Do Sunspot Cycles Causally Affect Social Tensions and Population Harm in Developing Countries?
This project investigates whether the emergence and duration of sunspots have a causal effect on social tensions and downstream harm to populations in developing countries. The core motivation is to distinguish true causal influence from spurious correlation driven by macroeconomic, political, climatic, and institutional confounders. We will combine time-series and panel-causal methods, including fixed effects and event-based designs, to estimate any direct or indirect pathways. The expected impact is a rigorous negative or positive finding that clarifies whether solar activity should be treated as a meaningful risk signal in conflict and social instability forecasting.
Problem Workspace
Problem Statement
Scope: The study will test a causal hypothesis linking solar activity (sunspot emergence, intensity, and duration) to measurable indicators of social tension and population-level harm in developing countries. The analysis will focus on country-month (or country-quarter) panels over a long horizon to capture multiple solar cycles. Outcomes will include protest incidence, conflict events, displacement, mortality proxies, and welfare stress indicators where available. Constraints: Establishing causality is difficult because solar activity may correlate with other geophysical and climatic variables, while social tensions are strongly influenced by governance quality, inflation, unemployment, commodity shocks, conflict spillovers, and historical fragility. Data quality is likely uneven across countries and years, with potential reporting bias in conflict and protest datasets. The design must therefore prioritize robustness checks, alternative operationalizations, lag…Read more
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Execution plan
Metrics: primary metrics are average treatment effect estimates (with confidence intervals), specification stability across model families, and out-of-sample predictive lift (AUC/PR or RMSE depending on target type). Baselines: (a) socioeconomic-political baseline model without solar variables, (b) climate-only baseline, and (c) naive temporal baseline using lagged outcomes. Data/splits: construct a country-time panel; use temporal train/validation/test splits (e.g., early years for training, recent years for testing) and rolling-window backtests; evaluate heterogeneity by region and fragility level. Acceptance criteria: claim causal support only if effect direction and magnitude remain consistent across predefined robustness checks, placebo tests show no systematic false positives, and adding solar variables yields statistically and practically meaningful improvement over baselines; otherwise conclude no reliable causal evidence.