Risk and Vulnerability in Nepal

Walker, Thomas; Kawasoe, Yasuhiro; Shrestha, Jui

doi:10.1596/33365

Cited by 8 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings demonstrate how households in this context were self‐reliant, with few households relying on aid or support from social networks. The findings are consistent with a 2019 pre‐pandemic study of risk and vulnerability in rural Nepal that shows that households typically take loans or use savings to deal with shocks (Walker et al, 2019). Figure 5 further unpacks how these three primary strategies were utilized over time.…”

Section: Coping With Shockssupporting

confidence: 87%

See 1 more Smart Citation

Coping with COVID‐19 shocks in rural Nepal

Ekstrom,

Janzen,

Magnan

2023

Applied Eco Perspectives Pol

View full text Add to dashboard Cite

We examine shocks experienced by rural Nepali households during the COVID‐19 pandemic. Households primarily experienced income and price shocks during a government‐imposed lockdown. During this time, households managed to effectively protect consumption, and mostly relied on credit (26%), asset sales (10%) and savings (8%). Debt levels nearly doubled, with limited changes to savings. We then leverage a long‐term randomized control trial (RCT) to assess whether beneficiaries of a livestock livelihood program are more resilient. Program beneficiaries are 6 percentage points less likely to take out new loans.

show abstract

Section: Coping With Shockssupporting

confidence: 87%

“…This was largely a matter of logistics. A typical rural village is 30 min walking distance from the closest paved road (Walker et al, 2019). Unlike in urban areas, police and security forces were not checking for compliance and enforcing the lockdown in rural areas, so small kiosks in rural areas often remained open.…”

Section: Context and Samplementioning

confidence: 99%

Coping with COVID‐19 shocks in rural Nepal

Ekstrom,

Janzen,

Magnan

2023

Applied Eco Perspectives Pol

View full text Add to dashboard Cite

show abstract

“…The data comes from Walker and Jacoby (2018), commonly known as the Nepal Household Risk and Vulnerability Survey (HRVS) Wave III. The survey interviewed 6051 households from 400 communities in non‐metropolitan areas per the 2010 Census definition covering 50 of the 75 districts in Nepal to collect a rich set of information at the household and individual level.…”

Section: Datamentioning

confidence: 99%

Social pensions and children's nutrition: Evidence from Nepal

Adhikari

2023

J of Intl Development

View full text Add to dashboard Cite

This paper investigates the impact of social pensions on child nutrition and health outcomes in Nepal. Empirical estimates suggest that the outcomes are heterogeneous with gender playing a key role, both of the social pension recipients as well as that of the children. The social pension received by females is found to have a positive influence on the weight‐for‐height of girls but no such impact is existent on that of boys. In contrast, social pensions received by male had no implications for children's nutrition irrespective of their gender.

show abstract

“…In this respect, SoVI scores are commonly used as a proxy of social vulnerability, which is independent of empirical data and which enables one to develop a more generic methodology that can be applied in different contexts. Within this scope, there are numerous studies that have examined the factors relating to social vulnerability in a hazard by using either descriptive statistics (Yücel and Arun, 2010;Walker et al, 2019) or traditional data analysis tools, such as linear or logistic regression (Fekete, 2009;Noriega and Ludwig, 2012;Syed and Kumar Routray, 2014;Llorente-Marrón et al, 2020;Mtintsilana et al, 2022). While the former lacks the incorporation of the relationships between the vulnerability indicators, the latter relies heavily on data assumptions.…”

Section: Background For Social Vulnerability Assessmentmentioning

confidence: 99%

Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study

Kalaycıoğlu

Akhanlı²,

Menteşe

et al. 2023

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. To what extent an individual or group will be affected by the damage of a hazard depends not just on their exposure to the event but on their social vulnerability – that is, how well they are able to anticipate, cope with, resist, and recover from the impact of a hazard. Therefore, for mitigating disaster risk effectively and building a disaster-resilient society to natural hazards, it is essential that policy makers develop an understanding of social vulnerability. This study aims to propose an optimal predictive model that allows decision makers to identify households with high social vulnerability by using a number of easily accessible household variables. In order to develop such a model, we rely on a large dataset comprising a household survey (n = 41 093) that was conducted to generate a social vulnerability index (SoVI) in Istanbul, Türkiye. In this study, we assessed the predictive ability of socio-economic, socio-demographic, and housing conditions on the household-level social vulnerability through machine learning models. We used classification and regression tree (CART), random forest (RF), support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), k-nearest neighbours (KNNs), and logistic regression to classify households with respect to their social vulnerability level, which was used as the outcome of these models. Due to the disparity of class size outcome variables, subsampling strategies were applied for dealing with imbalanced data. Among these models, ANN was found to have the optimal predictive performance for discriminating households with low and high social vulnerability when random-majority under sampling was applied (area under the curve (AUC): 0.813). The results from the ANN method indicated that lack of social security, living in a squatter house, and job insecurity were among the most important predictors of social vulnerability to hazards. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and savings of the household were found to be associated with social vulnerability. An open-access R Shiny web application was developed to visually display the performance of machine learning (ML) methods, important variables for the classification of households with high and low social vulnerability, and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can guide decision makers in identifying social vulnerability effectively and hence let them prioritise actions towards vulnerable groups in terms of needs prior to an event of a hazard.

show abstract

Risk and Vulnerability in Nepal

Cited by 8 publications

References 10 publications

Coping with COVID‐19 shocks in rural Nepal

Coping with COVID‐19 shocks in rural Nepal

Social pensions and children's nutrition: Evidence from Nepal

Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study

Contact Info

Product

Resources

About