8,593 research outputs found
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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
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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
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Social Norms as a Cost-Effective Measure of Managing Transport Demand: Evidence from an Experiment on the London Underground
In an effort to cope with increasing passenger demand on its network, Transport for London (TfL) implemented in the second half of 2017 an experiment on one of its busiest metro train platforms. The platform surface was painted to highlight the exact location of the train doors once it comes to a full stop and to direct passengers to wait in parts of the platform that would not obstruct passengers from alighting from the train and leaving the platform. We estimate the effect of this intervention to change passenger behaviour on the platform on train waiting and delay times. We use different sets of assumptions about what the counterfactual change in waiting and delay times would have been in the absence of the intervention. Depending on the assumptions, we find that the intervention has reduced train waiting times between 0 and 6.6%. We also find that this reduction came about mainly through reducing delay times of trains once they are delayed which were cut by between 4.6% and 12.6%. The reductions are not evenly distributed throughout the day, but tend to occur during peak traffic hours. The value of the implied time savings per year are £156,000 at a cost of £25,000, amounting to a return of £6 per £1 investment. If the dwell time reduction could increase train frequency on the affected line by 1 train per hour, however, then TfL could save another £3.6 million
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
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