Ethnic externalities and 2nd generation immigrants

I analyze the role of regional ethnic capital – defined as the average years of schooling of ethnic groups – in the educational attainment of young second generation immigrants in Germany and whether results are sensitive to regional aggregation. I find evidence for externalities of ethnic capital for ethnic groups at the regional level. A higher average education of ethnics makes attendance of higher-quality secondary schools more likely. Moreover, the marginal effect of the externality is increasing in the ethnic concentration in the region. However, if higher than regional aggregates are used for the measurement of ethnic capital, no externalities are detected

The costs of adjusting labor: Evidence from temporally disaggregated data

I estimate the costs for establishments of hires and separations using a dynamic labor demand framework and matched employer-employee data from Germany, which records the exact dates of start and end of an employment spell. I estimate adjustment costs under different assumptions of adjustment frequencies. Under the assumption that establishments revise their labor demand every month, GMM estimates suggest hiring costs per employee of approximately 5,000 Euros, and costs of separations of 1,000 Euros. Hiring costs vary considerably between skilled (8,000 to 28,000 Euros per hire) and unskilled (4,000 to 8,000 Euros) labor. Spatial aggregation (large establishments) is associated with lower cost estimates, and only monthly adjustment frequencies yield estimates consistent with theoretical predictions

Symbolic dynamics and relatively hyperbolic groups

We study the action of a relatively hyperbolic group on its boundary, by methods of symbolic dynamics. Under a condition on the parabolic subgroups, we show that this dynamical system is finitely presented. We give examples where this condition is satisfied, including geometrically finite kleinian groups.Comment: Revision, 16 pages, 1 figur

Transfer Learning for Device Fingerprinting with Application to Cognitive Radio Networks

Primary user emulation (PUE) attacks are an emerging threat to cognitive radio (CR) networks in which malicious users imitate the primary users (PUs) signals to limit the access of secondary users (SUs). Ascertaining the identity of the devices is a key technical challenge that must be overcome to thwart the threat of PUE attacks. Typically, detection of PUE attacks is done by inspecting the signals coming from all the devices in the system, and then using these signals to form unique fingerprints for each device. Current detection and fingerprinting approaches require certain conditions to hold in order to effectively detect attackers. Such conditions include the need for a sufficient amount of fingerprint data for users or the existence of both the attacker and the victim PU within the same time frame. These conditions are necessary because current methods lack the ability to learn the behavior of both SUs and PUs with time. In this paper, a novel transfer learning (TL) approach is proposed, in which abstract knowledge about PUs and SUs is transferred from past time frames to improve the detection process at future time frames. The proposed approach extracts a high level representation for the environment at every time frame. This high level information is accumulated to form an abstract knowledge database. The CR system then utilizes this database to accurately detect PUE attacks even if an insufficient amount of fingerprint data is available at the current time frame. The dynamic structure of the proposed approach uses the final detection decisions to update the abstract knowledge database for future runs. Simulation results show that the proposed method can improve the performance with an average of 3.5% for only 10% relevant information between the past knowledge and the current environment signals.Comment: 6 pages, 3 figures, in Proceedings of IEEE 26th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Hong Kong, P.R. China, Aug. 201

Homeownership and Unemployment Duration

We examine the effects of homeownership on individuals' unemployment durations in the USA. We take into account that an unemployment spell can terminate with a job or with a non-participation transition. The endogeneity of homeownership is addressed through the estimation of a full maximum likelihood function which jointly models the competing hazards and the probability of being a homeowner. Unobserved factors contributing to the probability of being a homeowner are allowed to be correlated with unobservable heterogeneity in the hazard rates. We find that unemployed homeowners are less likely to find a job than renters. The effect is small but statistically significant for most specifications. The effect is stronger for outright owners and weaker for mortgage holders. We also find that outright owners have a higher and mortgage holders a lower probability of exiting to non-participation than renters