Luis Aguiar: Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists

Platforms, Promotion, and Product Discovery:
Evidence from Spotify Playlists
(and Playlisting Favorites: Is Spotify Gender-Biased?)
Luis Aguiar1
Joel Waldfogel2
(Sarah Waldfogel3
)
1
University of Zurich
2 University of Minnesota and NBER
3 University of Wisconsin
Workshop on Designing Human-Centric MIR Systems
Delft – November 2, 2019

Introduction
Motivation: Digitization & Promotion in Music Industry
1 Decrease in costs of production and entry:
• Led to a huge amount of music being released
• ≈ 1 mil. new songs added to Spotify in 2017, over 35 mil. in total.
• Daunting product discovery challenge for consumers.
2 Change in market structure:
• Old days: Distribution and promotion were fragmented. Lots of radio stations as well as
record stores.
• Digitization: Few platforms now collectively dominate promotion and distribution.
• Spotify is a very important player (37% of streaming market).
Luis Aguiar (Univeristy of Zurich) Workshop on Designing Human-Centric MIR Systems Delft, November 2, 2019 1/45

Introduction
The Importance of Playlists
• Promotion mostly done via playlists (“playlists as the new radio”).
• Potentially informative lists of songs.
• Utility for playing listed songs, in ranked or random order.
• At Spotify, now a dominant curator/retailer:
• Free entry in playlists (anyone can create a playlist)
• But 25 most-followed lists are all Spotify-owned, with high share of total followers.
• Questions:
• Does Spotify have power to inﬂuence users’ listening decisions, via its playlists?
• Could this results in an abuse of power and biases in playlists?

Introduction
Today’s Plan
1 Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists
• Research Question: Does Spotify have the ability to influence users’ listening decisions?
Does playlist inclusion affect the number of streams that songs receive?
Does it affect consumers’ discovery of new songs and artists?
• We find large and significant effects of playlist inclusion.
2 Playlisting Favorites: Is Spotify Gender-Biased?
• Research Question: Are Spotify playlists biased, with respect to gender?
Concerns about treatment of women in - among many others -the entertainment industries
Develop tests to check for bias
• Evidence of pro-female bias on some playlists. Mostly no bias on others.

1 Introduction
2 Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists
• Global Curated Playlists
• Global Algorithmic Playlists
• New Music and Product Discovery
3 Playlisting Favorites: Is Spotify Gender-Biased?
• Conditioning on Observables - Global Lists
• Conditioning on Observables - New Music Friday
• Outcome Based Bias Test

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists
• Daily top 200 Spotify streams in 26 countries in 2016 and 2017
(https://spotifycharts.com).
• Playlist data: Observe rank as well as dates of entry/exit on major playlists
(Spotontrack.com).
• Focus on two broad types of playlists:
Playlist Name Followers (millions)
Today’s Top Hits 18.5
RapCaviar 8.6
¡Viva Latino! 6.9
Baila Reggaeton 6.3
Global Top 50 11.5
New Music Friday 6.4



Known Music – Global & Curated
Known Music – Global & Algorithmic
New Music – Country-Speciﬁc & Curated
Desc.

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists Global Curated Playlists
Empirical Strategy for Global Curated Playlists
• Study four biggest global and curated lists:
• Today’s Top Hits (18.5 mil. followers)
• RapCaviar (8.6 mil. followers)
• ¡Viva Latino! (6.9 mil. followers)
• Baila Reggaeton (6.3 mil. followers)
• Global playlists’ features:
• songs are already in streaming chart before add.
• number of followers jump/falls at add/drop.
• unlimited duration on the list.
• What happens to streams with the discontinuous jumps in followers after the add
(drop) to a playlist?

Daily Followers and US Streams for a Song added to Today’s Top Hits.
5
10
15
20
25
30
Followers(millions)
.15
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.25
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.35
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.45
.5
Streams(millions)
28m
ay201711jun201725jun201709jul201723jul2017
06aug2017
20aug2017
03sep2017
17sep201701oct201715oct201729oct2017
12nov2017
26nov2017
10dec2017
24dec2017
Date
Streams Followers Playlist Inclusion
What Ifs by Kane Brown

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists Global Curated Playlists
Implementation via regression
sict = γτ + µic + πd + εict
• sict is a measure of streaming for song i in country c on day t
• Normalize streams to make data comparable across countries:
sict = streamsict
i∈c
streamsi
× 1, 000, 000.
• γτ are leads and lags, where τ refers to the days since the event (until the event
when τ < 0).
• πd is a day of the week effect
• µic is a country-specific song fixed effect
• εict is an error term.
• We can then plot the coefficients γτ against τ.

Normalized streams before and after add/removal events at Today’s Top Hits.
sict = γτ + µic + πd + εict
−6−5−4−3−2−101234567
normalizedstreams
−30−28−26−24−22−20−18−16−14−12−10−8−6−4−2
0
2
4
6
81012141618202224262830
days around add
Add
−8−7−6−5−4−3−2−101234
normalizedstreams
−30−28−26−24−22−20−18−16−14−12−10−8−6−4−2
0
2
4
6
81012141618202224262830
days around drop
Drop
Note: 0 days around the event date corresponds to the last fully untreated day. 3 days after the event date
corresponds to the first fully treated day. Observations within the gray bands therefore correspond to partially
treated days.
The eﬀect estimate = the ﬁrst fully “treated” day (τ = 3) less the last fully “untreated” day (τ = 0)

Effect Estimates - Normalized Streams.†
Today’s Top Hits RapCaviar ¡Viva Latino! Baila Reggaeton
(add) (drop) (add) (drop) (add) (drop) (add) (drop)
Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e.
Add 3.346∗∗∗ 3.047∗∗∗ 3.211∗∗∗ 2.152∗∗
(0.28) (0.60) (0.75) (1.03)
Drop -2.757∗∗∗ -1.371∗∗∗ -1.863∗∗∗ -1.390∗∗
(0.09) (0.15) (0.37) (0.66)
R2 0.901 0.944 0.862 0.804 0.791 0.763 0.901 0.859
N 65650 85961 28896 35622 9807 13123 8428 11635
† The dependent variable is the total normalized streams defined as daily song streams in a country divided by the (country’s total
2017 streams/1,000,000). The sample includes song-country observations that fall within a 30 day window around the add (drop)
date. For the add specifications, the table reports the coefficient on an indicator variable equal to 1 one day after inclusion on
the list, as explained in the text. For the drop specifications, the table reports the coefficient on an indicator variable equal to
1 two days after exclusion from the list, as explained in the text. All specifications include song-country fixed effects and day of
the week fixed effects. Standard errors are clustered on the song-country level and are in parenthesis.
∗∗ Significant at the 5% level.
∗∗∗ Significant at the 1% level.
• Positive effect of an add, negative effect of a drop.
• Add effect larger than drop effect, suggesting decay.
• Assume that the effect evolves linearly and take the average of the add and removal effects for each
playlist.

Per-Song Value of Appearance on Global Lists.†
Worldwide Worldwide Daily Overall
Playlist Daily Streams Overall Streams Payment ($) Payment ($)
Today’s Top Hits 259,532 19,399,550 1,030 77,016
RapCaviar 187,862 10,044,227 746 39,876
¡Viva Latino! 215,777 50,507,751 857 200,516
Baila Reggaeton 150,615 27,384,199 598 108,715
†
The Worldwide Daily Streams column corresponds to the average daily effect (calculated as the average of the estimated
add and removal effects) times the total number of global streams in 2017 (85,047 million streams). The figures in the
Worldwide Overall Streams column are obtained by multiplying the worldwide daily streams by the average spell length
and by the number of spells per song. The Daily Payment (Overall Payment) column correspond to the worldwide daily
(overall) streams multiplied by our best available information on the Spotify payment per stream ($3.97 per thousand
streams).
• Large share of listed songs’ total streams is attributable to the playlist:
• E.g. for Today’s Top Hits: 19.4
86.0
= 22.6%
• Based on Spotify’s payment of $3.97 per thousand streams. Global

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists Global Algorithmic Playlists
Eﬀect of Inclusion on Global Top 50 Playlist
• A song’s playlist rank today is its streaming rank yesterday
• Do streams fall discontinuously between today’s playlist #50 and the song that just
missed the list (yesterday’s song whose streaming rank was #51)?
log
srt
sr−1,t
= θr + εrt,
• srt is global streams at rank r on day t
• θr is an estimated parameter
• εrt is an error term.
• If we plot these θr coeﬃcients in the neighborhood of θ51, is there a jump?

Percent reduction in streams moving from (r − 1)th
to rth
rank.
log srt
sr−1,t
= θr + εrt
−.1
−.08
−.06
−.04
−.02
0
.02
log(streams(r)/streams(r−1))
40
41
42
43
44
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46
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48
49
50
51
52
53
54
55
56
57
58
59
60
Global Top 50 Ranking
estimate Upper 95% confidence limit
Upper 95% confidence limit
Global Top 50 and Rank Streaming Gradient
−.1
−.08
−.06
−.04
−.02
0
.02
log(streams(r)/streams(r−1))
40
41
42
43
44
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46
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49
50
51
52
53
54
55
56
57
58
59
60
Global Top 50 Ranking
estimate Upper 95% confidence limit
Upper 95% confidence limit
Global Top 50 and Rank Streaming Gradient
Magnitudes

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists New Music and Product Discovery
New Music Friday Playlists and Product Discovery
• New Music Friday playlists present 50 new songs every week, by country
• Curated lists, songs are on for 7 days.
• Songs have no pre-list streaming history, so we cannot use before and after
approach
• Need diﬀerent empirical strategy
• Diﬀerent lists by country, although substantial overlap
• These are new songs and sometimes new artists, so results shed light on product
discovery.

Raw data: NM Ranks and Streaming Success
The higher the rank on New Music Friday,
the more likely to appear on the charts.
But two directions of causality: playlist ranks
affects success. And anticipated success
affects playlist inclusion and rank.
0
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.5
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.7
.8
.9
ShareAppearinginDailySpotifyCharts
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
New Music Friday Rank and Spotify Chart Appearance
top 200 top 100
Still, suggestive that higher recommendation ranks matter for performance.

Empirical strategy: Cross-country variation in song ranks.
R3HAB
Migos
Lost Kings
Aaron Carter
Moon Taxi
Delaney Jane
6ix9ine
Russ
Glades
Camila Cabello
Tove Lo
Camila Cabello
The Neighbourhood
G−Eazy
Moose Blood
The Wombats
Eminem
Ty Dolla
1
5
10
15
20
25
30
35
40
45
50
60
NewMusicFridayRankintheU.S.
1 5 10 15 20 25 30 35 40 45 50 60
New Music Friday Rank in Canada
Dec 10, 2017
Note: 60 indicates not ranked.
New Music Friday Ranks in US and Canada

Empirical strategy
• Rely on cross-country variation in song ranks.
• Take the view that countries have similar tastes but are treated with different
rankings.
• Measure effect of New Music Friday rankings by comparing streaming
performance of the same songs in different countries where they have received
different ranks.
• Streaming performance measured as the appearance in top streaming charts
(top 200).

Implement via song fixed effects regression
D200
ic = αr
δr
ic + µc + ηi + εic.
• D200
ic equals 1 if song i appears on country c’s Top 200 charts.
• δr
ic equals 1 when song i in country c is ranked rth
.
• ηi and µc are song and country fixed effects, respectively.
• If the unobserved quality of song i is the same in different countries, then ηi
controls for the unobserved heterogeneity.
• Then αr
shows the effect of being ranked r on the streaming success (relative to
the 50th
ranked song).

New Music Friday Rank and Streaming Success.
D200
ic = αr
δr
ic + µc + ηi +εic.
Song FE shrink effects a lot, but still
big for ranks 1−10.
Being ranked #1 on New Music Friday list
raises probability of appearing on the
streaming chart (among the daily top 200)
by about 50 percentage points.
0
.1
.2
.3
.4
.5
.6
.7
.8
.9
ShareAppearinginDailyTop200
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
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32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
New Music rank
OLS Song Fixed Effects
New Music Friday Rank Effects − Overall

Robustness & New Artists
• Underlying assumption is that perceived song quality is the same across countries,
so you might worry about:
1 Countries diﬀering in their tastes: perceived quality could diﬀer across countries
⇒ Consider subset of countries with similar preferences:
2 New Music Friday ranks may be subject to home bias
⇒ Remove domestic music and consider foreign songs only:
• Focus on new artists (i.e. exclude new songs from known artists) also leads to similar
results.
• New Music Friday lists play an important role in the discovery of new artists!
Robustness

New Music Friday Eﬀects Over Time
−.05
0
.05
.1
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.3
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0
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Top 5 Top 6−10 Top 11−30
Parameter Estimate Upper 95% Confidence Interval
Lower 95% Confidence Interval
ShareinDailyTop200
by Rank
Days since appearance
Graphs by Rank
Effect of Appearing in New Music Friday on Top 200 Charts
• Eﬀect lasts well beyond the 7 days on the list and is therefore not merely mechanical. Details

How Large is the New Music Friday Eﬀect?
sic = αr
δr
Essentially zero effect for ranks 10+
Being ranked #1 adds 550 normalized streams.
14 million additional streams for a song ranked #1
on the U.S. chart.
Worth about $55,000.
−50
0
50
100
150
200
250
300
350
400
450
500
550
600
650
Normalizedstreams
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New Music Rank
Parameter estimate Upper 95% confidence limit
Lower 95% confidence limit
New music rank and cumulative additional streams
Details

Platforms, Promotion, and Product Discovery: Evidence from Spotify Playlists
Conclusion
• Playlists have a substantial causal impact on streaming and songs’ success.
• Promotion - Playlists deliver substantial share of listening to already-famous songs.
• Eﬀect is a big share of total streams for songs on global curated playlists.
• Today’s Top Hits: $77,000.
• Discovery - raise the probability that new songs succeed, including those by new
artists
• Robust to a variety of approaches.
• Being #1 on US New Music Friday list is worth $55,000.

Playlisting Favorites: Is Spotify Gender-Biased?
Motivation
• Major concerns about treatment of women in - among many others - the motion
picture industry (Harvey Weinstein scandal, #MeToo movement ...).
• Recorded music industry has also come under scrutiny, raising concern about
anti-female bias.
• Explore gender-bias concerns in the music industry and develop tests to look for
gender bias in playlist inclusion.
• Focus on Spotify and look at their major playlists’ inclusion decisions
• Focus on Spotify’s own and most popular curated playlists (i.e. not algorithmic)
• Findings: Evidence of pro-female bias on some playlists. Mostly no bias on others.

Data - Gender Definitions
• We code individual artists as male or female if we can ascertain gender (unknown
if we cannot)
• Our measure is therefore of “perceived” gender.
• For bands: coded male or female if all prominent artists (e.g. those included in
official band photographs) are of the same gender. Otherwise: “mixed.”
• Gender definitions: 4 categories of songs (for the case of songs with 2 artists)
1 “All Female”: Both artists are female
2 “Female or Mixed”: First artist female, second female or mixed.
3 “First Artist Female”: First artist female, regardless of second.
4 “Either Female”: Either artist is female.

What share of playlists are occupied by female artists?
Gender Shares of Songs and Streams.†
All Female Female or Mixed First Artist Female Either Female Number of Songs
New Music Friday 21.3% 21.9% 28.0% 37.5% 18489
Today’s Top Hits 16.3% 17.7% 29.8% 41.1% 141
¡Viva Latino! 2.4% 2.4% 21.4% 40.5% 42
Baila Reggaeton 3.1% 3.1% 10.9% 23.4% 64
RapCaviar 1.2% 1.8% 7.9% 11.6% 164
Mint 0.0% 0.0% 4.5% 18.2% 44
Are & Be 5.3% 5.3% 10.5% 15.8% 19
Rock This 4.0% 4.0% 4.0% 4.0% 25
Top Artists Sample 13.0% 13.2% 16.4% 22.0% 6650
Top Artists Sample - Streams 12.4% 12.7% 22.5% 31.8% 6650
†
For each of the global playlists, the table includes songs that appeared in the Top Artists sample.
By Country

Testing for Bias
• Two broad ways to test for bias in the composition of a playlist.
1 Conditioning on observables and asking whether female-driven songs are more or less
likely to be included (or ranked) on playlists relative to male artists.
2 Examine the streaming success of playlisted songs (outcome-based test).
• Infer that songs that perform better (conditional on the rank they received) have faced bias.
• E.g. if two songs receive the same rank but one of them is more successful, then the latter will
have faced bias since it deserved a better rank.

Playlisting Favorites: Is Spotify Gender-Biased? Conditioning on Observables - Global Lists
Idea: Given past streams, are Male & Female songs similarly
likely to make Today’s Top Hits?
• Prob of inclusion on TTH rises
with past streams
• Prob of inclusion is higher for
female than male songs,
conditional on past streams.
0
.02
.04
.06
.08
.1
.12
smoothedprobonlist
10 12 14 16 18 20
2016 log streams
female non−female
First Artist Female
Past Streams and Appearance on Today’s Top Hits

Conditioning on observables: Global editorial lists
• Probability of appearing on global editorial list, conditional on observables:
• Dummy for female artist
• Past streams (2016, for the ﬁrst artist)
• Indicator for artist who lacks past streams
• Song characteristics: Origin, bpm, danceability, valence, etc.
• Music genre (ﬁrst artist’s reported genre in Allmusic).
• Use logits, but also:
• LASSO logits as a robustness to select control variables
• Linear probability models give similar results.

Streaming Success by New Music Friday Rank and Gender
• Some positive coeﬃcients
(indicating pro-female bias)
for Today’s Top Hits and Viva
Latino
• All coeﬃcients are negative
(anti-female bias) for Rock
This, although number of
sample songs on Rock This is
small.
• Overall: little systematic
evidence of gender bias in
the major global playlists.
−4.5
−4
−3.5
−3
−2.5
−2
−1.5
−1
−.5
0
.5
1
1.5
2
2.5
Pro−femaleBias Are & Be Baila
Raggaeton
Mint Rap
Caviar
Rock
This
Today’s
Top Hits
Viva
Latino
All Female Female or Mixed First Artist Female Either Female
Global List Inclusion − Logit

Playlisting Favorites: Is Spotify Gender-Biased? Conditioning on Observables - New Music Friday
Given past streams, do Male & Female songs get similar New
Music Friday ranks?
• Conditional on past streams,
New Music Friday ranks are
higher (worse) by about 1 for
male artists.
5
6
7
8
9
10
11
smoothedNMrank
12 14 16 18 20
log 2016 artist streams
Female Male
First Artist Female
Past Streams and US New Music Friday Ranks

Playlisting Favorites: Is Spotify Gender-Biased? Conditioning on Observables - New Music Friday
Conditioning on observables: New Music Friday lists
• Focus on Top 20 New Music Friday ranks
• ? found significant impact of list inclusion for Top 20 only.
• Regress New Music Friday rank on:
• Dummy for female artist
• Country fixed effects
• Past streams (2016, for the first artist)
• Indicator for artist who lacks past streams
• Song characteristics: Origin, bpm, danceability, valence, etc.

New Music Friday Playlist Rankings (among Top 20). †
(1) (2) (3) (4) (lasso)
Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e.
All Female -0.2516∗∗
-0.2529∗∗
-0.6489∗∗∗
-0.6844∗∗∗
-0.6524∗∗∗
(0.105) (0.105) (0.100) (0.102) (0.101)
Female or Mixed -0.3568∗∗∗
-0.3587∗∗∗
-0.7181∗∗∗
-0.7479∗∗∗
-0.7179∗∗∗
(0.104) (0.104) (0.099) (0.101) (0.100)
First Artist Female -0.6332∗∗∗
-0.6362∗∗∗
-0.7874∗∗∗
-0.7845∗∗∗
-0.7706∗∗∗
(0.095) (0.096) (0.091) (0.093) (0.092)
Either Female -0.7857∗∗∗
-0.7894∗∗∗
-0.7524∗∗∗
-0.7197∗∗∗
-0.7327∗∗∗
(0.088) (0.088) (0.084) (0.085) (0.085)
Country Fixed Eﬀects
Past Streams × Country FE
Song Characteristics
No. of Obs. 18233 18233 18233 18233 18233
†
The dependent variable is the ranking position on the New Music Friday playlist. Each row corresponds to a distinct regression.
∗ Signiﬁcant at the 10% level.
• Songs by women receive lower (i.e. better) ranks on New Music Friday, by roughly 0.7 rank
on average.

Playlisting Favorites: Is Spotify Gender-Biased? Outcome Based Bias Test
Outcome Based Bias Test
• Measure bias based on streaming outcomes.
• Suppose editor choosing what rank to give a song on New Music Friday.
• If biased against certain type of songs, they’ll give them higher (worse) ranks than they
warrant, in the sense of the songs’ tendency to be popular (streamed)
• A song facing bias will receive worse rank and will stream more (be more
successful) compared to songs with the same rank that don’t face bias.

Streaming Success by New Music Friday Rank and Gender
• Songs with better ranks are
more likely to appear in Top
200.
• Songs by female artists
ranked 11-20 tend to have
lower streaming success.
• Consistent with bias in favor
of female artists. 0
.1
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ShareappearinginstreamingTop200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
First Artist Female
Making the Top 200 by New Music
Friday Rank and Gender
Female Non Female

Streams Conditional on Rank and Gender
• Female songs ranked 11-20
achieve less streaming
success, consistent with
pro-female bias
• Female songs ranked 1-10
have lower streaming
success in only two of eight
tests.
−.09
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−.07
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Pro−MaleBias
top200
1−10
top100
1−10
top200
11−20
top100
11−20
All Female Female or Mixed First Artist Female Either Female
New Music Friday
Outcome−Based Test

Discussion Implications
1 Are these results plausible? Spotify has participated in initiatives to promote female
music listening, so could be strategic decision.
2 Spotify’s curated playlists decisions do not depress the female share of music
streaming on the platform (22.5 % according to “First Female Artist” measure).
• Possible explanations for this low share:
• Consumer preferences for male music
• Share of female music entering the platform: only 19% of songs entering Spotify in 2017
were by female artists.
• But women make up a higher share at musicians than songs entering Spotify (37%, BLS).

Conclusion
• Music streaming platforms have consolidated the roles of promotion and
distribution into the roles of a few players.
• Playlists have an important effect on streaming success
• Worry about possible bias in playlist placement.
• We find little or no evidence of gender bias at Spotify’s major global playlists.
• We find pro-female bias in the lower ranks of New Music Friday playlists
• Spotify does not appear to be responsible for the low female streaming shares.
Instead, Spotify appears to be affirmatively favoring female artists on its New Music
Friday lists.

Thank you!
luis.aguiar@business.uzh.ch
www.luis-aguiar.com
@luisaguiarw

Playlists Characteristics.†
Adjusted
Nb. of Songs not Mean Spell Mean Spell Mean Spell Median Mean
Playlist Name Songs Streaming Listings Duration Duration Per Song Streams Streams
Today’s Top Hits 226 26 12,152 54.2 74.4 1.004 29.9 86.0
RapCaviar 458 165 15,242 39.1 49.8 1.074 6.1 34.3
¡Viva Latino! 111 13 12,158 111.0 227.9 1.027 36.1 58.6
Baila Reggaeton 141 21 12,980 96.9 181.8 1.000 7.8 38.5
Global Top 50 434 0 18,250 30.2 37.1 1.383 37.5 92.8
New Music Friday 20,621 52,851
† Note: Streaming volumes and durations refer to songs that we observe streaming at some point during the 2017 sample period, across
all 26 sample countries. For the Global Top 50 playlist, streaming volumes and durations refer to songs that are included in the ﬁnal
estimation sample as explained in the text. Adjusted mean spell durations are derived from a censored regression of spell duration
on a constant. Songs already on the list at the start of the respective playlists sample, or still on the list at the end, are treated as
censored. New Music Friday followers are across 26 countries.
• Censored durations: some songs already on list at start of sample period, others still
on list at end.
• Use censored regression to uncover underlying mean duration
back

How large is this eﬀect in terms of streams?
log
srt
sr−1,t
= α + βRankt + δ D51 + εrt,
• D51 is a dummy = 1 for the 51st
rank.
• δ = −0.047 , s.e. = 0.008
• Average streams for a song ranked 50th
is 1,242,513.
• Average duration on Global Top 50 chart is 51.24 days.
⇒ inclusion raises streams by 0.047 × 1, 242, 513 × 51.24 = 3,021,867
• 3
92.8 ≈ 3.3 percent of streams arise from Global Top 50 charts.
back

D200
ic = α1 δ1-5
ic + α2 δ6-10
ic + α3 δ11-30
New Music Friday Rank Effects - Robustnes New Artists.†
US,GB, CO,ES, No New New Artist,
OLS Song FE CA MX Domestic Artist No Domestic
Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e. Coef./s.e.
NM Rank: 1-5 0.674∗∗∗ 0.401∗∗∗ 0.396∗∗∗ 0.266∗∗∗ 0.349∗∗∗ 0.459∗∗∗ 0.384∗∗∗
(0.05) (0.03) (0.06) (0.07) (0.03) (0.09) (0.12)
NM Rank: 6-10 0.351∗∗∗ 0.221∗∗∗ 0.240∗∗∗ 0.093∗∗ 0.194∗∗∗ 0.145∗∗∗ 0.129∗∗
(0.03) (0.03) (0.05) (0.04) (0.03) (0.05) (0.05)
NM Rank: 11-30 0.080∗∗∗ 0.048∗∗∗ 0.036 0.001 0.043∗∗∗ 0.015 0.022∗
(0.01) (0.01) (0.03) (0.02) (0.01) (0.01) (0.01)
Song Fixed Effects
R2 0.349 0.763 0.917 0.904 0.728 0.729 0.644
No. of Obs. 46184 46184 6373 5033 37507 2221 1745
† The dependent variable is an indicator for whether a song appears in the daily top 200 Spotify streaming charts. All specifications
include country fixed effects. Standard errors are clustered at the rank level and reported in parenthesis. The sample includes
only the weekly top 50 New Music Friday recommendations, as the lists usually but do not always include 50 songs.
Back

Effects over Time
Are New Music Friday effects persistent?
• If listeners use New Music Friday playlists as a utility for playing recommended
songs, we would expect a clear effect during the week that songs remain on the
list.
• If list inclusion provides information about songs, we should observe a continued
effect past the time on the list.
D200
icτ = αr
τ δr
ic + µc + ηi + εicτ .
• D200
icτ = 1 if song i appears on country c’s top charts τ days after appearance on
country c’s New Music Friday list.
Back

How large is the New Music Friday Eﬀect?
• What is the impact of New Music Friday list inclusion on total number of streams?
• Can construct measures of country-level (normalized) streams for each songs and
estimate:
sic = αr
δr
• Caveat: only observe streams when song is in the daily top 200
• Understates streaming, especially for lower-ranked songs.
Back

Who beneﬁts from playlist inclusion?
Characteristics of Streamed and Playlisted Songs.†
Country Global Today’s Rap ¡Viva Baila New Music
Streaming Data Streaming Data Top Hits Caviar Latino! Reggaeton Friday
Indie % of Listings 46.6% 21.9% 25.6% 28.7% 28.2% 41.2% 53.3%
Indie % of Songs 47.5% 24.1% 24.3% 33.8% 31.3% 43.3% 65.2%
Indie % of Streams 27.4% 19.0% 22.2% 17.9% 14.7% 15.0% -
US % of Listings 26.1% 72.5% 71.3% 96.6% 78.0% 78.7% 37.7%
US % of Songs 25.5% 71.1% 72.1% 95.4% 74.1% 76.6% 29.9%
US % of Streams 59.2% 71.2% 72.9% 98.3% 82.8% 81.9% -
Domestic % of Listings 27.0% - - - - - 18.0%
Domestic % of Songs 25.0% - - - - - 18.0%
Domestic % of Streams 25.2% - - - - - -
† For the country streaming data and the New Music Friday data, the domestic percentages reported correspond to the average of the
country-speciﬁc shares of domestic songs (as well as listings and streams).
• US artists and major labels are big on global curated lists.
• Non-US artists and indie labels are bigger on NM lists.
• (Bias - by label or origin - is an interesting question but hard to document).

Total Sample Streams during 2017 (in millions).
Country Streams
Brazil 6,663.5
Canada 3,107.3
Switzerland 475.0
Colombia 815.8
Germany 5,931.7
Denmark 1,486.5
Spain 3,671.8
Finland 1,223.8
France 3,060.8
Great Britain 7,018.6
Hong Kong 289.8
Indonesia 1,253.4
Iceland 79.4
Italy 2,322.6
Mexico 6,186.0
Malaysia 637.4
Netherlands 3,390.9
Norway 1,967.5
Philippines 3,253.6
Poland 764.4
Portugal 431.6
Sweden 3,316.2
Singapore 744.5
Turkey 899.2
Taiwan 435.8
United States 25,620.5
Total 85,047.3 back

New Music Friday lists, by country
0
.05
.1
.15
.2
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femaleshare
Pola
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exic
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C
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bia
D
enm
ark
N
eth
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U
nited
Sta
te
s
Portu
galIc
ela
ndSpain
Italy
BrazilC
anadaSw
edenFranceN
orw
ayFin
la
nd
G
reatBritainM
ala
ysia
G
erm
any
Sw
itzerla
nd
Sin
gapore
Philip
pin
esTaiw
an
In
donesiaTurkey
H
ong
Kong
All Female
0
.05
.1
.15
.2
.25
femaleshare
Pola
ndM
exic
o
D
enm
ark
C
olo
m
bia
N
eth
erla
nds
U
nited
Sta
te
s
Portu
gal
ItalyIc
ela
ndSpainC
anadaBrazilSw
edenFranceN
orw
ayFin
la
nd
G
reatBritainM
ala
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G
erm
any
Sw
itzerla
nd
Sin
gapore
Philip
pin
esTaiw
an
In
donesiaTurkey
H
ong
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Female or Mixed
0
.05
.1
.15
.2
.25
.3
.35
femaleshare
Pola
nd
D
enm
ark
N
eth
erla
nds
C
olo
m
biaM
exic
oPortu
gal
ItalyIc
ela
ndBrazil
U
nited
Sta
te
sC
anadaFin
la
ndSpainFranceSw
edenN
orw
ay
Sw
itzerla
nd
G
reatBritain
G
erm
any
In
donesiaTurkeyTaiw
an
M
ala
ysia
Sin
gapore
Philip
pin
es
H
ong
Kong
First Artist Female
0
.05
.1
.15
.2
.25
.3
.35
.4
.45
femaleshare
M
exic
o
N
eth
erla
nds
C
olo
m
biaPortu
gal
Italy
D
enm
arkBrazil
U
nited
Sta
te
sSpainPola
ndIc
ela
ndC
anadaFin
la
ndFrance
G
reatBritainSw
eden
Sw
itzerla
ndN
orw
ay
In
donesiaTaiw
an
G
erm
any
Philip
pin
esTurkey
Sin
gapore
H
ong
Kong
M
ala
ysia
Either Female
among the top 20
Female share of New Music Friday in 2017
Back

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