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Cultural participation in major Chinese cities

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We study the socioeconomic determinants of cultural participation in thirteen major Chinese cities for a broad range of indicators that cover highbrow and popular cultures. Consistent with previous studies from high-income countries, we find strong support for the elitism hypothesis: education and income increase participation in a broad range of cultural activities. There are also some exceptions. Interestingly, we also find a U-shaped relation between participation and city development for free and publicly supplied culture. Moreover, the impact of education, and to some extent also income, is weaker in richer cities. These findings contribute to understanding China’s key policy objective of promoting equal access to culture.

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  1. There were about 2K museums in China in year 2000. Twelve years later, this figure had increased to 3,8K (The Economist, 2014).

  2. According to China’s official statistics, the value added of culture (which includes sports and entertainment) increased in the same decade from 104.32 billion Yuan to 427.45 billion Yuan, corresponding to an average annual growth rate of 30.97%.


  4. For example, the National Basic Public Culture Service Guidance Standard (2015–2020) stipulates a fixed number of free channels in broadcasting.

  5. Some cultural industries, such as art performance, receive subsidies to support traditional culture and art, but still pursue profits.

  6. The literature has also used the terminology homology thesis or highbrow univore thesis. There is also a study of the demand for art. Demand studies focus specifically on price and cross-price elasticities (Garboua and Montmarquette 1996; Lévy-Garboua and Montmarquette 2003; Bonato et al. 1990). The issue of price is not relevant for free cultural activities.

  7. Since 2015, the Ministries of Culture and Finance jointly launched cultural pilot programs to promote cultural consumption in urban and rural areas. This survey was financed as a part of these pilot programs.

  8. Tables 10 and 11 in Appendix report age distributions among survey respondents and within the entire Chinese population. (We could not find age distribution for urban area only, but this is not crucial for our main point.) We tried to match the age categories used in the questionnaire as best as we could. There are important differences in the population fractions in the two tables. The survey covers a larger fraction of respondents in the mid-age category (age 18–40). Although there are small discrepancies and the overlap in categories between the two tables is not perfect, this is unlikely to explain the measured differences. It is likely that people in the mid-age category are more represented in public places where the survey was conducted.

  9. Rolando Y. Wee, 20 Biggest Cities In China,, April 13, 2017.

  10. Other surveys have grouped cultural activities together. For example, the EUROSTAT-SILC survey used by Falk and Katz-Gerro (2016) asks participants in 24 European countries how often they visited a museum, art gallery, historical monument or archaeological site in the past twelve months. It is arguable that our survey groups a broad range of activities that do not all qualify as highbrow culture. A concern with including libraries, for example, is that some respondents may use public libraries for educational rather than cultural purpose.

  11. Occupation ‘others’ corresponds to someone who is not employed or student. This includes unemployed, retiree, and those taking care of children or family members.

  12. Figures 10–13 in Online Appendix plot the coefficients with and without controlling for ‘other activities’ next to one another. Visualizing the point estimates along with their 95% confidence intervals reveals that adding controls does not have a big impact on the coefficient estimates.

  13. For some indicators, postgraduate education appears to lower participation, but these differences are not significant.

  14. Take the example of frequency of participation. Muñiz et al. (2017) use a Spanish survey with an integer answer, while the EUROSTAT-SILC survey has four intervals (see Table 7).

  15. They also report ‘The country dummy variables show large and significant differences in the probability and number of museum and historical site visits across the EU countries after controlling for individual and household factors. We find that the probability and number of visits are significantly higher for Sweden, Denmark, Finland, and the United Kingdom when compared to the benchmark country, Germany.’ (p.145)


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Correspondence to Pascal Courty.

Additional information

We are grateful for the financial support of the Art Program Funding from the National Social Science Foundation of China (No. 14CH141) and ‘the Pilot Program of Enlarging and Promoting Cultural Consumption of Residents in Urban and Rural Areas’ from the Ministry of Culture of China.

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Supplementary material 1 (pdf 381 KB)



1.1 Comparison of marginal effects with other countries

As discussed in Sect. 2.2, the literature on cultural participation covers a large number of countries and cultural activities. When comparing results across studies, one should keep in mind the following caveats: (a) The surveys are conducted in different years. (b) The surveys use different indicators of cultural participation (e.g., theater, museum, archaeological site) or a bundle of categories (e.g., see EUROSTAT-SILC below). (c) The questions sample different time windows (participation last month, last year). (d) The answers are coded differently.Footnote 14 (e) Differences in estimation models, which may be due to the way the variables are coded, further complicate the comparisons. (f) Some studies (e.g., Wen and Cheng 2013; Palma et al. 2013) do not report marginal effects.

These differences make it impossible to compare participation across most studies. Comparing the marginal effects is possible under the assumption that the differences across surveys and econometric models do not affect the margins. The marginal effects for education and income largely agree: Education has a large effect on cultural participation and income a smaller one. We focus here on education since it is widely reported as the variable with the greatest impact on cultural participation. The effects found in the literature are large. Recall Seaman (2006)’s review discussed in Sect. 2.2. Muñiz et al. (2017) look at participation to cultural events in Spain, where participation is broadly defined as visits to theater, ballet, classical dance, cinema, concert, museum, historical monument in the past four weeks. For the probability to participate, they report a 12% increase for primary education, 23% increase for high school education and 35% increase for university education (see Table 6, p. 87). These figures are larger than the marginal effects reported in Fig. 1.

Falk and Katz-Gerro (2016) use the EUROSTAT-SILC survey. The survey asks participants in 24 European countries how often they visited a museum, art gallery, historical monument or archaeological site in the past twelve months. We compare our results for China with their findings because the study is recent, covers a large number of countries and asks a similar participation question.

The left panel on Table 7 reports the marginal effects from an ordered Probit specification. See Table 3, p. 140. We run a similar specification using the ‘frequency’ categorical variable and report the results on the right panel. University education increases the probability to participate by 35.3% on average across the 24 European countries. This is a large effect. In contrast, university education increases participation by only 5.6–6.5% in China. Similarly, high school education has a much larger impact on participation in Europe than in China.

Table 7 Comparison of the impact of education on frequency of participation

We are not aware of any study that compares cultural participation across regions or countries with the exception of Falk and Katz-Gerro (2016). They were able to do so because their survey covers 24 European countries. They find large differences across the countries represented: ‘Our second main conclusion is that after accounting for socioeconomic and demographic correlates of cultural participation, there are still large differences in the probability of museum and historical site visits across countries.’ (p. 146).Footnote 15 Table 10 (p.159) reports large marginal effects: Greeks are 32% less, and Finnish are 13% more, likely to participate than Germans. In contrast, we find a small range of variation in participation across cities located in different Chinese regions. In our ordered Probit model, which matches their study on several points, the largest difference in participation between any city pair is 12.6 percent (Chengdu versus Zhengzhou).

1.2 Data description

See Tables 8, 9, 10, 11 and 12.

Table 8 Distribution of survey locations sampling by city
Table 9 Cultural participation conditional on survey location
Table 10 Summary of demographic variables
Table 11 Age distribution in Chinese population in 2014
Table 12 Cultural participation by city

1.3 LPM result

See Tables 13, 14, 15, 16 and 17.

Table 13 LPM: public and frequency
Table 14 LPM: time and media
Table 15 LPM: performance and craft
Table 16 LPM: movie and karaoke
Table 17 LPM: online

1.4 SEM result

See Table 18.

Table 18 SEM

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Courty, P., Zhang, F. Cultural participation in major Chinese cities. J Cult Econ 42, 543–592 (2018).

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