Details
Original language | English |
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Journal | Climate and Development |
Early online date | 1 Jan 2025 |
Publication status | E-pub ahead of print - 1 Jan 2025 |
Abstract
Given the importance of understanding the resilience of rural households to shocks and poverty in developing countries, we consider resilience as a capacity and estimate a (latent) variable reflecting household’s resilience capacity, and we investigate how better resilience capacity can help rural households mitigate the negative impacts of shocks and improve their welfare. We use panel data of 3367 households from two emerging economies in Southeast Asia collected in three survey waves for empirical analyses. We use a generalized structural equation model to estimate the latent variable of household’s resilience capacity. Then, the results of fixed-effects estimations show that the lagged resilience capacity of rural households is significantly and negatively correlated with losses caused by different types of shocks. Moreover, the results of fixed-effects estimations with a control function approach indicate that an improvement in resilience capacity can prevent rural households from falling into poverty in absolute and multidimensional terms.
Keywords
- control function, fixed-effects estimation, GSEM, instrumental variable, multidimensional poverty, Panel data
ASJC Scopus subject areas
- Environmental Science(all)
- Global and Planetary Change
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Development
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In: Climate and Development, 01.01.2025.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Insights on household’s resilience to shocks and poverty
T2 - evidence from panel data for two emerging economies in Southeast Asia
AU - Do, Manh Hung
AU - Nguyen, Trung Thanh
AU - Grote, Ulrike
N1 - Publisher Copyright: © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Given the importance of understanding the resilience of rural households to shocks and poverty in developing countries, we consider resilience as a capacity and estimate a (latent) variable reflecting household’s resilience capacity, and we investigate how better resilience capacity can help rural households mitigate the negative impacts of shocks and improve their welfare. We use panel data of 3367 households from two emerging economies in Southeast Asia collected in three survey waves for empirical analyses. We use a generalized structural equation model to estimate the latent variable of household’s resilience capacity. Then, the results of fixed-effects estimations show that the lagged resilience capacity of rural households is significantly and negatively correlated with losses caused by different types of shocks. Moreover, the results of fixed-effects estimations with a control function approach indicate that an improvement in resilience capacity can prevent rural households from falling into poverty in absolute and multidimensional terms.
AB - Given the importance of understanding the resilience of rural households to shocks and poverty in developing countries, we consider resilience as a capacity and estimate a (latent) variable reflecting household’s resilience capacity, and we investigate how better resilience capacity can help rural households mitigate the negative impacts of shocks and improve their welfare. We use panel data of 3367 households from two emerging economies in Southeast Asia collected in three survey waves for empirical analyses. We use a generalized structural equation model to estimate the latent variable of household’s resilience capacity. Then, the results of fixed-effects estimations show that the lagged resilience capacity of rural households is significantly and negatively correlated with losses caused by different types of shocks. Moreover, the results of fixed-effects estimations with a control function approach indicate that an improvement in resilience capacity can prevent rural households from falling into poverty in absolute and multidimensional terms.
KW - control function
KW - fixed-effects estimation
KW - GSEM
KW - instrumental variable
KW - multidimensional poverty
KW - Panel data
UR - http://www.scopus.com/inward/record.url?scp=85213709843&partnerID=8YFLogxK
U2 - 10.1080/17565529.2024.2446358
DO - 10.1080/17565529.2024.2446358
M3 - Article
AN - SCOPUS:85213709843
JO - Climate and Development
JF - Climate and Development
SN - 1756-5529
ER -