Assessment 2015/2016
Objective:
The purpose of this project is to help you develop skills in relation to the analysis and interpretation of data using techniques in statistics.
Marking Criteria:
Marking will be determined by the relevancy of the attempted statistical analysis, the clear presentation of the findings, the accuracy of your interpretations and comments on the findings.
Administration:
This assignment counts 50% of the total module result.
The assignment cannot exceed 2000 words (excluding tables, figures and references), and this is a limit not a guideline. You must include a word count. The assignment should be type written and word processed. You should use MS-Excel or a similar package to carry out statistical and graphical data analysis.
The assignment is due for submission no later than 16:00 on Tuesday 12th January 2016. Both electronic and hard copies are required to be submitted by the deadline. The electronic copy must be handed in via a link on T13301’s Moodle page. Once your work is successfully uploaded, a receipt will be sent to your university email address; this receipt constitutes proof of your submission date and time. The receipt number (referred to as a “Turnitin Paper ID”) will also appear on your Moodle homepage. Please contact the School Reception if you encounter any problems submitting work online.
Late submissions will be subject to normal penalties (i.e. -5% per working day). For example, work handed in after 16:00 on the deadline date will be subject to an absolute 5 mark deduction, as will work handed in before 16:00 on the following day. Work submitted after 16:00 on the day after the deadline will then incur 10 mark deduction, and so on until the mark reaches zero. For example, an original mark of 67% would be successively reduced to 62%, 57%, 52%, 47% etc. Normal working days include vacation periods, but not weekends or public holidays.
If referencing is needed, please follow the referencing guideline in the student handbook. Please note that any forms of academic misconduct (such as plagiarism, collusion, fabrication, etc.) may lead to zero mark of the assignment.
Data:
Download the datasets ‘wage.xls’ from the “Coursework” folder on Moodle.
The Task:
You are asked to work on a project for the Bureau of Human Resources and Social Security of city X in province Y in China. The project aims to analyse various issues related to employment in city X. The Bureau is particularly interested in issues related to wage inequality and the determinants of wage rates in this city.
You have been provided with the data from a survey of 359 employees in city X from three sectors, manufacturing, construction and other. The dataset provides information on the following characteristics: monthly wage rates, age, education, sex, occupation, Party member status, working experience, marital status, hukou, etc.
You are asked to address the following issues:
(1) How is wage inequality defined in the literature? Given the complex data set, explain how you will proceed in order to provide evidence of wage inequality. (30%)
(2) Provide the Bureau with the evidence of wage inequality you have found and interpret your results. (50%)
(3) Draw sensible conclusions based on your findings and provide suggestions to the Bureau. (20%)

List of Variables:

ID: identifier for the employee
WAGE: monthly wage (1000 RMB)
EDUCATION: number of years of education
SEX: indicator variable for sex, (1=Female, 0=Male)
EXPERIENCE: number of years of working experience
AGE: age of the employee (years)
PARTY MEMBER: indicator variable for Communist Party Member status (1= Party member, 0=not Party member)
SECTOR: sector of the employment (1=Manufacture, 2= Construction, 0= others)
MARR: Marital Status (0 = unmarried, 1=married)
OCCUPATION: occupational category (1=Management, 2=sales, 3=clerical, 4=service, 5=professional, 6=others)
HUKOU: indicator variable of Hukou (i.e. household registration) (1= local hukou, 0= hukou not in city X)