Standard Economics Template (SET)

The Standard Economics Template (SET) is a structured, opensource framework designed to make economic analysis, modelling, and reporting more consistent, transparent, and reproducible. By providing a set of predefined sections, datahandling conventions, and formatting guidelines, SET helps researchers, policy makers, and educators build documents that can be easily shared, reviewed, and updated.

Why SET Exists

Economic work often suffers from two recurring problems:

• Lack of standardisation: Different authors use different layouts, variable names, and citation styles, making it hard to compare results.
• Reproducibility gaps: Essential assumptions, data sources, and code snippets are frequently hidden in footnotes or appendices, which hampers verification.

SET addresses these issues by prescribing a clear hierarchy of sections, a consistent naming convention for tables and figures, and a recommended workflow for linking quantitative models (e.g., R, Python, Stata) to the written narrative.

Core Components of SET

1. Document Structure

SET divides an economic report into the following major blocks:

1. Executive Summary A concise overview (max 300 words) of objectives, methodology, key findings, and policy implications.
2. Introduction Context, research question, and relevance to the literature.
3. Methodology Detailed description of data sources, model specifications, estimation techniques, and any sensitivity analysis.
4. Results Presentation of primary outcomes, robustness checks, and subsample analyses.
5. Discussion Interpretation of results, comparison with prior studies, and policy relevance.
6. Conclusion & Recommendations Summarisation of findings, limitations, and suggested next steps.
7. References Full citation list following a chosen style (APA, Chicago, etc.).
8. Appendices Technical details, full data tables, code listings, and supplementary figures.

2. Naming Conventions

All tables, figures, and equations receive a standardized identifier that integrates the section number, e.g., Table 3.2 for the second table in Section 3 (Methodology). Variables in datasets follow snake_case (e.g., gdp_per_capita) and are documented in a data dictionary placed in the appendix.

3. Data & Code Integration

SET encourages the use of R Markdown, Jupyter Notebooks, or similar literateprogramming tools. This ensures the narrative, code, and output (tables, charts) are generated from a single source file, guaranteeing that any change to the data automatically updates the report.

4. Visualisation Standards

• Colorblindfriendly palettes (e.g., Tableau 10, ColorBrewer).
• All axes labelled with units; titles are concise.
• Figures are placed close to the text that references them; each figure has a caption that explains the key takeaway.

Benefits of Using SET

• Improved comparability: Researchers can lineup results across studies without reinterpreting different formats.
• Transparency: Code and data are embedded or linked directly, making peer review faster.
• Efficiency: Reusing the template reduces time spent on formatting and allows authors to focus on analysis.
• Educational value: Students learn best practices early, building habits that carry into professional work.

Implementation Steps

1. Download the template: A ZIP file containing a starter .Rmd (or .ipynb) file, a CSS stylesheet, and a datadictionary skeleton.
2. Populate the sections: Replace placeholder text with your own content while preserving the heading hierarchy.
3. Insert code chunks: Load your datasets, run estimations, and let the output render automatically into tables/figures.
4. Validate naming: Ensure every table/figure follows the Section.Subsection pattern.
5. Run a reproducibility check: Delete the generated PDF/HTML output, then reknit the document. If the final product matches the previous version, your workflow is reproducible.
6. Publish: Export to HTML, PDF, or Word and share via a repository (GitHub, OSF) that also hosts the raw code.

Case Study: Applying SET to a LabourMarket Impact Assessment

Consider a study estimating the effect of a minimumwage increase on employment using a differenceindifferences design.

Executive Summary (excerpt)

Objective: Measure employment change in the retail sector after a $2 raise in the state minimum wage (20222024).  Method: Differenceindifferences with countylevel fixed effects; robustness checks include propensityscore weighting.  Key Finding: A $1 increase reduces fulltime employment by 0.7% (p<0.05).  Policy Implication: Targeted wage subsidies could offset the observed labor loss.    

Methodology Highlights

• Data: Quarterly administrative payroll records (20202024); variable employees_fulltime.
• Model: lm(employees_fulltime ~ post*treated + county + quarter).
• Robustness: Added controls for local unemployment rate and industry mix.

Result Table (autogenerated)

Table 3.1: DifferenceinDifferences Estimates| Variable          | Coefficient | Std. Err. | tstat | pvalue ||-------------------|------------:|----------:|-------:|--------:|| PostTreated      |    -0.0070  |   0.0032  | -2.19  | 0.028   || Post (control)    |     0.0015  |   0.0011  |  1.36  | 0.174   || Treated (pre)     |    -0.0008  |   0.0010  | -0.80  | 0.424   || Constant          |     0.8623  |   0.0058  |149.35  | 0.000   |    

Adapting SET for Different Fields

While originally conceived for macroeconomic and policy analysis, SET can be customised for:

• Environmental economics adding a dedicated Carbon Accounting subsection.
• Health economics integrating costeffectiveness tables with standard ICER formats.
• Development economics including a Data Availability annex that follows the World Bank metadata guidelines.

Resources & Community

SET is maintained by a collaborative network of economists and datascientists. Useful links:

• SET GitHub repository source code, issue tracker, and contribution guide.
• Official documentation detailed style manual, FAQs, and video tutorials.
• Community forum discuss best practices, share templates, and request help.

Conclusion

The Standard Economics Template offers a practical, opensource solution for enhancing the clarity, reproducibility, and impact of economic research. By adopting a shared structure, consistent naming, and integrated codereport workflows, analysts can focus on substantive insights rather than formatting hurdles. Whether you are a graduate student drafting a thesis, a thinktank producing policy briefs, or a central bank publishing quarterly analyses, SET provides a robust foundation that aligns with modern scientific standards.