Assessing Reporting Delays in ACLED Conflict Event Data

TL;DR

This paper analyzes reporting delays in ACLED conflict data, revealing systematic biases based on event type, fatalities, location, and political context, which affect real-time conflict monitoring.

Contribution

It provides a statistical framework to understand and model reporting delays, enabling correction of biases in conflict event data analysis.

Findings

Over half of events are reported within two weeks.

Reporting delays vary systematically by event and country characteristics.

Delays are structured, not random, affecting real-time conflict analysis.

Abstract

Timely and accurate conflict event data are essential for real-time monitoring, forecasting, and policy response. Yet near-real-time conflict datasets such as the Armed Conflict Location \& Event Data Project (ACLED) are subject to reporting delays, that is, delays between event occurrence and first inclusion in the database. Such delays can introduce bias in short-term analyses and forecasts. This study provides a statistical analysis of reporting delays for African events recorded in ACLED's weekly releases from June 30, 2024, to June 1, 2025. Treating delay as a discrete time duration, we estimate grouped proportional hazards models with additive-linear and smooth terms incorporating event-level, spatial, and country-level covariates. Our results show that more than half of events are reported within two weeks, but delays vary systematically by event type, fatalities, geographic location, and political regime. Higher-fatality events are reported more quickly, while events in more restrictive political and informational environments tend to be reported more slowly. We also find substantial between-country heterogeneity, and country-specific analyses indicate that event-level effects differ across contexts. These findings show that reporting delays are structured rather than random and that real-time conflict analysis must account for them. More broadly, they provide an empirical foundation for developing nowcasting approaches to correct short-term underreporting in conflict event data.