Working Paper: CEPR ID: DP17220
Authors: Katarzyna Bilicka; Andre Seidel
Abstract: To understand how global firm networks operate, we need consistent information on their activities, unbiased by their reporting choices. In this paper, we collect a novel dataset on the light that factories emit at night for a large sample of car manufacturing plants. We show that nightlight data can measure activity at such a granular level, using annual firm financial data and high-frequency data related to Covid-19 pandemic production shocks. We use this data to quantify the extent of misreported global operations of these car manufacturing firms and examine differences between sources of nightlight.
Keywords: multinational firms; nightlight data; global firm networks
JEL Codes: H32; H26; F23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nightlight emissions (Y50) | production activities (L23) |
| 1% change in nightlight (O39) | 0.29% change in firm turnover (D21) |
| higher corporate tax rates (H29) | missing financial information (G32) |
| nightlight data (Y10) | variation in firm activities (D21) |
| COVID-19 factory closures (L69) | reduction in nightlight emissions (Q52) |
| factory closures (J65) | nightlight emissions in 2020 vs 2019 (Y10) |