Investigating the Prevalence of Child Labour and Child Trafficking in Gombe Local Government Area, Gombe State

Investigating the Prevalence of Child Labour and Child Trafficking in Gombe Local Government Area, Gombe State

Mahmud Muhammad¹, Sule Magaji², Yahaya Ismail³

¹Sustainable Development Centre, University of Abuja

^2,3Department of Economics, University of Abuja

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000208

Received: 28 July 2025; Accepted: 04 August 2025; Published: 04 September 2025

ABSTRACT

This study examines the prevalence of child labour and child trafficking in the Gombe Local Government Area (LGA) of Gombe State, Nigeria, employing a survey research design. Utilising a structured questionnaire, data were collected from 379 residents out of a target population of 551,000 in Gombe LGA, yielding a high response rate of 69%. The methodology incorporated both quantitative and qualitative approaches, with data analysed using frequency counts, simple percentages, and correlation analysis. A binary logistic model, derived from established frameworks, was specified to assess the influence of variables like household income, poverty, household size, and unemployment on child labour and trafficking. The findings indicate a substantial prevalence of child labour, with 74.67% of respondents reporting children engaged in work, and 56.20% having experienced child trafficking within their households. A majority of respondents (41.42%) believe both male and female children are affected. Poverty was identified as the leading cause by 38.79% of respondents, followed by unemployment (21.90%), and a combination of factors (25.33%). The demographic profile of respondents showed a slight majority of females (53.56%) and a significant concentration of middle-aged adults (54.35% aged 31-40). Household incomes were predominantly low, with 43.27% earning less than ₦5,000 and 47% earning between ₦5,000 and ₦50,000 per month. The study highlights the urgent need for interventions addressing socio-economic challenges to mitigate child exploitation in the region.

Keywords: Child Labour, Child Trafficking, Poverty, Gombe Local Government Area, Household Income

INTRODUCTION

Child labour and child trafficking are grave violations of children’s rights, particularly in developing countries where poverty and socio-economic challenges are prevalent (Enaberue, Musa & Magaji, 2024). These practices compromise the well-being, education, and development of children and are widely recognised as significant barriers to achieving the Sustainable Development Goals. Globally, it is estimated that 160 million children are engaged in child labour, with a substantial number subjected to forced labour and trafficking (International Labour Organisation [ILO], 2021). In Nigeria, these issues remain pressing, particularly in states with high poverty indices and limited access to social protection (Magaji, 2007).

The phenomenon of child labour in Gombe Local Government Area (LGA) has been linked to multifaceted factors, including economic hardship, large family sizes, cultural practices, and weak enforcement of child protection laws (Okafor & Amayo, 2020). Children are often found working in markets, motor parks, farms, and households, sometimes under hazardous and exploitative conditions. This situation deprives them of access to quality education and exposes them to physical, emotional, and psychological harm (Magaji, 2008). Furthermore, child trafficking – involving the recruitment, transportation, and exploitation of minors – has increasingly been reported in the region, often disguised under the guise of domestic help or apprenticeship (UNICEF, 2022).

Studies have shown that the prevalence of child trafficking in Nigeria is exacerbated by poor parental education (Gabdo, Magaji & Yakubu, 2025), unemployment, and the lure of financial gain (Ebigbo, 2003; Okojie, 2009). In Gombe LGA, these factors are compounded by insecurity and displacement caused by insurgency in the North-East, which heightens children’s vulnerability to exploitation. Many trafficked children end up in urban centres or other states (Lamiya, Magaji & Yakubu, 2025), are subjected to domestic servitude, street hawking, sexual exploitation, and other forms of abuse (National Agency for the Prohibition of Trafficking in Persons [NAPTIP], 2020). Despite national and international legal frameworks aimed at combating these practices, enforcement and grassroots awareness remain limited.

The need to investigate the prevalence of child labour and child trafficking in Gombe LGA is therefore both urgent and significant. By examining the extent and underlying causes of these social issues, this study aims to provide empirical evidence that informs policy formulation, intervention strategies, and community awareness. Such an investigation also aligns with Nigeria’s commitment to the United Nations Convention on the Rights of the Child (CRC) and the African Charter on the Rights and Welfare of the Child, both of which emphasise the protection of children from all forms of exploitation and abuse (United Nations, 1989).

Ultimately, this study contributes to the broader discourse on child welfare in Nigeria by focusing on a specific local context often underrepresented in academic literature. Understanding the dynamics of child labour and trafficking in Gombe LGA will not only help in assessing the effectiveness of current interventions but also highlight areas requiring urgent attention. It is hoped that the findings will assist stakeholders, governmental and non-governmental, in developing tailored approaches to protect vulnerable children in Gombe and beyond.

Conceptual Review

Child Labour

Child labour refers to the involvement of children in work that is mentally, physically, socially, or morally dangerous and harmful, and interferes with their schooling by depriving them of the opportunity to attend school, obliging them to leave school prematurely, or requiring them to attempt to combine school attendance with excessively long and heavy work (International Labour Organization [ILO], 2021). In Nigeria, child labour remains a significant concern, especially in sectors such as agriculture, domestic service, and street hawking, where children are often exploited under hazardous conditions (Okafor & Amayo, 2020).

Child Trafficking

Child trafficking is the recruitment, transportation, transfer, harbouring, or receipt of children for exploitation, often through force, deception, or coercion (United Nations, 2000). Unlike adult trafficking, child trafficking does not require evidence of force or coercion for it to be considered a crime (Jafaru, Magaji & Abdullahi, 2024). In Nigeria, trafficked children are commonly exploited for forced labour, domestic servitude, street begging, and sexual exploitation (Yunusa, 2024). This practice violates numerous international and national child rights laws and continues to threaten the safety and development of many Nigerian children (UNICEF, 2022).

Theoretical Framework: Human Capital Theory

Human Capital Theory, advanced by Gary Becker (1964), views education and health as investments that enhance individuals’ productivity and contribute to societal development. From this perspective, the engagement of children in labour or their trafficking undermines human capital development by depriving them of education, skill acquisition, and overall well-being. Children who are trafficked or subjected to hazardous labour often miss out on schooling, suffer long-term health consequences, and are less likely to break the cycle of poverty.

This theory is relevant to understanding how the exploitation of children in Gombe LGA affects not only their individual development but also the broader socio-economic progress of the region. The loss of educational opportunities due to child labour results in a poorly skilled workforce and limits future economic growth. Thus, Human Capital Theory provides a rationale for investing in policies that enhance educational access, social protection, and health services for vulnerable children

Theoretical Review

Household Economic Theory

Household Economic Theory. This theory posits that families, especially in low-income settings, make rational decisions to involve their children in labour as a means of coping with poverty (Musa, Ismail & Magaji, 2024) and economic hardship (Basu & Van, 1998). According to this theory, when households face financial constraints, the opportunity cost of sending children to school increases, leading to a preference for engaging them in income-generating activities, even at the cost of their education and well-being (Magaji & Musa, 2015). Where poverty and unemployment are prevalent, the theory explains why many parents may permit crime as a means of survival (Magaji, 2025). The theory is critical to understanding the socio-economic drivers behind the prevalence of child exploitation in the region.

Empirical Review

Musa, Magaji, and Tsauni (2022) explored the socioeconomic determinants influencing child labour across Northeastern Nigeria. They employed a multistage sampling technique to collect data from selected local government areas within Adamawa, Bauchi, and Yobe States. The study relied on structured questionnaires for data gathering, and the results were analysed using the Tobit regression model. Their findings revealed that several variables significantly impact child labour, including the child’s age and gender, the child’s relationship to the household head, the educational background and occupation of the household head, and poverty indicators. Poverty was assessed through factors such as household income, family size, access to potable piped water, and proximity to educational facilities. Some of these factors showed varying levels of statistical significance, indicating a complex interaction of influences on child labour.

Francis and Jellason (2022) focused on the root causes of child labour in Nigeria, with particular emphasis on the role of economic hardship. Their research applied a doctrinal methodology and found that key drivers of child labour include family size, economic condition, poverty, and joblessness. The study emphasised that these factors reinforce a persistent poverty cycle, which exposes children to a range of harmful consequences such as accidents, illness, disabilities, drug abuse, and sexual exploitation. The authors recommended that strong legislative frameworks be implemented to criminalise child labour, complemented by comprehensive rehabilitation programs and strict oversight by teachers to monitor school attendance and student welfare.

Ezeh and Oli (2021) conducted a study in Awka South Local Government Area of Anambra State to investigate the socio-economic drivers of children’s vulnerability to trafficking. Their research utilised both qualitative and quantitative research designs, incorporating a multistage sampling technique. A sample of 384 participants was determined using Cochran’s sample size formula, and the data collected were analysed using descriptive statistics. The study concluded that poverty and greed are the predominant factors increasing children’s risk of being trafficked in the area.

Oli and Nweke (2021) examined the dynamics of child labour in the Awka South Local Government Area. Their research design included both qualitative and quantitative methods, supported by a multistage sampling approach involving 200 adult respondents. Data collection tools included structured questionnaires and in-depth interviews. The study identified factors such as low household earnings, poverty, limited parental education, large family sizes, entrenched cultural norms, and substandard living conditions as the main drivers of child labour. Forms of child labour noted included hawking, street begging, domestic chores, farm work, and factory employment (Oli & Nweke, 2021). Another publication by the same authors confirmed similar findings, reinforcing that these variables commonly lead to exploitative child labour practices in the Eastern region of Nigeria.

Mackintosh and Wori (2021) investigate the impact of Parental socioeconomic status on Child Labour in Port Harcourt Metropolis. The study employed a 14-item questionnaire called “Parental Socioeconomic Status on Child Labour” (PSSCL) as the primary data collection tool. The study included a total sample size of 126 respondents, consisting of 45 parents and 81 children. The study employed a descriptive survey research design to collect dependable data. The data obtained from primary and secondary sources were analysed using statistical measures such as mean and standard deviation. The study’s results indicated a substantial correlation between parents’ socioeconomic position and the occurrence of child labour.

Abdu, Rabiu, and Usman (2020) investigate the impact of child employment on the educational outcomes of children in Northern Nigeria. Data analysis involved the utilisation of both descriptive and inferential statistics. The findings reveal a significant correlation between the level of engagement in child employment, its underlying contributing factors, and the perceived impact on education. Additionally, it demonstrates a notable correlation between family income and a mother’s work in terms of impact.

Gap in the Literature                                     

Despite the growing body of literature on child labour and child trafficking in various parts of Nigeria, a critical gap remains in location-specific, empirical investigations within the Gombe Local Government Area of Gombe State. While studies such as those by Musa, Magaji, and Tsauni (2022) and Francis and Jellason (2022) have provided broad insights into the socioeconomic drivers of child labour across Northeastern Nigeria, including states like Adamawa, Bauchi, and Yobe, they exclude Gombe State, thereby limiting the contextual understanding of the phenomenon in that locality. Similarly, research by Ezeh and Oli (2021) and Oli and Nweke (2021), which focused on southeastern states, is valuable; however, their findings may not fully capture the cultural, economic, and security dynamics unique to Gombe. Mackintosh and Wori (2021) focused on urban areas in Port Harcourt, while Abdu, Rabiu, and Usman (2020) examined educational outcomes in Northern Nigeria more broadly, without explicitly addressing the prevalence patterns and distinct challenges faced by children in Gombe LGA. Consequently, there is a clear research gap concerning the specific prevalence, manifestations, and community-based drivers of child labour and trafficking within the Gombe Local Government Area. This gap underscores the need for localised studies to inform targeted interventions and policymaking.

METHODOLOGY

Research Design

This study adopted a survey research design, aligning with established research practices that involve collecting information from a defined group through the use of questions (Check & Schutt, 2012). The survey approach offered a flexible structure for participant recruitment, data collection, and measurement, utilising various instruments. Quantitative approaches typically utilise structured questionnaires scored numerically for statistical analysis, whereas qualitative approaches rely on open-ended items to gather detailed, narrative responses. Additionally, a mixed-methods approach can be employed to combine both quantitative and qualitative data, providing a more nuanced understanding. Surveys are widely used in the social sciences due to their effectiveness in exploring and describing human behaviour (Singleton & Straits, 2009).

The choice of a survey method was appropriate for this study because it focused on social inquiries involving managerial elements. The structured nature of the design supported the investigation by ensuring systematic data collection and analysis. It enabled the quantification and interpretation of key constructs related to management, allowing for the generalisation of findings across similar settings. This methodological choice enabled the study to accurately assess complex managerial dynamics and draw evidence-based conclusions from the data collected.

One of the most significant benefits of using a survey strategy is its ability to reach a large, geographically dispersed population, making it ideal for analysing social issues that vary across regions and demographics (Ndiyo, 2016). The use of both digital and traditional survey administration techniques further improved accessibility and response rates. This wide-ranging reach strengthened the credibility and depth of the research findings. Overall, the survey design played a crucial role in achieving the study’s objectives and capturing comprehensive data on the managerial factors being examined.

The Study Area

Gombe State is located in Nigeria’s North-East geopolitical zone and shares its borders with Bauchi State to the west, Taraba State to the south, Borno State to the east, and Yobe State to the north. Established in 1996 following its separation from Bauchi State, Gombe is subdivided into eleven local government areas, with its administrative and political centre located in the capital city of Gombe. This central location enhances the state’s role as a key player in the cultural and economic exchanges across the surrounding states, making it strategically important in the northeastern region of Nigeria.

Within Gombe State, the Gombe Local Government Area (LGA), which encompasses the state capital, plays a pivotal role in driving the region’s economic activities. Situated in the Gombe Central Senatorial District, this urban area covers approximately 20,265 square kilometres. The state’s population has grown substantially, with projections indicating a total of about 3.96 million people as of 2022. Of this number, approximately 551,000 individuals are estimated to reside in the Gombe LGA, underscoring its urban appeal and economic significance. This growth highlights both the opportunities and the governance challenges posed by increasing urbanisation, as well as the demands it places on infrastructure and resources.

Population of the Study

The target population for this research consists of residents within the Gombe Local Government Area, where an estimated 551,000 people live (Wikipedia, 2022). This population is diverse, encompassing people from different ethnic, socio-economic, and age groups. As an urban centre, Gombe LGA serves as a significant hub for education, commerce, and social life, attracting individuals from nearby communities seeking employment, services, and improved living conditions. Studying this demographic provides a comprehensive understanding of local socio-economic behaviours and community interactions, which are central to the research objectives. Moreover, examining this specific population helps identify regional patterns, social issues, and development needs that affect residents’ daily experiences, thereby offering crucial insights that can guide policy interventions and social development programs.

Sample Size and Sampling Technique

This study will mostly utilise random sampling to examine the variables. The study’s sample size will be determined using the Taro Yamani Formula. The sample size for this study was established using the Yamani (1968) formula for estimating sample size, as follows:

n =   N   

     1+n(e)²

Where: n= sample size

N= 551,000 (population size)

e= 0.05(sample error level of significance)

1 = constant

n =      551,000                           n = 400

     1+551,000 (0.05)²

The sample size is 400 respondents

Instrument of Data Collection

The primary method of data gathering for this study will involve administering structured questionnaires to the respondents. This approach is chosen for its efficiency and ability to reach a broad audience within the Gombe Local Government Area. The questionnaire will be carefully divided into two distinct sections to facilitate a comprehensive analysis of the collected data. The first section is designed to capture essential socio-economic characteristics of the respondents, including demographic variables such as age, gender, educational background, and employment status. By gathering this information, the study aims to create a contextual understanding of the population, which is crucial for interpreting the subsequent findings and ensuring that the results reflect the diverse perspectives present in the community.

The second section of the questionnaire is focused on eliciting the participants’ perspectives regarding the influence of household income on two critical issues: child labour and child trafficking within the Gombe Local Government Area. This segment aims to explore the complex relationship between economic factors and the prevalence of these social issues, shedding light on how variations in household income may impact the likelihood of child exploitation. Respondents will be asked to provide their views on the extent to which financial constraints lead families to resort to child labour or become vulnerable to trafficking networks. By analysing these perspectives, the study seeks to identify patterns and correlations that can inform policymakers, social workers, and community leaders about the underlying causes of child labour and trafficking in the region, ultimately contributing to the development of effective interventions and support systems for vulnerable children and their families.

Nature and Source of Data

To effectively address the research objectives, both primary and secondary data sources were utilised, ensuring a comprehensive understanding of the issues at hand. The primary data sources consisted of field surveys conducted directly within the Gombe Local Government Area, allowing researchers to gather first-hand information from the respondents. These surveys facilitated the collection of relevant data on socio-economic characteristics, perceptions regarding household income, and its implications for child labour and trafficking. By engaging directly with the community, the study aimed to capture the lived experiences and insights of individuals who are directly affected by these pressing social issues. This approach not only enhances the validity of the findings but also fosters a deeper connection between researchers and the community, allowing for a nuanced exploration of the factors at play.

In addition to the primary data gathered from field surveys, secondary data was sourced from a variety of academic and professional publications, including books, journals, seminar papers, and relevant written reports. This secondary data served as a crucial backdrop for understanding existing literature and theoretical frameworks surrounding child labour and trafficking, as well as the socio-economic dynamics within the region. By integrating secondary sources, the research was able to contextualise its findings within broader academic discussions, compare new insights with established knowledge, and identify gaps that the current study aims to fill. Furthermore, these secondary data sources provided a historical perspective on the trends and patterns related to child exploitation, thereby enriching the analysis and enhancing the overall robustness of the research. This combined methodology, which utilises both primary and secondary data, ultimately strengthens the credibility of the study and its potential contributions to policy and practice in the region.

Method of Data Collection

In addition to the secondary sources previously mentioned, two primary data collection approaches were employed to gather information directly from the field, enhancing the depth and relevance of the research findings. The primary methods utilised were questionnaires and personal interviews, which together provide a rich data set for analysis. The questionnaires were designed to include a combination of closed-ended and open-ended questions, ensuring that respondents could provide specific, quantifiable data as well as express their thoughts and opinions in their own words. This structured approach enables the efficient collection of information while allowing participants to elaborate on their experiences and perspectives related to the study’s objectives. Personal interviews were also conducted to complement the questionnaire data, allowing for a more in-depth exploration of individual experiences and insights. These oral interviews provided a platform for subjects to share their stories and opinions conversationally, facilitating a deeper understanding of the nuances surrounding child labour and trafficking in the Gombe Local Government Area.

Furthermore, secondary sources of data played a pivotal role in supporting the primary research efforts. These sources refer to pre-existing documented information, such as books, academic journals, seminar proceedings, and various written reports that are relevant to the study’s focus. By reviewing these materials, researchers were able to contextualise their findings within existing literature, drawing connections between past studies and current observations. This comprehensive review of secondary data not only helped establish a theoretical foundation for the research but also identified knowledge gaps that the primary data collection aimed to address. Ultimately, the combination of primary and secondary data sources enriched the research process, ensuring a well-rounded investigation into the socio-economic factors influencing child labour and trafficking in Gombe State while providing insights that could inform effective policy and intervention strategies.

Method of Data Analysis

The data collected from the questionnaires was analysed using a range of statistical methods to ensure a thorough understanding of the information gathered. Frequency counts were employed to determine how often specific responses were given, providing a clear picture of the most common views and characteristics within the study population. This method helped to quantify the data, making it easier to identify patterns and trends. Additionally, simple percentage calculations were utilised to express the results in a more digestible format, allowing for straightforward comparisons across different demographic groups and variables. Correlation analysis was also conducted to examine the relationships between key variables, such as household income and the prevalence of child labour and trafficking. This statistical technique enabled researchers to identify significant associations and potential causal links, shedding light on how varying income levels may impact these critical social issues. By employing this multifaceted approach to data analysis, the study aimed to draw robust conclusions that could inform policymakers and stakeholders about the dynamics at play in the Gombe Local Government Area.

Model Specification

The analytical model utilised in this investigation was derived from the works of Basu and Van (1998), Fan (2011), and Zapata et al. (2011). The logit model is defined in its implicit form as:

Zi = βo +β1xik + ui ………………………………………… (1)

Where:

Zi = Financial Inclusion (dummy, 1= = Child Labour and trafficking and 0, otherwise).

Βo = constant

Β1 = coefficient

Xik = set of explanatory variables (i=1,2,..k)

Ui = random error disturbance term.

The explicit form of the model is specified as:

Zi=InPi ………………………………….. (2)

1 – Pi = βo+β1×1 +β2×2 +β3×3 +ui………………….…. (3)

Where: Zi = Child Labour (dummy, 1= Child Labour and 0, otherwise).

βo = constant term, βn = parameters to be estimated.

X1 household income (1=household income, 0, otherwise). X2=poverty (1=poverty, 0, otherwise). X3 = Unemployment

(1 = Unemployment, 0, otherwise).

Ui = random disturbance term.

The model is specified as follows;

Child Labour and Trafficking = f (Hs, E, S)

Where;

Hs= Household size

E= Employment

S= Skill

Child Labour and Trafficking = f (E)

Where; E= Employment

The binary logistic model was chosen due to its capacity for coherent interpretation and ability to accommodate numerous variables, in addition to its mathematical computational simplicity. The research aimed to identify crucial variables that influence a decision with a binary outcome, while also highlighting the model’s high level of flexibility and user-friendliness. Due to the binary nature of the dependent variable, the ordinary least squares (OLS) technique is more suitable for estimating the model.

Estimation and Evaluation Techniques and Procedures.

Omnibus Test: This diagnostic test determines whether to accept or reject the entire model.

We are analysing the P-value. The model fits the data well if the P-value is less than 0.05. Here, the null hypothesis is that the model does not provide a satisfactory fit. We shall then determine whether the entire model is accepted or rejected.

The goodness of fit refers to the accuracy of the predicted value. This statistic will demonstrate how the independent factors can explain variations in the dependent variables, indicating whether all the independent variables are significant in explaining these changes.

Odds Ratio: This represents the probability of an event happening. It denotes the consistent impact of predictor X on the probability of a particular outcome.

The term “relative risk” is also used to refer to a logit model. The measure quantifies the likelihood of y being equal to 1 compared to the likelihood of y being equal to 0. A ratio of 2 indicates that the probability of the outcome y=1 is twice as high as the probability of the outcome y=0. An odds ratio of 1 indicates equal likelihood, while a value greater than 1 suggests that y=1 is more probable, and a value less than 1 indicates that y=0 is more probable.

The Nagelkerke R-squared is a revised version of the Cox and Snell R-squared. It is considered the most appropriate Pseudo R-squared measure because it has a minimum value of zero (0) and a maximum value of one (1). This statement elucidates the extent to which the predictors in a model contribute to the variability observed in the dependent variable.

Predictive chance: Once the models have been estimated, we calculate the chance of y=1 for each observation based on their functional form. This guarantees accurate forecasting of the model.

P= pr [y=1/x] =F(x’β)

The predicted probability is limited between 0 and 1.

The predicted probability indicates the likelihood of y being 1.

Hypothesis Test: H0: β0 = 0 (the parameter estimate is statistically significant)

H1: β0 ≠ 0 (the parameter estimate is not statistically significant)

Decision Rule: For p values >0.05, reject H0

The determination of whether to accept or reject any hypothesis mentioned above will depend on the probability of the parameter’s predictive ability and the statistical significance of each parameter. When using the two-tailed test, we reject the null hypothesis if the p-value is less than 0.05; otherwise, we fail to reject the null hypothesis. If the p-value is less than 0.05, we reject the null hypothesis if the computed value is smaller than the tabular value in the standard distribution table; otherwise, we accept the null hypothesis. If the anticipated probability suggests the probability of y=1, and if the predicted probability is below 0.05, we can confidently predict that y=1; otherwise, we predict y=0.

Data Presentation

A total of 400 questionnaires were distributed to adult household members in the Gombe Local Government Area to assess the impact of household income on child labour and trafficking. The data collection process was highly effective, yielding 379 completed questionnaires, thanks to the efforts of data collectors who provided clarification and conducted interviews to support respondents. This high response rate and interactive approach ensured the accuracy and completeness of the data. The thorough engagement fostered trust and honest participation, resulting in a reliable dataset. The analysed responses offered significant insights into how household income influences child labour and trafficking in the area, providing valuable input for policy and community-based interventions.

Response rate

The results of the questionnaire conducted for the study are presented in Table 4.1. A total of 400 questionnaires were sent for completion and return. Of these, 379 questionnaires were completed and returned, resulting in a response rate of 95% across the four locations included in the study. All 379 questionnaires received were selected for analysis based on the determined sample size. Brooks (2008) states that a response rate of 60% or more is deemed satisfactory for academic research. Based on this criterion, a 95% response rate can be judged sufficient for this current academic study.

Descriptive Analysis

Table 4.2: Distribution of responses based on age of Respondents

Source: Field Survey, 2025

Table 4.1 above presents a detailed distribution of respondents categorised by age groups, highlighting the demographic characteristics of the study population. Among the participants, there are 41 respondents (10.82%) aged between 18 and 30, indicating a relatively small representation of younger adults in the sample. In contrast, a substantial 206 respondents (54.35%) fall within the 31 to 40 age bracket, which represents the largest segment of the population surveyed. This finding suggests that middle-aged adults are more prevalent in the study, likely reflecting their greater involvement in household income generation and decision-making processes that could influence child labour and trafficking issues. Additionally, 103 respondents (27.18%) are aged between 41 and 50, further emphasising the importance of middle-aged perspectives in understanding the socio-economic factors at play. Finally, there are 29 respondents (7.65%) who are over 50, indicating a smaller proportion of older individuals. Overall, the distribution of respondents suggests that the study captures a significant middle-aged demographic, which is crucial for understanding the implications of household income on child-related issues in the Gombe Local Government Area.

Table 4.2: Distribution of Responses based on Gender

Source: Field Survey, 2025

According to the data presented in Table 4.2 above, a total of 203 respondents, or 53.56%, identified as female, while 176 respondents, or 46.44%, identified as male. This distribution indicates that a slight majority of the study participants are female, reflecting the gender dynamics within the Gombe Local Government Area. The predominance of female respondents may suggest that women are more engaged in discussions related to household income and child welfare issues, possibly due to their traditional roles as caregivers and decision-makers within families. This gender imbalance in the sample is significant as it highlights the potential for gender-specific insights into the socio-economic factors influencing child labour and trafficking. The higher representation of women may also provide a platform for exploring how household income directly impacts their roles and responsibilities, as well as their perceptions of child welfare in their communities. Understanding these gender dynamics is essential for interpreting the study’s findings and ensuring that the voices and experiences of women are adequately represented in the discourse surrounding these critical social issues.

Table 4.3: Distribution of responses based on family Income

Source: Field Survey, 2025

Table 4.3 above presents a comprehensive breakdown of the respondents’ family income distribution, revealing significant insights into their financial backgrounds. Among the total respondents, 164 individuals, constituting 43.27%, reported a family income of less than $5,000, highlighting a considerable portion of the population struggling with low income. In contrast, a substantial majority of 178 respondents, or 47%, reported family incomes ranging from 5,000 to 50,000, indicating that this income bracket is the most common among the participants. Moreover, 30 respondents, accounting for 7.92%, fell into the income category of 50,000 to 100,000, suggesting a smaller segment of the population enjoys a relatively higher financial status. Finally, only seven respondents, representing 1.81%, reported an income of 100,000 or more, underscoring the rarity of higher income levels within this group. Overall, the data indicate that the majority of respondents’ household incomes are concentrated within the 5,000 to 50,000 range, suggesting a potential need for economic support and opportunities for those in lower income brackets.

Table 4.4: Distribution of responses based on Child Help

Source: Field Survey, 2025

Table 4.4 above provides a detailed overview of the role of children in supporting their parents’ enterprises or farms. The data reveal that a substantial 282 participants, representing 74.41% of the total respondents, benefit from their children’s assistance in these activities. This finding highlights the critical role that young family members play in contributing to the family business or agricultural operations, suggesting a strong culture of familial collaboration and support within these households. In contrast, 97 participants, or 25.59%, reported that their children do not assist, indicating that while a significant majority relies on their children for help, a notable minority does not involve their offspring in their work. This disparity suggests varying family dynamics and economic strategies, where the engagement of children in parental enterprises may be influenced by factors such as the nature of the business, the age of the children, or differing values regarding work and education. Overall, the data illustrate the importance of children as contributors to family livelihoods, emphasising their role in sustaining and enhancing household economic activities.

Table 4.5: Distribution of responses based on household Size of Respondents

Source: Field Survey, 2025

According to the data presented in Table 4.5 above, the distribution of household sizes among the respondents reveals intriguing insights into family structures within the surveyed population. A total of 19 respondents, accounting for 5.01%, reported a relatively small household size of 2 to 3 members, indicating that a small portion of participants may prefer or be constrained to nuclear family arrangements. In contrast, 82 respondents, representing 21.64%, reported a household size ranging from 4 to 8, indicating a moderate family size that typically includes parents and a few children. A significant majority, 186 respondents, or 49.08%, identified themselves as having larger households comprising between 9 and 14 members, which suggests a tendency towards extended families or communal living arrangements common in specific cultural contexts. Lastly, 92 respondents, accounting for 24.27%, reported having households with 15 or more members, further underscoring the prevalence of large family units. These findings highlight that most participants belong to households in the larger size category of 9-14 members, indicating a community dynamic where families may pool resources and share responsibilities, reflecting a social structure that emphasises collective support and interdependence among family members. This information is crucial for understanding the socio-economic conditions and needs of the participants, particularly about resource allocation, childcare, and economic support systems.

Table 4.6: Distribution of respondents based on Child Labour

Source: Field Survey, 2025

Based on the data presented in Table 4.6 above, there is a notable prevalence of child labourers among the respondents, as evidenced by the fact that 283 individuals, accounting for 74.67%, reported having children engaged in work within their households. This significant proportion suggests that child labour is a common practice in the community surveyed, likely reflecting socio-economic conditions where families rely on the additional income generated by their children. In contrast, 96 respondents, or 25.33%, indicated that they do not have child labourers, highlighting a minority that either does not engage their children in work or perhaps is in a better financial position to afford this choice. The findings point to a broader societal issue surrounding child labour, raising questions about the implications for education, health, and overall well-being of children involved in labour activities. The data emphasises the need for a deeper understanding of the factors that contribute to child labour in this context, as well as the potential impacts on family dynamics and economic sustainability. Overall, the high percentage of respondents with child labourers underscores the importance of addressing the socio-economic challenges faced by families and exploring solutions that prioritise the welfare and education of children.

Table 4.7: Distribution of responses based on child trafficking

Source: Field Survey, 2025

Table 4.7 provides a detailed overview of the respondents’ experiences regarding child trafficking within their households. Among the 379 participants surveyed, 213 respondents, which constitutes 56.20%, reported that they had children who had been trafficked. In contrast, 166 respondents, or 43.80%, indicated that they did not have trafficked children living in their homes. This data reveals that a majority of the respondents have directly encountered the issue of child trafficking, suggesting a significant prevalence of this distressing phenomenon within the population studied. Conversely, the nearly equal proportion of those without trafficked children highlights that a notable segment of the community remains untouched by this issue, which can inform targeted outreach and support strategies. Overall, these findings reflect the urgent need for further awareness and intervention initiatives that address the complexities and impacts of child trafficking, especially considering that more than half of the respondents are grappling with its consequences in their family environments.

Table 4.8: Distribution of responses based on the Gender of the child/children affected by child labour and trafficking

Source: Field Survey, 2025

The data presented in Table 4.8 provides a detailed breakdown of the respondents’ perceptions regarding the gender of children affected by child labour and trafficking in Gombe Local Government. Among the total respondents, 72 individuals, accounting for 19%, identified the affected child as female. In contrast, only 36 respondents, or 9.50%, indicated that the affected child was male. Notably, a significant portion of the participants, comprising 114 respondents or 30.08%, reported that they do not have any children affected by these issues. Furthermore, a substantial number of respondents, 157 in total, representing 41.42%, recognised that both male and female children are impacted by child labour and trafficking. This distribution of responses highlights a critical awareness among the participants, with the majority acknowledging that both genders are affected by these social issues. Overall, the findings suggest that the respondents in Gombe Local Government demonstrate a significant recognition of the dual impact of child labour and trafficking on both boys and girls, underscoring the need for comprehensive strategies to address these pervasive challenges in the community.

Table 4.9: Distribution of responses based on the causes of child labour and trafficking

Source: Field Survey, 2025

Table 4.9 illustrates the distribution of respondents’ opinions regarding the causes of child labour and trafficking in Gombe Local Government. The data reveals that a significant majority of respondents, 147 individuals or 38.79%, pinpointed poverty as the primary cause of these issues. This highlights a prevailing belief among participants that economic hardship plays a crucial role in driving children into labour and trafficking situations. Additionally, a smaller yet notable number of respondents identified unemployment, with 83 individuals (21.90%) recognising it as a contributing factor. Furthermore, only five respondents (1.32%) attributed the problem to a low level of employment, while three respondents (0.79%) identified greed as a cause. Other factors included large family size, cited by 27 participants (7.12%), and school dropout, reported by 18 respondents (4.75%). Interestingly, 96 respondents (25.33%) selected all of the above options as contributing factors, indicating a complex interplay of various issues leading to child labour and trafficking. Overall, the findings suggest that while poverty is viewed as the predominant cause, respondents acknowledge multiple interconnected factors that contribute to the prevalence of these social challenges in their community, necessitating a multifaceted approach to address the root causes effectively.

Table 4.15: Distribution of respondents based on Parents’ level of employment

Source: Field Survey, 2024

Table 4.10 provides insight into respondents’ perceptions of how parental employment status influences the likelihood of children becoming labourers and victims of trafficking in Gombe Local Government. Among the total respondents, 142 individuals, or 37.47%, indicated that they believed having “unemployed” parents is a significant factor contributing to children entering labour and trafficking situations. In contrast, a smaller segment, comprising 44 respondents (11.61%), selected “employed” as the parents’ employment status, which might also lead to such outcomes. However, the most striking finding is that 193 respondents, accounting for 50.92%, identified “underemployed” parents as the group most likely to see their children become labourers or victims of trafficking. This indicates a strong perception that while unemployment poses a risk, the condition of underemployment—where parents may have jobs that do not provide adequate financial support—presents a greater threat to child welfare. The data underscores a crucial link between the economic stability of families and the vulnerability of children, suggesting that underemployment may leave families in precarious situations where children are compelled to contribute economically, thereby increasing their risk of exploitation through labour and trafficking.

Table 4.11: Distribution of responses based on Parents’ Level of Skill

Source: Field Survey, 2025

Table 4.11 presents data on respondents’ perceptions regarding the skill levels of parents and how these levels may influence the likelihood of their children becoming labourers and victims of trafficking in Gombe Local Government. Among the total respondents, 62 individuals, or 16.36%, identified “skilled” as the level of skill possessed by parents that could lead to their children entering labour and trafficking situations. In contrast, a significant majority of respondents, 208 individuals, accounting for 54.88%, chose “unskilled” as the relevant parental skill level. Additionally, 109 respondents, representing 28.76% of the total, indicated “semi-skilled” as the applicable category. The findings suggest that most participants perceive unskilled labour as the primary parental skill level associated with children becoming labourers and victims of trafficking. This highlights a concerning trend where a lack of skills among parents may contribute to economic vulnerability, leading to increased chances of exploitation of their children. Overall, the data points to a clear recognition among participants of the role that parental skill levels play in influencing child labour and trafficking, emphasising the need for targeted interventions that address skills development and economic empowerment for families to reduce these risks in the community.

Presentation and Analysis of Regression Results

The regression analysis findings for each study’s objective are displayed in Tables 4.13, 4.14, and 4.15. The inferential statistics and the rationale for testing the study’s hypothesis are presented.

Test of Hypothesis One 

This study investigated the first hypothesis by employing the logit regression technique. The null hypothesis being tested is that:

H0: There is no significant relationship between the prevalence of child labour and child trafficking in Gombe Local Government.

Table 4.12: Proportional test of at least 50% prevalence of child labour and employment

Note: *** is Significant at 1%

Source: Authors’ Computation, 2025

The regression results presented in Table 4.12 provide a comprehensive analysis of the second objective of the study, which seeks to understand the respondents’ perceptions of the prevalence of child labour and child trafficking. Based on the responses of 379 participants, the findings reveal a concerning prevalence of child labour, characterised by a mean value of 0.81 and a standard error of 0.02. The associated Z-value of 9.44 is notably positive and statistically significant at the 1% level, indicating that the perceived prevalence of child labour is significantly higher than the average. This suggests that the issue is not only recognised by the respondents but also points to a critical concern within the community regarding the high rates of child labour, which could have profound implications for the well-being of children and the overall societal fabric.

In contrast, the analysis of child trafficking reveals a markedly different scenario. With a mean value of 0.42 and a standard error of 0.02, the prevalence of child trafficking is perceived to be relatively low. The Z-value of -0.84 further indicates that this prevalence falls below the average value, suggesting that while child trafficking is a recognised issue, it does not appear to be as widespread as child labour in the context of this study. However, it is important to note that this difference is not statistically significant at any conventional level of significance, which implies that the perception of child trafficking may not be robust enough to warrant immediate concern compared to the alarming rates of child labour. Collectively, these findings highlight the need for targeted interventions that address the critical issue of child labour while also considering the underlying factors that contribute to both child labour and trafficking in the community.

Implications of Findings 

The study examines the respondents’ responses about the prevalence of child labour and child trafficking in Gombe Local Government. The results indicate that out of the 379 respondents, 50% had the proportion perspective. The prevalence of child labour is considerable, with a mean value of 0.81 and a standard error of 0.02. The Z-value of 9.44 is positive and statistically significant at the 1% significance level. This suggests that the prevalence of child labour is higher than the average value.

The incidence of child trafficking is relatively low, with a mean value of 0.42 and a standard error of 0.02. The Z-value of -0.84 indicates that the prevalence of child trafficking is below the average value. However, it is not statistically significant at any significance level. These findings indicate that the prevalence of child labour surpasses that of child trafficking in Gombe Local Government.

CONCLUSION AND RECOMMENDATION

The findings of this study conclusively highlight the alarming prevalence of child labour and child trafficking within the Gombe Local Government Area, Gombe State. A significant majority of the surveyed households reported direct involvement in or exposure to these issues, with poverty identified as the most prevalent underlying cause. The demographic data, particularly the low household income levels and large family sizes among respondents, strongly correlate with the observed rates of child exploitation. This indicates that economic hardship forces many families to rely on children’s contributions, making them vulnerable to both child labour and trafficking. The widespread nature of these phenomena, affecting both male and female children, underscores a critical societal challenge that demands urgent attention and comprehensive interventions.

Based on these conclusions, it is strongly recommended that targeted socio-economic empowerment programs be implemented in the Gombe Local Government Area. These programs should focus on poverty alleviation through initiatives such as vocational training for adults, access to micro-credit facilities, and sustainable livelihood support. Furthermore, comprehensive public awareness campaigns are crucial to educate communities on the dangers of child labour and trafficking, emphasising the importance of child education and protection. Establishing robust monitoring and reporting mechanisms, alongside strengthening law enforcement’s capacity to prosecute offenders, is also vital. Ultimately, a multi-faceted approach involving government agencies, non-governmental organisations, and community leaders is essential to create a protective environment for children and address the root causes of their exploitation.

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