# Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity

Samuel Coavoux, Abel Aussant

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Samuel Coavoux, Abel Aussant. Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity. Sociological Science, 2025, 12, pp.572-600. 10.15195/v12.a24. hal-05249765

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# Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity

Samuel Coavoux,ª Abel Aussantᵇ

a) CREST, ENSAE, Institut Polytechnique de Paris; b) CRIS, Sciences Po

**Abstract:** Do digital technologies affect diversity in cultural tastes? Digital sociologists have warned of "filter bubbles," whereas sociologists of culture have shown that diversity in consumption is valued as a marker of upper-middle-class status. We estimate the effect of using streaming platforms on the diversity of cultural consumption using a matching technique applied to 2018 survey data from France. We find a statistically significant positive effect of using streaming platforms on the diversity of cultural consumption as well as on cosmopolitanism, on three domains, music, movies, and TV shows. The magnitude of this effect is much higher for TV shows. The study brings new evidence against the filter bubble thesis; it shows that platforms do reinforce cultural inequalities by increasing the social gap in consumption diversity. It further suggests that the effect of technology on cultural consumption might mainly operate through its impact on cultural markets rather than changes in cultural experience.

**Keywords:** culture; taste; inequality; platform; filter bubble

**Reproducibility Package:** A replication package containing all scripts necessary to reproduce the results presented in the article is available at OSF. The data are available on demand from the Progedo-Adisp repository.

STREAMING platforms provide a comfortable experience. Spotify keeps our favorite music at a click's length and Netflix pushes new shows it knows we will enjoy. Given how common this experience is, it is no surprise that ordinary consumers, as well as academics, tend to think that streaming platforms keep people in their own bubble of cultural taste (Hesmondhalgh 2022), reducing consumption diversity. Critical algorithm studies have labeled this the "filter bubble" thesis (Pariser 2012). The argument is that because personalized recommendations are based on consumption history and the proximity between consumers, streaming platforms reinforce a preexisting tendency of cultural consumers to stay within their established tastes (Lee and Hosanagar 2019). However, platforms argue that they value diversity and they increase the variety of users' consumption (Asmar, Raats, and Van Audenhove 2023). Streaming platforms have virtually unlimited shelf space and an incentive to cater to niche preferences (Anderson 2006). Moreover, they offer personalized recommendations, which may lower information costs and help users navigate toward unfamiliar products (Brynjolfsson, Hu, and Simester 2011).

The effect of technologies on consumption diversity is an important question for sociologists of cultural inequalities. In the past three decades, numerous studies have established that having varied tastes, that is, being a "cultural omnivore,"

**Citation:** Coavoux, Samuel and Abel Aussant. 2025. "Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity" Sociological Science 12: 572-600.

**Received:** May 29, 2025

**Accepted:** July 6, 2025

**Published:** September 4, 2025

**Editor(s):** Arnout van de Rijt, Bart Bonikowski

**DOI:** 10.15195/v12.a24

**Copyright:** © 2025 The Author(s). This open-access article has been published under a Creative Commons Attribution License, which allows unrestricted use, distribution and reproduction, in any form, as long as the original author and source have been credited. Ⓔ
is one of the most well-regarded contemporary cultural attitudes (Peterson and Kern 1996; Chan and Goldthorpe 2006; Coul angeon and Lemel 2007). It has been hypothesized that breadth of cultural consumption allows individuals to build social capital (Lizardo 2006), because cultural tastes affect social ties (Lewis and Kaufman 2018). Cosmopolitanism (openness to foreign cultures) is also rising among elites (Coulangeon 2017; Cicchelli et al. 2021), along with the international- ization of elite education and professional mobility (Bühlmann, David, and Mach 2013). Thus, the diversity of cultural consumption has become a dimension of cultural inequalities.

Streaming services, similar to many technological innovations, have first been adopted by highly educated, high-income consumers (Kim 2016). There are eco- nomic barriers at play, the cost of monthly subscriptions, as well as digital inequali- ties linked to the skills needed to navigate these platforms (Robinson 2012). Thus, there is a clear, plausible path from digital inequalities to cultural inequalities: if platforms have an effect on consumers’ preferences, they should affect upper- middle-class consumers first. A negative filter bubble effect would tend to entrap them more than the rest of the population, and therefore decrease cultural inequali- ties; in contrast, a positive effect would reinforce elite distinction by providing users with new ways to diversify their consumption.

This article proposes a method toward estimating the effect of streaming plat- forms on the diversity of cultural consumption. We analyze survey data on the adult population living in France in 2018 using a matching strategy that adjusts for all hypothesized confounders. We find a positive, significant effect of the use of streaming on the diversity of consumption across three domains, music, movies, and TV shows. However, we find that the magnitude of this effect is small for music, intermediate for movies, and substantively larger for TV shows.

These results provide evidence against the filter bubble thesis: the overall effect of streaming platforms on diversity is consistently positive. It also leads us to believe that platforms might worsen cultural inequalities. In contrast to the popular narrative that platforms are radically transforming the experience of cultural consumers, the gap between music, movies, and TV shows evidences that the effect of technological change might lie elsewhere. Music streaming platforms make music produced by other actors available to everyone. Compared with older services, they bring larger catalogs and personalized, automated recommendations. Video platforms also change the market itself, because many (such as Netflix, Disney+, or Prime Video) are also movie and TV show producers. The effect we estimate on music platforms is only due to changes in the experience of culture; the gap between music and video platforms is attributable to market changes. Thus, it appears that streaming platforms’ main effect on cultural diversity goes through the reconfiguration of cultural markets (Peterson and Anand 2004).

Streaming Platforms, Filter Bubbles, and Cultural
Inequalities

The filter bubble thesis we are addressing in this article is concerned with the effect
of digital platforms on the diversity of users’ exposure to content. Diversity is “a
property of the apportioning of elements or options in any system” combining “three basic properties [...] ‘variety’, ‘balance’ and ‘disparity’”(Stirling 2007, p. 709). Variety is the number of different elements, balance is a function of the proportion of each element, and disparity is a function of the dissimilarity of these elements. Interestingly, even though it is almost never framed as such, omnivore theory, the now-dominant theoretical account of cultural inequalities, is also a theory of diversity. It states that diverse tastes are better regarded than narrow tastes in contemporary post-industrial societies (Peterson and Kern 1996).

In this section, we link the two literatures. We first establish that the diversity of consumption is a central dimension of contemporary cultural inequalities. We then describe streaming platforms and show that they are unequally used. We present the filter bubble thesis and describe the various mechanisms that might constitute an effect of platforms on the diversity of consumption. Finally, we review the empirical literature on the effect of streaming platforms.

## Diversity as a Dimension of Cultural Inequalities

The effect of technologies on the diversity of consumption has implications for cultural inequalities. First proposed by Peterson and Simkus (1992), omnivore theory states that having varied tastes (i.e., being a “cultural omnivore,”) is one of the most well-regarded contemporary cultural attitudes. What counts as diverse varies across authors. Peterson and Simkus (1992) originally defined omnivorousness as diversity over cultural hierarchies (contemporary research using this definition include Friedman and Reeves 2020; Nault et al. 2021), but later works have redefined it as boundary-crossing consumption (Lizardo 2014). Upper-middle-class cultural consumers (Chan and Goldthorpe 2006; Coul angeon and Lemel 2007) as well as the economic and political elites (Friedman and Reeves 2020) display more diverse tastes than working class consumers. Another aspect of omnivorousness is cosmopolitanism, that is, openness to foreign cultures. It is also rising among the elites (Coul angeon 2017; Cicchelli et al. 2021), along with the internationalization of elite education and professional mobility (Bühlmann et al. 2013). Cosmopolitan cultural consumptions have been identified as an emerging form of cultural capital (Igarashi and Saito 2014).

Peterson (2004) explains the shift toward omnivorousness by upward social mobility, changing political norms, and ubiquity of cultural industries, but current research also point to the role of social networks. It has been hypothesized that the breadth of cultural consumption allows individuals to build social capital (Lizardo 2006), because cultural tastes affect social ties (Edelmann and Vaisey 2014; Lewis and Kaufman 2018). Diverse tastes permit the formation of ties with more diverse people, affecting one's chances of finding better employment (Rivera 2012), earning more money (Reeves and Vries 2019), or, possibly, finding a partner with a higher social status (DiMaggio and Mohr 1985).

Therefore, diversity constitutes a dimension of cultural inequalities. People with more diverse cultural consumption are more likely to be of higher social status and find benefit in terms of assigned status and social capital.
The Rise of Streaming Platforms

Streaming platforms are internet services giving unlimited access to cultural goods, generally music and videos such as movies, TV shows, and internet videos. Free platforms, such as YouTube, are supported by advertisement, whereas subscription-based platforms such as Spotify or Netflix sell access for a monthly fee. Hybrid solutions also exist, with a free advertisement-based tier and a paid subscription plan. In this article, we only consider the case of subscription-based platforms for music, movies, and TV shows. Music and video platforms share common characteristics. First, they give access to a very large catalog of cultural items, which is of a size orders of magnitude higher than the catalog of pre-internet brick-and-mortar stores. Second, they offer personalized recommendations, which are often prominently displayed on their homepage. These recommendations are based on users' consumption history: they propose items that are similar to previous consumption (content-based), items consumed by users with similar profiles (collaborative filtering), and editorially curated content perceived as relevant for a given user. Third, they are available on multiple devices, from computers to smartphones, smart speakers, and tablets. Finally, they offer similar subscription plans, ranging from 10 to 20 euros a month.

There are also considerable differences between music and video platforms that need to be emphasized. First, catalogs of music platforms are much larger than those of video platforms. Spotify claims to host more than 100 million songs¹. Netflix’s available content varies by country, but a ballpark number is between 3,000 and 9,000 titles². Movies are more expensive to produce than music tracks, and much fewer are made every year. Second, all music platforms give access to a similar catalog, representing a very large share of currently copyrighted recordings. In contrast, video platforms make exclusive deals with copyright holders and produce exclusive content that cannot be watched on a competing platform. On the music market, competition is focused on the user experience, whereas there is room for content differentiation on the video market. Third, listening to music and watching movies are two different activities. Music is more often listened to as a secondary activity at the same time as a consumer is doing something else (Fuentes, Hagberg, and Kjellberg 2019), whereas video consumption is on average more focused. Repeated consumption of an item is the norm for music, where previous familiarity with a track is a determinant for satisfaction, whereas consumers tend to prefer previously unseen movies (Margulis 2014).

Streaming platform use is socially unequal. Table S2 (online supplement) shows descriptive statistics of streaming use in 2018 from the *Pratiques culturelles* survey used in this study. The prevalence of streaming is similar in all three domains, just more than 30 percent of the sample. More recent data indicate that this practice is rising: 44 percent of music consumers used music streaming platforms in December 2022 (data from Louail et al. [2023], our analysis); and the share of people subscribing to a video streaming platform reached 56 percent in 2024 (CREDOC 2025, P. 232). Still, it indicates that, even now, a large number of cultural consumers living in France do not use these services. They are less prevalent than in other western countries such as Norway (Lüders, Sundet, and Colbjørnsen 2021), making France a good case to study the effect of streaming.
The use of streaming is strongly correlated with age, education level, and socioeconomic status (Table S2 in the online supplement). The average age of a music streaming platform user in our sample is 40, whereas the average age is 50 (the sample starting at 21 years old). Respondents with a graduate education (master's degree and above) make up 21 percent of streaming users but only 12 percent of the sample. There are comparable gaps in the use of movie and TV show streaming services. These inequalities persist, as shown in more recent surveys (Louail et al. 2023; CREDOC 2025). These findings are in line with previous research on digital inequalities that show discrepancies in the adoption of new technologies (Robinson 2012) and with research on digital cultural consumption (Weingartner 2020; Lüders et al. 2021).

Streaming Platforms as “Filter Bubbles”

The filter bubble thesis states that the personalization of internet content, through various algorithms reacting to information about a specific user, traps consumers in a narrow cognitive landscape (Pariser 2012). It has attracted attention as a mechanism to causally link two contemporary phenomena: the rise of social media and the increase in political polarization. The filter bubble thesis has been widely criticized. Research find mixed evidence as to the very existence of filter bubbles, as “larg[e] scale studies provide little evidence of negative effects of algorithms on the diversity of the content” (Helberger 2018, P. 20). Users of social media are exposed to a large variety of sources of information, albeit with a bias toward their political preference, except for a small minority consuming only partisan sources (Bakshy, Messing, and Adamic 2015; Dubois and Blank 2018). Moreover, a large part of this bias can be attributed to preexisting homophilic tendencies rather than the design of social media (Bakshy et al. 2015). Moreover, the causal chain that links social media algorithms to a lower diversity of information to political polarization might be unrealistic: polarization seems to precede rather than follow increased social media use (Nordbrandt 2023) and exposure to opposing views (diversity in information sources) may trigger more polarization than a decline in diversity (Bail et al. 2018). Finally, as recommendation algorithms are trained to maximize user retention, the mechanisms underlying filter bubbles are unclear. Platforms are incentivized to increase diversity for users who value it as much as to lower it for those who don't.

Despite these doubts, the filter bubble thesis is popular in journalistic and academic accounts of the digital world alike. It has become one of the main frames used by humanistic critics of streaming platforms (Reviglio 2017; Gómez-Aguilar 2020). Recommendation algorithms are similar to those filtering news on social media: they rely on collaborative filtering (recommending content consumed by like-minded users) and content-based filtering (recommending content similar to content consumed), trained to maximize user retention (Seaver 2018). They are similarly accused of limiting users' exposure to diverse content (Maasø and Spilker 2022).

The filter bubble thesis focuses on the way digital technologies curate access to information with algorithms. However, there is another pathway for the effect of digital services on diversity, which we call a market effect. Digital platforms have changed the way information and cultural goods are marketed, consumed, and profited from. For cultural production, the proposed mechanism is based
on low-attention consumption patterns. Distracted, passive consumers use music more and more as a “soundtrack” for other activities, and therefore favor music that “works” in specific contexts (Fuentes et al. 2019). Recommendation algorithms pick up on these uses. Thus, producers have an incentive to make background music that is less diverse and less innovative (Hesmondhalgh 2022; Pelly 2025). Moreover, the shift from the one-time purchase of an album to per-play revenues on streaming platforms has changed incentives to produce hit songs generating repeat play (Maasø and Spilker 2022). A similar argument has been made for video streaming platforms. Ubiquitous videos on smartphones means that movies and TV shows can be watched in low-attention settings, such as public transportation. Critics assert that platforms divert financial and human resources from regular movie production, hindering innovation, and that they tend to produce standardized content (Blondeau 2022).

However, a case could also be made for a positive effect of platforms on the production of culture. Video platforms have incentives, and sometimes legal obligations to invest in local content in every market they enter (Lobato 2019). This leads to the emergence of hit TV shows from small markets. Competing against traditional and premium TV channels, they may invest in niche genres (Afilipoaie, Iordache, and Raats 2021). It is also possible that incentives to produce content fine-tuned for success on platforms offer new venues for cultural innovation (Peterson and Anand 2004).

Therefore, the cultural bubble thesis describes two pathways for the effect of cultural platforms on diversity, one going through the *experience* of cultural consumption, notably via the curation of cultural goods proposed to consumers, and the other through the *market*, that is through changes in what cultural goods get produced.

## Empirical Studies of Diversity on Streaming Platforms

The filter bubble thesis predicts a decline in diversity in the age of streaming platforms. If content is becoming more standardized, we should first see less aggregate diversity. This is not the case: the most popular music is increasingly diverse in terms of country of origin (Im, Song, and Jung 2019; Bello and Garcia 2021) and aesthetic features (Bourreau, Moreau, and Wikström 2022). However, aggregate and individual diversity may diverge. It is possible, indeed, to find both an aggregate increase and an individual decrease in diversity: digital services could both ease access to niche products, leading consumers away from bestsellers and increasing aggregate diversity, while trapping each consumer into their own favorite niche, decreasing individual diversity.

Thus, the filter bubble thesis prediction of a declining diversity at the individual level should be studied separately. Each individual consumer would be falling into their own rabbit hole, dug by algorithms. Research on the effects of platforms on individuals has been rare. Datta, Knox, and Bronnenberg (2017) analyze data from lastfm, digital records that users voluntarily keep of their music listening activities across devices and platforms. They estimate a positive effect on consumption diversity of switching from a download-centered (iTunes) to a streaming platform-centered consumption (Spotify). However, their sample only includes
people who already use digital services, rather than the whole population, and is biased toward highly involved consumers subscribing to lastfm. Survey research has the potential to overcome the selection bias of studies using digital data. Lüders et al. (2021) use survey data from Norway to document a positive correlation between the frequency of use of streaming platforms and the diversity of genres that are consumed, for both music and television. However, no confounders are controlled for. Using survey data from Switzerland, Weingartner (2020) finds that using digital media is positively correlated with diversity of movie consumption, after controlling for demographic characteristics, cultural preferences, and opportunities, and concludes that digital media reinforce cultural inequalities. However, this study misses important confounders such as the underlying propensity toward diversity and introduces collider bias by controlling for variables such as consumption frequency and genre preferences.

To sum up, there are two reasons why we should want to measure the effect of streaming platforms on diversity: to add empirical evidence to current debates about the threat of filter bubbles in the cultural sector and to document the potential effect of technology on cultural inequalities. This section has theoretically grounded the causal pathway from streaming platforms to cultural inequalities. Streaming platform use increases with social status; streaming increases diversity; it therefore affects higher social status individuals more. Finally, as diversity in cultural consumption, omnivorousness, leads to better life outcomes, streaming increases cultural inequalities. Moreover, differences between music and video markets call for examining the two cases separately. On the music market, streaming platforms change the consumer experience (larger catalogs, more personalized music device) but do not directly affect the market; on the movie and especially the TV show market, streaming platforms change both consumer experience and the market.

This literature review has highlighted several conflicting mechanisms that might explain the effect of platforms on individual consumption diversity. On the one hand, streaming platforms might reduce search cost (ease of access through large catalogs) and information cost (personalized orientation devices), as well as increase the variety of items (exclusive video production), three mechanisms that should increase diversity. On the other hand, personalized recommendation might trap consumers into repeat consumption of the same kind of products, and the casualization of cultural experience, through mobile, distracted consumption, might favor standardized consumption patterns, thereby reducing diversity. Empirical evidence is still rare but rather points toward an increase in diversity linked to the use of streaming platforms. Our study adds new evidence that increases our confidence in a positive effect of streaming platforms on diversity. It also finds large difference between the music, movies and TV show markets, suggesting that market forces are more potent than changes in cultural experience.

# Data and Method

## Data

We use the 2018 "Pratiques Culturelles" (Cultural Practices) survey, hereafter PC18, the latest edition of a survey on cultural participation carried out in France every
10 years on average since 1973. It was commissioned by the French Ministry of Culture. The probability sample consists of 9,238 individuals. After weighting to correct for nonresponse bias, it is representative of the population living in metropolitan France aged 15 and above. We use the weights provided by the survey designers in all analyses. We restrict all analyses to the 8,692 respondents aged 21 and above because we use variables about cultural participation in childhood, specifically at 12, as a control, and we want to ensure that these variables could not be affected by the treatment. Respondents aged 21 and above turned 12 at the latest in 2009, a time when streaming services were not widely available in France. All analyses are restricted to cultural consumers, that is, respondents who answered the questions about frequency of consumption of music, movies, or TV shows and did not give the answer "Never or almost never." The questionnaire covers a wide variety of cultural consumption, from books to any kind of audiovisual product, with an emphasis on measuring taste through consumed, liked, and disliked cultural genres in each domain. It also includes systematic questions about modes of consumption (which devices and services are used to access cultural items).

## Treatment

We define the use of streaming services as

* For music, selecting the item "Streaming music on a specialized platform: Spotify, Deezer, Qobuz, ..." to the multiple-choice question, "During the last 12 months, what devices did you use to listen to music?" Other items were "CD or cassette tape," "Vinyl records," "Streaming music on another platform," "Digital files mp3 or other)," "Radio," and "Television." We understand the item "Streaming music on another platform" to refer to music played on YouTube, the largest music streaming platform, which is free (see Beuscart, Coavoux, and Garrocq 2023).

* For movies and TV shows, selecting the item "Video on Demand: Netflix, Canal Plus, myTF1vod" to the multiple-choice question, "During the last 12 months, what services did you use?" Other items were "DVD or Blu-ray," "TV (free or paid)," "Replay," "Video on streaming," and "Digital file." The item "Video on streaming" refers, in the French context, to websites with pirated content.

## Outcomes

Our main outcome is the diversity of cultural consumption, measured by the breadth of cultural genres consumed. We also measure the diversity of tastes, cultural tolerance (breadth of dislikes), and cultural cosmopolitanism (measured by the consumption of foreign-language items). The first three metrics are counts of genre, which means we only measure variety (Stirling 2007).

* Diversity of consumption. The outcome variable is the sum of binary answers (0 for no, 1 for yes) to the multiple-choice question "What are the music/movie/
TV shows genres you listen to/watch?" There were 13 music genres, 16 movie genres, and 16 TV show genres.

* *Diversity of tastes.* The sum of binary answers to the multiple-choice question, "In this list, are there genres that you particularly like?" with the same items.

* *Tolerance.* The sum of binary answers to the multiple-choice question, "In this list, are there genres that you particularly dislike?" with the same items.

* *Cosmopolitanism.* We used the following yes/no questions: "In the past 12 months, have you watched movies/TV shows/listened to music in another language than French?" French TV channels almost systematically broadcast dubbed versions of foreign movies and TV, as do cinema theaters. Moreover, there are legal quotas for French radio stations: 40 percent of songs played must be sung in French.

These questions were only asked to respondents who had declared that they listened to music, watched movies, or watched TV shows, respectively. In all analyses, we restrict the data set to this group.

## Analytical Strategy

The expected link between the use of streaming platforms and the diversity of consumption can be due to various confounders. Streaming users and non-users differ on many other dimensions than mere technology choice. For instance, streaming users have higher levels of education than non-users, and education is linked to cultural tastes. Moreover, it is plausible that a reverse causal link exists: an underlying taste for diversity might explain why some people choose to subscribe to streaming platforms. Moreover, cross-sectional survey data are the main source of information on cultural tastes.

However, the filter bubble thesis is a causal theory. To move toward estimating the effect of streaming platforms on the diversity of cultural consumption, despite being limited to cross-sectional, declarative data, we use matching to select respondents with no use of streaming into a control group, and we then compare streaming users (treated) and similar non-users (control) with regard to their tastes. This control group can be thought of as a counterfactual: it aims to be what the treated group would be, on aggregate, if they were not using streaming platforms. Matching presents several advantages. For one, it can be used on cross-sectional data. Research on cultural consumption is impaired by the lack of longitudinal data sets. Moreover, cross-sectional surveys on culture are only run every 10 years in France, making natural experiment approaches based on the staggered development of broadband access impossible, for lack of good quality data on the outcome. Matching is also advantageous compared with regression analysis as it is non-parametric, making it more robust to changes in model specification. Finally, it produces easy-to-read results. At the end of the matching procedure, we get a control group that has the same distribution of all control variables as the treated group. The comparison of outcomes is then straightforward: we present standardized differences between the two groups, with no further adjustment.
However, matching has one large drawback: all confounding factors should be observed (Caliendo and Kopeinig 2008). Note that only variables with a plausible causal link on both the treatment and outcome variables need to be taken into account. Variables that have an effect on only the treatment, or only the outcome, with no link to the other, do not need to be considered. Thus, the robustness of our results relies on the selection of controls, and especially in the idea that there are no unobserved confounders. We describe the selection in the following section. We also provide analyses estimating the sensitivity of our results to unobserved confounders in the limits section and the online supplement.

The primary goal of matching is to identify a sample of non-treated individuals that is as similar as possible to the treated group, with respect to the chosen confounders, that is achieving balance between the treated and control groups. Numerous matching techniques exist (Zhao 2004; Caliendo and Kopeinig 2008). In this article, we use the recently developed family of techniques known as cardinality matching (Visconti and Zubizarreta 2018), specifically template matching. This approach has been shown to outperform previous techniques in terms of covariate balance, particularly when the non-treated sample is not large³. The control group obtained through template matching is, by design, the largest obtainable that fulfills the balance criterion defined during the implementation of the method (Cohn and Zubizarreta 2022).

To balance covariate distribution, we define a maximum distance between the treated and control group. We opted for conservative thresholds of 0.05 standardized mean difference (SMD) for numeric variables or standardized proportion difference (SPD) for categorical variables. This means we allow the average of a continuous variable in the control group to be 0.05 standard deviation away from that in the treated group. Moreover, we chose an even stricter threshold for age, at 0.005, as we were concerned that this was the most important confounder. We use five-year bins to measure age rather than rely on mean difference because using a continuous variable may have resulted in widely different distributions with the same average. These thresholds are more conservative than those advocated by the literature of 0.1–0.2 SMD/SPD (Lenis et al. 2019; Zhang et al. 2019). See section B in the online supplement for detailed diagnostics of the matching procedure.

We perform the matching procedure for each domain (music, movies, and TV shows). Table 1 shows the size of the treated and control group for each model. We used the MatchIt R package to perform matching (Ho et al. 2011) and Gurobi to solve the complex computation needed (Gurobi Optimization, LLC 2023).

## Controls

We listed confounders of streaming use and taste that are established by the literature or that are plausible (given the lack of published research on correlations between tastes and digital technologies). We identified six main sets of confounding variables, all of which are measured in some way or another in the *Pratiques Culturelles* 2018 survey. We discuss the main factors in each set here and point the reader to Table S1 in the online supplement for an exhaustive list and the variables we used to measure them. The exact variables used vary from one domain to the
<table><caption>**Table 1:** Size of treated, non-treated, and control groups.</caption><thead><tr><th>Domain</th><th>Treated Group</th><th>Non-Treated Group</th><th>Control Group</th></tr></thead><tbody><tr><td>Music</td><td>1 913</td><td>5 791</td><td>1 333</td></tr><tr><td>Movie</td><td>1 954</td><td>4 923</td><td>1 207</td></tr><tr><td>TV shows</td><td>1 483</td><td>4 196</td><td>952</td></tr></tbody></table>

*Note:* “Treated group”: all individuals declaring they use streaming.

“Non-treated group”: all individuals declaring they do not use streaming.

“Control group”: subset of the non-treated group selected by the matching procedure to have the largest possible size while ensuring balance with the treated group on all covariates.

The size of treated and non-treated groups does not sum up to the total sample size as we remove respondents who declare not to consume any music/movies/TV shows.

other, for sets 4 and 5, but the overall principles stay the same. Parsimony is not a virtue when using matching techniques (Rubin and Thomas 1996); thus, we used sets of 55 (TV shows and movies) to 65 (music) variables in our model.

1. *Demographics* (age, gender, and marital status). Digital technologies have higher adoption rates in younger generations (Kim 2016); youth is also a time of higher cultural consumption levels and diversity (Falk and Katz-Gerro 2015). As we hypothesized that age would be the main confounder, we imposed stricter conditions on age for the matching procedure, allowing at most a 0.005 standardized proportion deviation between treated and control group (vs. 0.05 for other variables). Gender is also linked to technology adoption, through gendered representation of digital technologies use (Kim 2016), and with cultural tastes and participation (Christin 2012). Family status has an influence on availability and constraints on leisure time (Craig and Mullan 2013) and platform targets families with bundled plans, indicating that the presence of children at home might affect the probability of being treated.

2. *Social position and origin* (income, education, and socioeconomic status). Education is linked to higher cultural participation (Sullivan and Katz-Gerro 2006), more diverse tastes (Peterson and Kern 1996), and more intensive digital uses (Robinson 2012). Income facilitates the adoption of subscription-based digital services, and, through increased exposure to diverse social scenes, the diversity of cultural consumption. Socioeconomic status is important as cultural tastes are part of the symbolic boundaries between social groups (Bourdieu 1984). We also adjust for the education and socioeconomic status of parents at age 15 as the teenage years are important in the formation of cultural tastes (ter Bogt et al. 2011; Christin 2012; Nagel and Verboord 2012) as well as for digital skills (Robinson 2012).

3. *Digital uses*. We control for digital uses as it is related to the probability that a user takes up streaming and may also be related, because the internet gives access to other sources of information about culture, to the diversity in cultural consumption.
The next three sets of control variables mainly aim at solving two identification problems. First, we expect that part of the potential effect of streaming platforms on the diversity of taste goes through the intensity of consumption. Streaming platforms may increase time spent consuming cultural items (as they are designed to do, see Seaver 2018), which in turn increases the diversity of tastes. Therefore, we cannot directly control for the intensity of music/movies/TV show consumption (cf. Weingartner 2020), as this would block one indirect causal path that we want to estimate (Caliendo and Kopeinig 2008). To circumvent this problem, we look for variables that measure an underlying propensity toward intensive consumption. Second, reverse causation, that is, a causal effect of the diversity of cultural tastes on the likelihood of using streaming, is quite plausible. Consumers with a higher taste for diversity may be on the lookout for ways to discover new cultural items, and therefore be more willing to pay for streaming services. Here, again, we need to look for controls measuring the underlying propensity toward cultural diversity. We use three sets of control variables to achieve these two goals.

4. *Cultural participation during childhood*. First, we use measures of intensity of consumption and diversity of tastes during childhood. Except for the very youngest respondents (15-20 years old), we can be certain that these were not influenced by streaming platforms, as they did not exist at the time. This is the reason why we use a cutoff age of 20. We use variables related to participation and diversity of tastes in all available cultural domains, as well as variables on parents' tastes.

5. *Intensity of cultural participation*. Then, we take advantage of correlation of intensity and diversity of cultural participation across domains. In the literature, cultural consumption is taken as a whole as a manifestation of underlying dispositions (Bourdieu 1984; Lopez-Sintas and Garcia-Alvarez 2002; Coulangeon and Lemel 2009). Hence, to adjust the music consumption model without introducing bias, we use the intensity of movie, TV show, book, and fine art consumption as a proxy, all variables that we assumed are not caused by streaming on music platforms. We use different sets of variables for each domain, essentially removing variables pertaining to the domain from the set of controls: we remove movies but use music and TV shows in the movie model and remove TV shows but use music and movies variables for the TV show model. Moreover, we also control for the use of streaming platforms in other domains, because this may influence the likelihood of subscribing to a platform.

6. *Propensity toward diversity*. Finally, we use the same strategy to estimate an underlying propensity toward diversity, an idea championed by omnivore research (Peterson 2004). Namely, we introduce the main outcome variable of the model for music, the number of genre consumed, as a control variable for the models for movies and TV shows, and permute them for the two other models. We also include variables measuring diversity in other domains, such as books.
<figure><img src="image_2.png" /></figure>

**Figure 1:** Difference in number of genres consumed, liked, and disliked between streaming users and non-users. Streaming users consume more genres than non-users after controlling for confounders. The difference is small for music (0.1 sd), moderate for movies (0.2 sd), and high for TV shows (0.46 sd). However, differences in number of genres liked or disliked are small or not significant. SMD before (light) and after (dark) adjustment through matching, with error bars indicating 95 percent confidence interval.

# Results

In this section, we present estimates of the effect of streaming use on the diversity of taste across three domains, music, movies, and TV shows.

## Music

The raw difference in the number of different music genres consumed between streaming users and nonusers is fairly large (0.5 sd; see Fig. 1). After matching, only a small, significant net difference remains (0.1 sd). This corresponds to streaming users consuming on average 0.2 genres more than comparable non-users. Streaming users also declare liking slightly more genres than non-users (0.25 sd). The net difference is smaller (0.06 sd) and no longer statistically significant. Finally, while streaming users appear more tolerant than non-users in the whole population (-0.3 sd difference in the number of genres disliked), this difference also disappears when we control for confounders.

Aggregate data hide the heterogeneity of the effect of streaming on specific genres (see Fig. 2). Only three genres appear to be significantly affected by streaming: pop, rock, and electronic and dance music (EDM). EDM is the most affected, with a gap of 8 percentage points between the treated and control groups. All three are
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Difference in music genre consumption between streaming users and non-users. Streaming users have a 7.5 pp higher probability of consuming pop music than non-users after controlling for confounders, compared with 15 pp with no control. Differences in proportions before (light) and after (dark) adjustment through matching, with error bars indicating 95 percent confidence interval.</figcaption></figure>

heavily international, with leading artists usually from the United States or the United Kingdom (except EDM), and lyrics in English, compared with chanson and French rap, two of the most popular genres in France. Moreover, pop and EDM have a younger audience than other genres. Moreover, while streaming users are less likely to listen to chanson and folk than the rest of the population, the difference only remains statistically significant for folk after matching.

## Movies

The effect of streaming use on the diversity of movie consumption follows a similar pattern (see Fig. 1). Streaming users do watch more different genres (0.4 sd) than non-users, but the net difference is reduced (0.2 sd). The small raw difference in the number of genres liked (0.2 sd) is also smaller after adjustment (0.08 sd), while streaming users appear slightly more tolerant than non-users on the whole (-0.1 sd in number of genres disliked), this difference disappears after matching. Overall, streaming appears to have an intermediate, statistically significant, positive effect on movie tastes. Streaming users watch on average 0.6 more genres than comparable non-users.
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Difference in the consumption of foreign-language content between streaming users and non-users. Streaming users consume cultural content in foreign (non-French) languages more often than non-users, after controlling for confounders. The difference is small for music, intermediate for movies, and high for TV shows. Proportion difference before (light) and after (dark) adjustment through matching, with error bars indicating 95 percent confidence intervals.</figcaption></figure>

Moreover, streaming users watch movies in a foreign language (i.e., not originally in French, nor dubbed) more often than comparable non-users (7 percentage points significant net difference, see Fig. 3).

The heterogeneity between genres is again high (see Fig. 4). There is a statistically significant net difference in 9 out of 17 genres watched. The propensity for consuming science fiction, action, adventure, drama, horror, thrillers, and romantic comedies is significantly higher for streaming users (6–9 percentage point gaps); a smaller, positive, statistically significant effect appears for genres such as historical and political movies; interestingly, one genre, documentaries, is negatively impacted by streaming.

## TV Shows

The effect of streaming platforms' use on the diversity of TV show consumption is substantively larger than for music and movies (see Fig. 1). Like the two previous domains, the raw difference in the number of genres consumed is fairly large (0.54 sd). However, unlike the other domains, these differences remain after matching: streaming users watch significantly more TV genres (0.46 sd) than comparable non-users. This amounts to 1.2 supplementary genre liked on average. The two other measures of diversity follow the same pattern as music and movies. A small, statistically significant, positive net difference in the number of TV genres liked (0.18 sd) remains significant after matching, whereas the small tolerance that streaming users display compared to the whole population (-0.1 sd in the number of genres disliked) disappears when controlling for confounders. Finally, the effect on the propensity of cosmopolitan consumption, measured with a question on whether respondents watched shows in another language than French, is very large (56 percent of the treated group and 40 percent of the control group, see Fig. 3). This might be explained by the fact that there are very few subtitled TV shows on linear television, whereas platforms give a choice between subtitled and dubbed versions.
<figure><img src="image_5.png" /><figcaption>**Figure 4:** Difference in movie genre consumption between streaming users and non-users. Streaming users have a 7.5 pp higher probability of consuming Thriller movies than non-users after controlling for confounders, compared with 10 pp with no control. Differences in proportions before (light) and after (dark) adjustment through matching, with error bars indicating 95 percent confidence interval.</figcaption></figure>

Almost all TV genres appear to be favored by streaming users (see Fig. 5). Only two (musicals and erotic TV shows) do not. Conversely, science fiction, adventure, action, and historical shows are heavily influenced by streaming, reaching a 17 percentage point gap for the latter. No genre is less frequent in the matched population.

## Limits, Robustness Checks, and Sensitivity Analysis

The filter bubble theory that we are testing in this article is formally causal. It states that internet platforms reduce the diversity of content that consumers are exposed to too. The estimation of causal effect from cross-sectional, observational data is prone to omitted variable bias. In this situation, it is often advised to not make any causal claim and use a correlational vocabulary. This strategy leads to ambiguity and errors in data analysis and is now criticized (Hernán 2018). In contrast, we prefer to clearly state the question we are asking using a causal vocabulary, and to be transparent as to the limits of our identification strategy. Most limits we point to in this section have to do with these difficulties.

The first limit of our article is thus omitted variable bias. Our identification strategy relies on the assumption that selection into treatment is only linked to confounders that are measured in the survey. We did not identify any unobserved
<figure><img src="image_6.png" /><figcaption>**Figure 5:** Difference in TV shows genre consumption between streaming users and non-users. Streaming users have a 16 pp higher probability of consuming science fiction TV shows than non-users after controlling for confounders, compared with a raw 32 pp difference. Differences in proportions before (light) and after (dark) adjustment through matching, with error bars indicating 95 percent confidence interval.</figcaption></figure>

variables that can plausibly be related to both the treatment and the outcome during our literature review. Moreover, a sensitivity analysis with e-values (VanderWeele and Ding 2017) shows that unobserved confounders could explain away the positive effect on music but likely would not do so for movies and TV shows (Table 2). The set of unobserved confounders would need to increase both the treatment and outcome probability by at least 50 percent for movies and 118 percent for TV shows, compared with 20 percent for music, to reduce the lower bound of the 95 percent confidence interval to 0. We can therefore be confident in the existence of a positive, real effect of streaming on the diversity of TV show consumption, and fairly confident of its existence for movies.

To make sure that our results are not random noise, we also look at the effect of streaming music, movies, and TV shows, identified with matching, on three largely unrelated metrics: the number of genres of radio programs, news, and TV programs consumed (see Table S4 in the online supplement). We expect no effect as these should not be linked to the use of streaming, except for a link between streaming TV shows and the number of different kinds of TV programs consumed. Finding an effect would indicate a problem, likely a residual omitted variable. After
<table><caption>**Table 2:** Sensitivity analysis (EValue).</caption><thead><tr><td>Domain</td><td>SMD (se)</td><td>EValue (CI)</td></tr></thead><tbody><tr><td>Music</td><td>0.10 (0.04)</td><td>1.42 (1.20-)</td></tr><tr><td>Movies</td><td>0.20 (0.04)</td><td>1.70 (1.50-)</td></tr><tr><td>TV shows</td><td>0.46 (0.04)</td><td>2.42 (2.18-)</td></tr></tbody></table>

*Note:* EValue can be interpreted as the effect a set of confounders would need to have on both the treatment and outcome variables to explain away the measured difference.

The main EValue is the one that would reduce the point estimate to zero; in parentheses is the one that would reduce the lower bound of the 95% confidence interval to zero.

matching, all models are consistent with a null effect, except the hypothesized link between streaming TV shows and television programs. Streaming TV shows lowers the diversity of TV programs watched by approximately 0.1 sd, a result that might be explained by the substitution of watching shows rather than other kinds of TV programs.

Second, our strategy to control for the intensity of cultural engagement without introducing bias in the model relies on the assumption that engagement with various cultural domains is correlated and stems from the same generative process. This is the thesis of *Distinction* (Bourdieu 1984): cultural consumption is a product of an underlying habitus, a set of coherent dispositions, which develops early in life and tends to harmonize tastes across domains. Using this assumption, we can take literary tastes, for instance, as a proxy for an underlying cultural disposition that manifests in movies and use it to control for taste without introducing endogeneity. Some have cast doubts on the validity of this thesis (Lahire 2008). However, many studies have shown a high level of correlation across domains, which justifies this approach (e.g., Flemmen, Jarness, and Rosenlund 2018).

Third, while we consider digital uses as a confounder of streaming use and the diversity of tastes, one could argue that it is also an outcome of streaming use. Adopting digital technologies would have a snowballing effect on the use of other technologies. We rerun the analysis with a smaller set of controls, excluding all digital use control variables. The results, shown in Figure S5 in the online supplement, do not differ significantly from the results of the main analysis.

Similarly, we remove respondents aged 15–20 from our main analysis because controls describing their childhood cultural consumption may have been influenced by streaming platforms. We test whether this exclusion significantly impacts our main result by rerunning the analysis on the whole sample. We find no significant difference with the main analysis (see Fig. S6 in the online supplement).

Fourth, it is plausible that the taste and consumption habits of platform users have both a direct (through word-of-mouth) and an indirect (through weighing in on popularity rankings and sending signals to cultural producers) influence on non-users of streaming services (Moreau 2013; Im et al. 2019). Streaming users and non-users are not isolated populations with no interactions. We have no way to account for this effect. However, it would make the tastes of streaming users and non-users converge. This means that if our estimates are impacted by this bias, it is by being pulled toward zero. The true effect of streaming should therefore
be higher than what we estimate. This would not change our main finding that streaming has a positive effect on consumption diversity.

Fifth, our main outcome variable, the number of different genres consumed, corresponds to only one of the two main understanding of omnivorousness, that is, boundary-crossing cultural consumption. It does not measure the breadth of consumption on cultural hierarchies scales. We made this choice because we did not find good measures of cultural hierarchies in the data. The most common strategy for measuring these hierarchies consists of attributing a score to each genre based on the social status of its audience (Robette and Roueff 2014). This strategy suffers from two issues: cultural hierarchies are established by gatekeepers rather than just audiences (Baumann 2007), and there is a high within-genre variance in cultural hierarchies (Childress et al. 2021). Therefore, we use the operationalization of boundary-crossing cultural consumption most often used in the literature.

Finally, we use data from 2018, and from a country, France, with a relatively low prevalence of streaming, compared to other regions in the Western world such as Northern Europe (Lüders et al. 2021). We have no reasons to believe that French consumers would respond differently to the introduction of streaming than consumers from other post-industrial societies. The relatively balanced mix between users and non-users of streaming, moreover, is rather helpful for our analysis: there are many more non-users than in other countries, allowing us to build a more representative control group. Finally, while streaming has progressed since 2018, it is still used by no more than about half of the population, according to more recent survey data.

# Discussion

## Platforms and Diversity: Beyond “Filter Bubbles”

Our results contradict the filter bubble narrative on the effect of platforms on cultural consumption. This narrative, defended in critical academic studies and adopted by many creative workers (Hesmondhalgh 2022), states that platforms tend to lock down consumers in their own taste bubble, giving them more and more content of the same types.

We find that streaming users have more varied tastes and practices than non-users, even after adjusting for known confounders, in all three domains. None of our analysis of the effect of streaming on diversity yields a negative estimate. The literature on the effects of streaming on consumption diversity is contradictory. Our study brings new evidence in favor of a positive effect on streaming on diversity, in line with previous research on the switch to streaming (Datta et al. 2017) while using an entirely different methodology. These results are not incompatible with the findings of articles that study only some affordances of streaming platforms, such as algorithmic recommendation, that measure diversity in artists, or use aggregate measures. For instance, it might be that streaming on the whole increases diversity, whereas the use of algorithmic recommendations decreases it (Anderson et al. 2020), even more so when we consider that few music streaming platform users follow recommendations (Beuscart, Coavoux, and Maillard 2019; Shakespeare and Roth
2021). Moreover, few studies measure diversity over cultural genres and our results are in line with the only other one we could find (Weingartner 2020). Finally, an increase in genre diversity can go hand-in-hand with a decrease in artist diversity, especially because we know that category boundary crossing might be correlated with item exclusivity (Childress et al. 2021).

Our results challenge the “filter bubble” narrative. Most research on this topic has been focused on social media and tends to show that there is no greater network homophily or lack of diversity online compared with offline (Bail 2022). A puzzling fact remains: media and activist discourses still make great use of this narrative (Bruns 2019), in the cultural sector as in others (Hesmondhalgh 2022). Therefore, our results extend the literature on cultural platforms and call for a deflation of discourses around cultural bubbles. Indeed, just like the literature on social media, the focus on diversity may restrict the collective attention given to other, more pressing issues (Bruns 2019), such as the effect of cultural platforms on the production of culture (Pelly 2025).

Finally, we should keep in mind that the magnitude of the effect we measure is rather small, except for TV shows. The effect of streaming on the diversity of taste is rather small; streaming users are not significantly more culturally tolerant than non-users; effects on consumption are concentrated on specific genres, especially the most cosmopolitan ones; and in the only domain where we estimate these effects to be large, TV shows, we have reasons to believe that they should not be attributed primarily to catalog sizes and recommendation algorithms, but rather to platforms investing in movie and TV show production (see next section). Streaming might expand consumer horizons but it does not bring about a revolution in cultural tastes.

## Consumer Experience or Market Power?

We estimate that the effect of streaming platform use on tastes is higher for TV shows than for music and movies. This is surprising for two reasons. First, the literature suggests that streaming platforms primarily reconfigure consumer experience (Hanrahan 2018; Hesmondhalgh 2022). Music consumers have forgone albums in favor of playlists (Hagen 2015; Louail and Barthelemy 2017), music platforms encourage “soundtracking,” passive use of music (Fuentes et al. 2019), teenagers watch TV shows on their smartphones (Matrix 2014), et cetera. In short, the selection, consumption, and valuation of cultural items have drastically changed under the new streaming regime, and this would be an explanation for the widening in tastes. If this is true, we expect music to be the most affected domain, because mobile music consumption is much higher than mobile TV show and movie consumption, and because music consumption has changed more than video consumption. Our data document a 20 percentage point increase in daily music listening between 2008 and 2018 in France (Lombardo and Wolff 2020). Second, movies and TV shows are watched on the same platforms: we would expect both domains to follow similar trends.

Comparing effect size across all three domains in our results, a different picture emerges, with low estimates for music and high estimates for TV shows. We suggest that this can be explained by the producing role of video platforms on
the TV market. The competition in this sector, from private TV channels (HBO) to the many Subscription Video On Demand (SVOD) platforms (Netflix, Disney, Amazon, and Apple) is largely driven by the capacity to offer exclusive content. This means that video platforms are not only broadcasters but also producers of cultural goods. In markets such as Europe, they also have legal obligations to offer local contents, which often means making their own (Lobato 2019), even though, despite these obligations, their catalog is heavily skewed toward U.S. productions (Iordache 2022). There has been a marked increase in local European production after this regulation (Afilipoaie et al. 2021). Although they produce both movies and TV shows, they tend to favor the latter. SVOD consumers spend around 70 percent of their time on TV shows (CNC 2019, 173), and the production of TV shows is less regulated than that of movies. For instance, in France, the law requires that movies are released first in theaters before television. Finally, platforms tend to favor specific genres with less competition, such as science fiction and fantasy (Afilipoaie et al. 2021). In contrast, music platforms do not make music, but only give access to producers' catalogs, often the same from one platform to the other (cf. Pelly 2025).

Our results are consistent with the thesis that the market power of streaming platforms, that is, their influence on the overall offer of TV shows, is what primarily drives changes in consumer taste, rather than the new ways people navigate cultural markets as is hypothesized in the literature. Yet, the digital turn in experience receives most of the focus of current research. Although there are many other dimensions of experience than the diversity of taste, we think this insistence on experience is detrimental to our understanding of the digital turn of cultural consumption because it hinders works on the supply side (Peterson and Anand 2004).

Adjudicating between the two hypotheses, the consumer experience hypothesis and the market hypothesis, is beyond the scope of this article. However, our results suggest that the market hypothesis should be considered first. We are confident that our results rule out that changes in consumer experience explain all or even most of the difference in tastes between users and non-users of streaming platforms, as this would be reflected in equal effects for movies and TV shows. We can interpret the difference between the effect of streaming on TV shows tastes and movie tastes as the lower bound of the market hypothesis. Moreover, we cannot be sure that our estimates for music and movies are linked only to changes in consumer experience. In the music industry, the streaming platforms have not become producers, but they do contribute to changes in production (Pelly 2025). For instance, streaming favors shorter tracks, singles (over albums), and music genres prone to soundtracking (Fuentes et al. 2019).

## Digital and Cultural Inequalities

We find that the prevalence of streaming is higher among the youth and the most well-educated people. This result was expected: digital inequalities are not limited to access to the internet but encompass uses and skills (Deursen and Helsper 2015). Moreover, the sheer cost of streaming platforms hinders their use by poorer consumers. However, this finding is rarely woven together with the literature on
diversity (cf. Weingartner 2020). We also find that using streaming platforms in-
creases the diversity of cultural consumption. Having diverse tastes is now a valued
cultural attitude (Peterson and Kern 1996) adopted by the elites (Friedman and
Reeves 2020). Tastes might transform into social capital (Lizardo 2006; Lewis and
Kaufman 2018; Meuleman 2021) that itself facilitates access to rare resources, such
as high-paying jobs (Reeves and Vries 2019). Our results hold after adjusting for six
sets of confounders (demographics, social position and origin, cultural participation
during childhood, intensity of cultural consumption, the overall propensity toward
diversity, and digital uses). This suggests that the remaining difference is linked to
the use of streaming.

Thus, our study brings new evidence to the literature that suggests that stream-
ing might contribute to increasing cultural inequalities, through widening the gap
in cultural openness (Mihelj, Leguina, and Downey 2019; Weingartner 2020). This is
all the more true because we document a positive effect of streaming on cosmopoli-
tanism and that openness to foreign cultures is particularly well regarded in French
elites (Wagner 2003), while protectionist cultural policies hinder cultural cosmopoli-
tanism in France. The cosmopolitan, omnivore attitude of streaming consumer
parallels that of their curators (Bonini and Gandini 2019) and the cultural workforce
in general (O'Brien and Ianni 2023). In addition to the increased consumption of
foreign-language audiovisual products facilitated by video platforms, the use of
music streaming services similarly enhances exposure to globalized musical genres
(international pop, rock, and electronic music).

That technologies have an influence on cultural inequalities is nothing new,
but they are often thought to reduce these inequalities. Technological innovation
usually makes cultural items cheaper and allows for wider distribution: paperbacks,
vinyls, or VHS favored a wider circulation of books, music, or videos. Why is it
not the case for streaming? One idiosyncratic interpretation would be that digital
technologies are different, more exclusionary than previous innovations. Indeed,
there is a nexus of digital inequalities, from access to skill and participation, which
reinforce each other. However, another interpretation would be that innovations are
first adopted by consumers in the most favored positions, before they are diffused
in the whole social structure. The inequality that we document is clear in the short
term, but we do not know whether, and to what extent, it might persist.

Conclusion

In this article, we describe the differences between streaming users and non-users
in the diversity of cultural tastes in three domains, music, movies, and TV shows,
and we then estimate the effect of streaming on diversity, net of a large number
of confounders (demographics, social position and origin, cultural participation
during childhood, intensity of cultural consumption, the overall propensity toward
diversity, and digital uses), using a matching technique on a 2018 nationally repre-
sentative survey of French cultural consumers. We find that while there are large
raw differences in the diversity of cultural tastes between users and non-users,
the remaining difference, after adjusting for confounders, is always statistically
significant and positive, but small for music, intermediate for movies, and large
for TV shows. Streaming users listen to more music genres (0.1 sd, 0.2 genres) and movie genres (0.2 sd, 0.6 genres) than comparable non-users. They are not more tolerant, as measured by the number of genres disliked. They watch substantially more different TV shows genres (0.46 sd, 1.2 genres). Finally, streaming tends to favor cosmopolitan attitudes: streaming users watch more movies and more TV shows in foreign (non-French) languages.

These results carry several implications. First, the filter bubble thesis, the idea that platforms trap consumers inside their own taste, is probably wrong, at least for cultural platforms. Second, the positive effect of platforms on diversity might increase cultural inequalities, as streaming adoption is higher among the richest, most educated people, and as diversity is positively valued. Finally, the large difference in the magnitude of our estimates between domains calls for more attention to the mechanisms used to explain the effect of streaming on cultural consumption. It might be that the effect of the change in experience is small compared to the effect of streaming platforms on the market.

## Notes

1. According to Spotify's own figures, see https://newsroom.spotify.com/company-info/.

2. Figures from Statista https://www.statista.com/statistics/1013571/netflix-library-size-worldwide/.

3. The most common technique, propensity score matching, heavily relies on the “common support” assumption. For each treated individual, a non-treated individual with the same propensity to receive treatment must exist in the sample. This constraint is relaxed with cardinality matching techniques.

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**Acknowledgments:** This article benefited greatly from comments by Quentin Mazel, Patrick Präg, Léa Pessin, and anonymous reviewers, as well as from the audiences of AFS 2023, ESA-RN05 Midterm 2023, ECSR 2023, Culture in a digital context conferences, and the CREST sociology seminar.

**Samuel Coavoux:** CREST, ENSAE, Institut Polytechnique de Paris, Paris, France.
E-mail: samuel.coavoux@ensae.fr.

**Abel Aussant:** Sciences Po, CRIS, Paris, France. E-mail: abel.aussant@sciencespo.fr.