| Literature DB >> 30486452 |
Min Yang1, Martin Dijst2, Marco Helbich3.
Abstract
Massive rural⁻urban migration in China has drawn attention to the prevalence of mental health problems among migrants. Research on the mental health of Chinese migrants has a narrow focus on rural⁻urban migrants, emphasizing the institutional role of hukou in migrant mental health. We argue that the heterogeneity of migrants, including their place of origin and whether they are temporary or permanent migrants, should be taken into account when trying to understand the meaning of migration as an actual movement from one place to another. The data used for this study is from a cross-sectional survey (N = 855) conducted in Shenzhen to compare the differences in migrants' mental health that arise when using the two definitions (e.g., hukou and birthplace). Binary logistic regression models were estimated to assess the associations between people's mental health and migration, while controlling for settlement experiences, self-reported physical health, and sociodemographics. The results reveal inconsistent findings across both definitions: general migrants by birthplace were found to be unlikely to have mental problems compared to non-migrants, whereas temporary migrants were at higher risk of mental problems. The study provides important evidence that different migrant groups have different mental health outcomes. The choice of the definition used influences both migrant group selection and the actual linkage between migration and mental health.Entities:
Keywords: China; birthplace; hukou; mental health; migration
Mesh:
Year: 2018 PMID: 30486452 PMCID: PMC6313338 DOI: 10.3390/ijerph15122671
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Descriptive statistics and mental health prevalence rate.
| Variable | Category | Whole Sample | Prevalence of Mental Health Problems | |
|---|---|---|---|---|
| χ2 Test/ | ||||
|
| ||||
| Temporary migrants by hukou | Non-migrants | 398 (46.5%) | 106 (26.6%) | 1.66 * |
| Migrants | 457 (53.5%) | 140 (30.6%) | ||
| General migrants by birthplace | Non-migrants | 264 (30.9%) | 90 (34.1%) | 5.27 * |
| Migrants | 591 (69.1%) | 156 (26.4%) | ||
|
| ||||
| Length of residence in Shenzhen (years) | 25.5 | 23.8 | 2.83 * | |
| Housing ownership | No | 586 (68.5%) | 184 (31.4%) | 6.27 * |
| Yes | 269 (31.5%) | 62 (23.0%) | ||
| Residential mobility in Shenzhen | 1.7 | 2.0 | −2.87 ** | |
|
| ||||
| Self-reported physical health | Fair and poor | 295 (34.5%) | 138 (46.8%) | 72.08 ** |
| Good | 246 (28.8%) | 52 (21.1%) | ||
| Very good | 190 (22.2%) | 35 (18.4%) | ||
| Excellent | 124 (14.5%) | 21 (16.9%) | ||
|
| ||||
| Age (years) | 31.5 | 30.4 | 1.74 | |
| Gender | Female | 392 (45.8%) | 109 (27.8%) | 0.33 |
| Male | 463 (54.2%) | 137 (29.6%) | ||
| Education | High school or lower | 235(27.5%) | 78 (33.2%) | 3.09 |
| Bachelor’s or lower | 569 (66.6%) | 154 (27.1%) | ||
| Master’s or above | 51 (5.9%) | 14 (27.5%) | ||
| Personal income | <RMB 4000 | 199 (23.2%) | 76 (38.2%) | 11.72 ** |
| RMB 4001–8000 | 351 (41.1%) | 95 (27.1%) | ||
| >RMB 8000 | 305 (35.7%) | 75 (24.6%) | ||
| Number of jobs | None | 57 (6.7%) | 20 (35.1%) | 1.37 |
| One job | 664 (77.6%) | 186 (28.0%) | ||
| >One job | 134 (15.7%) | 40 (29.9%) | ||
* p < 0.05; ** p < 0.01.
Results of the binary logistic regressions (N = 855).
| Independent Variables | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|
| Coef. | Stand. Err. | Coef. | Stand. Err. | Coef. | Stand. Err. | Coef. | Stand. Err. | |
|
| ||||||||
| Migrants | 0.430 ** | 0.154 | 0.517 * | 0.206 | ||||
|
| ||||||||
| Migrants | −0.366 * | 0.160 | −0.630 ** | 0.203 | ||||
|
| ||||||||
| Length of residence in Shenzhen (years) | −0.029 * | 0.013 | −0.001 | 0.013 | ||||
| Housing ownership (reference: No) | ||||||||
| Housing ownership (Yes) | 0.023 | 0.211 | −0.367 | 0.202 | ||||
| Residential mobility | 0.137 ** | 0.047 | 0.130 ** | 0.047 | ||||
|
| ||||||||
| Good | −1.201 ** | 0.201 | −1.193 ** | 0.201 | ||||
| Very good | −1.333 ** | 0.229 | −1.358 ** | 0.230 | ||||
| Excellent | −1.590 ** | 0.283 | −1.660 ** | 0.285 | ||||
|
| ||||||||
| Gender (Female) | 0.298 | 0.171 | 0.354 * | 0.170 | ||||
| Age (years) | 0.011 | 0.015 | −0.011 | 0.015 | ||||
| Education (reference: High school and lower) | ||||||||
| Bachelor’s degree | −0.072 | 0.201 | −0.265 | 0.198 | ||||
| Master’s degree | 0.266 | 0.397 | 0.034 | 0.389 | ||||
| Personal monthly income (reference: <RMB 4000) | ||||||||
| RMB 4001–8000 | −0.487 * | 0.217 | −0.470 * | 0.217 | ||||
| >RMB 8000 | −0.407 | 0.255 | −0.420 | 0.255 | ||||
| No. of jobs (reference: None) | ||||||||
| One job | −0.145 | 0.325 | −0.061 | 0.328 | ||||
| >One job | 0.025 | 0.374 | 0.075 | 0.377 | ||||
|
| −1.146 ** | 0.117 | 0.012 | 0.522 | −0.659 ** | 0.130 | 0.910 | 0.511 |
|
| 0.013 | 0.164 | 0.019 | 0.169 | ||||
* p < 0.05; ** p < 0.01.