Online video sharing and revenues during the pandemic. Evidence from musical stream data

ABSTRACT

This study examines how instant online video sharing affects artists’ musical streams during the pandemic. On average, the use of the TikTok app significantly increases artists’ streams, by approximately 5%. This increase is even higher for male, European and dj Mag 2020 new entry artists.

KEYWORDS:

• Covid-19
• streams
• online video sharing
• culture

JEL CLASSIFICATION:

• I1
• L82
• Z10

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Dj Mag list provides an important measure of success in its own right as it provides a proxy for an artist’s fan base. This metric has become as important as traditional variables used to measure appeal.

2 https://thedjlist.com/djs

3 https://djrankings.org/about_rankings

4 Different effects of the pandemic on the subgroups can shed light on the long-run effect of the pandemic on the shift in sources of revenues in the music industry.

5 This voting list is the priming source on which new dj artists and their songs become discovered.

6 Based on reports of TikTok’s prevalence on dance music industry over the last 24 months, the use of this online tool by dj artists has a strong positive correlation with their rankings on dj Mag list for year 2021, in which no dj gigs took place due to Covid-19 restrictions.

7 Data drawn by https://chartmetric.com/

8 The large value for chi-square in the Goodness of fit (gof) was an indicator that the Poisson distribution is not a good choice. A significant (p < 0.05) test statistic from the gof indicates that the Poisson model is inappropriate. We used nbreg Stata command which fits a negative binomial regression model for a nonnegative count dependent variable. In this model, the count variable is believed to be generated by a Poisson-like process, except that the variation is allowed to be greater than that of a true Poisson. This extra variation is referred to as overdispersion.

9 We expect similar to OLS results because on the distribution of Spotify we had not many zeros and low values close to zero values.