RESEARCH
RESEARCH
Column (in German): DIW Wochenbericht 35/2024, Interview
Media: Wall Street Journal, MIT Sloan Management Review, Washington Post, F.A.Z., Harvard Business School Insights, IB Knowledge, Fast Company, Vanity Fair, Crisscrossed
This paper studies the impact of Generative AI technologies on the demand for online freelancers using a large dataset from a leading global freelancing platform. We identify the types of jobs that are more affected by Generative AI and quantify the magnitude of the heterogeneous impact. Our findings indicate a 21% decrease in the number of job posts for automation-prone jobs related to writing and coding, compared to jobs requiring manual-intensive skills, within eight months after the introduction of Chat-GPT. We show that the reduction in the number of job posts increases competition among freelancers while the remaining automation-prone jobs are of greater complexity and offer higher pay. We also find that the introduction of Image-generating AI technologies led to a 17% decrease in the number of job posts related to image creation. We use Google Trends to show that the more pronounced decline in the demand for freelancers within automation-prone jobs correlates with their higher public awareness of ChatGPT's substitutability.
Column (in German): DIW Wochenbericht 27/2023
Media (in German): taz, Table.Media, finanzen.net, and ntv
Tracking online user behavior is essential for targeted advertising and is at the heart of the business model of major online platforms. We analyze tracker-specific web browsing data to show how the prediction quality of consumer profiles varies with data size and scope. We find decreasing returns to the number of observed users and tracked websites. However, prediction quality increases considerably when web browsing data can be combined with demographic data. We show that Google, Facebook, and Amazon, which can combine such data at scale via their digital ecosystems, may thus attenuate the impact of regulatory interventions such as the GDPR. In this light, even with decreasing returns to data small firms can be prevented from catching up with these large incumbents. We document that proposed data-sharing provisions may level the playing field concerning the prediction quality of consumer profiles.
Freelancers face cold-start problems in online labor markets: getting hired is very difficult without ratings, while obtaining a rating is impossible unless already having been hired. According to economic theory and empirical evidence, advertising can serve as a signal of product quality for experience goods. As such, advertising might help skilled new freelancers without reputation on a platform to obtain a first job, by providing a quality signal to employers. This study empirically explores the role of advertising in online labor markets using transactional data from a major platform. While indeed newer freelancers tend to advertise, I find that buyers dislike ads once I control for the increased visibility of ads. This negative effect is amplified for new and unrated freelancers compared to already rated freelancers. Furthermore, I find that new freelancers who advertise do not perform significantly better in the long-run compared to similar freelancers who do not advertise. Taken together, my results contrast the hypotheses derived from signaling models of advertising.