Mohan-Gibbons, H., Vela, C., 2012. Why did you choose this pet? Adopters and pet selection preferences in five animal shelters in the United States. Animals 2, 144–159. Winograd, N.J., 2009. Redemption: The Myth of Pet Overpopulation and the No Kill Revolution in America. Almaden Books.
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Preventive Veterinary Medicine journal homepage: www.elsevier.com/locate/prevetmed
Economic impacts of adoption and fundraising strategies in animal shelters Emily Lord a , Nicole Olynk Widmar a , Annette Litster b,∗ a
Department of Agricultural Economics, Purdue University, 403 West State Street, West Lafayette, IN 47907, United States Department of Veterinary Clinical Sciences, Purdue University, School of Veterinary Medicine, 625 Harrison Street, West Lafayette, IN 47907, United States b
a r t i c l e
i n f o
Article history: Received 1 May 2013 Received in revised form 22 November 2013 Accepted 9 December 2013
Keywords: Adoption fees Animal-shelter adoptions Animal-shelter economics Adoption strategies Computer-based simulation modeling tool Reduction of adoption fees
a b s t r a c t The adoption strategies used in animal shelters can have a large impact on the total number of adoptions and donations that take place. Reducing adoption fees during peak kitten or puppy season is one way to reduce inventories and increase the number of open spaces to save more lives, but does not necessarily increase the financial well-being of the shelter if the per-animal costs exceed the revenues generated. We developed a stochastic model to simulate the expected costs, revenues, and net income of a hypothetical animal shelter for various alternative management strategies, based on US conditions. A total of 8 scenarios were developed and compared to the base-case scenario (BC). In the model, scenarios which decreased or waived adoption fees caused total costs to increase due to the escalating costs associated with increasing the total number and density of animals housed. This effect was especially pronounced when adoptions were free. When the return on money invested in additional fundraising was predetermined to be ‘good’ (rather than ‘fair’ or ‘poor’), net shelter income did exceed costs – but even ‘fair’ return increased net shelter income compared to the BC. Of the eight scenarios compared to BC, the mean monthly net income was significantly different from that in the BC in all eight scenarios (p < 0.01). In contrast, variances were different (p < 0.01) in five of the eight scenarios (and the uncertainty that comes with high variance would make planning difficult for shelter managers); however, the variance in net income did not differ from the BC for any of the scenarios investigating returns to additional spending on promotion and fundraising. In these scenarios, because the extra cost involved is relatively low compared to the other scenarios, the potential risk of a reduction in net shelter revenue is reduced. When shelters are aware of the positive and negative impacts of various adoption strategies on mean net income and variation in net income, shelter managers can better strategize saving animal lives and meeting shelter goals, while maintaining the financial health and functionality of the operation. © 2013 Elsevier B.V. All rights reserved.
1. Introduction The overall goal of shelters is to adopt animals into homes that are willing to provide the animal with love and long-term commitment (Weiss and Gramann, 2009).
∗ Corresponding author. Tel.: +1 765 418 3186; fax: +1 765 496 1108. E-mail address: [email protected] (A. Litster). 0167-5877/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.prevetmed.2013.12.002
Importantly, as stated by Winograd (2009), ‘adopting an animal means a shelter does not kill that animal’. Additionally, adoptions free space for additional animals to enter the shelter system, thereby enabling shelters to save more lives (because fixed assets such as buildings and cages can be reused). Many animal shelters share similarities in their mission statement; all of them have animals as their main focus, and many focus on rescuing, rehabilitating, and rehoming
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animals. A common mission (whether stated or implicit) of many animal shelters is to affect the largest “possible” number of animal lives. Mission statements are usually considered the cornerstone of an organization’s strategic plan and are designed to inspire and motivate the organization’s members; mission statements help determine the allocation of resources, but organizations must generate considerable managerial support to be successful (Bart, 1997). Adoptions are the core of how most animal shelters seek to achieve their goals and objectives, but sound strategies must underpin adoption protocols to maximize lives saved and achieve the shelter mission. To create a positive environment for adoptions, shelters must identify their optimal strategies – taking into account that each community is unique. Just as a business would need to tailor its strategies to operating in a suburban location where the vast majority of customers visit by car compared to a downtown location with considerable foot traffic, shelters must assess the communities in which they operate to determine the shelters’ optimal approaches for success. Furthermore, shelters must complete internal assessments of personnel to determine the unique skills or competencies of the people who work or volunteer for the organization to enhance success. Our aim was to analyze hypothetical adoption and fundraising strategies to determine their impacts on adoption numbers and finances, by developing a computerbased simulation modeling tool. The general shelter strategies investigated were: altering adoptions fees and associated adoption numbers; creating a ‘continued giving environment’; promoting adoption events; and re-evaluating adoption criteria. Specific scenarios investigated were: altering adoption fees and total numbers of animals handled; analyzing low, fair, and high returns to additional promotion spending; and investigating zerofee adoptions. Sensitivity analysis was performed on key shelter-performance and financial-performance variables. 2. Materials and methods 2.1. Model development We created an animal-sheltering model and simulation in Excel (Microsoft, Seattle, WA) and @RISK Version 5.7 (Palisade Corp., Newfield, NY). The model allows us to estimate the individual-animal costs to shelters for puppies, kittens, dog, and cats, and creates a hypothetical shelter. The development of a hypothetical shelter enabled investigation into the hypothetical shelter’s financial situation when various adoption strategies were used. Because animal shelters differ greatly (financially, geographically, and in their management strategies), a base-case scenario (BC) was generated to allow for discussion surrounding the positive and negative consequences of various adoption strategies. The model output can assist shelter managers in evaluating their current adoption strategies, as well as considering new adoption strategies to increase their shelter’s performance. Several key economic variables were incorporated into our model. Monthly animal-related costs, monthly fixed costs, and monthly revenues were included; each was
Table 1 Assumed variable costs for dogs and puppies. Dog
Cost ($)
Puppy
Cost ($)
Cost of spay (female)
15.00
15.00
Cost of neuter (male)
12.00
Food cost (for 36-day stay)
26.75
DA2 PP vaccine (two doses)
14.00
Bordetella vaccine Rabies vaccine Blood testing (≥6 years) Heartworm testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Dog supplies
6.00 1.25 4.00 3.00 11.00 12.00 2.00 7.00 6.00 3.15 10.00
Cost of spay (female) Cost of neuter (male) Food cost (for 21-day stay) DA2 PP vaccine (three doses) Bordetella vaccine Rabies vaccine Blood testing Heartworm testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Puppy supplies
12.00 7.23 20.00 6.00 1.25 0.00 3.00 10.00 12.00 2.00 5.00 4.00 1.25 10.00
adjusted according to the adoption strategy being simulated. Costs and revenues vary on a monthly basis due to numerous factors which are largely shelter-specific (and often related to seasonal patterns); however, for simplicity (and for lack of input data), we assumed that monthly revenues and expenses were consistent from month to month. 2.2. Revenue estimation Total revenues for the shelter included revenues from adoption fees for dogs, cats, puppies, and kittens, as well as any retail revenue, donations or sponsorships received, grants received, interest revenue, and income from special events. The hypothetical shelter represented in this analysis is representative of no particular shelter, but parameters were compared to various shelters reporting information on Charity Navigator (www.charitynavigator.org) to ensure that our hypothetical scenarios are feasible. The adoption fees used in the BC scenario were $150 for dogs, $175 for puppies, $50 for cats, and $95 for kittens. Further details of revenue estimation are provided in Appendix A. 2.3. Cost estimation Shelters have both animal-specific and fixed costs. The categories for individual-animal expenses we used included core vaccines recommended for shelters against: feline viral rhinotracheitis, calicivirus, and panleukopenia (FVRCP) for cats (Richards et al., 2006) and caninedistemper virus, canine parvovirus, canine adenovirus-2, and parainfluenza virus (DA2PP), and Bordetella bronchiseptica (intranasal) for dogs (AAHA et al., 2011). In addition to vaccine costs, key costs included in the model were for: food, spaying/neutering and other veterinary costs, and other general care for the dogs, cats, puppies, and kittens in shelter care. Vaccine costs were obtained from various supplier websites in January, 2013. Further details of cost estimation are provided in Tables 1 and 2 and in Appendix B. All costs were for US conditions.
E. Lord et al. / Preventive Veterinary Medicine 113 (2014) 423–429 Table 2 Assumed variable costs for cats and kittens. Cat
Cost ($)
Kitten
Cost ($)
Cost of spay (female)
12.00
12.00
Cost of neuter (male)
7.00
Food cost (for 36-day stay)
6.78
FVRCP vaccine (two doses)
8.00
Cost of spay (female) Cost of neuter (male) Food cost (for 21-day stay) FVRCP vaccine (three doses) Rabies vaccine Blood testing FeLV/FIV testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Kitten supplies
Rabies vaccine Blood testing (≥6 years) FeLV/FIV testing Topical flea control Microchip Dewormer Cost of euthanasia Cost of disposal Daily boarding cost Cat supplies
1.25 3.00 3.00 10.00 12.00 2.00 5.00 4.00 2.15 10.00
7.00 3.93 12.00 1.25 0.00 3.00 10.00 12.00 2.00 5.00 4.00 1.00 10.00
2.4. Stochastic budgeting Key parameters were stochastic (i.e. determined from a distribution using computer simulations) in our model. We used @RISK Version 5.7 (Palisade Corp., Newfield, NY) to evaluate the model and key outcomes: monthly revenue (MR), monthly costs (MC), and monthly net income (MNI). We ran 10,000 iterations for each adoption strategy investigated. We used triangular distributions to parameterize the yearly: adoption numbers for dogs, cats, puppies, and kittens; the probability of needing treatment in each species; the cost of treatment per stay in each species; and average number of days spent in the shelter for each species. Triangular distributions are used throughout this analysis; triangular distributions are used in cases which sample data are limited because only minimum, maximum, and most-likely values are needed to parameterize the model (Olynk and Wolf, 2009). Throughout this analysis, the BC was created as an example for comparison purposes only, and reflects no specific animal shelter. Assumptions for the BC are outlined in Appendices A and B through the construction of the base model. Means and variances were compared between the BC and every other scenario investigated by using a two-sample t-test with unequal variances (for comparing means) and a variance-ratio test (for variance) in Stata 12 (Stata Statistical Software: Release 12. College Station, TX: StataCorp LP.) All tests were evaluated 2-sided, at alpha = 0.01 (a “low” alpha, to adjust partially for multiplicity). Further details of stochastic budgeting are provided in Tables 3 and 4 and in Appendix C. 2.5. Simulations of adoption strategies employed Various scenarios were investigated to represent potential shelter situations under varying conditions. The price elasticity of demand is the percent change in quantity divided by the percent change in price (Varian, 2003). The sign of the elasticity of demand is generally negative, as demand curves slope downward (Varian, 2003). Therefore, it was assumed that as the adoption fee increases that fewer total adoptions would take place as fewer animals
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Table 3 Minimum, most likely, and maximum values for triangular distributions of stochastic variables in shelter model. Dog Annual numbers adopted 900 Minimum Most likely 1500 Maximum 2100 Probability of treatment (%) 2 Minimum 20 Most likely 40 Maximum Treatment cost per stay ($) 5 Minimum 50 Most likely 500 Maximum Days spent in shelter 2 Minimum 15 Most likely 90 Maximum
Cat
Puppy
Kitten
950 1200 1650
350 650 1050
950 1400 1800
2 20 40
5 20 55
5 20 55
5 40 350
5 50 500
5 40 350
2 15 90
2 15 45
2 15 45
are demanded at the higher price. Throughout the scenarios investigated, the adoption fees and adoption numbers considered represent an assumed elasticity of demand for shelter animals between −0.2 and −0.8.Scenarios investigated included: Scenario IfeeD10 (increased adoption fees with decrease in total adoption numbers by 10%) and Scenario IfeeD20 (increased adoption fees with decrease in total adoption numbers by 20%): adoption fees were increased from the BC to $200 per dog, $225 per puppy, $75 per cat, and $120 per kitten, and total adoption numbers were decreased by 10% and 20%, respectively, from the BC; Scenario DfeeI10 (decreased adoption fees with an increase in total adoption numbers by 10%) and Scenario DfeeI20 (decreased adoption fees with an increase in total adoption numbers by 10%): adoption fees were lowered from the BC scenario rates to $100 per dog, $125 per puppy, $25 per cat, and $70 per kitten, and total adoption numbers were increased by 10% and 20%, respectively, from the BC; Scenarios LprRet (low donation returns from promotions), FprRet (fair donation returns from promotions), and HprRet (high donation returns from promotions), in which returns on additional spending on promotions generated relatively, low, fair, or high, returns respectively in Table 4 Correlation matrix among stochastic parameters of the shelter model. Probability of treatment Probability of treatment Cost of treatment per stay Average days spent in shelter Annual number of animal adoptions
Cost of treatment per stay
Average days spent in shelter
1 0.3
1
0.3
0
1
0
0
−0.6
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Fig. 1. Mean monthly net income, adoption revenue, and animal costs across scenarios (thousands of $).
terms of donation revenue. Additional returns to promotion spending in this model was defined as a 1.5%, 13.6%, and 24.2% increase from the baseline monthly donations of $66,000. To assess returns to additional spending on promotions, the initial $10,000 of spending from the BC scenario continued to generate $6.60 per $1.00 spent while the additional $2000 was assessed at three levels generating $8.00, $4.50, and $0.50 in donations per $1.00 spent. Scenarios LFeeIncAn30 ($0 fee adoptions with 30% simultaneous increase in adoptions) in which adoption fees were reduced to $0.00 and investigated alongside the simultaneous increase in total animals adopted by 30%. 3. Results 3.1. Base-case (BC) scenario MAC was 51% of total costs. Summary statistics, including mean, range, and standard deviation of outcomes for the MNI, MC, and MR from 10,000 iterations of the BC scenario and all other scenarios investigated are in Table 5. Fig. 1 displays the mean monthly net income, adoption revenue, and animal costs for each of the scenarios investigated in this analysis. 3.2. Changing adoption fees and adoption numbers (scenarios IfeeD10, IfeeD20, DfeeI10 and DfeeI20) Minimum, most likely, and maximum values on the distributions for dogs, cats, puppies, and kittens are shown in Table 3 for the BC; changes in the minimum, most likely, and maximum adoption numbers used in simulations for increased and decreased adoption numbers were adjusted by 10% and 20%, as indicated by the scenario1 . Impacts of
1 Maximum, most likely, and minimum values on the distributions for dogs were 2310, 1650, and 990; for cats were 1815, 1320, and 1045; for puppies were 1155, 715, and 385; and for kittens were 1980, 1540, and 1045 when adoption numbers were increased by 10%. Correspondingly, maximum, most likely, and minimum values on the distributions for dogs
Fig. 2. Per animal revenue, cost, and average days in shelter by animal type in the base case and increased adoption fee scenarios (BC and IfeeD20 scenarios, respectively).
changes in adoption fees and associated changes in adoption numbers were assessed on MR, MC, and MNI. Under IfeeD10 mean MNI increased to $7.0K when adoption fees were increased (and an associated reduction in adoption by 10% was incorporated); the variance of MNI increased significantly over the BC. Mean MNI decreased to $-28.4K when adoption fees were decreased and an associated increase in adoptions by 10% was considered. Fig. 2 shows the per animal adoption revenue (under the BC and IfeeD20 scenarios), per animal total costs, and average days in the shelter by animal type. Dogs, cats, and kittens all have higher per-animal costs than per-animal adoption revenue; puppies generate only $2.43 more, on average, in adoption revenues in the BC scenario than they cost.
were 1890, 1350, and 810; for cats were 1485, 1080, and 855; for puppies were 945, 585, and 315; and for kittens were 1620, 1260, and 855 when adoption numbers were decreased by 10%.
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Table 5 Summary statistics of key outputs from the base-case scenario and alternative adoption strategies (10,000 iterations each scenario). Scenario
Monthly net income ($)
Total monthly revenue ($)
Total monthly costs ($)
Base-case scenario (BC)
Min Mean Max SD
−54,536 −9273 23,875 11,857
126,284 139,945 153,030 3983
119,400 149,218 198,987 11,621
Increased adoption fees with decrease in all animal adoption number parameters by 10% (Scenario IfeeD10)
Min Mean Max SD
−40,951 6994 40,615 11,029
133,582 148,608 164,865 4716
113,890 141,613 194,228 10,420
Increased adoption fees with decrease in all animal adoption number parameters by 20% (Scenario IfeeD20)
Min Mean Max SD
−32,928 8623 35,913 9629
128,080 142,650 156,015 4195
108,368 134,027 180,501 9167
Decreased adoption fees with increase in all animal adoption number parameters by 10% (Scenario DfeeI10)
Min Mean Max SD
−82,323 −28,442 8019 12,626
118,235 128,359 138,821 2997
118,319 156,802 213,191 12,587
Decreased adoption fees with increase in all animal adoption number parameters by 20% (Scenario DfeeI20)
Min Mean Max SD
−94,340 −33,017 3276 13,758
120,467 131,392 141,656 3313
124,398 164,409 228,833 13,762
Poor return from additional fundraising spending (Scenario LprRet)
Min Mean Max SD
−65,351 −10,293 23,156 11,848
126,782 140,945 154,610 3999
118,539 151,238 211,461 11,576
Fair return from additional fundraising spending (Scenario FprRet)
Min Mean Max SD
−51,068 −2257 29,122 11,751
136,042 148,944 163,185 4011
119,825 151,201 203,208 11,448
High return from additional fundraising spending (Scenario HprRet)
Min Mean Max SD
−38,393 4743 39,563 11,670
143,423 155,944 168,864 3990
119,784 151,202 199,377 11,400
Free adoptions with increase in all animal adoption number parameters by 30% (Scenario LfeeIncAn30)
Min Mean Max SD
−138,588 −76,986 −37,753 14,878
95,000 95,000 95,000 –
132,753 171,986 233,588 14,878
p-Value comparing meansa
p-Value comparing variancesb
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.941 <0.001 0.368 <0.001 0.111 <0.001 <0.001
a
The mean monthly net income in the base case scenario was compared to every other scenario via the use of a t-test. In all cases the p < 0.01, therefore we reject the null hypothesis that the means are equal. b The variance of the net income in the base case scenario was compared to every other scenario via the use of a variance ratio test; p < 0.01 indicates we reject the null hypothesis that the variances are equal.
3.3. Shelter promotions and events (scenarios LprRet, FprRet and HprRet)
to the increasing variable costs associated with handling more animals (Table 5, Figs. 1 and 2).
The additional $2000 spent on fundraising did not improve MNI when poor return on additional promotional spending was simulated; only the high return got the shelter into a positive MNI (although the variance in MNI did not differ from the BC scenario; all P ≥ 0.11).
4. Discussion
3.4. Free adoptions with simultaneous increased adoption numbers (scenarios LfeeIncAn30) When adoption fees were reduced to $0.00 and total adoption numbers were increased, the financial performance of the shelter was dramatically reduced – partly due to the elimination of adoption revenue and partly due
Simulation of animal-shelter economic outcomes allows shelter managers to ‘test run’ alternative adoption (or management) strategies and to determine the financial consequences. Empowering managers to model expected outcomes (including building in stochastic variables to take into account uncertainty and variation in key parameters) could help shelters to assess the outcomes of various management strategies efficiently and improve the shelters’ decision-making capabilities. In the hypothetical shelter we simulated, changing adoption fees and associated adoption numbers had a
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pronounced impact on the MNI of the shelter. Further, in many of the scenarios investigated, the variance in MNI increased significantly over the BC scenario. Beyond variation due to changing strategies, shelters might experience month-by-month variation in financial performance, which could lead to increased difficulty in financial management. It is often suggested, and expected, that the number of adoptions decrease when adoption fees are raised and increase when they are lowered. However, the exact relationship between fees and the number of adoptions is expected to vary depending on shelter-specific and location-specific factors. Reducing adoption fees to increase the number of adoptions (e.g. during an adoption fair or holiday season) could increase adoption rates of the shelter, ultimately having a positive impact on the number of lives saved. Recall that throughout this analysis when prices increased the total adoption numbers declined , with scenarios representing price elasticities between −0.2 and −0.8; variation from these assumptions may have significant impacts on adoption numbers resulting from changing prices. However, as we illustrated in this analysis, increasing the numbers of animals moved through the shelter could increase costs disproportionately to the associated increase in adoption-fee revenue, due to the escalating costs associated with the increased number and density of animals housed. Therefore, we suggest that reductions in adoption fees (with an increase in adoption numbers) must be considered in conjunction with strategies to raise funds or increase revenues through other avenues, and/or strategies to decrease costs. A financial dilemma is faced with the reduction of adoption fees. The question facing shelter managers is whether to allow the animal to be adopted for less than the total cost spent on the animal or to continue putting money into the animal to wait for another potential adopter, while also foregoing the opportunity to save an additional life. Considerations that must be taken into account are the sunk costs of each animal that is currently being housed at the shelter. Weiss and Gramann (2009) address the adoptionfee reduction stating, ‘implementing a free adult cat adoption program in shelters around the country could dramatically affect the lives of thousands of shelter cats who otherwise either would reside in the shelter for months awaiting adoption or be euthanized’. An example of reducing adoption fees can be found at Stray Rescue of St. Louis. Stray Rescue of St. Louis permanently reduced their adoption fees from $150 to $75 to encourage more adoptions (www.strayrescue.org, 2012). In addition to assessing adoption fees, adoption promotions are crucial to the success of a shelter. Adoption promotions also make shelter pets accessible to a wider audience of potential adopters and publicize the value of the human–animal bond and of saving the lives of homeless pets (Weiss and Gramann, 2009). The efficiency of promotion and event expenses in generating dollars of donations (or other forms of revenue for the shelter) or adoptions must be considered. The examples in this analysis held additional spending constant at $2000 and varied only the level of donations generated for every dollar of spending, resulting in substantial differences in MR and MNI (Fig. 1, Table 5).
Managing money well and demonstrating a positive impact on the community’s pet population, as well as demonstrating progress toward meeting stated shelter goals and objectives, can increase the likelihood of donations to flow into the shelter. Promotion includes adoption events that not only promote adoption, but also involve the community in the shelter’s purpose. According to Hibbert and Horne (1997), ‘the attitudes respondents talked about which guide donation decisions, related to three aspects of a charity’s operations: the work carried out by the charity, its efficient and effective use of funds, the nature of the request for a contribution and the associated feelings of obligation to give’. Once donors have gained confidence in the organization, barriers to giving generally dissipate; donations then become habitual and donors usually require little (if any) additional information (Hibbert and Horne, 1997). However, potential donors could consider withholding funding support for charities they felt were spending too large of a percentage of funds raised on activities that had no direct benefit to its beneficiaries; this was especially the case for groups that spent “too much” money on administrative costs, more specifically office space and staff (Hibbert and Horne, 1997). Shelter administrative and policy decisions can also impact adoption rates. These include providing weekend and evening opening hours for adoptions; encouraging interactions between potential adopters and pets; facilitating an effective and motivated volunteer network; asking why a pet is being relinquished; and providing an environment that is attractive and convenient for potential adopters (Fournier and Geller, 2004). The question of the quality of adoptive homes often arises when adoption fees are being set, but Weiss and Gramann (2009) point out that although there are plenty of potential homes available, unnecessarily stringent adoption criteria can prevent these animals from finding permanent homes and create negative feelings in the local community. Additionally, shelter workers sometimes create certain assumptions based on applications and can risk drawing conclusions that are incorrect, lessening the chance for potential adopters to save a life (Weiss and Gramann, 2009). Required review of adoption decisions by a group of shelter workers and/or volunteers ensures that one individual is not the sole determinant of the adoption and creates opportunities to realign adoption protocols with the mission of the shelter to maximize adoptions. The types of animals available for adoption are also an important determinant of adoption choices (Weiss et al., 2012) and this is in part determined by shelter-intake protocols. In order to maximize the ability to save lives and optimize the allocation of shelter resources, shelters must identify adoptable animals that are not likely to be delayed in the shelter due to medical reasons (Litster et al., 2011). Taking many high-risk animals into the shelter might change the cost of veterinary care, for example, beyond that which was modeled using the distributions presented in this analysis. It is important for shelters to assess their own costs and parameterize cost distributions which represent their own situation. Shelter managers are cautioned to take into account the expected MNI and variation in MNI resulting
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from various shelter management strategies. Both revenue and cost streams, as well as the variability in those streams, are expected to impact shelter performance and viability. In particular, shelters must pay careful attention to the management of fixed versus variable (predominantly animal-related) costs. In shelters that have higher per-animal costs than their adoption fees, increasing of adoption numbers without corresponding increases in revenues – via fees or donations – will result in a worsening financial outcome for the shelter. 5. Conclusions Increasing the total number of animals adopted might seem like a logical goal for shelters. However, our stochastic simulations showed that increasing animal numbers (without increasing adoption fees or donations) caused costs to increase faster than total revenues. This example illustrates the necessity for shelter managers to clearly understand variable or animal-related costs versus fixed costs and identify where their revenue is generated. Conflict of interest statement The authors declare that they have no financial or profession conflicts of interest that might inappropriately influence this paper. Acknowledgements This study was supported, in part, by a grant from the Maddie’s Fund® . The Purdue Maddie’s Shelter Medicine Program is underwritten by a grant from Maddie’s Fund® , The Pet Rescue Foundation (www.maddiesfund.org), helping to fund the creation of a no-kill nation.
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