ECON 1013 - Using Big Data for Economic and Social Problems I

North Terrace Campus - Semester 2 - 2020

This course will show how "big data" can be used to understand and solve some of the most important social and economic problems of our time. The course will give students an introduction to important relevant economic concepts and frontier research in applied economics and social science related to policy making. Topics may include equality of opportunity, discrimination, education, health care, and climate change besides others. The course will also provide an introduction to basic statistical methods and data analysis techniques relevant for big data approaches, which may include regression analysis, causal inference, and quasi-experimental methods.

  • General Course Information
    Course Details
    Course Code ECON 1013
    Course Using Big Data for Economic and Social Problems I
    Coordinating Unit School of Economics
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Course Description This course will show how "big data" can be used to understand and solve some of the most important social and economic problems of our time. The course will give students an introduction to important relevant economic concepts and frontier research in applied economics and social science related to policy making. Topics may include equality of opportunity, discrimination, education, health care, and climate change besides others. The course will also provide an introduction to basic statistical methods and data analysis techniques relevant for big data approaches, which may include regression analysis, causal inference, and quasi-experimental methods.
    Course Staff

    Course Coordinator: Dr Florian Ploeckl

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion of this course, students will be able to:

    1. Recognize suitable economic models and concepts to address major contemporary economic and social issues.

    2. Explain the relevance of causality in addressing policy questions.

    3. Identify suitable and appropriate empirical and statistical analysis approaches.

    4. Interpret and explain the application and outcomes of big data statistical techniques.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1,3
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    2,3
    Teamwork and communication skills
    • developed from, with, and via the SGDE
    • honed through assessment and practice throughout the program of studies
    • encouraged and valued in all aspects of learning
    2,4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    3,4
    Intercultural and ethical competency
    • adept at operating in other cultures
    • comfortable with different nationalities and social contexts
    • Able to determine and contribute to desirable social outcomes
    • demonstrated by study abroad or with an understanding of indigenous knowledges
    1
    Self-awareness and emotional intelligence
    • a capacity for self-reflection and a willingness to engage in self-appraisal
    • open to objective and constructive feedback from supervisors and peers
    • able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
    1,2
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course uses a blended teaching approach.

    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    On average beyond attending lectures and tutorials, students are expected to spend about 10 hours per week for reading, watching online material, preparing projects and studying. The time required may vary across students and topics.

    Learning Activities Summary
    Learning activities consist of weekly lectures and tutorials. Lectures require prior preparation with online materials and utilize active
    participation components in a blended teaching approach. Tutorials focus on practical exercises in data analysis with statistical software.

    Group projects are conducted during the whole semester based on lectures and tutorials.

    The following table provides a tentative overview about the economic topics and statistical methods covered in the weekly lectures and tutorials. The course focuses predominantly on an American experience but draws general lessons from there.

    The schedule and topic selection are tentative and might be adjusted during the semester
    Week Topic Lecture Topic Statistical Methods
    1-3 Equality of Opportunity Geography of mobility, Neighbourhood effects, etc Correlation, Regression, Experiments, etc
    4-5 Education Education and social mobility, Effects of schools and teachers Bayes Rule, Regression Discontinuity, etc
    6 Racial Disparities Disparities in Economic opportunities Dynamic Models
    7 Health Economics of Health Care, Improving Health Outcomes Hazard Models, Adverse Selection
    8 Mid-term
    9-10 Climate Change Impact of pollution, Mitigation policies Difference-in-Difference Externalities, etc
    11 Tax Policy Taxation, Behavioural Economics Supply & Demand
    12 Development & Institutions Institutions & Economic Development Historical Data Analysis
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Task Task Type Due Weighting Learning Outcome
    1 Participation Individual N/A 10% 1-4
    2 Group Projects Group TBC 30% 3-4
    3 Midterm Individual During Midterm period 15% 1-4
    4 Final Exam Individual During Exam period 45% 1-4
    Assessment Detail
    The assessment components are as follows:

    1)   Active participation is assessed during the weekly lectures through quizzes, polls and surveys.

    2)    Group projects involve explaining and analysing economic and social problems using data and statistical methods.

    3)    There will be a mid-semester test. Failure to sit the midterm examination will result in receiving zero points. The grade of the final examination will then account for 60% of the overall grade. If a medical certificate is provided, the final examination will also count for 60% of the overall grade.

    4)   Final/Replacement Exam: 2-hour exam as per examination schedule. Covering economic issues and statistical methods and using a combination of different question formats.

    To gain a pass for this course, a mark of at least 50% overall needs to be obtained. There is no hurdle or double pass requirement.

    Submission
    1) Submission of projects is to be done online through MyUni. Failure to submit an assignment on time will lead to a zero mark.

    2)  Extensions and alternative assessment conditions: It is your responsibility to contact the lecturer in the first 2 weeks of the semester to discuss extension or alternative assessment options. This applies to ALL students, included but not limited to those registered with the disability centre or the elite athletes program. Exceptional circumstances will be evaluated by your lecturer on a case-by-case basis and should be discussed whenever possible at least 48 hours before the due date

     

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

    This is a new course, no prior feedback is available.
  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.