Econ Visualization

Economics Visualization

Generate publication-quality figures that come out of code and \includegraphics{} cleanly into the paper or \frame{} into the slides. The default style follows DIME's Data Visualization and Reviewing Graphs checklist guidance.

Operating Principles

1. Figures are produced, never screenshotted. Same DIME four-tier replicability as tables: full replicability means the script writes a vector file (.pdf/.eps/.svg) directly to paper/figs/.
2. Vector first. Save as PDF (or EPS for some journals; SVG for web). PNG only for raster content (maps, photos, screenshots).
3. Color discipline. Use a colorblind-safe palette by default (Okabe-Ito, viridis); reserve color for information; ensure the figure still reads in grayscale; never red-green for treatment-vs-control.
4. Audience-aware annotation. A figure embedded in a paper relies on the caption; a standalone figure (slide, dashboard, blog) needs an in-figure title, takeaway annotation, and source citation.
5. Same dimensions everywhere. Define one figure-size convention per project (e.g. width = 6.5 in, height = 4 in for paper figures; width = 10 in, height = 5 in for 16:9 slides) so every figure aligns visually.

Decision Policy

This skill follows the repo-wide Agent Policy.

ASK before proceeding (blocking):

1. The single point the figure makes. (One figure, one claim.)
2. Audience: paper figure (rely on caption) vs standalone (needs in-figure title + source).
3. Chart type when more than one would work (e.g. event-study coefficient plot vs forest plot).
4. Color encoding — does color carry information, or is it decorative.

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

• Okabe-Ito colorblind-safe palette; never red-green for treatment/control.
• Vector PDF (device = cairo_pdf in R; default in matplotlib) for paper figures.
• Paper figure dimensions width = 6.5 in, height = 4 in; slide figures width = 10 in, height = 5.5 in.
• theme_paper() in R / paper-wide rcParams in matplotlib / scheme(white_tableau) in Stata.
• Output to paper/figs/<name>.pdf; the paper \includegraphics{} directly.

DOCUMENT and proceed (write into the figure script header):

• Sample / filter applied for this specific figure (when different from the main estimation sample).
• Source of any external macro variable (e.g. NBER recession dates).
• Color and shape encoding choices for any non-default series.

PROCEED items: figures produced by code (never screenshotted); save vector format; here::here() / pathlib for paths; one script per figure; rebuild path runs figure scripts before LaTeX.

Pre-flight Checklist

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

• Question the figure answers. A figure should make exactly one point.
• Chart type. Line / bar / scatter / coefficient plot / event study / binscatter / map / density / heatmap?
• Data source and sample. Where does the data come from; what filters apply?
• Audience. Paper figure (rely on caption) or standalone (needs title + source)?
• Output target. PDF for paper; PDF or PNG for slides; SVG for web.
• Dimensions. Width/height in inches; affects font sizing.
• Color encoding. Is color carrying information (yes → palette choice matters) or is it decorative (no → use a single accent color)?

Decision Tree (chart type by question)

Comparing two groups over time
└── Two-line plot with vertical reference line at the event.
    Add a shaded confidence band if you have one.

Treatment effect dynamics (event study)
└── Coefficient plot: x = relative period, y = coefficient,
    error bars = 95% CI, vertical line at -1 (omitted period).

Magnitude of one effect across many specifications
└── Coefficient plot: y axis lists specifications, x axis shows
    point estimate + 95% CI; vertical line at 0.

Distribution of a variable in two groups
└── Overlapping density plots, or a violin plot if N is large.

Continuous-X relationship (with many obs)
└── Binscatter (binsreg in Stata; binsreg / binscatter in R).

Heterogeneity across categories
└── Forest plot (coefplot per category, sorted by point estimate).

Geographic variation
└── Choropleth (sf + ggplot2 in R; geopandas in Python; spmap in Stata).

Cross-section relationship
└── Scatter + line of best fit; consider log scales for skewed vars.

Time series with macro events
└── Line plot with shaded recession bars (NBER dates) and
    annotated key events.

Project Layout

paper/
├── paper.tex
├── figs/                       # all .pdf figures; never hand-edited
│   ├── fig_event_study.pdf
│   ├── fig_balance.pdf
│   └── fig_time_series.pdf
└── code/
    ├── r/
    │   └── make_figures.R
    └── stata/
        └── make_figures.do

In the paper:

\begin{figure}[htbp]\centering
  \includegraphics[width=0.85\textwidth]{figs/fig_event_study.pdf}
  \caption{Event study of treatment effect on outcome Y.}
  \label{fig:event}
\end{figure}

Color Palettes (colorblind-safe defaults)

R, ggplot2:
  scale_color_manual(values = c("#0072B2", "#D55E00", "#009E73",
                                "#F0E442", "#CC79A7", "#56B4E9"))
  # Okabe-Ito, used by default in scientific publishing.

Python, matplotlib:
  plt.rcParams["axes.prop_cycle"] = cycler(
      color = ["#0072B2", "#D55E00", "#009E73", "#F0E442",
               "#CC79A7", "#56B4E9"])

Sequential numeric (viridis):
  ggplot2: scale_*_viridis_c()
  matplotlib: cmap = "viridis"
  Stata: graph twoway, ... scheme(white_tableau)

Never use a red-green encoding for treatment/control — fails for ~8% of male readers.

Output Skeleton (R, ggplot2)

# make_figures.R header
# Project: ProjectABC
# Inputs : data/processed/analysis.parquet
# Outputs: paper/figs/fig_event_study.pdf
# Author : First Last
# Notes  : One figure per script section. Run as a whole.

library(ggplot2)
library(arrow)
library(here)

FIGS <- here("paper", "figs")
dir.create(FIGS, recursive = TRUE, showWarnings = FALSE)

theme_paper <- function(base_size = 11) {
  theme_minimal(base_size = base_size) +
    theme(panel.grid.minor = element_blank(),
          plot.title       = element_text(face = "plain"),
          plot.title.position = "plot",
          legend.position  = "bottom")
}

# (figure code)

ggsave(file.path(FIGS, "fig_event_study.pdf"),
       width = 6.5, height = 4, units = "in", device = cairo_pdf)

Common Pitfalls

• Default ggplot/matplotlib themes for journal figures (gray panels, busy gridlines). Strip them.
• 3D bar charts and pie charts. Almost never the right choice.
• Two y-axes encoding different units. Hard to interpret; consider faceting instead.
• Tiny axis text on a paper figure that becomes unreadable when the page is printed.
• Inconsistent axis ranges between related figures. Force shared limits.
• Using "rainbow" colormaps (jet) — perceptually misleading. Use viridis/cividis.
• Forgetting to label units (Percent, USD, Log GDP).
• Including a legend with one entry. Use a direct title instead.
• Saving as PNG for a paper figure. Use PDF/EPS so the figure scales without pixelation.
• Pasting a figure that requires the audience to read tiny coefficient labels — replace with a coefficient plot.

Additional Resources

• reference.md — extended patterns: event-study coefplot recipes, binscatter, recession-shaded time series, balance plot, choropleth maps, faceting.
• examples/ — runnable scripts:
  • examples/event_study_ggplot.R — coefficient plot from fixest::iplot data
  • examples/balance_plot_ggplot.R — standardized differences plot
  • examples/time_series_recessions.R — line plot with shaded NBER recessions
  • examples/binscatter_ggplot.R — binsreg-style scatter
  • examples/event_study_matplotlib.py — same in Python
  • examples/coefplot_stata.do — Stata coefplot from eststo output
  • examples/twoway_stata.do — Stata twoway chart with shaded periods
  • examples/theme_paper.R — paper-wide ggplot2 theme

Requirements

• R: ggplot2, arrow, dplyr, here, viridis, sf, binsreg (optional).
• Python: matplotlib, seaborn, pandas, pyarrow, geopandas (for maps).
• Stata: coefplot, binsreg, spmap, colorpalette (in palettes package).
• LaTeX: graphicx, caption, subcaption.

References

DIME

• DIME Analytics, Data Visualization.
• DIME Analytics, Checklist: Reviewing Graphs.
• DIME Analytics, Exporting Analysis.
• DIME Analytics, Stata Visual Library for Impact Evaluation.

Style and Theory

• Tufte (2001), The Visual Display of Quantitative Information.
• Cleveland (1993), Visualizing Data.
• Wilke (2019), Fundamentals of Data Visualization — https://clauswilke.com/dataviz/.
• Healy (2018), Data Visualization: A Practical Introduction — https://socviz.co/.
• Datawrapper, What to Consider When Choosing Colors for Data Visualization.
• Okabe & Ito, Color Universal Design palette.

Tools

• ggplot2 — https://ggplot2.tidyverse.org/.
• matplotlib gallery — https://matplotlib.org/stable/gallery/.
• Stata coefplot (Jann) — https://repec.sowi.unibe.ch/stata/coefplot/.