1 84% 40 73% %(2015 ) (2015 ) (2012 ) 20 60% : :1 ;2 3 ;3 ;4 ;5 3 ;6 ; ; : :(1) ( 2013

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1 * : CHIP OLG : 12% ~13% 16% ~17% 12% ~ 20% 5% ~6% Logit : ( 2011; 2014; 2015) 16%~20%(Chenetal2015) ( ;Xu & Xie2015; 2015) ( ) ( )( ) % * : :[email protected] (16CJL014) (CHIP) 25

2 1 84% 40 73% %(2015 ) (2015 ) (2012 ) 20 60% : :1 ;2 3 ;3 ;4 ;5 3 ;6 ; ; : :(1) ( 2013; 2015) ;(2) ( 2014;Wu & Zhang 2015) ( 2006; 2013) ( ) ( )

3 ( ) ( ) ( 2015) (Meng2003;Giles & Yoo2007; 2008; 2009; 2010; Rosenzweig & Zhang2014; 2014) (Yuan & Zhang2015) (Chenetal2015) ( 2014) ( 2001) ( 2004) (Modigliani& Cao2004;Chamon & Prasad2010) ( 2013; 2014) (Zhao1999) ( 2014) ( 2014) : 1: (CHIP) : ( 2011) ; (Pro- pensityscorematchingpsm) (Xu & Xie2015) (Bias) 2 : 2: 27

4 (5) (6) : (3) : C 2 S(1+r)+P+w kh k (3) h p h k w p 2 w k S 1 r q ( ) h k w k Pi- S / g g =- Us U s / S =- g (1+r)(1+δ) <0 (7) racha & Zhu(2012) (7) g OLG 1 U=lnC 1+δlnC 2 (1) 2 U C 1 C δ 1 1 (2) : 2 (5) : C 1 w p h p -S-q (2) S / h 2 =- Us k h k U s / S =- w k (1+r)(1+δ) <0 1 P (8) 2 (8) ( ) h k g (Chi- q (4) : nesehouseholdincomeprojectchip) h k=h(gq) (4) CHIP2002 hk g >0 28 ;CHIP g h k (theinstituteforthe (2) StudyofLaborIZA) ;CHIP2013 (3)(4) ( ) ( ) (1) : U U s= S =0 -δ (1+r)C 1+C 2=0 (5) (5) g : U s h k g = C2 g =wk g >0 (6) CHIP 1 CHIP ( ) ( ) ( ) 1 28

5 15 18 ( ( ) ) 5 18 Logit ( ) 2 3. ln( ) ln( ) / 1. ln ( ) ( 1) 20 4 / / (2014) 20% 2 = / ln( ) ln( ) / / =1 =0 =3 =2 =1 =1 =0 ln( ) / : CHIP =1 =0 \ =0 =1 =2 \ \ \ =3 =4 29

6 ; ln( ) U ( ) 3 ln( ) ( 2012) Logit 95% /

7 / : CHIP ln( ) 11 Logit (2014) CHIP2007 Migkid=α+β Citylevel+χ Migspouse +γx+ε (9) Migkid Citylevel 1 Migspouse X 4 Logit 1% U

8 4 :Logit (2014) % 2002 (2) (3) (0.097) 1% *** ; Pseudo-R log-likelihood :* ** *** 10% 5% 1% ( ) Becker % OLS : Con=α+β Migkid+γX+ε (10) Con ln( ) ln( ) / Migkid U β β 32 (1) (2) (3) *** (0.198) *** (0.124) *** (0.249) 0.049*** (0.011) (0.125) (0.341) ln( ) ** *** (0.138) (0.002) (0.071) (0.137) *** (0.115) (0.092) *** (0.146) * (0.007) *** (0.084) *** (0.102) (0.083) ** (0.039) * (0.001) (0.052) ** (0.084) *** (0.262) (0.229) *** (0.439) *** (0.015) (0.221) (0.245) (0.176) *** (0.153) *** (0.002) ** (0.109) *** (0.132)

9 (Giles & Yoo2007;Chenetal2015;Chuetal 2015) X ε ( ) : 5 CHIP2002 CHIP OLS ln( ) ln( ) ln( ) U Rosenzweig & Zhang(2014) (2014) 1% ( ) : (4) (8) / 1% 10% 1 5 : (1) (2) (3) (4) (5) (6) (7) (8) ln( ) ln( ) / ln( ) ln( ) / *** (0.020) *** (0.026) *** (0.023) *** (0.010) *** (0.007) *** (0.016) *** (0.013) *** (0.005) ln( ) *** (0.025) *** (0.027) *** (0.030) *** (0.023) *** (0.013) *** (0.058) *** (0.015) (0.005) (0.013) *** (0.015) (0.014) * (0.008) (0.005) *** (0.012) * (0.009) (0.003) (0.008) *** (0.013) (0.009) *** (0.005) * (0.004) *** (0.010) (0.006) ** (0.002) *** * *** * *** ** Adj-R log-likelihood :(1)* ** *** 10% 5% 1% (2) CHIP CHIP2013 OLS PSM CHIP

10 9.2% 5 17% = E [Y 1i-Y 0i T i=1] 13.3% ATT 18.8% 26.5% + { E[Y 0i T i=1]-e[y 0i T i=0]} ( 11) T=1 T=0 Y 2 ( ) (PSM) (Rosenbaum & Rubin1983) : 1 2 ( ) ( ) OLS ATT: E[Y i T i=1]-e[y i T i=0] 6 : : (1) (2) (3) (4) :ln( ) (5) (6) (7) (8) *** (0.021) *** (0.016) *** (0.014) *** (0.024) *** (0.036) *** (0.028) *** (0.025) *** (0.035) ln( ) *** (0.018) *** (0.018) *** (0.013) *** (0.030) *** (0.042) *** (0.040) *** (0.024) *** (0.047) (0.017) (0.010) (0.009) (0.015) (0.025) (0.020) *** (0.014) (0.025) (0.011) (0.007) (0.008) *** (0.009) (0.019) (0.017) (0.012) *** (0.014) *** *** Adj-R log-likelihood :(1)* ** *** 10% 5% 1% ;(2) CHIP ;(3)

11 Logit 1% ATT OLS Logit 7 12%~ 1% 12%~20% 5%~6% Logit ( )ATT 7 (11) ATT 13% 16% ~17% :* ** *** 10% 5% 1% 7 Stata sensat ATT ATT T ATT T *** *** ln( ) *** *** ln( ) *** *** / *** *** :CHIP :CHIP

12 (Dehejia & Wah- 1% ba1999) ln( ) ln( ln( ) ) / 1 8 ATT 1% ln( ) ( ) (PSM) ATT T ATT T *** *** ln( ) *** *** *** *** *** *** *** *** *** *** ln( ) *** *** ln( ) *** *** *** *** ** *** :* ** *** 10% 5% 1% 9 : (PSM) (1) (2) (3) (4) (5) (6) (7) (8) ln( ) ln( ) / ln( ) ln( ) / ** (0.079) *** (0.034) *** (0.030) *** (0.015) *** (0.010) *** (0.021) *** (0.017) *** (0.007) ** (0.084) *** (0.063) ** (0.053) (0.017) (0.014) *** (0.034) (0.026) (0.011) * (0.128) (0.088) (0.076) (0.023) (0.020) (0.042) (0.035) (0.014) ln( ) *** (0.175) *** (0.032) *** (0.036) ** (0.028) *** (0.018) *** (0.038) *** (0.017) *** (0.004) ** (0.041) ** (0.017) (0.016) * (0.008) (0.006) *** (0.012) (0.009) (0.003) * (0.028) (0.015) (0.012) *** (0.005) (0.005) *** (0.010) (0.007) (0.002) * *** *** Adj-R log-likelihood :* ** *** 10% 5% 1% 36

13 :(1) CHIP ( OLG ) (2) (3) (4) (5) : 6 12% ~13% htp://edu.bjhd.gov.cn/xw/jwdt/ 16%~17% 12% ~20% /t html 5%~6% 5 (2010) ( 2016) 6 Stata 2% Logit 7 Kernel Radius : 2012: 8 ( 2013: ) : : : No.4(2016) 2013: ( ) : : (Balancing Assumption) / 18 (6) ( ) (7) (8) winsorize 6 : 2014: U A01 htp:// : : 2 (CommonSupportAssumption) : ( ) : 37

14 CHIP2008 ChuR.M.Liu & G.J.Shi(2015) Howrural-urbaniden : Chinesemigrantworkers Asia PacificJournalof Mar : ciation94(448): : networksandhouseholdconsumptiondecisions:anempirical : view of Economicsand Statistics89(3): : : andprecautionarysavinginurbanchina Journalof Com : ModiglianiF.& S.L.Cao(2004) The Chinesesaving : PirachaM.& Y.Zhu(2012) Precautionarysavingsbynatives : : tivitytoanunobservedbinarycovariateinanobservational 2008 CHIP : RosenzweigM.& J.Zhang(2014) Co-residencelife-cycle savingsand inter-generational supportin urban China : WuX. & Z.Zhang(2015) Population migration and : : PopulationStudiesCenterResearchReportNo : migrationonchildren s wel-beingin China European CHIP SociologicalReview31(4): ( ) 1 ChamonM.D.& E.S.Prasad(2010) Whyaresavingratesof urban householdsin China rising American Economic Journal:Macroeconomics2(1): ChenB.M.Lu & N.Zhong(2015) Howurbansegrega- tion distorts Chinese migrants consumption World Development70: tificationinfluencesconsumptionpaterns Evidencefrom keting and Logistics27(1): : DehejiaR.H.& S.Wahba(1999) Causalefectsinnon- experimentalstudies:reevaluatingtheevaluationoftrain- GilesJ.& K.Yoo(2007) Precautionarybehaviormigrant ingprograms Journalofthe American Statistical Aso- analysisusinghouseholdpaneldatafromruralchina Re- MengX.(2003) Unemploymentconsumptionsmoothing parative Economics31(3): puzzleandthelife-cyclehypothesis Journalof Economic Literature42(1): andimmigrantsin Germany Applied Economics44(21): RosenbaumP.R.& D.B.Rubin(1983) Assessingsensi- studywithbinaryoutcome Journalofthe RoyalStatis- ticalsocietyseriesb.45(2): NBER WorkingPaperNo children sschoolenrolmentsin China XuH.& Y.Xie(2015) Thecausalefectsofrural-to-urban YuanC.& L.Zhang(2015) Publiceducationspending andprivatesubstitutioninurbanchina Journalof De- velopmenteconomics115(c): ZhaoY.(1999) Leavingthecountryside:Rural-to-urban migrationdecisionsinchina American Economic Review 89(5): ( : ) 38

(2013) ( 2012; 2013) (2017) (2012) (2015) (1) ;(2) (PSM) (SEM) ( ) ; ; ; ( 2015) 1. (ConstantElasticityofSubstitution CES) Y t = [α(a Lt L t )

(2013) ( 2012; 2013) (2017) (2012) (2015) (1) ;(2) (PSM) (SEM) ( ) ; ; ; ( 2015) 1. (ConstantElasticityofSubstitution CES) Y t = [α(a Lt L t ) * 1998-2011 13864 61755 9.67% 14.24% 1 ; (Murphyetal1993;Guelec & vanpotelsberghe2003; 2016) ; (Lach2000; 2009; 2016; 2017) * 116025 tongbinzhang@ 126.com; 116025 [email protected] @163.com