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Record W2753884427 · doi:10.1002/psp.2086

The effects of labour migration on rural household production in inland China: Do landform conditions matter?

2017· article· en· W2753884427 on OpenAlexaff
Zehan Pan, Wei Xu, Zuyu Huang, Guixin Wang

Bibliographic record

VenuePopulation Space and Place · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Lethbridge
FundersShanghai Municipal Education CommissionFudan UniversityMinistry of Science and Technology of the People's Republic of China
KeywordsLandformChinaPovertyAgricultureRural areaGeographyProduction (economics)Agricultural productivityUrbanizationSocioeconomicsEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Abstract The large‐scale rural to urban migration, generating sizable remittances, is often considered as an important means to help reduce poverty in rural China. However, penury is still stubbornly haunting the mountainous rural areas of inland China where numerous rural–urban migrants originate. Neglected in the current literature, the landform conditions are vital to explain the diverse effects of labour migration on rural household production in China. By adopting a revised simultaneous equation model, this study explores empirically how variations in regional landform conditions configure the effect of labour migration on rural household production using the data from our 2013 survey of migration intentions among rural labourers in China. The results show that remittances exert a substitution effect on agricultural production of rural households in the mountainous areas but have a promotive effect in the plain and hilly areas. Labour cutback imposes a less negative influence on agricultural production of rural households residing in the plain and hilly areas than in the mountainous areas. The effects of remittances and labour cutback on nonagricultural production of rural households are positive and negative, respectively, although these effects are insensitive to the variation in landform conditions. As a consequence, local wage level of rural households is more difficult to be improved by labour migration in the mountainous areas. Therefore, it is likely to lead to excessive labour migration and poverty trap.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.280
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2017
Admission routes1
Has abstractyes

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