| Literature DB >> 35805229 |
Tao Xu1.
Abstract
Objective: Previous studies on settlement intentions have mainly focused on the explanations of social and economic rationality, culture, and institution, but insufficient attention had been paid to the relationship between health and settlement intentions. This study explored the relationship between changes in the health status of immigrants and their settlement intentions. Method: A cross-sectional survey was conducted both in 2018 and 2019. Foreigners who visited the Yiwu Municipal Exit-Entry Administration Office to extend their visas were invited to participate in the study. Quantitative data, such as the participants' sociodemographic characteristics, job status, employment, immigration experience, key factors associated with the intention to settle down, medical insurance coverage, and changes in health status, were collected by questionnaire.Entities:
Keywords: changes in health status; length of stay; social insurance; willingness to settle down
Mesh:
Year: 2022 PMID: 35805229 PMCID: PMC9266219 DOI: 10.3390/ijerph19137574
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Description of variables.
| Variables | Frequency | Percentage | Variables | Frequency | Percentage |
|---|---|---|---|---|---|
| Long-term settlement intention | Health problem | ||||
| Yes | 1375 | 68.3% | Yes | 418 | 20.4% |
| No | 638 | 31.7% | No | 1634 | 79.6% |
| Gender | Social insurance | ||||
| Male | 1681 | 89.6% | Yes | 976 | 42.5% |
| Female | 215 | 10.4% | No | 915 | 44.4% |
| Age group | Have no idea | 271 | 13.1% | ||
| Under 20 years old | 69 | 3.5% | Health change | ||
| 21–30 years old | 841 | 42.6% | Become very unhealthy | 45 | 2.2% |
| 31–40 years old | 689 | 34.9% | Become worse than before | 143 | 6.9% |
| 41–50 years old | 264 | 13.3% | The same as before | 1204 | 58.3% |
| Over 50 years old | 111 | 5.6% | Become better than before | 482 | 23.3% |
| Education | Become very healthy | 191 | 9.3% | ||
| Primary school | 69 | 3.5% | Length of stay in China | ||
| Junior high school | 841 | 42.6% | Less than 6 months | 904 | 48.6% |
| Senior high school | 689 | 34.9% | From 6 to12 months | 331 | 17.8% |
| College | 264 | 13.4% | From 13 to 18 months | 80 | 4.3% |
| Undergraduate and above | 111 | 5.6% | From 19 to 24 months | 156 | 8.4% |
| Income | From 25 to 32 months | 35 | 1.9% | ||
| Below CNY 2000 | 128 | 6.4% | From 33 to 36 months | 105 | 5.6% |
| CNY 2001–3000 | 396 | 19.6% | 37 months and above | 250 | 13.4% |
| CNY 3001–5000 | 367 | 18.2% | Employment status | ||
| CNY 5001–8000 | 455 | 22.6% | Yes | 1405 | 68.1% |
| Above CNY 8000 | 671 | 33.3% | No | 658 | 31.9% |
Regression analysis of long-term settlement intention.
| VARIABLES | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|
| Sex | |||||
| (Male = 0) | 0.109 | 0.113 | 0.147 | 0.183 | 0.166 |
| (0.189) | (0.190) | (0.191) | (0.192) | (0.209) | |
| Education Level | |||||
| Junior high school | −0.0955 | −0.125 | −0.138 | −0.0464 | 0.0113 |
| (0.711) | (0.713) | (0.715) | (0.723) | (0.726) | |
| Senior high school | 0.188 | 0.187 | 0.153 | 0.234 | 0.214 |
| (0.667) | (0.668) | (0.669) | (0.677) | (0.685) | |
| Junior college | −0.0736 | −0.0700 | −0.0887 | −0.0132 | 0.0474 |
| (0.619) | (0.621) | (0.621) | (0.628) | (0.633) | |
| Bachelor or above | −0.0706 | −0.0789 | −0.0867 | −0.0469 | 0.0336 |
| (0.610) | (0.611) | (0.611) | (0.618) | (0.622) | |
| Age group | |||||
| 21–30 years old | −0.103 | −0.101 | −0.0623 | −0.0375 | −0.0512 |
| (0.311) | (0.311) | (0.314) | (0.315) | (0.344) | |
| 31–40 years old | −0.619 * | −0.609 | −0.583 | −0.552 | −0.614 |
| (0.315) | (0.315) | (0.317) | (0.318) | (0.347) | |
| 41–50 years old | −0.967 ** | −0.955 ** | −0.930 ** | −0.848 * | −0.832 * |
| (0.333) | (0.334) | (0.336) | (0.338) | (0.369) | |
| Over 50 years old | −1.114 ** | −1.096 ** | −1.090 ** | −1.036 ** | −1.095 ** |
| (0.363) | (0.364) | (0.366) | (0.368) | (0.400) | |
| Income group | |||||
| CNY 1001–3000 | 0.0154 | 0.0400 | 0.0213 | 0.0540 | 0.000352 |
| (0.242) | (0.243) | (0.246) | (0.247) | (0.261) | |
| CNY 3001–5000 | 0.235 | 0.237 | 0.225 | 0.230 | 0.212 |
| (0.246) | (0.247) | (0.249) | (0.250) | (0.265) | |
| CNY 5001–8000 | 0.0726 | 0.0957 | 0.105 | 0.100 | 0.0805 |
| (0.238) | (0.240) | (0.241) | (0.243) | (0.257) | |
| Above CNY 8000 | −0.0319 | −0.0114 | 0.00776 | 0.00215 | −0.0104 |
| (0.231) | (0.232) | (0.234) | (0.235) | (0.248) | |
| No jobs | −0.544 *** | −0.520 *** | −0.508 *** | −0.435 *** | −0.349 ** |
| (Have jobs) | (0.122) | (0.123) | (0.123) | (0.125) | (0.132) |
| Area | |||||
| Middle East | 0.635 *** | 0.624 *** | 0.597 *** | 0.571 *** | 0.560 *** |
| (0.139) | (0.141) | (0.142) | (0.143) | (0.152) | |
| Asia | 0.603 *** | 0.595 *** | 0.582 *** | 0.552 *** | 0.530 ** |
| (0.150) | (0.150) | (0.151) | (0.152) | (0.162) | |
| No health problems | 0.0842 | 0.152 | 0.126 | 0.101 | |
| (Have health problem = 0) | (0.138) | (0.140) | (0.142) | (0.150) | |
| Health change | 0.233 *** | 0.248 *** | 0.230 ** | ||
| (0.0683) | (0.0690) | (0.0728) | |||
| Have medical insurance | 0.452 *** | 0.430 *** | |||
| (No insurance = 0) | (0.111) | (0.120) | |||
| Length of stay in China | 0.0975 *** | ||||
| (0.0278) | |||||
| Constant | 1.506 * | 1.357 | 0.487 | 0.107 | −0.133 |
| (0.752) | (0.776) | (0.818) | (0.830) | (0.862) | |
| N | 1760 | 1741 | 1737 | 1735 | 1584 |
* p < 0.05, ** p < 0.01, *** p < 0.001.
Regression analysis of long-term settlement intention (separated models).
| Model 6a | Model 6b | Model 6c | |
|---|---|---|---|
| Africa | Middle East | Asia | |
| No jobs | −0.0415 | −0.341 | −0.797 ** |
| (Have jobs = 0) | (0.269) | (0.221) | (0.278) |
| No health problems | −0.328 | 0.281 | −0.220 |
| (Have health problem = 0) | (0.408) | (0.216) | (0.299) |
| Health change | 0.561 *** | 0.157 *** | 0.117 *** |
| (0.0171) | (0.0108) | (0.0149) | |
| Have medical insurance | 0.860 ** | 0.358 * | 0.540 * |
| (No insurance) | (0.277) | (0.156) | (0.237) |
| Length of stay in China | 0.0918 *** | 0.03042 * | 0.03918 * |
| (−0.03366) | (−0.01551) | −0.01993 | |
| Control variables + | Yes | Yes | Yes |
| Constant | −1.984 | −0.129 | 1.087 |
| (1.454) | (1.061) | (1.581) | |
| N | 331 | 684 | 438 |
* p < 0.05, ** p < 0.01, *** p < 0.001; + Control variables in these models included age, age 2 education level, marital status, income, etc. More details can be seen in the Supplementary Files.
Moderating effect in the regression analysis of long-term settlement intention.
| Variables | Full Sample | Africa | Middle East | Asia | Full Sample | Africa | Middle East | Asia |
|---|---|---|---|---|---|---|---|---|
| Area | ||||||||
| Middle East | −0.347 ** | −0.340 * | ||||||
| (0.132) | (0.133) | |||||||
| Asia | 0.563 *** | 0.559 *** | ||||||
| (0.151) | (0.152) | |||||||
| No jobs | 0.534 *** | −0.0920 | −0.299 | −0.855 *** | 0.532 ** | −0.0648 | −0.306 | −0.828 *** |
| (Have jobs = 0) | (0.162) | (0.269) | (0.222) | (0.278) | (0.162) | (0.269) | (0.222) | (0.279) |
| No health problems | 0.106 | −0.355 | 0.295 | −0.226 | 0.103 | −0.355 | 0.292 | −0.242 |
| (Have health problems = 0) | (0.150) | (0.411) | (0.217) | (0.297) | (0.150) | (0.413) | (0.216) | (0.298) |
| Health change | 0.174 * | 0.461 *** | 0.108 | 0.0442 | 0.149 * | 0.427 ** | 0.0807 | 0.0691 |
| (0.0733) | (0.174) | (0.109) | (0.147) | (0.0745) | (0.173) | (0.111) | (0.152) | |
| Have medical insurance | 0.422 *** | 0.845 *** | 0.322 * | 0.550 ** | ||||
| (No insurance = 0) | (0.120) | (0.278) | (0.188) | (0.239) | ||||
| Have medical insurance × Health change | 0.134 *** | 0.271 *** | 0.113 ** | 0.139 * | ||||
| (0.0360) | (0.0847) | (0.0554) | (0.0707) | |||||
| Length of stay in China | 0.0964 *** | 0.169 *** | 0.0881 ** | 0.0477 | ||||
| (0.0278) | (0.0654) | (0.0420) | (0.0544) | |||||
| Length of stay in China × Health change | 0.0320 *** | 0.0574 *** | 0.0282 ** | 0.0164 | ||||
| (0.00853) | (0.0205) | (0.0125) | (0.0165) | |||||
| Control variables + | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Constant | 0.0550 | −1.849 | −0.148 | 1.487 | 0.118 | −1.691 | −0.0174 | 1.378 |
| (0.860) | (1.483) | (1.059) | (1.569) | (0.857) | (1.473) | (1.057) | (1.572) | |
| N | 331 | 684 | 438 | 1632 | 331 | 684 | 438 |
* p < 0.05, ** p < 0.01, *** p < 0.001; + Control variables in this model included age, age 2 education level, marital status, etc. More details can be seen in the Supplementary Files.
Figure 1The moderating effects of length of stay in China and insurance status on the association between health change and long-term settlement intention in China: (a) moderating effect of length of stay; (b) moderating effect of insurance status.