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At Policy Analysis Lab, we aim to be a valuable resource and discussion platform for public policy professionals interested in applied policy analysis. We welcome guest contributions that demonstrate the application of analytical methods, analysis tools, and evidence-based approaches to public policy analysis that are likely to be of interest to our wider community.

To find out more about making a submission, get in touch via our contact form.

Submission guidelines

Content philosophy

Our blog focuses on practical applications of analytical tools (R, Python, Excel, QGIS) to explore policy-relevant datasets and answer questions that matter to public policy professionals. Your contribution should:

  • Maintain objectivity and avoid prescriptive judgments.
  • Focus on exploring interesting policy questions or challenges using data.
  • Provide reproducible analysis with clear explanations.
  • Be accessible to a general audience familiar with data analysis.
  • Use only essential tools and techniques to answer the question at hand.

Length and format

  • 700-1,400 words (excluding code).
  • Well-written with clear descriptive subheadings and well-formatted diagrams and plots.
  • Formatted code snippets with short descriptive headings.
  • Well-documented and easy to read code.

Structure and style

Try to choose a topic that is likely to be relevant and interesting to policy analysts, researchers and experts from the policy space. This might include presenting interesting analysis on a public issue, policy intervention or an interesting approach for tackling a policy analysis problem. Informational posts are also welcome if they have relevance to data analysis. Some examples:

Try to follow a narrative arc in your article:

  • Open with a compelling ‘hook’ that draws in the reader. This might be an interesting question, surprising statistic, or vivid policy problem.
  • Establish context and background that helps readers understand why the topic matters.
  • Use narrative subheadings that relate to the theme covered by each section and guides the reader through your analysis.
  • Include a conclusion that ties everything together and leaves readers with a clear takeaway.

Strong submissions typically take one of these perspectives:

  • Counter-intuitive insights about policy analysis or analytical tools.
  • Fresh approaches to conventional analytical techniques.
  • Surprising discoveries from real-world policy data.
  • Clear articulations of complex policy analysis concepts.
  • Relatable experiences in policy analysis work.

Technical requirements

Originality and Attribution

  • You should be the primary author of your post and any associated code.
  • Where you have drawn on outside sources, these should be clearly referenced e.g. via Author, Date, Title, Publisher and URL.
  • Data should be appropriately licensed for use in the post.

Code and data

  • Analysis presented by your post should be reproducible by making all code, analysis files and software available to the reader, such a via a public GitHub repository. Policy Analysis Lab can also host files relevant to your analysis if preferred.
  • Articles that use freely available analysis solutions, such as Python or R, are preferred. Where commercial tools are used, a freely available alternative should also be provided so readers can reproduce your analysis (such as PSPP, Libreoffice, GRETL, QGIS etc).
  • Analysis should be fully reproducible. This means your post should include code or links to resources explaining how raw data used in your post was processed and cleaned.

Plots, diagrams and memes

Images, diagrams and memes relevant to your article are welcome. But make sure that they are of a reasonable quality and shared in a publishable format, such as .jpg or .png.

Make sure to include the source of external images using the format: Source: Author, Publisher, URL.

Submission notes

When submitting your article, make sure to include the following:

  • A short summary of your piece (one to three sentences).
  • The data and analysis files used (either as an attachment or via a link).
  • Any statistical packages and/or software used to produce the analysis.
  • A note on how AI was used to write the blog and produce the analysis.

Creating your author profile

Once your post has been accepted, a dedicated account will be created so you can create and customize your author profile (see example). To edit your profile, log in to your account, select ‘My Profile’ and ‘Edit Profile’. From here you can customize your bio, website, social links and your preferred display name.

To select a profile picture create a Gravatar.com account using the same email address as your Policy Analysis Lab account.

Once updated, your profile will appear alongside your guest articles. This will allow interested readers to find out more about you and get in touch to tell you how great your blog was!

Optional: The elements of analysis style

Follow the analysis style guidelines outlined below when submitting your code and analysis files. While not mandatory, adopting these practices will help maintain consistency with our instructional materials and make your work more accessible to other members.

Object prefixes

When assigning names, use prefixes that describe an purpose before providing a concise descriptive title. For example instead of ‘historical_stockmarket_data’ use ‘dta_stockmarket’. Prefix examples include:

  • Data: dta_
  • Temporary files: tmp_
  • Statistical summaries: sum_
  • Models (such as lm models): mod_
  • Custom functions: fn_
  • Plots and visualization: plt_
  • Reference tables: ref_
  • Results and analysis: rlt_
  • Consistency and accuracy checks: chk_

Structuring project directories

When your code requires reading or saving files, try to use the following directory structure:

  • Data: for storing all data sets used for analysis.
  • Outputs: for saving resulting analysis and data files.
  • Plots: for saving resulting diagrams and plots.
  • Scripts: for storing any relevant python or R scripts used by the project.

File names

Try to name your files using the format below:

  • Start with the date (YYMMDD)
  • Follow with a brief, descriptive title
  • Use underscores to separate words
  • Keep names concise but meaningful

Example: 240218_housing_price_analysis.R