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Using Multi-Level Modeling to Evaluate the Economic Status Attainments of Rural-to-Urban Migrants in China

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Abstract:

Using China General Social Survey 2006 data, I tried to evaluate the economic status attainments of the rural-urban migrants in China using Multi-Level Modeling. These migrants account for 27% of the total urban population, but are usually missed or seriously under-represented in surveys. In this paper, I will focus on an important factor that has been neglected by almost all quantitative studies on these migrants, namely, the variations existing among different hometowns and destinations. The hometown provinces and destination provinces have dramatically different economic developmental status, and I use multi-level models to capture the provincial level variations. The rural-urban migrants in the CGSS 2006 dataset came from 28 hometown provinces and were residents in 28 destination provinces. I categorized the 28 hometown provinces into 4 groups based on each province’s economical development level. Then I have these 4 groups interacted with the 28 destination provinces to have 112 interactive groups. Each migrant belonged to one of these groups. I want to make comparison of migrants with lifetime urban residents, so I assign evenly in each group the corresponding urban residents in the same destination province. With the multi-level models, I can capture the un-observed province level differences among my 112 interactive groups, which are almost always neglected in previous studies.

Most Common Document Word Stems:

effect (101), urban (97), model (86), rural (83), migrant (72), random (54), signific (49), resid (45), 0.000 (45), variabl (44), status (44), rural-urban (39), level (39), differ (38), attain (35), provinc (34), educ (33), incom (31), isei (31), one (30), china (28),
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Name: American Sociological Association Annual Meeting
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http://www.asanet.org


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URL: http://citation.allacademic.com/meta/p498449_index.html
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MLA Citation:

Yue, Yin. "Using Multi-Level Modeling to Evaluate the Economic Status Attainments of Rural-to-Urban Migrants in China" Paper presented at the annual meeting of the American Sociological Association Annual Meeting, Caesar's Palace, Las Vegas, NV, Aug 20, 2011 <Not Available>. 2014-11-25 <http://citation.allacademic.com/meta/p498449_index.html>

APA Citation:

Yue, Y. , 2011-08-20 "Using Multi-Level Modeling to Evaluate the Economic Status Attainments of Rural-to-Urban Migrants in China" Paper presented at the annual meeting of the American Sociological Association Annual Meeting, Caesar's Palace, Las Vegas, NV Online <PDF>. 2014-11-25 from http://citation.allacademic.com/meta/p498449_index.html

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Using China General Social Survey 2006 data, I tried to evaluate the economic status attainments of the rural-urban migrants in China using Multi-Level Modeling. These migrants account for 27% of the total urban population, but are usually missed or seriously under-represented in surveys. In this paper, I will focus on an important factor that has been neglected by almost all quantitative studies on these migrants, namely, the variations existing among different hometowns and destinations. The hometown provinces and destination provinces have dramatically different economic developmental status, and I use multi-level models to capture the provincial level variations. The rural-urban migrants in the CGSS 2006 dataset came from 28 hometown provinces and were residents in 28 destination provinces. I categorized the 28 hometown provinces into 4 groups based on each province’s economical development level. Then I have these 4 groups interacted with the 28 destination provinces to have 112 interactive groups. Each migrant belonged to one of these groups. I want to make comparison of migrants with lifetime urban residents, so I assign evenly in each group the corresponding urban residents in the same destination province. With the multi-level models, I can capture the un-observed province level differences among my 112 interactive groups, which are almost always neglected in previous studies.


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