By examining the effects of the CAR civil war on GDP per capita, NTL intensity, and various economic indicators such as industrial and manufacturing value added, as well as social indicators represented by the human asset index, it becomes possible to determine whether the observed shock to GDP is genuine or merely a statistical artifact. Additionally, this analysis helps evaluate the negative consequences of the civil conflict on crucial macroeconomic factors, ultimately affecting long-term development.

# **Data and variables**

As a proxy of economic development, we consider the GDP _per capita_ in purchasing power parity (in constant 2017 international US dollars) from the World Development Indicators (35) from 1990 (the first year available for CAR) up to 2017, as in Matta et al. (2019) (9).

When accounting for satellite NTL intensity, we use the Defense Meteorological Satellite Program (DMSP) data from the Earth Observation Group (EIG) at Colorado Schools of Mine’s (Ghosh et al., 2021) (22), as they provide original DMSP-OLS data from 1992 to 2013 but also an extended version of the DMSP NTL from 2013 to 2021. The regular DMSP NTL series has been discontinued after 2013 for two mains reasons. First the launch of a new generation of satellites equipped with more capable sensors, named Visible Infrared Imaging Radiometer Suite (VIIRS), providing more accurate NTL data. Second the orbital shifts from day/night to dawn/dusk of previously used DMSP satellites. The shift to NTL data collected by VIIRS prevented the use of NTL data for long temporal analysis and pushed researchers to produce harmonized data series combining DMSP and VIIRS data. Ghosh et al. (2021) provide consistent time series for the NTL from 1992 to 2021 by exploiting the constant orbital shift of satellites, which brought back older DMSP satellites (F15 and F16) into a usable day/night cycle to continue data collection with DMSP satellites. To have harmonized series for CAR, we select the period 2005-2017 as 2005 saw major structural reforms for Enerca (the main energy utility company of the CAR) and a policy shift towards liberalization of the electricity sector. We extract the sum of the NTL within a buffer zone of a 10 km radius around the capital location of each country. The sum of the NTL gives an aggregate that is a good proxy for electric power output (37) as well as economic activity (38) both at the national and local level. We use the capital as a reference city for a country rather than the all country to improve the signal-to-noise ratio of the NTL time series, as LICs tend to have poor electricity infrastructure, and the NTL data would measure large areas of noisy data. Capitals are usually the most infrastructure rich and economically dynamic locations (especially in LICs) and are therefore indicative proxies in the case of economic shocks.

Data on the share of industrial (including construction) and manufacturing value added over GDP are derived from the World Development Indicators from 2009 (the first year available for CAR) up to 2017. Due to lack of data the pre-treatment period for these two outcomes is limited, but the use of these indicators enables us to check consistency in our results. Due to large differences in sectoral composition among our donor pool of LICs and CAR, we convert the indicators in index base 100 in 2012 (i.e., just before the first clashes) which enables us to capture sectoral dynamics after the start of the civil war.

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