A3). This also suggests strong catch-up effects as the debt depreciates which we will turn towards in what follows.

The second aspect is that the link we find is not specific to the poverty measure we use. In the appendix we use the multi-dimensional poverty index developed in Alkire and Jahan (2018) to show that the strong link between debt burden and poverty also holds in that data (Appendix Table A4). We find extremely similar magnitudes to the results shown in Table 1. The multidimensional aspect of the data also allows to investigate the channels through which conflict debt might affect conflict. We find very little on the health dimension (nutrition and child mortality) but strong links to the education dimension and even stronger links to all standard of living categories. This is reasonable as it is less likely that, years after conflict ended, conflict history still affects harvests. But if it disrupts the economy and affects human capital accumulation then this will affect years of schooling and assets in the mid-term. We find that deprivation from public services like sanitation, electricity and drinking water increases with conflict burden which suggests that the state capacity link as a confounder could be relevant.

When interpreting Tables 1 and 2 it has to be kept in mind that the goal of our empirical exercise is not to show the dynamic response of poverty to innovations in conflict. Instead, we are interested to quantify broad trends at the country level as they are shown in Figures 1 and 2. What our empirical results show is that poverty trends change in line with a model in which conflict leaves a debt. We will bring this model to the cross-sectional data below and this is also where we see the strength of our statistical model – it provides a useful hypothesis for all levels of and for panel and cross-sectional data.

If we were to interpret the coefficient in Column (3) of Table 1 as causal – how economically significant is the relationship? The standard deviation of the debt variable is 0.6 which would imply that an increase of one standard deviation in conflict debt is associated with an increase of 1 percentage point (0.6*1.767). But the standard deviation is a bad measure of the burden on some countries with the maximum being at 5 which would imply an increase of almost 9 percentage points of the poverty rate.

One simple way to understand the dynamic relationship found in the regression is to simulate conflict debt for a conflict and plot the implied divergence in the poverty rate over time. In Figure 3 we show the implied increase of the poverty rate with a conflict of five years duration - symbolized by a series of five 1s on the X-axis. With the start of the conflict the poverty rate increases dramatically and to over 7 percentage points at the end of the five years of the conflict duration. It then falls gradually as the conflict debt falls but is still about 1 percentage points higher ten years after the end of the conflict. This kind of relationship leads to an image of catching up seen in Figure 2 but is also consistent with persistent divergence if a country enters the so-called violence trap, i.e. a repeated cycle of peace and violence. This is shown in a Figure 4. Divergence here is much more persistent. Poverty rates in countries like Afghanistan, the Democratic Republic of Congo or Somalia could therefore be explained by such a pattern.

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