The economic impact of peacekeeping. Evidence from South Sudan

Abstract

This paper investigates the effect of the deployment of United Nations Blue Helmets on economic activity in South Sudan with a special focus on agricultural production. Since UN troops are predicted to improve security, in particular, we expect a positive relationship between deployment of UN blue Helmets and cereal production. We test our hypothesis using an original data-set including all the 78 South Sudanese counties over the period 2009–2011. We control for the non-random assignment of UN troops through an Instrumental Variables approach. Our empirical results show that a 10% increase in the size of the troop allows the production of additional 600 tonnes.

Keywords:

• Peacekeeping
• United Nations
• Blue helmets
• South Sudan
• Cereal production
• Security

JEL Codes:

• C26
• D74
• F53
• H56

Acknowledgments

Earlier versions of this paper have been presented at the 2012 Workshop on the Economics of Conflicts at the Catholic University of the Sacred Heart, Milan, the 2013 Summer School in Development Economics, at the University of Verona, the 2013 Meeting of the Italian Economists Society held at the University of Bologna, the 17th World Congress of the International Economic Association, and the Development@Manchester Seminar Series. Useful comments and suggestions by an anonymous referee also helped improve the paper. Financial support from the University of Verona is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ For an encompassing overview of the history and challenges of South Sudan, see Thomas (2015).

² The conflict has severely undermined the local economy. See Ali (2013) that calculated that the cost of the conflict in Darfur for the government of Sudan was equal to totaling US$30.5 billion, equivalent to 171% of its 2003 GDP. In general, see Smith (2014) and Dunne and Tian (2014).

³ Up to 10,000 people are estimated to have been killed in the inter-ethnic Dinka-Nuer fighting, more than 1,000,000 people have been displaced inside South Sudan and more than 400,000 people have fled to neighboring countries. On 27 May 2014, the Security Council unanimously adopted resolution 2155 (2014) to reprioritize the mandate of UNMISS toward the protection of civilians, human rights monitoring, and support for the delivery of humanitarian assistance, and increased the Mission's troop strength to 12,500 and a police component to up to 1323 personnel.

⁴ Beardsley and Gleditsch (2015) show how peacekeepers are able to contain conflict in specific war-torn sub-national areas.

⁵ On recovery of war-torn countries see also Gil-Alana and Singh (2014).

⁶ Chowdhury and Murshed (2013) show that disruption of state capacity in war-torn societies.

⁷ On the evolution of SPLM see Metelits (2004).

⁸ See Security Council Resolution 1590.

⁹ United Nations (2011b).

¹⁰ Sector III seems to be the most high risk area in terms of threats. Sector III has three active RMG (rebel militia groups). RMG Mattew Pul Jang in Unity, RMG Ogat and Olony in Upper Nile, and South Sudan Democratic Movement (SSDM) under Lt Gen Chol Awan in Jonglei (UNMISS 2011b).

¹¹ For details on UNMISS mandates, see official UNMISS (2011a).

¹² Dinka is the dominant ethnic group in South Sudan, we experimented with a dummy equal to one when this group account for more than 40% of the population in a given province (and zero otherwise) but it was never significant.

¹³ The development of roads is a priority for the World Bank that promoted the Rural Roads Project for South Sudan in 2012 in the amount of US$38 million. The project will help to support the country’s attempts to develop its untapped agricultural potential to improve the livelihood of the rural population, reduce food insecurity, improve basic services delivery, and ensure peace. The support will help respond to the development challenges by providing reliable access to high agricultural production areas and enhancing agricultural marketing. The project will help the rural population in the project area to have access to market, employment opportunities, and to social and administrative services.

¹⁴ The null hypothesis of the Kleibergen-Paap rk LM statistic is that the model is under-identified.

¹⁵ The null hypothesis of the Hansen J test is that the model is not over-identified.