The internet has become an important data source for the Social Sciences because these data are available without lags, can be regarded as involuntary surveys and hence have no observer effect, can be geo-labeled, are available for countries across the globe and can be viewed in continuous time...

A large literature studies subjective beliefs about economic facts using unincentivized survey questions. We devise randomized experiments in a representative online survey to investigate whether incentivizing belief accuracy affects stated beliefs about average earnings by professional degree...

In markets with imperfect information and heterogeneity, the information technology affects the rate at which agents meet, which in turn affects the distribution of production technologies across firms. We show that in models for such markets there are typically multiple equilibria because...

How the internet affects job matching is not well understood due to a lack of data on job vacancies and quasi-experimental variation in internet use. This paper helps fill this gap using plausibly exogenous roll-out of broadband infrastructure in Norway, and comprehensive data on recruiters,...

We investigate whether the publicly available information on Facebook about job applicants affects employers' hiring decisions. To this end, we conduct a field experiment in which fictitious job applications are sent to real job openings in Belgium. The only characteristic in which these...

This paper presents a review of empirical methods used to assess the behavioral, economic, and political outcomes of Internet and social media usage. Instead of merely surveying the various impacts of the Internet, we examine the methods adopted to identify these impacts. We describe two main...

Most definitions of algorithmic bias and fairness encode decisionmaker interests, such as profits, rather than the interests of disadvantaged groups (e.g., racial minorities): Bias is defined as a deviation from profit maximization. Future research should instead focus on the causal effect of...

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...