WP1: Health impacts
WP lead: Anneli Eriksson, Johan von Schreeb, Debarati Guha
This WP will focus on understanding the health impact of armed conflict by analyzing the impact pathways. To define strategies to mitigate the health impact of conflict, such an understanding of causes of death and poor health are necessary. To achieve this, we will quantify impacts on mortality, causes of deaths, and basic public-health services, using a variety of methodological approaches. The package will have several components:
- We will develop the methods, concepts, and proxy indicators for vulnerability and exposure developed by the team [1, 2], working in close connection with CC3. We will define a set of proxy indicators for health and well-being in conflict contexts and their availability and aggregation level in regular surveys (DHS and others), extract data and assess progress over time through time-series analysis. To set a baseline, we will compare with similar vulnerability contexts without ongoing armed conflict. We will link statistically a broad range of indicators for health outcomes such as mortality, morbidities, levels of acute and chronic malnutrition, vaccination coverage, and provision of health services (deliveries and C-sections) to conflict exposure and intensity as defined in CC2 for 20–25 contemporary armed conflicts. We will also look more closely at two contemporary conflicts that differ in terms of pre-war income levels. Here, data will be studied at a regional level and related to geographic proximity to ongoing armed conflict. We will focus on malnutrition as an important driver of child mortality and measles vaccination coverage, as a proxy for health service availability and accessibility . This study will be complemented with a qualitative study seeking to understand more of the mechanisms behind the health impact, through focus group discussions and interviews with affected people.
- Verbal Autopsy and cause of death (CoD). Death registration data, particularly in com- plex emergencies, are notoriously sparse. Most deaths occur outside hospitals and remain unknown and unreported. Checchi et al.  have recently highlighted the absence of vali- dated methods as a main underlying reason for the lack of data on causes of death (CoD). To understand these patterns better in humanitarian settings, a variation of the verbal autopsy approach (VA) is a promising way to estimate CoD in populations where most deaths are undocumented. The quality and effectiveness of humanitarian response can only be improved by a more refined understanding of the underlying CoD, both in children and in adults. This information will also elucidate key inequalities related to other factors, such as age, gender and social status. We will undertake the testing experiment in Cox’s Bazar in Bangladesh.
- Estimating impact on key impact indicators by means of Bayesian models. Estimating conflict impact on mortality and other indicators is necessary but widely acknowledged as a challenging task. In many instances, estimations remain non-quantified and are often us- ing broad qualitative descriptors such as ‘tens of thousands’ deaths. We will work to improve estimates for Yemen, DR Congo and Ethiopia through Bayesian modeling, lever- aging the ability of this approach to combine data from several sources that are heterogeneous in their original structure. By pooling several sources of evidence, some of them with too few observations to stand well on their own, we will be able to considerably improve estimates of excess mortality and other indicators in these three countries. We will also estimate biases inherent in the presence of different data sources and use imputation techniques to fill in for missing data .
WP2: Social-psychology impacts
WP lead: Jonathan Hall
This WP aims to assess the impact of exposure to violence on cooperative behavior in the form of political and social trust, political participation, attitudes towards political and economic institutions, and subjective well-being (SWB). We will rely upon two complementary sources of data. The first regards pro-social behavior in experimen- tal games. We will provide the first truly global meta-analysis of the experimental games literature, incorporating data from conflict-affected societies, largely neglected in such analy- sis [cf. 6]. It will expand on the study of Bauer et al.  by incorporating new studies with clear in-group/outgroup treatments mapped onto war cleavages and measures of exposure to violence on the individual level. In combination with UCDP conflict data, we can examine the effects of war exposure on cooperative behavior in experimental games using multi-level modeling analytical strategies. The WP will make use of demographic characteristics such as gender included in surveys as well as experimental treatments designed to capture the in-group/outgroup dynamics of cooperative behavior. The second data source is the Life in Transition Survey [LiTS 8] that has recorded geographic location, socio-economic status, perceptions on social, economic and political issues for thousands of households and individuals across about 30 countries over 10 years. The dataset allows linking a rich set of measures of social cooperation at both the local and national level to several measures of conflict exposure. The work on impacts on SWB will be linked to other WPs through its importance for creativity, longevity and productivity  and political participation and voter turnout [10–12].
WP3: Micro-economic impacts
WP lead: Tilman Brück
This WP will review and estimate the micro-economic impacts of conflict across sectoral or topical domains. We will proceed in three steps. First, we will conduct a meta-analysis of the maturing literature on the micro-level impacts of violent conflict on people and households, updating and expanding Blattman & Miguel  and Verwimp et al. . Second, as this literature mostly disregards impacts across domains, we hypothesize that the sum of all known within-domain effects on say growth is lower than the sum of all effects (including the impacts and interactions across domains). We will thus postulate and estimate a micro-macro model of cross-sectoral impacts of armed violence, providing a novel micro-founded estimate of the aggregate costs of conflict. Third, we will feed the findings of this model into the extended ViEWS model (CC4) to predict the near-term human development impacts of armed violence across sectors.
WP4: The economic costs of conflict: uncertainty
WP lead: Hannes Mueller
Economic development and poverty are adversely affected both during and after armed con- flict, through the humanitarian crisis triggered and the contraction of investments due to instability. Risk perceptions play a key role here, as they spread the costs of armed conflict beyond the conflict period . If investments are affected by uncertainty and uncertainty increases before the conflict, they may decline even before violence breaks out. Conflicts will also make countries unable to attract investments after the conflict has ended because conflict risks stay high [16, 17]. In this WP, we will provide estimates of economic policy uncertainty at the monthly level for more than 180 countries to gauge the effect of conflict risk on investment incentives. Our methodology will build on two pillars. The first one is the literature on the economic cost of economic uncertainty . Such uncertainty is another impact of armed conflict, as shown for the Spanish civil war case in García-Uribe et al. . We will build on the seminal work of Baker et al.  who derive a monthly economic policy uncertainty (EPU) index for 22 countries. Their methodology is a dictionary-based method that counts economic, policy, and uncertainty terms in local newspaper sources and combines them in an index of uncertainty. We will reconstruct their index using a corpus of over 4 million newspaper articles covering 150 countries, training a machine-learning model on the 22 countries in Baker et al.  and predicting EPU across all the remaining countries. The results will allow us for the first time to track economic policy uncertainty in countries that are affected by armed conflict. We will validate our results by looking at sudden increases of the risk which were not followed by an actual outbreak of violence, i.e. the false positives in the conflict forecast model data. Next, we will then rely on the well-established literature on the effects of EPU on investments to gauge the costs of conflict in terms of foregone investment due to conflict risk.
WP5: The impact on the availability of water
WP lead: Ashok Swain
This WP will study how access to safe drinking water is affected by armed conflict, either due to destruction and contamination, or because insecurity hinders populations to reach the best sources. Among the mechanisms explored will be how disruptions to power supplies affect water storage and delivery systems, groundwater withdrawal or purification plants that depend on such supply [21, 22]. The WP will also study how large-scale forced population displacements lead to short-term (municipal- and industrial supply) and long-term (agricultural) changes in demand for water not only in conflict-affected areas but also in regions hosting these migrants. Post-conflict reconstruction efforts usually support and promote building large water infrastructure projects to stimulate food and energy production and economic recovery [23, 24]. The WP will investigate how water bodies, water transport systems, treatment plants, dams, and irrigation facilities are either intentionally or as collateral damage being destroyed or damaged by warring groups. For instance, in Iraq, ISIS used water as a weapon by withholding access, by flooding, and by contaminating water supplies [25, 26].
WP6: Impacts on political institutions
WP lead: Staffan Lindberg
Institutions that regulate access to power positions and how decisions are made and implemented are of crucial importance to many processes in the programme. This WP will contribute to several of these. First, it will study the impact of armed conflict on various aspects of political institutions. Using indicators from the Varieties of Democracy project , it will distinguish between the effects on elections, on institutions ensuring legislative or judicial constraints, and on civil society and freedom of speech and association. As a second goal, the WP will also explore how these institutions work to help preventing armed conflict in the first place [28-30], to establish a sound counterfactual for the impact of armed conflict, and to feed in to the conflict forecasting in CC4. Finally, the WP will work with CC3 to model how political institutions affect communities’ vulnerability to the impact of armed conflict.
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