Skill UI

A comprehensive guide for conducting rigorous empirical analysis on field and survey data in low- and middle-income settings. It covers advanced methods for handling clustered sampling, measurement error, differential attrition, and treatment-effect heterogeneity, ensuring results meet the high standards of reproducibility and robustness required by leading developmental economics journals.

This comprehensive guide establishes the rigorous methodological standards for developing credible causal identification strategies in development economics. It details best practices for modern quasi-experimental methods, including RCTs, DID, IV, and RDD. Authors learn how to meet the high empirical norms required by top-tier journals by ensuring robustness, addressing biases (e.g., TWFE), and validating external validity.

This guide provides best practices for presenting empirical results in development economics, emphasizing a 'figure-forward' approach suitable for top-tier journals. It teaches researchers how to transform complex statistical models (like DID, RDD, and ITT) into clear, self-contained, and impactful visual exhibits (plots and maps). The goal is to ensure that the headline findings are immediately legible to expert referees, moving beyond traditional, complex coefficient tables.

This comprehensive guide outlines the rigorous methodology required for empirical and theoretical growth studies, particularly for high-impact journals like JEG. It covers best practices for cross-country panel data analysis, calibration techniques, spatial inference (Conley SEs), and structuring robust manuscripts. Use this checklist when estimating growth outcomes, simulating transition paths, or conducting deep sensitivity analyses for development economics.

A comprehensive skill for authors targeting top development economics journals (like JDE). It guides the framing and methodological rigor of micro-development work, covering requirements for credible identification (RCTs, natural experiments), structuring the 'external lesson,' and adhering to modern academic publishing standards. Use this when refining a field experiment or policy paper for publication.

This specialized router guides the entire manuscript development process for submissions to The Review of Economics and Statistics (REStat). It does not replace specialized skills but systematically directs users through the full research pipeline—from initial topic selection and causal identification to robustness testing, figure creation, and rebuttal strategy—ensuring a structured approach to applied econometrics research.

A rigorous checklist and guide for conducting causal inference in development economics, specifically tailored to the high standards of top-tier journals like The World Bank Economic Review (WBER). It covers advanced econometric designs (RCT, DiD, RD, IV), focusing not just on estimation but on defining the estimand, addressing external validity, general equilibrium effects, and translating local findings into actionable policy recommendations.

Provides comprehensive guidelines for designing and auditing academic tables and figures, specifically for development economics journals. It teaches how to translate statistical coefficients into actionable policy magnitudes, validate research design (e.g., balance checks, pre-trends), and adhere to strict page limits while maintaining clarity for both economists and policymakers.

This skill is a specialized guide used by authors to evaluate the fit and required rigor for submitting development economics manuscripts to the World Bank Economic Review (WBER). It assesses the manuscript's alignment with the journal's focus on policy-relevant empirical micro and macro research, covering methodological standards (RCTs, IV, DiD), required policy framing, and structural formatting.

This guide outlines the rigorous standards for establishing causal claims in development economics and social sciences. It covers robust quantitative methods (like DID, IV, RDD, RCT) and structured qualitative/mixed-methods approaches. The core principle is that the logical route from evidence to claim must be explicitly defensible and matched to the strength of the assertion, avoiding over-claiming.