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10.1371/journal.pone.0249270 http://scihub22266oqcxt.onion/10.1371/journal.pone.0249270 33914745!8084142!33914745     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (4): e0249270 Nephropedia Template TP {{tp|p=33914745|t=2021. Socio-economic status, resilience, and vulnerability of households under COVID-19: Case of village-level data in Sichuan province |pdf=|usr=}}{{33914745}} gab.com Text Socio-economic status, resilience, and vulnerability of households under COVID-19: Case of village-level data in Sichuan province JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33914745 #FOAvip This paper investigates economic impacts of COVID-19 on households based on differences in the socio-economic status (SES). We determine the household-level effects of the COVID-19 shock using income sources, types of industries, communities' resilience, household susceptibility, and relevant policy measures. For this purpose, we used primary data of 555 households collected through snowball sampling technique using an online survey questionnaire from different villages mostly located in Sichuan Province, China. Using step-wise binary logistic regression analysis, we estimated and validated the model. Results suggest the use of SES as a better measure for understanding the impacts of COVID-19 on different households. We find that households with low SES tend to depend more on farmland income and transfer payments from the government. Contrarily, high SES households focus more on business and local employment as sources of income generation. Poor households were less resilient and more likely to fall back into poverty due to COVID-19, while the opposite stands true for non-poor households with high SES. Based on the estimations, policies encouraging employment and businesses complemented with loans on lower interest rates are recommended, which may increase the SES, thus minimizing vulnerability and enhancing the households' resilience towards poverty alleviation and economic shocks. Twit Text FOAVip Socio-economic status, resilience, and vulnerability of households under COVID-19: Case of village-level data in Sichuan province ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33914745 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33914745 #FOAvip English Wikipedia <ref>{{Cite pmid|33914745}}</ref> Socio-economic status, resilience, and vulnerability of households under COVID-19: Case of village-level data in Sichuan province #MMPMID33914745 Ur Rahman I; Jian D; Junrong L; Shafi M PLoS One 2021[]; 16 (4): e0249270 PMID33914745show ga This paper investigates economic impacts of COVID-19 on households based on differences in the socio-economic status (SES). We determine the household-level effects of the COVID-19 shock using income sources, types of industries, communities' resilience, household susceptibility, and relevant policy measures. For this purpose, we used primary data of 555 households collected through snowball sampling technique using an online survey questionnaire from different villages mostly located in Sichuan Province, China. Using step-wise binary logistic regression analysis, we estimated and validated the model. Results suggest the use of SES as a better measure for understanding the impacts of COVID-19 on different households. We find that households with low SES tend to depend more on farmland income and transfer payments from the government. Contrarily, high SES households focus more on business and local employment as sources of income generation. Poor households were less resilient and more likely to fall back into poverty due to COVID-19, while the opposite stands true for non-poor households with high SES. Based on the estimations, policies encouraging employment and businesses complemented with loans on lower interest rates are recommended, which may increase the SES, thus minimizing vulnerability and enhancing the households' resilience towards poverty alleviation and economic shocks. |*COVID-19/epidemiology[MESH] |*Employment[MESH] |*Poverty[MESH] |China/epidemiology[MESH] |Economic Status[MESH] |Family Characteristics[MESH] |Humans[MESH] |SARS-CoV-2/isolation & purification[MESH] |Social Class[MESH]Socio-economic status, resilience, and vulnerability of households und(epmc).pdf DeepDyve Pubget Overpricing