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Social benefits and individual costs of creativity in art and science: A statistical analysis based on a theoretical framework

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In this study, we statistically identified and characterized the relationship between the long-run social benefits of creativity and the in-life individual costs (in terms of happiness and health) of creativity. To do so, we referred to a theoretical framework that depicts a creator's life. We generated a balanced dataset of 200 creators (i.e., composers, painters, mathematicians and physicists, and biologists and chemists born between 1770 and 1879), and calculated standardized evaluations of the long-run social benefits in different domains (performances, exhibitions, citations). We performed regression analysis and identified the statistical determinants of the relationship between a creator's social benefits and the costs to their happiness and health. We found that creativity represented an individual cost for all four creator groups, with a larger impact on happiness than on health; the cost was greater if creativity was based more on divergent than on convergent thinking or if authors faced greater language issues. The impacts of long-run social benefits on individual happiness and health were similar in the arts and sciences if institutional differences were taken into account.
RESEARCH ARTICLE
Social benefits and individual costs of
creativity in art and science: A statistical
analysis based on a theoretical framework
Fabio ZagonariID
1
*, Elena Giacomoni
2
1Dipartimento di Scienze per la Qualitàdella Vita, Universitàdi Bologna, Rimini, Italy, 2Libera Accademia di
Belle Arti di Brescia, Rimini, Italy
*fabio.zagonari@unibo.it
Abstract
In this study, we statistically identified and characterized the relationship between the long-
run social benefits of creativity and the in-life individual costs (in terms of happiness and
health) of creativity. To do so, we referred to a theoretical framework that depicts a creator’s
life. We generated a balanced dataset of 200 creators (i.e., composers, painters, mathema-
ticians and physicists, and biologists and chemists born between 1770 and 1879), and cal-
culated standardized evaluations of the long-run social benefits in different domains
(performances, exhibitions, citations). We performed regression analysis and identified the
statistical determinants of the relationship between a creator’s social benefits and the costs
to their happiness and health. We found that creativity represented an individual cost for all
four creator groups, with a larger impact on happiness than on health; the cost was greater if
creativity was based more on divergent than on convergent thinking or if authors faced
greater language issues. The impacts of long-run social benefits on individual happiness
and health were similar in the arts and sciences if institutional differences were taken into
account.
Introduction
“What the common herd best appreciates is the work of artisans, the ultimate varnish
which makes each work trivial and non-artistic. It is the pleasure to add truth and knowl-
edge which makes me endeavor to finish a painting”
—Paul Cezanne
Recent neuroscience results based on functional magnetic resonance imaging have empirically
identified two main determinants of creativity from an individual perspective: divergent think-
ing [1] and convergent thinking [2]. In this paper, we have chosen the terminology convergent
thinking to represent the goal of finding a correct solution to a problem by following a particu-
lar set of logical steps (i.e., it resembles an artisan’s activity), whereas divergent thinking
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OPEN ACCESS
Citation: Zagonari F, Giacomoni E (2022) Social
benefits and individual costs of creativity in art and
science: A statistical analysis based on a theoretical
framework. PLoS ONE 17(4): e0265446. https://
doi.org/10.1371/journal.pone.0265446
Editor: Joydeep Bhattacharya, Iowa State
University, UNITED STATES
Received: October 7, 2021
Accepted: March 1, 2022
Published: April 27, 2022
Copyright: ©2022 Zagonari, Giacomoni. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data has been
uploaded to OSF: https://osf.io/qz73t.
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
represents the goal of generating creative new ideas by exploring many possible solutions (i.e.,
it resembles a creator’s activity). The ideas and information obtained from divergent thinking
can be organized and structured using convergent thinking.
To the best of our knowledge, only Zagonari [3] has numerically compared how improve-
ments in convergent thinking and in divergent thinking produce happiness from creativity
over an individual’s whole life. Once divergent thinking skill has increased sufficiently for a
creator to perceive a sufficient range of creative elements, convergent thinking becomes more
important for achieving happiness from creativity; that is, too much divergent thinking can
decrease happiness. Consider the psychological distress and social isolation that accompany a
situation in which divergent thinking seemed to prevail over convergent thinking (e.g., in
many of Van Gogh’s “self-portraits” as well as in his last painting, “Melancholia”). Consider
the psychological stability and social acceptability that accompany a situation in which conver-
gent thinking seemed to prevail over divergent thinking (e.g., Schoenberg’s Suite Op. 25 based
on dodecaphony in 1923 and his subsequent masterpieces). However, the impacts of creative
activity on health have not been considered from the perspectives of psychological and somatic
effects, and the arts have not been compared with the sciences in terms of the effects of their
institutions.
From a social perspective, creativity has been defined as a novel and valuable contribution
to a particular domain [4,5]. The degree and extent of novelty and value are negotiated among
participants in a specific domain (e.g., music, painting, theoretical sciences such as mathemat-
ics and physics, applied sciences such as biology and chemistry), since they depend on existing
knowledge and institutionalized rules of collaboration and evaluation. Consider the roles of
music and painting critics (i.e., the gatekeepers) and of the general population (i.e., the target
audience) for arts, and consider the roles of peer-scientific reviewers (i.e., the gatekeepers) and
the scientific community (i.e., the target audience) for sciences. A short-run evaluation (i.e., by
the creator’s contemporary generation in the general population or the scientific community)
might reveal different results from a long-run evaluation (i.e., by subsequent generations).
To our knowledge, only Liu et al. [6] have statistically compared contemporary evaluations
(i.e., careers) in the arts and sciences within a theoretical framework based on “hot streaks”. In
particular, a hot streak (i.e., a peak work or performance that leads to subsequent successful
works or performances due to increased reputation and recognition) that results in a unique
or innovative creation appears to occur randomly within a career (independently of the pro-
ductivity timing and shorter than the career length). However, the lasting impacts of works
(i.e., the long-run social benefits of creativity) and the origins of hot streaks (e.g., creativity
arising from divergent thinking) have been neglected by researchers.
The purpose of the present study was to statistically identify and characterize the relation-
ship between the costs of creativity to an individual (in terms of both happiness and health)
and the long-run social benefits of creativity (in terms of specific evaluations and institutions
in different domains). Note that we have chosen the words benefits and costs instead of positive
and negative impacts in order to test whether creators exhibit heroic behavior (i.e., positive
impacts for the whole society at the cost of negative impacts for the individual creator),
although quantifying the total benefits for society is beyond the scope of this study. In this con-
text, social benefits are theoretically grounded in the economic literature for composers and
painters (i.e., the willingness to pay for a performance or an exhibition ticket is at least equal to
the benefits people expect to receive from attending the performance or exhibition) and on the
scientific literature for scientists (i.e., subsequent scientists find benefits in a creator’s research
and cite these creators), although non-use values and distributional issues are disregarded. In
contrast, individual costs are empirically estimated from an original dataset, without a priori
positive or negative signs attached to the empirical variables that represent the costs, although
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these variables are based on a theoretical framework. To accomplish this goal, we refer to the
analytical framework developed in Zagonari [7] that depicts a creator’s life, by producing an
original dataset (i.e., the data used in the present study has not been previously used) of 200
creators (i.e., 50 composers, 50 painters, 50 mathematicians or physicists, and 50 biologists or
chemists). We chose the period between 1770, Beethoven’s birth year, and 1879, Einstein’s
birth year. This let us obtain relatively consistent estimates of health and happiness for each
creator. Because this period ends more than 100 years ago, we believe that the creators whose
works are still being performed, viewed, or cited represent long-run contributions to society
(i.e., social benefits). Note that these long-run social benefits should be distinguished from the
total social benefits, since the total benefits should consider all affected people from the time
when a work was created to the present day for each creation, by taking into account the
impacts of institutions on value formation over time. We then obtained standardized evalua-
tions of the long-run social benefits in the different domains (i.e., average number of global
performances, viewings, or citations of a creator’s works from 2009 to 2019, expressed as a per-
centage of the performances of Beethoven’s works, as a percentage of the number of Van
Gogh’s paintings exhibited in the world’s 10 most popular museums during this period, and as
a percentage of the number of citations of the works of Poisson, Einstein, Darwin, and Gibbs
to represent mathematics, physics, biology and chemistry, respectively). We conclude by per-
forming graphical regression analysis and identifying linear statistical determinants of the rela-
tionship between a creator’s long-run social impacts (i.e., over periods from around 200 to
around 100 years) and the creator’s happiness and health. This let us highlight differences and
similarities among the creators’ domains.
Note that we have not included interdisciplinary research, which we define as research con-
ducted across scientific disciplines or across artistic and scientific domains [8], since such
work has been relatively scarce [9] or has been unreliably evaluated [10]; it has also been rarely
funded [11]. However, Zeng et al. [12] have recently shown that success in science (in terms of
the average citations per paper) is negatively correlated with writing about diverse topics.
Moreover, we have not included the impacts of different research groups or art movements
[13] for individual successful works. Finally, we will focus on overall success in life, since the
timing and productivity during a creator’s career do not affect the long-run success of a crea-
tor’s works [14]. However, Yin et al. [15] have recently shown that ultimate success in science,
which they defined based on funded grant applications submitted to the National Institutes of
Health in the U.S., depends on the dynamics of past successes and failures.
Materials and methods
Any statistical analysis of the relationship between social benefits and individual costs in terms
of health (HEA) and happiness (HAP) requires consistent data for all creators in the dataset.
In this section, we will describe how we met this requirement by empirically estimating a theo-
retical model based on an original dataset.
The mathematical model
Zagonari [7] represented the dynamic interrelationship between happiness (hap[t]) and health
(hea[t]) at each time tby using two dynamic equations for an individual’s achievements (y[t]),
in which standardizations are applied to the original family income fy and to the individual’s
original health fh, while parameters are represented by the reference group’s average achieve-
ment ay, the education level ed, the feasible set for opportunities os, the ethical freedom fr, the
number of past periods that affect the current health me, the occupation type oc, and the
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employment status em:
hap½t ¼ ay½t  fsÞ=fsg þ by½t  y½t1Þ=y½t1g þ gy½t  ayÞ=ayg þ hea½t ð1Þ
hea½t ¼ os þXtme
t1hap½t  dðy½t  y½t1Þ þ y½t þ em þed þoc ð2Þ
where:
a¼1ðAristotleÞor b¼1ðEpicurusÞor g¼1ðZenoÞ
fs ¼fs½t  fs½t1¼fy þfh u½t þ fr;d0;oc 0;em 0;me 1; and u½tis in ½ u;þu
where αrepresents Aristotle’s contribution to happiness (achievements with respect to the
individual’s opportunity set), βrepresents Epicurus’ contribution (short-run achievements), γ
represents Zeno’s contribution (achievements with respect to the individual’s reference
group), such that α+β+γ= 1, u[t] is the personal uncertainty, δdepicts the level of psycho-
logical stress due to missed achievements, and uis the long-run equilibrium uncertainty.
Note that we set all coefficients at 1 to simplify the notation: their values could instead be
obtained by statistical analyses. Moreover, units for α,βand γare happiness over the specific
contribution to HAP they refer to, whereas the unit for δis health over its specific contribution
to HEA. Finally, we will refer to Eqs 1and 2as “the life model”, by using capital letters to stress
that we are moving from a theoretical to an empirical model.
The previous paragraphs highlighted which data are theoretically required at the individu-
al’s level to estimate the life model. Hereafter, we refer to the domains of creative endeavor as
CO = composers of music, PA = painters, MP = mathematicians or physicists to represent
more theoretical scientists, and BC = biologists or chemists to represent more applied scien-
tists. Supplementary Materials I in S1 Text presents the complete list of empirical variables
used in our analysis. To produce a balanced sample, we chose 50 creators for CO, 50 creators
for PA, 50 creators for MP, and 50 creators for BC (see Supplementary Materials II in S1 Text
for the complete list of creators). Note that we chose creators who could be ranked among the
best 50 in their domain based on criteria specified below, who were approximately equal nota-
ble, and for whom approximately the same level of detail was available for their lives. For each
creator, we recorded the birth year (BY), the death year (DY), and consequently the life years
(LY) = DY–BY. Note that these data and all individual data for each creator detailed below
were obtained by reading a total of 200 biographies and by coding the values of each parameter
based on the information provided in the biography according to objective and quantitative
criteria specified below. We have made the coding data available via the OSF repository under
reference number https://osf.io/qz73t. We analyzed composers (born between 1770 and 1879)
whose compositions were performed from 2009 to 2019 at a significant level (i.e., at a rate
equal to at least 1% of the number of Beethoven performances) around the world. We obtained
this data from bachtrack.com for concerts and operabase.com for operas. Note that we were
not forced to exclude many composers (e.g., De Falla, Glazunov, Rode, Spontini) to limit the
number of composers to 50 because we could not obtain a balanced sample of 100 creators in
each of the four domains (i.e., we limited our sample to 50 creators in each domain because
the domain with the fewest creators (i.e., composers) contained only 50 members who met our
criteria). In other words, to limit our sample to 50 composers, we included only the 50 com-
posers who were performed most often between 2009 and 2019.
Fraiberger et al. [16] studied artist exhibitions, including auction sales and primary market
quotes for around 500,000 artists. To select only 50 painters so that our sample for each
domain would be balanced, we considered only painters born between 1770 and 1879 whose
paintings are exhibited at a significant level (i.e., at 1% or more of the number of van Gogh
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paintings in the same museums) in the world’s 10 most popular museums (based on the num-
ber of visitors per year, www.theartnewspaper.com): the Louvre in Paris, the Metropolitan
Museum of Art in New York, the British Museum in London, the Tate Modern in London, the
National Gallery in London, the National Gallery in Washington, the Muse
´e D’Orsay in Paris,
the Victoria and Albert in London, the Museo Nacional Del Prado in Madrid, and the Hermit-
age in St. Petersburg. Note that these museums provide a good representation of painters from
the period under consideration, although the oldest painters in the sample such as Klee or
Kandinsky and subsequent painters are inevitably under-represented in these museums.
We analyzed the main scientists in the four disciplines (two theoretical vs. two applied) by
counting the number of citations of each scientist’s work, supplemented by including Nobel
winners in Physics and Chemistry from 1901 (the first year the prize was awarded) to 1921
(the year of Einstein’s award). Note that we did not account for the creators’ production over
their whole life (i.e., total number of compositions, paintings, or articles), since this was not
relevant for successful works in the CO, MP, and BC categories (i.e., a single work can be per-
formed many times in many places in the world, in general, and in particular, consider one-
opera composers such as Leoncavallo, with his opera “Pagliacci”, or Mascagni, with his opera
“Cavalleria Rusticana”). However, such lifetime production may be significant for PA (i.e., a
given painting cannot be exhibited simultaneously in two or more museums), in general, and
for the very productive Van Gogh, in particular. However, the outstanding productivity of
Van Gogh does not affect our results, since we used him as the reference painter to calculate
the long-run social benefits.
For health, the goal is to depict the potential impacts of artistic and scientific activities on
the individual’s health to represent the three main dynamic determinants of health (i.e., genet-
ics, chance, and behavior). We neglected accidents or illness (e.g., pneumonia, cholera, typhus,
syphilis, tuberculosis, meningitis). To do so, we included genetics in the health status (HS)
(i.e., reducing the HS value of 3 assigned to each creator as good health status by 1 point for
creators with diseases such as chronic nephritis, osteogenesis imperfecta, and chronic granulo-
matous: thus HS becomes 2 as medium health status) and included behaviors that could be
described as psychological problems (PP; i.e., reducing HS values by 1 in the case of PP such as
depression, hypersensitivity, paranoia, obsessive-compulsive disorder, bipolar disorder, ner-
vous breakdown) and somatic problems (SP; i.e., reducing HS by 1 in the case of SP such as
heart attack, cancers (e.g., throat, kidney, pancreatic, lung, colon), endocarditis, brain aneu-
rysm, diabetes, liver cirrhosis).
We defined the employment status (EM) as 1 if the creator was employed at a conservatory,
academy, or university, but as 0 otherwise. Note that Borodin was employed as a doctor, so for
him, EM = 0. In other words, we assumed that a creator who worked in a day job that did not
focus on their creative work was not employed.
For the Aristotle component of happiness, to which we applied the weight α, we set the eco-
nomic status of the creator’s birth family (FS) and the creator’s economic status (CS) at
1 = poor, 2 = medium, and 3 = rich, where FS or CS = 1 for a creator’s parents or creators who
were primary or secondary school professors or retail dealers, 2 if they were elected or
appointed as government officials and chair professors at a conservatory, academy, or univer-
sity, and 3 if they were lawyers, doctors, traders, land owners, or business owners.
We set the marital status (MA) to 1 if the creator was legally married. Note that Tchaikovsky
got married to cover his homosexuality, but we treated this as MA = 1.
For the Epicurus component of happiness, to which we applied the weight β, we set the year
of their first personal success (SY) to be the year of the first successful composition for com-
posers and painting for painters and as the year of the first appointment as a full professor or
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chair professor at a university for the two science categories. We used this to calculate the
number of years of successful professional life (i.e., DY–SY).
For the Zeno component of happiness, to which we assigned a weight of γ, we chose the
year of the first social endorsement (AY) as the year when the creator received their first social
award (e.g., the Legion of Honor in France, appointment to the Royal Society in Britain, Nobel
prize) for all four groups of creators. This let us calculate the number of years of successful
social life (i.e., DY–AY). Note that we treated the Nobel prize as a social award rather than as a
professional award for two reasons: it cannot be used as a primary criterion for professional
achievement, since otherwise the vast majority of scientists would not have achieved a success-
ful “professional” life, and it was introduced in 1901, so it cannot be used for the scientists
from previous periods in our sample.
Table 1 presents the main descriptive statistics for our sample of creators. For each statistic,
we tested for significant differences between pairs of creator groups using Student’s ttest with
a threshold value of 1.290 (i.e., P<0.1 with 98 degrees of freedoms). Two main conclusions
can be obtained. First, since the results for many of the features were expected, our sample
appears to be trustworthy. In particular:
The time lag between the creator’s birth and their success (i.e., SY–BY) were similar for the
four domains. This insight is consistent with Liu et al. [6].
Table 1. The descriptive statistics for the sample of 200 creators.
Factors CO PA MP BC CO PA CO MP CO BC PA MP PA BC MP BC
Statistics for each group t-test values for comparisons between groups
LY (years): Mean 62 65 67 71 -0.55 -0.99 -1.54 -0.44 -0.98 -0.54
Success lag (i.e., SY-BY, years): Mean 36 36 36 37 0.25 -0.08 -0.14 -0.34 -0.40 -0.06
FS in [1, 3]: Mean 1.68 2.12 2.04 1.96 -2.47 -2.08 -1.64 0.42 0.84 0.43
CS in [1, 3]: Mean 2.04 2.02 2.02 2.16 0.11 0.11 -0.62 0.00 -0.73 -0.74
HS in [1, 3]: Mean 2.86 2.84 2.78 2.86 0.08 0.33 -0.01 0.25 -0.09 -0.34
MA (%) 0.64 0.48 0.72 0.70 2.28 -1.05 -0.80 -3.40 -3.13 0.26
EM (%) 0.66 0.18 0.84 0.84 8.04 -2.33 -2.33 -10.29 -10.29 0.00
Success rate (i.e., SY <DY, %) 0.98 0.98 0.74 0.74 0.00 2.95 2.95 2.95 2.95 0.00
Gain rate (i.e., CS >FS, %) 0.40 0.18 0.18 0.26 4.30 4.30 2.57 0.00 -1.77 -1.77
Loss rate (i.e., CS <FS, %) 0.10 0.22 0.22 0.10 -3.09 -3.09 0.00 0.00 3.09 3.09
Award rate (i.e., AY >0, %) 0.48 0.22 0.60 0.76 4.67 -1.77 -3.92 -6.39 -8.48 -2.15
Good health status (i.e., HS = 3, %) 0.86 0.84 0.78 0.88 0.08 0.33 -0.08 0.25 -0.16 -0.41
Medium health status (i.e., HS = 2, %) 0.14 0.16 0.22 0.14 -0.27 -0.97 0.00 -0.71 0.27 0.97
Psychological problems (PP = 1, %) 0.26 0.16 0.04 0.04 2.26 5.90 5.92 3.89 3.92 0.04
Somatic problems (SP = 1, %) 0.36 0.24 0.1 0.1 2.30 5.68 5.72 3.51 3.56 0.06
Abbreviations: LY = life years, SY = success year, BY = birth year, FS = birth family economic status in [1, 3], DY = death year, CS = creator economic status,
MA = married, EM = employed, AY = award year, HS = health status in [1, 3], PP = psychological problems in [1, 3], and SP = somatic problems in [1, 3]. Creator
groups: CO = composers of music, PA = painters, MP = mathematicians or physicists, and BC = biologists or chemists. Notes: % values are expressed as decimals;
comparisons between creator groups are the values of Student’s t test, with a threshold value of 1.290 for significance and statistically significant values boldfaced.
Notes: HS = 2 for CO (Albeniz, Berlioz, Chopin, Elgar, Mendelssohn, Schubert, Johann Strauss), for PA (Friedrich, Manet, Matisse, Ranson, Renoir, Toulouse-Lautrec,
Troyon, Van Gogh), for MP (Cantor, Carnot, Clausius, Hamilton, Hermite, Hertz, Stark, Maxwell, Rayleigh, Riemann), and for BC (Bunsen, Fisher, Hess, Mendel,
Mendeleev, Pasteur, Sklodowska). PP = 1 for CO (Beethoven, Bizet, Bruckner, Chopin, Donizetti, Mendelssohn, Mussorgsky, Reger, Rossini, Schumann, Smetana,
Tchaikovsky, Wagner), for PA (Ce
´zanne, Constable, Friedrich, Gauguin, Gericault, Toulouse-Lautrec, Troyon, Van Gogh), for MP (Boltzmann, Cantor), and for BC
(Bosch, Schleiden). SP = 1 for CO (Bellini, Bizet, Borodin, Brahms, Busoni, Debussy, Donizetti, Elgar, Grieg, Mahler, Massenet, Paganini, Puccini, Rachmaninoff, Reger,
Respighi, Rimskij-Korsakov, Rossini), for PA (Cezanne, Church, Constable, Courbet, Degas, Matisse, Monet, Renoir, Sargent, Seurat, Signac, Sisley), for MP (Hamilton,
Hertz, Maxwell, Millikan, Poincare
´), and for BC (Cvet, Haber, Koch, Mendel, Nobel).
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The economic status of the creator’s birth family (FS) differed, as expected: PA >MP >
BC >CO. Consider, for example, that students who start painting because it increases their
health and happiness status and who continue painting because they can afford to do so
(e.g., Toulouse-Lautrec).
The creator’s economic status (CS) differed among the groups, as expected: BC >CO ~
MP = PA. Consider, for example, the money obtained by exploiting scientific breakthroughs
in biology or chemistry (e.g., Baekeland).
The marital status (MA) differed among the groups, as expected: BC >MP >CO >PA
(reflecting different life styles of creators in different domains during the period under
consideration).
The employment status (EM) differed among the groups, as expected: MP = BC >CO >
PA. Consider that university employment would be the main source of money for mathema-
ticians and physicists.
Success rates, which equal (DY–SY)/LY, as a %), were greater for the two art groups than for
the two science groups, as expected: CO >PA >MP = BC. A chair position at a university
at a success rate of 74% is reasonable; however, unsuccessful CO included Borodin, Faure
´,
and Mussorgsky and unsuccessful PA included Gauguin, Van Gogh, and Vuillard.
Gain rates, which equal (CS–FS)/FS, as a %), differed among the groups, as expected:
CO >BC >PA = MP. This might be due to the lowest economic status of the creator’s birth
family (FS) for CO, and the highest creator’s economic status (CS) for BC.
Loss rates, which equaled (FS–CS)/FS, as a %), differed among the groups, as expected:
PA = MP >CO = BC. This might be due to the highest economic status of the creator’s birth
family (FS) for PA and MP, the lowest economic status of the creator’s birth family (FS) for
CO, and the highest creator’s economic status (CS) for BC.
Award rates, which equaled (DY–AY)/DY, as a %), differed among the groups, as expected:
BC >MP >CO >PA. This might be due to a greater social endorsement for applied than
for theoretical science, and for music than for painting in the period under consideration.
Original health statuses (i.e., HS in [1,3], HS at 3 as a %, and HS at 2 as a %) were similar for
the four domains: CO = BC PA >MP. Nobody had HS = 1.
Psychological problems (i.e., PP at 1, as a %) differed among the groups, as expected:
CO >PA >MP = BC. Nobody had PP = 2 or 3.
Somatic problems (i.e., SP at 1, as a %) differed among the groups, as expected: CO >PA >
MP = BC. Nobody had SP = 2 or 3.
Note that the lowest value of the economic status of the creator’s birth family (FS) for CO is
plausible, since working in an orchestra was an option for young students of music, whereas
the lowest value of the creator’s economic status (CS) for PA is reasonable, since teaching in
schools was not an option for unsuccessful painters. Moreover, the life years (LY) for data
from all creator groups combined increased slowly but significantly with increasing birth year
(BY) (the slope of the linear regression was 0.068 with tat 1.97 and P<0.05), which represents
an increase in life expectancy of around 25 days per year during the period under consider-
ation. The life years (LY) also increased significantly with increasing health status (HS) (the
slope of the linear regression was 2.558 with tat 4.97 and P<0.01), which highlights the
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consistency of the health data for each creator. In particular, the life years (LY) was greater for
painters than for composers, but shorter in these groups than in the two science groups, and
was shorter for theoretical scientists than for applied scientists [17]. Finally, the health status
(HS) did not change significantly during the study period for data from all creators combined
(i.e., the birth year (BY) was positively but not significantly related to health status (HS), with a
slope of 0.003 and with tat 0.64 and P>0.52), which excludes both a lack of information
about older creators and an impact of medicine advances in the period under consideration.
Thus, our sample does not seem to be affected by significant spurious correlations.
Second, since many features were common between art and science (i.e., it was not possible
to group composers and painters on the one side and theoretic and applied scientists on the
other side), the statistical analyses of our sample must be developed by considering all four
domains (i.e., CO, PA, MP, and BC) instead of two domains (i.e., CO and PA in art, MP and
BC in science). Note that the vast majority of artists and scientists were male in our sample,
which prevented us from testing for a significant impact of gender on creativity, happiness, or
health (e.g., [18]).
The empirical model
Because we could not obtain data for all creators on the parameter values in the life model for
each year tin the study period, it was necessary to estimate Eqs 1and 2at the creator’s end of
life (year T) by implementing the following three steps:
First, since the health status (HS) takes values from 1 to 3 and we have information on psy-
chological problems and somatic problems for a creator’s whole life, we evaluated HEA at
death by applying the following formula:
HEA½T ¼ HS ð10:33 PPÞð10:33 SPÞ=3
Note that our choice of a scale from 1 to 3 will not affect the results, since it will be included
in the regression intercepts rather than in the regression slopes. In other words, we measured
HEA[T] as the health status (HS) reduced by possible psychological and somatic problems suf-
fered during a creator’s life.
Second, since both psychological and somatic problems affect life quality by about 25% [19
22], we calculated HAP[T] by applying the following formula:
HAP½T ¼ 0:25 faðCS FSÞ=FS þbðDY SYÞ=LY þgðDY AYÞ=LYg þ 0:75 HEA½Tð3Þ
Note that a change in HEA by 1 unit due to either a psychological or a somatic problem
(e.g., from 3 to 2) will affect HAP by 0.25 (i.e., 0.33 ×0.75). In other words, we measured hap-
piness as 0.25 times satisfaction (i.e., the three sources of happiness, namely α×an increase in
socioeconomic status with respect to the family status expressed as a percentage of FS, β×
years of professional success as a percentage of LY, and γ×years of social success as a percent-
age of LY) plus 0.75 times health. We did this to make the scale used for health status consis-
tent with the impact of psychological or somatic problems on the quality of life. Moreover, we
assumed β= 0.75 as the average empirical value in [0.5, 1], since the Epicurus component
(i.e., short-run achievements) is likely to be the most prominent happiness component for the
creators (i.e., β0.5): later in this section we test this parameter’s value in terms of the overall
strength (i.e., R
2
) of the regressions of HEA as a function of HAP, the economic status of the
creator’s birth family (FS), the creator’s economic status (CS), CO, PA, MP, and BC. Finally,
we assumed α=γ= 0.125 (i.e., the three weights sum to 1 when β= 0.75, and we assigned
them the same value because we had no reason to differentiate between them), since the Aris-
totle and Zeno components are likely to be the least prominent happiness components for the
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creators (indeed, creators are almost always the first creator in their birth family and they are
very often an outstanding creator within their reference group).
Third, we estimated the following empirical version of Eq (2):
HEA½T ¼ HAP½T þ FS þCS þOC þεð4Þ
where εrepresents the stochastic error term and Tis the year at a creator’s end of life. Note that
we approximated the creator’s education level using the economic status of the creator’s birth
family (FS) as a proxy, under the assumption that the ability of creators to obtain an overall edu-
cation depended on the socioeconomic status of their original family, and we assumed that this
was in addition to a good education in their own creator domain. In contrast, we neglected the
employment status (EM) in Eq 4 based on the collected data, since the vast majority of the
authors were employed at a conservatory, academy, or university. Indeed, the overall strength
of the model (i.e., R
2
) was not affected by introducing EM (see Supplementary Materials IV in
S1 Text). However, we will use EM to represent a social cost and an individual support for peo-
ple taking up a life of creative exploration (see the Results). Moreover, we treated the occupation
type OC as dummy variables (i.e., CO = 1 if the creator is a composer, PA = 1 if the creator is a
painter, MP = 1 if the creator is a mathematician or a physicist, and BC = 1 if the creator is a
biologist or a chemist). Finally, we disregarded personal uncertainty due to a lack of data, disre-
garded the psychological stress due to missed achievements (i.e., δ) because we focused on the
end of life (T), and disregarded ethical freedom due to a lack of data.
In other words, since we had no data to estimate the values of Eqs 1and 2at each time t, we
estimated Eq 2 at time T(i.e., Eq 4), by indirectly testing Eq 1 at time T(i.e., Eq 3) in terms of
changes of the R
2
value for Eq 4 due to changes in the weight parameters α,β, and γ. Supple-
mentary Materials III in S1 Text provides details of the transformation of Eqs 1and 2of the
theoretical model into Eqs 3and 4of the empirical model. Table 2 provides the linear regres-
sion results.
Note that applying βat 0.5, 0.75, and 1 produced R
2
= 0.61, 0.81, and 0.89, respectively. As
expected, the Epicurus component of happiness had the greatest importance for the creators.
However, we will refer to the intermediate level (0.75) in our subsequent analysis, since R
2
increases at a decreasing rate for βin [0.5, 1]. In other words, the greatest improvement of the
R
2
value occurred between the lowest value (β= 0.5) and the intermediate value of β(β= 0.75),
while the value of βthat produced the maximum R
2
(β= 1) treats the weights of αand γas
zero. Moreover, the economic status of the creator’s birth family (FS) and the creator’s eco-
nomic status (CS) had the expected positive and negative impacts (i.e., a richer family, denoted
Table 2. The empirical estimation of the life model’s regression coefficients.
HEA Coeff. Robust Std. Err. t P>|t| [95% Conf. Interval]
HAP 0.978772 0.1475821 6.63 <0.001 0.6876815 1.269863
FS 0.1059854 0.0969787 1.09 0.276 -0.085295 0.2972659
CS -0.1645908 0.1243237 -1.32 0.187 -0.4098065 0.0806249
CO -1.105051 0.3555441 -3.11 0.002 -1.806325 -0.403777
PA -0.9797172 0.2570708 -3.81 <0.001 -1.486763 -0.4726716
MP -0.7120002 0.1858725 -3.83 <0.001 -1.078615 -0.345386
BC -0.8250908 0.3019858 -2.73 0.007 -1.420727 -0.229455
CONS 2.526474 1.261352 2.00 0.047 0.0385869 5.014361
Sample size = 200. Adjusted R
2
= 0.81 (P <0.01). Abbreviations: CS, creator economic status; CONS, regression intercept; FS, birth family economic status; HAP,
happiness; HEA, health. Creator groups: CO = composers of music, PA = painters, MP = mathematicians or physicists, and BC = biologists or chemists.
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by a larger FS, and a better acceptance of the family status, denoted by a smaller CS minus FS,
will increase health). Finally, all creators achieve an expected lower level of health with respect
to a representative individual involved in other activities, which is consistent with the assump-
tions about the negative effects of the artistic and scientific activities under consideration on a
creator’s health.
Results
In the previous section, we obtained consistent data for each creator by empirically estimating
a theoretical model based on an original dataset. However, the statistical analysis of the rela-
tionship between long-run social benefits (SOC) and individual costs in terms of HEA and
HAP requires data on long-run social impacts for each creator. In this section, we will meet
this requirement by comparing the relationships estimated by graphical regression analysis
and the determinants identified by linear statistical analysis based on a consistent variable
across domains for long-run social benefits.
Since the targets of art and science are different (i.e., the general population is the audience
for music and painting, the scientific community is the audience for science), we standardized
the long-run social impacts within each creator group to allow comparisons between domains.
In particular, we calculated SOC for CO as the average number of works performed per year
between 2009 and 2019, divided by the number of performances of Beethoven (2975); for PA,
SOC was expressed as the number of paintings exhibited in the 10 most popular museums as a
percentage of the number of Van Gogh paintings exhibited in the same museums (67); for
mathematics, physics, biology, and chemistry, SOC was calculated as the average number of
citations between 2009 and 2019 reported in the Scopus database (scopus.com) divided by the
number of citations of Poisson (6001), Einstein (2345), Darwin (345), and Gibbs (2407),
respectively.
Note that we used citations that occurred in the title, abstract, or keywords of articles (i.e.,
we excluded reviews), since data are not available for citations of each single publication (i.e.,
books and articles) published in the 19th century. Moreover, the reference mathematician
(Poisson) was cited to a greater extent than other reference scientists (see the Discussion for
more details), although the long-run social benefit SOC did not increase significantly with
increasing birth year (BY) (the slope of the linear regression was –0.005 with tat –0.93 and
P>0.35), which highlights the creative homogeneity of the period under consideration.
Finally, Marie (Curie) Sklodowska belonged to both the MP and BC groups, since she
undoubtedly was both a physicist (her Nobel prize in 1911) and a chemist (her Nobel prize in
1903).
Different individual costs for social benefits in different domains
Let us make three reasonable assumptions (which we will subsequently test) that account for
the characteristics of the period under consideration. First, convergent thinking is greater in
CO, MP, and BC than in PA. In other words, PA requires a smaller investment in technique,
as innovative painters have relied on divergent thinking to a greater extent than on convergent
thinking. However, Seurat formally studied optics to guide his painting.
Second, composers have their works performed, painters have their works exhibited, and
scientists have their works cited for periods longer than 100 or 200 years (i.e., the selected crea-
tors produced long-run social benefits) if they were innovative during their life. Indeed,
reviewers for science and critics for art assess the novelty of creators, although with an impor-
tant difference: reviewers check scientific works before publication, without affecting citations
after publication; in contrast, critics affect performances and exhibitions both before and after
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their first performance and exhibition, although the impacts of critics for music and for paint-
ing after the first performance or exhibition of the works are typically for limited contexts
(e.g., the period when the creator served as a national composer or painter), limited times (e.g.,
rediscovery of a forgotten composer or painter, promotion of a composer or painter based on
changes in what is seen as fashionable) for some minor creators (i.e., creators with small per-
centages of performances and exhibitions relative to the numbers for Beethoven and Van
Gogh), but never for world-class artists in the long-run (i.e., art critics play only a small role in
the long-run).
Third, we assumed that the general population is more important for creators in CO and
PA than for creators in MP and BC. That is, composers and painters are creating for the gen-
eral population, not their peers, and therefor encounter greater communication (language)
issues with their audience, whereas scientists communicate primarily with their peers, and
therefore have fewer language problems. However, Schoenberg formally studied acoustics to
guide his music.
In other words, the main difference between CO and PA, which are characterized by similar
levels of language issues when communicating with non-colleagues, is the relatively larger
importance of convergent thinking in CO and divergent thinking in PA, whereas the main dif-
ference between CO and scientists, characterized by the same relative importance of conver-
gent thinking, is the more important language issues communicating with non-colleagues for
CO than for communicating with peers for scientists.
Some remarks on the role of aesthetics are worth considering here to support these three
assumptions. Although music and painting do not follow a set of codified and consistent rules
to communicate their meaning (i.e., they are emotional rather than semantic), they do use a
set of codified and consistent objects (e.g., dominant or diminutive seventh chords, major and
minor tonalities, warm and cold colors, perspective or plain drawing) to intentionally or unin-
tentionally evoke emotions,feelings or affects in their audience of listeners or viewers [23,24]:
these objects depend on the society in which they are produced (i.e., the sedimentation of
objects for composers and painters, the appreciation of works based on these objects for the
general population).
Note that the nature of art is irrelevant for the purposes of the present study, since we focus
on social benefits in terms of performances and exhibitions and the associated health and hap-
piness costs for the individual creator. Moreover, any referential language has some evocative
effect, apart from unequivocal technical definitions (e.g., “partial derivatives” has only one
meaning in mathematics, whereas “sea” evokes different meanings according to the individu-
al’s different experience and knowledge). Finally, the knowledge gained from art is marginal
for the present study, since “emotion” could be replaced with “understanding” without affect-
ing our analysis.
In particular, artistic creations are intentionally or unintentionally related to emotional
experiences (i.e., artists who compose or paint for themselves do not consider the emotions
evoked in others), although emotional connections are insufficient to explain the expressivity
of music and painting, which represents the capacity of artistic works to provoke emotions;
that is, there is no aesthetics of expression in philosophy (e.g., [25]), although there might be
an exhaustive computer description of emotional factors in informatics (e.g., [26]). This is
because emotions are based on synesthesia (i.e., interrelationships between senses that differ
among individuals) and because listeners hear what they have learned to hear and viewers see
what they have learned to see; that is, music and painting do not say something separate from
themselves but rather express something inside themselves, and they require individual inter-
pretation, which is closer to a mimetic operation rather than to an analytic operation. Thus,
the expressivity of music and painting (i.e., the capacity of artistic works to provoke emotions
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in the general population) changes over time (due to advances in artistic techniques and edu-
cation of viewers and listeners) and changes over space due to differences across and within
cultures. For example, there have been periods of popular appraisal by many members of soci-
ety rather than only its elite members, as in the case of the 5 days of the Milan insurrection in
1848, when young people sang contemporary operas by Verdi. There have also been periods of
popular appraisal when primarily the elite members of society appreciated the art, as in the
case of young people today, who sing neither the past operas by Verdi nor contemporary sym-
phonies by Busoni. However, advances in artistic techniques intermittently provide pressure
to improve education, as in the cases of the dodecaphonic music by Schoenberg, which pro-
moted the appraisal of diatonic music by Ravel.
These language issues do not exist in the sciences, where the language rules have been codi-
fied and are shared by all scientists. For example, the language of mathematics is standardized
and used by all scientists to communicate clearly.
However, composers whose works are still performed and painters whose works are still
exhibited after 200 years were clearly “authentic and autonomous” in Adorno’s [27] words;
that is, they were alienated by their creativity rather than being inauthentic and heteronomous.
Here, we have used authentic to mean original rather than derivative of previous works. In
other words, they use an original language and provoke original emotions. Thus, the study of
the grammar and syntax of music and painting is not only about Western art. For example, the
compositions of De Sarasate, who used a language close to that of Saint-Saens and Lalo (i.e.,
with similar evoked emotional experiences), are performed less often than those of the latter
two composers, since Saint-Saens and Lalo introduced that music language before De Sarasate,
as has been highlighted by critics. Similarly, Serusier used a language close to that of Gauguin
and Van Gogh (i.e., with similar evoked emotional experiences), but is exhibited to a smaller
extent, since Gauguin and Van Gogh introduced that visual language before Serusier, as has
been highlighted by critics.
In particular, since the number of potential innovative techniques that can be used to intro-
duce an original language and provoke original emotions is smaller for painters than for com-
posers, painters who want to be innovative rely to a relatively greater extent on divergent
thinking (e.g., Mondrian) and to a relatively smaller extent on convergent thinking (e.g.,
Turner), whereas composers rely to a relatively greater extent on convergent thinking (e.g.,
Schubert) and to a relatively smaller relative extent on divergent thinking (e.g., Beethoven).
Therefore, the creator’s contemporaries might have encountered difficulty feeling the emo-
tions intended by authentic artists, and as a result, these authentic artists did not achieve eco-
nomic, professional, or social success (i.e., they would have smaller HAP and HEA).
Conversely, the creator’s contemporaries might have no problems in feeling the emotions
intended by inauthentic artists, and inauthentic artists might therefore have achieved eco-
nomic, professional, or social success (i.e., larger HAP and HEA).
In summary, painters must rely to a greater extent on divergent thinking (which has a larger
impact on HEA and HAP) and painters encounter language issues (that decrease HAP and
HEA); composers can rely to a greater extent on convergent thinking (which has a smaller
impact on HEA and HAP) even though composers also face language issues; and scientists can
rely to a greater extent on convergent thinking (which has smaller impact on HEA and HAP)
and face few language issues. Based on the analysis described above, we defined the relation-
ships between happiness, health, and social benefit for the four groups of creators. Fig 1 shows
the relationship between HAP and SOC, and Fig 2 shows the relationship between HEA and
SOC. Supplementary Materials V in S1 Text (S1–S8 Figs in S1 Text) provides the graphical
regression equations used to generate these lines.
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Note that we have presented linear relationships, which is consistent with the linear theoret-
ical models developed in the Materials and Methods: these lines include the effects of different
institutions (e.g., employment status) for different groups of creators.
Several insights can be obtained by comparing the regression lines for the four domains in
terms of their intercepts and slopes:
Creativity (and social benefits SOC) has a cost to the individual creator in terms of HEA and
HAP in all domains (i.e., all slopes are negative).
The HAP line is lower (farther from the maximum value of 10) than the HEA line for all cre-
ator groups; indeed, the health status (HS) is an original beneficial stock for HEA (i.e., HS is
an essential component of HEA, as it represents the starting value for HEA), whereas HAP
does not benefit from a similar initial stock.
The PA lines are above the other lines for HAP and HEA at a low level of creativity (and of
social benefits SOC); thus, painting is good for health and happiness, with language issues
playing a smaller role for painters who are more conventional and less creative.
For both HEA and HAP, painters bear the largest cost of creativity (PA shows the largest
negative slope); this may be because painters rely to the greatest extent on divergent
thinking.
Fig 1. Social benefits (SOC) in [0, 10] vs. happiness (HAP) in [0, 10] if the relative contributions to happiness have values of α= 0.125, β= 0.75,
and γ= 0.125. For composers (blue) CO: HAP = -0.107 SOC + 6.785; for painters (purple) PA: HAP = -0.511 SOC + 8.20; for mathematicians and
physicists (yellow) MP: HAP = -0.062 SOC + 7.467; and for biologists and chemists (green) BC: HAP = -0.091 SOC + 7.466.
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The CO lines are below the MP and BC lines; indeed, composers face larger language issues
than scientists since they refer to the general population rather than to their colleagues (i.e.,
larger costs to the individual at each level of creativity). The larger values of psychological
and somatic problems (i.e., 31, 20, 7, and 7 for CO, PA, MP, and BC, respectively, in Table 1)
support this insight.
The CO lines are slightly steeper than the MP and BC lines, suggesting a greater cost to HAP
and HEA with increasing SOC: indeed, convergent thinking is more important than diver-
gent thinking in these domains, but language issues in CO are slightly smaller for less crea-
tive and more conventional composers.
MP had a slightly higher HAP than BC; indeed, the creations of mathematicians and physi-
cists are cited for longer durations, on average (see Fig 3) (i.e., in Fig 1, larger SOC at the
same level of HAP).
MP shows the same level of HEA as BC: indeed, mathematicians and physicists are cited lon-
ger, but biologists and chemists had a better health status (i.e., maximum HS at 80% and
86% for MP and BC, respectively, in Table 1; similarly, mean HS at 2.78 and 2.86 for MP and
BC, respectively, in Table 1).
The PA line is below the CO line (although PA and CO face similar language problems) and
the PA line is below the MP and BC lines (although MP and BC are not affected by language
Fig 2. Social benefits (SOC) in [0, 10] vs. health (HEA) in [0, 10] if the relative contributions to happiness have values of α= 0.125, β= 0.75, and γ
= 0.125. For composers (blue) CO: HEA = -0.189 SOC + 8.115; for painters (purple) PA: HEA = -0.578 SOC + 9.633; for mathematicians and physicists
(yellow) MP: HEA = -0.099 SOC + 9.2763; and for biologists and chemists (green) BC: HEA = -0.107 SOC + 9.355.
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problems) at a high level of creativity (and of social benefits SOC); indeed, a larger number
of PA than other creator groups had HS = 2 and PP = 1 (i.e., in Table 1, Chopin and Men-
delssohn for CO; Friedrich, Toulouse-Lautrec, Troyon, and Van Gogh for PA; and Cantor
for MP).
These results, combined with our three assumptions, lead to the following three insights.
First, if creativity in PA is based to a greater extent on divergent thinking than on convergent
thinking, since the PA line has the largest slope as a function of SOC, then creativity from
divergent thinking is more demanding in terms of HEA and HAP. In other words, creativity
based on divergent thinking increases the steepness. However, this may be because painters also
face language issues. Second, if creativity in CO is based to a greater extent on convergent
thinking, as it is in science, even though CO is characterized by larger language issues than MP
and BC, since the CO line has a similar slope to the MP and BC lines, but has a smaller inter-
cept, then creativity from convergent thinking has a lower cost in terms of HEA and HAP,
although language issues effect both HEA and HAP. In other words, language issues decrease
the intercept. Third, combining the two previous insights with Figs 1and 2at high levels of
SOC and at low levels of both HEA and HAP suggest that long-run social benefits arise to a
Fig 3. Social benefits (SOC) in [0, 10] vs. the number of creators in each group (N) in [0,50]. For composers (blue) CO: SOC = -2.319 ln[N] + 9.359;
for painters (purple) PA: SOC = -2.038 ln[N] + 8.263; for mathematicians and physicists (yellow) MP: SOC = -3.193 ln[N] + 12.005; and for biologists
and chemists (green) BC: SOC = -2.826 ln[N] + 9.792. S9–S12 Figs in S1 Text provides the graphical regression equations for the relationships between
SOC and N in the four domains.
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greater extent from creativity based on divergent thinking, regardless of any language issues that
characterize the different creator groups.
Similar individual costs for net social benefits in different domains
In the previous sub-section, we discussed the costs of creativity to the individual for the four
domains. However, Table 1 showed different characteristics for the four creator groups, with a
single variable representing a social cost (i.e., the employment status EM). In this sub-section,
we will test whether the relationships between SOC and the group’s HEA or HAP become sim-
ilar if they are separated from the impacts of the employment status (EM), and will also con-
sider the effect of the marital status (MA). Note that all variables other than SOC, HEA, and
HAP are dummy variables in [0,1]. We chose this approach to increase variability, since the
sample size is small (i.e., 200 observations). Tables 3and 4provide linear regression results of
the following equations:
HAP ¼CONS þSOC þEM þMA þCO þPA þMP þBC þzHAP ð5Þ
HEA ¼CONS þSOC þEM þMA þCO þPA þMP þBC þzHEA ð6Þ
where z
HAP
and z
HEA
represent stochastic error terms.
Table 3. The empirical estimation of the individual happiness (HAP) costs for social benefits (SOC) using Eq 5.
HAP Coeff. Robust Std. Err. t P>|t| [95% Conf. Interval]
SOC -0.1278457 0.0453425 -2.82 0.005 -0.217279 -0.0384123
EM 0.7351393 0.3010054 2.44 0.016 0.1414373 1.328841
MA 0.1018215 0.2480316 0.41 0.682 -0.3873953 0.5910382
CO -2.465993 0.4306435 -5.73 <0.001 -3.315392 -1.616593
PA -1.610017 0.4423877 -3.64 <0.001 -2.482581 -0.7374536
MP -1.734621 0.2892077 -6.00 <0.001 -2.305053 -1.164189
BC -1.447709 0.2277914 -6.36 <0.001 -1.897004 -0.9984138
CONS 8.751568 0.4073099 21.49 <0.001 7.948192 9.554945
Sample size = 200. Adjusted R
2
= 0.13 (P <0.01). Robust Standard Errors = Huber/White estimators. Abbreviations: EM, employment status; MA, marital status,
CONS, regression intercept. Creator groups: CO = composers of music, PA = painters, MP = mathematicians or physicists, and BC = biologists or chemists.
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Table 4. The empirical estimation of the individual health (HEA) costs for social benefits (SOC) using Eq 6.
HEA Coeff. Robust Std. Err. t P>|t| [95% Conf. Interval]
SOC -0.1652824 0.0524441 -3.15 0.002 -0.268723 -0.0618418
EM 0.5044576 0.350251 1.44 0.151 -0.1863763 1.195291
MA -0.1486146 0.2801023 -0.53 0.596 -0.7010874 0.4038582
CO -3.521048 0.4571447 -7.70 <0.001 -4.422719 -2.619377
PA -2.660581 0.4951863 -5.37 <0.001 -3.637285 -1.683877
MP -2.315452 0.2395622 -9.67 <0.001 -2.787963 -1.84294
BC -2.223893 0.2816868 -7.89 <0.001 -2.779491 -1.668295
CONS 11.33853 0.4636383 24.46 <0.001 10.42405 12.25301
Sample size = 200. Adjusted R
2
= 0.15 (P <0.01). Robust Standard Errors = Huber/White estimators. Abbreviations: EM, employment status; MA, marital status,
CONS, regression intercept. Creator groups: CO = composers of music, PA = painters, MP = mathematicians or physicists, and BC = biologists or chemists.
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Thus, controlling for other factors, the long-lasting social impact SOC measured by our
indexes (the relative numbers of performances, exhibitions, and citations) is associated with
decreased individual happiness HAP and health HEA (as measured by our indexes of happi-
ness and health), and the impacts were negative and statistically significant for all creator
groups, although each creator group was characterized by a different impact size.
Note that the employment status (EM) has a positive impact for both HAP and HEA (i.e., it
is a social cost that compensates for individual costs in terms of happiness and health), and
although this was significant for HAP (P= 0.016), it was not significant for HEA (P= 0.151).
The employment status (EM) can determine whether a creator has a lower potential loss from
embracing a life of exploration because they have a supplemental income that lets them be cre-
ative without having to worry about whether they can earn enough money to survive (e.g.,
employment at a conservatory for CO, an academy for PA, or a university for MP and BC).
The marital status (MA) had no significant impact on both HEA and HAP. We confirmed the
robustness of our results by using the birth year (BY) as a control variable. Indeed, BY did not
significantly affect HEA or HAP in Eqs 5and 6, and all significant variables included in Eqs 5
and 6were the same whether or not we included BY, with the impacts on HEA and HAP of
the four domains in the same order with or without BY, while the regression intercept was sig-
nificant in the estimations without BY but not in the estimations with BY. Supplementary
Materials IV in S1 Text provide a sensitivity analysis that supports these conclusions.
Thus, the slope of the regression for PA is between those of BC and MP in terms of HEA
and between those of MP and CO in terms of HEA. In other words, it is not possible to distin-
guish science from art in terms of their HAP and HEA costs.
Discussion
Many insights were provided by our study:
Creativity is a cost to the individual in all domains, but has a larger impact on HAP than on
HEA.
The cost is larger if creativity is based more on divergent thinking than on convergent
thinking.
The cost is larger if creators face greater language issues when they attempt to communicate
with their audience.
Psychological problems do not depend on the success lag (SY–BY; the Pearson’s correlation
rbetween this variable and PP was r= 0.11).
The duration of social benefits was larger for the two artistic groups than for the two scien-
tific groups, as expected, and the number of creators with an important SOC was ranked as
CO PA >MP >BC (Fig 3) based on the graphical regression equations presented in Sup-
plementary Materials V in S1 Text. Indeed, scientists are no longer cited after their suggested
methodologies or their obtained breakthroughs become common practice or common
knowledge. For example, the graphical framework (orthogonal xand yaxes) developed by
Descartes is no longer cited as his work in publications that rely on this framework.
Long-run social benefits do not depend on life years (DY–BY; the Pearson’s correlation r
between this variable and SOC was r= –0.07).
Note that there are both differences and similarities between the present study and the liter-
ature on exploration, which refers to search, variation, risk taking, experimentation, flexibility,
discovery, and innovation, and the literature on exploitation, which refers to refinement,
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choice, production, efficiency, selection, implementation, and execution [28]. In terms of the
differences, we lacked detailed information about the sequence of creation characteristics for
composers and painters and the sequence of research topics for scientists that would let us test
for a possible sequence of exploration followed by exploitation [29]. In addition, we focused
on long-run social benefits rather than on short-run individual careers [29]. In terms of simi-
larities, we could link exploration with divergent thinking and exploitation with convergent
thinking [30], and rephrase our positive results (i.e., explanatory insights) as long-run social
benefits arise to a greater extent from exploration than from exploitation (i.e., the social bene-
fits that arise from search and innovation are greater than those that arise from refinement
and efficiency), although creativity is based on both divergent and convergent thinking. In
addition, we could link risk taking with divergent thinking [30], and rephrase our normative
results (i.e., advisory insights) as society should bear the risk of a creator’s explorative life by
providing guaranteed employment (i.e., incentives should be offered to creators who take on
personal risks to produce results with social benefits), even though creative individuals appear
to be less risk averse than the average individual.
The main weaknesses of our study are:
1. It is based on a specific period. However, a period before the chosen period (i.e., 1770–1879)
would exclude all painters and many composers, since they would refer primarily to crea-
tors who have a single patron; that is, artistic works were thought to ex ante satisfy most of
the requirements of artistic demand, apart from some details deliberately introduced by the
artists, as in the case of Michelangelo’s Sistine Chapel. This contrasts with our (third)
assumption that the general population is more important for creators in CO and PA than
for creators in MP and BC, with creators in MP and BC having fewer language problems
than creators in CO and PA; that is, artistic works were mostly driven by artistic insights or
inspirations, which could ex post meet the expectations of the artistic demand to a greater
or smaller extent, as in the case of Bizet’s opera “Carmen”. Our motivation for choosing
this period was to include Beethoven and Einstein as the reference creators for music and
physics, respectively; choosing a longer period to increase the balanced sample would
require the addition of less-popular museums, since more recent painters are not well-rep-
resented in the 10 most popular museums.
2. The arts and sciences that we analyzed had attained different degrees of maturity during the
period under consideration. Both music and painting are depicted from adolescence to
maturity (i.e., from Beethoven to Schoenberg and from Turner to Klee) if we define music
as “the art of combining vocal and/or instrumental sounds to produce beauty of form, har-
mony, and expression of emotion” (Oxford English Dictionary) before Ligeti (when per-
formers could choose what to play within a specified range of sounds for a specified period
of time), and if we define painting as “the practice of applying paint, pigment, color, or
another medium to a solid surface” (Cambridge English Dictionary) before Fontana (when
a two-dimensional surface was replaced by a three-dimensional solid). In contrast, the sci-
ences are depicted from their infancy (e.g., biology and chemistry) to their adolesence (e.g.,
mathematics and physics). However, we considered social benefits today after 200 to 100
years have passed, which is a sufficiently long period to allow a comparison of these differ-
ent creator groups.
3. The role of critics for painting and music (as gate-keepers and fame-constructors) and the
role of musicians for music (as interpretation-prompters) are only considered implicitly.
The social benefits could be indirectly estimated by the willingness to pay for concert or
museum tickets or, in other words, the willingness to pay for the suggested interpretation of
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music in concerts performed by musicians and for the suggested interpretation of paintings
in exhibitions organized by critics (i.e., the estimated social benefits are conditional to crit-
ics and musicians). However, philological studies in both music and painting are likely to
lead to similar interpretations of art in the long-run, with critics and musicians unable to
affect the social benefits of art in the long-run. For example, many composers and painters
who had been dismissed by contemporary critics were rediscovered after their death,
whereas musicians with unusual performances were often ignored by subsequent genera-
tions of musicians.
Note that we could not rely on direct observation of a creator’s happiness during their life,
so we measured HAP indirectly by referring to economic, professional, and social achieve-
ments. However, possible dissatisfaction with these achievements could be depicted by the
health component of happiness.
The main strengths of the present study are:
1. Our approach let us compare very different groups of creators by accounting for very differ-
ent audiences (i.e., the scientific community for MP and BC; the general population for CO
and PA).
2. The analysis reveals the long-run impacts of creativity on social benefits instead of using
careers to judge individual benefits in the short-run.
3. The consideration of both HEA and HAP as individual costs incurred to obtain social bene-
fits reveals tight links with convergent thinking (prevalent in CO, MP, and BC) and diver-
gent thinking (prevalent in PA).
In addition, our observation that the employment status (EM) functions as a statistically
significant social cost for HAP (and HEA, to a lower extent) suggests that policies could be
developed to reduce institutional differences among the four creator groups. That is, creators
who are funded and who do not need to work outside their field to earn their living can
improve social benefits at a lower cost to their happiness (and to their health, to a lower
extent).
Conclusions
The main overall insight we obtained is that there is a significant statistical impact of the long-
run social benefits from creativity on an individual creator’s costs in terms of health and happi-
ness, and that this cost does not differ greatly between the arts and the sciences if institutional
differences are taken into account. In particular, painting was closer to science than it was to
music in terms of its personal costs. We explained this feature by stressing that composers face
language issues when communicating with non-colleagues (i.e., the general population), that
composing is not good for the creator’s health (i.e., it requires a large investment in technique
for even a low level of creativity), and that composers are employed at a conservatory or acad-
emy to a smaller extent than scientists are employed at a university. In other words, the differ-
ence among groups of creators depends on the institutional context rather than on the creative
process; that is, employment at a conservatory, academy, or university could reduce these
differences.
Moreover, creators in all four groups exhibit heroic behavior in the sense that they bear
individual costs to their happiness and health to provide social benefits for the rest of society,
although they are likely to be driven by an urge to explore or a desire for immortality.
Finally, there has been continuous support for all four groups of creators by the general
population and by expert colleagues across domains, although the former prevails in art and
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the latter in science. Consider Beethoven, who is both cited by other composers in their com-
positions and famous to the general population, and consider Einstein, who is both cited by
other scientists in their articles and famous to the general population. Since happiness depends
on short-run economic achievements (the Aristotle component), professional achievements
(the Epicurus component), and social achievements (the Zeno component), and since health
decreases if creativity is based to a greater extent on divergent thinking than on convergent
thinking, the high levels of both health and happiness at a low level of long-run social benefits
suggest that both the creator’s contemporary general population for art and the mainstream sci-
entific community for science appreciate creativity arising from convergent thinking to a
greater extent than creativity arising from divergent thinking. For example, consider the wan-
dering harmony in Beethoven’s work, and general relativity in Einstein’s work.
Supporting information
S1 Text. Benefits and costs of creativity.
(DOCX)
S1 Data.
(TXT)
Author Contributions
Formal analysis: Fabio Zagonari.
Supervision: Elena Giacomoni.
Writing – original draft: Fabio Zagonari.
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