"The Graying of Academia: Will It Reduce Scientific Productivity?", Stroebe 2010
"...This change resulted in a drop in retirements of older academics and has already altered the age structure at U.S. universities (Ashenfelter & Card, 2002; Clark & Ghent, 2008). On the basis of data obtained from 16,000 older faculty members at 104 colleges and universities across the United States, Ashenfelter and Card (2002) concluded that after the abolition of mandatory retirement, the percentage of 70-year-old professors continuing to work increased from 10% to 40%. In an analysis of data from the North Carolina university system, Clark and Ghent (2008) drew a similar conclusion:
> Prior to 1994, the retirement rate was 59 percent for faculty age 70, 67 percent for faculty age 71 and 100 percent for faculty age 72. After the policy of mandatory retirement was removed, 24 percent of faculty age 70, 19 percent of faculty age 71, and 17 percent of faculty age 72 retired. (pp. 156 –157)
As a result of such changes, the percentage of fulltime faculty members age 70 or older went up threefold (to 2.1%) between the years 1995 and 2006 (Bombardieri, 2006). However, at some universities the situation is more extreme. For example, in the Harvard University Faculty of Arts and Sciences, the percentage of tenured professors age 70 years and older has increased from 0% in 1992 to 9.1% in 2006 (Bombardieri, 2006). The impact of the changing age structure has also been felt at the National Institutes of Health (NIH), where the average age of principal investigators for NIH grants has increased from 30 – 40 years in 1980 to 48 years in 2007.
although creativity is moderately positively correlated with IQ up to intelligence levels that are approximately one standard deviation above the mean, the relationship becomes essentially zero for more intelligent individuals (Barron & Harrington, 1981; Feist & Barron, 2003). Thus, when IQ scores are correlated with some valid criterion of scientific distinction (e.g., number of citations), the correlations approach zero (e.g., Bayer & Folger, 1966; Cole & Cole, 1973). This makes it highly unlikely that a modest agerelated decrease in intelligence should impair a scientist’s ability to produce high-quality research. Similar reservations apply to measures of divergent thinking, which are considered more closely related to creativity than are traditional intelligence tests (e.g., Hennessey & Amabile, 2010). Although there is some evidence that age decrements in divergent thinking appear as early as in the 40s (e.g., McCrae, Arenberg, & Costa, 1987), age accounts for very little variance.
The most influential theory of the association of age, cognitive ability, and scientific achievement has been suggested by Simonton (e.g., 1985, 1988, 1997, 2002), undoubtedly the most important and prolific researcher in the area of the psychology of science. He developed an elegant quantitative model of the decline in creative potential, which predicts that the association between age and productivity is curvilinear and declines with career age rather than chronological age. The basic assumption of Simonton’s theory is that each creator starts off with a fixed amount of initial creative potential. This creative potential consists of “concepts, ideas, images, techniques, or other cognitions that can be subjected to free variation” (Simonton, 1997, pp. 67– 68). Of the possible combinations of these, only a subset are sufficiently promising to justify further elaboration. Some of them may fail the empirical test, but some may finally be worked out into finished products that might be published. Each time individuals produce new research, they use up part of their creative potential and reduce the ideational combinations that are available to them. According to Simonton (1997), productivity increases during the first 20 years of an individual’s career, when the individual still has a rich fund of creative potential and is getting better and better at turning these ideas into publishable output. However, approximately 20 years into an individual’s career, a peak is typically reached. After that, productivity begins to decline, because the individual has used up a substantial proportion of his or her initial creative potential.
It has been argued that the differences between scientists in research productivity are too extreme to be explained merely by differences in ability or motivation (Cole & Cole, 1973). For example, in a study of the scientific output of more than 1,000 American academic psychologists, Dennis (1954) found that the most productive 10% authored 41% of all publications, whereas the bottom 10% produced less than 1%. In fact, the top half were responsible for 90% of total output, and the bottom half, for only the remaining 10%. Similarly biased distributions have been shown for other sciences as well as for the arts and humanities (Simonton, 2002). Findings such as these led Price (1963), a historian of science, to propose Price’s law. According to this law, if k is the number of researchers who have made at least one contribution to a given field, the square root of k will be responsible for half of all contributions in this field. Thus, if there are 100 contributors in a field, the top 10% will be responsible for half of the contributions to this area.2
For example, in a study of publications by the 60 members of the editorial board of the Journal of Counseling Psychology in 2007, Duffy, Martin, Bryan, and Raque-Bogdan (2008) found number of publications and number of citations to correlate .80. This correlation is somewhat higher than the correlations typically found for psychology, which vary between .50 and .70 (Simonton, 2002). Simonton (2002) therefore concluded “that the quality of output is a positive function of quantity of output: the more publications one produces, the higher the odds that one will get cited” (p. 45). It is interesting to note that the same relationship has been observed in brainstorming research, where the number of ideas that are produced by an individual or a group is highly correlated with the number of good ideas (e.g., Diehl & Stroebe, 1987; Stroebe, Nijstad, & Rietzschel, 2010).
Because of the exponential growth of the scientific community during the last few centuries, there has always been an overrepresentation of younger scientists (Price, 1963). Thus, even if scientific achievement were unrelated to age, one would expect more eminent contributions from young rather than old scientists. The same bias arises with studies that use number of publications in top journals as their index of scientific achievement. For example, if one took the publications of 10 major scientific journals as one’s sample and then plotted the age distribution of the authors of these publications, the results would again be distorted by the fact that there are likely to have been more younger than older scientists in the population of scientists from which the successful publishers were drawn.
The classic study of Nobel laureates was published by Zuckerman (1977). It was based on 92 Nobel Prize winners who worked in the United States and won the Nobel Prize between 1901 and 1972. She found that the average age at which these individuals did their prize-winning research was 39 years, with winners of the prize in physics doing their research at 38.6 years and winners of the prize in medicine and physiology doing it at 41.1 years. Similar results were reported by Stephan and Levin (1993), who in an update and extension of Zuckerman’s (1977) study analyzed the 414 winners of the Nobel Prize in the natural sciences in the years 1901–1992. The average age for conducting the prize-winning research for all disciplines was 37.6 years, with physicists doing their research the earliest, at 34.5 years, and medical research being conducted by somewhat older researchers, at 38.0 years. Although this is not old, it is also not precociously young. However, before one draws any conclusions, one must remember that these findings inform us only of the proportion of Nobel Prizes won by scientists of different ages. They do not tell us at which age scientists are most likely to win that prize. For this, we need to know the age distribution of the population of scientists from which the Nobel Prize winners were selected. Although Stephan and Levin (1993) failed to make such a correction, Zuckerman (1977) did, and she compared the age distribution of her laureates to that of the general population of American scientists (see Figure 1). This comparison shows that the only substantial deviations from the general population occur for the age group of 35 to 44 years, which is clearly overrepresented among the Nobel laureates, and the age group of 55 years and older, which is underrepresented. Before one concludes from this evidence that great science is really the domain of the middle-aged, one should remember that during the period considered in these studies, even American scientists were subject to compulsory retirement. Most research in the natural sciences requires monetary resources, personnel, and laboratory facilities, which may have become unavailable to older scientists after their retirement. In anticipation of this fact, many scientists in their mid-50s may have already stopped initiating projects that they expected to be unable to finish before retirement.
For example, when Harvey Lehman, one of the most prolific researchers on age and scientific achievement, tabulated the ages at which a sample of 52 deceased philosophers had written their most significant work, a single-peaked function emerged: The mean age for producing a philosophical masterwork was 41.5 years. Practically the same age curve also describes the age at which significant works were produced in psychology (Lehman, 1966).
Lehman’s (1953, 1966) research can be criticized for his failure to take account of the age distribution of the population of philosophers and scientists from which he drew the sample of excellent contributions. The data were not corrected for the fact that there were likely to be many more younger than older individuals in the population of which the eminent individuals were a subsample. However, Wray (2004), who studied landmark discoveries in bacteriology between 1877 and 1899, also found that scientists 36 to 45 years of age were responsible for a disproportionate number of these discoveries, even after he corrected for the likely age distribution of scientists in the total population. In contrast, younger scientists (35 years and younger) and older scientists (46 to 65 years) were relatively underrepresented. Finally, Over (1988), who used publications in Psychological Review as his criterion for outstanding contributions (admittedly a less demanding criterion than that of landmark discoveries, even though Psychological Review is one of the top journals of our discipline), found a similar curvilinear distribution that peaked for individuals who were 12 to 17 years past their PhDs (i.e., ages 38 to 45 years) and declined thereafter. However, Over (1988) argued that because 60% of American psychologists active in research between 1965 and 1980 were under 40, one could expect that about 60% of the articles appearing in Psychological Review in this period would be authored by psychologists under the age of 40. In fact, 59.9% of the articles in his sample were published by authors who were 0 to 11 years past their PhDs. Thus, despite the less demanding criterion, the curvilinear relationship between age and scientific achievement reported here is similar to that found in studies of Nobel laureates.
The pattern of findings of these early studies is similar to that found in the studies of Nobel laureates and scientists with lesser achievements, with age being curvilinearly related to scientific productivity, which reaches a peak around ages 40 to 45 and then drops off (e.g., Bayer & Dutton, 1977; Cole, 1979; Dennis, 1956; Horner, Rushton, & Vernon, 1986; Kyvik, 1990; Over, 1982). This pattern was replicated in cross-sectional (Bayer & Dutton, 1977; Cole, 1979; Kyvik, 1990) and longitudinal or crosssequential studies (Dennis, 1956; Over, 1982; Horner et al., 1986) conducted in the United States (Bayer & Dutton, 1977; Cole, 1979; Horner et al., 1986) and Europe (Dennis, 1956; Kyvik, 1990; Over, 1982). However, not all disciplines showed this pattern (Levin & Stephan, 1989). But the only discipline in which a discrepant pattern has been replicated repeatedly is mathematics. Several studies of samples of mathematicians resulted in a linear relationship, with neither an increase nor a decline in productivity (Cole, 1979; Stern, 1978).
Three examples of studies suffice to illustrate the typical patterns found in this research area. In one of the most extensive cross-sectional studies, Cole (1979) compared the publication rates in the years from 1965 to 1969 of 2,460 scientists from six different disciplines, including psychology. Figure 2 presents the overall productivity for the six fields combined, as well as the overall citation rate. As the figure indicates, age is curvilinearily related to both productivity and citations. Overall, the rates for productivity and citations peaked around age 40 and then dropped off. This relationship was valid for all disciplines, except for mathematics, for which the relationship was linear, “supporting the conclusion that productivity does not differ significantly with age” (Cole, 1979, p. 965). Cole thus replicated the findings of Stern (1978), who concluded from her cross-sectional study that “the notion that younger mathematicians are, as it were, ‘physiologically’ more able to produce papers would appear to be in error. In general, we can state categorically that age explains very little, if anything, about productivity” (p. 134).
Two cross-sequential studies of psychologists were conducted by Over (1982) and Horner et al. (1986). Over (1982) analyzed the relationship between age and productivity of a small sample of British psychologists ranging in age from 26 to 65 years. These individuals were assessed twice, once in 1968 –1970 and a second time in 1978 – 1980. British psychologists in general published as frequently in 1978 –1980 as in 1968 –1970 (i.e., there was no period effect). However, both the cross-sectional and the longitudinal analyses indicated that psychologists over 45 years of age published significantly less frequently than their younger colleagues. The publication rates correlated .49 across the two times of measurement, indicating substantial stability of individual productivity. Over (1982) concluded that “a person’s previous research productivity was a far better predictor of subsequent research output than age was” (p. 519).
Another cross-sequential analysis on scientific productivity was based on 1,084 American academic psychologists and was conducted by Horner et al. (1986). Both the cross-sectional and the longitudinal analyses resulted in a curvilinear relationship between age and productivity. On average, the productivity at ages 35 to 44 was significantly higher than the productivity at younger and older ages. Again, the correlations between an individual’s number of publications at different periods indicated a great deal of stability. Finally, age accounted on average for only 6.9% of the variance across time (more for low than for high publishers).
The findings of these early studies allow four conclusions: (a) The overwhelming majority of studies reported an age-related decline in productivity (indicated by number of articles published), and most studies found the association to be curvilinear, with a peak somewhere around the ages of 40 to 45 years. (b) Even though there was a curvilinear relationship between age and productivity, age accounted for less than 8% of the variance in productivity. In mathematics, the relationship between age and productivity even appears to be linear, with age being unrelated to productivity. (c) In contrast, past performance was by far the best predictor of future productivity. As Simonton (2002) estimated, “Between one third to two thirds of the variance in productivity in any given period may be predicted from the individual difference observed in the previous period” (p. 86). (d) Finally, even if older researchers are somewhat less productive than their younger colleagues, the quality of their work (as reflected by citations) appears to be no less high. Over (1988) correlated the number of citations each article published in Psychological Review had received in the first five years after publication with the age of the article’s author and found that the correlation was not significantly different from zero. Similar findings were reported by Simonton (1985) in a study of the impact of the publications of 10 psychologists who had received the APA’s Award for Distinguished Scientific Contributions. He found that the ratio of high-impact publications to total output fluctuated randomly throughout their careers.
Although a recent longitudinal analysis of the association of age and productivity for 112 eminent members of the U.S. National Academy of Sciences also resulted in a nonlinear relationship (Feist, 2006), this relationship was different from that reported in most earlier studies. Three unconditional growth curve models were constructed. The best fit to the data was achieved with a cubic model, providing “population estimates on productivity that increase rapidly until approximately 20 years into one’s career, then flatten over the next 15 years, and then rise again over the last 5-year interval” (Feist, 2006, p. 29). Because these individuals started publishing their first articles between 22 and 25 years of age, they would have reached their first peak around age 45. After a 15-year leveling-off period, their productivity would increase again after age 60.
A somewhat different pattern was reported by Joy (2006), who examined the publication data of 1,216 faculty member from 96 schools ranging from elite research universities to minor undergraduate colleges. Data were collected in 2004. Figure 3 presents the mean number of publications per year by career age (i.e., years since receiving the PhD) of full-time faculty members at three homogeneous subgroups of institutions. In the context of the focus of this article, I restrict myself to discussing the data for the 399 faculty members associated with research universities (e.g., Princeton University, the University of Massachusetts at Amherst, Northeastern University). These academics published more during the first five years of their careers than in later years; their productivity remained essentially stable for the next 25 years, with perhaps a slight increase between the 26th and 30th years of their careers. Thus, the data for faculty members at research universities (or for those at other institutions) failed to show the pattern reported in earlier studies, in which productivity reached a peak around ages 40 to 45 and then dropped off (Bayer & Dutton, 1977; Cole, 1979; Dennis, 1956; Horner et al., 1986).
This study was based on 6,388 professors and researchers who had published at least one journal article over the eight-year period from 2000 to 2007. The study used 10-year age categories, ranging from age 20 to age 70. Two different sets of data were used in compiling average productivity, namely, the average productivity of all professors and that of active professors who had published at least one journal article at the age in question. Although the association between age and productivity was curvilinear for both samples, only the total sample showed a decline after age 50. For the active professors, productivity increased to age 50 and then stayed at the same level until age 70. (There were too few older professors to extend the study beyond age 70.) Thus, these active professors sustained their productivity at a high level throughout their careers. There was also no decline in quality for the group of active professors. In fact, the average number of articles they published in high-impact journals (i.e., the top 1% cited journals) rose steadily to age 70, and so did the average number of articles that were among the top 10% of highly cited articles. The findings of Gingras et al. (2008) are discrepant with practically all of the early research. Given that, as noted above, the province of Quebec had already abolished compulsory retirement in 1980, this change would offer a plausible explanation for the fact that productivity did not decline for the older age group."
"...This change resulted in a drop in retirements of older academics and has already altered the age structure at U.S. universities (Ashenfelter & Card, 2002; Clark & Ghent, 2008). On the basis of data obtained from 16,000 older faculty members at 104 colleges and universities across the United States, Ashenfelter and Card (2002) concluded that after the abolition of mandatory retirement, the percentage of 70-year-old professors continuing to work increased from 10% to 40%. In an analysis of data from the North Carolina university system, Clark and Ghent (2008) drew a similar conclusion:
> Prior to 1994, the retirement rate was 59 percent for faculty age 70, 67 percent for faculty age 71 and 100 percent for faculty age 72. After the policy of mandatory retirement was removed, 24 percent of faculty age 70, 19 percent of faculty age 71, and 17 percent of faculty age 72 retired. (pp. 156 –157)
As a result of such changes, the percentage of fulltime faculty members age 70 or older went up threefold (to 2.1%) between the years 1995 and 2006 (Bombardieri, 2006). However, at some universities the situation is more extreme. For example, in the Harvard University Faculty of Arts and Sciences, the percentage of tenured professors age 70 years and older has increased from 0% in 1992 to 9.1% in 2006 (Bombardieri, 2006). The impact of the changing age structure has also been felt at the National Institutes of Health (NIH), where the average age of principal investigators for NIH grants has increased from 30 – 40 years in 1980 to 48 years in 2007.
although creativity is moderately positively correlated with IQ up to intelligence levels that are approximately one standard deviation above the mean, the relationship becomes essentially zero for more intelligent individuals (Barron & Harrington, 1981; Feist & Barron, 2003). Thus, when IQ scores are correlated with some valid criterion of scientific distinction (e.g., number of citations), the correlations approach zero (e.g., Bayer & Folger, 1966; Cole & Cole, 1973). This makes it highly unlikely that a modest agerelated decrease in intelligence should impair a scientist’s ability to produce high-quality research. Similar reservations apply to measures of divergent thinking, which are considered more closely related to creativity than are traditional intelligence tests (e.g., Hennessey & Amabile, 2010). Although there is some evidence that age decrements in divergent thinking appear as early as in the 40s (e.g., McCrae, Arenberg, & Costa, 1987), age accounts for very little variance.
The most influential theory of the association of age, cognitive ability, and scientific achievement has been suggested by Simonton (e.g., 1985, 1988, 1997, 2002), undoubtedly the most important and prolific researcher in the area of the psychology of science. He developed an elegant quantitative model of the decline in creative potential, which predicts that the association between age and productivity is curvilinear and declines with career age rather than chronological age. The basic assumption of Simonton’s theory is that each creator starts off with a fixed amount of initial creative potential. This creative potential consists of “concepts, ideas, images, techniques, or other cognitions that can be subjected to free variation” (Simonton, 1997, pp. 67– 68). Of the possible combinations of these, only a subset are sufficiently promising to justify further elaboration. Some of them may fail the empirical test, but some may finally be worked out into finished products that might be published. Each time individuals produce new research, they use up part of their creative potential and reduce the ideational combinations that are available to them. According to Simonton (1997), productivity increases during the first 20 years of an individual’s career, when the individual still has a rich fund of creative potential and is getting better and better at turning these ideas into publishable output. However, approximately 20 years into an individual’s career, a peak is typically reached. After that, productivity begins to decline, because the individual has used up a substantial proportion of his or her initial creative potential.
It has been argued that the differences between scientists in research productivity are too extreme to be explained merely by differences in ability or motivation (Cole & Cole, 1973). For example, in a study of the scientific output of more than 1,000 American academic psychologists, Dennis (1954) found that the most productive 10% authored 41% of all publications, whereas the bottom 10% produced less than 1%. In fact, the top half were responsible for 90% of total output, and the bottom half, for only the remaining 10%. Similarly biased distributions have been shown for other sciences as well as for the arts and humanities (Simonton, 2002). Findings such as these led Price (1963), a historian of science, to propose Price’s law. According to this law, if k is the number of researchers who have made at least one contribution to a given field, the square root of k will be responsible for half of all contributions in this field. Thus, if there are 100 contributors in a field, the top 10% will be responsible for half of the contributions to this area.2
For example, in a study of publications by the 60 members of the editorial board of the Journal of Counseling Psychology in 2007, Duffy, Martin, Bryan, and Raque-Bogdan (2008) found number of publications and number of citations to correlate .80. This correlation is somewhat higher than the correlations typically found for psychology, which vary between .50 and .70 (Simonton, 2002). Simonton (2002) therefore concluded “that the quality of output is a positive function of quantity of output: the more publications one produces, the higher the odds that one will get cited” (p. 45). It is interesting to note that the same relationship has been observed in brainstorming research, where the number of ideas that are produced by an individual or a group is highly correlated with the number of good ideas (e.g., Diehl & Stroebe, 1987; Stroebe, Nijstad, & Rietzschel, 2010).
Because of the exponential growth of the scientific community during the last few centuries, there has always been an overrepresentation of younger scientists (Price, 1963). Thus, even if scientific achievement were unrelated to age, one would expect more eminent contributions from young rather than old scientists. The same bias arises with studies that use number of publications in top journals as their index of scientific achievement. For example, if one took the publications of 10 major scientific journals as one’s sample and then plotted the age distribution of the authors of these publications, the results would again be distorted by the fact that there are likely to have been more younger than older scientists in the population of scientists from which the successful publishers were drawn.
The classic study of Nobel laureates was published by Zuckerman (1977). It was based on 92 Nobel Prize winners who worked in the United States and won the Nobel Prize between 1901 and 1972. She found that the average age at which these individuals did their prize-winning research was 39 years, with winners of the prize in physics doing their research at 38.6 years and winners of the prize in medicine and physiology doing it at 41.1 years. Similar results were reported by Stephan and Levin (1993), who in an update and extension of Zuckerman’s (1977) study analyzed the 414 winners of the Nobel Prize in the natural sciences in the years 1901–1992. The average age for conducting the prize-winning research for all disciplines was 37.6 years, with physicists doing their research the earliest, at 34.5 years, and medical research being conducted by somewhat older researchers, at 38.0 years. Although this is not old, it is also not precociously young. However, before one draws any conclusions, one must remember that these findings inform us only of the proportion of Nobel Prizes won by scientists of different ages. They do not tell us at which age scientists are most likely to win that prize. For this, we need to know the age distribution of the population of scientists from which the Nobel Prize winners were selected. Although Stephan and Levin (1993) failed to make such a correction, Zuckerman (1977) did, and she compared the age distribution of her laureates to that of the general population of American scientists (see Figure 1). This comparison shows that the only substantial deviations from the general population occur for the age group of 35 to 44 years, which is clearly overrepresented among the Nobel laureates, and the age group of 55 years and older, which is underrepresented. Before one concludes from this evidence that great science is really the domain of the middle-aged, one should remember that during the period considered in these studies, even American scientists were subject to compulsory retirement. Most research in the natural sciences requires monetary resources, personnel, and laboratory facilities, which may have become unavailable to older scientists after their retirement. In anticipation of this fact, many scientists in their mid-50s may have already stopped initiating projects that they expected to be unable to finish before retirement.
For example, when Harvey Lehman, one of the most prolific researchers on age and scientific achievement, tabulated the ages at which a sample of 52 deceased philosophers had written their most significant work, a single-peaked function emerged: The mean age for producing a philosophical masterwork was 41.5 years. Practically the same age curve also describes the age at which significant works were produced in psychology (Lehman, 1966).
Lehman’s (1953, 1966) research can be criticized for his failure to take account of the age distribution of the population of philosophers and scientists from which he drew the sample of excellent contributions. The data were not corrected for the fact that there were likely to be many more younger than older individuals in the population of which the eminent individuals were a subsample. However, Wray (2004), who studied landmark discoveries in bacteriology between 1877 and 1899, also found that scientists 36 to 45 years of age were responsible for a disproportionate number of these discoveries, even after he corrected for the likely age distribution of scientists in the total population. In contrast, younger scientists (35 years and younger) and older scientists (46 to 65 years) were relatively underrepresented. Finally, Over (1988), who used publications in Psychological Review as his criterion for outstanding contributions (admittedly a less demanding criterion than that of landmark discoveries, even though Psychological Review is one of the top journals of our discipline), found a similar curvilinear distribution that peaked for individuals who were 12 to 17 years past their PhDs (i.e., ages 38 to 45 years) and declined thereafter. However, Over (1988) argued that because 60% of American psychologists active in research between 1965 and 1980 were under 40, one could expect that about 60% of the articles appearing in Psychological Review in this period would be authored by psychologists under the age of 40. In fact, 59.9% of the articles in his sample were published by authors who were 0 to 11 years past their PhDs. Thus, despite the less demanding criterion, the curvilinear relationship between age and scientific achievement reported here is similar to that found in studies of Nobel laureates.
The pattern of findings of these early studies is similar to that found in the studies of Nobel laureates and scientists with lesser achievements, with age being curvilinearly related to scientific productivity, which reaches a peak around ages 40 to 45 and then drops off (e.g., Bayer & Dutton, 1977; Cole, 1979; Dennis, 1956; Horner, Rushton, & Vernon, 1986; Kyvik, 1990; Over, 1982). This pattern was replicated in cross-sectional (Bayer & Dutton, 1977; Cole, 1979; Kyvik, 1990) and longitudinal or crosssequential studies (Dennis, 1956; Over, 1982; Horner et al., 1986) conducted in the United States (Bayer & Dutton, 1977; Cole, 1979; Horner et al., 1986) and Europe (Dennis, 1956; Kyvik, 1990; Over, 1982). However, not all disciplines showed this pattern (Levin & Stephan, 1989). But the only discipline in which a discrepant pattern has been replicated repeatedly is mathematics. Several studies of samples of mathematicians resulted in a linear relationship, with neither an increase nor a decline in productivity (Cole, 1979; Stern, 1978).
Three examples of studies suffice to illustrate the typical patterns found in this research area. In one of the most extensive cross-sectional studies, Cole (1979) compared the publication rates in the years from 1965 to 1969 of 2,460 scientists from six different disciplines, including psychology. Figure 2 presents the overall productivity for the six fields combined, as well as the overall citation rate. As the figure indicates, age is curvilinearily related to both productivity and citations. Overall, the rates for productivity and citations peaked around age 40 and then dropped off. This relationship was valid for all disciplines, except for mathematics, for which the relationship was linear, “supporting the conclusion that productivity does not differ significantly with age” (Cole, 1979, p. 965). Cole thus replicated the findings of Stern (1978), who concluded from her cross-sectional study that “the notion that younger mathematicians are, as it were, ‘physiologically’ more able to produce papers would appear to be in error. In general, we can state categorically that age explains very little, if anything, about productivity” (p. 134).
Two cross-sequential studies of psychologists were conducted by Over (1982) and Horner et al. (1986). Over (1982) analyzed the relationship between age and productivity of a small sample of British psychologists ranging in age from 26 to 65 years. These individuals were assessed twice, once in 1968 –1970 and a second time in 1978 – 1980. British psychologists in general published as frequently in 1978 –1980 as in 1968 –1970 (i.e., there was no period effect). However, both the cross-sectional and the longitudinal analyses indicated that psychologists over 45 years of age published significantly less frequently than their younger colleagues. The publication rates correlated .49 across the two times of measurement, indicating substantial stability of individual productivity. Over (1982) concluded that “a person’s previous research productivity was a far better predictor of subsequent research output than age was” (p. 519).
Another cross-sequential analysis on scientific productivity was based on 1,084 American academic psychologists and was conducted by Horner et al. (1986). Both the cross-sectional and the longitudinal analyses resulted in a curvilinear relationship between age and productivity. On average, the productivity at ages 35 to 44 was significantly higher than the productivity at younger and older ages. Again, the correlations between an individual’s number of publications at different periods indicated a great deal of stability. Finally, age accounted on average for only 6.9% of the variance across time (more for low than for high publishers).
The findings of these early studies allow four conclusions: (a) The overwhelming majority of studies reported an age-related decline in productivity (indicated by number of articles published), and most studies found the association to be curvilinear, with a peak somewhere around the ages of 40 to 45 years. (b) Even though there was a curvilinear relationship between age and productivity, age accounted for less than 8% of the variance in productivity. In mathematics, the relationship between age and productivity even appears to be linear, with age being unrelated to productivity. (c) In contrast, past performance was by far the best predictor of future productivity. As Simonton (2002) estimated, “Between one third to two thirds of the variance in productivity in any given period may be predicted from the individual difference observed in the previous period” (p. 86). (d) Finally, even if older researchers are somewhat less productive than their younger colleagues, the quality of their work (as reflected by citations) appears to be no less high. Over (1988) correlated the number of citations each article published in Psychological Review had received in the first five years after publication with the age of the article’s author and found that the correlation was not significantly different from zero. Similar findings were reported by Simonton (1985) in a study of the impact of the publications of 10 psychologists who had received the APA’s Award for Distinguished Scientific Contributions. He found that the ratio of high-impact publications to total output fluctuated randomly throughout their careers.
Although a recent longitudinal analysis of the association of age and productivity for 112 eminent members of the U.S. National Academy of Sciences also resulted in a nonlinear relationship (Feist, 2006), this relationship was different from that reported in most earlier studies. Three unconditional growth curve models were constructed. The best fit to the data was achieved with a cubic model, providing “population estimates on productivity that increase rapidly until approximately 20 years into one’s career, then flatten over the next 15 years, and then rise again over the last 5-year interval” (Feist, 2006, p. 29). Because these individuals started publishing their first articles between 22 and 25 years of age, they would have reached their first peak around age 45. After a 15-year leveling-off period, their productivity would increase again after age 60.
A somewhat different pattern was reported by Joy (2006), who examined the publication data of 1,216 faculty member from 96 schools ranging from elite research universities to minor undergraduate colleges. Data were collected in 2004. Figure 3 presents the mean number of publications per year by career age (i.e., years since receiving the PhD) of full-time faculty members at three homogeneous subgroups of institutions. In the context of the focus of this article, I restrict myself to discussing the data for the 399 faculty members associated with research universities (e.g., Princeton University, the University of Massachusetts at Amherst, Northeastern University). These academics published more during the first five years of their careers than in later years; their productivity remained essentially stable for the next 25 years, with perhaps a slight increase between the 26th and 30th years of their careers. Thus, the data for faculty members at research universities (or for those at other institutions) failed to show the pattern reported in earlier studies, in which productivity reached a peak around ages 40 to 45 and then dropped off (Bayer & Dutton, 1977; Cole, 1979; Dennis, 1956; Horner et al., 1986).
This study was based on 6,388 professors and researchers who had published at least one journal article over the eight-year period from 2000 to 2007. The study used 10-year age categories, ranging from age 20 to age 70. Two different sets of data were used in compiling average productivity, namely, the average productivity of all professors and that of active professors who had published at least one journal article at the age in question. Although the association between age and productivity was curvilinear for both samples, only the total sample showed a decline after age 50. For the active professors, productivity increased to age 50 and then stayed at the same level until age 70. (There were too few older professors to extend the study beyond age 70.) Thus, these active professors sustained their productivity at a high level throughout their careers. There was also no decline in quality for the group of active professors. In fact, the average number of articles they published in high-impact journals (i.e., the top 1% cited journals) rose steadily to age 70, and so did the average number of articles that were among the top 10% of highly cited articles. The findings of Gingras et al. (2008) are discrepant with practically all of the early research. Given that, as noted above, the province of Quebec had already abolished compulsory retirement in 1980, this change would offer a plausible explanation for the fact that productivity did not decline for the older age group."