Talent Gathering in Northeastern Provinces Policy Issues and

Countermeasures Based on Multiple Case Studies

Wei Wang

and Yunqin Wang

Harbin University of Commerce, Harbin, Heilongjiang, China

Keywords: Talent Aggregation, Fuzzy Set Qualitative Comparative Analysis Method, Policy Comparison.

Abstract: General Secretary Xi Jinping pointed out that "the competition for talents has become the center of the

competition for comprehensive national power"; the central talent work conference in 2021 also clearly

pointed out that we should accelerate the construction of the world's important talent center and innovation

highland, how to gather a large number of outstanding talents has become a problem that the government

needs to solve, excellent talent policy can attract a large number of What kind of talent policy can meet the

needs of different regions and gather high quality talents. The study uses the method of fuzzy set qualitative

comparative analysis to compare and analyze the text of talent attraction policy and talent retention policy of

the three northeastern provinces and Jiangsu, Zhejiang, Shanghai, Guangdong, Beijing Hainan, Chongqing, a

total of 10 provinces and cities. Suggestions are made that the talent policies of the three northeastern

provinces need to strengthen legal and institutional safeguards, adopt a multi-channel approach to introducing

talent, improve career support policies for maintaining female high-level talent, and improve career support-

type policies such as talent training and academic exchanges.

1 INTRODUCTION

From September 27 to 28, 2021, the Central

Conference on Talent Work was held in Beijing. Xi

Jinping, General Secretary of the CPC Central

Committee, President of the State and Chairman of

the Central Military Commission, attended the

conference and delivered an important speech,

emphasizing the need to adhere to the Party's

management of talent, adhere to the world's scientific

and technological frontier, the main battlefield of the

economy, the major needs of the country, the people's

life and health, in-depth implementation of the

strategy of strengthening the country with talent in

the new era, all-round training, introduction and use

of talent, accelerate the construction of the world's

important talent center and innovation highland, for

To provide talent support for the basic realization of

socialist modernization in 2035, and to lay the

foundation of talent for the full establishment of a

strong socialist modernization in 2050. Talent is an

indispensable factor for the development of every city.

Talent is an innovative element that can promote

https://orcid.org/0000-0001-9252-0051

https://orcid.org/0000-0003-0249-6049

economic and social development, and human beings

who can create value for social development.

According to the link of talent management, talent

policy can be divided into five categories: talent

attraction, talent retention, talent cultivation, talent

stimulation and talent utilization, and the four are

complementary to each other. The government will

make talent policy from talent attraction, talent

incentive, talent cultivation, talent management,

talent use, talent evaluation and other aspects. Talent

policies are formulated to attract talents and make

them gather in a region so as to promote regional

economic and technological development. About

talent gathering, Liu Sifeng (2008) believes that the

gathering of scientific and technological talents is a

unique phenomenon in the process of scientific and

technological talent flow, which generally refers to

the process of scientific and technological talents

flowing from different regions or units to a specific

region or unit due to various factors such as economic,

social, regional environment and unit conditions (Liu

2008) .Talent gathering policy refers to the policies

that can realize talent gathering, such as talent

154

Wang, W. and Wang, Y.

Talent Gathering in Northeastern Provinces Policy Issues and Countermeasures Based on Multiple Case Studies.

DOI: 10.5220/0012071400003624

In Proceedings of the 2nd International Conference on Public Management and Big Data Analysis (PMBDA 2022), pages 154-160

ISBN: 978-989-758-658-3

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

attraction policy, life security policy, social security

policy, talent training policy supporting innovation

and entrepreneurship policy and supporting

enterprise policy, etc. Because the three northeastern

provinces and Hainan Province are the more ordinary

regions of the country's development, and Jiangsu,

Zhejiang, Shanghai, Guangdong, Beijing and

Chongqing regions are the more developed regions, it

is beneficial to compare the differences between these

two types of regions to study the direction of

optimization of talent aggregation policies. Based on

the above understanding, based on the existing

literature and data summary and the need for talent

policy optimization, the talent aggregation policies of

ten provinces and cities are taken as the research

objects, and the commonalities and differences of ten

provinces in talent aggregation are studied and

compared and analyzed in terms of talent aggregation

policies.

2 LITERATURE REVIEW

Talent is a necessary factor for the prosperity and

development of a region, and the development of a

region cannot be separated from innovation, and

young talents and domestic and foreign high-end

talents are the sources of innovation, so the battle for

talent is also intensifying in major cities, which has

become a problem for the high-quality development

of each city in recent years (Yu 2018). Appropriate

talent introduction policies have a positive effect on

the size of urban labor market and urban human

capital accumulation (Mao, Zheng 2021). There are

many scholars who believe that a good talent policy

should contain the following factors: (1) a good

environment should be created when introducing

foreign high-level talents, enhancing the sense of

integration of foreign introduced talents (Pan 2021),

broadening channels to introduce high-level foreign

students (Chen, Liu 2021), expand the channels for

introducing talents, tilt the focus of attracting talents

to the basic discipline fields, and enrich the flexible

The new mode of flexible introduction of talents by

means of the Internet (Zhao, Huang 2021), and the

new mode of flexible introduction of intelligent

talents by means of the Internet (Li, Cao 2021).

3 STUDY DESIGN

3.1 Basic Ideas

Firstly, by analyzing the text of talent aggregation

policy policies in each province and city, we can

summarize and refine that talent aggregation policies

are divided into three major categories: financial

support policies, social life protection policies, and

career support policies. Secondly, we use the fuzzy

set qualitative comparative analysis method to get the

fuzzy set corresponding to the degree of talent

gathering and the categories of talent gathering

policies, and examine the different group states of

talent gathering policies to promote the degree of

talent gathering.

The samples are selected from only ten

representative provinces and cities in China,

including the three northeastern provinces, which

have been losing talents, Hainan Province, which has

just developed the degree of talent aggregation, and

Beijing, Shanghai, and Guangdong Province, which

have a high concentration of talent aggregation,

meeting the requirement of diverse case selection.

The fuzzy set qualitative comparative analysis

method in this paper is based on Excel and

fsQCA3.1b platform.

3.2 Data Collection

In order to conform to the data of the sixth and

seventh census, the indicators and data affecting the

degree of talent aggregation were selected by

collecting the texts about talent policies from 2010-

2020 from the official websites of provinces, cities

and national governments as a sample,

25 initial

classification codes were obtained, 9 secondary

classification codes were aggregated from 66

classifications, and finally 3 variable types were

refined through same-sex aggregation (as shown in

Table 1).

The indicators and data affecting the degree

of talent aggregation are selected as shown in Table 2.

Table 1: Summary of high-level talent gathering policy classification.

First-class classification Secondary Classification Variable Type

Financial support for talent attraction Financial incentives Financial Support Policy

Project funding grants

Settlement Allowance Life Security Social Life Security Policy

Housing Support

Talent Gathering in Northeastern Provinces Policy Issues and Countermeasures Based on Multiple Case Studies

155

Living allowance

Medical and Social Security Social Security

Talent Settlement

Obtaining a visa and residence permit

Family Settlement Relocation of family members

Child Enrollment

Family Medical and Social Security

Family work

Publicity Contribution Encourage respect Business Support Policy

Award and title evaluation

Promote project establishment Support innovation and

entrepreneurship

Promote the transformation of scientific

research results

Allow self-built project teams

Provide training opportunities Talent Development

Provide academic exchange opportunities

Encourage women to choose when to

retire

Team Assurance

Working hours for nursing mothers

Solve the difficulties in learning and

working of talents

Expand the two-way circulation

mechanism of the project enterprise

Enabling Companies

Reduced tax rate for high-tech enterprises

Guiding enterprises to participate in

training talents

Table 2: Table of variable measurement indicators and data sources.

Variable Type Variable Name Measurement indicators Data source

Result Variables Ten years of high-level

talent growth level

The number of high-

level talent growth in the

past ten years

National Bureau of

Statistics and provincial

and municipal statistical

offices

Cause Variables Financial Support Policy Frequency of financial

support polic

The official website of

the Human Resources

and Social Security

Bureau of each province

and city and the people's

government of each

rovince and city

Social Life Security

Polic

Social life security

olicy frequenc

Business Support Policy Frequency of business

support policies

According to the variable measurement index and

data source table, the raw data of the influence

variables and outcome variables of the degree of

talent aggregation in ten Chinese provinces and cities

were obtained, as shown in Table 3.

Table 3: Raw data table of the influence variables and outcome variables of the degree of talent aggregation in ten Chinese

provinces and cities.

Province and city variables Influence variables Result Variables

Frequency of

financial support

olic

Social life

security policy

frequenc

Frequency of

business support

olicies

The number of high-level

talent growth in the past ten

years (10,000 people)

Heilongjiang Province 6 13 6 123.76

Jilin Province 2 11 12 131.33

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Liaoning Province 3 10 2 252.42

Jiangsu Province 3 9 18 730.99

Zhejiang Province 3 7 16 589.25

Shanghai 4 17 13 337.11

Guangdong Province 20 14 16 1121.56

Beijing 3 10 14 301.27

Hainan Province 4 21 6 72.95

Chongqing 6 15 10 244.72

The key to the fuzzy set qualitative comparative

analysis method is data calibration, which transforms

the raw data into a fuzzy set and obtains the variables

to match the external criteria. For the analysis using

fsQCA, the antecedent condition and the outcome are

considered as a set respectively, and each case has a

corresponding affiliation score in the set, and the

direct method is used to calibrate the outcome and

condition variables into fuzzy sets. Referring to

existing studies, the 95%, 50%, and 5% quantile

values of the case data for the outcome variables and

antecedent conditions were set as three qualitative

anchor points for full affiliation, crossover point, and

full disaffiliation, respectively. The calibration

function calibrate (x, n1, n2, n3) provided in fsQCA

is used to calibrate the fuzzy set of the original data,

where x is the variable, n1 is the x value

corresponding to the fully affiliated in the target set,

n2 is the x value corresponding to the intersection

point in the target set, n3 is the x value corresponding

to the fully unaffiliated in the target set, and the fuzzy

set of each variable of the case is determined on the

basis of the above three anchor points affiliation, the

calibrated fuzzy set affiliation table is shown in Table

Table 4: Fuzzy set affiliation table of influence variables and outcome variables of talent aggregation degree in ten Chinese

provinces and cities.

Influence variables Result Variables

Provinceand

city

The frequency

offinancial support

oliciesfs

Social life security

policy frequencyfs

Frequent business

support policiesfs

The number of high-level talent

growth in the past ten years (10,000

eople)

Guangdong

rovince

0.99 0.7 0.91 0.98

Heilongjiang

Province

0.68 0.6 0.1 0.07

Chongqing

Municipalit

0.68 0.78 0.3 0.37

ShanghaiMu

nicipalit

0.54 0.89 0.58 0.57

Hainan

Province

0.54 0.98 0.1 0.03

Liaoningpro

vince

0.19 0.19 0.03 0.4

Jiangsu

rovince

0.19 0.1 0.97 0.88

ZhejiangPro

vince

0.19 0.03 0.91 0.8

Beijing 0.19 0.19 0.73 0.53

Jilin

rovince

0.01 0.32 0.46 0.08

The degree of explanation of the outcome

variables by individual influence variables was

analyzed to determine their explanatory power, and

variables with consistency greater than 0.9 were

considered essential variables and could explain the

outcome variables independently, while variables

less than 0.9 needed to explain the outcome variables

together with other variables. The consistency and

coverage of variables were analyzed by faQCA3.1b

software, and the results were shown in Table 5, and

Talent Gathering in Northeastern Provinces Policy Issues and Countermeasures Based on Multiple Case Studies

157

the consistency of all variables was is lower than the

threshold value of 0.9, indicating that the variables

alone cannot explain the outcome variables and are

not enough to become a necessary condition for the

degree of talent aggregation, and the group analysis

of the influencing variables needs to be continued.

Table 5: Consistency and coverage analysis.

Consistenc

Covera

The fre

uenc

of financial su

ort

oliciesfs 0.585987 0.657143

~The fre

uenc

of financial su

ort

oliciesfs 0.745223 0.605172

Social life security policy frequencyfs 0.494692 0.487448

~Social life security policy frequencyfs 0.723992 0.653257

Frequent business support policiesfs 0.891720 0.825147

~Fre

uent business su

ort

oliciesfs 0.392781 0.376782

Conditional group analysis using fsQCA3.1b, as

shown in Table 6, there are 2 different combinations

of influence variables combinations affecting the

degree of talent aggregation, and the agreement rate

is higher than 0.8, which indicates that these 2

combinations of influence variables have strong

explanatory power, in addition the total agreement

rate of conditional group analysis is higher than 0.8,

which indicates that among the cases that meet these

two combinations of influence variables, there are

more than 84.8% of talent aggregation degree is

higher, and the total coverage rate of 0.832 indicates

that the combination of the 2 influence variables can

explain 83.2% of the cases with talent aggregation

degree is higher, according to the combination of the

2 influence variables, the factors affecting the degree

of talent aggregation can be further analyzed, and it

can be seen through the group state 1 and group state

2 that the career support policy can promote talent

aggregation.

Table 6: Combination of influence variables of talent aggregation.

Configuration 1 Configuration 2

Frequency of financial support policy

~Frequency of financial support policy

Social life security policy frequency

●

~Social life security policy frequency

Social life security policy frequency

● ●

~Social life security policy frequency ○

Raw Coverage 0.662 0.445

Unique Coverage 0.383 0.169

Consistency rate 0.818 0.857

Overall coverage 0.832

Overall consistency 0.848

●indicates the presence of the influencing variable.○indicates that the influencing variable is not present.A blank indicates

that the influencing variable does not affect the result.●Indicates that the influence variable is more important.

4 RESEARCH CONCLUSIONS

AND RECOMMENDATIONS

4.1 Research Findings

Local governments attach great importance to talents

as shown by the policies formulated by each region

to gather talents. From the results of the analysis, it

can be seen that the talents in the three northeastern

provinces have been in the outflow state, while the

talents in the rest of the provinces and cities are in the

inflow state. One of the reasons for the outflow of

talents from the three northeastern provinces is the

bad climate and economy, and the other reason is the

lack of perfection in talent gathering and policy

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development. However, the development of talent

gathering policies is another major factor that causes

the outflow of talent. Although there are many similar

combinations of talent gathering policies developed

in the three northeastern provinces and the rest, there

are too few career support policies in the three

northeastern provinces, and because the basic

environment in the three northeastern provinces is not

as good as other provinces and cities, the talent

gathering policies in the three northeastern provinces

should be developed with more local characteristics

of their own. They should choose their own local

characteristics to attract talents according to local

conditions.

4.2 Talent Gathering Policy

Suggestions

4.2.1 Strengthen the Legal System

Protection

In response to the problem of no perfect talent policy

laws and regulations, the local government should

make local laws and regulations about talent in order

to keep the continuity and stability of talent policy,

because now there is no perfect talent policy laws and

regulations. The legal rights and interests of the

introduced talents cannot be protected. And because

the three northeastern provinces are mostly financial

support policies and social life protection policies,

they need to have perfect legal regulations to

guarantee the smooth implementation of the policies.

Local laws and regulations on talent policy will

increase the scientificity, legality and feasibility of

talent policy and ensure the smooth implementation

of the policy. Strengthening the legal system will also

guarantee that talents can stay in the region after

inflow.

4.2.2 Improve Career Support Policies for

the Maintenance of Female High-Level

Talents

While national policies have tried to preserve

women's rights and freedom to work as much as

possible, women's job opportunities are generally less

than men's due to marriage and pregnancy. It is true

that most of the high-level female talents have

problems during pregnancy and nursing. Policies

should be developed to address the difficulties

women face during pregnancy and nursing. For

example, special childcare services and flexible

working system should be set up so that female

executives can focus on their work without the

trouble of pregnancy and nursing. A comprehensive

policy to deal with the development of female high-

level talents and solve the problems of female high-

level talents in their working life can attract female

high-level talents to integrate.

4.2.3 Using Big Data to Gather Talents

Big data refers to the huge information data that has

a huge scale of information data volume and cannot

be managed, processed and applied by mainstream

software tools in a reasonable time. The huge number

of talents can rely on new processing technologies to

complete the processing of big data, such as: cloud

computing, distributed database, data mining,

massively parallel processing, etc. Big data

processing has comprehensive coverage, replacing

the traditional sample statistics, and the results are

more accurate and have personalization. The right

talent can be found better. With the emergence of

Artifici al Intelligence, Blockchain, Cloud

Computing, Big Data and other "ABC" technologies,

the era of human "digital intelligence". In this context,

the use of new science and technology to empower

talent training. At the same time to do a good job of

database information security protection, you can use

VPN technology (virtual private network), on the

basis of the public network to establish a private

network, connected to the database access channel, to

achieve encrypted communication, thus playing a

protective role in the security of database information.

4.2.4 Improve Career-Supporting Policies

Such as Personnel Training and

Academic Exchanges

To increase the cultivation of locally trained or

working in the local talent, for the local urgently

needed industries to focus on the training of talent,

the establishment of special projects. We support the

two-way circulation mechanism between project

members and enterprises, encourage senior personnel

of enterprises for lecture training for school students,

and support the system of distributing funds from the

project team after the transformation of scientific

research results to train talents needed by enterprises

and key and urgent industry.

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