{"id":"https://openalex.org/W4382046054","doi":"https://doi.org/10.1145/3598438.3598441","title":"The Impact of Precise Poverty Alleviation Policies on the Vulnerability of Farm Households to Poverty: Research Based on Micro Big Data","display_name":"The Impact of Precise Poverty Alleviation Policies on the Vulnerability of Farm Households to Poverty: Research Based on Micro Big Data","publication_year":2022,"publication_date":"2022-12-09","ids":{"openalex":"https://openalex.org/W4382046054","doi":"https://doi.org/10.1145/3598438.3598441"},"language":"en","primary_location":{"id":"doi:10.1145/3598438.3598441","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3598438.3598441","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3598438.3598441","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3598438.3598441","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068922010","display_name":"Shengze Qin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210111616","display_name":"Wuhan Business University","ror":"https://ror.org/0282ggx30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210111616"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shengze Qin","raw_affiliation_strings":["Wuhan Business University, China"],"raw_orcid":"https://orcid.org/0000-0001-5769-1234","affiliations":[{"raw_affiliation_string":"Wuhan Business University, China","institution_ids":["https://openalex.org/I4210111616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103009533","display_name":"Jia Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099437","display_name":"Hubei University Of Economics","ror":"https://ror.org/012a84b59","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210099437"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Guo","raw_affiliation_strings":["HUBEI UNIVERSITY OF ECONOMICS, China"],"raw_orcid":"https://orcid.org/0009-0006-7983-9643","affiliations":[{"raw_affiliation_string":"HUBEI UNIVERSITY OF ECONOMICS, China","institution_ids":["https://openalex.org/I4210099437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026509930","display_name":"Haimanot B. Atinkut","orcid":"https://orcid.org/0000-0002-1219-6405"},"institutions":[{"id":"https://openalex.org/I8764889","display_name":"University of Gondar","ror":"https://ror.org/0595gz585","country_code":"ET","type":"education","lineage":["https://openalex.org/I8764889"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Haimanot B Atinkut","raw_affiliation_strings":["University of Gondar, Ethiopia"],"raw_orcid":"https://orcid.org/0000-0002-1219-6405","affiliations":[{"raw_affiliation_string":"University of Gondar, Ethiopia","institution_ids":["https://openalex.org/I8764889"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013716639","display_name":"Chongjun Yang","orcid":"https://orcid.org/0009-0007-8004-3789"},"institutions":[{"id":"https://openalex.org/I4210111616","display_name":"Wuhan Business University","ror":"https://ror.org/0282ggx30","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210111616"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chongjun Yang","raw_affiliation_strings":["Wuhan Business University, China"],"raw_orcid":"https://orcid.org/0009-0007-8004-3789","affiliations":[{"raw_affiliation_string":"Wuhan Business University, China","institution_ids":["https://openalex.org/I4210111616"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5068922010"],"corresponding_institution_ids":["https://openalex.org/I4210111616"],"apc_list":null,"apc_paid":null,"fwci":0.3959,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75949812,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10446","display_name":"Income, Poverty, and Inequality","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10446","display_name":"Income, Poverty, and Inequality","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11886","display_name":"Agricultural risk and resilience","score":0.9559999704360962,"subfield":{"id":"https://openalex.org/subfields/1111","display_name":"Soil Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11711","display_name":"COVID-19 Pandemic Impacts","score":0.945900022983551,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/poverty","display_name":"Poverty","score":0.885084867477417},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.6832900643348694},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.5494189262390137},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4630439877510071},{"id":"https://openalex.org/keywords/socioeconomics","display_name":"Socioeconomics","score":0.427320659160614},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.37385886907577515},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.34709304571151733},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2573848366737366},{"id":"https://openalex.org/keywords/economic-growth","display_name":"Economic growth","score":0.21018832921981812},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12915244698524475},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.11070433259010315}],"concepts":[{"id":"https://openalex.org/C189326681","wikidata":"https://www.wikidata.org/wiki/Q10294","display_name":"Poverty","level":2,"score":0.885084867477417},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.6832900643348694},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.5494189262390137},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4630439877510071},{"id":"https://openalex.org/C45355965","wikidata":"https://www.wikidata.org/wiki/Q1643441","display_name":"Socioeconomics","level":1,"score":0.427320659160614},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.37385886907577515},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.34709304571151733},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2573848366737366},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.21018832921981812},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12915244698524475},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.11070433259010315},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3598438.3598441","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3598438.3598441","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3598438.3598441","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3598438.3598441","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3598438.3598441","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3598438.3598441","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 3rd International Symposium on Big Data and Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.7900000214576721,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4382046054.pdf","grobid_xml":"https://content.openalex.org/works/W4382046054.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W1537287213","https://openalex.org/W3082840371","https://openalex.org/W3123568397","https://openalex.org/W3125244926","https://openalex.org/W3136418157","https://openalex.org/W4229737049","https://openalex.org/W4294550781"],"related_works":["https://openalex.org/W2106092715","https://openalex.org/W616632114","https://openalex.org/W2379461907","https://openalex.org/W331143443","https://openalex.org/W4214505880","https://openalex.org/W2348994942","https://openalex.org/W93581272","https://openalex.org/W4285308928","https://openalex.org/W2378710205","https://openalex.org/W1535544643"],"abstract_inverted_index":{"Based":[0],"on":[1],"the":[2,15,23,49,56,67,105,113,131,141,145,151,155,163,173,181,196,202,209,213,217,221],"China":[3],"Household":[4],"Tracking":[5],"Survey":[6],"4-period":[7],"tracking":[8],"data":[9],"from":[10],"2012-2018,":[11],"this":[12],"paper":[13],"measures":[14,39],"poverty":[16,36,70,107,111,115,132,171,184,197,203],"vulnerability":[17,71,103,116,133,204],"of":[18,55,65,69,95,102,128,134,183,205,216],"Chinese":[19,73,135],"farm":[20,74,96,136],"households":[21,75,97,137],"using":[22,43],"VEP":[24],"method,":[25],"and":[26,47,51,59,88,148,157,175,208,220],"then":[27],"constructs":[28],"driving":[29,57,218],"variables":[30,33,219],"as":[31],"proxy":[32],"for":[34,82],"precise":[35],"alleviation":[37,198],"policies,":[38],"their":[40],"policy":[41,199],"effects":[42],"fuzzy":[44,190],"breakpoint":[45,52,191,222],"regression,":[46],"tests":[48],"continuity":[50,214],"regression":[53,192,210,223],"validity":[54,224],"variables,":[58],"finds":[60],"that":[61,195],"(1)":[62],"in":[63,86,99,122,140,150,162,172,180,185],"terms":[64,127],"time,":[66],"incidence":[68],"among":[72],"shows":[76,118],"an":[77],"overall":[78],"decreasing":[79,120],"trend":[80,121],"except":[81],"a":[83,100,119],"small":[84],"increase":[85],"2016,":[87],"by":[89,144],"2018,":[90],"there":[91,176],"are":[92,165,177],"still":[93],"10.11%":[94],"located":[98],"state":[101],"under":[104,109],"$1.3":[106],"line;":[108],"different":[110,186],"lines,":[112],"average":[114],"index":[117],"recent":[123],"years.":[124],"(2)":[125],"In":[126],"regional":[129],"dimensions,":[130],"is":[138],"highest":[139],"west,":[142],"followed":[143],"central":[146,158],"region,":[147],"lowest":[149],"east.":[152],"Compared":[153],"with":[154],"east":[156],"regions,":[159],"rural":[160,206],"residents":[161],"west":[164],"more":[166],"likely":[167],"to":[168],"fall":[169],"into":[170],"future,":[174],"large":[178],"differences":[179],"depth":[182],"regions.":[187],"(3)":[188],"The":[189],"model":[193],"found":[194],"significantly":[200],"reduced":[201],"households,":[207],"results":[211],"passed":[212],"test":[215],"test.":[225]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}