But there is also a temporal aspect to fear. Asset prices can be used to show how expectations change with political violence. Zussman and Zussman (2006) and Willard et al. (1996) show, for example, that asset prices during conflict react to important conflict events like battles or cease fire agreements. Besley and Mueller (2012) show that house prices seem to react to changes in expectations rather than violence itself. This implies that even once violence is over it can be that asset prices stay suppressed. Parts of the economy will then only recover when peace is regarded as stable. This generates huge problems in fragile states which, as the word suggests, are not regarded as stable. Poverty reduction in these countries might rely as much on changing perceptions of political stability as on economic help.

This fear effect also has a strong impact of how the effect of local violence should be captured at the macro level. If the effect of violence is only contemporaneous then we do not expect conflict to affect poverty beyond the conflict period. However, if expectations are important, we expect economic effects to persist as with a debt that needs to be repaid after conflict has ended. Expectations will then also lead to the sort of patterns we find in the poverty data.

# Poverty and the Cost of Crime

There is no robust empirical relationship between homicide rates on growth rates at the macro level, neither for regions within counties nor across countries. This is puzzling given the extreme levels of violence associated with crime in some regions. A possible reason for this lack of a relationship is that crime does not affect expectations in the same way as political violence. Also, crime rates move much more slowly and are therefore not causing the same dramatic disruptions or refugee waves as political violence.

This does not mean that violent crimes might not have a profound impact on the economy. There is a large related literature that calculates the cost of crime (See Soares (2009) for a review). The standard accounting approach is to estimate this cost simply by adding the costs and losses due to crime. For example, van Ours and Vollaard (2016) use estimates from an accounting approach to study the welfare gains from installing electronic engine immobilizers in the Netherlands. Their benchmark measures of the cost come from the UK Home Office. The methodology is based on Brand and Price (2000) which is a very detailed accounting exercise in which security expenditures, insurance costs, and damages are added up to derive a per case cost. Other approaches include individual valuations of counterfactuals, contingent-valuation, and changes in market prices to estimate the welfare costs. For example, Cook and MacDonald (2011) use both contingent-valuation surveys and jury awards to victims of violent crimes to calculate the welfare gains from crime reductions. And property prices are used to assess the cost of crime in London by Gibbons (2004).

Recent research on the economic effects of crime have shown that many of these studies will still constitute a lower bound of the cost of crime. Besley and Mueller (2018) show, for example, that twothirds of the economic impact of crime on firms comes through defensive measures taken by firms which might or might not be directly affected by crime. In other words, if one would focus on crime statistics to understand the impact of crime one would only capture one-third of its actual costs. Crime might disappear where firms are successful defending against it. But the effort exerted in defending against crime forms part of the cost.

There is therefore little doubt that crime will affect poverty rates through its impact on the allocation of jobs across firms and the arising disincentives for investment that arise from the lack of secure property rights. But this effect will only be visible in individual- and firm-level surveys or by calculating costs of criminal acts directly.

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