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Apply development economics research methods and data sources
Passed 21 of 23 test cases with the skill, and 20 of 23 without it.
1 fixed20 passed either way2 still failing0 broke
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A test case the skill fixed. Same model, same prompt, with and without the skill.
I am outlining a research proposal on child nutrition in Sub-Saharan Africa and need a methodological overview. Please provide a guide that lists the primary empirical identification methods ordered by causal strength, highlights major international household survey datasets relevant for development economics, and notes key measurement issues when analyzing household welfare.
| Checked | Without skill | With skill |
|---|---|---|
| Regression Discontinuity Design is ranked above Instrumental Variables in causal identification strength. | Missed | Passed |
| Consumption is identified as preferred over income for measuring household welfare in developing countries. | Passed | Passed |
| The guide lists the World Bank LSMS as a major international household survey dataset. | Passed | Passed |
The instructions your model reads when the skill loads.
A skill for conducting development economics research, covering impact evaluation methods, field experiment design, household survey analysis, key data sources, and the methodological toolkit used to study poverty, education, health, and institutions in developing countries.
Fundamental question: What is the causal effect of a program/policy?
Challenge: We observe outcomes for treated individuals, but we cannot
observe what would have happened to them without treatment
(the counterfactual).
Solutions (from strongest to weakest causal identification):
1. Randomized Controlled Trials (RCTs / field experiments)
2. Regression Discontinuity Design (RDD)
3. Instrumental Variables (IV)
4. Difference-in-Differences (DiD)
5. Matching / Propensity Score Methods
6. Cross-sectional regression with controls (weakest)
def design_field_experiment(intervention: str,
unit: str,
clusters: int,
expected_effect: float) -> dict:
"""
Design a cluster-randomized field experiment.
Args:
intervention: Description of the program/policy
unit: Unit of randomization (individual, household, village, school)
clusters: Number of clusters available
expected_effect: Expected effect size (standard deviations)
"""
test cases passed, vs 20 / 23 without
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