Unit information: Economic Data in 2020/21

Unit name	Economic Data
Unit code	EFIM10016
Credit points	20
Level of study	C/4
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Dr. Hans Sievertsen
Open unit status	Not open
Pre-requisites	None
Co-requisites	None
School/department	School of Economics
Faculty	Faculty of Social Sciences and Law

Description including Unit Aims

This unit focuses on obtaining, processing and presenting economic data. The unit consists of three elements. First, students are introduced to the software packages MS Excel and R, including fundamentals in programming. Second, we discuss principles of good and poor data visualization practice with applications in R and MS Excel. Third, the students learn concepts that are important for understanding and presenting economic data in a non-misleading way, for example the definition of GDP, price-indices, survey weights and log-scales. The main software for the unit is R (which is freely available). Methods and solutions using MS Excel will also be shown.

Topics covered will include

• How data is collected (e.g. issues with measurement error, missing values, survey weights).
• Fundamentals of programming (e.g. object types, control structures, functions).
• Principles of good and poor data visualization practice (e.g. the lie factor, data ink ratio)
• Definitions of internationally used variables (e.g. GPD, Unemployment rates, consumer price index/inflation, Purchasing Power Parity).
• Publicly available data sources from statistical agencies and international organizations.

The unit will draw on links that the department has with the Office for National Statistics.

Intended Learning Outcomes

Students will be able:

• To obtain, process and describe data using verbal, numerical and visual methods;
• To understand the conceptual issues in producing estimates of variables such as GDP, PPP or inflation;
• To select appropriate visualization methods.
• To select the appropriate software for a data task (i.e. spreadsheet software vs. programming software)
• To create small programming scripts and create advanced graphs

Teaching Information

Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities

Assessment Information

Coursework project (100%) This assesses all learning outcomes

Reading and References

Schwabish, J. A. 2014. "An Economist's Guide to Visualizing Data." Journal of Economic Perspectives, 28(1): 209-34. DOI: 10.1257/jep.28.1.209

Few, S. 2012. “Show Me the Numbers”, Analytics Press; 2nd New edition (30 Jun. 2012)

Grolemund, G. and H. Wickham 2016. “R for Data Science”, O’Reilly

Cairo, A. 2016. “The Turthful Art”, New Riders; 1st edition. Chapter 2.

Tufte, E. R. 2001. “The Visual Display of Quantitative Information”, Graphics Press USA; 2nd edition (31 Jan. 2001) (optional)