scholarly journals What Explains the Decline of the U.S. Labor Share of Income? An Analysis of State and Industry Level Data

2017 ◽  
Vol 17 (167) ◽  
Author(s):  
Yasser Abdih ◽  
Stephan Danninger
Keyword(s):  
Task Characteristics ◽  
Trade Integration ◽  
Data Set ◽  
Labor Share ◽  
Level Data ◽  
Routine Tasks ◽  
Labor Institutions ◽  
Using Data ◽  
High Level ◽  
The U.S

The U.S. labor share of income has been on a secular downward trajectory since the beginning of the new millennium. Using data that are disaggregated across both state and industry, we show the decline in the labor share is broad-based but the extent of the fall varies greatly. Exploiting a new data set on the task characteristics of occupations, the U.S. input-output tables, and the Current Population Survey, we find that in addition to changes in labor institutions, technological change and different forms of trade integration lowered the labor share. In particular, the fall was largest, on average, in industries that saw: a high initial intensity of “routinizable” occupations; steep declines in unionization; a high level of competition from imports; and a high intensity of foreign input usage. Quantitatively, we find that the bulk of the effect comes from changes in technology that are linked to the automation of routine tasks, followed by trade globalization.

Formation of Data Science Teams based on Scenario Characteristics

2021 ◽  
Vol 65 (1) ◽  
pp. 535-540
Author(s):  
Holly A. H. Handley ◽  
Candace Eshelman-Haynes
Keyword(s):  
Data Science ◽  
Team Member ◽  
Specific Data ◽  
Work Activities ◽  
Department Of Labor ◽  
Level Data ◽  
Team Design ◽  
Role Definitions ◽  
High Level ◽  
The U.S

The objective of this research was to identify a set of attributes to characterize data science scenarios to assists in the formation of an accompanying data science team. The six scenario characteristics were developed in consultation with a Subject Matter Expert (SME) to identify the important aspects of a data science endeavor. Concurrently, a generalizable role by task matrix was developed that captures the high-level data science functions and potential team member roles. This matrix was based on the NATO data science process function definitions, linked to the U.S. Department of Labor social science work activities, and data science role definitions. The mapping of the characteristics to the role by task matrix results in guidelines for forming a data science team; an example scenario with its characteristics and proposed team design is described. This work suggests methods to customize team information for specific data science needs based on scenario attributes.

scholarly journals A New Privacy Preserving Association Rule Mining Algorithm Based on Hybrid Partial Hiding Strategy

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 41-50 ◽  
Author(s):  
Jian-Ming Zhu ◽  
Ning Zhang ◽  
Zhan-Yu Li
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preservation ◽  
Original Data ◽  
Rule Mining ◽  
Data Set ◽  
Original Algorithm ◽  
Level Data ◽  
Frequent Items ◽  
High Level

Abstract Data mining is the progress of automatically discovering high level data and trends in large amounts of data that would otherwise remain hidden. In order to improve the privacy preservation of association rule mining, a hybrid partial hiding algorithm (HPH) is proposed. The original data set can be interfered and transformed by different random parameters. Then, the algorithm of generating frequent items based on HPH is presented. Finally, it can be proved that the privacy of HPH algorithm is better than that of the original algorithm.

scholarly journals Data Pre-Processing for Machine Learning Models using Python Libraries

2020 ◽  
Vol 9 (4) ◽  
pp. 1995-1999
Keyword(s):  
Machine Learning ◽  
Missing Values ◽  
Model Fitting ◽  
Learning Model ◽  
Data Sets ◽  
Data Set ◽  
Machine Learning Model ◽  
Level Data ◽  
Efficient Machine ◽  
High Level

Data pre-processing is the process of transforming the raw data into useful dataset. Data pre-processing is one of the most important phase of any machine learning model because the quality and efficiency of any machine learning model directly depends upon the data-set, if we skip this step and design a model with data sets containing missing values then the model we have designed will not be that efficient and will be inconsistent model. This paper describes the methodology for pre-processing the data in seven sequence of steps using python powerful libraries which are open source machine learning libraries that support both supervised and unsupervised learning like pandas is a high level data manipulation tool, scikit learn which provides various tools for model fitting, data pre-processing, model selection and many other utilities. These steps include dealing with missing value, categorical values, importing data sets etc. This analysis helps in cleaning and transforming the datasets which future applied to any learning model and produce a efficient machine learning model.

Immigration and the tech industry: As a labour shortage remedy, for innovation, or for cost savings?

2013 ◽  
Vol 10 (2) ◽  
pp. 201-227 ◽  
Author(s):  
Norman Matloff
Keyword(s):  
Immigration Policy ◽  
Brain Drain ◽  
Cost Savings ◽  
Foreign Workers ◽  
Service Firms ◽  
It Service ◽  
Labour Shortage ◽  
Labour Costs ◽  
Using Data ◽  
The U.S

The two main reasons cited by the U.S. tech industry for hiring foreign workers--remedying labour shortages and hiring "the best and the brightest"--are investigated, using data on wages, patents, and R&D work, as well as previous research and industry statements. The analysis shows that the claims of shortage and outstanding talent are not supported by the data, even after excluding the Indian IT service firms. Instead, it is shown that the primary goals of employers in hiring  foreign workers are to reduce labour costs and to obtain "indentured" employees. Current immigration policy is causing an ‘Internal Brain Drain’ in STEM.

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