William R. DI PIETRO

Human Trafficking and National Morality

The paper proposes that national morality is an important variable for explaining national anti-trafficking policy. It uses cross country regression analysis to see whether or not empirically national morality is a determinant of anti-trafficking policy. The findings of the paper are consistent with the notion that improved levels of national morality lead to better national anti-trafficking policy. National morality is found to be statistically relevant for national anti-trafficking policy when controlling for the extent of democracy, the share of the private sector in the economy, and the degree of globalization.
JEL ClassificationZ00, Z1

Full Article

1. Introduction

Human trafficking can be defined as the forced transportation of human beings against their will for the purpose of exploitation. While presumably, slavery was abolished long ago in most western countries, and, in the U.S.A. a horrendous civil war was fought to decide the issue of slavery, it is thoroughly reprehensible that, in our modern, technologically sophisticated, civilized world, human trafficking flourishes. Gaining some understanding of the causes of human trafficking and of the determinants of effective anti-trafficking policy is essential in order to begin to rationally address this inhumane problem.

This paper hypothesizes that higher levels of national morality can lead to better national anti-trafficking policy. If this proves to be the case, then improving national morality in countries around the world is one promising avenue for reducing human trafficking

Anticipating the results, upgrading national morality in order to better anti-trafficking policy can be undertaken by using two fundamental steps. The first is to make people more moral, and more sensitive to immoral behavior. Even from a strictly secular basis, a country can move in this direction by incorporating moral behavior as significant part of its national identity, and through its educational system. The second step is to make people more aware of the existence, the extent, and the horrific nature of human trafficking, that is to say, to bring it before their eyes, to the forefront of their consciousness. A hidden problem is a problem that is not likely to be addressed. Hiding the ugly consequences of immoral behavior is apt to increase immoral behavior by lowering its cost both in terms of personal guilt and with regard to social opprobrium.

The paper, aiming to see whether anti-trafficking policy depends on national morality, is divided into additional sections. The second section provides an overview of some of the recent literature with regard to anti-trafficking policy. The third section presents a model of anti-trafficking policy. The fourth section discusses the variables that will be included in the empirical analysis. The fifth presents and discusses the results of cross country regressions of anti-trafficking policy on national morality and on other variables. Lastly, the sixth section summarily concludes.

2. Some Relevant Literature

In addition to creating their index of anti-trafficking policy, Cho, Dreher, and Neumayer employ spatial autoregression on a panel consisting of one hundred seventy seven countries for the years 2000 through 2009 with their anti-trafficking index as the dependent variable to see if policy choices in one country are spatially dependent on policy choices made by other countries (Cho, Dreher, and Neumayer 2011). They find that both a contiguity weighted spatial variable and a voting weighted spatial variable are statistically relevant for overall country anti-trafficking policy. What is more, among their control variables, the level of democracy, the control of corruption, and the extent of woman’s rights also appear to be favorable forces for anti-trafficking policy, while U.S. aid, and the level of economic development do not appear to matter.

Rudolph and Schneider use a holistic multiple indicators multiple cause’s structural equation model to obtain measures of the unobservable variable of interest, human trafficking inflows, for the years 2000 to 2010 (Rudolph and Schneider 2013). For getting their human trafficking inflows estimates and for providing insights into human trafficking, their basic model considers four causes of human trafficking intensity (log of GDP per capita, log of FDI stock, percentage of agriculture to GDP, and language fractionalization), and three indicators of human trafficking intensity (the crime rate, the Cho, Dreher, and Neumayer’s policy index, and the number of identified victims).

Cho employs extreme bound analysis on an enormous number of regressions she runs on three different human trafficking data sets consisting of data on one hundred eighty countries for the years are 1995 through 2010 in order to identify robust determinants of outflows and of inflows of human trafficking (Cho 2012). Out of seventy eight push factors that she considers as potential sources of human trafficking outflows, her most robust negatively related push factors are log of per capita income, the percentage of Muslims in the population, the fertility rate, and the size of the food, beverage and tobacco industries. Her two most robust positively related push factors are the size of the information flow, and being a transition economy. From the sixty seven potential pull factors that she investigates as possible reasons for human trafficking inflow into countries, her single most robust negatively related pull factor is the size of the information flow, while her most robust positively related pull factors are the log of per capital income, the log of FDI to GDP, and language fractionalization.

Using averages over the period 2000 to 2010 for the Cho, Dreher, and Neumayer anti-trafficking policy index and its three components, Potrafke undertakes cross country regression analysis to investigate the effect of religion on anti-trafficking policy (Potrafke 2013). He finds that countries with greater Moslem majorities have more lax anti-human trafficking policies, and that the effect is more pronounced the more dictatorial (less democratic) the country. In addition, among the control variables the uses, he discovers that lower corruption and bigger population size are favorable for anti-trafficking policy, but that per capita GDP does not seem to be important.

Contrary to what one might expect on the basis of economic theory that greater scarcity of women relative to males would make women more valuable and thereby give women greater rights, Prakash proposes that, at least for India, given India’s particular cultural characteristics, that higher sexual imbalance (more females to males in the population) leads to greater trafficking of women (Prakash 2014). Consistent with his contention, his regressions on a panel of Indian states for the years 1980 through 2011 show that a higher child sex ratio, measured in terms of males per thousand in the population, is associated with increased trafficking between Indian States. 

In contrast to many who argue that internal factors, such as poverty, war, and cultural factors, are the major drivers of human trafficking in Africa, Njoh and Ayuk-Etang believe that external factors, such as the replacement of indigenous culture by foreign culture, are the real problem (Njoh and Ayuk-Etang 2012). Using data on forty seven African countries, they run regressions of Cho, Dreher, and Neumauyer’s overall anti-trafficking policy index and its three sub-indexes of prevention, prosecution, and protection on the percentage of Christians, the percentage of Moslems, the percentage of African Religion, and literacy rates. Their results show that while greater literacy rates, which they interpret as a marker of greater western influence, has, in line with their beliefs, a statistically significant negative influence on anti-trafficking policy, the percentage of the population with African Religion, contrary to their expectations, also has a statistically significant negative influence.

Hernandez and Rudolph use a gravity style model to try to explain the number of human trafficking victims per year for each of one hundred and twenty source countries to thirteen European host countries for the years 1998 to 2009 (Hernandez and Rudolph 2011). Among their findings are, first, that income differentials between source and host country, the total population of source and destination countries, the amount of origin country migrants in the destination country, and the number of origin country refugees in the destination country are all significantly positively related to trafficking from a source country to a destination country; second, that both geographic distance between origin and source country, and the extent of law and order in the host country are significantly negatively related to human trafficking from a source country to a destination country; and, finally, third, that neither prostitution regulation in the host country, the extent of women’s rights in the source country, the homicide rate in the host country, or the homicide rate in the source country are significant drivers of trafficking from a source country to a destination country. 

3. Simple Anti-Human Trafficking Policy Model

The theoretical model of anti-human trafficking policy consists of a single equation with an accompanying partial derivative. The equation with its associated partial derivative is as follows.

A = f(M, C) δA/δM>0

In the model’s equation, A stands for the extent of national anti-trafficking policy, M represents the level of national morality, and C is a set of control variables. The model’s lone partial derivative between national anti-trafficking policy and the level of morality is positive. As a whole, the model suggests that national anti-trafficking policy depends on national morality and on a set of control variables, and that, after adjusting for other relevant variables, the relationship between anti-trafficking policy and national morality is positive.

If morality means anything, then at a minimum it provides some constraint on individual undertaking of undesirable behavior. The whole notion of morality is to get people to behave in an ethically appropriate manner, and to avoid behaving in an ethically inappropriate manner. Morality develops and molds individual conscience, which becomes a force in individual decision making. It makes it more costly for an individual to engage in abhorrent behavior because of guilt, and because of perceived or actual negative views of others, and because of potential consequences from the judgment of others.

Besides national morality, three control variables are also considered as possible determinants of anti-trafficking policy.

The first is the extent of democratic government. Greater democracy is expected to have a positive effect on anti-trafficking policy. The reason is that, because democracy gives some political power to the common man, democracy has more concern for the common man, and it allows all groups, even highly marginalized groups, to have some impact on the government and on government policy. That is to say, as a general rule, a more democratic government is more sensitive and more responsive to the people.

The second control variable is the share of the private sector in the economy. It is anticipated that a bigger private sector share in the economy leads to better anti-trafficking policy. Culturally, societies can range all the way from an individualistic orientation, with individuals as individuals being important in and of themselves, to a more collective orientation, in which it is the nation that matters, and individuals are seen solely from the perspective of the nation as a whole. To say it a little differently, at the one extreme, there is no real society and society is simply a collection of individual human beings, and, at the other extreme, only the collective such as the nation matters, with individual identify completely subsumed in the collective identity. Thus, the more privatized an economy, the more capitalistic the economy, the more people define themselves as individuals , and as a consequence, the greater will be the tendency of the society to place emphasis on the individual and on individual rights.

Given the continuing integration of the world economy, the final control variable included is the extent of globalization. The expected sign on this variable is ambiguous. On the one hand, greater integration of a country with the world economy is likely to make a country more sensitive to world pressure, including world pressure in terms of improved human rights. This suggests a positive relationship between anti-trafficking policy and globalization. On the other hand, globalization increases the channels for movement of goods and services, including the trafficking of human beings, between nations. This latter suggests a negative relationship between anti-trafficking policy and globalization

4. Data Sources for Variables

The measure of the strength of anti-trafficking policy is the anti-trafficking policy index developed by Cho, Breher, and Neumayer (Cho, Dreher, and Neumayer 2011). The data is downloaded from the Human Trafficking website (human Trafficking 2014).The index ranges from a low value of three to a high value of fifteen with higher values representing better anti-trafficking policy. It is constructed by rating three component of anti-trafficking policy the prosecution of traffickers, the prevention of trafficking, and the protection of victims, from one to five, and adding the results.

National morality is quantified by using Crabtree’s measure of national morality for the first, and, at present, the only year for which it is available, 2013 (Crabtree 2013). Crabtree’s national morality measure is fairly comprehensive as it considers a wide variety of factors in its construction. For 2013, the Crabtree index ranges from 27.4 to 90.7. Higher values of the Crabtree index indicate greater national morality.

The measure of democracy is the 2010 democracy index of the economist intelligence unit of economist magazine (The Economist 2010). The democracy index can range from one to ten with higher values signifying greater levels of democracy.

The share of the private sector in the economy in 2010 is computed simply by subtracting the percentage of government spending to GDP for the year 2010 from one hundred percent. The numbers for the percentage of government spending to GDP for 2010 come from the World Bank (World Bank 2014).

Globalization is captured by utilizing the percentage of trade to GDP for the year 2010 with trade defined as exports plus imports. The data for the percentage of trade to GDP also comes from the World Bank.

5. Empirical Results

Table I shows the results of cross country regressions of the Human anti-trafficking policy index on national morality and on other variables.

Table 1. Cross Country Regressions of Anti-Trafficking Policy Index on National Morality and Other Variables

-1 -2 -3 -4
CONSTANT 3.612 4.500 (0.089) 0.075
(4.460) (6.020) (-.047) (0.034)
* *    
MORALITY 0.107 0.043 0.043 0.057
(8.040) (2.270) (2.410) (3.060)
* ** ** *
DEMOCRACY   0.582 0.588 0.532
(4.750) (4.740) (4.260)
* * *
PERPRIVATETOGDP     0.057 0.056
(2.910) (2.950)
* *
PERTRADETOGDP       (0.008)
(-2.35)
**
RSQ 0.275 0.411 0.435 0.459
N 173 159 144 143

The table consists of four different regression equations. The first is a simple regression using national morality as the sole explanatory variable. The next three equations cumulatively add a control variable as an additional explanatory variable, first starting with democracy in the second equation, followed by the percentage of the private sector to GDP in equation three, and, finally adding the percentage of trade to GDP in fourth equation.

The construction of the table is as follows. The first row numbers the regressions, the second to last row provides the r-squared values for the regressions, and the last row shows the number of countries (sample observations) for the regressions. The first column provides the potential explanatory variables. Each of the remaining columns shows the results of a single regression. For any variable that enters an equation the estimated coefficient is provided with its individual t-statistic underneath in parenthesis. Variables significant at the one percent level of significance or better in an equation are marked with a single asterisk under their individual t-statistic, while those significant at the five percent level or better are given two asterisks.

The results lend real support to paper’s central hypothesis that higher levels of national morality positively influence anti-trafficking policy. National morality, as measured by Crabtree’s morality index, is positive in all of the four regressions equations in table I. It is significant at the one percent level of significance or better in two of the four equations, equations one and four, and significant at the five percent level in the other two equations, equations two and three. When used on its own in the first equation, national morality explains over twenty seven percent of the cross country variation in the anti-trafficking index in a sample consisting of one hundred and seventy three countries.

The three control variables, democracy, the percentage share of the private sector in the GDP, and the percentage share of trade to GDP also appear to be relevant for anti-trafficking policy.

In line with theoretical expectations and the findings of past studies, the estimated coefficient on democracy is positive in the three equations that it enters, equations, two, three, and four. In every one of these equations democracy is significant at the one percent level of significance or better.

The percentage share of the private sector in the total economy is positive and significant at the one percent level of significance or better in both equations in which it appears, equations three and four, suggesting, as anticipated, that a more privatized economy with its greater focus on the importance of the individual is more conducive to better anti-trafficking policy.

Finally, in the single equation, equation four, that the measure of globalization, the percentage of trade to GDP, is employed, it is negative and significant at the five percent level of significance or better. This suggests that the negative channel enlargement effect on anti-trafficking policy due to globalization overwhelms any potential positive effect on anti-trafficking policy such as a country becoming more globally sensitive or globally influenced. Thus, while, perhaps, globalization may be favorable for economic development and economic growth, there appears to be a potential negative externality from globalization due to its negative effect on anti-trafficking policy that needs to be taken into consideration when a country considers greater globalization.

6. Conclusion

The cross country analysis of the paper provides support for the notion that greater country morality leads to better national anti-trafficking policy. Whether national morality is used alone in regressions to explain anti-trafficking policy or adjusting for various control variables, national morality proves to be a statistically significant positive determinant of anti-trafficking policy. In addition, the findings of the paper suggest that greater democracy and increased privatization of the economy are favorable for anti-trafficking policy, while greater globalization appears to be detrimental for anti-trafficking policy. Thus, in order to improve national anti-human trafficking policy it would be advisable for a country to work to improve national morality, to try to extend the scope of democracy and democratic institutions, and, culturally, to steer the country away from collective identity and more towards individual identity.

References

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Author(s)

William R. DI PIETRO
Daemen College, Amherst, New York, United Estates

Correspondence

William DiPietro, Daemen College, Amherst, New York, United States of America

Article History

Received: November 12, 2015
Accepted: November 30, 2015
Available Online: December 19, 2015

Cite Reference

DiPietro, W.R., 2015. Human Trafficking and National Morality. Expert Journal of Economics, 3(3), pp.161-166

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