Do Economic Growth and Education Matter in Combating Human Trafficking? An Analysis of State Level Panel Data in the U.S.

Do Economic Growth and Education Matter in Combating Human Trafficking? An Analysis of State Level Panel Data in the U.S.

Yuying Xie*¹, Meggie L. Chang², Zeyue Du³, Yubin Lu⁴

¹Shepherd University, WV, USA

²Richard Montgomery High School, MD, USA

³McLean High School, VA, USA

⁴Widener University, PA, USA

*Corresponding author

DOI: https://doi.org/10.51244/IJRSI.2024.1110003

Received: 20 September 2024; Accepted: 30 September 2024; Published: 28 October 2024

ABSTRACT

Human trafficking, consisting of all forms of nonconsensual forced or lured labor, is a violation of human rights and has severe impacts on the affected individuals, families, and society. In this paper, we investigate the factors of economic development, educational attainment, and states that potentially influence human trafficking in the U.S. Using panel data of human trafficking reports from all fifty states and D.C. from 2016 to 2021, we obtain interesting results. First, economic development has a small but significant negative effect on human trafficking within states, implying that economic development helps to reduce human trafficking, although richer states have slightly more cases. Second, it is surprising that high school graduation rates have a positive rather than negative effect, implying that education correlates with more identified trafficking cases. A possible explanation is that education increases the population’s awareness of trafficking activities, leading to higher reporting rates. Last, some states reveal significant effects, indicating geographical differences in the country. These findings have important policy and social implications.

Keywords: Human trafficking, economic development, education, states, public health

INTRODUCTION

Human trafficking, consisting of all forms of nonconsensual forced or lured labor, is an important social and economic issue (Koettl, 2009). In the U.S., the Trafficking Victims Protection Act (TVPA) defines trafficking in persons as “(a) sex trafficking in which a commercial sex act is induced by force, fraud, or coercion, or in which the person induced to perform such act has not attained 18 years of age; or (b) the recruitment, harboring, transportation, provision, or obtaining of a person for labor or services, through the use of force, fraud, or coercion for the purpose of subjection to involuntary servitude, peonage, debt bondage, or slavery.” Internationally, the United Nations (UN) defines human trafficking as the recruitment, transportation, transfer, harboring, or receipt of a person for the purpose of exploitation through the use of force, fraud, or deception.

Much of the social study literature has focused on the impacts on victims and the mental, social, and financial means to help them (e.g., Polizzi et al., 2024).  In the economics literature, Wheaton et al. (2010) use a rational-choice framework to understand the market of human trafficking. However, to our knowledge, there has been limited work on how important economic factors such as economic development, literacy, and geography affect the trafficking of human beings. This motivates us to identify the relevant factors and their effects. In this paper, panel data is collected on reported human trafficking cases, GDP per capita, high school graduation rate, and the percentage of college degree holders in the population from fifty states and the District of Columbia (D.C.) over the period from 2016 to 2021, and subsequently statistical test is performed using both fixed- and random-effect models are used to identify the relationships.

The remainder of this paper is organized as follows. A literature review is conducted in the next section. The section on Methodology details the model structure, hypotheses, and test results. Then, the Discussion section explores the possible implications of the statistical results. The last section concludes the paper.

LITERATURE REVIEW

Lack of development oftentimes is regarded as the root cause of human trafficking. However, Danailova-Trainor and Laczko (2010) mention that there is some evidence to suggest that victims of cross-border trafficking are more likely to originate from middle-income rather than lower-income countries. In the context of the U.S., whether there exists a concrete relationship between income level and trafficking and whether there exists geographical differences among the states remains unanswered.

Governments play a leading role in fighting human trafficking, but their ability is constrained by available resources, existing laws, and prevailing social norms. Danailova-Trainor and Laczko (2010) emphasize that different levels of government need to coordinate to increase the effectiveness of helping trafficking victims in personal development.

On the effect of education on trafficking, Spires (2015) studies two NGOs’ educational work and other measures to prevent human trafficking and protect youth in Thailand. Numerous other studies looked at the usefulness of educating health professionals on treating trafficking victims (e.g., Nordstrom, 2022; Miller et al., 2022). Much less of the literature has looked at how the education of the general public affects human trafficking, although it is generally believed that education increases the awareness of trafficking and helps lower the vulnerability of the at-risk population (Lesak et al., 2021). Some studies pointed out that the study of trafficking faces the problem of data accuracy, and the increased awareness of trafficking through education and policy efforts can transform the unreported hidden cases into documented reports (Brunovskis and Surtees, 2010; Van Dijk, 2024; Zhang, 2022). Therefore, education may be able to reduce trafficking but increase reports.

The exploitation of humans also exhibits geographical differences. Lo (2024) finds that organized crime and corruption impede the effectiveness of law on Hong Kong’s trafficking increases. Phon and Price (2024) find that climate change and disaster crises cause human migration and trafficking activities in Cambodia. Denton (2016) investigates who the traffickers and victims are in the U.S. using legal cases from 2006 to 2011 and found that the traffickers and victims are likely from the same community.

As pointed out by Goździak (2008), little research has been done on whether and how economic development affects human trafficking, whether education can build a firewall to prevent trafficking, and whether the states exhibit geographical differences in the U.S. In this paper, we try to answer these questions through an empirical study using state-level panel data from 2016 to 2021in the U.S. The findings draw a much clearer picture of the relationships between trafficking activities and these important factors and provide important policy implications.

METHODOLOGY

Empirical Model

Based on the previous analysis, we hypothesize the model as follows:

The reported trafficking cases are the dependent variable (y_s,t) where subscript s represents the state factor and t represents the time factor. Symbol α is the intercept. GDP_s,t is the state GDP per capita, which is used to present the economic development level and speed over the five years. We use two series of data to represent education level.  H_s,t is the state high school graduation rate, and C_s,t is the state college degree attainment rate. S_t captures the state factor.

The conventional thinking is that economic development brings economic opportunities and increases personal wealth, so it can reduce the population vulnerable to trafficking. Hence, we hypothesize the following relationship:

H1: GDP per capita is negatively correlated with human trafficking reports.

It is also commonly believed that education can increase the population’s awareness of and resistance to human trafficking, so we hypothesize the following relationships:

H2: High school graduation rate is negatively correlated with human trafficking reports.

H3: College degree attainment rate is negatively correlated with human trafficking reports.

Due to the legal, social, economic, geographical, and cultural differences among the states, we believe there exists regional differences across the states regarding human trafficking situations. Hence we have the following hypothesis:

H4: There are regional differences across the states in human trafficking.

Data Description

The number of identified human trafficking cases is extracted from the National Human Trafficking Hotline (NHTH).[1] We divide the total reported cases by the respective state population and then multiply by 100,000 to receive the reported cases per 100,000 people in order to eliminate the size effect. We obtain the most recent five years of data from 2016 to 2021 due to data availability and consistency.

GDP per capita is used to represent the state of the economy for the same periods. The values capture the information of both the economic development within the states and the different development levels between the states.

The state-level high school graduation rates and the college degree attainment rates are obtained from the U.S. Department of Education.[2][3]

The fifty states and D.C. are treated as categorical variables in the panel data. Both the fixed- and random- effect models are performed to investigate whether there are significant differences within and across the states.

The panel data is summarized in Table 1.

Table 1: Panel Data Summary

Statistical Results

We performed OLS regressions on the panel data with both the fixed-effect model and the random-effect model to better identify the correlations within and between states. The fixed effects model test results are in Tables 2 and 3.

Table 2: Fixed Effects Model Regression Summary
Dep.Variable:	human trafficking	R-squared:	0.291
Model:	OLS	Adj.R-squared:	0.141
Method:	Least Squares	F-statistic:	1.947
No.Observations:	306	Prob (F-statistic):	0.000364
Df Residuals:	252	Log-Likelihood:	-756.86
Df Residuals:	53	AIC:	1622
Covariance Type:	nonrobust	BIC:	1823

Table 3: Fixed Effects Model Regression Results
	coef	std err	t	P>|t|	[0.025	0.975]
Intercept	-104.2392	45.489	-2.292	0.023	-193.827	-14.651
C(State)[T.Alaska]	-4.0988	3.226	-1.27	0.205	-10.453	2.255
C(State)[T.Arizona]	-0.8569	2.097	-0.409	0.683	-4.987	3.273
C(State)[T.Arkansas]	1.6702	2.087	0.8	0.424	-2.439	5.78
C(State)[T.California]	7.8523	4.515	1.739	0.083	-1.039	16.744
C(State)[T.Colorado]	2.8653	3.884	0.738	0.461	-4.785	10.515
C(State)[T.Connecticut]	-1.1972	3.128	-0.383	0.702	-7.357	4.962
C(State)[T.DC]	23.8211	8.604	2.769	0.006	6.877	40.765
C(State)[T.Delaware]	0.5499	2.136	0.257	0.797	-3.657	4.757
C(State)[T.Florida]	-0.2659	2.415	-0.11	0.912	-5.023	4.491
C(State)[T.Georgia]	-3.9387	3.019	-1.305	0.193	-9.884	2.006
C(State)[T.Hawaii]	-4.1181	2.705	-1.522	0.129	-9.445	1.209
C(State)[T.Idaho]	-4.3063	3.064	-1.405	0.161	-10.342	1.729
C(State)[T.Illinois]	-0.9019	2.271	-0.397	0.692	-5.374	3.57
C(State)[T.Indiana]	-5.8092	3.218	-1.805	0.072	-12.148	0.529
C(State)[T.Iowa]	-3.4876	2.608	-1.337	0.182	-8.624	1.648
C(State)[T.Kansas]	2.3477	2.016	1.164	0.245	-1.623	6.319
C(State)[T.Kentucky]	2.4708	1.922	1.286	0.2	-1.314	6.256
C(State)[T.Louisiana]	-6.1468	3.194	-1.925	0.055	-12.436	0.143
C(State)[T.Maine]	-3.7679	4.377	-0.861	0.39	-12.388	4.853
C(State)[T.Maryland]	-3.7257	4.672	-0.797	0.426	-12.927	5.476
C(State)[T.Massachusetts]	-0.9063	2.775	-0.327	0.744	-6.371	4.558
C(State)[T.Michigan]	-7.3517	3.582	-2.052	0.041	-14.407	-0.297
C(State)[T.Minnesota]	5.5453	2.707	2.049	0.042	0.215	10.876
C(State)[T.Mississippi]	-3.6413	2.793	-1.304	0.193	-9.141	1.859
C(State)[T.Missouri]	-7.1862	3.592	-2.001	0.046	-14.26	-0.113
C(State)[T.Montana]	-2.3988	2.876	-0.834	0.405	-8.063	3.265
C(State)[T.Nebraska]	3.9311	2.225	1.767	0.078	-0.451	8.313
C(State)[T.Nevada]	-3.0123	3.297	-0.914	0.362	-9.505	3.48
C(State)[T.New Hampshire]	-0.9313	3.783	-0.246	0.806	-8.381	6.519
C(State)[T.New Jersey]	3.5849	2.321	1.544	0.124	-0.986	8.156
C(State)[T.New Mexico]	1.0483	4.107	0.255	0.799	-7.04	9.137
C(State)[T.New York]	1.8824	2.267	0.83	0.407	-2.583	6.348
C(State)[T.North Carolina]	-6.6087	3.421	-1.932	0.055	-13.346	0.129
C(State)[T.North Dakota]	4.9409	2.591	1.907	0.058	-0.162	10.043
C(State)[T.Ohio]	0.7775	2.07	0.376	0.707	-3.299	4.854
C(State)[T.Oklahoma]	-4.4388	2.81	-1.58	0.115	-9.973	1.096
C(State)[T.Oregon]	-2.727	2.367	-1.152	0.25	-7.388	1.934
C(State)[T.Pennsylvania]	-1.5745	2.6	-0.606	0.545	-6.694	3.545
C(State)[T.RhodeIsland]	-0.9866	1.883	-0.524	0.601	-4.694	2.721
C(State)[T.South Carolina]	-6.2055	3.205	-1.936	0.054	-12.517	0.106
C(State)[T.South Dakota]	0.9449	1.904	0.496	0.62	-2.804	4.694
C(State)[T.Tennessee]	4.1783	3.258	1.282	0.201	-2.238	10.595
C(State)[T.Texas]	-4.3369	2.836	-1.529	0.127	-9.923	1.249
C(State)[T.Utah]	-7.6987	3.777	-2.038	0.043	-15.138	-0.26
C(State)[T.Vermont]	-4.4086	3.92	-1.125	0.262	-12.13	3.312
C(State)[T.Virginia]	-4.3219	2.782	-1.553	0.122	-9.801	1.157
C(State)[T.Washington]	4.7536	3.57	1.332	0.184	-2.277	11.784
C(State)[T.West Virginia]	-7.8099	3.39	-2.304	0.022	-14.487	-1.133
C(State)[T.Wisconsin]	-7.264	3.925	-1.851	0.065	-14.995	0.467
C(State)[T.Wyoming]	-5.0288	3.895	-1.291	0.198	-12.699	2.642
GDP per capita	-0.0001	6.45E-05	-2.086	0.038	0	-7.50E-06
High School Graduation Rate	126.1617	58.932	2.141	0.033	10.1	242.224
College Degree Attainment Rate	10.1992	34.226	0.298	0.766	-57.207	77.606
Omnibus:	319.535	Durbin-Watson:	2.173
Prob(Omnibus):	0	Jarque-Bera(JB):	15405.93
Skew:	4.302	Prob(JB):	0
Kurtosis:	36.679	Cond.No.	28300000

Model Fit

The R-squared value is 0.291, indicating that approximately 29.1% of the variation in human trafficking rates is explained by the model. The p-value for F-statistic is 0.000364, indicating that the model is statistically significant overall.

Significant Factors

GDP per capita: The coefficient is -0.0001 with a p-value of 0.038. This suggests that an increase in GDP per capita is associated with a slight decrease in human trafficking rates within states. The p-value indicates that the small negative effect is significant. This finding is consistent with our expectation and H1 fails to be rejected.

High school graduation rate: The coefficient is 126.1617 with a p-value of 0.033. This indicates that higher high school graduation rates are statistically significantly associated with an increase in human trafficking rates within states. This finding seems to contradict the common belief that education helps to prevent the exploitation of trafficking. H2 is rejected.

College degree attainment rate: The coefficient is 10.1992 with a p-value of 0.766. This factor is not statistically significant. H3 is rejected.

State: Certain states have significant coefficients. Specifically, California has a coefficient of 7.8523 with a p-value of 0.083, which is borderline significant; the District of Columbia (DC) has a coefficient of 23.8211 with a p-value of 0.006; Utah has a coefficient of -7.6987 with a p-value of 0.043; and West Virginia has a coefficient of -7.8099 with a p-value of 0.022. This indicates that the state factor indeed affects human trafficking rates, so H4 fails to be rejected.

Table 4: Random Effects Regression Summary
Dep. Variable:	Human trafficking	R-squared:	0.0346
Estimator:	Random Effects	R-squared (Between):	0.1937
No. Observations:	306	R-squared (Within):	-0.0012
Date:	Mon, Sep 09 2024	R-squared (Overall):	0.0509
Time:	11:57:08	Log-likelihood	-787.48
Cov. Estimator:	Unadjusted
		F-statistic:	3.6118
Entities:	51	P-value	0.0137
Avg Obs:	6.0000	Distribution:	F(3,302)
Min Obs:	6.0000
Max Obs:	6.0000	F-statistic (robust):	3.6118
		P-value	0.0137
Time periods:	6	Distribution:	F(3,302)
Avg Obs:	51.000
Min Obs:	51.000
Max Obs:	51.000

Table 5: Random Effects Parameter Estimates
	Parameter	Std. Err.	T-stat	P-value	Lower CI	Upper CI
GDP per capita	2.93E-05	1.05E-05	2.7837	0.0057	8.59E-06	5.00E-05
High School Graduation Rate	9.7872	9.2844	1.0541	0.2927	-8.4832	28.057
College degree attainment rate	3.3736	4.8087	0.7016	0.4835	-6.0892	12.836
intercept	-8.6758	7.8826	-1.1006	0.2719	-24.188	6.836

The random effects model is better at explaining the differences between states. The R-square between states has a value of 0.1937, indicating that about 19.37% of the changes are explained by the model. The coefficient of GDP per capita is 2.93 E-05 with a p-value of 0.0057, indicating that the small coefficient is positive and statistically significant. The education variables have positive but insignificant coefficients, indicating that they do not have a clear relationship with human trafficking.

DISCUSSION

First of all, the fixed effects model results indicate that the growth of GDP per capita within the states helps to reduce trafficking, although the effect is small but significant. Several reasons can explain this encouraging relationship. Firstly, as GDP per capita increases, the typical standard of living in the region increases. This can reduce the vulnerability of individuals to human trafficking due to lower poverty, and improved access to resources. Secondly, a higher GDP per capita often correlates with lower unemployment rates and better job opportunities. When people have more access to legitimate employment, they are less likely to fall prey to traffickers who exploit economic desperation. Thirdly, the higher GDP per capita may also mean more social support systems and stronger law enforcement to protect individuals from becoming victims of trafficking. However, the random effects model results show that, across states, higher GDP per capita is associated with higher human trafficking rates. A possible explanation can be that the richer states have more resources to help victims reach out to authorities. This contrast suggests different dynamics at play within and between states.

Second, it is to our surprise that high school graduation rate has a statistically positive effect on trafficking. This seems counterintuitive because, with a higher level of education, people are more aware of the vulnerability of trafficking and can better protect themselves. Several possible explanations could account for this finding. The states with higher education levels may have better awareness and recognition of human trafficking which leads to higher reported rates. States with higher high school graduation rates may be more urbanized, and urban areas can have higher trafficking rates due to the higher density of population and higher chances of exploitation. It can also be that states with higher high school graduation rates have better law enforcement to better identify trafficking cases and better social support systems that motivate victims to step forward. If these are truly the reasons for the positive relationship between education and trafficking cases, then education plays an important role in converting the hidden cases into identified ones.

Third, the test shows that the college degree attainment rate is statistically insignificant. This might be because: 1. College graduates are more socially and economically established and less vulnerable to trafficking; 2. It represents a smaller segment of the population than high school graduates so it may not directly influence the factors that make individuals vulnerable to trafficking; 3. It may overlap other factors such as high school graduation rate and GDP per capita in explaining trafficking.

Fourth, certain states have significant coefficients, indicating that the state factor affects human trafficking rates. Specifically, California has a marginal significant and positive coefficient, the District of Columbia has a significant and positive coefficient, and Utah and West Virginia have significant and negative coefficients. This finding indicates that human trafficking is a complex mix of local conditions such as economic development, geographics, legal and social environments, policy, etc. States with lower income and higher poverty may have more vulnerable populations to human trafficking, while states with higher income may have more resources to combat trafficking but may also attract traffickers due to more economic activities and wealth. States with major transportation hubs might be more significant in trafficking networks, both for moving victims and for exploiting them within the state. This could partly explain why California and DC have positive coefficients and Utah and West Virginia have negative coefficients. The differences in state laws and the resources and efforts of law enforcement also affect the trafficking rates and detection rates.

CONCLUSION

In this paper, we investigated the potential factors that influence human trafficking in the U.S. Using panel data from all fifty states and D.C. from 2016 to 2021, we found that economic development reduces human trafficking activities to a small but significant degree. This indicates that economic well-being repels trafficking instead of fostering it. We also found that high school graduation rates have a positive correlation with trafficking but college degree attainment rates do not. This contradicts the common belief that education helps to prevent trafficking exploitation. A possible reason is that education increases the awareness of trafficking activities leading to higher reporting rates. Furthermore, we found differences in trafficking among states which were attributed to the regional economic, social, legal, and geographical variations. These findings have important social and policy implications.

Future research can provide deeper insight into the effects of the studied factors on human trafficking. First, conducting qualitative research is essential to understand the context and mechanisms in states with both high education and high trafficking rates. Second, while our model demonstrates statistically significant explanatory power, it only accounts for about thirty percent of the variation in trafficking. There are additional variables that help clarify the relationship, such as economic inequality, urbanization levels, and law enforcement practice. It is also worth studying the topic in other contexts overseas to investigate the related factors.

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FOOTNOTES

[1] https://humantraffickinghotline.org/en

[2] https://nces.ed.gov/programs/coe/indicator/coi/high-school-graduation-rates

[3] https://nces.ed.gov/programs/coe/indicator/ctr/undergrad-retention-graduation