Evolving a core-periphery pattern of manufacturing industries across Chinese provinces

doi:10.1007/s11442-014-1129-8

Abstract

Abstract:

This paper, concerning uneven development in China, empirically analyzes the core-periphery gradient of manufacturing industries across provinces (autonomous regions, municipalities), and assesses the extent to which these provinces have changed in recent years. Since China’s reform and opening-up, the spatial structure of the economy has presented a significant core-periphery pattern, the core evidently skewing towards east-coastal areas. With the deepening of market reforms and expansion of globalization, industrial location is gradually in line with the development advantages of provinces. The core provinces specialize in those industries characterized by strong forward and backward linkages, as well as a high consumption ratio, a high degree of increasing returns to scale, and labor or human-capital intensity. However, it is the opposite with regard to peripheral provinces, in addition, energy intensive industries are gradually concentrating in these areas. To a certain degree, the comparative advantage theory and new economic geography identify the underlying forces that determine the spatial distribution of manufacturing industries in China. This paper indicates that the industrialization of regions along different gradients becomes unsynchronized will be a long-term trend. Within a certain period, regions are bound to develop industrial sectors in line with their respective characteristics and development stage. A core-periphery pattern of industries also indicates that industrial development differentials across regions arise because of not only the uneven distribution of industries but also the inconsistent evolving trends of industrial structure for each province.

Key words: core-periphery pattern, spatial distribution, manufacturing industries, regional characteristic, industrial characteristic

Qiliang MAO, Fei WANG, Jun LI, Suocheng DONG. Evolving a core-periphery pattern of manufacturing industries across Chinese provinces[J].Journal of Geographical Sciences, 2014, 24(5): 924-942.

Figures/Tables 7

Table 1

Table 2

Figure 1

Figure 2

Table 3

Correlation coefficients between location quotient and centrality index (1980-2008)

Sector	1980	1985	1995	2004	2008
Food processing	-	-	-0.4105^**	-0.2229^*	-0.3899^**
Food production	-0.5044^***	-0.5248^***	-0.0590	-0.1254	-0.2046
Beverage production	-0.2515	-0.3488^*	-0.2516	-0.2970	-0.2676
Tabaco processing	-0.1824	-0.2025	-0.2215	-0.1815	-0.1810
Feed manufacturing	0.4954^***	0.0718	-	-	-
Textile industry	0.2534	0.2205	-0.0122	0.0159	0.0007
Garments & other fiber products	-	-	0.4604^**	0.3243^*	0.2247
Leather, furs, down & related products	-0.1927	-0.1982	-0.0908	0.0447	-0.0144
Timber processing, bamboo, cane, palm fiber & straw products	-0.1036	-0.1291	-0.1991	-0.1769	-0.2915^*
Furniture manufacturing	-0.3285^**	-0.2417	-0.1485	0.2593	0.2409
Papermaking & paper products	-0.1241	-0.1557	-0.2946	-0.0999	-0.0863
Printing & record pressing	-0.2045	-0.1197	-0.2138	-0.0184	-0.0246
Stationery, educational & sports goods	0.8618^***	0.7963^***	0.4633^***	0.4412^**	0.3442^*
Petroleum processing	-0.0587	-0.1353	-	-	-
Coking products, & gas production & supply	0.4626^**	0.4010^**	-	-	-
Petroleum processing, Coking products, & gas production & supply	-	-	-0.0575	-0.1457	-0.1552
Raw chemical materials & chemical products	0.1798	0.2416	-0.0781	-0.1218	-0.1950
Medical & pharmaceutical products	0.1947	0.0521	-0.0628	-0.2211	-0.2216
Chemical fibers	0.7643^***	0.7979^***	0.3251^*	0.0504	0.0064
Rubber products	0.0232	0.0796	-0.0312	0.0664	0.0776
Plastic products	0.1082	0.1829	0.0462	0.2367	0.2562
Nonmetal mineral products	-0.5341^***	-0.5941^***	-0.4634^***	-0.2413^*	-0.3005^*
Smelting & pressing of ferrous metals	0.1792	0.1601	0.1648	-0.0363	-0.0167
Smelting & pressing of nonferrous metals	-0.0692	-0.0783	-0.2389	-0.2733	-0.2877
Metal products	0.3268^*	0.4091^**	0.3639^**	0.5611^***	0.5404^***
Machinery manufacturing	0.0873	0.0730	-	-	-
Machinery & equipment manufacturing	-	-	0.2073	0.4077^**	0.4740^***
Special equipment manufacturing	-	-	0.0235	0.1615	0.2795
Transportation equipment manufacturing	-0.1849	-0.1822	0.1512	0.0274	0.1411
Electric equipment & machinery	0.3733^**	0.2818	0.3335^*	0.3674^**	0.3403^*
Electronic & telecommunications	0.5434^***	0.3652^**	0.4736^***	0.6469^***	0.7024^***
Instruments, meters, cultural & official machinery	0.2826	0.2894	0.5641^***	0.4860^***	0.5477^***

Table 3

Table 4

Table 5

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	Industrial characteristic	Measurement
j = 1	Intermediate input ratio	Intermediate inputs, % of output
j = 2	Ultimate consumption ratio	Domestic consumption, % of total sale
j = 3	Scale economy	Employment per enterprise
j = 4	Labor intensity	Percentage of labor wage in output, % of output
j = 5	Higher skills intensity	Share of higher educated workers in workforce
j = 6	Energy intensity	Energy consumption per output, ton standard coal
j = 7	Tax rate	Percentage of tax in output, % of output
j = 8	State-owned enterprise raito	Percentage of state-owned enterprise in total enterprise, % of total enterprise

	Regional characteristic ( )	Industrial characteristic ( )	Hypothesis
j = 1	Centrality (Centrality)	Intermediate input ratio (Input)	Hypothesis 1
j = 2	Centrality (Centrality)	Ultimate consumption ratio (Dmnd)	Hypothesis 2
j = 3	Centrality (Centrality)	Scale economy (Scale)	Hypothesis 3
j = 4	Labor endowment (L-endmnt)	Labor intensity (L-inten)	Hypothesis 4
j = 5	High skills- endowment (H-endmnt)	Higher skills intensity (H-inten)	Hypothesis 5
j = 6	Energy endowment (Eng-endmnt)	Energy intensity (Eng-inten)	Hypothesis 5
j = 7	Local protection (Local)	Tax rate (Tax)	Hypothesis 6
j = 8	Local protection (Local)	State-owned enterprise raito (State)	Hypothesis 6

	1985	1995	2004	2008
Interactions
Centrality×Input	0.1434 (0.0881)	0.1024^*** (0.0243)	0.0446^*** (0.0118)	0.0269^*** (0.0076)
Centrality×Dmnd	0.0292 (0.0268)	0.0309^*** (0.0071)	0.0160^*** (0.0034)	0.0101^*** (0.0022)
Centrality×Scale	0.0201^*** (0.0074)	-0.0021 (0.0030)	0.0010^* (0.0015)	0.0019^* (0.0028)
L-endmnt×L-inten	-0.0001 (0.0034)	0.0001^* (0.0000)	-0.0001 (0.0000)	0.0001 (0.0000)
Hc-endmnt×Hc-inten	-0.0016 (0.0049)	-0.0006 (0.0008)	-0.0007^* (0.0004)	-0.0002 (0.0002)
Eng-endmnt×Eng-inten	0.0717 (0.0500)	0.0778^*** (0.0301)	0.0849^*** (0.0264)	0.0582^*** (0.0187)
Other variables
Manu	0.8834^*** (0.1208)	0.7545^*** (0.0957)	0.6721^*** (0.1247)	0.5921^*** (0.1325)
Pop	0.1633 (0.1596)	0.3467^** (0.1258)	0.4683^*** (0.1539)	0.5977^*** (0.1670)
Fdm	-0.0550 (0.1084)	0.0030^* (0.0691)	0.1558^* (0.0898)	0.1879^** (0.0933)
Path	0.8939^*** (0.0127)	0.9083^*** (0.0207)	1.0111^*** (0.0285)	0.9258^*** (0.0177)
Local×Tax	22.6602 (15.6260)	19.7197 (10.7284)	2.4767 (2.7029)	1.4283 (1.3357)
Local×State	56.1146^* (36.8005)	35.7379^** (26.6363)	27.3633^* (16.6759)	2.7619 (3.3154)
Adjusted R-squared	0.8466	0.8614	0.8840	0.8295
S.E.	0. 653	0. 721	0. 701	0. 692
Number of obs	765	826	866	872

	1985	1995	2004	2008
Interactions
Centrality×Input	-0.1499 (0.3638)	0.1727^** (0.0448)	0.0483^*** (0.0133)	0.0277^*** (0.0077)
Centrality×Dmnd	-0.0013 (0.0403)	0.0337^** (0.0078)	0.0155^*** (0.0034)	0.0102^*** (0.0021)
Centrality×Scale	0.0314^*** (0.0098)	0.0097 (0.0076)	0.0146^** (0.0056)	0.0071^** (0.0038)
L-endmnt×L-inten	-0.0034 (0.0061)	-0.0001 (0.0001)	-0.0002^** (0.0001)	-0.0001^** (0.00003)
Hc-endmnt×Hc-inten	0.0015 (0.0055)	0.0009 (0.0010)	0.0011^* (0.0007)	0.0051^** (0.0002)
Eng-endmnt×Eng-inten	0.0850 (0.0522)	0.0787^** (0.0318)	0.0880^*** (0.0292)	0.0536^*** (0.0200)
Other variables
Man	0.8982^*** (0.1201)	0.7701^*** (0.0973)	0.6702^*** (0.1231)	0.6284^*** (0.1298)
Pop	0.1492 (0.1586)	0.3331^*** (0.1276)	0.4692^*** (0.1519)	0.5818^*** (0.1649)
Fdm	-0.0560 (0.1076)	0.0027^** (0.0700)	0.156^*** (0.0887)	0.1881^** (0.0411)
Path	0.8989^*** (0.0134)	0.9183^*** (0.0231)	1.0435^*** (0.0334)	0.9239^*** (0.0176)
Local×Tax	-7.8106 (26.3684)	9.0357 (6.7800)	0.3379 (2.9996)	1.4541 (1.3498)
Local×State	43.0539^** (44.0992)	37.4653^** (28.7800)	17.7382^* (9.3935)	2.6360^* (3.4418)
Adjusted R-squared	0.8411	0.8573	0.8278	0.8264
S.E.	0. 639	0. 711	0. 693	0. 671
Number of obs	765	826	866	872

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