The veterinary medical profession directly or indirectly affects nearly everyone. An estimated 59.5% of US households own pets,1 and most people are consumers of livestock and poultry products in the form of meat, dairy products, eggs, wool, or leather. The health and well-being of animals depend heavily on relationships with veterinarians. Veterinarians also contribute to public health through the FDA, CDC, USDA, and numerous other government agencies at the federal, state, and local levels. Issues of primary concern include food safety, biosecurity, and numerous emerging (and reemerging) infectious diseases that are zoonotic in nature. Veterinarians have an additional impact through their research contributions. Virtually all of the laboratory animals used in research are raised, housed, and managed under the care of veterinarians, and veterinary researchers regularly provide valuable contributions to the knowledge base of the biomedical sciences.
With such widespread impact, the economic health and viability of the veterinary medical profession assume broad societal importance. Results of research conducted in the late 1990s indicated that the economic health of the veterinary profession was not particularly strong.2 Although more recent anecdotal evidence indicates that the situation has improved slightly,3 the issue remains of general concern.
An estimated 78.1% of veterinarians are employed in private practice settings4; thus, prevailing economic conditions in this sector are of major importance for the overall economic health of the veterinary medical profession. Relatively little research has been conducted to elucidate key economic features of the market for veterinary medical services. In 1 study,5 investigators estimated a price elasticity of demand (ie, the percentage change in quantity demanded when price changes by 1%) of approximately 0.10% for the demand for veterinary-related services for dogs and 0.16% for the demand for veterinary-related services for cats. In another study,2 a real, positive trend in willingness to spend on pet health care was identified. Furthermore, it was found that price was not ranked high as a factor of importance for consumers seeking an animal healthcare provider, with factors such as reputation, caring attitude, and recommendation consistently viewed as being of greater importance.2 Similar results have been obtained in an additional study6 with a more limited scope. Periodically, the AVMA conducts surveys to provide descriptive information related to some of the demographic attributes of pet ownership.1,7
Considered together, these studies provide a useful foundation for formulating hypotheses related to the economic performance of the market for veterinary medical services. From this foundation, and because of the profession's substantial societal impact, the study reported here was designed to assess general trends in pet-related and veterinary service expenditures as well as factors associated with pet ownership and expenditures for veterinary medical services. Providing such key information on the sector of greatest economic importance will enhance the probability of sustained economic viability in the veterinary medical profession as a whole. The results will be useful for making strategic decisions related to the veterinary and pet markets by the entire profession as well as by individual practices.
Materials and Methods
Sample population—A large, comprehensive data set collected by the US Bureau of Labor Statistics was used to analyze expenditures on pets-pet supplies, pet services, and veterinary services by US consumers during the period from 1980 through 2005.
A goal of the CEX is to provide information on the buying habits of American consumers, including data on expenditures, income, and characteristics of consumer units (families and single consumers). A consumer unit consists of members of a household who are related or share at least 2 of 3 major expenditures (housing, food, and other living expenses). For the study reported here, consumer units will be referred to as households.
The CEX targets the total noninstitutionalized population (urban and rural) of the United States. Survey data have been collected quarterly since 1980, with approximately 5,000 households completing the survey each quarter from 1980 through 1998 and 7,500 households completing the survey beginning in 1999. The survey focuses on monthly out-of-pocket expenditures for items such as housing, apparel, transportation, health care, insurance, and entertainment. A rotating sample design is used. Households are interviewed once per quarter for 5 consecutive quarters. Thus, the intention is that 20% of the respondents will complete the fifth interview at the time that 20% have their first interview. The first interview is a bounding interview, and the data are not used. In general, 90% to 95% of all expenditures are covered by the survey.
The survey data serve as a basic source for revising the items and weights in the market basket of consumer purchases priced for the CPI. The information assists in construction of statistical measures of consumption, analysis of expenditure patterns by characteristics, market research studies, and economic research.
The major expenditure categories in the CEX include food, housing, medical, and entertainment. Among the specific expenditures collected are those for pet services (defined to include grooming, kennel fees, and license fees); purchase of pets, pet supplies, and medicine for pets (defined to include aquariums, tropical fish, birds, bird cages, cats, collars, dogs, dog houses, gerbils, guinea pigs, hamsters, hamster cages, and pet toys); and veterinarian expenses for pets. For this study, these expenditures will be referred to as petrelated services, pets-pet supplies, and veterinary services, respectively, and the sum of these expenditures will be referred to as total pet-related expenditures. Although the specific prices and quantities related to these expenditures are not known, these data allowed us to examine the amount expended and the relationship to socioeconomic variables, including income, family size, housing type (ie, owned or rented), race, urban or rural residence, and education.
Expenditure data were adjusted for inflation by use of the CPI and reported as real 2005 dollars. The CPI measures the general increase in price level. Growth in price level almost certainly explains part of the growth in expenditures. Because only expenditures were available, rather than the prices and quantities that compose expenditures, reporting expenditures in real terms did not allow us to control for the extent to which prices for veterinary services may have grown more (or less) than the prices for general consumer products. Expressing the values in 2005 dollars also did not allow us to control for changing quantities of veterinary services consumed.
In addition to the quarterly expenditures for these items, many socioeconomic variables that may have been related to these expenditures were collected. For these purposes, another definition for consumer unit was used.8 In that definition, consumer unit refers to all members of a particular household who are related by blood, marriage, adoption, or other legal arrangements; a person living alone or sharing a household with others or living as a roomer in a private home or lodging house or in permanent living quarters in a hotel or motel, but who is financially independent; or 2 or more persons living together who use their income to make joint expenditure decisions.8 The reference person owns or rents the home and will be referred to as the household head in the study reported here. Descriptive variables pertaining to the household and household head were defined (Appendix).
Data analysis—Analysis in the study comprised 2 methods: descriptive statistics on pet-related expenditures per household from the beginning to end of the study period and regression analysis of the socioeconomic factors related to the probability of an expenditure (any value > 0). There is a distinct difference between these 2 analyses because the first described mean pet-related and veterinary service expenditures per household, and the second evaluated the probability that any specific household would have a pet-related expenditure.
For the regression analysis, we hypothesized that family sociologic and economic characteristics would influence the likelihood for pet-related and veterinary service expenditures. The empirical model used to determine whether households participated in the market (ie, had an expenditure on that item) was a probit model of existence for pet-related or veterinary service expenditures. The probit model is defined as Pr(y ≠ 0|xj) = φ(xjβ), where Pr(y ≠ 0|xj) is the probability that y is nonzero given xj,φ is the cumulative standard normal distribution, and xjβ is the probit score. The probit model had the estimation equation y = β′xj + ϵ, where y is an indicator variable that equals 0 for households without the expenditure in question and 1 for households that had a positive expenditure for that category in that quarter, β is a vector of coefficients, xj is a vector of socioeconomic characteristics, and ϵ is an error term.
The estimated coefficients have a nonlinear relationship with the probability of expenditure. To facilitate intuitive interpretation, results were transformed into the change in probability of an expenditure caused by a change in that explanatory variable evaluated at the mean of the data. These marginal changes can aid us in evaluating the apparent effects of the regressors on the dependent variable. Marginal effects were calculated as a percentage change in the dependent variable caused by a 1-unit change in that independent variable at the mean value for continuous variables or as the change in the probability when that variable was true rather than false for categoric variables.
The explanatory variables (ie, x) were characteristics that included age, marital status, race, and education of the household head; household income; family size; whether the family owned or rented their residence; and whether the household was in an urban or rural community. Also included were the region of the United States as well as year and quarter of the interview to provide information on patterns for time and seasonality.
For a standard probit model, it is assumed that the cumulative normal distribution describes the probability. In accordance with another report,9 heteroskedasticity was assumed to be caused by the continuous variables in the model (ie, income, age, education, and family size). A heteroskedastic probit generalizes φ by not fixing the variance at 1 but allowing it to vary as a function of the independent variables. Robust SEs were acquired by accounting for the appropriate weighting and clustering of the data. Estimations were performed by use of a commercial software program.10,a
Considering all explanatory variables, the estimated probability of an expenditure by household i for quarter (or season) s of year t (ie, P[expenditure > 0]ist) was calculated by use of the following equation:
P(expenditure > 0)ist = β0 + (β1 • [year]t) + (β2 • [income]it) + (β3 • [age]it) + (β4 • [education]it) + (β5 • [family size]it) + (β6 • [family size]2it) + (β7 • [season]s) + (β8 • [region]i) + (β9 • [marital status]i) + (β10 • [race]i) + (β11 • [rent]i) + (β12 • [rural]i)
where β0 through β12 are coefficients.
Three estimations were calculated. First, we estimated the probability for any pet-related expenditure in total (ie, veterinary service expenditures, pet-related services, and pets-pet supplies). Second, we estimated the probability for veterinary service expenditures alone as a function of the explanatory variables. Finally, we estimated the probability for a veterinary service expenditure for those households that had any pet-related expenditures. Many of the available socioeconomic variables (eg, region, season, owned or rented, rural or urban, race, and marital status) were entered as categoric (dummy) dichotomous (0, 1) variables that shifted the intercept; a single category for each set was necessarily omitted. The referent household was designated as in the Northeast region, fall season, for a family that owned their residence in an urban area with a household head who was single and white. Therefore, effects reported in the tables should be interpreted as the marginal difference in expenditure as the household characteristics varied from that set of characteristics. Results were reported as marginal probabilities, rather than as coefficient estimates.
Household income was expected to be positively related to the probability of expenditure in every category. Households with an owned residence were expected to be more likely to have pets. Family size was expected to be quadratically related to the probability of owning a pet (ie, larger family size was expected to result in a greater probability of having a pet, up to a certain value; however, family size larger than that value was expected to result in a decreased probability of having a pet). Rural households were believed to be more likely to have pet-related expenditures. Other characteristics, including age, education, race, and marital status, were expected to help explain the probability, but the prior effect was not known.
Results
Results were provided in 2 sections. The section on descriptive statistics provided mean pet-related and veterinary service expenditures per household. In contrast, results for the regression analysis provided the probability that a specific household would have a petrelated expenditure.
Descriptive statistics—Because all values were adjusted (ie, inflated) to 2005 dollars (the final year examined), mean values can be meaningfully examined across the study period. Mean values for pet-related and veterinary service expenditures over time were calculated for 3 groups. Mean values were calculated for all households, households that had any pet-related expenditures (ie, value > 0 for pets-pet supplies, petrelated services, or veterinary services), and households with veterinary service expenditures > 0 (Table 1). Households with pet-related expenditures > 0 spent approximately 4 times as much on pets as the mean expenditure for all households. Heads of households with any reported pet-related expenditure appeared to be younger, were more educated, had larger families, and had a higher income after taxes and appeared to be more likely to own their residence, be married, and live in a rural area. Most of these characteristics were also shared by households with an expenditure on veterinary services in which the typical household had an even higher income.
Mean values for households of US consumers with regard to pet-related and veterinary service expenditure, 1980 through 2005.
| Variable | All households | Households with pet-related expenditures > $0 | Households with veterinary service expenditures >$0 |
|---|---|---|---|
| Quarterly expenditures ($) | |||
| Veterinary-related services | 15.69 | 62.09 | 155.90 |
| Pets-pet supplies | 4.98 | 19.71 | 22.68 |
| Pet-related services | 17.07 | 67.50 | 64.28 |
| Total pet-related expenditures | 37.74 | 149.30 | 242.86 |
| Region (%) | |||
| Northeast | 20.6 | 18.3 | 18.1 |
| Midwest | 24.1 | 25.5 | 26.3 |
| South | 33.8 | 33.0 | 32.9 |
| West | 21.6 | 23.2 | 22.7 |
| Annual income after taxes (S) | 38,256 | 50,627 | 54,091 |
| Age (y) | 47.5 | 45.5 | 46.1 |
| Education (y) | 12.6 | 12.9 | 12.9 |
| Family size (No.) | 2.6 | 2.8 | 2.8 |
| Owned residence (%) | 62.1 | 72.5 | 74.7 |
| Rural (%) | 13.0 | 14.3 | 14.3 |
| Race (%) | |||
| White | 85.1 | 93.5 | 95.9 |
| Black | 11.5 | 4.3 | 2.6 |
| Native American* | 0.9 | 0.8 | 0.6 |
| Asian† | 2.3 | 1.1 | 0.8 |
| Other | 0.2 | 0.3 | 0.2 |
| Marital status (%) | |||
| Married | 55.3 | 67.3 | 70.7 |
| Widowed | 5.2 | 1.7 | 1.0 |
| Divorced | 12.4 | 11.7 | 10.4 |
| Separated | 3.3 | 2.1 | 1.6 |
| Never married | 23.9 | 17.2 | 16.3 |
Expenditures and income were adjusted for inflation and are reported as 2005 dollars. All individual characteristics (ie, age, education, race, and marital status) refer to the head of the household.
Native American includes Aleut and Eskimo.
Asian includes Pacific Islander.
All categories for pet-related expenditures increased from 1980 through 2005. Mean annual pet-related expenditures for each household (2005 dollars) were averaged across all households (Figure 1). Expenditures for veterinary services for all households almost doubled between 1980 and 2005 (increase of 97%). Similarly, expenditures for pet-related services increased 110% and expenditures for pets-pet supplies increased 73% for all households.
Mean quarterly expenditure per household for petspet supplies (dashed green line), pet-related services (blue line), veterinary services (red line), or total pet-related expenditures (black line) for all households, 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Mean quarterly expenditure per household for petspet supplies (dashed green line), pet-related services (blue line), veterinary services (red line), or total pet-related expenditures (black line) for all households, 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Mean quarterly expenditure per household for petspet supplies (dashed green line), pet-related services (blue line), veterinary services (red line), or total pet-related expenditures (black line) for all households, 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
When restricted to only those households that reported a pet-related expenditure > 0, mean expenditures on veterinary services increased 33% between 1980 and 2005. Meanwhile, the mean expenditures for pet-related services for those same households increased 42% and expenditures for pets-pet supplies increased 16%. For only those households with a veterinary service expenditure > 0, mean veterinary service expenditures increased 124% during the study period.
Share of total pet-related expenditures was determined for each category (Figure 2). The largest share of total pet-related expenditures during the study period appeared to cycle between veterinary services and pets-pet supplies. Meanwhile, share of total pet-related expenditures spent for pet-related services was fairly stable. It should be remembered that expenditures on all 3 categories increased in terms of the absolute number of 2005-adjusted dollars.
Proportion of pet-related expenditures for petspet supplies (green bars), pet-related services (blue bars), and veterinary services (red bars) for all households, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of pet-related expenditures for petspet supplies (green bars), pet-related services (blue bars), and veterinary services (red bars) for all households, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of pet-related expenditures for petspet supplies (green bars), pet-related services (blue bars), and veterinary services (red bars) for all households, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
The percentage of households with an expenditure in each category and year was also considered (Figure 3). Households with any reported pet-related expenditure increased (from 19.7% to 29.1% of all households). Most of this increase was driven by an increase in households with expenditures for pets-pet supplies (from 11.2% to 23.7% of all households). The percentage of all households with a veterinary service expenditure remained fairly constant at approximately 10%. When only households with a pet-related expenditure were evaluated, those purchasing veterinary services decreased over time from 51% in 1980 to 30.4% in 2005 (Figure 4).
Proportion of households with an expenditure for petspet supplies (green line), pet-related services (blue line), veterinary services (red line), or any pet-related expenditures (dashed black line), 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of households with an expenditure for petspet supplies (green line), pet-related services (blue line), veterinary services (red line), or any pet-related expenditures (dashed black line), 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of households with an expenditure for petspet supplies (green line), pet-related services (blue line), veterinary services (red line), or any pet-related expenditures (dashed black line), 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of households with a veterinary service expenditure given any pet-related expenditures, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of households with a veterinary service expenditure given any pet-related expenditures, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Proportion of households with a veterinary service expenditure given any pet-related expenditures, 1980 through 2005.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
In dollar terms, the mean household expenditure on veterinary service expenditures for households that purchased veterinary services increased from $103/quarter in 1980 to $231/quarter in 2005 (Figure 5). In contrast, for those households that had any pet-related expenditure, mean household expenditure on veterinary service expenditures increased at a much slower rate from $52.50 to $70/quarter.
Mean quarterly expenditures per household for veterinary services for households with a pet (dashed line) or with any veterinary service expenditures (solid line), 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Mean quarterly expenditures per household for veterinary services for households with a pet (dashed line) or with any veterinary service expenditures (solid line), 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Mean quarterly expenditures per household for veterinary services for households with a pet (dashed line) or with any veterinary service expenditures (solid line), 1980 through 2005. Values have been adjusted for inflation and are reported as 2005 dollars.
Citation: Journal of the American Veterinary Medical Association 233, 3; 10.2460/javma.233.3.404
Regression analysis—Probit regression was conducted to explain the probability of a pet-related or veterinary service expenditure (Table 2). With other variables controlled, the overall trend was for the probability of expenditures for veterinary services to decrease by 0.31% each year. Income had a significant positive association with the probability of total pet-related and veterinary service expenditures, as did education of the household head. In contrast, age of the household head had a significant negative association.
Estimates of the probability of any pet-related expenditure, veterinary service expenditure, or veterinary service expenditure given any pet-related expenditure.
| Variable | Total pet-related expenditures | Veterinary service expenditures | Veterinary service expenditures when pet-related expenditures > 0 | |||
|---|---|---|---|---|---|---|
| Marginal effect | P value | Marginal effect | P value | Marginal effect | P value | |
| Year | −0.0001 | 0.529 | −0.0031 | < 0.001 | −0.0133 | < 0.001 |
| Income* | 0.0468 | < 0.001 | 0.0261 | < 0.001 | 0.0468 | < 0.001 |
| Age | −0.0022 | < 0.001 | −0.0004 | < 0.001 | 0.0013 | < 0.001 |
| Education | 0.0040 | 0.001 | 0.0036 | < 0.001 | 0.0084 | < 0.001 |
| Family size | 0.0706 | < 0.001 | 0.0246 | < 0.001 | −0.0207 | < 0.001 |
| (Family size)2 | −0.0087 | < 0.001 | −0.0036 | < 0.001 | 0.0003 | 0.634 |
| Season† | ||||||
| Winter | −0.0064 | < 0.001 | −0.0084 | < 0.001 | −0.0266 | < 0.001 |
| Spring | −0.0084 | < 0.001 | −0.0063 | < 0.001 | −0.0158 | < 0.001 |
| Summer | −0.0003 | 0.839 | 0.0016 | 0.115 | 0.0064 | 0.161 |
| Region‡ | ||||||
| West | 0.0362 | < 0.001 | 0.0129 | < 0.001 | −0.0019 | 0.711 |
| Midwest | 0.0324 | < 0.001 | 0.0156 | < 0.001 | 0.0185 | < 0.001 |
| South | 0.0238 | < 0.001 | 0.0125 | < 0.001 | 0.0125 | 0.009 |
| Marital status§ | ||||||
| Married | 0.0461 | < 0.001 | 0.0246 | < 0.001 | 0.0363 | < 0.001 |
| Widowed | 0.0238 | < 0.001 | 0.0123 | < 0.001 | 0.0157 | 0.022 |
| Divorced | 0.0420 | < 0.001 | 0.0119 | < 0.001 | −0.0110 | 0.094 |
| Separated | 0.0016 | 0.789 | −0.0084 | 0.014 | −0.0401 | 0.001 |
| Race∥ | ||||||
| Black | −0.1982 | < 0.001 | −0.0982 | < 0.001 | −0.1349 | < 0.001 |
| Native American¶ | −0.0559 | < 0.001 | −0.0381 | < 0.001 | −0.0734 | < 0.001 |
| Asian# | −0.2034 | < 0.001 | −0.0918 | < 0.001 | −0.1046 | < 0.001 |
| Housing** | ||||||
| Rented | −0.0713 | < 0.001 | −0.0286 | < 0.001 | −0.0378 | < 0.001 |
| Area†† | ||||||
| Rural | 0.0363 | 0.001 | 0.0089 | < 0.001 | 0.0043 | 0.515 |
All individual characteristics (ie, age, education, race, and marital status) refer to head of the household.
Represents natural logarithm of household income after taxes.
Categoric variable (ie, intercept shifter); fall is the omitted seasonal category.
Categoric variable; Northeast is the omitted category.
Cat-egoric variable; single is the omitted category.
Categoric variable; white is the omitted category.
Native American includes Aleut and Eskimo.
Asian includes Pacific Islander.
Categoric variable; owned is the omitted category.
Categoric variable; urban is the omitted category.
The probability of a pet-related or veterinary service expenditure in winter and spring was slightly but significantly lower than the probability of such an expenditure in the fall. For the most part, the probability of any pet-related expenditure was significantly lower for households in the Northeast than for households in other regions of the United States. Households in the West, Midwest, and South regions were each approximately 1.3% to 1.6% more likely to have an expenditure for veterinary services.
In general, the probability of having a pet-related or veterinary service expenditure significantly increased with family size up to 4 and then decreased as the negative effect of (family size)2 overcame the positive linear effect. Households in which the household head was married, widowed, or divorced were significantly more likely to have a pet-related expenditure and to have spent money for veterinary services than were households in which the household head was single.
Households in which the household head indicated race as white were significantly more likely to have petrelated and veterinary service expenditures. Relative to white households, black households were 9.8% less likely to have expenditures for veterinary services, whereas Native American households were 3.8% less likely, and Asian households were 9.2% less likely (Table 2).
Households that rented their residence were significantly less likely to have pet-related expenditures (approx 7.1% less) and less likely to purchase veterinary services (2.9% less). Households located in rural areas were significantly more likely to have pet-related expenditures, with approximately a 0.9% greater probability of veterinary service expenditures.
In addition to the general results, it was insightful to focus on the probability of a veterinary service expenditure in the subpopulation of only those households that had a pet-related expenditure (Table 2). Over time, the probability of an expenditure for veterinary services within this group decreased significantly (approx 1.3%/y). In addition, the probability of expenditures on veterinary services within this subgroup decreased significantly with increasing family size. In contrast, households that had a pet-related expenditure were more likely to have expenditures for veterinary services as age of the household head increased. Within this portion of the population, the probability of a veterinary service expenditure was not significantly different between households located in the West and Northeast. Similarly, no significant difference was found in this group between households with a single or divorced household head or between households located in rural or urban settings.
Discussion
With the number of households increasing by 45% between 1980 and 2005 and the mean real household expenditure on pet-related items and veterinary services increasing during that period, inflation-adjusted US consumer pet-related expenditures increased in aggregate approximately 186% from 1980 through 2005. The percentage of households with a pet-related expenditure also increased. In a broad sense, these trends speak to the evolving role of pets in American culture and support the widely held opinion that the human-animal bond is strengthening. However, the percentage of households with an expenditure on pet-related services was flat after controlling for confounders, and the percentage of households that had a veterinary service expenditure actually decreased during the study period. These results have some potentially important implications for the veterinary profession as it strives to meet the needs of an ever-changing society.
As expected, the probability of pet-related expenditures was positively associated with household income. Because income also increased significantly during the study period, results of the probit analysis suggest that the apparent overall increasing probability of spending on pets is primarily an income phenomenon (Figure 3). Once the effect of income was removed by the probit model, the effect of time on the probability of pet-related expenditures disappeared and the trend in probability of veterinary service expenditures was decidedly negative. In a sense, this indicates that increases in income have been a key enabler of the aforementioned ongoing evolution of the human-animal bond. Results of the analysis suggest that people may be spending more on their pets because they want to (human-animal bond effect) and because they have the means (income effect). However, these results do not indicate that the increase in spending would have happened in the absence of a steady increase in income. Further research on this topic would be useful.
Perhaps more importantly, these results contain a critical mixed message for veterinary service expenditures. Although overall spending for veterinary services increased, the probability of any particular household purchasing veterinary services actually decreased. This indicates that households that continue to purchase veterinary services are spending substantially more, but an increasing proportion of households are choosing not to spend any money for veterinary services.
With the exception of heads of household whose marital status was listed as separated, results indicated that those who were inclined to marriage may place a higher value on companionship afforded by pets. The fact that household heads who were separated had some inconsistency for this pattern is not surprising considering the major life transitions in which these individuals are often embroiled. Overall, these results do not differ substantially from results for data collected by the AVMA,1 which indicated that among households for which the household head was married, a higher proportion owned pets, and among households for which the household head was single, a lower proportion owned pets.
In a related item, it is interesting that pet-related expenditures peaked when family size was approximately 4. Data available for the study reported here did not allow further investigation of this phenomenon, but reasonable hypotheses may relate to a decreasing marginal value of the companionship afforded by pets once family size reaches 4. In combination, the availability of time and money to adequately care for pets also may be a limiting factor when family size increases above 4.
Consistency of our data with results of an AVMA study7 was also found in the relationship between education and pet-related expenditures. The positive association between pet-related expenditures and education of the head of household in the study reported here is similar to findings for the AVMA study7 in which it was reported that a greater percentage of pet-owning heads of households had attended college or attained a college degree. This relationship between pet-related expenditures and education was evident even when the effect of income was controlled in the estimation.
The concept of seasonality is not new to veterinary medicine. Individual practices and practitioners are aware that the demand for services waxes and wanes in relation to the seasons. Certainly, the rate of pet ownership does not change with season, nor does the value placed on pets fluctuate substantially throughout the year. Factors driving these results are more likely related to seasonal differences in availability of time and money (cash flow) for veterinary care along with some fundamental seasonal differences in the risk of disease within pet populations. In a broad sense, results of this study serve only to confirm previously held notions about the need for veterinary practices to manage resources in a manner that appropriately anticipates seasonal fluctuations.
Regional differences identified in the study suggested that households located in the Northeast were less likely than those in other regions to incur pet-related expenses. Data from the AVMA1 indicated that households in the Northeast are slightly less likely to own pets, with Rhode Island, Massachusetts, Maryland, New Jersey, New York, and the District of Columbia having some of the lowest rates of pet ownership. Therefore, our findings are consistent, in general, with results of the aforementioned AVMA data.
Similarly, households were less likely to incur petrelated expenses as the age of the household head increased, even when controlling for income. This finding may reflect a lower probability for pet ownership in aged populations. Alternatively, it may be reasonable to hypothesize that this apparent generational effect could be augmented by a hidden effect of income that was not fully evaluated in the model. Such an effect, if present, could conceivably stem from a smaller proportion of this group's income (probably fixed income) being available for pet-related expenses as a result of proportionately higher medical and housing costs.
The fact that renters were less likely and rural dwellers more likely to incur pet-related expenses is consistent with the respective feasibility of pet ownership for these 2 sets of circumstances. Pet ownership is comparatively more difficult in rented housing, but there are typically fewer restrictions in rural settings.
When considering race, the fact that persons of color were less likely to spend money on pets (when controlling for income) may indicate cultural differences with regard to the human-animal bond or the likelihood of pet ownership. However, these hypotheses cannot be substantiated or refuted by our study. Furthermore, anecdotal evidence associated with the aftermath of hurricane Katrina provides a strong reminder that such generalizations should not be made without supporting data. Alternatively, it may be that cultural norms related to pet ownership in these communities have heretofore not been fully understood or appreciated by the pets-pet supplies–pet services industry or veterinary profession. Regardless of the underlying cause or causes, the situation may evolve over time. Such evolution is likely to be enabled or enhanced by economic growth (ie, increasing income).
Many of the aforementioned findings can be readily understood by considering the demographics of pet ownership. However, the percentage of households with a pet-related expenditure in the study reported here was substantially lower than reported estimates of the percentage of households that own a pet. As mentioned previously, the AVMA estimates that 59.5% of US households own pets.1 This proportion is substantially higher than the 19.7% to 29.1% of households that reported pet-related expenditures in our study. Although the exact reason for this difference is uncertain, it may reasonably stem from one or more of the following factors:
• Pet food expenditures were reported under groceries in the CEX database, so members of pet-owning households whose only pet-related expenditure was on food for their pet would not be included in our study. It is reasonable that some pet-owning households may not have had any expenditures on pet-related services, pets, medicine, or veterinary services during the period of their participation in the consumer panel.
• Some pet-owning households in our study may not have reported all of their pet-related expenditures (ie, underreporting bias). Instances of underreporting related to pet-related expenditures may easily have resulted if panel participants failed to maintain sufficient detail on these particular categories when providing data.
• Sampling methods for data collected in our study differed substantially from those used in the AVMA study.1 Most notably, the CEX database included all households (total noninstitutionalized population), whereas lower income categories were truncated in the AVMA study.b
When these factors are considered alone or in combination, it is not surprising that the proportion of households with any pet-related expenditure in the study reported here differs substantially from the proportion of pet-owning households reported previously by the AVMA.1 Furthermore, it is reasonable to assume that the probability of spending money on any pet-related expense in our study may be considered as an acceptable general indicator of the probability of owning a pet. In that regard, results of our analysis involving only those households with a pet-related expenditure provided useful insights as to patterns of consumption for veterinary services within the petowning population. However, because it is not known whether the probability of pet owners incurring a petrelated expenditure is constant over time and across the other explanatory variables included in this study, inferences must be interpreted with some degree of caution.
Within the population of pet owners who spend money on their pets, the effect of time on probability of expenditure on veterinary services was decidedly negative, with the probability decreasing at a rate of approximately 1.3%/y. This finding is slightly alarming, and underlying causes are not known.
Further consideration of the pet-owning subgroup indicated that expenditures for veterinary services were less likely in households in the Northeast and West. Reasons for this disparity are not clear, but possible causes include inherent cultural difference in the human-animal bond in these regions, a lower relative availability of veterinarians, less available time to seek veterinary care, and regional differences in cost of living (which may lead to effective differences in disposable income). Additional research will be necessary to fully understand the potential contribution of these factors.
Although for the general population there was a decrease in the probability of a veterinary service expenditure with increasing age of the household head, when confined to households that spent money on pets, results of our analysis indicated that increasing age actually made it more likely that there would be a veterinary service expenditure. These findings suggested that older pet owners may place a higher priority on maintaining the health of their pets and should serve as a reminder that age is an increasingly important factor to consider in a rapidly aging US population in which older pet owners commonly live alone and have a strong emotional attachment to their pets.
A similar dichotomy was discovered when family structure was evaluated. In the general population, household heads who were divorced were similar to those who were married in that they were more likely than singles to have pet-related and veterinary service expenditures. However, among those households with a pet-related expenditure, divorced respondents were similar to single respondents in that they were less likely to have a veterinary service expenditure. The fact that divorced heads of household were more similar to single than to married household heads for this variable suggests that additional, unspecified constraints may exist in those divorced households owning pets that make it substantially more difficult to obtain veterinary care.
Racial disparities within the pet-spending population indicate the critical importance of enhancing diversity in the veterinary profession. The lack of diversity in the veterinary profession has been reported3 and may in fact be restricting the availability of veterinary health care in communities of color through geographic or cultural barriers to access. The fact that people of color in the pet-spending subgroup are 7% to 13% less likely to spend money on veterinary health care is alarming. Considering this fact in conjunction with documented and broad-based demographic trends that exist in the United States is even more disconcerting. As these trends continue and people of color steadily increase as a proportion of the total US population, their cultural, political, and economic importance grow as well. Consequently, understanding and addressing the needs of these potential clients deserve to be priorities for the veterinary profession. Enhancing diversity in the veterinary profession will be a vital step toward that end, as will expanding awareness of the vital importance of differences, enhancing cultural competency among veterinarians, and developing a broad-based inclusive environment across the veterinary profession.
Based on the decreasing probability for spending on veterinary services among the pet-owning subpopulation, it is apparent that veterinarians are not meeting the needs of an increasing proportion of the pet-owning population. Perhaps the veterinary profession has not sufficiently kept up with the increasing demand for upscale veterinary services that has accompanied the steady increase in income. On the other hand, it may be hypothesized that the rapid increase in available medical technology and the associated increase in real cost of veterinary services may actually be driving some pet owners from the market. Another potential explanation involves the insidious demographic shifts over time, which may translate into similar gradual changes in the needs and preferences for specific veterinary services. Still another explanation could be that improved preventative health-care programs during the past 25 years, including enhanced nutrition, parasite-control, and vaccination programs, have been sufficiently successful that the overall probability of visiting a veterinarian has decreased for pet owners.
Regardless of the cause, results suggest that an expanding proportion of animal owners are not seeking veterinary care for their pets and potentially viable niche markets for veterinary services are growing at a remarkable rate. The existence of these niche markets indicates that substantial potential exists for expanding the current market for veterinary services, with evidence indicating that the market for companion animal veterinary services is clearly not saturated. Obviously, the challenge for anyone considering strategies to delve into these potential markets will be to identify key characteristics of a target market and develop a profitable business model for offering quality veterinary medical care that meets accepted standards of practice and delivers value to the associated growing group of potential consumers. Further study of this issue would certainly be useful in developing a robust understanding of the market potential. In particular, it would be helpful to understand the potential relationship between improved preventative health-care programs and the frequency of visits to a veterinarian for companion animals.
Finally, results of this study are strategically important for organized veterinary medicine. Specifically, this study and potential follow-up studies have implications for each of the 5 strategic goals of the AVMA.
Economic viability—To ensure continued economic viability in the veterinary profession, it will be critical to address the apparent increasing proportion of pet-owning households that are choosing not to spend money on veterinary services. This apparent growing, untapped market potential represents a strategic opportunity to better meet societal needs for veterinary services, and the economic implications are obvious.
Animal welfare—With an increasing proportion of pet-owning households choosing not to spend money on veterinary services, the health and well-being of the pet population comes into question. With veterinarians as primary advocates of animal welfare, an obvious strategic opportunity exists to enhance the well-being of the nation's pet population.
Education—Practitioners, educators, and veterinary medical students should be made aware of the results of the study reported here, with strategic consideration of the potential impact on their professional opportunities. As additional information and understandings are developed related to the results of this study, effective broad-based education will allow veterinary medicine to adopt a strategic focus for meeting the needs identified.
Veterinary workforce—With an apparent growing, unmet market for companion animal veterinary services, strategic implications for the veterinary workforce are clear. Once this market potential is more fully understood and appropriate marketing and business models are developed, the accompanying growth in employment opportunities for veterinarians in the pet health-care arena should be substantial.
Advocacy—Addressing strategic opportunities related to economic viability, animal welfare, education, and the veterinary workforce will likely require effective advocacy at virtually every level. This advocacy will take 2 forms: legislative monitoring and vigilance to enable timely identification of federal and state legislative initiatives that could have positive or negative impacts, and legislative action to actively promote those initiatives with positive impacts and to actively oppose those initiatives with negative impacts.
As indicated, results of the study reported here have broad-based implications. On reflection, several noteworthy take-home messages emerged.
• Real spending on veterinary services is increasing at a substantial rate. This is good news for the veterinary profession from almost any perspective and reinforces widely held perceptions regarding evolution of the human-animal bond.
• Increases in expenditures by those who continue to spend on veterinary services has more than offset the loss of income from the increasing proportion of the pet-owning population who apparently are not seeking veterinary services. Successful practices clearly should continue to do what they have been doing because this study provides no indication that the potential for growth has been fully achieved.
• Increases in spending on veterinary services during the period of the study may not have happened in the absence of increases in income. The primary importance of this finding relates to planning for provision of veterinary services in situations where incomes might become constraining. For the most part, these situations would include periods in which income growth may not be sustained (eg, periods of recession) and specific communities where household incomes are inherently low or unstable.
• The finding that a decreasing proportion of households with pet-related expenditures are spending money on veterinary services requires further study to fully understand its origins, implications, and related opportunities. Although these results signal that a growing proportion of pet owners are not having their needs for veterinary services met, it also signals the existence of a growing, untapped potential market for veterinary services. This growing market represents a substantial growth opportunity for the veterinary profession and indicates clearly that the market for companion animal veterinary services is not saturated.
• The fact that non-white populations were less likely to spend money on veterinary services also requires further study to attain a robust understanding of the situation. However, because of the possible implications of findings from the study reported here and because of the unquestionable demographic trends related to race and ethnicity in the United States, the veterinary profession cannot afford to delay efforts to enhance its diversity and cultural competence toward a truly inclusive environment.
• Results of this study have strategic importance for veterinarians, veterinary practices, and organized veterinary medicine. Leaders in all segments of the veterinary profession should carefully consider implications for the future.
ABBREVIATIONS
| CEX | Consumer expenditure survey |
| CPI | Consumer price index |
Stata, version 7.0, StataCorp LP, College Station, Tex.
Flanigan J, Director of Marketing, Communications Division, AVMA, Schaumburg, Ill: Personal communication, 2007.
References
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Daneshvary N, Schwer K. The nature of demand for companion pet health care. J Appl Bus Res 1993;9:24–33.
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Lloyd J, Leeper D, Donovan D, et al. Prices and pricing policies for small animal and equine veterinary services: a study of teaching hospitals and Michigan private practices during 2000. Michigan State University staff paper 2001–40. East Lansing, Mich: Department of Agricultural Economics, Michigan State University, 2001.
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US Department of Labor, Bureau of Labor Statistics. Consumer expenditure survey glossary. Available at: www.bls.gov/cex/csxgloss.htm. Accessed Oct 4, 2007.
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Jensen H, Yen S. Food expenditures away from home by type of meal. Can J Agric Econ 1996;44:67–80.
Appendix
Explanatory variables used in analysis of US consumer expenditures for veterinary services, pets-pet supplies, and pet-related services.
| Variable | Description | Type |
|---|---|---|
| Year | Year in which interview was conducted | 1980 through 2005; continuous |
| Quarter | Quarter in which interview was conducted* | Winter, spring, summer, or fall; categoric variable with fall omitted† |
| Region | Region of the United States‡ | Northeast, Midwest, South, or West; categoric variable with Northeast omitted† |
| Income | Family income after taxes§ | Dollars transformed by use of the natural logarithm; continuous |
| Area | Description of area in which respondent lived∥ | Urban or rural area; categoric variable with urban omitted† |
| Age | Age of household head | No. of years; continuous |
| Education | Formal education of household head | No. of years; continuous |
| Housing | Family residence¶ | Owned or rented; categoric variable with owned omitted† |
| Family size | No. of members in consumer unit | No. of family members; continuous |
| Marital status | Marital status of household head | Married, divorced, separated, widowed, or never married; categoric variable with never married omitted† |
| Race | Race of household head | White, black, Native American (including Aleut and Eskimo), Asian (including Pacific Islander), or other; categoric variable with white omitted† |
Winter = January, February, and March; spring = April, May, and June; summer = July, August, and September, and fall = October, November, and December.
With respect to the characteristics represented by categoric variables, a series of (0,1) variables represented the category and shifted the constant.
Northeast = Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont; Midwest = Illinois, Indiana, jowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin; South = Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Virginia, and West Virginia; and West= Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.
Family income after taxes was the total money earnings and money receipts during the 12 months prior to the interview minus personal taxes (federal, state, and local income taxes).
Rural was defined as living outside a metropolitan statistical area and within an area with a population of < 2,500 people; the Office of Management and Budget defined a metropolitan statistical area as a large population nucleus together with adjacent communities that have a high degree of economic and social integration with that nucleus.
Whether the famijy's principal place of residence during the survey was owned or rented; rented also included those families living rent-free in lieu of wages.
