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Research on Legal Education Enabling New Quality Productivity Development in the Context of Big Data

Ping Liu

Ping Liu

Yunnan Open UniversityKunming, China
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Liu, Ping
  
24. März 2025
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Online veröffentlicht: 24. März 2025
Eingereicht: 23. Okt. 2024
Akzeptiert: 18. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0759
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© 2025 Ping Liu, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Legal talent drives the framework of new productivity
Legal talent drives the framework of new productivity

Figure 2.

Analysis of individual conditions necessity
Analysis of individual conditions necessity

Representative city

Conditional variable
High level traditional industrial technology innovation A1 Beijing, Shanghai, Guangzhou, Hangzhou State, Suzhou, Nanjing, Wuhan, Xi ‘an, Chengdu, Hefei and heavy Qing, Qingdao, Tianjin, Zhengzhou
A2 Dongguan, Foshan, Nantong
A3 Shaoxing, Wenzhou
A4 Shenzhen, Wuxi, Xiamen
High-tech innovation of high level strategic emerging industries B1 Beijing, Shanghai, Guangzhou, Hangzhou, Nanjing, Suzhou, Wuhan, Chengdu, Xi ‘an, Hefei, Chongqing, Qingdao, Tianjin
B2 Dongguan, Foshan, Nantong
B3 Shenzhen, Wuxi, Xiamen
High level future industrial technology innovation C1 Beijing, Shanghai, Hangzhou, Guangzhou, Nanjing, Wuhan, Suzhou, Chengdu, Xi ‘an, Hefei, Qingdao, Chongqing, Jinan,Tianjin
C2 Shenzhen, Wuxi, Xiamen

Random effect model

FGLS FGLS MLE MLE
LE 0.0475***(0.0144) 0.0357***(0.00972) 0.0468(.) 0.278***(0.0143)
CLP 0.0214*(0.0118) 0.0423(0.0345)
HT 0.0496***(0.00497) 0.0277***(0.00272)
SAT 0.322***(0.0765) 0.242***(0.0249)
LNATE 0.00245(0.00536) -0.00998*(0.00639)
_cons 0.0132(0.00896) -0.0189(0.0122) 0.0138***(0.00215) -0.125***(0.0178)
Sigma_u_cons 0.0597***(0.00163) 0.0522***(0.0122)
Sigma_e
_cons 0.0415***(0.000586) 0.0346***(0.000477)
N 2500 2500 2500 2500

Variable descriptive statistics

Variable N Mean SD Min Max
New productivity(NP) 2500 0.041 0.041 0.04 0.672
Legal education(LE) 2500 0.52 0.224 0 1
Education discipline setting (EDS) 2500 0.074 0.105 0 1
Talent training mode (TTM) 2500 0.043 0.0628 0 1
Complex legal personnel (CLP) 2500 0.0239 0 1
High-tech (HT) 2500 0.224 0.322 0 3.124
Science and technology (SAT) 2500 0.0826 0.0358 0 0.418
Large number according to education (LNATE) 2500 0.394 0.129 0.024 1.022

The condition group state sufficiency analysis

Conditional variable High level traditional industrial technology innovation High-tech innovation of high level strategic emerging industries High level future industrial technology innovation
A1 A2 A3 A4 B1 B2 B3 C1 C2
Leading talent ○ × × ○ ○ × ○ ● ●
Basic science ○ × × × ○ × × ○ ×
Talent ○ ○ × ○ ● ● ● ● ●
Technical talent ● ● ● ● ● ● ● ● ●
Social environment × ○ ○ × ○ ○
Education environment ○ × × × ○ × × ○ ×
Innovation support ○ ○ ○ ○ ● ● ● ● ●
Policy environment ○ × × ○ ○ × ○ ● ●
Original coverage 0.5143 0.1332 0.0826 0.0937 0.5128 0.1252 0.0922 0.5143 0.0915
Unique coverage 0.4336 0.0846 0.0326 0.0341 0.4534 0.0936 0.0306 0.4639 0.0352
consistency 0.9445 0.8667 0.9563 0.9847 0.9925 0.8475 0.9956 0.9985 0.9979
Overall resolution 0.6639 0.6422 0.5528
Overall solution consistency 0.9253 0.9635 0.9977

Fixed effect model results

Variable Individual fixation effect Individual fixation effect Individual fixation effect -LSDV Individual fixation effect -LSDV Two-way fixed effect - individual/time Two-way fixed effect - individual/time
NP NP NP NP NP NP
LE 0.0185***(0.00463) 0.0168***(0.00375) 0.0182***(0.00232) 0.0179***(0.00215) 0.245***(0.0620) 0.213***(0.0516)
CLP -0.00422***(0.00124) -0.00428(0.00587) -0.00268**(0.00117)
HT -0.0442**(0.0195) -0.0442***(0.0108) -0.0436**(0.0187)
SAT -0.0385(0.0472) -0.0385(0.0348) -0.0122(0.0467)
LNATE -0.0214***(0.00756) -0.0214***(000620) -0.0147*(0.00796)
2014 -0.0285**(0.00678) -0.0254***(0.0542)
2015 -0.0524***(0.0136) -0.0478***(0.0114)
2016 -0.0635***(0.0162) -0.0568***(0.0132)
2017 -0.0798***(0.0215) -0.0705***(0.0175)
2018 -0.0945***(0.0259) -0.0845***(0.0216)
2019 -0.125***(0.0305) -0.0985***(0.0285)
2020 -0.119***(0.0326) -0.115***(0.0263)
2021 -0.128***(0.0336) -0.112***(0.0274)
2022 -0.134***(0.0362) -0.115***(0.0294)
2023 -0.145***(0.0425) -0.134***(0.0322)
_cons 0.0272***(0.00258) 0.0489***(0.00685) 0.378***(0.0415) 0.436***(0.0415) 0.00512(0.00867) 0.0240***(0.00785)
N 2500 2500 2500 2500 2500 2500
R2 0.063 0.163 0.879 0.895 0.148 0.227
Adj.R2 0.063 0.162 0.874 0.896 0.137 0.225
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