代理变量测量中的聚合偏差：夜间灯光与地方经济活动

摘要

本文研究了当高分辨率信号聚合至行政单元时，能否恢复未观测的本地经济活动。我们构建了一个反向回归框架，用于处理由经济活动生成但被用于在较粗空间尺度上预测该活动的信号。主要定理将预测弹性分解为基本弹性、反向回归衰减以及由单元规模和单元内离散度驱动的空间聚合项，表明聚合使弹性趋近于一。蒙特卡洛证据验证了该分解，并阐明了可转移性条件。将VIIRS夜间灯光数据与巴西、意大利、美国、印度尼西亚和肯尼亚的本地GDP或收入数据相结合的应用表明，局部校准主要适用于较富裕地区。

English

This paper studies when high-resolution signals aggregated to administrative units can recover unobserved local economic activity. We develop a reverse-regression framework for signals generated by activity but used to predict it at coarser spatial supports. The main theorem decomposes predictive elasticity into elementary elasticity, reverse-regression attenuation, and a spatial aggregation term driven by unit size and within-unit dispersion, showing aggregation pulls elasticities toward one. Monte Carlo evidence confirms the decomposition and clarifies transferability conditions. Applications to VIIRS nighttime lights and local GDP or income in Brazil, Italy, the United States, Indonesia, and Kenya support local calibration mainly in richer contexts.

代理变量测量中的聚合偏差：夜间灯光与地方经济活动

Aggregation Bias in Proxy Measurement: Nighttime Lights and Local Economic Activity

摘要

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