Note to instructors

Target audience

Our target audience includes:

  • students at undergraduate and postgraduate level who are not taking economics as a major subject
  • anyone who wants to learn how to use economics to understand and articulate reasoned views on some of the most pressing policy problems facing our societies: inequality, financial instability, the future of work, wealth creation, and environmental degradation
  • anyone who wants practical training in understanding and using data to measure the economy and policy effectiveness
  • anyone interested in social and economic policy, who is taking a degree related to policy, or is hoping to have a policy-related job in the future.


  • no prior courses in economics or statistics are required
  • no knowledge of statistical programs (such as Python, R, Excel, or Google Sheets) is required, except a familiarity with the interface and how to enter and clear data
  • familiarity with basic mathematical operations, percentages, decimals, 2-D graphs.

The purpose of the empirical projects

  • provide hands-on experience, using real-world data, to investigate important policy problems
  • strengthen the link between real-world phenomena and economic concepts and models
  • help students to develop skills that are transferable to other courses and to the workplace.

The structure of the empirical projects

Empirical projects are designed to be completed in Excel, R, Google Sheets, or Python.

Each project contains:

  • clearly defined learning objectives related to statistical/economic concepts and data presentation
  • an introduction to the project and the economic topics it addresses
  • two to three parts, each containing multiple questions on a specific subtopic. Unless indicated otherwise, these can be done independently, or at the same time
  • step-by-step walk-throughs for conceptually difficult or challenging tasks in Excel, R, Python, and Google Sheets. These are videos and annotated screenshots (Excel and Google Sheets) and code with explanations (R and Python). The walk-throughs are designed so that beginners in Excel, R, Python, or Google Sheets will be able to learn the skills required to complete the project.

Students who come to these projects without any prior experience with the software tools should start with Project 1 as this will provide the easiest entry to the required software skills. This is essential for those using R or Python.

Solutions for all empirical projects are available, and the walk-throughs will also help students confirm that they have got the correct output. There are brief notes for some of the more interpretive questions. Note that these are not model answers. They are included to help students, including those doing the project outside a formal class, to check their progress working through the steps using Excel, Google Sheets, Python, or R. Students taking courses using Doing Economics should follow the guidance of their instructors.

Instructors are encouraged to develop additional tasks that help students critically evaluate data sources and definitions. Contact us if you’d like to share these with other users of CORE resources.

How to use the empirical projects

  • Each empirical project in Doing Economics is divided into two or three parts. Each part can be completed independently, or together (unless specified otherwise). The time needed to complete one part will depend on the structure and pace of the course, though as a rough guide, one project can be a term-long assignment. In each part, students will be guided through the steps to produce the charts or tables that can form the basis of a report.
  • The projects can be done independently, since the same key concepts are repeated in a number of projects. Each project has an introduction with information about the concepts that are prerequisites, for that project, as well as those that will be introduced in the project.
  • The empirical projects can be used to supplement units in Economy, Society, and Public Policy and The Economy, although this is not essential. Each project contains information about the unit to which the material is related, and has links to sections in the ebooks that may help students to understand the project.

ESPP and Doing Economics as part of a connected curriculum

If you are teaching a social science, engineering, business studies, or public policy program in which students have to take an economics course and a quantitative methods course, you can use ESPP and Doing Economics empirical projects to connect these parts of the curriculum.

ESPP Unit Title Doing Economics
1 Capitalism: Affluence, inequality, and the environment Empirical Project 1: Measuring climate change (datasets: Goddard Institute for Space Studies temperature data; US National Oceanic and Atmospheric Administration CO2 data)
2 Social interactions and economic outcomes Empirical Project 2: Collecting and analysing data from experiments (datasets: student-generated experimental data; Hermann et al. 2008)
3 Public policy for fairness and efficiency. Empirical Project 3: Measuring the effect of a sugar tax (datasets: Global Food Research Program’s Berkeley Store Price Survey; Silver et al. 2017)
4 Work, wellbeing, and scarcity. Empirical Project 4: Measuring wellbeing (datasets: UN GDP data; Human Development Index)
5 Institutions, power, and inequality Empirical Project 5: Measuring inequality: Lorenz curves and Gini coefficients (datasets: the Global Consumption and Income Project’s income data; Chartbook of Economic Inequality; Our world in data)
6 The firm: Employees, managers, and owners Empirical Project 6: Measuring management practices (dataset: World Management Survey)
7 Firms and markets for goods and services Empirical Project 7: Supply and demand (dataset: US market for watermelons (1930–1951); taken from Stewart (2018))
8 The labour market and the product market: Unemployment and inequality Empirical Project 8: Measuring the non-monetary cost of unemployment (dataset: European Values Study)
9 The credit market: Borrowers, lenders, and the rate of interest Empirical Project 9: Credit-excluded households in a developing country (dataset: Ethiopian Socioeconomic Survey)
10 Banks, money, housing, and financial assets Empirical Project 10: Characteristics of banking systems around the world (dataset: World Bank Global Financial Development Database)
11 Market successes and failures Empirical Project 11: Measuring willingness to pay for climate change mitigation (dataset: German survey data, taken from Uehleke (2016))
12 Governments and markets in a democratic society Empirical Project 12: Government policies and popularity: Hong Kong cash handout (datasets: University of Hong Kong Public Opinion Programme and the Hong Kong poverty situation report (published by the Hong Kong Census and Statistics Department))