Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2021 / Article
Special Issue

Big Data Modelling of Engineering and Management 2021

View this Special Issue

Research Article | Open Access

Volume 2021 |Article ID 1475781 | https://doi.org/10.1155/2021/1475781

Tsung-Xian Lin, Zhong-huan Wu, Xiao-xia Ji, Jia-jia Yang, "Research on the Operating Efficiency of Chinese Listed Pharmaceutical Companies Based on Two-Stage Network DEA and Malmquist", Mathematical Problems in Engineering, vol. 2021, Article ID 1475781, 18 pages, 2021. https://doi.org/10.1155/2021/1475781

Research on the Operating Efficiency of Chinese Listed Pharmaceutical Companies Based on Two-Stage Network DEA and Malmquist

Academic Editor: Yiwen Zhang
Received20 May 2021
Accepted16 Jun 2021
Published23 Jun 2021

Abstract

The development of pharmaceutical companies, which is an important part of the national economy and industry, is closely related to people’s livelihood issues. With the era of big data, this paper uses the two-stage DEA and Malmquist method to evaluate the efficiency of listed Chinese pharmaceutical companies. From a static and dynamic perspective, it analyses the total factor productivity index, pure efficiency change index, scale efficiency index, and so on. The results show that government subsidies have not had a positive impact on most Chinese pharmaceutical companies, and for films, diseconomies of scale caused by rapid expansion should be avoided.

1. Introduction

Since the outbreak of the COVID-19, medical health has become the primary topic of concern for the government and the public. The operation of pharmaceutical companies has become the focus of discussion. At present, China’s pharmaceutical manufacturing industry has long been in a state of low industry concentration, high product homogeneity, and weak technological innovation capabilities. It has a large gap with the international advanced level. In recent years, China’s various medical reform policies and regulations have been promulgated one after another. It has brought many uncertain factors to the development of the pharmaceutical industry. How to adapt to changes in the external environment and further improve operating efficiency has become an important issue facing the government and enterprises. Based on the above background, this article focuses on the research on the operating efficiency of pharmaceutical companies, innovatively combining the DEA method with Malmquist, taking 164 listed companies in China as a sample, and studying their operating conditions during the five-year period from 2015 to 2019.

The structure of the following parts of this article is as follows. Section 2 collects current scholars’ research on the efficiency evaluation of pharmaceutical companies; Section 3 introduces the related content of the two-stage network DEA and Malmquist index method in this article; Section 4 outlines the research of this article process, sample selection, and two-stage DEA data analysis. Section 5 uses Malmquist to analyse the dynamic effects of the data and finally summarizes the research results of this article.

2. Literature Review

At present, many scholars use the DEA method to measure the actual situation of the operating efficiency of pharmaceutical companies and provide suggestions for improvement of the company’s operations. For example, Wang [1] selected Chinese biopharmaceutical companies from 2017 to 2019 as a research sample and established a static DEA model to measure their financing efficiency. The results show that although the overall financing efficiency of Chinese biotech companies is not high, the level of it is increasing year by year. And Zhang Zicheng [2] innovatively combined the AHP method with the DEA method, finding that, compared with the scale factor, the deficiency of technology hinders the operating performance of companies such as Lunan Pharmaceutical firm. Meanwhile, Li et al. [3] used a two-step method of factor analysis and SE-DEA model to calculate the financial data of 58 listed pharmaceutical companies in China from 2009 to 2013 and concluded that the overall inefficiency of the pharmaceutical industry is also due to insufficient investment on scale and technology.

Among them, some scholars even divide medical companies into groups to study their operational status in different regions [4, 5]. The research found that there are indeed differences in the operating efficiency as well as in terms of technological innovation of pharmaceutical companies in different regions [6, 7]. For example, Xiong [8] used the panel data of technological innovation of medical companies listed in Guangdong, Shandong, Zhejiang, and Jiangsu as samples to study the allocation of technological innovation resources of pharmaceutical manufacturers in four provinces in China from 2015 to 2017 and finally came to the following conclusions: in the province, Jiangsu pharmaceutical companies have the advantage of pure technical efficiency in innovation activities, while Guangdong pharmaceutical companies performed better in scale efficiency with regard to technological innovation. However, the traditional DEA model cannot study the influencing factors in the process. Therefore, the research studies on the two-stage network DEA model and the Malmquist index method have received widespread attention from many scholars.

Regarding the two-stage network DEA, such method has been applied to plenty of fields. For example, Lewis et al. applied the undirected network DEA method to the efficiency evaluation of Major League Baseball [9]. Additionally, Liang et al. used a two-stage network DEA model to analyse the input-output efficiency of 50 universities in China [10], while Li et al. applied the DEA model under the two-stage expansion structure to the research on the efficiency of R&D in China’s provincial regions [11]. At the same time, some researchers also combine two-stage network DEA with other methods. For instance, Chen et al. [12] and Kao [13] combined it with the two-stage additive efficiency decomposition DEA method to study the relative efficiency of 24 non-life insurance companies in Taiwan. Lee and Johnson combined Malmquist under the network DEA structure to study the performance of the semiconductor manufacturing industry [14]. It can be seen that the two-stage network DEA has been widely used in insurance companies, universities, and other industry research. However, research in the pharmaceutical industry is still relatively rare.

Regarding the Malmquist index method, scholars have also made great achievements. In the field of sustainable e-agriculture, Pan and others used the 31 provinces as the research objects and explored the sustainable development efficiency of agriculture in mainland China through DEA and Malmquist productivity index models [15]. In the medical industry, Hashimoto and Haneda [16] used the conventional DEA method and the Malmquist productivity index method to measure the R&D efficiency of the Japanese pharmaceutical industry from the enterprise level and the industry level, respectively. Empirical evidence shows that the total factor productivity of Japan’s pharmaceutical industry is declining, and the main reason for the decline is the sharp decrease in technological changes. What is more, Pannu et al. [17] used the output-oriented VRS model and the Malmquist productivity index method to measure the increase in efficiency and productivity of the Indian pharmaceutical industry over a 10-year period, finding that the increase was mainly due to the growths in technical efficiency. Furthermore, Zhiyue and Qiu [18] also used the Malmquist index method to conduct an empirical analysis of the operating efficiency of China’s biopharmaceutical industry from both horizontal and vertical aspects. The results show that the overall operating efficiency of the biopharmaceutical industry is not ideal, and there is a large difference in efficiency between provinces and cities.

In summary, it can be seen that scholars have used many different methods to study the operating efficiency of pharmaceutical manufacturing enterprises, but the research still has the following shortcomings. Firstly, most research studies on the efficiency of pharmaceutical manufacturing enterprises use nonparametric methods. When measuring enterprise efficiency, some scholars only consider a certain aspect of static or dynamic and thus cannot comprehensively analyse the efficiency level and development trend of pharmaceutical manufacturing enterprises. Secondly, there are few literatures on the research of listed pharmaceutical companies using the two-stage network DEA and Malmquist index method, most of which focus on the traditional DEA method. Finally, in the literature on efficiency influencing factors, the selection of variables is relatively limited, and there are few literatures that consider the R&D capabilities of enterprises. For pharmaceutical manufacturing companies, environmental variables are very important and have a very large impact on the efficiency of the company. Therefore, the external environment of the company should be considered when studying the efficiency of the company. Based on the above deficiencies, this paper uses the two-stage network DEA and Malmquist index method to study the operating efficiency of enterprises from both static and dynamic perspectives. When studying the factors affecting the operating efficiency of enterprises, environmental variables have been added and considered from multiple angles in the article, striving for a more comprehensive selection of influencing factors.

3. Research Method

3.1. Two-Stage Network DEA

In the traditional DEA model, we only know the final efficiency values of the entire process, but the specific situation in the whole process is unknown. The information provided by the traditional DEA model is not enough, and the guidance to managers is limited. The two-stage network DEA model can open the “black box” of the production system, which can effectively measure the complex production network. Therefore, this paper also adopts the two-stage network DEA model for performance evaluation and pays more attention to the progressive relationship between the two stages based on the research results of other scholars. Its internal structure is shown in Figure 1.

Among them, represents the i-th input of in the first stage; represents the intermediate variable, namely, it is not only the d-th output of DMUj in the first stage, but also the d-th input of in the second stage; represents the k-th input of the newly added in the second stage; and represents the r-th output of in the second stage. First of all, calculate the efficiency of the first stage and then calculate the efficiency of the second stage on this basis, which means keeping the efficiency of the first stage unchanged. Finally, the product of the efficiency of the two stages is regarded as the total efficiency of the system. At this point, the model can be established as follows:The first-stage model (model 1) is given byModel 1 adds a constraint on the basis of the traditional CCR model, that is, the last constraint. Its purpose is to ensure that the optimal solution of the first stage makes the efficiency value of the second stage not more than 1, so as to ensure that the second stage model must have a feasible solution; otherwise, there may be no feasible solution. Therefore, this constraint is necessary, which was not considered in the previous two-stage DEA model. In model 1, , respectively, represent the weights of the corresponding variables, after considering the study conducted by Cheng and Zheng [19]. The efficiency of each DMU can be obtained by model 1. Record the efficiency of the as .Then second-stage model (model 2) is given by

Solving (5)–(9) can get the efficiency of the second-stage DMU, noting as the efficiency of the second stage. So far, it can be concluded that the total efficiency of the two-stage system is (10).

3.2. Malmquist Index

The two-stage network DEA model just horizontally compared the efficiency of listed pharmaceutical enterprises. So we will build the Malmquist index model to make a longitudinal analysis of efficiency and dynamically analyse the change of efficiency.TFP is total factor productivity index:PEC is pure efficiency change index:SE is scale efficiency index:TC is technical change index:The formula of total factor productivity is

4. Empirical Analysis

4.1. Sample Selection

With reference to the definition of pharmaceutical companies, combined with the description of the main business in the annual report of the A-share listed pharmaceutical company and the proportion of the main business in operating income, the study sample is determined. At the same time, to ensure the validity of the analysis results, ST, PT, and companies were excluded, and 164 listed pharmaceutical companies were finally identified as the research samples. The data of inputs and outputs comes from Cathay Pacific database and the annual public report of enterprise.

4.2. Variable Selection
4.2.1. The First Stage of Input and Output Variables

The selection of variables is based on character of listed pharmaceutical companies, and we fully inspect the business characteristics and operation of listed pharmaceutical companies. The inputs selected are gross costs , total number of employees , and net value of fixed assets , and the output is gross revenue .

4.2.2. The Second Phase of Input and Output Variables

This paper comprehensively examines the business characteristics and operation of listed pharmaceutical companies. Government subsidies as a new input are added to the second stage, and the other input is the gross revenue that is the output of first stage, and the final output is net profit . More details are shown in Table 1.


Variable typeVariable nameMeasure

The first stageOutputGross costsGross costs in corporate annual reports
InputTotal number of employeesTotal number of in-service staff
Net value of fixed assetsNet value of fixed assets in corporate annual reports
Gross revenueGross revenue in corporate annual reports

The second stageOutputNet profitTotal profit − income tax
InputGross costs
Government subsidiesGovernment subsidies that are included in the current profit and loss

4.3. DEA Efficiency Analysis in the First Stage

In first stage (shown in Table 2), no listed pharmaceutical company’s technical efficiency reached 1 during 2015–2019. There are 119 companies with technical efficiency between 0.6 and 1.0, accounting for 73%, indicating that the technical efficiency of these companies is good. The efficiency values of the eight securities companies (Chongqing Taiji Industry, Baiyunshan, Kelun, Huabei, Hisun, Harbin, China Medicine, and Renfu) are below 0.4, indicating that the technical efficiency level of these companies is relatively low, and they need to increase investment.


Company nameThe first stageThe second stageTotal

Adisseo0.7318074470.704276450.718041949
Anke Bio0.7012585450.656816190.679037367
Osaikon0.775171440.6777564860.726463963
Baiyunshan0.3837606110.3295685680.35666459
Bdyy0.6210632530.6681726570.644617955
Beilu0.7554567290.6625218950.708989312
Porton0.6264431980.6538941420.64016867
None0.7082054890.6447921190.676498804
Changchun High-Tech0.7541401170.7609207620.757530439
Changjiang Runfa Medicine0.6224442990.5819566940.602200497
Changshan Pharma0.7053799570.6632150030.68429748
DAJY0.6114166220.5700904910.590753556
Dezhan0.8001387090.7762217530.788180231
Jiao0.688130710.7612620560.724696383
DBBT0.7157380240.6415931590.678665591
VC0.4631213490.467031950.465076649
Dongcheng0.673841180.6807023720.677271776
Nhwa Pharm0.6557346490.6673497180.661542183
Ekzy0.6790414270.5787081060.628874767
Fangsheng0.7001944710.6566333680.678413919
Fengyuan0.581329290.491931380.536630335
Fczy0.6780399190.6519809450.665010432
Fayy0.647768470.5934469240.620607697
Fosun Pharm0.4696824420.7446472960.607164869
Fuxiang0.6992155670.6003480210.649781794
Guangji0.6864166930.6408386060.66362765
Kwong Sang Hong0.7399910670.6508356770.695413372
Guang Yuyuan0.6978039310.6435731720.670688552
Lark0.6303814640.6577467230.644064093
Glsj0.6771209870.6827962410.679958614
Sinopharm Hyundai0.4962214360.3975137510.446867593
Harbin Pharm0.344289280.4757911430.410040211
Haili Bio0.7278830950.6526821230.690282609
Hnhy0.6099837750.5285809960.569282386
Hepalink0.643895660.5944038590.619149759
Haishun New Pharma0.7509284240.6616757150.706302069
Haisco0.7204516960.4877439870.604097841
Hisoar0.6455446230.654604210.650074417
Haixin0.6275990870.6720977460.649848417
Hisun0.3534604940.4090155540.381238024
Han Sen Pharm0.6687777520.6638996460.666338699
Hybio0.6360330860.5808102370.608421662
Hengrui0.8374905270.9613027390.899396633
Chase Sun0.5477339190.4100160.478874959
NCPC0.362596570.4478019280.405199249
Huahai0.5439954090.5535336650.548764537
Hualan Bio0.8079312130.5958469140.701889064
Huaren0.5953284720.6596448380.627486655
HRSJ0.5417748610.5744257380.558100299
China Resources Double-Crane0.5680388280.546222750.557130789
Huashen Technology0.7191994180.6740355690.696617493
Walter Dyne0.6645965360.6871962190.675896377
Yanbian FC0.7781422580.635909630.707025944
Kyrgyzstan0.6016169260.4960279760.548822451
Jichuan0.6838072040.4878549070.585831056
Jimin0.6811853090.6573225040.669253906
JYPC0.6855629210.6496412110.667602066
Joincare pharm0.6100824570.4541422960.532112376
Jiangzhong0.6425678470.7365676640.689567756
Jincheng0.6397853890.5691783680.604481878
Jinhe Bio0.6233545770.6490286610.636191619
Jinling0.5462357190.6355768440.590906281
Jinshiya0.7157966670.6447578820.680277274
Jingxin0.685712910.5703881420.628050526
Jinghua0.6369449780.6416492870.639297132
Jingfeng0.6064889590.5435304950.575009727
Jiuqiang0.7869562280.7000462070.743501218
Jiuzhitang0.6452010260.5166030240.580902025
Jiuzhou0.6285671150.5841554220.606361268
CONBA0.5438613750.4810043480.512432862
Kanghong0.6652450110.5274608360.596352924
Kangyuan0.614142040.5779269920.596034516
Kangzhi0.6848160870.6564158710.670615979
KHB0.7279765070.5918412980.659908903
Kelun0.3787994410.4932316980.43601557
Sunflower0.5561700970.5114002440.533785171
Kunming Pharm0.5343244160.5519641910.543144303
Lummy0.6250562280.6281127550.626584491
LAYN0.7216144840.6615664790.691590481
Lisheng Pharma0.6573538550.6791771390.668265497
Livzon Pharm0.717381340.6288710940.673126217
LEADMAN0.7149442890.6244983450.669721317
Lianhuan pharm0.6988884110.6703671180.684627765
Lingkang0.7257438740.5846945180.655219196
Lingrui Pharm0.6669977470.6770707790.672034263
Long jin Pharm0.7535702720.6471331660.700351719
Lukang Pharm0.5090276380.4609652450.484996442
Mike Bio0.7424225130.6504076940.696415103
M.k.0.6542204370.5623562830.60828836
Palin Bio0.7032698360.651091860.677180848
PIEN TZE HUANG0.7638245580.7260050710.744914814
Julie Plec0.7096241660.4697892280.589706697
plyy0.4896138090.530540520.510077164
CHEEZHENGTTM0.7146898140.6135826460.66413623
Qidi0.6910539390.6549773340.673015636
Qianhong Biopharma0.702282140.6792034570.690742799
Qianjin Pharm0.6126471810.6075908240.610119003
Qianyuan0.6649734140.5568454970.610909455
Renfu0.3224064250.420165550.371285988
Renhe Pharmacy0.5963514720.6726796190.634515546
rpsw0.6511669250.59129460.621230762
Saisheng0.7619477780.7018368440.731892311
SAM0.6719263360.6597005130.665813424
Shanhe Pharmacy0.7347913490.6502211860.692506268
Shkb0.6980695210.6803878760.689228698
Shanghai RAAS Blood Products0.6764073780.6055619280.640984653
Shenqi0.6465891050.5819697420.614279423
Biological Stock0.7602029810.7560429240.758122952
Salvage Pharm0.4631448720.5024136140.482779243
Sts0.7397815670.6891492440.714465406
Scyy0.6999668110.6033802820.651673546
Beijing SL Pharm0.7737606540.688834420.731297537
Stellite0.6677601490.6427020480.655231099
Shsw0.736920570.6572038640.697062217
Tat0.602650230.5446972590.573673745
Taiji Group0.3992796710.4985186250.448899148
Taloph Pharm0.619242550.6444346230.631838587
Teyi0.6986673830.667166840.682917112
Tasly0.416668530.6302754870.523472009
Tiantan Biological0.7330106680.6576003280.695305498
Tianyao Pharm0.6462400880.6131433570.629691722
Thdb0.778833260.7065397640.742686512
Thjm0.5678091630.5643867420.566097953
TRT0.4996285210.6916214040.595624963
Wanbangde0.4274068520.5379926790.482699766
Wondfo0.7150095550.6317988680.673404211
WEDGE INDUSTRIAL0.7361843080.6433554390.689769873
Weiming0.7118260160.6503218290.681073922
Wowu0.7705670210.6758403620.723203692
Wohua0.7039033920.6611881320.682545762
Wosen0.6719124770.5529041430.61240831
AMD0.7509457810.610523090.680734436
Xianju Pharm0.6098038780.6075172620.60866057
Xiangxue Pharm0.568372510.5377400930.553056301
Sunflower0.558410590.5666836920.562547141
NHU0.6326549790.41225080.52245289
Xinhua0.4889473770.5567670520.522857214
Xinbang0.4402032470.3950792860.417641267
SALUBRIS0.7152400780.630262920.672751499
BROTHER0.6975948710.6215536040.659574237
Yabao0.5580604630.5670984430.562579453
Yatai0.594686080.5686730890.581679584
Yananbikang0.5850800920.5033401820.544210137
Yiling Pharm0.5918149930.6352128970.613513945
Yifan0.6598800290.6090508790.634465454
Yibai0.5640933230.4373306990.500712011
Yisheng0.6616469270.6037370310.632691979
Yiduoli0.6480993840.5976238450.622861614
Chinataurine0.6928811080.6473704140.670125761
Gloria Pharm0.4797399630.4332187530.456479358
Baiyao0.503625580.8191774690.661401524
Zhejiang Medicine0.5008731090.5153435680.508108338
Zhenbao Island0.664680480.4947830020.579731741
zdzy0.5689093210.5617124430.565310882
Zhifei0.8210438440.7835855990.802314721
Zhongguancun0.6032265180.5469173390.575071929
China Medicine0.3260221750.6199022350.472962205
Zhongheng Group0.6993364930.7164319820.707884238
Zhongmu0.5340610490.6252195520.579640301
Zhongxin0.5236562260.647056750.585356488
Zsyy0.7283083940.6119063740.670107384
JLZX0.700326430.5542901010.627308266
Zuoli0.6584877790.4921436860.575315732

Five securities companies, Adisseo, Changchun High-Tech, Hualan Biological Engineering, Hengrui, and Zhifei, have achieved technology effectiveness in some years. Adisseo’s technical efficiency is effective in 2016 and has dropped significantly after 2016, and the technical efficiency can be improved by referring to the operation method of 2016 when the technology is effective (see Table 3 for specific annual data).


Company nameYear
2015-20162016-20172017-20182018-2019

Adisseo10.5388263770.7487696220.639633789
Anke Bio0.698938920.7316204950.6711131970.703361568
Osaikon0.7130773610.756737850.8133390880.817531461
Baiyunshan0.3285421110.3563205230.5465070450.303672764
Bdyy0.5897612320.628838860.5923204920.673332429
Beilu0.7011529730.7699646230.7185344930.832174827
Porton0.6175094150.6220153190.6063509450.659897114
None0.7095275870.7498396740.6806415170.692813179
Changchun High-Tech0.6442389010.6938758560.6784457111
Changjiang Runfa Medicine0.6610739210.6338585320.5776282410.617216502
Changshan Pharma0.7025744350.7374444560.6548969140.726604025
DAJY0.6080341810.6125626730.5656494460.659420187
Dezhan0.7782480140.88597130.8011729530.735162569
Jiao0.6958760140.7525551960.7846275330.519464099
DBBT0.6984274020.7329104560.6837998710.747814366
VC0.3567469510.3696051750.5966200540.529513216
Dongcheng0.6852985290.7133563570.652240540.644469293
Nhwa Pharm0.5660507440.5958888790.5596296120.90136936
Ekzy0.6958931350.6421426880.561110620.817019263
Fangsheng0.6904306350.70723660.6682348420.734875807
Fengyuan0.5079540550.5133820480.5003125290.803668528
Fczy0.6733187220.7065597340.6372875490.694993671
Fayy0.6401991620.6228825810.474879850.853112289
Fosun Pharm0.4408291610.4531778320.4916471360.49307564
Fuxiang0.695091720.7133198610.6553176770.733133012
Guangji0.6658758450.6854651310.6665093770.727816419
Kwong Sang Hong0.7312119490.7581188760.7035989270.767034517
Guang Yuyuan0.7057274470.7288027560.6726831650.684002357
Lark0.5920698790.6076908080.5481446010.77362057
Glsj0.6460763120.702916050.6426426340.716848951
Sinopharm Hyundai0.5505003140.319274420.5867020330.528408978
Harbin Pharm0.2329040040.2397505040.4522435840.452259029
Haili Bio0.7209588250.7557240920.6833838260.751465636
Hnhy0.5937232490.6156636810.5339027850.696645384
Hepalink0.6136246890.5949133190.5650386310.802005999
Haishun New Pharma0.7308584790.7705998060.713685730.788569679
Haisco0.6949507190.6896407950.6277248180.86949045
Hisoar0.5361605140.5983451450.580534810.867138023
Haixin0.5989218520.6516766240.604134510.655663364
Hisun0.2794623810.2912529820.3969708050.446155807
Han Sen Pharm0.6226411450.6703382490.6382640520.743867561
Hybio0.7069900210.7620957360.5698158410.505230747
Hengrui0.6599088760.69005323311
Chase Sun0.5906167290.5463918650.4850560580.568871023
NCPC0.2614786010.2732947640.4765316440.439081271
Huahai0.5109397720.5329789020.4224502720.70961269
Hualan Bio0.7244890530.759038680.748197121
Huaren0.5478119230.606553410.5730184290.653930126
HRSJ0.4218067480.4247801280.6423461210.678166446
China Resources Double-Crane0.3963382040.4120179520.7462953590.717503796
Huashen Technology0.6922828970.7486442240.6731213940.762749156
Walter Dyne0.649057810.6873201560.6043319680.717676209
Yanbian FC0.7284347350.7963582270.6229735960.964802474
Kyrgyzstan0.6906485630.7237214350.6765582690.315539437
Jichuan0.6545934030.6877524640.6589171810.733965769
Jimin0.6672073120.7152031450.6390620340.703268743
JYPC0.6761634290.6605518780.6745643380.730972038
Joincare Pharm0.426099370.7795567090.420131060.814542688
Jiangzhong0.5779830920.6602344970.6217714610.710282338
Jincheng0.6043499750.6356280240.5403280790.778835478
Jinhe Bio0.6204884570.6223158270.5931638620.657450163
Jinling0.4576936470.4644854340.4853000030.77746379
Jinshiya0.7395794610.7948848420.6420739720.686648392
Jingxin0.6150130470.6453711620.6008493030.881618128
Jinghua0.6542920460.6957230850.6449251340.552839647
Jingfeng0.6554708790.6251557810.561687050.583642128
Jiuqiang0.7755370260.805767690.7413535970.825166597
Jiuzhitang0.6384322510.6323456930.5268163110.783209851
Jiuzhou0.5311299290.5630826830.5532271640.866828683
CONBA0.4249595720.4480919240.763183620.539210385
Kanghong0.6496789020.6769134820.6178013130.716586348
Kangyuan0.5471736590.5384124050.5219744360.84900766
Kangzhi0.6814598750.7188562050.6585834090.68036486
KHB0.6799092010.706015260.6462197810.879761787
Kelun0.3094400680.3066321610.4813700930.417755441
Sunflower0.5034686250.5427127140.5411647650.637334286
Kunming Pharm0.4877527660.4945924690.4567842070.698168222
Lummy0.594773780.6501483250.6201394760.63516333
LAYN0.7066662810.7642503450.6584840630.757057246
Lisheng Pharma0.6523582130.6582255850.6338026480.685028972
Livzon Pharm0.4831278880.901473950.7658543450.719069177
LEADMAN0.6852778680.7396936090.6854796690.749326011
Lianhuan Pharm0.6725992940.7117572690.6701595410.741037539
Lingkang0.6989029050.7434022310.6965862790.764084079
Lingrui Pharm0.6744016360.6723890240.6149061340.706294196
Long Jin Pharm0.7318451120.7704524440.7187670220.793216512
Lukang Pharm0.4274684860.465706820.4603327690.682602477
Mike Bio0.7080274830.723198520.6451497420.893314305
M.k.0.7083959240.7264829260.568737590.613265307
Palin Bio0.6713913140.7181819860.6664252420.757080803
PIEN TZE HUANG0.6849306110.7504056590.6610391960.958922766
Julie Plec0.7077043290.7237140450.6734627260.733615566
Plyy0.4069597480.4148537780.4277358390.708905869
CHEEZHENGTTM0.6866829510.7251353570.6782378650.768703082
Qidi0.6668065470.682369240.644983980.770055989
Qianhong Biopharma0.719386820.7193850250.6565600980.713796618
Qianjin Pharm0.5306567910.5486290690.5227137540.848589111
Qianyuan0.6385064670.6715433680.627910260.721933561
Renfu0.3342188030.4132727440.1492314870.392902669
Renhe Pharmacy0.5332684790.5069148130.5199994970.8252231
Rpsw0.6380390280.6549743980.6178454970.693808777
Saisheng0.7544733920.7967484890.727706480.76886275
SAM0.6809094310.6895983580.5940206140.72317694
Shanhe Pharmacy0.7132629810.7477558430.7012916180.776854953
Shkb0.6906136540.7210269370.6553619280.725275566
Shanghai RAAS Blood Products0.8159372160.7116329660.295122330.882937
Shenqi0.6465158920.6651229010.6049057170.669811909
Biological Stock0.7763003640.8577319610.7333105110.673469086
Salvage Pharm0.4054239810.4711474110.5705804080.405427689
Sts0.7453428680.7898095760.6959434430.728030381
Scyy0.6213249920.7366247640.670142760.771774728
Beijing SL Pharm0.7549595690.8224792510.7390666370.778537157
Stellite0.646998610.6797910020.6487082420.695542742
Shsw0.7064906880.7490479720.7018121010.790331518
Tat0.5833020350.5798028330.5306127460.716883304
Taiji Group0.3686979530.2883734470.5055066690.434540617
Taloph Pharm0.605054210.6447078820.5694537810.657754327
Teyi0.6856393880.7101370440.6673097160.731583384
Tasly0.3447363230.3643982460.5475079610.410031592
Tiantan Biological0.566854930.7351396850.7055972370.924450822
Tianyao Pharm0.5677840750.6401007990.5683663280.808709151
Thdb0.6904201520.760737240.6920586340.972117015
Thjm0.7007593080.6820660650.620990780.2674205
TRT0.4499416370.4525618560.5785653130.517445278
Wanbangde0.3503981530.4108983120.4645474950.483783447
Wondfo0.7060339330.7491632590.6663774870.738463539
WEDGE INDUSTRIAL0.7180411810.7683976980.6902601110.76803824
Weiming0.7481770090.7691283210.6198534860.710145248
Wowu0.7392984510.7842439220.73861250.820113211
Wohua0.6743229380.7101570450.6590120710.772121516
Wosen0.6193034120.5714918750.7973938210.699460802
AMD0.6899665690.7899412630.713295270.810580022
Xianju Pharm0.5144648570.5664906220.5401095860.818150446
Xiangxue Pharm0.5377836670.5400686230.4812752330.714362519
Sunflower0.5400295230.5989986320.3885320540.706082152
NHU0.5176920550.6038835170.660537920.748506424
Xinhua0.4021098630.4301563220.4371749290.686348395
Xinbang0.430116240.4288081920.3612372550.540651302
SALUBRIS0.7448816150.7792941360.6976514880.639133074
BROTHER0.6633838530.7195289770.5835981780.823868476
Yabao0.4534187060.5079143920.5150889770.755819777
Yatai0.6876246160.7234979460.6586610440.308960713
Yananbikang0.6150882740.5986679780.635097450.491466664
Yiling Pharm0.5353998970.546293010.5210046940.764562368
Yifan0.621372480.7061452320.5434275560.768574846
Yibai0.6018199830.5832459520.3444749020.726832453
Yisheng0.6407080570.6792030140.6235393810.703137255
Yiduoli0.618786470.6010978540.5669682910.80554492
Chinataurine0.653922480.7158652760.6745135720.727223103
Gloria Pharm0.6198024620.5831614650.5347497760.181246147
Baiyao0.4077032920.4153258320.5986832780.592789916
Zhejiang Medicine0.396941490.3939987310.6274378980.585114319
Zhenbao Island0.6156370110.6304671450.5654641870.847153576
Zdzy0.539305680.5415024350.4472816590.74754751
Zhifei0.6590112630.7793839240.845780191
Zhongguancun0.6079849420.5976610570.5591899380.648070137
China Medicine0.2982937190.2843217850.3893965590.332076638
Zhongheng Group0.65833690.7308237380.6608844530.747300882
Zhongmu0.466975420.5057017280.4807530410.682814008
Zhongxin0.4258699970.4786045660.4753363450.714813998
Zsyy0.6722847420.7218394710.641258140.877851223
JLZX0.6580452750.7247155090.5966661890.821878746
Zuoli0.6470506890.6671067220.6163724590.703421246

4.4. DEA Efficiency Analysis in the Second Stage

In the second stage (shown in Table 2), there is no company that has reached technical efficiency of 1 during 2015–2019, indicating that all the companies were not effective. There are 97 companies with a technical efficiency value of 0.6–1.0, accounting for 59.5%, which is significantly lower than the first stage. Only 3 companies had technical efficiency below 0.4 (Sinopharm, Xinbang, and Baiyunshan). The technical efficiency in second stage is generally low, but companies with lower efficiency have been promoted, which may be correlated with government subsidies.

Adisseo, Dezhan Health, Jiao, Hengrui, Livzon Group, Shanghai RAAS, and Zhifei Biotechnology have achieved technical efficiency of 1 in some years. And Adisseo is the same as the first stage, the technical efficiency is effective in 2016 and has dropped dramatically after 2016. The technical efficiency of Dezhan Health and Jiao pharmaceutical companies in the second stage has increased; we believe that the first stage is relevant to the second stage in these companies. However, the average technical efficiency of Hualan Biological is below 0.6 in the two stages, for the resources cannot be well utilized (see Table 4 for specific annual data).


Company nameYear
2015-20162016-20172017-20182018-2019

Adisseo10.6498375450.6257374310.541530825
Anke Bio0.7651620260.4761890620.7722551490.613658522
Osaikon0.8437780660.4414459850.8507807310.575021161
Baiyunshan0.1280297310.3384728090.446815410.404956323
Bdyy0.8908343950.4452227540.7276765090.608956969
Beilu0.8085449910.4477814740.7352022660.658558851
Porton0.7906068560.4528643560.7411518930.630953461
None0.8995533520.4122247690.6921647380.575225616
Changchun High-Tech0.6973316340.5340868020.9742107310.83805388
Changjiang Runfa Medicine0.7582782020.4572085970.7571782280.355161748
Changshan Pharma0.8394309130.4522683920.7229494260.63821128
DAJY0.4987498120.4530467710.7360621020.592503277
Dezhan10.5401445160.9073256080.65741689
Jiao10.69565735510.349390869
DBBT0.791609790.4438872790.7254361610.605439407
VC0.521766850.4525604750.49232620.401474275
Dongcheng0.8756323340.4569548620.7686388570.621583435
Nhwa Pharm0.9026855340.4811017410.8125063470.47310525
Ekzy0.6639656940.4921396530.7415891750.417137904
Fangsheng0.837283480.4459126680.7294483770.613888946
Fengyuan0.3817007720.4482481780.7292552550.408521314
Fczy0.8108294550.4503230960.7336124140.613158813
Fayy0.8538510880.4626521380.6266760330.430608438
Fosun Pharm0.4058795890.8876084740.8630396040.822061518
Fuxiang0.6861479370.403026210.659687930.652530008
Guangji0.7817837440.4388624220.7281055840.614602675
Kwong Sang Hong0.8336008980.4452354210.7218736050.602632782
Guang Yuyuan0.8735446330.4671737160.789871180.443703159
Lark0.8563597230.5073552750.8345416430.432730249
Glsj0.7628644980.4989327750.8024015530.666986138
Sinopharm Hyundai0.3043264410.4678727650.5226099030.295245894
Harbin Pharm0.4719078320.4989279740.518929830.413398936
Haili Bio0.8459815940.4529635860.7157619690.596021344
Hnhy0.5461322860.4512090710.739299060.377683569
Hepalink0.5706928290.4557735860.8337482270.517400795
Haishun New Pharma0.8751911210.4412839290.7185837190.61164409
Haisco0.2652477590.4620945740.7704046380.453228976
Hisoar0.8067776930.4837330430.8412845760.486621529
Haixin0.8695993120.451220250.7488380020.618733421
Hisun0.2953306770.4699350510.4346721280.436124359
Han Sen Pharm0.8427922740.4490549270.7392697820.624481603
Hybio0.7411908630.4821594270.649406530.450484128
Hengrui0.9940066190.85120433511
Chase Sun0.5109347680.2529636320.3881831450.487982456
NCPC0.4469581380.4428936230.4850195120.416336438
Huahai0.4836314190.5178198760.7445625470.468120819
Hualan Bio0.4880186880.4632751330.8251558670.606937968
Huaren0.8583224420.4459579890.726142990.608155932
HRSJ0.390559960.6062967310.6598274560.641018804
China Resources Double-Crane0.5320238860.5442029860.5886791290.519985
Huashen Technology0.8994237230.4618629090.7239069590.610948685
Walter Dyne0.8425026960.4972427210.7671432010.641896259
Yanbian FC0.4426233860.6573294080.8878159630.555869763
Kyrgyzstan0.4551808570.4668361490.7630562610.299038639
Jichuan0.6924560780.2690194830.4808911910.509052877
Jimin0.8431759820.4472301930.725934010.612949828
JYPC0.9022645040.4057529650.7107645030.579782874
Joincare Pharm0.3206793440.4460191870.4423653250.607505328
Jiangzhong0.9685781560.4889101630.8069956810.681786657
Jincheng0.6805876260.4468837250.7264634330.422778688
Jinhe Bio0.7675559710.4548008530.7518967260.621861095
Jinling0.8821518040.4626325320.7771498180.420373223
Jinshiya0.8188249750.4303377380.7080294090.621839405
Jingxin0.7542717860.39972610.6690112250.458543459
Jinghua0.8188964140.464798970.7713051730.511596589
Jingfeng0.6596288370.4625869120.7562454340.295660795
Jiuqiang0.8888000590.4751141260.7795411590.656729486
Jiuzhitang0.6829395940.3883852960.5762211750.418866032
Jiuzhou0.700683030.4593764940.750175960.426386203
CONBA0.4508403670.5320075340.5721598010.36900969
Kanghong0.6308619170.3838742540.6329104240.462196751
Kangyuan0.5698894620.4838125620.8000758310.457930115
Kangzhi0.8604485120.4470777290.7228074240.595329819
KHB0.758740450.4462622530.7318655530.430496937
Kelun0.3921925540.4918780680.5739393150.514916857
Sunflower0.3970694080.4022218670.6777471890.568562512
Kunming Pharm0.4847125390.4828525570.7878632720.452428396
Lummy0.7575919110.4460242940.7356994240.57313539
LAYN0.826164770.466737080.7350881040.618275961
Lisheng Pharma0.8922898980.448238650.7437826060.632397402
Livzon Pharm0.33047651910.6212691090.563738749
LEADMAN0.7444911320.4396741970.7085872780.605240773
Lianhuan Pharm0.8833816640.4498826860.7335903380.614613785
Lingkang0.4873426480.4611473150.7556094230.634678684
Lingrui Pharm0.8205702940.4691082870.7684032760.650201258
Long Jin Pharm0.8252695050.4454742960.72134830.596440566
Lukang Pharm0.7093075440.2719677640.4484059640.41417971
Mike Bio0.8296724910.4919635440.8165164520.463478288
M.k.0.7025401710.4617985680.7505047230.334581671
Palin Bio0.8003666880.442994070.7337465580.627260125
PIEN TZE HUANG0.90453430.5382908450.9475361740.513658965
Julie Plec0.5495210850.3051118150.4999393270.524584684
Plyy0.5334028940.4219727280.7080182130.458768245
CHEEZHENGTTM0.5651486590.4654294480.7614718670.662280612
Qidi0.861178920.4415271530.7095082070.607695056
Qianhong Biopharma0.8746060480.4563155370.7505635730.635328671
Qianjin Pharm0.7392461080.4718145040.7799248670.439377818
Qianyuan0.8313389850.4045752770.6551546680.336313058
Renfu0.3344142050.6180459990.1760554830.552146513
Renhe Pharmacy0.8881118730.4960370760.8370408150.469528713
Rpsw0.5943945680.4338808690.7099304870.626972475
Saisheng0.9296829340.4761577780.775990030.625516632
SAM0.9052646820.4511502870.6805558610.60183122
Shanhe Pharmacy0.8479248440.4329281460.7100087650.610022991
Shkb0.8480897110.4714991570.7589092870.643053348
Shanghai RAAS Blood Products10.544708280.4095203540.468019077
Shenqi0.7147620960.3813445610.6176130410.61415927
Biological Stock0.9653593570.5492231790.8706204830.638968678
Salvage Pharm0.6893644330.4153082010.7067494770.198232344
Sts0.9357575170.4721413330.743570520.605127606
Scyy0.7391535330.4264859930.6764147710.571466831
Beijing SL Pharm0.7337499390.5069477810.8328428480.681797114
Stellite0.8033788960.4373709930.7152512620.614807042
Shsw0.8904106160.4347617690.7001775970.603465476
Tat0.8425862940.3608079230.5848232440.390571577
Taiji Group0.679199530.4529807770.4740015410.387892653
Taloph Pharm0.8426368240.4382247810.6891615250.607715363
Teyi0.8363586940.4536820770.7490143520.629612238
Tasly0.7172991630.6157350650.6752241490.512843572
Tiantan Biological0.6786135590.5910612540.8602467740.500479724
Tianyao Pharm0.8576771160.4483939480.7372409470.409261416
Thdb0.9006881310.5457224150.8887417770.491006733
Thjm0.7449761470.473113690.7853343150.254122817
TRT0.8361779720.6561516090.7035607230.570595311
Wanbangde0.7928518610.4536006640.4823064060.423211786
Wondfo0.7182341540.4236541280.7123946550.672912535
WEDGE INDUSTRIAL0.786233380.4507056670.7271736660.609309043
Weiming0.8018467210.4890195590.6975306620.612890373
Wowu0.9151782810.4442288650.7325301560.611424148
Wohua0.8499510670.4511581650.7266572430.616986053
Wosen0.2935388340.368962970.9275819150.621532855
AMD0.5657896970.466053090.7559410060.654308568
Xianju Pharm0.7221298260.469752240.7886108220.449576159
Xiangxue Pharm0.565944130.4502466090.7286015950.406168036
Sunflower0.7757272470.4351194950.4726275810.583260445
NHU0.7444461920.0847924090.1745175150.645247085
Xinhua0.6861500260.4185801310.6914756440.430862405
Xinbang0.371466410.4823792560.297065990.42940549
SALUBRIS0.8289506890.455146340.7440677550.492886897
BROTHER0.8673615670.4912909660.7229691930.404592688
Yabao0.625382230.4659814240.7761641460.400865973
Yatai0.7918699110.4585799050.7494885010.274754037
Yananbikang0.6095922910.5257548390.4958899030.382123694
Yiling Pharm0.7321997420.5057486710.835209040.467694136
Yifan0.8953803660.4281631950.6144912150.498168738
Yibai0.6228202740.327751120.3862509870.412500415
Yisheng0.6158185610.4502338590.7338561060.615039599
Yiduoli0.7683702150.4541516980.7490928340.418880633
Chinataurine0.7905722540.4454969580.7347871510.618625291
Gloria Pharm0.4253751380.4730218260.7351987570.099279291
Baiyao0.6764208970.8312255190.8985342610.870529199
Zhejiang Medicine0.6489776210.4709282380.5088772620.43259115
Zhenbao Island0.3667142140.447969520.7190058350.445442437
Zdzy0.6664627770.4772720750.6880043430.415110578
Zhifei0.7943125610.4885837410.851446092
Zhongguancun0.6384314870.3610484750.6008297230.58735967
China Medicine0.6003856610.6329357670.7050127280.541274783
Zhongheng Group0.7794883370.5164317250.8429807720.726827094
Zhongmu0.8917914260.4615958810.7586295870.388861314
Zhongxin0.7842991660.5000359960.8337342880.470157549
Zsyy0.7122365560.4937766550.8058818330.435730452
JLZX0.568394670.4873518220.75384120.407572713
Zuoli0.5068306350.3278428370.5300452260.603856046

4.5. Overall Efficiency Analysis

In the overall efficiency analysis (shown in Table 2), there are 109 companies with efficiency between 0.6 and 1.0, accounting for 66.9%, and it shows that the technical efficiency of the second stage is less than that of the first stage.

Comparing Hengrui (the highest efficiency) and Baiyunshan (the lowest efficiency), we found that Hengrui did not receive government subsidies in the 2018 and 2019, but the technical efficiency reached 1, and Baiyunshan has received government subsidies, but the technical efficiency rises first and then decreases.

For Zhifei Bio with the second highest efficiency, its efficiency in 2018 and 2019 was significantly higher than in 2016 and 2017, and the government subsidies received by Zhifei Bio in 2018 and 2019 were significantly lower than before. The second-to-last-ranked company, Medicare, reached a low point in 2018, followed by a significant rebound next year, when it was not subsidized by the government in 2019.

It can be concluded that government subsidies have no obvious effect for most companies, but it has a positive impact on enterprises with low efficiency in a short term. The government may need to reconsider the way of subsidies to pharmaceutical companies, such as the capital subsidies to equipment upgrades and talent introduction (see Table 5 for specific annual data).


Company nameYear
2015-20162016-20172017-20182018-2019

Adisseo10.5943319610.6872535270.590582307
Anke Bio0.7320504730.6039047780.7216841730.658510045
Osaikon0.7784277130.5990919170.8320599090.696276311
Baiyunshan0.2282859210.3473966660.4966612280.354314544
Bdyy0.7402978130.5370308070.65999850.641144699
Beilu0.7548489820.6088730490.7268683790.745366839
Porton0.7040581350.5374398380.6737514190.645425288
None0.8045404690.5810322220.6864031270.634019398
Changchun High-Tech0.6707852680.6139813290.8263282210.91902694
Changjiang Runfa Medicine0.7096760620.5455335640.6674032350.486189125
Changshan Pharma0.7710026740.5948564240.688923170.682407653
DAJY0.5533919960.5328047220.6508557740.625961732
Dezhan0.8891240070.7130579080.854249280.696289729
Jiao0.8479380070.7241062760.8923137670.434427484
DBBT0.7450185960.5883988670.7046180160.676626886
VC0.43925690.4110828250.5444731270.465493745
Dongcheng0.7804654310.585155610.7104396980.633026364
Nhwa Pharm0.7343681390.538495310.6860679790.687237305
Ekzy0.6799294150.5671411710.6513498980.617078583
Fangsheng0.7638570570.5765746340.698841610.674382377
Fengyuan0.4448274140.4808151130.6147838920.606094921
Fczy0.7420740880.5784414150.6854499820.654076242
Fayy0.7470251250.5427673590.5507779410.641860364
Fosun Pharm0.4233543750.6703931530.677343370.657568579
Fuxiang0.6906198280.5581730350.6575028030.69283151
Guangji0.7238297950.5621637760.6973074810.671209547
Kwong Sang Hong0.7824064230.6016771490.7127362660.684833649
Guang Yuyuan0.789636040.5979882360.7312771720.563852758
Lark0.7242148010.5575230410.6913431220.603175409
Glsj0.7044704050.6009244120.7225220940.691917544
Sinopharm Hyundai0.4274133770.3935735920.5546559680.411827436
Harbin Pharm0.3524059180.3693392390.4855867070.432828982
Haili Bio0.7834702090.6043438390.6995728980.67374349
Hnhy0.5699277670.5334363760.6366009220.537164476
Hepalink0.5921587590.5253434520.6993934290.659703397
Haishun New Pharma0.80302480.6059418680.7161347250.700106885
Haisco0.4800992390.5758676850.6990647280.661359713
Hisoar0.6714691030.5410390940.7109096930.676879776
Haixin0.7342605820.5514484370.6764862560.637198392
Hisun0.2873965290.3805940160.4158214670.441140083
Han Sen Pharm0.7327167090.5596965880.6887669170.684174582
Hybio0.7240904420.6221275820.6096111850.477857438
Hengrui0.8269577480.77062878411
Chase Sun0.5507757480.3996777490.4366196020.528426739
NCPC0.3542183690.3580941940.4807755780.427708854
Huahai0.4972855950.5253993890.583506410.588866754
Hualan Bio0.6062538710.6111569060.7866764930.803468984
Huaren0.7030671830.52625570.6495807090.631043029
HRSJ0.4061833540.515538430.6510867890.659592625
China Resources Double-Crane0.4641810450.4781104690.6674872440.618744398
Huashen Technology0.795853310.6052535660.6985141760.686848921
Walter Dyne0.7457802530.5922814380.6857375850.679786234
Yanbian FC0.585529060.7268438180.755394780.760336119
Kyrgyzstan0.572914710.5952787920.7198072650.307289038
Jichuan0.6735247410.4783859740.5699041860.621509323
Jimin0.7551916470.5812166690.6824980220.658109286
JYPC0.7892139670.5331524210.6926644210.655377456
Joincare Pharm0.3733893570.6127879480.4312481920.711024008
Jiangzhong0.7732806240.574572330.7143835710.696034497
Jincheng0.64246880.5412558740.6333957560.600807083
Jinhe Bio0.6940222140.538558340.6725302940.639655629
Jinling0.6699227260.4635589830.631224910.598918507
Jinshiya0.7792022180.612611290.675051690.654243898
Jingxin0.6846424160.5225486310.6349302640.670080794
Jinghua0.736594230.5802610280.7081151530.532218118
Jingfeng0.6575498580.5438713460.6589662420.439651462
Jiuqiang0.8321685430.6404409080.7604473780.740948042
Jiuzhitang0.6606859220.5103654950.5515187430.601037942
Jiuzhou0.6159064790.5112295880.6517015620.646607443
CONBA0.437899970.4900497290.6676717110.454110037
Kanghong0.6402704090.5303938680.6253558680.589391549
Kangyuan0.5585315610.5111124830.6610251340.653468887
Kangzhi0.7709541930.5829669670.6906954160.63784734
KHB0.7193248250.5761387570.6890426670.655129362
Kelun0.3508163110.3992551140.5276547040.466336149
Sunflower0.4502690170.472467290.6094559770.602948399
Kunming Pharm0.4862326520.4887225130.6223237390.575298309
Lummy0.6761828460.5480863090.677919450.60414936
LAYN0.7664155260.6154937130.6967860830.687666604
Lisheng Pharma0.7723240560.5532321180.6887926270.658713187
Livzon Pharm0.4068022040.9507369750.6935617270.641403963
LEADMAN0.71488450.5896839030.6970334740.677283392
Lianhuan Pharm0.7779904790.5808199770.701874940.677825662
Lingkang0.5931227770.6022747730.7260978510.699381382
Lingrui Pharm0.7474859650.5707486550.6916547050.678247727
Long Jin Pharm0.7785573080.607963370.7200576610.694828539
Lukang Pharm0.5683880150.3688372920.4543693660.548391093
Mike Bio0.7688499870.6075810320.7308330970.678396297
M.k.0.7054680470.5941407470.6596211570.473923489
Palin Bio0.7358790010.5805880280.70008590.692170464
PIEN TZE HUANG0.7947324550.6443482520.8042876850.736290866
Julie Plec0.6286127070.514412930.5867010260.629100125
Plyy0.4701813210.4184132530.5678770260.583837057
CHEEZHENGTTM0.6259158050.5952824020.7198548660.715491847
Qidi0.7639927330.5619481960.6772460940.688875522
Qianhong Biopharma0.7969964340.5878502810.7035618360.674562645
Qianjin Pharm0.6349514490.5102217870.6513193110.643983464
Qianyuan0.7349227260.5380593220.6415324640.529123309
Renfu0.3343165040.5156593710.1626434850.472524591
Renhe Pharmacy0.7106901760.5014759440.6785201560.647375907
Rpsw0.6162167980.5444276340.6638879920.660390626
Saisheng0.8420781630.6364531340.7518482550.697189691
SAM0.7930870560.5703743230.6372882380.66250408
Shanhe Pharmacy0.7805939120.5903419940.7056501920.693438972
Shkb0.7693516820.5962630470.7071356070.684164457
Shanghai RAAS Blood Products0.9079686080.6281706230.3523213420.675478039
Shenqi0.6806389940.5232337310.6112593790.641985589
Biological Stock0.8708298610.703477570.8019654970.656218882
Salvage Pharm0.5473942070.4432278060.6386649430.301830016
Sts0.8405501920.6309754550.7197569820.666578994
Scyy0.6802392630.5815553780.6732787660.671620779
Beijing SL Pharm0.7443547540.6647135160.7859547430.730167136
Stellite0.7251887530.5585809970.6819797520.655174892
Shsw0.7984506520.5919048710.7009948490.696898497
Tat0.7129441650.4703053780.5577179950.553727441
Taiji Group0.5239487410.3706771120.4897541050.411216635
Taloph Pharm0.7238455170.5414663310.6293076530.632734845
Teyi0.7609990410.5819095610.7081620340.680597811
Tasly0.5310177430.4900666560.6113660550.461437582
Tiantan Biological0.6227342440.663100470.7829220060.712465273
Tianyao Pharm0.7127305960.5442473730.6528036370.608985283
Thdb0.7955541420.6532298280.7904002060.731561874
Thjm0.7228677280.5775898770.7031625470.260771659
TRT0.6430598050.5543567330.6410630180.544020294
Wanbangde0.5716250070.4322494880.4734269510.453497617
Wondfo0.7121340430.5864086940.6893860710.705688037
WEDGE INDUSTRIAL0.752137280.6095516830.7087168890.688673642
Weiming0.7750118650.629073940.6586920740.661517811
Wowu0.8272383660.6142363940.7355713280.715768679
Wohua0.7621370030.5806576050.6928346570.694553785
Wosen0.4564211230.4702274220.8624878680.660496829
AMD0.6278781330.6279971770.7346181380.732444295
Xianju Pharm0.6182973410.5181214310.6643602040.633863302
Xiangxue Pharm0.5518638990.4951576160.6049384140.560265277
Sunflower0.6578783850.5170590630.4305798180.644671298
NHU0.6310691240.3443379630.4175277170.696876755
Xinhua0.5441299450.4243682260.5643252860.5586054
Xinbang0.4007913250.4555937240.3291516230.485028396
SALUBRIS0.7869161520.6172202380.7208596220.566009985
BROTHER0.765372710.6054099710.6532836860.614230582
Yabao0.5394004680.4869479080.6456265610.578342875
Yatai0.7397472640.5910389260.7040747720.291857375
Yananbikang0.6123402830.5622114090.5654936760.436795179
Yiling Pharm0.6337998190.5260208410.6781068670.616128252
Yifan0.7583764230.5671542140.5789593850.633371792
Yibai0.6123201290.4554985360.3653629450.569666434
Yisheng0.6282633090.5647184370.6786977440.659088427
Yiduoli0.6935783420.5276247760.6580305620.612212776
Chinataurine0.7222473670.5806811170.7046503620.672924197
Gloria Pharm0.52258880.5280916460.6349742660.140262719
Baiyao0.5420620950.6232756760.748608770.731659557
Zhejiang Medicine0.5229595550.4324634840.568157580.508852734
Zhenbao Island0.4911756120.5392183320.6422350110.646298006
Zdzy0.6028842280.5093872550.5676430010.581329044
Zhifei0.7266619120.6339838320.9228900950.925723046
Zhongguancun0.6232082150.4793547660.580009830.617714904
China Medicine0.449339690.4586287760.5472046430.43667571
Zhongheng Group0.7189126180.6236277310.7519326120.737063988
Zhongmu0.6793834230.4836488040.6196913140.535837661
Zhongxin0.6050845810.4893202810.6545353160.592485773
Zsyy0.6922606490.6078080630.7235699870.656790838
JLZX0.6132199720.6060336660.6752536940.61472573
Zuoli0.5769406620.4974747790.5732088420.653638646

5. Dynamic Effect Analysis

The efficiency of the two-stage network DEA model varies from year to year, and the efficiency value of different years is not comparable, so time series analysis cannot be carried out. To make up for the shortcomings of the traditional two-stage network DEA, this paper adds the Malmquist index to study the total factor productivity of listed pharmaceutical companies in 2015–2019 and quantify its decomposition limit. The results are shown in Table 6.


ECSCTCPCTFP

2015-20161.18412.79101.01090.42421.1970
2016-20170.51621.44721.12110.35670.5787
2017-20180.98152.46761.00180.39780.9833
2018-20190.51881.21761.00340.42600.5205
Mean0.80011.98081.03430.40120.8219

The average total factor productivity (TFP) is 0.8219 that has fallen by an average 17.81%. Viewed from the decomposition index, the mean of EC is 0.8001; that is, EC has decreased by an average of 19.99%. The mean of PC is 0.4012, with the rate of decline in each averaging over 59.88% a year. SC is 1.9808, with an average annual growth rate of 98.08%. It shows that the operating efficiency of listed pharmaceutical enterprises depends on the scale expansion and makes up for low management efficiency. The average technology change (TC) is 1.0343, and it has risen by nearly 3.43% per year. The technology change has been improved between 2015 and 2019.

The listed pharmaceutical companies rely on product development and can be combined with innovative technologies. For the pharmaceutical industry, the level of research and development of products indirectly affects the level of industry development. The drugs or pharmaceutical equipment is very important; if the level of medical technology research and development is not advanced enough, the progress of medical level will be affected. Therefore, the listed pharmaceutical enterprises should rely on the existing advanced technology achievements, improving their own technology, to improve operating efficiency.

6. Conclusion

This paper firstly divides the two subsystems by using the two-stage network DEA and analyses the operating efficiency of 1,63 listed pharmaceutical companies in China from 2015 to 2019. Secondly, Malmquist index is used for dynamic analysis; the total factor productivity and decomposition limit were obtained. Finally, we make some suggestions based on the results of the study.

From the results, the technical efficiency of the second phase is less than that of the first stage; government subsidies have no positive impact on most companies. It is possible that enterprises move government subsidies elsewhere rather than pharmaceutical companies. It is also possible that government subsidies have increased, enterprises are more willing to invest in product development and enterprises expansion, and it is difficult to see the improvement of operational efficiency in the short term. However, the government subsidies have a positive impact on enterprises with low efficiency in a short term. To ensure the efficiency of investment and avoid waste of resources, government needs to choose the object of subsidies carefully and reformulate policies that encourage pharmaceutical listed companies. And according to the Malmquist index results, enterprises should pay attention to risk prevention and avoid rapid expansion bringing in diseconomies of scale. All in all, enterprises should improve management level and technological capabilities and shift scale growth to total factor productivity.

However, there are also some limitations. Regarding the data resources, the data we chose cannot exactly predict the operational situations among current Chinese medical firms, since there is more uncertainty in the market, especially during the COVID-19 period, which is likely to be a potential direction that other scholars can study further in the future. Concerning variables, this article analyses the operational efficiency of 164 firms; researchers can only choose several companies to make an in-depth analysis instead of the whole industrial analysis.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was financially supported by Guangdong Planning Office of Philosophy and Social Sciences Project (Youth): Research on cross-border social responsibility of private foreign trade enterprises in Guangdong and the reconstruction of legitimacy—Perspective of the organization to piece together. (Project number: GD20YGL09). And it also supported by Department of Education of Guangdong Province “Innovative projects with characteristics of ordinary universities” project: Research on Sustainable Development of Foreign Trade in Guangdong Province Based on Energy Footprint (Project no. 2019WTSCX158). Moreover, it is also supported by Key Discipline-International Business Construction and Development Project (Project no. HS2019CXQX17).

References

  1. Y. Wang, “Research on the financing efficiency of biomedicine industry based on DEA model,” Hebei Enterprise, no. 01, pp. 104-105, 2021. View at: Publisher Site | Google Scholar
  2. Z. Zhang, Research on Performance Evaluation of Pharmaceutical Enterprises Based on AHP-DEA Method, Tianjin University, Tianjin, China, 2018.
  3. Z. Li, R. Liu, H. He, and J. Qin, “An empirical study on the efficiency of the pharmaceutical industry based on the SE-DEA model,” Accounting Communications, no. 14, pp. 26–28, 2016. View at: Google Scholar
  4. X. Zhou and C. Luo, “Research on the relative effectiveness of China’s pharmaceutical industry technological innovation based on DEA model,” Science and Technology Management Research, no. 9, pp. 252–254, 2009. View at: Google Scholar
  5. P. Shi and L. Tao, “Evaluation of innovation performance of pharmaceutical industry based on DEA model,” Economic Perspective, no. 3, pp. 28-29, 2011. View at: Publisher Site | Google Scholar
  6. H. Chen, Research on Technology Innovation Efficiency of China’s Pharmaceutical Industry—Analysis Based on Panel Data, Jinan University, Guangzhou, China, 2011.
  7. J. Liu, “Study on performance evaluation of science and technology investment in China’s pharmaceutical high-tech industry,” Science Management Research, vol. 27, no. 4, pp. 35–38, 2009. View at: Google Scholar
  8. A. Xiong, Research on Technological Innovation Efficiency of Pharmaceutical Companies Based on DEA Method, Guangdong Pharmaceutical University, Guangzhou, China, 2020.
  9. H. F. Lewis, S. Mallikarjun, and T. R. Sexton, “Unoriented two-stage DEA: the case of the oscillating intermediate products,” European Journal of Operational Research, vol. 229, no. 2, pp. 529–539, 2013. View at: Publisher Site | Google Scholar
  10. L. Liang, Z.-Q. Li, W. D. Cook et al., “Data envelopment analysis efficiency in two-stage networks with feedback,” IIE Transactions, vol. 43, no. 5, pp. 309–322, 2010. View at: Google Scholar
  11. L. Liang, D. Wade, and J. Z. Cook, “DEA models for two-stage processes: game approach and efficiency decomposition,” Naval Research Logistics, vol. 55, no. 7, pp. 643–653, 2008. View at: Google Scholar
  12. Y. Chen, J. Du, H. David Sherman et al., “DEA model with shared resources and efficiency decomposition,” European Journal of Operational Research, vol. 207, no. 1, pp. 339–349, 2010. View at: Google Scholar
  13. C. Kao, “Efficiency decomposition in network data envelopment analysis with slacks-based measures,” European Journal of Operational Research, vol. 192, no. 3, pp. 949–962, 2009. View at: Google Scholar
  14. C.-Y. Lee, L. Andrew, and Z. Johnson, “A decomposition of productivity change in the semiconductor manufacturing industry,” International Journal of Production Research, vol. 49, no. 16, pp. 4761–4785, 2011. View at: Google Scholar
  15. W. T. Pan, M. E. Zhuang, Y. Y. Zhou et al., “Research on sustainable development and efficiency of China’s E-agriculture based on a data envelopment analysis-malmquist model,” Technological Forecasting and Social Change, vol. 162, Article ID 120298, 2021. View at: Publisher Site | Google Scholar
  16. A. Hashimoto and S. Haneda, “Measuring the change in R&D efficiency of the Japanese pharmaceutical industry,” Research Policy, vol. 37, no. 10, pp. 1829–1836, 2008. View at: Google Scholar
  17. H. Pannu, U. Kumar, and J. Farooqui, Impact of Innovationon the Performance of Indian Pharmaceutical Industry Using Data Envelopment Analysis, IIMB working paper, Karnataka, India, 2010.
  18. X. Zhiyue and J. Qiu, “Evaluation and analysis of the operating efficiency of my country’s biopharmaceutical industry,” Shanghai Medicine, vol. 36, no. 13, pp. 50–53, 2015. View at: Google Scholar
  19. P. Cheng and C. Zheng, “Research on my country’s science and technology input efficiency based on decision preference two-stage network DEA,” Science and Technology Progress and Policy, vol. 31, no. 08, pp. 125–129, 2014. View at: Publisher Site | Google Scholar

Copyright © 2021 Tsung-Xian Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views269
Downloads379
Citations

Related articles

No related content is available yet for this article.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.