Workforce prediction

by Nikhil Gupta

Institution: San Diego State University
Year: 2015
Posted: 02/05/2017
Record ID: 2085967
Full text PDF: http://hdl.handle.net/10211.3/163431


It sounds counterintuitive, but by 2030, many of the world's largest economies will have more jobs than citizens to do those jobs. Countries ought to look across borders for mobile and willing job seekers. But to do that, they need to start by changing the culture in their businesses. The aim of the thesis is to find the impact of factors like Labor force participation, Government debt to GDP, GDP, Labor costs, Inflation Rate, and Job vacancy on Unemployment rate using Regression model of the R language and to predict the Labor force participation and Job vacancies for the upcoming years using two R models ??? ARIMA model and Holt winters model. 4 places have been targeted - United States of America, Germany, China and Singapore. A Web application has been designed using JavaScript, JQuery, HTML, CSS, and JSON and the predicted values of Labor force participation and Job vacancies for each place are plotted on an interactive world map. The application gives the user a way to select the year and to view the forecast value of that year by hovering the mouse over that region. The user can also see all the predicted values and R plots by clicking on that place on the map. The application can be accessed using any standard web browser and Maps are implemented to make the application more responsive to the user. The study will help to analyze the workforce crisis of these places and suggest ways to prevent it. Language and Technologies used are R, JavaScript, JQuery, HTML, CSS, and JSON. Advisors/Committee Members: Eckberg, Carl, Riggins, Alan, Tsou, Ming-Hsiang.