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Kidnap risks and migration: evidence from Colombia

Abstract

Using a unique data set from the major Colombian cities collected between 2000–2003 and with information on more than 12,000 households, this paper studies the relationship between the kidnap risk a household faces with its migration decisions. We find evidence that exposure to such risk induces households to react sending some of their members to an international destination but not necessarily to a domestic one. Estimates are robust to the inclusion of several household characteristics usual in the migration literature, other crime risks, reported feelings of insecurity of the household, and an alternative measure of kidnap risk.

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Notes

  1. For example, the rapid increase of this type of crime in Mexico, which is now the country with the highest kidnap rate in the world, has induced current President Calderon to suggest the introduction of lifetime prison for people that commit this crime. Moreover, the private sector has now developed a micro-chip that is implanted in peoples’ skin so they can be traced in case they are kidnapped (http://news.bbc.co.uk/hi/spanish/latin_america/newsid_7562000/7562903.stm).

  2. Retreat from: http://www.castlerockinternational.com/news/casualty-insurance/casualty-categories/47-kidnap-and-ransom-insurance/104-top-10-kidnap-rated-countries-with-ransom-stats?start=1

  3. For instance, approximately 64% of the reported 23,666 kidnaps in Colombia had an economic motive in which a monetary ransom was demanded.

  4. Examples include among others: Stark and Bloom (1985), Borjas (1987), Chiquiar and Hanson (2005), Epstein and Gang (2006), and Mckenzie and Rapoport (2007).

  5. The indirect utility function is derived by maximizing Eq. 1 subject to

    $$ c_{i}+o_{i}\varpi _{a}\left( S_{i}\right) +Pd_{i}=W_{i,a} $$

    in c i , L i  ≡ T i  − o i , and d i . The demands are c i  = αP, \(o_{i}=\frac{P\left( 1-\alpha \right) }{\varpi _{a}}\), and \( d_{i}=\max \left( 0\mathbf{,}\frac{W_{i,a}}{P}-1\right) \). This last function makes sense because only households with high enough wealth relative to P will demand the durable good.

  6. This assumption goes well with anectodal evidence that suggest household members may not migrate all at the same time. For instance, households living in violent environments send their children to another country searching for safer environments while the head of the household stays behind to look after their business’.

  7. According to our conceptual framework, we should have considered as the dependent variable of the model the number of household members that migrate instead of the dichotomic variable of whether some member has migrated. This is because a prediction of the model is that more members would migrate if the perceived kidnap risk is higher. Unfortunately, the data do not allow us to test this hypothesis. The question asked in the survey is whether any member of the household migrated in the last 6 months. To this question, they answered either yes or no. No information on the number of members who migrated is available. Similarly, there is no information of the number of members of the household that were victims of a given crime. Again for this topic, the survey asked only whether any member was a victim or not.

  8. A recent example of such constructed regressors with clustered bootstrap standard errors may be found in Banerjee et al. (2009).

  9. Even though further survey rounds have been applied, we use all those that are available to the general public.

  10. Previous studies of violence have focused on explaining cross country or cross city differences using macro-level data sets. Examples include Bourguignon (1998, 1999), and Fajnzylber et al. (1998, 2000, 2002a, b). Similarly, Schultz (1971) and Morrison and May (1994) also use macro-level data.

  11. It is estimated that the total population of Colombia is around 43 million in 2003.

  12. It should be noted that this pattern of migration is representative only for urban households in major cities of Colombia. Hence, they might differ from the actual migration statistics at the national level.

  13. Since the estimations are done by pooling cross sections by the round of the surveys, we consider as two different neighborhoods the same strata within a city in two different time periods.

  14. Given that some control variables are absent for some households in the estimations of migration, we end with 12,627 observations.

  15. We thank an anonymous referee for this suggestion.

  16. According to statistics from Pais Libre, there are only two Colombian provinces out of the 32 that exist where no victim of this crime was reported. The two provinces are Amazonas and San Andres y Providencia. The former is a low density province in the southern part of the country where the Amazon River flows, while the latter is a Caribbean island close to Nicaragua.

  17. It should be mentioned that even though Colombia has one of the highest number of displaced households in the world, most of them are rural households that leave their homes due to the direct threat of the armed groups within the country. The sample from Encuesta Social used in this study covers only urban households, and hence, such rural displacement, which does in fact occur at a national level as shown in Engel and Ibañez (2007), is not studied in this paper.

  18. To obtain these measures, we considered a household being a victim of a kidnap if the predicted kidnap probability obtained after the probit estimation was higher than the actual kidnap likelihood in the sample (around 0.18%).

  19. All columns in the table report bootstrap standard errors in brackets using as seed number 1 and 1,000 replications. It should be noted that the significance level of the coefficient of interest remains if we estimate cluster standard errors at the neighborhood level. The results with this alternative specification for the standard errors are available upon request.

  20. See Borjas (1987) and Taylor (1986).

  21. A typical example is that in which one parent migrates first, finds a job, establishes him or herself, and only later does the spouse and kids migrate. Another case, which occurs specially under a violent environment, is that in which parents in order to protect their children from harm send them out of the country while they stay behind taking care of their business’ and assets.

  22. Alternatively, such non-parametric likelihood can be thought of measuring the effect of how the possibility of knowing others that have been kidnapped influence the household’s migration decisions. This view follows the ideas behind Epstein and Gang (2006).

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Acknowledgements

We specially thank the valuable comments of V. Aguirregabiria, K. Lang, D. Mookherjee, and R. Bernal. We also thank the participants of the Empirical Micro-Workshop at Boston University, the Third Conference on Inequality at the World Bank, LACEA-LAMES, and the Ninth GDN Conference. We are grateful to Fedesarrollo and CEDE for providing the data used in this research. We specially thank the two anonymous referees for their comments and suggestions. All remaining errors are our own.

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Correspondence to Edgar Villa.

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Responsible editor: Junsen Zhang

This paper is based on one chapter of our Ph.D. dissertations at Boston University 2006.

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Rodriguez, C., Villa, E. Kidnap risks and migration: evidence from Colombia. J Popul Econ 25, 1139–1164 (2012). https://doi.org/10.1007/s00148-011-0358-8

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Keywords

  • Kidnaps
  • Migration

JEL Classification

  • O15
  • O54