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The myth of salary discrimination in public relations
Public Relations Review 31 (2005) 73–83
The myth of salary discrimination in public relations James G. Hutton∗ Silberman College of Business and the Corporate Communications Institute, Fairleigh Dickinson University, Teaneck, NJ 07666, USA Received 6 March 2004; received in revised form 23 October 2004; accepted 1 November 2004
Abstract One of the more puzzling dimensions of the public relations literature is the persistent claim of salary discrimination against women in the field, despite the absence of even a single comprehensive study on the topic. Even more disturbing is that closely related fields like marketing and advertising—as well as more professionalized fields like medicine—have made no such claims of discrimination, despite larger salary gaps and larger correlations between salary and gender than in the PR field. Detailed statistical analysis of a major salary survey and a review of existing studies both indicate that there is no empirical reason to believe that there is gender-based salary discrimination in the PR field. Findings from the PR salary survey show that there is little or no discrimination, even without considering factors (e.g., employment breaks-in-service) that were shown to have substantial explanatory power in salary surveys from other fields. Circumstantial evidence of PR salary discrimination existed 20 years ago, but the evidence available since that time suggests strongly that there has been little or no salary discrimination for at least the past decade. © 2004 Elsevier Inc. All rights reserved. Keywords: Public relations; Gender; Salary discrimination
1. Introduction Given the longstanding claims of salary discrimination in public relations, one would assume those assertions are based on a solid foundation of scientific evidence. Remarkably, however, not a single comprehensive study concerning the role of gender in public relations salaries has ever been conducted. ∗
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J.G. Hutton / Public Relations Review 31 (2005) 73–83
Circumstantial evidence of salary discrimination existed 20 years ago, but the evidence available since that time suggests very strongly that there has been little or no salary discrimination for at least the past decade.
2. Background One of the first major attempts to study issues of gender in public relations was the Velvet Ghetto study (Cline et al., 1986), published by the International Association of Business Communicators (IABC), consisting almost entirely of anecdotal evidence and very small-scale studies that lacked statistical validity. It presented circumstantial evidence to suggest that there may have been significant gender discrimination when it came to salaries in the PR field. Unfortunately, however, it included no comprehensive or statistically significant studies capable of providing a benchmark or presenting a scientific argument. Work by Broom and Dozier in the 1980s provided a more scientific basis for claims of discrimination (see Broom, 1982; Broom & Dozier, 1986). They made a fairly persuasive circumstantial case for salary discrimination, but, again, their studies were not comprehensive, focusing rather narrowly on public relations roles (i.e., managers versus technicians). More important, Broom and Dozier’s own follow-up research in 1991 (Dozier & Broom, 1995) came to a very different conclusion than their earlier research. Their research did not indicate why the relationship between salary and gender had changed, but their research indicated that the world had changed from the early 1980s to the early 1990s, as explained by Toth (2001): [Broom and Dozier] reported that roles still predicted salaries, with the managerial role being valued more than the technical role. However, they concluded that, based on their survey, “the relation between gender and salary is not statistically significant after controlling for the influences of professional experience, manager role enactment and decision-making participation.” This was a significant break in the previous research of these and other authors who had found a direct relationship between salary and gender. Later studies were fraught with conceptual and methodological mistakes, as well as bias. An oft-cited 1991 monograph published by the PRSA Foundation (Wright, Grunig, Springston & Toth, 1991), for example, included numerous inconsistencies, numbers that (literally) did not add up, misuse of research techniques, many inaccurate statements, and biased presentation of findings (see Hutton, 1993). In some cases, the statements and conclusions of the monograph contradicted the data contained in the report itself. The bias of the PRSA monograph and its authors was perhaps best illustrated by its interpretation of the results of focus groups. In an article summarizing the findings of the monograph (Wright & Springston, 1991), it was asserted that “Results from . . . the focus group stages of this research project show that gender discrimination exists in American PR.” Anyone who carefully read the monograph itself, however, discovered that the claim of pervasive discrimination was based on the opinions of a single, non-randomly selected, four-woman focus group, whose views were directly contradicted by a single, 11-man focus group. In other words, the authors of the monograph completely dismissed the comments of the 11 men, while extrapolating the comments of four women to the entire U.S. PR profession.
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3. Methodological deficiencies One of the most puzzling and disturbing aspects of the rhetoric is the enormous disconnection between the claims made in the field of public relations compared with the absence of claims made in other professional disciplines. The correlation between salary and gender (i.e., the gap between men’s and women’s salaries) is even greater in professions like medicine (Baker, 1996) and marketing/advertising (Kiecker, Hunt & Chonko, 1991), yet scholarly claims of discrimination in those fields are generally very tame, when they exist at all. A major reason, it seems, is that the general level of understanding of research methods is substantially higher in those professions. In public relations, both educators and practitioners commonly assume that, just because men make more money than women, there is salary discrimination. No so. To simply assume that the salary differential is the result of discrimination is illogical, even on its face. The question is not whether men make more money than women, but whether the reasons for the difference are legitimate or illegitimate. Therefore, the approach in many other fields has been to dig deeper, to ascertain the specific reasons for the salary differential. In marketing/advertising, for example, while the correlation between salary and gender was 0.34 (substantially higher than in PR), Kiecker et al. (1991) found that the difference was explained primarily by factors such as age, experience, education and job continuity. They concluded that adding gender to the control variables increased the explanatory power of their salary model only very slightly (from R2 = 0.45 to R2 = 0.47), even without considering other possible explanatory variables not included in their model, such as family background and knowledge about how to be successful (e.g., work strategies and use of mentors). In medicine, a 1996 study in the New England Journal of Medicine (Baker, 1996) found a substantial 41% difference in the income of male physicians ($155,000) versus female physicians ($110,000). The lion’s share of the difference, however, was attributable to the difference in hours worked. Male doctors worked an average of 62 h per week versus 51 h per week for female doctors, reducing the pay gap to only 14% on a per-hour basis. This finding is consistent with a number of studies that suggest men work more hours than women, and that women more often prefer to sacrifice pay in exchange for fewer work hours. For example, Bureau of Labor statistics show that more than twice as many men as women work more than 60 h per week, and a 1996 study by the Employment Policy Foundation found that, at every income level, women are more likely than men to desire fewer hours for less pay (“Worker Dilemma, 1997”). The remainder of the gap in physicians’ pay was found to be the result of women participating less in group practice and in higher-paying specialties. At least some of those differences were attributed to the conscious choices made by women. The bottom line: The study could not rule out the possibility of a small amount of gender discrimination in physicians’ salaries, but neither could it rule it in. Another common conceptual and methodological error that has been made routinely is the examination of one variable at a time (using simple two-way cross-tabulations), and then reaching the conclusion that “anyway you look at it” there is discrimination in PR salaries. One study, for example (Turk, 1986), not only used this approach, but suggested erroneously that, in doing so, it was “holding the other variables constant” (a term that has a very different and specific meaning in statistics). The result was that the study amounted to little more than a shell game, arriving at wholly unsupported conclusions because the variables have to be considered simultaneously, using a technique like regression analysis. That same conceptual mistake continues to be made repeatedly, including in a report by the National Association for Female Executives in December 2003.
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4. The PR Week study A detailed analysis of the data from PR Week’s 2001 annual salary survey provides the best evidence yet that there is little or no gender-based salary discrimination. Almost 5,000 PR practitioners on the publication’s e-mail database responded to the electronic survey, representing a response rate of about 15%. The survey covered dozens of topics, ranging from job satisfaction to job training to family issues to number of business lunches per week to whether or not respondents had been contacted by a headhunter. For purposes of the analysis, all the variables that might relate to salary were explored. A few were not included in the final analysis because they played little or no role in salary, or because they did not make much sense theoretically (e.g., whether the respondent had been contacted recently by a headhunter). The final list of variables included gender, age, experience in PR, tenure in current organization, tenure in current position, number of hours worked, type of organization worked for (e.g., agency versus corporation), type of PR practiced, type of industry (e.g., manufacturing versus services) number of children, marital status, size of company and “commitment” to their current job (i.e., whether they were likely to leave their job in the next six months). Unlike most salary surveys, which require respondents to check off a salary range, the survey required respondents to provide their specific salary. Thus, the survey was unusual and significantly more accurate than the typical salary survey, to the extent that its dependent variable was continuous rather than derived from “range” data. The variables for PR experience, hours worked, age, and type of PR practiced were operationalized as indicated in Figs. 2–6, respectively. Respondents were given the following choices for “type of organization”: nonprofit, PR agency, corporate, education, government/politics, “other.” (Fig. 4 shows only the first three categories, which were by far the most frequently cited types of organizations.) “Type of industry” provided 14 possible categories in the original survey, consolidated to seven categories (PR agency, nonprofit, “technical,” services, heavy industry, government/public service and “other”) for the analysis because the cell sizes were very small in several of the original categories. A close inspection of the salaries resulted in the elimination of nine extreme cases. Five cases were eliminated because they reported annual salaries of less than $200 (on the premise that they were essentially unemployed), and four cases were eliminated because they reported salaries of $3.9–$5.5 million, which appeared (based on those cases’ demographic information) to be typographical errors. That left a database with a salary range of $3,800–$865,000.
5. Hypotheses Based on prior salary studies conducted in public relations and other fields, the study hypothesized: H1. The most prominent variable in explaining the salary differential would be years of work experience in the field. H2. The second most important factor explaining the salary differential would be number of hours worked. H3. The third most significant force explaining the salary differential would be “occupational factors,” a group of variables describing the nature of the organization worked for and the type of work
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performed—type of organization (corporation versus agency versus nonprofit), type of PR practiced (e.g., investor relations versus media relations) and type of industry (e.g., high tech versus manufacturing). Conceptually, a variety of other variables would have been hypothesized as correlating with salary (e.g., type and amount of education, breaks in employment), but the salary survey existed as a secondary database, and such data were either missing from the survey design or included in a form that was not usable for the analysis. The survey data also included some variables that were studied but about which no hypotheses were formed (e.g., the relationship between salary and (a) marital status, and (b) number of children). 6. Analysis Simple frequency analysis of the PR Week data indicated that the average salary for men was more than $15,000 higher than the average salary for women. Because of the relatively high intercorrelations among the variables, and the fact that a number of variables correlated strongly with both gender and salary, multiple regression was used to help determine whether the reasons for the so-called “gender gap” might be the result of discrimination. (Regression analysis has been accepted as a legal standard in court cases of gender discrimination.) Essentially, multiple regression analysis “explains” a dependent variable (in this case, salary) in terms of a set of independent variables (in this case, years of experience, gender, number of hours worked, etc.). Based on the raw correlations between the dependent variable and each of the independent variables, the (stepwise) regression software program first picks the independent variable that best explains or predicts the dependent variable (salary). After the effects of that variable have been accounted for, the program then picks the next most important variable, then the next most important, and so forth. 6.1. Step A As Fig. 1, line “A” shows, the most important variable in explaining PR salaries is “years of experience in the field.” The “R2 ” in Fig. 1 is a statistical measure that refers to the percent of variation in salary that is explained by each factor. The “0.26” indicates that 26% of the variation in PR salaries is explained or accounted for by experience in the PR field. (In this study, with such a large sample size, the “adjusted R2 ,” which is a more accurate and appropriate measure to use with a small sample size and a large number of independent variables, was the same as R2 .) Fig. 2 shows graphically that a major reason men make more money is that they have substantially more experience than women. This finding is not a surprise to anyone, given that about 80% of the PR students in the U.S. are female, and that the percentage of women in the field has been increasing methodically for the past two or three decades. If current trends stabilize, the percentage of women will eventually be about 80%—at all levels of experience—as older men retire from the PR work force. 6.2. Step B Line “B” in Fig. 1 shows that, once the effects of experience in the field have been considered, the next most important factor is “number of hours worked.” Adding this factor explains an additional 4% of the variation in salary.
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Fig. 1. Factors that predict salary.
Fig. 2. Salary by years of experience in PR.
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Fig. 3. Salary by hours worked per day.
Fig. 3 shows graphically that another reason men make more money is that they work longer hours, on average, than women. This finding is consistent with studies in other fields, such as medicine, mentioned earlier. 6.3. Step C Line “C” in Fig. 1 shows that, after considering the effects of experience and hours worked, the most important factor explaining salary differential was “type of organization.” Fig. 4 displays the salary differences among the three most common types of organizations—nonprofit, agency and corporate—and indicates that men tend to work in the types of organizations that pay somewhat better. The change in R2 of 0.04 means that the type of organization a person works for explains an additional 4% of the variation in salaries. 6.4. Step D Line “D” in Fig. 1 shows that adding age to the mix explains another 2% of the variation in salaries. Note that this factor might better be considered “maturity” since it represents the effects of age that are above-and-beyond the effects of work experience in the field. (The raw correlation between age and salary is almost as high as the raw correlation between PR experience and salary.) While most of the effect of age has already been accounted for by the “PR experience” factor at this stage of the analysis, Fig. 5 depicts the strong correlation between age and salary, and between age and gender. Thus, another significant reason that men make more money than women in PR is that they are much older.
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Fig. 4. Salary by type of organization.
Fig. 5. Salary by age.
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Fig. 6. Salary by type of PR practiced.
6.5. Step E Line “E” in Fig. 1 shows that adding “type of PR practiced” to the equation explains another 2% of the variation in salaries. Fig. 6 indicates the average salaries of various PR specialties. Once again, men tend to work in specialties that pay somewhat better. It should be noted here, as well as in Step C, that, conceptually, discrimination could result in women being barred from certain types of PR specialties, as well as certain types of organizations, resulting in a form of de facto salary discrimination. There is little or no evidence, however, that women are being prevented from working in corporate or agency PR, nor practicing the better paying specialties like investor relations (indeed, PR educators generally find it extremely difficult to get any student—male or female—to express an aptitude or interest in the more lucrative PR specialties like investor relations). In any case, both “type of organization” and “type of PR practiced” are relatively minor factors in predicting salaries. 6.6. Step F Line “F” in Fig. 1 shows that adding gender to the mix explains 1% of the variation in salaries. Beyond the very modest role that gender appears to play, after the effects of other variables are considered, it is important to note that this 1% explanatory power of gender does not necessarily represent discrimination. Indeed, there is substantial reason to believe that there are at least two or three non-discriminatory factors not included in the study that would explain all or part of the tiny remaining piece of the gender gap. For example, “job continuity” (based on “breaks in service,” which were more typical for women
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than men), was a major factor explaining salary differentials in a study of the marketing/advertising field (Kiecker et al., 1991); interestingly, job continuity was not just a women’s issue in that study, given that the correlation between salary and job continuity was significantly higher than the correlation between salary and gender. 6.7. Step G Finally, lumping all of the other, less-important variables together explains another 2% of the variation in salaries. Altogether, then, the variables in the study explained a grand total of 40% of the variation in salaries. Presumably—while there is no practical way to measure some of them in a survey—a variety of important factors such as education, luck, skill, intelligence, creativity, negotiating ability, self-confidence and ambition explain the remaining 60% of variation in salaries. It might be noted that multi-category nominal variables, which are treated as dummy variables (e.g., “type of organization” and “type of PR practiced,” in this study), can sometimes be problematic in regression analysis. In this case, the role of the nominal variables was minor, leaving little room for distortion of the findings. Nonetheless, further checks were conducted to be sure the nominal variables did not mask or distort the role of gender in the study. For example, an exploratory analysis found that adding the effect of gender to the effects of the most important variables (“years of PR experience” and “hours worked”) increased the R2 only from 0.300 to 0.316 if the nominal variables were thrown out of the equation altogether. In other words, the effect of gender on salary was very minor and similar in both cases (i.e., whether or not the nominal variables like “type of PR practiced” and “type of organization” were included in the analysis), thus demonstrating that the use of multiple-category nominal variables did not distort the gender analysis in any significant way. One can see simply from a visual inspection of Figs. 4 and 6 that these two nominal variables correlated highly with both gender and salary.
7. Conclusion In line with the very limited number of serious studies that have been conducted in the past 10–15 years (and contrary to popular belief and many unfounded assertions), a sophisticated analysis of the PR Week salary data demonstrates that gender plays little or no role in PR salaries. Clearly, factors such as experience and hard work are far more important than gender. As hypothesized, the most important factor explaining salary was years of PR experience, followed by number of hours worked per week. Among the “occupational” variables studied, “type of organization” (corporate versus agency versus nonprofit) was the most significant. Perhaps the most unexpected finding was that age (perhaps better described as “maturity”) still explained a small amount of the variation in salaries even after the effect of “years in the field” was already considered. Conceptually, it is still possible that public relations salaries could include a tiny amount of gender discrimination, but it is also possible that potentially important factors not included in the study could more than account for the residual gender difference in salaries (i.e., it is possible that consideration of all relevant factors might demonstrate that there is a small amount of salary discrimination against men). For example, when the following year’s PR Week salary survey (“Salary Survey, 2002”) added a question
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about whether respondents had a graduate degree, 31% of men versus only 20% of women responded “yes.” In any circumstance, more comprehensive and methodologically sophisticated studies are needed, which would include other factors that have been demonstrated or hypothesized to correlate with both gender and salary—factors such as presence of a mentor, willingness to relocate for a new job, type and level of education, and negotiation skills (see Babcock & Laschever, 2003).
References Babcock, L., & Laschever, S. (2003). Women don’t ask: Negotiation and the gender divide. Princeton, NJ: Princeton University Press. Baker, L. C. (1996). Differences in earnings between male and female physicians. New England Journal of Medicine, 334(15), 960–964. Broom, G. M. (1982). A comparison of sex roles in public relations. Public Relations Review, 8, 17–22. Broom, G. M., & Dozier, D. M. (1986). Advancement for public relations role models. Public Relations Review, 12, 37–56. Cline, C. G., Masel-Walters, L., Toth, E. L., Turk, J. V., Smith, H. T., & Johnson, N. (1986). The velvet ghetto: The impact of the increasing percentage of women in public relations and organizational communication. San Francisco: IABC Foundation. Dozier, D. M., & Broom, G. M. (1995). Evolution of the manager role in public relations practice. Journal of Public Relations Research, 7, 3–26. Hutton, J. G. (1993, January). Pay discrimination versus women in pr not proven. O’Dwyer’s PR Services Report, 7(1), 19–24. Kiecker, P. L., Hunt, S. D., & Chonko, L. B. (1991). Gender, income differences and marketing: Examining the ‘earnings gap’ in three areas of marketing. Journal of the Academy of Marketing Science, 19(2), 77. Salary survey 2002 (2002, March 25). PR Week, 17–22. Toth, E. (2001). How feminist theory advanced the practice of public relations. In R. L. Heath (Ed.), Handbook of public relations. Thousand Oaks, CA: Sage Publications. Turk, J. V. (1986). The changing face of CASE. CASE Currents, 12(6), 8–20. Worker dilemma, cash or time off? (1997, May 5). Star-Tribune, D-1 (Minneapolis). Wright, D. K., Grunig, L. A., Springston, J. K., & Toth, E. L. (1991). Under the glass ceiling (monograph). New York: Public Relations Society of America (PRSA) Foundation. Wright, D. K., & Springston, J. K. (1991, June). Gender gap narrowing. PR Journal, 22.
The myth of salary discrimination in public relations James G. Hutton∗ Silberman College of Business and the Corporate Communications Institute, Fairleigh Dickinson University, Teaneck, NJ 07666, USA Received 6 March 2004; received in revised form 23 October 2004; accepted 1 November 2004
Abstract One of the more puzzling dimensions of the public relations literature is the persistent claim of salary discrimination against women in the field, despite the absence of even a single comprehensive study on the topic. Even more disturbing is that closely related fields like marketing and advertising—as well as more professionalized fields like medicine—have made no such claims of discrimination, despite larger salary gaps and larger correlations between salary and gender than in the PR field. Detailed statistical analysis of a major salary survey and a review of existing studies both indicate that there is no empirical reason to believe that there is gender-based salary discrimination in the PR field. Findings from the PR salary survey show that there is little or no discrimination, even without considering factors (e.g., employment breaks-in-service) that were shown to have substantial explanatory power in salary surveys from other fields. Circumstantial evidence of PR salary discrimination existed 20 years ago, but the evidence available since that time suggests strongly that there has been little or no salary discrimination for at least the past decade. © 2004 Elsevier Inc. All rights reserved. Keywords: Public relations; Gender; Salary discrimination
1. Introduction Given the longstanding claims of salary discrimination in public relations, one would assume those assertions are based on a solid foundation of scientific evidence. Remarkably, however, not a single comprehensive study concerning the role of gender in public relations salaries has ever been conducted. ∗
Tel.: +1 201 692 7241; fax: +1 201 692 7241. E-mail address: [email protected].
0363-8111/$ – see front matter © 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.pubrev.2004.11.007
74
J.G. Hutton / Public Relations Review 31 (2005) 73–83
Circumstantial evidence of salary discrimination existed 20 years ago, but the evidence available since that time suggests very strongly that there has been little or no salary discrimination for at least the past decade.
2. Background One of the first major attempts to study issues of gender in public relations was the Velvet Ghetto study (Cline et al., 1986), published by the International Association of Business Communicators (IABC), consisting almost entirely of anecdotal evidence and very small-scale studies that lacked statistical validity. It presented circumstantial evidence to suggest that there may have been significant gender discrimination when it came to salaries in the PR field. Unfortunately, however, it included no comprehensive or statistically significant studies capable of providing a benchmark or presenting a scientific argument. Work by Broom and Dozier in the 1980s provided a more scientific basis for claims of discrimination (see Broom, 1982; Broom & Dozier, 1986). They made a fairly persuasive circumstantial case for salary discrimination, but, again, their studies were not comprehensive, focusing rather narrowly on public relations roles (i.e., managers versus technicians). More important, Broom and Dozier’s own follow-up research in 1991 (Dozier & Broom, 1995) came to a very different conclusion than their earlier research. Their research did not indicate why the relationship between salary and gender had changed, but their research indicated that the world had changed from the early 1980s to the early 1990s, as explained by Toth (2001): [Broom and Dozier] reported that roles still predicted salaries, with the managerial role being valued more than the technical role. However, they concluded that, based on their survey, “the relation between gender and salary is not statistically significant after controlling for the influences of professional experience, manager role enactment and decision-making participation.” This was a significant break in the previous research of these and other authors who had found a direct relationship between salary and gender. Later studies were fraught with conceptual and methodological mistakes, as well as bias. An oft-cited 1991 monograph published by the PRSA Foundation (Wright, Grunig, Springston & Toth, 1991), for example, included numerous inconsistencies, numbers that (literally) did not add up, misuse of research techniques, many inaccurate statements, and biased presentation of findings (see Hutton, 1993). In some cases, the statements and conclusions of the monograph contradicted the data contained in the report itself. The bias of the PRSA monograph and its authors was perhaps best illustrated by its interpretation of the results of focus groups. In an article summarizing the findings of the monograph (Wright & Springston, 1991), it was asserted that “Results from . . . the focus group stages of this research project show that gender discrimination exists in American PR.” Anyone who carefully read the monograph itself, however, discovered that the claim of pervasive discrimination was based on the opinions of a single, non-randomly selected, four-woman focus group, whose views were directly contradicted by a single, 11-man focus group. In other words, the authors of the monograph completely dismissed the comments of the 11 men, while extrapolating the comments of four women to the entire U.S. PR profession.
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3. Methodological deficiencies One of the most puzzling and disturbing aspects of the rhetoric is the enormous disconnection between the claims made in the field of public relations compared with the absence of claims made in other professional disciplines. The correlation between salary and gender (i.e., the gap between men’s and women’s salaries) is even greater in professions like medicine (Baker, 1996) and marketing/advertising (Kiecker, Hunt & Chonko, 1991), yet scholarly claims of discrimination in those fields are generally very tame, when they exist at all. A major reason, it seems, is that the general level of understanding of research methods is substantially higher in those professions. In public relations, both educators and practitioners commonly assume that, just because men make more money than women, there is salary discrimination. No so. To simply assume that the salary differential is the result of discrimination is illogical, even on its face. The question is not whether men make more money than women, but whether the reasons for the difference are legitimate or illegitimate. Therefore, the approach in many other fields has been to dig deeper, to ascertain the specific reasons for the salary differential. In marketing/advertising, for example, while the correlation between salary and gender was 0.34 (substantially higher than in PR), Kiecker et al. (1991) found that the difference was explained primarily by factors such as age, experience, education and job continuity. They concluded that adding gender to the control variables increased the explanatory power of their salary model only very slightly (from R2 = 0.45 to R2 = 0.47), even without considering other possible explanatory variables not included in their model, such as family background and knowledge about how to be successful (e.g., work strategies and use of mentors). In medicine, a 1996 study in the New England Journal of Medicine (Baker, 1996) found a substantial 41% difference in the income of male physicians ($155,000) versus female physicians ($110,000). The lion’s share of the difference, however, was attributable to the difference in hours worked. Male doctors worked an average of 62 h per week versus 51 h per week for female doctors, reducing the pay gap to only 14% on a per-hour basis. This finding is consistent with a number of studies that suggest men work more hours than women, and that women more often prefer to sacrifice pay in exchange for fewer work hours. For example, Bureau of Labor statistics show that more than twice as many men as women work more than 60 h per week, and a 1996 study by the Employment Policy Foundation found that, at every income level, women are more likely than men to desire fewer hours for less pay (“Worker Dilemma, 1997”). The remainder of the gap in physicians’ pay was found to be the result of women participating less in group practice and in higher-paying specialties. At least some of those differences were attributed to the conscious choices made by women. The bottom line: The study could not rule out the possibility of a small amount of gender discrimination in physicians’ salaries, but neither could it rule it in. Another common conceptual and methodological error that has been made routinely is the examination of one variable at a time (using simple two-way cross-tabulations), and then reaching the conclusion that “anyway you look at it” there is discrimination in PR salaries. One study, for example (Turk, 1986), not only used this approach, but suggested erroneously that, in doing so, it was “holding the other variables constant” (a term that has a very different and specific meaning in statistics). The result was that the study amounted to little more than a shell game, arriving at wholly unsupported conclusions because the variables have to be considered simultaneously, using a technique like regression analysis. That same conceptual mistake continues to be made repeatedly, including in a report by the National Association for Female Executives in December 2003.
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4. The PR Week study A detailed analysis of the data from PR Week’s 2001 annual salary survey provides the best evidence yet that there is little or no gender-based salary discrimination. Almost 5,000 PR practitioners on the publication’s e-mail database responded to the electronic survey, representing a response rate of about 15%. The survey covered dozens of topics, ranging from job satisfaction to job training to family issues to number of business lunches per week to whether or not respondents had been contacted by a headhunter. For purposes of the analysis, all the variables that might relate to salary were explored. A few were not included in the final analysis because they played little or no role in salary, or because they did not make much sense theoretically (e.g., whether the respondent had been contacted recently by a headhunter). The final list of variables included gender, age, experience in PR, tenure in current organization, tenure in current position, number of hours worked, type of organization worked for (e.g., agency versus corporation), type of PR practiced, type of industry (e.g., manufacturing versus services) number of children, marital status, size of company and “commitment” to their current job (i.e., whether they were likely to leave their job in the next six months). Unlike most salary surveys, which require respondents to check off a salary range, the survey required respondents to provide their specific salary. Thus, the survey was unusual and significantly more accurate than the typical salary survey, to the extent that its dependent variable was continuous rather than derived from “range” data. The variables for PR experience, hours worked, age, and type of PR practiced were operationalized as indicated in Figs. 2–6, respectively. Respondents were given the following choices for “type of organization”: nonprofit, PR agency, corporate, education, government/politics, “other.” (Fig. 4 shows only the first three categories, which were by far the most frequently cited types of organizations.) “Type of industry” provided 14 possible categories in the original survey, consolidated to seven categories (PR agency, nonprofit, “technical,” services, heavy industry, government/public service and “other”) for the analysis because the cell sizes were very small in several of the original categories. A close inspection of the salaries resulted in the elimination of nine extreme cases. Five cases were eliminated because they reported annual salaries of less than $200 (on the premise that they were essentially unemployed), and four cases were eliminated because they reported salaries of $3.9–$5.5 million, which appeared (based on those cases’ demographic information) to be typographical errors. That left a database with a salary range of $3,800–$865,000.
5. Hypotheses Based on prior salary studies conducted in public relations and other fields, the study hypothesized: H1. The most prominent variable in explaining the salary differential would be years of work experience in the field. H2. The second most important factor explaining the salary differential would be number of hours worked. H3. The third most significant force explaining the salary differential would be “occupational factors,” a group of variables describing the nature of the organization worked for and the type of work
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performed—type of organization (corporation versus agency versus nonprofit), type of PR practiced (e.g., investor relations versus media relations) and type of industry (e.g., high tech versus manufacturing). Conceptually, a variety of other variables would have been hypothesized as correlating with salary (e.g., type and amount of education, breaks in employment), but the salary survey existed as a secondary database, and such data were either missing from the survey design or included in a form that was not usable for the analysis. The survey data also included some variables that were studied but about which no hypotheses were formed (e.g., the relationship between salary and (a) marital status, and (b) number of children). 6. Analysis Simple frequency analysis of the PR Week data indicated that the average salary for men was more than $15,000 higher than the average salary for women. Because of the relatively high intercorrelations among the variables, and the fact that a number of variables correlated strongly with both gender and salary, multiple regression was used to help determine whether the reasons for the so-called “gender gap” might be the result of discrimination. (Regression analysis has been accepted as a legal standard in court cases of gender discrimination.) Essentially, multiple regression analysis “explains” a dependent variable (in this case, salary) in terms of a set of independent variables (in this case, years of experience, gender, number of hours worked, etc.). Based on the raw correlations between the dependent variable and each of the independent variables, the (stepwise) regression software program first picks the independent variable that best explains or predicts the dependent variable (salary). After the effects of that variable have been accounted for, the program then picks the next most important variable, then the next most important, and so forth. 6.1. Step A As Fig. 1, line “A” shows, the most important variable in explaining PR salaries is “years of experience in the field.” The “R2 ” in Fig. 1 is a statistical measure that refers to the percent of variation in salary that is explained by each factor. The “0.26” indicates that 26% of the variation in PR salaries is explained or accounted for by experience in the PR field. (In this study, with such a large sample size, the “adjusted R2 ,” which is a more accurate and appropriate measure to use with a small sample size and a large number of independent variables, was the same as R2 .) Fig. 2 shows graphically that a major reason men make more money is that they have substantially more experience than women. This finding is not a surprise to anyone, given that about 80% of the PR students in the U.S. are female, and that the percentage of women in the field has been increasing methodically for the past two or three decades. If current trends stabilize, the percentage of women will eventually be about 80%—at all levels of experience—as older men retire from the PR work force. 6.2. Step B Line “B” in Fig. 1 shows that, once the effects of experience in the field have been considered, the next most important factor is “number of hours worked.” Adding this factor explains an additional 4% of the variation in salary.
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Fig. 1. Factors that predict salary.
Fig. 2. Salary by years of experience in PR.
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Fig. 3. Salary by hours worked per day.
Fig. 3 shows graphically that another reason men make more money is that they work longer hours, on average, than women. This finding is consistent with studies in other fields, such as medicine, mentioned earlier. 6.3. Step C Line “C” in Fig. 1 shows that, after considering the effects of experience and hours worked, the most important factor explaining salary differential was “type of organization.” Fig. 4 displays the salary differences among the three most common types of organizations—nonprofit, agency and corporate—and indicates that men tend to work in the types of organizations that pay somewhat better. The change in R2 of 0.04 means that the type of organization a person works for explains an additional 4% of the variation in salaries. 6.4. Step D Line “D” in Fig. 1 shows that adding age to the mix explains another 2% of the variation in salaries. Note that this factor might better be considered “maturity” since it represents the effects of age that are above-and-beyond the effects of work experience in the field. (The raw correlation between age and salary is almost as high as the raw correlation between PR experience and salary.) While most of the effect of age has already been accounted for by the “PR experience” factor at this stage of the analysis, Fig. 5 depicts the strong correlation between age and salary, and between age and gender. Thus, another significant reason that men make more money than women in PR is that they are much older.
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Fig. 4. Salary by type of organization.
Fig. 5. Salary by age.
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Fig. 6. Salary by type of PR practiced.
6.5. Step E Line “E” in Fig. 1 shows that adding “type of PR practiced” to the equation explains another 2% of the variation in salaries. Fig. 6 indicates the average salaries of various PR specialties. Once again, men tend to work in specialties that pay somewhat better. It should be noted here, as well as in Step C, that, conceptually, discrimination could result in women being barred from certain types of PR specialties, as well as certain types of organizations, resulting in a form of de facto salary discrimination. There is little or no evidence, however, that women are being prevented from working in corporate or agency PR, nor practicing the better paying specialties like investor relations (indeed, PR educators generally find it extremely difficult to get any student—male or female—to express an aptitude or interest in the more lucrative PR specialties like investor relations). In any case, both “type of organization” and “type of PR practiced” are relatively minor factors in predicting salaries. 6.6. Step F Line “F” in Fig. 1 shows that adding gender to the mix explains 1% of the variation in salaries. Beyond the very modest role that gender appears to play, after the effects of other variables are considered, it is important to note that this 1% explanatory power of gender does not necessarily represent discrimination. Indeed, there is substantial reason to believe that there are at least two or three non-discriminatory factors not included in the study that would explain all or part of the tiny remaining piece of the gender gap. For example, “job continuity” (based on “breaks in service,” which were more typical for women
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than men), was a major factor explaining salary differentials in a study of the marketing/advertising field (Kiecker et al., 1991); interestingly, job continuity was not just a women’s issue in that study, given that the correlation between salary and job continuity was significantly higher than the correlation between salary and gender. 6.7. Step G Finally, lumping all of the other, less-important variables together explains another 2% of the variation in salaries. Altogether, then, the variables in the study explained a grand total of 40% of the variation in salaries. Presumably—while there is no practical way to measure some of them in a survey—a variety of important factors such as education, luck, skill, intelligence, creativity, negotiating ability, self-confidence and ambition explain the remaining 60% of variation in salaries. It might be noted that multi-category nominal variables, which are treated as dummy variables (e.g., “type of organization” and “type of PR practiced,” in this study), can sometimes be problematic in regression analysis. In this case, the role of the nominal variables was minor, leaving little room for distortion of the findings. Nonetheless, further checks were conducted to be sure the nominal variables did not mask or distort the role of gender in the study. For example, an exploratory analysis found that adding the effect of gender to the effects of the most important variables (“years of PR experience” and “hours worked”) increased the R2 only from 0.300 to 0.316 if the nominal variables were thrown out of the equation altogether. In other words, the effect of gender on salary was very minor and similar in both cases (i.e., whether or not the nominal variables like “type of PR practiced” and “type of organization” were included in the analysis), thus demonstrating that the use of multiple-category nominal variables did not distort the gender analysis in any significant way. One can see simply from a visual inspection of Figs. 4 and 6 that these two nominal variables correlated highly with both gender and salary.
7. Conclusion In line with the very limited number of serious studies that have been conducted in the past 10–15 years (and contrary to popular belief and many unfounded assertions), a sophisticated analysis of the PR Week salary data demonstrates that gender plays little or no role in PR salaries. Clearly, factors such as experience and hard work are far more important than gender. As hypothesized, the most important factor explaining salary was years of PR experience, followed by number of hours worked per week. Among the “occupational” variables studied, “type of organization” (corporate versus agency versus nonprofit) was the most significant. Perhaps the most unexpected finding was that age (perhaps better described as “maturity”) still explained a small amount of the variation in salaries even after the effect of “years in the field” was already considered. Conceptually, it is still possible that public relations salaries could include a tiny amount of gender discrimination, but it is also possible that potentially important factors not included in the study could more than account for the residual gender difference in salaries (i.e., it is possible that consideration of all relevant factors might demonstrate that there is a small amount of salary discrimination against men). For example, when the following year’s PR Week salary survey (“Salary Survey, 2002”) added a question
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about whether respondents had a graduate degree, 31% of men versus only 20% of women responded “yes.” In any circumstance, more comprehensive and methodologically sophisticated studies are needed, which would include other factors that have been demonstrated or hypothesized to correlate with both gender and salary—factors such as presence of a mentor, willingness to relocate for a new job, type and level of education, and negotiation skills (see Babcock & Laschever, 2003).
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