Accounting Horizons Vol. 26, No. 3 2012 pp. 417–437
American Accounting Association DOI: 10.2308/acch-50167
Client Variables Associated with Four Key Determinants of Demand for Tax Preparer Services: An Exploratory Study Gary M. Fleischman and Teresa Stephenson SYNOPSIS: There is a rich literature associated with the dichotomous decision to hire/ not-hire tax preparation services. There is also some research assigning motivation to the decision to hire tax preparation services. However, no research has examined the underlying demographics and key perceptions of clients that do hire tax preparation professionals to determine which are associated with specific motivations to hire. We focus on the four most common motivations to hire a preparer found in extant literature (i.e., saving money, saving time, legal compliance, and protection from the IRS). Using survey data, we perform exploratory analysis using MANCOVA to discover what client demographics and perceptions underlie each motivation. Our most noteworthy finding is that client perceptions of tax advocacy are positively associated with all four focal motivations to hire. Our results also suggest that female clients, more than male clients, choose tax preparers with a desire to save time and to be legally compliant. In addition, taxpayers with children tend to be more concerned with legal compliance. We also find that taxpayers with relatively complex returns are less likely to hire a preparer to provide a legally compliant return and to gain protection from the IRS. Keywords: demand for tax preparer services; saving money; saving time; legal compliance; protection from IRS. Data Availability: The data are available from the authors upon request.
BACKGROUND
T
ax preparation services constitute a considerable portion of most public accounting practices in terms of both revenues and litigation (Yancey 1996; Rufus and Sennetti 2007). Due to the importance of effective tax preparation to society, Congress has considered
Gary M. Fleischman is a Professor and Teresa Stephenson is an Associate Professor, both at the University of Wyoming. We thank participants at the 2009 National Tax Association annual meeting, the two anonymous reviewers, and the editor of Accounting Horizons for their valuable comments on this research.
Submitted: November 2010 Accepted: January 2012 Published Online: March 2012 Corresponding author: Gary M. Fleischman Email:
[email protected]
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legislation regulating tax preparation services as part of an overall effort to reduce the tax gap.1 Similarly, the IRS has recently established requirements whereby tax preparers acquire a Preparer Tax Identification Number (PTIN) beginning in 2011 to further monitor these services. About 87 million individual tax returns are prepared professionally (Internal Revenue Service [IRS] 2009), further underscoring the importance of this practice, yet unresolved questions remain about client demand for these services. Specifically, we know little about client perceptions of tax preparer advocacy or about client demographic particulars such as gender or relative tax knowledge in terms of the motivations that drive demand for professional tax services, i.e., who hires for purposes of saving money, saving time, legal compliance, and/or protection from the IRS. In short, it is only possible to answer such questions using survey data that allow such specific inquiry. There is a rich literature regarding client demographics associated with the general decision whether or not to hire a tax preparer (e.g., Collins et al. 1990; Dubin et al. 1992). There is also a literature regarding client self-reported motivations for hiring a tax preparer (e.g., Yankelovich, Skelly and White, Inc. 1984 [hereafter, YSW 1984]; Collins et al. 1990). However, this study makes an additional contribution by exploring the link between these two bodies of knowledge that has not been addressed previously. Specifically, our purpose is to explore how the demographics and perceptions of taxpayers who hire tax preparers are associated with the four key motivations to hire (i.e., saving money, saving time, legal compliance, and protection from the IRS). This exploratory study contributes to the literature in two specific ways: (1) by identifying client demographics and perceptions associated with each of the four demand constructs, and (2) by utilizing a unique survey-based dataset that allows us to investigate explanatory variables that are difficult, if not impossible, to measure using tax return archival sources. This study is also pragmatically useful since it further facilitates tax preparers’ understanding of why clients demand their services, which should enhance preparer-client communication, minimize tax service misunderstandings, and therefore ultimately boost professional tax service effectiveness. Our focal research question is as follows: What client perceptions and demographic variables are significantly associated with the four key motivations that drive the demand for tax preparation services? Consistent with our overall research question, our dataset includes only taxpayers who have hired professional tax preparers in the five years preceding the survey. Our most noteworthy finding is that client perceptions of tax advocacy are positively associated with all four focal motivations to hire. Our results also suggest that female clients hire preparers with the desire to save time and to be legally complaint more than their male counterparts do. Also, taxpayers with children tend to be more concerned with saving time and with legal compliance. In addition, taxpayers with relatively complex returns are less likely to hire a preparer to provide a legally compliant return or to gain protection from the IRS. Higher income clients are also less concerned with being protected from the IRS. The remainder of this manuscript is organized as follows. The next section briefly discusses our research questions. Then we present the study method and related literature followed by a presentation of the study’s results. Next, we provide a discussion of the findings before concluding with implications for professional practice and public policy, while also highlighting study contributions and limitations. RESEARCH QUESTIONS Several studies conclude that the four key construct variables in this study (i.e., saving money [SM], saving time [ST], legal compliance [LC], and protection from the IRS [IRS]) strongly and 1
For example, the Taxpayer Assistance and Protection Act of 2007 and the Taxpayer Assistance Act of 2010 were not passed, but Congress is still considering different components of these acts.
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positively predict client demand for tax preparer services (YSW 1984; Christensen 1992; Collins et al. 1990, 1992). However, we know very little about what drives these constructs in particular. Therefore, the purpose of this study is to address this informational gap. Because this study is exploratory, we present generalized research questions instead of formal hypotheses. This is also consistent with the pragmatic nature of this study. As mentioned earlier, the overall research question in the study is as follows: What client perceptions and demographic variables are significantly associated with demand for tax preparation services? We break this overall research question into four subparts (RQ1–RQ4) in order to address each of the four key demands for tax preparation service constructs that are predominant in the literature and were developed into a scale for use in such research (Stephenson 2010): RQ1: What client perceptions and demographic variables are significantly associated with the demand for the tax preparation services construct ‘‘saving money’’(SM)? RQ2: What client perceptions and demographic variables are significantly associated with the demand for the tax preparation services construct ‘‘saving time’’ (ST)? RQ3: What client perceptions and demographic variables are significantly associated with the demand for the tax preparation services construct ‘‘legal compliance’’ (LC)? RQ4: What client perceptions and demographic variables are significantly associated with the demand for the tax preparation services construct ‘‘protection from the IRS’’ (IRS)? In order to answer these four research questions, the remainder of this paper investigates client-specific variables that are significantly associated with the four key focal variables, and these four constructs serve as dependent measures in the subsequent exploratory MANCOVA analysis. The next section discusses the method used in the present study, including data collection and variable measures. METHOD Data Collection We use a mail survey to collect usable and complete data (for MANCOVA) from a total of 340 taxpayers who hired tax preparation professionals.2 We employ the taxpayer motivation scale (TMS) developed by Stephenson (2010) to measure the four key tax service demand determinants that serve as study dependent variables: SM, ST, LC, and IRS. We asked tax preparers to allow us to survey their clients, obtaining permission from 21 located in the southeastern United States, and completing the surveys over a five-month period in 2005. In return, we provided each preparer with a summary of client responses. Nine of the 21 preparers are CPAs; seven are enrolled agents; the rest are uncertified preparers. Sixteen of them have over 21 year’s experience, but none have less than five. Five prepare more than 500 returns per year; ten prepare between 100 and 500; one prepares less than 25. Eleven are self-employed or work for a local tax preparation firm; one works for a national tax preparation firm; and the other nine work for local CPA firms. Surveys were sent to each household only one time, and it requested that the person with the primary responsibility for tax matters be the one to fill out the questionnaire. The response rate for each preparer ranges from 17.7 percent to 100 percent. Our usable (complete) data consist of 340 2
Our full dataset includes 374 observations, but due to missing data, our final MANCOVA results that contain complete data for all ten independent variables reduce our sample to 340 usable observations.
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taxpayers. Our overall response rate is 20.5 percent. We tested the data for nonresponse bias and concluded that this issue was not a concern.3 We encouraged completion of the entire survey by allowing those who did so to vote for a charity to which we sent a donation. Measures This study employs four dependent and ten independent variables to assess our research questions and to conduct our exploratory MANCOVA analysis. The following section describes how we measure these variables. Figure 1 presents the individual items that comprise the taxpayer motivation scale (TMS) that Stephenson (2010) used to create the composite scores for the four focal client motivations to hire a tax preparer (i.e., SM, ST, LC, and IRS). These four items comprise our dependent variables. Each focal variable construct item is measured using a Likert scale anchored by ‘‘1’’ ¼ strongly disagree and ‘‘7’’ ¼ strongly agree. We used confirmatory factor analysis on the 14 items that make up the scale to ensure validity in this setting. There are four factors with eigenvalues greater than 1, and those four factors explain more than 68 percent of the variance in the data. The factors were rotated using a Varimax rotation with Kaiser Normalization and converged in six iterations. The factor loadings along with Cronbach’s alphas for our dataset are shown in Figure 1. Saving Money (SM) The literature suggests that taxpayers often employ the services of tax professionals because their relatively superior knowledge of the tax law combined with associated tax planning services enables them to save money for the client through tax liability minimization. Although clients must pay preparers a fee for their services, there is an anecdotal assumption that this fee is usually less than the reduction in tax liability. Numerous studies find a positive association between a desire to save money and the demand for tax preparation services (YSW 1984; Long and Caudill 1987; Klepper et al. 1991; Christensen 1992; Collins et al. 1990, 1992; Christian et al. 1994; Hite et al. 1995; Frischmann and Frees 1999). The SM construct measures the importance of saving money on the tax return for the client by having the return professionally prepared. The variable is composed of three items (see Figure 1). We sum and average each of the item scores to obtain the composite score. The Cronbach’s alpha is 0.80 for this scale. Confirmatory factor analysis shows that this factor is orthogonally distinct from the other dependent variables with factor loadings similar to those from the original scale development (Stephenson 2010). Saving Time (ST) Many taxpayers would rather pay a professional to prepare their tax return than take the time from their personal schedules to prepare the return themselves. Taxpayer time often is limited for a number of reasons that include both work and personal demands. The literature suggests that the taxpayer’s desire to save time is positively associated with demand for the services of a tax preparation professional (Slemrod and Sorum 1984; YSW 1984; Long and Caudill 1987; Christian et al. 1994; Hite et al. 1995; Frischmann and Frees 1999). The ST construct measures the importance of saving the client time by having the return professionally prepared. The variable is composed of four items (see Figure 1). The last two items in the scale juxtapose money against time forcing the taxpayer to determine which is most important. Therefore, even though the construct is simply called ‘‘saving time,’’ there is a richness 3
We tested for this bias using ANOVA by comparing responses received shortly after mailing to those received last.
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FIGURE 1 Taxpayer Motivation Scale (TMS) (Stephenson 2010) Money Time Legal Saving Money (alpha ¼ 0.80) Even though I pay a fee, I come out ahead financially with a tax professional. My tax professional saves me money. I have my taxes professionally prepared because it saves me money overall. Saving Time (alpha ¼ 0.73) It takes so long to do tax returns; I would rather let a professional handle it. It’s okay to pay a little more to have a professional prepare my taxes as long as it doesn’t take my time. I have my taxes professionally prepared because I don’t have time to do it myself. I have my taxes professionally prepared and although it costs me money, it saves me valuable time. Legal Compliance (alpha ¼ 0.80) I would rather be protected from penalties than save money on my taxes. I would rather pay a little more in taxes and make sure I haven’t broken any laws. I would rather be protected from penalties than save money on tax preparation charges. IRS Protection/Avoidance (alpha ¼ 0.85) The IRS won’t prosecute me personally if my tax professional is wrong about something. I have my taxes professionally prepared so that I know I’ll never have to face the IRS. If I paid enough to have my taxes prepared, I could end up without any tax liability. I have my taxes professionally prepared so that if the IRS questions it, I won’t be the one who gets in trouble.
IRS
0.839 0.814 0.802
0.839 0.724 0.693 0.515
0.871 0.832 0.683
0.849 0.826 0.804 0.677
The factor loadings for each of the 14 items that compose the final scale are shown here along with Cronbach’s alpha for each factor. Values below 0.50 are deleted for ease of interpretation.
to the definition that includes a trade-off between time and money that partially explains the correlation between the two constructs. Again, we sum and average each of the item scores to obtain the composite score. The Cronbach’s alpha is 0.73 for this scale. The confirmatory factor analysis loadings are also shown in Figure 1 and are similar to the loadings in the original scale development (Stephenson 2010). Legal Compliance (LC) The literature suggests that most taxpayers employ professional tax preparation services because they desire an ‘‘accurate’’ return. We contend this means that they desire a return that complies with the complicated tax code in an unbiased manner—one that is legally compliant. Five Accounting Horizons September 2012
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papers in particular conclude that this is a primary motivation (YSW 1984; Collins et al. 1990, 1992; Christensen 1992; Hite et al. 1995). The LC construct measures the importance to the client of having a legally compliant tax return prepared by a tax professional. The variable is composed of three items (see Figure 1). Although the TMS dubs these items collectively as ‘‘legal compliance,’’ it really is a construct that captures a taxpayer’s desire to comply with the law and avoid penalties even at the expense of tax preparation fees and/or the tax liability itself. We sum and average each of the item scores to obtain the composite score. The Cronbach’s alpha is 0.80 for the scale. The confirmatory factor analysis shows LC to be an independent construct with loadings similar to that in the original scale development (Stephenson 2010). Protection from the IRS (IRS) In general, the literature suggests that there is a positive association between the desire of taxpayers to gain protection from the IRS in general, and audits specifically, and the resultant demand for professional tax preparation services (Duncan et al. 1989; Collins et al. 1990, 1992; Christensen 1992; Dubin et al. 1992; Hite and McGill 1992; Christian et al. 1994; Long and Caudill 1993; Frischmann and Frees 1999; Hite and Hasseldine 2003; Nichols and Price 2004). The literature also suggests that if the IRS audits a tax client, the client often views this as failure by the tax preparer regardless of outcome (Schisler and Galbreath 2000). The IRS construct measures the importance to the client of having their tax return prepared professionally in order to protect them from, or help them avoid, the IRS. Figure 1 shows that the variable is composed of four items. Interestingly, these four items are all technically not true. For example, there is no guarantee that a tax professional can reduce one’s tax liability to zero. In addition, the use of a tax professional does not eliminate personal culpability as the third and fourth items indicate. Confirmatory factor analysis, however, does show that these items do in fact load together on a separate item with similar loadings to those shown in the original scale development (Stephenson 2010). Interestingly, although fewer respondents find IRS protection to be a key reason for hiring a tax professional (i.e., Table 2’s mean of 3.54 is less than the scale midpoint of 4), this model explains more of the variance than the other three (i.e., Table 4, Panel D). We sum and average each of the item scores to obtain the composite score. The Cronbach’s alpha is 0.85 for this scale. Our ten independent predictor variables include marital status, age, taxpayer gender, child, income, IRS contact, college degree, tax complexity, clients’ self-assessed tax knowledge, and perceptions of the preparer tax advocacy. We include IRS contact as an exploratory variable because anecdotal evidence suggests that this variable may significantly explain the variance. Most of the literature regarding the demand for tax preparation services focuses on the dichotomous hire/not hire decision. The coefficient sign of the predictor variables in these studies may not agree with those in the present study because we are focusing solely on taxpayers who choose to hire a professional. Additionally, because this is the first study to examine the demographics behind particular motivations to hire preparers, it is exploratory in nature. Therefore, we are hesitant to predict directional associations on an a priori basis. Marital Status/Filing Status Research shows that married taxpayers who (do not) itemize are (more) less likely to use a preparer than other taxpayers (Long and Caudill 1987; Christian et al. 1993); this result likely suggests that one spouse is reasonably knowledgeable about taxes. However, our data only include participants who choose to hire professional preparers, and we ask that the person in charge of taxes for the household answer the survey. This dummy variable is coded ‘‘0’’ for unmarried clients and Accounting Horizons September 2012
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‘‘1’’ for married clients. The variable also proxies for filing status as well, since nearly 88 percent of our sample file as either single or married filing jointly. In this study, we code dummy variables as fixed factors in our MANCOVA analysis because they are categorical in nature. Age Several studies investigate how age affects the hiring of a tax preparer. In general, tax preparer use increases with age (Slemrod and Sorum 1984; Long and Caudill 1987; Christian et al. 1993). Additionally, Collins et al. (1990) find that the desire for tax minimization increases with age, and Dubin et al. (1992) conclude that age shifts the demand for preparers from chains such as H&R Block to CPAs and attorneys. To simplify our model, we collapse our six age groupings into a dummy variable. This variable is coded as ‘‘0’’ for persons less than 45 years old and ‘‘1’’ for persons over 44 years old.4 Taxpayer Gender Several studies show that female taxpayers tend to be more risk averse and more compliant than their male counterparts (Jackson and Milliron 1986; Sanders and Wyndelts 1989; Chung and Trivedi 2003). This research, by analogy, suggests that female taxpayers’ demand for tax preparer services is motivated by both the LC and IRS constructs. However, anecdotal evidence suggests that saving time should also be important. This study directly tests the gender association with each of the four primary motivations to hire. In this study, gender is a dummy variable coded ‘‘0’’ for male clients and ‘‘1’’ for female clients. Child Extant literature indicates that taxpayers who claim numerous return deductions, who have a greater number of taxpayer exemptions, and who have more dependent children are more likely to hire a tax preparer (Dubin et al. 1992; Long and Caudill 1987, 1993; Frischmann and Frees 1999). Christian et al. (1993) find that although paid preparer use increases with the number of dependents, among taxpayers who itemize the relationship is inversely related. However, anecdotal evidence suggests that persons with a greater number of children are more conservative in matters dealing with taxation in general, and the IRS in particular, compared with those with few or no children. In general, we expect that the child variable will be positively associated with our four dependent variables. The child variable measures whether or not respondents have one or more children. We measure dependent children as opposed to dependents in general because dependent children are likely to take more of the taxpayers’ time and resources than other types of dependents (i.e., dependent parents). To simplify the categories in our measure, we collapse our five child categories into a dummy variable where ‘‘0’’ represents respondents with no children, and ‘‘1’’ represents taxpayers with one or more children. Income The preponderance of literature suggests that higher-income persons are more likely to employ professional tax preparers (Slemrod and Sorum 1984; Long and Caudill 1987, 1993; Christian et al. 1993). Collins et al. (1990) conclude that higher income is associated with tax minimization motivations in hiring preparers. As the marginal cost of preparing one’s own return increases, the taxpayer is more likely to engage the services of a tax preparer in order to save time and money, 4
We utilize age 45 as the cutoff point because it represents mid-career for most professionals, as well as middle age.
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since a client with relatively more income faces a greater opportunity cost to prepare their own return. Similarly, a higher-income person generally faces greater audit risk, so they should be more concerned about legally complying with the law as well as being protected from the IRS. The income variable measures the relative self-reported gross income of surveyed clients. This ordinal categorical scale is anchored by ‘‘1’’ (less than $25,000) and ‘‘4’’ ($100,000 or more). We elect to code this as a categorical (range of income) variable as opposed to a continuous one for three reasons. First, it enhances the parsimony of the scale. Second, taxpayers are more likely to check a range than to write a number down (based on anecdotal evidence) and are not any more likely to be accurate when doing so. Third, these categories are mandated by the university Institutional Review Board (IRB). IRS Contact Schisler and Galbreath (2000) conclude that taxpayers consider an audit to be a failure on the part of the tax preparer regardless of whether the outcome is favorable or unfavorable. We include this variable because we reason clients who have been previously contacted by the IRS in the form of a letter, office or field audit have motivations that differ from other taxpayers. On one hand, the experience could have been distasteful enough for taxpayers to more highly value legal compliance and/or protection from the IRS. On the other hand, the experience could have alleviated fear of the IRS and increased the importance for saving time and/or money. This exploratory variable measures whether or not the client has had previous contact with the IRS. The dummy variable designates ‘‘0’’ for no previous IRS contact and ‘‘1’’ for previous IRS contact in the form of a letter, office, or field audit. College Degree (Education) Arena et al. (2002) conclude that tax preparer use decreases with education; however, this study focuses only on taxpayers who choose to hire a tax professional. Therefore, we are unsure on an a priori basis of the directional influence of this measure. This variable measures the extent of the client’s formal education. Our initial education measure identifies education categories anchored by ‘‘1’’ less than high school, and ‘‘6’’ doctorate degree. However, to simplify the number of categories in the variables, we create a dummy variable where ‘‘0’’ ¼ no college degree, and ‘‘1’’ ¼ college degree or graduate experience. Tax Return Complexity Tax return complexity is frequently an important reason for hiring a tax preparer (Slemrod and Sorum 1984; Long and Caudill 1987; Christian et al. 1993; Ashley and Segal 1997; Arena et al. 2002). Additionally, Collins et al. (1990) find that return complexity is inversely correlated with finding accuracy to be a primary motivation in hiring a preparer. Dubin et al. (1992) conclude that tax return complexity increases the demand for CPAs and attorneys. This independent variable measures how complex a client’s tax return is based on the number of income and deduction items on the return. For example, the complexity variable measures the sum of 17 potential different income items and 9 deduction items that a client’s return contains. Therefore, this variable is continuous based on the sum of the taxpayer’s income and deduction items. Because the variable is continuous, we code it as a covariate.5 5
For MANCOVA assessment, qualitative categorical variables, which are not ordinal, are generally coded as fixed factors. Variables that have ordinal categories may be coded either as fixed factors or as covariates. Variables that are continuous ( purely quantitative) are coded as covariates.
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Self-Assessed Tax Knowledge The literature suggests that tax knowledge is negatively associated with the demand for tax preparer services (Long and Caudill 1987, 1993; Jackson et al. 1988; Reckers et al. 1991; Dubin et al. 1992; Christian et al. 1993; Frischmann and Frees 1999). Collins et al. (1990) find that the desire for both tax minimization and accuracy decreases with tax knowledge. Therefore, those who do hire a tax preparer despite greater tax knowledge are likely to be doing so to save time or money. These persons are likely less concerned with legal compliance because their enhanced tax knowledge causes them to be more aware of the low audit rate. We also expect subjects with greater tax knowledge to be negatively associated with IRS protection because they presumably recognize that the statements making up this construct are false, and such persons likely are more willing to play the ‘‘audit lottery.’’ The self-assessed tax knowledge variable measures how much a client perceives that he/she knows about tax matters in general. This five-item ordinal scale is anchored by ‘‘1’’ (clueless about tax matters) and ‘‘5’’ (expert in tax matters). Perceptions of Preparer Tax Advocacy The related literature also investigates the advocacy of tax preparers, finding that when the law is unambiguous, professionals follow the law, but when it is ambiguous, they tend to be more aggressive on behalf of their clients (cf. Klepper et al. 1991). Attorneys and CPAs are the most aggressive tax professionals (Ayres et al. 1989; Erard 1993; Cuccia 1995). Tax practitioners often are more aggressive than tax clients really want them to be (Jackson et al. 1988; Hite and McGill 1992; Stephenson 2007). However, there is also evidence to suggest that clients have influence over the aggressiveness of the tax preparer as well (Reckers et al. 1991; Schisler 1994, 1995; Kahle and White 2004). The literature further suggests that tax professionals who have recent audit success with the IRS tend to be more aggressive than other tax professionals (Kaplan et al. 1988; Duncan et al. 1989) even though clients consider audits of any kind to be a failure of the preparer regardless of outcome (Schisler and Galbreath 2000). We modify the Mason and Levy (2001; hereafter, Mason-Levy) advocacy scale to be a measure of client perceptions of preparer tax advocacy in the current study. Our modified measure of tax advocacy of the preparers as perceived by their clients should generally be positively associated with the four-client demand focal variables in the study on an a priori basis. Simply stated, clients generally want their paid professionals to be their advocate in tax matters. Although the original use of the Mason-Levy (2001) scale was to determine the self-reported levels of advocacy or aggressiveness exhibited by tax preparers, we modify the instructions to have the clients predict how their preparers would answer. This strategy encourages the client to think more carefully about their perceptions of their specific preparer as opposed to simply answering based on their own experiences or on tax preparers in general. The eight6 items we use are based on a seven-point Likert scale anchored by ‘‘1’’ (strongly disagree) and ‘‘7’’ (strongly agree). The Cronbach’s alpha is 0.85 for our modified scale. Figure 2 presents all nine items of the Mason-Levy (2001) scale. Sample Our sample has higher income, more clients filing ‘‘Married Filing Jointly,’’ more Caucasians, older, and more college-educated participants than the national averages. Our gender mix approximates national averages. Because the sample is only of clients who use preparers, these demographics are not surprising, although we find it interesting that about the same proportion of 6
Item 7 of the scale has been dropped for this analysis because it is not consistent with the other eight items. Item 7 is the only item that pertains to loyalty to the tax system as opposed to loyalty to the client.
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FIGURE 2 Mason-Levy Client Advocacy Scale (Mason and Levy 2001)a 1. When examining a tax return, I tend to point out to taxpayers reasonable positions they could have taken which would have contributed to minimizing their tax liability. 2. The taxpayer has the right to structure transactions in ways that yield the best tax result, even if the law is unclear in an area. 3. It is important to use trends in the law by trying to establish a pattern of more favorable treatment for the taxpayer and then extending this pattern to the taxpayer’s position. 4. I always interpret unclear/ambiguous laws in favor of the taxpayers. 5. Where no judicial authority exists with respect to an issue, I feel that the taxpayer is entitled to take the most favorable tax treatment. 6. I feel I should apply ambiguous tax law to the taxpayer’s benefit. 7. Generally speaking, my loyalties are first to the tax system, then to the taxpayer. 8. I believe it is important that I encourage taxpayers to pay the least amount of taxes possible. 9. In an instance where no judicial authority exists with respect to an issue and where the Code and Regulations are ambiguous, I feel that the taxpayer is entitled to take the most favorable tax treatment. a
Taxpayers were asked to answer these questions the way that they believed their tax preparers would.
households have females versus males as the primary person in charge of tax matters. Prior research shows that paid preparer use increases with income, age, number of dependents claimed, and marginal tax rate, and decreases with greater education and tax knowledge and marriage (Slemrod and Sorum 1984; Long and Caudill 1987; Klepper et al. 1991; Christian et al. 1993; Arena et al. 2002). Therefore, aside from race and the married filing jointly status, our data are similar to the subset of the population that uses paid preparers; however, statistics for taxpayers who hire preparers are not available for a more critical comparison. Because we gathered our data on a nonrandom basis, we suggest caution when generalizing our findings. Table 1 summarizes fixed-factor categorical predictor variables that are used in our subsequent MANCOVA empirical assessment. The mean and standard error for each of our four dependent variables (i.e., SM, ST, LC, and IRS) are included for each category to better familiarize the reader with subject responses. Key demographical variables are presented to highlight study participant fixed-factor sample percentages. RESULTS We employ multivariate analysis of covariance (MANCOVA) to investigate associations between our four focal dependent variable constructs and our ten predictor variables. This method is appropriate because our four dependent constructs have significant intercorrelation. MANCOVA allows us to measure the impact of the ten independent variables on all four dependent variables simultaneously, and also determines whether there are interaction effects.7 The next section reviews the statistical results followed by sensitivity analysis, discussion, and conclusions. 7
For readers who wish to learn more about MANCOVA (similar to MANOVA), please refer to Hair et al. (1998, 326–386). MANCOVA is especially appropriate when analyzing multiple-correlated metric (quantitative) dependent variables that are predicted by nonmetric qualitative categorical (coded as fixed factors) and metric quantitative (coded as covariates) independent variables. MANCOVA permits an exploitation of the data that permit reductions in type 1 errors (e.g., probability of incorrectly rejecting the null hypothesis, which is similar to saying a difference or correlation exists when it actually does not). The method also enhances the probability of finding significant predictor variables, which likely is not the case with a univariate analysis (e.g., ANCOVA).
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TABLE 1 Sample Characteristics for MANCOVA Fixed Factors Variable Marital/Filing Status Age
Category
Unmarried Married Older than 44 Under 45 Taxpayer Gender Male Female Child No child One or more children Income Less than $25,000 $25,000–$49,999 $50,000–$99,999 $100,000 or more IRS Contact No IRS Contact IRS Contact College Degree No degree Bachelor’s or higher
Sample Count % 95 245 217 123 172 168 170 170 21 88 143 88 236 104 166 174
28 72 64 36 51 49 50 50 6 26 42 26 69 31 49 51
Moneya
Timeb
Legalc
IRSd
M
SE
M
SE
M
SE
M
SE
5.90 5.95 5.97 5.87 5.88 5.96 5.82 6.02 5.96 5.92 5.94 5.85 6.01 5.82 5.94 5.90
0.11 0.08 0.10 0.08 0.09 0.08 0.09 0.09 0.20 0.10 0.09 0.11 0.07 0.11 0.09 0.09
5.43 5.33 5.44 5.32 5.20 5.56 5.26 5.50 4.97 5.43 5.55 5.58 5.49 5.27 5.35 5.41
0.12 0.09 0.11 0.09 0.10 0.10 0.10 0.10 0.23 0.12 0.10 0.13 0.08 0.12 0.10 0.10
5.47 5.70 5.58 5.59 5.44 5.74 5.41 5.77 5.70 5.80 5.53 5.32 5.50 5.67 5.70 5.48
0.14 0.11 0.13 0.10 0.12 0.11 0.12 0.12 0.27 0.14 0.11 0.15 0.10 0.14 0.12 0.12
3.55 3.68 3.60 3.64 3.50 3.74 3.51 3.72 4.07 3.77 3.42 3.22 3.64 3.59 3.67 3.57
0.16 0.12 0.15 0.12 0.14 0.13 0.13 0.14 0.30 0.15 0.13 0.17 0.11 0.16 0.14 0.13
a
Save Money: 3 item seven-pt. Likert construct (see Figure 1) where ‘‘1’’ ¼ SD and ‘‘7’’ ¼ SA. Save Time: 4 item seven-pt. Likert construct (see Figure 1) where ‘‘1’’ ¼ SD and ‘‘7’’ ¼ SA. c Legal Compliance: 3 item seven-pt. Likert construct (see Figure 1) where ‘‘1’’ ¼ SD and ‘‘7’’ ¼ SA. d IRS Protect: 4 item seven-pt. Likert construct (see Figure 1) where ‘‘1’’ ¼ SD and ‘‘7’’ ¼ SA. Where: M ¼ mean; SE ¼ standard error; SD ¼ strongly disagree; and SA ¼ strongly agree. Other variable definitions are in Table 2. b
Descriptive Statistics and Correlations Table 2 summarizes the descriptive statistics and correlations for the study. We identify alpha , 0.10 as a measure of marginal significance, which is reasonable for an exploratory behavioral study. Not surprisingly, the four focal constructs are significantly intercorrelated. However, Stephenson’s (2010) scale development demonstrates that each construct loads on a separate and distinct factor, and paired samples t-tests (not shown) demonstrate that all pairs of the construct means are statistically different (p , 0.001) except for the ST-LC comparison. Table 2 also highlights a number of other meaningful correlations. Although the four dependent constructs, SM, ST, LC, and IRS, are correlated, factor analysis ensures that they are measuring four distinct concepts, and the significant loadings are shown in Figure 1. Other noteworthy correlations include perceptions of tax advocacy positively correlated with SM, ST, LC, and IRS, while child is positively correlated with SM, ST, and LC. In addition, taxpayer gender is positively correlated with ST, LC, and IRS, but negatively correlated with self-assessed tax knowledge, income, and IRS contact.8 It is also noteworthy that college degree and tax complexity are highly correlated with income. Not surprisingly, child is negatively correlated with age, and 8
This implies that men are correlated with higher income, have had more previous IRS contact, and perceive themselves as having greater tax knowledge compared with women in our sample.
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TABLE 2 Descriptive Statistics, Correlations, and Variable Definitions Panel A: Save Money (DV) to Age (IV) VN
M
StD
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12 13 14
5.95 5.49 5.57 3.54 0.72 0.64 0.49 0.50 2.87 0.31 0.51 8.33 2.73 4.90
0.91 1.05 1.23 1.45 0.45 0.48 0.50 0.50 0.87 0.46 0.50 2.97 0.76 3.10
— 0.47** 0.27** 0.21** 0.06 0.09^ 0.05 0.13* 0.03 0.10^ 0.04 0.08 0.04 0.23**
— 0.36** 0.34** 0.03 0.11* 0.19** 0.13* 0.03 0.13* 0.01 0.01 0.09 0.21**
— 0.45** 0.02 0.05 0.17** 0.12* 0.20** 0.03 0.18** 0.16** 0.18** 0.11*
— 0.04 0.02 0.15** 0.02 0.25** 0.09^ 0.15** 0.18** 0.20** 0.32**
— 0.02 0.24** 0.24** 0.33** 0.07 0.02 0.22** 0.09 0.03
— 0.13* 0.34** 0.07 0.25** 0.01 0.02 0.07 0.07
Panel B: Taxpayer Gender (IV) to Perceptions of Preparer Tax Advocacy (IV) VN
M
StD
7
8
9
10
11
12
13
14
7 8 9 10 11 12 13 14
0.49 0.50 2.87 0.31 0.51 8.33 2.73 4.90
0.50 0.50 0.87 0.46 0.50 2.97 0.76 3.10
— 0.06 0.23** 0.18** 0.14** 0.10^ 0.19** 0.01
— 0.10^ 0.08 0.04 0.19** 0.04 0.09^
— 0.18** 0.34** 0.28** 0.22** 0.07
— 0.18** 0.16** 0.19** 0.04
— 0.15** 0.21** 0.04
— 0.25** 0.03
— 0.04
—
*, **, ^; p , 0.05, p , 0.01, and p , 0.10, respectively. n ¼ 340. VN ¼ variable number; M ¼ mean; StD ¼ standard deviation; SD ¼ strongly disagree; SA ¼ strongly agree; DV ¼ dependent variable; and IV ¼ independent variable. Variable Definitions: 1 ¼ Save Money (DV): 3 item seven-pt. Likert construct (see Figure 1), where ‘‘1’’ ¼ SD, and ‘‘7’’ ¼ SA; 2 ¼ Save Time (DV): 4 item seven-pt. Likert construct (see Figure 1), where ‘‘1’’ ¼ SD, and ‘‘7’’ ¼ SA; 3 ¼ Legal Compliance (DV): 3 item seven-pt. Likert construct (see Figure 1), where ‘‘1’’ ¼ SD, and ‘‘7’’ ¼ SA; 4 ¼ IRS Protect (DV): 4 item seven-pt. Likert construct (see Figure 1), where ‘‘1’’ ¼ SD, and ‘‘7’’ ¼ SA; 5 ¼ Marital Status (IV): dummy variable, where ‘‘0’’ ¼ unmarried, and ‘‘1’’ ¼ married; 6 ¼ Age (IV): dummy variable, where ‘‘0’’ ¼ under 45, and ‘‘1’’ ¼ over 44; 7 ¼ Taxpayer Gender (IV): dummy variable, where ‘‘0’’ ¼ male, and ‘‘1’’ ¼ female; 8 ¼ Child (IV): dummy variable, where ‘‘0’’ ¼ no children, and ‘‘1’’ ¼ one or more children; 9 ¼ Income (IV): measured by four items anchored by ‘‘1’’ ¼ less than $25,000, and ‘‘4’’ ¼ $100,000 or more; 10 ¼ IRS contact (IV): dummy variable, where ‘‘0’’ ¼ no previous client IRS contact, and ‘‘1’’ ¼ previous IRS contact; 11 ¼ College Degree (IV): dummy variable, where ‘‘0’’ ¼ no college degree, and ‘‘1’’ ¼ college degree or higher education; 12 ¼ Tax Complexity (IV): measured by the sum of 17 income items and 9 deduction items; 13 ¼ Self-Assessed Tax Knowledge (IV): measured by 5 items, where ‘‘1’’ ¼ client clueless about tax issues, and ‘‘5’’ ¼ client is a tax expert; and 14 ¼ Perceptions of Preparer Tax Advocacy (IV): 8 item seven-pt. Likert construct (see Figure 2), where ‘‘1’’ ¼ SD, and ‘‘7’’ ¼ SA.
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positively correlated with marital status. Finally, respondents with more complex returns and higher incomes are more likely to have had previous IRS contact. MANCOVA Data Analysis Our initial goal is to analyze our ten independent variables in association with all four dependent variables in two stages. The first stage uses a full factorial (including all interactions for fixed factors) MANCOVA model. Stage two then includes the ten focal predictor variables in a main effects MANCOVA along with significant interaction variables. Stage One: Full Factorial MANCOVA For purposes of stage one of our analysis, we split the fixed factors into two separate categories because it is not possible to run a full factorial MANCOVA with such a large number of variables. Full factorial MANCOVA is important because it efficiently assesses fixed factor interactions on both a multivariate and univariate basis. Our two categories are based on logical categorizations. For example, category one variables represent fundamental demographics including marital status (which also proxies for filing status), age, taxpayer gender, and child. Category two variables are either cognitively based or relate to the tax return, and include income, tax complexity, IRS contact, and college degree. We assess the two categories using MANCOVA. Stage one provides a few interactions that are significant predictors, along with a number of our demographic predictors.9 Stage Two: Main Effects MANCOVA The purpose of stage two of our MANCOVA analysis is to assess the final combined (category 1 and category 2) model that includes the ten predictor variables as well as the significant interaction variables identified in the first stage of the analyses. However, our diagnostic tests indicate that the interaction terms introduce severe multicollinearity, so we removed the interaction terms to ensure that our main effects MANCOVA analysis has statistical validity.10 All predictor variables are coded as fixed factors with the exception of three metric (quantitative) variables: tax complexity, self-assessed tax knowledge, and perceptions of preparer tax advocacy, which are coded as covariates. Table 3 provides the multivariate analysis of the final model using Wilk’s Lambda for assessment. The most significant predictor variable in the multivariate analysis is perception of preparer advocacy, but other significant predictors include taxpayer gender, income, and tax complexity. Child, IRS contact, and self-assessed tax knowledge are marginally significant (p , 0.10). Table 4 presents the univariate results of the final model for each dependent variable (e.g., SM, ST, LC, and IRS). Panel A presents the findings for SM. This model is the weakest of the four. The model is significant (p ¼ 0.000; R2 ¼ 0.10; Adjusted R2 ¼ 0.07).11 The model is primarily driven by the positive association with perception of preparer advocacy (p ¼ 0.000). There is also a 9
10
11
We do not show these initial findings in a table due to space limitations as well as conciseness and readability concerns. Variance inflation factors (VIF) that measure multicollinearity approached 20 to 30 for some interaction terms. After removing interaction terms, no variable has a VIF in excess of 1.4. Generally, it is desirable for VIFs to be less than 10. Note that the model R2 varies depending on how the predictor variables are coded in our MANCOVA analysis. For example, if all predictor variables were coded as fixed factors (would then be a MANOVA), the model R2 for save money increases from 0.10 to 0.28; the model R2 for save time increases from 0.13 to 0.28; the model R2 for legal compliance increases from 0.13 to 0.32; and the model R2 for IRS protect increases from 0.20 to 0.40.
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TABLE 3 Multivariate Analysis of Covariance (MANCOVA) Effects of Money, Time, Legal Compliance, and IRS on Tax Client Demographics and Perceptions Source Intercept Marital Statusa Agea Taxpayer Gender Child Income IRS Contact College Degreea Tax Complexityb SA Tax Knowledgeb Per. Preparer Advocacyb
Wilk’s k
F
Partial g2c
Observed Powerd
0.871 0.987 0.995 0.965 0.975 0.926 0.975 0.988 0.967 0.974 0.854
11.979*** 1.027 0.377 2.919* 2.059^ 2.119* 2.050^ 1.003 2.740* 2.185^ 13.857***
0.129 0.013 0.005 0.035 0.025 0.025 0.025 0.012 0.033 0.026 0.146
1.000 0.324 0.137 0.783 0.612 0.905 0.610 0.316 0.753 0.642 1.000
*, **, ***, ^; p , 0.05, p , 0.01, p , 0.001, and p , 0.10, respectively. a Although not significant at the multivariate level, Marital Status, Age, and College Degree are deemed to be important variables to be retained in the final model based on subject matter knowledge and previous literature. b Tax Complexity, Self-Assessed Tax Knowledge, and Perceptions of Preparer Advocacy are coded as covariates. The other independent variables are all coded as fixed factors. c Partial g2 is measured on a scale of 0 to 1 and explains how much variance in the dependent variable is explained by the independent variable. d Observed power is the ability to detect an effect if there is one. The measure is on a scale of 0 to 1. For example, a power of .950 suggests there is a 5% chance of failing to detect an effect that actually exists. n ¼ 340 Variables are defined in Table 2.
marginally significant positive association with child (p ¼ 0.07) and a negative association with previous IRS contact (p ¼ 0.09). Evidently, persons who have had previous IRS contact have less desire to save money. Panel B of Table 4 presents univariate results for the dependent variable ST. The model is also significant (p ¼ 0.000; R2 ¼ 0.13; Adjusted R2 ¼ 0.10). This model is primarily driven by the positive associations with perception of preparer advocacy (p ¼ 0.000) and taxpayer gender (p ¼ 0.002). The latter finding suggests that female clients are more concerned with saving time by having their tax return prepared as compared with their male counterparts. As with SM, there is again a marginally significant positive association with child (p ¼ 0.057) and a negative association with previous IRS contact (p ¼ 0.09). Panel C of Table 4 presents univariate results for the dependent variable LC. Again, this model is significant (p ¼ 0.000; R2 ¼ 0.13; Adjusted R2 ¼ 0.10). There are three predictor variables that provide statistically significant associations with LC. For example, taxpayer gender (p ¼ 0.027) and number of children (p ¼ 0.012) are both positively associated with LC, suggesting that females as well as those with a larger number of dependents value a legally compliant return more than persons without these demographic characteristics. Clients with complex returns (p ¼ 0.031) are generally less concerned with legally compliant returns. Clients who desire their preparers to be advocates for them (p ¼ 0.066) want them to prepare accurate returns. Panel D of Table 4 presents univariate results for IRS, which is the strongest model of the four. Once again, this model is significant (p ¼ 0.000; R2 ¼ 0.20; Adjusted R2 ¼ 0.17). Clients with higher income (p ¼ 0.036) and complex returns (p ¼ 0.042) are less likely to be concerned with IRS Accounting Horizons September 2012
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TABLE 4 Univariate Analysis of Covariance (ANCOVA) Effects of Tax Client Demographics and Perceptions on Money, Time, Legal Compliance, and IRS Panel A: Dependent Measure is Saving Money (n ¼ 340; R2 ¼ 0.10; Adjusted R2 ¼ 0.07) Source (Sign)
df
MSb
F
Partial g2c
Observed Powerd
Model Intercept Marital Status (þ) Age () Taxpayer Gender (þ) Child (þ) Income () IRS Contact () College Degree () Tax Complexitya (þ) SA Tax Knowledgea (þ) Per. Preparer Advocacya (þ)
12 1 1 1 1 1 3 1 1 1 1 1
2.374 8.900 0.133 0.541 0.451 2.491 0.136 2.207 0.166 1.517 1.432 17.453
3.061*** 11.476** 0.172 0.697 0.581 3.212^ 0.175 2.846^ 0.214 1.956 1.846 22.504***
0.101 0.034 0.001 0.002 0.002 0.010 0.002 0.009 0.001 0.006 0.006 0.064
0.993 0.922 0.070 0.132 0.118 0.431 0.082 0.391 0.075 0.286 0.273 0.997
Panel B: Dependent Measure is Saving Time (n ¼ 340; R2 ¼ 0.13; Adjusted R2 ¼ 0.10) Source (Parameter Sign)
df
MSb
F
Partial g2c
Observed Powerd
Model Intercept Marital Status () Age () Taxpayer Gender (þ) Child (þ) Income (þ) IRS Contact () College Degree (þ) Tax Complexitya () SA Tax Knowledgea () Per. Preparer Advocacya (þ)
12 1 1 1 1 1 3 1 1 1 1 1
4.050 24.733 0.499 0.852 9.403 3.636 1.978 2.859 0.253 0.000 0.767 20.676
4.065*** 24.827*** 0.501 0.856 9.439** 3.650^ 1.986 2.870^ 0.254 0.000 0.770 20.755***
0.130 0.071 0.002 0.003 0.028 0.011 0.018 0.009 0.001 0.000 0.002 0.060
0.999 0.999 0.109 0.152 0.856 0.478 0.510 0.393 0.079 0.050 0.141 0.995
Panel C: Dependent Measure is Legal Compliance (n ¼ 340; R2 ¼ 0.13; Adjusted R2 ¼ 0.10) Source (Parameter Sign)
df
MSb
F
Partial g2c
Observed Powerd
Model Intercept Marital Status (þ) Age (þ) Taxpayer Gender (þ) Child (þ) Income () IRS Contact (þ) College Degree () Tax Complexitya () SA Tax Knowledgea () Per. Preparer Advocacya (þ)
12 1 1 1 1 1 3 1 1 1 1 1
5.457 27.019 2.709 0.014 6.698 8.695 2.612 1.895 3.395 6.398 3.476 4.616
4.024*** 19.926*** 1.998 0.010 4.940* 6.412* 1.926 1.398 2.504 4.718* 2.564 3.404^
0.129 0.057 0.006 0.000 0.015 0.019 0.017 0.004 0.008 0.014 0.008 0.010
0.999 0.994 0.291 0.051 0.601 0.714 0.496 0.218 0.351 0.582 0.358 0.452 (continued on next page)
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TABLE 4 (continued) Panel D: Dependent Measure is Protection From the IRS (n ¼ 340; R2 ¼ 0.20; Adjusted R2 ¼ 0.17) Source (Parameter Sign)
df
MSb
F
Partial g2c
Observed Powerd
Model Intercept Marital Status (þ) Age (þ) Taxpayer Gender (þ) Child (þ) Income () IRS Contact () College Degree () Tax Complexitya () SA Tax Knowledgea () Per. Preparer Advocacya (þ)
12 1 1 1 1 1 3 1 1 1 1 1
11.809 57.491 0.940 0.108 4.315 2.873 5.050 0.164 0.620 7.288 5.700 63.653
6.744*** 32.833*** 0.537 0.061 2.465 1.641 2.884* 0.094 0.354 4.162* 3.255^ 36.353***
0.198 0.091 0.002 0.000 0.007 0.005 0.026 0.000 0.001 0.013 0.010 0.100
1.000 1.000 0.113 0.057 0.347 0.248 0.686 0.061 0.091 0.530 0.436 1.000
*, **, ***, ^; p , 0.05, p , 0.01, p , 0.001, and p , 0.10, respectively. a Tax Complexity, Self-Assessed Tax Knowledge, and Perceptions of Preparer Advocacy are coded as covariates. The other independent variables are all coded as fixed factors. b MS ¼ mean square: Represents the average of the square of the observations. c Partial g2 is measured on a scale of 0 to 1 and explains how much variance in the dependent variable is explained by the independent variable. d Observed power is the ability to detect an effect if there is one. The measure is on a scale of 0 to 1. For example, a power of .950 suggests there is a 5% chance of failing to detect an effect that actually exists. Variables are defined in Table 2.
protection. Similarly, persons who perceive they know more about the tax law are somewhat less concerned with being protected from the IRS at the margin (p ¼ 0.072). Although perception of preparer advocacy is positively and significantly associated with all four dependent variables, Panel D of Table 4 underscores that the association with IRS is by far the strongest (e.g., p ¼ 0.000; partial g2 ¼ 0.100; power ¼ 1.000). This suggests that clients are especially desirous of having their tax preparer be their advocate in a manner that shields them from the IRS. Overall, the results of Table 4, Panels A–D, answer RQ1–RQ4 pertaining to key client perceptual and demographic variables that are significantly associated with the four dependent variables (e.g., SM, ST, LC, and IRS). In addition, the Table 3 and Table 4 results corroborate the Table 1 and Table 2 presentation. The next section of the manuscript provides a summary and discussion of the findings, followed by conclusions, contributions, limitations, and suggestions for future research. DISCUSSION Summary Our main purpose is to investigate, using MANCOVA, four key demand-for-tax-preparation determinants (SM, ST, LC, and IRS) to fill the previously identified gaps in the literature pertaining to what client perceptions and demographic variables drive these four focal constructs in particular. The following sub-section focuses on our specific findings that have implications for public policy and professional practice. Accounting Horizons September 2012
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Specific Findings and Discussion of Pragmatic and Public Policy Implications Our MANCOVA analysis in Table 3 (multivariate) and Table 4, Panels A–D (univariate) answers RQ1–RQ4, and also provides new information that assists understanding of the association of client perceptions and demographic characteristics with our four focal dependent variables. For example, client perceptions of preparer tax advocacy, which measures client perceptions about the degree of support or aggressiveness that their tax preparer exhibits, are positively and significantly associated with all four dependent focal variables. Although Stephenson (2007) concludes that clients generally perceive tax professionals to be less tax aggressive than the preparers perceive themselves to be, a desire to obtain a tax advocate is evidently by far the strongest motivator given our four dependent variables for hiring a tax preparer based on our sample. The following discussion highlights new information besides this consistent tax advocacy finding and related insights by dependent variable. Saving Money (RQ1) The dependent variable saving money (SM) is marginally negatively associated with IRS contact. Therefore, clients who have had previous contact with the IRS are somewhat less likely to focus on saving money. This finding also has interesting public policy implications. For example, the negative sign suggests that IRS audits and other such contact cause taxpayers to be less concerned with saving money, which means that they are less willing to take risks on their return. Because Schisler and Galbreath (2000) conclude that clients view contact with the IRS (such as audits) as failure by their tax preparer, tax practitioners should consider that new clients who have endured a tax audit in their recent past are less concerned with saving money and instead wish to take less tax risks in the future. It is not surprising that respondents with children are somewhat more concerned with saving money. Saving Time (RQ2) The finding that females are more likely than males to hire a preparer to save time is noteworthy. It is possible that women responsible for the tax return value saving time disproportionately to men because they likely are responsible for other time consuming tasks as well, such as domestic responsibilities, although this is purely conjecture. Respondents with children are also marginally more concerned with saving time, which is not surprising. Although marginally significant, it is noteworthy that subjects with previous IRS contact are less concerned with saving time. Given such contact is often time consuming; this could have moderated their time expectations for annual tax filings. Legal Compliance (RQ3) Our results underscore that females are more concerned with having a legally compliant tax return, and thus appear more conservative. This finding is consistent with several studies that demonstrate that female taxpayers tend to be more risk averse and more compliant than their male counterparts (Jackson and Milliron 1986; Sanders and Wyndelts 1989; Chung and Trivedi 2003). Similarly, our findings corroborate anecdotal evidence that suggests respondents with children tend to also be more conservative.12 12
It is interesting to note that females in our sample are not more likely than men to have one or more dependent children.
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Conversely, our findings demonstrate that respondents with more complex returns are less likely to desire an accurate return. This finding is consistent with Collins et al. (1990), who conclude complexity is inversely related to accuracy. We can only surmise that this negative association indicates that these respondents are aware of the very low audit rates that approximate an average of only 1 percent, so these more sophisticated taxpayers are more willing to play the ‘‘audit lottery.’’ This is likely true even though these subjects do not perceive that they possess great knowledge of tax law in general. Protection from the IRS (RQ4) The literature suggests that protection from the IRS is very important to taxpayers in general (Duncan et al. 1989; Collins et al. 1990, 1992; Christensen 1992; Dubin et al. 1992; Hite and McGill 1992; Christian et al. 1994; Long and Caudill 1993; Frischmann and Frees 1999; Hite and Hasseldine 2003; Nichols and Price 2004). In fact, protection from the IRS is the most often researched construct of the four we discuss. However, the Table 2 mean values for each construct highlight that the IRS mean of 3.54 for this construct (which is less than the scale midpoint of 4), is less important to the surveyed taxpayers than the other three dependent measures in our study. However, it is also noteworthy that this model is the strongest of the four we assess (F ¼ 6.744).13 Our results suggest that clients with relatively complex returns, those with higher incomes, as well as those who self-assess a greater tax knowledge, are less likely to hire a tax preparer to protect them from IRS conflict, which is also consistent with Collins et al. (1990). We presume that this negative association is related to the ‘‘sophisticated taxpayer’’ explanation that we provided earlier regarding the demand for a legally compliant tax return, although this is conjecture. CONCLUSION Implications for Professional Practice Our findings provide implications for professional tax practitioners. For example, our statistical results suggest indirectly that it benefits professional tax preparers to tactfully inquire of their clients what particular motive is driving the decision to employ them. Most preparers ask their clients to complete intake sheets with their tax information. These intake sheets could ask clients about their perceptions that deal with advocacy beliefs as well as issues relating to our four dependent constructs. In short, we contend our findings underscore the importance of enhanced communication between the client and tax professional, while avoiding misunderstandings regarding tax preparation services and underlying objectives. Tax preparers should be especially careful to make conservative decisions when dealing with clients who wish to have the most accurate return possible and who also gain protection from IRS audits. It is also helpful for professionals to be cognizant that women are generally more risk averse than men (as suggested by Jackson and Milliron [1986], and Sanders and Wyndelts [1989]), and are relatively more concerned with time savings. Conversely, taxpayers with higher incomes, complex returns, and who are relatively more knowledgeable about the tax system are less concerned with conservative tax positions and protection from the IRS, and therefore wish for their tax preparers to be more aggressive regarding areas that are ambiguous in the tax law. Ultimately, enhanced client versus tax professional communication about tax return objectives and expectations, within ethical and legal boundaries, should enhance service effectiveness and client satisfaction. 13
We contend this model strength is likely due to the variability of subject responses. For example, the Table 2 standard deviation is the highest for IRS compared to the other three dependent variables.
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Study Contributions This study contributes to the demand-for-tax-preparer literature using a unique behavioral tax sample. This is also the first study of which we are aware that deconstructs key client explanatory demographic and perceptual variables associated with demand for tax preparation services (i.e., SM, ST, LC, and IRS). We find that the majority of the research dealing with demand for tax preparer services focuses on the drivers of that demand, such as our four dependent variables. This study, in contrast, investigates explanatory client perceptions and demographic variables that impact the four key demand variables themselves using a focused sample that hired tax professionals. Our most noteworthy finding is that perception of preparer advocacy explains the largest share of the variance (see partial g2 values in Tables 3 and 4) in taxpayers’ motivations for using a professional preparer. Limitations and Suggestions for Future Research The usual limitations that apply to any behavioral survey research apply to this study as well, including possible same source bias, self-selection bias, social desirability bias, and demand effects. Our data were not gathered randomly, and demographically reflects a subset of the country that is not necessarily representative of the nation as a whole. Our sample specifically does not include anyone that did not hire a paid preparer, so in a sense we report on a truncated sample. Also, because of university Institutional Review Board requirements, we were forced to collect data such as age and income based on categories rather than using actual specific data that would provide continuous measures as we had initially desired. Our survey is composed of self-reported information, so it is also possible that this introduced bias or otherwise inaccurate information, and we did not control for personal characteristics such as honesty and social responsibility. Furthermore, we do not intend to imply causality regarding any of the statistical associations that we discuss. We tested for nonresponse bias by comparing responses received shortly after mailing to those received last but found no statistically significant differences. Although the findings are new and interesting, the explained variance is modest, and suggests that more research is needed to augment predictor variables associated with the key taxpayer motivations to hire a tax preparer investigated in this study. Future research could focus on data obtained from the Big 4 international firms to complement our sample that specifically focuses on smaller, regional accounting firms. Furthermore, research should investigate client versus tax preparer perceptual gaps relating to motivations that drive demand for professional tax services. Finally, determining which taxpayer characteristics would indicate a demand for tax software would help to complete our knowledge of the demand for tax services.
REFERENCES Arena, P., J. F. O’Hare, and M. P. Stavrianos. 2002. Measuring taxpayer compliance burden: A microsimulation approach. National Tax Association—Proceedings: 333–341. Ashley, T., and M. A. Segal. 1997. Paid tax preparer determinants extended and reexamined. Public Finance Review 25 (3): 267–284. Ayres, F. L., B. R. Jackson, and P. S. Hite. 1989. The economic benefits of regulation: Evidence from professional tax preparers. The Accounting Review 64 (2): 300–312. Christensen, A. L. 1992. Evaluation of tax services: A client and preparer perspective. Journal of the American Taxation Association 14 (2): 60–87. Christian, C. W., S. Gupta, and S. Lin. 1993. Determinants of tax preparer usage: Evidence from panel data. National Tax Journal 46 (4): 487–503. Accounting Horizons September 2012
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