Finding Soldiers of Peace by Uzonyi Gary;

Author:Uzonyi, Gary; [Неизв.]
Language: eng
Format: epub
Publisher: Georgetown University Press
Published: 2020-11-22T20:00:00+00:00

Research Design

The theory I present in chapter 2 produces four observable implications. Each of these implications is a testable hypothesis of the theory, and each hypothesis needs to be tested separately. However, there are some commonalities between the tests. In each test, I use a directed-mission-member-state-year unit of analysis for all post–Cold War United Nations peacekeeping missions from 1992 to 2015. This means that each UN member state that is not the mission’s target country is paired with each mission authorized by the Security Council. Kathman (2013) provides data on member state participation in each mission.1 To test Hypothesis 1 on the likelihood of Participation, I use a probit regression to analyze whether each state participated in a given mission, coded 1 if yes and 0 if no.2 To test Hypothesis 2 on a state’s Contribution Size, I use zero-inflated negative binomial (ZINB) models to examine the maximum number of troops a state contributes to each UN peacekeeping mission each year.3 Contribution sizes range from 0 to over 7,000 (Pakistan in UNOSOM). To test Hypothesis 3, I use a piecewise exponential regression to examine each member’s Duration until Contribution in months. States that participate at the beginning of the mission are assigned a value of 1. For example, Niger contributed to MINUSMA at its beginning date and thus has a duration of 1. A noncontributing state remains in the data set until it either contributes or the mission ends. Finally, to test Hypothesis 4, I use a piecewise exponential regression to examine a contributing member’s Duration of Contribution in months, which begins counting the month the state contributes its first solider until the state withdraws its final peacekeeper.

Each of the four analyses uses the same key independent variable, Ln(Refugee Inflows). This variable captures the size of the refugee flows between the country receiving the peacekeeping mission and each potential contributor state in each year of an ongoing mission. By measuring dyadic refugee flows from each conflict area to each potential donor state, I am able to calculate the direct externalities received by each third party from the conflict rather than capturing a general measure of conflict severity, or the overall magnitude of the humanitarian crisis for the international community. I collect data on directed refugee flows for each dyad from the Office of the United Nations High Commissioner for Refugees (UNHCR 2018). I use the natural log of the refugee flows because these dyadic flows are highly skewed. In these data, refugee inflows range from 0 for many dyads—for example, Haiti to Guyana in 2000—to 1.9 million from Afghanistan to Pakistan in 2010. This variable, and all other time-varying independent variables, is lagged one year in the nonduration models.

I compare the effects of these direct refugee inflows to Ln(Total Refugee Outflows) to capture the total refugee outflows from each conflict each year (UNHCR 2018). Conflicts that create severe externalities for the international community may be a significant threat to international security. Given this concern, and the humanitarian motives such crises

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