Jde Data Analysis

---
name: jde-data-analysis
description: "Use when estimation, heterogeneity, attrition, measurement, or inference choices need to meet Journal of Development Economics (JDE) empirical norms — clustered field data, survey measurement error, and treatment-effect heterogeneity in low- and middle-income settings. Covers the analysis itself, not the identifying design."
category: backend-and-data
source_repo: brycewang-stanford/Awesome-Journal-Skills
source_path: "Journal-of-Development-Economics-Skills/skills/jde-data-analysis/SKILL.md"
source_url: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Development-Economics-Skills/skills/jde-data-analysis/SKILL.md
---


# Data Analysis (jde-data-analysis)

## When to trigger

- The identification is settled but the estimation, inference, or heterogeneity analysis is unconvincing
- A referee would question standard errors, attrition, measurement error, or sample construction
- You need to decide how to present treatment-effect heterogeneity across subgroups
- You are unsure the analysis would survive JDE's replication scrutiny

## JDE empirical norms

JDE referees are experienced with the realities of **field and survey data in developing countries** — clustered sampling, panel attrition, noisy self-reports, seasonality, and small effective sample sizes. Analysis that ignores these reads as naive. Hold the work to these standards:

- **Inference matched to the data structure.** Cluster at the level of treatment assignment or sampling (village, school, market); with few clusters use wild-cluster bootstrap or randomization inference rather than naive cluster-robust t-stats.
- **Attrition and missing data.** Document panel attrition, test whether it is differential by treatment, and bound effects (Lee bounds) when it is. Survey non-response and refusal patterns belong in the appendix.
- **Measurement.** Be explicit about how key variables (consumption, income, yields, test scores, health) were measured and constructed; address recall error, social-desirability bias, and unit/seasonal issues. Pre-specify or transparently justify index construction.
- **Heterogeneity, disciplined.** Development audiences care about *for whom* effects bind, but data-mined subgroups are penalized. Pre-specify subgroups where possible; otherwise treat heterogeneity as exploratory and adjust for multiple comparisons.
- **Magnitudes in welfare terms.** Report effects in policy-comparable units (share of the poverty gap, cost-effectiveness per dollar, standard deviations) — see jde-contribution-framing.

Because JDE's **replication policy** lets editors or referees request data, programs, and computational details **at the review stage**, every number in a table must be reproducible from a script the day you submit. Build the analysis to be auditable, not just presentable.

## Robustness expected

- Alternative specifications, samples, and functional forms in an extensive online appendix
- Sensitivity to outliers, winsorization, and index/aggregation choices
- Placebo / falsification outcomes that should not move
- Spillover/SUTVA checks where treatment may leak across units (common in village-level interventions)

## Worked analysis (illustrative)

Hypothetical: a cluster-randomized health-extension experiment, 80 villages, ~30 households each, with 12 percent endline attrition.

- **Inference:** cluster at the village level; with 80 clusters report wild-cluster-bootstrap p-values too (*illustrative* ITT = +0.14 SD on a child-health index, p = 0.03).
- **Attrition:** 12 percent overall but 9 vs 15 percent across arms — differential, so add Lee bounds; the effect survives the lower bound (~+0.06 SD).
- **Measurement:** the health index mixes recall and anthropometric items; pre-specify weights and show robustness to inverse-covariance vs simple-average aggregation.
- **Heterogeneity:** only the baseline-poverty split was pre-registered; report it Romano–Wolf-adjusted, demote the rest.

## Empirical-credibility pushback and the fix

| Referee objection                                  | The JDE-norm response                                        |
|----------------------------------------------------|--------------------------------------------------------------|
| "SEs ignore the clustered randomization"           | Re-cluster at the randomization unit; wild-cluster boot / RI |
| "Differential attrition biases the effect"         | Report attrition by arm; add Lee bounds; show the bound holds |
| "Spillovers violate SUTVA in your villages"        | Distance-ring / treated-neighbor checks; bound the leakage    |

## Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map:
[`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). Development economics leans on RCTs and observational designs alike; field experiments demand the many-outcome family-wise correction (`romano_wolf`).

- **Many outcomes / specifications:** `romano_wolf` (step-down FWER, accounts for
  cross-test correlation) or `benjamini_hochberg` — report the adjusted threshold.
- **OVB sensitivity:** `oster_delta` / `sensemakr` — the confounder strength that would
  overturn the headline.
- **Inference:** `wild_cluster_bootstrap` (few clusters), `twoway_cluster` / `conley`.
- **Re-fit off one handle:** `audit_result(result_id)` lists the missing checks and the
  exact `suggest_function` for each — no guessing the battery.
- **Exhibits:** `etable` / `did_summary_to_latex` from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive (now actually-run) battery in
the appendix. See the executed chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).
## Anti-patterns

- Standard errors not clustered at the design level; ignoring few-cluster bias
- Silently dropping attritors or trimming the sample without reporting it
- A wall of unadjusted subgroup interactions presented as confirmatory
- Effects reported only in raw units, leaving importance unclear
- Tables that cannot be regenerated from the submitted code


## Evidence pass for Journal of Development Economics

Treat this skill as an executable review pass, not a prose hint. First lock the development constraint, identification, welfare or distribution margin, and implementation context; then judge whether the current manuscript answers the venue's real reader: development economists who expect a development mechanism, credible design, and policy-relevant external validity.

- **Do the pass:** Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
- **Return a ledger:** give `claim / evidence / risk / manuscript location` rows, so the next agent can edit rather than rediscover the issue.
- **Sibling guard:** compare against World Development for broader policy audience, JPubE for fiscal/public-finance mechanisms, AER/AEJ Applied for field-wide reach; if a sibling owns the contribution, recommend re-routing before polishing format.
- **Stop condition:** do not give submission-ready advice until the pack's `resources/official-source-map.md` has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.

## Output format

```
【Estimator】+ why it fits the design
【Inference】clustering level / few-cluster method / MHT
【Attrition】rate, differential? bounds?
【Measurement】key variable construction + error handling
【Heterogeneity】pre-specified vs exploratory
【Robustness done / missing】[...]
【Reproducible from code today?】[Y/N]
【Next step】jde-tables-figures
```

---

**Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Development-Economics-Skills/skills/jde-data-analysis/SKILL.md`