coordinate efforts to improve security (which means coordinating with security operations) with projects to improve service delivery.<sup>3</sup>

# Data

Summary statistics for our data sources are provided in Appendix Table A1.

## Poverty Data

Throughout this report, we employ three poverty data sets to explore the relationship between poverty and conflict. The first data set is an unbalanced panel of the poverty headcount ratio at $1.90 a day (2011 PPP) at the country level (PovcalNet) from the World Bank. The second data set is the Global Subnational Atlas of Poverty (GSAP). It provides a cross-section of international poverty measures circa 2013 at the subnational level. The third data set we rely on is a short panel from Uganda in 2012 and 2016. This panel consists of around 1,500 sub-counties in the country.

## Conflict Data

Two conflict databases are used in the study. The Georeferenced Event Dataset (GED) Global version 19.1 from the Uppsala Conflict Data Programme (UCDP), henceforth called UCDP, and the Armed Conflict Location & Event Data Project (ACLED) are databases of geolocated conflict events around the world with a number of fatalities. The two data sets are different in terms of coverage and data collecting process. The UCDP has larger coverage with more countries so it delivers a broader illustration of the global situation between 1989-2018 when we implement the cross-country study. But UCDP’s different inclusion criteria, such that a conflict dyad is only coded once it crosses the 25 battle-related deaths threshold in a given year, makes it understate fatalities from conflict in many areas (see Raleigh and Kishi 2019 comparing the two data sets in detail). When we analyze the withincountry relationship between poverty and conflict, ACLED provides a very rich set of data and we therefore show results for our within-country case study and the African continent using the ACLED data.

# Empirical Relationship between Conflict and Poverty

In what follows we will take an agnostic view regarding the channel through which the correlation of violence on poverty rates arises. Instead, we will rely on the related literature for identification and simply try to understand the involved magnitudes in macro relationship at the country and regional level. If conflict prevents children from building their human capital or hinders investments in the recovery period, then past conflict should be related to poverty rates today. The channels of human capital accumulation and expectations will then lead to a correlation between poverty rates and past conflict. Theoretically, we expect reverse causality from poverty to conflict to generate a contemporaneous relationship. If poverty triggers conflict, then increases in poverty should increase contemporaneous conflict risk.

We will assume that the relationship between violence and poverty works through the local level and re-code conflict counts into per capita counts, i.e. divide fatalities by the overall population of the country or region. This assumes that the effect of violence on the poverty rate is more severe if a conflict is affecting a larger share of the population. In a country like India a fatality count will mean that a much smaller share of the population is affected than in a small country like Burkina Faso.

> 3 The case of the Arab Republic of Egypt shows how difficult this is in practice. Fiscal reforms were accompanied by a World Bank project for social security. However, a government change from Morsi damaged the project because the new government tried to establish control and, according to the national data, poverty has increased. Ironically, sharing fiscal resources with the population is also a way of building fiscal control.

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