Journal of Global Change Data & Discovery2020.4(1):55-60

[PDF] [DATASET]

Citation:Zhang, Y., Ma, T. L.SAM Table Dataset for Four Municipalities of China (2012, 2015)[J]. Journal of Global Change Data & Discovery,2020.4(1):55-60 .DOI: 10.3974/geodp.2020.01.08 .

DOI: 10

SAM Table Dataset for Four Municipalities of China (2012, 2015)

Zhang, Y.*  Ma, T. L.

School of Economics, Hunan University of Finance and Economics, Changsha 410205, China

Changsha Environmental Protection College, Changsha 410004, China

 

Abstract: The Computable General Equilibrium (CGE) model is one of the important research methods in economics. In recent years, it has been applied widely in the fields of tariff, trade barriers, resources, and environment. The CGE model data come from the Social Accounting Matrix (SAM), which is based on an input-output table. However, most input-output tables are only prepared at national and provincial levels, which is not conducive to the study of urban problems using the CGE model. This paper, based on input-output theory, matches 41 industrial sectors in China to five types of state-owned construction land, including land for transport, in the first-level classification of landuse status. According to the latest input-output table, in combination with the “Land and Resources of China Yearbook”, “Beijing Statistical Yearbook”, “Shanghai Statistical Yearbook”, “Tianjin Statistical Yearbook”, “Chongqing Statistical Yearbook”, and the Beijing, Shanghai, Tianjin, and Chongqing Social Accounting Matrix (SAM) in 2012–2015 are compiled and balanced using the direct cross-entropy method. This dataset can provide data support for the construction of an urban land use CGE model, and the study of urban land use and urban economics.

Keywords: CGE model; Social Accounting Matrix; Urban Economics; Economic Geography

1 Introduction

The economist Wassily Leontief proposed the input-output model, also known as the Leontief model, which uses simultaneous linear equations to accurately describe the interrelationship and interaction between economy and technology in various sectors of the national economy[1]. The input-output method has greatly promoted and influenced the development of modern economics. The full name of the CGE Model is the Computable General Equilibrium Model; it involves the deepening and development of input-output analysis. The traditional input-output model only analyzes intermediate products/intermediate inputs, factor inputs, final products, and other links without considering the economic activities of residents, enterprises, governments, and other sectors, as well as social and economic accounts such as savings, investments, imports, and exports. Therefore, its role has certain limitations[1]. According to Walras’ general equilibrium idea[2], the price variable is solved, and the analysis result is more comprehensive and systematic. Therefore, the CGE Model has played a huge role in international trade, government taxation, resources and environment, public policy, and other fields, and has been widely applied, gradually becoming an important tool in modern economic research[1].

CGE model data are derived from the Social Accounting Matrix (SAM). It reflects the relationship between capital flow and proportion among various sectors of the social economy, and describes social and economic activities in a more detailed way. Only municipalities directly under the central government and provincial-level administrative regions in China compile input-output tables at every mantissa of 2, 7 years, with a preparation interval of 5 years. To keep the data updated, the input-output extension table is prepared for every year with mantissa 0 and 5. The input-output extension table updates the data based on the input-output table. The department classification is completely consistent with the input-output table, and the data are extendable. However, the data are derived from statistical data, rather than from a direct survey, like the data in the input-output table. Therefore, the input-output extension table is consistent with the input-output table, and both can be used together. Since the National Bureau of Statistics has not yet compiled the input-output table for the municipalities directly under the central government in 2017, the latest input-output table for the Chongqing municipality was the input-output extension table for 2015. Therefore, a SAM table for Chongqing in 2015 was compiled by taking Beijing, Tianjin, Shanghai, and Chongqing as examples and SAM tables for Beijing, Tianjin, Shanghai, and Chongqing in 2012 according to relevant data.

2 Metadata of Dataset

The metadata of “SAM table dataset for four municipalities of China (2012, 2015)”[3] is summarized displayed in Table 1.

Table 1  Metadata summary of SAM table dataset for four municipalities of China (2012, 2015)

Items

Discription

Dataset full name

SAM table dataset for four municipalities of China (2012, 2015)

Dataset short name

SAM_4MunicipalitiesChina

Authors

Zhang, Y., Hunan University of Finance and Economics, 520andylau@sina.com

Geographical region

Beijing, Tianjin, Shanghai, and Chongqing

Years

2012-2015           Data format   .xlsx        Data size   25.2 KB

Data files

6 .xlsx worksheets: (1) SAM for Beijing in 2012; (2) SAM for Shanghai in 2012; (3) SAM for Tianjin in 2012; (4) SAM for Chongqing in 2012; (5) SAM for Chongqing in 2015; (6) Code, meaning, and unit in SAM.

Foundation

Scientific research project of the Hunan Education Department (18C0964)

Data publisher

Global Change Research Data Publishing and Repository http://www.geodoi.ac.cn

Address

No. 11A, Datun Road, Chaoyang District, Beijing, 100101, China

Data sharing policy

Data from the Global Change Research Data Publishing and Repository include metadata,

datasets (data products), and publications (in this case, in the Journal of Global Change

Data and Discovery). Data sharing policy includes (1) Data are openly available and can be

freely downloaded via the Internet; (2) End users are encouraged to use Data subject to citation;(3) Users, who are by definition also value-added service providers, are welcome to

redistribute Data subject to written permission from the GC data PR Editorial Office and the

issuance of a Data redistribution license, and; (4) If Data are used to compile new datasets,

the ‘ten percent principle’ should be followed so that Data records utilized should not

surpass 10% of the new dataset contents, while sources should be clearly noted in suitable

places in the new dataset[4]

Communication and searchable system

DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS

3 Methodology

3.1 Definitions of elements

The 10 elements in SAM’s table include industrial and mining warehousing, transportation, housing, commercial services, public administration, labor, capital, land, residents, and government. The corresponding relationship between industrial land types and input-output departments is shown in Table 2.

Table 2  Comparison of land use types and input-output departments in China

Land use types

Definition of land use types

Corresponding sections of IO table

Land for industry, mining, and warehousing

This refers to the land mainly used for industrial production and warehousing

Mining and Washing of Coal Products

Extraction of Petroleum and Natural Gas Products

Mining and Processing of Metal Ores Products

Mining and Processing of Non-metal Ores Products

Food and Tobacco

Textiles

Clothing, Hats, Leather, Feather, and Related Products

Processing of Timber Products and Furniture

Paper, Printing, Culture, Education, and Sport Products

Products of Petroleum, Coking, and Processing of Nuclear Fuel

Chemical Products

Products of Non-metallic Minerals

Products of Metals Smelting and Pressing

Metal Products

General Equipment

 

 

Special Equipment

 

 

Transportation Equipment

 

 

Electrical Machinery and Apparatus

 

 

Communication, Computers and other Electronic Equipment

 

 

Measuring Instruments and

 

 

Other Manufactured Products

 

 

Waste and Flotsam

 

 

Repair Services of Metal Products, Machinery, and Equipment

 

 

Electric Power and Heat Production and Supply

 

 

Gas Production and Supply

 

 

Water Production and Supply

 

 

Construction

Land for transport

This refers to the land mainly used for ground lines and stations for transportation and passage. It includes land used for civil airports, harbors, wharves, ground transport pipelines, and all types of roads

Transport, Storage, and Post

Land for residential uses

This refers to the land used for house sites and the affiliated facilities for people’s daily life and dwelling

Real Estate

Land for commercial and services uses

This refers to land mainly used for commerce and service use

Wholesale and Retail Trades

Hotel and Catering Services

Information Transmission, Software, and Information Technology

Financial Intermediation

Leasing and Business Services

Land for public management and public services

This refers to the land mainly used for government agencies and public organizations, press and publication, science, education, culture and health, scenic spots, historical sites, and public facilities

Scientific Research and Technical Services

Management of Water Conservancy, Environment, and Public Facilities

Services to Households, Repair, and other Services

Education

Health and Social Work

Culture, Sports and Entertainment

Public Management, Social Security and Social Organization

 

Similar to the input-output table, the SAM table is a matrix with rows and columns equal to each other, and the same rows and columns correspond to a sector in the national economy. Where the production/activity sector reflects the relationship between intermediate demand and intermediate input among industrial sectors, it can be divided into two departments of production and activity, or one department can be combined as needed. As shown in Table 3, to simplify the analysis, the two departments of production and activity should be merged into the production/activity department.

Table 3  Components of the SAM table

 

Production/activities

Production factors

Households

Government

Total

Production/activities

Intermediate input/Use Part

 

Household consumption

Government consumption

 

Production factors

Value-Added

 

 

 

Factor income

Households

 

Resident factor income

 

 

Total resident income

Government

 

 

Individual income tax

 

Total government revenue

Total

Total Inputs

Factor expenditure

Household spending

Government consumption

 

 

It can be found that the 42 industrial sectors analyzed by input-output analysis in China basically correspond to five types of state-owned construction land, such as transportation land, industrial and mining land, and storage land, in the classification of land use status in China (GB/T21010-2007) (Table 2). According to the division of construction land and the definition of the industry in the land classification, five industries, including information transmission, software, and information technology services in the input-output table can correspond to commercial service land. 27 industries were compared with industrial and mining storage land. Seven industries, including scientific research and technical services, water conservancy, environment, and public facilities management, will be matched with public management and public service land. The real estate industry and residential land are corresponding. Input-Output table 42 has some overlapping fields, so it cannot fully correspond to the classification of land use status, such as the classification of warehousing. Since the warehousing industry and the transportation industry are merged into the transport, storage and post industry (code 30) in the input-output table of China, they cannot be separated. Here, warehousing industry refers to goods transportation, transit warehousing, and goods distribution mainly engaged in warehousing. Its main business belongs to the same category as transportation, so the transportation and storage industry and transportation land will be corresponding. The special land in the state-owned construction land includes the land for military facilities, embassies, and consulates; the water area and water conservancy facilities in the construction land include the water surface of reservoirs, coastal beaches, and other land including idle land. Taking Beijing as an example, in 2012, the special land, water and water conservancy facilities land, and other land in Beijing were 3.33, 6.83, and 0 hm2 respectively (data source: 2013 “China Land and Resources Yearbook”), which accounted for a small proportion and were not closely related to the city’s social economy, so they were not included in the research scope. Since this data set is mainly used to study the structure of urban construction land, the data for agriculture, forestry, animal husbandry, and fishery and corresponding agricultural land data in the input-output table are excluded. Other departments in the SAM table refer to Zhang, X. (2010) [1] and match the standard CGE model, including elements, residents, government, and summary departments.

3.2 Statistical Approach

According to the above analysis, the original data in the input-output table for Beijing, Shanghai, Tianjin, and Chongqing municipality [5-8] in 2012 and the input-output extension table for Chongqing municipality [9] in 2015 were first combined horizontally and vertically according to the classification of departments in Table 2, and then the other data in the SAM table were supplemented according to the statistical yearbook of each province and city. Data on land for industry, mining, warehousing, transport, residential uses, commercial and services uses, public management and public services of Beijing, Shanghai, and Tianjin in 2012 and Chongqing in 2012 and 2015 come from the “State-owned Land for Construction Use Supplied by Land Use Type and by Province, Autonomous Region and Municipality” section in the “China Land and Resources Statistical Yearbook” in 2013 and 2016[1011]; data on Value Added for SAM come from the IO table for Beijing, Shanghai, and Tianjin in 2012 and Chongqing in 2012 and 2015[59]; data on Resident Factor Income for SAM come from the “People’s Life and Price” section in the “Beijing Statistical Yearbook,” the “Shanghai Statistical Yearbook,” the “Tianjin Statistical Yearbook” for 2013, and the “Chongqing Statistical Yearbook” for 2013 and 2016[1216];data on Household Consumption and Government Consumption of SAM come from the IO table for Beijing, Shanghai, and Tianjin in 2012 and Chongqing in 2012 and 2015[59]; data on individual income tax for SAM come from the “Government Finance” section in the “Beijing Statistical Yearbook”, the “Shanghai Statistical Yearbook,” the “Tianjin Statistical Yearbook” for 2013 and the “Chongqing Statistical Yearbook” for 2013 and 2016[1216].

At the same time, similar to the input-output table, the total number for each column of the SAM table is equal to the total number for each row, thereby achieving “balance” in the SAM table. However, primary SAM tables are not balanced in the usual sense, so they need to be balanced in different ways. The cross-entropy method is a more balanced method adopted in the modern CGE model, including a direct cross-entropy method and a coefficient cross-entropy method, in which the coefficient the cross-entropy method is mainly used to update the existing SAM table data. Therefore, to retain the idea of economics and overcome its subjectivity, this data set is adopted to balance the SAM table with a direct cross-entropy method as the primary method and a manual balance method as the supplement. The cross-entropy method builds the model according to the entropy information proposed by information economics, and its objective function is:

                                                                                  (1)

                                                               (2)

Where  is the original data of the SAM table. is the balanced data, and all are positive.

4 Data Files

The files consist of one Excel file with six worksheets, respectively: (1) SAM for Beijing in 2012; (2) SAM for Shanghai in 2012; (3) SAM for Tianjin in 2012; (4) SAM for Chongqing in 2012; (5) SAM for Chongqing in 2015; (6) Code, meaning, and unit in SAM. The data set is stored in XLSX format and consists of one file with a data volume of 25.2 k.

5 Discussion and Conclusion

The SAM table is not only the basic data of the CGE model but also a further development of the input-output table. Based on the input-output table, government, savings-investment, import and export, and other departments and modules are added to reflect the capital flow and proportion relationship between various sectors of the social economy and describe social and economic activities in a more detailed way.

Taking the SAM table for Beijing as an example, the general equilibrium analysis analyzes the changes in prices and the total amount for various factors such as GDP increase, labor force, capital and land, and predicts the impact on various sectors of Beijing’s economy and society, to make more accurate policy evaluations and adjustments. For example, when GDP growth was 6.5%, land prices rose 20% and the total labor force increased by 20%, the SAM table data input GAMS (general algebraic modeling system) software, set up a corresponding CGE model to get the following conclusion.

With economic development, the structure of new construction land in Beijing changed significantly under three scenarios (Table 4), including a steady increase in GDP, a rising price for land, and a significant increase in labor supply caused by the two-child policy.

Table 4  Structural changes in optimal construction land in Beijing under three scenarios (unit: km2)

Types of landuse

Industry, Mining, and Warehousing

Transport

Residential

Commerce and Services

Public Management and Services

Actual value

5.31

0.33

6.14

1.49

3.00

Scenario 1

0.03

24.42

86.26

0.01

28.82

Change

-99.48%

7398.97%

1305.85%

-99.08%

861.16%

Scenario 2

0.48

0.66

5.78

0.15

0.21

Change

-90.88%

103.22%

-5.88%

-90.25%

-92.85%

Scenario 3

0.38

0.62

5.02

0.12

0.18

Change

-92.87%

89.89%

-18.17%

-92.13%

-94.06%

When GDP is expected to grow steadily by 6.5%, the supply of land for transport, residential use, public management, and public services should be effectively increased compared to the actual value. When the price of land increases by 20%, due to the apparent increase in land use cost, in addition to land for transport, the approval and supply of land for industry, mining and warehousing, residential uses, commercial and services uses, public management, and public services should be strictly controlled. When the total labor force is expected to increase by 20%, the simulation results are similar to Scenario 2. Therefore, the preparation of a SAM table for Beijing, Shanghai, Tianjin, and Chongqing, using the CGE model, is conducive to a more comprehensive and in-depth analysis of the social and economic problems of each municipality directly under the central government.

References

[1]     Zhang X. Principles of Computable General Equilibrium (CGE) Modeling and Programming [M]. Shanghai: Truth & Wisdom Press, 2010: 1–272.

[2]     Gao H.Y. Economics [M]. BeijingChina Renmin University Press, 2014: 1–685.

[3]    Zhang Yang. Dataset on SAM table of municipalities directly under the central government of China (2012, 2015) [DB/OL]. Global Charge Data Repository, 2019. DOI: 10.3974/geodb.2019.05.12.V1.

[4]    GCdataPR Editorial Office. GC data PR data sharing policy [OL]. DOI: 10.3974/dp.policy.2014.05 (Updated 2017).

[5]    Beijing Municipal Bureau of Statistics. Input-Output Tables (2012) [Z].Beijing: Beijing Municipal Bureau of Statistics.

[6]     Shanghai Municipal Bureau of Statistics. Input-Output Tables (2012) [Z]. Shanghai: Shanghai Municipal Bureau of Statistics.

[7]    Tianjin Municipal Bureau of Statistics. Input-Output Tables (2012) [Z]. Tianjin: Tianjin Municipal Bureau of Statistics.

[8]     Chongqing Municipal Bureau of Statistics. Input-Output Tables (2012) [Z]. Chongqing: Chongqing Municipal Bureau of Statistics.

[9]     Chongqing Municipal Bureau of Statistics. Chongqing Statistical Yearbook 2017[M]. Chongqing: China Statistics Press, 2017.

[10]  Ministry of Land and Resources P.R.C. China Land and Resources Statistical Yearbook 2013[M]. Beijing: Geological Publishing House. P830–831.

[11]  Ministry of Land and Resources P.R.C. China Land and Resources Statistical Yearbook 2016[M]. Beijing: Geological Publishing House. P110–111.

[12]  Beijing Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Beijing. Beijing Statistical Yearbook 2013[M]. Beijing: China Statistics Press, 2013.

[13]  Tianjin Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Tianjin. Tianjin Statistical Yearbook 2013[M]. Beijing: China Statistics Press, 2013.

[14]  Shanghai Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Shanghai. Shanghai Statistical Yearbook 2013[M]. Beijing: China Statistics Press, 2013.

[15]  Chongqing Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Chongqing. Chongqing Statistical Yearbook 2013[M]. Beijing: China Statistics Press, 2013.

[16]  Chongqing Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Chongqing. Chongqing Statistical Yearbook 2016[M]. Beijing: China Statistics Press, 2016.

Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum