国家触达范围的不均衡：地方国家存在映射的新方法

中文摘要

国家行使权威的能力在其境内往往存在显著差异，但我们尚缺乏可靠的实证指标来衡量国家触达范围的不均衡。本文提出了一种以精细空间分辨率预测国家存在的方法，并使用撒哈拉以南非洲的数据展示这一方法。我们将一系列国家存在指标（例如基础设施数据）与有关居民次国家层级治理体验的地理定位调查数据相结合。随后，我们采用机器学习算法，使其学习输入变量与居民所体验到的国家存在之间的关系，并将预测外推至撒哈拉以南非洲全境。我们通过一系列检验验证这一预测指标，并考察地方国家存在如何影响发展结果。• 我们提出了一种绘制地方国家存在空间分布的新方法。• 机器学习模型利用地理定位调查数据进行训练，以预测国家存在。• 模型特征包括道路基础设施等反映国家存在的变量。• 该指数使用有关国家存在的替代数据进行验证。• 国家存在调节石油财富冲击与冲突之间的关系。

Abstract

The ability of states to exercise authority often varies considerably within their borders, yet we lack reliable empirical measures of the uneven reach of states. In this paper, we develop a methodology to predict state presence at granular spatial resolutions and demonstrate the approach using data from Sub-Saharan Africa. We link a range of indicators of state presence, e.g., infrastructural data, with geolocated survey data of residents’ experiences with subnational governance. Then, we employ a machine learning algorithm that learns how the input variables relate to experienced state presence and extrapolates the predictions to all of Sub-Saharan Africa. We validate the predicted measure through a range of tests and document how local state presence influences development outcomes. • We present a novel approach to mapping local state presence. • A machine learning model trains on geo-located survey data to predict state presence. • The features comprise variables that signal state presence, e.g. road infrastructure. • The index is validated against alternative data on state presence. • State presence moderates the relationship between oil wealth shocks and conflict.