Neoliberal Policies and Human Trafficking for Labor: Free Markets,Unfree Workers?

Regulatory Climate and Labor Trafficking

A primary purpose of neoliberal policies is to increase voluntary market exchanges. Along these lines, a suitable “business climate” characterized by decreased regulation of business and labor markets is viewed as vital to economic competitiveness. While such an environment might yield some economic benefits, it creates an environment conducive to labor trafficking.

First, neoliberal policies can amplify insecurities within the labor force, which can increase the potential supply of vulnerable labor. While a competitive business climate might lead to increased economic efficiency, meeting the needs of the market may not be synonymous with protecting the human security of the poor and vulnerable, especially women and children (Cho 2013; see also Peksen and Detraz 2016). Neoliberal reforms can accentuate these negative effects and “increase the divide between the winners and losers of globalization” (Cho 2013, 684). This can create an economic environment in which vulnerable populations are ripe for trafficking. For example, Kara (2009, 26) cites the “cataclysmic results” of International Monetary Fund (IMF) programs that center on the rapid construction of a free-market climate as an important contributing factor to the growth of trafficking in the former Soviet Republics and East Asia. ¹ He contends that the IMF programs hastened economic collapses and created a ready pool of labor for potential traffickers.

A “business-friendly” regulatory climate also has a direct negative impact upon the rights enjoyed by workers. Arguments in support of neoliberal policies have long viewed labor rights as contradictory to economic competitiveness, as such rights “infringe upon the economic freedoms of employees and employers” (Gwartney, Lawson, and Hall 2014, 6) and purportedly hamper productivity (Aidt and Tzannatos 2002). Neoliberal reformers may thus see labor rights as an obstacle to be overcome (Berg and Kucera 2008) and seek to roll back worker protections to enhance competitiveness. Indeed, there is empirical support for these negative linkages, as extant work has found neoliberal regulatory climates to be at odds with core worker rights (Blanton and Peksen 2016).

While most violations of labor rights fall short of actual trafficking, there is growing agreement that there is no definitive “categorical distinction between free and unfree labor” (Barrientos, Kothari, and Phillips 2013, 1038). Rather, violations “comprise a spectrum of relations” (LeBaron 2015, 4) in which “there is a continuum including both what can clearly be identified as forced labor and other forms of labor exploitation and abuse” (ILO 2009, 8–9; see also Bravo 2007). Along these lines, the same business and regulatory climate that encourages milder forms of labor repression may also be conducive to more extreme violations of labor rights entailed in labor trafficking. This leads to the first hypothesis:

Labor Trafficking and Government Size

Neoliberal orthodoxy calls for a limited role of the state, arguing that “the primary role of government is to protect individuals and their property from aggression by others” (Gwartney, Lawson, and Hall 2014, 1). The ideal is a so-called “small” state whose operation requires relatively limited resources relative to a country’s total economy and exerts minimal intervention in the domestic marketplace. In practice, decreased investment in state institutions reflects a reduced role for the public sector. Instead, privatization often prevails with the assumption being that a “bloated public sector” is a “drag on economic growth” (Lee and Strang 2006, 886).

Concomitantly, reduced government size is associated with reduced social spending, including reduction or removal of economic and social safety nets (i.e., Chwieroth 2010). Although such policies might stimulate economic growth, they also might have negative outcomes for labor trafficking. First, reduced safety nets might be less able to address the detrimental effects incurred by neoliberal reforms, such as short-term rises in unemployment and increased cost of public services (Friman and Reich 2007; see also Abouharb and Cingranelli 2007), and thus leave dislocated workers more vulnerable to these shifts.

More directly, a “small” state might lack the capability to control trafficking flows. While states might readily decry labor trafficking and slavery in principle, the actual enforcement of laws and regulations prohibiting such trafficking requires a great deal of state investment and is often a labor- and cost-intensive enterprise. Given problems with the enforcement or “justiciability” of labor rights (Whelan and Donnelly 2007), there has been a proliferation of private regulation regimes that involve corporations and various nongovernmental organizations (NGOs; Locke 2013). Such regimes might yield some successes (Amengual 2010). However, extant scholarship finds that improvements in labor rights are unlikely unless the state is willing (and able) to establish and support a credible inspection regime (Locke 2013). Simply put, state’s willingness and ability to provide substantial resources is ultimately necessary for the protection of labor rights.

Bringing this to bear on trafficking, effective public regimes may be lacking in countries where the economic role of the state is limited along the lines prescribed by neoliberalism. As one investigative report noted, while antislavery activists were “telling the Western companies to audit their suppliers, the World Bank was telling them (developing host countries) that government inspectors didn’t need to” (Hobbes 2015). There are limits to what private inspection regimes or NGO “shaming” may accomplish in preventing trafficking; ultimately states “have a vital role to play in ending trafficking and worst forms of child labor” (Campbell 2008, 5). Along those lines, policies aimed specifically at limiting the role of the state in its domestic market may render it less able to implement effective anti-trafficking policies. This leads us to our second hypothesis:

Another facet of neoliberalism is economic openness, specifically the reduction of restrictions to foreign trade and investment. Increased participation in global supply chains and capital flows is seen as a way to stimulate economic growth (Wolf 2004). However, such openness may also be conducive to labor trafficking. The industrial context of a state can play an important role in the use of trafficked labor. Crane (2013, 54) argues that “value trap slavery” is encouraged in supply chains “where margins are narrow and where value is captured further downstream,” such as textiles and agriculture. In such cases, firms may “perceive the necessity of coerced labor brought as close as possible to zero cost to survive” (Crane 2013, 54). This “cost calculus” for cheap labor and the “avoidance of legal regulations concerning labor” (LeBaron and Ayers 2013, 883) might be endemic in countries focused on exports, as they are seeking to attract firms in these sectors. As a result, forced labor may be a key part of “the strategic use of export platforms” (LeBaron and Ayers 2013, 885), with labor trafficking used as a shortcut to “reinforce a country’s comparative advantage” (Belser 2009, 24).

Empirically, there is some support for these dynamics. Mosley and Uno (2007) find a negative relationship between trade openness, measured in terms of trade flows, and collective bargaining rights. Busse (2002) found that the use of forced labor created a comparative advantage in unskilled export markets, though later work (Busse and Braun 2003) found a much more qualified linkage between the two. This leads us to a third hypothesis:

Our discussion thus far has focused on three major facets of neoliberal policies—a “business-friendly” regulatory environment, a limited state, and economic openness. While each suggests somewhat distinct mechanisms through which market-friendly policies can embolden labor trafficking, they are still components of the broader neoliberal policy “playbook.” These policies are thus often used in conjunction with one another and might have synergistic impacts on the prevalence of labor trafficking. For example, in a wide-reaching study of forced labor, the ILO (2005, 63) notes that “strong pressures to deregulate labor markets and to downsize labor inspection services may have allowed the proliferation of unregistered agencies which can operate beyond the boundaries of state control.” The result can be seen as “the combined failure of labor markets, institutions and regulations” that allows for labor trafficking to occur. The overall impact of neoliberalism on labor trafficking can thus also be understood in terms of the combined impact of these factors. This leads to our final hypothesis:

Outcome Variables: Human Trafficking for Labor

We operationalize our outcome variables using data from the Human Trafficking Indicators (HTI) database, which provides comprehensive data on various forms of trafficking of human beings (Frank 2013). The human trafficking data are originally collected based on the U.S. State Department’s annual Trafficking in Persons (TIP) reports. ² While there are multiple forms of human trafficking, we focus our analysis on two facets of labor trafficking, namely, Forced Labor and Forced Child Labor. The forced labor and child labor variables are coded one if a significant number of people are being trafficked for labor purposes, and zero otherwise. More specifically, “significant” denotes more than hundred people trafficked in a given year. ³ The common types of forced labor are agricultural work, construction, sweatshops, domestic servitude, involuntary servitude, begging, and bonded labor. The forced child labor variable is coded one if there is widespread illicit trafficking in children to be employed in such economic sectors as construction, agriculture, and sweatshops.

In terms of overall patterns, the data attest to the global nature and continued prevalence of this problem. During the time period of our analysis, there were 105 different countries designated at least once as a source country of forced labor trafficking. There were 116 countries designated at least once as a destination country, while ninety-eight countries experienced major internal trafficking for forced labor. The number of countries designated at least once as a major source country of child labor trafficking is ninety-seven. There were 106 countries designated at least once as destination countries for child labor and eighty-seven countries that experienced internal trafficking for child labor. The number of countries engaged in labor trafficking has increased steadily throughout the time period covered by the data. For example, in our sample, whereas seventy-four countries were designated as source countries for forced labor in 2006, there were 115 source countries in 2011. While there is a significant amount of variation across countries, the number of source and destination countries, as well as the pervasiveness of human trafficking for labor and other purposes, has increased over time in the data (for a detailed discussion of the trends in the human trafficking data, see Frank 2013). ⁴

The human trafficking variables in the HTI are categorical measures. It is difficult to provide more detailed information on the extent of the illicit trafficking because its victims and perpetrators are “hidden populations” (Tyldum and Brunovskis 2005), and there is no comprehensive cross-national source that contains more detailed data. It is therefore more practical and appropriate to code the trafficking data based on the extensiveness of trafficking rather than assign indeterminate numbers of people trafficked. The HTI also provides data on whether a country is a source, transit, or destination country of human trafficking, or has internal human trafficking. Our theoretical argument suggests that free-market policies might increase the degree of internal trafficking as well as the probability that a country will become a major source of, or destination for, labor trafficking. Thus, we report models assessing whether pro-market reforms increase the probability of the designation of countries as source or destination. As trafficking can also take place within the borders of a country, we run models examining the possible impact of neoliberal policies on the probability of internal trafficking. ⁵

Explanatory Variables: Market-Friendly Policies

The data for neoliberal policies come from the Fraser Institute’s Economic Freedom of the World dataset (Gwartney, Lawson, and Hall 2014). It provides a comprehensive and multifaceted index of market-friendly policies, and its measures are widely used as proxies for neoliberalism. It includes an aggregate Economic Freedom Index, which is based on forty-two different variables that capture five major aspects of pro-market policies, including market regulation, government size, pro-trade policies, property rights, and monetary policies. We use the index variable to test our hypothesis on the overall impact of market-friendly policies on labor trafficking. The Economic Freedom Index, as well as its component measures, ranges from zero (no economic freedom) to ten (extensive economic freedom).

To assess the empirical merits of the hypotheses on market deregulation, we use the Regulation subcomponent of the index variable. It captures the extent to which labor, product, and credit markets are regulated, including minimum wage and labor flexibility rules as well as the administrative costs associated with starting a business. To analyze the possible role of government involvement and spending in the economy, we include the Government Size variable. It measures the degree to which countries depend on markets and personal choices rather than government spending to allocate resources, goods, and services, as evidenced by such measures as government consumption, the size of the public sector, and tax rates. We include the Freedom to Trade variable in the analysis to test the impact of trade liberalization. It specifically accounts for the degree of protectionist measures such as tariffs, quotas, controls on capital, and hidden administrative restrictions of international economic exchanges. ⁶

Control Variables

To more fully capture the various stages of the labor trafficking “supply chain,” we examine the causes of trafficking at the source and destination stages, as well as internal trafficking. Although the specific dynamics behind a country being a source or destination for labor trafficking might vary, many of the key variables underlying these processes are the same. For example, we include the natural log of GDP Per Capita to account for the impact of economic wealth—in this case, the expectation is that trafficking is likely to originate in lower income countries and that trafficked individuals are likely to go to higher income countries (Jakobsson and Kotsadam 2013). Another broadly used variable is Population, as it connotes an increased labor pool as well as a larger potential market for trafficked individuals (Cho, Dreher, and Neumayer 2013). The data for economic wealth and population come from the World Bank’s (2014) World Development Indicators dataset.

In addition to the economic freedoms outlined earlier, it might also be the case that political freedoms influence labor trafficking, as countries with more responsive governments and increased civil liberties might be better able to prevent trafficking. The higher level of institutional readiness typically embodied in democratic political systems reflects greater state capacity to thwart trafficking in persons. We thus incorporate a measure of Democracy. This variable comes from the Polity IV dataset and is coded based on a twenty-one-point scale ranging from +10 indicating most democratic to −10 indicating most authoritarian (Marshall, Jaggers, and Gurr 2012). ⁷

Instability and conflict might facilitate trafficking, as both can increase the number of displaced and vulnerable citizens (Akee et al. 2010). Conversely, the lack of armed conflict can be a significant pull factor, as people might be drawn to states free of such instability. We thus include both International Conflict and Civil Conflict variables, obtained from the Major Episodes of Political Violence (MEPV) database (Marshall 2015). The Interstate Conflict variable accounts for the severity of all major interstate conflicts a country faces. It ranges from zero indicating no interstate violence to ten indicating severe interstate violence. Similarly, the Civil Conflict variable assesses the magnitude of ongoing societal conflicts (i.e., civil and ethnic wars) and ranges from zero to ten.

Human trafficking holds a relatively recent, though visible, place within extant human rights law, most notably with the passage of the UN Protocol to Prevent, Suppress, and Punish Trafficking in Persons (also called the Trafficking Protocol) in 2000, which has since been ratified by a vast majority of states. Yet the impact of human rights treaties remains contentious. While some studies have found evidence that states may use such treaties as a cover for further human rights violations—so-called “radical decoupling” (Hafner-Burton and Tsutui 2005)—others have found that treaty ratification is associated with improved human rights conditions (Fariss, forthcoming). To account for the impact of such treaties, we use the Ratified Conventions variable. It is the total number of the five core conventions relevant to human trafficking ratified by each country. The conventions included in the variable are as follows: the Protocol to Prevent, Suppress, and Punish Trafficking in Persons; the Forced Labour Convention; the Abolition of Forced Labour Convention; the Convention on the Elimination of All Forms of Discrimination against Women; and the Convention on the Rights of the Child. We gathered the data from various UN websites and other online sources.

To account for the possible positive effect of respect for human rights on the prevention of labor trafficking, we include two human rights measures in the model. The first variable, Freedom from Repression, captures the overall level of respect for four main types of physical integrity rights, namely, extrajudicial killings, disappearances, political imprisonment, and torture (Cingranelli and Richards 2012). It ranges from zero to eight, where zero indicates extensive abuse of physical integrity rights and eight denotes full respect for those rights. As noted earlier, it might be the case that countries that repress the rights of their workforce are also more likely to have forced and child labor. Therefore, we incorporate a second variable, Worker Rights, to gauge the overall level of respect for core labor rights. The variable accounts for the extent to which workers enjoy and exercise core labor rights, including collective bargaining rights and prohibitions on forced and child labor (Cingranelli and Richards 2012, 65). It is an ordinal measure ranging from zero to two, with zero representing no respect for core labor rights and two indicating strict enforcement and protection of core labor rights.

As many trafficking routes are concentrated within a geographic region, there may be region-specific factors that influence trafficking flows (Cho, Dreher, and Neumayer 2013; Shelley 2010). We thus include six dichotomous region dummies. Finally, to control for the path-dependent nature of trafficking problems—that is, countries that have a history of trafficking issues are more likely to experience labor trafficking flows—as well as temporal dependence (Beck, Katz, and Tucker 1998), we add a Years since Last HT Incidence variable. ⁸ This variable is coded based on the outcome variable used in each model reported below and indicates the number of years since the last major trafficking incident for forced labor or child labor occurred in a country.

Given the relatively short time span of the human trafficking data (2000–2011), we use a generalized estimating equation (GEE) with the logit link function. The GEE is considered the most appropriate method for temporally limited data with a large number of spatial units (Horton and Lipsitz 1999; Zorn 2001). It is worth noting that we find no major change in the main findings when we estimate logit models without the GEE specification. We lag all the time-variant explanatory variables one year to reduce the simultaneity bias and ensure our independent variables precede the outcome variables.