countries might be catching up with the non-conflict countries. This is not surprising as the conflict countries include countries like Colombia, Uganda and Angola which all are increasingly peaceful.

This observation has an extremely important implication: conditional on the absence of contemporaneous or recent conflict there seems to be some convergence in poverty rates. This finding is also confirmed by Corral et al. (2020) who show dramatic poverty reduction in “escaped FCS”. We therefore need to use a more nuanced framework to understand the dynamic effects of conflict on poverty reduction. A simple cumulative conflict history without discounting will not be a good representation of the fact that countries seem to be able to escape high poverty rates once conflict ends.

To capture this empirically we build an empirical model of the conflict debt and its impact on poverty rates consisting of the following two equations: P P P P P i C i i _(1)_ where are country and time fixed effects and PP = ββ CCPPC CCPPCCPPbbPP + μμii + μμii + εε μμii, μμii C i C i −1 C i _(2)_ where C CCPPCi is a dummy that is equal to 1 in years in which there are more than 0.08 CCPPCCPPbbPP = δδ CCPPC CCPPCCPPbbPP + CCPPC CCPPCCP PP C i C i −1 C i _(2)_ where C i is a dummy that is equal to 1 in years in which there are more than 0.08 fatalities per 1,000 population. CCPPC CCPPCCP PP Equation (2) captures the build-up of a stock of conflict debt with ongoing conflict. C i increases by 1 in a conflict year and increases by δ with past conflict debt. The parameter δ is a decay CCPPC CCPPCCPPbbPP parameter which will be smaller than 1 if conflict in the past affects current poverty less and less over time, i.e. if countries are able to catch up and “repay their debt” after conflict is over. In the context of the literature discussion the parameter δ is best thought of as the (mental) health and skills of the affected population recovering in the years following conflict or as investors regarding a region or country as increasingly stable with lasting peace. We follow a two-stage procedure. We first calculate the value of C i for every country year under different assumptions on . We then estimate by running the regression in (1) repeatedly with different values of and taking the regression that maximizes the fit of the model. As a result, CCPPC CCPPCCPPbbPP an estimated equal to 0.88 is used throughout the study. This implies that, with stable peace, a δδ δδ δδ conflict debt shrinks to 52% or its original size after 5 years and to 28% of its original size in 10 years. δδ This is in itself interesting as it suggests a relatively quick recovery of poverty rates after conflict ends.

In Table A1 we show the evaluation of the conflict data using the conflict debt model. In the crosscountry data we show that the mean debt value is 0.177 but debt concentrates in few countries. Conflict debt takes a maximum value of almost 5 as the debt accumulates without peace.

# Cross-Country Results

The results provided by our conflict debt model are shown in Table 1. There is a robust relationship between conflict debt and the poverty rate where an increase by 1 in the debt is associated with an increase in the poverty rate of 1.767 percentage points in our preferred specification in column (3) which features country and year fixed effects. What is important to note here is how the country fixed effects reduce the size of the coefficient. This indicates that level differences in conflict and poverty between countries are a key driver of the relationship we see in Figure 1A. Once this is partialed out, the relationship becomes much weaker.

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