The following end notes accompany our article, “Why Don’t They Come?” published on May 6, 2015:
(1) On opportunity cost:
Another way to look at this issue is through the lens of opportunity cost. In basic microeconomics, an individual’s wellbeing is a function of consumption (or how much stuff you can buy, which depends on your wage rate and how many hours you choose to work) plus leisure (the hours you spend not working). This means that the “price” — or opportunity cost — of leisure is a function of your wage rate: choosing to spend an hour that you might have spent working engaged in a leisure activity.
Attending an arts event is both consumption and leisure in the sense that you have to buy a ticket and set aside the time to attend an event, and both considerations are relevant to considering how an individual might react to the price of an arts event. It’s not obvious which will be the stronger driver of demand in the market for theatre, so to answer this question, we turn to the data. In her 2009 study “Full-Income and Price Elasticities of Demand for German Public Theatre,” Marta Zieba examines this question empirically using data from German public theaters. She finds that that an increase in income is associated with an increase in demand for theatre, meaning that there is a positive “income effect” on theatre demand. However, the positive income effect on theatre attendance is offset to some degree by a negative price of leisure effect, meaning that while those with higher incomes are more likely to buy tickets based on their income alone, the higher price that they “pay” for their leisure time has a negative association with their tendency to buy theatre tickets. This also suggests that we would expect someone’s attendance rate to increase as their amount of leisure time increases. If we know that low-SES people have more time and a lower opportunity cost to attend arts events, it follows that cost may be a more significant barrier for this group than time.
(2) Methodological explanation:
To further explore the rate at which cost is a barrier for interested non-attendees, we used data from the General Social Survey (GSS) to calculate simple cross-tabulations of respondents who reported cost as a barrier to arts attendance for population groups at different education and income levels.
To consider the impact of cost as a barrier to arts attendance among different subsets of the population, we created a model that can theoretically equalize the rate at which populations of different socioeconomic statuses perceive cost as a barrier. In our calculations, we compared those with less than a high school education and those with at least a bachelor’s degree, and those in the bottom and top quartile of the income distribution. We used the following model to calculate these proportions:
where refers to a subset of interested non-attendees in the GSS respondent pool with a particular income or education level, is the number of interested non-attendees within the subset who cited cost as a barrier, and is the number of attendees with that income or education level who attended an arts event. This model essentially takes the additional interested non-attendees who cited cost as a barrier to attendance among the lower socioeconomic status group (subset1) compared to the high-SES group (subset2), and converts them to attendees. To get the rate at which the lower socioeconomic status group might potentially attend arts events with this adjustment, we divide this number by the total count of respondents in the lower socioeconomic status subset in the General Social Survey who participated in the cultural module. Variations of this calculation involved converting all low-SES interested non-attendees who cited cost as a barrier to attendees, and all low-SES interested non-attendees who cited any barrier to attendees.
The GSS includes a number of survey weights to adjust for potential areas of bias. Because the raw counts of respondents were important to this exercise, we used unweighted data for these calculations. Readers are thus advised to consider these numbers approximations.
(3) On “informal arts”:
It’s important to remember here that not every form of non-media-based participation in the arts is captured by these figures. The statistics on barriers and motivations from “When Going Gets Tough” exclude any form of making art oneself, as opposed to experiencing the art of others, and the question about performances doesn’t count those held in K-12 settings. (It’s also worth contemplating the culturally loaded nature of the terms “exhibit” and “performance” and how those may influence the readiness of survey participants from different walks of life to connect experiences they’ve had to those words.) The range of activities considered in the SPPA is much broader, but neglects to incorporate several common modes like singing to oneself, displaying art in one’s home, or listening to music as part of, for example, a church service.
What little research does exist on these more “informal” activities suggests that they may be relatively more successful in attracting engagement from low-SES populations, although the data is hardly conclusive on that front. The Irvine Foundation, in collaboration with researcher Jennifer Novak-Leonard, has led the charge in recent years to learn more about a broad range of participation types. “Cultural Engagement in California’s Inland Regions,” a 2008 Irvine-funded study by Alan Brown and Novak-Leonard (then known as Jennifer Novak) found that the home was the most common location for three of the four arts genres measured (70% of music, 34% of dance, 51% of visual arts activities), with “alternative” spaces such as parks, community centers, and places of worship also important to the Californians participating in the study. Notably, the authors’ analysis indicated that “inventive” and especially “interpretative” modes of participation did not skew towards more-educated individuals to nearly the same extent as “observational” participation, whether live or via media. A similar finding held for “arts learning,” “faith-based,” “heritage-based,” and “family-based” arts activities. Irvine and Novak-Leonard et al. have just released the results of a new survey of Californians that mostly corroborates these insights. An earlier, more qualitative investigation by Alaka Wali found evidence that people of all ages, races, incomes, and occupations participate in informal arts in Chicago, although even in that context there was still a small education gap between participants and non-participants. While these results suggest that informal arts experiences may play an important role low-SES individuals who do not attend exhibits and performances, it is important to note that the underlying surveys are somewhat more vulnerable to bias than the SPPA or GSS due to their arts-specific focus, lower response rates, and (in some cases) use of nonrandom samples.
(4) On leisure:
There exists a substantial literature on the concept of “leisure”: i.e., how we are spending the free hours we have, and how much those choices cost. Researchers define leisure in different ways, but one common approach uses the “third-person criterion,” which defines leisure as something that you cannot pay someone to do for you and that is not a biological necessity. For example, you can’t pay someone to or attend the symphony on your behalf and expect that the benefit that person receives from that experience will transfer to you.
In their study of leisure inequality, Sevilla et al. examine how the quality of leisure has changed since 1965 for different populations in relation to the quantity of leisure. Using the third-person criterion, survey data that examines how much utility people tend to get out of certain activities, and an examination of leisure time from time-use diary datasets, they propose that types of leisure are divided into three categories: pure leisure (where leisure is the main activity, so watching TV while making dinner doesn’t count); co-present leisure (time spent with a spouse or other adults); and fragmented leisure (in which leisure time is split up with non-leisure activities.) The categories are not mutually exclusive, so a leisure activity like attending a concert with a friend is considered both pure and co-present leisure. The authors, based on their review of survey data and prior work on leisure quality, deem fragmented leisure to be a lower-quality category than the other two. As a medium that is more likely to fall prey to multitasking, Sevilla et al. argue that television falls into this last category. Naturally, people do not all have the same habits when it comes to watching TV, but as long as there is at least some truth to the argument presented here, it suggests that the increase in hours of leisure time for low-SES people gives an inflated view of their relative wellbeing.
(5) Methodological explanation No. 2:
For this exercise, we modeled the total low-SES population of the United States who would have attended an arts event were cost no more likely to be a barrier than for high-SES populations, using numbers from the 2012 GSS and the 2010 US Census. In this instance we created a composite measure of low socioeconomic status that includes individuals who have no more than a high school diploma and are in the bottom half of the income distribution.
Since many adults aged 18-24 are in the process of attending college, standard education statistics cover adults aged 25 and above. To obtain a rough estimate of the number of 18-to-24-year-olds with low socioeconomic status, we used the proportion of adults aged 25+ who fit the definition described above and applied it to the total count of adults aged 18-24. We then calculated the difference in rates at which cost is a barrier for lower and higher socioeconomic status respondents and multiplied it by the proportion of low-SES interested non-attendees in the sample, using a survey weight to adjust for nonresponse. This number is then multiplied by the total adult US population to arrive at the estimated number of low-SES adults who are differentially affected by cost as a barrier to arts attendance.
(6) On the increase in leisure time:
Recent studies show that there has been a marked decrease in paid work for men and in unpaid work for women across all demographics in recent years; the gain in discretionary time across social strata can likely be attributed to this decrease. Since the low-SES population presumably includes people who work fewer hours for a lower wage, a decrease in work hours ostensibly indicates a decrease in available discretionary income. Since consumption inequality, or how much stuff people are able to buy, has largely kept pace with income inequality over the years, it follows that less discretionary income means less buying power.
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