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收敛型蛛网

经济理论

  收敛型蛛网是指供给价格弹性小于需求价格弹性,则价格、产量波动幅度逐渐减小,最终趋于平衡。

目录

收敛型蛛网概述

  需求曲线斜率的绝对值小于供给曲线斜率的绝对值时,即需求曲线较为平缓,供给曲线较为陡峭时,才能得到蛛网稳定的结果,所以,供求曲线的上述关系是蛛网趋于收敛的条件,相应的蛛网被称为“收敛型蛛网”。

  收敛型蛛网的特点和过程

  (1)特点:需求曲线较供给曲线平坦,即需求曲线斜率绝对值<供给曲线斜率绝对值。

  (2)过程:因市场收外在干扰偏离原来均衡状态,实际价格和实际产量围绕均衡价格和均横产量上下波动,波动幅度变小,最终回到原来均衡状态。

收敛型蛛网示意图

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" alt="" />

蛛网理论

  蛛网理论由美国的舒尔茨、意大利的里西和荷兰的丁伯根于1930年各自提出,是一种引入时间因素考察价格和产量均衡状态变动过程的动态均衡分析。由于均衡变动过程反映在二维坐标图上形如蛛网,故1934年英国的卡尔多将其命名为蛛网理论。蛛网理论认为均衡状态被打破之后不一定可以自动恢复,假定前提是在完全竞争条件下,如果某种商品的生产时间较长,生产过程中的生产规模无法改变,那个体单位只能影响产品产量而无法改变商品的价格。本期产量决定本期价格,本期价格决定下期产量。

  蛛网理论多用于分析农产品,特别是农产品的价格波动对下个周期产量的影响时,所发生的均衡变动情况。根据需求弹性和供给弹性的对比关系,价格和供给量的变化可分为三种情况:①收敛型蛛网;②发散型蛛网;③封闭性蛛网

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