The temporal and spatial evolution of market segmentation and its spatial spillover in the Yangtze River Delta Region

doi:10.3969/j.issn.1004-9479.2022.04.2020380

Abstract

Abstract:

The elimination of market segmentation is the primary goal of regional integration in the Yangtze River Delta region, and it also meets the requirement of promoting high-quality development of regional economy. Therefore, it is necessary to depict and analyze the temporal and spatial evolution of market segmentation in the Yangtze River Delta region in recent years and explore its spatial externalities. Based on market segmentation of the circulation of commodities, labor migration "two sides" perspective, this article integrated using the method of relative price method, the spatial econometric model, to 35 cities in Yangtze river delta, 2008-2017 panel data analysis, to explore market segmentation in Yangtze river delta geography process, pattern, as well as its spatial spillover effects on regional economic growth. The research shows that:① in the past ten years, the segmentation of commodity and labor market in the Yangtze River Delta region presents a downward trend in fluctuation, with a decrease of 63% and 48% respectively; ② Spatial differentiation of market segmentation varies from province (municipality) to province (municipality) and also city to city. At the provincial level, the segmentation of commodity and labor market in all provinces(municipalities) has a downward trend. The annual trend of commodity market segmentation between provinces(municipalities) is consistent, while the labor market segmentation shows great difference after 2012. At the municipal level, the decrease of commodity market segmentation in core cities is 65.8%, which is larger than that in non-core cities, and the decrease of labor market segmentation is only 37.2%, which is significantly smaller than that in non-core cities. As a result, the rank of the commodity market segmentation in the core cities moves down, while the rank of the labor market segmentation moves up; ③ The results of the spatial econometric model show that labor market segmentation has a significant negative space spillover effect on regional economic growth, which is mainly reflected by direct effect, while the indirect effect coefficient is negative but not significant outside the region. Although the influence coefficient of commodity segmentation on regional economic growth is not statistically significant, its negative coefficient still reflects its spatial inhibitory effect on economic growth to some extent.

Key words: market segmentation, economic growth, spatial econometrics, spatial-temporal evolution, Yangtze River Delta Region

摘要：

消除市场分割是长三角区域一体化发展的题中之义。基于商品流通、劳动力流动的市场分割“一体两面”视角，综合运用相对价格法、空间计量模型等方法，对2008—2017年35个长三角地区城市面板数据进行分析，刻画长三角地区市场分割地理过程、格局，探究其对地区经济增长的空间溢出效应。研究发现：①近十年间，长三角地区商品与劳动力市场分割水平在波动中呈现下降趋势，降幅分别为63%和48%。②长三角地区的市场分割凸显多尺度空间分异特征。在省级尺度上，商品和劳动力市场分割水平保持总体下降趋势。各省的商品分割水平年际变化趋势较为一致，而劳动力市场分割则在2012年后出现较大差异。在市级尺度上，核心区城市的商品市场分割水平降幅较大，分割水平位序下移；非核心区城市劳动力市场分割水平降幅较大，分割水平位序下移。③空间计量模型的结果表明，长三角地区的两类市场分割均对地区经济增长形成负向空间溢出效应。其中，劳动力市场分割的空间溢出主要通过直接效应体现，而间接抑制效应并不显著。

关键词: 市场分割, 经济增长, 空间计量, 时空演变, 长三角地区

Xiang KONG, Kaihang CHEN, Yiman LI. The temporal and spatial evolution of market segmentation and its spatial spillover in the Yangtze River Delta Region[J]. World Regional Studies, 2022, 31(4): 827-836.

孔翔, 陈开航, 李一曼. 长三角地区市场分割的时空演变及其空间溢出效应[J]. 世界地理研究, 2022, 31(4): 827-836.

Figures/Tables 8

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城市	商品			劳动力
城市	2008	2017	变化幅度	2008	2017	变化幅度
核心区城市	10.8	3.70	-65.8%	141.6	88.9	-37.2%
非核心区城市	9.17	3.65	-60.2%	163.7	71.6	-56.3%

城市	商品			劳动力
城市	2008	2017	变化幅度	2008	2017	变化幅度
核心区城市	10.8	3.70	-65.8%	141.6	88.9	-37.2%
非核心区城市	9.17	3.65	-60.2%	163.7	71.6	-56.3%

指标	检验结果	指标	检验结果
Hausman（OLS）	154.46^***	Hausman（SDM）	-11.35
LM（SEM）	128.47^***	LM（SAR）	48.02^***
Robust-LM（SEM）	91.28^***	Robust-LM（SAR）	10.83^***
LR（SEM）	111.34^***	LR（SAR）	16.93^***
Wald（SEM）	67.45^***	Wald（SAR）	17.05^***

指标	检验结果	指标	检验结果
Hausman（OLS）	154.46^***	Hausman（SDM）	-11.35
LM（SEM）	128.47^***	LM（SAR）	48.02^***
Robust-LM（SEM）	91.28^***	Robust-LM（SAR）	10.83^***
LR（SEM）	111.34^***	LR（SAR）	16.93^***
Wald（SEM）	67.45^***	Wald（SAR）	17.05^***

变量名称	计量模型
变量名称	OLS（固定效应）	SAR（随机效应）	SEM（随机效应）	SDM（随机效应）
seg-go	-0.034^***（-3.78）	-0.004（-0.68）	-0.013（-1.43）	-0.005（-0.57）
seg-lab	-0.022^**（-2.30）	-0.011（-1.57）	-0.025^***（-2.99）	-0.016^**（-2.04）
ind	-0.196^**（-2.03）	-0.225^***（-3.40）	-0.203^***（-3.31）	-0.227^***（-3.37）
fa	0.290^***（11.02）	0.132^***（5.38）	0.156^***（4.81）	0.112^***（4.17）
fe	0.263^***（10.19）	0.108^***（5.38）	0.081^***（3.35）	0.080^***（3.74）
hr	0.129^***（6.33）	0.088^***（6.09）	0.102^***（6.11）	0.098^***（6.16）
常数项	8.25^***（24.79）	1.57^***（7.95）	5.91^***（19.35）	1.67^***（6.40）
R²	0.923	0.946	0.912	0.952
ρ/λ	-	0.577^***（16.68）	0.884^***（34.43）	0.516^***（10.68）
Log-L		379.40	332.19	387.86