Risk Factors for Forced Migrant Flight

Abstract

An important type of medical study seeks to establish the risk factors for contracting various diseases. A similar, but very small, vein of research exists in peace and conflict studies, and we seek to contribute to it. Our study evaluates whether variables shown to explain variance in numbers of forced migrants can serve as risk factors that might aid contingency planning for such humanitarian crises. We study a cross-national sample of cases over the period from 1985 through 1994. Our findings indicate that annual, country-level indicators of civil war, a forced migrant episode, and human rights violations are candidate risk factors for forced migration in the following year. Interestingly, when using country-years as the unit of observation genocide is not a useful risk factor for forced migration.

Keywords:

• early warning
• forced migration
• refugees

A previous version of this paper was presented at the 2006 annual meeting of the International Studies Association, 22–25 March, San Diego. We thank Howard Adelman, Andreas Beger, Christian Davenport, Kristin Kosek, Idean Saleyhan, Steve Shellman, Jeff Weber, and Joe Young for comments. The replication data is available as study #1319 at the ICPSR's Publication Related Archive.

Notes

¹We adopt the conventional definition of a forced migrant as one who, due to a fear of persecution, has abandoned his or her home in favor of an uncertain future elsewhere.

²Rather than focus on the probability of observing a forced migration event one could focus on the intensity of such an event. Doing so would certainly be useful. However, like those in the medical field who focus on the probability of contracting a medical condition rather than the severity of that condition, we believe that we are considerably more likely to find useful risk factors for the probability of observing a forced migration event than for the intensity of such an event.

³See Gurr and Harff (1998) and Davies and Gurr (1998). Adelman (1998) and Schmeidl and Jenkins (1998) provide a concise and comprehensive discussion of the concepts underlying this type of effort, and the potential problems it presents.

⁴The list includes 14 factors prompting departure, including ethnic/racial tensions, social tensions, religious tension, human rights abuses, political instability including opposition movements, external factors (e.g., influence of foreign groups and governments), relations with neighboring countries, demographic factors, ecological devastation and other natural events, economic instability (including labor disputes), corruption and drug trafficking, military intervention and interferences, historical probability, and a favorable situation in neighboring countries. It includes seven intervening factors, including alternatives to international flight, international relief in place of origin, international protection force in place of origin, obstacles to flight, unfavorable asylum policies in nearby countries, closed borders, and uncertain living conditions in asylum country. Finally, it includes nine triggering events, including new types of people affected, problems spreading to new geographic regions, significant increases in the intensity of a situation, changes in the viability of flight (including open borders and new neighboring governments), the departure of key political figures or changes in political party, increased peer group pressure, natural disasters, mass demonstrations or riots, and seasonal factors.

⁵For example, Schmeidl (1997) finds that ethnic rebellion and foreign intervention into civil wars are significant predictors of refugee exodus. In addition, she argues that economic development may serve as an accelerator in the presence of political conflict, so that forced migrant flight is more likely in conflicting areas with low levels of development than in areas of greater development and equivalent conflict. Neumayer's (2005) study includes a series of economic variables absent from Moore and Shellman's model, of which growth and discrimination against ethnic minorities exerted a significant impact on asylum-seeking in Western Europe. He also found geographic proximity of the destination state to be an important determinant of asylum-seeking. Any of these variables might be considered as potential risk factors for forced migration.

⁶ Moore and Shellman (2004) construct a dichotomous measure of foreign troops on territory to capture this possible influence on p. The variable never gains significance in their analyses, and we expect the same here. Nonetheless, we did run models including the measure. As expected, it was statistically insignificant and did not change any of our other results.

⁷We are grateful to Barbara Harff for drawing our attention to the fact that, as she put it, “post genocide people do not move because they are dead.”

⁸ Schmeidl (1997) also has variables that measure some of these concepts, but not all of them. The ones she measures are also supported in her data.

⁹The literature also proposes an inverted U-shape for the relationship between institutional freedom (opportunities) and political dissent (Jenkins, 1983; Tilly, 1978). We estimated models that included such a specification; the nonlinear variable, democracy², was not significant in either model. Including it also failed to change the significance or substantive implications of the model's other variables.

¹⁰ Schmeidl (1997) is an exception. She finds that economic underdevelopment and population pressures have little impact on subsequent refugee migration.

¹¹They bound the lower value at zero by recoding all negative scores.

¹²Although our current research interests have led us to a dichotomous dependent variable, one might be interested in the distribution of the count data that generated our current dependent variable. Given forced migration (e.g., forced migrants 0), we observe a range from 10 to 3.5 million refugees and IDPs in a given unit. The average country-year produced 175,649 refugees and IDPs (standard deviation = 416,480). 16 country-years experienced forced migration in excess of 1 million refugees and IDPs.

¹³To aid in interpreting results, we recode the physical integrity index so that higher values indicate higher levels of human rights abuse.

¹⁴The data are available at the State Failure project website: http://www.cidcm.umd.edu/inscr/ stfail/. We adopt Moore & Shellman's revised scaling of this variable: 0 = 0 deaths; 1 = 1 to 999; 2 = 1,000 to 1,999; 3 = 2,000 to 3,999; 4 = 4,000 to 7,999; 5 = 8,000 to 15,999; 6 = 16,000 to 31,999; 7 = 32,000 to 63,999; 8 = 64,000 to 127,999; 9 = 128,000 to 255,999; 10 ≥ 256,000.

¹⁵ Polity's democracy measure is an additive 10-point scale derived from codings of the competitiveness of political participation and executive recruitment, the openness of executive recruitment, and constraints on the chief executive. For our purpose, the democracy scale captures the extent to which citizens can channel discontent through political institutions. On the other hand, the autocracy scale is derived from the lack of regulated political competition and political participation, the lack of competitiveness and openness in executive recruitment, and the lack of constraints on the chief executive. This second measure captures the extent to which citizens are isolated from their government. As measured, both democracy and autocracy may directly affect the ability or willingness of a regime to repress its citizens.

When the autocracy score is subtracted from the democracy score, the resulting −10 to 10 variable captures the government's ability to imbibe discontent through nonviolent means, adjusted for its autonomy and lack of accountability: its ability to repress. This is what we want to capture when we hypothesize that institutional design may influence a government's ability or willingness to repress its citizenry, and as a result we use the −10 to 10 measure rather than the 0 to 10 democracy option.

¹⁶Natural cubic splines fit cubic polynomials to a predetermined number of subintervals of a variable (Beck et al., 1998, p. 1270).

¹⁷There are at least two ways one can employ fixed effects to account for unit-level heterogeneity. First, we can specify our model to vary across units by including a dummy variable for each unit in the analysis: Y _it = α _it + β′ X _it + ε _it . This is the least-squares dummy variable (LSDV) approach. Second, we can remove country-specific effects by reducing each observation by its country-specific mean: Y _it – Y _i(mean) = β′ (X _it – X _i(mean)) + (ε _it – ε _i(mean)). This is the conditional fixed-effects specification. We estimated parameters using both specifications, and the results were not meaningfully different. However, the LSDV specification failed to report the Wald chi-square statistic, while the conditional fixed-effects option produced all statistics of interest. Therefore, the models reported in this paper use the conditional fixed-effects specification. We used Stata 8 to estimate the models (see the clogit command).

¹⁸ Many of the most important international events seldom occur. For example, revolutions, economic shocks, and wars are rarely observed, but are of great interest to international scholars. One consequence of the rarity of these events is that our data often contain many more 0 s (nonevents) than 1 s (events). King and Zeng's (2001a; 2001b) demonstration of the dangers of rare events data call on a dataset of national dyads with 303,814 observations. Of those, 1,042 dyads (0.3%) were at war. The rarity of war in the data is accurate; the problem arises with the consideration that “the substantive information in the data lies much more with the 1's than the 0's” (King and Zeng, 2001a, p. 695). With the data overwhelmed by the absence of war, meaningful covariates of war's presence are lost. As a result, logit analyses underestimate the probability of war, while the probability of peace is overestimated.

¹⁹The odds ratio is the exponentiated coefficient multiplied by the degree of change in the independent variable. Thus, for a change of δ in x, the odds are expected to change by a factor of e^(β∗δ).

²⁰In all cases, variables other than the specified variable of interest are held at their modes (for dichotomous and ordinal variables) or means (for continuous variables).

²¹Because we reversed the coding of this variable so that higher values represented more serious physical integrity abuse, the contents of the table are similarly reversed from Cingranelli and Richard's original table.

²²We have argued that the different findings across measures of human rights abuse are attributable to CIRI's systematic treatment of abuse. Both the Political Terror Scale and the CIRI physical integrity index used here combine an inherently categorical index into a single variable. Rubin is currently working on a paper that investigates the multidimensional implications of physical integrity abuse, including the possibility that the contemporaneous consequences of that abuse vary across repressive method. In the future, extending that possibility to a risk factor model could provide further insight into the phenomena that precede or predict refugee flight.

²³Several anonymous reviewers questioned whether the genocide finding is a consequence of multicollinearity among the variables on the right side of our equations. We examined the possibility in two ways. First, we looked at the correlations among our independent variables. Only 6 two-way correlations in a 9×9 matrix exceeded 0.28, and the highest of these was between the Political Terror Scale and civil war, which correlated at 0.54. Second, we examined the variance-inflation factor (VIF) for each independent variable: VIF = 1/1 − R _k ². “The VIF for a variable shows the increase in [the variance of any independent variable] that can be attributable to the fact that this variable is not orthogonal to the other variables in the model” (Greene, 2003, p. 57). No variable in our analyses had a VIF greater than 1.74, and the mean VIFs were 1.3 (for model 1) and 1.27 (for model 2). On these grounds, we are as confident as we can be that our findings are not statistical artifacts, but meaningful estimates from which we may draw inference.

²⁴See footnote 13.