1、Human Capital Externalities in China Edward L. GLAESER and Ming LU（Harvard University, NBER; Fudan University）I. INTRODUCTION Are human capital externalities (HCE) important in swiftly-growing economies like China? HCE generate the increasing returns that enable long-run economic growth (Lucas (1988

2、). Strong correlation across American metropolitan areas, between area-level human capital and individual earnings (Rauch (1993) and Moretti (2004a) ) Chinas average years of schooling have increased from 3.7 to 7.5 (Barro and Lee, 2011) Chinas per capita GDP has gone up by more than 1200%. Challeng

3、es in Estimation of HCE Significant problems with interpreting the OLS coefficients (Acemoglu and Angrist (2001) and others). Areas may have attracted more educated workers with greater economic opportunity - upward bias. If more educated people are attracted to areas because of consumption amenitie

4、s, then the extra labor elicited by these amenities should depress wages. downward bias. If workers differ in unobserved ways, and if workers with more unobserved ability tend to sort with workers that have more observed ability- upward bias.IV estimation of HCEIV: Acemoglu and Angrist (2001) : Usin

5、g area level policies, like compulsory schooling laws. Little evidence for human capital spillovers across U.S. States. Moretti (2004a): The location of land grant colleges in the U.S. prior to 1940 Eevidence of large HCE, similar in magnitude to the OLS estimates.Land grant colleges operate by incr

6、easing the upper end of the human capital distribution, while compulsory schooling laws operate by raising the lower end of the human capital distribution. If the spillovers came from learning from the more skilled, as in Glaeser (1999), then increasing the most skilled creates spillovers while incr

7、easing the least skilled does not. One or both of the instruments are contaminated by correlation with omitted variables, either at the state-cohort level (in the case of Acemoglu and Angrist, 2001) or that the metropolitan area level (Moretti, 2004a).II. A MODEL OF SKILLS AND LOCATION (Skipped)III.

8、 DATA DESCRIPTION AND ORDINARY LEAST SQUARES RESULTS Individual-level data from the 2002 and 2007 Chinese Household Income Project Surveys (CHIP2002, CHIP2007) for urban households. The 2002 survey covers 70 cities and county towns from 10 provinces, 6,835 households and 20,632 individuals The 2007

9、sample covers 19 cities and county towns from seven provinces, 5,000 households and 14,699 individuals. The data of city-level per capita schooling are from population census data in 2000. Other city level characteristics are from China City Statistical Yearbook.Modellnwij is the logarithm of indivi

10、dual-level hourly wage or monthly cityj is the average years of schooling at city level. eduij is individual-level years of schooling. Xij is a vector of individual characteristics, including: (1) Experience, defined as the difference between age and years of schooling minus 6; (2) Gender du

11、mmy, male=1; (3) Marital status, denoted by dummies of being married, being divorced, being widowed and other; (4) Ethnic group dummy, minority=1; (5) Dummy variables of occupation, sector, and ownership types of their working units.Cityj : City level characteristics. (1) Public goods. averoad2000,

12、road area per capita in 2000. avebus2000, the number of buses per capita in 2000; (2) university, city-level number of universities. (3) City size. medium and big; (4) Dummy variables of provincial capitals and municipalitiesijjijijjijcityXedueducitycw4321lnHuman Capital Externality (N=11,556 )(1)(2

13、)(3)(4)loghrsallogsalloghrsallogsaleducity0.197*0.184*0.119*0.100*(0.0459)(0.0467)(0.0466)(0.0446)gender0.169*0.212*0.167*0.210*(0.0166)(0.0169)(0.0156)(0.0161)edu0.0469*0.0403*0.0494*0.0430*(0.00427)(0.00360)(0.00403)(0.00330)exp0.00218*0.0005280.00319*0.00174(0.00130)(0.00142)(0.00132)(0.00137)min

14、ority-0.0483-0.0322-0.0236-0.00978(0.0470)(0.0458)(0.0403)(0.0399)City level var.NNYYR-squared0.7820.4980.7900.520Heterogeneity of Human Capital Externality by Education Group (1)(2)(3)(4)(5)(6)Dep. Var.Log hourly salary Log monthly salaryedu129edu12edu129edu12edu200Working hour7Salary200Working hou

15、r7educity0.119*0.112*0.100*0.0986*(0.0466)(0.0480)(0.0446)(0.0448)Observations11,55611,15611,55611,156R-squared0.7900.8020.5200.521IV. UNIVERSITY RELOCATION AND SHIFTING EDUCATION LEVELSUniversity Relocation Motivation: First, having learned the economic system from the Soviet Union, the Chinese lea

16、ders also wanted to follow their university system which is highly specialized to serve the economic development. Second, to widespread the communism ideology, the Party was strongly motivated to remove the influence of the education system in the period of the Republic of China. Moving across citie

17、s Among 502 departments moved out, 282 were across cities, while 333 among 623 departments moved in were from a different city. For 314 top scientists who experienced the movement of the relocation of university departments 232 among them, 74% of the 314, were relocated to other universities, colleg

18、es or institutions. 43 out of 158 top scientists who changed their working units within university system were moved across cities during the relocation of university departments. 38 out of 74 top scientists who were moved out of universities to other institutions migrated to other cities, while for

19、 those 17 from other units to universities, 10 of them left their living cities. For balance? Not significantly. A simple correlation between the city-level numbers of departments moved in and moved out. However, the correlation coefficient is actually 0.44.The Quantity of Universities in the 1950s

20、The Quantity of University Departments Moved In The Quantity of University Departments Moved Out The Net Number of University Departments Moved In University Relocation and Regional Characteristics Departments inDepartments outNet departments in No. of Universities1.170*0.748*0.421(0.242)(0.214)(0.3

21、34)Population in 1953 0.0267*-0.002010.0287(in 10,000)(0.0125)(0.0111)(0.0173)Northeast-3.097-2.466-0.631(3.403)(3.009)(4.694)North-6.480*-4.381-2.099(3.148)(2.784)(4.343)East-4.920*-1.244-3.676(2.700)(2.387)(3.724)Southwest-3.2791.621-4.899(3.249)(2.873)(4.481)Northwest-6.158*-5.781*-0.378(3.599)(3

22、.182)(4.964)Observations535353R-squared0.6760.4040.278F-value13.404.352.48Other channels? Social network? A case: Fudan vs. Zhejiang U. Through investment in the 1950s and 1960s? Formally tested.The Determinants of Per Capita Fixed Asset Investment in the 1950s and 1960s (1)(2)(3)(4)(5)(6)department

23、_net0.0130.0020.0170.013department_out0.0200.0020.0200.016department_in0.0270.0040.0170.014fix_49520.408*0.408*0.403*0.0770.0770.080Constant-15.036*-15.144*-15.183*-8.069*-8.081*-8.180*0.1360.1750.1611.3121.3471.397Observations484848484848R-squared0.0130.0210.0550.3950.3950.395The Determinants of Pe

24、r Capita Infrastructure Investment in the 1950s and 1960s(1)(2)(3)(4)(5)(6)department_net0.0150.0040.0170.013department_out0.012-0.0070.0220.017department_in0.022-0.0000.0160.014infra_49520.387*0.397*0.391*0.0750.0750.078Constant-15.075*-15.134*-15.192*-8.444*-8.226*-8.372*0.1360.1760.1601.2811.3141

25、.363Observations454545454545R-squared0.0190.0080.0420.4030.4040.402V. HUMAN CAPITAL EXTERNALITIES BASED ON UNIVERSITY RELOCATIONIV Estimation for Human Capital Externality First stageSecond stageDep. Vcityloghrsallogsaldepartment_in0.0342*educity0.219*0.162(0.00680)(0.123)(0.119)department_out

26、-.0270*edu0.0477*0.0420*(.00909)(0.00374)(0.00337)exp0.00283*0.00152(0.00137)(0.00143)gender0.166*0.209*(0.0153)(0.0159)F test20.119Observations11,55611,556R-squared0.7870.516IV Estimation for Heterogeneity of Human Capital Externality by Education Group (1)(2)(3)(4)(5)(6)Dep. Var.Log hourly salary

27、Log monthly salaryedu129edu12edu129edu12edu9educity0.224*0.2080.245*0.1960.1400.177*(0.132)(0.146)(0.104)(0.124)(0.150)(0.0851)Obs.3,5334,5203,5033,5334,5203,503R-squared0.7780.7820.7690.4720.4840.492IV Estimation for Heterogeneity of Human Capital Externality by Industry (1)(2)(3)(4)(5)(6)Dep. Var.Log hourly salary Log monthly salaryabstractmanualManuf.abstractmanualMcity0.231*0.250*0.214*0.197*0.1570.194*(0.123)(0.145)(0.0908)(0.119)(0.152)(0.0887)edu0.0295*0.006620.02470.0307*-0.005360.00994(0.00786)(0.0345)(0.0159)(0.00725)(0.0310)(0.0140)exp0.00755*0.0007370.0029