Quantifying Fiscal Space After Energy Subsidy Reform

The dataset comprises a balanced panel of 71 countries from 2000 to 2024 and is structured for a Difference-in-Differences (DiD) event-study analysis using Tow-Way-Fixed-Effect (TWFE-DiD) baseline model and Callaway and Sant’Anna (CS-DiD) estimator as a biase corrector and robustness checker. Core variables such as CountryID, Year, ReformYear, and EventTime, define treatment cohorts and capture the relative timing of fossil fuel subsidy reforms. The empirical focus is on two key outcome variables: government expenditure and government revenue (e.g., SOE profits and fuel-related taxation), typically expressed as a percentage of GDP to assess the fiscal-neutrality. A set of control variables, including macroeconomic fundamentals (e.g., GDP and inflation), is incorporated to absorb shocks and broader economic fluctuations. The full coding workflow should clearly document the construction of the event-time panel and the implementation of the DiD estimator, culminating in cohort-adjusted, time-varying Average Treatment Effect on the Treated (ATT) plots, and marginal effects.