By Julia Fischer[1]
Armed conflict remains a daily reality in many parts of the world. While the trend of conflict outbreaks is increasing (Pettersson et al., 2021), there is reason to expect that peace eventually materializes after war. Within the past 25 years, approximately half of African countries experienced war, followed by periods of peace, the so-called post-conflict periods. However, due to the acute instability, a country’s economy contracts during war because of factors such as capital flight, disinvestment in human capital and physical property destruction. This not only shapes a country’s post-conflict development as a whole, but these consequences of war are likely very local. While some locations within a country experience an influx of displaced people and their savings are spent on necessities there, other locations experience higher destruction of physical property and infrastructure, increased capital flight and a large outflow of human capital.
Furthermore, a location’s economic outlook after a war is not necessarily clear. There are two competing hypotheses on how countries fare after war. The war ruin hypothesis posits that, on average, institutions are weaker after war, the economy is destroyed, and any post-conflict reconstruction is lengthy and costly, hindering growth. In contrast to this grave outlook, the phoenix factor hypothesizes that such locations arise from the ashes. After war, functional and efficient structures supersede outdated institutions, infrastructure is rebuilt better and with new technologies, and devastated economies are replaced by competitive systems (Nkurunziza, 2019). Existing findings are either at the aggregate level (Collier, 1999; Collier & Hoeffler, 2004; Donaubauer et al., 2019) or on a case-study basis (Miguel & Roland, 2011; Redding & Sturm, 2024; Yamada & Yamada, 2021). As local dynamics potentially matter, this article studies how those past local conflict dynamics shape economic conditions.
For any development interventions targeting post-conflict locations, it is important to be aware of these differing local economic settings. Thus, I apply this local post-conflict framework to investigate how this affects the impact of post-conflict interventions. So far, previously conflict-ridden and peaceful countries compete for the same pool of development assistance. However, anecdotal evidence from aid agencies themselves indicates a lack of systematic conflict analysis for any location-specific context in post-conflict countries. This pattern is also visible in the data. The data show that locally disbursed World Bank aid mainly goes to peaceful destinations or countries with minor conflict. Even countries with outright war — defined as more than a thousand fatalities a year — receive larger disbursed amounts than post-conflict countries. A look within post-conflict countries gives a similar picture, locations within post-conflict countries that did not experience any involvement in fighting during wartime receive more than 50% of the locally disbursed foreign aid by the World Bank in the period from 1995 to 2020[2].
The first step is to identify post-conflict countries and subsequently, within these countries, a location’s exposure to fighting during wartime. To do so, all African countries are categorized based on the number of conflict fatalities in a year into a state of war, minor conflict or peace. Periods after war, during which they do not experience any fighting anymore, are considered post-conflict periods. Data for conflict fatalities is based on the Uppsala Conflict Data Program (UCDP) georeferenced event dataset. In the next step, an intensity index for past local war exposure is calculated. For each subnational location, it measures the exposure to fighting, respectively fatalities, during the war period[3]. Using a district-level panel with more than 5400 African districts for the years from 1995 to 2020, I estimate the effect of a location’s indirect exposure to war on growth, as well as the differential effect for those locations that were directly exposed to different intensities of fighting on growth, namely the change in nighttime lights. Additionally, I estimate the effect of post-conflict foreign aid interventions on growth dependent on a location’s exposure to fighting[4].
Results indicate that the so-called post-conflict peace dividend largely depends on local involvement in past fighting. Locations that were only indirectly involved in the country’s past war and never experienced any fatalities on their territory do experience significantly higher growth in nighttime lights in comparison to fully peaceful entities. Also the impact of aid is significant and positive in these cases. However, for those who did experience fighting, not only is growth hindered, but also post-conflict development interventions have a smaller impact and are less effective in pushing growth.
These findings show that local context matters. And this is particularly relevant to policies in post-conflict locations. Within-country dynamics and local war exposure can lead to differential outcomes and differing growth trajectories. When development projects are designed, implemented and evaluated, decision-makers have to explicitly account for wartime exposure and local conditions, as disregarding these differences can lead to adverse effects.
References
Collier, P. (1999). On the economic consequences of civil war. Oxford Economic Papers, 51 (1), 168–183.
Collier, P., & Hoeffler, A. (2004). Aid, policy and growth in post-conflict societies. European Economic Review, 48 (5), 1125–1145.
Donaubauer, J., Herzer, D., & Nunnenkamp, P. (2019). The effectiveness of aid under post conflict conditions: A sector-specific analysis. The Journal of Development Studies, 55 (4), 720–736.
Miguel, E., & Roland, G. (2011). The long-run impact of bombing Vietnam. Journal of Development Economics, 96 (1), 1–15.
Nkurunziza, J. D. (2019). Growth in fragile states in Africa: Conflict and post-conflict capital accumulation. Review of Development Economics, 23 (3), 1202–1219.
Pettersson, T., Davies, S., Deniz, A., Engström, G., Hawach, N., Högbladh, S., Sollenberg, M., & Öberg, M. (2021). Organized violence 1989–2020, with a special emphasis on Syria [Publisher: SAGE Publications Ltd]. Journal of Peace Research, 58 (4), 809– 825. https://doi.org/10.1177/00223433211026126
Redding, S. J., & Sturm, D. M. (2024, April). Neighborhood effects: Evidence from wartime destruction in london (Working Paper No. 32333). National Bureau of Economic Research.
Yamada, T., & Yamada, H. (2021). The long-term causal effect of u.s. bombing missions on economic development: Evidence from the ho chi minh trail and xieng khouang province in lao p.d.r. Journal of Development Economics, 150, 102611.
[1] University of Lucerne, Switzerland.
[2] The data is sourced from the Geocoded Official Development Assistance Dataset (GoDAD) and refers to any World Bank aid disbursements that can be localized at the ADM2 (district) level for the period from 1995 to 2020.
[3] To do so, it accounts for all battle deaths over the whole war period, while discounting battle deaths that are longer in the past and considering the intensity of past war experiences in the case of war recurrences.
[4] The empirical model uses extensive fixed effects such as district, country and year fixed effects, and additionally controls for district- and country-specific trends. Furthermore, controls such as population, precipitation and temperature at the district and total population at the country level are included. Results are robust to a battery of robustness checks as well as alternatively using two different instrumental variable strategies.
