Remittance dependence, support for taxation and quality of public services in Africa

IF 2.4 2区 经济学 Q2 ECONOMICS
Maty Konte, Gideon Ndubuisi
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Abstract

We explore the heterogeneous effect of migrant remittances on citizens' support for taxation using a sample comprising 45,000 individuals from the Afrobarometer survey round 7 [2016–2018] across 34 African countries. To correct for unobserved heterogeneity, we endogenously identify latent classes/subtypes of individuals that share similar patterns on how their support for taxation is affected by their unobserved and observed characteristics, including remittance dependency. We apply the finite multilevel mixture of regressions approach, a supervised machine learning method to detect hidden classes in the data without imposing a priori assumptions on class membership. Our data are best generated by an econometric model with two classes/subtypes of individuals. In class 1 where more than two-thirds of the citizens belong, we do not find any significant evidence that remittance dependence affects support for taxation. However, in class 2 where the remaining one-third of the citizens belong, we find a significant negative effect of remittance dependence on support for taxation. Furthermore, we find that citizens who have a positive appraisal of the quality of the public service delivery have a lower probability of belonging to the class in which depending on remittance reduces support for taxation. The findings emphasize the need for efficient public services provisioning to counteract the adverse effect of remittances on tax morale.
非洲对汇款的依赖、对税收的支持和公共服务质量
我们使用非洲晴雨表调查第 7 轮[2016-2018 年]中来自 34 个非洲国家的 45000 个样本,探讨了移民汇款对公民税收支持的异质性影响。为了校正未观察到的异质性,我们内生地识别出个人的潜在类别/亚类,这些类别/亚类在税收支持如何受其未观察到和观察到的特征(包括汇款依赖性)影响方面具有相似的模式。我们采用有限多层次混合回归方法,这是一种有监督的机器学习方法,用于检测数据中的隐藏类别,而无需对类别成员资格进行先验假设。我们的数据最好由具有两类/亚类个体的计量经济学模型生成。在超过三分之二的公民属于的类别 1 中,我们没有发现汇款依赖性影响税收支持的显著证据。然而,在剩下的三分之一公民所属的第二类中,我们发现汇款依赖对税收支持有显著的负面影响。此外,我们还发现,对公共服务质量有积极评价的公民属于依赖汇款降低税收支持率的阶层的概率较低。研究结果强调,需要提供高效的公共服务来抵消汇款对纳税士气的不利影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.80
自引率
18.20%
发文量
45
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