2016 International Conference on Computer, Mechatronics and Electronic Engineering (CMEE 2016) ISBN: 978-1-60595-406-6

Study on Input Measuring Method of Government Public Resources in Beijing, and Regions Ya-wei JIANG1, Hua-li CAI1,* and Ji-rong NING3 1China National Institute of Standardization, Beijing. 2The Standardization Institute of Xinjiang, Xinjiang, China *Corresponding author

Keywords: Public resources input, Z-score standardization, Beijing, Tianjin and Hebei regions.

Abstract. Public resources are of external features as the services provided by the government. Scientific and rational distribution of public resources could drive industry development, promote the process of urbanization and increase human capital. More importantly, it can make economic development fruits equally shared by the public and help the achievement of the current goals of China in terms of reform and development. This paper, by using Z-score standardization method, conducts non-dimensional processing of statistical indexes concerning public resources input, enables the level of public resources input in different regions to be quantified and calculated, and measures the input intensity coefficients of public resources in Beijing, Tianjin and Hebei regions.

Introduction Public resources are of external features as the services provided by the government. Rational input of public resources could promote urbanization process, quicken the adjustment of industry structure, guide governments’ input into key industries and increase human capital, thus producing a significant impact on the economy [1]. As an important national strategy, the integration of Beijing-Tianjin-Hebei Regions could promote equal access to public services in these regions, effectively ensure economic development fruits to be equally shared by the public and help the achievement of the current goals of China in terms of reform and development. The current research on public resources input mainly concentrates on resources efficiency, resources configuration, indexes concerning equal access to resources and relationship between resources and fiscal input, etc. and lacks the quantification and measurement of public resources input level. For example, Ren Fuxiang established the management activity performance evaluation index system and distributed public resources inputs in both value and quantity through linear planning, and measured the efficiency of public resources input [2]. Sun Qingguo defined basic indexes concerning equal access to public services, including public service governance system, input system and result system, etc. from three dimensions including input, output and result according to the requirements for government performance evaluation [3]. Liu Haiying measured and studied the configuration of urban and rural public health resources input based on the traditional method on non-parameter DEA input-output measuring efficiency [4]. This paper aims to solve the difficulty in quantifying and comparing resources inputs by measuring public resources input level according to Z-score standardization method and calculates input intensity coefficients of public resources in Beijing, Tianjin and Hebei regions.

Method of Measurement on Z-score Public Resources Input When measuring public resources input, it tends to be difficult in conducting a direct calculation and comparative study of data due to the difference in statistical index dimensions. In order to eliminate the impacts caused by difference in statistical index dimensions and in their values, this paper standardizes relevant indexes via Z-score method and converts indexes into pure non-dimensional values so that all indexes are subject to weighting calculation and comparison without dimensions. The processed data conforms to standard normal distribution, with average value of 0 and standard deviation of 1. The Z-score measurement method is specifically described as follows: The transfer function for standardization of Series 휒1 , 휒2 , 휒3 … … 휒푛 is shown as follows:

휒 −μ 휒∗ = 푖 (1) 푖 휎

1 σ = √ ∑푛 (휒 − 휇)2 (2) 푛−1 푖=1 푖 1 where, μ = ∑푛 휒 means the average value of sample data in all cities; σ means standard 푛 푖=1 푖 deviation of sample data in all cities. The Z-score standardization method is applicable to the case where the maximum and minimum of sample data are unknown or there is any outlier beyond the range of values. It can be known from the Z interval of standard normal distribution in statistics that the conversion result in this ∗ method is 휒푖 휖[-4，4]. In order to control the conversion result within the interval of [0, 1], the following conversion measures are adopted: 휒∗+4 (1) If the index is a positive, then휒∗′ = 푖 ; 푖 8 4−휒∗ (2) If the index is a negative, then휒∗′ = 푖 . 푖 8 ∗′ After conversion, 휒푖 휖[0，1]. Thus, the Z-score standardization method is adopted to convert the indexes whose reference values (specified standard value, sensed standard value) cannot be determined into the interval of [0, 1] so as to ensure the consistency, comparability and additivity of ∗′ all non-dimensional indexes in the index system. Also, 휒푖 is called public service resources intensity coefficient, which is used to reflect the access to public resources by residents of a region.

Measurement of Public Resources Input in Beijing, Tianjin and Hebei regions Through collection of data from statistical yearbooks in Beijing, Tianjin, Hebei regions, the cities under their jurisdiction or the districts under the jurisdiction of Beijing and Tianjin, and data from statistical yearbooks in Chinese cities, measurements were conducted on the input level of public resources in ten fields including public facility service, environmental governance, information technology service, public transport, city outlook, compulsory education, medical health, elderly care, public culture and sports and social guarantee by using Z-score standardization method. Measuring indexes are listed as follows: The public resources intensity coefficients of Beijing, Tianjin and Hebei regions are shown in Tables 2 and 3 below.

Acknowledgment This work was funded by the Dean fund project of China National Institute of Standardization under grant No. 552016Y-4667, the National Key Technology R&D Program of the Ministry of Science and Technology under grant No. 2015BAK46B02 and 2015BAK46B03-3.

References [1] Song Fei. Analysis on the Impacts of Public Resources Policy Selection over Economic Growth, Economic Research Guide, 2015 [4]: 5-10. [2] Ren Fuxiang, Scientific Analysis on the Input of Public Resources, China Market, 2014 [20]: 85-86. [3] Sun Qingguo. On Measuring Indexes of Equal Access to Basic Public Services, Journal of China Executive Leadership Academy Pudong, January 2009, Volume III, No. 1: 57-61. [4] Liu Haiying, Insufficient Input or Unbalanced Configuration of Public Health Resources in Urban and Rural Areas of China, Chinese Health Economics, 2012, Volume 31, No. 8: 12-15.

Table 1. Public Resource Monitoring Fields and Measuring Indexes in Beijing, Tianjin and Hebei Regions.

Monitoring Field Measuring Index

Public facility Percentage of urban maintenance and construction service funds over public fiscal expenditure (%) Annual average value of PM2.5 (mg/m3) Comprehensive utilization rate of common industrial Environmental solid waste (%) governance Centralized treatment rate of sewage plant (%) Harmless domestic waste treatment rate (%) Information technology Share of internet broadband users (%) service Number of Public vehicles per 10,000 persons Public transport Urban road area per capita (m2) Greening area per capita (ha./10,000 persons) City outlook Statistical Greening coverage in the built-up area (%) Indexes Number of full-time teachers per 10,000 primary Compulsory school students education Number of full-time teachers per 10,000 high school students Number of hospital and clinics beds per 10,000 persons Medical health Number of doctors per 10,000 persons Share of participants in basic endowment insurance Elderly care among urban employees (%) Public culture and Number of library book collections per 100 persons sports Share of participants in basic medical insurance among urban employees (%) Social guarantee Share of participants in unemployment insurance (%) Unemployment rate (%) Regional GDP per capita (RMB yuan) Basic Economic Urban Growth rate in regional GDP (%) development Indexes Percentage of educational funds over public fiscal expenditure (%) Table 2. Resources Input Intensity Coefficients of Beijing, Tianjin and Hebei. Resources Input Intensity Region Coefficient

Beijing 0.52 Tianjin 0.57 Hebei 0.42

Table 3. Resources Input Intensity Coefficients of Areas in Beijing, Tianjin and Hebei Regions.

Resources Resources Rank Area Intensity Region Rank Area Intensity Region Coefficient Coefficient New 1 0.64 Tianjin 23 Hebei 0.49 Tianjin Area Shijingshan 2 Heiping District 0.62 Tianjin 24 0.49 Beijing District Dongli 3 Qin Huangdao 0.61 Hebei 25 0.49 Tianjin District Dongcheng 4 0.58 Beijing 26 Ji County 0.49 Tianjin District 5 Xicheng District 0.58 Beijing 27 0.48 Tianjin Hongqiao 6 0.56 Tianjin 28 Miyun County 0.48 Beijing District Mentougou 7 0.55 Beijing 29 Xingtai 0.48 Hebei District 8 Shijiazhuang 0.53 Hebei 30 Langfang 0.47 Hebei Chaoyang Fangshan 9 0.53 Beijing 31 0.47 Beijing District District Fengtai 10 Tangshan 0.53 Hebei 32 0.47 Beijing District 11 Cangzhou 0.53 Hebei 33 Handan 0.46 Hebei Wuqing 12 Huairou District 0.52 Beijing 34 0.46 Tianjin District Beichen 13 Hexi District 0.52 Tianjin 35 0.46 Tianjin District Daxing 14 Haidian District 0.51 Beijing 36 0.46 Beijing District Xiqing 15 Pinggu District 0.51 Beijing 37 0.46 Tianjin District 16 Ninghe County 0.49 Tianjin 38 Baoding 0.46 Hebei Hedong 17 Yanqing County 0.49 Beijing 39 0.45 Tianjin District Shunyi 18 0.49 Tianjin 40 0.45 Beijing District 19 0.49 Tianjin 41 Hengshui 0.44 Hebei Tongzhou 20 Chengde 0.49 Hebei 42 0.43 Beijing District Jinghai 21 Zhang Jiakou 0.49 Hebei 43 0.41 Tianjin County Changping 22 0.49 Beijing District