Metadata for the climate change and macroeconomic asymmetric analysis dataset

Rwanda's economy is highly climate-exposed: rain-fed agriculture, nature-based tourism, and climate-sensitive extractives together account for roughly 45% of GDP, 65% of employment, and 40% of exports. Yet the macroeconomic consequences of climate variability remain poorly quantified, particularly the asymmetric, nonlinear dynamics linking climate shocks to macroeconomic outcomes in small, climate-vulnerable economies. This study applies the Nonlinear Autoregressive Distributed Lag (NARDL) model to Rwandan annual time series (1980–2023), estimating six models pairing extreme maximum temperature and rainfall anomalies with GDP growth, inflation, and unemployment.The dataset used comprised 44 annual observations (1980–2023) for Rwanda, combining three macroeconomic indicators (GDP growth, inflation, and unemployment rate), three climate-anomaly indicators (extreme maximum temperature anomaly, extreme minimum temperature anomaly, and annual rainfall anomaly), and six control variables (exchange rate, trade openness, gross capital formation, government expenditure, agriculture value added, and population growth), merged from the World Bank Indicators and the Climate Change Knowledge Portal into a single documented file.