Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data

doi:10.1007/s11442-022-2039-9

Abstract

Abstract:

Industrial agglomeration is a highly prominent geographical feature of economic activities, and it is an important research topic in economic geography. However, mechanism-based explanations of industrial agglomeration often differ due to a failure to distinguish properly between the spatial distribution of industries and the stages of industrial agglomeration. Based on micro data from three national economic censuses, this study uses the Duranton-Overman (DO) index method to calculate the spatial distribution of manufacturing industries (three-digit classifications) in the Beijing-Tianjin-Hebei region (BTH region hereafter) from 2004 to 2013 as well as the hurdle model to explain quantitatively the influencing factors and differences in the two stages of agglomeration formation and agglomeration development. The research results show the following: (1) In 2004, 2008, and 2013, there were 124, 127, and 129 agglomerations of three-digit industry types in the BTH region, respectively. Technology-intensive and labor-intensive manufacturing industries had high agglomeration intensity, but overall agglomeration intensity declined during the study period, from 0.332 to 0.261. (2) There are two stages of manufacturing agglomeration, with different dominant factors. During the agglomeration formation stage, the main locational considerations of enterprises are basic conditions. Agricultural resources and transportation have negative effects on agglomeration formation, while labor pool and foreign investment have positive effects. In the agglomeration development stage, enterprises focus more on factors such as agglomeration economies and policies. Internal and external industry linkages both have a positive effect, with the former having a stronger effect, while development zone policies and electricity, gas, and water resources have a negative effect. (3) Influencing factors on industrial agglomeration have a scale effect, and they all show a weakening trend as distance increases, but different factors respond differently to distance.

Key words: manufacturing agglomeration, hurdle model, DO index, Beijing-Tianjin-Hebei region

HUANG Yujin, SHENG Kerong, SUN Wei. Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data[J].Journal of Geographical Sciences, 2022, 32(10): 2105-2128.

Figures/Tables 13

Figure 1

Figure 2

Table 1

Table 2

Figure 3

Table 3

Table 4

Table 5

Probit regression results of the first stage in the hurdle model

Model	2004‒2008				2004‒2013
Model	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
S	50 km	100 km	150 km	194 km	50 km	100 km	150 km	194 km
RES_AGR	-3.813**	-4.153**	-4.106**	-3.703**	-3.042**	-3.158**	-3.119**	-2.837*
	(1.695)	(1.734)	(1.709)	(1.694)	(1.467)	(1.488)	(1.489)	(1.509)
RES_MIN	1.325	1.262	1.201	0.915	0.249	0.162	0.153	0.202
	(2.039)	(2.099)	(2.067)	(1.995)	(1.412)	(1.432)	(1.424)	(1.413)
RES_ENE	-13.264	-14.357*	-13.884	-10.649	6.624	7.071	7.579	7.808
	(8.470)	(8.597)	(8.643)	(8.524)	(5.391)	(5.520)	(5.511)	(5.437)
AGG_EMP	0.287***	0.321***	0.328***	0.373***	0.240***	0.252***	0.258***	0.311***
	(0.086)	(0.083)	(0.084)	(0.085)	(0.058)	(0.057)	(0.057)	(0.058)
AGG_INI	1.086	1.716	1.720	1.459	-0.637	-0.345	-0.428	-0.570
	(1.706)	(1.701)	(1.690)	(1.698)	(1.278)	(1.293)	(1.298)	(1.318)
AGG_INT	0.206	0.516	0.395	0.570	0.929	1.133	1.119	1.268
	(1.609)	(1.632)	(1.608)	(1.611)	(1.377)	(1.396)	(1.399)	(1.433)
AGG_TEC	-0.014	-0.006	-0.028	-0.044	-0.061	-0.060	-0.069*	-0.067
	(0.077)	(0.084)	(0.084)	(0.094)	(0.041)	(0.042)	(0.042)	(0.043)
GOV_NAT	0.651	0.756	0.224	0.883	-1.233	-1.382	-1.634	-0.937
	(3.807)	(4.088)	(4.056)	(3.903)	(2.271)	(2.284)	(2.299)	(2.266)
GOV_LEV	-0.007	-0.010	-0.006	-0.002	-0.001	-0.002	-0.001	-0.001
Model	2004‒2008				2004‒2013
Model	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	(0.006)	(0.006)	(0.006)	(0.007)	(0.003)	(0.003)	(0.003)	(0.003)
GLO_EXP	0.611	0.551	0.608	0.456
	(0.585)	(0.627)	(0.621)	(0.594)
GLO_ FOR	5.552**	6.333**	6.367**	4.989*	4.690**	4.830**	4.891**	3.434*
	(2.780)	(3.052)	(3.089)	(2.979)	(2.003)	(2.132)	(2.150)	(2.034)
SPA_BJ	2.568***	2.583***	2.592***	2.204***	2.501***	2.491***	2.515***	2.357***
	(0.672)	(0.712)	(0.713)	(0.706)	(0.517)	(0.533)	(0.534)	(0.539)
SPA_TJ	1.751**	1.945**	1.658**	1.118	2.580***	2.769***	2.623***	2.316***
	(0.694)	(0.756)	(0.735)	(0.718)	(0.562)	(0.583)	(0.579)	(0.583)
RES_TRA	-13.759	-15.439*	-15.359*	-13.606	-12.833**	-13.414**	-13.757**	-12.867**
	(8.473)	(8.735)	(8.670)	(8.390)	(6.412)	(6.547)	(6.547)	(6.558)
Constant	-2.321**	-2.652**	-2.656**	-2.771**	-2.218**	-2.353***	-2.342***	-2.525***
	(1.118)	(1.100)	(1.092)	(1.092)	(0.894)	(0.896)	(0.897)	(0.910)
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	324	324	324	324	492	492	492	492
Pseudo R²	0.224	0.247	0.241	0.219	0.223	0.237	0.236	0.229

Table 5

Table 6

Table 7

OLS regression results of the second stage in the hurdle model

Model	2004-2008				2004-2013
Model	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
S	50 km	100 km	150 km	194 km	50 km	100 km	150 km	194 km
RES_AGR	-0.001	-0.009	-0.025	-0.027	-0.086	-0.041	-0.055	-0.058
	(0.196)	(0.210)	(0.223)	(0.225)	(0.217)	(0.220)	(0.228)	(0.227)
RES_MIN	0.413	0.439	0.389	0.402	-0.120	-0.089	-0.133	-0.126
	(0.263)	(0.285)	(0.299)	(0.290)	(0.184)	(0.191)	(0.197)	(0.198)
RES_ENE	-4.905***	-4.938***	-5.619***	-5.443***	-3.143***	-3.165***	-3.724***	-3.403***
	(1.191)	(1.290)	(1.289)	(1.307)	(0.925)	(0.962)	(0.972)	(0.998)
AGG_EMP	-0.011	-0.001	0.002	0.005	-0.015	-0.005	-0.001	-0.001
	(0.013)	(0.014)	(0.014)	(0.013)	(0.009)	(0.010)	(0.010)	(0.010)
AGG_INI	0.746***	0.800***	0.829***	0.857***	0.365**	0.470***	0.498***	0.524***
	(0.199)	(0.209)	(0.211)	(0.207)	(0.182)	(0.180)	(0.181)	(0.179)
AGG_INT	0.350**	0.414**	0.481***	0.527***	0.155	0.288	0.338*	0.385**
	(0.163)	(0.177)	(0.183)	(0.184)	(0.189)	(0.187)	(0.189)	(0.188)
AGG_TEC	0.008	0.004	0.013	0.014	-0.002	-0.005	-0.000	-0.001
	(0.009)	(0.010)	(0.010)	(0.010)	(0.007)	(0.007)	(0.007)	(0.007)
GOV_NAT	0.248	0.070	0.074	0.090	0.140	-0.127	-0.102	-0.121
	(0.504)	(0.533)	(0.453)	(0.443)	(0.477)	(0.532)	(0.496)	(0.497)
GOV_LEV	-0.001	-0.002*	-0.002**	-0.002**	-0.001	-0.001**	-0.002***	-0.002***
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
GLO_EXP	-0.043	-0.028	-0.027	-0.018
	(0.086)	(0.096)	(0.096)	(0.094)
GLO_ FOR	0.581	0.421	0.411	0.445	0.611**	0.443	0.443	0.499
	(0.379)	(0.416)	(0.412)	(0.410)	(0.290)	(0.309)	(0.312)	(0.312)
SPA_BJ	0.224*	0.311**	0.342**	0.325**	0.198*	0.274**	0.315***	0.305***
	(0.123)	(0.136)	(0.136)	(0.135)	(0.103)	(0.109)	(0.110)	(0.110)
SPA_TJ	0.259	0.413**	0.462***	0.441***	0.223	0.349**	0.395***	0.384***
	(0.171)	(0.178)	(0.165)	(0.162)	(0.139)	(0.146)	(0.139)	(0.137)
RES_TRA	-1.484	-2.003	-1.807	-1.899	-0.196	-0.846	-0.554	-0.580
	(1.608)	(1.688)	(1.682)	(1.663)	(1.204)	(1.268)	(1.284)	(1.311)
Constant	0.061	-0.039	-0.080	-0.115	0.190	0.074	0.027	-0.001
	(0.134)	(0.142)	(0.146)	(0.143)	(0.122)	(0.127)	(0.128)	(0.125)
Time fixed effect	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	248	253	255	267	377	382	384	398
R²	0.345	0.307	0.346	0.348	0.240	0.231	0.264	0.262

Table 7

Figure 4

Figure 5

Figure 6

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Year	Agglomerated industries		Dispersed industries		Randomly distributed industries		Total number of industries	Average agglomeration intensity
Year	Number	Proportion	Number	Proportion	Number	Proportion	Total number of industries	Average agglomeration intensity
2004	124	76.5%	22	13.6%	16	9.9%	162	0.332
2008	127	78.4%	27	16.7%	8	4.9%	162	0.307
2013	129	76.8%	29	17.3%	10	6.0%	168	0.261

Industry classification	2004	Industry classification	2008	Industry classification	2013
Aerospace vehicle manufacturing	1.22	Leather tanning	1.09	Manufacturing of wire rope and its products	1.09
Electronic computer manufacturing	1.12	Bicycle manufacturing	1.07	Bicycle manufacturing	1.06
Bicycle manufacturing	1.10	Other unspecified manufacturing	0.94	Motorcycle manufacturing	0.90
Bookbinding and other printing service activities	1.07	Aerospace vehicle manufacturing	0.92	Cultural and office machinery manufacturing	0.78
Other electronic equipment manufacturing	1.01	Ship and floating device manufacturing	0.90	Metal furniture manufacturing	0.74
Ship and floating device manufacturing	1.01	Bookbinding and other printing service activities	0.84	Electronic component manufacturing	0.71
General equipment manufacturing	0.92	Electronic computer manufacturing	0.80	Leather goods manufacturing	0.69
Manufacturing of special instruments	0.91	Electronic component manufacturing	0.75	Aerospace vehicle and equipment manufacturing	0.66
Leather tanning	0.90	Medical instrument and equipment manufacturing	0.75	Fur tanning and product processing	0.66
Biological and biochemical products manufacturing	0.83	Metal furniture manufacturing	0.65	Leather tanning	0.65

Influencing factor	Variable	Quantitative indicator	Name	Data source
Resource endowment (RES)	Agriculture	Intermediate inputs in agriculture, forestry, animal husbandry and fishery as a proportion of total industry inputs	RES_AGR	Regional input- output tables
	Mining	Intermediate inputs in coal, petroleum, metal, and non-metal as a proportion of total industry inputs	RES_MIN
	Electricity, gas, water	Intermediate inputs in electricity, gas, and water supply as a proportion of total industry inputs	RES_ENE
Agglomeration economies (AGG)	Labor pool	Number of employees in the industry	AGG_EMP	Economic census data
	Internal links of industries	Intermediate inputs in industries as a proportion of total inputs	AGG_INI	Regional input-output tables
	External links of industries	Intermediate inputs of other manufacturing products as a proportion of total inputs	AGG_INT	Regional input-output tables
	Knowledge spillover	Number of industry patents	AGG_TEC	PatSnap patent platform
Government behavior (GOV)	Local protectionism	State-owned enterprises in the industry as a proportion of all enterprises	GOV_NAT	Economic census data
Government behavior (GOV)	Development zone policies	Number of times the industry has become the target of a development zone	GOV_LEV	Catalogue of China Development Zones
Globalization (GLO)	Foreign trade	Industry exports value as a proportion of total sales value	GLO_EXP	Industrial enterprise database
Globalization (GLO)	Foreign investment	Foreign-invested enterprises in the industry as a proportion of all enterprises	GLO_FOR	Economic census data

Industry	Proportion of agricultural resources input	Agglomeration intensity within each distance
Industry	Proportion of agricultural resources input	50 km	100 km	150 km	194 km
Agricultural and sideline food processing industry	0.301	0.018	0.021	0.021	0.022
Food manufacturing	0.301	0.022	0.023	0.023	0.024
Beverage manufacturing	0.301	0.000	0.000	0.000	0.000
Textile industry	0.245	0.144	0.156	0.169	0.193
Textile clothing, shoes, hats manufacturing	0.126	0.161	0.250	0.258	0.258
Average of all industries	0.057	0.152	0.180	0.195	0.200