Research Division

Report #42

NATIONAL

ENDOWMENT

FOR THE ARTS

Seven Locks Press

Santa Ana, California

AGE AND APIS

PA1RTICIpR110N :

198-1997

Pichard R . Peterson

Pamela C . Hull

PogerM . Kern
AGE AND RATS

PATITICIPATION :

1g82 1997


TABLE Of CONTENTS

Chapter 1 .

Chapter 2.

Chapter 3.

Chapter 4.

Chapter 5.

Notes

Bibliography

List of TablesVii

Executive Summary 1

Introduction : The "Aging Arts Audience" Question 10

The Changing Age of the Arts Audience 16

The Contribution of Baby Boomers to

the Arts Audience 29

The Importance of Age as a Determinant

of Arts Participation 42

Correlates of Baby Boomer, Pre-Boomer,

and Post-Boomer Participation 55

67

71
Age and Arts Participation : 1982-1997 is Report #42 in a series on matters of interest to the arts

community commissioned by the Research Division of the National Endowment for the Arts .

First printed in 2000

Library o f Congress Cataloging-in-Publication Data

Peterson, Richard A ., 1932-

Age and arts participation : 1982-1997 / Richard A . Peterson, Pamela C . Hull, Roger M . Kern

p.cm.

Includes bibliographical references .

ISBN 0-929765-86-9

1 . Arts audiences--United States--Statistics . 2 . Arts surveys--United States . 3 . Baby boom

generation--Statistics . I . Hull, Pamela C ., 1973- II . Kern, Roger M ., 1967- III . Title .

NX220 .P48 2000

381' .457'0097309048--dc21

00-045625

Printed in the United States of America

Seven Locks Press

Santa Ana, California

800-354-5348
LIST Of TABLES

Table 1 .1 .

Table 1 .2 .

Table 2.1 .

Table 2.2 .

Table 2.3 .

Table 2.4 .

Table 2.5 .

Table 2.6 .

Table 2.7 .

Table 2.8 .

Table 2.9 .

Table 2.10.

Table 2.11 .

Table 2.12.

Table 2 .13.

Table 2.14.

Median Age of Attendees, Overall Sample

and Benchmark Arts

13

Expected Age Group and Cohort Distributions 15

Age Group % Contribution to Total

Classical Music Attendances, by Year 18

Age Group % Contribution to

Total Opera Attendances, by Year 19

Age Group % Contribution to

Total Musical Attendances, by Year 20

Age Group % Contribution to

Total Jazz Attendances, by Year 21

Age Group % Contribution to

Total Theater Attendances, by Year 21

Age Group % Contribution to

Total Ballet Attendances, by Year 23

Age Group % Contribution to

Total Art Museum Attendances, by Year 23

Classical Music - Difference between % of

Total Times Attended and % of People Attending25

Opera Music - Difference between % of

Total Times Attended and % of People Attending25

Musical - Difference between % of

Total Times Attended and % of People Attending

26

Jazz - Difference between % of

Total Times Attended and % of People Attending26

Theater - Difference between % of

Total Times Attended and % of People Attending26

Ballet - Difference between % of

Total Times Attended and % of People Attending

27

Art Museum -Difference between % of

Total Times Attended and % of People Attending 27
vm

1 0 q e and Arts Participation : 1982-1997

Table 2.15. Sum of Seven Benchmark Arts with Attendees

Attending more Frequently than Expected

(Possible Range 0-7) 27

Table 3.1 . Cohort % Contribution to Total Classical Music

Attendances, by Year 31

Table 3.2. Cohort % Contribution to Total Opera

Attendances, by Year 33

Table 3.3. Cohort % Contribution to Total Musical

Attendances, by Year 33

Table 3.4. Cohort % Contribution to Total Jazz

Attendances, by Year 34

Table 3.5. Cohort % Contribution to Total Theater

Attendances, by Year 34

Table 3.6 . Cohort % Contribution to Total Ballet

Attendances, by Year 35

Table 3.7. Cohort % Contribution to Total Art Museum

Attendances, by Year 35

Table 3.8 . Classical Music - Difference between % of

Total Times Attended and % of People Attending 38

Table 3.9 . Opera Music - Difference between % of

Total Times Attended and % of People Attending 39

Table 3.10. Musical - Difference between % of

Total Times Attended and % of People Attending 39

Table 3.11 . Jazz - Difference between % of Total Times

Attended and % of People Attending 40

Table 3.12. Theater - Difference between % of Total Times

Attended and % of People Attending 40

Table 3.13. Ballet - Difference between % of Total Times

Attended and % of People Attending - 40

Table 3.14. Art Museum - Difference between % of Total Times

Attended and % of People Attending 41

Table 4.1 . Regression Results of Number of Attendances

on Age (standardized coefficients) 48
list of Tables I ix

Table 4.2 . Regression Results of Whether One Attends or

Not on Age (standardized coefficients) 49

Table 4.3 . Regression Results of Summary Measure of

Attendance on Age (standardized coefficients) 53

Table 5.1 . Regression Results of Classical Music Attendances

on Age by Cohort (standardized coefficients) 57

Table 5.2 . Regression Results of Opera Attendances on

Age by Cohort (standardized coefficients) 58

Table 5.3. Regression Results of Musical Theater Attendances

on Age by Cohort (standardized coefficients) 58

Table 5.4. Regression Results of Jazz Attendances on Age

by Cohort (standardized coefficients) 59

Table 5.5 . Regression Results of Theater Attendances on

Age by Cohort (standardized coefficients) 59

Table 5.6 . Regression Results of Ballet Attendances on Age

by Cohort (standardized coefficients) 60

Table 5.7. Regression Results of Art Museum Attendances

on Age by Cohort (standardized coefficients) 60

Table 5.8 . Regression Results of Summary Arts Attendances

on Age by Cohort (standardized coefficients) 64

Table 5.9 . Regression Results of Summary Arts Attendances

(without jazz) on Age by Cohort

(standardized coefficients) 64
2

1

a9e and arts Participation 1982-1997

Chapter 1

Chapter 1 sets the scene for the monograph by taking a first look at the age of

the audiences for the seven benchmark performing art forms in 1982, 1992, and

1997. Since the average age of the United States population has been increasing

over this span of years, the age of the arts audience for each of the seven benchmark art forms is compared with the age of the sample as a whole in each year .

The evidence for the three following conclusions can be found in Table 1 .1 .

•

The audiences for all art forms, except opera, are aging faster than did the

entire sample .

• In 1982 only the opera audience was older than the entire sample . By 1997

the audiences for all art forms were older than the sample except for jazz,

and museum-goers have the same average age as the entire sample .

•The jazz audience is aging most rapidly. In 1982 it was eleven years

younger than that of the whole sample, by 1997 it was just two years

younger.

Chapter 2

In Chapter 2 we ask what distribution of young and older people we "see" if

we look out over the average audience for each benchmark art form in 1982, in

1992, and again in 1997? Is there indeed, a higher proportion of the audience with

graying hair, or are the larger numbers of young people born since World War H

taking the places of their elders in arts audiences over the span of years from 1982

to 1997? The answer depends on which of the seven art forms one is talking about,

so each will be discussed separately . The data from which the following conclusions are drawn are from Tables 2 .1 through 2 .7.

•

The classical music audience is aging faster than the population as a whole .

In 1982 those under thirty years of age comprised 26 .9 percent of the audience and by 1997 comprised just 13 .2 percent of the audience. Over this

same span of years, those over sixty years of age rose from 15 .6 percent to

30 .3 percent of the classical music audience . •

By 1997, a higher proportion of the classical music audience was over sixty

than was the audience for any other performing art form . • In 1982 those under thirty years of age comprised just 17 .8 percent of the

opera audience and by 1997 comprised only 13 .3 percent of the audience

for opera. Over this same span of years, audience members over sixty rose

from 16 .6 percent to 23 .5 percent of the opera audience .
EXECUTIVE SUMMARY

On February 12, 1996 an article titled "As Patrons Age, Future of Arts is

Uncertain" appeared in the New York Times (Miller 1996) . It galvanised attention

on the question of the aging of arts audiences in the United States . The findings of

the National Endowment for the Arts Research Division Report #34, Age and Arts

Participation, released that same year, largely supported this assertion and helped

to energize the debate over the aging of arts audiences. While many interested in

arts policy echoed the fears of aging, the findings were hotly contested by some arts

presenters who said that they did not perceive their audiences as aging .

To bring further light to these issues, the National Endowment for the Arts commissioned Demographic Data Consultants of Nashville to revisit the issue of the

age of arts audiences with the newly available data from the 1997 Survey of Public

Participation in the Arts . Since Research Division Report #34 had made particular

note of the low rates of arts participation of the baby boom generation (those in

the United States born between 1946 and 1965), the Endowment asked

Demographic Data Consultants to pay particularly close attention to the arts participation of baby boomers .

This executive summary highlights the prime findings of that study . It is divided

into five parts, each highlighting the key findings of the corresponding chapter in

the full report .
Eucutiue Summary 1

3

•While the opera audience was the oldest in 1982 and aged somewhat

through the years to 1997, it was the one art form whose audience aged

less rapidly than did the population as a whole . •

The dynamics of the Broadway musical theater audience aging is similar to

that seen above but not as dramatically as for classical music. In 1982,

those under thirty years of age comprised 27.1 percent of the audience and

by 1997 comprised just 16 .2 percent of the audience . Over this same span

of years, those over sixty rose from 16 .4 percent to 22 .7 percent of the

musical theater audience . • In 1982 the jazz audience was unusually young in that just 5.0 percent of

the 1982 jazz audience was over sixty, while for all the other benchmark

arts, between 15 percent and 17 percent of the audiences was above sixty

years of age . •

In 1982 fully 56.7 percent of the jazz audience was under thirty years of

age, but by 1997 these younger age groups had fallen dramatically to 23 .2

percent of the jazz audience . Over the same span of years those over sixty

rose from 5.0 percent to 15 .5 percent of the jazz audience.

•In 1982 those under thirty years of age comprised 29.1 percent of the theater audience, and by 1997 young people comprised just 16.7' percent of

the audience for theater . Over this same span of years, those over sixty rose

from 15 .5 percent to 22.8 percent of the theater audience .

•In 1982 34 .1 percent of the ballet audience was younger than thirty years

of age ; this was the second highest proportion after jazz . By 1997 the proportion of the ballet audience who were under thirty dropped to 16.1

percent, a level comparable to the other performing arts . Those sixty and

over comprised 15.4 percent of the ballet audience in 1982, and by 1997

had risen to 22 .0 percent, a change comparable with the that taking place

for arts audiences generally . •

In 1982 30.6 percent of arts museum attendances were by young people,

and by 1997 just 19.2 percent. This change is comparable with that of the

other art forms, but by 1997 art museums attracted the second highest proportion of attendances by people under thirty (after jazz) .

•Unlike all of the other benchmark arts, the proportion of museum-goers

sixty and over has not increased appreciably over the fifteen years from

1982 to 1997. Later evidence introduced suggests the reason that not as

many older people frequent art museums is their impaired ability to walk

and stand for extended periods of time .
4

1 flue and WS Participation : 1982-1997

Not all arts attendees attend with equal frequency . Observers have suggested

that older attendees are likely to buy season tickets while attendees under thirty

buy tickets event-by-event as time, money, and inclination dictate and thus generally attend less frequently. The information presented in Tables 2 .8 through 2 .14

show a somewhat more complex pattern :

•For opera, the predicted pattern held true in 1982 and 1997 with young

attendees attending somewhat less often than older attendees .

• For classical music, theater, and ballet, the pattern was the opposite in

1982 from what was expected . That is, young attendees attended more

often than older attendees . For all three forms, however, young attendees

attended less often than older attendees by 1997.

•In the case of jazz where season tickets are seldom offered, attendees in

their twenties attended more often than did both teenage attendees and

those sixty and above .

• Looking at all the art forms together (as seen in Table 2 .15), we find that

arts attendees in their twenties get quite involved in the art forms of

their choice and attend often, while attendees in their thirties and forties

do not go as often, perhaps because of the competing demands of family and work. Finally, in their later years, attendees come back to

attending more often .

Chapter 3

In Chapter 3 we follow respondents who are in the same birth cohort across the

survey years from 1982 to 1997 . Two distinct predictions have been made about

the observed lower level of arts participation of baby boomers relative to their elders . One expectation is that they will "age into" arts participation as they embrace

midlife obligations and perspectives . The alternative prediction is that the lower

level of arts participation is a consequence of their early liberal experience and

will persist over the coming decades, while post-boomer cohorts, raised in a

more conservative atmosphere, will enjoy levels of arts participation comparable to pre-boomers .

In 1982, baby boomer respondents were eighteen to thirty-six years of age, so

it was impossible to know what their mid-life experience would be . Now, fifteen

years later, is the appropriate moment to test these assertions . By 1997 postboomers comprised 21 .5 percent, boomers 43 .0 percent, and pre-boomers 35 .5

percent of survey respondents .
Enecutiue Summary 1

5

The question asked in Chapter 3 is the same as the one asked in Chapter 2,

namely, what distribution of ages would be seen if you looked out at the typical

audience of each specific art form in 1982, in 1992, and again in 1997 . The difference is that here we ask what cohorts, rather than age groups would

predominate . The following points highlight the findings shown in Tables 3 .1

through 3 .7 . Starting with the youngest, cohorts are considered in turn .

•Post-boomer cohort 1976-1980 : It is encouraging for future arts participation that this youngest group in 1997 attended five of the seven

benchmark arts somewhat more often than the sample as a whole, and

they were under-represented in the audience of only one art form, opera . •

Post-boomer cohort 1966-1975 : This young cohort attended six of the

seven art forms less often than the sample as a whole, but their underrepresentation was less pronounced in 1997 than in 1992 for three forms,

opera, musicals, and art museums .

•Late-baby boomers 1956-1965 : Except for jazz, which more often appeals

to the young, late-boomers are clearly under-represented in arts audiences . •

Early-baby boomers 1946-1955 : In marked contrast to late-boomers,

early boomers, contrary to all expectations, were over-represented in the

audiences of six of seven forms in 1982 and in 1997 as well . This starkly

contradicts the finding of Research Report #34 . The reason for the difference is that in that analysis the authors took into account the participation

rates that the various cohorts should have attained given their education

and income. Given the boomer cohort's better education and higher

income relative to prior cohorts, their arts attendance was markedly less

than would be expected . Considering together the findings of these two

studies, demonstrates that early boomers attended the arts more often than

earlier cohorts but not nearly so often as would be expected given their

educational and financial advantages .

• Neither early- nor late-boomers are attending the arts more often as they

age, bringing into question the assumption that the boomer and later

cohorts will age into arts participation . • War and Great Depression cohorts 1926-1945 : Members of these cohorts

attend most art forms more often than the sample as a whole with the

exception of jazz .

• Roaring 20s cohort 1916-1925 : Members of this cohort were at least

seventy-two in 1997, so it is not surprising that their arts attendance was

lower than for respondents as a whole in five of the seven art forms . The
6

1 Age and Arts Participation 1982-1997

only two forms in which they were somewhat over-represented in 1997

were classical music and opera .

• Pre-World War I cohort born before 1916 : The youngest of this

group were sixty-seven in 1982 and eighty-two in 1997, so their underrepresentation in audiences for all the art forms is not surprising .

Attendance does not go down gradually with advancing age, instead, it

plummets as one approaches seventy years of age.

In a fashion parallel to Chapter 2 where the focus was on age groups, the next

question is whether arts attendees of each of the cohorts attend more frequently

than would be expected by comparing their attendance rates with the average rate

of attendance of the entire sample . Because the findings suggest such groupings,

we will focus on three groups of adjacent cohorts : baby boomers, those born

before the baby boomers (pre-boomers), and those born since the baby boomers

(post-boomers) . The conclusions drawn here are based on data in Tables 3 .8

through 3 .14.

•Among post-boomers, it was only for jazz that a few respondents account

for the cohorts' attendance . For all the rest of the art forms, a larger number of Generation X attendees attended fewer times on average . This

means a large number of people were sampling widely.

•For baby boomers generally, a large number of attendees attend infrequently and this trend grew more pronounced from 1982 to 1997 . That

means that these boomers, like the post-boomers noted above, tend to

sample widely without showing a strong commitment to any arts form .

•In marked contrast, the attendance figures for pre-boomers are accounted

for largely by the frequent attendance of a relatively few people in these

cohorts.

Chapter 4

What is the importance of age relative to other factors in determining arts attendance? This is the question addressed in Chapter 4 . Ordinary Least Squares

regression analysis was used to look at the effects of age while controlling for the

effects of a number of other measured variables for each of the seven benchmark

arts . Also used, was a summary measure of the attendance at all the arts together .

It was important to control for the effects of the other factors because the

direct relationship between age and arts attendance was inconsistent and weak .

This was not unexpected because it is well known that arts participation tends
to rise gradually from the thirties through the sixties and then falls rapidly after

that age. However, with the controls in place, the results became clear and

strong as can be seen in Tables 4 .1, 4 .2 and 4 .3 . These results suggest that it is

not age per se but the many factors often associated with stages in the life-cycle

that influence arts participation .

•

There is a significant positive relationship between age and arts attendance

for each of the art forms except jazz when the effects of the control factors

are taken into account . This result is even stronger when considering the

summary measure of arts attendance . This means that older persons attend

all the art forms except jazz more often than do younger people of the same

education, gender, marital status, income, etc . •

Education is, in every instance, the best predictor of participation in each

art form separately and also when they are combined together in the summary measure .

•Age is the second best predictor of arts attendance in four of the benchmark forms, and it is a significant predictor in every case but jazz . • Age is the fourth most important predictor of the summary arts participation measure after education, income, and gender.

• The importance of the other control variables varies from art form to art

form in interesting ways . Since these findings are not directly relevant to

the contribution of age to arts participation, the reader is referred to the

text and tables to see their place in the mix of arts predictors .

Chapter 5

To what extent do the same factors determine arts participation for persons of

differing ages? A number of potential differences come to mind . For example,

respondents in their thirties are more likely to have their arts participation reduced

due to the presence of young children in the home . At the same time, chronic ill

health is likely to be a factor for more older people than for those who are young .

To assess the importance of influences during major phases of the life-cycle . As in

Chapter 4, the sample was split the into three parts : baby boomers, pre-boomers,

and post-boomers .

The same sort of analysis was performed as in Chapter 4 with one small variation, post-boomers under the age of 25 were asked whether they were full-time

students at the time they were surveyed . This variable is included for the postboomer regression analyses . The results of the OLS regression analyses for each of

the three age-groups on the seven benchmark arts are shown in Tables 5 .1 through

Eaeculiue Summary 17
8

1Nqe and Orts Participation 1982-1997

5.7 . For a succinct summary of the findings for each art form please refer to the

text of Chapter 5 . The most significant general findings are as follows :

• The set of variables taken together proved to be better predictors of preboomers' arts participation than that of boomers or post-boomers . The set

of predictors of participation for those born during World War II or before,

including education, gender, and income, are not as important for younger

cohorts. The findings of Research Report #34 suggest that this is due to the

differences between the cohorts and is not simply a function of the age of

the respondents . Thus those interested in increasing the arts participation

of younger people will do well to look to factors other than those measured here .

•

Even after dividing the sample into three parts based on age, being older is

a significant determinant of arts participation among pre-boomers or

boomers for five of the seven benchmark arts . The finding for pre-boomers

is surprising because, within that older group of respondents, age is negatively correlated with arts participation . The finding of the regression

analyses mean that age is not, in itself, a deterrent to arts participation .

Rather, age is often associated with other causal factors such as health, education, and income, which do correlate with arts participation .

•

In twenty of twenty-one regression models, education is the best single predictor of participation in each of the arts . The single exception is ballet for

pre-boomers, where income, father's education, and being female are the

best predictors of attendance .

As in Chapter 4, a summary measure of arts participation was also created . The

respondent was given one point for each art form they had attended in the prior

year. Thus, since there are seven art forms, the variable ranges from 0 to 7 . The

results for this summary measure of arts participation are shown in Table 5 .8

• Even though the sample was divided into three parts on the basis of age,

age is still positively and significantly associated with arts attendance for

baby boomers and pre-boomers . • The respondents' education was far and away the best predictor of arts

participation for all three age groups. The importance of education is further underlined by the fact that the respondents father's education is

significantly associated with participation even for the pre-boomers who
EHecutiue Summary 1

9

were at least in their mid-fifties at the time of the survey . Further underlining the importance of education, the second most important predictor of

arts participation among post-boomers was being a full-time student .

•Family income is the second most important predictor for boomers and

pre-boomers, and sixth most important for post-boomers .

• Being female is the third most important predictor of the summary arts

measure for baby boomers and for pre-boomers, but gender hardly seems

salient for post-boomers. • Chronic health problems is the fourth best predictor for the pre-boomers

where we expected it to be most salient, but it is also the sixth best predictor for boomers . As expected, health is not significantly related to arts

participation for post-boomers .

•Finally, not being currently married, as expected, is significantly related to

arts participation but only for baby boomers and post-boomers .
CHAPTER 1

INTRDDUCTIDN : THE "RGING RRTS RUDIENCE"" QUESTIDN

Concern over the aging of performing and visual arts audiences in the United

States and elsewhere has been mounting over the past several decades . The question was directly confronted in the 1996 National Endowment for the Arts,

Research Division Report #34 (hereafter referred to as Research Report #34) . In

brief, the report found that in the decade from 1982 to 1992 there was clear evidence of an aging audience in several of the performing arts disciplines, most

notably classical music and opera. This aging was due, the study found, largely to

the fact that "baby boomers" (people born between 1946 and 1965) did not attend

as often as would be expected from the participation rates of those horn earlier .

The report evoked considerable comment and some criticism . Performing arts

professionals called for renewed efforts to attract younger people, and others promoters questioned the findings, saying they did not see such a trend in their own

venues. At the same time, museum managers pointed with satisfaction to the finding that museum audiences were not aging.

The recently completed National Endowment for the Arts 1997 Survey of

Public Participation in the Arts (hereafter SPPA) provides a good opportunity to

revisit the aging audience question and to take a fresh look at the question . For the

most part, therefore, we do not make the same kinds of analyses contained in

Research Report #37. There are three reasons for taking a different tack . First, the

1997 survey was fielded just five years after the prior one, so changes in the fundamental relationships reported in Research Report #37 cannot be great .

The second reason is to give those interested in arts audiences a more intuitively

clear way of seeing the aging question . Many practitioners in the arts world considered the complex multi-variant analyses reported in Part I, authored by Richard

Peterson and Darren Sherkat, to be incomprehensible, while they found Part II,

authored by Judy Balfe and Rolf Meyersohn too cluttered with tables relating age

of arts participants to other variables such as gender or education . Thus, though

many commentators accepted the conclusion that the performing arts audience is

aging, practitioners found it difficult to really understand the information they

needed and to apply the findings directly to their own situations .

The third reason why different methods are used here is that the 1997 arts participation figures cannot be directly compared with those reported in the prior

surveys . This is because the methods of drawing the sample, choosing respondents,

and administering the questionnaire were all markedly different . For a detailed discussion of the differences see (NEA 1998a, 1998b) . In consequence, the absolute

levels of arts participation in 1997 cannot be directly compared with those in
Chapter 1 1

11

earlier years . The numerous comparisons we do make across years all compare the

proportion of the audience of a given age in one survey year with the proportion

in another survey year. Thus, to take one example from Table 1 .1, which can be

found following the text of this chapter, the average age of the opera attendees was

43.5 years in 1982 and 45 in 1997, an increase of one and one half years . Over the

same span of years, the average age of all SPPA respondents -like that of the population of the United States - increased three years, thus, while the opera audience

aged, it did so only one half as fast as did the respondent sample as a whole .

Because the 1997 sample is not directly comparable with the other years, Chapters

4 and 5 focus entirely on the data from 1997.

In this and subsequent chapters we will use both the word "attendance" and the

word "attendee ." Attendee refers to the individuals in the SPPA sample who report

participating in a particular arts activity at least once during the prior year .

Attendance refers to the aggregate of the participation of attendees in the particular arts activity. Thus, a person who reports going to four classical music concerts

in the prior year is counted as an attendee and contributes four units to the aggregate attendance at classical music concerts for the year.

CHAPTER TOPICS

Chapter 2 : The Changing Age o f Arts Audiences . Chapter 2 shows the age distribution of the national audience for each of the benchmark art forms . Thus, for

the first time we will be able to see clearly the proportions of young, middle-aged,

and older people in the national audience for each art form in 1982, 1992 and in

1997. This kind of analysis makes any changes in the age of the audience for the

arts easier to see and to understand . In addition, focusing on the proportion of the

audience who are less than 20, 20-29, 30-39, etc . i n each survey year circumvents

the problem, mentioned above, that the rates of reported participation vary widely

from survey year to year.

After first focusing on the age composition of arts attendance, the focus then

shifts to look at the age distribution of arts attendees . If young, mid-life, and older

attendees, on average, attend an art form the same number of times during a year,

the figures for "attendance" and "attendee" will be the same . If, however, as some

commentators assert, younger attendees sample a wide range of activities, attending any one art form less often than older participants who are more likely to buy

season tickets, then more young people and less older people will account for the

aggregate attendance .

Chapter 3 : The Contribution o f the Baby Boomers to Arts Audiences . One of

the prime concerns addressed in Research Report #34 was what seemed to be the
1 2

1age and arts Participation : 1982-1997

lower arts participation of the baby boom birth cohort relative to their elders . A

birth cohort includes all those born in the same span of years, and the baby boom

cohort includes all those born between 1946 and 1965 . Unlike an age group whose

members change every year, the members of a birth cohort always remain the same

as they age together year-by-year. Chapter 3 tracks the arts participation of the

baby boom, younger, and older cohorts as they age from 1982 to 1997 . As in

Chapter 2 the national arts audiences for the seven benchmark arts is shown, but

here birth cohorts are focal, and the central question is, Do baby boomers form an

ever larger part o f the arts audience as they mature? In a way parallel to Chapter

2, the focus is first on the cohort composition of the aggregate audience attendance

and then on the cohort composition of attendees .

Chapter 4 : Age and Other Factors in Determining Arts Participation. As

Research Report #34 clearly showed, numerous other factors, such as gender, education, income, health, combine with age in determining arts participation . To

more clearly distinguish the influence of age per se relative to the other factors,

regression analyses of participation are performed for each of the seven benchmark

arts . The focus is exclusively on the 1997 data to alleviate the incomparability

problem discussed above . The first section of Chapter 4 shows the influence of age

on participation relative to each of the other controlled factors . The final section

of Chapter 4 looks at the contribution of each of the control variables to predicting arts participation .

Chapter 5 : Factors Differentially Affecting Baby Boomers, Pre-Boomers, and

Post-Boomers Arts Participation . Different factors influence arts participation

over the life course . Divorce or death of a spouse is not often a problem for the

young, and children in the home is not often a problem for those past mid-life, for

example. To show the differential influence of the control factors for baby boomers

in comparison with pre- and post-boomers, regression analyses of participation are

performed showing the independent influence of age on participation, net of the

controlled factors . The focus here again is on the 1997 data to alleviate the incomparability problem discussed above .

FIRST LOOK AT ARTS AUDIENCES

The Median Age of Arts Attendees

While most of this monograph looks at age groups and birth cohorts, it is useful to begin inspecting the data by looking at the median age' of the audience for

each of the benchmark arts in the survey years 1982, 1992, and 1997 . In effect, a

survey of the audiences at classical music concerts, ballet performances, opera, etc .

i n 1982, 1992, and 1997 to see if they have aged over this period of time .
Chapter 1 1

13

The average age of the United States population has been increasing, so the key

question is whether the audience for each of the art forms is rising slower or

faster than is the population as a whole . The top line of Table 1 .1 shows that

the median age of the survey respondent was 40 in 1982, 42 in 1992, and 43 in

1997. 2 These changes mean that, like the United States population as a whole,

the average age of survey respondents was rising one year in every five years

between 1982 and 1997 .

Table 1 .1 .

Median Age of Attenders, Overall Sample and Benchmark Arts*

*Values in parentheses indicate years of difference from median age

These three median ages, 40 in 1982, 42 in 1992, and 43 in 1997, can be used

as the "expected" age of arts attendees for the three survey years . These ages would

be found if persons of all ages had attended the same amount. Any departure from

these expected figures signals that the audience for a benchmark art form is older

or younger than expected in the particular survey year . To be able to see these differences at a glance, departures from the expected age are noted in parenthesis in

Table 1 .1 .

Taking a more detailed look at table 1 .1, we can see the changing age of arts

audience in several ways. 3 Looking at the column for 1982 we find that jazz, with

an audience averaging 29 years old, had by far the youngest of any of the bench1982 1992 1997 Net Gain

Overall Sample 40 42 43 3

Opera 43.5 45 45 1 .5

(+3 .5) (+3) (+2)

Classical 40 45 46 6

(0) (+3) (+3)

Musical 39 43 44 5

(-1) (+1) (+1)

Theater 39 44 44 5

(-1) (+2) (+1)

Ballet 37 40 44 7

(-3) (-2) (+ 1)

Museum 36 40 43 7

(-4) (-2) (0)

Jazz 29 37 41 12

(-11) (-5) (-2)
1 4

1 Ape and Arts Participation1982-199?

mark arts . Opera at 43 .5 years had the oldest audience, and the audiences of the

other five forms were at or below the average age of the sample as a whole .

The 1997 column shows quite a different picture . While the sample as a whole

has aged three years, the audience for jazz has aged twelve years, nearly reaching

the age of the sample as a whole . What is more, the age of the audiences for all of

the other benchmark arts in 1997 is at, or above, the age of the sample as a whole .

Comparing the differences between the left-hand column for 1982 with the righthand column for 1997 shown in the "Net Gain" column at the right, we find that,

except for opera, the average age of audience members in all the arts has risen

faster than that for the sample as a whole, and that after jazz, this relative rise has

been greatest for the ballet, art museum, and classical music audiences .

Expected Age and Cohort Distributions

Just as it is possible to form an "expected" average age of arts participants, it is

possible to find the "expected" proportion of participants in each age group and

cohort during each of the three survey years. Thus, if each age group and cohort

contain the same proportion of attendees as all of the others in the survey year we

will find the proportions shown in Table 1 .2 . For example in 1982 we expect 20 .6

percent of arts audiences to be in their thirties and in 1997 we expect 22 .9 percent

of arts audiences in their thirties .

Linking these expected figures to those observed in the survey samples will be

focal in Chapters 2 and 3, but the expected distributions should be inspected before

moving to those chapters. Looking first at the upper half of Table 1 .2, note the

clear decline of those between eighteen and twenty-nine, seen by comparing the

1997 column with that for 1982 and the corresponding rise of those in their forties . The decline in the youth and increase in mid-lifers reflect the aging of the large

baby boom cohort over this fifteen-year period .'

Chapter 3 focuses on the changing contribution of Baby Boomers (respondents

born in the twenty years between 1946 and 1965), to the arts audiences over the

years from 1982 to 1997, and it compares these figures with the proportions that

would be expected if members of every cohort, on average, attended equally. These

expected frequencies are shown in the bottom half of Table 1 .2 . Over these fifteen

years boomers have comprised just over 40 percent of SPPA respondents . In 1982,

post-boomers were not old enough to be surveyed, but by 1997 they comprised

21 .5 percent of the survey sample and pre-boomers ranks shrank proportionately

over the same time period .
Table 1 .2 .

Expected Age Group and Cohort Distributions*

Age Group Distribution of Samples (%)

*Some column percentages do not total to 100 .0 due to rounding .

Chapter 1 1

15

1982 1992 1997

18-19

20-29

30-39

40-49

50-59

60-69

70 & over

4 .8

24.0

20.6

14.9

14.4

11 .6

9 .6

3.0

18.2

23.0

18.5

13.1

11 .8

12.4

2 .1

15.8

22.9

21 .6

14.6

10.7

12.4

Total 99.9 100.0 100.1

Cohort Distribution of Samples (%)

1982 1992 1997

1976-1980 4 .6

1966-1975 15.2 17.4

1956-1965 22.1 22.5 23.5

1946-1955 21 .8 20.3 20.7

1936-1945 16.0 14.3 12 .9

1926-1935 14.5 12.0 10.6

1916-1925 12.9 10.0 7 .7

Before 1916 12 .7 5 .6 2 .6

Total 100.0 99.9 100.0
CHAPTER 2: THE CHRNGIHG RGE Of THE HIS RUDIENCE

In Chapter 1 it was observed that the average age of the audience for each of

the benchmark arts has increased between 1982 and 1997 . Here in Chapter 2 this

observation is examined in greater detail by dividing the total sample into age

groups of ten years in length-twenty year-olds, thirty year-olds, etc. We focus on

the changes for each ten-year-long age group . In the first section the proportion of

the total audience represented by each of the age groups is discussed, that is to say

"attendance" as the word is defined in the 1997 Survey of Public Participation in

the Arts. In this analysis, the total number of attendances reported by survey

respondents is tallied, and the degree to which this representation is greater or less

than would be expected by chance is observed . In the second section of Chapter 2,

the focus is on attendees to see the degree to which a few respondents account for

the aggregate audience for the year by attending the arts many times and whether

the average number of attendees varies by age and art form . Only those relationships that are statistically significant are discussed in the text.

PART IRTTEHRRHCE

The concept of attendance used here represents what one is likely to see looking out over the audience in each of the arts and observing what percent of the

audience is in their twenties, thirties, etc . This look is taken for the survey years

1982, 1992, and 1997 . This seems such a simple way of asking about aging of the

audience; it is a wonder that it has not been focal in earlier survey reports . The reason is that prior reports have asked other questions of the data .' What is more,

though conceptually simple, answering this question involves a great deal of manual calculation ." To get this value we first ran cross-tab frequencies of age group

or birth cohort by the number of attendances, to learn how many respondents in

each age group or cohort attended each art form every number of times . For example, in the 20-29 age group in 1997, 95 respondents attended jazz one time, 75

attended two times, 48 attended three times, etc ., up to the maximum possible

value of 72 times in one year . Next, each value in the frequency cell was multiplied

by the corresponding number of attendances for each age group . (In the same

example, 95 times 1, 75 times 2, 48 times 3, etc .) Then these products were

summed for each age group or cohort to represent the total number of times that

respondents in this age group or cohort attended that benchmark art in the previous year. (For example, the 20-29 year old respondents attended jazz a total of
Chapter 2 1

17

1,084 times in 1997 .) Finally, the sum for each age group or cohort was divided

by the summed total of every age group or cohort's attendances, that is, the total

number of attendances by all respondents in that year, to reflect the proportion of

attendances reported by each age group or cohort in relation to the others in each

sample year. (For example, the total for the 20-29 age group of 1,084 was divided

by the grand total of 5,123 attendances for the whole sample in 1997 to reflect the

21 percent share of total jazz attendances for this age group .)

Looking at the age group composition of arts audiences, recall that, as found in

Chapter 1, the survey population has aged somewhat from 1982 to 1997 . To take

this aging of the population into account, the calculated percentage for each age

group with the expected percentages discussed in Chapter 1 and shown in Table

1 .2 will be compared . In each of the tables displayed in Chapter 2, there is a number in parenthesis below each percentage . This represents the difference (plus or

minus) between the observed percentage and what would be expected if people of

all age groups attended the art venue with equal frequency .

Here and throughout the monograph, the art forms will be discussed in the following order: classical music, opera, musical theater, jazz, theater, ballet, and art

museum attendance . The tables are also presented in this same order.

The Classical Music Audience

Table 2.1 shows the age distribution of the audience attending classical music

concerts in 1982, 1992, and 1997 . Looking across the second and third row of figures, the proportion of the audience in their twenties and thirties has gone down

dramatically over this fifteen year period, from 21 .5 percent to 11 .4 percent and

from 24 .5 percent to 13 .7 percent respectively.' Over the same years the proportion

of respondents sixty years of age and older has nearly doubled, going from 15 .6

percent to 30 .3 percent!

As noted above, the population as a whole has aged over this same period, but

an inspection of the Table 2 .1 figures in parentheses shows that the audience for

classical music has aged faster than has the entire sample of respondents . The negative sign for those in their twenties shows that their attendance has always been

significantly below what is expected, and this difference has increased between

1982 and the two 1990s surveys . In addition, those in their thirties, whose attendance in 1982 was 3 .9 percent above what would be expected, had become 9 .2

percent below what would be expected by 1997 . The picture is reversed at the

other end of the age spectrum. In 1982, those over sixty were under represented,

but by 1997 the two oldest age groups attended classical music concerts more

than would be expected, given their proportions in the sample population . Taken
18

I

Aqe and Arts Participation : 1962-1997

Table 2 .1 .

Age Group % Contribution to Total

Classical Music Attendances, by Year

Values in parentheses indicate the difference of this observed percentage from the "expected"

percentage of a group in a total sample . These differences are statistically significant where indicated (*p< .05 ; **p< .01) . A statistically significant difference means that the probability of this

difference occuring merely by chance is less than 5% (for p< .05) or less than 1 % (for p< .01),

based on this sample . Therefore, we can reasonably conclude that this difference actually exists .

together, these findings show that the classical music audience is aging, and is aging

more rapidly than is the population as a whole .

The Opera Audience

A number of commentators have suggested that more young people have been

going to opera performances in the 1990s than were in the 1980s . The age distribution of the audience attending opera performances in 1982, 1992, and 1997 are

shown in Table 2.2 . Indeed, eighteen and nineteen year-olds composed one percent

of the audiences in 1982 and 1 .6 percent in 1997. Though this increase is small,

the numbers in parenthesis are encouraging because in 1997 teens more nearly

approximate the expected contribution to the total audience for opera . Those in

their twenties show a different pattern . Their percent of the audience has fallen

from 16.8 to 11 .7, but the differences from their expected attendance has narrowed from -7 .2 to -4 .1 .

1982 1992 1997

18-19 5 .4 1 .2 1 .8

(+0.6)** (-1 .8)** (-0 .3)

20-29 21 .5 12.9 11 .4

(-2 .5)** (-5 .3)** (-4 .4)**

30-39 24.5 18.6 13.7

(+3 .9)** (-4 .4)** (-9 .2) --

40-49 17.7 21 .4 23.3

(+2 .8)** (+2 .9)** (+1 .7)**

50-59 15.4 16.9 19.5

(+1 .0)** (+3.8)** (+4.9)**

60-69 8 .5 15.5 14.5

(-3 .1)** (+3 .7)** (+3 .8)**

70 & over 7 .1 13.4 15 .8

(-2 .5)** (+1 .0)* (+3 .4)**

Total 100.0 100.0 100.0
Table 2.2 .

Age Group % Contribution to Total Opera Attendances, by Year

1982

1992 1997

18-19 1 .0 1 .2 1 .6

(-3 .8)** (-1 .8)** (-0 .5)

20-29 16.8 11 .1 11 .7

(-7 .2)** (-7 .1)** (-4 .1) --

30-39 22.4 20.3 17.2

(+1 .8)* (-2 .7) (-5 .7) --

40-49 22.9 23.1 27.5

(+8 .0)** (+4.6)** (+5 .9)**

50-59 20.2 18.7 18.4

(+5.8)** (+5 .6)** (+3 .8)**

60-69 5 .9 17 .1 11 .7

(-5 .7)** (+5 .3)** (+1 .0)

70 & over 10.7 8.6 11 .8

(+1 .1) (-3 .8)** (-0 .6)

Total 100.0 100.0 100.0

See footnote on Table 2 .1 .

The proportion of those in their thirties has declined steadily, and their participation relative to the expected has moved from slightly positive (+1 .8) to quite

negative (-5 .7) . Those in their forties have risen from 22 .9 percent of the audience

to 27.5 percent, while the proportion in their fifties has remained nearly the same.

The proportion of the audience in their sixties and above has risen from 16 .6 to

23 .5 percent, and they have moved from being less represented in the audience

than would be expected (-4 .4), to being a bit more than expected (+ .4) . Taken

together these figures suggest that the opera audience is older than that of the population, but it is not aging more rapidly than the sample as a whole .

The Audience Attending Musicals

The figures for the average age of the musical theater audience in Table 1 .1

showed that, on average the audience for musicals is about the same as the total

sample and it has been aging at about the same rate as the total . The figures for the

musical theater audience by age group can be found in Table 2 .3 . They show that

both younger and older people are under represented . Unlike both classical music

and opera, the audience for musical theater is composed primarily of those from

30 to 59 years of age .

Chapter 2 1

19
20

1 Aqe and Arts Participation : 1982-1997

Table 2 .3.

Age Group % Contribution to Total Musical Attendances, by Year

1982

1992 1997

18-19 4 .6 2 .1 2 .1

(-0 .2) (-0 .9)** (+0 .0)

20-29 22.5 14.2 14.1

(-1 .5)** (-4 .0)** (-1 .7) --

30-39 23.3 22.0 21 .1

(+2 .7)** (-1 .0) (-1 .8) --

40-49 16.6 21 .8 22.8

(+1 .7)** (+3 .3)** (+1 .2)*

50-59 16.6 15.7 17 .1

(+2.2)** (+2 .6)** (+2 .5)**

60-69 10.3 13 .8 12 .1

(-1 .3)** (+2 .0)** (+1 .4)**

70 & over 6 .1 10 .5 10 .6

(-3 .5)** (-1 .9)** (-1 .8) --

Total 100.0 100.0 100.0

See footnote on Table 2 .1 .

The Jazz Audience

The average age of the jazz audience is the youngest of all the benchmark arts,

as seen in Table 1 .1 . At the same time, jazz has experienced the greatest degree of

aging between 1982 and 1997. As Table 2 .4 shows, in 1982 half of the audience

was in their twenties (49.6 percent), and 57.7 percent were under thirty years of

age. In 1997, however, barely one fifth (21 .2 percent) were in their twenties and

less than one quarter (23.2 percent) were under thirty . All of the groups aged forty

and above have gained in their proportion of the jazz audience, but considering the

figures in parenthesis, all age groups fifty and older are still under represented in

the jazz audience, attesting to the fact that the audience for live jazz performances

is still younger than in the other art forms .

The Theater Audience

As seen in Table 1 .1 the average age of those in the theater audience increased

five years, while the sample as a whole has aged three years between 1982 and

1997. This change, as seen in Table 2.5, is reflected across all the age groups . Those

age groups less than forty have a lower percentage representation in the audience

in 1997 than in 1982. Meanwhile, all those groups forty and older have increased

their representation in the audience, even those over seventy years of age, who in

1997 represented 11 .5 of the audience, just less than one percent below the

expected percentage .
Table 2.4 .

Age Group % Contribution to Total Jazz Attendances, by Year

See footnote on Table 2 .1 .

Table 2.5 .

Age Group % Contribution to Total Theater Attendances, by Year

See footnote on Table 2 .1 .

Chapter 2 121

1982 1992 1997

18-19 3 .7 1 .9 1 .8

(-1 .1)** (-1 .1)** (-0 .3)

20-29 25.4 13.7 14 .9

(+1 .4)** (-4 .5)** (-0 .9)

30-39 23.9 22.9 19.6

(+3 .3)** (-0 .1) (-3 .3)**

40-49 17.9 21 .7 23.8

(+3 .0)** (+3 .2)** (+2 .2)**

50-59 13.7 16 .6 17.2

(-0 .7) (+3.5)** (+2 .6)**

60-69 9 .8 15.2 11 .3

(-1 .8)** (+3 .4)** (+0.6)

70 & over 5 .7 7 .9 11 .5

(-3 .9)** (-4 .5)** (-0 .9) -

Total 100.0 100 .0 100.0

1982 1992 1997

18-19 7 .1 1 .7 2.0

(+2 .3)** (-1 .3)** (-0 .1)

20-29 49.6 25.4 21 .2

(+25 .6)** (+7 .2)** (+5.4)**

30-39 20.8 33.2 23.8

(+0 .2) (+10 .2)** (+0 .9)

40-49 10.0 21 .7 24.9

(-4 .9)** (+3 .2)** (+3 .3)**

50-59 7 .5 8.7 12 .6

(-6 .9)** (-4 .4)** (-2 .0) --

60-69 2 .8 6.7 8.7

(-8 .8)** (-5 .1)** (-2 .0)**

70 & over 2 .2 2.6 6.8

(-7 .4)** (-9 .8)** (-5 .6) --

Total 100.0 100.0 100.0
I Age and Arts Participation : 198-1997

The Ballet Audience

As seen in Table 1.1 the average age of the audience for ballet was thirty seven

in 1982 and forty four in 1997, moving from three years younger than the entire

sample to one year older between 1982 and 1997 . And just as we have seen in

looking at the theater audience, Table 2 .6 shows that this aging has taken place

across all age groups . Those below forty represented 60 .7 percent of the ballet

audience in 1982 and just 34 .4 percent fifteen years later. At the same time, all age

groups forty and older have increased their proportion of the ballet audience . Only

those above seventy have not increased faster than would be expected from the

aging of the sample as a whole .

Art Museum Attendance

As seen in Table 1 .1, the audience for art works has aged faster than for any

other art form except jazz . For the younger age groups particularly, as seen in Table

2.7, the pattern is like the audience for the other art forms . Respondents under

forty accounted for 56 .1 percent of art museum goers in 1982 but just 39 .7 percent in 1997. Over the same period of years the proportion of respondents forty to

fifty nine have increased the most, increasing from 28 .5 percent to 43 .6 percent of

the museum audience. And, to a degree not seen for the other art forms, the

museum audience sixty and older has increased only slightly from 15 .4 percent to

16.7 percent, far less than expected when compared to from the aging of the total

sample. Perhaps the physical activity involved in going through museum exhibits

deterred and continues to deter the participation of older people in this art form .
Table 2.6.

Age Group % Contribution to Total Ballet Attendances, by Year

See footnote on Table 2 .1 .

Table 2.7 .

Age Group % Contribution to Total Art Museum Attendances, by Year

18-193.5 1 .9 1 .7

(-1 .3)** (-0 .4) --

20-29 27.1 19.4 17.5

(+3 .1)** (+1 .2)** (+1 .7)**

30-39 25.5 25.6 20.5

(+4 .9)** (+2 .6)** (-2 .4) --

40-49 15.5 21 .3 24.5

(+0.6)** (+2 .8)** (+2 .9)**

50-59 13.0 12 .2 19 .1

(-1 .4)** (-0 .9)** (+4 .5)**

60-69 9 .9 12.1 9.3

(-1 .7)** (+0 .3) (-1 .4)**

70 & over 5.5 7 .5 7 .4

(-4 .1)** (-4 .9)** (-5 .0)**

Total 100.0 100.0 100.0

See footnote on Table 2 .1 .

19821992 1997

Chapter 2 123

1982 1992 1997

18-19 2 .4 3 .8 2 .0

(-2 .4)** (+0 .8) (-0 .1)

20-29 31 .7 16.1 14.1

(+7 .7)** (-2 .1) (-1 .7)

30-39 26.6 26.1 19.3

(+6.0)** (+3 .1)* (-3.6)**

40-49 11 .6 20.2 27.1

(-3 .3)** (+1 .7) (+5.5)**

50-59 12.4 10.4 15.5

(-2 .0)** (-2 .7)** (+0 .9)

60-69 9 .0 16.0 12.6

(-2 .6)** (+4.2)** (+1 .9)*

70 & over 6.4 7 .5 9 .4

(-3 .2)** (-4 .9)** (-3 .0) --

Total 100.0 100.0 100.0
24

1Hge and Hrts Participation : 1992-1997

Part 2 DTTENDEES

Having looked at the age distribution of the arts audience as it has changed over

time, attention now turns from the question of "attendances" to the question of

"attendees ." The question is whether attendees of all ages, on average, attend the

same number of times per year. This question is of importance because some commentators have observed that the older people who do attend, attend more often

than young attendees do because, for example, they are more likely than younger

people to buy season tickets . Thus, to draw one hypothetical example, it may be

that fifty year old attendees are likely to attend five times each while those in their

twenties, on average, attend twice . If this were the case, two fifty-year-olds would

account for (2x5) =10 attendances, while it would take five twenty year olds to

account for 10 attendances (5x2) =10.

To quickly show the degree to which a few attendees may account for the lion's

share of the attendance, the proportion of attendees of a given age group is compared with their contribution to the entire number of attendees . These comparisons

for the age groups in each benchmark art form are shown in Tables 2 .8 to 2.14 .

The figures in the tables are "difference scores"-that is the difference between an

age group's percentage of the total audience minus its percentage of all attendees .

The (+) sign in the upper-left-most cell of Table 2.8, for example, means that in

1982 relatively few teens accounted for the attendance of this age group at classical music concerts, because they represented a larger percentage of attendances

than of attendees . The (-) sign in the upper right-hand cell of the same table means

that in 1997 those teens who attended classical music concerts did so less often

than attendees in other age groups that year. The size of the numbers suggests

the degree to which a few or many people of an age group contributed to the

audience .'

The numerous no-zero differences scores shown in Tables 2 .8 through 2.14

show that, indeed, attendees in different age groups do not all attend equally often .

The pattern of plus and minus scores, however, is more complex than the relationship suggested above. Table 2.8 shows that, in 1982, younger attendees went

to classical music concerts more often, while in 1997 older attendees went more

often. Tables 2.12 and 2 .13 show that this pattern is roughly the same for theater

and ballet goers . Tables 2.9 and 2.10 show that opera and musicals attendees fit

the pattern described above with younger attendees going less often while other

attendees went more often all three survey years . The figures for jazz reported in

Table 2.11 show that attendees in their twenties consistently attended more often

than do those who are younger or older.
See footnote on Table 2 .8 .

Chapter 2 125

In order to get an impressionistic summary measure of these difference scores,

the number of times attendees of a particular age attended more frequently than

would be expected across all seven benchmark arts in 1982 and 1997 was exammed. The results of these tabulations are found in Table 2 .15. The figures suggest

that attendees in their twenties attend often, while attendees in their thirties and

forties tend to attend less often . Finally, the figures show that those attendees who

are seventy years of age and older attend often as do those in their fifties and sixties in 1997 .

Table 2.8 .

Classical Music - Difference between % of Total Times Attended

and % of People Attending

These differences are statistically significant where indicated (*p< .05 ; **p< .01) . A statistically significant difference means that the probability of this difference occuring merely by chance is less

than 5% (for p< .05) or less than 1 % (for p< .01), based on this sample . Therefore, we can reasonably conclude that this difference actually exists . Table 2.9 .

Opera Music - Difference between % of Total Times Attended

and % of People Attending

1982 1992 1997

Under 20 -2 .2** -0 .9 -0 .7

20-29 -1 .5 -2 .1 -3.2*

30-39 +3.3 -2.4 -2 .1

40-49 +2.9 +1 .8 +2.4

50-59 +1 .7 +2.5 +0.6

60-69 -6 .4** +2.7 +1 .3

70 & over +2.1 -1 .4 +1 .6

Total 100.0 100.0 100.0

1982 1992 1997

Under 20 +1 .0* -0 .5 -0 .3

20-29 +0.4 -1 .4 -1 .4

30-39 +0.7 -2 .4* -3 .5

40-49 -0 .2 -1 .5 -0 .8

50-59 -0 .1 +0.5 +0.9

60-69 -1 .9** +2.7** +1 .9

70 & over +0.3 +2.4* +3.2

**

Total 100.0 100.0 100.0
See footnote on Table 2 .8 .

Table 2.11 .

Jazz - Difference between % of Total Times Attended

and % of People Attending

See footnote on Table 2 .8 .

Table 2.12.

Theater - Difference between % of Total Times Attended

and % of People Attending

See footnote on Table 2 .8.

26I Bqe and Bits Participation : 198?-1997

Table 2 .10 .

Musical - Difference between % of Total Times Attended

and % of People Attending

1982 1992 1997

Under 20 +0.4 -0 .3 -0 .4

20-29 -0 .2 -1 .6 +0.0

30-39 -0 .8 -1 .6 -0 .8

40-49 -0 .8 +0.1 -0 .9

50-59 +0.5 +0.3 +1 .0

60-69 +0.2 +0.8 +0.7

70 & over +0.6 +2.3** +0.3

1982 1992 1997

Under 20 -2 .0** -0 .8 -0 .2

20-29- +7 .7** +2.0 +3.1 **

30-39 -1 .6 +2.9 -0 .3

40-49 -1 .6* +1 .0 -1 .4

50-59 -2 .1 ** -2 .1 * -2 .2*

60-69 -1 .1* -2 .3* +0.2

70 & over +0.5 -0 .7 +0.9

1982 1992 1997

Under 20 -0 .5 -0 .7 -0 .9*

20-29 +3.0** -2 .6* +0.6

30-39 -0.1 -0 .0 -1 .5

40-49 +0.4 +0.2 +0.0

50-59 -2 .3** +1 .0 +0.9

60-69 -0 .2 +2.7** +0.5

70 & over -0 .2 -0 .6 +0.4
Table 2.13.

Ballet - Difference between % of Total Times Attended

and % of People Attending

See footnote on Table 2 .8 .

Table 2.14.

Art Museum - Difference between % of Total Times Attended

and % of People Attending

See footnote on Table 2 .8 .

Table 2.15.

Sum of Seven Benchmark Arts with Attenders Attending more

Frequently than Expected (Possible Range 0-7)

Chapter 2 127

1992 1997

Under 20 2 0

20-29 5 4

30-39 3 0

40-49 2 2

50-59 2 6

60-69 2 6

70 & over 6 5

1997

1982 1992

Under 20 -1 .7* +0.8 -0 .4

20-29 +7.6** -2 .1 +1 .3

30-39 -2 .0 -1 .4 -2 .9

40-49 -4 .4** +1 .2 +0.6

50-59 -0 .1 -3 .0 +0.3

60-69 -0 .6 +4.3* +1 .5

70 & over +1 .3 +0.3 -0 .3

1982 1992 1997

Under 20 -1 .0** -0 .7* -0 .8**

20-29 +0 .2 -0 .5 +1 .1

30-39 +0.1 -0 .8 -2 .4**

40-49 -0 .8 +0.4 +0.0

50-59 -0 .7 -0 .8 +3.4**

60-69 +1 .2* +1 .7** -0 .4

70 & over +0.9* +0.8 -1 .1*
28

1 Age aril Arts Participation 1982-1991

Taken together, these patterns suggest that attendees in their twenties get quite

involved with one or more art forms, while attendees in their thirties and forties do

not go as often, perhaps because of the competing demands of family and job .

Furthermore, in their later years, attendees come back to attending more often .

There will be a better chance to understand why the frequency of attendance varies

by age in Chapter 5 where the predictors of arts attendance over the life-course are

explored.
CHAPTER 3 THE CONTRIBUTION Of BRBY BOOMERS TO

THE ARTS AUDIENCE

Are baby boomers (those Americans born between 1946 through 1965) less

likely than their elders to participate in the arts, as asserted in Research Report

#34? The 1997 data make it possible to see whether boomers are "aging into" arts

participation as some have predicted . In other words, if participation is due more

to the respondent's age than to their birth cohort, then the arts participation of

baby boomers will increase for the next twenty years .

This chapter focuses on the arts participation of boomers and the other birth

cohorts over the years from 1982 to 1997. Thus, while the changing age composition of each benchmark art's audience from 1982 to 1997 was central in

Chapter 2, here in Chapter 3 the focus is on the changing contribution of each

birth cohort to the arts audience .

In 1982, baby boomers were ei teen to thirty-six years of age, the youngest

respondents to that SPPA survey. By 1997, baby boomers had become fully established adults in the middle third of their lives, aged thirty-two to fifty one . In 1997,

boomers comprised 44 percent of the SPPA survey respondents, while the postboomers comprised 22 percent of respondents, and the pre-boomers comprised 34

percent of those surveyed .

As noted in Chapter 1, the term "birth cohort" or just "cohort" is the term used

by social scientists to refer to all those born in the same span of years . Unlike an

age group, say all 21 year olds, whose membership changes every year, the members of a birth cohort always remain the same as they age together year-by-year,

decade-by-decade ; thus once a baby boomer, always a boomer. It has been shown

that . the experience of "late boomers" (those born between 1956 and 1965) has

been quite different from those of "early boomers" those born between 1946 and

1955 (Newman 1993). The early boomers grew up in the excitement of newfound

prosperity, the exuberance of the counterculture and the feeling that their lives

could make a difference in society. The late boomers grew up in the disillusioned

backwash of many of these ideas . While early boomers tended to easily find jobs

on the New Frontier or in the booming war plants, the late boomers entered a

much more competitive job market with most of the best jobs already taken by the

millions of early boomers . Since their formative experience was so different, we

analyze the arts participation of early and late boomers separately in this chapter .

To keep this same level of detail through out the age range, all birth cohorts are

defined as being those born in the ten years between the sixth year of a decade and
3 0

1Age aid Arts Participation : 1982-199?

the fifth year of the next decade . The only exceptions to this rule are for the cohorts

at the ends of the age range . Because of its rapid depletion, all those born in 1915

or before were lumped into one cohort . Those in this cohort were at least sixtyseven years of age in 1982, and by 1997 they were at least eighty-two years old .

New cohorts have been added over the years, of course . In 1982 no cohort younger

than the boomers were old enough to be surveyed, but by 1997 there were two

new cohorts represented . Note however that this youngest 1997 cohort represents

only those born over a five-year period, this because only those born between 1976

and 1980 were at least eighteen years of age and thus eligible to be respondents .

Chapter 3 tracks the arts participation of all the cohorts as they age from 1982

to 1997 . As in Chapter 2 the national arts audiences for the seven benchmark arts

is shown, but here birth cohorts are focal, and the central question is : do baby

boomers form a larger part o f the arts audience relative to their proportion in the

whole sample as they mature? To get at this question the same approach is taken

here as that in Chapter 2 where the focus was on age groups . Our goal here is to

see what proportion of the arts audience comes from each of the birth cohorts .

Again, as in Chapter 2, the question is whether these proportions are greater or less

than would be expected if all cohorts had contributed equally to the audiences for

the arts . The focus is shifted briefly from the composition of the audience to ask to

what extent a few respondents tend to increase the cohort's contribution to the

audience by attending many times, and whether the average number of attendances

per attendee varies significantly by cohort .

Thus, the prime difference between Chapters 2 and 3 is that in the former we

focused on the changing age of audiences in each of the art forms . Here in Chapter

3 the focus is on the arts attendance of each of the birth cohorts over the years from

1982 to 1997.

Part 1Cohort Rttendance Rates

Birth cohorts vary greatly in size, so, for example, there were far more baby

boomers born in the United States than there were members of the two cohorts

that follow them . Indeed, that is why these Generation Xers, as they are now

called, have been referred to as the "baby bust" cohort . At the same time there

are relatively few people in the cohorts born early in the 20th century because

a considerable number of those born into them have already died . Given the

unequal size of cohorts, the arts audience of each cohort relative to their proportion in the sample will be examined, so it is possible to see whether the

contribution a cohort would be expected to make if each cohort contributed

equally to the arts audience ."
1982

1992 1997

1976-1980 5 .4

(+0 .8)**

1966-197510.1 9.6

(-5 .1)** (-7 .8) --

1956-1965 20.3 17.5 15.9

(-1 .8)** (-5 .0)** (-7 .6) --

1946-1955 23.4 19.5 24.7

(+1 .6)** (-0 .8) (+4.0)**

1936-1945 21 .0 19 .2 16.4

(+5.0)** (+4 .9)** (+3.5)**

1926-1935 15.1 15 .4 15.3

(+0 .6)* (+3 .4)** (+4.7)**

1916-1925 10.9 14.8 10.2

(-2 .0)** (+4 .8)** (+2 .5)**

Before 1916 9 .3 3 .4 2 .5

(-3 .4)** (-2 .2)** (-0 .1)

100.0 100.0 100.0

Chapter 3 131

Tables 3 .1 through 3.7 present the contributions of each of the cohorts to one

of the benchmark arts . In each table, the three upper left-hand cells are empty

because persons in these cohorts were too young at the time to be surveyed . The

figure in the upper right-hand cell of Table 3.1, for example, means that in 1997,

5 .4 percent of the audience for classical music was from the youngest cohort sampled, those born between 1976 and 1980." The numbers in parentheses in these

same seven tables show the contribution of each cohort to the audience relative to

the contribution it would make if all cohorts participated equally. 12 The figure of

+0.8 in parenthesis in the upper right-hand cell of Table 3 .1, for example, means

that the 1976-1980 cohort contributes eight tenths of a percent more to the 1997

audience for classical music audience than expected ." Now each cohort will be

considered in turn .

Table 3 .1 .

Cohort % Contribution to Total Classical Music Attendances, by Year

Values in parentheses indicate the difference of this observed percentage from the "expected" percentage of group in total sample . The differences are statistically significant where indicated (* p< .05 ;

**p< .01) . A statistically significant difference means that the probability of this difference occurring

merely by chance is less than 5% (for p< .05) or less than 1% (for p< .01), based on this sample.

Therefore, we can reasonably conclude that this difference actually exists.
3 2

1Bge and BrIs Participation : 1982-1997

Post-Boomer Cohort 1976-1985 (-1980) Only in the 1997 survey were members

of the 1976-1985 cohort old enough to be surveyed for the SPPA, and only those

in the older half of the cohort years were at least eighteen years of age and thus eligible to take part in the SPPA survey. Very little can be said about their arts

attendance . It is potentially encouraging for the future of the arts audience that

these youngsters attend five of the seven benchmark arts more often than would be

expected, and they under-attend only one art form, opera . It may be that their elevated rate of attendance is due in part to their participation in conjunction with

school-related activities that give many of them easy access to arts performances .

Certainly, such a decline is in line with the attendance of younger baby boomers

whose attendance dropped from that of their youthful days in 1982 to 1997. However, it will be possible to discuss this possibility further in Chapter 5 where

the focus is placed on the impact of school attendance on arts participation among

post-boomers.

Post-Boomer Cohort 1966-1975 The trend in the arts participation of these early

Generation Xers in the five years between 1992 and 1997 is very clear. They attend

six of the seven benchmark arts less than expected in 1992 and in 1997 as well .

What is more, their attendance, though still less than expected as shown by the

negative signs, improved for opera, musicals, and theater attendance . Jazz is, as we

have shown in Chapter 2, the one benchmark art that attracts more young people

than those who are older. And it is the only art form that this cohort attended more

than expected.

Later Baby boomers : 1956-1965 This cohort and all of the ones born earlier were

surveyed in 1982 as well as in 1997, so for these cohorts all comparisons of cohort

rates of participation will cover the full fifteen year time span ."

The arts participation of the later boomers, those born between 1956 and 1965

is well below their proportion in the sample as a whole . The only exception is jazz,

the form that distinctively appeals to young people, and even the cohort's jazz

attendance went down from 1982 to 1997. These late boomers were under represented in the audience of the other six art forms in 1982 and in 1997 as well .

What is more, their under-representation increased from 1982 to 1997. The

later boomers clearly show the low arts participation that has been widely

observed in earlier studies, and they do not seem to be attending more as they

mature into mid-life .
Table 3.2 .

Cohort % Contribution to Total Opera Attendances, by Year

See footnote on Table 3 .1 . Table 3.3 .

Cohort % Contribution to Total Musical Attendances, by Year

See footnote on Table 3 .1 .

Chapter 3 133

1982 1992 1997

1976-1980 4.6

(+0 .0)

1966-1975 11 .6 15.1

(-3 .6)** (-2 .3)**

1956-1965 19.4 19.5 23.0

(-2 .7)** (-3 .0)** (-0 .5)

1946-1955 24.6 21 .4 22.3

(+2 .8)** (+1 .1) (+1 .6)**

1936-1945 18.6 19 .1 14.7

(+2 .6)** (+4 .8)** (+1 .8)**

1926-1935 16 .1 14.3 12.1

(+ 1 .6)** (+2.3)** (+1 .5)**

1916-1925 12.1 11 .7 6 .7

(-0 .8)** (+1 .7)** (-1 .0) --

Before 1916 9 .3 2 .5 1 .6

(-3 .4)** (-3 .1)** (-1 .0)--

100.0 100.0 100.0

1982 1992 1997

1976-1980 3 .4

(-1 .2)*

1966-1975 8.7 11 .7

(-6 .5)** (-5 .7)**

1956-1965 11 .7 19.6 20.8

(-10 .4)** (-2 .9) (-2 .7) -

1946-1955 19 .3 18.4 25.7

(-2 .5)** (-1 .9) (+5 .0)**

1936-1945 25.4 22.4 16 .8

(+9.4)** (+8 .1)** (+3 .9)**

1926-1935 17.1 17 .1 12.1

(+2.6)** (+5 .1)** (+1 .5)

1916-1925 14.4 10 .9 8 .0

(+1 .5)* (+0.9) (+0 .3)

Before 1916 12 .2 2 .9 1 .5

(-0 .5) (-2 .7)** (-1 .1) -

100.0 100.0 100.0
34

189e and 8rts Participation : 1982-1997

Table 3.4.

Cohort % Contribution to Total Jazz Attendances, by Year

See footnote on Table 3 .1 .

Table 3.5.

Cohort % Contribution to Total Theater Attendances, by Year

See footnote on Table 3 .1 .

1982 1992 1997

1976-1980 5 .0

(+0 .4)

1966-1975 10.6 14.8

(-4 .6)** (-2 .6) --

1956-1965 22.8 20.6 20.9

(+0 .7) (-1 .9)** (-2 .6)**

1946-1955 22.3 22.1 23.7

(+0 .5) (+1 .8)** (+3 .0)**

1936-1945 20.5 18.7 15.5

(+4.5)** (+4.4)** (+2 .6)**

1926-1935 15.1 16.0 10 .8

(+0 .6) (+4.0)** (+0 .2)

1916-1925 11 .6 9 .6 7 .0

(-1 .3)** (-0 .4) (-0 .7)

Before 1916 7 .7 2 .4 2 .3

(-5 .0)** (-3 .2)** (-0 .3)

100.0 100.0 100.0

1982 1992 1997

1976-1980 5 .2

(+0 .6) *

1966-1975 18.2 23.0

(+3 .0)** (+5 .6)**

1956-1965 44.1 32.2 25.8

(+22 .0)** (+9.7)** (+2 .3)**

1946-1955 29.6 26.4 21 .8

(+7 .8)** (+6.1)** (+1 .1)

1936-1945 10.5 10.5 10 .8

(-5 .5)** (-3 .8)** (-2.1) --

1926-1935 9 .2 8 .4 8.3

(-5 .3)** (-3 .6)** (-2 .3)**

1916-1925 3.9 3 .3 4.7

(-9 .0)** (-6 .7)** (-3.0)**

Before 1916 2 .6 0 .9 0.4

(-10 .1)** (-4 .7)** (-2 .2)**

100.0 100.0 100.0
Table 3.6.

Cohort % Contribution to Total Ballet Attendances, by Year

See footnote on Table 3 .1 . Table 3.7.

Cohort % Contribution to Total Art Museum Attendances, by Year

See footnote on Table 3 .1 .

Chapter 3 135

1982 1992 1997

1976-1980 5.2

(+0 .6)**

1966-1975 15 .1 17.2

(-0 .1) (-0 .2)

1956-1965 22.8 23.8 21 .8

(+0.7)** (+1 .3)** (-1 .7)**

1946-1955 25.1 22.8 24.5

(+3 .3)** (+2 .5)** (+3 .8)**

1936-1945 20.3 15 .4 16 .3

(+4 .3)** (+1 .1)** (+3 .4)**

1926-1935 12.3 13 .4 9.4

(-2 .2)** (+1 .4)** (-1 .2) --

1916-1925 12.0 7 .8 4.5

(-0 .9)** (-2 .2)** (-3 .2)**

Before 1916 7 .4 1 .6 1 .1

(-5 .3)** (-4 .0)** (-1 .5) --

100.0 100.0 100.0

1982 1992 1997

1976-1980 5 .6

(+1 .0)

1966-1975 13 .9 12.6

(-1 .3) (-4.8)**

1956-1965 25.5 25.7 22.3

(+3 .4)** (+3 .2)* (-1 .2)

1946-1955 25.3 17.2 26.7

(+0 .5) (+1 .8)** (+3 .0)**

1936-1945 18.8 16.8 14.4

(+4.5)** (+4.4)** (+2 .6)**

1926-1935 11 .8 11 .0 12 .0

(-2 .7)** (-1 .0) (+1 .4)

1916-1925 9 .6 13 .2 4.9

(-3 .3)** (+3 .2)** (-2 .8)**

Before 1916 9.0 2 .3 1 .4

(-3 .7)** (-3 .3)** (-1 .2)' -

100.0 100.0 100.0
3 6

I Age and Arts Participation : 1982-1997

Early Baby boomers : 1946-1955 The early boomers show a pattern of participation that is starkly in contrast with the picture of low arts participation of the

later boomers just discussed . Indeed their participation rate was higher than the

sample as a whole in six of the art forms in 1982, and that was true for all seven

forms by 1997. This finding jibes with the personal observations of many artsorganization managers, but it goes against the conclusions drawn from NEA

Research Report #34. 15 The possibility that younger and older boomers are so different from each other will be taken up again in Chapter 4.

Late Depression and World War II Cohort : 1936-1945 Looking across Tables

3 .1-3 .7, the pattern of arts participation of the Great Depression and World War

H cohort is very clear. In 1982 and in 1997 as well, they were a larger part of the

audience than would be expected by their proportion of the sample in six out seven

of the benchmark art forms . It is also notable that over this span of years their over

representation in the arts audience became less pronounced. The only form for

which their participation was less than expected is jazz .

Early Depression Cohort: 1926-1935 Following the pattern just observed, the

arts participation of this cohort, who experienced the Great Depression in their

childhood and came to maturity during World War II, was high, being greater than

their proportion in the sample as a whole in four of the benchmark arts in 1982

and five in 1997. Their participation in jazz and museum attendance was less then

expected. The figures for jazz fit the pattern of a youthful jazz audience noted in

Chapter 2, but no explanation for their continuing low art museum attendance

comes readily to mind.

Roaring Twenties Cohort : 1916-1925 Members of this cohort, whose early

experience was the boom times following World War I, were between seventy-two

and eighty-one years-of-age in 1997, so it is not surprising, given their advanced

age, that their arts participation was below average for five of the seven benchmark

arts . Their participation was higher than expected only for classical music and

opera.

Pre-World War I Cohort: Born Before 1916 This "cohort" includes all those

born before 1916, thus they were at least 67 in 1982 and at least 82 in 1997. It is

not surprising, given their advanced age and lower education relative to baby

boomers, that they are not over represented in the audience of any of the benchmark arts. It is more surprising at first glance that their arts participation, while
Chapter 3 1

37

still low, more nearly approached, and in the case of classical music reached, the

level that would be expected in terms of their proportion of the sample as a whole .

The attendance rates of these older persons may be due to the fact that the factors

that make for more arts participation, such as higher education, greater wealth,

and an active mind, also make for better health so that by this time in life, surviving members of the cohort are more likely to have been inclined to arts

participation all along than is true of still young cohorts .

Part 2 :

Attendees' Rates of Attendance across Cohorts

Having looked at the contribution of each cohort to the arts audience as it has

changed over time ; focus now turns from the question of "attendance" to those

who attend the arts . Do members of all cohorts, on average, attend the same number of times per year? This question is of importance because some commentators

have observed that those in older cohorts, who do attend, attend more often than

do attendees in the more recent cohorts . Thus, to draw one hypothetical example,

it may be that those in a pre-World War II cohort are likely to attend five times a

year while baby boomers, on average, attend twice a year. If this were the case two

fifty-year olds would account for (2x5) =10 attendances, while it would take five

twenty year olds to account for 10 attendances (5x2) =10 .

To quickly show the degree, to which a few attendees account for the lion's

share of the attendance of a cohort, the proportion of attendees of a given cohort

is compared with their contribution to the entire audience . These comparisons for

the cohorts in each benchmark art form are shown in Tables 3 .8 to 3 .14. These

tables are parallel to Tables 2 .8 to 2.14 analyzed in Chapter 2 . The figures in the

tables are "difference scores ."" The (+) sign in the upper-right-most cell of Table

3 .8, for example, means that in 1997 attendees in this cohort, on average, attended

more often than did attendees in the sample as a whole . In other words, a relatively

few in the youngest cohort accounted for much of the attendance of this cohort at

classical music concerts . The (-) sign in the cell just below in the same table means

that in 1997 those respondents under twenty years of age who attended did so less

often than did all attendees that year. The size of the numbers suggests the degree

to which a few or many people of a cohort contributed to the audience ." The

numerous no-zero differences scores in Tables 3 .8 through 3.14 show that, indeed,

attendees in different cohorts often do not all attend equally often .

Focusing briefly on the youngest cohort who were old enough to be surveyed

only in 1997, those born between 1976 and 1980, there is no clear pattern in

the frequency of their attendance, since a few attendees accounted for the

cohort's attendance in about half the art forms, while a larger number of attendees
38

1 Age and Arts Participation : 1982-1997

Table 3.8 .

Classical Music - Difference between % of Total Times Attended and

of People Attending

These differences are statistically significant where indicated (* p< .05 ; **p< .01) . A statistically significant difference means that the probability of this difference occurring merely by chance is less than 5%

(for p< .05) or less than 1 % (for p< .01), based on this sample. Therefore, we can reasonably conclude

that this difference actually exists .

participated less often in the rest. A pattern is, however, much clearer for the early

Generation Xers, those born between 1966 and 1975 . Only for jazz did a few aficionados account for the cohort's attendance in 1992 and in 1997 . For all the rest

of the art forms, a larger number of Generation Xers attended less often .

Now focus turns to the baby boom and pre-baby boom cohorts that participated in all rounds of the SPPA survey . Here, rather than focusing on each cohort

individually, a contrast is made between the two baby-boom cohorts on the one

hand and the three cohorts that preceded them, because these two groups of

cohorts show quite different patterns ." In order to get an impressionistic summary

measure of these difference scores, the number of times attendees of a particular

cohort attended more or less often than would be expected from the total sample

rate is examined for the seven benchmark arts in 1982 and 1997 .

Focusing on the two baby boom cohorts, very interesting variations are found .

A larger number of attendees tend to participate infrequently, and this trend

became greater from 1982 to 1997 . This means that baby boom arts attendees, at

least over the span of years covered in the survey, tend to sample an art form rather

than showing a strong commitment to arts participation as shown by their infrequent attendance .

Turning to the three pre-baby boom cohorts, there is a clear trend, and it is the

opposite of the pattern for baby boomers . While in 1982 more infrequent samplers

were found, by 1997 the attendance for these cohorts was contributed by relatively

few attendees who tended to go more often . While this trend is apparent in all three

1982 1992 1997

1976-1980 +1 .1

1966-1975 -1 .0 -3 .7**

1956-1965 +1 .4 -1 .4 -3 .2**

1946-1955 +0.3 -2 .8* +0.0

1936-1945 +0.6 -0 .2 +0.7

1926-1935 -0 .9 +1 .6 +2.8**

1916-1925 -1 .4* +3.2** +1 .8*

Before 1916 -0 .1 +0.5 +0.6
Chapter 3 1

39

pre-boomer cohorts, it is shown most dramatically by those in the cohort born

between 1926 and 1935, since across all art forms, attendees in this cohort tended

to be samplers in 1982 and aficionados in 1997 . There is a better chance to understand why the frequency of attendance varies so dramatically between baby

boomers and pre-boomer cohorts when, in later chapters, the predictors of arts

attendance across cohorts are examined .

Table 3.9.

Opera Music - Difference between % of Total Times Attended and

of People Attending

See footnote on Table 3 .8.

Table 3.10.

Musical - Difference between % of Total Times Attended and % of

People Attending

See footnote on Table 3 .8 .

1982 1992 1997

1976-1980 +0.0

1966-1975 1 .3 -0 .8

1956-1965 -0 .2 -2 .1* -0 .4

1946-1955 +0.3 -1 .2 -0 .9

1936-1945 -0 .8 +1 .1 +1 .4*

1926-1935 -0 .1 +0.8 +0.8

1916-1925 -0.7 +2.7** -0 .1

Before 1916 +1 .5** +0.2 +0.3

1982 1992 1997

1976-1980 -1 .7

1966-1975 -2 .5 -3 .6*

1956-1965 -3 .0 -1 .1 -0 .7

1946-1955 -1 .3 -2 .5 +2.0

1936-1945 +5.6** +3.6 +1 .1

1926-1935 -1 .4 +2.0 +1 .5

1916-1925 -0 .5 +0.2 +1 .1

Before 1916 +0.8 +0.1 +0.1
40

I Age and Arts Participation

1982-1997

Table 3.11 .

Jazz - Difference between % of Total Times Attended and % of People

Attending

See footnote on Table 3 .8 .

Table 3.12.

Theater - Difference between % of Total Times Attended and % of

People Attending

See footnote on Table 3 .8 .

Table 3.13.

Ballet - Difference between % of Total Times Attended and % of

People Attending

See footnote on Table 3 .8 .

1982 1992 1997

1976-1980 +0.3

1966-1975 -0 .8 -0 .4

1956-1965 +5.0** -0 .7 -2 .4

1946-1955 -2 .4 -4 .6* +1 .7

1936-1945 -1 .9 +2.3 +0.4

1926-1935 -1 .3 -1 .1 +1 .7

1916-1925 -1 .5 +5.1 ** -1 .0

Before 1916 +1 .9 +0.1 -0 .5

1982 1992 1997

1976-1980 -0 .6

1966-1975 -2 .7** -0 .1

1956-1965 +2.8** -0 .5 -0 .9

1946-1955 -1 .5 -0 .5 -0 .1

1936-1945 +1 .5 +1 .4 +1 .4

1926-1935 -2 .0* +2 .1* +0.0

1916-1925 -0 .2 -0 .1 +0.2

Before 1916 -0 .6 +0.3 +0.2

1982 1992 1997

1976-1980 -0 .3

1966-1975 +0.2 +3.7**

1956-1965 +4.0** +3 .2* -0 .1

1946-1955 +1 .2 +1 .8 -2 .8*

1936-1945 -2 .5** -2 .9** -1 .2

1926-1935 -0 .9 -1 .3 +0.0

1916-1925 -2 .0** -1 .2* +0.6

Before 1916 +0.2 +0.0 -0 .1
Table 3.14.

Art Museum - Difference between % of Total Times Attended and

of People Attending

See footnote on Table 3 .8 .

Chapter 3 1

41

1982 1992 1997

1976-1980 +0.4

1966-1975 -0 .5 -0 .6

1956-1965 -0 .2 -1 .3 -2 .4**

1946-1955 -1 .9* -1 .1 +1 .0

1936-1945 +1 .5* +0.4 +2.8**

1926-1935 -1 .4* +2.0** +0.1

1916-1925 +1 .3* +0.4 -0 .9**

Before 1916 +0.6 -0 .0 -0 .3
Chapter 4

THE IMPOPTRHCE Of RGE RS R OETEPMIHRHT Of

RPTS PRPTICIPRTIOH

In Chapters 2 and 3 we looked at the changing age and cohort composition of

the audience for each of the seven benchmark arts in the United States over the

years from 1982 to 1997. Here, focussing on the data of the 1997 survey, we seek

to find the importance of age in the context of other factors as a determinant of

arts participation . First we ask how well age predicts participation for each of the

art forms; next, we ask the importance of age when other measured factors that

have been shown to influence arts participation are taken into account ; and which

of the other variables are the most important in predicting participation in addition to age . Finally, we ask the same set of questions when considering a summary

measure of participation in all of the seven benchmark arts together.

The reason for controlling for the effects of other variables when looking at the

importance of age is to show the effects of age, per se . after statistically eliminating the effect of these other factors . As will be shown, the influence of nine other

factors is considerable . For example, as seen in Chapters 2 and 3, persons in their

middle years tend to go more often than those who are younger or older . The data

presented in this chapter, however, show that when the other factors are taken into

account, arts participation rises significantly with age . The meaning of these results

is discussed in detail in the concluding section of the chapter.

METHODS

A series of ordinary least squares regression analyses are used to compare the

relationship between arts participation and age, with the relationship between participation and age after controlling for the effects of nine other relevant variables .

Following our focus in Chapters 2 and 3 on both attendance and attendees, we are

interested in explaining both whether respondents attend an art form in the prior

twelve months and also their frequency of attendance . Thus, parallel sets of regression analyses are presented, one with the frequency of attendance as the dependent

variable, and the other with whether or not the respondent is an attendee as the

dependent variable .'

Independent Variable

In this chapter, "Age" is measured as the respondent's year of age at the time

they took the survey.
Chapter 4 1

43

Dependent Variables

"Attendances" is measured as the number of times R attended an art form during the last year. A few respondents attended one or another art form 50 to 150

times in a year. While it is perfectly possible to attend this many times in a year,

such persons are not ordinarily what we think of as audience members in the usual

sense of the term . In all likelihood these very frequent attendees are art critics, art

teachers, or managers . Accordingly, to correct for the skewing effect of these few

respondents, we set 24 as the highest frequency, so that the top of the attendance

measure scale is "24 or more ."

"Attendee" is simply whether the respondent attended the art form during the

last year (O=No, 1=Yes) .

"Summary Arts Participation" is the number of art forms that the respondent

attended during the last year (range : 0 to 7) . This measure does not take into

account how often the respondent attended arts events . The measure here is the

respondent's range of arts attendance and not their total number of attendances at

a given form . Thus the person who attended seven jazz concerts and no other art

form gets a score of 1 while the person who participated in each form just once

attains a score of 7. Since the age composition of the audience for jazz is younger

than that for the other arts, and since its audience differs in other ways as well,

excluding jazz from the summary measure was considered, but the results were virtually the same as when all seven art forms were included in the summary measure .

Control Variables

Gender was measured as Female=l, Male=O because prior research has shown

that women more often attend the arts than do men . Accordingly, the variable is

designated "Female" in Tables 4.1, 4 .2 and 4.3 .

Race was coded as Black=l, Other=0 because "Other" is a mixture of several

different categories including white, Asian, and non-white Hispanic . Accordingly,

the variable is designated "Black" in the tables .

Marital status was reported as Never Married, Married, Divorced, Separated,

or Widowed . The rates of arts participation of each of these groups was compared

in a preliminary analysis and the groups most alike in their participation were

grouped together. Never Married, Divorced, and Separated had similarly high rates

of participation and accordingly were grouped together and called "Not Married ."

They were coded =1 . Those who were Married or Widowed generally had lower

rates of participation and were coded =0 .
4 4

1 A ;e and Arts Participation 1982-1997

Household income is designated "Income" in the tables . It was coded 1-7 on

the survey responses, and these correspond to the following income ranges:

1=$10,000 or less

2=$10,001 to $20,000

3=$20,001 to $30,000

4=$30,001 to $40,000

5=$40,001 to $50,000

6=$50,001 to $75,000

7=$75,001 to $100,000

8=Over $100,000

"Education" indicates the highest level completed by the respondent . It has a

range of 1-13 as follows :

1=7th grade or less

2=8th grade

3=9th to 11th grade

4=12th grade but no diploma

S=High school diploma/equivalent

6=Vocational/Technical program after high school

7=Some college but no degree

8=Associate's degree

9=Bachelor's degree

10=Graduate or professional school but no degree

11 =Master's degree

12=Doctorate (PhD, EDD)

13=Professional (Law LLB or JB ; Medicine/MD ; Dentistry/DD)

The ordering is the same as in the questionnaires. Initially it was thought that

respondents with a Doctorate should be ranked higher those than with a professional degree, so a number of preliminary runs were made, and, in the case of most

art forms, respondents with professional degrees were more likely to attend than

were those with a Doctorate .

As in the case of the respondent's education, "Father's Education" was measured as the highest grade or degree earned by the respondent's father, using the

same scale ranging from 1 to 13 . Before selecting Father's education as the best

measure of the educational atmosphere in the home, a number of preliminary

analyses were made . Father's education was found to correlate somewhat better

with the respondent's arts attendance than did Mother's education, and including

both did not appreciably increase the ability to predict respondent's arts participation over using Father's education, alone .
Chapter 4 1

45

"Children" comprises a continuous measure of the number of children 18 or

under in household . Earlier SPPA surveys had shown that having children six and

under in the household was a much better predictor of lower arts participation, but

alas, the 1997 survey did not ask about children six or younger .

"Health" is a five point ordinal scale of self-reported health status with the following levels :

1=Poor

2=Fair

3=Good

4=Very Good

5=Excellent

Several more focussed measures of health status were available in the survey .

These included self-reports of eyesight, hearing, and the ease of walking . Hearing

ability was a good predictor of Classical Music concert attendance, and the ease of

walking was a good predictor of art museum attendance, but overall none of these

singly or in combination significantly increased the predictive value of the global

health measure alone .

"Metro" is the dichotomous indicator of the size of the respondent's place of

residence . It is approximately equivalent to the "size of place" variable in the

three earlier surveys . Metro was coded I if the respondent lives in any of the

following eleven major metropolitan areas as operationalized in the survey, and

0 if they do not :

Boston-Worchester-Lawrence,

Chicago-Gary-Kenosha,

Dallas-Ft . Worth,

Detroit-Ann Arbor-Flint,

Houston-Galveston-Bazoria,

Los Angeles,

Miami-Ft. Lauderdale,

New York-Northern New Jersey-Long Island,

Philadelphia-Wilmington-Atlantic City,

San Francisco-Oakland-San Jose,

Washington DC-Baltimore

In a preliminary analysis we also tried to use the "state and counties" variable

in the 1997 survey to approximate the "region" variable of earlier surveys . The

level of detail provided in the 1997 data made it possible to see that the range of
4 6

I Age and Arts Participation : 198?-1997

variation within regions and states was as wide as the variation between regions .

For example, both Florida and Texas proved to be quite different from the rest of

the South, and the rates of arts participation in the Central Valley of California was

among the lowest in the nation, while those of the San Francisco and Los Angeles

areas were among the highest.

No conceptually justifiable division among geographic areas was any clearer

than the metropolitan area measure just described, so it is used in the regression

analyses .

RESULTS

The three tables below show the results of the regression analyses. Just as in

Chapters 2 and 3, we are interested in both whether the respondent attended an

art form in the previous year and the respondent's frequency of attendance.

Accordingly, the dependent variable in Table 4.1 is the frequency of attendance,

while the dependent variable in Table 4.2 measures whether the respondent

attended a particular art form during the prior year or not .

Reading the Tables

A good deal of information is condensed in Tables 4.1, 4 .2 and 4.3 . Take the

left-hand portion of Table 4.1 for example . This shows the results for Classical

Music attendance . The "Beta" is the standardised coefficient so its range is from

+1 .0 to -1 .0 . The focus is on the Betas because their relative strength is comparable across variables and across models as well . The relationships are all positive

unless a negative sign precedes them . Thus, for example both being black and having children 18 or under in the household tends to reduce the level of Classical

Music attendance .

The measure "R-square" refers to the amount of variance in the dependent variable that is explained by the independent and control variables included in the

model. The one or two stars (* or *) indicate whether the variable in that row of

the table contributes significantly to predicting the dependent variable with the difference being obtained by chance in less than one case in fifty (p< .05) or one chance

in a hundred (p.< .01) respectively. Unless otherwise noted, just the statistically significant relationships will be discussed . Finally, note that the smaller top part of the

column represents the bivariate relationship between arts attendance and age,

while the longer, bottom part of the column shows the predictive value of age when

the contribution of the other nine control variables is taken into account .

The corresponding right-hand part of Table 4.2 shows the same set of information, in this case for being an "attendee" rather than the frequency of "attendance"
Chapter 4 1

47

shown in Table 4 .1 . As an inspection of the three tables shows, the pattern of relationship for attendance (shown in Table 4 .1) and attendee (Shown in Table 4 .2) are

very similar. Accordingly, the focus will be on attendance except where noted .

Table 4 .3 presents the results for the "Summary Arts Participation" measure

defined above .

The Effect of Age on Arts Participation With and Without Controls

Here the figures in the top two rows of Table 4 .1 are compared with the age and

R-square figures in the bottom part of the table . Focussing first on the top row all

the way across the table, one can see that when the influence of other factors is not

taken into account the relationship between age and arts attendance is positive for

classical music and opera, and negative for jazz and art museum attendance . As

indicated by the lack of a significance, however, there is no apparent relationship

between the age of the respondent and attendance at musicals, theater, and ballet .

The R-squares are all less than one percent ( .01) suggesting that, while there is a

statistically significant association between age and participation in classical music,

opera, jazz, and art museums, the relationships are substantively trivial .

With the exception of jazz, the Betas for age are positive and significant for all

the art forms when the effects of the nine control variables are taken into account .

Taken together, these results suggest that the direct effects of age on arts participation is masked by the effects of other factors including those measured by the

control variables . When these variables are taken into account, arts attendance

increases with age. This means that older persons attend the art forms more often

than do younger people of the same gender, education, income, etc.

Contribution of Controls to Predicting Participation in Each Benchmark Art

In this section we look at Tables 4 .1 and 4 .2 in order to identify the control variables that are most important in predicting participation in each of the benchmark

arts . Please note that each of the statements made in this section is predicated on

controlling for the effects of age and the other measured variables .

The figures for Classical Music are shown in the first column of these two tables .

On inspection, they show that age is the second-most important variable in predicting arts attendance . The respondent's amount of formal education is the most

potent predictor of classical music attendance . What is more, father's education is

the third most important predictor. Interestingly, respondent's health, race, gender,

and number of children do not importantly affect classical music attendance . The

relative magnitude of the figures in Table 4 .2 is very much like those of Table 4 .1

with the exception of household income . Income is a better predictor of whether

one attends classical music concerts (Table 4 .2) than of how often one attends
Table 4.1 .

Regression Results of Number of Attendances on Age (standardized coefficients)

These effects are statistically significant where indicated (*p< .05; **p< .01) . A statistically significant effect means that the probability of this effect

occurring merely by chance is less than 5% (for p< .05) or less than 1 % (for p< .01), based on this sample . Therefore, we can reasonably conclude

that this effect actually exists .

Classical Opera Musical Jazz Theater Ballet Art Museum

Bivariate Model

Age 0 .075** 0 .020* 0.013 -0 .058** 0 .014 0 .005 -0 .033**

R-square 0 .006 0 .000 0.000 0 .003 0 .000 0 .000 0.001

Multivariate Model

Age 0 .143** 0.062** 0.065** -0 .024 0 .078** 0 .050** 0.046**

Female 0 .023* 0.027* 0.069** -0 .017 0 .023* 0 .046** 0 .032**

Black -0 .024* -0 .019 0.012 0.054** -0 .001 -0 .015 -0 .010

Not Married 0 .066** 0.049** 0.042** 0.077** 0 .069** 0 .038** 0 .092**

Income 0 .054** 0.043** 0.082** 0.050** 0 .039** 0 .044** 0 .068**

Education 0 .171** 0.097** 0.136** 0.088** 0 .145** 0 .064** 0 .185**

Father's Educ 0 .070** 0.031 * 0.030* 0.038** 0 .037** 0 .045** 0 .085**

Children -0 .008 -0 .015 -0 .040** -0 .064** -0 .029* 0 .004 -0 .036**

Health 0 .024* 0.025* 0.052** 0.004 0.023* 0 .021 0 .026*

Metro 0 .030** 0.047** 0 .038** 0.016 0.042** 0 .041 ** 0 .073**

R-square 0 .076 0.030 0 .060 0.037 0.049 0 .021 0 .097
Table 4.2 .

Regression Results of Whether One Attends or Not on Age (standardized coefficients)

See footnote on Table 4 .1 .

Classical Opera Musical Jazz Theater Ballet Art Museum

Bivariate Model

Age 0 .058** 0.000 -0 .007 -0 .083** 0 .001 -0 .005 -0 .078**

R-square 0 .003 0.000 0.000 0.007 0 .000 0.000 0.006

Multivariate Mode

Age 0 .133** 0.049** 0 .064** -0 .003 0 .078** 0 .045** 0.018

Female 0 .069** 0.055** 0.098** -0 .004 0 .045** 0.089** 0.050**

Black -0 .027** -0 .027* 0.021 * 0.083** 0 .024* -0 .017 0.015

Not Married 0 .065** 0 .044** 0 .041 ** 0.104** 0.071 ** 0.042** 0 .051 **

Income 0 .082** 0.065** 0.146** 0.100** 0.073** 0.058** 0 .106**

Education 0 .215** 0 .129** 0 .190** 0 .140** 0.193** 0 .105** 0 .262**

Father's Educ 0 .094** 0 .036** 0 .036** 0 .067** 0.044** 0.053** 0 .074**

Children -0 .035** -0 .019 -0 .037** -0 .055** -0 .042** 0.013 -0 .030**

Health 0.039** 0 .032** 0 .050** 0 .018 0.040** 0 .022 0 .047**

Metro 0 .022* 0 .052** 0 .047** 0 .010 0.025* 0 .029** 0 .057**

R-square 0.124 0 .049 0 .116 0 .081 0 .084 0 .040 0 .153
50

1OAr and Arts Participation : 198?-1997

(Table 4.1) . This attendee/attendance difference is discussed at greater length

below.

Looking at the Betas for Opera in Tables 4.1 and 4.2, we find again that age is

the second most important predictor of attendance, and education is the most

important. Not having children in the home is the third most important predictor

of opera attendance. Residence in one of the largest metropolitan areas has more

to do with predicting opera attendance than with any other art form, with the

exception of visiting art museums .

Looking at the Betas for Musicals in Tables 4.1 and 4.2, the respondent's education and household income are again most important in predicting attendance .

Unlike the art forms discussed so far, however, gender is the third most important

variable in predicting arts attendance, women going to musicals significantly more

often than men.

Again, education is the most important predictor of attending jazz events as

seen by the Betas for jazz in Tables 4 .1 and 4.2, but beyond that the pattern is different from the other arts forms . The second most important predictor is marital

status, with those not currently married more likely to attend. The third most

important variable in predicting jazz attendance is the lack of children living in the

respondent's home . The fourth most important is being African-American . Neither

age nor gender are significant predictors, but they are interesting, nonetheless,

because unlike their coefficients for any of the other art forms, attendance tends to

be more frequent for men than for women and tends to go down rather than up

with age.

The pattern of best predictors of Theater attendance is the same as for opera

going as can be seen by looking at the relevant Betas in Tables 4.1 and 4.2 .

Education is the most important predictor, age is second, being unmarried is third,

and residence in one of the larger metropolitan areas is fourth . Being female follows education and age as the most important predictor of Ballet attendance .

Income is the fourth . As for all the other art forms, education is the most important predictor of Art Museum attendance, and as for classical music, father's

education is the third most important . The second most important predictor is

being unmarried . Residence in a metropolitan area, income, and age follow in that

order. It is notable that art museum attendance is less dependent on age than is

attendance at any of the other benchmark arts .

The Relative Contribution of Each of the Control Variables

Here looking across the rows of Betas in Tables 4.1 and 4 .2 focus is put on the

contribution of each of the control variables in turn . The goal is to predict partic-
[hapter 4 1

51

ipation in each of the various art forms, when the effects of age and each of the

other measured control variables are taken into account . Again, the results for

attendance shown in the Betas of Table 4.1 and attendee shown in Table 4.2 are

very similar, and so they will be considered separately only in the case of Income

where the figures are clearly different .

Looking first across the row for Gender ("Female") it can be seen that with the

other variables taken into account, women are significantly more likely to attend

each of the art forms, except for jazz . The predominance of females is most pronounced for ballet and musical theater attendance . As for jazz, men attend

somewhat (but not significantly) more often (as indicated by the negative but not

significant sign of the Beta -0 .017) .

Of all the control variables, Race is least often a significant predictor of arts

attendance. Only in the case of classical music where African-Americans attend less

often and jazz where they attend more often than others, is race a significant predictor of attendance .

Being single, divorced, or widowed, that is to say, Not Married is a significant

predictor of attendance at each of the seven art forms . What is more, currently

being without a companion is the second most important predictor of both jazz

and art museum attendance .

Higher Income significantly influences attendance at each of the forms, but it is

the second most important predictor for only one of them, attending musicals . In

the common stereotype, wealth is associated with classical music attendance,

opera, and art museum going, but income is only the fifth most important control

variable for these art forms . As noted earlier, the relative magnitude of the Betas in

Table 4.2 is very much like that of Table 4.1 with the exception of household

income. Income is a better predictor of whether one attends each of the seven art

forms (Table 4.2) than of how often one attends (Table 4.1) . This suggests that having sufficient money is important to arts attendance, but, after this threshold is

reached, other factors are more important in predicting how often one attends . In

other words, respondents with higher levels of income are more likely to attend the

arts, but not necessarily to participate more often than respondents with lower

incomes, all other things being equal .

Education is far and away the best predictor of attendance at every one of the

seven benchmark arts . It is the only control variable that is consistently more

important than age. The Beta coefficients in Table 4.1 show that education is most

important for art museum attendance, classical music, and theater attendance in

that order. The general importance of education in fostering arts attendance is further underlined by the fact that, even controlling for respondent's education,
52

1 0 9 e and Hits Participation : 1962-1997

Father's Education is a significant predictor for all the art forms, and it is the third

most important predictor for classical music and art museum going .

Earlier SPPA reports have shown the importance of having young children in

reducing arts participation . The measure inserted in the 1997 survey, Children 18

and Under in the household, has proved a less potent depressant . Children significantly reduce attendance at musicals, theater, and art museums . Only in the case

of jazz does children in the household become the third most important predictor

coming just behind education and being unmarried.

While it is riot among the leading predictors of arts attendance, Good Health

understandably contributes significantly to arts participation . This holds true in all

the art forms except jazz and ballet . The audiences of these forms are younger, on

average, but since the effects of age has been taken into account, it must be that the

older people who do attend these art forms must be, on average, in better health

than are older participants in the other art forms .

Finally, Metropolitan Place o f Residence adds significantly to predicting participation in each of the art forms with the exception of jazz, yet jazz would seem to

be as much an urban form as the others . It may be that the costs of production and

the number of people involved in production are lower than for the other art

forms, so it is easier to tour outside the metropolitan areas . In addition, it may be

that the younger audience for jazz is more likely to be college students and students

are more likely to be concentrated in a few places such as Madison, Wisconsin ;

Chapel Hill, North Carolina ; Austin, Texas; and western Massachusetts, outside

metropolitan areas . Such places present greater opportunities to attend jazz concerts than do most other non-metropolitan places who include fewer young adults

in school .

The Summary Arts Participation Measure

As noted above, in order to get a picture of the contribution of age to arts participation in general, the variable "Summary Arts Participation" was created by

summing the number of benchmark arts attended by the respondent . Then an

analysis parallel to those discussed so far in this chapter was performed . At one

extreme of the summary measure are those who have attended none of the art

forms in the prior year. At the other are respondents who have attended all seven

in the prior year.

To measure the contribution of age to predicting general arts participation with

and without controls, an OLS regression analysis parallel to the attendee measures

shown in Table 4 .2 were performed . Table 4 .3 shows the results of this analysis .

The left-hand side of the table shows the results when all seven benchmark arts are
Chapter 4 1

53

considered together. The right side shows the same analysis made without taking

into account attendance at jazz concerts . We inspected this latter measure because,

as shown in Tables 4 .1 and 4 .2, many of the predictors of jazz participation are different from those of the other art forms .

Table 4.3 .

Regression Results of Summary Measure of Attendance on Age

(standardized coefficients)

The figures for arts participation with and without jazz prove to be remarkably

similar. There are only two differences of note . The first has to do with the bivariate relationship between arts participation and the age of the respondent . As seen

in the top row of Table 4 .3, age does not significantly predict arts participation

when jazz attendance is not included in the measure. When jazz is included in the

arts participation measure, however, there is a significant negative relationship

between arts participation and age .

The only control variable showing a significant difference is the race measure,

Black. When jazz is not included in the measure, there is no difference between the

number of forms attended by African-Americans and attended by others . When

jazz, which has a significantly higher attendance rate among African-Americans

than among others is included in the summary arts participation measure, blacks

attendance is significantly higher than for others in the sample . Since, with this one

Sum of 7 Arts Sum of 6 Arts (w/o Jazz)

Bivariate Model

Age -0 .03050957** -0 .014

R-square 0 .001 0

Multivariate Model

Age 0 .090** 0.102**

Female 0 .094** 0.106**

Black 0 .021* 0 .003

Not Married 0 .098** 0 .084**

Income 0.153** 0 .147**

Education 0.302** 0 .304**

Father's Educ 0.095** 0 .090*

Children -0.052** -0 .044**

Health 0 .059** 0 .063**

Metro 0 .057** 0 .061 **

R-square 0 .236 0.233

See footnote on Table 4 .1 .
CHAPTER 5 COPPELRTES Of BABY BOOMED, PPE-800M,

RHD POST-BDOMEP PRPTICIPRTION

Here in Chapter 5 is concluded the exploration of the causes of arts participation begun in Chapter 4 . There the focus was on the whole 1997 sample, here the

focus is on each of three major birth cohorts, baby boomers, those born before

them, and those born after the boomers .

The sample is divided into three parts because some of the other measured factors are more likely to affect young people, and some are more likely to affect those

who are older. For example, adults under the age of forty are more likely than their

elders to have young children in the home, so the presence of young children in the

home is more likely to depress the arts participation of young people, likewise,

older people are more likely to have their arts participation reduced by ill health .

In addition, the same variable may mean quite different things depending on the

respondent's age . For example, for young people "not married" means being single, separated, or divorced . Such people are more likely to actively seek partners

and engage in arts participation, while their married age-mates are likely to have

their leisure activities taken up in family activities . For older cohorts, "not married" is likely to mean widowed which may mean these individuals have no one to

accompany them to arts events.

METHODS

Birth Cohorts

As noted in Chapter 1, birth cohorts consist of all those persons born within the

same span of years . The number of years to be included in a cohort depends on the

research question at hand . In Chapter 3 cohorts were divided into persons born in

the same ten-year period because of an interest in the differential experience of

those born at historically specific moments . Focal here are the factors differentially

influencing the arts participation of those in different stages of their lives, with a

special interest in baby boomers . Consequently, the respondents are divided into

three groups : baby boomers-those born between 1946 and 1965, pre-baby

boomers-those born before 1946, and post-baby boomers-those born after

1965. The number of respondents in each of these three groups is as follows : preboomers: 4076, boomers : 5317, and post-boomers: 2653.
5 4

1 Hge and Orts Participation : 1982-1997

exception, the results are so similar, only the results for the seven arts together will

be considered in the following discussion .

The importance of controlling for the influence of other variables when considering the relationship between age and general arts attendance is dramatically clear

in comparing its direct relationship with its relationship after the effects of the control variables are taken into account . As the left side of the top line of Table 4 .3

shows, the slight but statistically significant Beta is negative, meaning that attendance tends to go down as age goes up . Taking the nine control variables into

account, however, the relationship between arts participation and age becomes

positive and significant . This means that, other variables taken into account, the

number of art forms attended tends to go up as people get older .

Examining the Beta coefficients in the left-hand column can see the relative

importance of age in predicting arts participation . As we have seen for each of the

art forms considered separately, education, not age, contributes the most to predicting the number of art forms attended . Interestingly, household income is the

second most potent predictor . Next gender, marital status, father's education, along

with age are all about equally important predictors . Then children in the home,

health, and residence in a metropolitan area are all about equally important, and

finally race, while being significant, is the least substantively important predictor

of the number of art forms attended .
5 6

1

Age and 8rts Participation : 1982-1992

Independent and Dependent Variables

As in Chapter 4, the independent variable is "Age" measured as the respondents' years of age at the time they participated in the survey .

The dependant variable is "Attendances." It is measured as the number of times

R attended an art form during the last year-a few respondents attended one or

another art form 50 to 150 times in a year. While it is perfectly possible to attend

this many times in a year, such persons are not ordinarily thought of as audience

members in the usual sense of the term . In all likelihood these outliers are art critics, art teachers, or managers . Accordingly, to correct for the skewing effect of these

few respondents, 24 was set as the highest frequency, so that the top of the attendance measure scale is "24 or more ."

"Summary Arts Participation" is the number of art forms that the respondent

attended during the last year (range : 0 to 7) . This measure does not take into

account how often the respondent attended arts events .

Control Variables

Gender, Race, Marital Status, Household Income, Education, Father's

Education, Children in the Home, Health, and Metropolitan Place of Residence are

measured in exactly the same way as they were in Chapter 4. The reader is referred

to that discussion .

One additional control variable, "Student Status," is added for those respondents in the post-boomer sample . Since a goodly number of post-boomer

respondents are still in school, it was good to be able to take this into account in

evaluating the impact of education on arts participation . SPPA surveyors were

instructed to ask all respondents 18 to 25 years old: "During the last 12 months,

were you enrolled in a high school, college, or university?" Since the oldest persons asked this question were 25, those post-boomers born between 1966 and

1971 were not asked this question. Consequently, this measure misses the fact that

some of the older post-boomers have not completed their formal education .

FINDINGS

Three lines of findings are discussed in searching for differences in the predictors of arts attendance of boomers, pre-boomers and post-boomers . First, how

successfully the measured variables in aggregate predict arts participation in the

three age groups is examined . Second, the best predictors of arts participation in

each art form and in the summary measure of arts participation are found .

Finally, looking across the tables, the focus will be on the relative importance of

age and each of the control variables in predicting arts attendance at all of the

benchmark arts .
Chapter 5 1

57

The results of the analyses are summarized in Tables 5 .1 through 5 .8 . They can

be read in exactly the same way as the three tables in Chapter 4, so refer to the discussion "Reading the Tables" offered there .

Table 5.1 .

Regression Results of Classical Music Attendances on Age by Cohort

(standardized coefficients)

These effects are statistically significant where indicated (* p< .05; **p< .01) . A statistically significant effect means that the probability of this effect occurring merely by chance is less than 5%

(for p< .05) or less than 1 % (for p< .01), based on this sample . Therefore, we can reasonably

conclude that this effect actually exists .

The Aggregate Explanation of Arts Attendance

The row of numbers across Tables 5 .1 through 5 .8 gives the R-square for each

of the twenty-four OLS regression analyses . R-square, as noted above in Chapter

4, is a measure of how well the variables together predict arts attendance. If the

variables together perfectly predicted attendance then the R-square would be 1 .00.

The bottom line of Table 5 .1 shows that the measured variables account for 10

percent of the variance in the classical music attendance of pre-boomers, 8 .2 percent of the variance for baby boomers and just 4 .9 percent of the variance for

post-boomers. This means that the set of demographic variable was twice as successful in predicting attendance at classical music concerts for pre-boomers as for

post-boomers .

Pre-Boomers Boomers Post-Boomers

Age 0.088** 0 .093** -0 .019

Female 0.080** 0.011 -0 .017

Black -0.022 -0 .019 -0 .036

Not Married 0 .027 0.077** 0 .026

Income 0 .126** 0.043* -0 .012

Education 0 .192** 0.172** 0.132**

Father's Educ 0 .065** 0.083** 0.047

Children 0.012 0.013 -0 .015

Health 0.028 0.026 -0 .006

Metro 0.026 0 .052** -0 .003

Student 0.129**

R-square 0.100 0.082 0.049
5 8

1

099 and arts Participation : 1982-1997

Table 5.2 .

Regression Results of Opera Attendances on Age by Cohort

(standardized coefficients)

Age

Female

Black

Not Married

Income

Education

Father's Educ

Children

Health

Metro

Student

R-square

See footnote on Table 5 .1 .

Table 5.3 .

Regression Results of Musical Theater Attendances on Age by

Cohort (standardized coefficients)

Quickly inspecting the other seven tables, shows the same pattern, better prediction for the older age group and considerably attenuated prediction for younger

respondents, with boomers intermediate, is found also for attendance at musicals,

theater, art museums, and also for the summary arts measure . Like the forms discussed so far, opera attendance is best predicted for the older age group but the

Pre-Boomers Boomers Post-Boomers

Age 0 .044 0 .035* 0 .003

Female 0 .114** 0.051 ** 0.057*

Black -0 .025 0 .039* -0 .004

Not Married 0 .012 0 .050** 0.038

Income 0 .122** 0.082** 0.046

Education 0 .126** 0.161** 0.090**

Father's Educ 0 .021 0 .016 0 .058*

Children -0 .016 -0 .021 -0 .053*

Health 0 .095** 0 .050** -0 .020

Metro 0 .030 0 .066** -0 .010

Student 0 .032

R-square 0 .081 0 .073 0 .034

See footnote on Table 5 .1 .

Pre-Boomers Boomers Post-Boomers

0 .052* 0 .008 0 .000

0 .093** -0 .002 0 .006

-0 .012 -0 .018 -0 .045*

0.047* 0.047** 0.025

0.124** 0.020 -0 .010

0.080** 0 .115** 0.076**

0.067** -0 .001 0.064*

-0.007 -0 .022 0.008

0.014 0 .026 0 .029

0.054* 0 .039* 0 .066**

0 .072*

0.055 0 .025 0 .029
prediction for the other two age groups is equally poor . The pattern is similar for

ballet attendance but the degree of explanation is so low that it is not worth considering. Only in the case of jazz is the pattern reversed . Here the R-square for the

pre-boomers is very low, but is somewhat higher for the other two age groups .

Table 5.4.

Regression Results of Jazz Attendances on Age by Cohort

(standardized coefficients)

Chapter 5 159

Table 5.5.

Regression Results of Theater Attendances on Age by Cohort

(standardized coefficients)

Pre-Boomers Boomers Post-Boomers

Age 0 .007 -0.012 0 .050

Age 0 .079** 0 .020 0.014

Female 0 .060** 0 .004 0.037

Black 0 .005 -0 .006 0.003

Not Married 0.033 0 .071 ** 0.070**

Income 0.117** 0 .032 -0 .031

Education 0.185** 0 .145** 0 .057*

Father's Educ -0 .003 0.038* 0 .094**

Children 0 .005 -0 .037* -0 .026

Health 0 .044* 0.027 -0 .035

Metro 0 .048* 0.052** 0 .015

Student 0 .058

R-square 0.080 0 .051 0.031

See footnote on Table 5 .1 .

Pre-Boomers Boomers Post-Boomers

Age 0.007 -0 .012 0 .050

Female -0 .019 -0 .011 -0 .027

Black 0.037 0.075** 0 .031

Not Married 0.012 0.102** 0 .068**

Income 0.057* 0.058** 0 .045

Education 0.081** 0.095** 0 .084**

Father's Educ -0 .001 0.047** 0 .035

Children -0 .003 -0.054** -0 .074**

Health 0 .044 -0 .004 -0 .018

Metro 0 .022 0 .013 0.017

Student 0.067*

R-square 0 .023 0 .045 0.042

See footnote on Table 5 .1 .
60

1 Bge and fins Participation1982-1997

Table 5.6.

Regression Results of Ballet Attendances on Age by Cohort

(standardized coefficients)

Age

Female

Black

Not Married

Income

Education

Father's Educ

Children

Health

Metro

Student

R-square

See footnote on Table 5 .1 .

Table 5.7.

Regression Results of Art Museum Attendances on Age by Cohort

(standardized coefficients)

The difference in predictive power seen across all birth cohorts means that those

interested in increasing arts participation cannot focus on the same set of variables

across all age groups . In most art forms the usual list of predictors of arts attendance for the population born before World War II, variables including education,

Pre-Boomers Boomers Post-Boomers

Age 0.003 0.030 -0.002

Female 0.095** 0.022 -0.009

Black -0 .023 0.004 -0.026

Not Married 0.057** 0.093** 0.090**

Income 0.150** 0.053** -0.006

Education 0.194** 0.180** 0.168**

Father's Educ 0.074** 0.103** 0.062*

Children -0 .007 -0 .037* -0 .027

Health 0 .047* 0.009 0.022

Metro 0 .070** 0.064** 0.094**

Student 0.037

R-square 0 .142 0.096 0.073

See footnote on Table 5 .1 .

Pre-Boomers Boomers Post-Boomers

0.039 0 .041 * -0 .031

0.060** 0 .060** 0 .002

-0.020 -0 .009 -0 .023

0.045* 0 .040* -0 .012

0.094** 0 .029 0 .012

0 .033 0 .080** 0 .052

0 .077** 0.047** -0 .008

0 .034 0.008 -0 .028

0 .019 0.027 0.000

0 .074** 0.052** -0 .023

0.026

0.038 0.028 0.007
Chapter 5 1

61

income and gender, are no longer as important in determining attendance . The

findings reported in Research Report #37 suggest that this difference is due in part

to differences between cohorts and is not simply a function of the age of respondents. If Research #37 showed that baby boomers are different from the cohorts

born before them, the findings reported here suggest that the post-boomer cohorts

are different again .

THE RELATIVE CONTRIBUTION OF AGE AND THE CONTROL

VARIABLES TO PARTICIPATION IN EACH BENCHMARK ART

FORM

Here the predictors of arts attendance in each of the art forms are examined

in turn . These results are found in Tables 5 .1 through 5 .7 . As in Chapter 4, to

facilitate comparisons, the focus here in Chapter 5 is on the Betas (standardized

coefficients) .

Classical Music Looking first at the Betas across the top line of Table 5 .1, even

after separating the whole sample into the three age groups (pre-boomers,

boomers, and post-boomers), age is still an important predictor of classical music

attendance for the older two groups . This means that even within these age groups,

older pre-boomers and older boomers are more likely to attend than are their

somewhat younger colleagues. Age is the third most important predictor for preboomers, and the second most important predictor among boomers .

Reflecting the findings of Chapter 4, the respondents' years of education is by

far the most important predictor of classical music attendance . Among preboomers and boomers the importance of the respondents education and,

independently, by their father's education as well . Among post-boomers education

along with student status are the only two significant predictors of classical music

attendance .

Household income is the second most important predictor for pre-boomers but

falls to sixth most important for boomers . Only among pre-boomers are women

more likely to attend classical music concerts than are men . Finally, being married,

and metropolitan residence, are the only significant predictors of classical music

attendance among boomers .

Opera Only among pre-boomers, as is seen in the Betas of Table 5 .2, is age a significant predictor of opera attendance . Income is by far the best predictor among

pre-boomers but is unimportant among the younger age groups .
62

1 A e and Arts Participation : 1982-1997

Education is the most important predictor of boomer and post-boomer opera

attendance . Metropolitan residence is the second most important predictor for

post-boomers and is also a significant predictor among the older age groups . As

with classical music, gender is important only for pre-boomers . Being married is a

significant predictor of not attending the opera among pre-boomers and boomers,

as is being black among post-baby boomers .

Musical Theater As seen in the Betas of Table 5 .3, education is far and away the

best predictor of musical theater attendance for all three age groups, and father's

education is also important for post-boomers.

For pre-boomers and boomers, income is the second most important predictor

of attending musicals but is not significantly important among post-boomers .

Women of all ages are more likely to go to musicals than are men . Among baby

boomers, blacks, and those living in metropolitan areas are more likely to attend

musicals than are whites and those living outside the metropolitan areas .

Jazz The Betas in Table 5 .4 show that among pre-boomers, few of whom attend

jazz concerts, higher education and income are the only significant predictors of

attending jazz concerts . Education is of prime importance among boomers and

post-boomers as well .

For boomers and post-boomers, both being married and having young children

in the home significantly reduce jazz attendance . Finally, the respondents being

black is significantly correlated with jazz attendance, but only among baby

boomers.

Theater Education is the most important predictor of theater attendance for preboomers and boomers alike as shown by the Betas. But beyond this point the

predictors of theater attendance are quite different across birth cohort groups .

Among pre-boomers, family income, age, and gender follow in importance . In contrast, not being married and living in a metropolitan area are the second and third

most important predictors for boomers .

Among post-boomers, education is the all-important predictor, but interestingly,

the best single predictor is father's education, followed by student status and

respondents education . The only other significant predictor of theater attendance

among post-boomers is not being married .

Ballet In contrast to the art forms discussed so far, income is the best predictor of

ballet attendance among pre-boomers, as can be seen by the relevant Betas in Table
Chapter 5 1

63

5.6 . Curiously, father's education is the second best predictor and the respondent's

education is not a significant predictor . Being female is an important predictor, and

it may well be that many of these older female ballet fans born before the Second

World War came from well educated families but, because of their gender, did not

so often have educational opportunities themselves . Living in a metropolitan area

and not currently being married are the two other variables important in predicting ballet attendance among pre-boomers .

Father's education is still important for baby boomers but respondent's education is the most important predictor followed by being female and living in a

metropolitan area . Unlike several of the other art forms already examined, having

children does not depress boomer attendance . This may be because many young

people, especially girls, are regularly taken to ballet performances .

The results for post-boomers are strikingly different, because none of the measured variables contribute significantly to ballet attendance .

Art Museum As Table 5 .7 shows, the predictors of art museum attendance are

roughly the same across all three age groups . These include both respondent's and

father's education, being unmarried, and metropolitan area residence . In addition

to these four, household income is important for boomers and pre-boomers, gender and health are important just for pre-boomers, and having children in the home

is important just for boomers .

Summary Arts Participation Recall that summary arts participation measures the

number of benchmark forms that the respondent has participated in during the

prior year. The results for the summary arts participation measure shown in Table

5.8 . Considerably more variables prove to be significant predictors than seen in the

earlier tables for each of the art forms alone . This is not surprising because the

range of this measure is from 0 to 7 while the range of all of the single discipline

measures is 0 and 1 . Respondent's education, household income, gender, father's

education, and metropolitan residence are all significant for all three age groups .

Redolent of the findings for several of the individual art forms, age is a significant predictor of attendance for pre-boomers and boomers but not for

post-boomers . Finally, reflecting stage-of-life exigencies, health is a predictor of

summary arts participation for pre-boomers and boomers ; children in the home

are significant for boomers and post-boomers, and student status is important

for post-boomers .
1 age and arts Participation : 1982-1997

Table 5 .8 .

Regression Results of Summary Arts Attendances on Age by Cohort

(standardized coefficients)

Table 5.9 .

Regression Results of Summary Arts Attendances (without Jazz) on

Age by Cohort (standardized coefficients)

Pre-Boomers Boomers Post-Boomers

Age 0.066** 0.060** 0.007

Female 0.154** 0.107** 0.059**

Black 0.007 0.005 -0 .008

Not Married 0.040* 0.074** 0.095**

Income 0.203** 0.153** 0.054*

Education 0 .305** 0.318** 0.268**

Father's Educ 0 .059** 0.077** 0.138**

Children -0 .010 -0 .029 -0 .049*

Health -0 .103** -0 .047** -0 .024

Metro 0 .061 ** 0.067** 0.053*

Student 0.141**

R-square 0 .268 0.245 0.204

See footnote on Table 5 .1 .

Pre-Boomers Boomers Post-Boomers

Age 0.057** 0 .054** 0.019

Female 0.145** 0 .092** 0.049*

Black 0.018 0 .024 0.014

Not Married 0 .043* 0.096** 0.107**

Income 0 .208** 0.163** 0.057**

Education 0 .308** 0.311** 0 .273**

Father's Educ 0 .063** 0.082** 0 .137**

Children -0 .011 -0 .035* -0 .060**

Health 0 .101** 0.043** 0 .022

Metro 0 .054** 0.063** 0 .051 *

Student 0.152**

R-square 0 .273 0.246 0.217

See footnote on Table 5 .1 .
Chapter 5 1

65

THE RELATIVE IMPORTANCE OF AGE AND INDIVIDUAL

CONTROL VARIABLES IN PREDICTING PARTICIPATION

ACROSS THE BENCHMARK ARTS

Looking again at Tables 5.1 through 5.7, now the focus is on the predictive

importance of each variable in turn across all seven forms . Before examining the

individual variables, however, it is worthwhile noting the consistency in the results

across the tables . Among the 217 coefficients,20 across all seven art forms the sign

of significant relationships is consistent with one single exception . 21 This consistency attests the reliability of the measures in predicting arts attendance . Thus,

variability across tables has to do with differences in the strength of the predictive

value of variables by art form and by age, but they do not show any difference in

the directions of predictions .

Turning now to the individual variables, we see that having divided the sample

into three parts by age, the predictive power of age has been attenuated, but not

completely. Older pre-boomers and baby boomers are still more likely to attend

several of the art forms than are the younger members of their cohorts .

The importance of education for socialization to arts participation is very

apparent in the figures in Tables 5.1-5 .7 . Among pre-boomers, the respondent's

education is the most important predictor of arts participation for all forms except

ballet . It is the most important among boomers for all the arts, and among postboomers for five of the forms. Likewise, father's education is also independently

important in four arts forms for each of the age groups, and finally, student status

is important among post-boomers .

Household income is a significant predictor for all seven of the art forms among

pre-boomers, and it is also important for four of the seven among baby boomers .

Yet it is without significance for post-boomers . It may be that differences in wealth

are becoming less important over these birth cohorts . It may also be that the variable "household income" is a poor measure among young adults in any time

period because the current income of those who went on the job market early,

and rarely attend arts events, is still as high as that for those who spend many

more years in formal schooling and are more likely to attend. The importance

of education, father's education, and student status gives credibility to this latter life-cycle interpretation .

The two ascribed status variables, race and gender, show contrasting results .

Controlling for the other measured factors, Black respondents generally show the

same pattern of arts attendance as do non-Blacks. Only in the case of jazz attendance among baby boomers, are Black rates higher than for the rest, and only for

post-boomers is attendance at opera lower than for non-Blacks .
66

1 Age and Arts Participation 1962-199?

The results for gender show a pattern that is clearly consistent with changes

between cohorts . Controlling for the other factors, pre-boomer women are

more likely to attend every art form more often than are men except jazz .

Among baby boomers, women are more likely to attend only two forms, musicals and ballet, and among post-boomers the attendance rates for women and

men are virtually the same for every form except musicals, which women attend

more often than men .

Four of the variables measure the effects of exigencies that affect arts participation . The first is marital status . Those not married that is to say the never married,

the divorced, and the separated-show higher rates of arts participation in four art

forms among pre-boomers . in all seven among boomers, and in three forms among

post-boomers as compared with those who are married or widowed . These findings suggest that those married persons who have available companionship and

those who are involuntarily alone, are less likely to seek out the arts than are those

who are unmarried or otherwise voluntarily living alone .

Large cities provide more arts participation opportunities than do smaller cities

and towns, so it is reasonable to expect that metropolitan place of residence should

equally influence all age groups . Metropolitan residence is important for participation in four of the arts forms for pre-boomers, and six forms for baby boomers.

But among post-boomers, it is important for only two art forms, opera and

museum attendance . It may be that due to wider travel, more education, and

greater exposure to the arts via the media, metropolitan residence is becoming less

important. If this is the case, this is a clear cohort effect . It may also be that more

of the well-educated and arts-oriented younger people living outside metropolitan

areas are located in college towns and other high-technology towns .

Finally, the participation of two variables, children in the household and health,

should be correlated with their stage of life, and, indeed, that is seen in the data .

Presence of children in the home is an irrelevant consideration among most of the

older pre-boomers, but significantly depresses participation in several art forms for

boomers and post-boomers cohorts, many of whose members were in 1997m' the

midst of child raising . In parallel but opposite direction, health is irrelevant for

post-boomers, is somewhat important for boomers and is even more important for

the more elderly pre-boomers . These patterns are reflected exactly in the summary

arts participation shown in Table 5 .8 . The presence of children in the home is

important only among the two younger cohorts and (poor) health is important

only among the two older cohorts . Thus the findings for these two variables reflect

changes associated with life-stages .
NOTES

' As a measure of "average age" we use the "median ." The median value of a

set of measures means that half the individuals in the sample are older and half are

younger than the median age . Another way to measure "average age" is to use the

mean values. The mean takes into account the distance of all the individual ages

from the mean . The expected mean age for 1982 is 43 .1 years, for 1992 45 .4, and

for 1997 46 .1 . These mean ages are three and a fraction years older than their corresponding medians for each of the survey years reported in Table 1 .1 because

those below the mean cannot be younger than 18 while those above the mean can

be in their 80s or older . Therefore, we find it more appropriate to use the median

as the measure of central tendency, of "average ."

2 SPPA respondents are older on average than the United States civilian population because only those 18 years of age or older were sampled (NEA 1998a) .

3 It is worth noting that in all cases the ages of art-form audiences in 1992 is

between, or equal to, those for 1982 and 1997, giving support to the assertion that

the comparison of proportions method used here makes the 1997 figures comparable with those of earlier survey years .

4 It is also worth noting at the outset that those in the sample who are 60 and

over have gone from 21 .2 percent in 1982 to 23 .1 percent in 1997 of survey

respondents . This is an increase of just under two percent, but as we will see in

Chapter 2, the proportion of these elders in arts audiences has risen considerably

more.

5 They have, for example, linked age with the changing size of the audience and

the influence of age relative to other factors on the rate of audience attendance over

time.

6 A calculation was made of the relative percentage that each age group and

birth cohort represented in the total number of "attendances" (the number of times

each respondent attended the art form) in each sample . A variable in each data set

represented the number of times the respondent attended that benchmark art in the

last twelve months (with the exception of 1982, in which the question covered only

the last month . This value was assumed to represent an "average" month and was

multiplied by twelve to reflect the number of attendances in a year) .

Figures for attendees under twenty years of age are shown in all the tables in

this Chapter, but they will not be discussed here because the numbers are based just

on those respondents 18 and 19 years of age while all the other age groups (except

for the eldest) span ten years .
68

1 Age and Arts Participation : 1982-1997

8 These figures are reached by adding 8 .5 and 7.1 percent = 15 .6 percent and

14.4 and 15 .7 percent = 30.1 percent . The computations for other combined age

groups will be made in the same fashion without note.

9 If you wish to reconstruct a table of the proportion of the audience of any particular age for any of the art forms who are attendees, simply subtract, if positive,

or add, if negative, the appropriate number in the difference tables (Tables

2.8-2 .14) from the corresponding cell in the attendance tables (Tables 2.1-2.7) .

Thus, for example, to find the proportion of the symphony orchestra audience that

were teens in 1982 subtract 1 .0 (found in the upper left-hand cell of Table 2 .1 )from

5 .4 (found in the upper left-hand cell of Table 2.8) . The resulting 4.4 is the proportion of all classical music attendees in 1982 who were teens .

1° The relative percentage that each birth cohort contributed to the total number of "attendances" (the number of times one attended a benchmark art) in each

benchmark art form for each year were calculated . A variable in each 1992 and

1997 data set represents the number of times the respondent attended that particular benchmark art in the last twelve months . In 1982, the question covered only

the last month, so this value was assumed to represent an "average" month and

was multiplied by twelve to reflect the attendances in a year . To calculate the attendance figures shown in Tables 3 .1 through 3 .7 cross-tab frequencies of the cohort

by the number of attendances were made in order to know how many respondents

in each cohort attended that benchmark art form every possible number of times .

Using the example of jazz attendance mentioned in Note 2 of Chapter 2, in 1997

the 20-29 age group, 95 respondents attended jazz one time, 75 attended two

times, 48 attended three times, etc ., up to a maximum possible value of 72 times

in one year. Next, each value in the frequency cell was multiplied by corresponding

the number of attendances for each cohort (in the same example, 95 times 1, 75

times 2, 48 times 3, etc .) . Then these products were summed for each cohort to represent the total number of times that respondents in this cohort attended that

benchmark art in the previous year (e .g ., the 20-29 year old respondents attended

jazz a total of 1,084 times in 1997) . Finally, the sum for each age group or cohort

was divided by the summed total of every age group or cohorts' attendances (the

global number of attendances by all respondents in that year) to reflect the proportion of attendances reported by each cohort in relation to the others within each

sample year (e .g ., the 1,084 total for the 20-29 group was divided by the grand

total of 5,123 attendances for the whole sample in 1997, showing that 21 percent

of total attendances at jazz concerts were 20 to 29 years old) .

1 Unlike the other decade-long cohorts, this one includes cohorts, this one

include just those born over a five-year period . This is because in the survey year,
Notes 1

69

1997, those born in 1981-1985 were under 18 years of age and thus were too

young to be part of the SPPA survey sample .

12 The observed participation for each cohort is found in the appropriate Table

from 3.1 to 3 .7 . The expected participation for each cohort is expected in the

appropriate row of Table 1 .2 . The difference figures in parentheses are obtained by

subtracting the expected participation from the observed participation .

13 In this instance, the obtained percent is 5 .2 and the expected percentage is 4 .5,

and since the observed is larger than the expected, the sign of the figure is positive .

14 The data for 1992 are available, but they are not explicitly examined here .

The 1992 values are intermediate between those of 1982 and 1997 in 20 of the 42

possible comparisons (six cohorts x seven art forms) . And in most of the rest of the

cases where a figure for 1992 was not intermediate, it was roughly equal to that of

1982 or 1997 . The only notable exceptions are the audiences for theater and for

musical theater. The 1992 audience for theater tended to be older, and the audience

for musical theater tended to be younger, in 1992 than in the survey years before

and after 1992 . These variations from the norm probably have to do with changes,

beyond the scope of this monograph, taking place in these art forms during the

early 1990s .

15 To be sure, that report found that the participation rate of these earlier

boomers was low compared to earlier cohorts after their relatively high education,

income, and other factors were taken into account . That is to say, their participation rate was low given their relatively advantaged status .

16 Scores in Tables 3 .8-3 .15 reflect the proportion of attendance minus the proportion of attendees.

" If you wish to construct a table of the proportion of the audience of any particular age for any of the art forms who are attendees, simply subtract, if positive,

or add, if negative, the appropriate number in the difference tables (Tables

2 .8-2.14) from the corresponding cell in the attendance tables (Tables 2.1-2 .7) .

Thus, for example, To find the proportion of the symphony orchestra audience that

were teens in 1982 subtract 1 .0 (found in the upper left-hand cell of Table 2 .1)from

5.4 (found in the upper left-hand cell of Table 2.8) . The resulting 4.4 is the proportion of all classical music attendees in 1982 who were teens .

18 The earliest cohort, the one including all those born before 1916 will not

be considered here because, while its participants tend to attend less often than

average, its rates of participation do not show any coherent pattern across all

the art forms .

19 We chose to use simple OLS regression with the dichotomous dependent variable "attendee" rather than logistic regression in order to facilitate interpretation
Bibliography

Balfe, Judith H . (1989). "The Baby-boom Generation : Lost Patrons, Lost

Audience?" Pp . 9-25 in Margaret Wyszomirski and Pat Clubb (eds .) The

Cost of Culture . New York: ACA Books .

Davis, James A . (1994) . "What's Wrong with Sociology?" Sociological Forum 9: 179-197.

DiMaggio, Paul (1982) . "Cultural Entrepreneurship in Nineteenth-century Boston :

the Creation of an Organizational Base for High Culture in America."

Media, Culture, and Society 4: 4-33 ; 303-320.

	. (1991) . "Constructing an Organizational Field as a Professional Project:

The Case of Art Museums, 1920-1940 ." In Walter W Powell and Paul

DiMaggio, (eds .) The New Institutionalism in Organizational Analysis . Chicago : University of Chicago Press .

Esterlin, Richard A . (1987) . Birth and Fortune . Chicago : University of Chicago

Press .

Glenn, Norval D . (1989) . "A Flawed Approach to Solving the Identification

Problem in the Estimation of Mobility Effect Models ." Social Forces

69 :789-795.

Loomis, Laura and Mary Collins (1998) . "Changes in Survey Procedures and their

Potential Effects on Estimates of Arts Participation : 1997 Survey of Public

Participation in the Arts ." Westat, unpublished .

Mather, John (1993) . "A Challenging New Audience-'Baby Boomers ."' Bulletin

o f the Association for Performing Arts Presenters . March: 3 .

Miller, Judith (1996) . "As Patrons Age, Future of Arts is Uncertain ." New York

Times p Al, C12 February 12 .

National Endowment for the Arts (1993) . Research Division Report #27 . Arts

Participation in America: 1982-1992. National Endowment for the Arts :

Washington D.C.

National Endowment for the Arts (1996). Research Division Report #37 . Age and

Arts Participation : With a Focus on the Baby Boom Cohort . Richard A.

Peterson, Darren Sherkat, Judith Balfe, and Rolf Meyersohn . Santa Ana, CA:

Seven Locks Press . National Endowment for the Arts : Washington D .C.

National Endowment for the Arts (1998a) . Research Division Report #39 . 1997

Survey of Public Participation in the Arts : Summary Report. National

Endowment for the Arts : Washington D .C.
72

I

89e and Arts Participation 1982-1991

National Endowment for the Arts (1998b) . Research Division Note #70.

"Technical Appendix" to "Comparing 1997 Survey of Public Participation

in the Arts (SPPA) Results with Prior SPPAs (1992,1985,1982) ." National

Endowment for the Arts : Washington D .C.

National Endowment for the Arts (1999) . Research division Note #71 .

Demographic Characteristics of Arts Attendance : 1997 ." National Endowment for the Arts : Washington D .C.

Newman, Katherine S . (1993) . Declining Fortunes : The Withering o f the American

Dream. New York: Basic Books .

Peterson, Richard A. (1992) . "The Battle for Classical Music in the Air ." Pp.

271-286 in Judith H . Balfe (ed .) Paying the Piper: Causes and Consequences

o f Arts Patronage . Urbana, Illinois : University of Illinois Press .

	. and Albert Simkus (1992) . "How Musical Taste Groups Mark

Occupational Status Groups ." Pp. 152-168 in Michele Lamont and Marcel

Fournier, editors, Cultivating Differences: Symbolic Boundaries and the

Making o f Inequality . Chicago : University of Chicago Press .

Rodgers, Willard L . (1982) . "Estimable Functions of Age, Period, and Cohort Effects ." American Sociological Review 47: 774-787.