Paywalls: Monetizing Online Content

Marketing Science Institute Working Paper Series 2017 Report No. 17-107

Adithya Pattabhiramaiah, S. Sriram, and Puneet Manchanda

MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not to be reproduced or published in any form or by any means, electronic or mechanical, without written permission. Report Summary

While consumption of digital content (news, music, TV, etc.) is growing by leaps and bounds, consumer willingness to pay for this content is still low. Nevertheless, in the context of newspapers, the paywall instituted by the New York Times in March 2011 is a well-publicized case of an increasingly popular trend toward online monetization in the industry. While a paywall may generate a new source of income in the form of subscription revenue, the externalities that might arise as a consequence of this pricing change are unclearmaking it hard to pinpoint the overall impact of paywall implementation.

Here, Adithya Pattabhiramaiah, S. Sriram, and Puneet Manchanda use a difference-in-difference estimation strategy to study three potential externalities of newspaper paywalls, and compare them against the new direct subscription revenue generated. The first externality is the effect of a paywall on the engagement of its online reader base. Any possible differences in engagement likely impact the newspaper’s ad revenues; this is termed the indirect effect of the paywall. The net indirect effect of paywalls is likely dependent on the relative magnitudes of the changes in the quantity and quality of ad impressions subsequent to the paywall. Finally, charging a price for the digital newspaper may have a positive effect on print newspaper consumption (termed the spillover effect of the paywall), especially if readers view the print and online versions of a newspaper as substitutes.

Findings Overall, the authors find that within two years of its inception, the New York Times’ paywall was responsible for at least a 13.5% increase in total revenues, only about half of which was from incremental digital subscriptions. The authors’ analyses reveal that the number of unique visitors decreased by 13.1% as a result of the paywall, although, on average, there was no statistically significant effect on engagement metrics such as visits, pages consumed, and duration per visitor. Moreover, the paywall had an adverse effect on the behavior of heavy, as opposed to light, users. As regards the indirect effect, they find that advertising revenues declined by 48% in the period following the paywall mainly on account of the lower quantity of ad impressions served. On the other hand, they find a positive spillover effect wherein the introduction of the paywall arrested the decline of print subscriptions for the New York Times by about 27%.

These findings have two broad implications. First, the results suggest that monetization of online content, especially in the form of metered paywalls, might suppress usage among loyal consumers, which could have implications for the firm’s future growth potential. Second, for legacy firms, the monetization of online content can have positive spillover effects for offline consumption. In situations where the offline channel is significantly more lucrative than its online counterpart (which is the case for newspapers and television), charging a fee for online content might arrest the erosion of offline revenues.

Adithya Pattabhiramaiah is Assistant Professor of Marketing, Scheller College of Business, Georgia Tech. S. Sriram is Associate Professor of Marketing, Ross School of Business, University of Michigan. Puneet Manchanda is Isadore and Leon Winkelman Professor of Marketing, Ross School of Business, University of Michigan.

Marketing Science Institute Working Paper Series

Acknowledgements The authors thank Pradeep Chintagunta for helping with the acquisition of the data used in this paper. The authors thank Xu Zhang, participants at the 2015 TPM conference, the 2015 Marketing Science conference, and the seminar participants at Johns Hopkins University, Temple University, and the University of Maryland for their feedback. The standard disclaimer applies.

Marketing Science Institute Working Paper Series

The movement of content (news, music, TV etc.) to digital media has had a large and (mostly) negative impact on the economics of multiple industries over the last 15 years. This impact is largely a function of the fact that while consumption of digital content is growing by leaps and bounds, the willingness to pay for this content is very low. As a result, firms in these industries have been exploring different options to monetize this content. For example, in the newspaper industry, firms have been scrambling to find ways to monetize their online platforms, mainly to offset the loss in circulation and advertising revenue from their legacy offline (print) properties (George, 2008; Sweney, 2015; Seamans and Zhu, 2014). Since access to the online version of newspapers has traditionally been provided free to readers, revenue from the online channel has come solely from advertising. Although online ad revenues have been growing steadily, this growth has not been sufficient to compensate for the offline ad revenue losses (industry estimates suggest for every advertising dollar gained online, newspapers lose 16 advertising dollars offline (Thompson, 2013)). Thus, in recent years, newspapers have tried to tap into a new source of online revenue via imposing an access and/or a consumption fee, commonly implemented as a “paywall.” As of 2014, nearly 75% of all newspapers had implemented paywalls (Marsh, 2014), including some high-profile national newspapers such as the New York Times, the LA Times and the Washington Post. However, the expectation that paywalls provide an additional source of revenue needs to be evaluated in the light of the fact that newspapers are complex businesses, with multiple interconnected parts. Specifically, newspapers are platforms bringing readers and advertisers together, with the two sides having a direct impact on each other. This effect is even more nuanced as online user type (loyal versus casual) may react differentially to any changes (Tornoe, 2016, Mutter, 2015, Masnick, 2016). In addition, for (legacy) newspapers the online and offline properties do not exist in isolation. The heterogeneity in usage and the interconnectedness of these various parts leads to multiple externalities that arise when a paywall is implemented, making it hard to pinpoint its overall impact. In this paper, we do this by explicitly considering three externalities. The

1 Marketing Science Institute Working Paper Series first externality is the engagement effect, or the impact of the paywall on reader engagement (measured via activity and consumption patterns on its website) for both loyal and casual readers.1 Note that both sets of readers are important to the newspapers. Broadly speaking, loyal users bring in more subscription revenue while causal readers (typically a majority of traffic) bring in more advertising revenue while also increasing the “footprint” of the newspaper. Second, any change in reader engagement is likely to have an impact on online advertising revenues. We call this the indirect effect of the paywall. Finally, and perhaps most important, we consider the effect of the online paywall on print (offline) readership and subscriptions as well as potentially on the corresponding advertising revenues. We call this the spillover effect. While there is some discussion in the popular press regarding the first and second externalities, there is little documentation about their direction and magnitude. In addition, the third externality, or the spillover effect, has been virtually ignored. A useful baseline to examine the impact of these externalities, especially the latter two, is the incremental online subscription revenue from paywalls which we call the direct effect. Thus, our approach provides a holistic assessment of the implementation of the paywall. This is in contrast to popular press reports (highlighted above) that while mentioning the direct and indirect effects, do not provide estimates of the direction and magnitude of these. More important, they typcially ignore the spillover effect completely. The first externality, the engagement effect, is likely to arise because erection of the paywall can adversely affect the number of visitors to a newspaper’s website (Chiou and Tucker, 2013). For example, prominent national (San Francisco Chronicle, Dallas Morning News) and international newspapers (Sun, Toronto Star) have withdrawn their paywalls reportedly due to big losses in traffic.2 Similarly, several local U.S. newspapers (e.g., Memphis Commercial Appeal, Columbia Tribune) also witnessed considerable decrease in traffic with the erection of the paywall (Blankenhorn, 2013). Moreover, as some paywalls, e.g., the New York Times’, limit the number of articles that can be viewed for free, they can also reduce

1http://www.experian.com.au/blogs/marketing-forward/2012/06/29/paywalls-drive-readers-away/ 2http://www.reuters.com/article/us-newscorp-sun-paywall-idUSKCN0SO25R20151030

2 Marketing Science Institute Working Paper Series reader engagement by lowering both the average number of pages viewed by a visitor, as well as the duration of their visits. The second externality, the indirect effect, can impact advertising revenue in multiple ways. Lower engagement and traffic leads to a lower quantity of ad impressions that can be served on the newspaper’s website. Thus, relative to the period before the paywall, this will lead to lower advertising revenue. However, as a result of the paywall, the newspaper is likely to have richer information on subscribing visitors, increasing its ability to serve targeted ads. Moreover, subscribing visitors, by virtue of their revealed willingness to pay for digital content, are likely to be more attractive to advertisers. In the absence of the paywall, advertisers would not have been able to directly identify such high willingness to pay users. Therefore, the paywall can potentially help a newspaper charge higher ad rates per impression (typically measured in terms of cost per mille or CPM) as a result of the improved quality of the served ad impressions. There are early indications from the results of survey-based journalism research that advertisers are willing to pay higher advertising rates for their ads in paid online newspapers.3 Therefore, the net indirect effect of paywalls is likely to depend on the relative magnitudes of the changes in the quantity and quality of ad impressions subsequent to the paywall. The third externality of the paywall, the spillover effect, can arise if readers view print and online versions of a newspaper as substitutes. In that case, increasing the price of the latter is likely to increase demand for the former. Moreover, many newspaper paywalls, including the one instituted by the New York Times, offer print subscribers free access to the online newspaper. Therefore, the value that a reader derives from print subscription is likely to have increased subsequent to the erection of the paywall. As a result, paywalls can have a positive spillover effect on print subscription, and consequently circulation. Moreover, this positive subscription spillover can be enhanced by the fact that due to higher CPMs for advertising in the print newspaper, a print reader is considerably more valuable than an

3http://sabramedia.com/blog/newspapers-battle-between-paywall-and-advertising

3 Marketing Science Institute Working Paper Series online reader - between 16 to 228 times (Thompson, 2013, Blodget, 2011).4 We investigate the three externalities for the paywall commissioned by the New York Times (henceforth, NYT) in March 2011. Ideally, there are two possible research designs that could help to identify the causal impact of the paywall. In the first such design, if there are a large number of newspapers serving independent markets, we could assign a random sample of these newspapers to the treatment condition by introducing a paywall and compare the quantities of interest for the treated newspapers with those experienced by the control newspapers. For the second ideal design, we could treat a random sample of readers of the NYT to the paywall and compare their subsequent response to those of the untreated readers. However, the implementation of either design is unrealistic. We therefore attempt to infer the effect of the paywall using a difference-in-difference estimator on non-experimental behavioral/market data by considering the change in online readership metrics and print circulation for the NYT subsequent to the commission of the paywall. In order to parse out temporal trends in online news consumption that are common to all newspapers, we compare these changes for the New York Times against those experienced by national newspapers of similar popularity and political inclination, i.e., USA Today and the Washington Post (henceforth, USAT and WP, respectively). We validate the appropriateness of the control newspapers by verifying that they exhibit similar pre-treatment temporal trends as the NYT (i.e., prior to the introduction of the NYT paywall). Furthermore, since we perform our empirical investigation with two “control” newspapers, we can verify the robustness of our results. In order to infer the indirect effect of the paywall on online visitation, we use web analytics data from comScore, which tracks the visitation behavior of panelists to NYT and the control newspapers. We conduct our investigation of the engagement and indirect effects at two levels of aggregation: individual panelist level and the DMA level for over 200 DMAs in the U.S.

4Given the proliferation of digital devices on which news content can be consumed, the paywall could induce switching behavior within online channels e.g., the website versus a mobile app Dhillon and Aral (2016).

4 Marketing Science Institute Working Paper Series market. For the spillover effect, we obtain the weekday and weekend print circulation data for NYT, WP and USAT from the annual audit reports published by the Alliance of Audited Media. These data are available at the DMA level for over 200 DMAs in the U.S. market. We control for cross-sectional differences in taste for newspaper readership by including controls in the form of unit-specific (i.e., individual-specific when we use disaggregate data and DMA- specific when we aggregate the data to the DMA level) fixed effects for each newspaper. In addition, we include controls for temporal evolution in preference for consuming online and print news. Therefore, our identification relies on how the differences in the variables of interest between NYT and the control newspaper changed subsequent to the paywall, after controlling for cross-sectional differences between these newspapers as well as broader temporal trends in news consumption. Our results reveal that the number of unique visitors decreased by 13.1% as a result of the paywall, although, on average, there was no statistically significant effect on engagement metrics such as visits, pages consumed, and duration per visitor. However, we do find significant differences between light and heavy users of the NYT website defined based on their pre-paywall usage - while heavy users significantly reduced their visits, pages viewed, and the time spent on the NYT website subsequent to the paywall, there was no significant effect on the behavior of light users. Therefore, the paywall had an adverse effect on the behavior of heavy, as opposed to light users. Further, we leverage the combination of web visitation and referring domain information available in our dataset to offer some conjectures on the paywall’s impact on the activity of NYT subscribers. We find evidence suggesting that subscribing to the paywall did not dampen this adverse effect of the intervention among heavy users. Together, these results suggest that the quantity of impressions that could be served at the NYT website decreased as a result of the paywall. Although CPM rates increased subsequent to the implementation of the paywall, online ad revenues at the NYT decreased by 48.9% as a result of the paywall. As regards the spillover effect, we find that the introduction of the paywall arrested the

5 Marketing Science Institute Working Paper Series decline of print subscriptions for the NYT. As a result, compared to the counterfactual scenario without the paywall, the newspaper gained between .20 - .50 share points (an approximately 27% lift in subscriptions, considering the total population in these markets) in both weekday and weekend subscriptions when we use national newspapers such as the USAT and the WP as the control group. On the other hand, when we use local newspapers in individual DMAs as the control group, the effect is even larger at 1.6 share points. To put these results in context, for every $1 generated in online subscription revenue as a result of the paywall, the NYT lost $.13 in online advertising revenue as a result of the indirect effect. At the same time, it gained $.54 via the spillover effect by arresting the decline in print subscription revenue. If we consider the additional print advertising revenue that is likely to have been generated as a result of this positive effect on print circulation, the spillover effect is bound to be larger in magnitude. Overall, our analysis reveals that the net effect on revenue of the NYT paywall was positive during the period of our analysis. More importantly, our results suggest that it is important to consider spillover effects on other channels when we evaluate the implications of policy changes, such as institution of the paywall. There are two broad implications of our findings. First, the results suggest that monetization of online content, especially in the form of metered paywalls might suppress usage among loyal consumers. This can have implications for future growth potential of the firm. Second, for legacy firms, the monetization of online content can have positive spillover effects for offline consumption. In situations where the offline channel is significantly more lucrative than its online counterpart (which is the case for newspapers and television), charging a fee for online content might arrest the erosion of offline revenues. The rest of the paper is organized as follows. First, we investigate the effect of the paywall on online readership. Next, we consider the effect of the paywall on print readership. Sub- sequently, we perform back of the envelope calculations to document the relative economic impacts of the subscription revenue from the paywall, the indirect effect of the paywall on

6 Marketing Science Institute Working Paper Series online advertising revenue, and the spillover eﬀect of the paywall on the revenues from the print version of the newspaper. We conclude with some comments regarding the implications of these ﬁndings for the broad issue of monetizing online content.

Engagement Eﬀect: Paywall and Online Readership

Data

We use the web analytics data collected by comScore from Jan 2010 through Dec 2013 for our investigation. Given that NYT launched its paywall in the last week of March 2011, our data span a reasonably wide window before and after the intervention. The web analytics data track the online activities of comScore panelists and include information on the websites visited by each panelist, date and time of the visit, number of pages viewed and the time spent on each website. In addition, we have information on the ZIP code where each panelist resides. To provide a benchmark for inferring the effect of the paywall on traffic to the news website, we extracted information regarding activities on three websites: NYTimes.com, Washingtonpost.com, and USAToday.com. This resulted in a sample of 75,174 representative individuals identified by comScore’s sampling strategy.5 Our primary interest is in studying how news consumption at NYT changed after its paywall was erected. In order to parse out any changes in consumption that might have occurred as a result of the general trend in news consumption, we use WP and USAT as the benchmark. We chose these two newspapers as reasonable “controls” because similar to NYT, WP and USAT are national newspapers. Moreover, the readership bases of the three newspapers are comparable (see Table 1; Tables follow References throughout) and represent the top three U.S. news websites in terms of online traffic (2015 PEW Journalism Report).6

5For details on the sampling strategy, see http://www.comscore.com/Media/Files/Misc/comScore- Uniﬁed-Digital-Measurement-Methodology-PDF. 6http://www.journalism.org/ﬁles/2015/04/FINAL-STATE-OF-THE-NEWS-MEDIA1.pdf

7 Marketing Science Institute Working Paper Series In addition, both WP and USAT did not operate paywalls during the period of our analysis.7 Thus, we intend to use the readership trends in WP and USAT after March 2011 to project the trend that NYT would have experienced had it not instituted the paywall. This, in turn, would help us understand the causal effect of the paywall instituted by NYT. We perform our investigation by considering the online visitation data at two levels of aggregation. First, we aggregate the web visitation data across panelists within a geographic market (designated market areas or DMAs) to a monthly level. We restrict our analysis to the panelists residing in the top 202 DMAs, which, per Nielsen’s DMA classification scheme, together account for 99.84% of the potential readership base for national newspapers. Upon aggregation, we obtain the following four metrics of online news consumption, and subsequently use those in our analyses: number of unique visitors, number of visits per visitor, pages viewed per visitor, and the average time spent per visitor. Of these, we treat the last three metrics as proxies for online engagement. Engagement can be broadly defined in terms of the specific nature of consumers’ use of and interactions with online content hosted by the news site. Examples include sharing content with others on social media, writing comments, and participating in polls. Since we do not have such detailed data on engagement, we follow the precedence in the literature (e.g., Lambrecht and Misra, 2016) by defining engagement based on the frequency of visits, number of pages viewed, and time spent on the website. In the second set of analyses, we use individual-level disaggregate online visitation data. Given that online activity can be sparse at the individual panelist level, we aggregate these data to the quarterly level for each reader. As with aggregate data, we consider the three engagement metrics: number of visits per visitor, pages viewed per visitor, and the average time spent per visitor.8 The two levels of aggregation have their own advantages and defi- ciencies. Individual-level data preserve information on changes in behavior of each panelist

7WP eventually launched a paywall in June 2013 though the paywall was not eﬀectively enforced until Dec 2013 (Volokh, 2014). 8Note that the number of unique visitors is not a useful metric when we consider disaggregate data.

8 Marketing Science Institute Working Paper Series without losing such variation to aggregation. Moreover, when we aggregate data, we need to choose the level of aggregation, such as geographic markets and usage. We do not need to make such arbitrary decisions when we consider individual-level data. In addition, the ability to track news consumption behaviors across websites at the individual level allows us to comment on the extent of potential substitution that may exist between the chosen newspapers (we discuss why this may be relevant, subsequently). On the other hand, aggregate data have a few advantages too. At the individual panelist level, visits to individual newspaper websites can be relatively sparse. Therefore, we need to aggregate these data to a quarterly level. This might render it difficult to consider the effect of short-term policy changes such as temporary suspension of the NYT paywall for a few weeks during the 2012 elections. Moreover, comparing the results from aggregated and disaggregated data will help us evaluate the robustness of our findings. The validity of our inference is predicated on WP and USAT providing reasonable proxies for the readership trends at NYT in the counterfactual scenario. If the trends pre-paywall were not significantly different, we can be more confident in projecting the counterfactual trends in NYT news consumption based on those at WP and USAT. In order to verify this, we compared the linear and quadratic terms corresponding to the pre-period trends (after accounting for potential within-unit serial correlation) in our dependent measures across the treated and control newspapers and found no statistically significant differences in these trends (details are available from the authors on request). Furthermore, since we perform our empirical investigation with two “control” newspapers, we can verify the robustness of our results across multiple analyses. The idea is that it is unlikely that two control newspapers will be markedly different from NYT and yet yield qualitatively and quantitatively similar results.

9 Marketing Science Institute Working Paper Series Model-free evidence

We present summary statistics for the four metrics for the three newspapers in Table 2. The raw data indicate that the average number of unique visitors increased weakly for all newspapers in our sample. We notice a similar increase from pre to post paywall for all newspapers in two of our engagement proxies (pages per visitor as well as duration per visitor). In addition, NYT appears to have witnessed a weaker increase in the number of visits per visitor in the post paywall period, compared to other newspapers. However, while these patterns provide a valuable ﬂavor for our data, we recognize that the cross- sectional as well as seasonal diﬀerences in consumption levels across the newspapers may make drawing inferences based on raw data summaries alone tenuous. In order to keep the metrics comparable across newspapers, we present the de-meaned and deseasonalized values in Figure 1 (Figures follow References throughout). The number of unique visitors to the NYT appears to have dropped sizably in periods following launch of the paywall, while page views increased over time following a temporary downward blip. In comparison, the unique visitors and page views of USAT’s website increased.9 Next, we stratify our user base into two groups using a median split, based on the number of online NYT pages accessed in the pre period, to better understand the nature of where the attrition in users to NYT is coming from. We present the percent change (from the pre to post paywall period) in our online visitation metrics in Figure 2. As such, the number of unique visitors and the engagement metrics seem to have declined for NYT among its light users subsequent to the paywall. At the same time, the NYT appears to have

9To assess whether these patterns were a result of each newspaper’s subscription-focused advertising efforts, we collected time-series data on advertising expenditures focusing on subscription-drives by these newspapers, from Kantar Media’s Ad$pender database. When we regressed the demeaned and deseasonalized ad expenditure on a linear time (month) trend for each newspaper, we found them to be insignificant (all t-values ranged in the 1.10 - 1.30 range). In addition, we estimated alternative models with monthly acquisition-focused ad expenditure as DV, where we included newspaper fixed effects and allowed for differential trends in the pre- and post-paywall periods, and found the trends to be insignificant (t-values were in the .1-.2 range), as was the interaction term (NYT x paywall) - which had a t-value of 1.5. These results are available with the authors on request. Therefore, we believe it is unlikely that these observed visitation patterns were a consequence of the newspapers’ acquisition-focused advertising efforts.

10 Marketing Science Institute Working Paper Series performed better along these dimensions when we considered heavy users. However, given that USAT and WP experienced considerable changes in the number of unique visitors and engagement metrics, we need to be cautious about attributing the changes experienced by NYT solely to the erection of the paywall. For example, although heavy users increased their activity at NYT subsequent to the paywall, we see similar increases at WP and USAT among these users. Therefore, it is premature to draw causal inferences without systematically exploring the nature of the effect of NYT’s paywall on its active vs. ad-hoc visitors, which we undertake in the subsequent section. Thus, our systematic analysis allows us to both provide a quantification of the paywall effect as well as employ stronger temporal controls which would be essential to estimate a causal effect of the paywall.

Empirical Analysis

Aggregate-level analysis

In this analysis, we aggregate the individual visitation information up to the DMA-month level. We use a difference in difference (DiD) specification to infer the effect of the paywall instituted by the NYT in March 2011 on the three metrics: number of visits, pages visited, and the time spent on the news site. To this end, we use USAT and WP, two national newspapers that did not operate a paywall during our analysis period as control groups. As noted above, we perform separate analyses for each control group. This would enable us to evaluate the robustness of the results to alternative definitions of the control group. Our objective here is to infer the effect of the NYT paywall on unique visitors to the news site, as well as verify the robustness of our paywall effect on engagement proxies (number of visits per visitor, pages visited per visitor, and the duration per visitor).

ln(Qkjnt) = αkjn + β1kIτ + β2kIn=NYT × Iτ + ςknt + εkjnt (1)

where αkjn is a DMA-newspaper ﬁxed eﬀect aimed at controlling for the preference for newspaper n in market j for metric k. Iτ is an indicator variable that turns on for all

11 Marketing Science Institute Working Paper Series months following the introduction of the NYT paywall. The coeﬃcient β1k captures the overall change in visitation behavior k post-paywall for both NYT and the control newspaper.

The parameter β2k is the main coeﬃcient of interest and captures the diﬀerential impact (with respect to the control group) of the paywall on NYT visitation behavior k. Since the dependent variable is in logarithms, we can compute the percentage change in the readership

metric for NYT as a result of the paywall as exp(β2k) − 1. As part of ςknt in the aggregate analysis, we first include non-parametric controls in the form of newspaper year fixed effects to account for year-on-year changes in online visitation that are specific to each newspaper.10 Second, we include month of the year fixed effects to capture any within-year seasonal variation in online visitation.11 Lastly, we include newspaper specific dummy variables for the election quarters in 2010 and 2012 to account for any changes to newspaper readership during the election quarters in those years. The identifying assumption behind the DiD specification is that the control newspapers can help us infer the counterfactual visitation behavior that would have accrued at the NYT post paywall, had the paywall not been launched. Therefore, the premise is that the post-paywall trends at NYT under the counterfactual scenario would have been similar to those experienced by the control newspapers. Following standard practice in the literature employing DiD estimation approaches (Bronnenberg et al., 2010; Algesheimer et al., 2010; Manchanda et al., 2015; Angrist and Pischke, 2009), we test for common trends across the treated and two control group newspapers in the pre-paywall period. If NYT and the control newspapers experienced similar temporal trends prior to the erection of the paywall, one can

10Including newspaper website-specific year fixed effects would pick up any structural shift in online visitation at NYT subsequent to the launch of the paywall. Therefore, identification of the effect of the paywall would rely on how visits to NYT shifted differentially for NYT vis-a-vis the control newspaper within 2011. This identification is similar in spirit to the regression discontinuity approach. Since this might be a conservative estimate of the effect of the paywall, we also consider an alternative specification that allows for common year fixed effects across both newspapers. The premise is that these year fixed effects would account for year-on-year changes in online news consumption that are common to all newspaper websites. 11As we note subsequently, we are unable to separate out the effect of the paywall’s introduction from the effect of changes to the newspaper’s quality if the two occurred simultaneously. There isn’t much anecdotal support for the thesis that the quality of NYT’s online offering changed markedly following the paywall (Doctor, 2013), reinforcing our confidence in the temporal controls. Note also that the separation of the two effects is not very relevant for the firm’s managers.

12 Marketing Science Institute Working Paper Series make the case that these trends would have been similar for the entire time horizon if the paywall intervention had not occurred. Furthermore, verifying the robustness of the findings across multiple definitions of control groups and alternative controls for temporal trends in online news consumption gives us more confidence in our findings. A second issue to consider is whether the design and timing of the NYT paywall were chosen strategically by the firm. For example, the structure and timing of the paywall might have been chosen to minimize adverse effects on online visits. Similarly, the advent of the paywall might have been associated with changes in online content and promotional activities. If these concerns are true, our analysis would enable us to comment on the impact of: (a) this particular design of the paywall and (b) the advent of the paywall as well as the associated changes in content and promotion. Regarding the latter point, if the associated changes in content and promotion were a consequence of introduction of the paywall (i.e., not coincidental), they can help us understand the causal effect of the paywall and the associated execution. Related to the above, a third issue is whether the inferred effect of the paywall can be generalized to other contexts. In order to evaluate this, we use a similar research design to evaluate the effect of the paywall that the LA Times (LAT) launched in March 2012. To the exent that the estimated effects for the LAT paywall are similar to those for NYT, we can gain some confidence that the results can be generalized to similar newspapers.

Results from aggregate-level analysis

We first discuss the results of the first cut model intended to explore the paywall effect on online readership, without considering usage heterogeneity (Table 3). These results suggest that the NYT paywall did not have a statistically significant effect on engagement metrics, i.e., number of visits, pages visited, and the duration per visitor. This result is robust to our choice of the control newspaper, i.e., WP or USAT. However, the number of unique visitors decreased by 13.1% as a result of the paywall.12

12The eﬀect of the paywall on unique visitors was signiﬁcant when we consider USAT as control, but not WP.

13 Marketing Science Institute Working Paper Series The above analysis ignores the possibility that the paywall might have had differential effects on light vs. heavy consumers of online news. As such, there are two alternative views. On the one hand, Mutter (2015) suggests that paywalls are likely to deter casual visitors and/or readers with low willingness to pay for online content.13 On the other hand, since a metered paywall such as the one erected by NYT is likely to impose a constraint only for heavy users. Therefore, the paywall is likely to have an adverse effect on the more engaged readers of NYT. Notwithstanding the ambiguity regarding whether the paywall is likely to have a greater effect on visits among heavy or light users, the debate highlights the importance of considering the effects on these groups separately. We investigate whether the paywall had a differential impact on light vs. heavy users by dividing panelists into two groups based on their pre-paywall usage. Specifically, we classify a panelist as a heavy user if their pre-paywall average number of pages accessed at NYT was higher than the median value (see Table 4). In addition to the engagement metrics, the aggregate analysis enables us to comment on the effect of the paywall on the number of unique visitors. The results in Table 4 reveal that the paywall adversely impacted the number of unique visitors by 11%-20% among light users and 16% among heavy users, although the effect is consistently significant in a statistical sense among light users. We further verify the sensitivity of the results to alternative characterizations of heterogeneity in usage. First, rather than classifying panelists into heavy vs. light users based on a median split, we perform this classification based on their actual usage. Specifically, we classify a panelist as a heavy user if their average number of visits to NYT is greater than a certain number of pages. In our empirical analysis, we use three thresholds: 20, 30 and 40 pages.14 Note that the definition of heavy usage becomes more stringent as we move from a threshold of 20 pages to 40 pages. Therefore, comparing the results across alternative

13Casual visitors may especially perceive the popup reminders intimating them of the available number of free articles (before encountering the paywall) as detrimental to their experience. 14We chose these thresholds because they are roughly equivalent to the 20 articles that could be accessed for free post-paywall. Since our data contain information on the number of pages accessed by each panelist, not the number of articles, we try diﬀerent thresholds.

14 Marketing Science Institute Working Paper Series thresholds will help us assess how the effect of the paywall changes with the degree of heavy usage. We present the results from this analysis in Tables 5 and 6. Overall, we find that our results are very consistent across all of these alternative thresholds. In addition, we find that the effect appears to get stronger in magnitude in some cases (visits per visitor and duration per visitor) with stricter definition of usage levels. Overall, we find that the paywall instituted by NYT had an adverse effect on engagement among heavy users. There are two potential explanations for the stronger adverse effect of the paywall among heavy users. First, heavy users are more likely to be constrained by the limit on the number of free articles that imposed by the paywall. Therefore, it is conceivable that this “capacity constraint” drove the asymmetric reduction in engagement among heavy users. However, note that such a capacity constraint should not exist for heavy users who subscribe to the paywall. The second explanation is that the paywall can deter the ability of engaged users to share their content with others (Maher, 2015).15 This, in turn, could have an adverse effect on their engagement irrespective of their subscription status. In order to understand whether the constraint imposed by the paywall on non-subscribers drove the decrease in engagement among heavy users, we need to study how subscribers and non-subscribers responded to the erection of the paywall. However, the comScore data do not contain information on whether a panelist was a subscriber to the NYT. Therefore, we adopt an alternative approach by inferring subscribers based on post-paywall usage of users. As noted above, the NYT paywall allows a user to view 20 articles a month without subscription. Thus, we can identify a panelist as a subscriber based on whether they accessed more than 20 articles in a month. However, there are two problems with such an inference. First, NYT operated a leaky paywall wherein articles that were accessed via Social Media Portals such as Facebook, as well

15Alternatively, having to repeatedly login to verify subscription status could also serve as a deterrent though the use of cookies (which helps subscribers circumvent the need to repeatedly login) might ameliorate this concern to an extent.

15 Marketing Science Institute Working Paper Series as from Google and other search engines, were not counted towards the free article limit.16 Fortunately, the comScore data contain information on the source (referring) website from which a user accessed NYT. This enables us to identify the number of accessed pages that would be counted towards the limit for each user. Second, as noted above, our data contain information on the number of pages accessed by each panelist in a given month. On the other hand, the limit imposed by the paywall is in terms of the number of articles that a user can read in a given month. Since some articles can span multiple pages, we do not have accurate information regarding the number of articles accessed. Therefore, we treat the number of pages accessed as a noisy metric of the number of articles consumed. In light of this noisiness in the data, we use a range of cutoffs for the number of directly accessed pages to identify whether a user accessed more than 20 articles in a month. As before, in our analysis, we use three cutoffs: 20, 30, and 40 directly accessed pages to characterize a user as having accessed more than 20 articles.17 Correspondingly, once a user reaches this 20 article limit in a given month, we classify her as a subscriber for all subsequent periods.18 We perform a DiD analysis similar to those described above. However, we now consider the effect of the paywall for four groups of customers: (heavy vs. light users defined based on their pre-paywall usage) x (subscribers vs. non-subscribers defined based on their post- paywall activity). We classify users as heavy if they accessed more than a certain number of pages (20, 30 or 40) in the pre-paywall period, and we classify users into subscribers and non-subscribers similarly based on whether they accessed a threshold number of pages (20,

16In addition, a 2013 study found that nearly 66% of users reported social media as their primary source of news, with 47% of users surveyed identifying Facebook as their main source of news (Lichterman, 2016), highlighting the importance of accounting for the referring medium while analyzing page visits. 17Given that an article can span multiple pages implies that we may only need to consider users that access more than 20 pages as a subscriber. However, given that subscribers do not necessarily need to cross the cutoff imposed by NYT, we will be leaving out some subscribers if we adopt a strict definition. As such, our intention is to verify if the results are robust to a wide range of definitions, while internalizing the noisiness in our classification scheme. Though we only present results with 20 and 30 pages here, we performed these analysis with 40 pages as an alternative threshold and found very similar results (readily available with the authors on request). 18The premise behind treating a user as a subscriber for all month subsequent to reaching the 20 article limit is that subscribers do not necessarily need to cross the 20 article limit every month. However, we acknowledge that the definition assumes that users do not terminate their subscription subsequently.

16 Marketing Science Institute Working Paper Series 30 or 40 respectively) in the post period - in this case, we only count visits that arose directly (not sourced indirectly via a search engine/news aggregator/social media portal). The idea here is to understand if the adverse eﬀect of the paywall is restricted to non-subscribers. We report the results from this analysis in Tables 7 and 8. These results suggest that the adverse eﬀect of the paywall persists even when we consider users that were potential subscribers to the paywall. Overall, the implication is that the paywall reduced engagement metrics by 18%-32% among heavy users.

Individual-level analysis

We use a DiD specification similar to the one adopted in the aggregate level analysis. Follow- ing Equation (1), we characterize the logarithm of the online readership metric k, k = 1, 2, 3, corresponding to user i’s visit to newspaper n in quarter t as dependent variables in our analysis. In the individual level analysis, we include individual newspaper fixed effects to account for heterogeneous tastes for newspaper consumption at the individual level. As discussed before, we aggregate the individual data up to quarters to avoid sparseness problems at the individual level. Similar to the aggregate analysis, Iτ is an indicator variable that turns on for all quarters following the introduction of the NYT paywall. As before, given that online newspaper readership is likely to have evolved during the period of our analysis, we need to employ strict temporal controls to parse out the effect of the paywall from the changes that would have been realized even if the paywall was not commissioned. We include fixed effects for each quarter following quarter 1 (which serves as the baseline quarter) in our data to account for any changes over time in online visitation. To avoid contamination of our standard errors arising from possible serial correlation (Bertrand et al., 2004), we estimate heteroskedasticity-robust standard errors by clustering observations at the individual level (we also find no material differences when we cluster SEs at the individual/newspaper level).

17 Marketing Science Institute Working Paper Series Results from individual-level analysis

Similar to our approach in the aggregate analysis, we classify individuals into light and heavy (using a median split, based on their pre-paywall usage levels). We present the results from this analysis in Table 9. These results suggest that the engagement metrics increased among light users of NYT. On the other hand, the results imply that the paywall had an adverse effect among heavy users for the engagement metrics. This result is largely consistent when we consider either WP or USAT as the control newspaper. We also present the results from the analysis where we classify users based on the number of pages accessed prior to the paywall in Table 9. These results suggest that the engagement metrics declined as a result of the paywall only among heavy users. This result is consistent across alternative definitions of (a) the control newspaper as well as (b) heavy vs. light users. The results also reveal that the adverse effect of the paywall increases as we define heavy usage more strictly. Therefore, the adverse effect of the paywall on the engagement metrics was numerically higher among very heavy users compared to that on moderately heavy users. In order to verify this intuition, we divided panelists into three groups based on the average number of pages of NYT accessed per month pre-paywall. We performed an analysis that was similar to the one discussed above. We present the results in Table 10. These results reinforce our intution that the adverse effect of the paywall on the engagement metrics is stronger among very heavy users.19 As with the aggregate analysis, we explore the effect of the paywall on subscribers and non-subscribers using individual data. We present the results from this analysis in Table 11. Consistent with our earlier results, we find that the paywall had an adverse effect on the three engagement metrics among heavy users. Moreover, the results suggest that the negative effect exists for both subscribers and non-subscribers, although the effect on subscribers is statistically significant on a consistent basis only with WP as the control

19We also verify the robustness of our results to the inclusion of non-parametric controls in the form of newspaper-year ﬁxed eﬀects - results are presented in Appendix Table A.3.

18 Marketing Science Institute Working Paper Series newspaper. Nevertheless, these results imply that subscribing to the paywall might not have rendered NYT immune to decrease in engagement among its heavy users. Lastly, we explore whether the paywall had a differential effect of different demographic groups. We do this by interacting the treatment effect with the demographic variables available to us through the comScore database: income (split into terciles), age of head of household (split into terciles), and the presence of children in the household. We present these results in Table 12 (the results using the WP as a control are qualitatively similar). Similar to Chiou and Tucker (2013), we find that the paywall had a significant negative effect on visitation and engagement among younger readers, although this effect is persistant only among heavy users. However, we do not find a persistant and significant influence of income or the presence of children in the household on the effect of the paywall.

Robustness checks

Assessing substitution between the New York Times and control newspapers A potential concern with our analysis is that the limits imposed by the NYT paywall might have induced some of its readers to substitute to the control newspapers. If such substitution exists, we will be double counting the eﬀect of the paywall in our DiD analysis wherein we treat the control newspapers as being unaﬀected by the intervention. We verify if substitution is bound to be problematic in our context in two ways. First, we examine whether there is model-free evidence of substitution by considering how users change their online reading habits of the control newspapers when they modify their online usage at NYT subsequent to the paywall. If NYT and the control newspapers are substitutes, we should observe that a decrease (increase) in usage of NYT should be associated with an increase (decrease) in usage of the control newspapers. In Table 13, we present a two- way frequency tabulation of the number of individuals in our sample that demonstrated an increase, decrease or no change (within 5%) in their consumption levels (as measured by the number of pages consumed) from the pre paywall period to the post paywall period across

19 Marketing Science Institute Working Paper Series treated and control newspapers. The numbers indicate that the majority of users did not change their usage of the control newspapers even when they changed their consumption of news content at NYT subsequent to the paywall. This gives us confidence that substitution is unlikely to have biased our results. Second, we estimated our DiD model on a restricted sample of exclusive users in our dataset who accessed either the NYT or the control group newspaper (but not both newspapers) in either period. Approximately 27% of users in our sample accessed both treated and control newspapers in either the pre/post paywall periods. We present summary statistics in Table 14, comparing the full sample with the exclusive sample - the two datasets are alike on our key measures of interest, in the pre-period. If the results are robust for this set of exclusive consumers of each newspaper for whom we can rule out substitution, we can infer that substitution is unlikely to have biased the results from the analysis based on the broader sample of users. We present these results based on disaggregate data in Table 15 which suggest that (a) the paywall adversely impacted engagement among heavy users of NYT and (b) the adverse effect is not mitigated by subscription still hold. Moreover, the magnitude of the adverse effect on visits/pages/duration for the heavy group, which ranges between 55%-82% for the exclusive sample, is consistent with the earlier results of 48%-75% for the full sample. Therefore, we contend that our key results are not driven by users substituting between newspapers as a result of the paywall instituted by NYT. Effect of temporary breaks in NYT’s paywall While NYT introduced the paywall in March 2011, it instituted a few changes in subsequent periods. Specifically, NYT removed the restriction imposed by the paywall temporarily on two different occasions: during the last week of October 2012, following Hurricane Sandy and for nearly a week in November 2012, due to the upcoming Elections (Fiegerman, 2012). This change may have resulted in higher visits and/or page views to both newspapers’ websites as users did not face the paywall upon encountering their free article limit. The effect of such

20 Marketing Science Institute Working Paper Series a temporary upward shift in demand would be picked up by the newspaper specific election year quarter dummies in our aggregate online readership demand models. However, as we may be more interested in accurately attributing these temporary shifts in demand to the paywall, we re-estimate the model by conservatively switching off the post-paywall indicator variable (“Paywall”) for these two months. With this change, the effect of NYT’s paywall on its visitation drops to 72.4% (77.8%) from the earlier 74.1% (78.3%) in pages per visitor (duration per visitor) when we define activity levels based on whether users accessed 20 pages in the pre-paywall period. This result is robust across the different thresholds used to define usage-based cutoffs (20, 30, and 40 pages). By and large, we find that the results are substantively invariant to attributing these temporary changes to the paywall or to seasonal demand variations. Revisions to the free article limit Between 2011-2013, the NYT introduced two policy changes to the number of free news articles offered to users, as follows. When the paywall was first introduced on March 28, 2011, the NYT limited the number of free articles each month to 20. The 20 free article limit was set to ensure that its more loyal user base paid a fee to access news content (generating a new source of revenue to NYT). The limit was set high to preserve visits to the newspaper by ad-hoc visitors, which, in turn, generated advertising revenues. In April 2012, the NYT revised the number of free articles downwards to 10 in order to motivate a higher proportion of online readers to start paying for online access. To capture the influence of this policy change, we estimate two treatment effects corresponding to when the two changes to the free article limits were introduced (Mar 2011 and Apr 2012). Specifically, we use an indicator variable corresponding to the paywall’s commission in Mar 2011 that stays on for all subsequent periods, and a separate indicator that turns on for all periods following the free-article limit revision in Apr 2012. Thus, the second dummy variable measures the effect of the downward revision in the free limit, over and beyond the influence of the paywall. We present the results from this analysis in Table 16. We find that while the paywall had a

21 Marketing Science Institute Working Paper Series sizable impact on unique visitors in Mar 2011, when users encountered the paywall, plausibly after reading 20 free articles, the downward revision in the number of articles in Apr 2012 did not have a significant impact.20 Thus, we choose to focus on the introduction of the paywall in Mar 2011 for our analyses. Generalizability Our results suggest that the paywall instituted by NYT adversely affected engagement among its heavy users. However, since our analysis is based on data from one newspaper, it is not clear if they can be generalized to other contexts. In order to explore whether similar results are likely to hold for other national newspapers, we consider the paywall instituted by LA Times in March 2012. To this end, we adopt a research design similar to the one discussed above by using data on online visitation to LATimes.com among comScore panelists. As before, we use USAT and WP as control group newspapers. We present the results from this analysis in Appendix Table A.1. These results suggest that the key results that (a) the paywall adversely impacted engagement among heavy users of NYT and (b) the adverse effect is not mitigated by subscription, are replicated for the LA Times (see Appendix Table A.1). This provides us some confidence that the key findings documented in this paper may not be unique to NYT.

The Spillover Eﬀect: The Paywall and Print

Readership

Data

We obtained data on print circulation from the Alliance for Audited Media’s annual Audit Reports for the years 2005 through 2013. As in the case of online visitation data, we collected this information for the NYT and three other newspapers with similar circulation - USAT,

20With USAT as the control newspaper, while the “Light” group indicates responsiveness to the downward revision of 20 articles to 10 in Apr 2012, this eﬀect is not robust when we consider WP as the control group.

22 Marketing Science Institute Working Paper Series WP, and the Wall Street Journal (WSJ).21 Alliance for Audited Media reports the circulation data at the annual level.22 Therefore, we have six years of data prior to the erection of the paywall (i.e. 2005 through 2010) and three years after the paywall (i.e., 2011 through 2013). We collected these data at the DMA level for the same 202 DMAs that we used to study the eﬀect of the paywall on online readership. In addition to the circulation numbers, these data contain information on subscription prices. Both the circulation data and the corresponding prices are further broken down by weekdays vs. weekends. Next, we also collected these circulation data for the most popular local newspaper in the 25 largest DMAs. The idea is to verify the robustness of the results by treating local newspapers (as opposed to the national newspapers listed above) as the control group.

Model-free evidence

We present the average circulation numbers before and after the paywall for NYT and the three other national newspapers in Table 17. Based on pre period circulation as an evaluation metric, USAT was the most popular newspaper with 2.45 million subscribers, followed by the WSJ (1.92 million) and the NYT (1.69 million). Table 18 provides a list of the top 25 DMAs for NYT by circulation. These DMAs account for approximately 75% of print NYT’s circulation, in the average year in our data. Across the 202 DMAs in our sample, the average DMA had about 11,091 (7,734) USAT subscribers compared with 5,146 (4,080) NYT subscribers in the pre (post) period. Turning to the temporal pattern, paid circulation of U.S. print newspapers decreased consistently during our analysis period.23 Our data from the four national newspapers: the NYT, WP, USAT, and the WSJ exhibit a similar pattern. In Table 17, we present the average annual (i.e., year-on-year) growth rates for these newspapers. These data suggest

21We use the print newspaper circulation and subscriptions as equivalent, in the following sections. 22In our analyses of the spillover eﬀect, we use data on NYT, USAT and WSJ - the three newspapers for which DMA level circulation data were available in the AAM database. 23http://www.marketingcharts.com/traditional/global-newspaper-circulation-and-advertising-trends-in- 2013-43338/attachment/wan-ifra-newspaper-circs-ad-trends-in2013-june2014/

23 Marketing Science Institute Working Paper Series that on average, the three newspapers saw their circulation figures decline by 1.9% to 6.5% during the period of our analysis. Next, we consider the average annual growth rates for these newspapers prior to the NYT paywall (i.e., 2005 through 2010) and post paywall (2011 through 2013). We present these results in Table 17. These results highlight two aspects of the print circulation data. First, prior to the erection of the paywall, NYT saw its circulation decrease at rates similar to those experienced by other national newspapers. Second, we find that between 2011 and 2013, WP, USAT, and WSJ saw steeper declines in their circulation than during the 2005-2010 period. This pattern is consistent with the steep decline in print circulation experienced by the U.S. newspaper industry in the last decade (Edmonds, 2016). Contrary to this pattern, the results in Table 17 imply that NYT experienced lower declines or even increases during this period. Together, these data patterns are suggestive of a positive spillover effect of the paywall on print circulation. In Figure 3, we present a histogram of the average percentage year-on-year change in weekday print circulation for the NYT between 2005-2013 to develop a feel for the cross-sectional variation in our data - the plot indicates that the majority of markets experienced a small percentage decline in circulation over time (the average percentage change across markets in NYT’s weekday print circulation is approximately 2% as shown in Table 17). Thus, the effect is unlikely to be driven by the presence of outliers. While these summaries provide a valuable flavor for the data, they also suggest the need for a detailed analysis that controls for possibly differential rates of evolution of print circulation across the newspapers and changes in print subscription prices. In what follows, we include subscription prices of the newspapers in each period, as a covariate in order to parse out the role of changes in prices in driving print readership. In addition, we include DMA specific linear and quadratic time trends to account to differential temporal evolution in readership across the DMAs in our data. Thus, we estimate the effect of the paywall intervention on print readership by exploiting the residual variation in shares after accounting for those motivated by changes in print prices and seasonal changes to print readership at the market

24 Marketing Science Institute Working Paper Series level.

Empirical analysis

We employ a panel model, similar in speciﬁcation to the one we used for online readership, to examine the eﬀect of the paywall on print readership of newspaper n in market j in year t:

2 Rnjt = λn + λj + µIτ + δIn=NYT × Iτ + ϑpnjt + ℘j t + Υj t + εnjt (2)

where Iτ : is a time-indicator signifying pre/post paywall launch and takes on the value of 1 post-paywall and 0 otherwise.24 We use print readership of USAT and the WSJ to establish a baseline for the effect of NYT’s paywall. We use two dependent variables (as part of the vector R) consisting of the weekday and weekend newspaper print circulation share (i.e., the % readership in each market, which is constructed by dividing the market level circulation by the number of households) for the analysis. The terms λj and λn capture reader preference for newspaper consumption in market j and for newspaper n, respectively, while µ captures any time specific effects of the post-period (common to both newspapers). δ is our coefficient of interest and measures the causal average effect of the paywall on NYT print newspaper readership. We also control for market specific time trends in newspaper readership using a

2 parametric function [ the (℘j t + Υj t ) term in equation (2)]. The basic premise behind the DiD speciﬁcation is that the temporal trends in print circulation for the control newspapers post-paywall will inform us about the corresponding trends for NYT had the paywall not been instituted. Since we cannot conﬁrm the validity of this assumption directly, we verify if NYT and the control newspapers experienced similar temporal trends prior to the erection of the paywall. Recall that the model-free evidence which showed that the print readership of these newspapers experienced similar trends prior to the paywall. To test this more formally, we regress pre-period Sunday readership for

24Given our annual data on print readership, we treat years 2011-2013 as the post paywall period, and years 2005-2010 as the pre-period.

25 Marketing Science Institute Working Paper Series all newspapers on a newspaper specific linear year-time trend, after including DMA and newspaper fixed effects and clustering standard errors across DMAs to account for any serial correlation in readership. We see no significant differences between annual circulation trends for NYT and USAT (F(1,200) = .04, p = .83) and NYT and WSJ (F(1,200) = 1.05, p = .31). A second issue that we need to consider in our empirical analysis is the possibility that newspapers set their subscription prices in anticipation of changes in demand conditions. Although our newspaper-specific temporal controls account for the general trends in newspaper readership, they may not capture all the demand fluctuations. If newspapers set their subscription prices in anticipation of these demand fluctuations, this would result in an endogeneity problem. We account for the potential endogeneity of price using instrumental variables: e.g., producer price indices for printing ink manufacturing (NAICS 32591), the cost of output from paper mills (NAICS 32212), and producer price indices of firms in industries that share similar cost structures, such as book publishers (NAICS 511130). We present estimates from the first stage regressions of endogenous prices on instruments in Table 19. Overall, our instruments are reasonably strong in terms of their contribution to incremental R-squared (Rossi, 2014).

Results

We employ a panel estimator to measure the influence of the paywall on NYTs weekday and weekend print circulation by using the corresponding readership levels at USAT and the WSJ. We report the results with USAT and WSJ as controls in Table 20. The results reveal a significantly positive coefficient on the (NYT x paywall) interaction term for both weekday and weekend shares. This implies that the the paywall had a positive effect on the offline readership of NYT, either in terms of slower rate of decline compared to the control newspapers or even growth. This result is consistent with the model-free evidence presented in the previous section. As a result, the estimates suggest that the NYT paywall had a

26 Marketing Science Institute Working Paper Series positive effect on its print circulation to the extent of between .20-.50 share points. This represents at least a 27% lift in print subscriptions, considering the total population in these markets. In order to assess the robustness of our estimates to the choice of control group, we consider an alternative analysis with the most popular local newspaper in each market as the baseline. Since we could not obtain credible circulation numbers for local newspapers in each of the 202 DMAs, we restrict our analysis to the top 25 DMAs. We present the results from this analysis in Table 21. Consistent with the results with the above analysis with national newspapers used as controls, we find that the NYT paywall had a positive effect on the offline circulation of NYT. However, note that the magnitude of this effect is larger at 1.98 share points when we use the local newspapers as controls (for reference, the effect of the paywall for the top 25 DMAs, considering USA Today as the control newspaper, is between .36-.47 share points for weekday and weekend circulation). This larger effect can perhaps be rationalized by the steeper drop in print circulation witnessed by local newspapers in relation to national newspapers.25 Generalizability As in the case of our analysis of online visitation, these results are based on data from one newspaper. Therefore, we explore whether these results are generalizable to other newspapers of similar size. To this end, we compiled print circulation data for the LA Times (LAT), in the period surrounding its paywall commission (May 2012). To enable the investigation of the effect of LAT’s paywall on its print circulation, we collected print circulation data at the zipcode level for both the LAT and a control newspaper, the WP (that did not operate a paywall during our analysis window). We chose to collect zipcode level data both because it was the finest level of aggregation reported in the AAM Audit Reports for LAT, and also because the relatively smaller national coverage of the LAT implied limited power for our

25http://www.nytimes.com/2010/04/27/business/media/27audit.html

27 Marketing Science Institute Working Paper Series regressions using DMA data.26 We present the results of this analysis in Appendix Table A.2. We find a significant positive spillover effect (of between 4.5-4.8 share points, higher in magnitude than the corresponding number for NYT) for the paywall erected by LAT. These results are similar in spirit to our finding for the NYT paywall. These results helps us place more confidence in our documentation of a positive spillover effect of the NYT paywall on its print circulation. Overall, these results suggest a positive effect of the paywall on print newspaper circulation for NYT - a positive significant spillover effect of the paywall. Thus, our results are consistent with the view that the paywall may be serving a very important objective for this industry: stemming the decline in print readership. As discussed earlier, preserving a print reader is believed to be at least 16 times as valuable, in revenue terms, than an online reader. Thus, newspaper paywalls may also stand to provide an important allied benefit of slowing the decline in print readership, thus helping preserve a legacy revenue source.27

Relative Magnitudes of the Direct, Indirect and

Spillover Eﬀects

Our empirical analyses have documented that the paywall instituted by NYT had: (a) an adverse effect on engagement among its heavy users and (b) a positive spillover effect on its print readership. From a managerial point of view, it would be useful to quantify the net effect of these two effects. In this section, we perform back-of-the-envelope calculations to document the relative magnitudes of the direct effect of the paywall on subscription revenues, indirect effect on online advertising revenues, and the spillover effect on the revenues from

26After removing outliers on the extreme left tail of the distribution, we were left with fewer than 10 DMAs for our analysis using LAT print circulation data. Note also that we were able to perform this analysis considering WP as the only control newspaper because data for the USAT are not available at the zipcode level. 27As discussed earlier, nearly 65-80% of revenues are believed to accrue from print editions, underscoring the importance of this legacy revenue source for newspaper publishers.

28 Marketing Science Institute Working Paper Series the print version.

Background

In each quarter following the launch of its paywall in March 2011, NYT witnessed a steady increase in the number of paid subscribers (Figure 4). NYT is reported to be successful in amassing a sizable base of over 500,000 digital subscribers in just eighteen months after the paywall was set up (Haughney, 2013). Our online advertising data, which were also obtained from comScore, consist of information on the number of impressions served and aggregate monthly ad expenditure by advertisers who advertise on NYT’s website. In order to provide a baseline for the temporal evolution of NYT’s online ad expenditure, we compiled data on online ad expenditure on all U.S. newspapers from the Newspaper Association of America’s website (naa.org).

NYT’s advertising revenues

On the advertising side, our data consist of ad expenditure and advertising levels. We ﬁrst provide a plot of the temporal evolution of ad revenues for the NYT as well as for the broad category (total ad revenues invested in online newspapers in the U.S., as reported by the Newspaper Association of America) in Figure 5. We see that NYT’s total digital ad revenue increased in the period following the paywall, just as the category spending did. The seasonal nature of online newspaper advertising decisions implies seasonal ﬂuctuations in ad revenues, indicating the importance of controlling for trends in assessing the growth of NYT ad revenues in our model. We also present the total number of advertising impressions served and the average online advertising rate (CPM or the cost per 1000 impressions) for the NYT in Figure 6. The average online ad rate increased by around 32% in the period following the paywall. As such, this is in contrast to the decrease in CPMs experienced by online display advertising during this period (Johnston, 2014). Prima facie, this might indicate that advertisers were

29 Marketing Science Institute Working Paper Series willing to pay premium CPMs for ads placed at NYTimes.com, possibly as a result of superior quality of ad impression served on NYTimes.com. However, the fact that CPMs increased post-paywall does not necessarily imply that advertisers were willing to pay higher rates per impression; it also conceivable that the NYT increased its CPMs in anticipation of the changes in the quality of impressions, although advertisers did not perceive such quality improvements. In order to understand the impact of the paywall on NYT’s online ad revenues, we track online advertising in a panel regression of logged online ad expenditure at the NYT considering corresponding ad spend on all US online newspapers as a comparison group (data we compiled from NAA.org). This allows us to track the evolution of NYT’s online advertising relative to that experienced by the overall category of online newspaper advertising. Formally, we used the following speciﬁcation:

2 Ant = αIτ + θ1 t + θ2 t + νt + nt, (3) where the dependent variable A consists of the logarithm of the ratio of total online ad spending (in $) on the NYT in each month t to the corresponding category level ad spending (advertising spending on all online newspapers in the U.S.). In essence, this allows us to track how NYT ad revenues evolved in relation to that of U.S. online newspapers. We control for temporal changes in advertising by using a parametric function of linear and quadratic month trends. We also include month of the year fixed effects to capture seasonal variations in advertising behavior. In addition, we explore the impact of including separate month of the year fixed effects for the pre/post periods, as a robustness test of whether the paywall motivated seasonal shifts in ad spending levels on the NYT. The coefficient α captures the effect of the paywall on NYT’s ad revenues, which we term the indirect effect of the paywall. We present the results in Table 22. We find that the paywall had a negative effect of around 48.90% of post-period ad expenditure. Considered against the backdrop of the finding

30 Marketing Science Institute Working Paper Series that the paywall had a negative impact on the number of impressions served following the paywall (on account of the loss of the heavy user segment), one can rationalize the drop in advertising as arising from the reduced quantity of impressions. This suggests that the effect of the paywall on online advertising due to changes in quantity of impressions served (the quantity effect) dominated the corresponding change to advertising due to the quality effect. In sum, our results of lower online engagement of NYT’s readers after the paywall are consistent with the observed decrease in online advertising. An essential caveat to these results may be in order. Our choice of NYT was motivated mainly based on media reports of its success with executing a paywall. While these media reports did not discuss specifics of the advertising gains for NYT from the paywall, our results appear to be inconsistent with the broad claims in media reports lauding the all-around success of NYT’s paywall (Doctor, 2013). So what is the impact of the NYT’s paywall on its overall revenues? In order to answer this question, we employ industry data as well as our model estimates to compute the revenue impact of the paywall. We first consider online subscriptions, which is a new source of revenue to newspapers on account of the paywall. At the end of our analysis period in 2013, 500,000 readers had signed up for NYT’s paid membership. While the NYT offered various pricing tiers for different subscription plans ($3.75 per week for access to NYTimes.com, $5 per week for online+iPad access, $8.75 per week for unlimited access on all devices),28 we do not have information on how many consumers signed up for each of these plans. Thus, we use the price of the cheapest plan ($3.75 per week) to arrive at the most conservative estimate for NYT’s online subscription revenues. Using this metric, we compute that the NYT gained approximately $97.5 million in online subscription revenues in 2013. We next discuss the impact on online advertising. Given that the NYT lost approximately 48.90% in online revenues (compared to the category level baseline of online newspaper advertising), we can attribute $7.34 million (48.90% of the $15.02 million in online ad revenues) to the paywall.

28http://www.nytimes.com/subscriptions/Multiproduct/lp5558.html – retrieved May 2013.

31 Marketing Science Institute Working Paper Series To quantify the effect on print readership, we multiply NYT’s paid subscriber base in 2013: 1.35 million, with the revenue per reader: $405.6 per year (subscription price of $7.8 per week * 52 weeks). We then multiply this with the effect of the paywall on print readership (considering the conservative estimate of .20 share points) to arrive at the revenue estimate for the paywall’s effect on print subscription. Thus, the paywall contributed $94.9 million in print subscription revenue to NYT. It is important to note that the above revenue impact does not consider the effect of print advertising, due mainly to non-availability of print advertising data. Given that we find a positive effect of the paywall on print readership, it is likely that the paywall also had a positive effect on print advertising revenues (with a reasonable assumption that advertisers, all else equal, prefer a larger reach). However, if advertisers are actively switching between print and online newspaper versions, it is possible that the observed growth in online advertising due to the paywall may be in part due to advertiser substitution away from print advertising. The net benefit from the paywall can be computed as the sum of the three revenue components: online subscription, online advertising and print subscription, which together amount to a gain of approximately $182.16 million. This revenue increment represents 13.53% of NYT’s total revenues in 2013. Thus, this research is one of the first to offer empirical evidence for a positive economic return from newspaper paywalls, by documenting that the NYT paywall was responsible for at least a 13% gain in its total revenues within a period of two years since its inception.

Discussion

The notion of monetizing online content is a problem that extends beyond the context of newspapers that we study. Recently, television content providers and educational institutions have been grappling with the issue of designing appropriate monetizing strategies for their online content. Our study reveals that one needs to consider the spillover effect of online monetization on offline content consumption. Especially, in our context, we find that the

32 Marketing Science Institute Working Paper Series spillover effect of moving from free online content to a paid model is beneficial to offline content consumption. This could have arisen either because consumers view the two channels as substitutes or as a result of the bundling strategy adopted by the NYT. We conjecture that a bundling strategy that provides free access to online content with the subscription to offline content might be reasonable when (a) the marginal cost of online content delivery is relatively low, (b) the offline channel is significantly more lucrative in terms of ad revenues, and (c) it is important to prevent channel partners for offline content (e.g., cable companies for television content) from feeling threatened by the online content. Our findings also shed light on how consumers of online content react when one moves from free to a paid format, especially in the form of a metered plan. We find that the institution of the paywall decreased engagement among loyal users, which could potentially have long-term implication for the media platform. Moreover, the design of metered plans brings to surface the debate regarding whether loyal users (as opposed to transients) should be penalized by the platform in its effort to monetize online content. It can be argued that the freemium model, which relies on a few heavy users, while subsidizing the larger light user population, has been successful. However, loss of heavy users might hamper user generated content creation, which might be detrimental to platforms such as newspapers who may rely on such content in the future.

Conclusion

Newspaper paywalls are becoming an increasingly prevalent phenomenon, with over 50% of newspapers in the U.S. having either implemented or actively considering setting up a paywall. The popular belief is that paywalls may provide a much welcome new source of revenue: online subscriptions. However, as suggested by various surveys of newspaper readers, newspapers stand the risk of driving away readers who are not willing to pay for online news. As online ad revenues are heavily linked to newspaper readership, newspapers

33 Marketing Science Institute Working Paper Series also stand to putting these revenues at risk if the paywall leads to heavy reader attrition. Thus, the overall impact of setting up newspaper paywalls is far from conclusive. In this study, we employ data on online readership and advertising, and print readership of New York Times to assess the overall impact of the paywall it instituted in March 2011. We find that NYT’s paywall appears to have driven away some readers, as evidenced by a decline in the number of unique visitors to its website after the paywall. In addition, our results suggest that following the paywall, previously active readers of the NY Times visit the website less often and also spend a shorter time on the website, implying that paywalls may pose a challenge with the greater objective of generating increased engagement of online readers. We find a positive significant effect of the paywall on the newspaper’s print circulation, indicating that the spillover effect serves as a sizeable benefit, in addition to the incremental online subscription revenues generated by the paywall. Overall, this research is the first of its kind to offer empirical evidence for positive overall economic returns accrued to information media firms from the decision to charge readers for access to online news content. A few caveats and limitations are in order. First, two explanations could be responsible for the observed revenue bump: higher engagement of online readers, and self-selection. The higher engagement explanation would state that the average paying subscriber stays longer on the NYT website, and visits more frequently, thus possibly contributing more advertising, both of which we don’t observe. On the other hand, the paywall could also work as a sorting mechanism between low and high willingness to pay readers, with low WTP readers switching away upon encountering the paywall (the selection explanation). While we are unable to explicitly disentangle these two effects in our data, owing mainly to our inability to separate out paying and non-paying readers’ contributions to online page views as well as advertising, there is a stronger rationale for the selection explanation. However, note that from the news publisher’s more pragmatic perspective, it probably matters less which of these two explanations is driving the economic returns from the paywall. Second, an important overarching question pertains to the timing of the paywall launch.

34 Marketing Science Institute Working Paper Series Given the fast evolving market conditions and shrinking industry revenues, it is possible, and indeed likely, that the paywall’s launch was an endogenously determined decision by the newspaper. Compared to the newspaper industry’s heyday in the period before it faced steep competition for consumer/advertiser attention from new media sources (such as Google, Craigslist, news aggregators, Facebook etc.), online news was kept free, possibly with a view of monetizing the gains in online ad revenues on account of the larger readership base accessing the newspaper’s free online offering. Over the last five years, however, motivated mainly by a steep decline in their primary source of revenues: advertising, newspapers have actively pursued alternative revenue streams to ensure survival. Newspaper paywalls can be viewed as a prominent effort in this direction. Rather than offering normative nor prescriptive guidelines for setting up paywalls or managing their timing, this research aims to empirically quantify the impact on the firm’s revenues conditional on its decision to charge readers for online news content. The fact that close to 75% of all newspapers are said to be operating paywalls makes studying the revenue impact of such strategies a topical and important question.

35 Marketing Science Institute Working Paper Series References

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37 Marketing Science Institute Working Paper Series Volokh, Eugene (2014), ‘Paywall reprieve: Volokh content will be free and easy for everyone until Dec. 31, free for many (and free for all via RSS/Twitter/Facebook) after that’. URL: https://www.washingtonpost.com/news/volokh-conspiracy/wp/2014/07/25/paywall- reprieve-volokh-content-will-be-free-and-easy-for-everyone-until-dec-31-free-for-many-and- free-for-all-via-rsstwitterfacebook-after-that/

38 Marketing Science Institute Working Paper Series Tables and Figures

Comparison of NYT, WP and USAT’s newspaper readership (print + online) Newspaper Rank in Rank in Rank in Circulation Circulation 2010 2011 2013 in Jan 2010 in Dec 2013

Wall Street Journal 1 1 1 1,752,693 2,378,827 USA Today 2 2 3 1,671,539 1,674,306 New York Times 3 3 2 1,086,293 1,865,318 LA Times 4 6 4 1,078,186 653,868 Washington Post 5 5 8 763,305 474,767

Table 1: Top Newspapers in the U.S. by circulation Source: Alliance for Audited Media’s annual Newspaper Audit Reports; http://www.thepaperboy.com/usa-top-100-newspapers.cfm

39 Marketing Science Institute Working Paper Series NYT USAT WP WSJ Pre Post Pre Post Pre Post Pre Post Uniq Visitors (per month) 2,224 2,236 1,050 1,386 1,001 1,168 599 674 Visits per Visitor 2.05 2.02 1.92 2.32 1.94 2.03 1.74 2.18 Page Views per Visitor 4.65 5.19 3.63 7.73 5.22 5.27 3.54 7.46 Duration Per Visitor 8.92 12.87 5.35 14.24 6.54 1.36 5.25 13.77

Table 2: Summary statistics before and after the paywall

40 Marketing Science Institute Working Paper Series USAT as Control ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor) group Est. SE Est. SE Est. SE Est. SE Est. SE NYT -.03 .02 .62 .08 .06 .05 .65 .07 .57 .09 paywall .20 .03 .46 .08 .23 .05 .26 .07 .50 .09 NYT x paywall -.14** .04 -.06 .09 -.01 .05 .09 .07 -.02 .10

√ √ √ √ √ Newspaper x DMA fix ef √ √ √ √ √ Newspaper x Year fix ef √ √ √ √ √ Month of the year fix ef √ √ √ √ √ Newspaper x election quarter dummies for 2010 and 2012 R2 .86 .63 .30 .32 .35 ** p<.01, *p<.05, + p<.1

WP as Control group ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor) Est. SE Est. SE Est. SE Est. SE Est. SE NYT .24 .02 .49 .06 .19 .04 .25 .05 .19 .07 paywall .12 .03 .41 .07 .26 .04 .29 .06 .55 .08 NYT x paywall -.03 .03 -.02 .08 -.06 .04 .01 .07 -.1 .09

√ √ √ √ √ Newspaper x DMA fix ef √ √ √ √ √ Newspaper x Year fix ef √ √ √ √ √ Month of the year fix ef √ √ √ √ √ Newspaper x election quarter dummies for 2010 and 2012 R2 .87 .65 .33 .34 .37 ** p<.01, *p<.05, + p<.1

Table 3: Eﬀect of the paywall on NYT Online Visitation, aggregate data

41 Marketing Science Institute Working Paper Series USAT as Control group ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x Light -.23** .04 -.22** .11 .00 .06 .01 .09 -.11** .12

NYT x paywall x Heavy -.17** .04 -.39** .09 -.20** .05 -.22** .08 -.28** .10

√ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election

quarter dummies for

2010 and 2012 √ √ √ √ √ Newspaper x activity

level (Light, Heavy) √ √ √ √ √ Paywall x activity level

(Light, Heavy)

R2 .82 .66 .36 .32 .28 ** p<.01, *p<.05, + p<.1

WP as Control group ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x Light -.12** .03 -.07 .07 -.02 .03 .04 .06 .03 .08

NYT x paywall x Heavy -.03 .03 -.08 .07 -.16** .03 -.05 .05 −0.12+ .07

√ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election

quarter dummies for

2010 and 2012 √ √ √ √ √ Newspaper x activity

level (Light, Heavy) √ √ √ √ √ Paywall x activity level

(Light, Heavy)

R2 .81 .66 .30 .25 .25 ** p<.01, *p<.05, + p<.1

Table 4: NYT paywall on Online Visitation - breakup by Activity Level, aggregate data

42 Marketing Science Institute Working Paper Series USAT as control WP as control

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .32** .07 .55** .08 .56** .09 .21** .06 .41** .09 .42** .11

Light

NYT x paywall x -.19** .07 -.40** .08 -.32** .11 -.19** .05 -.33** .08 -.28** .10

Medium

NYT x paywall x -.68** .10 -.91** .12 -.90** .14 -.64** .09 -.91** .11 -.94** .13

Heavy

√ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .92 .90 .91 .93 .92 .92 ** p<.01, *p<.05, + p<.1

Table 10: Online Visitation by Activity Level, Three Groups (Split by Tercile on # pre pages of NYT), individual data

43 Marketing Science Institute Working Paper Series USAT as Control group 20 pages 30 pages Marketing

ln(Unique ln(Pages) ln(Visits ln(Pages ln(Duration ln(Unique ln(Pages) ln(Visits ln(Pages ln(Duration

Vi- per per per Visitors) per per per

Science soitors) visitor) visitor) visitor) visitor) visitor) visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

Light x NYT x paywall -.15** .04 -.21** .10 -.13** .06 -.06 .08 -.12 .10 -.16** .05 -.21* .11 -.17* .08 -.05 .10 -.11 .12 Institute Heavy x NYT x paywall .35 .43 -1.01 .68 -1.03** .34 -1.35** .38 -1.53** .39 .09 .26 -1.29** .52 -1.22** .36 -1.39** .46 -2.18** .61 √ √ √ √ √ √ √ √ √ √ Newspaper x DMA fix ef Working x Year fix ef √ √ √ √ √ √ √ √ √ √ Month of the year fix ef √ √ √ √ √ √ √ √ √ √ Newspaper x election Paper

quarter dummies for 2010

Series and 2012 √ √ √ √ √ √ √ √ √ √ Paywall x activity level

interactions

R2 .87 .74 .36 .42 .39 .80 .58 .37 .45 .46

USAT as Control group 40 pages

ln(Unique ln(Pages) ln(Visits per ln(Pages ln(Duration

Visitors) visitor) per per visitor)

visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

Light x NYT x paywall -.15** .05 -.19* .09 -.13* .06 -.03 .08 -.09 .11

Heavy x NYT x paywall -.08 .27 -1.86** .71 -1.26** .45 -1.78** .57 -2.70** .66 √ √ √ √ √ Newspaper x DMA ﬁx ef x Year ﬁx

ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election quarter

dummies for 2010 and 2012 √ √ √ √ √ Paywall x activity level

interactions

R2 .81 .60 .39 .47 .48 ** p<.01, *p<.05, + p<.1 44 Table 5: NYT paywall on Online Visitation of users with varying activity levels, aggregate data, USAT as control group Marketing WP as Control group 20 pages 30 pages

ln(Unique ln(Pages) ln(Visits per ln(Pages per ln(Duration ln(Unique ln(Pages) ln(Visits ln(Pages ln(Duration

Visitors) visitor) visitor) per Visitors) per per per Science visitor) visitor) visitor) visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

Institute Light x NYT x paywall -.02 .04 -.20** .07 -.25** .04 -.18** .05 -.23** .07 .01 .05 .04 .08 -.18** .05 .03 .07 -.04 .09

Heavy x NYT x paywall .89* .47 -1.19 .90 -1.15** .32 -2.07** .47 -2.25** .62 0.31+ .18 -1.39** .46 -1.32** .25 -1.70** .41 -2.29** .50 √ √ √ √ √ √ √ √ √ √ Newspaper x DMA ﬁx ef x Year ﬁx Working

ef √ √ √ √ √ √ √ √ √ √ Month of the year ﬁx ef

Paper √ √ √ √ √ √ √ √ √ √ Newspaper x election quarter

dummies for 2010 and 2012

Series √ √ √ √ √ √ √ √ √ √ Paywall x activity level

interactions

R2 .86 .71 .27 .29 .29 .77 .55 .33 .41 .42

WP as Control group 40 pages

ln(Unique ln(Pages) ln(Visits ln(Pages ln(Duration

Visitors) per per per

visitor) visitor) visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

Light x NYT x paywall .00 .05 .01 .08 -.16** .05 .01 .06 -.05 .09

Heavy x NYT x paywall .25 .23 -2.20** .54 -1.77** .32 -2.45** .45 -3.32** .52 √ √ √ √ √ Newspaper x DMA ﬁx ef x Year ﬁx

ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election quarter

dummies for 2010 and 2012 √ √ √ √ √ Paywall x activity level

interactions

R2 .78 .56 .35 .42 .44 ** p<.01, *p<.05, + p<.1

45 Table 6: NYT paywall on Online Visitation of users with varying activity levels, aggregate data, WP as control group Marketing Science

USAT as Control group 20 pages 30 pages

ln(Unique Visotors) ln(Pages) ln(Visits per ln(Pages per visitor) ln(Duration ln(Unique Visotors) ln(Pages) ln(Visits per ln(Pages per visitor) ln(Duration Institute

visitor) per visitor) visitor) per visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Working Light x Non-Subs x NYT -.20** .05 -.34** .1 -.21** .06 -.14** .08 -.19** .09 -.17** .05 -.32** .10 -.19** .06 −0.15+ .08 -.20* .10

x paywall

LightPaper x Subs x NYT x .02 .3 -.90** .47 -1.03** .29 -.93** .33 -1.05** .49 .09 .26 -1.28** .52 -1.21** .36 -1.37** .46 -2.16** .61

paywall

HeavySeries x Non-Subs x NYT -.29 .22 -2.11** .45 -.86** .25 -1.82** .32 -2.00** .5 -.13 .37 -2.11** .81 -.70 .49 -1.98** .57 -2.36** .81

x paywall

Heavy x Subs x NYT x -.01 .37 -2.13** .52 -1.62** .28 -2.12** .47 -2.5** .37 -.15 .37 -1.83** .55 -1.44** .25 -1.68** .42 -2.20** .45

paywall √ √ √ √ √ √ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ √ √ √ √ √ Newspaper x election quarter dummies for 2010

and 2012 √ √ √ √ √ √ √ √ √ √ Paywall x activity level interactions for Subs and

Non-Subs

R2 .77 .54 .35 .44 .42 .80 .58 .38 .46 .46 ** p<.01, *p<.05, + p<.1

Table 7: NYT paywall on Online Visitation - Tracking behaviors of Subscribers and Non-Subscribers, aggregate data, USAT as control group 46 Marketing Science

WP as Control group 20 pages 30 pages

ln(Unique Visotors) ln(Pages) ln(Visits per ln(Pages per visitor) ln(Duration ln(Unique Visotors) ln(Pages) ln(Visits per ln(Pages per visitor) ln(Duration Institute

visitor) per visitor) visitor) per visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Working Light x Non-Subs x NYT -.06 .05 -.17* .08 -.23** .05 −0.11+ .06 -.17* .07 .00 .04 -.12 .08 -.21** .05 −0.12+ .06 -.16* .08

x paywall

LightPaper x Subs x NYT x .33* .17 -1.26** .36 -1.28** .23 -1.59** .29 -1.86** .35 0.30 .17 -1.40** .44 -1.32** .25 -1.71** .39 -2.29** .49

paywall

HeavySeries x Non-Subs x NYT -.19 .19 -1.59** .45 -.7** .26 -1.4** .35 -1.59** .48 -.27 .25 -2.30** .41 -1.08** .27 -2.02** .36 -2.27** .45

x paywall

Heavy x Subs x NYT x 0.3+ .17 -1.8** .5 -1.47** .26 -2.09** .37 -2.66** .58 .98** .38 -.87 .63 -1.56** .25 -1.86** .38 -2.41** .53

paywall √ √ √ √ √ √ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ √ √ √ √ √ Newspaper x election quarter dummies for 2010

and 2012 √ √ √ √ √ √ √ √ √ √ Paywall x activity level interactions for Subs and

Non-Subs

R2 .76 .52 .32 .41 .40 .78 .55 .33 .42 .42 ** p<.01, *p<.05, + p<.1

Table 8: NYT paywall on Online Visitation - Tracking behaviors of Subscribers and Non-Subscribers, aggregate data, WP as control group 47 USAT as Control Median Split 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) Marketing

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .14** .05 .23** .06 .28** .07 -.02 .04 .02 .05 .06 .06 -.03 .04 .00 .05 .05 .06 -.03 .04 .00 .05 .04 .06

Science Light

NYT x paywall x -.51** .07 -.76** .09 -.72** .11 -.78** .25 -.90** .36 -.85* .41 -.99** .30 -1.18** .39 -1.20** .43 -1.05** .34 -1.22** .43 -1.30** .48

Heavy Institute

√ √ √ √ √ √ √ √ √ √ √ √ Individual x

NewspaperWorking ﬁx ef √ √ √ √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ √ √ √ Paywall x Paper

activity level

(Light,Series Heavy)

interactions

R2 .92 .90 .91 .92 .90 .91 .92 .90 .91 .92 .90 .91 ** p<.01, *p<.05, + p<.1

WP as Control Median Split 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .11* .05 .23** .07 .25** .08 -.02 .03 .02 .05 .05 .06 -.04 .03 .00 .05 .02 .06 -.04 .03 -.01 .05 .01 .06

Light

NYT x paywall x -.50** .06 -.77** .07 -.77** .09 -.67** .19 -1.11** .28 -1.37** .31 -.64** .22 -1.02** .32 -1.34** .32 -.73** .27 -1.07** .39 -1.39** .38

Heavy

√ √ √ √ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R248 .93 .92 .92 .93 .92 .91 .93 .92 .91 .93 .92 .91

** p<.01, *p<.05, + p<.1

Table 9: NYT paywall on Online Visitation - breakup by Activity Level (Varying Cutoﬀs based on Pre-Period # Pages accessed) USAT as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

Light x Non-Subs -.01 .04 .06 .05 .10 .06 -.02 .04 .03 .05 .07 .06 -.03 .04 .01 .05 .06 .06 x NYT x paywall

Light x Subs x .07 .26 .05 .34 -.09 .44 .09 .27 .09 .37 .02 .46 .01 .31 .02 .42 -.15 .52

NYT x paywall

Heavy x -.36* .16 -.78** .21 -.65** .24 -.43* .21 -.78** .28 -.65* .31 −0.39+ .22 -.75** .27 -.67* .30

Non-Subs x NYT

x paywall

Heavy x Subs x -.51* .26 -.43 .35 -.38 .39 −0.52+ .28 -.40 .38 -.31 .45 −0.57+ .30 -.42 .43 -.33 .52

NYT x paywall

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

interactions for

Subscribers and

Non-Subscribers

R2 .92 .90 .91 .92 .90 .91 .92 .90 .91 ** p<.01, *p<.05, + p<.1

WP as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

Light x Non-Subs -.02 .03 .04 .05 .06 .06 -.03 .03 .02 .05 .04 .06 -.03 .03 .01 .05 .02 .06 x NYT x paywall

Light x Subs x .14 .21 .15 .27 .24 .33 .09 .24 .15 .33 .17 .39 -.15 .28 -.13 .39 .05 .47

NYT x paywall

Heavy x -.35** .16 -.72** .18 -.71** .26 -.39* .17 -.68** .26 -.95** .32 -.37* .19 -.72* .31 -.88** .34

Non-Subs x NYT

x paywall

Heavy x Subs x -.53** .18 -.71** .27 -.95** .28 -.64** .19 -.92** .24 -1.05** .29 -.61** .20 -.93** .26 -1.06** .32

NYT x paywall

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

interactions for

Subscribers and

Non-Subscribers R2 .93 .92 .91 .93 .92 .91 .93 .92 .9149 Marketing Science Institute Working Paper Series** p<.01, *p<.05, + p<.1 Table 11: NYT paywall on Online Visitation - Tracking behaviors of Subscribers and Non- Subscribers, individual data # Users NYT 4Pages (P re−P ost) NYT 4Pages (P re−P ost) USAT Increased No Change Decreased WP Increased No Change Decreased Increased 2,386 3,918 59 Increased 2,052 2,158 75 Control group No Change 5,941 57,459 3,899 No Change 6,267 59,028 3,740 4(P re−P ost) Decreased 29 1,069 414 Decreased 37 1,260 557

Table 13: Two-way frequency table of change in newspaper visitation from pre to post Exploring Substitution across Treated and Control Newspapers

50 Marketing Science Institute Working Paper Series Marketing USAT as control group 20 pages 30 pages 40 pages

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Science Light xNYT x paywall -.09 .09 -.09 .11 -.01 .14 -.09 .09 -.09 .11 -.01 .14 -.09 .09 -.09 .11 -.01 .14

Heavy x NYT x paywall -.47 .49 .39 .59 .59 .74 .29 .70 1.09 .62 .73 .84 .20 .73 .99 .67 .65 .88

Institute Light x NYT x paywall x Income .02 .11 .07 .14 .10 .16 .01 .11 .05 .14 .08 .16 .00 .11 .04 .14 .07 .16

bottom tercile

Working Light x NYT x paywall x Income .04 .09 .05 .11 .04 .14 .04 .09 .04 .11 .03 .14 .04 .09 .04 .11 .02 .14

middle tercile

Heavy x NYT x paywall x Income -.73 .65 -.68 .95 -.35 1.03 -1.16** .37 -1.43** .52 -1.16* .56 0.00# . 0.00# . 0.00# . Paper bottom tercile

Heavy x NYT x paywall x Income .51 .43 -.98 .79 -.37 .72 .19 .66 -1.61 .40 -.36 .71 .29 .68 -1.51** .46 -.22 .75 Series middle tercile

Light x NYT x paywall x -.05 .10 -.04 .13 -.10 .15 -.05 .10 -.06 .13 -.12 .15 -.05 .10 -.06 .13 -.12 .15

Age_Head_HH bottom tercile

Light x NYT x paywall x .05 .09 .16 .12 .11 .14 .04 .09 .14 .12 .09 .14 .04 .09 .13 .12 .08 .14

Age_Head_HH middle tercile

Heavy x NYT x paywall x -1.97** .76 -1.29 .90 -2.48** .85 -1.98** .70 -.92 .59 -2.66** .83 -2.09** .70 −1.05+ .61 -2.83** .85

Age_Head_HH bottom tercile

Heavy x NYT x paywall x -.81* .42 -.96 .67 -1.56** .74 -1.25* .64 -1.04* .52 -1.68** .76 -1.34* .69 −1.09+ .60 -1.88** .81

Age_Head_HH middle tercile

Light X NYT x paywall x children .12 .08 .08 .11 .05 .13 .12 .08 .08 .11 .05 .13 .12 .08 .08 .11 .05 .13

absent

Heavy x NYT x paywall x children .45 .47 -.47 .53 -.32 .63 -.33 .64 -1.19 .46 -.45 .72 -.27 .67 -1.16* .51 -.44 .75

absent √ √ √ √ √ √ √ √ √ Individual x Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x activity level (Light,

Heavy) interactions

R2 .92 .90 .91 .92 .90 .91 .92 .90 .91 ** p<.01, *p<.05, + p<.1 ; #: insuﬃcient observations to reliably identify an eﬀect.

51 Table 12: NYT paywall eﬀect on Online Visitation Patterns of diﬀerent Demographic groups, Individual data (Pre period, per quarter) Full Sample Non-overlapping users NYT USAT WP NYT USAT WP Visits 15.87 7.83 8.39 16.04 7.91 8.47 Pages 37.58 25.05 29.92 37.9 25.39 3.23 Duration 102.79 41.68 29.51 103.68 42.28 29.81

Table 14: Summary statistics for Full Sample and Exclusive Sample (users who accessed either NYT and the control newspaper, but not both)

52 Marketing Science Institute Working Paper Series USAT as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .01 .10 .10 .13 .21 .15 .00 .10 .08 .13 .19 .15 -.01 .10 .08 .13 .18 .15

Light

NYT x paywall x -.90** .20 -1.12** .25 -.97** .27 -.79** .22 -1.01** .29 -.89** .33 -.90** .24 -1.14** .33 -1.06** .35

Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .96 .95 .95 .96 .95 .95 .96 .95 .95 ** p<.01, *p<.05, + p<.1

WP as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .02 .10 .06 .12 .13 .15 .00 .10 .04 .12 .12 .15 .00 .10 .04 .12 .12 .15

Light

NYT x paywall x -.84** .27 -1.10** .31 -.98** .38 -.83** .31 -1.18** .36 -1.07** .47 -1.26** .30 -1.70** .34 -1.74** .40

Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .96 .94 .95 .96 .94 .95 .96 .94 .95

** p<.01, *p<.05, + p<.1

Table 15: NYT paywall on Online Visitation - Restricted sample of users who access the NYT or the control newspaper (but not both)

53 Marketing Science Institute Working Paper Series USAT as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x -.02 .05 .01 .06 .05 .07 -.04 .05 -.01 .06 .02 .07 -.04 .05 -.02 .06 .01 .07

Light

NYT x paywall x -.96** .33 -1.16** .38 -1.33** .46 -1.25** .36 -1.29** .48 -1.41** .57 -1.34** .42 -1.40** .55 -1.49** .66

Heavy

NYT x free 0.07+ .04 −0.08+ .05 .02 .06 0.07+ .04 −0.08+ .05 .02 .06 0.07+ .04 −0.08+ .05 .02 .06

article limit

revision x Light

NYT xfree article -.51 .54 -.75 .74 -.88 1.02 -.44 .71 -.62 .96 -.83 1.36 -.60 .78 -.86 1.06 -1.09 1.54

limit revision x

Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .92 .90 .91 .92 .90 .91 .92 .90 .91 ** p<.01, *p<.05, + p<.1

WP as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration) ln(Visits) ln(Pages) ln(Duration)

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .00 .04 .06 .05 .08 .07 -.02 .04 .03 .05 .04 .07 -.02 .04 .02 .05 .03 .07

Light

NYT x paywall x -.76** .23 -1.17** .34 -1.51** .39 -.72** .27 -1.05** .40 -1.49** .39 -.81** .32 -1.10** .48 -1.48** .45

Heavy

NYT x free .03 .03 -.02 .04 .04 .05 .03 .03 -.02 .04 .04 .05 .03 .03 -.02 .04 .04 .05

article limit

revision x Light

NYT x free -.56 .5 -.9 .57 -1.15 .81 -.62 .62 -1.16 .70 -1.62 1.00 -.77 .67 -1.41 .77 -1.91 1.15

article limit revision x Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Quarter ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions R2 .93 .92 .91 .93 .92 .91 .93 .92 54.91 Marketing Science Institute Working Paper Series** p<.01, *p<.05, + p<.1 Table 16: NYT paywall on Online Visitation - Eﬀect of NYT’s revisions to the Free Article Limit Print Circulation for each Newspaper NYT NYT USAT USAT WSJ WSJ Weekend Weekday Weekend Weekday Weekend Weekday Pre 1,686,020 1,034,263 2,454,332 2,207,041 1,919,427 2,039,218 Post 1,407,170 819,372 1,742,403 1,554,420 1,474,160 1,502,907 Percent change (pre to -16.54% -2.78% -29.01% -29.57% -23.20% -26.30% post)

Avg year on year -3.28% -1.89% -6.54% -6.19% -4.39% -5.20% percent change (2005-2013) Avg year on year -3.18% -4.19% -4.41% -3.47% -2.59% -1.59% percent change (2005-2010) Avg year on year -3.44% 1.93% -1.11% -1.72% -7.37% -11.23% percent change (2011-2013)

Table 17: Print circulation trends for each newspaper

55 Marketing Science Institute Working Paper Series Rank DMA Rank DMA Rank DMA Rank DMA Rank DMA 1 New York 6 Washington 11 Minneapolis 16 Tampa - St. 21 Cleveland D.C. - St. Paul Pete - Sarasota 2 Los Angeles 7 San Fran - 12 Phoenix 17 Orlando - 22 Pittsburgh Oakland - Daytona San Jose Beach - Melbourne 3 Chicago 8 Dallas - Ft. 13 Detroit 18 Indianapolis 23 Miami - Ft. Worth Lauderdale 4 Boston 9 Atlanta 14 Houston 19 Denver 24 Sacramento - Stockton - Modesto 5 Philadelphia 10 Seattle - 15 Portland 20 Hartford - 25 Charlotte Tacoma New Haven

Table 18: Top 25 DMAs for NYT

56 Marketing Science Institute Working Paper Series Weekday Price Sunday Price Est. SE Est. SE Est. SE Est. SE PPI Paper Mills .142** .007 .162** .016 PPI Book Publishers -.003 .008 .016 .035 PPI Printing Ink .204** .017 .321** .036 Manufacturing √ √ √ √ Newspaper dummies R2 .68 .96 .90 .98 ** p<.01, *p<.05, + p<.1

Table 19: First stage regression of price on instruments

57 Marketing Science Institute Working Paper Series All DMAs, USA Today as control All DMAs, WSJ as control group group DV = Weekday Weekend Weekday Weekend circulation circulation circulation circulation share (%) share (%) share (%) share (%) Est. SE Est. SE Est. SE Est. SE NYT -1.43** .02 -1.41** .02 -.85** .02 -.50** .02 Paywall -.24** .03 -.20** .04 -.23** .03 -.14** .03 NYT x Paywall .46** .03 .50** .04 .26** .03 .20** .03

p[ -.33** .07 -.19** .07 .05 .04 -.01 .04

√ √ √ √ DMA dummies, DMA speciﬁc linear and quadratic time trends

R2 .79 .72 .72 .70 [: using instruments to resolve the endogeneity in price; ** p<.01, *p<.05, + p<.1

Table 20: Eﬀect of paywall on print readership

58 Marketing Science Institute Working Paper Series Top 25 DMAs, most popular local newspaper in each market as control group DV = Weekday circulation Weekend circulation share (%) share (%) Est. SE Est. SE NYT -1.18** .07 -1.84** .09 Paywall -.95 4.59 -.04 3.64 NYT x Paywall 1.98** .74 1.98+ 1.13

p[ -.41 1.99 1.08 8.87 Time trend 1.26 6.18 -1.37 4.55

√ √ DMA dummies

R2 .86 .87 [: using instruments to resolve the endogeneity in price; ** p<.01, *p<.05, + p<.1

Table 21: Eﬀect of the paywall on print readership - using Local Newspapers as the control group

59 Marketing Science Institute Working Paper Series DV = (1) (2) (3)

ln(ad expenditureNYT/ad expenditureonl nps)

Parameter Est. SE Est. SE Est. SE

Paywall -.67** .25 -.59* .29 -1.45* .73

Month Trend .25** .02 .25** .02 .26** .02

Month Trend - Quadratic -3.1e-3** 3.0e-4 -3.1e-3** 3.3e-4 -3.2e-3** 3.4e-4 √ Month of the year ﬁx ef. √ Separate month of the year ﬁx. ef for

pre/post

R2 .89 .90 .92 ** p<.01, *p<.05, + p<.1 ; Robust SE’s are reported.

Table 22: Online Advertising Regression

60 Marketing Science Institute Working Paper Series USAT as Control group ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

LAT x paywall x Light −0.06+ .04 .10 .08 .18** .04 .16* .07 .32** .09

LAT x paywall x Heavy .04 .03 -.11 .09 -.05 .05 -.15* .08 -.09 .10

√ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election

quarter dummies for

2010 and 2012 √ √ √ √ √ Paywall x Activity Level

(Light, Heavy)

interactions

R2 .79 .58 .40 .36 .31 ** p<.01, *p<.05, + p<.1

WP as Control group ln(Unique Visitors) ln(Pages) ln(Visits per Visitor) ln(Pages per Visitor) ln(Duration per Visitor)

Est. SE Est. SE Est. SE Est. SE Est. SE

LAT x paywall x Light -.22** .03 -.46** .08 -.14** .04 -.23** .06 -.29** .09

LAT x paywall x Heavy -.19** .04 -.56** .09 -.22** .05 -.37** .08 -.45** .10

√ √ √ √ √ Newspaper x DMA ﬁx ef

x Year ﬁx ef √ √ √ √ √ Month of the year ﬁx ef √ √ √ √ √ Newspaper x election

quarter dummies for

2010 and 2012 √ √ √ √ √ Paywall x Activity Level

(Light, Heavy)

interactions

R2 .77 .54 .32 .26 .25 ** p<.01, *p<.05, + p<.1

Table A.1: Appendix - Assessing Generalizability: examining the eﬀect of the LA Times Paywall.

61 Marketing Science Institute Working Paper Series WP as Control group Weekend Weekday Est. SE Est. SE Paywall -6.32** .60 -4.72** .94 LAT x Paywall 4.58** .87 4.79** 1.36 √ √ Zipcode speciﬁc linear and quadratic trends √ √ Newspaper dummies R2 .89 .84 ** p<.01, *p<.05, + p<.1

Table A.2: Appendix - Assessing Generalizability: examining the eﬀect of the LA Times Paywall on print readership.

62 Marketing Science Institute Working Paper Series USAT as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration ) ln(Visits) ln(Pages) ln(Duration ) ln(Visits) ln(Pages) ln(Duration )

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x .00 .04 .06 .05 .09 .06 -.02 .04 .04 .05 .07 .06 -.02 .04 .03 .05 .06 .06

Light

NYT x paywall x -.79** .24 -.88** .34 -.86** .38 -.97** .29 -1.15** .39 -1.18** .41 -1.04** .32 -1.19** .43 -1.29** .46

Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Newspaper x Yr

ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .92 .90 .91 .92 .90 .91 .92 .90 .91 ** p<.01, *p<.05, + p<.1

WP as Control 20 pages 30 pages 40 pages

group

ln(Visits) ln(Pages) ln(Duration ) ln(Visits) ln(Pages) ln(Duration ) ln(Visits) ln(Pages) ln(Duration )

Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE Est. SE

NYT x paywall x -.03 .03 .00 .05 .03 .06 -.04 .03 -.03 .05 .01 .06 -.04 .03 -.03 .05 .00 .06

Light

NYT x paywall x -.65** .19 -1.10** .27 -1.35** .31 -.63** .22 -1.01** .31 -1.33** .32 -.71** .26 -1.06** .38 -1.37** .38

Heavy

√ √ √ √ √ √ √ √ √ Individual x

Newspaper ﬁx ef √ √ √ √ √ √ √ √ √ Newspaper x Yr

ﬁx ef √ √ √ √ √ √ √ √ √ Paywall x

activity level

(Light, Heavy)

interactions

R2 .93 .92 .91 .93 .92 .91 .93 .92 .91

** p<.01, *p<.05, + p<.1

Table A.3: Appendix - NYT paywall on Online Visitation: breakup by Activity Level, using newspaper year ﬁxed eﬀects, individual data

63 Marketing Science Institute Working Paper Series Figure 1: Summary Statistics before and after the paywall - Demeaned and Deseasonalized

64 Marketing Science Institute Working Paper Series Figure 2: Online Visitation Summaries - by Activity Level

65 Marketing Science Institute Working Paper Series Figure 3: Change in NYT Weekday Print Circulation

66 Marketing Science Institute Working Paper Series Source: http://www.cjr.org/the_audit/the_new_york_timess_paywall_ha.php

Figure 4: Growth in paid subscribers to NYT’s website

67 Marketing Science Institute Working Paper Series Figure 5: Temporal Evolution of NYT’s Online Ad Revenues when compared with the Online Newspaper Industry’s.

68 Marketing Science Institute Working Paper Series Online Advertising − NY Times

Total # Ad Impressions Total Ad Revenue (right axis) Avg Ad Rate (right axis) 25 4000 20 3000 15 2000 10 1000 5 Total Ad Impressions (millions)

0 Paywall 0

1 13 25 37 49 Total Ad Expenditure ($ millions); Avg Rate (CPM) Month

Figure 6: NYT # Online Advertising Impressions and Ad Rates

69 Marketing Science Institute Working Paper Series