Latex Tables

LaTeX Tables

Generate publication-ready LaTeX tables that come out of code (esttab, modelsummary, pyfixest.etable, stargazer) and \input{} cleanly into the paper. The default style follows DIME's "full replicability" tier: no manual editing between code and PDF.

Operating Principles

1. Tables are produced, never typed. DIME identifies four levels of replicability: full, good, basic, and none. "None" (copy-paste from a console window) is unacceptable for any output that leaves the analyst. Default to full: code writes a .tex file that the paper \input{}s directly.
2. One source of truth per table. Every numeric cell, star, sample size, and footnote comes from the same script that ran the regression. Never edit the .tex file by hand — re-run the script and rebuild the paper.
3. booktabs styling. Three horizontal rules (\toprule, \midrule, \bottomrule), no vertical lines, consistent decimal alignment via siunitx S columns when precision matters.
4. Notes via threeparttable. Significance levels, standard-error type, sample restrictions, and clustering belong in a single notes block at the bottom — not in the caption.
5. Cross-reference with \Cref{}. \label{tab:main} + \Cref{tab:main} reads "Table 2" in body text and behaves correctly at sentence start.

Decision Policy

This skill follows the repo-wide Agent Policy.

ASK before proceeding (blocking):

1. Source language of the regression (Stata / R / Python) — determines the canonical table package.
2. Table type (regression / summary stats / balance / multi-panel / DiD-estimator comparison).
3. Standard-error type and cluster level (these belong in the table notes).
4. Output target (LaTeX .tex, Word .rtf, Excel .xlsx).

DEFAULT + flag (use this default; tell the user how to override):

• esttab (Stata), modelsummary or fixest::etable (R), pyfixest.etable or stargazer (Python).
• booktabs styling: \toprule, \midrule, \bottomrule; no vertical lines.
• Three-star convention * 0.10 ** 0.05 *** 0.01; pin once per paper.
• threeparttable for the notes block.
• Output path paper/tabs/<name>.tex; the paper \input{}s it.
• Within-R² (r2_within) reported instead of overall R² for FE models.

DOCUMENT and proceed (write into the decisions log of the table-generating script):

• The estimating equation, sample, cluster level, and FE included for each column.
• Variable label mapping (coef_map / coeflabels).
• Any subsample restriction shown in a column.

PROCEED items: tables produced by code (never copy-paste); generated .tex never hand-edited; rebuild path runs the table script before LaTeX.

Pre-flight Checklist

Before generating code, confirm with the user — and write the answers in the script header:

• Source language. Stata, R, or Python? (Each has its own canonical table package.)
• Table type. Regression (multi-spec), summary statistics, balance / difference-in-means, descriptive cross-tabs, or model-comparison panel?
• Sample. Single or stratified? If panel, which fixed effects are on/off across columns?
• Standard errors. Cluster-robust at which level? Wild-bootstrap p-values?
• Stars and notes. Conventional *** ** * cutoffs (1/5/10), or journal-specific (some require none)?
• Output path. Almost always paper/tabs/{name}.tex; the paper \input{}s that file.
• Compile target. \begin{table} floating environment in body, or naked tabular snippet to be included from a parent macro?

Decision Tree (which table package to use)

Source = Stata
└── esttab (from estout) is canonical.
    eststo store; esttab using "tabs/x.tex", booktabs label se ...

Source = R
├── fixest models (most common in 2024-26): use fixest::etable
└── Mixed model classes: use modelsummary (works across lm, glm,
    PanelOLS, fixest, brms, ...). modelsummary writes .tex via
    `output = "tabs/x.tex"`.

Source = Python
├── pyfixest models: use pyfixest.etable
├── linearmodels models: use compare(...) + manual to_latex(), or
    pass models through stargazer
└── statsmodels OLS / GLM: use stargazer (Python port).

Output for Word
└── Same packages with .rtf or .docx output (esttab supports rtf;
    modelsummary supports docx via flextable).

Output Path Convention

paper/
├── paper.tex
├── tabs/                       # all .tex tables; never hand-edited
│   ├── table_main.tex
│   ├── table_balance.tex
│   ├── table_summary.tex
│   └── table_robustness.tex
├── figs/
└── code/
    ├── stata/
    │   └── make_tables.do
    └── r/
        └── make_tables.R

Body text:

\Cref{tab:main} reports our preferred specification.
\input{tabs/table_main.tex}

The .tex file should produce the entire \begin{table} ... \end{table} environment so you only have one \input{} per table in the paper.

Common Pitfalls

• Hand-editing the .tex file after generation. The next run will overwrite or diverge from the published numbers.
• Mixing significance-star conventions across tables (* at 0.05 in one, at 0.10 in another).
• Using \hline everywhere; readers expect three-line booktabs tables.
• Vertical lines in regression tables — never necessary, always ugly.
• Putting key information in the caption (e.g. "Standard errors clustered by firm") instead of in the notes block, where it can be longer and less awkward.
• Using _ in variable labels without \_ escaping; LaTeX silently interprets them as subscripts.
• Including raw column names like treat_post instead of human-readable labels.
• Numbers with inconsistent decimal precision across columns.
• Missing N row at the bottom of regression tables; reviewers will ask.
• Not exporting at all — running the regression in interactive mode and screenshotting the result. (This is "no replicability" in DIME's framework.)

Additional Resources

• reference.md — extended patterns: balance tables, summary stats, panel tables, threeparttable notes, siunitx S-column alignment, multi-panel tables, journal-specific deviations.
• examples/ — runnable scripts that produce real .tex files:
  • examples/make_tables_esttab.do — Stata regression + summary + balance via esttab
  • examples/make_tables_modelsummary.R — R via modelsummary + fixest::etable
  • examples/make_tables_pyfixest.py — Python via pyfixest.etable
  • examples/make_tables_stargazer.py — Python statsmodels via stargazer
  • examples/table_main_template.tex — minimal copy-paste-able tabular for "good replicability" cases
  • examples/table_balance_template.tex — DIME-style balance table layout

Requirements

• LaTeX: TeX Live 2022+ or MacTeX 2022+; packages booktabs, threeparttable, siunitx, caption, cleveref.
• Stata: ssc install estout (provides esttab, eststo, estadd, estpost).
• R: install.packages(c("modelsummary", "fixest", "kableExtra")).
• Python: pip install stargazer pyfixest pandas.

References

DIME

• DIME Analytics, Exporting Analysis — four levels of replicability.
• DIME Analytics, Checklist: Submit Table.
• DIME Analytics, Stata Coding Practices — estout family.

Style

• AEA, Manuscript Style Guide.
• Jansen & Persson, Nice and fast tables in Stata for LaTeX and Excel — https://osf.io/78nuc/.
• Lukas Püttmann, esttab cheat sheet.
• booktabs documentation — https://ctan.org/pkg/booktabs.
• siunitx — https://ctan.org/pkg/siunitx (S columns for decimal alignment).