Gender Differences in Nutritional Intake among Rural-Urban Migrants in China
Abstract
:1. Introduction
2. Context of the Study Area in China
3. Data and Methods
3.1. Data
3.1.1. Description of CHNS
3.1.2. Measurement of Nutritional Intake
3.2. Methods
4. Results
4.1. Descriptive Statistics
4.2. Nutritional Intake Trends among Migrants
4.3. Empirical Results
4.3.1. Factors Influencing Nutritional Intake of Rural-Urban Migrants
4.3.2. Gender Differences in Nutritional Intake among Rural-Urban Migrants
5. Discussion
6. Conclusions and Policy Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Migrant Type | Male Migrants (MMs) | Female Migrants (FMs) | Treatment Effects |
---|---|---|---|
Male Migrants | (a) | (c) | |
Female Migrants | (d) | (b) | |
Heterogeneity Effects |
Variables | Description of Variables | Total | By Gender of Migrants | Difference (t-Stat.) a | |
---|---|---|---|---|---|
Mean (Male) | Mean (Female) | ||||
Energy | Energy intake per capita per day (kcal/p/d) | 2193.914 (748.202) | 2412.491 (779.408) | 2003.511 (663.722) | 408.980 *** (24.678) |
Protn | Protein intake per capita per day (g/p/d) | 66.077 (26.729) | 72.419 (28.179) | 60.552 (24.077) | 11.867 *** (19.781) |
Fat | Fat intake per capita per day (g/p/d) | 68.528 (44.331) | 73.505 (47.660) | 64.193 (40.725) | 9.312 *** (9.177) |
Sprotn | The share of energy from protein | 0.122 (0.028) | 0.122 (0.028) | 0.123 (0.028) | −0.001 * (−1.653) |
Sfat | The share of energy from fat | 0.281 (0.125) | 0.274 (0.123) | 0.287 (0.127) | −0.013 *** (−4.439) |
Pcinc | Per capita household income inflated to 2015 (CNY) | 8833.117 (11,950.54) | 8877.683 (11,645.62) | 8794.297 (12,211.29) | 83.386 (0.300) |
Age | Age of individual (years) | 44.068 (15.689) | 43.260 (15.294) | 44.772 (15.992) | −1.512 *** (−4.237) |
Married | Married: 1 = Yes, 0 = No | 0.889 (0.314) | 0.857 (0.350) | 0.917 (0.275) | b 71.540 *** (0.000) |
Nonfarm | Nonfarm employment: 1 = Yes, 0 = No | 0.26 (0.438) | 0.328 (0.469) | 0.201 (0.400) | b 161.659 *** (0.000) |
Education Level | c −16.776 *** (0.000) | ||||
Illiterate | Illiterate: 1 = Yes, 0 = No | 0.307 (0.461) | 0.212 (0.408) | 0.390 (0.488) | |
Primary | Primary school: 1 = Yes, 0 = No | 0.21 (0.407) | 0.214 (0.410) | 0.205 (0.404) | |
Middle | Middle school: 1 = Yes, 0 = No | 0.337 (0.473) | 0.396 (0.489) | 0.285 (0.451) | |
High | High or vocational school: 1 = Yes, 0 = No | 0.133 (0.34) | 0.164 (0.370) | 0.106 (0.308) | |
University | University or higher: 1 = Yes, 0 = No | 0.013 (0.114) | 0.013 (0.115) | 0.013 (0.114) | |
Hhsize | Household size (number) | 4.178 (1.455) | 4.197 (1.441) | 4.160 (1.466) | 0.037 (1.092) |
Community Characteristics | |||||
Market | Traditional market score (0–10) | 5.662 (3.296) | 5.630 (3.287) | 5.690 (3.304) | −0.060 (−0.798) |
Trans | Transportation score (0–10) | 5.531 (2.387) | 5.495 (2.398) | 5.562 (2.377) | −0.067 (−1.238) |
Mart | Modern market score (0–10) | 4.414 (2.957) | 4.389 (2.968) | 4.437 (2.948) | −0.048 (−0.710) |
Sani | Community sanitation score (0–10) | 5.556 (2.977) | 5.428 (2.983) | 5.668 (2.966) | −0.240 *** (−3.550) |
Year | Dummy variable | - | - | - | |
Obs. | Sample size | 7752 | 3609 | 4143 |
Year | Energy (kcal/p/d) | Sprotn (%) | Sfat (%) | Sample Size | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Overall | MMs | FMs | Gap | Overall | MMs | FMs | Gap | Overall | MMs | FMs | Gap | Overall | MMs | FMs | |
1991 | 2638.8 | 2890.0 | 2418.2 | 471.8 | 11.3 | 11.3 | 11.2 | 0.15 | 19.27 | 19.0 | 19.5 | −0.48 | 586 | 274 | 312 |
1993 | 2487.3 | 2735.9 | 2268.6 | 467.3 | 11.7 | 11.6 | 11.8 | −0.17 | 22.25 | 21.8 | 22.6 | −0.83 | 921 | 431 | 490 |
1997 | 2375.7 | 2606.0 | 2166.6 | 439.4 | 11.6 | 11.5 | 11.7 | −0.15 | 25.65 | 25.0 | 26.3 | −1.33 | 1099 | 523 | 576 |
2000 | 2236.5 | 2453.3 | 2046.2 | 407.1 | 12.0 | 11.9 | 12.1 | −0.19 | 29.76 | 29.1 | 30.3 | −1.16 | 1059 | 495 | 564 |
2004 | 2176.5 | 2402.7 | 1983.7 | 419.0 | 12.1 | 12.0 | 12.2 | −0.18 | 26.56 | 25.6 | 27.3 | −1.71 | 941 | 433 | 508 |
2006 | 2136.7 | 2344.9 | 1950.3 | 394.6 | 12.0 | 11.9 | 12.1 | −0.20 | 29.04 | 28.4 | 29.6 | −1.13 | 978 | 462 | 516 |
2009 | 2030.1 | 2243.5 | 1843.8 | 399.7 | 12.8 | 12.8 | 12.8 | −0.04 | 32.67 | 31.9 | 33.3 | −1.40 | 1021 | 476 | 545 |
2011 | 1726.5 | 1877.2 | 1603.7 | 273.5 | 13.6 | 13.7 | 13.6 | 0.14 | 34.41 | 33.8 | 34.9 | −1.17 | 1047 | 515 | 632 |
Variables | Ln(energy) | Sprotn | Sfat | |||
---|---|---|---|---|---|---|
(1) MMs | (2) FMs | (3) MMs | (4) FMs | (5) MMs | (6) FMs | |
Ln(pcinc) | 0.014 * | 0.018 ** | 0.007 | 0.013 *** | 0.080 *** | 0.061 *** |
(0.007) | (0.008) | (0.005) | (0.005) | (0.012) | (0.012) | |
Age | −0.013 | 0.052 ** | 0.0002 | 0.0001 | 0.001 | 0.002 * |
(0.025) | (0.024) | (0.0004) | (0.0004) | (0.001) | (0.001) | |
Married | 0.062 ** | 0.037 | 0.004 | 0.021 | 0.009 | −0.028 |
(0.025) | (0.037) | (0.015) | (0.015) | (0.038) | (0.037) | |
Nonfarm | 0.031 ** | −0.030 * | 0.049 *** | 0.047 *** | 0.055 ** | 0.098 *** |
(0.015) | (0.018) | (0.010) | (0.012) | (0.023) | (0.025) | |
Education Level (Illiterate) | ||||||
Primary | 0.006 | 0.027 | 0.026 * | 0.015 | 0.049 | 0.068 ** |
(0.028) | (0.027) | (0.013) | (0.013) | (0.039) | (0.032) | |
Middle | 0.027 | 0.015 | 0.060 *** | 0.024 * | 0.095 ** | 0.057 * |
(0.035) | (0.036) | (0.013) | (0.013) | (0.039) | (0.034) | |
High | −0.021 | 0.002 | 0.060 *** | 0.056 *** | 0.021 | −0.003 |
(0.046) | (0.052) | (0.016) | (0.016) | (0.046) | (0.040) | |
University | 0.013 | −0.034 | 0.117 *** | 0.053 | 0.001 | −0.085 |
(0.083) | (0.122) | (0.044) | (0.040) | (0.080) | (0.083) | |
Hhsize | −0.008 | −0.009 | 0.005 | 0.007 ** | −0.008 | −0.012 |
(0.006) | (0.006) | (0.003) | (0.003) | (0.009) | (0.008) | |
Market | 0.005 ** | 0.005 *** | −0.002 | 0.001 | 0.008 ** | 0.015 *** |
(0.002) | (0.002) | (0.002) | (0.002) | (0.004) | (0.003) | |
Trans | 0.004 | −0.0002 | −0.004 * | −0.004 ** | −0.005 | −0.005 |
(0.003) | (0.003) | (0.002) | (0.002) | (0.005) | (0.005) | |
Mart | 0.005 | 0.005 * | 0.003 * | 0.004 ** | −0.007 | −0.003 |
(0.003) | (0.003) | (0.002) | (0.002) | (0.005) | (0.004) | |
Sani | 0.012 ** | 0.008 * | 0.015 *** | 0.015 *** | 0.047 *** | 0.043 *** |
(0.005) | (0.005) | (0.002) | (0.002) | (0.005) | (0.005) | |
Year (1991) | ||||||
1993 | −0.067 | −0.174 *** | 0.015 | 0.034 *** | 0.083 ** | 0.099 *** |
(0.055) | (0.055) | (0.015) | (0.012) | (0.039) | (0.037) | |
1997 | −0.105 | −0.456 *** | 0.002 | 0.023 | 0.228 *** | 0.284 *** |
(0.152) | (0.149) | (0.016) | (0.014) | (0.041) | (0.039) | |
2000 | −0.161 | −0.726 *** | 0.019 | 0.043 *** | 0.411 *** | 0.456 *** |
(0.225) | (0.224) | (0.016) | (0.014) | (0.043) | (0.041) | |
2004 | −0.140 | −0.987 *** | 0.045 ** | 0.075 *** | 0.212 *** | 0.278 *** |
(0.323) | (0.319) | (0.020) | (0.018) | (0.044) | (0.043) | |
2006 | −0.153 | −1.116 *** | 0.022 | 0.048 *** | 0.284 *** | 0.336 *** |
(0.373) | (0.366) | (0.018) | (0.016) | (0.045) | (0.043) | |
2009 | −0.163 | −1.340 *** | 0.093 *** | 0.102 *** | 0.443 *** | 0.507 *** |
(0.447) | (0.441) | (0.020) | (0.017) | (0.046) | (0.044) | |
2011 | −0.382 | −1.626 *** | 0.145 *** | 0.144 *** | 0.441 *** | 0.480 *** |
(0.496) | (0.488) | (0.019) | (0.018) | (0.048) | (0.047) | |
Constant | 8.160 *** | 5.850 *** | −2.249 *** | −2.311 *** | −2.290 *** | −2.094 *** |
(0.806) | (0.822) | (0.049) | (0.047) | (0.127) | (0.120) | |
R2 (within) | 0.226 | 0.207 | ||||
Log pseudolikelihood | −1252.019 | −1437.475 | −1973.208 | −2301.933 | ||
Wald χ2 (20) | 404.30 | 511.51 | 591.79 | 746.66 | ||
p-value a | 0.096 | 0.005 | ||||
Observations | 3394 | 3876 | 3394 | 3876 | 3394 | 3876 |
Migrants Type | Lnenergy | ||
---|---|---|---|
MMs | FMs | Treatment Effect | |
MMs | (a) 7.742 | (c) 7.495 | 0.247 *** (0.015) |
FMs | (d) 7.727 | (b) 7.552 | 0.175 *** (0.015) |
Heterogeneity effect | 0.015 *** (0.005) | −0.057 *** (0.020) |
Migrants Type | Sprotn | ||
---|---|---|---|
MMs | FMs | Treatment Effect | |
MMs | (a) 0.1212 | (c) 0.1230 | −0.0017 *** (0.0002) |
FMs | (d) 0.1200 | (b) 0.1222 | −0.0022 *** (0.0002) |
Heterogeneity effect | 0.0012 *** (0.0002) | 0.0008 *** (0.0002) |
Migrants Type | Sfat | ||
---|---|---|---|
MMs | FMs | Treatment Effect | |
MMs | (a) 0.2759 | (c) 0.2913 | −0.0154 *** (0.0013) |
FMs | (d) 0.2747 | (b) 0.2898 | −0.0150 *** (0.0013) |
Heterogeneity effect | 0.0012 (0.0013) | 0.0016 (0.0013) |
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Sun, Q.; Li, X.; Rahut, D.B. Gender Differences in Nutritional Intake among Rural-Urban Migrants in China. Int. J. Environ. Res. Public Health 2021, 18, 9821. https://doi.org/10.3390/ijerph18189821
Sun Q, Li X, Rahut DB. Gender Differences in Nutritional Intake among Rural-Urban Migrants in China. International Journal of Environmental Research and Public Health. 2021; 18(18):9821. https://doi.org/10.3390/ijerph18189821
Chicago/Turabian StyleSun, Qian, Xiaoyun Li, and Dil Bahadur Rahut. 2021. "Gender Differences in Nutritional Intake among Rural-Urban Migrants in China" International Journal of Environmental Research and Public Health 18, no. 18: 9821. https://doi.org/10.3390/ijerph18189821