preference for the SCM compared to Difference-in-Difference (DiD) approaches. First, a DiD approach could conflate differences in outcomes associated with the war with differences due to pre-treatment characteristics associated with war if the control group was not similar enough to the CAR. The SCM is guaranteed to be at least as similar to CAR as a simple weighted mean of control states or any one other control country (19). Second, traditional DiD methods assume unobserved confounders are time constant, whereas the effects of pre-war confounders do not have to be time constant in the synthetic control method (20). Third, traditional DiD methods provide asymptotic large-sample inference that is inappropriate given the comparative case study design, whereas the synthetic control method uses exact inference (21).

Compared to the previous studies, the contributions of this paper are threefold. First, we assess the costs of conflict in the specific context of the CAR civil war from 2013, mainly through a descriptive analysis. Second, we take advantage of recent progress in satellite nighttime lights, or NTL measurements (22)) to bypass the lack of data inherent to low-income countries, or LICs (like the CAR), and to control for potentially artificially inflated GDP figures due to political economy reasons (23). Finally, we expand the analysis beyond the potential loss of GDP (and lights intensity) and explore the potential detrimental impact of the civil war on the sectoral composition of the economy, and key social indicators (acknowledging the quantitative literature on the social costs of conflicts is much scarcer (10,24,25)) to establish the diagnostic of a possible conflict trap.

We recognize however some limitations in the analysis. The choice to select all other LICs for the potential donor pool to build our counterfactual is explained both for practical and transparency reasons (see the Methodology section for more details) and for structural reasons (although each country is unique and has a specific national context, all LICs face the challenges of structural vulnerability and development trap (26)). These states, however, are also more likely to experience large-scale social unrest and localized armed conflicts (27). If some of the LICs in the donor pool are selected _ex-post_ in the synthetic control, while they also suffered consequences of conflicts during 2013-17, the estimates provided here can be considered conservative.

We did not find any other paper assessing the socio-economic impacts of the CAR civil war using the SCM, so far. Although SCM has been previously used in the literature to assess the economic impact of local conflicts and revolts (8–10) and the social and institutional impacts of civil war (10), we expand the analysis on a variety of key macro-socio indicators, including the innovative satellite NTL extended time series (22), the sectoral composition of the economy, and a reduced version of the human asset index (28) to establish the formal diagnostic of a conflict trap (29), or fragility trap (1). Over the period 2012-17, we find that the civil war led to a significant drop in GDP _per capita_ (41.6percent), NTL intensity (33.9 percent), industrial production (34 percent), manufacturing value added (33.7 percent), and human asset index (20.2 percent). These results are consistent with Abadie and Gardeazabal (2003) and Matta _et al_ . (2017) (8,9), although the magnitude of the adverse socio-economic impacts of the CAR’s civil war seem significantly worse, with cumulative GDP _per capita_ loss found to be between 2.9 times and 7.3 times larger than in 4