scholarly journals A Note on labor Search Models

2017 ◽  
Vol 31 (2) ◽  
Author(s):  
Orhan Torul
Keyword(s):  
2019 ◽  
Vol 11 (3) ◽  
pp. 327-357 ◽  
Author(s):  
R. Jason Faberman ◽  
Marianna Kudlyak

We use online job application data to study the relationship between search intensity and search duration. The data allow us to control for job seeker composition and the evolution of available job openings over the duration of search. We find that, within an individual search spell, search intensity declines continuously. We also find that longer-duration job seekers search more intensely throughout their search. They tend to be older, male, nonemployed, and live in areas with weaker labor markets. Our findings contradict standard assumptions of labor search models. We discuss how to reconcile the theory with our evidence. (JEL E24, J24, J63, J64)


Author(s):  
Audra J. Bowlus ◽  
Nicholas M. Kiefer ◽  
George R. Neumann

2018 ◽  
Vol 74 (a1) ◽  
pp. a355-a355
Author(s):  
Dmytro Guzenko ◽  
Jose M. Duarte ◽  
Stephen K. Burley

2018 ◽  
Vol 74 (4) ◽  
pp. 290-304 ◽  
Author(s):  
Claudia Millán ◽  
Massimo Domenico Sammito ◽  
Airlie J. McCoy ◽  
Andrey F. Ziem Nascimento ◽  
Giovanna Petrillo ◽  
...  

Macromolecular structures can be solved by molecular replacement provided that suitable search models are available. Models from distant homologues may deviate too much from the target structure to succeed, notwithstanding an overall similar fold or even their featuring areas of very close geometry. Successful methods to make the most of such templates usually rely on the degree of conservation to select and improve search models.ARCIMBOLDO_SHREDDERuses fragments derived from distant homologues in a brute-force approach driven by the experimental data, instead of by sequence similarity. The new algorithms implemented inARCIMBOLDO_SHREDDERare described in detail, illustrating its characteristic aspects in the solution of new and test structures. In an advance from the previously published algorithm, which was based on omitting or extracting contiguous polypeptide spans, model generation now uses three-dimensional volumes respecting structural units. The optimal fragment size is estimated from the expected log-likelihood gain (LLG) values computed assuming that a substructure can be found with a level of accuracy near that required for successful extension of the structure, typically below 0.6 Å root-mean-square deviation (r.m.s.d.) from the target. Better sampling is attempted through model trimming or decomposition into rigid groups and optimization throughPhaser'sgyrerefinement. Also, after model translation, packing filtering and refinement, models are either disassembled into predetermined rigid groups and refined (gimblerefinement) orPhaser's LLG-guided pruning is used to trim the model of residues that are not contributing signal to the LLG at the target r.m.s.d. value. Phase combination among consistent partial solutions is performed in reciprocal space withALIXE. Finally, density modification and main-chain autotracing inSHELXEserve to expand to the full structure and identify successful solutions. The performance on test data and the solution of new structures are described.


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