Unemployed Needn’t Apply: Unemployment Status, Legislation, and Interview Requests

2019 ◽  
Vol 46 (8) ◽  
pp. 1380-1407 ◽  
Author(s):  
Tiffany M. Trzebiatowski ◽  
Connie R. Wanberg ◽  
Karyn Dossinger

This research investigates whether and when a job applicant’s unemployment status (i.e., employed, short-term unemployed, or long-term unemployed) affects the probability of receiving an interview request by examining interview request rates in the presence of versus absence of unemployment status antidiscrimination legislation. In response to 3,335 fictitious resumes sent to 1,237 online job postings in Los Angeles and New York City, we received an overall interview request rate of 10.37. Long-term unemployed applicants were less likely to receive an interview request than short-term unemployed applicants in Los Angeles but not in New York City, which has unemployment status antidiscrimination legislation. These findings are supplemented with self-report survey data about perceptions of the unemployed from 200 hiring personnel in New York City and Los Angeles. Practical and theoretical implications are discussed for the unemployment, job search, and selection literatures.

Author(s):  
Shay Lehmann ◽  
Alla Reddy ◽  
Chan Samsundar ◽  
Tuan Huynh

Like any legacy subway system that first opened in the early 1900s, the New York City subway system operates using technology that dates from many different eras. Although some of this technology may be outdated, efforts to modernize are often hindered by budgetary limits, competing priorities, and managing the tradeoff between short-term service disruptions and long-term service improvements. At New York City Transit (NYCT), the locations of all trains on all lines are not visible to any one person in any one place and, for much of the system, train locations can only be seen at field towers for the handful of interlockings in its operational jurisdiction as result of the legacy signal system, which may come as a surprise to many daily commuters or personnel at newer metros. In 2019, developers at NYCT gained full access to the legacy signal system’s underlying track circuit occupancy data and developed an algorithm to automatically track trains and match these data with schedules and manual dispatchers’ logs in real time. This data-driven solution enables real-time train identification and tracking long before a full system modernization could be completed. This information is being provided to select personnel as part of a pilot program via several different tools with the aim of improving service management and reporting.


2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.


2017 ◽  
Vol 62 ◽  
pp. 3-11 ◽  
Author(s):  
Nicholas E. Johnson ◽  
Olga Ianiuk ◽  
Daniel Cazap ◽  
Linglan Liu ◽  
Daniel Starobin ◽  
...  

Author(s):  
Jenny S. Guadamuz ◽  
G. Caleb Alexander ◽  
Shannon N. Zenk ◽  
Genevieve P. Kanter ◽  
Jocelyn R. Wilder ◽  
...  

Author(s):  
Jenny S. Guadamuz ◽  
Ramon A. Durazo-Arvizu ◽  
Martha L. Daviglus ◽  
Gregory S. Calip ◽  
Edith A. Nutescu ◽  
...  
Keyword(s):  
New York ◽  

2000 ◽  
Vol 32 (5) ◽  
pp. 237 ◽  
Author(s):  
Lisa D. Lieberman ◽  
Heather Gray ◽  
Megan Wier ◽  
Renee Fiorentino ◽  
Patricia Maloney

2015 ◽  
Vol 43 (8) ◽  
pp. 839-843 ◽  
Author(s):  
Alison Levin-Rector ◽  
Beth Nivin ◽  
Alice Yeung ◽  
Annie D. Fine ◽  
Sharon K. Greene

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