Battle of the ballet household decisions on arts consumption

Abstract

Women and men differ in their tastes for the performing arts. Gender differences have been shown to persist after accounting for socioeconomic factors. This paper uses this difference to shed light on how decisions on arts consumption are made in households. Based on relatively recent theoretical developments in the literature on household decision-making, we use three different so-called distribution factors to show for the first time that the relative bargaining power of spouses affects their arts consumption. Using a sample from the US Current Population Survey, which includes data on the frequency of visits to cultural activities, we regress attendance on a range of socioeconomic variables using a count data model. The distribution factors consistently affect attendance by men at events such as the opera, ballet and other dance performances, which are more frequently attended by women than by men. We conclude that when men have more bargaining power, they tend to attend such events less frequently.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    To see how important joint constraints are consider an example: A wife’s evening at the local jazz club is of concern to her husband since it will oblige him to spend the evening taking care of the children. At the same time, she is spending money that will not be available for the next family vacation. Time and money constraints are not independent here since the couple may hire a babysitter at additional expense.

  2. 2.

    Age is also likely indicative of time constraints, as mid-career parents will tend to be more pressed for time than retirees.

  3. 3.

    Lévy-Garboua and Montmarquette (1996) use indicators of the use of a parking facility and of whether a theater attendance was followed by a meal as indicators of quality of the outing. Zieba (2009) use measures related to artists’ wages, production cost and guest performances.

  4. 4.

    Other attempts to explain the difference in high-brow cultural consumption between men and women have been made. For instance, Lizardo (2006) suggests that it is caused by a difference in relative importance of cultural knowledge in the professional environments of men and women.

  5. 5.

    Although marital sorting on observed characteristics will also produce such correlations, the effect would be netted out by controlling for own observed attributes.

  6. 6.

    This typically refers to local sex ratios, which affect the ease with which a new partner could be found.

  7. 7.

    Though the question of why these patterns arise may be interesting, it is beyond the scope of this paper.

  8. 8.

    1: less than high school, 2: high school, 3: some college—no degree, 4: associate degree, 5: bachelor’s degree, 6: postgraduate degree.

  9. 9.

    As is typical of American data, a "race" variable was available but was not included in the results as preliminary regressions showed that it was consistently not significant. Childhood exposure to the arts and arts education was available for only a small subsample and was therefore not included in order to preserve the sample size which was especially important for the relatively complex zero-inflated negative binomial.

  10. 10.

    Additionally, the appendix contains results from regressions on attendance in each of the nine categories of cultural activities.

  11. 11.

    Such a two-stage conception of decisions on arts consumption and participation also shows up in the Heckman-selection models used e.g., in Lévy-Garboua and Montmarquette (1996) and Lazzaro and Frateschi (2017). While the latter investigates time use, which is continuous, a count data model is more appropriate in the present analysis.

  12. 12.

    This means that a coefficient of 2 can be interpreted as indicating that an increase in the explanatory variable by one unit is associated with a twofold increase in the expected value of the dependent variable; A coefficient of 0.66 means that an increase in the explanatory variable by one unit is associated with a decrease in the expected value of the dependent variable of one third.

  13. 13.

    In choosing these two variables for the logit part of the model, we are essentially proposing a mechanism for the generation of the excess zeros. Some individuals simply do not have the possibility either for financial or geographic reasons to engage in a given type of cultural consumption. Only those who can attend will then make decisions about how often to do so (and may still choose to attend zero times).

  14. 14.

    We are not alone in not finding strong results here (see Borgonovi 2004)

References

  1. Ateca-Amestoy, V. (2008). Determining heterogeneous behavior for theater attendance. Journal of Cultural Economics, 32(2), 127–151.

    Google Scholar 

  2. Ateca-Amestoy, V. (2010). Cultural participation patterns: evidence from the Spanish time use survey. In ESA Research Network Sociology of Culture Midterm Conference: Culture and the Making of Worlds.

  3. Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75, 493–517.

    Google Scholar 

  4. Becker, G. S. (1991). A treatise on the family. Cambridge, Mass: Harvard.

    Google Scholar 

  5. Bihagen, E., & Katz-Gerro, T. (2000). Culture consumption in Sweden: The stability of gender differences. Poetics, 27(5), 327–349.

    Google Scholar 

  6. Borgonovi, F. (2004). Performing arts attendance: an economic approach. Applied Economics, 36(17), 1871–1885.

    Google Scholar 

  7. Bourdieu, P. (1973). Cultural reproduction and social reproduction. London: Tavistock, 178.

  8. Bourdieu, P. (1984). Distinction: A social critique of the judgement of taste. Cambridge: Harvard University Press.

    Google Scholar 

  9. Bourguignon, F., Browning, M., & Chiappori, P. A. (2009). Efficient intra-household allocations and distribution factors: Implications and identification. The Review of Economic Studies, 76(2), 503–528.

    Google Scholar 

  10. Browning, M., & Chiappori, P. A. (1998). Efficient intra-household allocations: A general characterization and empirical tests. Econometrica, 66(6), 1241–1278.

    Google Scholar 

  11. Browning, M., Bourguignon, F., Chiappori, P. A., & Lechene, V. (1994). Income and outcomes: A structural model of intrahousehold allocation. Journal of Political Economy, 102(6), 1067–1096.

    Google Scholar 

  12. Cherchye, L., De Rock, B., & Vermeulen, F. (2012). Married with children: A collective labor supply model with detailed time use and intrahousehold expenditure information. The American Economic Review, 102(7), 3377–3405.

    Google Scholar 

  13. Chiappori, P. A. (1992). Collective labor supply and welfare. Journal of Political Economy, 100(3), 437–467.

    Google Scholar 

  14. Chiappori, PA., & Donni, O. (2009). Non-unitary models of household behavior: A survey of the literature. IZA Discussion Papers.

  15. Christin, A. (2012). Gender and highbrow cultural participation in the United States. Poetics, 40(5), 423–443.

    Article  Google Scholar 

  16. DiMaggio, P., & Mukhtar, T. (2004). Arts participation as cultural capital in the United States, 1982–2002: Signs of decline? Poetics, 32(2), 169–194.

    Article  Google Scholar 

  17. Falk, M., & Katz-Gerro, T. (2016). Cultural participation in Europe: Can we identify common determinants? Journal of Cultural Economics, 40(2), 127–162.

    Article  Google Scholar 

  18. Frateschi, C., & Lazzaro, E. (2008). Attendance to cultural events and spousal influences: the Italian case. “Marco Fanno” Working Paper N 84, University of Padua.

  19. Gary, S., & Becker, K. M. M. (1988). A theory of rational addiction. Journal of Political Economy, 96(4), 675–700.

    Article  Google Scholar 

  20. Kalmijn, M., & Bernasco, W. (2001). Joint and separated lifestyles in couple relationships. Journal of Marriage and Family, 63(3), 639–654.

    Google Scholar 

  21. Katz-Gerro, T., & Jæger, M. M. (2015). Does women’s preference for highbrow leisure begin in the family? Comparing leisure participation among brothers and sisters. Leisure Sciences, 37(5), 415–430.

    Google Scholar 

  22. Kraaykamp, G., van Gils, W., & Ultee, W. (2008). Cultural participation and time restrictions: Explaining the frequency of individual and joint cultural visits. Poetics, 36(4), 316–332.

    Google Scholar 

  23. Kracman, K. (1996). The effect of school-based arts instruction on attendance at museums and the performing arts. Poetics, 24(2), 203–218.

    Google Scholar 

  24. Lazzaro, E., & Frateschi, C. (2017). Couples’ arts participation: Assessing individual and joint time use. Journal of Cultural Economics, 41, 47–69.

    Google Scholar 

  25. Lévy-Garboua, L., & Montmarquette, C. (1996). A microeconometric study of theatre demand. Journal of Cultural Economics, 20(1), 25–50.

    Google Scholar 

  26. Lewis, G. B., & Seaman, B. A. (2004). Sexual orientation and demand for the arts. Social Science Quarterly, 85(3), 523–538.

    Google Scholar 

  27. Lizardo, O. (2006). The puzzle of women’s highbrow culture consumption: Integrating gender and work into Bourdieu’s class theory of taste. Poetics, 34(1), 1–23.

    Google Scholar 

  28. Lundberg, S. J., Pollak, R. A., & Wales, T. J. (1997). Do husbands and wives pool their resources? evidence from the United Kingdom child benefit. The Journal of Human Resources, 32(3), 463–480.

    Google Scholar 

  29. Manser, M., & Brown, M. (1980). Marriage and household decision-making: A bargaining analysis. International Economic Review, 21(1), 31–44.

    Google Scholar 

  30. McElroy, M. B., & Horney, M. J. (1981). Nash-bargained household decisions: Toward a generalization of the theory of demand. International Economic Review, 22(2), 333–349.

    Google Scholar 

  31. Nagel, I., & Ganzeboom, H. B. (2002). Participation in legitimate culture: Family and school effects from adolescence to adulthood. The Netherlands Journal of Social Sciences, 38(2), 102–120.

    Google Scholar 

  32. Seaman, BA. (2005). Attendance and public participation in the performing arts: A review of the empirical literature. Andrew Young School of Policy Studies Research Paper Series (06–25).

  33. Seaman, B. A. (2006). Empirical studies of demand for the performing arts. Handbook of the Economics of Art and Culture, 1, 415–472.

    Google Scholar 

  34. Stigler, G. J., & Becker, G. S. (1977). De gustibus non est disputandum. The American Economic Review, 67(2), 76–90.

    Google Scholar 

  35. Taylor, L. D., & Houthakker, H. S. (2009). Consumer demand in the United States: Prices, income, and consumption behavior. Springer Science & Business Media.

  36. Throsby, D. (1994). The production and consumption of the arts: A view of cultural economics. Journal of Economic Literature, 32(1), 1–29.

    Google Scholar 

  37. Upright, C. B. (2004). Social capital and cultural participation: Spousal influences on attendance at arts events. Poetics, 32(2), 129–143.

    Article  Google Scholar 

  38. Vermeulen, F., Bargain, O., Beblo, M., Beninger, D., Blundell, R., Carrasco, R., et al. (2006). Collective models of labor supply with nonconvex budget sets and nonparticipation: A calibration approach. Review of Economics of the Household, 4(2), 113–127.

    Article  Google Scholar 

  39. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57(2), 307–333.

    Google Scholar 

  40. Zieba, M. (2009). Full-income and price elasticities of demand for German public theatre. Journal of Cultural Economics, 33(2), 85–108.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Caterina Adelaide Mauri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 8 shows results of a regression similar to that reported in Table 5. The difference is in the link function chosen. Instead of a zero-inflated negative binomial, here a negative binomial model was used. This model is reported here as a robustness check. Notably, the effect of the distribution factor on attendance at female-dominated events remains significant and very similar in magnitude, still implying the same \(4\%\) reduction in the number of visits per year for a \(10\%\) shift in income in favor of the husband.

Table 8 Determinants of cultural consumption—Negative binomial

Table 9, which comes in two parts, shows results for regressions on attendance by either member of the couple for each of the nine activities available in the data. This allows for comparisons of individual attendance across activities. Unfortunately, the effects of the distribution factor are much harder to detect here, both because visits by both members of the household are considered and because there are very few nonzero observations for some of the activities. This results in a lack of variation in the dependent variable on which to base the estimation. Especially Latin and salsa performances, the opera and ballet are attended so rarely overall that inference becomes difficult. For the same reason, inclusion of state fixed effects becomes hard here, and they are omitted.

Table 9 Annual attendance by either partner—Zero-inflated negative binomial

The income share variable is significant at the 1% level only once, for jazz music, and though the sign is consistent with the previous result that relatively wealthier men depress attendance in all categories, there is no particular reason why this effect should be strong for jazz and not for the others. Again, the relatively small numbers of nonzero observations may not be sufficient to see through the noise here.

Other effects are strong enough though, to be detected in the majority of categories. These are the usual suspects, log income and education. Log income very consistently reduces the probability of never going in the inflate part of the models. The fact that this variable has this prominent role in determining who does and does not go at all limits the interpretability of its coefficient in the main part of the model, where it is found significant only in the case of classical music. Theoretically, this means that higher income increases the probability of going at least once per year while decreasing the expected number of attendances among those who have the means.

Education, both of the man and the woman, has a similarly consistent role. The associated coefficients are mostly significant, greater than one whenever they are significant and often very large. One exception to this rule is the Latin and Salsa category, which is unusual in other ways also. This may be partly due to the fact that though it is performance art, it is less high-brow in character than most of the other activities. But again, the oddity may also be the result of the small number of nonzero observations (or of the omission of a variable indicating Hispanic origin, which may be important for this category but not for the others).

The effects of education are especially strong for very high-brow activities such as classical music, the opera and dance performances, where the latter two are especially influenced by female and male education, respectively.

Another trend that is relatively consistent comes from the dummy for data from the year 2012, the second wave considered here. Coefficients are mostly below one, indicating a negative trend in the number of visits across activities.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mauri, C.A., Wolf, A.F. Battle of the ballet household decisions on arts consumption. J Cult Econ (2020). https://doi.org/10.1007/s10824-020-09395-z

Download citation

Keywords

  • Arts consumption
  • Household bargaining
  • Count data
  • Bargaining power
  • High culture

JEL Classification

  • Z11
  • D13
  • D12