Estimating Deprivation Cost Functions for Power Outages During Disasters: A Discrete Choice Modeling Approach

TL;DR

This paper develops a methodology to estimate deprivation cost functions for power outages during disasters using discrete choice models and survey data, revealing that costs increase convexly with outage duration and vary across sociodemographic groups.

Contribution

It introduces a systematic approach to measure deprivation costs of power outages, incorporating heterogeneity and advanced modeling techniques, filling a key gap in infrastructure risk assessment.

Findings

Deprivation cost functions are convex and strictly increasing with outage duration.

Heterogeneity exists in how individuals value power loss across income groups.

Flexible discrete choice models effectively capture taste variation in outage valuation.

Abstract

Systems for the generation and distribution of electrical power represents critical infrastructure and, when extreme weather events disrupt such systems, this imposes substantial costs on consumers. These costs can be conceptualized as deprivation costs, an increasing function of time without service, quantifiable through individuals' willingness to pay for power restoration. Despite widespread recognition of outage impacts, a gap in the research literature exists regarding the systematic measurement of deprivation costs. This study addresses this deficiency by developing and implementing a methodology to estimate deprivation cost functions for electricity outages, using stated preference survey data collected from Harris County, Texas. This study compares multiple discrete choice model architectures, including multinomial logit and mixed logit specifications, as well as models incorporating BoxCox and exponential utility transformations for the deprivation time attribute. The analysis examines heterogeneity in deprivation valuation through sociodemographic interactions, particularly across income groups. Results confirm that power outage deprivation cost functions are convex and strictly increasing with time. Additionally, the study reveals both systematic and random taste variation in how individuals value power loss, highlighting the need for flexible modeling approaches. By providing both methodological and empirical foundations for incorporating deprivation costs into infrastructure risk assessments and humanitarian logistics, this research enables policymakers to better quantify service disruption costs and develop more equitable resilience strategies.