The role of statistical distributions in vulnerability to poverty analysis
dc.contributor.author | Poghosyan, Armine | en |
dc.contributor.committeechair | Van Mullekom, Jennifer H. | en |
dc.contributor.committeemember | Benami, Elinor | en |
dc.contributor.committeemember | Moeltner, Klaus | en |
dc.contributor.department | Statistics | en |
dc.date.accessioned | 2024-09-05T18:00:37Z | en |
dc.date.available | 2024-09-05T18:00:37Z | en |
dc.date.issued | 2024-04-11 | en |
dc.description.abstract | 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. | en |
dc.description.abstractgeneral | In drought-prone regions where many families rely on rainfed farming, extreme weather can devastate livelihoods. Governments have created aid programs to assist the most vulnerable households during these climate crises, but identifying who needs help is extremely challenging. Part of this difficulty lies in selecting the right statistical methods for analyzing weather data and household information. In this paper, we focus on Niger, a country that experiences frequent droughts and where over 80% of the population depends on rainfed agriculture. By evaluating household consumption data, we aim to assist in identifying the households who has high probability of becoming poor as a result of unfavorable weather events and thus needs support from social protection programs. In our analysis, we systematically evaluate how each input factor (including household characteristics and statistical distributions) affects households likelihood of becoming poor in the event of weather crises. We find that compared to alternative statistical distributions, using a conventional normal distribution could lead to misclassifying around 150,000 households as non-vulnerable, leaving them without vital assistance. Similarly, using different sets of household characteristics can result in up to 10 percentage points which equivalents to 2 million households that would miss out on much-needed support. Understanding these sensitivities is crucial for policymakers in refining how aid programs identify the vulnerable populations and include them into the protection programs. The improved targeting approach will enhance the resilience of communities in semi-arid regions facing increasing weather variability. | en |
dc.description.degree | Master of Science | en |
dc.format.medium | ETD | en |
dc.format.mimetype | application/pdf | en |
dc.identifier.uri | https://hdl.handle.net/10919/121080 | en |
dc.language.iso | en | en |
dc.publisher | Virginia Tech | en |
dc.subject | statistical distribution | en |
dc.subject | satellite remote sensing | en |
dc.subject | gridded weather | en |
dc.subject | consumption | en |
dc.subject | survey data | en |
dc.title | The role of statistical distributions in vulnerability to poverty analysis | en |
dc.type | Thesis | en |
thesis.degree.discipline | Data Analysis and Applied Statistics | en |
thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
thesis.degree.level | masters | en |
thesis.degree.name | Master of Science | en |