doi: 10.3934/dcdss.2020273

Financial deepening and premium of China's higher education based on the hypothesis of capital-skill complementarity

1. 

College of Accounting and Finance, Jiangxi University of Engineering, Xinyu 338000, China

2. 

School of Electronic Commerce, Jiangxi University of Engineering, Xinyu 338000, China

3. 

School of Marxism, Hainan University, Haikou 570228, China

*Corresponding author: Hong-Ping Yan

Received  May 2019 Revised  May 2019 Published  February 2020

The capital-skills complementary hypothesis had been widely verified and used abroad, domestic scholars started paying special attention to it and trying to use it to make relevant researches. In this paper, the capital-skills complementary hypothesis was firstly used to study the spatial heterogeneity of financial deepening and Chinese higher education premium; its primary purpose is to provide new thinking to the supply side reform of Chinese higher education. Through the improved Mincer equation and the empirical research, it is found that the person of higher education level prefers to the developed regions. The primary cause of such phenomenon is the one that developed region provides higher return of education (hereinafter referred to as "ROE"). Rightly, it is the regional difference of ROE that affects the flow direction and mode of labor force. In the region with higher financial development degree, enterprises prefer to borrow and loan money to use the capital to start their production. However, the weaker substitution of skilled labor and capital than the non-skilled labor and capital forced enterprises to increase their demands on skilled labor force and decrease their demands on the non-skilled labor force. In the context of a relative stable supply, the distortion from the demanding party improves the wage premium of skilled labor force. Therefore, the higher the financial development degree in a region is, the higher the ROE is. For Chinese higher education supply side reform, it is preferred to consider the spatial difference features of Chinese financial development.

Citation: Shurong Yan, Hongli Huang, Weihong Li, Lina Wang, Dehua Liu, Manwen Tian, Hong-Ping Yan. Financial deepening and premium of China's higher education based on the hypothesis of capital-skill complementarity. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020273
References:
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show all references

References:
[1]

D. Acemoglu, A microfoundation for social increasing returns in human capital accumulation, Quarterly Journal of Economics, 111 (1996), 779-804.  doi: 10.2307/2946672.  Google Scholar

[2]

Y. K. Al-Yousif, Education expenditure and economic growth: Some empirical evidence from the gcc countries, Journal of Developing Areas, 42 (2008), 69-80.   Google Scholar

[3]

D. H. AutorL. F. Katz and M. S. Kearney, The polarization of the u.s. labor market, American Economic Review, 96 (2006), 189-194.  doi: 10.3386/w11986.  Google Scholar

[4]

J. Brenner, Life-cycle variations in the association between current and lifetime earnings: Evidence for german natives and guest workers, Labour Economics, 17 (2010), 392-406.  doi: 10.1016/j.labeco.2009.03.006.  Google Scholar

[5]

C. C. Chase and D. Klahr, Invention versus direct instruction: For some content, it's a tie, Journal of Science Education and Technology, 26 (2017), 582-596.  doi: 10.1007/s10956-017-9700-6.  Google Scholar

[6]

C. Cobbinah and A. Bayaga, Physics content and pedagogical changes: Ramification of theory and practice, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 1633-1651.  doi: 10.12973/eurasia.2017.00689a.  Google Scholar

[7]

L. DingC. Kim and M. Orey, Studies of student engagement in gamified online discussions, Computers and Education, 115 (2017), 126-142.  doi: 10.1016/j.compedu.2017.06.016.  Google Scholar

[8]

W. Gao and W. Wang, New isolated toughness condition for fractional (g, f, n) - critical graph, Colloquium Mathematicum, 147 (2017), 55-65.  doi: 10.4064/cm6713-8-2016.  Google Scholar

[9]

W. Gao and W. Wang, A tight neighborhood union condition on fractional (g, f, n ', m)-critical deleted graphs, Colloquium Mathematicum, 149 (2017), 291-298.  doi: 10.4064/cm6959-8-2016.  Google Scholar

[10]

W. GaoL. ZhuY. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent and Fuzzy Systems, 33 (2017), 3153-3163.  doi: 10.3233/JIFS-169367.  Google Scholar

[11]

E. H. GellerY. S. Ji and J. W. Stigler, Conceptual explanations and understanding fraction comparisons, Learning and Instruction, 52 (2017), 122-129.  doi: 10.1016/j.learninstruc.2017.05.006.  Google Scholar

[12]

G. A. Gunter and J. L. Reeves, Online professional development embedded with mobile learning: An examination of teachers' attitudes, engagement and dispositions, British Journal of Educational Technology, 48 (2017), 1305-1317.  doi: 10.1111/bjet.12490.  Google Scholar

[13]

O. Guterman and A. Neuman, What makes a social encounter meaningful: The impact of social encounters of homeschooled children on emotional and behavioral problems, Education and Urban Society, 49 (2017), 778-792.  doi: 10.1177/0013124516677009.  Google Scholar

[14]

J. HeckmanL. Lochner and P. E. Todd, Earnings functions and rates of return, Journal of Human Capital, 2 (2008), 1-31.  doi: 10.3386/w13780.  Google Scholar

[15]

V. Hogan and I. Walker, Education choice under uncertainty and public policy, Labour Economics, 14 (2007), 894-912.  doi: 10.1016/j.labeco.2007.06.009.  Google Scholar

[16]

M. JohnJ. M. Molepo and M. Chirwa, South african learners' conceptual understanding about image formation by lenses, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 1723-1736.  doi: 10.12973/eurasia.2017.00694a.  Google Scholar

[17]

T. Judd and K. Elliott, Methods and frequency of sharing of learning resources by medical students: Sharing of learning resources by medical students, British Journal of Educational Technology, 48 (2017), 1345-1356.  doi: 10.1111/bjet.12481.  Google Scholar

[18]

Z. Liu, Teaching reform of business statistics in college and university, Eurasia Journal of Mathematics, Science and Technology Education, 13 (2017), 6901-6907.  doi: 10.12973/ejmste/78537.  Google Scholar

[19]

N. Luo and M. Zhang, Effects of different interactions on students' sense of community in e-learning environment, Computers and Education, 115 (2017), 153-160.  doi: 10.1016/j.compedu.2017.08.006.  Google Scholar

[20]

J. A. F. Machado and J. Mata, Counterfactual decomposition of changes in wage distributions using quantile regression, Journal of Applied Econometrics, 20 (2010), 445-465.  doi: 10.1002/jae.788.  Google Scholar

[21]

A. H. Malumfashi, Education expenditure and economic growth in nigeria: Co-intergration and correction technique, International Journal of Research in Commerce, Economics and Management, 2 (2012), 34-37.   Google Scholar

[22]

S. Murugan and M. Sundar, Investigate safety and quality performance at construction site using artificial neural network, Journal of Intelligent and Fuzzy Systems, 33 (2017), 2211-2222.  doi: 10.3233/JIFS-16497.  Google Scholar

[23]

V. Paredes, Grading system and student effort, Education Finance and Policy, 12 (2017), 107-128.  doi: 10.1162/EDFP_a_00195.  Google Scholar

[24]

L. P. Rieber, Participation patterns in a massive open online course (mooc) about statistics: Mooc participation, British Journal of Educational Technology, 48 (2017), 1295-1304.  doi: 10.1111/bjet.12504.  Google Scholar

[25]

L. Si and H. Qiao, Performance of financial expenditure in china's basic science and math education, Eurasia Journal of Mathematics, Science and Technology Education, 13 (2017), 5217-5224.   Google Scholar

[26]

A. Taggart, The role of cultural discontinuity in the academic outcomes of latina/o high school students, Education and Urban Society, 49 (2017), 731-761.  doi: 10.1177/0013124516658522.  Google Scholar

[27]

V. K. Teles and J. Andrade, Public investment in basic education and economic growth, Social Science Electronic Publishing, 35 (2010), 352-364.  doi: 10.2139/ssrn.573301.  Google Scholar

[28]

M. W. TianS. R. Yan and H. Peng, Research on the differences of ecological efficiency of low-carbon manda among enterprises under the education of ecological civilization, EURASIA Journal of Mathematics, Science and Technology Education, 13 (2017), 5233-5245.   Google Scholar

[29]

K. VajraveluR. Li and M. Dewasurendra, Effects of second-order slip and drag reduction in boundary layer flows, Applied Mathematics and Nonlinear Sciences, 3 (2018), 291-302.  doi: 10.21042/AMNS.2018.1.00022.  Google Scholar

[30]

M. YeX. Zheng and B. Wang, Econometric analysis of the contribution of education to economic growth, The Journal of Quantitative and Technical Economics, 20 (2003), 89-92.   Google Scholar

[31]

A. Yokuş and S. Gülbahar, Numerical solutions with linearization techniques of the fractional harry dym equation, Applied Mathematics and Nonlinear Sciences, 4 (2019), 35-42.  doi: 10.2478/AMNS.2019.1.00004.  Google Scholar

Table 1.  Descriptive
Variable Observed value Mean value Standard deviation Minimum value Maximum value
Wage index (log value, Yuan)
(1) = monthly basic wage 4959 7.46 0.681 0 10.98
(2) = hourly basic wage 4942 2.14 0.817 $ -5.12 $ 7.78
(3) = (1) + variable wage 4911 7.50 0.71 0.74 10.79
(4) = (3) + year-end bonus converted to month 4897 7.51 0.731 0.71 10.86
(5) = (4) + converted cash from other welfares 4889 7.53 0.741 0.73 10.79
(6) Annual income 7301 9.54 1.25 0 13.36
Individual characteristic
Education years 7809 9.23 4.57 0 27
Education degree (junior college or above = 1) 7814 0.24 0.42 0 1
Age 7814 39.41 10.52 17 65
Sex (Male =1) 7814 0.51 0.52 0 1
Years of working 7809 24.43 12.89 0 55
Party member or not (Yes = 1) 7814 0.12 0.31 0 1
Marital status (Married = 1) 7814 0.87 0.38 0 1
Financial development index
Total quantity of credit and loan/GDP (2015) 7814 1.06 0.38 0.71 2.13
Total quantity of credit and loan/GDP (average value of 2012–2015) 7814 0.98 0.34 0.67 1.98
Total market value of stock/GDP (2015) 7814 0.77 1.34 0.17 10.58
Total market of circulating stock/ GDP (2015) 5693 0.67 0.68 0.17 3.89
Net quantity of cash put into circulation (2015) 7774 0.03 0.03 $ -0.07 $ 0.08
Financial costs/total liabilities (%, 2014) 7814 12.97 9.73 0.60 33.72
Job characteristics
Working time (hour/month) 5340 222.61 75.40 1 753
Management personnel or not (Yes = 1) 5580 0.16 0.37 0 1
Leader or not (Yes = 1) 5561 0.19 0.396 0 1
Other variables
Spouse at the education level above junior college or not (Yes = 1) 7814 0.14 0.358 0 1
Parent at the education level above senior high school or not (Yes = 1) 7814 0.16 0.359 0 1
Unmarried children (Yes = 1) 7814 0.75 0.467 0 1
Child at the age under 6 years old (Yes =1) 7814 0.19 0.39 0 1
Family scale 7814 3.89 1.58 1 17
Note: the data of financial development indexes were excerpted from Chinese Financial Yearbook 2015 and Chinese Industrial Enterprise Database (they had already been calculated by the author); the other data were excerpted from CFPS (2015).
Variable Observed value Mean value Standard deviation Minimum value Maximum value
Wage index (log value, Yuan)
(1) = monthly basic wage 4959 7.46 0.681 0 10.98
(2) = hourly basic wage 4942 2.14 0.817 $ -5.12 $ 7.78
(3) = (1) + variable wage 4911 7.50 0.71 0.74 10.79
(4) = (3) + year-end bonus converted to month 4897 7.51 0.731 0.71 10.86
(5) = (4) + converted cash from other welfares 4889 7.53 0.741 0.73 10.79
(6) Annual income 7301 9.54 1.25 0 13.36
Individual characteristic
Education years 7809 9.23 4.57 0 27
Education degree (junior college or above = 1) 7814 0.24 0.42 0 1
Age 7814 39.41 10.52 17 65
Sex (Male =1) 7814 0.51 0.52 0 1
Years of working 7809 24.43 12.89 0 55
Party member or not (Yes = 1) 7814 0.12 0.31 0 1
Marital status (Married = 1) 7814 0.87 0.38 0 1
Financial development index
Total quantity of credit and loan/GDP (2015) 7814 1.06 0.38 0.71 2.13
Total quantity of credit and loan/GDP (average value of 2012–2015) 7814 0.98 0.34 0.67 1.98
Total market value of stock/GDP (2015) 7814 0.77 1.34 0.17 10.58
Total market of circulating stock/ GDP (2015) 5693 0.67 0.68 0.17 3.89
Net quantity of cash put into circulation (2015) 7774 0.03 0.03 $ -0.07 $ 0.08
Financial costs/total liabilities (%, 2014) 7814 12.97 9.73 0.60 33.72
Job characteristics
Working time (hour/month) 5340 222.61 75.40 1 753
Management personnel or not (Yes = 1) 5580 0.16 0.37 0 1
Leader or not (Yes = 1) 5561 0.19 0.396 0 1
Other variables
Spouse at the education level above junior college or not (Yes = 1) 7814 0.14 0.358 0 1
Parent at the education level above senior high school or not (Yes = 1) 7814 0.16 0.359 0 1
Unmarried children (Yes = 1) 7814 0.75 0.467 0 1
Child at the age under 6 years old (Yes =1) 7814 0.19 0.39 0 1
Family scale 7814 3.89 1.58 1 17
Note: the data of financial development indexes were excerpted from Chinese Financial Yearbook 2015 and Chinese Industrial Enterprise Database (they had already been calculated by the author); the other data were excerpted from CFPS (2015).
Table 2.  Benchmark regression
Dependent Variable: Monthly Basic Wage Log Value
(1) (2) (3) (4) (5) (6) (7)
Education years $ 0.0330***\\(0.013) $ $ 0.0209*\\(0.010) $ $ 0.0129\\(0.010) $ $ 0.0128\\(0.010) $ $ 0.0190*\\(0.012) $ $ 0.0078\\(0.011) $ $ -0.0112 \\(0.0076) $
Financial development $ 0.189*\\(0.11) $ $ 0.219*\\(0.10) $ $ 0.260**\\(0.11) $ $ 0.267**\\(0.11) $ $ 0.341***\\(0.11) $
Education years * financial development $ 0.0260***\\(0.0089) $ $ 0.0270***\\(0.0087) $ $ 0.0189**\\(0.0086) $ $ 0.0188**\\(0.0085) $ $ 0.0129\\(0.0079) $ $ 0.0204**\\(0.0089) $ $ 0.0178**\\(0.0079) $
Work experience $ 0.0190***\\(0.0028) $ $ 0.0088***\\(0.0032) $ $ 0.00779**\\(0.0031) $ $ 0.00789***\\(0.0031) $ $ 0.00890***\\(0.0030) $ $ 0.00951***\\(0.0029) $ $ 0.0153***\\(0.0029) $
Work experience square $ $-0.0297$ ***\\(0.0058) $ $ $-0.0240$ ***\\(0.0059) $ $ $-0.0220$ ***\\(0.0061) $ $ $-0.0230$ ***\\(0.0049) $ $ $-0.0231$ ***\\(0.0048) $ $ $-0.0258$ ***\\(0.0048) $ $ $-0.361$ ***\\(0.068) $
Education years * per capita GDP 0.0230***
(0.0031)
Education years * urbanization rate 0.0612
(0.028)
Education years * secondary industry percentage 0.0028
(0.0015)
Individual characteristics Y Y Y Y Y Y
Job characteristics Y Y Y Y Y
Dummy variable of ownership Y Y Y Y
Dummy variable of industry Y Y Y
Provincial-level fixed effect Y Y
Observed value 5216 5216 5216 5216 5216 5216 5216
$ R^{2} $ 0.19 0.27 0.38 0.37 0.36 0.38 0.39
Note: in view of the length of this paper, the coefficient and standard error beyond the work experience were not reported herein. The individual characteristics include sex, marital status and party member or not; the job characteristics include work time, management personnel or not, leader or not and dummy variable of workplace. Y means the dummy variable is being controlled. The standard errors in the brackets are gathered at the county-level ones. *, **, *** represents respectively the significance at the level of 10%, 5% and 1% (the same below).
Dependent Variable: Monthly Basic Wage Log Value
(1) (2) (3) (4) (5) (6) (7)
Education years $ 0.0330***\\(0.013) $ $ 0.0209*\\(0.010) $ $ 0.0129\\(0.010) $ $ 0.0128\\(0.010) $ $ 0.0190*\\(0.012) $ $ 0.0078\\(0.011) $ $ -0.0112 \\(0.0076) $
Financial development $ 0.189*\\(0.11) $ $ 0.219*\\(0.10) $ $ 0.260**\\(0.11) $ $ 0.267**\\(0.11) $ $ 0.341***\\(0.11) $
Education years * financial development $ 0.0260***\\(0.0089) $ $ 0.0270***\\(0.0087) $ $ 0.0189**\\(0.0086) $ $ 0.0188**\\(0.0085) $ $ 0.0129\\(0.0079) $ $ 0.0204**\\(0.0089) $ $ 0.0178**\\(0.0079) $
Work experience $ 0.0190***\\(0.0028) $ $ 0.0088***\\(0.0032) $ $ 0.00779**\\(0.0031) $ $ 0.00789***\\(0.0031) $ $ 0.00890***\\(0.0030) $ $ 0.00951***\\(0.0029) $ $ 0.0153***\\(0.0029) $
Work experience square $ $-0.0297$ ***\\(0.0058) $ $ $-0.0240$ ***\\(0.0059) $ $ $-0.0220$ ***\\(0.0061) $ $ $-0.0230$ ***\\(0.0049) $ $ $-0.0231$ ***\\(0.0048) $ $ $-0.0258$ ***\\(0.0048) $ $ $-0.361$ ***\\(0.068) $
Education years * per capita GDP 0.0230***
(0.0031)
Education years * urbanization rate 0.0612
(0.028)
Education years * secondary industry percentage 0.0028
(0.0015)
Individual characteristics Y Y Y Y Y Y
Job characteristics Y Y Y Y Y
Dummy variable of ownership Y Y Y Y
Dummy variable of industry Y Y Y
Provincial-level fixed effect Y Y
Observed value 5216 5216 5216 5216 5216 5216 5216
$ R^{2} $ 0.19 0.27 0.38 0.37 0.36 0.38 0.39
Note: in view of the length of this paper, the coefficient and standard error beyond the work experience were not reported herein. The individual characteristics include sex, marital status and party member or not; the job characteristics include work time, management personnel or not, leader or not and dummy variable of workplace. Y means the dummy variable is being controlled. The standard errors in the brackets are gathered at the county-level ones. *, **, *** represents respectively the significance at the level of 10%, 5% and 1% (the same below).
Table 3.  Verification of different wage definitions
Hourly basic wage (1) Monthly basic wage plus variable wage (2) Plus year-end bonus (3) Plus other welfare (4) Annual income (5)
Education years 0.0319***
(0.0079)
0.0280***
(0.0081)
0.0249***
(0.0069)
0.0261***
(0.0080)
0.0460***
(0.0079)
Financial development 0.449***
(0.076)
0.462***
(0.070)
0.431***
(0.081)
0.430***
(0.069)
0.598***
(0.081)
Education years * financial evelopment 0.0130*
(0.0064)
0.0107*
(0.0063)
0.0109*
(0.0071)
0.0109**
(0.0061)
0.0094*
(0.0056)
Observed value 4911 4863 4849 4840 4970
$ R^{2} $ 0.35 0.35 0.37 0.37 0.39
Note: all dependent variables are in the logarithmic form. In view of the limited length of the paper, the education years, financial development degree and the coefficient of their cross term are only reported. All regressions have controlled the control variable and the dummy variable contained by line (7) of Table 2 (except for the provincial-level fixed effect). The one in the brackets is the standard error, and all standard errors are gathered at the level of county. *, **, *** represents respectively the significance at the level of 10%, 5% and 1%.
Hourly basic wage (1) Monthly basic wage plus variable wage (2) Plus year-end bonus (3) Plus other welfare (4) Annual income (5)
Education years 0.0319***
(0.0079)
0.0280***
(0.0081)
0.0249***
(0.0069)
0.0261***
(0.0080)
0.0460***
(0.0079)
Financial development 0.449***
(0.076)
0.462***
(0.070)
0.431***
(0.081)
0.430***
(0.069)
0.598***
(0.081)
Education years * financial evelopment 0.0130*
(0.0064)
0.0107*
(0.0063)
0.0109*
(0.0071)
0.0109**
(0.0061)
0.0094*
(0.0056)
Observed value 4911 4863 4849 4840 4970
$ R^{2} $ 0.35 0.35 0.37 0.37 0.39
Note: all dependent variables are in the logarithmic form. In view of the limited length of the paper, the education years, financial development degree and the coefficient of their cross term are only reported. All regressions have controlled the control variable and the dummy variable contained by line (7) of Table 2 (except for the provincial-level fixed effect). The one in the brackets is the standard error, and all standard errors are gathered at the level of county. *, **, *** represents respectively the significance at the level of 10%, 5% and 1%.
Table 4.  Robustness test: using different indexes and different models
Monthly basic wage (1) Hourly basic wage (2) Monthly basis wage plus variable wage (3) Plus year-end bonus (4) Plus other welfares (5) Annual income (6)
Panel A: Using different financial development indexes financial development indexes * education years
Total quantity of credit and loan/ GDP (average of 2012–2015) 0.0151**
(0.0059)
0.0129*
(0.0070)
0.0070
(0.0068)
0.0128**
(0.0059)
0.0141*
(0.0069)
0.0130**
(0.0058)
Net cash supply/ GDP 0.301***
(0.059)
0.189***
(0.065)
0.180**
(0.064)
0.301***
(0.068)
0.302***
(0.069)
0.146**
(0.062)
Total market value of stock/ GDP 0.0061**
(0.0021)
0.0076***
(0.0028)
0.0078***
(0.0019)
0.0084***
(0.0024)
0.0083***
(0.0028)
0.0203***
(0.0029)
Total market value of circulating stock/ GDP 0.0206**
(0.0050)
0.0217***
(0.0048)
0.0307***
(0.0037)
0.0194***
(0.0048)
0.0195***
(0.047)
0.0268***
(0.0049)
Financial costs/total liabilities $ -0.185 $***
(0.038)
$ -0.150 $***
(0.041)
$ -0.203 $***
(0.039)
$ -0.182 $***
(0.039)
$ -0.0168 $***
(0.041)
$ -0.131 $**
(0.043)
Panel B: Using different education indexes education indexes * relative scale of credit market
Dummy variable of higher education 0.172***
(0.061)
0.133**
(0.060)
0.031**
(0.054)
0.162***
(0.048)
0.160***
(0.047)
0.181***
(0.059)
Dummy variable of parent at the education level of senior high school or above 0.180***
(0.059)
0.151**
(0.057)
0.161**
(0.053)
0.184***
(0.058)
0.192***
(0.057)
0.0861
(0.083)
Panel C: Heckman selection model
Education years * financial development 0.0143**
(0.0057)
0.124**
(0.0046)
0.0107
(0.0052)
0.0205**
(0.0058)
0.0216**
(0.0056)
0.0127*
(0.0057)
Lambda $ -0.0985 $
(0.14)
$ -0.946 $***
(0.17)
$ -0.405 $**
(0.16)
$ -0.325 $*
(0.18)
$ -0.407 $**
(0.13)
$ -1.764 $***
(0.46)
Note: all dependent variables are in logarithmic form. Panel A uses education years to measure education level, but the financial development index to be used is different. Panel B uses the specific value of provincial total quantity of credit and loan 2014 and GDP as the proxy of financial development, but the education indexes to be used are different. Panel C uses education years to measure education level, and uses the specific value of provincial total quantity of credit and loan 2014 and GDP as the proxy of financial development. Lambda of Panel C is the Inverse Mills Ratio $ \lambda $ of Heckman model.
Monthly basic wage (1) Hourly basic wage (2) Monthly basis wage plus variable wage (3) Plus year-end bonus (4) Plus other welfares (5) Annual income (6)
Panel A: Using different financial development indexes financial development indexes * education years
Total quantity of credit and loan/ GDP (average of 2012–2015) 0.0151**
(0.0059)
0.0129*
(0.0070)
0.0070
(0.0068)
0.0128**
(0.0059)
0.0141*
(0.0069)
0.0130**
(0.0058)
Net cash supply/ GDP 0.301***
(0.059)
0.189***
(0.065)
0.180**
(0.064)
0.301***
(0.068)
0.302***
(0.069)
0.146**
(0.062)
Total market value of stock/ GDP 0.0061**
(0.0021)
0.0076***
(0.0028)
0.0078***
(0.0019)
0.0084***
(0.0024)
0.0083***
(0.0028)
0.0203***
(0.0029)
Total market value of circulating stock/ GDP 0.0206**
(0.0050)
0.0217***
(0.0048)
0.0307***
(0.0037)
0.0194***
(0.0048)
0.0195***
(0.047)
0.0268***
(0.0049)
Financial costs/total liabilities $ -0.185 $***
(0.038)
$ -0.150 $***
(0.041)
$ -0.203 $***
(0.039)
$ -0.182 $***
(0.039)
$ -0.0168 $***
(0.041)
$ -0.131 $**
(0.043)
Panel B: Using different education indexes education indexes * relative scale of credit market
Dummy variable of higher education 0.172***
(0.061)
0.133**
(0.060)
0.031**
(0.054)
0.162***
(0.048)
0.160***
(0.047)
0.181***
(0.059)
Dummy variable of parent at the education level of senior high school or above 0.180***
(0.059)
0.151**
(0.057)
0.161**
(0.053)
0.184***
(0.058)
0.192***
(0.057)
0.0861
(0.083)
Panel C: Heckman selection model
Education years * financial development 0.0143**
(0.0057)
0.124**
(0.0046)
0.0107
(0.0052)
0.0205**
(0.0058)
0.0216**
(0.0056)
0.0127*
(0.0057)
Lambda $ -0.0985 $
(0.14)
$ -0.946 $***
(0.17)
$ -0.405 $**
(0.16)
$ -0.325 $*
(0.18)
$ -0.407 $**
(0.13)
$ -1.764 $***
(0.46)
Note: all dependent variables are in logarithmic form. Panel A uses education years to measure education level, but the financial development index to be used is different. Panel B uses the specific value of provincial total quantity of credit and loan 2014 and GDP as the proxy of financial development, but the education indexes to be used are different. Panel C uses education years to measure education level, and uses the specific value of provincial total quantity of credit and loan 2014 and GDP as the proxy of financial development. Lambda of Panel C is the Inverse Mills Ratio $ \lambda $ of Heckman model.
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