Healthcare and Development: Based on Principal Component

Analysis

Jing Chen and Chiping Yuan

Institute of Guangdong, Hong Kong and Macao Development Studies, Sun Yat-sen University, Xingang West Road,

Guangzhou, China

Keywords: Public Health Emergency Management Capability, Nighttime Light, China.

Abstract: Governments are paying increasing attention to public health emergency management, which is a cruel part

of sustainable development and a form of public goods, in that various public health emergencies can bring

serious threats to local economic growth. This paper proposes a framework to assess local public health

emergency management capability using principal component analysis method and investigates its

relationship with regional economic development. Through econometric analysis, we found apparent regional

gap regarding public health emergency management capacity in China, which is highly related to economic

development. However, the relationship of this capacity with regional economic development varies due to

regional differences.

1 INTRODUCTION

Seventeen sustainable development goals have been

put forward in the “Transforming our World: The

2030 Agenda for Sustainable Development” issued

by the United Nations, the eleventh of which is to

“Make cities and human settlements inclusive, safe,

resilient and sustainable”. There’s no doubt that

emergency management has become a part of

sustainable development that cannot be ignored.

From SARS to H1N1 to COVID-19 pandemic, public

health events continue to threaten local economic

development, local governments have to increase

public health emergency management capacity to

effectively prepare for and respond to such

emergencies. According to the State Overall

Emergency Response Plan for Public Emergencies,

the research on the classification of emergencies in

China is based on the occurrence mechanism of

emergencies, which divides public emergencies into

four categories: natural disasters, accident disasters,

public health events and social security events. Early

in 2003, Beijing has started constructing a

comprehensive governments’ emergency

management system with "one case, three systems"

as the core. It was further strengthened in 2009 during

the H1N1 epidemic. In 2020, the outbreak of

COVID-19 has brought severe challenge to China’s

emergency public health emergency management

system, and China has undoubtedly handed over a

satisfactory answer. We have seen that China's

emergency management capacity has made great

progress in the past decades. Therefore, we hope to

make a proper quantitative analysis of this changing

process and evaluate it scientifically.

Whereas from the experience of COVID-19

pandemic, we see that fighting against such

emergency is so complex that it is not only a case of

government management, but also a systematic

project involving the joint efforts of every aspect of

society including all kinds of social organizations.

This cross-sector collaboration in emergency

response is defined by Mojir (2019) as a process in

which different autonomous actors from different

societal sectors attempt to create a new setting by

establishing new ways of sharing information,

resources and capabilities and by performing joint

response operations in order to achieve common

goals, including saving lives and minimising

environmental damages (Mojir et al. 2019). Cross-

sector collaboration is essential in addressing public

health (Johnston and Finegood 2015), dealing with

climate change (Ingold and Fischer 2014), improving

traffic quality (Bryson et al. 2009), fighting against

poverty by securing food management (Hamann et al

2011) and so on. Thus, we must take these resource

Chen, J. and Yuan, C.

Healthcare And Development: Based on Principal Component Analysis.

DOI: 10.5220/0011188600003444

In Proceedings of the 2nd Conference on Artiﬁcial Intelligence and Healthcare (CAIH 2021), pages 109-116

ISBN: 978-989-758-594-4

109

endowments into account when measuring local

public health emergency management capability.

Specifically, for the evaluation of emergency

management system, some scholars establish an

evaluation system from the perspective of crisis

response process. Zhang et al. (2003) believe that the

urban emergency management system should cover

three systems, namely, emergency warning

preparation, emergency response and crisis recovery

and reconstruction. Dong (2005) further proposed

that the guarantee system of emergency management

mechanism includes twelve aspects: information

guarantee, communication guarantee, command

technology guarantee, engineering guarantee,

command technology guarantee, team guarantee,

transportation guarantee, medical rescue guarantee,

public security guarantee, material guarantee, fund

guarantee, scientific research guarantee and

legislative guarantee. With the outbreak of COVID-

19, the number of articles discussing on the

management of public health emergencies has

increased, and research methods have become

increasingly complex, digital, and multidisciplinary.

On the one hand, however, an important shortcoming

of these methods is their cruel requirement on data,

which makes it difficult to form a long-term

evaluation system. On the other hand, there are many

articles about the impact of public health emergencies

on economy, and most of them take specific epidemic

situations as research objects, while there are few

literatures concentrate on the relationship between

the public health emergency management capability

and economic development.

Our most related literature is by chen et al. (2021)

which evaluates capability of rural public health

emergency management in China, and draws one

conclusion that the level of rural public health

emergency management capability is strongly

correlated to economic development. Following this

paper, we set up a similar evaluation system to further

expand the study of rural areas to urban and rural

areas by using provincial panel data, and further

explore the relationship of public health emergency

management capacity and economic development

through econometric analysis.

The remainder of the paper will proceed as

follows. Section 2 will set up our evaluation system

and compare the capacity of public health emergency

management horizontally and vertically. Section 3

will specify the empirical models and present the

econometric results and heterogeneity analysis.

Section 4 will provide conclusion.

2 EVALUATION OF PUBLIC

HEALTH EMERGENCY

MANAGEMENT CAPABILITY

2.1 Construction of Evaluation System

As an aspect of sustainable development, responding

to major public health emergencies is also a reflection

of the regional ability to integrate and dispatch

resources. Public health emergency management is a

complex systematic project which needs cross-sector

cooperation involving transportation, information

and communication, grassroots organizations, social

stability, medical security and other aspects. In order

to measure local public health emergency

management capability as comprehensively as

possible, we established an index system from the

four levels of multi-agent cooperation, medical and

health security, social and economic stability, and

infrastructure construction, covering 24 secondary

indicators in total.

Table 1: Evaluation indexes for public health emergency management.

Targert layer Criteria layer Index layer Unit

capacity of public health

emergency management

Multi-agent

cooperation

Leadership of grassroots organizations

erson/unit

Student-full-time teacher ratio -

Number of em

ersons 10,000

erson

Gross de

endenc

ratio %

opulation 10,000 person

Medical and health

care

Number of beds in health institutions

er 10000

ersons

bed/person

Number of medical technical personnel

er 10000 persons

person

Number of health care institutions unit

Number of beds in health care

institutions

bed

Persons in

ension insurance 10,000

erson

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

110

Persons in health

rams 10,000

erson

Social and economic

stability

General public budget revenue

100 million

uan

Public budgetary expenditure

100 million

yuan

Per ca

ita dis

osable income

uan

Tertiar

industr

-GDP ratio %

Numbers of traffic accidents case

registered unemployment rate %

Infrastructure

constructions

Population density

erson/sq.

Ratio of sewage treatment %

Per ca

ita area of roads s

Water

enetration rate %

Ratio of garbage harmlessly treatment %

Viewe

-coverage rate %

Listene

-coverage rate %

ularization rate of tele

hone sets/100

erson

2.2 Data Resources

To make a horizontal and vertical comparison of local

public health emergency management capacity in

China, we use panel data covering 29 provinces and

regions from 2005 to 2019. The data in this paper are

mainly from the national statistical yearbook and

CEInet statistics database released by the National

Bureau of Statistics, some regional data are from

local statistical bulletin, and a few incomplete values

are filled using Conditional Mean Completer method.

2.3 Health Emergency Management

Capability Assessment

Principal Component Analysis (PCA) is applied to

create a public health emergency management

capability index for rural regions for each year in the

study period (2005-2019). The original data are

standardized as observed variables are in different

dimensions, positive indicators are normalized on the

interval [0,1] according to equation (1).





































(1)

While negative indicators are normalized

according to equation (2).





































(2)

KMO and Bartlett’s test of sphericity are

conducted, the value of former is 0.831 and both

confirmed the suitability of PCA. According to the

factor analysis result, 5 common factors whose

characteristic root is greater than 1 are extracted, with

contribution rate of cumulative variance being

77.89%. Factor 1 mainly explains the information of

six indicators: the number of persons in pension

insurance, general public budget revenue, the number

of health care institutions, population, the ratio of

sewage treatment, and the number employed persons.

Factor 2 mainly explained four indicators:

popularization rate of telephone, per capita

disposable income, Tertiary industry-GDP ratio, and

the number of medical technical personnel per 10000

persons. Factor 3 mainly explains five indicators:

listener-coverage rate, gross dependency ratio,

leadership of grassroots organizations, viewer-

coverage rate, and registered unemployment rate.

Factor 4 explained per capita area of roads, student-

full-time teacher ratio, water penetration rate and

ratio of garbage harmlessly treatment. Factor 5

explained the number of traffic accidents, the number

of beds in health institutions per 10000 persons,

public budgetary expenditure, the number of persons

in health programs, and population density.

Table 2: Factor load matrix after rotation.

Factor1 Factor2 Factor3 Factor4 Factor5 Index

x1 -0.1129 0.1147 -0.0549 -0.0749 0.3092 Numbers of traffic accidents

x2 -0.0573 0.1224 0.032 0.0635 0.1418 Number of beds in health institutions per

10000

ersons

x3 -0.030 -0.0066 0.2885 0.1182 -0.0342 Listene

-coverage rate

x4 -0.0081 -0.0033 0.3514 -0.1961 -0.0628 Gross dependency ratio

x5 0.1714 -0.0126 -0.0175 -0.0888 -0.0288 Persons in pension insurance

Healthcare And Development: Based on Principal Component Analysis

111

x6 0.1016 0.0536 -0.0866 0.0065 0.1591 Public bud

etar

enditure

x7 0.1361 0.0595 -0.0703 -0.0899 0.0335 General public budget revenue

x8 0.0108 0.1037 -0.3973 0.0158 -0.0905 Leadership of grassroots organizations

x9 0.0592 -0.0475 -0.0501 0.1541 0.2484 Persons in health

rams

x10 0.1676 -0.0022 0.0089 -0.1263 -0.0036 Number of health care institutions

x11 -0.0149 -0.0013 -0.0077 0.1915 -0.6127 Po

ulation densit

x12 0.0034 0.1379 0.0562 -0.0647 -0.0578 Popularization rate of telephone

x13 0.0128 0.14705 -0.06007 -0.01077 0.06410 Per capita disposable income

x14 0.15524 -0.1144 -0.0253 0.0598 0.0481

opulation

x15 -0.056 -0.0327 0.0036 0.4891 -0.2384 Per ca

ita area of roads

x16 -0.0045 0.2059 -0.1399 -0.1454 -0.0539 Tertiar

industr

-GDP ratio

x17 0.1555 -0.0144 -0.0223 -0.0312 -0.0822 Ratio of sewa

e treatment

x18 -0.0363 -0.0751 0.1064 0.2153 0.1958 Student-full-time teacher ratio

x19 -0.0282 0.1536 0.0550 -0.0808 -0.0098 Number of medical technical personnel per

10000

ersons

x20 -0.0388 0.1219 -0.1497 0.2447 -0.0444 Ratio of garbage harmlessly treatment

x21 -0.0366 0.0129 0.2501 0.135 -0.0175 Viewe

-coverage rate

x22 0.0241 0.1334 -0.1543 0.0097 -0.1024 registered unemployment rate

x23 -0.0458 0.0947 -0.0151 0.1735 -0.0573 Water

enetration rate

x24 0.1696 -0.0310 0.0015 -0.0726 0.0007 Number of em

ersons

The values of five common factors were

calculated according to the component score

coefficient matrix of factor analysis, and the score

regression equation of comprehensive factor F was

constructed by combining the variance contribution

rate and cumulative variance contribution rate of each

component factor:

∑

𝑓



𝑊







(3)

Where W

𝑃

𝑖

𝐶

, and W



is the ratio of the

variance contribution rate of factor i to the cumulative

variance contribution rate of the five common factors.

Now we get the scores of public health emergency

management mechanism in 29 regions of mainland

China in the past 15 years (Xinjiang and Tibet

excluded).

Table 3: Index score of 29 regions in 2005 and 2019.

region

2019 2005

F-value ranking GDP ranking F-value ranking GDP ranking

Shanghai 0.930093 6 10 -0.02443 2 7

Yunnan 0.372891 16 18 -0.79152 24 23

Neimenggu 0.276175 23 20 -0.80677 25 19

Beijing 1.087905 4 12 0.259063 1 10

Jilin 0.198572 25 25 -0.60617 15 22

Sichuan 0.877105 7 6 -0.53519 12 9

Tianjin 0.323722 20 23 -0.2888 6 21

Nin

xia 0.015994 28 28 -1.06179 29 29

Anhui 0.463785 14 11 -0.70065 18 15

Shandong 1.065953 5 3 -0.37225 9 2

Shanxi 0.355786 17 21 -0.6867 17 18

Guangdong 1.920924 1 1 -0.0724 3 1

Guangxi 0.300613 22 19 -0.84313 26 17

Jiangsu 1.314929 2 2 -0.09225 4 3

Jiangxi 0.337264 19 16 -0.71013 19 16

Hebei 0.645215 9 13 -0.38037 10 6

Henan 0.827317 8 5 -0.36702 8 5

Zhejiang 1.124411 3 4 -0.19255 5 4

Hainan 0.039474 27 27 -0.73328 20 27

Hubei 0.601304 11 7 -0.56932 13 13

Hunan 0.635282 10 9 -0.78122 23 12

Gansu 0.164195 26 26 -0.76491 21 26

Fujian 0.409762 15 8 -0.45402 11 11

Guizhou 0.208629 24 22 -1.03386 28 25

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

112

Liaoning 0.533621 12 15 -0.33313 7 8

Chon

0.350023 18 17 -0.77314 22 24

Shaanxi 0.495465 13 14 -0.67268 16 20

Qinghai -0.01875 29 29 -0.85483 27 28

Heilon

ian

0.313462 21 24 -0.5748 14 14

Great improvement has been seen in the past

decades regarding to the public health emergency

management capability in China, which is apparently

consistent with economic fact. In terms of the overall

spatial layout, the overall emergency management

capability in southeast China has always been in the

leading level, with Guangdong and Shanghai leading

the way. Among East, Central and Western Regions,

the Central region has the fastest growth rate,

represented by Hunan Province. Specifically, the

ranking of public health emergency management

capability in some regions, such as Tianjin, Jilin,

Hainan and Heilongjiang, declined significantly.

Another fact worth noticing is that, the capacity of

public health emergency management is somewhat

not positively correlated with local economic

development as we expected. Some regions possess a

relatively high economic development speed but a

relatively decreasing emergency management

capacity. In cases like Tianjin, a steady GDP ranking

can also be accompanied by a rapidly dropping

capacity ranking. This raises our interest in how this

capability reacts with economic growth.

3 ECONOMETRIC MODEL AND

RESULTS

From the discussion above, relatively developed

areas generally possess a high level of emergency

management capacity, but a high speed of GDP

development does not guarantee a huge increase in

health emergency management capability. On the one

hand, high management level is conducive to

attracting more investment and talents, and providing

a stable environment for economic development, so

as to enhance economic development. On the other

hand, for regions with limited resources, great

difficult in crisis management may occupy public

resources originally used for other economic

construction, thus inhibiting economic development.

However, which kind of power is stronger may

depends, therefore the relationship between the two

needs further empirical verification.

3.1 Basic Analysis

Since there is an obvious causal relationship between

the level of local public health emergency

management and the level of regional economic

development, the endogenous problem is not

alleviated. Thus, we use nighttime lights datasets as

the proxy measure for economic activity. We set the

model as follow:

𝑙𝑖𝑔ℎ𝑡



=𝛽𝑓



+𝜃ln𝑋





+𝜖 (4)

Where light_it is nighttime light of province i at

period t, f



is its index score, X represents other

control variables, and ϵ is random error. According to

Solow model, human capital and social capital stock

are important components of economic growth.

Urbanization level, industrial structure and foreign

trade are also major factors affecting regional

economic growth (LYU 2015). Variables above are

included in X with all original data from the National

Bureau of Statistics. Capital stock (cap) is calculated

according to Shan (2008) using fixed capital

formation, investment price index, base capital stock,

depreciation rate data from the national statistical

yearbook. Human capital (hr) is calculated according

to Peng (2005), we calculate the average number of

years of schooling in each province over the years and

convert it into human capital, combined with the

return on education. The urbanization rate (urb) is

expressed by the proportion of urban population in

the total population. Variable serv and eo represent

the proportion of tertiary industry in GDP and Total

import and export respectively.

Table 4: Effects of public health emergency management capacity on regional economic development.

endent variable: ni

httime li

(1) (2) (3) (4) (5) (6) (7)

OLS OLS OLS OLS FE OLS FE

L.light 0.023 -0.005

(1.50) (-0.64)

f 2.301*** 2.233*** 3.948*** 1.373* 0.494 2.127*** 3.960***

(5.14) (4.51) (7.31) (1.90) (0.69) (3.16) (7.26)

Healthcare And Development: Based on Principal Component Analysis

113

lncap -0.895*** -1.084*** 0.161 -1.233*** -0.060

(-2.95) (-3.51) (0.46) (-4.26) (-0.16)

lneo -0.076 0.042 -0.106 0.388** -0.115

(-0.59) (0.31) (-0.85) (2.34) (-0.86)

lnhr 0.234 1.102*** 0.728 0.124 0.147

(0.60) (2.69) (1.56) (0.35) (0.28)

lnserv 0.883 -0.111 -0.060 4.870*** 0.509

(

1.23

)

(

-0.15

)

(

-0.08

)

(

5.08

)

(

0.69

)

lnurb 0.227 -0.757 -6.432*** 0.800* -3.582**

(0.29) (-0.96) (-4.86) (1.79) (-2.26)

_cons 1.764*** 1.737*** 7.239 6.981 -6.090 -10.195* -1.256

(6.89) (4.20) (1.41) (1.32) (-1.00) (-1.74) (-0.19)

N 174 174 174 174 174 174 174

Time FE NO YES NO YES YES NO NO

NOTE: The sample includes mainland of China except Tibet and Xizang due to lack of dataset, since the statistical

caliber of lighting data has changed since 2013, so data of this table covers only from 2014 to 2019. Time FEs are

years. FEs are location FEs. All controlling variables are logged. * p<0.1 ** p<0.05 *** p<0.01

It seems that public health emergency

management capacity does have a positive effect on

local economic development, and it is not weakened

as we adding more controlling variables. Further, we

add in one-period lagged values of the dependent

variables in (6) and (7), and the conclusion still holds.

To directly investigate the relationship between

public health crisis management capacity and

regional economic development, we use GDP data

and introduce the lagging term of regional economic

development indicators into the model as an

explanatory variable:

ln 𝑔𝑑𝑝



=𝛼ln𝑔𝑑𝑝



+𝛽𝑓



+𝜃ln𝑋



+ϵ

(5)

All control variables remain unchanged.

Table 5: Effects of public health emergency management capacity on GDP growth.

Dependent variable: log (GDP)

(1) (2) (3) (4)

OLS FE GMM FD-GMM

L.lngdp 1.023*** 0.842*** 0.610***

(81.56) (30.06) (10.48)

L2.lngdp 0.629***

(6.66)

ff -0.052*** 0.065*** 0.330*** 0.203***

(-4.60) (3.02) (5.01) (3.01)

X √ √ √ √

N 406 406 377 348

NOTE: The sample includes mainland of China from 2005 to 2019, with Tibet and Xizang excluded due to lack

of data. In column (1) and (2) we simply applied an OLS and FE regression. In column (3) GMM model is applied

and in column (4) we applied a first-difference GMM and added two-period lagged values of the dependent

variable in. All controlling variables are logged.

* p<0.1 ** p<0.05 *** p<0.01

In order to solve the endogeneity problem of

dynamic panel model, since the OLS regression of

first-order difference data cannot get consistent

estimation, we adopt the generalized moment

estimation (GMM) method for regression on the basis

of first-order difference. The results show that there

is a significant positive correlation between public

health emergency management capacity and regional

economic growth rate.

3.2 Heterogeneity Analysis

As we discussed before, the relationship between the

level of public health emergency management

capacity and economic development may not be

simply same positive through all regions in China, so

we come up with further analysis to see how it differs

through different regions.

CAIH 2021 - Conference on Artiﬁcial Intelligence and Healthcare

114

Table 6: Heterogeneity analysis.

Region

East West Central

(1) (2) (3) (4) (5) (6)

Dependent

variable

light Log(gdp) light Log(gdp) light Log(gdp)

L.light -0.004 0.040 0.034

(-0.30) (0.61) (1.23)

L2.lngdp 0.720*** 0.427*** 0.465***

(14.01) (5.84) (4.34)

ff 2.428* 0.080** 2.825*** 0.421*** 1.577 -0.076

(1.93) (2.52) (3.18) (5.98) (1.24) (-0.87)

X √ √ √ √ √ √

N 66 132 54 108 54 108

Hansenp 1.000 1.000 1.000

sarganp 0.000 0.001 0.000

ar1p 0.328 0.032 0.887

ar2p 0.005 0.013 0.160

NOTE: We performed regression on the eastern, central and western regions respectively, and took GDP and

nighttime light data as explained variables to ensure the robustness of conclusion. The dependent variable of

column (1), (3), (5) are nighttime light data, which is regressed with FE model, with study period from 2014 to

2019. The dependent variable of column (2), (4), (6) are log of GDP, which is regressed in FD-GMM model, with

data from 2005 to 2019. All controlling variables are logged. * p<0.1 ** p<0.05 *** p<0.01

We were surprised to find that there was a

statistically significant positive correlation between

the capacity of public health emergency management

and regional economic development for the eastern

and western regions, while for the central region, this

economic driving effect was not significant. This

difference is mainly related to the characteristics of

each region. With concentrated resources and large

population density, the eastern region has sufficient

manpower and material resources to carry out the

capacity construction of public health emergency

management, and the improvement of such capacity

can promote the regional economic growth by

attracting funds, attracting talents and providing a

stable environment for economic development. The

western region is relatively undeveloped, with a

smaller population density, and facing less pressure

from public health crisis management, so dealing

with public health events is far less a pressure of the

government, meanwhile the western region with a

relatively higher emergency management capacity

will also attract more funds and talents. For the

central region, due to the scarcity of resources, the

construction of public health emergency management

capacity may run counter to the regional economic

development in the short term, which means that the

government may have to give up some economic

benefits when investing in improving its capability of

emergency management.

4 CONCLUSIONS

Based on our research, the conclusions are obtained

as below:

(1) There exists great regional gap of public health

emergency management capacity in China, showing

the spatial distribution characteristics that the

southeast is the best, the northwest worst.

(2) The more developed one region is, the better

able it is to build a better public health crisis

management system. However, as a part of public

services, public health emergency management

ability can enhance the regional economic vitality to

a certain extent though, it may also be a burden in

some areas, so we see that its relationship with

regional economic development varies due to

regional differences.

Though we hope to promote sustainable

development through crisis management capacity-

building, we cannot ignore the fact that in short term,

such construction may require sacrificing part of

economic growth.

Healthcare And Development: Based on Principal Component Analysis

115

ACKNOWLEDGEMENTS

This work was funded by the “Public Management”

Construction Project of Characteristic Key Discipline

from Guangdong Province, China in

2016(F2017STSZD01).

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