Acta Geographica Sinica ›› 2017, Vol. 72 ›› Issue (8): 1347-1360.doi: 10.11821/dlxb201708002
• Industrial Development • Previous Articles Next Articles
Received:
2016-05-02
Revised:
2017-01-30
Online:
2017-08-20
Published:
2017-08-20
Supported by:
Xiangnan WANG. The geographical expansion and profit of property insurers[J].Acta Geographica Sinica, 2017, 72(8): 1347-1360.
Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks
Tab. 1
Spatial stratified heterogeneity for the development degree and loss ratio of main property insurance categories in China in 2013
区域所含地级 单位数 | 东北 | 华北 | 华东 | 华南 | 华中 | 西北 | 西南 | q统计量(%)(显著性) 基于省级单位计算 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
40 | 31 | 66 | 37 | 55 | 53 | 54 | |||||||||
机动车险 | 占比(%) | 70.3 | 80.1 | 77.2 | 76.6 | 79.0 | 71.6 | 74.0 | 51.0 (0.000) | ||||||
深度(%) | 0.53 | 0.91 | 0.83 | 0.73 | 0.62 | 0.78 | 0.97 | 40.8 (0.000) | |||||||
赔付率(%) | 52.8 | 52.4 | 56.0 | 51.2 | 53.2 | 50.6 | 51.4 | 18.9 (0.000) | |||||||
企业 财产险 | 占比(%) | 4.4 | 4.4 | 4.3 | 3.7 | 2.8 | 4.1 | 2.3 | 24.6 (0.000) | ||||||
深度(%) | 0.04 | 0.05 | 0.05 | 0.04 | 0.02 | 0.05 | 0.03 | 22.2 (0.000) | |||||||
赔付率(%) | 43.9 | 43.5 | 47.4 | 102.2 | 61.7 | 52.2 | 54.6 | 19.4 (0.000) | |||||||
农业保险 | 占比(%) | 13.0 | 4.5 | 2.4 | 3.0 | 6.0 | 9.6 | 9.2 | 45.2 (0.000) | ||||||
深度(%) | 0.09 | 0.06 | 0.03 | 0.03 | 0.04 | 0.10 | 0.11 | 31.0 (0.000) | |||||||
赔付率(%) | 78.5 | 44.2 | 46.6 | 62.5 | 63.2 | 50.0 | 45.0 | 27.6 (0.000) | |||||||
信用 保证险 | 占比(%) | 2.4 | 6.2 | 5.3 | 5.7 | 4.6 | 4.0 | 3.7 | 21.5 (0.000) | ||||||
深度(%) | 0.05 | 0.03 | 0.05 | 0.08 | 0.04 | 0.06 | 0.06 | 17.9 (0.000) | |||||||
赔付率(%) | 10.2 | 29.6 | 31.1 | 19.7 | 22.5 | 15.2 | 22.4 | 23.1 (0.000) | |||||||
责任险 | 占比(%) | 2.3 | 2.7 | 3.8 | 3.4 | 3.5 | 2.8 | 2.6 | 38.7 (0.000) | ||||||
深度(%) | 0.02 | 0.03 | 0.03 | 0.03 | 0.03 | 0.05 | 0.05 | 37.0 (0.000) | |||||||
赔付率(%) | 47.2 | 38.1 | 40.8 | 40.4 | 45.8 | 35.4 | 36.1 | 21.1 (0.000) | |||||||
其他险 | 占比(%) | 5.1 | 5.3 | 8.1 | 10.0 | 7.4 | 9.6 | 9.1 | 48.3 (0.000) | ||||||
深度(%) | 0.04 | 0.05 | 0.08 | 0.08 | 0.06 | 0.12 | 0.11 | 42.0 (0.000) | |||||||
赔付率(%) | 46.5 | 53.1 | 47.3 | 52.6 | 47.3 | 52.2 | 45.2 | 13.6 (0.000) |
Tab. 3
Descriptive statistics of the input and output variables
度量 | 均值 | 标准差 | ||
---|---|---|---|---|
投入项数量 | 劳动 | 员工人数(人) | 5796.36 | 8845.45 |
物料 | 固定资产(百万元) | 369.61 | 826.15 | |
金融资本 | 资本金+公积金(百万元) | 2354.21 | 4390.93 | |
投入项价格 | 劳动 | 职工薪酬/员工人数(百万元/人) | 0.25 | 0.37 |
物料 | “业务及管理费用–职工薪酬”/固定资产 | 1.98 | 8.14 | |
金融资本 | 税后利润/“资本金+公积金”(拟合值) | 0.06 | 0.10 | |
产出项数量 (百万元) | 损失补偿 | 赔付支出+准备金增量(百万元) | 4687.37 | 15666.72 |
资金融通 | 投资资产(百万元) | 9472.91 | 28495.60 |
Tab. 4
Descriptive statistics and correlation of the variables measuring geographical expansion
描述统计 | 简单相关系数 | ||||||||
---|---|---|---|---|---|---|---|---|---|
均值 | 标准差 | 最小值 | 最大值 | ||||||
0.78 | 0.41 | 0 | 1 | 1 | |||||
11.36 | 10.78 | 1 | 31 | 0.51*** | 1 | ||||
0.54 | 0.39 | 0 | 1 | 0.69*** | 0.81*** | 1 | |||
0.56 | 0.38 | 0 | 0.95 | 0.78*** | 0.82*** | 0.95*** | 1 | ||
583.58 | 482.69 | 0 | 2604.54 | 0.56*** | 0.74*** | 0.86*** | 0.80*** | 1 |
Tab. 6
The effect of geographical expansion on geographically-weighted profit efficiency
被解释变量:Profitit (地理加权) | ||||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
0.0224*** | 0.0053*** | 0.0016 | 0.0010*** | 0.0004** | 0.0002* | |
(0.0057) | (0.0009) | (0.0045) | (0.0002) | (0.0001) | (0.0001) | |
时变公司特征变量 | × | × | √ | × | × | √ |
公司固定效应 | × | √ | √ | × | √ | √ |
年度固定效应 | √ | √ | √ | √ | √ | √ |
(7) | (8) | (9) | (10) | (11) | (12) | |
0.0311*** | 0.0113*** | 0.0055*** | 0.0335*** | 0.0127*** | 0.0053*** | |
(0.0059) | (0.0017) | (0.0017) | (0.0060) | (0.0017) | (0.0017) | |
时变公司特征变量 | × | × | √ | × | × | √ |
公司固定效应 | × | √ | √ | × | √ | √ |
年度固定效应 | √ | √ | √ | √ | √ | √ |
(13) | (14) | (15) | ||||
0.0050*** | 0.0021*** | 0.0010*** | ||||
(0.0012) | (0.0003) | (0.0003) | ||||
时变公司特征变量 | × | × | √ | |||
公司固定效应 | × | √ | √ | |||
年度固定效应 | √ | √ | √ |
Tab. 7
The estimate results for time-varied company characteristic variables
(3) | (6) | (9) | (12) | (15) | |
---|---|---|---|---|---|
–0.0025 | 0.0001 | 0.0002 | 0.0001 | 0.0001 | |
(0.0031) | (0.0006) | (0.0005) | (0.0006) | (0.0006) | |
–0.0490*** | 0.0012 | 0.0033 | 0.0026 | 0.0025 | |
(0.0144) | (0.0027) | (0.0027) | (0.0027) | (0.0027) | |
0.0067 | 0.0082*** | 0.0077*** | 0.0069*** | 0.0074*** | |
(0.0069) | (0.0013) | (0.0012) | (0.0013) | (0.0013) | |
–0.0049 | –0.0002 | –0.0001 | –0.0003 | –0.0002 | |
(0.0037) | (0.0007) | (0.0006) | (0.0007) | (0.0007) | |
0.0200*** | 0.0026* | 0.0023* | 0.0022* | 0.0024* | |
(0.0072) | (0.0014) | (0.0014) | (0.0013) | (0.0013) |
Tab. 8
The effect of geographical expansion on ordinary profit efficiency
被解释变量:Profitit (普通估计) | ||||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
0.0708*** | 0.0032 | –0.0016 | 0.0038*** | 0.0008 | 0.0000 | |
(0.0114) | (0.0040) | (0.0045) | (0.0004) | (0.0005) | (0.0006) | |
时变公司特征变量 | × | × | √ | × | × | √ |
公司固定效应 | × | √ | √ | × | √ | √ |
年度固定效应 | √ | √ | √ | √ | √ | √ |
(7) | (8) | (9) | (10) | (11) | (12) | |
0.1040*** | 0.0129 | 0.0043 | 0.1090*** | 0.0136* | –0.0031 | |
(0.0114) | (0.0080) | (0.0097) | (0.0117) | (0.0079) | (0.0098) | |
时变公司特征变量 | × | × | √ | × | × | √ |
公司固定效应 | × | √ | √ | × | √ | √ |
年度固定效应 | √ | √ | √ | √ | √ | √ |
(13) | (14) | (15) | ||||
0.0231*** | 0.0014 | –0.0015 | ||||
(0.0024) | (0.0014) | (0.0016) | ||||
时变公司特征变量 | × | × | √ | |||
公司固定效应 | × | √ | √ | |||
年度固定效应 | √ | √ | √ |
Tab. 9
Profit efficiency before and after the initial geographical expansion
Profitit(地理加权) | ||||||
---|---|---|---|---|---|---|
首次跨省区市经营的前后比较 | 首次跨大区域经营的前后比较 | |||||
系数 | 标准误 | t统计量 | 系数 | 标准误 | t统计量 | |
D-3t | 0.006 | (0.006) | [0.99] | 0.006 | (0.007) | [0.92] |
D-2t | 0.005 | (0.006) | [0.95] | 0.008 | (0.006) | [1.40] |
D-t | 0.001 | (0.004) | [0.32] | 0.001 | (0.005) | [0.12] |
Dt | 0.016 | (0.004) | [3.59] | 0.015 | (0.004) | [3.22] |
D2t | 0.013 | (0.004) | [2.74] | 0.010 | (0.004) | [2.00] |
D3t | 0.015 | (0.004) | [3.13] | 0.012 | (0.005) | [2.52] |
公司固定效应 | √ | √ | √ | √ | √ | √ |
年度固定效应 | √ | √ | √ | √ | √ | √ |
R2 | 0.96 | 0.96 | ||||
观察值数 | 288 | 295 |
Annexed Table
Composition of the sample and geographically-weighted profit efficiency in 2015
序号 | 公司名称(作为地理基准点的公司) | 总部地 | 利润效率 | 序号 | 公司名称 (作为地理基准点的公司) | 总部地 | 利润效率 |
---|---|---|---|---|---|---|---|
1 | 中国人民 | 北京 | 0.682 | 33 | 信达 | 北京 | 0.742 |
2 | 中国人寿 | 北京 | 0.733 | 34 | 泰山 | 山东 | 0.777 |
3 | 中国大地 | 上海 | 0.692 | 35 | 锦泰 | 四川 | 0.617 |
4 | 太平 | 上海 | 0.696 | 36 | 众诚汽车 | 广东 | 0.639 |
5 | 中国太平洋 | 上海 | 0.727 | 37 | 长江 | 湖北 | 0.672 |
6 | 中国平安 | 广东 | 0.715 | 38 | 诚泰 | 云南 | 0.627 |
7 | 中华联合 | 北京 | 0.624 | 39 | 富德 | 广东 | 0.690 |
8 | 阳光 | 北京 | 0.740 | 40 | 鑫安汽车 | 吉林 | 0.442 |
9 | 华泰 | 北京 | 0.702 | 41 | 北部湾 | 广西 | 0.542 |
10 | 天安 | 上海 | 0.685 | 42 | 众安在线 | 上海 | 0.542 |
11 | 史带 | 上海 | 0.540 | 43 | 中意 | 北京 | 0.507 |
12 | 华安 | 广东 | 0.766 | 44 | 国泰 | 上海 | 0.672 |
13 | 永安 | 陕西 | 0.653 | 45 | 美亚 | 上海 | 0.718 |
14 | 永诚 | 上海 | 0.696 | 46 | 东京海上日动 | 上海 | 0.564 |
15 | 安信农业 | 上海 | 0.467 | 47 | 瑞再企商 | 上海 | 0.716 |
16 | 安邦 | 北京 | 0.589 | 48 | 丘博 | 上海 | 0.823 |
17 | 安华农业 | 吉林 | 0.592 | 49 | 三井住友海上 | 上海 | 0.444 |
18 | 阳光农业 | 黑龙江 | 0.286 | 50 | 三星 | 上海 | 0.386 |
19 | 安盛天平 | 上海 | 0.542 | 51 | 安联 | 广东 | 0.551 |
20 | 渤海 | 天津 | 0.738 | 52 | 日本 | 上海 | 0.652 |
21 | 都邦 | 吉林 | 0.744 | 53 | 利宝互助 | 重庆 | 0.709 |
22 | 华农 | 北京 | 0.678 | 54 | 中航安盟 | 四川 | 0.862 |
23 | 民安 | 广东 | 0.661 | 55 | 苏黎世 | 北京 | 0.789 |
24 | 安诚 | 重庆 | 0.685 | 56 | 现代 | 北京 | 0.327 |
25 | 中银 | 北京 | 0.658 | 57 | 劳合社 | 上海 | 0.458 |
26 | 英大泰和 | 北京 | 0.584 | 58 | 爱和谊日生同和 | 天津 | 0.682 |
27 | 长安责任 | 北京 | 0.701 | 59 | 日本兴亚 | 广东 | 0.607 |
28 | 国元农业 | 安徽 | 0.299 | 60 | 乐爱金 | 江苏 | 0.390 |
29 | 鼎和 | 广东 | 0.444 | 61 | 富邦 | 福建 | 0.749 |
30 | 中煤 | 山西 | 0.689 | 62 | 信利 | 上海 | 0.593 |
31 | 紫金 | 江苏 | 0.690 | 63 | 天平汽车 | 上海 | 0.739 |
32 | 浙商 | 浙江 | 0.579 | 64 | 安盛 | 上海 | 0.688 |
[1] |
He Canfei, Fu Rong.An empirical study on the locational choices of foreign banks in China. Acta Geographica Sinica, 2009, 64(6): 701-712.
doi: 10.3321/j.issn:0375-5444.2009.06.007 |
[贺灿飞, 傅蓉. 外资银行在中国的区位选择. 地理学报, 2006, 64(6): 701-712.]
doi: 10.3321/j.issn:0375-5444.2009.06.007 |
|
[2] |
Wu Wei, Liu Weidong, Liu Yi.Regional differences of local banking systems in China. Acta Geographica Sinica, 2007, 62(12): 1235-1243.
doi: 10.3321/j.issn:0375-5444.2007.12.001 |
[武巍, 刘卫东, 刘毅. 中国地区银行业金融系统的区域差异. 地理学报, 2007, 62(12): 1235-1243.]
doi: 10.3321/j.issn:0375-5444.2007.12.001 |
|
[3] |
Qian Minghui, Hu Ridong.Research on the spatial radiation ability of regional financial center in China. Geographical Research, 2014, 33(6): 1140-1150.
doi: 10.11821/dlyj201406014 |
[钱明辉, 胡日东. 中国区域性金融中心的空间辐射能力. 地理研究, 2014, 33(6): 1140-1150.]
doi: 10.11821/dlyj201406014 |
|
[4] |
Dai Zhimin, Zhu Liya.The influence of geographical distribution of commercial bank loan on the bank profit efficiency in China. Acta Geographica Sinica, 2015, 70(6): 955-964.
doi: 10.11821/dlxb201506009 |
[戴志敏, 朱莉妍. 中国商业银行贷款地理分布对银行利润效率的影响. 地理学报, 2015, 70(6): 955-964.]
doi: 10.11821/dlxb201506009 |
|
[5] | Deng S E, Elyasiani E.Geographic diversification, bank holding company value, and risk. Journal of Money, Credit and Banking, 2008, 40(6): 1217-1238. |
[6] | Berger A N, DeYoung R. The effects of geographic expansion on bank efficiency. Journal of Financial Services Research, 2001, 19(2/3): 163-184. |
[7] |
Brickley J A, Linck J S, Smith C W Jr. Boundaries of the firm: Evidence from the banking industry. Journal of Financial Economics, 2003, 70(3): 351-383.
doi: 10.1016/S0304-405X(03)00170-3 |
[8] |
Hayden E, Porath D, Westernhagen N.Does diversification improve the performance of German banks? Evidence from individual bank loan portfolios. Journal of Financial Services Research, 2007, 32(3): 123-140.
doi: 10.1007/s10693-007-0017-0 |
[9] |
Schmid M M, Walter I.Do financial conglomerates create or destroy economic value? Journal of Financial Intermediation, 2009, 18(2): 193-216.
doi: 10.1016/j.jfi.2008.07.002 |
[10] | Wang Qiang, Wu Wei, Huang Juan.Trans-regional operation of city commercial bank: Credit expansion, risk level and bank performance. Journal of Financial Research, 2012(1): 141-153. |
[王擎, 吴玮, 黄娟. 城市商业银行跨区域经营: 信贷扩张、风险水平及银行绩效. 金融研究, 2012(1): 141-153.] | |
[11] |
Goetz M R, Laeven L, Levine R.Identifying the valuation effects and agency costs of corporate diversification: Evidence from the geographic diversification of US banks. Review of Financial Studies, 2013, 26(7): 1787-1823.
doi: 10.1093/rfs/hht021 |
[12] | Li Guangzi.Trans-regional operation and small and medium-sized banks' performance. Journal of World Economy, 2014(11): 119-145. |
[李广子. 跨区经营与中小银行绩效. 世界经济, 2014(11): 119-145.] | |
[13] |
Sun Qixiang, Bian Wenlong, Wang Xiangnan.The roles of business concentration in the profit and risks of life insurance companies. Modern Economic Science, 2015, 37(3): 27-38.
doi: 10.3969/j.issn.1002-2848.2015.03.004 |
[孙祁祥, 边文龙, 王向楠. 业务集中度对寿险公司利润和风险的作用研究. 当代经济科学, 2015, 37(3): 27-38.]
doi: 10.3969/j.issn.1002-2848.2015.03.004 |
|
[14] |
Millo G, Carmeci G.Non-life insurance consumption in Italy: A sub-regional panel data analysis. Journal of Geographical Systems, 2011, 13(3): 273-298.
doi: 10.1007/s10109-010-0125-5 |
[15] |
Wang J F, Li X H, Christakos G, et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 2010, 24(1): 107-127.
doi: 10.1080/13658810802443457 |
[16] |
Wang J F, Zhang T L, Fu B J.A measure of spatial stratified heterogeneity. Ecological Indicators, 2016, 67: 250-256.
doi: 10.1016/j.ecolind.2016.02.052 |
[17] |
Wang Shaojian, Wang Yang, Lin Xueqin, et al.Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties. Acta Geographica Sinica, 2016, 71(8): 1329-1324.
doi: 10.11821/dlxb201608004 |
[王少剑, 王洋, 蔺雪芹, 等. 中国县域住宅价格的空间差异特征与影响机制. 地理学报, 2016, 71(8): 1329-1342.]
doi: 10.11821/dlxb201608004 |
|
[18] | Insurance Association of China. China Motor Insurance Market Development Report 2014 |
(Chapters 4-5) 4-5). Beijing: China Financial Publishing House, 2015. | |
[中国保险行业协会. 中国机动车辆保险市场发展报告2014(第四、五章). 北京: 中国金融出版社, 2015.] | |
[19] | Shi Peijun.The Atlas of China's Natural Disaster Hazard. Beijing: Science Press, 2011. |
[史培军. 中国自然灾害风险地图集. 北京: 科学出版社, 2011.] | |
[20] | People's Bank of China. China Financial Stability Report 2016. Beijing: China Financial Publishing House, 2016. |
[中国人民银行.中国金融稳定报告2016. 北京: 中国金融出版社, 2016.] | |
[21] | Cummins J D, Weiss M A.Analyzing firm performance in the insurance industry using frontier efficiency and productivity methods//Dionnes G. Handbook of Insurance. Boston: Kluwer Academic Publishers, 2000. |
[22] | Cummins J D, Weiss M A.Analyzing firm performance in the insurance industry using frontier efficiency and productivity methods//Dionnes G. Handbook of Insurance. 2nd ed. Boston: Kluwer Academic Publishers, 2013. |
[23] | Eling M, Luhnen M.Frontier efficiency methodologies to measure performance in the insurance industry: Overview, systematization, and recent developments. Geneva Papers on Risk and Insurance-Issues and Practice, 2010, 35(2): 217-265. |
[24] |
Cummins J D, Weiss M A, Zi H.Organizational form and efficiency: The coexistence of stock and mutual property-liability insurers. Management Science, 1999, 45(9): 1254-1269.
doi: 10.1287/mnsc.45.9.1254 |
[25] |
Tabak B M, Miranda R B, Fazio D M.A geographically weighted approach to measuring efficiency in panel data: The case of US saving banks. Journal of Banking & Finance, 2012, 37(10): 3747-3756.
doi: 10.1016/j.jbankfin.2013.05.022 |
[26] |
Battese G E, Coelli, T J.Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 1992, 3(1/2): 153-169.
doi: 10.1007/BF00158774 |
[27] |
Bian Wenlong, Wang Xiangnan.A literature review on the stochastic frontier analysis in panel data. Statistical Research, 2016, 33(6): 13-20.
doi: 10.19343/j.cnki.11-1302/c.2016.06.002 |
[边文龙, 王向楠. 面板数据随机前沿分析的研究综述. 统计研究, 2016, 33(6): 13-20.]
doi: 10.19343/j.cnki.11-1302/c.2016.06.002 |