农村居民人均消费性支出影响因素分析

农村居民人均消费性支出影响因素分析摘要:运用Eviews软件建立我国农村居民全年人均消费性支出的计量经济模型,对影响我国农村居民全年人均消费性支出的可能因素进行分析,发现农村居民全年人均纯收入、农村居民消费价格指数、人均实际纯收入、人均实际消费性支出对我国农村居民全年人均消费性支出具有重要的影响关键字:农村居民人均消费性支出影响因素多元线性回归一、问题的提出今年以来，全国上下认真贯彻落实科学发展观，以农业增产、农民增收为目的，加大各项惠农政策措施落实力度，多措并举做好农村劳动力转移就业工作，克服金融危机和严重干旱等自然灾害带来的不利影响，使全市农村经济保持了稳定发展的良好态势，农民现金收入持续增长，生活消费水平继续提高。我国是一个农业大国，至今仍有9亿农村人口，占全国人口总数的70%，农民是我国最大的群体，农村消费能力的提升直接关系到国民经济的全局。从农村市场看，中国有近六成人口生活在农村。农村城镇化的进程对经济增长的带动作用是非常明显的，世界上还没有哪个国家有规模如此巨大的城镇化。农村居民的收入虽然低于城市居民，但是基数巨大，且农村人口的收入也在稳定增长。随着经济的发展，我国农民的收入水平和消费水平的结构也发生了很大变化，农民生活水平的提高和消费的增加对于实现国民经济又好又快发展、正确处理好内需和外需的关系至关重要。但从总体来看，农民消费水平仍然较低，调查显示有的地区都不及城市居民人均消费支出的三分之一。而且消费结构不合理，局限于食品类等生存基本需求品，消费在衣着装饰等方面的极少。而影响农民消费水平的根本原因是农民的收入。农民生活消费支出主要包括食品、衣着、医疗卫生、教育文化、家庭设备、交通等方面，本文只挑选了五种典型的消费支出作为代表来分析农村居民的消费结构。二、理论依据（一）、1.三、模型设定（一）、影响因素的分析1、.农民收入偏低，增收困难抑制消费。消费的多少在很大程度上决定于可支配收入，虽然农村居民近几年人均收入增长明显，但相对仍处于较低水平。2、为下一代消费支出过大影响其他消费。一是教育费用支出比重过大。调查显示，高中、大学教育负担沉重严重影响其他消费支出。3、.医疗支出不确定性，使农村居民不敢轻易消费。农村生活水平近几年有大幅提升，在解决了基本温饱问题之后，越来越多的农村居民对医疗养老增加了关注度，对医疗保险的投入度也较以往有所增加，特别是在新农村合作医疗保险开办之后。4、主动负债消费理念还未普及，社会需求潜能被隐藏。改革开放后，我国居民长期保持着量人为出的消费理念，特别是对于普通农村居民长期以来形成了勤俭持家的习惯，消费观念较为保守，提倡“量人为出、知足常乐”的消费观念（二）、影响因素的选择影响农民人均生活消费的因素有很多。经分析有如下：变量选择和说明：被解释变量即自变量：农民人均生活消费支出F；解释变量即因变量：农民人均收入用，农民人均食品消费支出禺，衣着消费支出用2,农民人均交通和通讯消费支出蛊3,农民人均医疗保健消费支出^4。（三）、模型形式的设计为此设定了如下对数形式的计量经济模型：=pi+p2Xt+p3X2t+p4X3t+p5X4t+|it其中农民人均生活消费支出农民人均收入农民人均食品消费支出X2衣着消费支出X3农民人均交通和通讯消费支出X4农民人均医疗保健消费支出咸----随机干扰四、数据的收集一）、全国各地区农村基本情况—人均消费情况（2011）单位：元指标农村居民家庭人均收入X农村居民家庭平均每人生活消费支出F食品衣着交通和通讯医疗保健全国合计6977.35221.12107.3341.3547436.8

北京市14735.711077.73593.5862.61228.21035.2天津市12321.26725.42376611.7781.6571.7河北省7119.74711.21579.7334.1520.2434.7山西省5601.445871729.9401.9458.8349.3内蒙古自治区6641.65507.72067395.2728.9534.2辽宁省8296.55406.42116.3446.1577.7482.9吉林省7510.05305.81872.1397.5564.3673.6黑龙江省7590.75333.62072.4473.8576.3573.6上海市16053.811049.34517.2644.51308.9908.6江苏省10805.08094.62839.9554.8923.9645.6浙江省13070.79965.13714.8717.51380.6921.3安徽省6232.24957.32055.2297475.2440.5福建省8778.66540.93032.2395.4728.5321.2江西省6891.64659.92106.3233.6393.3346.7山东省8342.15900.62107.1399.8753.1508.4河南省6604.043201559.7362.8427.9399.7湖北省6897.95010.71954.6272.1414.4438.2湖南省6567.15179.42343.1260.4421.7396.5广东省9371.76725.63301.1277.3682.5398.5广西壮族自治区5231.34210.91844.9123.9384.8301.3海南省6446.04166.12137.9139.8370.3290.1重庆帀6480.44502.12108.6309401.7375.3四川省6128.64675.52161.7281.9431.1413.1贵州省4145.43455.81646.5186.2304.5246.3云南省4722.03999.91884209.1393309.3西藏自治区4904.32741.61384.7331.2348.965.8陕西省5027.94491.71345285.4406.7533.4甘肃省3909.43664.91548.2246.7366.6339.3青海省4608.54536.81716.4347.5450.9308.1宁夏回族自治区5410.04726.61762.5380483.4444.7新疆维吾尔自治区5442.24397.81589.5372.1530.6376.9二）、农村居民家庭基本情况（1990-2011）单位：元指标19901995200020102011调查户数（户）66960.0067340.0068116.0068190.0073630.00调查户人口（人）平均每户常住人口4.804.484.203.953.90平均每户整半劳动力2.922.882.762.852.78平均每个劳动力负担人口（含本人）1.641.561.521.391.40平均每人年收入（元）总收入990.382337.873146.218119.519833.14工资性收入138.80353.70702.302431.052963.43家庭经营收入815.791877.422251.284937.485939.79财产性收入35.7940.9845.04202.25228.57转移性收入65.77147.59548.74701.35现金收入676.671595.562381.607088.768638.51工资性收入136.43352.88700.412427.892959.74家庭经营收入481.191116.731498.813955.364810.37财产性收入59.0538.1938.89168.33185.76转移性收入87.76143.49537.18682.64纯收入686.311577.742253.425919.016977.29工资性收入138.80353.70702.302431.052963.43家庭经营纯收入518.551125.791427.272832.803221.98财产性收入}40.9845.04202.25228.57J28.96转移性收入57.2778.81452.92563.32平均每人年支出（元）总支出903.472138.332652.426991.798641.63家庭经营费用支出241.09621.71654.271915.622431.05购置生产性固定资产20.2962.3363.90193.26265.75税费支出38.6688.6595.528.5711.67消费支出584.631310.361670.134381.825221.13财产性支出18.8055.2819.7449.2512.27转移性支出148.86443.27699.76现金支出639.061545.812140.376307.437984.94家庭经营费用支出162.90454.74544.491757.582269.19购买生产性固定资产20.4662.3263.91193.26265.75税费支出33.3776.9689.818.5611.65消费支出374.74859.431284.743859.334733.35财产性支出47.5992.359.8249.2512.27转移性支出147.60439.45692.73五、模型的估计与调整（一）、模型估计1、农村家庭总收入单线图，农村家庭总收入逐年增加。（X-农村家庭总收入Y-年份）2、利用Eviews软件，输入Y、X、X2、X3、X4、X5等数据，采用这些数据对模型进行OLS回归，结果如表：DependentVariable:YMethod:LeastSquaresDate:06/14/14Time:22:01Sample(adjusted):132

Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-136.9270154.0293-0.8889670.3822X0.0245480.0418350.5867710.5624X11.0650020.1451907.3352420.0000X21.7912960.6041662.9649080.0064X31.5035120.1796868.3674600.0000X41.5841930.4843303.2709000.0030R-squared0.992351Meandependentvar5495.281AdjustedR-squared0.990880S.D.dependentvar1995.508S.E.ofregression190.5708Akaikeinfocriterion13.50529Sumsquaredresid944248.1Schwarzcriterion13.78011Loglikelihood-210.0846Hannan-Quinncriter.13.59638F-statistic674.6076Durbin-Watsonstat1.933440Prob(F-statistic)0.000000图1由此可见，该模型R2=0.9924,F=674.608则，我国农村居民全年人均消费性支出模型的估计式为：Y=-136.927+0.02455Xt+1.065Xlt+1.7913X2t+1.5035X3t+1.5842X4t+“t（二）、模型检验1、经济意义检验。模型估计结果说明:农村居民全年人均纯收入、农村居民消费价格指数、人均实际消费性支出的增加都将带来我国农村居民全年人均消费性支出的增加，与理论分析和经验判断一致。该模型通过了经济意义上的检验，系数符号均符合经济意义。2、统计意义检验。R2=0.9924说明模型的拟合优度较好，F=674.608符合F检验，因而农民人均收入、农民人均食品消费支出、衣着消费支出、农民人均交通和通讯消费支出、农民人均医疗保健消费支出五个解释变量对农村居民全年人均消费性支出的99.2%的离差做出解释，且解释变量联合起来对被解释变量有显著影响。3、多重共线性的检验X、X1、X2、X3、X4的相关系数如表：XX1X2X3X4X10.79440.74500.74590.7258X10.794410.65360.74500.7705X20.74500.653610.78580.7830X30.74590.74500.785810.7500X40.72580.77050.78300.85001表2通过简单相关系数检验法：由表2可知任意两个解释变量之间的零阶相关系数V0.8。由此知该模型不存在多重共线性用Y分别对X、X2、X3、X4、X5作一元线性回归，结果如图：图2-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:20Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C735.9259290.90292.5297990.0169X0.6375970.03625817.584760.0000R-squared0.911563Meandependentvar5495.281AdjustedR-squared0.908615S.D.dependentvar1995.508S.E.ofregression603.2415Akaikeinfocriterion15.70297Sumsquaredresid10917008Schwarzcriterion15.79458Loglikelihood-249.2476Hannan-Quinncriter.15.73334F-statistic309.2239Durbin-Watsonstat1.913390Prob(F-statistic)0.000000由此可见，该模型R2=0.9116图2-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:19Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-55.22996445.3505-0.1240150.9021X12.5310380.19305013.110760.0000R-squared0.851406Meandependentvar5495.281AdjustedR-squared0.846453S.D.dependentvar1995.508S.E.ofregression781.9424Akaikeinfocriterion16.22190Sumsquaredresid18343018Schwarzcriterion16.31351Loglikelihood-257.5504Hannan-Quinncriter.16.25227F-statistic171.8921Durbin-Watsonstat1.155038Prob(F-statistic)0.000000

由此可见，该模型R2=0.8514图2-3DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:18Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1606.443476.08733.3742620.0021X210.464241.1768778.8915330.0000R-squared0.724920Meandependentvar5495.281AdjustedR-squared0.715751S.D.dependentvar1995.508S.E.ofregression1063.905Akaikeinfocriterion16.83774Sumsquaredresid33956823Schwarzcriterion16.92935Loglikelihood-267.4039Hannan-Quinncriter.16.86811F-statistic79.05937Durbin-Watsonstat1.422873Prob(F-statistic)0.000000由此可见，该模型R?=0.7249图2-4DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:16Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C700.9945425.02831.6492890.1095X34.7462160.39424912.038630.0000R-squared0.828502Meandependentvar5495.281AdjustedR-squared0.822785S.D.dependentvar1995.508S.E.ofregression840.0476Akaikeinfocriterion16.36526Sumsquaredresid21170400Schwarzcriterion16.45686Loglikelihood-259.8441Hannan-Quinncriter.16.39562

F-statisticProb(F-statistic)144.92870.000000Durbin-Watsonstat1.400608由此可见，该模型R2=0.8285图2-5DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:15Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1416.370220.41406.4259550.0000X46.9555910.34080920.409050.0000R-squared0.932815Meandependentvar5495.281AdjustedR-squared0.930576S.D.dependentvar1995.508S.E.ofregression525.7865Akaikeinfocriterion15.42813Sumsquaredresid8293544.Schwarzcriterion15.51974Loglikelihood-244.8501Hannan-Quinncriter.15.45849F-statistic416.5292Durbin-Watsonstat1.863327Prob(F-statistic)0.000000由此可见，该模型R2=0.9328由图2-1、2-2、2-3、2-4、2-5知X4的R?最大所以以以yx4作为基础再用Y分别对XX4、X1X4、X2X4、X3X4作线性回归；结果如图图3-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:32Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C960.5453206.43104.6531060.0001X0.2831880.0668344.2371550.0002X44.1282080.7207495.7276600.0000R-squared0.958504Meandependentvar5495.281AdjustedR-squared0.955643S.D.dependentvar1995.508S.E.ofregression420.2775Akaikeinfocriterion15.00877

Sumsquaredresid5122363.Schwarzcriterion15.14618Loglikelihood-237.1403Hannan-Quinncriter.15.05432F-statistic334.9350Durbin-Watsonstat2.190815Prob(F-statistic)0.000000图3-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C625.1550245.52052.5462440.0165X10.9279560.2056684.5119250.0001X44.8346390.5399728.9534930.0000R-squared0.960526Meandependentvar5495.281AdjustedR-squared0.957803S.D.dependentvar1995.508S.E.ofregression409.9148Akaikeinfocriterion14.95884Sumsquaredresid4872875.Schwarzcriterion15.09625Loglikelihood-236.3414Hannan-Quinncriter.15.00438F-statistic352.8259Durbin-Watsonstat2.316899Prob(F-statistic)0.000000图3-3DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C1421.820240.08235.9222180.0000X2-0.0799051.260403-0.0633960.9499X46.9969360.7385559.4738130.0000R-squared0.932824Meandependentvar5495.281AdjustedR-squared0.928192S.D.dependentvar1995.508

S.E.ofregression534.7379Akaikeinfocriterion15.49049Sumsquaredresid8292394.Schwarzcriterion15.62790Loglikelihood-244.8478Hannan-Quinncriter.15.53604F-statistic201.3524Durbin-Watsonstat1.872625Prob(F-statistic)0.000000图3-4DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:33Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C877.1122205.59884.2661340.0002X31.6767130.3608014.6472010.0001X44.9869800.49831310.007720.0000R-squared0.961492Meandependentvar5495.281AdjustedR-squared0.958836S.D.dependentvar1995.508S.E.ofregression404.8647Akaikeinfocriterion14.93404Sumsquaredresid4753548.Schwarzcriterion15.07146Loglikelihood-235.9447Hannan-Quinncriter.14.97959F-statistic362.0467Durbin-Watsonstat1.414208Prob(F-statistic)0.000000由图可知R2=0.9615最大，则以YX3X4为基础用Y分别对XX3X4、X1X3X4、X2X3X4作多元线性回归结果如图图4-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:38Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.647.4620179.50723.6068860.0012

647.4620179.50723.6068860.0012X0.2133450.0554933.8445210.0006X31.3230200.3109804.2543570.0002X43.2721890.6060335.3993570.0000R-squared0.974796Meandependentvar5495.281AdjustedR-squared0.972096S.D.dependentvar1995.508S.E.ofregression333.3395Akaikeinfocriterion14.57267Sumsquaredresid3111227.Schwarzcriterion14.75589Loglikelihood-229.1627Hannan-Quinncriter.14.63340F-statistic360.9839Durbin-Watsonstat1.470526Prob(F-statistic)0.000000图4-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:38Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C110.9190151.00290.7345490.4687X10.9102580.1147217.9345630.0000X31.6458320.2037648.0771630.0000X42.9427360.3815087.7134360.0000R-squared0.988146Meandependentvar5495.281AdjustedR-squared0.986876S.D.dependentvar1995.508S.E.ofregression228.6073Akaikeinfocriterion13.81836Sumsquaredresid1463317.Schwarzcriterion14.00157Loglikelihood-217.0937Hannan-Quinncriter.13.87909F-statistic778.0155Durbin-Watsonstat1.865370Prob(F-statistic)0.000000图4-3DependentVariable:Y

Method:LeastSquaresDate:06/15/14Time:15:39Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C914.0456213.00304.2912340.0002X2-0.7256660.971530-0.7469310.4613X31.7157550.3673214.6710000.0001X45.3166220.6685297.9527140.0000R-squared0.962244Meandependentvar5495.281AdjustedR-squared0.958199S.D.dependentvar1995.508S.E.ofregression407.9865Akaikeinfocriterion14.97681Sumsquaredresid4660683.Schwarzcriterion15.16003Loglikelihood-235.6290Hannan-Quinncriter.15.03754F-statistic237.8710Durbin-Watsonstat1.582733Prob(F-statistic)0.000000由图可知R2=0.9881最大，则以YX1X3X4为基础用Y分别对XX1X3X4、X1X2X3X4作多元线性回归结果如图图5-1DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:41Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C125.6046143.06640.8779460.3877X0.0854750.0413652.0663730.0485X10.7835040.1246896.2836610.0000X31.5084280.2039607.3957170.0000X42.5403810.4101746.1934310.0000R-squared0.989765Meandependentvar5495.281AdjustedR-squared0.988248S.D.dependentvar1995.508S.E.ofregression216.3246Akaikeinfocriterion13.73404Sumsquaredresid1263501.Schwarzcriterion13.96306Loglikelihood-214.7446Hannan-Quinncriter.13.80995F-statisticF-statistic652.7227Durbin-Watsonstat1.966234Prob(F-statistic)0.000000Prob(F-statistic)0.000000图5-2DependentVariable:YMethod:LeastSquaresDate:06/15/14Time:15:42Sample(adjusted):132Includedobservations:32afteradjustmentsVariableCoefficientStd.Errort-StatisticProb.C-165.6481144.2608-1.1482550.2609X21.9654310.5198263.7809390.0008X11.1199860.10954710.223830.0000X31.5329740.1704208.9952750.0000X41.5789130.4783303.3008870.0027R-squared0.992249Meandependentvar5495.281AdjustedR-squared0.991101S.D.dependentvar1995.508S.E.ofregression188.2426Akaikeinfocriterion13.45594Sumsquaredresid956752.1Schwarzcriterion13.68496Loglikelihood-210.2951Hannan-Quinncriter.13.53185F-statistic864.1597Durbin-Watsonstat1.886714Prob(F-statistic)0.0000004、异方差性的检验White检验结果如下:F-statistic3.562028Prob.F(20,11)0.0173Obs*R-squared27.71987Prob.Chi-Square(20)0.1162ScaledexplainedSS20.21128Prob.Chi-Square(20)0.4448TestEquation:DependentVariable:RESIDEMethod:LeastSquaresDate:06/15/14Time:15:59Sample:435Includedobservations:32VariableCoefficientStd.Errort-StatisticProb.C-629464.1234087.0-2.6890180.0211X-120.502761.84256-1.9485400.0773XA20.0048490.0106720.4543730.6584X*X10.0403980.0530730.7611760.4626X*X20.1107580.2181600.5076900.6217X*X3-0.0259560.067862-0.3824840.7094X*X4-0.1027320.207896-0.4941530.6309X1881.4267357.99612.4621130.0316X1A2-0.1830450.131816-1.3886350.1924X1*X2-3.1660960.815849-3.8807400.0026X1*X3-0.3470850.237409-1.4619690.1717X1*X41.8261670.7002162.6080060.0243X24813.7931054.9284.5631480.0008X2A2-9.4608611.743218-5.4272400.0002X2*X3-3.1690440.945138-3.3529940.0064X2*X419.441643.4849345.5787680.0002X395.26270167.21940.5696870.5803X3A20.5241290.2212402.3690490.0372X3*X41.6849551.2594451.3378540.2079X4-2789.149760.8548-3.6658100.0037X4A2-7.5541641.373581-5.4996120.0002R-squared0.866246Meandependentvar29507.75AdjustedR-squared0.623057S.D.dependentvar44557.74S.E.ofregression27356.53Akaikeinfocriterion23.51596Sumsquaredresid8.23E+09Schwarzcriterion24.47785Loglikelihood-355.2553Hannan-Quinncriter.23.83480F-statistic3.562028Durbin-Watsonstat1.928746Prob(F-statistic)0.017282表3由表3可以看出，nR?=27.71987,由White检验知，在a=0.05下，查X?分布表,得临界值=30.1435，比较计算的X彳统计量与临界值，因为nR2=27.71987V30.1435，表明模型不存在异方差。5、自相关的检验通过DW检验法由表1知该模型的DW统计量=1.9334查DW分布表可得临界值dL=1.144dU