Association for Public Policy Analysis & Management

Following a natural or manmade disaster, it is not uncommon to witness an outpouring of generosity directed toward the relief and recovery of those affected. While this phenomenon has been investigated in numerous studies, previous literature has taken for granted a potentially key intermediate variable between disasters and donations, namely public attention. Using data from Google Trends and the National Center on Charitable Statistics, our analysis employs econometric models to directly study the impact of attention cycles on charitable giving, specifically in the case of contributions to nonprofits following the terrorist attacks of September 11^th, 2001. Because Google Trends data are not available for 2001, we model attention cycles using a strategy introduced by McLaughlin (1982). This strategy requires identifying Google Trends data for events that are expected to affect public attention similarly to 9/11 and mapping an average attention cycle for those types of events. Using that expected attention cycle, we then estimate the donative response to the modeled disaster over time. Our analysis introduces a method for measuring public attention that is driven by an individual’s interest in searching for the phenomenon as opposed to other measures that assume interest. In terms of effect on donations, we anticipate that donations will follow the attention cycle. That is, immediately following the disaster, attention will surge and so will disaster-related donations. As the attention subsides, disaster-related donations will also decrease and giving will return to pre-disaster equilibrium. In the wake of predictions of future increases in the frequency and severity of disasters, it is important to begin to understand how disasters mobilize public support. By unpacking the role attention plays in that dynamic, we not only strengthen our understanding of the causal mechanism between disasters and donations, but we also provide practically useful information concerning which nonprofit subsectors are most vulnerable to funding decreases following disasters. Both nonprofit managers and policy makers could use such information to inform risk management strategies and financial resilience planning.