Skill UI

A comprehensive guide for constructing and applying structural models in health economics, particularly for interpreting empirical estimates or running policy counterfactuals. It covers core archetypes—including demand-selection, provider incentives, and human capital—and emphasizes critical rigor, such as parameter identification, adherence to institutional rules (e.g., risk adjustment), and defending policy-invariance (Lucas critique).

This guide helps authors structure and strengthen their contribution claims for top-tier journals in economics and human resources. It emphasizes moving beyond simply stating new evidence by focusing on policy relevance, translating coefficients into natural human-capital units (like dollars or percentage points), and rigorously reconciling findings with prior literature to maximize impact.

A comprehensive guide for constructing and auditing empirical data pipelines, specifically tailored for high-impact journals. It covers advanced causal inference techniques (DID, RDD, IV, RCT), detailed sample management, robustness testing, and creating structured comparative estimates to withstand intense academic scrutiny.

A comprehensive guide for stress-testing causal identification in empirical microeconometrics. It covers advanced methods like RCT, DID, RDD, and IV, emphasizing robustness checks, identifying selection bias, and structuring results for clear public policy interpretation in high-impact academic journals.

This guide provides an advanced framework for positioning empirical research findings against established academic literature, particularly for high-impact journals. It instructs authors to move beyond simple citation by quantitatively reconciling differences in estimates (magnitude, sign, significance). By explicitly identifying disparities in sample design, population, or underlying mechanisms, this methodology significantly strengthens the paper's academic rigor and perceived contribution.

This guide provides a comprehensive strategy for authors responding to a "Revise and Resubmit" (R&R) decision, particularly for high-stakes journals. It details the structure of the rebuttal letter, including handling conflicting referee comments, performing necessary reconciliation with prior literature, and integrating rigorous sensitivity tests (e.g., comparative estimation, pre-trend analysis). It focuses on strategic communication and method validation.

This comprehensive guide outlines the mandatory data and replication policies for submissions to the Journal of Human Resources (JHR). It details best practices for creating robust submission packages, including Data Availability Statements, code packages, and plans for public repository deposit. It ensures maximum reproducibility and adherence to CC0 licensing standards for academic research.

A detailed guide to the Journal of Human Resources (JHR) editorial standards. It helps authors understand the unique hurdles, including the single-anonymous review process, passing the fast desk-reject screen, fulfilling the critical reconciliation requirement with prior literature, and optimizing the review-recycling shortcut. Essential preparation for academic submissions.

This comprehensive guide provides meticulous standards for presenting quantitative empirical results, particularly for academic journals like JHR. It covers the structure for main estimates, design diagnostics (pre-trends, balance), reconciliation tables, and organizing the online appendix. Learn how to maximize clarity and impact while adhering to strict page limits.

This tool guides researchers on whether their academic topic aligns with the scope of an empirical microeconomics journal, specifically the Journal of Human Resources (JHR). It emphasizes that the research must focus on policy-relevant, causal questions in core fields (e.g., labor, health, education), strictly differentiating it from purely organizational behavior or 'HR management' studies. It serves as a critical gatekeeper before formal submission.

This comprehensive guide provides rigorous methodological guidelines for executing and reporting empirical analysis on cross-national, multi-level, or panel data, specifically tailored for top-tier international business journals. It covers essential checks like measurement invariance (configural to scalar), Common Method Variance (CMV), dynamic endogeneity, and appropriate estimation techniques (e.g., Multilevel SEM, GMM), ensuring research meets the highest academic standards.

A specialized guide for structuring rigorous empirical studies, particularly for journals like JIBS. It helps researchers map their research phenomenon to the correct cross-country, multilevel, or panel design. Critically, it mandates planning for advanced econometric issues—including common-method variance, measurement equivalence, and dynamic endogeneity—at the initial design stage to ensure methodological rigor.