Dynamic response analysis of economic development and infant mortality rate in China from 1991 to 2018
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摘要:
目的 探讨我国经济发展与婴儿死亡率之间的相互影响关系，预测我国婴儿死亡率的变化趋势。 方法 依据我国1991-2018年国民生产总值(gross domestic product, GDP)、卫生总费用(total expenditure on health, TEH)和婴儿死亡率(infant mortality rate, IMR)数据，构建向量自回归(vector autoregression, VAR)模型，并以此预测我国2030年婴儿死亡率水平。 结果 VAR(4)模型R2=0.86, AIC=-20.37, SBC=-18.44; GDP是IMR降低和TEH增长的格兰杰原因(χ2=20.97, P < 0.001)，IMR和GDP是TEH增长的格兰杰原因(χ2=18.07, P < 0.001)；GDP、TEH的新息冲击对婴儿健康水平产生正向中长期响应，12期时对IMR变化的贡献度分别是11.04%和69.49%。GDP受IMR和TEH新息冲击产生正向响应。预测至2030年时我国的IMR为2.13‰(95% CI: 0.93‰~4.90‰)。 结论 经济发展和卫生投入的增加使我国婴儿死亡率有效下降，而相应地，婴儿死亡率下降和卫生投入的增加也促进了我国经济发展。 Abstract:Objective To explore the interactional relationship between economic development and infant mortality in China, and to predict the changing trend of infant mortality in China. Methods Based on gross domestic product (GDP), total expenditure on health (TEH) and infant mortality rate (IMR) data of China from 1991 to 2018, a vector autoregressive model was constructed to predict the infant mortality level of China in 2030. Results The results of VAR (4) model showed that R2=0.86, AIC=-20.37 and SBC=-18.44. The Granger cause of IMR decrease and TEH increase was GDP (χ2=20.97, P < 0.001). The Granger causes of TEH increase were IMR and GDP (χ2=18.07, P < 0.001). The impact of innovations in GDP and TEH had a positive medium-to-long-term response to infant health, and the contribution to IMR changes at 12 periods were 11.04% and 69.49 %, respectively. GDP was positively affected by the impact of IMR and TEH innovation. It was expected that IMR of China would drop to 2.13 ‰ (95% CI: 0.93 ‰-4.90‰) by 2030. Conclusions The increase of economic development and health investment has effectively reduced infant mortality in China. accordingly, the decline in infant mortality and increase in health investment have also promoted the economic development of China. -
图 1 数据转换和1阶差分处理后GDP、TEH及IMR序列图
Figure 1. Sequence diagram of GDP, TEH and IMR after data conversion and first order
表 1 我国1991-2018年GDP、TEH及IMR的变化情况
Table 1. Changes of GDP, TEH and IMR in China from 1991 to 2018
年份 GDP名义值(亿元) GDP平减指数a(%) GDP实际值b(亿元) TEH名义值(亿元) TEH实际值c(亿元) IMR(‰) 1991 22 005.6 308.1 7 142.36 893.49 290.00 50.2 1992 27 194.5 351.9 7 727.91 1 096.86 311.70 46.7 1993 35 673.2 400.7 8 902.72 1 377.78 343.84 43.6 1994 48 637.5 453.0 10 736.75 1 761.24 388.79 39.9 1995 61 339.9 502.6 12 204.52 2 155.13 428.80 36.4 1996 71 813.6 552.5 12 997.94 2 709.42 490.39 36.0 1997 79 715.0 603.5 13 208.78 3 196.71 529.70 33.1 1998 85 195.5 650.8 13 090.89 3 678.72 565.26 33.2 1999 90 564.4 700.7 12 924.85 4 047.50 577.64 33.3 2000 100 280.1 760.2 13 191.28 4 586.63 603.35 32.2 2001 110 863.1 823.6 13 460.79 5 025.93 610.24 30.0 2002 121 717.4 898.8 13 542.21 5 790.03 644.20 29.2 2003 137 422.0 989.0 13 895.05 6 584.10 665.73 25.5 2004 161 840.2 1 089.0 14 861.36 7 590.29 697.00 21.5 2005 187 318.9 1 213.1 15 441.34 8 659.91 713.87 19.0 2006 219 438.5 1 367.4 16 047.86 9 843.34 719.86 17.2 2007 270 092.3 1 562.0 17 291.44 11 573.96 740.97 15.3 2008 319 244.6 1 712.8 18 638.76 14 535.40 848.63 14.9 2009 348 517.7 1 873.8 18 599.51 17 541.91 936.17 13.8 2010 412 119.3 2 073.1 19 879.37 19 980.39 963.79 13.1 2011 487 940.2 2 271.1 21 484.75 24 345.91 1 071.99 12.1 2012 538 580.0 2 449.6 21 986.45 28 119.00 1 147.90 10.3 2013 592 963.2 2 639.9 22 461.58 31 668.94 1 199.63 9.5 2014 643 563.1 2 835.9 22 693.43 35 312.39 1 245.19 8.9 2015 688 858.2 3 035.6 22 692.65 40 974.64 1 349.80 8.1 2016 746 395.1 3 243.5 23 012.03 46 344.89 1 428.85 7.5 2017 832 035.9 3 468.8 23 986.27 52 598.28 1 516.32 6.8 2018 91 9281.1 3 703.0 24 825.31 59 121.90 1 596.59 6.1 注：aGDP平减指数：以1978年GDP指数=100; bGDP实际值：某年GDP实际值=GDP名义值*100/该年GDP指数; cTEH实际值：某年TEH实际值=TEH名义值*100/该年GDP指数。 
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表 2 1阶差分处理后各序列ADF检验结果
Table 2. ADF test results of each sequence after first-order difference processing
变量 类型 Rho值 P值 GDP 单均值 -15.84 0.014 趋势 -18.26 0.038 TEH 单均值 -14.36 0.024 趋势 -15.34 0.093 1/IMR 单均值 -13.97 0.027 趋势 -17.11 0.055
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表 3 不同滞后期数p的信息准则变化
Table 3. Changes in the information criterion for different lag numbers p
滞后期数p AICC值a AIC值b SBC值c HQC值d FPEC值e 0 -19.27 -19.28 -19.14 -19.24 4.23*10-9 1 -19.60 -19.77 -19.19 -19.60 2.61*10-9 2 -19.13 -19.78 -18.76 -19.50 2.67*10-9 3 -18.28 -20.06 -18.59 -19.67 2.27*10-9 4 -17.04 -21.45 -19.52 -20.96 7.61*10-10 5 -11.46 -23.10 -20.72 -22.54 3.01*10-10e 注：a是修正的AICC下选择最优滞后阶数；b是AIC下选择最优滞后阶数；c是SBC下选择最优滞后阶数；d是HQ准则(hannan-quinn criterion, HQC)下选择最优滞后阶数；e是最终预测误差准则(Final prediction error criterion, FPEC)下选择最优滞后阶数。
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表 4 VAR(4)模型参数估计
Table 4. Parameter estimation of VAR (4) model
参数 y1 y2 y3 估计值 P值 估计值 P值 估计值 P值 C 0.02 0.650 0.04 0.252 0.04 0.249 y1, t-1 0.68 < 0.001 -0.01 0.946 0.04 0.170 y2, t-1 0.65 0.066 0.79 0.002 0.61 0.037 y3, t-1 1.21 < 0.001 -0.33 0.079 0.01 0.965 y1, t-2 -0.17 0.295 -0.31 0.022 -0.07 0.683 y2, t-2 -1.47 < 0.001 -0.23 0.414 -0.19 0.612 y3, t-2 -0.53 0.038 0.23 0.267 0.18 0.486 y1, t-3 0.57 < 0.001 0.30 0.014 -0.03 0.824 y2, t-3 0.80 0.006 0.18 0.435 0.08 0.769 y3, t-4 0.41 0.134 0.003 0.991 -0.29 0.263 y1, t-4 -0.66 < 0.001 0.11 0.378 0.17 0.329 y2, t-4 -0.82 0.001 -0.31 0.149 0.12 0.621 y3, t-4 0.08 0.727 -0.33 0.107 0.20 0.412
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表 5 y1不同滞后预测方差分解
Table 5. Variance decomposition of y1 with different lag predictions
滞后 y1 y2 y3 1 100.00 0.00 0.00 2 40.66 4.45 54.89 3 37.01 5.55 57.44 4 28.82 5.44 65.74 5 22.51 4.21 73.28 6 22.32 6.85 70.83 7 21.58 8.78 69.64 8 20.95 8.57 70.48 9 20.81 8.56 70.63 10 20.22 9.17 70.61 11 19.79 10.49 69.72 12 19.49 11.04 69.47
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