A Holland perspective on the U.S. workforce from 1960 to 2000
Robert C. Reardon
The authors analyze civilian occupations and employment data collected by the U.S. Census Bureau in 1960, 1970, 1980, 1990, and 2000 with respect to 6 kinds of work (Holland’s RIASEC [Realistic, Investigative, Artistic, Social, Enterprising, Conventional] classification), employment, and gender. For the 1990 and 2000 censuses, kinds of work, gender, and income are analyzed, and for the 2000 census, kinds of work, age, and gender are examined. Past employment trends developed from census data are further analyzed with respect to Bureau of Labor Statistics employment projections through 2012. Implications for further research, employment policy, and career services are offered.
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Holland’s (1997) typological theory specified a theoretical connection between vocational personalities and work environments that makes it possible to use the same RIASEC (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) classification system for both persons and jobs. Use of the typology enables individuals to categorize their interests and personal characteristics in terms of the six types and combinations of the types. In Holland’s theory, persons can be categorized as one of six personality types: Realistic, Investigative, Artistic, Social, Enterprising, or Conventional. In a similar way, the environments of college campuses, fields of study, work positions, and occupations can be classified using the same RIASEC system.
Holland’s RIASEC typology has become a common tool for classifying persons and environments in career guidance and counseling. It is now incorporated into the Occupational Information Network (O*NET), a comprehensive database that provides information about 975 occupations, worker skills, and job training requirements. O*NET, sponsored by the U.S. Department of Labor’s Employment and Training Administration (U.S. Department of Labor, 1998), is the primary source of U.S. occupational information, and its data are included in many computer-based career information delivery systems (e.g., Choices Planner, Career Information System). Moreover, most career assessment instruments for individuals report scores based on Holland codes because this has become a standard method for linking persons and occupational alternatives (e.g., Self-Directed Search [SDS; Holland, 1994], Strong Interest Inventory [SII; Strong, 1994], Kuder Career Search With Person Match [Zytowski & Kuder, 1999]).
Given the extensive use of the RIASEC typology in career guidance services, we believe it is important to examine the actual distribution of jobs in the U.S. economy. Persons contemplating career decisions could benefit from understanding the scope and nature of jobs in the economy. Are there income, gender, or age differences across these six areas? Moreover, we think it important to determine whether the distribution of jobs is changing as a result of various socioeconomic developments. For example, has the distribution of jobs in RIASEC categories changed in the past 50 years? An analysis of occupational employment, then, could be beneficial to career counselors and other career services providers.
Downes and Kroeck (1996) reported discrepancies between existing jobs and a person’s vocational interests. Using SDS normative data from the 1994 edition for both high school students and adults, together with employment figures for the 292 occupations listed in the June 1993 issue of the Monthly Labor Review, the authors reported a lack of person-environment match with respect to some individual interests and employment. For example, they reported low interest in the Enterprising and Conventional areas relative to the large number of jobs for both high school and adult groups. However, the two norm groups differed in their interest across the six RIASEC areas. For example, high school students showed little interest in the Realistic area, in contrast to adults, who had more interest in this area.
Beginning in the early 1970s, researchers began to examine the U.S. labor market using the RIASEC classification system. This work was important for both theoretical and practical reasons. For example, if the number of annual job openings is strongly related to the number of people currently working in that area, then knowing the number of persons employed is of practical importance in job hunting because of the need to replace workers. In a series of studies, G. D. Gottfredson and colleagues (G. D. Gottfredson & Daiger, 1977; G. D. Gottfredson & Holland, 1996; G. D. Gottfredson, Holland, & Gottfredson, 1975) and L. S. Gottfredson and colleagues (L. S. Gottfredson, 1978, 1980; L. S. Gottfredson & Brown, 1978) analyzed U.S. employment using data provided by the decennial censuses in 1960, 1970, and 1980. In similar fashion, Arbona (1989) examined 1980 census data to explore gender, educational level, and ethnicity (e.g., Black, Hispanic, White) with respect to population employment distribution. Altogether, these studies examined a variety of variables with respect to the Holland RIASEC classification, including percentage of men and women working in hundreds of occupations, ethnicity of workers in occupations, salaries earned during the preceding year by incumbents, educational and training levels associated with occupations, occupational prestige, and the levels or complexity ratings for occupations. As a result of these studies, practitioners and scholars obtained more detailed information about work environments and the characteristics of workers.
After a 15-year hiatus in research on census data and Holland codes, Reardon, Vernick, and Reed (2004) analyzed the 1990 census data in relation to data from 1960, 1970, and 1980. These authors considered the variables of gender, income, and cognitive complexity (Cx). They found stability in the census data for the use of occupational titles for six kinds of work from 1960 to 1990 (e.g., the Realistic area included many more named occupations in the census than did the other five areas, averaging between 46% and 50% of all named occupations over the 40-year period). Cx is an estimate of the cognitive skill and ability associated with an occupation (e.g., the number of years of formal education required for an occupation or the time required for on-the-job training [OJT] to master a job; G. D. Gottfredson & Holland, 1996). Cx scores range from 70 or above to 40 or below, and a Cx rating of 65 or higher is associated with a college degree and possibly postgraduate work and 4-10 years of OJT, whereas a Cx rating of 50 is typically associated with a high school degree and a year or more of OJT. G. D. Gottfredson and Holland (1996) found that Investigative and Artistic areas were associated with the highest ratings, and the Conventional area was associated with the lowest ratings. They found that those employed in the Investigative area had Cx ratings only in the highest two levels of cognitive ability, whereas individuals employed in the Realistic, Social, Enterprising, and Conventional areas had Cx ratings in all six levels.
Reardon et al. (2004) found that whereas employment declined by 18% in the Realistic area relative to other areas, it remained the largest area of employment through 1990 and actually increased in real numbers. Only 1% of the U.S. population was employed in the Artistic area. They reported marked differences in employment between men and women across the six areas from 1960 to 1990. Reardon et al. (2004) examined income and gender with respect to six kinds of work and found that the average income profile ranging from highest to lowest was IESARC. The discrepancy across the six areas was very large, with the average Investigative income more than 2 times larger than the average Conventional income.
The data included in these studies are unique in several ways and have special implications for career counselors. First, as an independent branch of the federal government, the U.S. Census Bureau reports actual numbers of people working in different occupations based on an accounting of persons in households. Ideally, census data would become a standard component of occupational and labor market information used in career interventions. Second, these data provide a retrospective look at the labor markets, and by examining them over time, it is possible to examine changes in the economic lives of persons in the United States. Third, the occupational titles included in the census have remained constant over the years, only changing slightly, and this reinforces the use of occupational schema in matching persons and environments. Career counselors can use census data organized by Holland codes to illustrate and explain where jobs exist in relation to their clients’ interests. For example, a client may have a strong interest in Artistic occupations, and census data may help a counselor to explain the relatively small number of persons working in Artistic fields.
The purpose of the present study was to reexamine employment trends reported in earlier research using census data and to add new analyses based on the 2000 census. The present study further examined areas of work, age, gender, and income in terms of the 1990 and 2000 census data. Our research questions were as follows:
1. What were the number and percentage of persons employed in 1960, 1970, 1980, 1990, and 2000 in relation to six kinds of work?
2. What were the number and percentage of persons employed in 1960, 1970, 1980, 1990, and 2000 for men and women in relation to six kinds of work?
3. What were the incomes for different kinds of work for men, women, and the total population in 1990 and 2000?
4. What was the age and gender distribution of workers in six kinds of work in 2000?
5. What do census and other labor market data suggest regarding future employment trends?
Method
The occupational data collected by the U.S. Census Bureau are based on census researchers’ analysis of hundreds of thousands of jobs reported by persons in each census period. Researchers then categorize the detailed job information into occupational groups using the census occupational codes (U.S. Census Bureau, 1992b).
Employment Data
In the 1960 census, the sampling unit was the housing unit, or the person in the case of group housing. This sampling method provided information about 297 detailed occupational categories. L. S. Gottfredson and Brown (1978) described the methods they used to derive Holland codes for 1960 census data using 1970 census data as a point of reference. In the 1970 census, the sampling unit was the housing unit, and 440 detailed occupational titles were included in these data, 143 more than in 1960. As with the 1960 census, the data included only employed persons and excluded members of the armed forces. G. D. Gottfredson et al. (1975) analyzed data from the 1970 census involving 424 occupations and excluded men (5.6%) and women (6.6%) not classified according to one of the detailed occupations. Information about the 1980 census was taken primarily from G. D. Gottfredson and Holland (1989) and G. D. Gottfredson (1984). The 1980 analysis was based on 503 selected occupations.
Comprehensive information about the 1990 census was provided by the U.S. Census Bureau (1992a, 1992b) and was based on 500 selected occupations. G. D. Gottfredson and Holland (1996) indicated that this classification was most closely related to the Standard Occupational Classification (SOC; U.S. Department of Commerce, 1980). The U.S. population count in 1990 was 283,928,233 (U.S. Census Bureau, 1992a). Comparing the 1980 and 1990 census data, G. D. Gottfredson and Holland (1996) noted that four new categories of work were added in the 1990 census and six were eliminated from the 1980 census. Data for individuals who were classified as salaried and self-employed were collapsed into one occupational entry for both of these census codes.
The 2000 census counted 281,421,906 people in the 50 states and the District of Columbia. As in the past, short and long forms were used with about 17% of households, or 1 in 6, receiving the latter (U.S. Census Bureau, 2002). Four hundred seventy-one occupations were classified from the 2000 census. Researchers used the SOC (U.S. Department of Labor, 2000) to classify occupations in the 2000 census.
We obtained Holland codes from several different resources for the 2000 census using the following sources: (a) the Dictionary of Holland Occupational Codes (DHOC; G. D. Gottfredson & Holland, 1996), (b) the Dictionary of Occupational Titles (U.S. Department of Labor, 1991), and (c) the O*NET online database. The majority of the occupations (n = 391; 83%) were coded using the DHOC. Approximately 80 (17%) occupations were coded according to the information provided by O*NET OnLine. Of those occupations, 9 (2%) were classified as “data not available” because of the lack of information available about these occupations (e.g., logistician, network and computer systems administrator). Eggerth, Bowles, Tunick, and Andrew (2005) investigated the rates of agreement between Holland code classifications from O*NET, the SII, and the DHOC. They found the highest mean pairwise agreement rate for first letters was 70.6%, followed by 60.2% for two letters and 32% for three letters. Only first letters of occupational codes were used in the present study.
A Note About Data Analysis
Previous studies of employment using census data and Holland codes have reported frequency and percentage frequency distributions, and we have chosen to continue that procedure. As earlier researchers have noted (G. D. Gottfredson & Daiger, 1977), the sample sizes are so large that the magnitude of observed differences is more important than statistical differences. However, the fourth research question in our study had not been explored earlier, and we used chi-square tests for this analysis. The U.S. Census Bureau (1992b) has reported detailed methods for interpreting data based on procedural errors and standard errors in sampling. Information about confidence intervals in estimates about population characteristics are quite technical and beyond the scope and precision of our study. For these reasons, we have rounded numbers in this report to the nearest percent or thousand in order to avoid communicating a misplaced sense of precision in the findings.
Results
Employment in Six Kinds of Work, 1960-2000
The first question of interest concerned the number and percentage of persons employed in six kinds of work from 1960 to 2000. Table 1 indicates that the total estimated employment increased over the 5 decades from 63.8 million in 1960 to 121.0 million in 2000. Table 1 also reveals that the Realistic area had the largest number of individuals employed and that the Artistic area had the fewest number employed. Noteworthy is the finding demonstrating that the gap between the number of people employed in the Realistic and Enterprising areas decreased from 38% in 1960 to 11% in 1990, and by the year 2000, approximately equal numbers of people were employed in both interest areas. Employment in the Realistic area declined by 25% from 1960 to 2000, about 5% to 7% each decade, but it remained the largest area of employment over the 5 decades. In contrast, employment in the Enterprising area increased by 13% between 1960 and 2000 (11.1 million to 36.0 million) in relation to the other five areas, and the percentage of individuals employed in the Investigative area more than doubled between 1960 and 2000 (from 3% to 8%, or 2.0 million to 9.3 million). Employment in the other four areas remained more stable.
Employment, Gender, and Kinds of Work, 1960-2000
The second question concerned employment for men and women over 5 decades in six kinds of work (see Table 1). Most men were employed in the Realistic area, followed by the Enterprising area. Over the 5 decades, between 75% and 85% of male workers were employed in these two areas. This means that only 15% to 25% of men were employed in the other four areas. Table 1 shows that male employment in the Realistic area decreased over the 5 decades, whereas the total number and percentage of men and women employed in the Realistic area remained the highest for the six kinds of work (44%). The number of individuals employed in the Realistic area was only 0.4 million higher in 2000 than in 1960.
As compared with men, women have been employed in more varied kinds of work, including Conventional; Realistic; Social; and, more recently, Enterprising areas. Indeed, the percentage of women employed in the Enterprising area more than doubled over the 5 decades, from 13% to 28%. In contrast, there were slight decreases in female employment in the Conventional and Realistic areas. For example, the percentage of women employed in Realistic occupations decreased from 33% in 1960 to 15% in 2000 in spite of some efforts to encourage nontraditional work for women. Yet the percentage of women in Investigative occupations increased from 1% in 1960 to 6% in 2000. From 1960 to 1990, Conventional was the area of work in which most women were employed, but in 2000, that shifted to the Enterprising area. In 2000, 28% of women were employed in Enterprising occupations and 26% were employed in Conventional occupations. The percentage of women employed in the Social area remained relatively constant over the 5 decades, although the actual number employed increased from 3.8 million in 1960 to 13.6 million in 2000. The Artistic area consistently showed the smallest percentage of employment for women from 1960 to 2000.
Kinds of Work, Gender, and Income in 1990 and 2000
The third question concerned the income for men, women, and the total population in different kinds of work in 1990 and 2000. Table 2 presents the income levels for men and women in different kinds of work. Because the method for calculating the income levels differed for the 1990 and 2000 census years, comparisons between the 2 years should be made with caution. The more accurate way of viewing Table 2 is to focus on the continued discrepancy with regard to income among the Holland types in each of the two census periods. These numbers show wide variations in income levels among different groups.
In 1990, the income for women was lower than the income for men in all six categories, and the discrepancy became greater as income levels rose. Table 2 shows that, in 2000, the income for women was lower than the income for men in all categories except Conventional. The average income profile for six kinds of work ranging from highest to lowest was IESARC for men in 1990, ISEACR for women in 1990, ISAERC for men in 2000, and ICSAER for women in 2000. The discrepancy between the average Investigative income ($41,499) was more than 2 times that of the average Conventional income ($16,179) in 1990. However, this was only true for men in 2000, because the Conventional income for women in 2000 rose dramatically. On the other hand, in 2000, the average income for women in Investigative work ($39,358) was more than 2 times larger than the average Realistic income ($18,082).
Kinds of Work, Age, and Gender in 2000
The fourth question concerned the age and gender distribution of workers in the six kinds of work according to the 2000 census. This issue was not addressed in previous studies. We speculated that workers might be disproportionately represented across the six areas in terms of age. For example, we anticipated that younger men would not be employed in the Realistic area because it is an area of declining employment. Table 3 shows the number and percentage of men and women over and under the age of 40 years according to the six types of work. We conducted chi-square tests to determine whether there was a relationship in the percentage of persons over and under 40 across the six types. The results indicated that there was no statistically significant relationship between age and kind of work for either men, [chi square](5, N = 64,414) = 470.58, p < .001, or women, [chi square](5, N= 56,565) = 225.23, p < .001. (Note that the Ns reported are to the nearest thousand.)
For male workers under 40 and over 40, the percentage employed in the Realistic area was 48% and 41%, respectively, which was the opposite of what we expected. Otherwise, Table 3 shows very little discrepancy between the percentage of male and female workers employed in the various kinds of work with regard to age. For women under 40, 30% are working in Enterprising occupations as opposed to only 26% for the over-40 group. For men under 40, 29% are working in Enterprising occupations as opposed to 33% in the over-40 group. These discrepancies do not suggest large shifts in employment across the six areas with respect to age.
Future Employment Trends
Our final question explored census employment data relative to employment trends reported by the Bureau of Labor Statistics (BLS). The distribution of employment over 50 years indicates some shifts across the six kinds of work. The RECSIA profile for 2000 actual employment (most to fewest jobs) reported by the U.S. Census Bureau (2002) stands in contrast to the SERCIA profile of the 20 occupations projected to have the most new jobs through 2012 by the BLS (Horrigan, 2003-2004). Regardless of which method of analysis is used, Realistic, Enterprising, Social, and Conventional areas of work appear to have the largest number of employment opportunities. Reardon, Lenz, Sampson, and Peterson (2005) also found that the 20 occupations with the fastest projected percentage of employment growth had a profile of SRICEA. It is somewhat surprising to see that the Realistic area has become so prominent in these latest projections by the BLS. Moreover, the BLS predicts that the professional and related occupations group, which employs large numbers of college graduates, will grow faster and add more workers than any other occupational group through 2010 (Dohm & Wyatt, 2002). The most significant source of these jobs will be the replacement of workers leaving their positions. Millions of baby boomers will retire by 2010, and college graduates will fill their positions.
Discussion
Several limitations in the present study should be noted. First, the occupational titles included in the census have changed slightly over the years, and we were required to exercise some limited interpretation in assigning Holland codes to occupations in the 1980, 1990, and 2000 census data. In a similar manner, codes for occupations have also changed somewhat over the years in the three editions of the DHOC (G. D. Gottfredson & Holland, 1996). However, we believe this issue had a minimal impact on our findings because changes in occupational codes are least likely to affect the first letter of a code.
Second, the classification of hundreds of thousands of jobs into 350 to 500 occupational categories requires considerable judgment and skill. Occupational analysts base these judgments on the application of classification criteria, and there is the possibility of error in the use of the classification such that the classification itself may need revision. The National Research Council (1999) recommended that the tools and systems of occupational analysis should be updated frequently to keep pace with changes in the structure of work.
Third, we used the first letter in the Holland code in our analysis in order to simplify our reporting. Although this decision reduced some of the precision inherent in the Holland classification when three-letter codes are used, it increased the accuracy of occupational classification. Fourth, our analysis was based on a sampling procedure used by the U.S. Census Bureau over 5 decades, and we have generalized from this sample to the entire U.S. population. We assume that the sampling procedure used by the U.S. Census Bureau is appropriate for use in this study. Finally, it is possible that occupations may be shifting within or among industry groups, which would mask some of the findings we are reporting in our analysis.
These limitations notwithstanding, the results of this analysis of six kinds of work and employment over 5 decades have implications for career service providers. Holland (1997) noted several “rules” to use in interpreting the SDS, such as the “Rule of Asymmetrical Distribution of Types and Subtypes.” This rule reminds counselors and clients that the distribution of types across the six RIASEC areas is very uneven and unequal. Codes associated with small employment numbers may have fewer jobs and new openings. Our research underscores the validity of this rule. In each census period, the Artistic area was the smallest area of employment at 1%. At the other extreme, the Realistic area was the largest area of employment, ranging from 55% in 1960 to 30% in 2000. The distribution of jobs across the six types is, indeed, not symmetrical or equal.
Some of these findings may be interpreted in different ways. For example, the Realistic area employed the most persons in 2000, but employment in the Realistic area had dropped by 25% over the 5 decades to the same percentage of employment as the Enterprising area (30%). The loss of jobs in the Realistic area is greater than in any other area, from 42.7 million in 1990 to 36.7 million in 2000. The Investigative area almost tripled in employment from 1960 to 2000, from 3% to 8%, but less than 10% of U.S. jobs are in that area (9.3 million). This finding seems akin to the issue of “big growth” and “fast growth” jobs described by Horrigan (2003-2004), where few occupations appear on the top of both lists. Career information used in career guidance programs often touts the rapid growth in information and technology jobs; however, this emphasis needs to be balanced with the understanding that only 8% of U.S. jobs are in the Investigative area.
Other findings are clearer and not open to alternative interpretations. For example, the Enterprising area nearly doubled in employment from 1960 to 2000, from 17% to 30%. The Social area almost doubled in the same period, from 9% to 16%. At the same time, stability, not change, characterized the Artistic and Conventional areas, with the percentages of jobs for both areas staying the same in 1960 and 2000. With almost 46% of workers employed in the Enterprising and Social areas in 2000, it is apparent that the occupations that will employ large numbers of people in the future will draw upon the social and enterprising skills of workers, their “people” skills (Reardon et al., 2005).
Male employment has been concentrated in the Realistic and Enterprising areas, and there has been an increase of employment in the Investigative area from 4% to 10% over the 5 decades (1.7 million to 6.1 million). There was a corresponding increase for women from 1% to 6% in the Investigative area (0.3 million to 3.1 million). However, it must be noted that in 2000, only 11% of male employment and 7% of female employment were associated with work in the combined Investigative and Artistic areas. In general, there is a wide disparity regarding employment in 2000 across the six kinds of work for men and women, ranging from 44% for men in the Realistic area to 1% for both men and women in the Artistic area. There is a relatively stable pattern of employment for men and women in six kinds of work over 5 recent decades.
Compared with women, men are more segregated with respect to the six areas of work, with 8 out of 10 working in either the Realistic or Enterprising area. Career guidance programs might encourage men to explore occupations that are frequently dominated by women (e.g., Social). On the other hand, women, who are more equally distributed across the Conventional, Realistic, Social, and Enterprising areas, might be encouraged to pursue occupations associated with higher Cx levels (e.g., Investigative).
Although a large portion of women continue to be employed in the Conventional and Social areas, the Enterprising area now employs the largest number of female workers for the first time in the 5 decades. Indeed, the percentage of women employed in the Investigative and Enterprising areas is now 34%. Almost 30 years ago, L. S. Gottfredson (1978) suggested that it might be advantageous for women to explore more fully occupations in the Investigative and Enterprising areas because these areas are marked by occupations with higher levels of income and prestige. It appears that this has now begun to occur.
Our findings also indicate that income for men and women is not equitable across the six areas, with men reporting more income in every area except Conventional. (It is unclear from our study why the income level for women in the Conventional area increased in the 2000 census.) Gilbert and Brownson (1998) reported that women earn 76 cents on the dollar compared with men. The analysis of the reasons for these discrepancies in employment and income across the six areas is beyond the scope of this article, but it is important to note that Holland’s (1997) classification of occupations provides a potentially useful perspective for analyzing these social and economic issues.
Altogether, we do not see an extraordinary shift in employment across the six areas of work, but this is a matter of conjecture as we noted earlier. Obviously, the percentage of the U.S. population employed in the six areas of work has changed. Yet the number of people employed in the occupations has not changed. This means Realistic types still have as many Realistic jobs to consider. There does not yet seem to be a need for career counselors to begin discouraging “R people” from exploring Realistic occupations.
Much of the focus in Holland’s (1997) person–environment matching model involves the study of persons and measuring their interests and personality. This study demonstrates how the theory can be used to assess environments. Counselors might help clients see how adding some Investigative or Enterprising aspects to their occupational pursuits may enhance the income associated with future occupational activities. Moreover, career assessment inventories using Holland codes to provide occupational information could use census data in the interpretive report information provided to users.
Although we speculated that employment across the six areas might differ dramatically across age groups, this was not apparent in the 2000 census data; indeed, we found no significant discrepancy between male and female workers of different ages across the six areas of work. Contrary to our expectations, there are slightly more people under 40 than over 40 working in the Realistic area. Perhaps this finding underscores the fact that (a) a large number of jobs actually involve the replacement of older workers and (b) the Realistic area is the largest area of employment in the U.S. economy. Career counselors should be aware of this when advising clients about ongoing job opportunities.
G. D. Gottfredson et al. (1975) found that people aspired to Enterprising jobs at a rate far below the rate of actual employment in that kind of work. At the same time, they aspired to Social and Artistic jobs at a rate well above the rate of actual employment. Career guidance programs might properly help participants understand the economic realities of current employment data, rather than relying exclusively on projections of expected future occupational activity (U.S. Department of Labor, 2003-2004).
Our findings indicate that most people are employed in Realistic, Enterprising, and Conventional occupations. Public attention regarding employment and career preparation is often directed at occupations with combinations of codes in the Investigative, Artistic, and Social areas because the rate of employment increase is sometimes greater there than in the Realistic, Enterprising, and Conventional areas. The Investigative, Artistic, and Social areas may also require or provide higher levels of Cx, social prestige, and income but employ fewer people. However, we noted that the BLS projects that most new jobs through 2012 will be in the Social and Enterprising areas, followed by the Realistic area. It must be remembered that these are projected new jobs, which seem to capture more public attention and interest than the census data regarding actual employment. Given that 1 out of 3 jobs is related to replacement (Mittelhauser, 1998), it would also appear wise to inform individuals about employment in areas that offer more employment opportunities, especially in specific occupations in which older workers will soon be retiring.
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Robert C. Reardon, Emily E. Bullock, and Katie E. Meyer, Career Center, The Florida State University. Emily E. Bullock is now at Department of Psychology, The University of Southern Mississippi. Katie E. Meyer is now at Division of Workforce Development, Florida Community College. A longer version of this article is available as Technical Report No. 45 at www.career.fsu.edu/techcenter. A PowerPoint with graphs that was presented at the 2005 meeting of the National Career Development Association is also available at this Web site. The authors thank Janet Lenz, Corey Reed, and Christen Perry for comments on this article and Betty Brown for assistance with data analysis. Correspondence concerning this article should be addressed to Robert C. Reardon, Career Center UCA4150, The Florida State University, Tallahassee, FL 32306-2490 (e-mail: rreardon@admin.fsu.edu).
TABLE 1 Number (Nearest Thousand) and Percentage of Persons Employed by
Gender and Kinds of Work, 1960-2000
1960 1970
Kind of Work Men Women Total Men Women Total
Realistic
Number 27,986 6,986 34,972 26,853 7,488 34,341
Percentage 65 33 55 60 28 48
Investigative
Number 1,723 259 1,982 3,203 486 3,689
Percentage 4 1 3 7 2 5
Artistic
Number 474 279 753 671 299 970
Percentage 1 1 1 1 1 1
Social
Number 1,861 3,772 5,633 2,622 5,772 8,394
Percentage 4 18 9 6 21 12
Enterprising
Number 8,476 2,616 11,092 9,131 3,023 12,154
Percentage 20 13 17 20 11 17
Conventional
Number 2,550 7,066 9,316 2,563 10,093 12,656
Percentage 6 34 15 6 37 17
Total 43,070 20,978 63,748 45,043 27,161 72,204
1980 1990
Kind of Work Men Women Total Men Women Total
Realistic
Number 32,871 9,378 42,249 32,176 10,535 42,711
Percentage 56 22 42 52 20 37
Investigative
Number 3,185 984 4,169 4,745 1,993 6,738
Percentage 6 2 4 8 4 6
Artistic
Number 827 577 1,404 780 772 1,552
Percentage 1 1 1 1 1 1
Social
Number 3,348 7,468 10,816 3,816 11,167 14,983
Percentage 6 17 11 6 21 13
Enterprising
Number 15,170 10,480 25,650 16,585 13,083 29,668
Percentage 26 25 25 27 24 26
Conventional
Number 3,022 14,518 17,540 3,771 16,316 20,087
Percentage 5 33 17 6 30 17
Total 58,424 43,675 101,828 61,873 53,866 115,739
2000
Kind of Work Men Women Total
Realistic
Number 28,427 8,271 36,698
Percentage 44 15 30
Investigative
Number 6,129 3,185 9,314
Percentage 10 6 8
Artistic
Number 858 764 1,622
Percentage 1 1 1
Social
Number 5,252 13,569 18,821
Percentage 8 24 16
Enterprising
Number 19,963 15,982 35,945
Percentage 31 28 30
Conventional
Number 3,782 14,792 18,574
Percentage 6 26 15
Total 64,411 56,563 120,974
Note. The data in columns 1-6 are from “Using a Classification of
Occupations to Describe Age, Sex, and Time Differences in Employment
Patterns,” by G. D. Gottfredson and D. C. Daiger, 1977, Journal of
Vocational Behavior, 10, p. 131. Copyright 1977 by Academic Press, Inc.
Reprinted with permission from Elsevier.
TABLE 2 Annual Income (Dollars) of Men and Women in Six Kinds of Work,
1990 and 2000 Censuses
1990 2000
Kind of Work Men Women Total Men Women Total
Realistic 23,139 16,196 21,529 29,830 18,082 27,215
Investigative 43,795 27,250 41,499 53,703 39,358 48,592
Artistic 27,873 16,330 22,057 37,338 27,588 32,724
Social 30,543 21,187 25,095 42,731 28,764 32,506
Enterprising 31,561 20,145 27,493 30,899 19,577 26,109
Conventional 20,208 14,422 16,179 22,875 35,802 33,223
TABLE 3 Population (Nearest Thousand), Age (Over and Under 40), and
Gender by Kinds of Work, 2000
40+ Years Old 16-39 Years Old
Kind of Work Men Women All Men Women All
Realistic
Number 13,030 4,283 17,313 15,398 3,988 19,386
Percentage 41 15 29 48 14 32
Investigative
Number 3,273 1,495 4,767 2,857 1,691 4,547
Percentage 10 5 8 9 6 7
Artistic
Number 409 367 776 450 397 847
Percentage 1 1 1 1 1 1
Social
Number 3,007 7,369 10,376 2,245 6,200 8,445
Percentage 9 26 17 7 22 14
Enterprising
Number 10,660 7,363 18,023 9,303 8,620 17,923
Percentage 33 26 30 29 30 30
Conventional
Number 1,693 7,295 8,988 2,089 7,497 9,586
Percentage 5 26 15 6 26 16
Total 32,072 28,172 60,244 32,342 28,393 60,735
Total
Kind of Work Men Women All
Realistic
Number 28,428 8,271 36,669
Percentage 44 15 30
Investigative
Number 6,130 3,185 9,315
Percentage 10 6 8
Artistic
Number 859 764 1,622
Percentage 1 1 1
Social
Number 5,252 13,569 18,821
Percentage 8 24 16
Enterprising
Number 19,963 15,982 35,946
Percentage 31 28 30
Conventional
Number 3,782 14,792 18,574
Percentage 6 26 15
Total 64,414 56,565 120,979
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