Research on the evolution and influencing factors of regional economic spatial pattern in Germany

doi:10.3969/j.issn.1004-9479.2024.11.20230401

Abstract

Abstract:

Since the reunification of Germany and East Germany, the increasingly prominent regional economic development imbalance has become a bottleneck limiting Germany's economic development and social equity, and scholars of economic geography have been analyzing the regional economic development problems in Germany to summarize the experiences and lessons of coordinated regional development. This paper focuses on the evolution of the regional economic pattern in Germany from 2000 to 2020. Based on the data of GDP per capita, the spatial distribution and regional pattern are summarised at the federal-l?nder-county (city) level, and the spatial error model (SEM) is used to further analyse the drivers of the evolution of the regional economic pattern in Germany. The spatial error model (SEM) was used to further analyse the drivers of the evolution of regional development patterns in Germany. The results of the study show that: (1) Germany's regional economic pattern shows a spatial distribution pattern of "high in the west and low in the east, high in the south and low in the north", and the high GDP per capita areas are mostly located in the metropolitan cores. (2) The gap between the former East Germany and the former West Germany is gradually narrowing, andregional economic differences in the north-south direction of Germany still exist. (3) There are significant scale differences and regional heterogeneity in the driving factors of regional economic development in Germany. The regional economic pattern and its evolution in Germany can provide references for the future of China's regional development. China should attach importance to the construction of scientific and technological innovation capabilities in the Midwest, and manufacturing industry matters a lot to cultivate competitive advantages of the region. The paper can provide useful perspectives for a better understanding of development and experience of Germany.

Key words: Germany, regional economic patterns, influencing factors, Spatial Error Models

摘要：

自两德统一以来，东西德日益突出的区域不平衡问题制约着德国经济发展和社会公平，经济地理学者纷纷通过剖析德国区域经济发展问题以总结区域协调发展的经验与教训。本文基于2000—2020年德国的县级人均GDP数据，对其区域经济发展格局特征和演变过程进行梳理归纳，并使用空间误差模型（SEM）进一步分析德国区域发展格局演变的驱动因子。研究结果显示：（1）德国的区域经济格局呈现出“西高东低、南高北低”的空间分布形态，且人均GDP高值区多分布在大都市核心区。（2）原东德与原西德之间的差距正逐渐缩小，但德国南北方向的区域间经济差异依然存在。（3）德国区域经济发展的驱动因子存在明显的尺度差异性和区域异质性。德国的区域经济格局及其演变对我国区域发展具有借鉴意义，我国应重视中西部地区的科技创新能力建设，其中，实体经济和制造业的发展是培育区域竞争优势的重要手段。本文能为更好地理解德国发展与汲取德国经验提供有益补充。

关键词: 德国, 区域经济格局, 影响因素, 空间误差模型

Yuefang SI, Qi MENG, Xianzhi HU. Research on the evolution and influencing factors of regional economic spatial pattern in Germany[J]. World Regional Studies, 2024, 33(11): 41-52.

司月芳, 孟琦, 胡贤之. 德国区域经济空间格局演变与影响因素研究[J]. 世界地理研究, 2024, 33(11): 41-52.

Figures/Tables 8

Fig.1 Administrative divisions of Germany

Tab.1 Description of variables

变量名称	变量符号	变量含义	均值	标准差
经济发展水平	$L n G D P$	德国各县市人均GDP（千欧元）的对数值	1.510	0.152
产业结构	$L n T L$	每百人中第二产业从业人员数量的对数值	1.493	0.161
创新	$L n R & D$	每百人中R&D人员数量的对数值	1.413	0.158
教育	$L n E d u$	大学生人数的对数值	1.483	0.161
环境	$L n E R$	人均废物处理量的对数值	3.197	0.447
公共财政	$L n F i n$	人均税收（欧元）的对数值	2.581	0.149
出口	$L n E x p$	人均出口额（欧元）的对数值	3.077	0.354

Tab.1 Description of variables

变量名称	变量符号	变量含义	均值	标准差
经济发展水平	$L n G D P$	德国各县市人均GDP（千欧元）的对数值	1.510	0.152
产业结构	$L n T L$	每百人中第二产业从业人员数量的对数值	1.493	0.161
创新	$L n R & D$	每百人中R&D人员数量的对数值	1.413	0.158
教育	$L n E d u$	大学生人数的对数值	1.483	0.161
环境	$L n E R$	人均废物处理量的对数值	3.197	0.447
公共财政	$L n F i n$	人均税收（欧元）的对数值	2.581	0.149
出口	$L n E x p$	人均出口额（欧元）的对数值	3.077	0.354

Fig.2 Distribution of per capita GDP in Germany in 2020

Tab.2 Basic statistics for the eastern and western regions of Germany in selected years (West Germany=100)

变量	2000年	2005年	2010年	2015年	2020年
人口	23	22	21	20	20
人均GDP	60	66	67	67	71
劳动成本	77	78	80	79	83
生产力	69	78	75	77	81
出口比率	56	63	67	74	66
人均专利	27	26	28	22	23
失业率	239	202	188	166	125

Tab.3 Basic statistics for the southern, northern and central regions of Germany from 2000 to 2020 (South Germany=100)

变量	2000年		2005年		2010年		2015年		2020年
变量	北部	中部	北部	中部	北部	中部	北部	中部	北部	中部
人口	65	196	64	191	64	187	63	184	62	181
人均GDP	97	75	97	76	94	76	90	74	91	75
劳动成本	97	85	97	86	93	84	91	84	92	85
生产力	97	85	97	86	93	84	91	84	92	85
出口比率	86	54	94	56	93	56	99	56	90	57
人均专利	25	34	27	35	28	36	32	37	43	48
失业率	106	158	103	135	103	125	102	112	105	97

Tab.4 Basic information of metropolitan regions in Germany in 2020

大都市区	主导产业	人均GDP /欧元	就业人数/人	失业率/%
柏林-勃兰登堡	航空航天工程、交通、医疗技术、科学研究	72 470	3 196 255	8.3
法兰克福- 莱茵-美因	金融、咨询、运输、媒体、信息与通信、汽车工业、自动化技术、化学制药、生物技术	85 720	3 275 341	5.4
汉堡	港口业、民航工业、生命科学、纳米技术与化工行业	80 006	2 930 017	6.4
汉诺威-布伦瑞克-哥廷根-沃尔夫斯堡	以汽车为代表的重工业、科学研究、艺术和文化产业	82 322	2 046 264	6.4
德国中部	汽车与机械工程、电子信息与通信	64 401	1 510 915	7.1
慕尼黑	医疗健康行业、信息与通信、纳米技术、汽车工程	92 281	3 726 029	3.6
西北	港口业、汽车与航空航天、食品工业、旅游业	68 107	1 495 615	6.5
纽伦堡	物流业、信息与通信、医药行业、能源、电子技术	73 729	2 043 243	3.9
莱茵-内卡	汽车与化学工业	79 351	1 291 565	5.4
莱茵-鲁尔	文化与工业旅游	76 360	6 818 385	8.4
斯图加特	汽车制造业、电子与精密仪器	86 187	3 134 192	4.1

Tab.5 Estimation results of regression models

解释变量	普通面板模型	空间固定模型	时间固定模型	双固定模型
产业结构	0.443^*	0.491^*	0.454^*	0.492^*
产业结构	（1.673）	（1.662）	（1.684）	（1.661）
创新	0.072^*	0.131^***	0.153^***	0.191^***
创新	（0.017）	（0.019）	（0.017）	（0.018）
教育	0.003	0.002	0.001	0.002
教育	（0.002）	（0.002）	（0.002）	（0.002）
环境	-0.052	-0.002	-0.001	-0.071^**
环境	（-0.001）	（-0.001）	（-0.001）	（-0.032）
公共财政	0.493^***	0.492^***	0.312^***	0.251^***
公共财政	（0.003）	（0.003）	（0.012）	（0.021）
出口	0.004	0.012	0.03^*	0.04^**
出口	（0.021）	（0.023）	（0.021）	（0.022）
R²	0.808	0.868	0.872	0.889

Tab.6 Estimation results of regional regression models

0.006

(0.007)

0.404^***

(0.139)

0.003

(0.029)

0.006

(0.007)

0.059^***

(0.021)

5.692^**

（2.463）

3.511

（2.731）

创新

0.124^***

(0.037)

0.209^***

(0.022)

0.129^**

(0.051)

0.115^***

(0.026)

0.302^***

(0.034)

0.212

（2.644）

1.343

（2.731）

教育

0.014

(0.013)

0.003

(0.002)

0.000

(0.003)

0.031^***

(0.008)

0.014^**

(0.006)

1.593^***

（0.393）

2.449^***

（0.631）

环境

-0.006^**

(-0.003)

0.004^**

(0.002)

0.000

(0.003)

0.000

(0.002)

0.003

(0.003)

0.962^***

（0.191）

0.399^*

（0.223）

公共财政

0.444^***

(0.007)

0.514^***

(0.005)

0.485^***

(0.013)

0.473^***

(0.007)

0.515^***

(0.006)

13.061^***

（0.634）

11.721^***

（0.695）

出口

0.001

(0.004)

0.003

(0.003)

0.016^***

(0.006)

0.000

(0.004)

0.012

(0.003)

1.962^***

（0.323）

0.679^*

（0.391）

R²

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