Military-Civil fusion and optimisation of urban industrial structure—an evidence from China

Figures & data

Figure 1. Cities with MCFNDBs in mainland China.

Table 1. Summary Statistics of Key Variables

Variable	Sample size	Mean	SD	Min	Max
Dependent viable
Upgrading	3420	0.8662	0.4734	0.0943	5.3401
Employment	3420	1.4550	1.0985	0.1823	21.2791
Rat	3420	0.2629	0.2128	0.00014	1.7205
Degree of MCF
MCF level	3420	0.0136	0.0814	0	1
MCF frequency	3420	0.5833	0.6402	0	2
Urban economic characteristics
Gdpp (RMB)	3420	4.4948	0.3187	3.4420	6.8077
Technology (%)	3420	19.63	4.53	1.58	45.56
Unemployment (%)	3420	5.69	03.24	0	55.99
Investment (%)	3420	83.89	77.10	0	145.933
Finance (%)	3420	274.55	189.57	64.88	183.985
Urban politics and geography characteristics
Rank	3420	2.8877	0.3575	1	3
Area	3420	1.8702	0.8382	1	3

Figure 2. Parallel Trend of MCFNDBs and Optimisation of Urban Industrial Structure.

Figure 3. Partial effect based on the annual dummy variable.

Table 2. Optimisation Effects of MCFNDBs

Panel A. Whole sample
	Upgrading(1)			Rat(2)
Treat×post	0.0904*(0.0545)			0.0052(0.0224)
Control	YES			YES
Year FE	YES			YES
City FE	YES			YES
Constant	3.6864***(0.8343)			0.7227***(0.1441)
R^2	0.4031			0.1815
Observations	3,420			3,420
Panel B. By economic zoning
	Upgrading			Rat
	Developed areas(1)	Less developed areas(2)		Developed areas(3)		Less developed areas(4)
Treat×post	0.1426(0.1089)	0.0800*(0.0476)		−0.0070(0.0203)		0.0114(0.0332)
Control	YES	YES		YES		YES
Year FE	YES	YES		YES		YES
City FE	YES	YES		YES		YES
Constant	3.9811***(0.8158)	3.2375***(1.0387)		0.7305***(0.2547)		0.7184***(0.1778)
R^2	0.4411	0.4230		0.1507		0.2124
Observations	1,452	1,968		1,452		1,968
Panel C. By administrative ranks
	Upgrading			Rat
	Municipality(1)	Capital(2)	Prefecture(3)	Municipality(4)	Capital(5)		Prefecture(6)
Treat×post	0.1943(0.1759)	0.1312*(0.0723)	0.0126(0.0382)	0.0676(0.0364)	0.0458*(0.0244)		0.0100(0.0284)
Control	YES	YES	YES	YES	YES		YES
Year FE	YES	YES	YES	YES	YES		YES
City FE	YES	YES	YES	YES	YES		YES
Constant	5.9574 (11.3870)	7.7561***(1.7054)	3.4192***(0.8223)	−0.5284(1.2651)	−0.0354(0.3389)		0.7545***(0.1529)
R^2	0.9162	0.6184	0.3878	0.7583	0.2056		0.1886
Observations	48	288	3,084	48	288		3,084

Note: Due to space limitations, the table does not list the regression results of urban economic characteristics as expressed in the “control”. Year FE represents the time fixed effect. City FE represents the city fixed effect. The regression is mis-estimated using the robust standard. The value in parentheses is the standard error; the same conditions are applied below.

Table 3. IV Estimation Results

	First stage of regression	Second stage of regression
	Treat×post(1)	Upgrading(2)	Rat(3)
IV×post	0.9368***(0.0062)
Treat×post		0.0776**(0.0319)	0.0083(0.0145)
Control	YES	YES	YES
Year FE	YES	YES	YES
City FE	YES	YES	YES
ConstantR^2	0.0440(0.0427)0.9360	3.6849***(0.2069)0.4031	0.7231***(0.0942)0.1815
Observations	3,420	3,420	3,420
First-stage F-statistics	12.82

Table 4. Placebo Test

Panel A. Parallel trend regression
	Upgrading				Rat
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Treat×post	0.0904* (0.0545)	0.0904 (0.0550)	0.0890 (0.0558)	0.0890 (0.0558)	0.0052 (0.0224)	0.0054 (0.0226)	0.0051 (0.0230)	0.0051 (0.0230)
Treat×year 2011		-0.0010 (0.0288)	0.0023 (0.0311)	0.0023 (0.0311)		-0.0057 (0.0153)	-0.0050 (0.0175)	-0.0050 (0.0175)
Treat×year 2010			0.0315 (0.0316)	0.0315 (0.0316)			0.0066 (0.0247)	0.0066 (0.0247)
Treat×year 2009				0.0000(.)				0.0000(.)
Control	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
Constant R^2	3.6864*** (0.8343) 0.4031	3.6864*** (0.8344) 0.4031	3.6851*** (0.8345) 0.4031	3.6851*** (0.8345) 0.4031	0.7227*** (0.1441) 0.1815	0.7228*** (0.1442) 0.1815	0.7225*** (0.1443) 0.1815	0.7225*** (0.1443) 0.1815
Observations	3,420	3, 420	3, 420	3, 420	3, 420	3, 420	3, 420	3, 420
Panel B. By randomly reselected treatment groups
	Upgrading				Rat
	Whole sample (1)	Developed areas (2)	Less developed areas (3)		Whole Sample (4)	Developed areas (5)	Less developed areas (6)
Rantreat×post	-0.0250* (0.0129)	-0.0304 (0.0196)	-0.0159 (0.0164)		0.0102 (0.0082)	-0.0020 (0.0088)	0.0157 (0.0123)
Control	YES	YES	YES		YES	YES	YES
year FE	YES	YES	YES		YES	YES	YES
city FE	YES	YES	YES		YES	YES	YES
Constant R^2	3.6778*** (0.8317) 0.4019	4.0049*** (0.8180) 0.4379	3.2244*** (1.0331) 0.4218		0.7212*** (0.1437) 0.1821	0.7293*** (0.2551) 0.1506	0.7114*** (0.1755) 0.2134
Observations	3, 420	1,452	1,968		3, 420	1,452	1,968

Note: “Rantreat” represents “randomly selected treatment groups”.

Table 5. Robustness Test

Panel A. Substitution of dependent variable
	Employment
	Whole sample	Developed areas		Less developed areas
	(1)	(2)		(3)
Treat×post	0.1601**(0.0813)	0.0587(0.1826)		0.2245***(0.0842)
Control	YES	YES		YES
Year FE	YES	YES		YES
City FE	YES	YES		YES
ConstantR^2	4.1800***(0.8245)0.0845	5.1360***(0.9357)0.1709		3.6528***(0.9788)0.0800
Observations	3,420	1,452		1,968
Panel B. Choice of dependent variables that are unrelated to the MCF policy
	Gdpp(1)	Technology(2)	Unemployment(3)		Finance(4)
Treat×post	0.0077(0.0182)	0.0074(0.0058)	−0.0018(0.0036)		0.1971(0.1604)
Control	YES	YES	YES		YES
Year FE	YES	YES	YES		YES
City FE	YES	YES	YES		YES
ConstantR^2	4.2436***(0.0191)0.8582	0.3184***(0.0396)0.1642	0.1591***(0.0276)0.0793		8.7783***(1.3515)0.1485
Observations	3,420	3,420	3,420		3,420

Appendix I. MCFNDBs Identified in China

Year	MCFNDB Location	Year	MCFNDB Location
2010	Xi’an District, Shaanxi Province	2012	Ningbo District, Zhejiang Province
	Xiaogan District, Hubei Province		Handan District, Hebei Province
	Guangyuan District, Sichuan Province	2013	Xiangtan District, Hunan Province
	Mianyang District, Sichuan Province		Hanzhong Industry Park, Shaanxi Province
	Zhuzhou District, Hunan Province		Bishan Industrial Park, Chongqing
	Baotou District, Inner Mongolia Municipality		Luoyang District, Henan Province
	Xiangyang District, Hubei Province		Tiding District, Liaoning Province
	Guiyang District, Guizhou Province	2014	Danyang District, Jiangsu Province
2012	Daxing District, Beijing		Zunyi District, Guizhou Province
	Jingdezhen District, Jiangxi Province	2015	Xi’an Weapon Industry Base, Shaanxi Province
	Kunming District, Yunnan Province	2016	Liangjiang New Area, Chongqing
	Wuhu District, Anhui Province		Zhanjiang District, Guangdong Province
	Pinjiang Industrial Park, Hunan Province		Guan Industry Zone, Hebei Province
	Lanzhou District, Gansu Province		Dalian Industrial Zone, Liaoning Province
	Minhang District, Shanghai		Ningde District, Fujian Province
	Harbin District, Heilongjiang Province		Changzhi Industrial Park, Shanxi Province

Appendix III. Definitions of Key Variables

Variables	Definition
Dependent variables
Upgrading	Upgrading of urban industrial structure, ratio of output in urban tertiary industry to secondary industry
Employment	Ratio of employment in urban tertiary industry to employment in secondary industry
Rat	Rationalisation of the urban industrial structure, calculated by the Theil index
Degree of MCF
MCF level	Ratio of the MCFNDB’s gross industrial output to the urban gross industrial output. Because the amount of data is very small, we use data standardisation
MCF frequency	Number of MCFs appears in the central government’s work report each year
Urban economic characteristics
Gdpp	Gross urban GDP/Gross population, CNY 10,000, and take the log value
Technology	Ratio of urban education and science and technology expenditure to financial expenditure
Unemployment	Ratio of urban registered unemployed population to employed population
Investment	Ratio of urban fixed asset investment to gross industrial output
Finance	Ratio of urban financial expenditure to financial revenue
Urban geographical and political characteristics
Rank	Administrative level of the city. Municipality, Rank = 1; Provincial capital, Rank = 2; General level, Rank = 3
Area	Level of economic development. Developed region = 1; Less developed region>1 (central region, Area = 2; western region, Area = 3)