{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/135886"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/135886","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"Essays in industrial organization","abstract":"User data is extensively used for ad targeting on online platforms, and a higher volume of data allows platforms to improve targeting and may incentivize mergers. This research quantifies the impact of data on match quality (measured by average user click rates) in the online advertising industry, examining how potential mergers between platforms with complementary data could affect market outcomes. Such a merger improves the match quality through two different mechanisms: First, increasing the number of observations the merged entity has access to allows it to predict expected click rates more accurately. Second, the merged entity can observe user browsing history and ad exposures across more websites, allowing it to know more about user preferences and target ads more effectively. The analysis utilizes the conditionally random allocation of ads in the data provided by Iran's largest online advertising platform and employs causal forest, a novel causal machine learning method, to estimate heterogeneous user click rates based on extensive browsing and ad exposure data from more than 1.6 million internet users. Simulations of user and platform behavior reveal that hypothetical mergers could boost match quality by 14-25\\%. In addition, this research finds that the majority of gains arise from an increase in the number of observations, and observing more browsing and ad exposure history has minimal gains.","abstract_html":"User data is extensively used for ad targeting on online platforms, and a higher volume of data allows platforms to improve targeting and may incentivize mergers. This research quantifies the impact of data on match quality (measured by average user click rates) in the online advertising industry, examining how potential mergers between platforms with complementary data could affect market outcomes. Such a merger improves the match quality through two different mechanisms: First, increasing the number of observations the merged entity has access to allows it to predict expected click rates more accurately. Second, the merged entity can observe user browsing history and ad exposures across more websites, allowing it to know more about user preferences and target ads more effectively. The analysis utilizes the conditionally random allocation of ads in the data provided by Iran's largest online advertising platform and employs causal forest, a novel causal machine learning method, to estimate heterogeneous user click rates based on extensive browsing and ad exposure data from more than 1.6 million internet users. Simulations of user and platform behavior reveal that hypothetical mergers could boost match quality by 14-25\\%. 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Simulations of user and platform behavior reveal that hypothetical mergers could boost match quality by 14-25\\%. In addition, this research finds that the majority of gains arise from an increase in the number of observations, and observing more browsing and ad exposure history has minimal gains."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Essays in industrial organization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ackerberg, Daniel A."],"dc:contributor.committeemember":["Town, Robert","Dorsey, Jackson"],"dc:creator":["Akhbari, Mehdi"],"dc:date.accessioned":["2026-04-09T20:15:00Z"],"dc:date.issued":["2025-12"],"dc:description.abstract":["User data is extensively used for ad targeting on online platforms, and a higher volume of data allows platforms to improve targeting and may incentivize mergers. 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