Does public pension impact migrant workers’ nutrition: evidence from urban pension insurance in China

He, Zhipeng; Deng, Ye; Yu, Kang; Li, Lei

doi:10.1590/0103-8478cr20200829

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

RURAL SOCIOLOGY • Cienc. Rural 51 (12) • 2021 • https://doi.org/10.1590/0103-8478cr20200829 copy

Does public pension impact migrant workers’ nutrition: evidence from urban pension insurance in China

A previdência pública impacta a nutrição de trabalhadores migrantes: evidências do seguro de pensão urbana na China

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

Based on the data of “employment and food demand of urban migrant workers”, this paper empirically analyzed the impact of urban pension insurance on the nutritional intake structure of migrant workers. The results showed that participating in urban pension insurance can change the nutritional intake structure of migrant workers. Additionally, fat and protein replace carbohydrate as the main nutrition sources for migrant workers. After controlling the income and labor intensity of migrant workers and other factors, urban pension insurance has a positive effect on the intake of fat and protein of migrant workers for they increase by 13.5% and 8.8% respectively. There is no significant effect on the intake of carbohydrates of migrant workers. The calorie intake of migrant workers increases by 6.8% accounting for the change of nutritional intake structure. Endogenous and robustness tests showed that the above conclusions are robust. Heterogeneity analysis showed that there is no significant difference in the effect of urban pension insurance on calorie intake of migrant workers in different income levels and age groups.

Key words:
urban pension insurance; migrant workers; calorie intake; nutritional structure; uncertainty

Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] E-mail: yukang888@163.com. ^*Corresponding author

Variables		Mean	Unit	Std. Err.
Dependent variables	Carbohydrate	305.75	g/day	122.07
	Fat	65.06	g/day	53.36
	Protein	67.27	g/day	30.61
	Calorie	2069.40	kcal/day	811.30
Independent variables	Urban pension insurance	0.17	%	0.37
Control variables	Monthly income（RMB）	3340.43	RMB	2859.41
	Light manual labour	0.16	%	0.37
	Middle manual labour	0.57	%	0.50
	Heavy manual labour	0.27	%	0.44
	BMI	22.23	Kg/m²	3.06
	Gender	0.58	%	0.49
	Age	35.84	year	11.10
	Middle school and below	0.66	%	0.47
	High school or junior college	0.30	%	0.46
	Bachelor and above	0.04	%	0.19
	Beijing	0.19	%	0.39
	Guang Dong	0.20	%	0.40
	Shan Dong	0.20	%	0.40
	Si Chuan	0.20	%	0.40
	Zhe Jiang	0.21	%	0.41
Observations	------------------------------------------------------------3315--------------------------------------------------------

Variables	Carbohydrate	Fat	Protein	Calorie
Urban pension insurance	0.026(0.021)	0.135^***(0.043)	0.088^***(0.021)	0.068^***(0.019)
Log of average	-0.019(0.018)	0.222^***(0.039)	0.075^***(0.019)	0.051^***(0.016)
Middle manual labour	0.012(0.021)	0.008(0.045)	0.011(0.021)	0.006(0.018)
Heavy manual labour	0.072^***(0.025)	0.115^**(0.056)	0.034(0.026)	0.061^***(0.022)
BMI	0.003(0.002)	-0.002(0.005)	0.001(0.003)	0.002(0.002)
Gender	0.164^***(0.017)	0.132^***(0.036)	0.131^***(0.017)	0.147^***(0.015)
Age	0.019^***(0.004)	0.014(0.009)	0.017^***(0.004)	0.016^***(0.004)
Age squared	-0.000^***(0.000)	-0.000^**(0.000)	-0.000^***(0.000)	-0.000^***(0.000)
High school or junior college	0.000(0.017)	0.088^**(0.035)	0.043^***(0.016)	0.021(0.015)
Bachelor and above	-0.023(0.038)	0.147^*(0.087)	0.080^**(0.040)	0.047(0.034)
Guangdong	-0.020(0.023)	0.596^***(0.051)	0.136^***(0.024)	0.142^***(0.021)
Shandong	-0.023(0.021)	-0.208^***(0.055)	-0.002(0.023)	-0.044^**(0.020)
Sichuan	-0.206^***(0.024)	0.477^***(0.048)	-0.012(0.023)	-0.010(0.020)
Zhejiang	-0.114^***(0.019)	0.029(0.050)	-0.067^***(0.023)	-0.087^***(0.019)
Constant term	5.279^***(0.156)	1.574^***(0.335)	3.069^***(0.158)	6.698^***(0.137)
Observations	3315	3315	3315	3315
R-squared	0.102	0.130	0.071	0.094

Variables	Carbohydrate	Fat	Protein	Calorie
Urban pension insurance	-0.004 (0.040)	0.199^** (0.081)	0.095^** (0.039)	0.083^** (0.034)
Urban pension insurance ^* middle-income group	0.054 (0.045)	-0.111 (0.091)	-0.024 (0.045)	-0.029 (0.039)
Urban pension insurance ^* high income group	-0.003 (0.071)	0.001 (0.139)	0.046 (0.071)	0.012 (0.062)
Log of average monthly income	-0.020 (0.019)	0.225^*** (0.042)	0.073^*** (0.019)	0.051^*** (0.017)
Other control variables	controlled	controlled	controlled	controlled
Observations	3315	3315	3315	3315
R-squared	0.102	0.130	0.072	0.094

Variables	Carbohydrate	Fat	Protein	Calorie
Urban pension insurance	0.024 (0.023)	0.131^*** (0.047)	0.077^*** (0.023)	0.065^*** (0.021)
Urban pension insurance ^* above 45 years old	0.013 (0.051)	0.022 (0.103)	0.058 (0.048)	0.015 (0.044)
Log of average monthly income	-0.019 (0.018)	0.222^*** (0.039)	0.076^*** (0.019)	0.051^*** (0.016)
Other control variables	controlled	controlled	controlled	controlled
Observations	3315	3315	3315	3315
R-squared	0.102	0.130	0.072	0.094

Carbohydrate	Fat	Protein	Calorie
H₀：ρ=0	H₀：ρ=0	H₀：ρ=0	H₀：ρ=0
ρ=-0.054 χ²(1)=0.40 p[χ²(1)>0.40]=0.52	ρ=-0.044 χ²(1)=0.09 p[χ²(1)>0.09]=0.77	ρ=-0.053 χ²(1)=0.22 p[χ²(1)>0.22]=0.64	ρ=-0.054 χ²(1)=0.24 p[χ²(1)>0.24]=0.62
accept the null hypothesis	accept the null hypothesis	accept the null hypothesis	accept the null hypothesis

Variables		Mean		Standardized mean difference（%）	Reduction in absolute value standardized difference（%）	T value
		Treated	Control
Ln monthly income	U	7.9259	7.9911	-14.5		-3.03^***
Ln monthly income	M	7.9262	7.9279	-0.4	97.5	-0.06
Gender	U	0.53916	0.59328	-10.9		-2.35^**
Gender	M	0.53931	0.55096	-2.4	78.5	-0.39
Age	U	35.559	35.896	-3.2		-0.65
Age	M	35.547	35.352	1.8	42.3	0.32
Age square	U	1362.3	1416.7	-6.7		-1.37
Age square	M	1361.7	1348.8	1.6	76.4	0.28
BMI	U	22.122	22.256	-4.3		-0.94
BMI	M	22.122	22.106	0.5	88.2	0.09
High school or junior college	U	0.44627	0.27585	36.0		8.00^***
High school or junior college	M	0.44607	0.46694	-4.4	87.8	-0.69
Bachelor and above	U	0.08015	0.03145	21.3		5.37^***
Bachelor and above	M	0.07861	0.07827	0.2	99.3	0.02
Middle manual labour	U	0.69217	0.54194	31.3		6.53^***
Middle manual labour	M	0.69104	0.68106	2.1	93.4	0.36
Heavy manual labour	U	0.0929	0.30694	-55.5		-10.47^***
Heavy manual labour	M	0.09324	0.10367	-2.7	95.1	-0.58
Guang Dong	U	0.27322	0.18619	20.8		4.67^***
Guang Dong	M	0.27422	0.26827	1.4	93.2	0.22
Shan Dong	U	0.2204	0.19812	5.5		1.19
Shan Dong	M	0.22121	0.21314	2.0	63.8	0.32
Si Chuan	U	0.25865	0.18547	17.7		3.94^***
Si Chuan	M	0.25594	0.27146	-3.7	78.8	-0.58
Zhe Jiang	U	0.13479	0.22343	-23.3		-4.68^***
Zhe Jiang	M	0.13528	0.13053	1.2	94.6	0.23

Variables	-------------------------------------The influence of urban pension insurance-------------------------------------
	Carbohydrate	Fat	Protein	Calorie
The nearest neighbour matches	0.020 (0.029)	0.155^** (0.074)	0.082^** (0.034)	0.067^** (0.029)
Radius matching	0.014 (0.026)	0.146^** (0.063)	0.081^*** (0.028)	0.065^** (0.026)
Nuclear matching	0.014 (0.026)	0.145^** (0.061)	0.083^*** (0.029)	0.065^** (0.026)
OLS	0.026 (0.021)	0.135^*** (0.043)	0.088^*** (0.021)	0.068^*** (0.019)