ESSAYS ON EDUCATION AND HEALTH REFORMS IN RURAL CHINA LI LI (M.A. ZHEJIANG UNIVERSITY) THESIS IS SUBMITTED FOR THE DOCTOR OF PHILOSOPHY DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2013 DECLARATION I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. Li Li 28 May 2013 Acknowledgement I would like to take this opportunity to retrospect the journey past and thank all the people who have helped and supported me along this long but fulfilling road. Firstly, I am indebted to my supervisor, Associate Professor Liu Haoming, for his excellent guidance and deep knowledge in applied econometrics. His rigorous scholarship and dedication in academic works, both teaching and researching, encourage me to work harder. I would like to express my heartfelt gratitude to him. It is my honor to be under his supervision. Secondly, I would like to thank Associate Professor Zeng Jinli for his encouragement and suggestions when I hit rock bottom in my research. It is because of him that I walked out of fog and finally found my research direction. Moreover, I would like to thank my committee members, Doctor Lu Yi and Doctor Jessica Pan, for their constructive comments and suggestions on my thesis and Professor Chen Songnian, Zhang Jie, Associate Professor Aditya Goenka, Luo Xiao, Doctor Zhu Shenghao, Eric Fesselmeyer, and Peter James McGee for their help and suggestions during my study at NUS. Importantly, I also thank all my friends and colleagues at the department of Economics for their friendship and suggestions, especially Mun Lai Yoke, Miao Bin and Jiao Qian. Finally, I would like to dedicate this thesis to my dear father, mother, and husband. Their love and support have accompanied me along the journey and helped me get close to my dream. Contents Summary vi List of Tables ix List of Figures x Primary School Availability and Middle School Education in Rural China 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Basic education in rural China . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Identification strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.5.1 Effect of having a local primary school . . . . . . . . . . . . . . . . . 11 1.5.2 Effect of opening a local primary school . . . . . . . . . . . . . . . . . 14 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.6.1 School quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.6.2 School accessibility and school choice . . . . . . . . . . . . . . . . . . 17 1.6.3 Sample attrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.6.4 Geographical boundary changes . . . . . . . . . . . . . . . . . . . . . 19 1.6.5 Sample with wave 1989 and Liaoning province added . . . . . . . . . 19 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.6 1.7 ii 1.8 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.8.1 Construction of educational attainment . . . . . . . . . . . . . . . . . 23 1.8.2 Grade repetition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.8.3 Distance to school . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.8.4 Measurement error in school availability . . . . . . . . . . . . . . . . . 25 New Cooperative Medical Scheme and Health Expenditure in Rural China 41 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.2 NCMS and the data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 2.2.1 New Cooperative Medical Scheme (NCMS) . . . . . . . . . . . . . . . 43 2.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.4 Identification strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.5.1 Reduced form results . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.5.2 Household in NCMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.6.1 Difference-in-differences with propensity score matching . . . . . . . . 53 2.6.2 Missing reported health expenditure . . . . . . . . . . . . . . . . . . . 55 2.6.3 Income level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.6.4 Health status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 2.6.5 Household size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.6.6 Evaluation of NCMS . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.6.7 Continuously insured participators . . . . . . . . . . . . . . . . . . . . 58 2.6.8 Price of health care . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.6.9 Choice of birth place and birth expenditure . . . . . . . . . . . . . . . 59 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 2.6 2.7 iii Choice of Doctor Type and Children’s Height in Rural China 77 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 3.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 3.3 Identification strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 3.4.1 Results from OLS and FE models . . . . . . . . . . . . . . . . . . . . 84 3.4.2 Results from 2SLS and FE-2SLS models . . . . . . . . . . . . . . . . 86 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.5.1 School availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.5.2 Duplicate observations dropped . . . . . . . . . . . . . . . . . . . . . 87 3.5.3 School age children . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.5.4 Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 3.5 3.6 iv Summary This thesis aims to contribute to the empirical analysis of the impact of the education and health reforms in rural China. The first chapter presents the impact evaluation of change in school availability on children’s educational attainment. The latter two chapters present the effect analysis of health reform. Chapter two analyzes the effect of health insurance expansion on household total health care expenditure. The third chapter analyzes the effect of choice of doctor type on children’s height. We provide below individual synopses for each chapter of my thesis. Chapter 1: Primary School Availability and Middle School Education in Rural China To improve primary school accessibility, the Chinese government built many primary schools in rural areas in the late 1980s and early 1990s. At the same time, it also closed many schools due to the declining number of school age children. These changes provide us a unique opportunity to examine the impact of primary school accessibility on children’s educational attainment. Using data extracted from the China Health and Nutrition Survey and a two-way fixed-effects linear probability model, we find that improved primary school accessibility has a significant positive effect on girls’ middle school attendance rate and completion rate, but has no significant impact on boys’ education. Our results suggest that the large-scale campaign of school mergers in the past 30 years might have an unintended effect on children’s education, particularly for girls. Chapter 2: New Cooperative Medical Scheme and Health Expenditure in Rural China v The New Cooperative Medical Scheme (NCMS) was launched in rural China in 2003, aiming to safeguard rural households against catastrophic disease. The expansion of the NCMS over the country has been surrounded by the concern for its sustainability since the very beginning. Increasing health care utilization after the NCMS has been documented (Lei and Lin, 2009; Wagstaff et al., 2009). Direct evidence on the relationship between the NCMS and total health expenditure is needed to evaluate the sustainability of the NCMS. To address this issue, we use a panel data set combined from the Rural Fixed-point Survey (RFPS) 2003-2006 and a supplemented NCMS survey conducted in 2007 and a household fixed-effects model with the endogeneity of household participation considered. We find that joining the NCMS did not increase household total health expenditure, which could be attributed to conservative policy design and low operation efficiency. Chapter 3: Choice of Doctor Type and Children’s Height in Rural China China is the only country in the world where Western medicine and traditional Chinese medicine (TCM) work alongside each other at every level of the health care system (Hesketh and Zhu, 1997). However, the effectiveness of TCM is controversial and the contraction of TCM in the whole health system has been observed. If the application of TCM has undesirable effect, it can be detected from the health of children who normally take TCM when sick and those who not take. Using data extracted from the China Health and Nutrition Survey and a community fixed-effects model, I examine the effect of choice of doctor type on children’s height. It is found that whether household consulting Western doctor or Chinese doctor does not affect rural Children’s height. This finding suggests that TCM would be as effective as Western medicine in maintaining children’s health. vi List of Tables 1.1 Numbers of communities that had, gained or lost schools . . . . . . . . . . . . 29 1.2 Summary statistics for the entire sample of children . . . . . . . . . . . . . . . 30 1.3 Effect of primary school availability on middle school attainment (Girls) . . . . 31 1.4 Effect of primary school availability on middle school attainment (Boys) . . . . 32 1.5 Effect of school open on middle school attainment . . . . . . . . . . . . . . . . 33 1.6 Participation rate of in-school activities . . . . . . . . . . . . . . . . . . . . . 34 1.7 Effect of school availability on home leaving decision . . . . . . . . . . . . . . 35 1.8 Effect of primary school availability on middle school attainment (sample attrition considered) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 36 Effect of primary school availability on middle school attainment in communities without boundary changes . . . . . . . . . . . . . . . . . . . . . . . . . . 37 1.10 Effect of primary school availability on middle school attainment with wave 1989 and Liaoning added . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 1.11 Effect of distance to primary school on middle school attainment . . . . . . . . 39 1.12 Effect of primary school availability on primary school grade repetition . . . . 40 2.1 Summary statistics of county variables . . . . . . . . . . . . . . . . . . . . . . 62 2.2 Summary statistics of household variables . . . . . . . . . . . . . . . . . . . . 63 2.3 County and household participation pattern . . . . . . . . . . . . . . . . . . . 64 2.4 Determinants of county in NCMS . . . . . . . . . . . . . . . . . . . . . . . . 65 vii 2.5 Determinants of household in NCMS . . . . . . . . . . . . . . . . . . . . . . . 66 2.6 Effect of county in NCMS on household total health expenditure . . . . . . . . 67 2.7 Effect of household in NCMS on household total health expenditure . . . . . . 68 2.8 Households on support and off support for each matching . . . . . . . . . . . . 69 2.9 Balancing test after propensity score matching . . . . . . . . . . . . . . . . . . 70 2.10 Effect of household in NCMS using the regression adjusted matching . . . . . 71 2.11 Effect of household in NCMS with the selection of missing expenditure adjusted 72 2.12 Robustness tests for the effect of NCMS on health expenditure . . . . . . . . . 73 2.13 Evaluation of NCMS in NCMS counties . . . . . . . . . . . . . . . . . . . . . 74 2.14 Joining the NCMS and the cost for cold . . . . . . . . . . . . . . . . . . . . . 75 2.15 Effect of household in NCMS on delivery behavior . . . . . . . . . . . . . . . 76 3.1 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 3.2 Effect of doctor type on 3-6 year-old boys’ height . . . . . . . . . . . . . . . . 91 3.3 Effect of doctor type on 3-6 year-old girls’ height . . . . . . . . . . . . . . . . 92 3.4 Effect of doctor type on children’s height (instrumental variable) . . . . . . . . 93 3.5 Effect of doctor type on children’s height (school availability controlled for) . . 94 3.6 Effect of doctor type on children’s height (duplicate observation dropped) . . . 95 3.7 Effect of doctor type on 6-9 year-old children’s height . . . . . . . . . . . . . . 96 3.8 Effect of doctor type on 3-6 year-old children’s weight . . . . . . . . . . . . . 97 viii Table 3.3: Effect of doctor type on 3-6 year-old girls’ height Dep. var.: Height Z-score OLS (1) Child Number of siblings OLS (2) FE (3) OLS (5) FE (6) .016 .017 .035 .010 .011 .041 (.044) (.044) (.044) (.044) (.044) (.046) .266∗∗ .280∗∗ .218 (.133) (.132) (.135) Daily protein intake (g) Household Father’s height OLS (4) .035∗∗∗ .037∗∗∗ .047∗∗∗ .039∗∗∗ .040∗∗∗ .045∗∗∗ (.007) (.008) (.008) (.007) (.008) (.009) .048∗∗∗ .050∗∗∗ .046∗∗∗ .048∗∗∗ .050∗∗∗ .046∗∗∗ (.009) (.010) (.011) (.009) (.009) (.011) Schooling of the better educated parent .035 .035 .037∗ .034 .035 .040∗ (.022) (.022) (.021) (.021) (.021) (.021) Both parents farmer -.070 -.061 -.070 -.055 -.042 -.061 (.120) (.124) (.128) (.118) (.121) (.131) .007 .007∗ .005 .008∗ .008∗ .006 (.004) (.004) (.005) (.004) (.004) (.005) Mother’s height Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Community Average household income per capita Years of schooling Non-farm employment rate .146 .140 .124 .103 .092 .108 (.116) (.114) (.100) (.110) (.107) (.101) .033 .024 .009 -.023 -.036 -.032 (.121) (.123) (.132) (.124) (.126) (.137) .096 .096 .149 .070 .070 .128 (.105) (.105) (.112) (.109) (.109) (.118) .178 .183 .333∗∗ .120 .129 .259 (.144) (.145) (.166) (.145) (.143) (.169) .094 .116 .268 .105 .137 .289 (.081) (.097) (.266) (.083) (.100) (.281) -.298 -.382 -2.815∗∗∗ -.218 -.359 -3.055∗∗∗ (.516) (.589) (1.056) (.502) (.583) (1.049) Average male adult height in 1989 Const. Wave dummies and birth year dummies Province dummies Community fixed-effects Obs. y -.030 -.042 (.048) (.045) -17.888∗∗∗ -13.405∗ -20.670∗∗∗ -18.925∗∗∗ -12.777∗ -20.820∗∗∗ (2.106) (7.132) (2.736) (2.023) (6.643) (2.742) Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 808 -2.033 808 -2.033 808 -2.033 Yes 745 -2.037 Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. Standard errors in parentheses are clustered at community level in OLS and FE models. 92 745 -2.037 745 -2.037 Table 3.4: Effect of doctor type on 3-6 year-old children’s height, instrumental variable Dep. var. Instrumental variable Height Z-score 2SLS (1) FE-2SLS (2) 2SLS (3) FE-2SLS (4) Community % consulting West in 1989 2SLS (5) -.027 -.006 -.026 -.004 -.032 -.031 .008 (.072) (.063) (.073) (.063) (.074) (.074) (.068) -.039 -.023 -.039 -.024 -.043 -.038 -.020 (.037) (.028) (.037) (.028) (.038) (.036) (.036) .269∗∗∗ .223∗∗∗ .269∗∗∗ .222∗∗∗ .284∗∗∗ .269∗∗∗ .255∗∗∗ (.085) (.082) (.085) (.082) (.081) (.087) (.090) .045∗∗∗ .048∗∗∗ .045∗∗∗ .049∗∗∗ .045∗∗∗ .044∗∗∗ .046∗∗∗ (.006) (.006) (.006) (.006) (.006) (.006) (.006) Mother’s height .039∗∗∗ .039∗∗∗ .038∗∗∗ .039∗∗∗ .038∗∗∗ .039∗∗∗ .039∗∗∗ (.007) (.006) (.007) (.006) (.008) (.007) (.007) Schooling of the better educated parent .038∗∗ .039∗∗∗ .039∗∗ .040∗∗∗ .043∗∗∗ .037∗∗ .040∗∗ (.016) (.014) (.016) (.014) (.015) (.017) (.016) Both parents farmer -.022 -.044 -.017 -.038 -.007 -.039 -.036 (.078) (.081) (.079) (.081) (.098) (.114) (.087) .004 .004 .004 .004 .002 .004 .002 (.003) (.003) (.003) (.003) (.003) (.003) (.003) Method Child Male Number of siblings Daily protein intake (g) Household Father’s height Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Community Average household income per capita Community % consulting West County % consulting West County % consulting West in 1989 2SLS (6) Community number of Chn in 1989 2SLS (7) .101 .078 .015 -.019 -.373 .372 .498 (.167) (.149) (.228) (.191) (.760) (1.195) (.465) -.027 -.098 -.057 -.134 -.160 .068 .149 (.086) (.099) (.092) (.109) (.274) (.414) (.197) .060 .021 .052 .010 .019 .086 .104 (.080) (.091) (.080) (.092) (.113) (.117) (.103) -.0005 .053 .190 .054 .195 .081 .049 (.118) (.120) (.118) (.120) (.116) (.113) (.104) Years of schooling .016 .048 .019 .055 .030 .009 -.014 (.062) (.124) (.063) (.124) (.057) (.066) (.061) Non-farm employment rate .785∗ -.248 .802∗ -.190 .871∗ .732 .964∗∗ (.438) (.591) (.438) (.596) (.521) (.555) (.401) -17.189∗∗∗ -18.757∗∗∗ -17.154∗∗∗ -18.787∗∗∗ -17.193∗∗∗ -17.302∗∗∗ -17.183∗∗∗ (1.384) (1.888) (1.383) (1.890) (1.453) (1.464) (1.495) Yes Yes Yes Yes Yes Yes Yes Yes Yes 1648 -1.994 1648 -1.994 1648 -1.994 1648 -1.994 1634 -1.986 1648 -1.994 1522 -2.017 Const. Wave dummies and birth year dummies Community fixed-effects Obs. y Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. Standard errors in parentheses are clustered at community level in 2SLS model and conventional in FE-2SLS model. 93 Table 3.5: Effect of doctor type on 3-6 year-old children’s height with school availability controlled for Dep. var.: Height Z-score Boys Child Number of siblings FE (2) -.083∗∗ -.088∗∗ .030 .043 (.042) (.043) (.045) (.047) Daily protein intake (g) Household Father’s height Mother’s height Schooling of the better educated parent Both parents farmer Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Community Primary school in community Average household income per capita Years of schooling Non-farm employment rate Const. Wave dummies and birth year dummies Community fixed-effects Girls FE (1) FE (3) FE (4) .273∗∗ .269∗ (.125) (.160) .050∗∗∗ .049∗∗∗ .051∗∗∗ .051∗∗∗ (.009) (.009) (.010) (.010) .042∗∗∗ .038∗∗∗ .044∗∗∗ .045∗∗∗ (.010) (.010) (.012) (.012) .031 .034 .024 .031 (.022) (.023) (.024) (.025) -.024 .009 -.084 -.038 (.122) (.125) (.137) (.141) -.0007 .0004 .002 .002 (.005) (.005) (.005) (.005) .040 .076 .087 .079 (.105) (.107) (.119) (.123) -.176 -.157 -.007 -.049 (.130) (.133) (.158) (.164) -.079 -.091 .147 .128 (.132) (.136) (.149) (.154) -.494∗∗ .022 .049 -.474∗∗ (.178) (.183) (.214) (.229) .275 .317 .420∗∗ .349∗ (.188) (.194) (.192) (.201) .057 .083 .346 .356 (.192) (.198) (.222) (.229) 1.635∗ 1.216 -3.131∗∗∗ -3.245∗∗∗ (.886) (.913) (.983) (1.055) -19.792∗∗∗ -19.620∗∗∗ -22.826∗∗∗ -23.636∗∗∗ (2.666) (3.060) (3.412) (3.621) Yes Yes Yes Yes Yes Yes Yes Yes Obs. 786 758 637 y -1.869 -1.874 -1.957 Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. Standard errors in parentheses are clustered at community level. 94 599 -1.962 Table 3.6: Effect of doctor type on 3-6 year-old children’s height, duplicate observation dropped Dep. var.: Height Z-score Boys Child Number of siblings FE (2) -.116∗∗ -.118∗∗ .033 .036 (.046) (.047) (.044) (.048) Daily protein intake (g) Household Father’s height Girls FE (1) FE (3) FE (4) .295∗∗ .165 (.123) (.142) .046∗∗∗ .046∗∗∗ .045∗∗∗ .046∗∗∗ (.009) (.010) (.008) (.009) .042∗∗∗ .038∗∗∗ .047∗∗∗ .046∗∗∗ (.013) (.013) (.011) (.011) Schooling of the better educated parent .029 .038 .035∗ .035∗ (.024) (.024) (.021) (.019) Both parents farmer -.100 -.012 -.057 -.060 (.134) (.135) (.126) (.129) -.003 -.002 .004 .005 (.005) (.006) (.005) (.005) Mother’s height Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Community Average household income per capita Years of schooling Non-farm employment rate Const. Wave dummies and birth year dummies Community fixed-effects .080 .064 .140 .140 (.097) (.101) (.119) (.119) -.133 -.169 .140 .065 (.149) (.154) (.141) (.154) .021 .013 .277∗ .249∗ (.105) (.111) (.142) (.149) .281 .287 .274 .221 (.215) (.245) (.194) (.194) -.049 -.007 .253 .299 (.235) (.238) (.257) (.263) 1.240 1.171 -2.332∗∗ -2.861∗∗∗ (1.351) (1.410) (1.102) (1.099) -19.088∗∗∗ -20.017∗∗∗ -20.377∗∗∗ -20.746∗∗∗ (3.057) (3.188) (2.848) (2.908) Yes Yes Yes Yes Yes Yes Yes Yes Obs. 803 761 670 y -1.948 -1.958 -2.045 Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. Standard errors in parentheses are clustered at community level. 95 621 -2.047 Table 3.7: Effect of doctor type on 6-9 year-old children’s height Dep. var.: Height/median height for age Boys Child Number of siblings FE (2) FE (3) FE (4) -.002 -.002 .0003 .0008 (.002) (.002) (.002) (.002) Daily protein intake (g) Household Father’s height Mother’s height Schooling of the better educated parent Both parents farmer Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Girls FE (1) .012∗∗∗ .013∗ (.005) (.007) .002∗∗∗ .002∗∗∗ .001∗∗ .001∗∗ (.0003) (.0003) (.0005) (.0005) .002∗∗∗ .002∗∗∗ .002∗∗∗ .002∗∗∗ (.0003) (.0003) (.0004) (.0004) 7.83e-06 -.00002 .002∗ .001 (.0008) (.0008) (.0009) (.0009) -.002 -.0005 -.003 -.003 (.004) (.004) (.004) (.004) -.0001 -.0001 .0002 .0001 (.0001) (.0001) (.0002) (.0002) -.004 -.004 -.007 -.007 (.004) (.004) (.004) (.004) -.007 -.007∗ -.002 -.0006 (.004) (.004) (.006) (.006) .003 .0007 -.002 -.003 (.005) (.005) (.006) (.006) -.003 -.003 .014∗ .014∗ (.008) (.008) (.008) (.008) -.011 -.007 -.003 -.0003 (.007) (.007) (.010) (.010) .074∗∗∗ .073∗∗ .030 .012 (.028) (.030) (.029) (.029) .450∗∗∗ .441∗∗∗ .332∗∗ .277∗ (.084) (.100) (.154) (.163) Wave dummies and birth year dummies Community fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes Obs. y 935 .933 899 .933 798 .932 767 .932 Community Average household income per capita Years of schooling Non-farm employment rate Const. Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. Standard errors in parentheses are clustered at community level. 96 Table 3.8: Effect of doctor type on 3-6 year-old children’s weight Dep. var.: Weight Z-score Boys FE (1) Child Number of siblings FE (3) .003 .013 -.004 .021 (.032) (.035) (.047) (.048) Daily protein intake (g) Household Father’s height Mother’s height Schooling of the better educated parent Both parents farmer Housing floor area per capita (sq.m.) Consulting Western doctor Clinic as an option Hospital as an option Community Average household income per capita Years of schooling Non-farm employment rate Const. Wave dummies and birth year dummies Community fixed-effects Girls FE (2) FE (4) .190∗ .486∗∗∗ (.101) (.121) .029∗∗∗ .030∗∗∗ .024∗∗∗ .019∗∗∗ (.008) (.008) (.007) (.007) .034∗∗∗ .031∗∗∗ .036∗∗∗ .035∗∗∗ (.009) (.009) (.011) (.010) -.005 -.0002 .020 .025 (.020) (.021) (.021) (.022) -.132 -.103 -.003 .049 (.083) (.084) (.105) (.104) -.0002 -2.32e-06 .005 .004 (.004) (.004) (.003) (.004) .108 .111 .205∗∗ .140∗ (.067) (.071) (.088) (.085) -.030 -.016 -.058 -.152 (.096) (.101) (.121) (.134) -.004 -.001 .090 .077 (.103) (.103) (.107) (.109) -.050 -.070 .096 .065 (.141) (.149) (.144) (.158) .311∗ .271 .390∗∗ .319∗ (.183) (.182) (.154) (.167) .107 -.077 .182 .032 (.796) (.844) (.611) (.582) -13.401∗∗∗ -13.351∗∗∗ -15.411∗∗∗ -14.595∗∗∗ (2.174) (2.153) (2.580) (2.252) Yes Yes Yes Yes Yes Yes Yes Yes Obs. 953 899 811 y -1.145 -1.136 -1.226 Note: * means significant at 10%, ** significant at 5%, and *** significant at 1%. 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Economics of Education Review, 29(3):451–460. 107 [...]... analyses are conducted in Section 1.6, and Section 1.7 concludes 3 1.2 Basic education in rural China In rural China, basic education is provided almost entirely by local government It normally consists of 6 years of primary education and 3 years of secondary education According to the statistics published by Chinese Ministry of Education, there were 512,993 rural primary schools in 1997, but only 1,012... (2010) are the only ∗ This research uses data from the China Health and Nutrition Survey (CHNS) We thank the National Institute of Nutrition and Food Safety, China Center for Disease Control and Prevention; the Carolina Population Center, University of North Carolina at Chapel Hill; the National Institutes of Health (NIH; R01-HD30880, DK056350, and R01-HD38700); and the Fogarty International Center,... rural sample of the China Health and Nutrition Survey (CHNS) to analyze the impact of school accessibility on children’s educational attainment The CHNS covers nine provinces that vary substantially in geography, economic development, public resources, and health indicators Among these nine provinces, Heilongjiang was added in 1993 while Liaoning was not surveyed in 1997 A multistage random cluster process... augmented sample are consistent with that from the main sample 1.7 Conclusion In this paper, we study the impact of local school availability on children’s education in rural China Educational attainment is measured by middle school attendance and middle school completion We find that having a primary school in the community has a strong positive effect on girls’ schooling after controlling for community... children living in remote rural areas while the latter is mostly driven by the dwindling number of school age children According to the information extracted from various issues of China Rural Statistical Yearbooks, the number of primary schools in rural China declined from 798 thousand in 1984 to 253 thousand in 2008 However, the decline is far from universal across provinces The number of rural primary... Information on school availability has been collected since 1991 To minimize the impact of changes in sample composition on our estimates, we focus on 102 rural communities that were surveyed every wave As a result, all households from Heilongjiang and Liaoning provinces are excluded The rationale for our sample extraction is that we need information on both primary and middle school education While... Foundation (CYDF) in 1989 for the development of fundamental education in the economic backward regions of China and the healthy growth of younger generation By 2009, 5.67 billion yuan (approximately 810 million US dollars) have been raised in donations, 3.46 million students from poverty-stricken families have been aided to go or return to schools (China Youth Development Foundation, 2009) 4 As stated in. .. observed in two adjacent waves, if a child was absent in one wave, his/her chance of being surveyed in later waves was very small Hence, we restrict our sample to students who were enrolled in primary school in Party of China in 1985, and the Decision on Basic Education Reform and Development announced by the State Council in 2001, province government distributes the administration authority of basic education. .. children By reducing the number of schools, the resources for surviving schools can be increased, which promotes education in rural China Nevertheless, whether the positive impact of the potential increase in school quality can dominate the negative impact of deteriorating accessibility is worth further investigation 22 1.8 Appendix 1.8.1 Construction of educational attainment A child’s primary and middle... estimated effect of having a local primary school on middle school attainment is positive and significant for girls, and the effect magnitude is smaller than that from the main sample Again, no effect on boys can be detected Children from Liaoning province were not included in our main sample, as we focus on communities that were surveyed every wave and the 1997 survey was not conducted in Liaoning While the middle . are conducted in Section 1.6, and Section 1.7 concludes. 3 1.2 Basic education in rural China In rural China, basic education is provided almost entirely by local government. It normally consists. Availability and Middle School Education in Rural China ∗ 1.1 Introduction During the past thirty years, many new schools were constructed and even more were closed in China, particularly in rural areas ESSAYS ON EDUCATION AND HEALTH REFORMS IN RURAL CHINA LI LI (M.A. ZHEJIANG UNIVERSITY) THESIS IS SUBMITTED FOR THE DOCTOR OF PHILOSOPHY DEPARTMENT OF ECONOMICS NATIONAL UNIVERSITY OF SINGAPORE 2013 DECLARATION I