Polarization vs antipolarization
Social media is often accused of dividing society apart. Sustain (2017) in his book argues that social media facilitates the creation of echo chambers that expose people to those who are similar to them. This effect makes their initial slant more strong. However, Gentzkow, M., and J. Shapiro (2011) show that the Internet is not as polarized as face-to-face contacts though they do not provide analysis for social media. C. Bail provides experimental evidence on the polarizing effect of exposure. However, these findings are not supported by R. Levy (2021). Indeed, the answer to this question is not obvious since one could come up with alternative mechanisms which justified the decrease in polarization, for instance, lower cost of searching for alternative explanations. I will briefly describe the findings of both papers and provide some arguments to explain this two-sided effect of social media.
Bail et. al. (2018) experimented on Twitter by suggesting users follow a bot that retweets posts opposed to their views. R. They select accounts of elected officials, opinion leaders, media organizations, and nonprofit groups, and regularly randomly select an account to make a retweet. Comparing the surveys before, within, and after the experiment authors make the following conclusions:
— Republicans who were exposed to a liberal Twitter bot became substantially more conservative post-treatment.
— Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot. However, these results are not statistically significant.
In his book, C. Bail talks about his research based on in-depth interviews. Here he explained the channel of polarization. First, the polarization after exposure to the opposite opinion if initially, people have more extreme views. Second, he shows that there are discrepancies between online and offline behavior, particularly, people could be restricted to share their opinion online because offline or, on the contrary, be freer on social media compensating for the lack of social approval and admiration in real life. Third, he provides suggestions to improve communications online by creating a new discussion platform where people of different slants were incentivized to discuss various topics.
R. Levy conducted an online experiment suggesting users subscribe to opposing news outlets. He managed to gather data on subscriptions to outlets, exposure to news on Facebook, visits to online news sites, and sharing of posts, as well as changes in political opinions and attitudes from the surveys. This study has four main findings:
1. News sites visited through social media, and specifically, Facebook, are associated with more segregated and extreme news, compared to other news sites. Nevertheless, exposure to opposing news outlets helps to diversify the news consumption of an individual.
2. Exposure to counter-attitudinal news decreases negative attitudes toward the opposing political party, i.e. affective polarization. This is in line with Bail’s finding of exposing people to those with opposite views on the discussion platform.
3. Exposure does not affect people's beliefs and ideological slant.
4. Facebook’s algorithm is less likely to supply individuals with posts from counter-attitudinal outlets, conditional on individuals subscribing to them. Thus, social media algorithms may limit exposure to counter-attitudinal news.
Social media is often accused of dividing society apart. Sustain (2017) in his book argues that social media facilitates the creation of echo chambers that expose people to those who are similar to them. This effect makes their initial slant more strong. However, Gentzkow, M., and J. Shapiro (2011) show that the Internet is not as polarized as face-to-face contacts though they do not provide analysis for social media. C. Bail provides experimental evidence on the polarizing effect of exposure. However, these findings are not supported by R. Levy (2021). Indeed, the answer to this question is not obvious since one could come up with alternative mechanisms which justified the decrease in polarization, for instance, lower cost of searching for alternative explanations. I will briefly describe the findings of both papers and provide some arguments to explain this two-sided effect of social media.
Bail et. al. (2018) experimented on Twitter by suggesting users follow a bot that retweets posts opposed to their views. R. They select accounts of elected officials, opinion leaders, media organizations, and nonprofit groups, and regularly randomly select an account to make a retweet. Comparing the surveys before, within, and after the experiment authors make the following conclusions:
— Republicans who were exposed to a liberal Twitter bot became substantially more conservative post-treatment.
— Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot. However, these results are not statistically significant.
In his book, C. Bail talks about his research based on in-depth interviews. Here he explained the channel of polarization. First, the polarization after exposure to the opposite opinion if initially, people have more extreme views. Second, he shows that there are discrepancies between online and offline behavior, particularly, people could be restricted to share their opinion online because offline or, on the contrary, be freer on social media compensating for the lack of social approval and admiration in real life. Third, he provides suggestions to improve communications online by creating a new discussion platform where people of different slants were incentivized to discuss various topics.
R. Levy conducted an online experiment suggesting users subscribe to opposing news outlets. He managed to gather data on subscriptions to outlets, exposure to news on Facebook, visits to online news sites, and sharing of posts, as well as changes in political opinions and attitudes from the surveys. This study has four main findings:
1. News sites visited through social media, and specifically, Facebook, are associated with more segregated and extreme news, compared to other news sites. Nevertheless, exposure to opposing news outlets helps to diversify the news consumption of an individual.
2. Exposure to counter-attitudinal news decreases negative attitudes toward the opposing political party, i.e. affective polarization. This is in line with Bail’s finding of exposing people to those with opposite views on the discussion platform.
3. Exposure does not affect people's beliefs and ideological slant.
4. Facebook’s algorithm is less likely to supply individuals with posts from counter-attitudinal outlets, conditional on individuals subscribing to them. Thus, social media algorithms may limit exposure to counter-attitudinal news.
Thus, a comparison of the focuses of these works could help to shed a light on this divergent effect. First, the extreme belief which is emphasized in Bail's works could be the main driver of polarization. Since the share of such people is not high enough, this is not reflected in Levy's findings. The second reason lies in the dichotomy of active interaction and consumption. In my opinion, the first should affect beliefs and affective polarization compared to pure consumption of news since in the interaction with others more tools of persuasion are used. The third potential explanation, which is close to the first one, is the coherence of beliefs. As C. Bail shows in his book and is established in political science literature, people could have contradictory and incomplete beliefs which also could change the way how they are responsive to the exposure of opposite opinions.
This question lies if the area of my research interest, therefore this small post could be treated as a motivation for my thesis. I hope to provide a more rigorous argument on these counter effects later on.
References:
Levy, Ro'ee. 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment." American Economic Review, 111 (3): 831-70.
C. A. Bail et. al. 2018. Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37): 9216-9221
C. A. Bail 2021. Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing. Princeton University Press.
This question lies if the area of my research interest, therefore this small post could be treated as a motivation for my thesis. I hope to provide a more rigorous argument on these counter effects later on.
References:
Levy, Ro'ee. 2021. "Social Media, News Consumption, and Polarization: Evidence from a Field Experiment." American Economic Review, 111 (3): 831-70.
C. A. Bail et. al. 2018. Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37): 9216-9221
C. A. Bail 2021. Breaking the Social Media Prism: How to Make Our Platforms Less Polarizing. Princeton University Press.
Dynamic polarization
Polarization is a widespread phenomenon in the political sphere, and it affects different groups differently. Particularly, we notice the polarization between elites (parties) and masses (voters). There are great papers that empirically investigate polarization among people (to be reviewed), but there was no one that tries to catch polarization at both levels and provide a theoretical concept to explain polarization dynamics. S.Callander and J. C. Carbajal fill this gap by providing a dynamic voting model with voters who update their beliefs.
They build upon a classic model of electoral competition adding behaviorally justified change to the nature of voter preference. Namely, the voter updates their preferences in a way to justify their choice which is called a causal loop, i.e. they vote for a party that is closer to them and then they become even closer to this party in terms of preferences. Also, unlike other models, they introduce the tolerance of voters which guarantees that voters vote not just for the closest party but the closest party within a particular range. This helps to derive equilibrium with the so-called “missing middle” of the electorate or “disappearing center” Abramowitz (2010) – moderate voters who are left behind because of parties’ polarization.
Since parties face a trade-off between the probability of winning and the policy outcome, the authors find a new type of equilibrium where parties do not compete with each other but with abstention. This strategy is called “no-voter-left-behind”. It means that parties polarize incrementally such that in each step, no voters are lost to abstention. This implies that the speed of polarization is tightly linked to the degree voters update their preferences after voting: the smaller is updating, the more incremental the updating, and the slower and more iterative party polarization. This dynamic reflects a shift in the nature of electoral competition over time: from competition for the swing voter to competition for turnout.
The dynamic nature of this model allows us to conclude that parties would converge faster to their ideal points while voters will update their beliefs until they reach the position of their favorite parties. Note that abstainers do not update their ideals points, so the gap in the middle opens up. This result is supported by empirical literature that finds evidence for elite polarization and very moderate polarization within the masses.
[1] Steven Callander & Juan Carlos Carbajal, 2022. "Cause and Effect in Political Polarization: A Dynamic Analysis," Journal of Political Economy, Vol. 130, N. 4
[2] Abramowitz, A. I. (2010). The Disappearing Center: Engaged Citizens, Polarization, and American Democracy. Yale University Press.
Polarization is a widespread phenomenon in the political sphere, and it affects different groups differently. Particularly, we notice the polarization between elites (parties) and masses (voters). There are great papers that empirically investigate polarization among people (to be reviewed), but there was no one that tries to catch polarization at both levels and provide a theoretical concept to explain polarization dynamics. S.Callander and J. C. Carbajal fill this gap by providing a dynamic voting model with voters who update their beliefs.
They build upon a classic model of electoral competition adding behaviorally justified change to the nature of voter preference. Namely, the voter updates their preferences in a way to justify their choice which is called a causal loop, i.e. they vote for a party that is closer to them and then they become even closer to this party in terms of preferences. Also, unlike other models, they introduce the tolerance of voters which guarantees that voters vote not just for the closest party but the closest party within a particular range. This helps to derive equilibrium with the so-called “missing middle” of the electorate or “disappearing center” Abramowitz (2010) – moderate voters who are left behind because of parties’ polarization.
Since parties face a trade-off between the probability of winning and the policy outcome, the authors find a new type of equilibrium where parties do not compete with each other but with abstention. This strategy is called “no-voter-left-behind”. It means that parties polarize incrementally such that in each step, no voters are lost to abstention. This implies that the speed of polarization is tightly linked to the degree voters update their preferences after voting: the smaller is updating, the more incremental the updating, and the slower and more iterative party polarization. This dynamic reflects a shift in the nature of electoral competition over time: from competition for the swing voter to competition for turnout.
The dynamic nature of this model allows us to conclude that parties would converge faster to their ideal points while voters will update their beliefs until they reach the position of their favorite parties. Note that abstainers do not update their ideals points, so the gap in the middle opens up. This result is supported by empirical literature that finds evidence for elite polarization and very moderate polarization within the masses.
[1] Steven Callander & Juan Carlos Carbajal, 2022. "Cause and Effect in Political Polarization: A Dynamic Analysis," Journal of Political Economy, Vol. 130, N. 4
[2] Abramowitz, A. I. (2010). The Disappearing Center: Engaged Citizens, Polarization, and American Democracy. Yale University Press.
Attitudes change via social media: example of xenophobia
The literature provides evidence that social media could influence opinions and attitudes. Among others, it could drive the recent increase in the expression of hate and xenophobia. Bursztyn et. al. (2019) provides causal evidence in the Russian context. [1] I will briefly summarize the research design, theoretical model, and results interpretation.
To establish the causal relationship between social media usage and xenophobia, they follow the (Enikolpov, at. al. (2020)) approach using the penetration of VK as an instrument for social media usage. [2] Then, the approximate xenophobic views by the city-level vote share of Rodina, an explicitly nationalist and xenophobic party. The authors documented that the impact of social media on hate crime is positive and significantly depends on the strength of pre-existing support of nationalists in the city which is consistent with previous literature about traditional media (Adena et al., 2015). [3] Then, the authors examine the potential mechanism via the online survey.
Channels through which social media could affect the expression of hate are the following: facilitation of coordination that is relevant for illegal and stigmatized activities like hate crimes, change of opinion (exposure to intolerant views that are prone to be exacerbated via echo chambers), and effect on people’s perceptions of the acceptability of expressing hate (and, consequently, the willingness to express hate). The survey was designed to test the last channel, i.e. perceptions of the social acceptability of xenophobia. The authors find a positive effect of social media penetration on elicited ethnic hostility, i.e. the share of respondents that hold xenophobic attitudes, regardless of whether they are willing to openly report them. Bursztyn, et. al. also look at the effect of social media on self-reported ethnic hostility, but the coefficients are mostly insignificant and negative. The difference between elicited and reported ethnic hostility could be used as a proxy for the perceived stigma associated with the expression of such attitudes in a survey. According to the authors' results, there is no evidence that social media reduced that perceived stigma.
In addition to empirical evidence, the authors build a simple theoretical model which captures the idea that social media increases the propensity of individuals to meet like-minded people, thereby resulting in higher polarization of opinions. In this model, individuals have a particular position (for instance, about aborts) which is drawn from some distribution. In each period, they update their views as the result of interactions with others. The change proceeds by the following scheme: the new position incorporates the current one with weight ω, and the positions of other people they talk to with weight 1 − ω. Also, each individual interacts with others whose share r is similar to an individual, and share 1-r is a random set of individuals from the society. Thus, r is considered to be a proxy for penetration of social networks. Also, to avoid convergence, the authors assume that each individual’s position is subject to a random additive shock with the normal distribution of zero mean. This model predicts convergence of the normal distribution of preferences with a mean equal to the mean of the original distribution. The predictions of the model are the following: the more individuals are influenced by people with random opinions (lower r) the faster these preference shocks dissipate leading to the smaller the variance of the limit distribution. Conversely, if people are mostly influenced by themselves (higher ω) or like-minded people (higher r ), as in ‘echo chambers, so we observe the persistence of beliefs.
[1] Bursztyn, L., Egorov , G., Enikolopov, R., Petrova, M. (2019). Social media and xenophobia: evidence from Russia. NBER Working Paper 26567
The literature provides evidence that social media could influence opinions and attitudes. Among others, it could drive the recent increase in the expression of hate and xenophobia. Bursztyn et. al. (2019) provides causal evidence in the Russian context. [1] I will briefly summarize the research design, theoretical model, and results interpretation.
To establish the causal relationship between social media usage and xenophobia, they follow the (Enikolpov, at. al. (2020)) approach using the penetration of VK as an instrument for social media usage. [2] Then, the approximate xenophobic views by the city-level vote share of Rodina, an explicitly nationalist and xenophobic party. The authors documented that the impact of social media on hate crime is positive and significantly depends on the strength of pre-existing support of nationalists in the city which is consistent with previous literature about traditional media (Adena et al., 2015). [3] Then, the authors examine the potential mechanism via the online survey.
Channels through which social media could affect the expression of hate are the following: facilitation of coordination that is relevant for illegal and stigmatized activities like hate crimes, change of opinion (exposure to intolerant views that are prone to be exacerbated via echo chambers), and effect on people’s perceptions of the acceptability of expressing hate (and, consequently, the willingness to express hate). The survey was designed to test the last channel, i.e. perceptions of the social acceptability of xenophobia. The authors find a positive effect of social media penetration on elicited ethnic hostility, i.e. the share of respondents that hold xenophobic attitudes, regardless of whether they are willing to openly report them. Bursztyn, et. al. also look at the effect of social media on self-reported ethnic hostility, but the coefficients are mostly insignificant and negative. The difference between elicited and reported ethnic hostility could be used as a proxy for the perceived stigma associated with the expression of such attitudes in a survey. According to the authors' results, there is no evidence that social media reduced that perceived stigma.
In addition to empirical evidence, the authors build a simple theoretical model which captures the idea that social media increases the propensity of individuals to meet like-minded people, thereby resulting in higher polarization of opinions. In this model, individuals have a particular position (for instance, about aborts) which is drawn from some distribution. In each period, they update their views as the result of interactions with others. The change proceeds by the following scheme: the new position incorporates the current one with weight ω, and the positions of other people they talk to with weight 1 − ω. Also, each individual interacts with others whose share r is similar to an individual, and share 1-r is a random set of individuals from the society. Thus, r is considered to be a proxy for penetration of social networks. Also, to avoid convergence, the authors assume that each individual’s position is subject to a random additive shock with the normal distribution of zero mean. This model predicts convergence of the normal distribution of preferences with a mean equal to the mean of the original distribution. The predictions of the model are the following: the more individuals are influenced by people with random opinions (lower r) the faster these preference shocks dissipate leading to the smaller the variance of the limit distribution. Conversely, if people are mostly influenced by themselves (higher ω) or like-minded people (higher r ), as in ‘echo chambers, so we observe the persistence of beliefs.
[1] Bursztyn, L., Egorov , G., Enikolopov, R., Petrova, M. (2019). Social media and xenophobia: evidence from Russia. NBER Working Paper 26567
[2] Enikolopov, Ruben & Makarin, Alexey & Petrova, Maria. (2020). Social Media and Protest Participation: Evidence from Russia. Econometrica,Vol. 88, Issue 4, pp. 1479-1514
[3] Adena, M., Enikolopov, R., Petrova, M., Santarosa, V., Zhuravskaya, E. (2015) Radio and the Rise of The Nazis in Prewar Germany , The Quarterly Journal of Economics, Volume 130, Issue 4, November 2015, Pages 1885–1939, https://doi.org/10.1093/qje/qjv030
[3] Adena, M., Enikolopov, R., Petrova, M., Santarosa, V., Zhuravskaya, E. (2015) Radio and the Rise of The Nazis in Prewar Germany , The Quarterly Journal of Economics, Volume 130, Issue 4, November 2015, Pages 1885–1939, https://doi.org/10.1093/qje/qjv030
Factors of online news demand
Insights into the factors influencing people's choice of news sources could help understand better important questions such as propaganda effectiveness, the dynamic of slants, fakes’ spreading, and many others. Simonov and Rao (2022) investigated the demand drivers in the context of authoritarian Russia which is known for its control of media.
The authors separate two drivers of demand for government-controlled (GC) outlets. One of the drivers is pro-government bias. The second factor includes tastes for other features of the outlets, for instance, website design, video content, referrals by news aggregators, or accumulated brand capital. All these factors authors call “persistent preferences”. Also, they differentiate between two types of content: sensitive and non-sensitive topics. For the non-sensitive topics, there is no difference in terms of ideological slants, while news on sensitive topics is framed following the official position.
Simonov and Rao ground their research in a model of news production and consumption. In this model, consumers choose news outlets relying on their outlet-level persistent preferences and tastes for outlets’ ideological coverage. The importance of outlets’ ideological coverage changes depending on the volume of realized sensitive-news events. For instance, on days with no sensitive-news events, people do not pay attention to news outlets’ ideological positions.
On the production side, news outlets decide on shares of sensitive news commodities in their product. The authors assume that news commodities are costly to produce. Also, it is less costly on days when a lot of topic-related events happen. Given that the government dislikes sensitive-news publications, it exercises censorship by imposing additional costs for the production of sensitive news on independent outlet A.
From this model, two observations could be derived. First, a controlled outlet would choose to produce less sensitive news than an independent outlet. Second, since the government is assumed to care mainly about the first few sensitive stories reported, we should expect that the difference in the amount of sensitive news produced by the independent and controlled outlets is increasing in the share of sensitive news to report. For instance, if there is no sensitive news, it can be very costly for both news outlets to produce sensitive news, so both outlets produce very low sensitive content. However, when there is a lot of sensitive news to report, the cost of sensitive-news production is low, and censorship (as costs) plays a more important role.
Next, Simonov and Rao turn to empirical analysis. They consider the top 48 Russian-language news outlets, classified by the degree and type of government influence. They end up with 3 groups: outlets that are owned by the government or members of the incumbent political party (GC), “potentially influenced” outlets, and independent outlets (owned by either journalists, international media companies, or the opposition to the government). They gathered the content published by these outlets during the reference period. From this data, they found 2 topics of sensitive news: POC (political protests, opposition, and corruption) and news about Ukraine. Also, they conclude that international outlets closely resemble Ukrainian outlets in their ideological slant on sensitive topics, and independent outlets have more “neutral” ideological positions. Some potentially influenced outlets closely resemble independent outlets, while others resemble GC outlets.
Using structural estimation, the authors show that news outlets that report more news about the Ukraine crisis and have a more pro-Ukraine ideological position get the highest increases in their market shares on days with a high share of sensitive news realizations.
Insights into the factors influencing people's choice of news sources could help understand better important questions such as propaganda effectiveness, the dynamic of slants, fakes’ spreading, and many others. Simonov and Rao (2022) investigated the demand drivers in the context of authoritarian Russia which is known for its control of media.
The authors separate two drivers of demand for government-controlled (GC) outlets. One of the drivers is pro-government bias. The second factor includes tastes for other features of the outlets, for instance, website design, video content, referrals by news aggregators, or accumulated brand capital. All these factors authors call “persistent preferences”. Also, they differentiate between two types of content: sensitive and non-sensitive topics. For the non-sensitive topics, there is no difference in terms of ideological slants, while news on sensitive topics is framed following the official position.
Simonov and Rao ground their research in a model of news production and consumption. In this model, consumers choose news outlets relying on their outlet-level persistent preferences and tastes for outlets’ ideological coverage. The importance of outlets’ ideological coverage changes depending on the volume of realized sensitive-news events. For instance, on days with no sensitive-news events, people do not pay attention to news outlets’ ideological positions.
On the production side, news outlets decide on shares of sensitive news commodities in their product. The authors assume that news commodities are costly to produce. Also, it is less costly on days when a lot of topic-related events happen. Given that the government dislikes sensitive-news publications, it exercises censorship by imposing additional costs for the production of sensitive news on independent outlet A.
From this model, two observations could be derived. First, a controlled outlet would choose to produce less sensitive news than an independent outlet. Second, since the government is assumed to care mainly about the first few sensitive stories reported, we should expect that the difference in the amount of sensitive news produced by the independent and controlled outlets is increasing in the share of sensitive news to report. For instance, if there is no sensitive news, it can be very costly for both news outlets to produce sensitive news, so both outlets produce very low sensitive content. However, when there is a lot of sensitive news to report, the cost of sensitive-news production is low, and censorship (as costs) plays a more important role.
Next, Simonov and Rao turn to empirical analysis. They consider the top 48 Russian-language news outlets, classified by the degree and type of government influence. They end up with 3 groups: outlets that are owned by the government or members of the incumbent political party (GC), “potentially influenced” outlets, and independent outlets (owned by either journalists, international media companies, or the opposition to the government). They gathered the content published by these outlets during the reference period. From this data, they found 2 topics of sensitive news: POC (political protests, opposition, and corruption) and news about Ukraine. Also, they conclude that international outlets closely resemble Ukrainian outlets in their ideological slant on sensitive topics, and independent outlets have more “neutral” ideological positions. Some potentially influenced outlets closely resemble independent outlets, while others resemble GC outlets.
Using structural estimation, the authors show that news outlets that report more news about the Ukraine crisis and have a more pro-Ukraine ideological position get the highest increases in their market shares on days with a high share of sensitive news realizations.
Next, Simonov and Rao describe the news consumption patterns. They show that consumers are more likely to arrive at the GC outlets from third-party websites: direct navigation plays a lower role for the GC outlets (50.68%), especially compared to the independent ones (56.28%). Particularly, the GC outlets get more than a quarter of their traffic from Yandex (25.57%), compared to only 15.5% for the independent outlets. GC outlets also tend to cross-refer each other more than other types of news outlets. In addition, a large share of consumers arrives directly at their news article pages and “other pages” such as special projects and videos that support the notion of persistent preferences. Also, they conclude that there is almost no outlet-topic specialization. The authors also estimate the inputs of various third parties on the popularity of GC outlets as well as provide contrafactual analysis for market shares.
To sum up, the authors build a theoretical framework to investigate the importance of two channels for online news demand: ideological preferences and factors related to the design, brand, and others. They conclude that the main source of demand for the GC news outlets comes from persistent preferences, which are largely supported by third-party referrals and the nonsensitive news content on GC outlets’ websites. This could potentially help the government in the propaganda making the customers stick to the controlled outlet.
The role of third-party referrals was discussed in the context of the war in Ukraine which was followed by shutdowns of independent news outlets. Also, one of the critical parts was again the role of Yandex as one of the main news aggregators in Russia.
One of the important comments about this paper is that the authors could measure only visitings of the outlets, not the customers' opinion (or its shift). For instance, some of the people, who follow independent outlets, could read the referred GC news not because they agree with the official position but because they want to critically assess the arguments of the opposite side. Therefore, some of the visits do not construct the demand for GC outlets but, for instance, show the demand for comparison and gaining additional information for the critique.
Simonov, A. & Rao, J. (2022) Demand for Online News under Government Control: Evidence from Russia. Journal of Political Economy, 130(2), 259-309
To sum up, the authors build a theoretical framework to investigate the importance of two channels for online news demand: ideological preferences and factors related to the design, brand, and others. They conclude that the main source of demand for the GC news outlets comes from persistent preferences, which are largely supported by third-party referrals and the nonsensitive news content on GC outlets’ websites. This could potentially help the government in the propaganda making the customers stick to the controlled outlet.
The role of third-party referrals was discussed in the context of the war in Ukraine which was followed by shutdowns of independent news outlets. Also, one of the critical parts was again the role of Yandex as one of the main news aggregators in Russia.
One of the important comments about this paper is that the authors could measure only visitings of the outlets, not the customers' opinion (or its shift). For instance, some of the people, who follow independent outlets, could read the referred GC news not because they agree with the official position but because they want to critically assess the arguments of the opposite side. Therefore, some of the visits do not construct the demand for GC outlets but, for instance, show the demand for comparison and gaining additional information for the critique.
Simonov, A. & Rao, J. (2022) Demand for Online News under Government Control: Evidence from Russia. Journal of Political Economy, 130(2), 259-309
Ideological bias and trust
People with different ideological preferences disagree not only about particular issues but also about the trustworthiness of each other's information sources. Thus, the root of the polarization might be grounded in both people’s biases and their trust in different sources of information. This mechanism is addressed in the theoretical model proposed by M. Gentzkow, M. Wong, and A. Zhang. Their contribution to this work is explaining the biases in the learning process without an assumption about any behavioral deviation, i.e. with Bayesian individuals. The model is built upon Berk (1966), who provides a general statement that beliefs need not converge in the long run under misspecified learning, and Acemoglu, Chernozhukov, and Yildiz (2016) conclude that arbitrarily small differences in beliefs about the interpretation of signals can generate large disagreements about an underlying state.
The model describes the learning process of agents who gain knowledge about a sequence of unobservable states in several periods. There is an ideological valence on the state of the world, i.e. consistency of a particular ideological platform. The agent observes signals from information resources that are associated with parameters reflecting the accuracy and bias of signals. In some cases, the agent receives feedback that she believes is unbiased. The authors look at single-homing when there is only one course and multi-homing cases when agents observe all the signals. Introducing the relationship structure between parts of the model, the authors look at the dynamic of several key variables described below.
The results could be divided into two groups. The first group comprises the learning about parameters including correlation between variables, i.e. how the signal of the source is correlated with the state of the world. Another group of the results is devoted to learning about the state of the world. Below I list the main conclusions about different facets of these two directions.
Learning about parameters of signals and feedback
— Confidence: beliefs about the accuracy of her feedback). The authors show that learning from either one or all resources under some conditions overconfidence arises. It means that people could rely more on the feedback than they should.
— Trust: beliefs about the accuracy of the sources. The authors show the trade-off between accuracy and bias and argue that trust-maximizing sources will lie on the border of all possible sets of parameters standing for accuracy and bias. Thus, if the bias of a source is sufficiently high relative to accuracy, the agent will prefer a source with a bias close to one and accuracy close to zero, i.e. a source that reports the ideological valence.
— Ideology and perceived bias: beliefs about the correlation ideological valence and state of the world. Considering the agent with the right-leaning bias, the authors derive that she perceives an unbiased source as oppositely biased. Besides, she perceives a like-minded biased source as less right-biased than it actually is.
People with different ideological preferences disagree not only about particular issues but also about the trustworthiness of each other's information sources. Thus, the root of the polarization might be grounded in both people’s biases and their trust in different sources of information. This mechanism is addressed in the theoretical model proposed by M. Gentzkow, M. Wong, and A. Zhang. Their contribution to this work is explaining the biases in the learning process without an assumption about any behavioral deviation, i.e. with Bayesian individuals. The model is built upon Berk (1966), who provides a general statement that beliefs need not converge in the long run under misspecified learning, and Acemoglu, Chernozhukov, and Yildiz (2016) conclude that arbitrarily small differences in beliefs about the interpretation of signals can generate large disagreements about an underlying state.
The model describes the learning process of agents who gain knowledge about a sequence of unobservable states in several periods. There is an ideological valence on the state of the world, i.e. consistency of a particular ideological platform. The agent observes signals from information resources that are associated with parameters reflecting the accuracy and bias of signals. In some cases, the agent receives feedback that she believes is unbiased. The authors look at single-homing when there is only one course and multi-homing cases when agents observe all the signals. Introducing the relationship structure between parts of the model, the authors look at the dynamic of several key variables described below.
The results could be divided into two groups. The first group comprises the learning about parameters including correlation between variables, i.e. how the signal of the source is correlated with the state of the world. Another group of the results is devoted to learning about the state of the world. Below I list the main conclusions about different facets of these two directions.
Learning about parameters of signals and feedback
— Confidence: beliefs about the accuracy of her feedback). The authors show that learning from either one or all resources under some conditions overconfidence arises. It means that people could rely more on the feedback than they should.
— Trust: beliefs about the accuracy of the sources. The authors show the trade-off between accuracy and bias and argue that trust-maximizing sources will lie on the border of all possible sets of parameters standing for accuracy and bias. Thus, if the bias of a source is sufficiently high relative to accuracy, the agent will prefer a source with a bias close to one and accuracy close to zero, i.e. a source that reports the ideological valence.
— Ideology and perceived bias: beliefs about the correlation ideological valence and state of the world. Considering the agent with the right-leaning bias, the authors derive that she perceives an unbiased source as oppositely biased. Besides, she perceives a like-minded biased source as less right-biased than it actually is.
Learning about the state of the world
— Accuracy: the position of posterior beliefs about the state of the world compared to the true state. The paper concludes that with the tight priors of an agent the lack of bias leads to perfect accuracy. However, the positive bias distorts the posterior beliefs.
— Polarization: the extent to which agents with opposite biases come to disagree about the value of the state of the world. Supporting the previous empirical evidence, the model predicts that the differences in view on facts between people with the opposite view can arise as a result of information processing biases even when accurate information is widely available and agents’ only motivation is to learn the truth.
— Single- vs. Multi-Homing: The authors test the intuitive result that exposure to an ideologically diverse set of information could reduce polarization. They show that it is possible to have such an effect in multi-homing cases, but there are also negative outcomes with the same or even exacerbated polarization. As for the single-homing setting, posterior beliefs depend on the observed source in each period. If the source has less bias, there is less expected disagreement.
— Learning without feedback: the influence on asymptotic learning of the presence of feedback (ex-ante believed to be the unbiased source). In such a setting, the agent cannot rule out the extreme possibilities that any of the sources are perfectly positively correlated, uncorrelated, or perfectly negatively correlated with the true state. It means, that conditional on a received signal, the expected state of the world is zero.
Thus, this paper provides a rich set of conclusions that explain the underlying reasons for disagreements between representatives of various ideological platforms and elaborate on the forces for the dynamic of polarization related to media.
Matthew Gentzkow, Michael B. Wong, and Allen T. Zhang. (2021) “Ideological Bias and Trust in Information Sources”. Working Paper.
This post opens the series of reviews on theoretical models of learning and dynamic of beliefs. Besides, some literature about network models might be covered.
— Accuracy: the position of posterior beliefs about the state of the world compared to the true state. The paper concludes that with the tight priors of an agent the lack of bias leads to perfect accuracy. However, the positive bias distorts the posterior beliefs.
— Polarization: the extent to which agents with opposite biases come to disagree about the value of the state of the world. Supporting the previous empirical evidence, the model predicts that the differences in view on facts between people with the opposite view can arise as a result of information processing biases even when accurate information is widely available and agents’ only motivation is to learn the truth.
— Single- vs. Multi-Homing: The authors test the intuitive result that exposure to an ideologically diverse set of information could reduce polarization. They show that it is possible to have such an effect in multi-homing cases, but there are also negative outcomes with the same or even exacerbated polarization. As for the single-homing setting, posterior beliefs depend on the observed source in each period. If the source has less bias, there is less expected disagreement.
— Learning without feedback: the influence on asymptotic learning of the presence of feedback (ex-ante believed to be the unbiased source). In such a setting, the agent cannot rule out the extreme possibilities that any of the sources are perfectly positively correlated, uncorrelated, or perfectly negatively correlated with the true state. It means, that conditional on a received signal, the expected state of the world is zero.
Thus, this paper provides a rich set of conclusions that explain the underlying reasons for disagreements between representatives of various ideological platforms and elaborate on the forces for the dynamic of polarization related to media.
Matthew Gentzkow, Michael B. Wong, and Allen T. Zhang. (2021) “Ideological Bias and Trust in Information Sources”. Working Paper.
This post opens the series of reviews on theoretical models of learning and dynamic of beliefs. Besides, some literature about network models might be covered.
Theoretical perspective on news consumption and beliefs formation
Here, I decided to share short summaries of several theoretical works about news consumption and belief formation that are relevant to my thesis. I think understanding the learning process is an important key to understanding the roots of peoples' behavior.
The working paper by G. Tabellini and L. D'Amico studies how people comment on political news on Reddit political forums. The authors provide a theoretical model that shows how people allocate their attention to signals about two candidates to form a ranking between them. To test the model predictions, the authors use the comments to post on political forums on Reddit. The paper concludes that partisan users behave very differently from independents: partisan comments on bad news are less frequent on the own candidate, and more frequent on the opponent. Applying text analysis, the authors show that partisan users are less likely to accept bad news about their candidate, and more likely on the opponent that is consistent with motivated reasoning. They point out that emotions play an important role in the propensity to comment on political news. The authors mention that a potential interpretation of their results is that a partisan user tries to protect their self-identity, rationalizing the candidate’s behavior, finding excuses for it, or attenuating its relevance.
In this regard, Fryer et. al. suggest a model explaining the behavior of people when they receive ambiguous news such that different people might interpret them differently. They show that double updating (the interpretation of ambiguous signals, and then the formation of a posterior) leads to confirmation bias and polarization. They also conducted an online experiment in which individuals interpret research summaries about climate change and the death penalty. The authors show a significant relationship between an individual’s prior and their interpretation of the summaries as was predicted by the model.
Another pattern of news consumption leading to polarization is selective sharing presented in (Bowen et. al., 2023). The authors assume that people tend to share only the signals which are in line with their priors. Also, they consider a not fully rational person who does not estimate properly the probability of not receiving a signal. That distorts her updating process and leads to polarization.
(Gentzkow et. al., 2021) considers trust as one of the driving forces of polarization. The authors present a learning process where agents do not know the accuracy of sources ex-ante and rely on noisy feedback. This paper shows that small biases in this feedback can cause large ideological differences both the in the trust in information sources and beliefs about the states of the world.
One more mechanism was presented by (DeMarzo, et. al., 2003). The authors assume that people underestimate or completely ignore the fact that signals they receive are correlated with each other. This fact distorts the way they form their beliefs, and it leads to overconfidence.
References
D’Amico, L., & Tabellini, G. (2022). Online Political Debates (tech. rep.). Center for Economic Studies & Ifo Institute
DeMarzo, P. M., Vayanos, D., & Zwiebel, J. (2003). Persuasion Bias, Social Influence, and Unidimensional Opinions. The Quarterly Journal of Economics, 118 (3)
Fryer Jr, R. G., Harms, P., & Jackson, M. O. (2018). Updating Beliefs when Evidence is Open to Interpretation: Implications for Bias and Polarization. Journal of the European Economic Association, 17 (5)
Gentzkow, M., Wong, M. B., & Zhang, A. T. (2021). Ideological Bias and Trust in Information Sources (Working paper). https://www.matthewgentzkow.com/papers/
T Renee Bowen, Danil Dmitriev, Simone Galperti, Learning from Shared News: When Abundant Information Leads to Belief Polarization, The Quarterly Journal of Economics, 2023; qjac045, https://doi.org/10.1093/qje/qjac045
Here, I decided to share short summaries of several theoretical works about news consumption and belief formation that are relevant to my thesis. I think understanding the learning process is an important key to understanding the roots of peoples' behavior.
The working paper by G. Tabellini and L. D'Amico studies how people comment on political news on Reddit political forums. The authors provide a theoretical model that shows how people allocate their attention to signals about two candidates to form a ranking between them. To test the model predictions, the authors use the comments to post on political forums on Reddit. The paper concludes that partisan users behave very differently from independents: partisan comments on bad news are less frequent on the own candidate, and more frequent on the opponent. Applying text analysis, the authors show that partisan users are less likely to accept bad news about their candidate, and more likely on the opponent that is consistent with motivated reasoning. They point out that emotions play an important role in the propensity to comment on political news. The authors mention that a potential interpretation of their results is that a partisan user tries to protect their self-identity, rationalizing the candidate’s behavior, finding excuses for it, or attenuating its relevance.
In this regard, Fryer et. al. suggest a model explaining the behavior of people when they receive ambiguous news such that different people might interpret them differently. They show that double updating (the interpretation of ambiguous signals, and then the formation of a posterior) leads to confirmation bias and polarization. They also conducted an online experiment in which individuals interpret research summaries about climate change and the death penalty. The authors show a significant relationship between an individual’s prior and their interpretation of the summaries as was predicted by the model.
Another pattern of news consumption leading to polarization is selective sharing presented in (Bowen et. al., 2023). The authors assume that people tend to share only the signals which are in line with their priors. Also, they consider a not fully rational person who does not estimate properly the probability of not receiving a signal. That distorts her updating process and leads to polarization.
(Gentzkow et. al., 2021) considers trust as one of the driving forces of polarization. The authors present a learning process where agents do not know the accuracy of sources ex-ante and rely on noisy feedback. This paper shows that small biases in this feedback can cause large ideological differences both the in the trust in information sources and beliefs about the states of the world.
One more mechanism was presented by (DeMarzo, et. al., 2003). The authors assume that people underestimate or completely ignore the fact that signals they receive are correlated with each other. This fact distorts the way they form their beliefs, and it leads to overconfidence.
References
D’Amico, L., & Tabellini, G. (2022). Online Political Debates (tech. rep.). Center for Economic Studies & Ifo Institute
DeMarzo, P. M., Vayanos, D., & Zwiebel, J. (2003). Persuasion Bias, Social Influence, and Unidimensional Opinions. The Quarterly Journal of Economics, 118 (3)
Fryer Jr, R. G., Harms, P., & Jackson, M. O. (2018). Updating Beliefs when Evidence is Open to Interpretation: Implications for Bias and Polarization. Journal of the European Economic Association, 17 (5)
Gentzkow, M., Wong, M. B., & Zhang, A. T. (2021). Ideological Bias and Trust in Information Sources (Working paper). https://www.matthewgentzkow.com/papers/
T Renee Bowen, Danil Dmitriev, Simone Galperti, Learning from Shared News: When Abundant Information Leads to Belief Polarization, The Quarterly Journal of Economics, 2023; qjac045, https://doi.org/10.1093/qje/qjac045
Now that I have completed my bachelor's degree, it presents a perfect opportunity to revitalize this channel. With my research interests already well-defined, I intend to focus primarily on Political Economy, and I hope to make not only summaries of papers. To kick things off, today I will be sharing some notes and thoughts from the recent Workshop on Political Economy held at BSE on June 15-16. I will provide short summaries of these yet not published works and then point out what I like the most.
My Top Picks of the Workshop*:
1) Bureaucracy as a Tool for Politicians: Evidence from Germany by Leander Heldrin
The authors look at the instrumental value of bureaucrats. Primarily, they argue, whether a better bureaucratic system would be equivalently efficient for both good and bad policies. To address this question, the authors look at Nazi Germany, and, to nail down the role of bureaucracy in the deportation of Jews, they compare the regions of Germany that previously belonged to Prussia with those which were attached later on. The Prussian bureaucratic system is known for its efficiency, and indeed, they perform tax collection with the least delays. Importantly, this professionalism stays when the policy changes, meaning that previously Prussian territories were more productive in the departure of Jews. The results hold accounting for many alternative explanations. Lastly, the question is, how do bureaucrats know about the goals of their actions comply with the policy? The answer suggested is the ‘Banality of Evil’ introduced by H. Arend. The argument is that in the more specialized system (that also is considered the most efficient), agents can hide behind the principle to mentally absolve themselves from the responsibility.
First, I believe that this work provides evidence for the very important idea of how good tools could be dangerous in the wrong hands. Besides, it highlights how a big and specialized system could allow its parts to turn on a blind eye to any atrocities. Second, the authors did great work in terms of data collection to build this database allowing them to nail down the channel of bureaucracy efficiency. Third, this paper has a rich set of robustness checks that makes it quite persuasive.
2) Keep your Enemies Closer: Strategic Candidate Adjustments in U.S. and French Elections by Caroline Le Pennec
This paper explores the empirical implications – the convergence of parties’ platforms to the median voter’s preferences. Indeed, we do not see parties with identical platforms, but, as the authors argue, we can observe a partial convergence. To prove this, the authors analyze candidates’ websites in the US and political manifestos in France. Using the two-stage nature of the elections, the authors show that indeed parties change their platform to be closer to the opponent. It is easier to see in the case of France whether the winner of the first round would debate with the second candidate that could be either on the right or left relative to the first-round winner. The authors show that the political identity of the opponent shifts the platform of the central candidate.
My Top Picks of the Workshop*:
1) Bureaucracy as a Tool for Politicians: Evidence from Germany by Leander Heldrin
The authors look at the instrumental value of bureaucrats. Primarily, they argue, whether a better bureaucratic system would be equivalently efficient for both good and bad policies. To address this question, the authors look at Nazi Germany, and, to nail down the role of bureaucracy in the deportation of Jews, they compare the regions of Germany that previously belonged to Prussia with those which were attached later on. The Prussian bureaucratic system is known for its efficiency, and indeed, they perform tax collection with the least delays. Importantly, this professionalism stays when the policy changes, meaning that previously Prussian territories were more productive in the departure of Jews. The results hold accounting for many alternative explanations. Lastly, the question is, how do bureaucrats know about the goals of their actions comply with the policy? The answer suggested is the ‘Banality of Evil’ introduced by H. Arend. The argument is that in the more specialized system (that also is considered the most efficient), agents can hide behind the principle to mentally absolve themselves from the responsibility.
First, I believe that this work provides evidence for the very important idea of how good tools could be dangerous in the wrong hands. Besides, it highlights how a big and specialized system could allow its parts to turn on a blind eye to any atrocities. Second, the authors did great work in terms of data collection to build this database allowing them to nail down the channel of bureaucracy efficiency. Third, this paper has a rich set of robustness checks that makes it quite persuasive.
2) Keep your Enemies Closer: Strategic Candidate Adjustments in U.S. and French Elections by Caroline Le Pennec
This paper explores the empirical implications – the convergence of parties’ platforms to the median voter’s preferences. Indeed, we do not see parties with identical platforms, but, as the authors argue, we can observe a partial convergence. To prove this, the authors analyze candidates’ websites in the US and political manifestos in France. Using the two-stage nature of the elections, the authors show that indeed parties change their platform to be closer to the opponent. It is easier to see in the case of France whether the winner of the first round would debate with the second candidate that could be either on the right or left relative to the first-round winner. The authors show that the political identity of the opponent shifts the platform of the central candidate.
❤1
I like the way authors measure the proximity via text analysis of candidates’ platforms. However, to strengthen the work, I would prefer (at least as a robustness check) to use more novel techniques of NLP to identify the platforms and sentiments. In general, the paper provides a direct illustration of the Downsian model and derives insights into the dynamic behavior of political candidates.
3) Judicial Independence, Local Protectionism, and Economic Integration: Evidence from China by Shaoda Wang
The independence of the judicial system is believed to be an important factor in economic development. However, until recently, in China local courts that are responsible for the majority of economic disputes were funded by local governments that exploit that dependence to establish a paternalism for local firms in their disputes with firms from other municipalities. Admitting this issue, the government started the reform that implied shifting the funds from local to province level. As the authors show using the time variation in the reform’s adoption, the winning rate of local firms against the firms from the other municipalities dropped, also, the effect is driven by the firms who had tights with local governments via procurement contracts. Besides, the reform did not create any other inefficiencies in improving the decisions (as measured by the number of complaints and approval of signature verification procedures that were previously highly forbidden to hide the falsification of local firms).
I think that the paper elegantly exploits the institutional environment to answer the question. Also, the richness of the gathered dataset allows authors to nail down the suggested mechanism of patronage for local firms by identifying the firms connected to different layers of government. It also enriches the literature showing the importance of judicial independence.
4) Control through Empowerment: Evidence from Soviet Nation-Building in Central Asia by Ekaterina Zhuravskaya
This paper tackles the question of whether the improvement of colonization mitigates and exacerbates the conflict. Particularly, the authors look at the context of the relationship between the Soviet government and Central Asia. The paper exploits the creation of borders in this region to select a proper control group. Using the objectives of the Bolsheviks, the author creates ideal borders that result in 6 republics instead of the actual 5. Importantly, the overlap between ideal and real borders is pretty high, meaning that the government did a good job in terms of accounting for language groups. Next, the authors look at the reports of secret agencies to create a measure of conflict in these regions. It turns out that the localization of conflicts changed from the southern regions which were a beneficiary of the reform to the north one where the situation worsened for Russians who would ideally constitute the 6th republic. Also, the share of titular nationals increased over time and more so due to the reform and due to massive nation-building investments, such as school construction. Overall, these reforms could be considered successful for the Soviets by the criteria of keeping control. However, the population of North Kazakhstan was worse off.
The part of the paper I like the most is the construction of artificial borders to identify the appropriate control groups. Also, the use of names to identify the ‘Soviet’ or national identity of draftees is a very nice and smart strategy. Of course, besides the elegance of these technical elements, the paper provides a rigorous argument regarding colonial strategies and its implication for the ruling government.
* I put the names of presenters, the lists of authors could be found there. The summaries are made from my understanding of the presentations since the texts are not available yet, so there could be some inaccuracies.
3) Judicial Independence, Local Protectionism, and Economic Integration: Evidence from China by Shaoda Wang
The independence of the judicial system is believed to be an important factor in economic development. However, until recently, in China local courts that are responsible for the majority of economic disputes were funded by local governments that exploit that dependence to establish a paternalism for local firms in their disputes with firms from other municipalities. Admitting this issue, the government started the reform that implied shifting the funds from local to province level. As the authors show using the time variation in the reform’s adoption, the winning rate of local firms against the firms from the other municipalities dropped, also, the effect is driven by the firms who had tights with local governments via procurement contracts. Besides, the reform did not create any other inefficiencies in improving the decisions (as measured by the number of complaints and approval of signature verification procedures that were previously highly forbidden to hide the falsification of local firms).
I think that the paper elegantly exploits the institutional environment to answer the question. Also, the richness of the gathered dataset allows authors to nail down the suggested mechanism of patronage for local firms by identifying the firms connected to different layers of government. It also enriches the literature showing the importance of judicial independence.
4) Control through Empowerment: Evidence from Soviet Nation-Building in Central Asia by Ekaterina Zhuravskaya
This paper tackles the question of whether the improvement of colonization mitigates and exacerbates the conflict. Particularly, the authors look at the context of the relationship between the Soviet government and Central Asia. The paper exploits the creation of borders in this region to select a proper control group. Using the objectives of the Bolsheviks, the author creates ideal borders that result in 6 republics instead of the actual 5. Importantly, the overlap between ideal and real borders is pretty high, meaning that the government did a good job in terms of accounting for language groups. Next, the authors look at the reports of secret agencies to create a measure of conflict in these regions. It turns out that the localization of conflicts changed from the southern regions which were a beneficiary of the reform to the north one where the situation worsened for Russians who would ideally constitute the 6th republic. Also, the share of titular nationals increased over time and more so due to the reform and due to massive nation-building investments, such as school construction. Overall, these reforms could be considered successful for the Soviets by the criteria of keeping control. However, the population of North Kazakhstan was worse off.
The part of the paper I like the most is the construction of artificial borders to identify the appropriate control groups. Also, the use of names to identify the ‘Soviet’ or national identity of draftees is a very nice and smart strategy. Of course, besides the elegance of these technical elements, the paper provides a rigorous argument regarding colonial strategies and its implication for the ruling government.
* I put the names of presenters, the lists of authors could be found there. The summaries are made from my understanding of the presentations since the texts are not available yet, so there could be some inaccuracies.
❤1🔥1
Social media and mental health
The introduction and widespread use of social media have had diverse effects on society, including implications for mental health. In this discussion, I aim to highlight key studies that explore the causal relationship between social media usage and mental well-being. Previous research has yielded conflicting results, with some studies suggesting positive effects due to increased engagement, while others argue that exposure to unfavorable social comparisons has a negative impact on subjective well-being. However, from the experiments I will discuss, it appears that the latter effect tends to dominate, although its magnitude may not be very high.
One of the initial experiments, conducted by R. Mosquera and colleagues, aimed to estimate the economic value of Facebook by asking participants to deactivate their accounts for a week. Using the BDM mechanisms, they found that individuals valued Facebook at $67 per week, which amounted to 30% of students' weekly income. Comparing surveys before and after the treatment, they observed higher subjective well-being among the treatment group compared to the control group. Importantly, the treatment group also started to allocate more time to healthy activities. Consequently, it becomes challenging to disentangle the effect of spending free time on healthy pursuits from the effect of Facebook usage itself.
To address some of the limitations of previous studies, such as small reductions in Facebook usage and the lack of post-experiment monitoring, H. Alcott et al. conducted a similar experiment with a larger sample size, where participants deactivated their accounts for up to 8 weeks. They also explored a broader range of parameters of interest. Their findings corroborated the notion that abstaining from Facebook improves subjective well-being. Furthermore, when considering usage after the experiment, the authors demonstrated that the treatment group reduced social media usage by 12 minutes (a 23% decrease compared to the control group's average of 53 minutes).
While randomized controlled trials (RCTs) are considered the gold standard for causal studies, the experiments discussed above highlight local effects on specific samples and specific designs. Another study, conducted by L. Braghieri, R. Levy, and A. Makarin, took advantage of the natural experiment provided by the staggered introduction of Facebook in American colleges. Their results align with previous findings, indicating that Facebook usage negatively affects the mental health of students. The magnitude of this effect is comparable to the 22% of impact of job loss documented in literature. As an alternative baseline, the authors argue that exposure to Facebook increases the proportion of students suffering from depression by 2% (with the mean share of such students being 25%). The proposed mechanism for this effect is the negative comparison of oneself with others. This argument is supported by two observations: first, the effect is more pronounced among students who are more susceptible to negative comparisons, and second, the introduction of Facebook influences beliefs about others' behavior.
The introduction and widespread use of social media have had diverse effects on society, including implications for mental health. In this discussion, I aim to highlight key studies that explore the causal relationship between social media usage and mental well-being. Previous research has yielded conflicting results, with some studies suggesting positive effects due to increased engagement, while others argue that exposure to unfavorable social comparisons has a negative impact on subjective well-being. However, from the experiments I will discuss, it appears that the latter effect tends to dominate, although its magnitude may not be very high.
One of the initial experiments, conducted by R. Mosquera and colleagues, aimed to estimate the economic value of Facebook by asking participants to deactivate their accounts for a week. Using the BDM mechanisms, they found that individuals valued Facebook at $67 per week, which amounted to 30% of students' weekly income. Comparing surveys before and after the treatment, they observed higher subjective well-being among the treatment group compared to the control group. Importantly, the treatment group also started to allocate more time to healthy activities. Consequently, it becomes challenging to disentangle the effect of spending free time on healthy pursuits from the effect of Facebook usage itself.
To address some of the limitations of previous studies, such as small reductions in Facebook usage and the lack of post-experiment monitoring, H. Alcott et al. conducted a similar experiment with a larger sample size, where participants deactivated their accounts for up to 8 weeks. They also explored a broader range of parameters of interest. Their findings corroborated the notion that abstaining from Facebook improves subjective well-being. Furthermore, when considering usage after the experiment, the authors demonstrated that the treatment group reduced social media usage by 12 minutes (a 23% decrease compared to the control group's average of 53 minutes).
While randomized controlled trials (RCTs) are considered the gold standard for causal studies, the experiments discussed above highlight local effects on specific samples and specific designs. Another study, conducted by L. Braghieri, R. Levy, and A. Makarin, took advantage of the natural experiment provided by the staggered introduction of Facebook in American colleges. Their results align with previous findings, indicating that Facebook usage negatively affects the mental health of students. The magnitude of this effect is comparable to the 22% of impact of job loss documented in literature. As an alternative baseline, the authors argue that exposure to Facebook increases the proportion of students suffering from depression by 2% (with the mean share of such students being 25%). The proposed mechanism for this effect is the negative comparison of oneself with others. This argument is supported by two observations: first, the effect is more pronounced among students who are more susceptible to negative comparisons, and second, the introduction of Facebook influences beliefs about others' behavior.
However, these studies also have some limitations. Firstly, they do not account for potential adaptation as social media becomes an integral part of our lives. Secondly, apart from the impact on mental health, these studies attempt to estimate the value of Facebook, considering its broader benefits such as easier communication with friends and relatives, as well as access to news and entertainment content. Lastly, while these works primarily focus on Facebook, other forms of social media that involve different types of interactions may have distinct effects on mental health. For example, platforms like Instagram may act as sources of dopamine due to social approval, potentially leading to positive effects. Therefore, while there is evidence alerting us to potential threats to mental well-being, we must also acknowledge the surplus generated by the functionality of social media and thoughtfully alocate our time.
Social media addiction and self-restriction
Although social media's harmful effects on mental health are acknowledged, individuals often struggle to reduce their time spent on these platforms. Alcot et al. delve into the analysis of whether social media can be considered an addictive good. Their model incorporates the concept of projection bias, wherein agents make choices without fully considering habit formation and exhibit naivety towards self-control issues. When people are aware of the potential for habit formation, they tend to reduce consumption to avoid negative consequences. However, projection bias undermines this effect. Furthermore, if individuals recognize their self-control problems, they may desire commitment mechanisms to limit future consumption.
To test their theoretical predictions, the authors conduct an experiment employing a specialized application that participants must install to regulate their social media usage. Two experimental groups are established. The first group offers participants a temporary subsidy of $2.50 for each hour they reduce their social media consumption over a three-week period. The second group is asked to self-impose time limits using the installed application, adhering to their chosen maximum time allocation for social media. The latter group does not receive any monetary incentives. The results reveal that the first group, on average, reduces their social media consumption by 56 minutes per day, corresponding to a 39% reduction compared to the control group. Even after the experiment concludes, participants continue to spend 19 minutes less time on social media on average. These findings support the argument that social media can form addictive habits.
The analysis of the behavior of the second group supports the presence of self-control issues, and the results confirm individuals' awareness of these problems. Over the course of 12 weeks, the participants in this group reduced their social media usage by 22 minutes per day (a 16% reduction). However, the effectiveness of this effect diminishes during the experiment, potentially due to a decline in motivation. Despite the absence of a reward for setting limits, 78% of participants in this group restricted their social media consumption and maintained these restrictions until the experiment's conclusion. Notably, while there is a significant demand for self-restraint, the authors observe a relatively low demand (5% of the sample) for commercial tools that control app usage time. This discrepancy may stem from expensive or ineffective applications available on the market.
The authors continue this research focusing on the optimal design for apps restricting phone usage. The preliminary findings of this follow-up work were presented by M. Gentzkow in a keynote speech at the 2nd CEPR Workshop on Media, Technology, Politics, and Society (to be reviewed). Presently, phone-usage apps allow users to establish time restrictions on selected applications, but these limitations can be easily extended. To address this issue, the authors explore the concept of flexibility in commitment devices by considering delays for limits’ extensions. They conduct a similar experiment, comparing incentivized groups with a group focused on setting limits. However, in the latter group, the delays before extending the limits are randomized, with some participants waiting for 0, 2, 5, or 20 minutes before being able to increase their allotted time, while others have no flexibility to extend their limits. The results indicate that even a short delay can incentivize individuals to reduce their consumption by 22-24 minutes per day. At the same time, the absence of any flexibility leads to approximately 27 minutes of reduction. Fully flexible commitment devices (with no delay) prove insufficient for most consumers within the sample.
Although social media's harmful effects on mental health are acknowledged, individuals often struggle to reduce their time spent on these platforms. Alcot et al. delve into the analysis of whether social media can be considered an addictive good. Their model incorporates the concept of projection bias, wherein agents make choices without fully considering habit formation and exhibit naivety towards self-control issues. When people are aware of the potential for habit formation, they tend to reduce consumption to avoid negative consequences. However, projection bias undermines this effect. Furthermore, if individuals recognize their self-control problems, they may desire commitment mechanisms to limit future consumption.
To test their theoretical predictions, the authors conduct an experiment employing a specialized application that participants must install to regulate their social media usage. Two experimental groups are established. The first group offers participants a temporary subsidy of $2.50 for each hour they reduce their social media consumption over a three-week period. The second group is asked to self-impose time limits using the installed application, adhering to their chosen maximum time allocation for social media. The latter group does not receive any monetary incentives. The results reveal that the first group, on average, reduces their social media consumption by 56 minutes per day, corresponding to a 39% reduction compared to the control group. Even after the experiment concludes, participants continue to spend 19 minutes less time on social media on average. These findings support the argument that social media can form addictive habits.
The analysis of the behavior of the second group supports the presence of self-control issues, and the results confirm individuals' awareness of these problems. Over the course of 12 weeks, the participants in this group reduced their social media usage by 22 minutes per day (a 16% reduction). However, the effectiveness of this effect diminishes during the experiment, potentially due to a decline in motivation. Despite the absence of a reward for setting limits, 78% of participants in this group restricted their social media consumption and maintained these restrictions until the experiment's conclusion. Notably, while there is a significant demand for self-restraint, the authors observe a relatively low demand (5% of the sample) for commercial tools that control app usage time. This discrepancy may stem from expensive or ineffective applications available on the market.
The authors continue this research focusing on the optimal design for apps restricting phone usage. The preliminary findings of this follow-up work were presented by M. Gentzkow in a keynote speech at the 2nd CEPR Workshop on Media, Technology, Politics, and Society (to be reviewed). Presently, phone-usage apps allow users to establish time restrictions on selected applications, but these limitations can be easily extended. To address this issue, the authors explore the concept of flexibility in commitment devices by considering delays for limits’ extensions. They conduct a similar experiment, comparing incentivized groups with a group focused on setting limits. However, in the latter group, the delays before extending the limits are randomized, with some participants waiting for 0, 2, 5, or 20 minutes before being able to increase their allotted time, while others have no flexibility to extend their limits. The results indicate that even a short delay can incentivize individuals to reduce their consumption by 22-24 minutes per day. At the same time, the absence of any flexibility leads to approximately 27 minutes of reduction. Fully flexible commitment devices (with no delay) prove insufficient for most consumers within the sample.
Thus, social media usage might be considered as an addictive good. Assuming that people have self-control problems, the commitment for reducing social media usage might help to improve overall welfare. It was proved that fully-flexible commitments are not an option, so nudging people to reduce the consumption via delay before limits’ extensions should be efficient. However, authors cannot assess the long-term effect of such restrictions. In principle, people can put higher limits to avoid delay and spend even more time in the future. Moreover, it is not clear that the effect is persistent and whether it leads to the optimal consumption point.
Reflections on the Women in Empirical Microeconomics Conference
Thanks to my boss, I had the opportunity to attend this enriching conference. While I remain unconvinced about its significant impact on promoting women's research and representation in the profession, the overall atmosphere was notably different - fewer questions during presentations, impeccable timing, and more positive feedback from discussants (from my perspective). Nonetheless, the conference proved to be interesting due to the high-quality research presented and the diverse range of fields covered. Here, I'd like to highlight three works that particularly caught my interest.
Pricing and Efficiency in a Decentralized Ride-Hailing Platform by Renata Gaineddenova
The author of this paper essentially juxtaposes capitalism with socialism by analyzing the efficiency of common designs in rideshare platforms (e.g., Uber, Lyft, Yandex taxi), which are centralized and socialistic in nature. The study explores whether the current pricing models, determined by internal algorithms and not incorporating individual willingness to pay or sell, create an information asymmetry. Using novel data from inDriver, a platform with decentralizing mechanisms ('capitalistic' one), where clients and drivers can set their own prices, the study observes demand and supply dynamics as they naturally occur. This decentralized approach increases overall efficiency, as it allows high-valuation riders to benefit from setting their prices, enhancing the match between drivers and clients. However, a critical aspect that needs further consideration is the welfare of the platform itself. Companies like Uber, Lyft, and Yandex, while focusing on matching riders and drivers, also aim to maximize profits through various strategies. Differentiating these strategies and their impact on consumer and driver welfare would enrich the analysis.
Missing Tariffs, False Imputation, and the Trade Elasticity by Feodora Teti
This paper is absolutely striking and posed a significant challenge that greatly surprised the audience. Essentially, the authors explore how previous researchers utilized tariff data and why their approaches were flawed. The importance of data quality is emphasized by the fact that tariff data is pivotal in calculating trade elasticity, a crucial factor in estimating trade gains. Furthermore, it serves as a vital tool for understanding trade shocks in various literature.
There are two broad categories of tariffs: those uniformly imposed within the WTO across all countries, guided by non-discriminatory principles, and those resulting from preferential agreements between specific countries aimed at establishing improved conditions. The primary challenge lies in creating a comprehensive tariff database, given that these tariffs are not adequately published. Another equally significant issue is that the active tariff is consistently calculated as the minimum between the two, without accounting for missing observations in a particular year. Consequently, this approach results in a graph of active tariffs that appears quite erratic.
Additionally, there are issues of selection bias stemming from the poorer quality of data from developing countries. Theoretically, these biases lead to an attenuation bias in estimation. In fact, upon replicating a couple of recent papers, the authors demonstrate a change in trade elasticity from approximately -2.6 to around 0.6. Moreover, some of the results lose statistical significance. I was truly taken aback by the realization that nobody had previously recognized the problem associated with taking the minimum in the case of missing data.
Thanks to my boss, I had the opportunity to attend this enriching conference. While I remain unconvinced about its significant impact on promoting women's research and representation in the profession, the overall atmosphere was notably different - fewer questions during presentations, impeccable timing, and more positive feedback from discussants (from my perspective). Nonetheless, the conference proved to be interesting due to the high-quality research presented and the diverse range of fields covered. Here, I'd like to highlight three works that particularly caught my interest.
Pricing and Efficiency in a Decentralized Ride-Hailing Platform by Renata Gaineddenova
The author of this paper essentially juxtaposes capitalism with socialism by analyzing the efficiency of common designs in rideshare platforms (e.g., Uber, Lyft, Yandex taxi), which are centralized and socialistic in nature. The study explores whether the current pricing models, determined by internal algorithms and not incorporating individual willingness to pay or sell, create an information asymmetry. Using novel data from inDriver, a platform with decentralizing mechanisms ('capitalistic' one), where clients and drivers can set their own prices, the study observes demand and supply dynamics as they naturally occur. This decentralized approach increases overall efficiency, as it allows high-valuation riders to benefit from setting their prices, enhancing the match between drivers and clients. However, a critical aspect that needs further consideration is the welfare of the platform itself. Companies like Uber, Lyft, and Yandex, while focusing on matching riders and drivers, also aim to maximize profits through various strategies. Differentiating these strategies and their impact on consumer and driver welfare would enrich the analysis.
Missing Tariffs, False Imputation, and the Trade Elasticity by Feodora Teti
This paper is absolutely striking and posed a significant challenge that greatly surprised the audience. Essentially, the authors explore how previous researchers utilized tariff data and why their approaches were flawed. The importance of data quality is emphasized by the fact that tariff data is pivotal in calculating trade elasticity, a crucial factor in estimating trade gains. Furthermore, it serves as a vital tool for understanding trade shocks in various literature.
There are two broad categories of tariffs: those uniformly imposed within the WTO across all countries, guided by non-discriminatory principles, and those resulting from preferential agreements between specific countries aimed at establishing improved conditions. The primary challenge lies in creating a comprehensive tariff database, given that these tariffs are not adequately published. Another equally significant issue is that the active tariff is consistently calculated as the minimum between the two, without accounting for missing observations in a particular year. Consequently, this approach results in a graph of active tariffs that appears quite erratic.
Additionally, there are issues of selection bias stemming from the poorer quality of data from developing countries. Theoretically, these biases lead to an attenuation bias in estimation. In fact, upon replicating a couple of recent papers, the authors demonstrate a change in trade elasticity from approximately -2.6 to around 0.6. Moreover, some of the results lose statistical significance. I was truly taken aback by the realization that nobody had previously recognized the problem associated with taking the minimum in the case of missing data.
It’s a Man’s World: Culture of Abuse, #MeToo and Worker Flows by Caroline Coly
This paper addresses how sexual harassment could affect the labor supply. The authors use #MeToo as a shock to perceived social norms and examine job resignations in French firms. The most exciting aspect of this paper is the thoughtful and elegant use of data. They utilize survey data to create a measure of the risk of sexual harassment in a particular firm. Then, using an administrative census of all firms, they track labor allocation over time. It turns out that indeed, firms with a higher risk of harassment lose more workers. These workers tend to find new jobs in less toxic firms with a higher proportion of females, and what is interesting is that this is true not only for female employees. Naturally, there is heterogeneity by industry – male-dominated industries such as construction are associated with a higher risk of harassment, as well as firms led by a male CEO.
This paper addresses how sexual harassment could affect the labor supply. The authors use #MeToo as a shock to perceived social norms and examine job resignations in French firms. The most exciting aspect of this paper is the thoughtful and elegant use of data. They utilize survey data to create a measure of the risk of sexual harassment in a particular firm. Then, using an administrative census of all firms, they track labor allocation over time. It turns out that indeed, firms with a higher risk of harassment lose more workers. These workers tend to find new jobs in less toxic firms with a higher proportion of females, and what is interesting is that this is true not only for female employees. Naturally, there is heterogeneity by industry – male-dominated industries such as construction are associated with a higher risk of harassment, as well as firms led by a male CEO.
Social Economics Conference
The conference title prompts a reconsideration of one's academic identity: am I a political, development, or labor economist, or do I identify as a social economist? It's not merely a subfield but an encompassing category for those intrigued by the societal influences shaping human behavior. Here are my personal favorites from the event:
Social Media Trap
Our lab's brilliant work challenges the conventional method of estimating social welfare from products. The study questions the assumption that the absence of a product equates to zero utility. Instead, it argues that non-consumption of popular goods may have negative utility due to non-user spillovers. For instance, abstaining from social media might cause a fear of missing out, or a rejection of certain brands signal lower status. Experimental evidence reveals negative adjusted welfare measures for TikTok and Instagram. Survey suggests that even owners of luxury brands prefer a world without them, challenging the standard framework.
Socializing Alone: How Online Homophily Has Undermined Social Cohesion in the US
This project explores how online networks influence interpersonal interactions. While social media's intent is to bridge gaps and unite communities, it also could lead to fragmentation and polarization. The study examines how homophily within online networks affects offline behavior. Using Facebook data, authors show that people from counties, that are connected more to socially similar ones via Facebook, use this social media more and visit bars and restaurants less. Political opinions within counties become more diverse, diminishing the probability of two voters supporting the same political party. The 'death-of-distance' technology appears to compromise short-distance social cohesion.
Decomposing the Rise of the Populist Radical Right
This work quantifyes the factors behind the rise of populism. The study provides a structural estimation of the weights for shifts in party positions, changes in voter attributes, changes in voter priorities, and a residual as key determinants of populist support. Surprisingly, shifts in party positions and changes in voter attributes play a minor role. Instead, voters' changing priorities, especially among older, nonunionized, low-educated men, prioritize nativist cultural positions. This challenges the notion that misinformation is the primary issue, suggesting that agenda setting and manipulation of salience play a more significant role. It was very inspiring project for me personally, and I hope to work on a follow-up!
Changing Harmful Norms through Information and Coordination: Experimental Evidence from Somalia
This study sheds light on how social norms can negatively impact public health, using the context of Somalia. The research explores methods to halt harmful practices, focusing on two components: providing corrective information and implementing coordination tools. Results show that informational treatment influences behavior and reduces harmful practices. However, coordination tools are less effective, succeeding only in communities with negative views on such practices. The study emphasizes the need for context-specific tools, advocating for pilot programs before full-scale implementation.
The conference title prompts a reconsideration of one's academic identity: am I a political, development, or labor economist, or do I identify as a social economist? It's not merely a subfield but an encompassing category for those intrigued by the societal influences shaping human behavior. Here are my personal favorites from the event:
Social Media Trap
Our lab's brilliant work challenges the conventional method of estimating social welfare from products. The study questions the assumption that the absence of a product equates to zero utility. Instead, it argues that non-consumption of popular goods may have negative utility due to non-user spillovers. For instance, abstaining from social media might cause a fear of missing out, or a rejection of certain brands signal lower status. Experimental evidence reveals negative adjusted welfare measures for TikTok and Instagram. Survey suggests that even owners of luxury brands prefer a world without them, challenging the standard framework.
Socializing Alone: How Online Homophily Has Undermined Social Cohesion in the US
This project explores how online networks influence interpersonal interactions. While social media's intent is to bridge gaps and unite communities, it also could lead to fragmentation and polarization. The study examines how homophily within online networks affects offline behavior. Using Facebook data, authors show that people from counties, that are connected more to socially similar ones via Facebook, use this social media more and visit bars and restaurants less. Political opinions within counties become more diverse, diminishing the probability of two voters supporting the same political party. The 'death-of-distance' technology appears to compromise short-distance social cohesion.
Decomposing the Rise of the Populist Radical Right
This work quantifyes the factors behind the rise of populism. The study provides a structural estimation of the weights for shifts in party positions, changes in voter attributes, changes in voter priorities, and a residual as key determinants of populist support. Surprisingly, shifts in party positions and changes in voter attributes play a minor role. Instead, voters' changing priorities, especially among older, nonunionized, low-educated men, prioritize nativist cultural positions. This challenges the notion that misinformation is the primary issue, suggesting that agenda setting and manipulation of salience play a more significant role. It was very inspiring project for me personally, and I hope to work on a follow-up!
Changing Harmful Norms through Information and Coordination: Experimental Evidence from Somalia
This study sheds light on how social norms can negatively impact public health, using the context of Somalia. The research explores methods to halt harmful practices, focusing on two components: providing corrective information and implementing coordination tools. Results show that informational treatment influences behavior and reduces harmful practices. However, coordination tools are less effective, succeeding only in communities with negative views on such practices. The study emphasizes the need for context-specific tools, advocating for pilot programs before full-scale implementation.
Missing component in policy demand
People tend to blame politicians and bureaucrats in inefficiencies within policies they implement. However, part of the effect might be hidden in demand, which politicians try to satisfy. Indeed, Ernesto Dal Bo and coauthors show that this might happen because people underestimate the equilibrium effects when assessing the outcomes of a policy.
To verify this hypothesis, the authors conducted lab experiments because analyzing policy preferences in reality would not allow them to highlight specific channels due to the complexity of the environment. Then, so speak toward the systematic nature of this bias, they provide a theoretical framework that helps clearly disentangle components of policy preferences.
The analysis is built upon two games: prisoner dilemma (PD) and ‘harmony game’ (HG). The former is a classic game where two players select between cooperate and defect, where in equilibrium, both players defect and get the lower pay-off. In the latter, we take the prisoner's dilemma and introduce two taxes – on cooperation and defection – such that cooperation becomes the dominant strategy. In this setting, agents anticipating equilibrium behavior should prefer a harmony game over the prisoner dilemma, and in the case of voting, agents should impose taxes to overcome the social dilemma.
However, if people fail to anticipate change in behavior, they can support a ‘bad’ policy. The authors highlight three components of expected gain – the direct benefit of change in pay-offs assuming that behavior is not changed across games, indirect benefit due to adjustment of individual behavior in harmony game, and last, an indirect benefit when others adjust their behavior too. Thus, an agent might prefer a prisoner dilemma if she significantly underestimates the third term.
In an experimental setting, authors ask people to play this game and divide them into 6 treatment arms:
1. Control group: play PD and randomly switch to HG in the second period
2. Reverse control: play HG and randomly switch to PD in the second period
3. Random dictator: play PD and randomly pick vote determine the next game
4. Reverse Random Dictator: play HG and randomly pick vote determine the next game
5. Majority one: play PD and decide by majority rule on the next game
6. Majority repeated: play PD and decide by majority rule every time after 6th game on the next game
‘Reverse’ treatments are included to avoid status quo bias. The inclusion of the dictator and majority was done to ensure robustness across the voting institutions. A repeated majority rule was added to see whether people can learn and make better choices.
Experimental results show that voters underestimate how much others will change their behavior. Besides, a share of voters fail to appreciate that their own behavior will differ across games. Can learning help? Authors show that under repeated, the majority of people do switch to choosing a better game, but a substantial share remains picking PD. Overall, that mechanism might explain why voters are reluctant to support policies that mostly entail indirect benefits but support populist proposals that could be, in opposite, costly due to equilibrium effects.
Ernesto Dal Bó, Pedro Dal Bó, Erik Eyster, The Demand for Bad Policy when Voters Underappreciate Equilibrium Effects, The Review of Economic Studies, Volume 85, Issue 2, April 2018, Pages 964–998, https://doi.org/10.1093/restud/rdx031
P.s. Yes, this is another attempt to resurrect this channel, hopefully, more successful one!
People tend to blame politicians and bureaucrats in inefficiencies within policies they implement. However, part of the effect might be hidden in demand, which politicians try to satisfy. Indeed, Ernesto Dal Bo and coauthors show that this might happen because people underestimate the equilibrium effects when assessing the outcomes of a policy.
To verify this hypothesis, the authors conducted lab experiments because analyzing policy preferences in reality would not allow them to highlight specific channels due to the complexity of the environment. Then, so speak toward the systematic nature of this bias, they provide a theoretical framework that helps clearly disentangle components of policy preferences.
The analysis is built upon two games: prisoner dilemma (PD) and ‘harmony game’ (HG). The former is a classic game where two players select between cooperate and defect, where in equilibrium, both players defect and get the lower pay-off. In the latter, we take the prisoner's dilemma and introduce two taxes – on cooperation and defection – such that cooperation becomes the dominant strategy. In this setting, agents anticipating equilibrium behavior should prefer a harmony game over the prisoner dilemma, and in the case of voting, agents should impose taxes to overcome the social dilemma.
However, if people fail to anticipate change in behavior, they can support a ‘bad’ policy. The authors highlight three components of expected gain – the direct benefit of change in pay-offs assuming that behavior is not changed across games, indirect benefit due to adjustment of individual behavior in harmony game, and last, an indirect benefit when others adjust their behavior too. Thus, an agent might prefer a prisoner dilemma if she significantly underestimates the third term.
In an experimental setting, authors ask people to play this game and divide them into 6 treatment arms:
1. Control group: play PD and randomly switch to HG in the second period
2. Reverse control: play HG and randomly switch to PD in the second period
3. Random dictator: play PD and randomly pick vote determine the next game
4. Reverse Random Dictator: play HG and randomly pick vote determine the next game
5. Majority one: play PD and decide by majority rule on the next game
6. Majority repeated: play PD and decide by majority rule every time after 6th game on the next game
‘Reverse’ treatments are included to avoid status quo bias. The inclusion of the dictator and majority was done to ensure robustness across the voting institutions. A repeated majority rule was added to see whether people can learn and make better choices.
Experimental results show that voters underestimate how much others will change their behavior. Besides, a share of voters fail to appreciate that their own behavior will differ across games. Can learning help? Authors show that under repeated, the majority of people do switch to choosing a better game, but a substantial share remains picking PD. Overall, that mechanism might explain why voters are reluctant to support policies that mostly entail indirect benefits but support populist proposals that could be, in opposite, costly due to equilibrium effects.
Ernesto Dal Bó, Pedro Dal Bó, Erik Eyster, The Demand for Bad Policy when Voters Underappreciate Equilibrium Effects, The Review of Economic Studies, Volume 85, Issue 2, April 2018, Pages 964–998, https://doi.org/10.1093/restud/rdx031
❤2
Formalizing Narratives in Economics
Information has long stood at the center of economic reasoning. From markets to political decisions, beliefs shape behavior, and beliefs are shaped by how we interpret available data. A key contribution is the Bayesian persuasion model by Kamenica and Gentzkow (2011), which shows that, under fully rational Bayesian agents, a sender can strategically reveal information to change the receiver’s action in a preferred way. This persuasion is effective due to two key features: the absence of constraints on the distribution of posteriors, and the non-linearity of actions in beliefs.
While powerful, this framework falls short in explaining many real-world behaviors. Behavioral models stepped in to address this gap by assuming that people process information imperfectly, leading to deviations from the Bayesian benchmark. However, these approaches are often criticized for being too flexible, offering too many degrees of freedom in modeling how people “misinterpret” information.
A more recent strand of literature takes a different route. Rather than focusing on what information is revealed or how it is misread, it explores how framing commonly known information can influence even perfectly rational agents. This is the growing field of studying narratives or mental models, and this post outlines two major approaches to modeling them, along with a recent theoretical extension.
Before delving into the two main approaches to modeling narratives, it’s helpful to outline the shared conceptual framework.
A Common Foundation: Rational Persuasion Through Models
In both approaches, a receiver must make decisions under uncertainty, based on data about the world. She doesn’t observe the true underlying process that generates outcomes, but she does see the outcomes themselves. To make sense of them, she relies on a model through which data is interpreted and beliefs are updated in the classical Bayesian style.
A persuader, who knows the true data-generating process, observes the same data and proposes a model intended to influence the receiver’s beliefs and, ultimately, her choices. Both receiver and persuader begin from a common prior and update their beliefs using Bayes’ rule. The receiver chooses between her default model and the one offered by the persuader, based on which one better explains the observed outcomes.
This shared structure underpins two influential formalizations of narratives:
1. Narratives as Models (Schwartzstein & Sunderam, 2021)
In this approach, narratives are simplified likelihood models used to interpret data. The persuader offers a model; the receiver adopts it only if it explains the data more convincingly than her default model.
The persuader uses his knowledge of the true model to craft a proposal that nudges the receiver toward an action that benefits him. If the receiver’s default model is weak or uninformative, she may actually benefit from persuasion. But if the persuader’s model is misleading—yet fits the data well enough—she may end up worse off, despite behaving rationally throughout.
Even in the presence of truthtellers, misleading models can dominate if they fit past data better than the true model. Competition among persuaders favors overfitting, causing beliefs to underreact to new evidence. Unlike cherry-picking, which hides facts, model persuasion reframes all available data, leading receivers to interpret it through a biased lens.
The authors also extend the model to accommodate multiple receivers and competing persuaders, thereby expanding its relevance to media environments and political competition.
Information has long stood at the center of economic reasoning. From markets to political decisions, beliefs shape behavior, and beliefs are shaped by how we interpret available data. A key contribution is the Bayesian persuasion model by Kamenica and Gentzkow (2011), which shows that, under fully rational Bayesian agents, a sender can strategically reveal information to change the receiver’s action in a preferred way. This persuasion is effective due to two key features: the absence of constraints on the distribution of posteriors, and the non-linearity of actions in beliefs.
While powerful, this framework falls short in explaining many real-world behaviors. Behavioral models stepped in to address this gap by assuming that people process information imperfectly, leading to deviations from the Bayesian benchmark. However, these approaches are often criticized for being too flexible, offering too many degrees of freedom in modeling how people “misinterpret” information.
A more recent strand of literature takes a different route. Rather than focusing on what information is revealed or how it is misread, it explores how framing commonly known information can influence even perfectly rational agents. This is the growing field of studying narratives or mental models, and this post outlines two major approaches to modeling them, along with a recent theoretical extension.
Before delving into the two main approaches to modeling narratives, it’s helpful to outline the shared conceptual framework.
A Common Foundation: Rational Persuasion Through Models
In both approaches, a receiver must make decisions under uncertainty, based on data about the world. She doesn’t observe the true underlying process that generates outcomes, but she does see the outcomes themselves. To make sense of them, she relies on a model through which data is interpreted and beliefs are updated in the classical Bayesian style.
A persuader, who knows the true data-generating process, observes the same data and proposes a model intended to influence the receiver’s beliefs and, ultimately, her choices. Both receiver and persuader begin from a common prior and update their beliefs using Bayes’ rule. The receiver chooses between her default model and the one offered by the persuader, based on which one better explains the observed outcomes.
This shared structure underpins two influential formalizations of narratives:
1. Narratives as Models (Schwartzstein & Sunderam, 2021)
In this approach, narratives are simplified likelihood models used to interpret data. The persuader offers a model; the receiver adopts it only if it explains the data more convincingly than her default model.
The persuader uses his knowledge of the true model to craft a proposal that nudges the receiver toward an action that benefits him. If the receiver’s default model is weak or uninformative, she may actually benefit from persuasion. But if the persuader’s model is misleading—yet fits the data well enough—she may end up worse off, despite behaving rationally throughout.
Even in the presence of truthtellers, misleading models can dominate if they fit past data better than the true model. Competition among persuaders favors overfitting, causing beliefs to underreact to new evidence. Unlike cherry-picking, which hides facts, model persuasion reframes all available data, leading receivers to interpret it through a biased lens.
The authors also extend the model to accommodate multiple receivers and competing persuaders, thereby expanding its relevance to media environments and political competition.