Econ Data Analysis

Economic Data Analysis

Stage-Scoped References

Load per stage, not all at once:

The Iron Law

NO TRANSFORMATION WITHOUT PRIOR DESCRIPTION

Transformed without describing first? Undo it and describe fresh from the current data state — not from a previous session, not "later at the end."

Three Concurrent Disciplines: Describe–Analyze–Validate

Concurrent, not sequential — every analysis step exercises all three, with documentation alongside.

Shared checklist: implementer before DONE, reviewer within its focus. These items apply to every analysis task; operation-conditional items live in §Pitfalls.

• [BLOCKING] — must fix to earn APPROVE.
• [ADVISORY] — recorded; never blocks APPROVE.

Committed diagnostics, row-count logs, and output files are the evidence. A missing diagnostic is a finding, not a prompt to generate it yourself. When fixing, re-run the changed step and its downstream dependents; unaffected upstream outputs stand as committed.

Describe

Describe before and after every transformation. The post-transformation describe feeds Sanity checks below.

After loading any dataset:

• [BLOCKING] Every input described before the first transformation on it.
• [BLOCKING] Panel structure — first priority for panel/longitudinal data, the common case. Pure cross-section: note it, skip these.
  • IDs identified — panel ID (firm, fund, country, individual) and time ID (year, quarter, month, day).
  • Counts verified — unique IDs and unique time periods, against expectations.
  • Date range — min and max noted.
  • Balancedness characterized — periods-per-unit distribution (mean, median, min, max) and balanced ratio (actual rows / N_ids × T_periods).
    • Unbalanced: pattern characterized — entry/exit, mid-panel gaps, expanding coverage.
• [BLOCKING] Variable diagnostics on key variables — do NOT blanket-describe() all columns:
  • Continuous (returns, prices, GDP, weights): mean, median, std, min, max, tail percentiles (p1, p5, p95, p99).
  • Categorical/binary (sector codes, indicators, country): value counts and shares; check unexpected categories or near-zero frequencies.
  • Identifiers: panel ID × time uniquely identifies rows; check duplicates.
• [BLOCKING] Data types and missing values:
  • Column types correct — dates as dates, numerics as numerics, not object/string.
  • Missing values counted, and share per variable.
  • Missingness pattern noted — random vs systematic. Interpretation: §Validate §Missing-data as signal.

Data already imported and validated upstream: read the existing diagnostics instead of re-running full validation.

Outlier flagging:

• [BLOCKING] Observations beyond p1/p99 flagged and assessed.
  • Data error vs genuine extreme — naturally skewed variables (firm size, wealth, trade volumes) have real extremes.
  • Keep/winsorize/trim decision documented.
• [ADVISORY] If winsorizing, cutoff documented; robustness with alternatives considered (see references/data-robustness-checklist.md).

After every major transformation (re-describe):

• [BLOCKING] Descriptive statistics re-run on affected variables, compared against pre-transformation values.
  • Applies after merges, filters, variable construction, aggregations, reshaping, deduplication.
  • An unexplained distribution shift is silent corruption — nothing downstream uses the variable until it is understood.

Visualization for key variables — plot what summary statistics hide:

• [BLOCKING] A companion figure wherever visualization is feasible, which covers most analyses. A key variable carried through the analysis without one is a finding.
  • Continuous variable, before transforming, winsorizing, or filtering it — histogram.
  • Time-series variable — line plot against time, where structural breaks and seasonality live.
  • A correlation you are about to rely on — scatter plot.
• [ADVISORY] Visualization not feasible — a lone scalar, too many series to plot individually, a figure that would not clarify: say which and move on.

Analyze

Transform data with integrity. Operation-specific traps: §Pitfalls.

• [BLOCKING] One logical operation per step. One verb per step — merge, filter, construct, aggregate, reshape, deduplicate. No chaining merge + filter + construct.
• [BLOCKING] Row-count logging at every sample-changing operation. Print before → after for every merge, filter, drop, deduplication, or sample restriction.
  • Major operations warrant their own cell; minor ones can share a cell as long as the count is printed.

Validate

Numbers must make economic sense — checked against priors, literature, cross-variable relationships, and alternative specifications. Runs on the output of every Analyze step, not at the end.

Sanity checks (after every Analyze step; minimum bar before proceeding):

• [BLOCKING] Row count matches a stated expectation, not just logged.
  • Left join: matches the left table when the right side is m:1.
  • Inner join and filter: drop counts explained, drop rate defensible.
• [BLOCKING] Economic sense.
  • Magnitudes plausible — GDP growth of 300% is wrong.
  • Signs correct; correlations match known stylized facts.
  • Constructed variables and growth rates spot-checked by hand on a few observations.
• [BLOCKING] Task objective expectations comparison. Task objective states Expected Results or Hypotheses: compare findings explicitly, flag divergences before moving on.

Anything unexpected: STOP and investigate.

Multi-source validation (key variables and headline numbers):

• [BLOCKING] Every key variable and headline number checked against at least one external reference, for scale, sign, and relationship.
  • References: a published benchmark (IMF WEO, World Bank, central-bank data), prior literature, or a known related measure.
  • Two proxies for the same construct should correlate.
  • Conditional means across obvious subgroups (developed vs emerging, pre/post crisis) should behave as expected.
  • A surprising relationship is a signal to investigate, not to explain away.

Missing-data as signal (interrogate before handling — how-to in §Pitfalls §Missing data handling):

• [BLOCKING] Systematic missingness (concentrated in time, geography, or correlated with other variables) investigated — true absence vs construction error.
• [BLOCKING] "Missing" meaning disambiguated. No position (→ zero) vs didn't report (→ truly missing). Missing returns treated as zero is almost always wrong.

Sensitivity analysis (design in references/planning.md; menu in references/data-robustness-checklist.md):

• [ADVISORY] Sensitivity checks run on robustness-sensitive tasks, one alternative specification at a time — sample cutoff, variable definition, winsorization, leave-one-out.
  • "Robust enough" is an economic judgment: a coefficient moving 5% is usually fine, one that flips sign is not.
  • The question is whether the researcher tells the same story under the alternative.
• [BLOCKING] Divergence escalated. A sensitivity check with a meaningfully different result: STOP and AskUserQuestion.
  • Meaningfully different — sign flip, lost significance on a headline coefficient, magnitude change large enough to change the interpretation.
  • Divergence is a methodology question, not an RA decision.

Implementation standards

• [BLOCKING] Analysis scripts follow the notebook-compatible format per references/notebook-format.md.
• [BLOCKING] Major decisions (filter threshold, join type, variable definition, sample period) carry a markdown-cell justification; minor decisions carry an inline comment.
• [BLOCKING] Outputs (tables, figures) are generated from committed code, not ad-hoc REPL state.

Documentation and handoff

• [BLOCKING] In a superRA task, task-specific result-producing code follows using-superra/references/task-companion-files.md.
• [BLOCKING] Markdown cells explain what each block does and why.
• [BLOCKING] Headline findings presented visually. Each headline result as a figure — a distribution, a relationship, or a time path.
  • Exception: a figure would not clarify it — a lone scalar, a small table that already reads clearly.
• [BLOCKING] Figures saved under the task's attachments/ directory and embedded in task ## Results as attachments/fig.png per superRA:communicate.

Stage-scoped discipline (not walked at every implementation dispatch)

• integration stage — references/integration.md (codebase consistency, data discipline preserved through refactoring, utility reuse, documented deviations).
• End-of-workflow completion verification — orchestrator-owned, not dispatched. In superRA, superimplement/references/completion.md §Verify Pipeline and Reproducibility.

Pitfalls

Operation-conditional — walk a subsection only when the task performs that operation. Severity markers match the main checklist.

Merges and joins

• [BLOCKING] Before — describe both sides. Row counts and unique join-key values in both tables; key overlap and type compatibility.
  • A merge without join-key inspection on both sides is an Iron Law violation.
• [BLOCKING] Join type declared. Decide 1:1, m:1, or 1:m before writing the merge; confirm the post-merge row count against it.
  • Many-to-many is almost always a bug — a Cartesian product that silently inflates row counts, and the reason a left join can come back longer than its left table.
• [BLOCKING] Unmatched rows logged. How many rows from each side did not match; whether non-matching is random or systematic.

Time-series operations (lag, lead, diff, cumsum, fill)

• [BLOCKING] Sort first. By panel ID + time, before any lag, lead, diff, or cumsum. Joins destroy sort order — re-sort after every merge.
• [BLOCKING] Check for gaps before lags/leads/diffs.
  • If unit i is missing period t, a naive shift(1) treats period t+1's lag as t-1's value — silently wrong.
  • Diagnose gaps per unit first, then spot-check a few units after the shift, especially near panel entry and exit.
• [BLOCKING] Use time-aware operators when available.
  • Julia: PanelShift.jl handles gaps.
  • Python: merge on lagged time index, or reindex to a full time grid before shifting.
  • Positional-shift-only framework: verify no gaps, or fill gaps explicitly — with NaN, not interpolation.

Reshaping

• [BLOCKING] After pivot: unique IDs × unique time periods should match original shape.
• [BLOCKING] Check for unintended NAs from unbalanced panels going wide.

Aggregations

• [BLOCKING] Function matches content. Sum dollar amounts, average rates — never the reverse.
• [BLOCKING] Group-by keys match intended level (country-year, not country-month).
• [BLOCKING] Weights verified. Weighted average: weights sum to expected values.
• [BLOCKING] Duplicates handled before aggregating — dupes cause double-counting.

Deduplication

• [BLOCKING] Uniqueness checked before operations that assume it (merges, index-setting).
• [BLOCKING] Which duplicate kept, and why (first, last, highest value).

Filtering

• [BLOCKING] Rows dropped logged — count, reason, before/after.
• [BLOCKING] Non-randomness of drops checked. Drops concentrated in certain countries, periods, or variable ranges are sample-selection-bias risk.
• [BLOCKING] Boolean logic verified. & vs | is a common silent bug; chained filters compound.

Variable construction

• [BLOCKING] Transformation order: log → winsorize → standardize. Log after standardize fails on negative standardized values.
• [BLOCKING] Ratio denominators checked for zero/near-zero; extreme ratios often come from small denominators.
• [BLOCKING] Growth rates: spot-checked against published benchmarks; first differences amplify measurement error — inspect for implausible spikes.
• [BLOCKING] Standardization: mean ≈ 0, std ≈ 1 within the relevant sample; cross-sectional vs time-series vs pooled stated.

Missing data handling

Handling missingness (for interpretation, §Validate §Missing-data as signal):

• [BLOCKING] Handling is visible and auditable — .fillna(0), .dropna(), and filters explicit; package defaults that silently ignore NaN in aggregations checked against the analytical objective.
• [BLOCKING] Prefer passing missing through the pipeline over filling silently; fill or coalesce only with explicit justification.

Common Rationalizations

Excuses that precede Iron Law violations. Catch yourself forming one: undo the transformation and describe first.

Key References

• Gentzkow & Shapiro (2014), "Code and Data for the Social Sciences"
• AEA Data Editor, "Guidance for Replication Packages"