The role of statistical distributions in vulnerability to poverty analysis

In regions characterized by semi-arid climates where households’ welfare primarily relies on rainfed agricultural activities, extreme weather events such as droughts can present existential challenges to their livelihoods. To mitigate these risks, numerous social protection programs have been established to assist vulnerable households affected by weather events. Despite efforts to monitor environmental changes through remotely sensed technology, estimating the impact of weather variability on livelihoods remains challenging. This is compounded by the need to select appropriate statistical distribution for weather anomaly measures and household characteristics. We address these challenges by analyzing household consumption data from the Living Standards Measurement Study survey in Niger and systematically evaluating how each input factor affects vulnerability estimates. Our findings show that the choice of statistical distribution can significantly alter outcomes. For instance, using alternative statistical distribution for vegetation index readings could lead to differences of up to 0.7%, which means around 150,000 more households might be misclassified as not vulnerable. Similarly, variations in household characteristics could result in differences of up to 10 percentage points, equivalent to approximately 2 million households. Understanding these sensitivities helps policymakers refine targeting and intervention strategies effectively. By tailoring assistance programs more precisely to the needs of vulnerable households, policymakers ensure that resources are directed where they can make the most impact in lessening the adverse effects of extreme weather events. This enhances the resilience of communities in semi-arid regions.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Data Analysis and Applied Statistics

Department dc:contributor.department

Statistics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Poghosyan, Armine

Chair dc:contributor.committeechair

• Van Mullekom, Jennifer H.

Committee members dc:contributor.committeemember

• Benami, Elinor
• Moeltner, Klaus

• statistical distribution
• satellite remote sensing
• gridded weather
• consumption
• survey data

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/121080

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121080