贫困动态的测度方法与演化机制

doi:10.11821/dlxb202110007

摘要/Abstract

摘要：

将贫困视为一个动态现象与过程逐渐成为学术界共识。尽管国内外相关成果丰硕,但迄今鲜有对贫困动态研究进行系统梳理。本文立足于现有文献,开展贫困动态研究的回顾与展望。首先,解析了贫困动态的内涵及类型;其次,分别从单维和多维角度归纳了贫困动态测度方法的原理、优缺点及数据来源;第三,从持续时间、家庭生命周期变化、代际传递和时空演变四方面系统梳理了贫困动态研究脉络;第四,基于经济学、社会学和地理学视角,分别从经济、发展和空间层面对贫困动态发生机制进行了解析。在对以上内容进行简要评述的基础上,展望了未来值得进一步探究的若干贫困动态议题,希望能够为贫困动态研究提供有益借鉴,并为巩固扶贫成果、完善扶贫政策提供理论支撑。

关键词: 贫困动态, 贫困地理, 贫困持续时间, 代际传递, 家庭生命周期

Abstract:

Studying poverty as a dynamic process has gradually become a consensus in the research of poverty. In this paper, we conduct a comprehensive review on the existing poverty dynamics. We first introduce various poverty dynamics, as well as their connotations. Then we review the theory, feature and data collection method of the poverty dynamic measurements, from both uni-dimensional and multi-dimensional perspectives. Third, we provide a thorough categorization for poverty dynamics in four aspects, namely duration, inter-generational transmission, family life cycle, and spatio-temporal evolution. Finally, we interpret the mechanism of poverty dynamics in terms of economy, development and space from an interdisciplinary perspective, which involves economics, sociology and geography. The review helps us identify issues for further exploration of poverty dynamics. Our systematical review would shed light on future research on poverty dynamics and provide theoretical support for poverty alleviation policies.

Key words: poverty dynamics, geography of poverty, duration of poverty, intergenerational transmission, family life cycle

严小燕, 祁新华. 贫困动态的测度方法与演化机制[J]. 地理学报, 2021, 76(10): 2425-2438.

YAN Xiaoyan, QI Xinhua. The measurement method and evolution mechanism of poverty dynamics[J]. Acta Geographica Sinica, 2021, 76(10): 2425-2438.

图/表 7

表1

表2

图1

表3

贫困动态的主要测度方法

方法		主要原理		优点	缺点	文献编号
单维
贫困率		测算一段时间内贫困率变化, $H = q / n$ 式中：H为贫困率;q为贫困人口数;n为总人口数		反映贫困广度	不能反映贫困深度、差异度变化	[20, 47]
转移矩阵法		测度不同年份农户在贫困和非贫困之间的转换概率		直观测算贫困与非贫困的转换规模	无法了解贫困程度变化	[34-35]
贫困指数法	Sen指数	通过测算一段时间内贫困指数的变化显示贫困动态	$Sen = 2 (q + 1) n ∑ i q z - y i z × (q + 1 - i)$ ( $i = 1,2, …, n$ )式中：y为贫困人口收入;z为贫困线	满足单调公理和加性分解公理	缺乏连续性和复制不变性,强转移性公理不足	[28-29, 47]
	SST指数		$SST=\frac{1}{n^{2}} \sum_{i=1}^{q}\left[\frac{z-y_{i}}{z}\right] \times(2 n-2 i+1)$	调整Sen指数权重函数	不符合加性分解公理,不满足转移—敏感性公理
	FGT指数		$FGT=\frac{1}{n} \sum_{i=1}^{q}\left[\frac{z-y_{i}}{z}\right]^{\alpha}$ α ≥ 0,α为分布敏感参数	克服Sen指数和SST指数所缺乏的加性分解公理	难以确定α最优值
Spells法		考察特定时段个体贫困持续时间,以及依据多个时段内处于贫困状态时间是否长于某界线来区分慢性和暂时性贫困		简单直观、易操作	侧重贫困进入和退出转变,未考虑贫困深度	[36-37]
组分法		基于“永久性收入假说”前提,将收入/消费分为永久性和暂时性部分,若前者低于贫困线则被认为是慢性贫困,其余为暂时性贫困,总贫困是二者总和		采用回归模型避免慢性和暂时性贫困的区分在时间长度上不统一	对长期贫困识别受贫困深度影响;贫困时间长度不敏感;对数据质量要求高	[21-22, 48]
生活史法		通过参与式调查,访谈个人生活史,绘制生活史图表,以预先设定的等级标准衡量生活轨迹中的幸福水平		能获取个人生命历程中详细的生活福祉及幸福感变化的信息	难获取长时间序列面板数据,受主观性因素影响大	[38-39]
多维
A-F双界线法		设置指标和缺失得分两个临界值,计算多维贫困指数(MPI)动态,并对广度、深度、维度和子群进行分解		运用广,克服多维贫困识别中“并集法”和“交集法”弊端	指标选取、临界值和权重设置具有主观性	[30-31, 49]
指标综合法		通过加权求和、主成分分析、人工神经网络或多面体法等方法汇总得出综合贫困指数,分析动态趋势或结合ESTDA法探索时空交互		灵活性较强,可与多元数据、多种方法相结合	受指标选择和赋权影响大;不好衡量各指标或维度贡献	[41, 50-51]
地理信息技术辅助法		借助3S技术提取多源遥感数据,社交媒体大数据,融合传统普查或调研数据,运用机器算法(随机森林、卷积神经网络等)测算、描述和预测多维贫困及其时空动态,实现可视化		时效性好、精度高、信息量大,有效弥补传统方法的滞后性和不充分性	对不同年份、不同来源的数据整合、精度校准技术要求高	[44-45, 52]

表3

图2

图3

图4

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分类视角	类型	文献编号
识别对象	个体贫困动态(个人、家庭或群体);区域/贫困动态	[15, 19-20]
时间尺度	慢性贫困/长期贫困/持续贫困;暂时性贫困;贫困个人/家庭生命周期;贫困代际传递	[7, 21-25]
状态性质	贫困进入;贫困退出;贫困持续;返贫	[18, 26-27]
贫困程度	贫困广度动态;贫困深度动态;贫困差异度动态;综合贫困动态	[28-29]
贫困类型	单维贫困动态;多维贫困动态;绝对贫困动态;相对贫困动态;主观贫困动态;客观贫困动态	[25, 30-32]