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«The Spread of Anti-trafficking Policies – Evidence from a New Index Seo-Young Cho Axel Dreher Eric Neumayer ISSN: 1439-2305 The Spread of ...»

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Number 119– March 2011

The Spread of Anti-trafficking Policies –

Evidence from a New Index

Seo-Young Cho

Axel Dreher

Eric Neumayer

ISSN: 1439-2305

The Spread of Anti-trafficking Policies – Evidence from a New Index

Seo-Young Choa

Axel Dreherb

Eric Neumayerc

March 2011

Abstract: We analyze the spread of policies dealing with international trafficking in human

beings. Arguing that countries are unlikely to make independent choices, we identify pressure,

externalities and learning or emulation as plausible diffusion mechanisms for spatial dependence in anti-trafficking policies. We develop a new index measuring governments’ overall anti-trafficking policies for 177 countries over the 2000-2009 period. We also assess a country’s level of compliance in the three main constituent dimensions of anti-trafficking policies – prosecution, protection and prevention. Employing a spatial autoregressive model, we find that, with the exception of victim protection measures, anti-trafficking policies diffuse across contiguous countries and main trading partners due to externality effects. We find evidence for learning or emulation effects in all policy domains, with countries looking toward peers with similar political views or cultural values. Surprisingly, major destination countries do not seem to exert pressure on relevant main countries of origin or transit to ratchet up their policies.

Keywords: human trafficking, human rights, spatial dependence of policies JEL codes: O15, F22, P41 Acknowledgements: We thank Nina Breitenstein, Laura Felfeli, Ulrike Heyken, Veronika Kling, Marleen Knipping, Tabea Lakeman and Lukas Semmler for excellent research assistance, Scott Jobson for excellent proof-reading and Krishna Vadlamannati and seminar participants at the University of Goettingen for excellent comments. The authors cordially acknowledge the generous funding provided by the European Commission (JLS/2009/ISEC/AG/005).

a University of Goettingen, Department of Economics, Platz der Goettinger Sieben 3, D-37073 Goettingen, Germany, Telephone: +49 (0)551 39-7368, Email: scho@uni-goettingen.de b University of Heidelberg, Alfred-Weber Institute for Economics, Bergheimer Strasse 58, D-69115 Heidelberg, Germany, University of Goettingen, Germany, CESifo, Germany, IZA, Germany, and KOF Swiss Economic Institute, Switzerland, Telephone: +49 (0)6221 54 2921, E-mail: mail@axel-dreher.de c London School of Economics and Political Science, Department of Geography and Environment, Houghton Street, London WC2A 2AE, UK, Telephone: +44 (0)207 955 7598, Email: e.neumayer@lse.ac.uk 

1. Introduction In the last few decades, human trafficking has become a growing phenomenon worldwide.

The illicit trade in human beings across borders violates the human rights of victims, threatens national security and deteriorates the health of the affected economies and societies by increasing the size of the shadow economy and organized criminal activities (Belser 2005).

Although the exact magnitudes and dimensions of the problem are unknown, available statistics suggest that human trafficking is one of the most serious transnational crimes in the 21st century. According to the U.S. Department of State (2010), there are more than 12 million victims of human trafficking worldwide. Interpol (2009) estimates that human trafficking is a multi-billion-dollar business, amounting to the third largest transnational crime following drug and arms trafficking.

Human trafficking can be seen as one of the dark sides of globalization. As advancements in technology and transportation connect countries more closely regardless of geographical distances, illicit flows of human beings have also become a global phenomenon.

Anecdotal evidence suggests that traffickers recruit victims worldwide and transfer them from one country to another, often across continents (U.S. Department of State 2010). For instance, according to the UNODC (2006), trafficking victims found in the United States came from 66 countries in different regions (China, Mexico and Nigeria for example). Germany, another major destination, receives trafficking victims from at least 51 countries, including many from outside Europe (Afghanistan, Colombia, the Dominican Republic, etc.).

Given the growing significance of international human trafficking, it is no surprise that the international community has adopted several measures in the past ten years, including the United Nations Convention against Transnational Organized Crime and its Protocol to Prevent, Suppress and Punish Trafficking in Persons, especially Women and Children (2000, hereinafter the “Convention” or “Protocol”). Accordingly, social scientists have started to turn their attention towards policies enacted to combat human trafficking (Akee et al. 2010; Auriol and Mesnard 2010; Avdeyeva 2010; Bartilow 2010; Cho and Vadlamannati 2011; Di Tommaso et al. 2009; Friebel and Guriev 2006; Mahmouda and Trebesch 2009; Simmons and Lloyd 2010). One of the problems scholars face is the lack of reliable data on countries’ antitrafficking policies which can be compared over time and between countries. The U.S.

Department of State reports a ranking of countries with respect to their actions in fighting human trafficking. They use a scale of 1-3, 1 which is based on the level of compliance with

                                                            





The tier-ranking consists of tier 1, 2, 2-watchlist and 3. “Tier 2” and “tier 2-watchlist” reflect the same level of compliance (with ‘watchlist’ providing information about a country’s development relative to the previous year).

the United States 2000 Victims of Trafficking and Violence Protection Act (TVPA). However, the tier ranking has several drawbacks, which limit its reliability and relevance. 2 In particular, while the tier ranking provides an aggregate score of compliance with anti-trafficking policies, it fails to recognize the different levels of compliance in the three main policy dimensions – prosecution, protection and prevention. Separating the three dimensions is important. Theory and evidence indicate that better protection policy may encourage potential victims to risk illegal migration, which could lead them to fall prey to traffickers. Human trafficking inflows might therefore increase as a consequence, contradicting the objectives of prosecution and prevention policies (Akee et al. 2010). Countries can thus have the same overall ranking on the index, but for very different reasons. 3 We make two important contributions to the growing literature on human trafficking.

First, we develop novel and original indices of anti-trafficking policies around the world, providing better, more detailed and disaggregated measures of the three prime policy dimensions enacted by countries. Specifically, we use raw data from two reports on human trafficking – the Annual Reports of Trafficking in Persons (United States State Department, 2001-2010) and the Reports on Trafficking in Persons: Global Patterns (United Nations Office on Drugs and Crime, 2006 and 2009) – to construct separate indices on the three policy dimensions (prosecution, protection and prevention), as well as one overall aggregate antitrafficking policy index for up to 177 countries over the 2000-2009 period. The index provides a score from 1 to 5 for the level of compliance, with each dimension of antitrafficking policies for each country and year. Second, we argue that policy choices across countries are very unlikely to be independent from each other. Major destination countries will wish to push for policy changes in relevant transit and origin countries. More generally, international human trafficking creates significant cross-country externalities and countries will also want to learn from or emulate the policies enacted by other countries. Because of these cross-country spillover effects, we argue that countries spatially depend on each other in

                                                            

The decision rule of the tier-ranking is not transparent to the public. It is not clear how the three levels of the ranking – full compliance, significant efforts and no significant efforts – are assessed and determined, making the ranking vulnerable to subjectivity (GAO 2006). It has been argued the tier-ranking is largely a tool of the U.S.

government to influence other country’s policies through ‘naming’ and ‘shaming’ (Simmons and Lloyd 2010). It is determined based on evaluation of compliance with the United States’ domestic anti-trafficking law – the Victims of Trafficking and Violence Protection Act (TVPA 2000) – rather than international law. Its relevance for evaluating international standards is therefore limited.

A number of countries in full compliance with the tier-ranking fail to ensure the basic legal rights of victims, punishing and deporting them, while demonstrating sound policy interventions in the other dimensions (prosecution and prevention). For instance, in the tier 1 group, victims in France and the United Kingdom were reportedly imprisoned and deported due to their actions related to the situations in which they were trafficked in 2008 and 2009 (U.S. Department of State, 2009 and 2010).

their respective policy choices. We empirically investigate this hypothesis with a spatial autoregressive estimation model.

To foreshadow our results, we find evidence for spatial dependence in anti-trafficking policies. In particular, policies diffuse via externality effects across contiguous countries and main trading partners – with the exception of protection policies, for which one would not expect any externality effect. Policies also diffuse via learning or emulation effects as countries look for cues (or information) from other countries sharing political and cultural similarities. However, we do not find any significant effect of pressure from the United States via aid. Nor do we find evidence that major destination countries pressurize relevant major transit and origin countries to enact stricter anti-trafficking policies.

We proceed as follows. In section 2, we develop theoretical arguments as to why antitrafficking policies are not independently chosen by countries. In section 3, we introduce our indices on anti-trafficking policies. The method of estimation and data are described in section 4, while we discuss our results in section 5. Section 6 tests for the robustness of our results. The final section concludes the paper.

2. Spatial Dependence in Anti-trafficking Policies Spatial dependence in policy choices has become a key concept in the recent literature analyzing policy diffusion across countries (Neumayer and Plümper 2010; Gassebner et al.

2011; Gauri 2011; de Soysa and Vadlamannati 2010; Greenhill et al. 2009; Eichengreen and Leblang 2008; Pitlik 2007; Blonigen et al. 2007). Spatial dependence exists whenever the marginal utility of one unit of observation (here: a country) is affected by the decision-making of other units of observation (Neumayer and Plümper 2010). For example, if policies enacted in one country are influenced by policy choices in other countries, then they are said to spatially depend on each other. From a theoretical perspective, spatial dependence can result from pressure, externalities, learning and emulation (Elkins and Simmons 2005; Simmons and Elkins, 2004). 4 The major destination countries of internationally trafficked persons are likely to exert pressure onto countries which function as major sources of transit and/or origin for people trafficked into these major destinations. Major destination countries will be averse to illegal migration into their territories (as international trafficking always is) and will resent the increase in other transnational criminal activities (such as drug and arms trafficking) that typically accompany human trafficking (Bartilow 2010). Moreover, human trafficking creates

                                                            

They list coercion, rather than pressure, and add competition. However, coercion is incompatible with policy choice and competition can be subsumed under externalities. On the other hand, emulation could be subsumed under learning unless countries blindly follow others in their policy choices.

a shadow economy of illegal labor markets and businesses with estimated annual profits of some one billion dollars in industrialized countries (Belser 2005) – money which is not taxed and is likely to be used for illegal activities. Yet, the effectiveness of policies undertaken in destination countries will be undermined if other countries, particularly relevant transit and origin countries, do not follow suit. The strictest anti-trafficking policies in destination countries may be ineffective if countries of origin and transit have lax policies in place. Hence, successful anti-trafficking policies in destination countries depend on a ratcheting up of policies in origin and transit countries, as well as major destination countries exerting pressure on laggards.

In addition to pressure, externalities are rampant in this policy area (Simmons and Llyod 2010). Anti-trafficking policies enacted by one country create significant externalities that other countries cannot simply ignore. Stricter policies in one destination country will deflect some of the flows of trafficked persons into other destination countries, while stricter policies in one origin or transit country will prompt transnational trafficking networks to increasingly resort to other origin or transit countries. Similar to international drug-trafficking, unless policies can address the underlying supply and demand factors driving international trafficking (which they typically cannot), stricter anti-trafficking policies in one country will merely deflect the problem onto other countries with weaker policies in place, such that there is an incentive to ratchet policies upwards over time. In other words, by predicting externality effects of such transnational crime, countries will be able to update their anti-trafficking measures, following relevant policy changes of other countries which share certain characteristics, such as geographic proximity and economic similarity.



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