Predicting Risk Factors Associated with Forced Migration: An Early Warning Model of Haitian Flight

Abstract

This study predicts forced migration events by predicting the civil violence, poor economic conditions, and foreign interventions known to cause individuals to flee their homes in search of refuge. If we can predict forced migration, policy-makers can better plan for humanitarian crises. While the study is limited to predicting Haitian flight to the United States, its strength is its ability to predict weekly flows as opposed to annual flows, providing a greater level of predictive detail than its ‘country-year’ counterparts. We focus on Haiti given that it exhibits most, if not all, of the independent variables included in theories and models of forced migration. Within our temporal domain (1994–2004), Haiti experienced economic instability, low-intensity civil conflict, state repression, rebel dissent, and foreign intervention and influence. Given the model's performance, the study calls for the collection of disaggregated data in additional countries to provide more precise and useful early-warning models of forced migrant events.

Notes

 1. Clair Apodaca, ‘Human rights abuses: Precursor to refugee flight?’ Journal of Refuge Studies 11/1(1998) p.81.

 2. UNHCR, Handbook for Emergencies (Geneva: UNCHR 2000) p.36 < www.unhcr.org/cgibin/texis/vtx/publ/opendoc.pdf?tbl = PUBL&id = 3bb2fa26b >.

 3. Glen Dunkley, Mika Kunieda and Atsushi Tokura, ‘Evaluation of UNHCR's contribution to emergency preparedness, contingency planning and disaster management in the Asia Pacific region (2000–2003)’, The Tokyo Centre and Jakarta Partnership 2004).

 4. UNHCR (note 2) p.26.

 5. Susanne Schmeidl and J. Craig Jenkins, ‘The Early Warning of Humanitarian Disasters: Problems in Building an Early Warning System’, International Migration Review 32 (1998) p.472.

 6. Susanne Schmeidl and J. Craig Jenkins, ‘The Early Warning of Humanitarian Disasters: Problems in Building an Early Warning System’, International Migration Review 32 (1998) p.472

 7. See Susanne Schmeidl, ‘Exploring the Causes of Forced Migration: A Pooled Time-Series Analysis, 1971–1990’, Social Science Quarterly 78/2 (1997) pp.284–308; Susanne Schmeidl, ‘ComparativeTrends in Forced Displacement: IDPs and Refugees, 1964–1996’, in Janie Hampton (ed.), Internally Displaced People: A Global Survey (London: Earthscan 1998) pp.24–33; Susanne Schmeidl, ‘The Quest for Accuracy in the Estimation of Forced Migration’, in Stephen C. Lubkemann, Larry Minear and Thomas G. Weiss (eds.), Humanitarian Action: Social Science Connections (Providence, RI: Watson Institute, Occasional Paper Series 2000) pp.127–59. Christian Davenport, Will H. Moore and Steven C. Poe, ‘Sometimes You Just Have to Leave: Domestic Threats and Forced Migration, 1964–1989’, International Interactions 29 (2003) pp.27–55; Will H. Moore and Stephen M. Shellman, ‘Fear of Persecution, 1952–1995’, Journal of Conflict Resolution 48/5 (2004) pp.723–45; Eric Neumayer, ‘Bogus Refugees? The Determinants of Asylum Migration to Western Europe’, International Studies Quarterly 49/3 (Sept. 2005) pp.389–410.

 8. Dan Wood, ‘Principals, Bureaucrats, and Responsiveness in Clean Air Enforcements’, American Political Science Review 82/1 (1988) p.229.

 9. Schmeidl, ‘Exploring the Causes of Forced Migration’ (note 6).

10. Davenport, Moore and Poe, ‘Sometimes You Just Have to Leave’ (note 7).

11. Moore and Shellman, ‘Fear of Persecution, 1952–1995’ (note 7).

12. Neumayer, ‘Bogus Refugees?’ (note 6).

13. William Deane Stanley, ‘Economic Migrants or Refugees from Violence? A Time-Series Analysis of Salvadoran Migration to the United States’, Latin American Research Review 22/1 (1987) pp.132–54.

14. Andrew R. Morrison, ‘Violence of Economics: What Drives Internal Migration in Guatemala?’, Economic Development and Cultural Change 41/4 (July 1993) pp.817–31.

15. Stephen M. Shellman and Brandon M. Stewart, ‘Political Persecution or Economic Deprivation? A Time Series Analysis of Haitian Exodus, 1990–2004’, Conflict Management & Peace Science 24/3 (2007), pp. 1–17.

16. Davenport, Moore and Poe (note 7).

17. Eric Neumayer, ‘The Impact of Violence on Tourism – Dynamic Econometric Estimation in a Cross-National Panel’, Journal of Conflict Resolution 48/2 (April 2004) pp.259–81.

18. Moore and Shellman ‘Fear of Persecution, 1952–1995’ (note 7); Will H. Moore and Stephen M. Shellman, ‘Whither Will They Go? A Global Analysis of Refugee Flows, 1955–1995’ Paper presented at the annual meeting of the Midwest Political Science Association, Chicago, 15–April 2006; Will H. Moore and Stephen M. Shellman (2006) ‘Refugee or Internally Displaced?’ Forthcoming in Comparative Political Studies 39(5): 599–622.

19. Shellman and Stewart (note 15).

20. Moore and Shellman, ‘Fear of Persecution, 1952–1995’ (note 7).

21. Thomas Bauer and Klaus Zimmermann, ‘Modeling International Migration: Economic and Econometric Issues’, in Rob van der Erf and Liesbeth Heering (eds.), Causes of International Migration (Brussels: Statistical Office of the European Communities 1994).

22. George J. Borjas, ‘The Economics of Immigration’, Journal of Economic Literature 32/4 (Dec. 1994) pp.1667–1717.

23. Douglas S. Massey, Joaquín Arango, Graeme Hugo, Ali Kouaouci, Adela Pellegrino and J. Edward Taylor, ‘Theories of International Migration: A Review and Appraisal.’ Population and Development Review 19/3 (1993) p.431ff.

24. Schmeidl, ‘Exploring the Causes of Forced Migration’ (note 7).

25. Moore and Shellman, ‘Fear of Persecution, 1952–1995’ (note 7); Neumayer, ‘The Impact of Violence on Tourism’ (note 17).

26. Moore and Shellman, ‘Whither Will They Go?’ (note 18).

27. Stanley (note 13).

28. Shellman and Stewart (note 15).

29. Neumayer, ‘The Impact of Violence on Tourism’ (note 17).

30. Moore and Shellman, ‘Fear of Persecution, 1952–1995’ (note 7).

31. Shellman and Stewart (note 15).

32. Shellman and Stewart (note 15)

33. Christian A. Davenport, ‘Multi-Dimensional Threat Perception and State Repression: An Inquiry into Why States Apply Negative Sanctions’, American Journal of Political Science 39/3 (Aug. 1995) pp.683–713.

34. Will H. Moore, ‘Repression and Dissent: Substitution, Context and Timing’, American Journal of Political Science 42/3 (July 1998) pp.851–73; Will H. Moore, ‘The Repression of Dissent: A Substitution Model of Government Coercion’, Journal of Conflict Resolution 44/1 (2000) pp.107–27.

35. Stephen M. Shellman, ‘Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis’, Political Analysis 12/1 (2004) pp.97–104. Stephen M. Shellman, ‘Leaders and Their Motivations: Explaining Government-Dissident Conflict-Cooperation Processes’, Conflict Management & Peace Science 23/1 (Spring 2006) pp.73–90.

36. Michael D. McGinnis, and John T. Williams. ‘Change and Stability in Superpower Rivalry.’ The American Political Science Review 83/4 (Dec. 1989) pp.1101–23; Michael D. McGinnis and John T. Williams, Compound Dilemmas: Democracy, Collective Action, and Superpower Rivalry (Univ. of Michigan Press 2001).

37. John T. Williams, and Michael D. McGinnis, ‘Sophisticated Reaction in the U.S.-Soviet Arms Race: Evidence of Rational Expectations’, American Journal of Political Science 32/4 (Nov. 1988) pp.968–95.

38. S. Gates, S.B. Quiñones, and C.W. Ostrom, Jr., ‘The Role of Reciprocity in Maintaining Peace among Spheres of Influence: An Empirical Assessment Utilizing Vector Autoregression’, Unpublished manuscript, Michigan State Univ. 1993).

39. D. Snyder and C. Tilly, ‘Hardship and Collective Violence in France: 1830–1960’, American Sociological Review 37 (Oct. 1972) pp.520–32; C. Tilly, From Mobilization to Revolution (New York: Random House 1978); Moore, ‘The Repression of Dissent’ (note 34); Moore, ‘Repression and Dissent’ (note 34); Ronald A. Francisco, ‘The Relationship between Coercion and Protest: An Empirical Evaluation of Three Coercive States’, Journal of Conflict Resolution 39/2 (June 1995) pp.263–82;

Ronald A. Francisco, ‘Coercion and Protest: An Empirical Test in Two Democratic States’, American Journal of Political Science 40/4 (Nov. 1996) pp.1179–1204; M. Lichbach, ‘Deterrence or Escalation? The Puzzle of Aggregate Studies of Repression and Dissent’, Journal of Conflict Resolution 31/2 (1987) pp.266–97.

40. T. R. Gurr, Why Men Rebel (Princeton UP 1970); D. A. Hibbs Jr., Mass Political Violence (New York: Wiley 1973); Francisco ‘Coercion and Protest: An Empirical Test in Two Democratic States’ and ‘The Relationship between Coercion and Protest: An Empirical Evaluation of Three Coercive States’ (note 39).

41. K. Rasler, ‘Concessions, Repression, and Political Protest in the Iranian Revolution’, American Sociological Review 61/1 (1996) pp.132–52.

42. M. Krain, Repression and Accommodation in Post-Revolutionary States (New York: St. Martin's 2000); S. Carey, ‘The Dynamic Relationship between Protest, Repression, and Political Regimes’, Political Research Quarterly 59/1 (2006) pp.1–11.

43. Robert O. Keohane, ‘Reciprocity in International Relations’, International Organization 40/1 (1986) p.8.

44. Joshua S. Goldstein, and John R. Freeman, Three-Way Street: Strategic Reciprocity in World Politics (Univ. of Chicago Press 1990) p.23.

45. Dipak K. Gupta, Harinder Singh and Tom Sprague, ‘Government Coercion of Dissidents: Deterrence or Provocation?’, Journal of Conflict Resolution 37/2 (June 1993) pp.301–39.

46. McGinnis and Williams ‘Change and Stability in Superpower Rivalry’ (note 36); McGinnis and Williams, Compound Dilemmas (note 36); Williams and McGinnis ‘Sophisticated Reaction in the U.S.-Soviet Arms Race’ (note 37).

47. Will H. Moore, ‘Action-Reaction or Rational Expectations? Reciprocity and the Domestic-International Conflict Nexus during the ‘Rhodesia Problem’, Journal of Conflict Resolution 39/1 (March 1995) pp.129–67.

48. For details on the GECM see: Anindya Bannerjee, Juan Dolado, John W. Galbraith and David F. Hendry, Integration, Error Correction, and the Econometric Analysis of Non-Stationary Data (Oxford: OUP 1993); Suzanna De Boef, ‘Modeling Equilibrium Relationships: Error Correction Models with Strongly Autoregressive Data’, Political Analysis 9/1 (2001) pp.78–94.

49. Kristian Gleditsch and Kyle Beardsley, ‘Nosy Neighbors: Third-Party Actors in Central American Conflicts’, Journal of Conflict Resolution 48/3 (2004) pp.78–94.

50. Kristian Gleditsch and Kyle Beardsley, ‘Nosy Neighbors: Third-Party Actors in Central American Conflicts’, Journal of Conflict Resolution 48/3 (2004) pp.78–94

51. Goldstein and Freeman (note 44) p.23.

52. The Clinton intervention is an example of typical of a US response to the situation in Haiti.

53. Nils Petter Gleditsch, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg and Håvard Strand, ‘Armed Conflict 1946–2001: A New Dataset’, Journal of Peace Research 39/5 (2002) pp.615–37. The codebook is available here: < http://www.prio.no/cwp/armedconflict/current/Codebook_v4-2006b.pdf>.

54. Jackie Rubin and Will H. Moore, ‘Risk Factors for Forced Migrant Flight’, Conflict Management & Peace Science 24/3 (2007).

55. Gary King, Robert O. Keohane and Sidney Verba, Designing Social Inquiry (Princeton UP 1994).

56. Wood (note 8) p.215.

57. See Shellman, ‘Time Series Intervals and Statistical Inference’ (note 35) for a review of the economics and political science literatures on temporal aggregation.

58. Massimiliano Marcellino, ‘Some Consequences of Temporal Aggregation in Empirical Analysis’, Journal of Business and Economic Statistics 17/1 (1999) p.133.

59. R.J. Rosanna and J.J. Seater, ‘Temporal Aggregation and Economic Time Series’, Journal of Business and Economic Statistics 13/4 (Oct. 1995) p.441.

60. Joshua Goldstein and Jon C. Pevehouse, ‘Reciprocity, Bullying, and International Cooperation: Time-series Analysis of the Bosnia Conflict’, American Political Science Review 91/3 (1997) p.207.

61. Roberto Franzosi, ‘Computer-Assisted Content Analysis of Newspapers: Can We Make an Expensive Research Tool More Efficient?’, Quality and Quantity No. 29 (1995) p.172.

62. Shellman, ‘Time Series Intervals and Statistical Inference’ (note 35); Stephen M. Shellman, ‘Measuring the Intensity of Intranational Political Interactions Event Data: Two Interval-Like Scales’, International Interactions 30/2 (2004) pp.109–41.

63. Wood (note 8).

64. Rubin and Moore (note 54).

65. Goldstein and Pevehouse (note 60).

66. We filed a written request to obtain the US Coast Guard's logs.

67. See < www.uscg.mil/hq/g-cp/comrel/factfile/>, accessed 5 Sept. 2005.

68. See < www.uscg.mil/hq/g-cp/comrel/factfile/>, accessed 5 Sept. 2005

69. Jarol B. Manheim and Richard C. Rich, Empirical Political Analysis (White Plains, NY: Longman 1995) pp. 73–8.

70. Moore and Shellman ‘Fear of Persecution, 1952–1995’ (note 7).

71. Weekly-level refugee/migration data is not available.

72. Stephen M. Shellman, Brandon Stewart, and Andrew Reeves, ‘Project Civil Strife Codebook’, v1.0 (Typescript, University of Georgia) for more information on coding rules and procedures.

73. Joshua S Goldstein, ‘A Conflict-Cooperation Scale for WEIS Events Data’, Journal of Conflict Resolution 36 (1992) p.369.

74. Such projects include: Cooperation and Peace Data Bank – COPDAB, World Events Interaction Survey – WEIS, Integrated Data for Events Analysis – IDEA, Protocol for the Assessment of Nonviolent Direct Action – PANDA, Intranational Political Interactions Project.

75. See < http://web.ku.edu/keds/index.html> for information on the KEDS and TABARI projects.

76. TABARI recognizes pronouns and dereferences them. It also recognizes conjunctions and converts passive voice to active voice (Philip A. Schrodt, ‘KEDS: Kansas Event Data System Manual’, Typescript 1998.)

77. These particular data are coded from Associated Press reports available from Lexis-Nexis.

78. See ‘World Event/Interaction Survey (WEIS) Project, 1966–1978,’ ICPSR Study No. 5211.

79. KEDS has introduced new codes in addition to those used by McClelland and the WEIS project. Most of these are borrowed from the Protocol for the Assessment of Nonviolent Direct Action (PANDA) project. The KEDS project investigators assigned weights to the new codes that are comparable to the Goldstein weights, and we used those weights in tandem with the Goldstein weights to create the scaled event data series analyzed in this study. See < http://web.ku.edu/keds/data.html> for WEIS codes and adaptations PANDA.

80. The verbs rebel and force are coded as -9.0.

81. The verb torture and its variations are coded by TABARI.

82. We also experimented with separating out the military from the government.

83. See < http://laborsta.ilo.org/>.

84. However, the data range from similar starting and ending values and have similar means.

85. It would not be surprising to find that there is a level shift, since it is clear from looking at the series that there is a clear downward shift in the series though the means are similar and share a similar range.

86. If we had merged the level in first and then taken the first difference, this would not be the case as several observations would be zero since the monthly value did not change from week 3 to week 4.

87. The leadership and groups remain consistent from 1997 to 2004 so we feel that using the existing dictionaries rather than creating new ones does not pose great threat to the data's reliability and validity.

88. Given that the GECM assumes contemporaneous correlation and we are predicting using past values, we lag the contemporaneous ‘change’ term.

89. Suzanna De Boef and Keele, ‘Taking Time Seriously’, prepared for 2005 Political Methodology Meeting, Florida State Univ., p.12 < http://polisci.wustl.edu/retrieve.php?id = 585>.

90. Banerjee et al. (note 48).

91. De Boef (note 48).

92. Scott Long, Regression Models for Categorical and Limited Dependent Variables (Thousand Oaks, CA: Sage 1997) p.219.

93. Shellman and Stewart (note 15).

94. Gary King, ‘Variance Specification in Event Count Models: From Restrictive Assumptions to a Generalized Estimator’, American Journal of Political Science 33/3 (1989) pp.764–9.

95. Long (note 92) p.217.

96. These models are widely used for forced migration counts [see Moore and Shellman, ‘Fear of Persecution, 1952–1995’ (note 6); Moore and Shellman, ‘Refugee or Internally Displaced?’ (note 17); Shellman and Stewart (note 15)].

97. Gupta, Singh and Sprague (note 45).

98. Shellman and Stewart (note 15).

99. Shellman and Stewart (note 15)