Reputation transferability across contexts: Maintaining cooperation among anonymous cryptomarket actors when moving between markets.

BACKGROUND: Buyers and sellers of illegal drugs in cryptomarkets have been found to overcome trust issues created by anonymity and the lack of legal protection with the help of reputation systems. Cryptomarkets rarely operate for longer than a year before closing or getting shut down due to external shocks, such as law enforcement operations. This results in large flows of users migrating between market platforms. An important question in order to better understand why cryptomarkets recover quickly after external shocks is: to what extent can reputation be carried over between different markets? This problem is non-trivial given the anonymity of cryptomarket users and the fact that reputation is tied to a user's online identity. Here we analyze conditions under which sellers choose to migrate with the same identity and whether reputation history from previous cryptomarkets yields benefits in new contexts. METHODS: We analyze sellers' migration in three cryptomarkets (Abraxas, Agora and AlphaBay) and follow their reputation history by linking user accounts between marketplaces using the Grams database. We use longitudinal multi-level regression models to compare market success of migrant and non-migrant sellers. In total, the data contains more than 7,500 seller account and 2.5 million buyers' reputational feedback messages over a period of 3 years. FINDINGS: It is predominantly the successful sellers with a large number of sales and high reputation who choose to migrate and maintain their identity using cryptographic methods after market closures. We find that reputation history from previous markets creates a competitive advantage to migrant sellers compared to market entrants. CONCLUSION: Reputation transferability embeds cryptomarket users beyond a single market platform, which incentivizes cooperative behavior. The results also suggest that reputation transferability might contribute to a quick recovery of online drug trade after shutdowns and accumulation of market share in the hands of a small fraction of successful sellers.

Unemployment and the Division of Housework in Europe.

Unemployment, especially in insecure times, has devastating effects on families, but it is not clear what happens to domestic work. On the one hand, unemployment frees up time for more housework by both men and women. On the other hand, once unemployed, women may take on more additional housework than men do, either because they capitalize on their time to act out traditional gender roles or because unemployment compounds women's general disadvantage in household bargaining. Multi-level analyses based on the European Social Survey show that both men and women perform more housework when unemployed. However, the extra domestic work for unemployed women is greater than for unemployed men. They also spend more time on housework when their husband is unemployed. Compared to their employed counterparts, unemployed women, but not men, perform even more housework in a country where the unemployment rate is higher.

Offline constraints in online drug marketplaces: An exploratory analysis of a cryptomarket trade network.

BACKGROUND: Cryptomarkets, or illegal anonymizing online platforms that facilitate drug trade, have been analyzed in a rapidly growing body of research. Previous research has found that, despite increased risks, cryptomarket sellers are often willing to ship illegal drugs internationally. There is little to no information, however, about the extent to which uncertainty and risk related to geographic constraints shapes buyers' behavior and, in turn, the structure of the global online drug trade network. In this paper, we analyze the structure of a complete cryptomarket trade network with a focus on the role of geographic clustering of buyers and sellers. METHODS: We use publicly available crawls of the cryptomarket Abraxas, encompassing market transactions between 463 sellers and 3542 buyers of drugs in 2015. We use descriptive social network analysis and Exponential Random Graph Models (ERGM) to analyze the structure of the trade network. RESULTS: The structure of the online drug trade network is primarily shaped by geographical boundaries. Buyers are more likely to buy from multiple sellers within a single country, and avoid buying from sellers in different countries, which leads to strong geographic clustering. The effect is especially strong between continents and weaker for countries within Europe. A small fraction of buyers (10%) account for more than a half of all drug purchases, while most buyers only buy once. CONCLUSION: Online drug trade networks might still be heavily shaped by offline (geographic) constraints, despite their ability to provide access for end-users to large international supply. Cryptomarkets might be more "localized" and less international than thought before. We discuss potential explanations for such geographical clustering and implications of the findings.