Conference sessions in Teaching with data
Teaching using ‘Measuring the Economy’
Workshop at DEE 2021,Eradicating data phobia in students
Presentation at DEE 2019,Many aspects of everyday life, including economic, financial, social and environmental dimensions can be described by data, particularly quantitative data. Unsurprisingly therefore, the skills and knowledge associated with data analysis are an important part of the toolkit for any economics graduate. Indeed, repeated surveys of employers of economists show that they particularly value students who are comfortable analysing economic data, for example, using popular spreadsheet packages. Unfortunately, economics curricula across UK Higher Education tend frequently to ‘ghettoise’ the teaching of quantitative analysis so that students perceive data analysis as separate from general economic analysis. Quantitative analysis tends to default to econometrics, and overly complex and abstract presentation fuels many students’ phobia of data with many eschewing the analysis of quantitative data altogether. Furthermore, curricula often fail to develop students’ skills in the use of spreadsheet software that are valued by employers. This paper proposes ways in which students’ data competency can be enhanced across the curriculum. Core economics teaching can incorporate the sourcing and analysis of economic data in their teaching, learning and assessment. This will help prevent students from viewing such analysis as distinct from ‘other’ economic analysis or, worse still, either largely irrelevant to the subject or something to be avoided. It can be embedded from introductory level, making students comfortable accessing data from credible sources and using contemporary spreadsheet software. Further, the analysis need not default to econometrics after introductory level. Effective integration should mean using all the analytical tools in the economist’s toolkit, including charting and descriptive statistics. Adopting this approach will produce economics graduates who are more comfortable in working with data and who, as a result, can make sense of economics data more easily and have developed a series of associated transferrable skills valued by employers.
