Tutorial 5

Roadmap

1. Present the goal of the research project
2. Review the main steps to write a research paper, with some examples
3. Useful resources (inspiration for research questions, data sources, etc.)
4. Discussion, Q&A

The research project

Goals of the project

The goal of the project is to write an original research output. This means that you will need to:

• Identify a research question in economics (in whatever field, as long as it regards an economic mechanism)
• Find open data sources to answer it
• Prepare and justify an empirical strategy and perform a simple econometric analysis

Expected output

1. A 8 to 10 page report (including tables and figures, not include bibliography or appendix). It must be written in .tex.
2. A slide deck (that you will present at the end of the semester)
3. The code to go from the raw data to the output (+ a link to the raw data). I need to be able to replicate all your results using R and only R!

Report structure

Introduction (2-3 pages)

The introduction should include the following elements:

1. A motivation (2/3 paragraphs) State why the question you tackle is important, why is it a subject that is relevant for the policy leaders.

2. A research question (1 paragraph) Precisely state what is the research question.

3. A brief summary of the paper (3 paragraphs) Precise: the data you use, the context, the method you used, the key results.

4. A literature review (2 paragraphs) Cite some papers that are close to what you do and state how you compare to them in terms of results and methods.

Descriptive evidence (2-3 pages)

1. Data description: Data sources (with the appropriate references), the time span, the geographical level of analysis, the countries/contexts. Say some words about the context you are working on (developing/developed countries, political contexts, etc.)

2. Summary statistics: Make a table with the summary statistics (min,Q1,mean,median,Q3,max,nb.observations) of the key variables in your analysis

3. Descriptive evidence: You should make non-causal graphs (scatter or line) and/or maps to support your intuition

Causal evidence (2-3 pages)

1. Model specification: State the econometric model you want to estimate. Describe the threats to identification (simultaneity, OVB, or measurement error…)

2. Strategy: Write your method to address them (IV, DiD, etc.) and the assumptions needed for it to be valid, the specification.

3. OLS results: Export the results with coefficient tables with the relevant information (standard errors, N, R2, proper labelling, etc.). Interpret the results.

4. Extensions results: If you think you can/need to implement an IV or another strategy, you should present and interpret the results.

Conclusion

The conclusion is short and quickly summarizes the results.

Dissemination

You will need to make slides (7 to 8’ presentation) to present your key results. The structure should be the following

1. Motivation
2. Research question + contribution to the literature
3. Data description
4. Descriptive evidence
5. Causal evidence
6. Conclusion

Replication package

You will need to provide a replication package in a zip file containing:

1. The raw data
2. The code to replicate all the results (graphs, tables, etc.) present in your paper
3. A readme file with instructions to run the code and replicate the results.

Useful resources

Ideas for research questions

• American Economic Association classification system: decomposition of economics into fields and subfields. It is used to classify research papers and to find relevant literature
• Policy reports/briefs from research and international organizations:
  • IZA World of Labor: provides summaries of empirical research in labor economics
  • CEPII policy briefs: international economics, trade
  • IFO policy briefs: macroeconomics, public economics, labor economics
  • OECD policy briefs: all fields
  • VoxEU columns: all fields
  • …
• Find related literature: Google Scholar, Research rabbit…

Open data sources: international databases

• World Bank Open Data: GDP, poverty, inequality, education, health, environment
• OECD Data: labor markets, productivity, innovation, education, taxation
• IMF Data: inflation, public debt, government finance, balance of payments
• United Nations Data: population, gender, health, development
• Our World in Data: climate, inequality, health, energy, education
• Observatory of Economic Complexity: international trade, export specialization, product-level trade data
• World Values Survey: values, social attitudes, income, political preferences
• Pollution Data: air pollution data (PM2.5) at the global level
• FAO Data: agriculture, food security, land use, fisheries
• Climate data: weather and climate data (temperature, precipitation, etc.)

Open data sources: European databases

• Eurostat: employment, prices, regional statistics, migration, environment
• European Social Survey: political attitudes, trust in institutions, inequality perceptions
• EU Public Procurement Data: government contracts across Europe

Open data sources: National databases

Mostly French examples, but you can find similar databases for other countries:

• INSEE: income, employment, prices, demographics, firms
• French Government Open Data: housing, transport, public spending, environment
• Environmental and Transport Data: air pollution, transport, energy
• Banque de France Statistics: credit, banking, financial markets
• US Census data: mostly census tract-level data on demographics, income, employment, etc. (for the US)

Data Aggregator Platforms

These platforms combine many datasets.

• Google Public Data Explorer
• Dbnomics

Q&A