Differences Between Male and Female Pharmacists in Part-time Status and Employment Setting

Differences Between Male and Female Pharmacists in Part-time Status and Employment Setting

[J Am Pharm Assoc 41(5):703-708, 2001. © 2001 American Pharmaceutical Association, Inc.]
Surrey M. Walton and Judith A. Cooksey

Abstract and Introduction

Abstract

Objective: To assess differences across gender in part-time status and employment setting in the labor market for pharmacists and to examine other factors associated with employment choices.
Design: Retrospective analysis of data on pharmacists from national population surveys from 1979 through 1998. Differences in pharmacists' demographic characteristics and employment status were examined across gender. Logistic regression analysis was used to measure the impact of gender and other demographic characteristics on part-time status and employment in the hospital setting.
Main Outcome Measures: Part-time status was defined as 35 or fewer self-reported hours worked weekly; half-time status was defined as fewer than 20 hours worked weekly. The outcome measure for employment setting was work in the hospital industry.
Results: Female pharmacists were more than four times as likely as male pharmacists to work part-time, and 70% more likely to work in the hospital industry, even when other factors were controlled for. Overall, women worked 84% of the weekly hours that men worked. Older pharmacists, both men and women, were more likely to work part-time and less likely to work in hospitals, and the effects of age on employment setting were much greater for men than for women. The effect of survey year on part-time work and hospital employment was small for both men and women.
Conclusion: Predictive models of labor supply should take into account the percentage of women in the pharmacy labor force, and labor supply models for pharmacists should allow for variance in entry across employment settings.

Introduction

Concern about the supply and distribution of pharmacists in the United States heightened during the late 1990s as many employers faced a growing gap between the number of open pharmacist positions and the availability of candidates to fill them. In 1999 this discrepancy prompted Congress to call for a study to assess and characterize a possible pharmacist shortage.^[1] The current tight labor market for pharmacists stands in sharp contrast to projections of a pharmacist surplus that had been made in the mid-1990s.^[2]

Several factors have been identified as contributors to the present high demand for pharmacists, including growth in the use of prescription medications, market expansions in the chain pharmacy industry, career opportunities for pharmacists in nontraditional settings, and the growing number of women in pharmacy.^[3] Despite an overall increase in the number of pharmacists over the past 20 years, the effective supply can be modified by career choices and work patterns. Since pharmacists are expected to play an increasing role in health care, gaining a better understanding of how their choices with respect to employment affect the labor market is important for health workforce analysis and planning.

The number of women in the pharmacist workforce has increased steadily over the past 30 years.^[3,4] In 1980, 18% of the estimated 142,400 active pharmacists were women; by 1996 this proportion had increased to 41% of the 185,000 active pharmacists.^[5] As of 2000, women are expected to make up 50% of practicing pharmacists within the next 5 years.

Studies have shown differences between male and female pharmacists in hours worked and part-time status. In studies from the late 1970s through the late 1980s, female pharmacists reported working 85% to 91% of the average weekly hours reported by men.^[6,7] Results from periodic surveys have shown higher rates of part-time employment by female pharmacists, although few of these studies actually defined part-time status in terms of hours worked. An analysis of work patterns among pharmacists who graduated from pharmacy school in the years 1959 through 1989 found higher rates of part-time work among women overall, although the proportions of women working full-time were higher among more recent cohorts.^[8] In their 1995 survey of part-time pharmacists' practice, Quiñones and Mason^[9] identified two groups of part-time workers and noted gender differences. More women than men were working less than full-time in one or more part-time jobs (29% of women versus 11% of men); however, men were more often working a second part-time job (moonlighting) in addition to a full-time job (16% of men versus 10% of women).^[9] In a survey conducted in 2000 by the Pharmacy Manpower Project,^[10] 17% of working pharmacists -- 12% of men and 22% of women -- worked part-time (fewer than 30 hours per week). This survey also revealed that, among full-time pharmacists, men were more likely to work a second job than were women (14% of men versus 10% of women).

Selection of a work setting may be subject to many professional and personal preferences, as well as job opportunities. About two-thirds of pharmacists are presently employed in retail settings (predominantly chain and independent pharmacies, and including supermarkets and mass merchandisers), with hospitals employing about one-quarter and lesser numbers in long-term care, manufacturing, business, education, and government.^[3] Gender differences have been reported among work settings. A 1991 survey found women working in hospitals at higher rates than men (34% versus 22%, respectively).^[11] The 2000 survey found that, among full-time pharmacists, 28% of women versus 22% of men worked in hospitals (among part-time workers these numbers were 24% and 14%, respectively).^[10]

While these studies show consistent findings with respect to gender differences in part-time work and employment setting, little research on the pharmacy workforce has gone beyond the descriptive level of analysis. Therefore, assessment of possible relationships among work choices, gender, and other factors has been limited. For example, how do age, marital status, and having children affect the work choices of male and female pharmacists?

To probe these questions, we used regression analysis, a tool commonly applied by social scientists and labor economists to test relationships among multiple relevant factors using large data sets. Estimates from regression analyses can allow one to assess how much of an overall difference in part-time employment between men and women can be explained by differences in demographic factors or a particular time period. Conversely, regression analysis allows one to examine a "pure" effect of gender differences while controlling for these other factors.

Objective

The objective of this study was to use a nationally representative data set covering the years 1979 through 1998 to study the impact of gender on part-time status and employment setting and to explore relationships between employment choices and other factors such as age, marital status, years of education, having children, and time period.

Methods

We used the Current Population Survey's (CPS) Outgoing Rotation Group (ORG) data covering 1979 through 1998. To assess the impact of gender and other measurable demographic characteristics on the odds of working part-time and being employed in a hospital, we used descriptive statistics and logistic regression analyses. In addition, we examined how the effect of gender interacted with the effects of age and the time period of employment on part-time status and employment setting.

Data

The CPS is a survey sent monthly by the Bureau of Labor Statistics to roughly 50,000 households nationwide. It involves a combination of personal and telephone interviews. The survey provides unique and important employment-related data.

The ORG data are compiled each year from a subset of CPS respondents who receive a more detailed questionnaire regarding their current occupation, industry of employment, labor force participation, and earnings, as well as standard demographic questions such as education, age, race, sex, and marital status. The ORG data are based on a sample of approximately 300,000 individuals per year, yielding 250 to 350 pharmacists per year. ORG data are available for purchase.^[12]

We identified employed pharmacists through self-reported occupation based on detailed Census Bureau occupational codes adapted for the CPS. From 1979 through 1998, 6,495 individuals reported their occupation as pharmacist. To improve validity, we dropped respondents whose responses fell outside of an expected range for employed pharmacists (less than 16 years of schooling, age less than 20 years, or hours worked last week less than 1). This left 6,051 usable respondents for the analysis on employment setting and 5,395 respondents (due to missing values in hours worked) for the analysis on part-time status. The analyses of the effect of having children included only those respondents surveyed in the years when these questions were asked (1984 through 1993), resulting in sample sizes of 2,832 for part-time status and 3,178 for employment setting. In all of the analyses, we used sampling weights from the ORG data that adjust the data to be representative of the U.S. population.

Data Analysis

Variables. The independent variables used in the analyses were sex, age, years of education, marital status, having children less than 18 years of age, and an indicator for whether the particular survey was given in 1990 or later. For the regression analyses, we converted age into three categories: 20 to 34 years, 35 to 55 years, and greater than 55 years. Years of education were as reported by respondents up to a maximum of 18 years. However, for data
collected in years after 1993 the education question had changed to reflect final degree achieved, and we recoded responses to reflect years of education using 16 for college graduates and 18 for professional and graduate degrees. Indicator variables (1 for yes and 0 for no) were set for being married and having children under 18 years of age.

The dependent variable for working part-time was an indicator variable for fewer than 35 reported hours worked the week prior to completing the survey. This definition of working part-time is consistent with those used in various other studies and with analyses done by the Bureau of Labor Statistics.^[13] Note, however, that this definition does not include moonlighters (defined as full-time workers with a second, part-time position) because they would indicate working more than 30 hours per week.^[9] We did include, as a further assessment of part-time status, a second indicator variable for working half-time, defined as working fewer than 20 hours per week. Finally, the dependent variable for employment setting was an indicator for working in the hospital industry.

Statistical Methods. We calculated mean values of selected demographic and labor market variables for all respondents and across gender. Next, we used logistic regression analyses to examine the multivariate relationships among the independent variables and the three dependent dichotomous variables: whether pharmacists selected part-time or half-time employment and whether pharmacists selected a hospital as their employment setting. We also conducted a separate logistic regression analysis that added an independent variable indicating having children under 18 years of age. We performed two regression analyses because the variable for having children was only available for a subset of the years. We felt that having children would prove to be an important influence on work choices, but that it would also be worthwhile to examine the broader set of years and consequently larger sample size without this variable. Finally, we conducted an analysis on the interactive effects of gender with the time period of employment (captured using survey dates) and with pharmacists' age on the key outcome variables of part-time status and hospital employment.

All regression results are expressed as odds ratios (OR), which measure how much the odds of the dependent variable (e.g., part-time status) were affected by the independent variables in the model. A Wald X² test was used to determine statistical significance for each independent variable. We used statistical software by STATA for all of the analyses.

Results

The demographic and labor force characteristics of pharmacists are shown in Table 1. The table shows averages weighted using CPS sampling weights. Women were on average 10 years younger than men (34.5 years versus 43.9 years). Only 5% of women were 55 years or older, compared with 21% of men. Almost 60% of female pharmacists were younger than 35 years old, compared with 29% of men. We found no differences in education level, although this may reflect the narrow range of allowable responses, only between 16 and 18 years. Of men, 80% were married, compared with 65% of women. About 40% of all respondents reported having children younger than 18 years of age, with no difference between men and women.

Average hours worked weekly among all pharmacists over the study period was 41.8 hours, with men reporting 44.1 hours and women 37.2 hours. Thus, women reported working 84% of the average hours reported by men. Overall, 17% of pharmacists reported working part-time -- 28% of women and 11% of men. Of all pharmacists, 6% reported working half-time -- almost 10% of women and 4% of men.

Overall, 27% of pharmacists reported working in a hospital, 62% in a drugstore, 6% in drugstores located in supermarkets or department stores, 2% in government, 1% in drug manufacturing or wholesale, and the remaining 3% in other industries. Thirty-six percent of women reported working in a hospital, compared with 21% of men, and 50% of women reported working in a drugstore, compared with 68% of men. The differences between men and women in part-time and half-time employment as well as employment setting were substantial and statistically significant.

Tables 2 and 3 show the results of the three logistic regression analyses. For each analysis, the results include the OR for each independent variable and the probability of the OR. In Table 2, the OR of 4.24 for women in the part-time analysis indicates that after controlling for all other variables, women were more than four times as likely as men (the reference group) to work
part-time. Pharmacists over 55 years of age were 3.61 times as likely as the reference age group (20 to 34 years old) to work part-time. The middle age group (35 to 55 years old) was no more likely than the reference group to work part-time. Being married, which increased the odds of part-time status in the regression in Table 2, became an insignificant variable when the presence of children was included in the model (Table 3).

The regression for working half-time revealed that the impact of the independent variables on the odds of working half-time was similar to working part-time. Only 6% of pharmacists worked half-time, but women were roughly four times as likely to do so than were men, after controlling for other factors. Older pharmacists (over 55 years) were about six times as likely to work half-time than were younger pharmacists (younger than 34 years old, the reference group), after controlling for other factors.

The effect of adding the variable of having children to the regression models can be seen in Table 3. Pharmacists of both sexes who had children younger than 18 years of age were 1.8 times as likely to work part-time and about 1.6 times as likely to work half-time compared with pharmacists without children. The effect of gender was still strong, although slightly diminished, with women being almost four times as likely to work part-time or half-time, after controlling for having children. These analyses revealed a consistently strong and statistically significant effect of gender and age on both part-time status measures, after controlling for other characteristics.

The results for hospital employment reveal that women were 1.7 times as likely as men to select the hospital industry, controlling for all other variables. Age had a significant effect, with older pharmacists being less likely to work in the hospital, after controlling for gender and other variables. Pharmacists over the age of 55 were less likely (0.4 times as likely) to work in a hospital as pharmacists under the age of 35 years. Education also had a small but significant effect. An additional year of school corresponded to being about 1.2 times as likely to work in a hospital. The presence of children was insignificantly related to the decision to work in a hospital (Table 3). Married pharmacists were less likely (about 0.7 times) to work in a hospital compared with unmarried pharmacists, after controlling for having children.

The effect of the 20-year survey time period was examined in several ways. In analyses not shown, we assessed the effect of a time trend on the dependent variables using indicator variables for each year of the survey. No effects were observed. We then tried to look for effects of larger periods of time, from 1978 through 1989, and 1990 through 1998. As the tables show, after 1989 the year of the survey had no effect on part-time or half-time employment. The effect of the survey time period on hospital employment was relatively weak and not consistently significant in the two regressions. Specifically, respondents in the years after 1989 had a relatively small but statistically significant increased likelihood of hospital employment in the regression without the presence of children variable (Table 2); yet the effect was smaller and not statistically significant when the presence of children variable was included. Overall, after controlling for the other variables, there did not appear to be a large difference in hospital employment across the survey periods.

We performed a second level of analysis to explore whether the impact of gender changed across survey period. To do this, we used interaction terms in the regression analysis (not shown for ease of exposition) and tested them for significance. The results indicated that the difference between male and female pharmacists in part-time status and hospital employment was consistent across the survey period, after controlling for other factors.

The results of an analysis of the interaction between age and gender showed that older men were much less likely than younger men to work in a hospital; however, older women were only slightly less likely to work in a hospital than were younger women. Also, older men were much more likely to work part-time than were younger men, but older women were only slightly more likely to work part-time than were younger women. These results indicate a significant age and gender interaction with respect to employment setting and part-time work.

Discussion

Our major findings reinforce findings from other studies in noting differences in employment choices between male and female pharmacists. However, our study adds new information on other personal characteristics that influence work choices, information that holds significance for workforce planning. For example, we observed that the greater likelihood of women working part-time and half-time held consistently throughout the 20-year period of 1979 through 1998, after we controlled for age, being married, and having children. For both men and women, having children younger than 18 years of age increases the odds of working part-time. The finding that older pharmacists, men and women alike, are more likely to work part-time, but with a greater age effect for men than women, is also significant for workforce planning. The differences revealed when age and gender were considered together are important findings that support the value of using analyses that include multiple variables and allow for testing interactions among factors to explore workforce participation.

Our findings extended from a study design that used a clear and standard definition of part-time work in terms of hours. This definition is useful to labor force planning, and it should be adopted more frequently to facilitate comparisons among current and future studies of part-time workers. In addition, we included a second measure, half-time work, to examine differences across gender in labor force participation when the hours worked were substantially less than full-time.

Future research on how gender affects part-time work choices is critical to workforce supply modeling efforts, as earlier research groups have stated.^[14] Our study explored gender differences after controlling for other personal characteristics (marital status, having children, age). Quinoñes and Mason suggested that certain preferences influence female pharmacists' decisions to work part-time.^[9] Further studies should examine such preferences in more detail and explore ways in which employers could best respond to those preferences.

We found that women across all age groups were more likely to work in hospitals. Our study could not distinguish whether, all else being equal, employers in hospitals were more likely to choose women relative to employers associated with drugstores or whether women were more likely to choose to work in a hospital. Nevertheless, regardless of whether the employment setting decision originates with employers or pharmacists, our results demonstrate that predictive models for the pharmacist workforce should distinguish among employment settings and account for gender, particularly when making predictions involving various settings.

Future research should include a more detailed examination of the effect of educational background on employment setting. More recent graduates, and, consequently, female pharmacists in general, are more likely to have completed the PharmD degree. Between 1960 and 1998, 11,170 men and 13,680 women received the PharmD degree, and in recent years the numbers of women receiving the PharmD have outpaced the numbers of men by more than two to one (e.g., 1,802 women versus 830 men in 1998).^[15] Although education had no discernible effect on employment setting in our study, our measure of education was limited. It is reasonable to think that the PharmD degree, which includes hospital-based clinical pharmacy training, makes pharmacists more attractive to hospital employers. Future studies of gender and workforce should include education years or degree, since the increase in the numbers of women entering pharmacy training and practice overlaps the time period of the movement toward the PharmD degree.

Finally, although technically beyond the scope of this study, an important consideration is that labor force behavior is likely to change as market conditions, such as wages, change. One problem with supply models is that they concentrate on the number of workers and make rigid assumptions regarding entry into and exit from the profession. Empirical evidence, as well as economic theory, indicates that the numbers of workers and the hours supplied per worker change in response to financial incentives.^[16,17] For example, as pharmacists' wages increase or as overtime pay increases, some pharmacists will likely increase their hours worked and may even switch from part-time to full-time status. These responses to wage increases are likely to vary with gender and other sociodemographic factors as well as between part-time and half-time workers.

In the current market, with a pharmacist shortage, extensive anecdotal evidence indicates that pharmacists' salaries are rising and other financial incentives are being used to increase hours worked and the effective supply of pharmacists.^[3] Increases in supply can be rapid if there are large numbers of part-time employees or licensed but nonworking members of the profession, but increases may be delayed considerably when substantial training is required of new entrants, which is the case for prospective students of pharmacy.

Limitations

Because this study included only pharmacists who reported being employed, it did not allow for a full description of the potential labor force of pharmacists. Moreover, pharmacists' roles are expanding, and individuals trained as pharmacists may self-report being engaged in another occupation, such as health care administrator.

The ORG data do not capture pharmacists reporting other occupations. In addition, standard industry codes in the CPS (e.g., hospital, drugstore) are relatively coarse measures of employment setting that do not necessarily correspond to definitions of setting used in other pharmacy workforce studies (e.g., independent or chain pharmacy). Finally, our analyses on hours worked and industry of employment did not examine whether pharmacists were working at multiple sites or working a part-time job in addition to full-time employment (i.e., moonlighting). Other researchers have found that 10% to 15% of pharmacists moonlight and that the rate of moonlighting varies by sex.^[9]

Conclusion

Study of the labor force participation of pharmacists should continue to be a high priority for the profession for the foreseeable future, particularly given the current shortage of pharmacists. The present study provides evidence that pharmacy workforce models need to consider gender and age in predicting effective supply. Whenever possible, models that attempt to predict supply and/or shortages of pharmacists should examine the impact of gender and other demographic characteristics on the odds of working part-time or on average hours worked. Furthermore, such models should take into account different rates of entry into different industries and continue to adjust counts of individuals in the workforce by hours worked.

Ultimately, a better understanding of how pharmacists' behaviors adjust to wages and other incentives, and how those adjustments vary by gender, is necessary to understand and predict the supply of pharmacists. Other relevant areas of study include understanding how employers use other labor (such as pharmacy technicians) and automated technology for duties traditionally done by pharmacists, and the effect such substitutions have on productivity. Ongoing changes in the gender distribution and education level of new pharmacists will continue to have ramifications for the effective supply of pharmacists across various industries, which highlights the importance of continued assessment. Further study of these matters is imperative to predicting market trends for pharmacists and pharmaceutical care delivery in the future.

Table 1. Demographic and Labor Force Characteristics of Male and Female Pharmacists, 1979-1998^a

Variables	All	Men	Women	P
Average age (years)	40.8	43.9	34.5	< .001^b
Age 35-55 (%)	45.2	50.4	34.8	< .001^c
Age > 55 (%)	15.7	21.0	5.1	< .001^c
Education (years)	16.8	16.8	16.8	.821^b
Married (%)	74.8	79.7	65.2	< .001^c
Children < 18 years (%)^d	41.7	42.5	40.3	.373^c
Hours worked/week^e	41.8	44.1	37.2	< .001^b
Work part-time (%)^e	16.8	11.4	27.9	< .001^c
Work half-time (%)^e	6.1	4.2	9.9	< .001^c
Work in hospital (%)^e	26.5	21.4	36.4	< .001^c
Work in drugstore (%)	61.6	67.5	49.9	< .001^c
n	6,051	4,072	1,979

^aAverages weighted using Current Population Survey sampling weights.
^bP value corresponds to a standard t test for different means of continuous variables.
^cP value corresponds to Pearson's X² test for differences in frequencies (for indicator variables).
^dSample size was 3,178 (all), 2,129 (men), and 1,049 (women).
^eSample size was 5,395 (all), 3,665 (men), and 1,730 (women).

Table 2. Effect of Demographic Characteristics on Part-time Status and Hospital Employment, 1979-1998^a

	Part-time		Half-time		Hospital
	OR	P	OR	P	OR	P
Women	4.24	< .0001	4.33	< .0001	1.71	< .001
Age 35-55 years	0.95	.533	0.75	.094	0.71	< .001
Age > 55 years	3.61	< .0001	5.84	< .0001	0.37	< .001
Years of education	1.01	.816	1.05	.560	1.16	< .001
Married	1.24	.021	1.57	.007	0.78	< .001
Survey year > 1989	1.00	.961	0.92	.507	1.15	.024
n	5,395		5,395		6,051
P value for X² all variables	<. 001		<. 001		<. 001

OR = odds ratio.^aLogistic regressions were run using Stata.

Table 3. Effect of Demographic Characteristics, Including the Presence of Children, on Part-time Status and Hospital Employment, 1984-1993^a

	Part-time		Half-time		Hospital
	OR	P	OR	P	OR	P
Women	3.86	< .0001	3.90	< .0001	1.73	< .001
Age 35-55 years	0.86	.253	0.78	.3300	0.75	.003
Age > 55 years	4.51	< .0001	6.42	< .0001	.41	< .001
Years of education	1.05	.786	1.02	.846	1.22	< .001
Married	1.06	.6690	1.34	.198	0.68	< .001
Survey year > 1989	1.06	.607	.88	.467	1.11	.205
Children < 18 years	1.82	< .0001	1.57	.032	1.02	.781
n	2832		2832		3178
P value for X² all variables	< .001		< .001		< .001

OR = odds ratio.
^aLogistic regressions were run using Stata.

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Acknowledgements

The authors would like to acknowledge the editorial review and useful comments offered by Katherine Knapp, Richard Campbell, Carol Simon, James Cultice, and four anonymous reviewers.

Funding Information

Funding was provided by the Health Resources and Services Administration, Bureau of Health Professions, #U76MB10004, received through the Illinois Center for Health Workforce Studies.

Article Disclaimer

The authors declare no conflicts of interest or financial interests in any product or service mentioned in this article, including grants, employment, stock holdings, gifts, or honoraria. Dr. Cooksey served as a consultant to the Health Resources and Services Administration on the national pharmacist shortage study.

Surrey M. Walton, PhD, is assistant professor, College of Pharmacy, University of Illinois-Chicago. Judith A. Cooksey, MD, MPH, is director, Illinois Center for Health Workforce Studies, University of Illinois- Chicago.

Correspondence: Surrey M. Walton, PhD, College of Pharmacy, University of Illinois-Chicago, Department of Pharmacy Administration (M/C 871), 833 South Wood Street, Room 241, Chicago, IL 60612-7231. Fax: 312-996-0868. E-mail: walton@uic.edu.