We follow Besley and Mueller (2015a) and analyze international investment flows in a fixed effects Pseudo Poisson framework. The investment flow at the country level is given by where _xit_ is the inflow of investment in country _i_ in year _t_ . We use the exposure model which controls for global investment flows through _Xt_ . The regression with total inflows as an exposure variable can be thought of as modeling the annual _rate_ of investment inflows into a country in each year.<sup>40</sup> The variable _peaceit_ is a dummy that takes a value of 1 in all years with peace. We lag this variable by one year to allow for the fact that investment needs some planning and will not react immediately to changes in the host country. We expect _γ_ 0 _>_ 0 if inflows increase after the end of conflict.

It is likely that effects of violence will be most visible if the conflict has been intense in terms of battle related deaths per capita. Yet, the right cut-off for the peace dummy is a priori not clear. India, for example, is coded as in conflict throughout the period if we choose a very low threshold. Choosing a higher cut-off means we treat low intensities as experiencing no conflict. In order to understand the role of threshold we use the fact that we have seven different data sets on investment flows from four different sources.<sup>41</sup> We can therefore take a pragmatic approach and run regressions at different cutoffs with all the different data sets we have and discuss the results jointly. Results are presented in Figure 17. Each point in the graph is a point estimate of _γ_ 0.<sup>42</sup> The Y-axis displays the magnitude of this coefficient.

The message from Figure 17 is quite clear. The end of civil war is associated with large positive changes in foreign investment inflows - regardless which dataset we use. Estimates vary somewhat but lie around 0.5 which implies that investment inflows increase by 50 percent on average. The second observation is that at a cut-off between 0.005 and 0.02 battle-related deaths per 1000 population all point estimates move up and very close together. In other words, across a large variation of investment measures results are fairly similar.<sup>43</sup> This means that at these cutoffs we have divided countries with similar experiences above and below the threshold. In what follows we focus on the threshold of 0.008 battle related deaths per 1000 population. Observations below this threshold are, for example, from China, the United Kingdom, the Russian Federation, India and Bangladesh. In all these cases violence probably did not affect the entire economy notably. In what follows we focus on positive net flows, i.e. we subtract outflows from inflows and code negative numbers as 0s. Our results are robust to using gross inflows but as these are not provided by all sources.

> 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discussion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year fixed effects instead of exposure.

> 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net flows and gross flows.

> 42We also distinguish two different ways of calculating the cut-off of intensity using contemporaneous and average population in a country. In total we therefore have 14 different estimates per cut-off.

> 43Each coefficient is also estimated quite precisely at this cut-off.

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