The detection dilemma of marginally non‐poor households in poverty alleviation evaluation: Evidence from a linear quantile mixed model

Abstract

Poverty alleviation remains one of the most pressing problems, and China has made considerable advancements toward poverty alleviation in recent years. Considering village as a random effect, this paper proposes a linear quantile mixed model to analyze the effects of household type, village type, and their interactions on household income, suggesting a test of who benefits more or less from anti‐poverty policies. Results indicate that there has been a somewhat unbalanced development between poverty‐stricken households who are scheduled to be out of poverty earlier and poverty‐stricken households who are scheduled to be out of poverty later. The imbalance is slightly different across village types. Results also show that anti‐poverty policies have been equally implemented among village types, but there is some unbalanced development of poverty‐stricken and non‐poverty‐stricken households at lower income distribution quantiles depending on village type. Previously unidentified, marginally non‐poor households are found to benefit disproportionately less from policy measures irrespective of village type.

Assessment of the effects of targeted poverty alleviation policy on differently poverty‐stricken households: A multi‐propensity score weighting model

“…Scholars have explored the components of the TPA strategy and its implementation mechanism (Li et al, 2022), distilled the TPA strategy from a geographical perspective (Yang & Liu, 2021), and examined the TPA policy process using a relational poverty governance theoretical framework (Xu et al, 2022). Besides, empirical studies have evaluated the impacts of China's poverty alleviation programs, which can be summarized into two main aspects: (1) the overall effectiveness of poverty reduction (Ascher, 2021; Hu et al, 2022); and (2) the impact of specific poverty reduction measure (Liao et al, 2021; Rahman et al, 2021). For instance, Chang et al (2022) found that the TPA program contributed to reducing absolute poverty by increasing incomes.…”

Study on the Spatial Distribution Characteristics and Poverty Inducements of Poverty-Stricken Villages in Henan Province

“…In addition to qualitative research methods, methods using geography, economics, statistics, and other disciplines have also been introduced. For example, the G-TOPSIS model [25], logical regression [26], multi-standard model [27], linear quantitative, mixed model [28], correlation analysis [29], space-time statistics [30], and other methods have been used to study the causes of poverty in poverty-stricken villages. The spatial autocorrelation method is used to analyze the distribution characteristics of poverty-stricken villages, establish a comprehensive evaluation mechanism, and establish an early warning mode to prevent poverty-stricken villages from returning to a state of poverty.…”