Author Affiliations
1College of Urban and Environmental Sciences, Peking University, Beijing 100871, China2Peking University-Lincoln Institute Center for Urban Development and Land Policy, Beijing 100871, Chinashow less
Fig. 1. The proximity between markets and firm agglomeration
Fig. 2. The spatial distribution of Guangdong’s firms which exported to Norway in 2016
Fig. 3. The export trade flows of exporters in Guangdong province (2016) and the cumulative amounts of exporters to each country/region(2000—2016)
Fig. 4. The annual change of Guangdong’s export value and the proportion of each continent
Fig. 5. The value of Guangdong’s exports to other countries/ regions
Fig. 6. The point density distribution of Guangdong’s exporters
Fig. 7. LISA clusters of Guangdong’s exporters
Fig. 8. The annual change of Shenzhen and Zhuhai’s export value as well as their ratio
Fig. 9. The kernel density curve of Guangdong’s exporters (0~800 km)
Fig. 10. The kernel density curve of Guangdong’s exporters which exported to Mexico, Belarus and the Philippines in 2016
Fig. 11. The correlation of localization index and export value of Guangdong’s exporters
变量类型 | 变量名称 | 变量含义 | 预期符号及显著性 |
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因变量 | Localization_index | 广东省出口企业的集聚指数 | | 核心自变量:多维邻近性 | Geo_PROX | 地理邻近性(距离):广东省至出口目的地的航班数 | 显著为负 | Econ_PROX | 经济邻近性(距离):广东省与出口目的地的人均GDP之差除以两地GDP总量乘积 | 显著为正 | Pol_PROX | 政治邻近性(距离):广东省与出口目的地的VA得分之差 | 显著为正 | Insti_PROX | 制度邻近性(距离):广东省与出口目的地的RQ得分之差 | 显著为正 | Cul_PROX | 文化邻近性(距离):广东省与出口目的地Hofstede六维度文化综合得分差 | 显著为正/不显著 | 控制变量 | control | Pop | 出口目的地的人口 | | GDP_total | 出口目的地的GDP总量 | Belt_road | 出口目的地是否属于一带一路成员国。若是,该变量取值为1,否则,该变量取值为0 |
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Table 1. Variable description and the expected results
自变量 | 全样本 | 发达地区 | 发展中地区 |
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模型(1) | 模型(2) | 模型(3) | 模型(4) | 模型(5) | 模型(6) | 模型(7) | 模型(8) |
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Geo_PROXit | 0.0016 | | | | | 0.0014 | 0.0054 | 0.0005 | (0.0011) | | | | | (0.0011) | (0.0045) | (0.0006) | Econ_PROXit | | 0.0054** | | | | 0.0059** | 0.0136*** | 0.0370** | | (0.0024) | | | | (0.0026) | (0.0035) | (0.0180) | Cul_PROXit | | | -0.0058 | | | -0.0056 | -0.0266 | -0.0046 | | | (0.0067) | | | (0.0067) | (0.0168) | (0.0046) | Pol_PROXit | | | | 0.0071* | | 0.0081** | 0.0365** | 0.0049* | | | | (0.0040) | | (0.0040) | (0.0178) | (0.0027) | Insti_PROXit | | | | | 0.0054** | 0.0056** | 0.0389** | 0.0018 | | | | | (0.0026) | (0.0026) | (0.0163) | (0.0018) | Pop | -0.0004 | 0.0023 | -0.0006 | 0.0023 | 0.0022 | 0.0045 | 0.0692*** | 0.0045 | (0.0036) | (0.0037) | (0.0038) | (0.0037) | (0.0037) | (0.0040) | (0.0200) | (0.0032) | GDP_total | 0.0140*** | 0.0138*** | 0.0155*** | 0.0129*** | 0.0127*** | 0.0107*** | -0.0560*** | 0.0163*** | (0.0032) | (0.0032) | (0.0034) | (0.0033) | (0.0033) | (0.0035) | (0.0197) | (0.0030) | Belt_road | 0.0276*** | 0.0302*** | 0.0232* | 0.0335*** | 0.0307*** | 0.0249** | 0.00464 | 0.0159** | (0.0096) | (0.0094) | (0.0124) | (0.0094) | (0.0094) | (0.0122) | (0.0390) | (0.0079) | 年份固定效应 | √ | √ | √ | √ | √ | √ | √ | √ |
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Table 2. The relationship between market proximity and localization index of firms