Bo Cowgill

Using Prediction Markets to Track Information Flows: Evidence from Google

Since 2005, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information. We show that market participants are not typical of Google’s workforce, and that market participation and success is skewed towards Google’s engineering and quantitatively oriented employees.

Corporate prediction markets: evidence from google, ford, and firm X

Despite the popularity of prediction markets among economists, businesses and policymakers have been slow to adopt them in decision making. Most studies of prediction markets outside the lab are from public markets with large trading populations. Corporate prediction markets face additional issues, such as thin- ness, weak incentives, limited entry and the potential for traders with ulterior motives raising questions about how well these markets will perform. We examine data from prediction markets run by Google, Ford and Firm X (a large private materials company). Despite theoretically adverse conditions, we find these markets are relatively efficient, and improve upon the forecasts of experts at all three firms by as much as a 25% reduction in mean squared error. The most notable inefficiency is an optimism bias in the markets at Google and Ford. The inefficiencies that do exist generally become smaller over time. More experienced traders and those with higher past performance trade against the identified inefficiencies, suggesting that the markets efficiency improves because traders gain experience and less skilled traders exit the market.

Does Online Trade Live Up to the Promise of a Borderless World? Evidence from the EU Digital Single Market

An important EU Digital Single Market policy objective is to achieve an open and integrated market for online e-commerce in the EU, to make it easy for consumers to go outside their domestic market and shop online in other EU Member States. This study applies a standard gravity model of international trade to Google e-commerce data to estimate the prevalence of home bias in online shopping in the EU. It compares how much EU Member States trade domestically and with other Member States, and how much the EU trades with itself and with the rest of the world. The research confirms the findings of the (offline) international trade literature, according to which there is strong home bias. There is no unambiguous evidence about the strengths or weaknesses of the EU Digital Single Market. Strong intra-EU home bias suggests that online consumers have a tendency to stay in their home country market. Equally strong extra-EU home bias suggests that online consumers who do decide to shop abroad have a tendency to stay in the EU however, rather than going to a non-EU country. There are indications that online home bias is lower in a comparable cross-border trade setting in North America. Data and methodological limitations do not allow a more detailed analysis.