Introduction

Child trafficking is a heinous crime that poses a severe threat to the sustainable development of the international community. The 2024 Global Trafficking in Persons Report revealed that among globally identified victims, 60% of girls were trafficked for sexual exploitation, approximately 45% of boys were trafficked for forced labor, and another 47% of victims were exploited in other forms, including forced crime and begging (United Nations Office on Drugs and Crime, 2024). Almost every nation in the world has been affected by child trafficking, as either a country of origin, transit, or destination (Warria et al., 2015). Many children were illegally transported by traffickers from underdeveloped areas to developed areas across local, regional, and inter-provincial borders, which reflects that child trafficking is also a fundamentally spatial phenomenon (Blazek et al., 2019; Yea, 2021). The measures taken in response to the severe crackdown on trafficking have further pushed child trafficking underground (Xiong, 2021). Understanding and addressing child trafficking are challenging tasks because of its clandestine and covert nature (Zhou et al., 2022). This has created obstacles to the international criminal justice system and public security systems.

In China, child trafficking remains a persistent issue. It has not only caused severe harm to the children themselves but also dealt a devastating blow to their families and posed a serious threat to social stability (Jin et al., 2024). To curb this phenomenon, the Ministry of Public Security of the People’s Republic of China has launched a nationwide special campaign against the trafficking of women and children. According to the latest report, over 550 trafficking cases were detected and a number of human trafficking suspects were arrested in 2024, effectively safeguarding the legitimate rights and interests of women and children (People’s Daily, 2025). Although the number of child trafficking cases has decreased year by year (Li et al., 2018; Jin et al., 2024), the issue remains unresolved. A key priority in addressing child trafficking is to examine the full range of driving factors underlying this crime. These factors include not only push factors in origin regions, such as poverty, social unrest, and unemployment (Hilson, 2008; Hill and Carey, 2010; Hurtado et al., 2017), but also pull factors in destination regions, such as better economic prospects, demand for children, birth policies, and migration, as well as a number of facilitating factors (e.g. social tolerance, lack of criminal justice cooperation, high profits, low risks) (Goldring and Landolt, 2012; Strauss and McGrath, 2017).

In China, in contrast to the larger body of qualitative scholarship on the causes of child trafficking, very few quantitative analyzes exist targeting the origin areas. In this light, this study attempted to examine the various factors affecting child trafficking in Southwest China and explore in depth the relationship between child trafficking and its origin areas. GIS mapping holds significant potential for analyzing the spatial dynamics of child trafficking (Li et al., 2018; Zhou et al., 2023a). Firstly, we used spatial analysis to identify spatial clusters of child trafficking. Secondly, socioeconomic statistics were utilized to measure factors influencing its occurrence. As child trafficking is a long-term historical phenomenon, the unique impacts of various factors on its occurrence need to be distinguished. With this in mind, this study focused on the following two research questions (RQ):

RQ.1: Was there spatial clustering of child trafficking in Southwest China? If so, how did the footprints of these spatial clusters evolve over time?

RQ.2: How did local socioeconomic conditions, population changes, and policy adjustments affect child trafficking in the Chinese context?

The study is structured as follows. Section 2 outlines relational perspectives on child trafficking in China and globally. Section 3 covers the study area and methods, and presents the data employed in this study. Section 4 presents the quantitative findings. Section 5 presents the discussion and concluding remarks. We also propose possible solutions in practice.

Review of related work

Internationally, child trafficking is a human rights violation of epidemic proportions globally (Scannell et al., 2018). According to the United Nations Palermo Protocol, child trafficking refers to the recruitment, transportation, transfer, harboring, or receipt of any person below the age of 18 for the purpose of sexual exploitation, forced labor or services, slavery, or the removal of organs (United Nations 2000). The 2024 Global Trafficking in Persons Report showed that vulnerabilities brought about by poverty, conflict, and the climate crisis had further exacerbated the situation of child trafficking (United Nations Office on Drugs and Crime, 2024). Some scholars have argued that child trafficking violates human rights and advocated examining this issue through the lens of criminal justice (Albrecht, 2019). Others considered child trafficking as a public health emergency. Scholars have incorporated factors associated with public health to explain child sex trafficking (Gutfraind et al., 2023; Zimmerman and Kiss, 2017). HIV/AIDS has been identified as a key contributor to child sex trafficking and is unlikely to diminish in the short term (Chris and Beyrer, 2004; Wirth et al., 2013). Poverty is widely recognized as the most common risk factor for child sex trafficking (Cole and Sprang, 2015). Another critical factor contributing to sex trafficking is gender inequality and discrimination, particularly the devaluation of girls (Reid, 2012). This phenomenon is prevalent in India and Nepal, with sex trafficking linked to child marriage being particularly severe (McDougal et al., 2020; Shakya et al., 2020).

In addition, socioeconomic status, ethnic background, or lack of parental care may exacerbate the risk of child trafficking (IOM, 2015). Children were forced to engage in dangerous activities such as labor exploitation, military conscription, armed conflict, street begging, organ extraction, and petty theft (Hill and Carey, 2010; OSCE, 2010). Scholars have focused on exploitation related to immigration status, migration status, and labor rights (Goldring and Landolt, 2012; Strauss and McGrath, 2017). A report showed that among Thailand’s 3.4 million immigrants, approximately 11% of children were at risk of being trafficked or subjected to labor exploitation (Jampaklay, 2011). One key driving force was child labor, the demand for labor and widespread tolerance of exploiting child labor may allow child trafficking to certainly continue (Rafferty, 2013). In sub-Saharan Africa, poverty was the underlying cause exacerbating the prevalence of child labor (Hilson, 2008). Another driving factor was the use of forced child soldiers. A study from Colombia confirmed that children were increasingly being used by armies, militias, and paramilitary organizations in zones of social disruption and chaos (Hurtado et al., 2017). Especially in border areas, traffickers were more likely to recruit and transport victims (Yagci Sokat, 2024). For instance, there was widespread trafficking between the transportation corridors in Tanzania and Uganda (Green et al., 2023).

Child trafficking has a long history in China, and it is mainly inter-provincial trafficking (Huang and Weng, 2019). According to Chinese Criminal Law 1997 under Section 240, child trafficking occurs when a person kidnaps, abducts, purchases, trades, facilitates and transfers a child less than 14 years of age for the purpose of sale (National People’s Congress, 2024). Previous literature has extensively investigated a series of factors associated with child trafficking (Wang, 2015; Fu, 2023). Scholars have called for a comprehensive understanding of the connections among the one-child policy, the traditional culture of son preference, and barriers to legal adoption as key factors contributing to child trafficking (Shen et al., 2013). China implemented a strict family planning policy in the early 1980s, allowing each couple to have only one child (Hesketh et al, 2005). Chinese parents generally preferred boys, believing that only sons could carry on the family line; moreover, legal adoption procedures were not easy in the past (Zhang, 2006). This situation drove a sharp increase in the number of child trafficking cases (Zhou et al., 2023a). More than 99% of trafficked children in China were adopted by buyers (Xin and Cai 2018). They were often taken in as sons, daughters, or child brides, and some were also forced to work as laborers at home or subjected to abuse and exploitation (Chu, 2011). Forced labor or sexual exploitation was only considered an aggravating factor in the overall trafficking process (Shen, 2016). A study showed that Southwest China served as a source region for child trafficking (Li et al., 2017), while eastern China’s high economic development correlated with a high incidence of child purchases (Xin and Cai, 2018). In this sense, the huge economic gap is another important factor influencing child trafficking. This has gradually reinforced the stereotype linking socioeconomic factors to child trafficking. The research gap in this area may be attributed to difficulties in collecting primary data on child trafficking and partly to differing research perspectives in academia. Notably, child trafficking is a complex process, and many countries face similar challenges, such as unstructured and spatially fragmented data (Cockbain et al., 2022).

After reviewing the above literature, we found that the definitions of child trafficking in China differed from international legal frameworks in terms of age thresholds, purposes, means, and associated influencing factors. Understanding these factors and formulating targeted strategies can effectively mitigate the occurrence of trafficking to a certain extent. Among the many complex factors that contribute to child trafficking, it may not always be applicable to generalize these factors across all countries or regions, as they exert distinct impacts in different social contexts (Rafferty, 2007). For example, poverty and low educational attainment cannot be considered general causes of child trafficking, although they may apply in specific circumstances (Hynes, 2010). Existing research remains limited but important, effectively highlighting a broader set of factors that facilitate trafficking. Academic understandings of child trafficking in China tend to rely on the single interpretation of the typical cases (Zhou et al., 2022). A robust body of qualitative studies has explored the social causes of child trafficking in great detail (Shen et al., 2013; Fu, 2023), but corresponding quantitative analyzes are notably absent (Wang, 2015; Zhou et al., 2022). Although the content is insightful, existing literature predominantly focuses on pull factors, neglecting push factors in trafficking origin areas (Li et al., 2018; Xu et al., 2022b). The unfolding of child trafficking depends on interactions between multiple actors (from origin and destination regions); however, the factors influencing child trafficking in origin areas remain the most elusive and understudied aspects, requiring further empirical verification.

Study area, date and methods

Study area

Southwest China was selected as the study area based on several considerations (Fig. 1), including its coverage of Yunnan, Guizhou, Sichuan, and Chongqing (Tibet is not considered in our study). Geographically, this region is complex and diverse, sharing borders with Vietnam, Laos, and Myanmar, neighboring regions with relatively low-income levels. It is also home to numerous ethnic groups, each with unique customs and festivals. By the end of 2022, a total of 25373 records had been retrieved from a database of missing children (Li et al., 2022), of which 9016 were from Southwest China, accounting for over 35% of all trafficked children in the dataset. The latest study also confirmed that Southwest China remains a major origin and supplier region for child trafficking (Xue et al., 2020), and that numerous traffickers have congregated in this area (Xia et al., 2022).

Fig. 1: Diagram of study area.
Fig. 1: Diagram of study area.The alternative text for this image may have been generated using AI.
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The inset shows the location of the study area in this paper.

Date

The data used in this study comes from a widely recognized public welfare website for missing persons in China (www.baobeihuijia.com). This platform is supported by numerous volunteers across the country, who assist registrants in collecting leads, publishing announcements, and locating their families through multiple channels. Based on registration types (including trafficking, adoption, abandonment, and martyr ancestry tracing, etc.), volunteers assign a unique case number to each record and verify the information, thereby enhancing the accuracy of self-reported data. The public can register their seeking information on this site for free, and there is no limitation for public to browse items. The platform’s data are categorized into three types:

  1. (1)

    Parents searching for children: Data submitted by parents who have lost their children. These records contain information about the initial missing location of children, and were thus used to identify trafficking origins in this study. A total of 9016 valid records were obtained as origin cases for analysis, which include details such as the child’s age, sex, date and location of disappearance, etc.

  2. (2)

    Children searching for families: Data submitted by trafficked children themselves. These records were used to identify the destinations of child trafficking.

  3. (3)

    Successful reunification cases: It records the basic information about the reunion between trafficked children and their families.

The second and third types of records were not used in this study.

Methods

Spatial autocorrelation

Spatial autocorrelation provides a useful measure of spatial patterns. Moran’s I is a widely used statistic for spatial autocorrelation, encompassing two core variants: Global Moran’s I (for overall spatial clustering) and Local Moran’s I (for location-specific clustering) (Lin, 2023). Local Moran’s I can identify similar values (High-High & Low-Low) or dissimilar values (High-High & Low-Low) at specific locations (Anselin, 1995). In this study, Local Moran’s I analysis was used to identify cluster locations of child trafficking. It is calculated by Equation:

$$I=\frac{n({x}_{i}-\bar{x}){\sum }_{j=1}^{n}{w}_{{ij}}({x}_{j}-\bar{x})}{{\sum }_{i}{({x}_{i}-\bar{x})}^{2}}$$
(1)

where \({x}_{i}\) and \({x}_{j}\) are divided into the number of child trafficking in space units i and j, Wij is the spatial weight matrix, and n is the number of space units. If Moran’s I > 0, it indicates that child trafficking has a spatial cluster. If Moran’s I < 0, it indicates that child trafficking has a spatial outlier. If Moran’s I = 0, it shows that child trafficking is randomly distributed in space.

Stepwise regression model

To explore the various factors influencing child trafficking, the stepwise regression model was employed in this study to select the optimal combination of variables. The acquired data was summarized using simple descriptive statistics to examine the relationships between the dependent and independent variables within the SPSS environment. The model equation is computed:

$$Y={\beta }_{0}+{\beta }_{1}{X}_{1}+{\beta }_{2}{X}_{2}+\cdots +{\beta }_{k}{X}_{k}+\varepsilon$$
(2)

where Y is the dependent variable, X(1…k) is the independent, β0 is the intercept, β(1…k) is the estimated regression coefficient, and ε is the error.

Independent variables

The economic, cultural, and policy factors vary across different eras, which directly affect the number of child trafficking and generate distinct spatial distribution patterns. Given that the number of child trafficking was very small before 1985 and thus insufficiently representative for this study, records from this period were excluded from the analysis. The number of child trafficking increased sharply between 1986 and 2000, while it continued to decline between 2001 and 2022. This study focused on the factors influencing child trafficking during these two periods, using provincial-level trafficking cases as the dependent variable and twelve socioeconomic indices as independent variables. The rational model of criminal behavior argues that crime risk rises with wider income inequality gaps (Kang, 2016). Low-income individuals who attempt to keep up with wealthier groups are more likely to engage in criminal activities (Distefano et al., 2018; Sugiharti et al., 2022). To measure the relationship between socioeconomic conditions and trafficking, we selected the GDP gross (X1), urban-rural income gap (X2) and Engel coefficient (X3). Crime is more prevalent in areas where residents have low levels of education and income (Pieszko, 2016). Public transit plays a key role in facilitating criminal diffusion, as offenders are sensitive to transportation costs (Phillips and Sandler, 2015). Accordingly, the registered urban unemployment rate (X4), illiteracy rate (X5) and passenger turnover (X6) were used as proxy variables for social status and development. Regarding demographic variables, population density (X7) and sex ratio (X8) are closely linked to crime generators and attractors (Zhou et al., 2023b). The child dependency ratio (X9) relates to guardianship quality, while minority share (X10) serves as a proxy for population heterogeneity. In addition to these ten variables, this study examined the impact of policy on child trafficking. The birth rate (X11) was a vital indirect proxy for assessing the link between the one-child policy and child trafficking. We also used the proportion of public security spending (X12) to reflect the intensity of legal policies against child trafficking. It is worth noting that this use of proxy indicators is a simplification due to the absence of direct data. Nevertheless, this approach provides a logical starting point for exploring the effects of social change on trafficking over the past few decades (Zhou, et al., 2023b).

Results

Local Moran’s I analysis

The Local Moran’s I values are consistently positive and statistically significant (p value < 0.001), indicating a high degree of spatial clustering of child trafficking. Figure 2 exhibits the locations of High-High (HH), Low-Low (LL), Low-High (LH), and High-Low (HL), with color-coded values for 5 each grid. The non-significant zones in the LISA results occupy the majority of the study area, suggesting that child trafficking typically occurs in a few key regions. Considering the characteristics of the data, we divided the period from 1949 to 2022 into three phases (Supplementary Fig. 1).

Fig. 2: Cluster map of Local Moran’s I result for child trafficking.
Fig. 2: Cluster map of Local Moran’s I result for child trafficking.The alternative text for this image may have been generated using AI.
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Dark-red grids represent a significant high-value grid surrounded by a high-value grid (HH), light-red grids represent a high-value surrounded by a low-value (HL), dark-blue grids represent a significant low-value grid crash surrounded by another low-value grid (LL), and light-blue grids represent low-value grid surrounded by high-value grid (LH), light-green grids represent not significant.

From 1949 to 1980 (Fig. 2a), a scattered pearl-like pattern can be identified for child trafficking in the ‘High-High’ zones, which were mainly distributed in the northeastern of Sichuan Province and the southwestern of Chongqing Municipality, covering over 20 counties. The number of trafficking cases in the ‘Low-High’ Zones were scattered across northeast Sichuan Province, involving 8 counties. The number of cases in both the “High-Low” zones and “Low-Low” zones was relatively small. From 1986 to 2000 (Fig. 2b), a concentrated cluster pattern can be observed for child trafficking in the “High-High” zones, which were roughly distributed in the border area of Yunnan, Guizhou, Sichuan and Chongqing, covering more than 20 counties. The number of trafficking cases in the “Low-High” zones was distributed in areas bordering Guizhou Province and the Guangxi Zhuang Autonomous Region, involving over 10 counties. As shown in Fig. 2c, the trafficking pattern from 2001 to 2022 was similar to that of the previous period.

On the whole, the spatial clustering of child trafficking shifted from the adjacent regions of Sichuan and Chongqing to the border regions of Yunnan, Guizhou, Sichuan, and Chongqing between 1949 and 2022 (Fig. 2d), indicating that child trafficking was spatially unevenly distributed.

Influencing factors of child trafficking

As shown in Tables 1 and 2, the R-squared values for the two periods are 0.759 and 0.615 respectively, indicating that these factors have strong explanatory power for Y. All VIF values in the model are less than 5, suggesting no collinearity among the variables and thus confirming the model’s validity. The model results show that X4, X7, X9, X10, and X12 were the main factors between 1986 and 2000, X4, X5, X6, X7, X11, and X12 were the key factors between 2001 and 2022. These findings are further elaborated below.

Table 1 Stepwise regression model results between 1986 and 2000 (n = 61).
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Table 2 Stepwise regression model results between 2001 and 2022 (n = 79).
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In terms of economic factors, the high unemployment rates were direct drivers of child trafficking, threatening the livelihoods of local populations. Over the past four decades, Chinese society has undergone unprecedented transformations, accompanied by a massive rural-to-urban migration wave for employment. These migrants lacked official urban residency status and thus did not enjoy the same welfare benefits as local residents, including fair wages, housing support, healthcare access, and educational opportunities (Du et al., 2018). These issues have widened resource distribution gaps and reduced returns to legal work (Muryani and Esquivias, 2021), thereby further increasing unemployment. Compounding these challenges was the prevalence of low educational attainment. Specifically, illiteracy rates in Yunnan and Guizhou provinces fluctuated between 14% and 27% during this period, while those in Sichuan and Chongqing ranged from 7% to 20%. The poorly educated faced severe shortages of formal employment opportunities. To improve their living conditions, unemployed individuals were often lured by illicit jobs promising high profits and perceived low risks (Jiang and Sánchez-Barricarte, 2013). In such contexts, child trafficking emerged as an illicit means for some to generate income and alleviate economic hardship.

In the process of child trafficking, supply and demand locations tend to be geographically separated. Transportation affecting child trafficking depends on space-related characteristics. Studies have proved that denser transit stations may attract more crimes (Tay et al., 2013), as offenders normally seek to escape crime scenes immediately after committing a crime. Densely populated transport stations could further facilitate criminals in covering their tracks, reducing their risk of arrest and thus increasing the likelihood that potential offenders will commit crimes (Wang and Li, 2022). In the 1990s, transportation hubs in Southwest China were mainly located in provincial capitals. The lack of surveillance cameras at high-traffic transport stations, such as train stations, bus stations, airports, ports, and highway tollbooths, created more opportunities for child trafficking. Traffickers used many different modalities to transport children from origin to destination, including air, marine, and land transportation. Traffickers used various modes to transport children from the origin to the destination, including air, marine, and land transportation. Children were moved to areas where they were socially, culturally, and linguistically isolated from their home environment, making it easier for offenders to maintain control over their victims (Yagci Sokat et al., 2024).

As for the demographic factors, there was a significant nexus between the child dependency ratio and child trafficking. The child dependency ratio in the Southwest reached 40%-50% between 1980 and 1990, indicating that the majority of families were multi-child families and faced a heavy dependency burden. In rural areas, guardians were usually busy with farm work, so younger children were cared for by older siblings at home. Some guardians left their villages and migrated to cities for work. They usually left their children in the care of elderly parents or grandparents. Because rural infrastructure was poor, they could not install surveillance cameras in every activity area. Moreover, guardians couldn’t keep an eye on their children all the time. This kind of free-range and intergenerational care neglected children’s safety to a certain extent, thus providing more exploitable opportunities for child trafficking. In addition, some parents or guardians may also have acted as perpetrators (Xu et al., 2022a), and children were sold or transferred as ‘fertility property’ for illegal profit (Wang, 2014).

Furthermore, population diversity proxied by the minority share was associated with higher rates of child trafficking. Southwest China is a region inhabited by ethnic minorities with diverse customs, cultures, and festivals, such as social and recreational festivals, farming-related festivals, annual festivals, and sacrificial festivals. Studies have discovered that 46% of children in Yunnan Province were trafficked during folk festivals around the 1990s (Yang, 2020), and 69.02% of children in Guizhou Province were trafficked during traditional festivals (Xue, 2021). Specifically, social and recreational festivals are very grand with participants ranging from 200 to 10,000 people, such as Flower-jumping Festival, Local Drama Festival and Lusheng Festival, etc. Children usually followed their families into bustling fairs to celebrate, while traffickers could also approach and enter these open fairs unhindered. Farming-related festivals, such as the Eat New Festival, Yi Nationality Tea-Picking Festival, and Hmong Assembly, were mostly attended by adults. Parents usually left their children at home to play alone, making this an ideal ‘hunting ground’ for traffickers. Annual festivals, like the Yi Nationality New Year, generally coincided with the National Day holiday. Children spent more time away from home playing, and guardians could easily neglect to supervise their children due to festival preparations. For sacrificial festivals such as the Dong Nationality New Year, men, women, and children all participated in singing to honor their ancestors, which provided opportunities for acquaintances to commit crimes.

In addition to the above-mentioned factors, policy in a specific period also played an important role in child trafficking. Statistics showed that birth rates in Yunnan and Guizhou provinces remained above 20% in the 1990s, while those in Sichuan and Chongqing were above 16% and 12% respectively. Due to the lag in implementing the family planning policy, birth rates in the southwest were generally higher than those in eastern provinces during the same period. These excess births children provided a ‘source’ for the ‘buyer’s market’ in the eastern provinces, which stimulated child trafficking to reach its peak. Subsequently, the Chinese government issued a series of laws to launch large-scale anti-trafficking campaigns nationwide. In 2008, the General Office of the State Council issued China’s Action Plan against Trafficking in Women and Children. In 2013 and 2021, the Chinese government promulgated the Action Plan against Human Trafficking twice, aiming to prevent and combat child trafficking using modern technology. The 2015 amendment to China’s Criminal Law stipulates that buyers shall be held criminally responsible, which has become a milestone in China’s anti-trafficking work. In 2019, the Ministry of Public Security launched the Reunion campaign to rescue trafficked children nationwide.

Discussion

This exploratory paper highlights the necessity of quantitative study, aiming to advance understanding of the spatial pattern of child trafficking. In addition, our paper responded to more compelling explanations of the factors affecting child trafficking. We focused on variable selection across different dimensions, including economic, social, demographic, and policy-related factors. Over the years, scholars have been striving to find better methods to reveal the clustered locations of child trafficking (Xin and Cai, 2018; Huang and Weng, 2019; Li et al., 2022). Previous studies have focused on hot spot identification and spatial pattern analysis (Xue et al., 2020; Zhou et al., 2023b), neglecting the spatial autocorrelation of child trafficking and missing valuable insights into influencing factors (Shen et al., 2013; Zhou et al., 2023a). In this study, Local Moran’s I statistics were utilized to investigate the spatial autocorrelation of child trafficking. We identified specific locations of clusters with similar high or low values using LISA maps to gain better insights into the spatial patterns of child trafficking. As expected, all the Moran’s I indices were statistically significant. Before 1985, child trafficking in the ‘High-High’ zones was mainly distributed in the areas adjacent to Sichuan and Chongqing. After that, these clusters shifted to the junction areas of Yunnan, Guizhou, Sichuan, and Chongqing. The number of child trafficking cases were more spatially clustered than would be expected by chance alone. This result well answers the first question raised in this study.

With respect to the second question, the widening income gap and high unemployment were the direct factors driving child trafficking. This issue was exacerbated by low education levels. Underlying child trafficking were some harsh realities, the unemployed were more likely to become potential offenders and increased their motivation for trafficking. This result differs from the conclusion in previous literature that unemployment may increase guardianship (D’Alessio et al., 2012), thereby reducing the risk of child trafficking. Regarding the impact of transportation, it was evident that it played a critical role in combating child trafficking. Such effects on crime have also been observed in studies of international child trafficking (Yagci Sokat, 2022). Some families with more children faced a higher risk of trafficking due to weaker guardianship. Another finding is that the impact of population diversity on child trafficking in the Chinese context differs from the link between high racial heterogeneity and increased crime rates observed in Western criminological research. Multi-ethnic communities have nurtured diverse customs and festivals. High population mobility during festivals provided good visual cover and generated more crime generators/attractors, while the absence of guardians offered more targets for crime, creating opportunities for traffickers. These findings align with the pattern theory of crime but contradict the ‘eyes on the street’ theory, which posits that areas with high population density are conducive to increased mutual surveillance and protection, potentially inhibiting crime (Cahill and Mulligan, 2007). Additionally, unlike in other regions, child trafficking in China was influenced by the one-child policy. Although the policy has now been abolished, its lingering effects deserve serious consideration. A series of laws and policies that have been constantly revised and promulgated are playing an increasingly significant role in combating child trafficking. These new findings provide robust evidence that the factors influencing child trafficking in China differ significantly from those in international contexts. Unfortunately, these factors were not accounted for in previous research.

The result of spatial analysis substantiated the idea that child trafficking clustered in provinces border areas. Thus, geographically focused prevention efforts may be warranted. The results of the regression analysis suggested that we should engage a broader range of partners to take more targeted action. At the macro level of society, the State should encourage farmers to develop characteristic industries aligned with the rural revitalization strategy, thereby promoting rural economic development. Such measures will help narrow the wealth gap between urban and rural and to some extent reduce the incidence of child trafficking in rural areas. In practice, the government should improve the social security system and provide more job training and guidance for unemployed individuals to secure livelihoods, which will help reduce the risk of child trafficking by the unemployed. Local communities can strengthen cultural and legal publicity through radio, posters, and other channels, which is an indispensable part of mobilizing people from all walks of life to participate in anti-trafficking efforts. Furthermore, authorities should revise relevant laws and policies, while public security departments should strengthen joint law enforcement between neighboring cities and counties to build a cross-regional anti-trafficking network. The ‘Internet+anti-trafficking’ model could be a new initiative to integrate online and offline anti-trafficking efforts. For example, modern technologies like big data and artificial intelligence should be used to crackdown on child trafficking. Surveillance cameras should be installed at high-traffic stations. Additionally, geographic information technology and facial recognition technology should be employed to enhance the tracking of traffickers, thereby improving the detection rate of trafficking cases.

The above analysis also suggested that improving economic conditions and police actions is not a universally effective approach to curbing trafficking. Instead, it is necessary to take into account the circumstances of individuals and families, such as children and guardians. At the micro level, anti-trafficking knowledge needs to be integrated into school classrooms through animated demonstrations or interactive games. This is conducive to cultivating children’s awareness of anti-trafficking and anti-fraud from an early age and enhancing their self-protection skills. In addition, guardians’ responsibilities must be enforced, especially during busy festivals or in high-traffic areas to keep children as far as possible within the guardian’s protective line of sight. In some remote areas, we should enhance supervision of left-behind children and migrant children, and effectively ensure the safe growth of children at the family level.

We have demonstrated that there are clear benefits to conducting quantitative studies of child trafficking, but our study has some data- and variable-related limitations. Our study collected data from a public welfare website for missing persons in China. Firstly, the data relied on self-reports and memories from parents or relatives, which may have been affected by recall bias. Secondly, the sample only included families who were actively searching for their children or knew their children had been trafficked, leading to inevitably limited representativeness. To address this issue in future research, larger samples and more complete data should be collected. Finally, the selection of proxy indicators affecting child trafficking in study without accounting for those limitations may produce unreliable and misleading results, thus undermining anti-trafficking strategy, policy and practice. In subsequent research, more direct variables should be considered to explore more deeply the potential correlations between child trafficking and other factors, in order to obtain less biased estimates.