Feature Selection of Post-Graduation Income of College Students in the United States

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure

Flexible production and entry: institutional, technological, and organizational determinants

Academics, the media, and policymakers have all raised concerns about the implications of human workers being replaced by machines or software. Few have discussed the implications of the reverse: firms’ ability to replace capital with workers. We show that this flexibility can help new firms overcome uncertainty and increase entrepreneurial entry. We develop a simple real options model where permissive labor regulations allow firms to take advantage of capital-labor substitutability by replacing ‘rigid’ capital with ‘flexible’ labor. The model highlights institutional, technological, and organizational preconditions to using this flexibility. Using a large and comprehensive dataset on entry by standalone firms and group affiliates, we provide evidence in support of the model

The illusion of routine as an indicator for job automation with artificial intelligence

The resurgence of artificial intelligence (AI) has empowered organizations to concentrate their research efforts on enhancing decision-making and automation capabilities. This is being pursued with the goal of increasing productivity, whilst reducing costs. With this, it is perceived that the jobs within these organizations that are considered subject to ‘routine’, or repetitive and mundane tasks, are more likely to be automatable. However, it may be recognised that these jobs are more than a simple set of routine tasks. This study aims to address the concept of routineness from the perspective of the job occupants themselves. The findings reveal that jobs which are considered routine from an organizational perspective, realistically require a degree of human intervention. This suggests that the fear of mass unemployment at the hands of AI may be an unrealistic notion. Rather, the introduction of AI into jobs paves the way for collaborative methods of working which could augment current jobs and create new jobs. Furthermore, this paper accentuates that the acceptance of AI by stakeholders requires an alignment of the technology with their own unique contextual needs

Automation in human-machine networks: how increasing machine agency affects human agency

© 2018, Springer International Publishing AG. Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change

User-Friendly MES Interfaces:Recommendations for an AI-Based Chatbot Assistance in Industry 4.0 Shop Floors

The purpose of this paper is to study an Industry 4.0 scenario of ‘technical assistance’ and use manufacturing execution systems (MES) to address the need for easy information extraction on the shop floor. We identify specific requirements for a user-friendly MES interface to develop (and test) an approach for technical assistance and introduce a chatbot with a prediction system as an interface layer for MES. The chatbot is aimed at production coordination by assisting the shop floor workforce and learn from their inputs, thus acting as an intelligent assistant. We programmed a prototype chatbot as a proof of concept, where the new interface layer provided live updates related to production in natural language and added predictive power to MES. The results indicate that the chatbot interface for MES is beneficial to the shop floor workforce and provides easy information extraction, compared to the traditional search techniques. The paper contributes to the manufacturing information systems field and demonstrates a human-AI collaboration system in a factory. In particular, this paper recommends the manner in which MES based technical assistance systems can be developed for the purpose of easy information retrieval

The contribution of virtual enterprises to competence-based learning: an assessment from the students’ perspective: Case study

The use of virtual enterprises has evolved from secondary, vocational and professional education to the university level, becoming in recent times a relevant experiential e-learning tool based on the simulation of the functioning of a company. The purpose of this study is to analyze the perspective of students taking part in virtual enterprises about their acquisition of generic and specific managerial skills fostered by this e-learning methodology. We analyzed data of 76 students from Faculties of Business and Economics, who had participated during the academic year 2014-2015 in a virtual company during the development of their degrees at two different Italian universities, University of Bologna and Parma. Our results show that the most valued generic skills were related to the capacity of learning and adaptation, problem solving and teamwork, whilst the least valued ones were related to the skills of communication and interaction with people of other countries and cultures. In the case of specific managerial skills, the most valued ones were about understanding managerial concepts and the role and functions of economic agents, and the least valued were skills related to providing managerial advice, dealing with risks and analyzing financial statements. Besides, the analyses conducted to determine the existence of a learning profile in the students reveal that the skills acquired were quite similar, not being affected by the students’ gender or the economic activity developed by the virtual enterprise. This study makes a contribution in terms of the effectiveness of virtual enterprises for competence-based learning

The political economy of the disability insurance: theory and evidence of gubernatorial learning

Abstract The dramatic rise in the disability insurance (DI) rolls in the last 20 years has been the subject of much controversy. While the relationship between DI and labor force participation has been the subject of a growing literature, the mechanism of this transition from employment to DI remains unclear. We hypothesize that one mechanism is the state-level administration of the program which creates a classic principal-agent problem. We analyze the conflict of interests for Disability Determination Services agencies between Social Security Administration (SSA) standards and state gubernatorial political interests interacted with the increased demand for disability insurance as an alternative for low-skilled employment during the period of 1982 to 2013. We find evidence that multi-term governors allow a greater fraction of applicants than do first-term governors, but only up to year 2000, when allowance rates started to decrease over time. We develop a model that illustrates how these differences can be due to the type of monitoring conducted by the SSA. We provide additional evidence supporting this hypothesis analyzing how the effects interact with economic and political constraints. JEL codes H55, I18, I38, G22</jats:p