doi: 10.3934/dcdss.2020265

The stochastic frontier analysis of the continuous data platform development's influence on industrial economy- a case study of China's Guizhou province

1. 

The School of Accounting, Guizhou University of Finance and Economics, China

2. 

Department of Politics and Economics, King's College London, United Kingdom

3. 

Department of Accounting, Wenzhou Business College, China

*Corresponding author: Jie Yang

Received  April 2019 Revised  May 2019 Published  February 2020

This study utilizes the city-level continuous data in Guizhou Statistical Yearbook 2011 and 2014. From the perspective of production performance evaluation and governmental subsidies, the study discusses the influence of data platform development on the urban industrial efficiency of Guizhou Province. In order to further analyze the impact of big data industry policies or related developments on Guizhou, this paper applies stochastic frontier analysis (SFA) to estimate the data. It is found that investment factors such as fixed asset investments, employed population in the information industry and government's expenditures in science and technology have significant impact on output values. After comparison of efficiency values among different regions, it turns out that the Qianxinan Buyi and Miao Autonomous Prefecture has the highest overall economic efficiency; Bijie City has the strongest increase in performance between 2011 and 2014, followed by Qianxinan Buyi and Miao Autonomous Prefecture.

Citation: Huabing Wang, An Li, Jie Yang. The stochastic frontier analysis of the continuous data platform development's influence on industrial economy- a case study of China's Guizhou province. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020265
References:
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D. AignerC. A. Lovell and P. Schmidt, Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6 (1977), 21-37.  doi: 10.1016/0304-4076(77)90052-5.  Google Scholar

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B. BrownM. Chui and J. Manyika, Are you ready for the era of `big data', McKinsey Quarterly, 4 (2011), 24-35.   Google Scholar

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A. DasM. Chowdhury and S. Khan, The dynamics of electricity consumption and growth nexus: Empirical evidence from three developing regions, Margin: The Journal of Applied Economic Research, 6 (2012), 445-466.  doi: 10.1177/0973801012462121.  Google Scholar

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M. J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society: Series A (General), 120 (1957), 253-281.  doi: 10.2307/2343100.  Google Scholar

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A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35 (2015), 137-144.   Google Scholar

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H. Harraga and M. Yebdri, Attractors for a nonautonomous reaction-diffusion equation with delay, Applied Mathematics and Nonlinear Sciences, 3 (2018), 127-150.  doi: 10.21042/AMNS.2018.1.00010.  Google Scholar

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T. Klymchuk, Regularizing algorithm for mixed matrix pencils, Applied Mathematics and Nonlinear Sciences, 2 (2017), 123-130.  doi: 10.21042/AMNS.2017.1.00010.  Google Scholar

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W. Meeusen and J. van Den Broeck, Efficiency estimation from cobb-douglas production functions with composed error, International Economic Review, 18 (1977), 435-444.  doi: 10.2307/2525757.  Google Scholar

[12]

A. Mehta, Big data: Powering the next industrial revolution, Tableau Software White Paper, 16. Google Scholar

[13]

M. Nerlove, Returns to Scale in Electricity Supply, En "Measurement in Economics-Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld", 1963. Google Scholar

[14]

M. M. Pitt and L.-F. Lee, The measurement and sources of technical inefficiency in the indonesian weaving industry, Journal of Development Economics, 9 (1981), 43-64.  doi: 10.1016/0304-3878(81)90004-3.  Google Scholar

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F. Wang and F. Chen, Openness and exploitation of government big data in the course of state governance, China Public Administraction, 11 (2015), 6-12.   Google Scholar

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F. Wang and M. Lai, The study on the current development of e-government and corresponding measures, Electronic Government, 8 (2009), 41-52.   Google Scholar

show all references

References:
[1]

D. AignerC. A. Lovell and P. Schmidt, Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6 (1977), 21-37.  doi: 10.1016/0304-4076(77)90052-5.  Google Scholar

[2]

G. Altinay and E. Karagol, Electricity consumption and economic growth: Evidence from turkey, Energy Economics, 27 (2005), 849-856.  doi: 10.1016/j.eneco.2005.07.002.  Google Scholar

[3]

G. E. Battese and G. S. Corra, Estimation of a production frontier model: With application to the pastoral zone of eastern australia, Australian Journal of Agricultural Economics, 21 (1977), 169-179.  doi: 10.1111/j.1467-8489.1977.tb00204.x.  Google Scholar

[4]

F. Bauer and M. Kaltenböck, Linked open data: The essentials a quick start guide for decision makers, edition mono/monochrom, Vienna, Austria, 23. Google Scholar

[5]

B. BrownM. Chui and J. Manyika, Are you ready for the era of `big data', McKinsey Quarterly, 4 (2011), 24-35.   Google Scholar

[6]

A. DasM. Chowdhury and S. Khan, The dynamics of electricity consumption and growth nexus: Empirical evidence from three developing regions, Margin: The Journal of Applied Economic Research, 6 (2012), 445-466.  doi: 10.1177/0973801012462121.  Google Scholar

[7]

M. J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society: Series A (General), 120 (1957), 253-281.  doi: 10.2307/2343100.  Google Scholar

[8]

A. Gandomi and M. Haider, Beyond the hype: Big data concepts, methods, and analytics, International Journal of Information Management, 35 (2015), 137-144.   Google Scholar

[9]

H. Harraga and M. Yebdri, Attractors for a nonautonomous reaction-diffusion equation with delay, Applied Mathematics and Nonlinear Sciences, 3 (2018), 127-150.  doi: 10.21042/AMNS.2018.1.00010.  Google Scholar

[10]

T. Klymchuk, Regularizing algorithm for mixed matrix pencils, Applied Mathematics and Nonlinear Sciences, 2 (2017), 123-130.  doi: 10.21042/AMNS.2017.1.00010.  Google Scholar

[11]

W. Meeusen and J. van Den Broeck, Efficiency estimation from cobb-douglas production functions with composed error, International Economic Review, 18 (1977), 435-444.  doi: 10.2307/2525757.  Google Scholar

[12]

A. Mehta, Big data: Powering the next industrial revolution, Tableau Software White Paper, 16. Google Scholar

[13]

M. Nerlove, Returns to Scale in Electricity Supply, En "Measurement in Economics-Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld", 1963. Google Scholar

[14]

M. M. Pitt and L.-F. Lee, The measurement and sources of technical inefficiency in the indonesian weaving industry, Journal of Development Economics, 9 (1981), 43-64.  doi: 10.1016/0304-3878(81)90004-3.  Google Scholar

[15]

F. Wang and F. Chen, Openness and exploitation of government big data in the course of state governance, China Public Administraction, 11 (2015), 6-12.   Google Scholar

[16]

F. Wang and M. Lai, The study on the current development of e-government and corresponding measures, Electronic Government, 8 (2009), 41-52.   Google Scholar

Table 1.  The definition of variables
The year of 2014 Amount Unit Definition
Governmental expenditure on science and technology 44.34 Million RMB Spending from the government and other relative departments to support science and technology activities
Employed population in the information industry 32921 Person The number of the people employed in the information industry in Guizhou Province
Fixed asset investments 8778.4 Million RMB The process in which an investment entity advances money or materials to obtain operational or service fixed assets
Source: collected by this study
The year of 2014 Amount Unit Definition
Governmental expenditure on science and technology 44.34 Million RMB Spending from the government and other relative departments to support science and technology activities
Employed population in the information industry 32921 Person The number of the people employed in the information industry in Guizhou Province
Fixed asset investments 8778.4 Million RMB The process in which an investment entity advances money or materials to obtain operational or service fixed assets
Source: collected by this study
Table 2.  OLS results
The interpreted variables: The logarithm of the regional production output value
Coefficient Standard error P value
The logarithm of governmental expenditures on science and technology 1.332 0.244 0.00
The logarithm of the employed population in the information industry 0.985 0.310 0.00
The logarithm of fixed asset investments 0.311 0.160 0.051
Constant term 15.401 1.899 0.00
The number of samples 176
R2 0.717
The interpreted variables: The logarithm of the regional production output value
Coefficient Standard error P value
The logarithm of governmental expenditures on science and technology 1.332 0.244 0.00
The logarithm of the employed population in the information industry 0.985 0.310 0.00
The logarithm of fixed asset investments 0.311 0.160 0.051
Constant term 15.401 1.899 0.00
The number of samples 176
R2 0.717
Table 3.  Estimation of the overall efficiency of all samples
The interpreted variablesthe logarithm of the regional production output value
Coefficient Standard error P value
The logarithm of governmental expenditures on science and technology 1.256 0.041 0.00
The logarithm of the employed population in the information industry 0.142 0.084 0.092
The logarithm of fixed asset investments 0.021 0.058 0.719
Constant term 11.394 0.184 0.00
sigma_v 0.092 0.008
sigma_u 0.217 0.013
The number of samples 176
The value of $ \lambda $ 2.363 0.019
Log likelihood 258.275
Source: collected by this study
The interpreted variablesthe logarithm of the regional production output value
Coefficient Standard error P value
The logarithm of governmental expenditures on science and technology 1.256 0.041 0.00
The logarithm of the employed population in the information industry 0.142 0.084 0.092
The logarithm of fixed asset investments 0.021 0.058 0.719
Constant term 11.394 0.184 0.00
sigma_v 0.092 0.008
sigma_u 0.217 0.013
The number of samples 176
The value of $ \lambda $ 2.363 0.019
Log likelihood 258.275
Source: collected by this study
Table 4.  Descriptive statistics of efficiency values
2011 average 2014 average Value added
Guiyang City (samples20) 0.756 0.842 0.086
Liushuipan City (samples8) 0.772 0.840 0.068
Zunyi City (samples28) 0.755 0.829 0.074
Anshun City (samples12) 0.753 0.824 0.071
Bijie City(samples16) 0.762 0.857 0.095
Tongren City (samples20) 0.798 0.858 0.060
The Qianxinan Buyi and Miao Autonomous Prefecture (samples16) 0.785 0.862 0.077
The Qiandongnan Miao and Dong Autonomous Prefecture (samples32) 0.769 0.835 0.066
The Qiannan Buyi and Miao Autonomous Prefecture (samples24) 0.735 0.824 0.089
Source: collected by this study
2011 average 2014 average Value added
Guiyang City (samples20) 0.756 0.842 0.086
Liushuipan City (samples8) 0.772 0.840 0.068
Zunyi City (samples28) 0.755 0.829 0.074
Anshun City (samples12) 0.753 0.824 0.071
Bijie City(samples16) 0.762 0.857 0.095
Tongren City (samples20) 0.798 0.858 0.060
The Qianxinan Buyi and Miao Autonomous Prefecture (samples16) 0.785 0.862 0.077
The Qiandongnan Miao and Dong Autonomous Prefecture (samples32) 0.769 0.835 0.066
The Qiannan Buyi and Miao Autonomous Prefecture (samples24) 0.735 0.824 0.089
Source: collected by this study
Table 5.  multicollinearity check for variance inflation factor (VIF), heteroscedasticity check for Breusch-Pagan test
Dependent variable The logarithm of the regional production output value
The logarithm of governmental expenditures on science and technology 1.40
The logarithm of the employed population in the information industry 2.95
The logarithm of fixed asset investments 1.19
Constant term 1.22
Mean VIF 1.86
Breusch-Pagan test ($ H_{0} $: Constant variance) 0.4155
Dependent variable The logarithm of the regional production output value
The logarithm of governmental expenditures on science and technology 1.40
The logarithm of the employed population in the information industry 2.95
The logarithm of fixed asset investments 1.19
Constant term 1.22
Mean VIF 1.86
Breusch-Pagan test ($ H_{0} $: Constant variance) 0.4155
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